NA
b) Accompany the Combined Work with a copy of the GNU GPL and this license document.
c) For a Combined Work that displays copyright notices during execution, include the copyright notice for the Library among these notices, as well as a reference directing the user to the copies of the GNU GPL and this license document.
d) Do one of the following:
- 0) Convey the Minimal Corresponding Source under the terms of this
- License, and the Corresponding Application Code in a form
- suitable for, and under terms that permit, the user to
- recombine or relink the Application with a modified version of
- the Linked Version to produce a modified Combined Work, in the
- manner specified by section 6 of the GNU GPL for conveying
- Corresponding Source.
-
- 1) Use a suitable shared library mechanism for linking with the
- Library. A suitable mechanism is one that (a) uses at run time
- a copy of the Library already present on the user's computer
- system, and (b) will operate properly with a modified version
- of the Library that is interface-compatible with the Linked
- Version. 0) Convey the Minimal Corresponding Source under the terms of this
+ License, and the Corresponding Application Code in a form
+ suitable for, and under terms that permit, the user to
+ recombine or relink the Application with a modified version of
+ the Linked Version to produce a modified Combined Work, in the
+ manner specified by section 6 of the GNU GPL for conveying
+ Corresponding Source.
+
+ 1) Use a suitable shared library mechanism for linking with the
+ Library. A suitable mechanism is one that (a) uses at run time
+ a copy of the Library already present on the user's computer
+ system, and (b) will operate properly with a modified version
+ of the Library that is interface-compatible with the Linked
+ Version.e) Provide Installation Information, but only if you would otherwise be required to provide such information under section 6 of the GNU GPL, and only to the extent that such information is necessary to install and execute a modified version of the Combined Work produced by recombining or relinking the Application with a modified version of the Linked Version. (If you use option 4d0, the Installation Information must accompany the Minimal Corresponding Source and Corresponding Application Code. If you use option 4d1, you must provide the Installation Information in the manner specified by section 6 of the GNU GPL for conveying Corresponding Source.)
- Combined Libraries. @@ -224,7 +224,7 @@
- column 3 defines the variance of the distribution (or length of uniform distribution).
Study 1 and 2 from table 2 in: Gibiansky, L., Gibiansky, E., & Bauer, R. (2012). Comparison of Nonmem 7.2 estimation methods and parallel processing efficiency on a target-mediated drug disposition model. Journal of Pharmacokinetics and Pharmacodynamics, 39(1), 17–35. https://doi.org/10.1007/s10928-011-9228-y↩︎
-Nyberg, J., Bazzoli, C., Ogungbenro, K., Aliev, A., Leonov, S., Duffull, S., Hooker, A.C. and Mentré, F. (2014). Methods and software tools for design evaluation for population pharmacokinetics-pharmacodynamics studies. British Journal of Clinical Pharmacology, 79(1), 1–32. https://doi.org/10.1111/bcp.12352↩︎
+Study 1 and 2 from table 2 in: Gibiansky, L., Gibiansky, E., & Bauer, R. (2012). Comparison of Nonmem 7.2 estimation methods and parallel processing efficiency on a target-mediated drug disposition model. Journal of Pharmacokinetics and Pharmacodynamics, 39(1), 17–35. https://doi.org/10.1007/s10928-011-9228-y↩
+Nyberg, J., Bazzoli, C., Ogungbenro, K., Aliev, A., Leonov, S., Duffull, S., Hooker, A.C. and Mentré, F. (2014). Methods and software tools for design evaluation for population pharmacokinetics-pharmacodynamics studies. British Journal of Clinical Pharmacology, 79(1), 1–32. https://doi.org/10.1111/bcp.12352↩
- mrgsolve
- RxODE -
- send a pull request on: https://github.com/andrewhooker/PopED -
- compose a friendly e-mail to: andrew.hooker@farmbio.uu.se +
- compose a friendly e-mail to: andrew.hooker@farmbio.uu.se
Updated the website at https://andrewhooker.github.io/PopED
@@ -151,9 +151,9 @@ Added the ability to predict and plot model prediction intervals by computing the expected variance (using an FO approximation) and then computing a prediction interval based on an assumption of normality. See
?model_predicitonand?plot_model_prediction. The computation is faster but less accurate compared to usingDV=TRUE(andgroupsize_sim = 500) in the two functions.New and improved vignettes (#30, @giulialestini)!
@@ -171,18 +171,18 @@ Bug fixing. See https://github.com/andrewhooker/PopED/commits/master for more information.
Exported the
summarymethod for the results ofpoped_optimin the PopED NAMESPACE, so that the method can actually be used! Just usesummary(output).Fixed some old bugs that used
returnas a varible in functions, a la MATLAB.Added a vignette to introduce PopED!
@@ -199,9 +199,9 @@ Various small changes and bug fixes.
Added new optimization methods and tools, see
?poped_optim(). This function incorporates the new optimization routinesoptim_ARS()andoptim_LSwhich are optimized versions of previous optimization algorithms used in PopED. Both can be run with parallelization.poped_optim()also incorporates the genetic algorithm fromGA::ga(), which can also be run with parallelization, and the “L-BFGS-B” method fromstats::optim().poped_optim()should be more efficient and faster thanpoped_optimize().
@@ -209,9 +209,9 @@ Various small changes and bug fixes.
Fixed
plot_efficiency_of_windows()bug that had wrong headers on each subplot.
@@ -220,9 +220,9 @@ Added example 10 describing a PKPD design of hepatitis C virus (HCV) kinetics to the
system.file("examples",package="PopED")directory of the PopED installation.Updated model_prediction() to allow for creation of NONMEM datasets.
@@ -238,9 +238,9 @@Various small bug fixes.
Updated package author list
@@ -253,9 +253,9 @@ Fixed a bug where get_rse failed when a parameter had a value of 3.
- PopED has been translated to R from MATLAB and this is the initial release. @@ -278,7 +278,7 @@
Contents
diff --git a/docs/articles/examples.html b/docs/articles/examples.html index 6385079b..228853f1 100644 --- a/docs/articles/examples.html +++ b/docs/articles/examples.html @@ -93,13 +93,13 @@ -Examples
Giulia Lestini, Martin Fink and Andrew Hooker
-27 March, 2020
+23 April, 2020
Source:vignettes/examples.Rmd
examples.Rmd27 March, 2020
Introduction
In this vignette, we try to highlight PopED features that may be useful. Only code related to specific features we would like to highlight is described here in this vignette. These features (and more) are presented as r-scripts in the “examples” folder in the PopED installation directory. You can view a list of these example files using the commands:
-ex_dir <- system.file("examples", package="PopED") ++#> [24] "one_comp_oral_CL.c" +#> [25] "tmdd_qss_one_target.c" +#> [26] "two_comp_oral_CL.c"ex_dir <- system.file("examples", package="PopED") list.files(ex_dir) #> [1] "ex.1.a.PK.1.comp.oral.md.intro.R" #> [2] "ex.1.b.PK.1.comp.oral.md.re-parameterize.R" @@ -137,19 +137,11 @@#> [21] "ex.8.tmdd_qss_one_target_compiled.R" #> [22] "ex.9.PK.2.comp.oral.md.ode.compiled.R" #> [23] "HCV_ode.c" -#> [24] "HCV_ode.o" -#> [25] "HCV_ode.so" -#> [26] "one_comp_oral_CL.c" -#> [27] "one_comp_oral_CL.o" -#> [28] "one_comp_oral_CL.so" -#> [29] "tmdd_qss_one_target.c" -#> [30] "tmdd_qss_one_target.o" -#> [31] "tmdd_qss_one_target.so" -#> [32] "two_comp_oral_CL.c" -#> [33] "two_comp_oral_CL.o" -#> [34] "two_comp_oral_CL.so"
You can then open one of the examples (for example, ex.1.a.PK.1.comp.oral.md.intro.R) using the following code
file_name <- "ex.1.a.PK.1.comp.oral.md.intro.R" +file_name <- "ex.1.a.PK.1.comp.oral.md.intro.R" ex_file <- system.file("examples",file_name,package="PopED") file.copy(ex_file,tempdir(),overwrite = T) @@ -525,7 +517,7 @@Analytic solution of PKPD model, multiple study arms
The full code for this example is available in
ex.4.PKPD.1.comp.emax.R.Here we define a PKPD mode using analytical equations. The PK is a one compartment model with intravenus bolus administration and linear elimination. The PD is an ordinary Emax model driven by the PK concentrations. The expected output of each measurement (PK or PD) is given in the vector
-model_switch(see below for details).library(PopED) +library(PopED) f_pkpdmodel <- function(model_switch,xt,parameters,poped.db){ with(as.list(parameters),{ y=xt @@ -544,7 +536,7 @@}) }
The error model also has to accommodate both response models.
-## -- Residual Error function +## -- Residual Error function ## -- Proportional PK + additive PD f_Err <- function(model_switch,xt,parameters,epsi,poped.db){ returnArgs <- do.call(poped.db$model$ff_pointer,list(model_switch,xt,parameters,poped.db)) @@ -562,7 +554,7 @@return(list( y= y,poped.db =poped.db )) }
In the
-poped.dbobject the vector we specifymodel_switchin order to assign the sampling times defined in the vectorxtto the PK (=1) or PD (=2) model.poped.db <- create.poped.database( +poped.db <- create.poped.database( # Model ff_fun=f_pkpdmodel, @@ -586,7 +578,7 @@maxa=c(DOSE=10), mina=c(DOSE=0))
The model predictions below show typical PK and PD profiles for three dose groups and the expected 95% prediction interval of the data. The initial design, as shown in the
-poped.dbobject, consists of 3 arms with doses of 0, 1, and 2 mg; PK sampling times are 0.33, 0.66, 0.9, and 5 hours/days; PD sampling times are 0.1, 1, 2, and 5 hours/days. Withmodel.names=c("PK","PD")one can name the outputs in the graph.plot_model_prediction( +plot_model_prediction( poped.db,PI=TRUE, facet_scales="free", separate.groups=TRUE, @@ -598,9 +590,9 @@ODE solution of PK model, multiple dosing
The full code for this example is available in
ex.9.PK.2.comp.oral.md.ode.compiled.R.In this example, the
-deSolvelibrary needs to be installed for computing solutions to a system of differential equations. For faster solutions one can use pre-compiled code using theRcpplibrary (see below).+library(deSolve)library(deSolve)Here we define the two compartment model in R using deSolve notation
-PK.2.comp.oral.ode <- function(Time, State, Pars){ +PK.2.comp.oral.ode <- function(Time, State, Pars){ with(as.list(c(State, Pars)), { dA1 <- -KA*A1 dA2 <- KA*A1 + A3* Q/V2 -A2*(CL/V1+Q/V1) @@ -609,7 +601,7 @@}) }
Now we define the initial conditions of the ODE system
-A_iniwith a named vector, in this case all compartments are initialized to zeroc(A1=0,A2=0,A3=0). The dosing input is defined as a data.framedose_datreferring to the named compartmentvar = c("A1"), the specifieddose_timesandvalue=c(DOSE*Favail)dose amounts. Note that the covariatesDOSEand the regimenTAUcan differ by arm and be optimized (as shown inex.1.a.PK.1.comp.oral.md.intro.R). For more information see the help pages for?deSolve::odeand?deSolve::events.ff.PK.2.comp.oral.md.ode <- function(model_switch, xt, parameters, poped.db){ +ff.PK.2.comp.oral.md.ode <- function(model_switch, xt, parameters, poped.db){ with(as.list(parameters),{ # initial conditions of ODE system @@ -638,7 +630,7 @@}) }
When creating a PopED database.
-ff_funshould point to the function providing the solution to the ODE. Further, the names in the parameter definition (fg) function should match the parameters used in the above two functions.poped.db <- create.poped.database( +poped.db <- create.poped.database( # Model ff_fun="ff.PK.2.comp.oral.md.ode", @@ -661,10 +653,10 @@a=c(DOSE=100,TAU=24), discrete_a = list(DOSE=seq(0,1000,by=100),TAU=8:24))
We plot the population prediction of the model for the initial design
-+plot_model_prediction(poped.db,model_num_points = 500)plot_model_prediction(poped.db,model_num_points = 500)
Faster computations with Rcpp: We could also define the system using Rcpp, which will produce compiled code that should run faster (further examples in
-ex.2.c.warfarin.ODE.compiled.R). First we redefine the ODE system using Rcpp.library(Rcpp) +library(Rcpp) cppFunction( 'List two_comp_oral_ode_Rcpp(double Time, NumericVector A, NumericVector Pars) { int n = A.size(); @@ -682,7 +674,7 @@return List::create(dA); }'
)Next we add the compiled function (
-two_comp_oral_ode_Rcpp) in the ODE solver.ff.PK.2.comp.oral.md.ode.Rcpp <- function(model_switch, xt, parameters, poped.db){ +ff.PK.2.comp.oral.md.ode.Rcpp <- function(model_switch, xt, parameters, poped.db){ with(as.list(parameters),{ # initial conditions of ODE system @@ -713,14 +705,14 @@}) }
Finally we create a poped database to use these functions by updating the previously created database.
-poped.db.Rcpp <- create.poped.database( +poped.db.Rcpp <- create.poped.database( poped.db, ff_fun="ff.PK.2.comp.oral.md.ode.Rcpp")We can compare the time for design evaluation with these two methods of describing the same model.
-tic(); eval <- evaluate_design(poped.db); toc() -#> Elapsed time: 2.892 seconds. ++#> Elapsed time: 1.091 seconds.tic(); eval <- evaluate_design(poped.db); toc() +#> Elapsed time: 2.407 seconds. tic(); eval <- evaluate_design(poped.db.Rcpp); toc() -#> Elapsed time: 1.539 seconds.The difference is noticeable and gets larger for more complex ODE models.
@@ -728,7 +720,7 @@ODE solution of TMDD model with 2 outputs, Multiple arms, different dose routes, different number of sample times per arm
The full code for this example is available in
ex.8.tmdd_qss_one_target_compiled.R.In the function that defines the dosing and derives the ODE solution, the discrete covariate
-SC_FLAGis used to give the dose either intoA1orA2, the sub-cutaneous or the IV compartment.tmdd_qss_one_target_model_compiled <- function(model_switch,xt,parameters,poped.db){ +-tmdd_qss_one_target_model_compiled <- function(model_switch,xt,parameters,poped.db){ with(as.list(parameters),{ y=xt @@ -765,8 +757,8 @@}) }
Two different sub-studies are defined, with different sampling times per arm - in terms of total number of samples and the actual times1. Due to this difference in numbers and the relatively complicated study design we define the sample times (
-xt), what each sample time will measure (model_switch) and which samples should be taken at the same study time (G_xt) as matricies. Here three variablesxt,model_switch, andG_xtare matrices with each row representing one arm, and the number of columns is the maximum number of samples (for all endpoints) in any of the arms (i.e.,max(ni)). To be clear about which elements in the matriicies should be considered we specify the number of samples per arm by defining the vectorniin thecreate.poped.databasefunction.xt <- zeros(6,30) +Two different sub-studies are defined, with different sampling times per arm - in terms of total number of samples and the actual times1. Due to this difference in numbers and the relatively complicated study design we define the sample times (
+xt), what each sample time will measure (model_switch) and which samples should be taken at the same study time (G_xt) as matricies. Here three variablesxt,model_switch, andG_xtare matrices with each row representing one arm, and the number of columns is the maximum number of samples (for all endpoints) in any of the arms (i.e.,max(ni)). To be clear about which elements in the matriicies should be considered we specify the number of samples per arm by defining the vectorniin thecreate.poped.databasefunction.xt <- zeros(6,30) study_1_xt <- matrix(rep(c(0.0417,0.25,0.5,1,3,7,14,21,28,35,42,49,56),8),nrow=4,byrow=TRUE) study_2_xt <- matrix(rep(c(0.0417,1,1,7,14,21,28,56,63,70,77,84,91,98,105),4),nrow=2,byrow=TRUE) xt[1:4,1:26] <- study_1_xt @@ -784,7 +776,7 @@G_xt[1:4,1:26] <- study_1_G_xt G_xt[5:6,] <- study_2_G_xt
These can then be plugged into the normal
-poped.dbsetup.poped.db.2 <- create.poped.database( +poped.db.2 <- create.poped.database( # Model ff_fun=tmdd_qss_one_target_model_compiled, @@ -818,9 +810,9 @@discrete_a = list(DOSE=seq(100,1000,by=100), SC_FLAG=c(0,1)))
Now we can plot population predictions for each group and evaluate the design.
-+plot_model_prediction(poped.db.2,facet_scales="free")plot_model_prediction(poped.db.2,facet_scales="free")-
eval_2 <- evaluate_design(poped.db.2) +eval_2 <- evaluate_design(poped.db.2) round(eval_2$rse) # in percent
@@ -924,7 +916,7 @@ Model with continuous covariates
The R code for this example is available in
ex.12.covariate_distributions.R.Let’s assume that we have a model with a covariate included in the model description. Here we define a one-compartment PK model that uses allometric scaling with a weight effect on both clearance and volume of distribution.
-mod_1 <- function(model_switch,xt,parameters,poped.db){ +mod_1 <- function(model_switch,xt,parameters,poped.db){ with(as.list(parameters),{ y=xt @@ -946,7 +938,7 @@return( parameters ) }
Now we define a design. In this case one group of individuals, where we define the individuals’ typical weight as 70 kg (
-a=c(WT=70)).poped_db <- +poped_db <- create.poped.database( ff_fun=mod_1, fg_fun=par_1, @@ -964,10 +956,10 @@a=c(WT=70) )
We can create a plot of the model prediction for the typical individual
-+plot_model_prediction(poped_db)plot_model_prediction(poped_db)
And evaluate the initial design
-evaluate_design(poped_db) +evaluate_design(poped_db) #> Problems inverting the matrix. Results could be misleading. #> Warning: The following parameters are not estimable: #> WT_CL, WT_V @@ -990,7 +982,7 @@#> 3.247502 3.317107 NA NA 21.026264 21.950179 10.061292
From the output produced we see that the covariate parameters can not be estimated according to this design calculation (RSE of WT_CL and WT_V are
NA). Why is that? Well, the calculation being done is assuming that every individual in the group has the same covariate (to speed up the calculation). This is clearly a poor assumption in this case!Distribution of covariates: We can improve the computation by assuming a distribution of the covariate (WT) in the individuals in the study. We set
-groupsize=1, the number of groups to be 50 (m=50) and assume that WT is sampled from a normal distribution with mean=70 and sd=10 (a=as.list(rnorm(50, mean = 70, sd = 10)).poped_db_2 <- +-poped_db_2 <- create.poped.database( ff_fun=mod_1, fg_fun=par_1, @@ -1007,10 +999,10 @@bUseGrouped_xt=1, a=as.list(rnorm(50, mean = 70, sd = 10)) )
+ev <- evaluate_design(poped_db_2) +-ev <- evaluate_design(poped_db_2) round(ev$ofv,1) -#> [1] 42.1+#> [1] 41.9round(ev$rse)round(ev$rse)
@@ -1027,11 +1019,11 @@
WT_CL -30 +31 WT_V -23 +24 d_CL @@ -1049,7 +1041,7 @@
Here we see that, given this distribution of weights, the covariate effect parameters (
WT_CLandWT_V) would be well estimated.However, we are only looking at one sample of 50 individuals. Maybe a better approach is to look at the distribution of RSEs over a number of experiments given the expected weight distribution.
-nsim <- 30 +nsim <- 30 rse_list <- c() for(i in 1:nsim){ poped_db_tmp <- @@ -1088,8 +1080,8 @@
0% 3.25 3.32 -23.90 -18.32 +24.30 +18.62 21.02 21.95 10.06 @@ -1098,8 +1090,8 @@
25% 3.25 3.32 -27.39 -20.99 +27.34 +20.95 21.03 21.95 10.07 @@ -1108,30 +1100,30 @@
50% 3.26 3.33 -29.79 -22.83 +29.38 +22.52 21.03 21.96 10.07 75% -3.30 -3.37 -31.80 -24.36 +3.28 +3.36 +31.13 +23.86 21.03 21.96 10.07 @@ -1148,7 +1140,7 @@ 100% -3.36 -3.44 -37.61 -28.82 +3.57 +3.64 +37.13 +28.45 21.03 -21.96 +21.97 10.08 Model with Inter-Occasion Variability (IOV)
The full code for this example is available in
ex.14.PK.IOV.R.The IOV is introduced with
-bocc[x,y]in the parameter definition function as a matrix with the first argumentxindicating the index for the IOV variances, and the second argumentydenoting the occasion. This is used in the example to derive to different clearance values, i.e.,CL_OCC_1andCL_OCC_2.sfg <- function(x,a,bpop,b,bocc){ +sfg <- function(x,a,bpop,b,bocc){ parameters=c( CL_OCC_1=bpop[1]*exp(b[1]+bocc[1,1]), CL_OCC_2=bpop[1]*exp(b[1]+bocc[1,2]), V=bpop[2]*exp(b[2]), @@ -1158,7 +1150,7 @@return( parameters ) }
These parameters can now be used in the model function to define the change in parameters between the occacions (here the change occurs with the 7th dose in a one-compartment model with first order absorption).
-cppFunction( +cppFunction( 'List one_comp_oral_ode(double Time, NumericVector A, NumericVector Pars) { int n = A.size(); NumericVector dA(n); @@ -1198,7 +1190,7 @@}) }
The within-subject variability variances (
-docc) are defined in the poped database as a 3-column matrix with one row per IOV-parameter, and the middle column giving the variance values.poped.db <- +poped.db <- create.poped.database( ff_fun=ff.ode.rcpp, fError_fun=feps.add.prop, @@ -1219,7 +1211,7 @@mina=c(DOSE=0,TAU=24) )
We can visualize the IOV by looking at an example individual. We see the PK profile changes at the 7th dose (red line) due to the change in clearance.
-library(ggplot2) +library(ggplot2) set.seed(123) plot_model_prediction( poped.db, @@ -1233,7 +1225,7 @@) + geom_vline(xintercept = 24*6,color="red")
We can also see that the design is relatively poor for estimating the IOV parameter:
-ev <- evaluate_design(poped.db) +ev <- evaluate_design(poped.db) round(ev$rse)
@@ -1277,7 +1269,7 @@ Full omega matrix
The full code for this example is available in
ex.15.full.covariance.matrix.R.The
-covdobject is used for defining the covariances of the between subject variances (off-diagonal elements of the full variance-covariance matrix for the between subject variability).poped.db_with <- +poped.db_with <- create.poped.database( ff_file="ff", fg_file="sfg", @@ -1294,7 +1286,7 @@a=70 )
What do the covariances mean?
-(IIV <- poped.db_with$parameters$param.pt.val$d) +(IIV <- poped.db_with$parameters$param.pt.val$d) #> [,1] [,2] [,3] #> [1,] 0.07 0.03 0.10 #> [2,] 0.03 0.02 0.09 @@ -1306,15 +1298,15 @@#> [3,] 0.4879500 0.8215838 1.0000000
They indicate a correlation of the inter-individual variabilities, here of ca. 0.8 between clearance and volume, as well as between volume and absorption rate.
We can clearly see a difference in the variance of the model predictions.
-library(ggplot2) +library(ggplot2) p1 <- plot_model_prediction(poped.db, PI=TRUE)+ylim(-0.5,12) p2 <- plot_model_prediction(poped.db_with,PI=TRUE) +ylim(-0.5,12) gridExtra::grid.arrange(p1+ ggtitle("No covariance in BSV"), p2+ ggtitle("Covariance in BSV"), nrow = 1)
Evaluating the designs with and without the covariances:
-ev1 <- evaluate_design(poped.db) +-ev1 <- evaluate_design(poped.db) ev2 <- evaluate_design(poped.db_with)round(ev1$rse) +
@@ -1377,7 +1369,7 @@
Note, that the precision of all other parameters is barely affected by including the full covariance matrix. This is likely to be different in practice with more ill-conditioned numerical problems.
Evaluate the same designs with full FIM (instead of reduced)
-ev1 <- evaluate_design(poped.db, fim.calc.type=0) +ev1 <- evaluate_design(poped.db, fim.calc.type=0) ev2 <-evaluate_design(poped.db_with, fim.calc.type=0) round(ev1$rse,1) @@ -1447,7 +1439,7 @@Include a prior FIM, compute power to identify a parameter
In this example we incorporate prior knowledge into a current study design calculation. First the expected FIM obtained from an experiment in adults is computed. Then this FIM is added to the current experiment in children. One could also use the observed FIM when using estimation software to fit one realization of a design (from the $COVARIANCE step in NONMEM for example). The full code for this example is available in
ex.11.PK.prior.R.Note that we define the parameters for a one-compartment first-order absorption model using a covariate called
-isPediatricto switch between adult and pediatric models, andbpop[5]=pedCLis the factor to multiply the adult clearancebpop[3]to obtain the pediatric one.sfg <- function(x,a,bpop,b,bocc){ +sfg <- function(x,a,bpop,b,bocc){ parameters=c( V=bpop[1]*exp(b[1]), KA=bpop[2]*exp(b[2]), @@ -1459,7 +1451,7 @@return( parameters ) }
The design and design space for adults is defined below (Two arms, 5 sample time points per arm, doses of 20 and 40 mg,
-isPediatric = 0). As we want to pool the results (i.e. add the FIMs together), we also have to provide thepedCLparameter so that both the adult and childrems FIM have the same dimensions.poped.db <- +poped.db <- create.poped.database( ff_fun=ff.PK.1.comp.oral.md.CL, fg_fun=sfg, @@ -1477,10 +1469,10 @@c(DOSE=40, TAU=24,isPediatric = 0)) )
Create plot of model without variability
-+plot_model_prediction(poped.db, model_num_points = 300)plot_model_prediction(poped.db, model_num_points = 300)
To store the FIM from the adult design we evaluate this design
-(outAdult = evaluate_design(poped.db)) +(outAdult = evaluate_design(poped.db)) #> Problems inverting the matrix. Results could be misleading. #> Warning: The following parameters are not estimable: #> pedCL @@ -1511,7 +1503,7 @@#> 6.634931 8.587203 4.354792 NA 33.243601 55.689432 27.133255
It is obvious that we cannot estimate the pediatric covariate from adult data only; hence the warning message. You can also note the zeros in the 4th column and 4th row of the FIM indicating that
pedCLcannot be estimated from the adult data.We can evaluate the adult design without warning, by setting the
-pedCLparameter to be fixed (i.e., not estimated):evaluate_design(create.poped.database(poped.db, notfixed_bpop=c(1,1,1,0,0))) +evaluate_design(create.poped.database(poped.db, notfixed_bpop=c(1,1,1,0,0))) #> $ofv #> [1] 29.70233 #> @@ -1529,7 +1521,7 @@#> 6.634931 8.587203 4.354792 33.243601 55.689432 27.133255
One obtains good estimates for all parameters for adults (<60% RSE for all).
For pediatrics the covariate
-isPediatric = 1. We define one arm, 4 sample-time points.poped.db.ped <- +poped.db.ped <- create.poped.database( ff_fun=ff.PK.1.comp.oral.md.CL, fg_fun=sfg, @@ -1546,10 +1538,10 @@a=list(c(DOSE=40,TAU=24,isPediatric = 1)) )
We can create a plot of the pediatric model without variability
-+plot_model_prediction(poped.db.ped, model_num_points = 300)plot_model_prediction(poped.db.ped, model_num_points = 300)
Evaluate the design of the pediatrics study alone.
-evaluate_design(poped.db.ped) +evaluate_design(poped.db.ped) #> Problems inverting the matrix. Results could be misleading. #> $ofv #> [1] -Inf @@ -1579,7 +1571,7 @@#> 77.2918849
Clearly the design has problems on its own.
We can add the prior information from the adult study and evaluate that design (i.e., pooling adult and pediatric data).
-diff --git a/vignettes/model_def_other_pkgs.Rmd b/vignettes/model_def_other_pkgs.Rmd index eb6a1dfc..4f73efb7 100644 --- a/vignettes/model_def_other_pkgs.Rmd +++ b/vignettes/model_def_other_pkgs.Rmd @@ -399,42 +399,27 @@ ff_ode_mrg <- function(model_switch, xt, p, poped.db){ ## ODE solution using RxODE The model written for RxODE: -```{r rxode} -ode_rx <- " -d/dt(DEPOT) = -KA*DEPOT; -d/dt(CENT) = KA*DEPOT - (CL/V)*CENT; -CP=CENT/V; -" -``` - -Compile and load the model with `RxODE` ```{r} -modrx <- RxODE(model = ode_rx, modName = 'modrx') +modrx <- RxODE({ + d/dt(DEPOT) = -KA*DEPOT; + d/dt(CENT) = KA*DEPOT - (CL/V)*CENT; + CP=CENT/V; +}) ``` ```{r} ff_ode_rx <- function(model_switch, xt, p, poped.db){ + times_xt <- drop(xt) + et(0,amt=p[["DOSE"]], ii=p[["TAU"]], until=max(times_xt)) %>% + et(times_xt) -> data - times_xt <- drop(xt) - times <- sort(unique(c(0,times_xt))) - - ev <- eventTable() - ev$add.dosing( - dose = p[["DOSE"]], - dosing.interval = p[["TAU"]], - nbr.doses = floor(max(times)/p[["TAU"]])+1, - dosing.to = 1, - start.time = 0 - ) - ev$add.sampling(times) - Params <- c(CL = p[["CL"]], V = p[["V"]], KA = p[["KA"]]) + out <- rxSolve(modrx, p, data, atol=1e-8, rtol=1e-8,maxsteps=5000, + returnType="data.frame") - out <- modrx$solve(Params, ev,atol=1e-8,rtol=1e-8) + y <- out$CP[match(times_xt,out$time)] - y = out[match(times_xt,out[,"time"]),"CP",drop=F] - - return(list(y=y,poped.db=poped.db)) + return(list(y=matrix(y,ncol=1),poped.db=poped.db)) } ``` @@ -614,7 +599,7 @@ kable( # Version information {-} ```{r session, cache.rebuild=TRUE} -sessionInfo() +devtools::session_info() ```poped.db.all <- create.poped.database( +poped.db.all <- create.poped.database( poped.db.ped, prior_fim = outAdult$fim ) @@ -1611,7 +1603,7 @@#> 6.381388 8.222819 4.354761 12.591940 31.808871 52.858399 25.601551
The pooled data leads to much higher precision in parameter estimates compared to either study separately.
One can also obtain the power for estimating the pediatric difference in clearance (power in estimating bpop[5] as different from 1).
-diff --git a/docs/reference/zeros.html b/docs/reference/zeros.html index 59d28d70..b300236c 100644 --- a/docs/reference/zeros.html +++ b/docs/reference/zeros.html @@ -206,7 +206,7 @@evaluate_power(poped.db.all, bpop_idx=5, h0=1, out=out.all) +diff --git a/docs/reference/tryCatch.W.E.html b/docs/reference/tryCatch.W.E.html index 29b40d8a..79eadad7 100644 --- a/docs/reference/tryCatch.W.E.html +++ b/docs/reference/tryCatch.W.E.html @@ -180,7 +180,7 @@evaluate_power(poped.db.all, bpop_idx=5, h0=1, out=out.all) #> $ofv #> [1] 34.96368 #> @@ -1645,7 +1637,7 @@
Design evaluation including uncertainty in the model parameters (robust design)
-In this example the aim is to evaluate a design incorporating uncertainty around parameter values in the model. The full code for this example is available in
+ex.2.d.warfarin.ED.R. This illustration is one of the Warfarin examples from software comparison in: Nyberg et al.2.In this example the aim is to evaluate a design incorporating uncertainty around parameter values in the model. The full code for this example is available in
ex.2.d.warfarin.ED.R. This illustration is one of the Warfarin examples from software comparison in: Nyberg et al.2.We define the fixed effects in the model and add a 10% uncertainty to all but Favail. To do this we use a
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:@@ -1654,7 +1646,7 @@
Here we define a log-normal distribution
-diff --git a/docs/reference/toc.html b/docs/reference/toc.html index 11ed553e..769f61fc 100644 --- a/docs/reference/toc.html +++ b/docs/reference/toc.html @@ -183,11 +183,11 @@bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1) +bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1) bpop_vals_ed <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution bpop_vals, @@ -1667,7 +1659,7 @@#> KA 4 1.00 0.010000 #> Favail 0 1.00 0.000000
With this model and parameter set we define the initial design and design space. Specifically note the
-bpop=bpop_vals_edand theED_samp_size=20arguments.ED_samp_size=20indicates the number of samples used in evaluating the E-family objective functions.poped.db <- +poped.db <- create.poped.database( ff_fun=ff, fg_fun=sfg, @@ -1685,7 +1677,7 @@maxa=100, ED_samp_size=20)
You can also provide
-ED_samp_sizeargument to the design evaluation or optimization arguments:tic();evaluate_design(poped.db,d_switch=FALSE,ED_samp_size=20); toc() ++#> Elapsed time: 0.13 seconds.tic();evaluate_design(poped.db,d_switch=FALSE,ED_samp_size=20); toc() #> $ofv #> [1] 55.41311 #> @@ -1714,9 +1706,9 @@#> 5.030673 2.983917 14.014958 29.787587 36.758952 26.753311 31.645011 #> SIGMA[2,2] #> 25.341368 -#> Elapsed time: 0.225 seconds.
We can see that the result, based on MC sampling, is somewhat variable with so few samples.
-tic();evaluate_design(poped.db,d_switch=FALSE,ED_samp_size=20); toc() ++#> Elapsed time: 0.119 seconds.tic();evaluate_design(poped.db,d_switch=FALSE,ED_samp_size=20); toc() #> $ofv #> [1] 55.42045 #> @@ -1745,14 +1737,14 @@#> 5.021700 2.980981 14.068646 29.765030 36.691675 26.754137 31.469425 #> SIGMA[2,2] #> 25.311870 -#> Elapsed time: 0.199 seconds.
Design evaluation for a subset of model parameters of interest (Ds optimality)
Ds-optimality is a criterion that can be used if one is interested in estimating a subset “s” of the model parameters as precisely as possible. The full code for this example is available in
ex.2.e.warfarin.Ds.R. First we define initial design and design space:For Ds optimality we add the
-ds_indexoption to thecreate.poped.databasefunction to indicate whether a paramter is interesting (=0) or not (=1). Moreover, we set theofv_calc_type=6for computing the Ds optimality criterion (it is set to 4 by default, for computing the log of the determiant of the FIM). More details are available by running the command?create.poped.database.diff --git a/docs/articles/intro-poped.html b/docs/articles/intro-poped.html index d4bf2ea8..783aaa0a 100644 --- a/docs/articles/intro-poped.html +++ b/docs/articles/intro-poped.html @@ -93,13 +93,13 @@ -poped.db <- +poped.db <- create.poped.database( ff_fun=ff, fg_fun=sfg, @@ -1771,9 +1763,9 @@ds_index=c(0,0,0,1,1,1,1,1), # size is number_of_non_fixed_parameters ofv_calc_type=6) # Ds OFV calculation
Design evaluation:
-diff --git a/docs/articles/examples_files/figure-html/simulate_IOV_with_IIV-1.png b/docs/articles/examples_files/figure-html/simulate_IOV_with_IIV-1.png index 041ca7cd..b5164a83 100644 Binary files a/docs/articles/examples_files/figure-html/simulate_IOV_with_IIV-1.png and b/docs/articles/examples_files/figure-html/simulate_IOV_with_IIV-1.png differ diff --git a/docs/articles/examples_files/figure-html/simulate_ODE_model-1.png b/docs/articles/examples_files/figure-html/simulate_ODE_model-1.png index d06ec15e..35c5c102 100644 Binary files a/docs/articles/examples_files/figure-html/simulate_ODE_model-1.png and b/docs/articles/examples_files/figure-html/simulate_ODE_model-1.png differ diff --git a/docs/articles/examples_files/figure-html/simulate_adult-1.png b/docs/articles/examples_files/figure-html/simulate_adult-1.png index 24003566..ad5fcbc6 100644 Binary files a/docs/articles/examples_files/figure-html/simulate_adult-1.png and b/docs/articles/examples_files/figure-html/simulate_adult-1.png differ diff --git a/docs/articles/examples_files/figure-html/simulate_different_dose_regimen-1.png b/docs/articles/examples_files/figure-html/simulate_different_dose_regimen-1.png index 63c57b7a..c701307a 100644 Binary files a/docs/articles/examples_files/figure-html/simulate_different_dose_regimen-1.png and b/docs/articles/examples_files/figure-html/simulate_different_dose_regimen-1.png differ diff --git a/docs/articles/examples_files/figure-html/simulate_multi-response_model-1.png b/docs/articles/examples_files/figure-html/simulate_multi-response_model-1.png index dfaa653e..c408bab7 100644 Binary files a/docs/articles/examples_files/figure-html/simulate_multi-response_model-1.png and b/docs/articles/examples_files/figure-html/simulate_multi-response_model-1.png differ diff --git a/docs/articles/examples_files/figure-html/simulate_pediatrics-1.png b/docs/articles/examples_files/figure-html/simulate_pediatrics-1.png index afdd1d19..9ca4d0c5 100644 Binary files a/docs/articles/examples_files/figure-html/simulate_pediatrics-1.png and b/docs/articles/examples_files/figure-html/simulate_pediatrics-1.png differ diff --git a/docs/articles/examples_files/figure-html/simulate_with_cov_matrix-1.png b/docs/articles/examples_files/figure-html/simulate_with_cov_matrix-1.png index d2f10172..b8a496b4 100644 Binary files a/docs/articles/examples_files/figure-html/simulate_with_cov_matrix-1.png and b/docs/articles/examples_files/figure-html/simulate_with_cov_matrix-1.png differ diff --git a/docs/articles/examples_files/figure-html/unnamed-chunk-24-1.png b/docs/articles/examples_files/figure-html/unnamed-chunk-24-1.png index 820d5259..9fbf0074 100644 Binary files a/docs/articles/examples_files/figure-html/unnamed-chunk-24-1.png and b/docs/articles/examples_files/figure-html/unnamed-chunk-24-1.png differ diff --git a/docs/articles/examples_files/figure-html/unnamed-chunk-61-1.png b/docs/articles/examples_files/figure-html/unnamed-chunk-61-1.png index 363af242..ab929e7a 100644 Binary files a/docs/articles/examples_files/figure-html/unnamed-chunk-61-1.png and b/docs/articles/examples_files/figure-html/unnamed-chunk-61-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index f8056e0f..b6dad535 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -163,7 +163,7 @@evaluate_design(poped.db) +evaluate_design(poped.db) #> $ofv -#> [1] 14534520 +#> [1] 16.49204 #> #> $fim #> CL V KA d_CL d_V @@ -1807,13 +1799,13 @@
The full code for this example is available in “ex.13.shrinkage.R”.
To evaluate the estimation quality of the individual random effects in the model (the b’s) we use the function
-shrinkage().shrinkage(poped.db) ++#> d_KA d_V `D[3,3]` type group +#> <dbl> <dbl> <dbl> <chr> <chr> +#> 1 0.504 0.367 0.424 shrink_var grp_1 +#> 2 0.295 0.205 0.241 shrink_sd grp_1 +#> 3 0.710 0.303 0.206 se grp_1shrinkage(poped.db) #> # A tibble: 3 x 5 -#> `d[1]` `d[2]` `d[3]` type group -#> <dbl> <dbl> <dbl> <chr> <chr> -#> 1 0.504 0.367 0.424 shrink_var grp_1 -#> 2 0.295 0.205 0.241 shrink_sd grp_1 -#> 3 0.710 0.303 0.206 se grp_1The output shows us the expected shrinkage on the variance scale (\(shrink_{var}=1-var(b_j)/D(j,j)\)) and on the standard deviation scale (\(shrink_{sd}=1-sd(b_j)/sqrt(D(j,j))\)), as well as the standard errors of the \(b_j\) estimates.
@@ -1837,8 +1829,8 @@@@ -1858,7 +1850,7 @@
-
All vignettes
+diff --git a/docs/reference/tic.html b/docs/reference/tic.html index 73804404..6280dfc1 100644 --- a/docs/reference/tic.html +++ b/docs/reference/tic.html @@ -186,8 +186,8 @@diff --git a/docs/reference/test_mat_size.html b/docs/reference/test_mat_size.html index 04a1de47..1aef35c4 100644 --- a/docs/reference/test_mat_size.html +++ b/docs/reference/test_mat_size.html @@ -193,7 +193,7 @@Introduction to PopED
Andrew Hooker
-27 March, 2020
+23 April, 2020
Source:vignettes/intro-poped.Rmd@@ -117,12 +117,12 @@intro-poped.Rmd27 March, 2020
There are a number of functions to help you with these tasks. See
?popedfor more information.Below is an example to introduce the package. This example and several other examples, are available as r-scripts in the “examples” folder in the PopED installation directory located at:
-+system.file("examples", package="PopED")system.file("examples", package="PopED")You can view a list of the example files using the commands:
-ex_dir <- system.file("examples", package="PopED") +ex_dir <- system.file("examples", package="PopED") list.files(ex_dir)You can then open one of the examples (for example,
-ex.1.a.PK.1.comp.oral.md.intro.R, the code found in this vignette) using the following codediff --git a/docs/reference/summary.poped_optim.html b/docs/reference/summary.poped_optim.html index b2f21f19..1e61be2a 100644 --- a/docs/reference/summary.poped_optim.html +++ b/docs/reference/summary.poped_optim.html @@ -190,7 +190,7 @@file_name <- "ex.1.a.PK.1.comp.oral.md.intro.R" +@@ -131,10 +131,10 @@file_name <- "ex.1.a.PK.1.comp.oral.md.intro.R" ex_file <- system.file("examples",file_name,package="PopED") file.copy(ex_file,tempdir(),overwrite = T) file.edit(file.path(tempdir(),file_name))27 March, 2020
Define a model
Here we define a one-compartment pharmacokinetic model with linear absorption using an analytical solution. In this case the solution is applicable for both multiple and single dosing. Note that this function is also predefined in PopED as
-ff.PK.1.comp.oral.md.CL(see?ff.PK.1.comp.oral.md.CLfor more information).diff --git a/docs/reference/start_parallel.html b/docs/reference/start_parallel.html index e84ba4a2..6fe85b59 100644 --- a/docs/reference/start_parallel.html +++ b/docs/reference/start_parallel.html @@ -214,7 +214,7 @@library(PopED) +-library(PopED) packageVersion("PopED") #> [1] '0.4.0.9002'ff <- function(model_switch,xt,parameters,poped.db){ +ff <- function(model_switch,xt,parameters,poped.db){ with(as.list(parameters),{ N = floor(xt/TAU)+1 y=(DOSE*Favail/V)*(KA/(KA - CL/V)) * @@ -144,7 +144,7 @@}) }
Next we define the parameters of this function, in this case the between-subject variability (BSV) for each parameter is log-normally distributed (parameter
-Favailis assumed not to have BSV).DOSEandTAUare defined as covariates (in vectora) so that we can optimize their values later.diff --git a/docs/reference/shrinkage.html b/docs/reference/shrinkage.html index ab60b8a3..4c62a88e 100644 --- a/docs/reference/shrinkage.html +++ b/docs/reference/shrinkage.html @@ -217,7 +217,7 @@sfg <- function(x,a,bpop,b,bocc){ +sfg <- function(x,a,bpop,b,bocc){ parameters=c( V=bpop[1]*exp(b[1]), KA=bpop[2]*exp(b[2]), CL=bpop[3]*exp(b[3]), @@ -153,7 +153,7 @@TAU=a[2]) }
Now we define the residual unexplained variability (RUV) function, in this case the RUV has both an additive and proportional component.
-diff --git a/docs/reference/randn.html b/docs/reference/randn.html index fd162a2a..57cd999f 100644 --- a/docs/reference/randn.html +++ b/docs/reference/randn.html @@ -206,7 +206,7 @@feps <- function(model_switch,xt,parameters,epsi,poped.db){ +feps <- function(model_switch,xt,parameters,epsi,poped.db){ returnArgs <- ff(model_switch,xt,parameters,poped.db) y <- returnArgs[[1]] poped.db <- returnArgs[[2]] @@ -170,7 +170,7 @@
In this example, the parameter values are defined for the fixed effects (
bpop), the between-subject variability variances (d) and the residual variability variances (sigma). We also fix the parameterFavailusingnotfixed_bpop, since we have only oral dosing and the parameter is not identifiable. Fixing a parameter means that we assume the parameter will not be estimated (and is know without uncertainty). In addition, we fix the small additive RUV term, as this term is reflecting the higher error expected at low concentration measurements (limit of quantification measurements) and would typically be calculated from analytical assay methods (for example, the standard deviation of the parameter might be 20% of the limit of quantification).For the initial design, we define two groups (
m=2) of 20 individuals (groupsize=20), with doses of 20 mg or 40 mg every 24 hours (a). The initial design has 5 sample times per individual (xt).For the design space, which can be searched during optimization, we define a potential dose range of between 0 and 200 mg (
-minaandmaxa), and a range of potential sample times between 0 and 10 hours for the first three samples and between 240 and 248 hours for the last two samples (minxtandmaxxt). Finally, we fix the two groups of subjects to have the same sample times (bUseGrouped_xt=TRUE).diff --git a/docs/reference/poped_optimize.html b/docs/reference/poped_optimize.html index a0f8ce3b..275243a5 100644 --- a/docs/reference/poped_optimize.html +++ b/docs/reference/poped_optimize.html @@ -423,7 +423,7 @@poped.db <- create.poped.database( +diff --git a/docs/reference/poped_optim_3.html b/docs/reference/poped_optim_3.html index 334bc0c3..82c1c20f 100644 --- a/docs/reference/poped_optim_3.html +++ b/docs/reference/poped_optim_3.html @@ -409,7 +409,7 @@poped.db <- create.poped.database( # Model ff_fun=ff, fg_fun=sfg, @@ -198,17 +198,17 @@
Design simulation
First it may make sense to check your model and design to make sure you get what you expect when simulating data. Here we plot the model typical values:
-+plot_model_prediction(poped.db, model_num_points = 300)plot_model_prediction(poped.db, model_num_points = 300)
Next, we plot the expected prediction interval (by default a 95% PI) of the data taking into account the BSV and RUV using the option
-PI=TRUE. This option makes predictions based on first-order approximations to the model variance and a normality assumption of that variance. Better (and slower) computations are possible with theDV=T,IPRED=Tandgroupsize_sim = some large numberoptions.diff --git a/docs/reference/poped_optim_2.html b/docs/reference/poped_optim_2.html index 8bf82c57..eb48f74d 100644 --- a/docs/reference/poped_optim_2.html +++ b/docs/reference/poped_optim_2.html @@ -414,7 +414,7 @@plot_model_prediction(poped.db, +plot_model_prediction(poped.db, PI=TRUE, separate.groups=T, model_num_points = 300, sample.times = FALSE)
We can get these predictions numerically as well:
-dat <- model_prediction(poped.db,DV=TRUE) +diff --git a/docs/reference/poped_optim.html b/docs/reference/poped_optim.html index 3c04dedb..e2ae4a4e 100644 --- a/docs/reference/poped_optim.html +++ b/docs/reference/poped_optim.html @@ -421,7 +421,7 @@dat <- model_prediction(poped.db,DV=TRUE) head(dat,n=5);tail(dat,n=5) #> ID Time DV IPRED PRED Group Model DOSE TAU #> 1 1 1 0.09659927 0.08034839 0.05325024 1 1 20 24 @@ -227,7 +227,7 @@
Design evaluation
Next, we evaluate the initial design
-diff --git a/docs/reference/poped_gui.html b/docs/reference/poped_gui.html index 46bc0feb..2252d304 100644 --- a/docs/reference/poped_gui.html +++ b/docs/reference/poped_gui.html @@ -166,7 +166,7 @@(ds1 <- evaluate_design(poped.db)) +(ds1 <- evaluate_design(poped.db)) #> $ofv #> [1] 39.309 #> @@ -257,7 +257,7 @@
Evaluate alternative design
We can compare the initial design to a similar design with sparse sampling, i.e. having only 3 time points: xt = c(1,2,245).
-poped.db.new <- create.poped.database( +-poped.db.new <- create.poped.database( # Model ff_fun=ff, fg_fun=sfg, @@ -280,7 +280,7 @@minxt=c(0,0,240), maxxt=c(10,10,248), bUseGrouped_xt=TRUE)
(ds2 <- evaluate_design(poped.db.new)) +diff --git a/docs/reference/pargen.html b/docs/reference/pargen.html index 0c794f3b..61fcf887 100644 --- a/docs/reference/pargen.html +++ b/docs/reference/pargen.html @@ -215,7 +215,7 @@(ds2 <- evaluate_design(poped.db.new)) #> $ofv #> [1] 29.66484 #> @@ -310,7 +310,7 @@
Comparison of designs
The precision on CL is similar with the alternative design but the other parameters are less well estimated.
-+(design_eval <- round(data.frame(design_1=ds1$rse,design_2=ds2$rse)))(design_eval <- round(data.frame(design_1=ds1$rse,design_2=ds2$rse)))
@@ -356,7 +356,7 @@
Comparing the objective function value (OFV), we see that the alternative design (less samples per subject) has a smaller OFV (=worse). We can compare the two OFVs using efficiency, which tells us the proportion extra individuals that are needed in the alternative design to have the same information content as the original design (around 4 times more iindividuals than are currently in the design).
-diff --git a/docs/reference/optimize_n_dist-1.png b/docs/reference/optimize_n_dist-1.png index 680519a6..8591bfed 100644 Binary files a/docs/reference/optimize_n_dist-1.png and b/docs/reference/optimize_n_dist-1.png differ diff --git a/docs/reference/optimize_n_dist.html b/docs/reference/optimize_n_dist.html index c31ad009..b54eb2fb 100644 --- a/docs/reference/optimize_n_dist.html +++ b/docs/reference/optimize_n_dist.html @@ -235,7 +235,7 @@efficiency(ds2$ofv,ds1$ofv,poped.db) +@@ -365,9 +365,9 @@efficiency(ds2$ofv,ds1$ofv,poped.db) #> [1] 3.965919 #> attr(,"description") #> [1] "(exp(ofv_final) / exp(ofv_init))^(1/n_parameters)"
diff --git a/docs/reference/optim_LS.html b/docs/reference/optim_LS.html index 622597dc..2d8242f8 100644 --- a/docs/reference/optim_LS.html +++ b/docs/reference/optim_LS.html @@ -297,7 +297,7 @@Design optimization
-Now we can optimize the sample times of the origianl design by maximizing the OFV1.
--output <- poped_optim(poped.db, opt_xt=TRUE)@@ -406,10 +406,10 @@summary(output) +Now we can optimize the sample times of the origianl design by maximizing the OFV1.
++output <- poped_optim(poped.db, opt_xt=TRUE)summary(output) #> =============================================================================== #> FINAL RESULTS #> Optimized Sampling Schedule @@ -390,7 +390,7 @@#> d_CL 0.0625 28 26 #> sig_prop 0.04 14 15 #> -#> Total running time: 18.058 seconds +#> Total running time: 15.568 seconds plot_model_prediction(output$poped.db)
We see that there are four distinct sample times for this design. This means that for this model, with these exact parameter values, that the most information from the study to inform the parameter estimation is with these sample times.
@@ -398,7 +398,7 @@
Examine efficiency of sampling windows
Of course, this means that there are multiple samples at some of these time points. We can explore a more practical design by looking at the loss of efficiency if we spread out sample times in a uniform distribution around these optimal points (\(\pm 30\) minutes).
-+plot_efficiency_of_windows(output$poped.db,xt_windows=0.5)plot_efficiency_of_windows(output$poped.db,xt_windows=0.5)
Here we see the efficiency (\((|FIM_{optimized}|/|FIM_{initial}|)^{1/npar}\)) drops below 80% in some cases, which is mostly caused by an increase in the parameter uncertainty of the BSV parameter on absorption (om_KA). Smaller windows or different windowing on different samples might be needed. To investigate see
?plot_efficiency_of_windows.
Optimize over a discrete design space
In the previous example we optimized over a continuous design space (sample times could be optimized to be any value between a lower and an upper limit). We could also limit the search to only “allowed” values, for example, only samples taken on the hour are allowed.
-diff --git a/docs/reference/optim_ARS.html b/docs/reference/optim_ARS.html index 6c3e346a..581e6607 100644 --- a/docs/reference/optim_ARS.html +++ b/docs/reference/optim_ARS.html @@ -405,7 +405,7 @@poped.db.discrete <- create.poped.database(poped.db,discrete_xt = list(c(0:10,240:248))) +-poped.db.discrete <- create.poped.database(poped.db,discrete_xt = list(c(0:10,240:248))) output_discrete <- poped_optim(poped.db.discrete, opt_xt=TRUE)summary(output_discrete) +summary(output_discrete) #> =============================================================================== #> FINAL RESULTS #> Optimized Sampling Schedule @@ -432,7 +432,7 @@#> d_CL 0.0625 28 27 #> sig_prop 0.04 14 15 #> -#> Total running time: 9.961 seconds +#> Total running time: 9.069 seconds plot_model_prediction(output_discrete$poped.db, model_num_points = 300)
Here we see that the optimization ran somewhat quicker, but gave a less efficient design.
@@ -441,7 +441,7 @@
Optimize ‘Other’ design variables
One could also optimize over dose, to see if a different dose could help in parameter estimation .
-+output_dose_opt <- poped_optim(output$poped.db, opt_xt=TRUE, opt_a=TRUE)output_dose_opt <- poped_optim(output$poped.db, opt_xt=TRUE, opt_a=TRUE)In this case the results are predictable … higher doses give observations with somewhat lower absolute residual variability leading to both groups at the highest allowed dose levels (200 mg in this case).
@@ -449,17 +449,17 @@diff --git a/docs/reference/ones.html b/docs/reference/ones.html index cc10601b..a21e6c2f 100644 --- a/docs/reference/ones.html +++ b/docs/reference/ones.html @@ -205,7 +205,7 @@Cost function to optimize dose
Optimizing the dose of a study just to have better model parameter estimates may be somewhat implausible. Instead, let’s use a cost function to optimize dose based on some sort of target concentration … perhaps typical population trough concentrations of 0.2 and 0.35 for the two groups of patients at 240 hours.
First we define the criteria we use to optimize the doses, here a least squares minimization.
-crit_fcn <- function(poped.db,...){ +crit_fcn <- function(poped.db,...){ pred_df <- model_prediction(poped.db) sum((pred_df[pred_df["Time"]==240,"PRED"] - c(0.2,0.35))^2) } crit_fcn(output$poped.db) #> [1] 0.01469712Now we minimize the cost function
-output_cost <- poped_optim(poped.db, opt_a = TRUE, opt_xt = FALSE, +-output_cost <- poped_optim(poped.db, opt_a = TRUE, opt_xt = FALSE, ofv_fun=crit_fcn, maximize = FALSE)diff --git a/docs/articles/intro-poped_files/figure-html/simulate_cost_optmization-1.png b/docs/articles/intro-poped_files/figure-html/simulate_cost_optmization-1.png index 407d876f..4271432b 100644 Binary files a/docs/articles/intro-poped_files/figure-html/simulate_cost_optmization-1.png and b/docs/articles/intro-poped_files/figure-html/simulate_cost_optmization-1.png differ diff --git a/docs/articles/intro-poped_files/figure-html/simulate_discrete_optimization-1.png b/docs/articles/intro-poped_files/figure-html/simulate_discrete_optimization-1.png index 76233d8e..593b0f63 100644 Binary files a/docs/articles/intro-poped_files/figure-html/simulate_discrete_optimization-1.png and b/docs/articles/intro-poped_files/figure-html/simulate_discrete_optimization-1.png differ diff --git a/docs/articles/intro-poped_files/figure-html/simulate_efficiency_windows-1.png b/docs/articles/intro-poped_files/figure-html/simulate_efficiency_windows-1.png index 6a6f1ad7..69752efe 100644 Binary files a/docs/articles/intro-poped_files/figure-html/simulate_efficiency_windows-1.png and b/docs/articles/intro-poped_files/figure-html/simulate_efficiency_windows-1.png differ diff --git a/docs/articles/intro-poped_files/figure-html/simulate_optimal_design-1.png b/docs/articles/intro-poped_files/figure-html/simulate_optimal_design-1.png index 9237666d..135af057 100644 Binary files a/docs/articles/intro-poped_files/figure-html/simulate_optimal_design-1.png and b/docs/articles/intro-poped_files/figure-html/simulate_optimal_design-1.png differ diff --git a/docs/articles/intro-poped_files/figure-html/simulate_with_BSV-1.png b/docs/articles/intro-poped_files/figure-html/simulate_with_BSV-1.png index f6728bb7..7c654093 100644 Binary files a/docs/articles/intro-poped_files/figure-html/simulate_with_BSV-1.png and b/docs/articles/intro-poped_files/figure-html/simulate_with_BSV-1.png differ diff --git a/docs/articles/intro-poped_files/figure-html/simulate_without_BSV-1.png b/docs/articles/intro-poped_files/figure-html/simulate_without_BSV-1.png index ca809841..e4666d6a 100644 Binary files a/docs/articles/intro-poped_files/figure-html/simulate_without_BSV-1.png and b/docs/articles/intro-poped_files/figure-html/simulate_without_BSV-1.png differ diff --git a/docs/articles/model_def_other_pkgs.html b/docs/articles/model_def_other_pkgs.html index e92a139a..3982834a 100644 --- a/docs/articles/model_def_other_pkgs.html +++ b/docs/articles/model_def_other_pkgs.html @@ -93,13 +93,13 @@ -summary(output_cost) +@@ -514,7 +514,7 @@summary(output_cost) #> =============================================================================== #> FINAL RESULTS #> @@ -483,7 +483,7 @@#> d_CL 0.0625 28 28 #> sig_prop 0.04 14 14 #> -#> Total running time: 3.65 seconds +#> Total running time: 3.34 seconds get_rse(output_cost$FIM, output_cost$poped.db) #> V KA CL d_V d_KA d_CL sig_prop #> 8.207404 10.070674 4.399378 39.808012 60.478363 27.549531 13.800551 @@ -494,7 +494,7 @@
+diff --git a/docs/reference/mfea.html b/docs/reference/mfea.html index 1efeeb23..3d54121e 100644 --- a/docs/reference/mfea.html +++ b/docs/reference/mfea.html @@ -336,7 +336,7 @@Defining models for PopED using R based PKPD simulators
Andrew Hooker, Ron Keizer and Vijay Ivaturi
-27 March, 2020
+23 April, 2020
Source:vignettes/model_def_other_pkgs.Rmd@@ -120,7 +120,7 @@model_def_other_pkgs.Rmd
diff --git a/docs/reference/mf7.html b/docs/reference/mf7.html index 80636c2d..7e8b977f 100644 --- a/docs/reference/mf7.html +++ b/docs/reference/mf7.html @@ -227,7 +227,7 @@library(PopED) +diff --git a/docs/reference/mf3.html b/docs/reference/mf3.html index e525f7c1..5dbe829e 100644 --- a/docs/reference/mf3.html +++ b/docs/reference/mf3.html @@ -229,7 +229,7 @@library(PopED) library(PKPDsim) library(mrgsolve) library(deSolve) @@ -136,7 +136,9 @@
The model
In this example we will use a one-compartment linear absorption model
-+
+@@ -150,7 +152,7 @@+
\end{split} (\#eq:ode) \] -
All compartment amounts are assumed to be zero at time zero (\(\boldsymbol{A}[t=0]=0\)). Inputs to the system come in tablet form and are added to the amount in \(A_{0}\) according to
+All compartment amounts are assumed to be zero at time zero (\({\boldsymbol{A}}[t=0]=0\)). Inputs to the system come in tablet form and are added to the amount in \(A_{0}\) according to
\[ \text{Input}(t,D,t_D) = \begin{cases} @@ -173,7 +175,7 @@
-y = A_1/V_1 \cdot (1+\varepsilon_1) + \varepsilon_2 (\#eq:ruv) \]
elements of \(\boldsymbol{\varepsilon}_{j}\) vary accross observations and come from normal distributions with means of zero and variances of \(\sigma^2_{j}\).
+elements of \({\boldsymbol{\varepsilon}}_{j}\) vary accross observations and come from normal distributions with means of zero and variances of \(\sigma^2_{j}\).
Parameter values are shown in Table @ref(tab:values).@@ -181,7 +181,7 @@
Model parameter values. @@ -262,7 +264,7 @@
Analytic solution
First we implement an analytic solution to the model in a function that could be used in
-PopED.ff <- function(model_switch,xt,parameters,poped.db){ +ff <- function(model_switch,xt,parameters,poped.db){ with(as.list(parameters),{ y=xt N = floor(xt/TAU)+1 @@ -277,14 +279,14 @@
ODE solution using deSolve
Model using ODEs defined in deSolve
-diff --git a/docs/reference/evaluate.e.ofv.fim.html b/docs/reference/evaluate.e.ofv.fim.html index 54a348ef..47bd8d6e 100644 --- a/docs/reference/evaluate.e.ofv.fim.html +++ b/docs/reference/evaluate.e.ofv.fim.html @@ -353,7 +353,7 @@PK_1_comp_oral_ode <- function(Time, State, Pars){ +-PK_1_comp_oral_ode <- function(Time, State, Pars){ with(as.list(c(State, Pars)), { dA1 <- -KA*A1 dA2 <- KA*A1 - (CL/V)*A2 return(list(c(dA1, dA2))) }) }ff_ode_desolve <- function(model_switch, xt, parameters, poped.db){ +ff_ode_desolve <- function(model_switch, xt, parameters, poped.db){ with(as.list(parameters),{ A_ini <- c(A1=0, A2=0) @@ -318,7 +320,7 @@
ODE solution using deSolve and compiled C code
Model using ODEs defined in deSolve with compiled code
-diff --git a/docs/reference/ed_laplace_ofv.html b/docs/reference/ed_laplace_ofv.html index d81618be..20260300 100644 --- a/docs/reference/ed_laplace_ofv.html +++ b/docs/reference/ed_laplace_ofv.html @@ -313,7 +313,7 @@cat(readChar(system.file("examples/one_comp_oral_CL.c", package="PopED"), 1e5)) +-cat(readChar(system.file("examples/one_comp_oral_CL.c", package="PopED"), 1e5)) #> /* file tmdd_qss_one_target.c */ #> #include <R.h> #> static double parms[3]; @@ -346,11 +348,11 @@#> } #> #> /* END file tmdd_qss_one_target.c */
diff --git a/docs/reference/downsizing_general_design.html b/docs/reference/downsizing_general_design.html index 93d57abe..e0175deb 100644 --- a/docs/reference/downsizing_general_design.html +++ b/docs/reference/downsizing_general_design.html @@ -194,7 +194,7 @@file.copy(system.file("examples/one_comp_oral_CL.c", package="PopED"),"./one_comp_oral_CL.c") +-file.copy(system.file("examples/one_comp_oral_CL.c", package="PopED"),"./one_comp_oral_CL.c") #> [1] FALSE system("R CMD SHLIB one_comp_oral_CL.c") dyn.load(paste("one_comp_oral_CL", .Platform$dynlib.ext, sep = ""))diff --git a/docs/reference/diag_matlab.html b/docs/reference/diag_matlab.html index 6077289e..f3ede203 100644 --- a/docs/reference/diag_matlab.html +++ b/docs/reference/diag_matlab.html @@ -221,7 +221,7 @@ff_ode_desolve_c <- function(model_switch, xt, parameters, poped.db){ +diff --git a/docs/reference/design_summary.html b/docs/reference/design_summary.html index f085b207..3f9e2535 100644 --- a/docs/reference/design_summary.html +++ b/docs/reference/design_summary.html @@ -182,7 +182,7 @@ff_ode_desolve_c <- function(model_switch, xt, parameters, poped.db){ with(as.list(parameters),{ A_ini <- c(A1=0, A2=0) @@ -388,7 +390,7 @@
ODE solution using deSolve and compiled C++ code (via Rcpp)
Here we define the ODE system using inline C++ code via Rcpp
-diff --git a/docs/reference/create_design_space.html b/docs/reference/create_design_space.html index 50a165d0..ac7f27b6 100644 --- a/docs/reference/create_design_space.html +++ b/docs/reference/create_design_space.html @@ -401,7 +401,7 @@cppFunction('List one_comp_oral_rcpp(double Time, NumericVector A, NumericVector Pars) { +-cppFunction('List one_comp_oral_rcpp(double Time, NumericVector A, NumericVector Pars) { int n = A.size(); NumericVector dA(n); @@ -400,7 +402,7 @@dA[1] = KA*A[0] - (CL/V)*A[1]; return List::create(dA); }'
)ff_ode_desolve_rcpp <- function(model_switch, xt, p, poped.db){ +diff --git a/docs/reference/create.poped.database.html b/docs/reference/create.poped.database.html index d856e7e3..d23258c4 100644 --- a/docs/reference/create.poped.database.html +++ b/docs/reference/create.poped.database.html @@ -1041,7 +1041,7 @@ff_ode_desolve_rcpp <- function(model_switch, xt, p, poped.db){ A_ini <- c(A1=0, A2=0) #Set up time points for the ODE @@ -432,7 +434,7 @@
ODE solution using PKPDsim
The same model written as a set of ODEs using PKPDsim:
-diff --git a/docs/reference/convert_variables.html b/docs/reference/convert_variables.html index 387e9429..9a4f0acf 100644 --- a/docs/reference/convert_variables.html +++ b/docs/reference/convert_variables.html @@ -191,7 +191,7 @@pk1cmtoral <- PKPDsim::new_ode_model("pk_1cmt_oral") # take from library +pk1cmtoral <- PKPDsim::new_ode_model("pk_1cmt_oral") # take from library ff_ode_pkpdsim <- function(model_switch, xt, p, poped.db){ #Set up time points for the ODE times_xt <- drop(xt) @@ -465,7 +467,7 @@
ODE solution using mrgsolve
The same model written as a set of ODEs using mrgsolve:
-code <- ' +code <- ' $PARAM CL=3.75, V=72.8, KA=0.25 $CMT DEPOT CENT $ODE @@ -475,9 +477,9 @@$CAPTURE CP '
Compile and load the model with
-mcodemoda <- mcode("optim", code, atol=1e-8, rtol=1e-8,maxsteps=5000) +-moda <- mcode("optim", code, atol=1e-8, rtol=1e-8,maxsteps=5000) #> Building optim ... done.ff_ode_mrg <- function(model_switch, xt, p, poped.db){ +ff_ode_mrg <- function(model_switch, xt, p, poped.db){ times_xt <- drop(xt) dose_times <- seq(from=0,to=max(times_xt),by=p[["TAU"]]) time <- sort(unique(c(0,times_xt,dose_times))) @@ -505,34 +507,22 @@
ODE solution using RxODE
The model written for RxODE:
--ode_rx <- " -d/dt(DEPOT) = -KA*DEPOT; -d/dt(CENT) = KA*DEPOT - (CL/V)*CENT; -CP=CENT/V; -"Compile and load the model with
-RxODE-modrx <- RxODE(model = ode_rx, modName = 'modrx')@@ -540,7 +530,7 @@ff_ode_rx <- function(model_switch, xt, p, poped.db){ - + +ff_ode_rx <- function(model_switch, xt, p, poped.db){ times_xt <- drop(xt) - times <- sort(unique(c(0,times_xt))) - - ev <- eventTable() - ev$add.dosing( - dose = p[["DOSE"]], - dosing.interval = p[["TAU"]], - nbr.doses = floor(max(times)/p[["TAU"]])+1, - dosing.to = 1, - start.time = 0 - ) - ev$add.sampling(times) - Params <- c(CL = p[["CL"]], V = p[["V"]], KA = p[["KA"]]) + et(0,amt=p[["DOSE"]], ii=p[["TAU"]], until=max(times_xt)) %>% + et(times_xt) -> data - out <- modrx$solve(Params, ev,atol=1e-8,rtol=1e-8) + out <- rxSolve(modrx, p, data, atol=1e-8, rtol=1e-8,maxsteps=5000, + returnType="data.frame") - y = out[match(times_xt,out[,"time"]),"CP",drop=F] + y <- out$CP[match(times_xt,out$time)] - return(list(y=y,poped.db=poped.db)) + return(list(y=matrix(y,ncol=1),poped.db=poped.db)) }
Common model elements
Other functions are used to define BSV and RUV.
-diff --git a/docs/reference/blockheader.html b/docs/reference/blockheader.html index 69a208c6..444a6060 100644 --- a/docs/reference/blockheader.html +++ b/docs/reference/blockheader.html @@ -312,7 +312,7 @@+@@ -418,7 +418,7 @@sfg <- function(x,a,bpop,b,bocc){ parameters=c( KA=bpop[1]*exp(b[1]), @@ -563,7 +553,7 @@Create PopED databases
Next we define the model to use, the parameters of those models, the intial design design and design space for any design calculation. Here we create a number of databases that correspond to different model implementations.
The initial design is a 2 group design, with doses of 20 mg or 40 mg every 24 hours. Each group has the same sampling schedule, with 3 samples in the first day of the study and 2 on the 10th day of the study.
-poped_db_analytic <- create.poped.database(ff_fun =ff, +poped_db_analytic <- create.poped.database(ff_fun =ff, fg_fun =sfg, fError_fun=feps, bpop=c(KA=0.25,CL=3.75,V=72.8), @@ -592,10 +582,10 @@Model predictions
So are there difference in the model predictions between the different implementations?
Here is a visual representation of the model predictions for this study design, based on the analytic solution:
-+plot_model_prediction(poped_db_analytic,model_num_points = 500,PI=T,separate.groups = T)plot_model_prediction(poped_db_analytic,model_num_points = 500,PI=T,separate.groups = T)
We can compare the different predictions in this plot accross model implementations. Here we see that the accuracy of the different methods are within machine precision (or very small).
-diff --git a/docs/index.html b/docs/index.html index ff62a27e..d2d0a236 100644 --- a/docs/index.html +++ b/docs/index.html @@ -101,9 +101,9 @@pred_std <- model_prediction(poped_db_analytic,model_num_points = 500,include_sample_times = TRUE,PI = TRUE) +-pred_std <- model_prediction(poped_db_analytic,model_num_points = 500,include_sample_times = TRUE,PI = TRUE) pred_ode_desolve <- model_prediction(poped_db_ode_desolve, model_num_points = 500, @@ -623,7 +613,7 @@include_sample_times = TRUE, PI = TRUE) all.equal(pred_std,pred_ode_pkpdsim) -#> [1] "Component \"PI_l\": Mean relative difference: 3.593837e-08" +#> [1] "Component \"PI_l\": Mean relative difference: 3.592993e-08" pred_ode_mrg <- model_prediction(poped_db_ode_mrg, model_num_points = 500, @@ -643,7 +633,7 @@
Evaluate the design
Here we compare the computation of the Fisher Information Matrix (FIM). By comparing the \(ln(det(FIM))\) (the lnD-objective function value, or ofv).
-(eval_std <- evaluate_design(poped_db_analytic)) +(eval_std <- evaluate_design(poped_db_analytic)) #> $ofv #> [1] 48.98804 #> @@ -673,17 +663,17 @@#> sig_add #> 24.339781
All the computations give very similar results:
-eval_ode_desolve <- evaluate_design(poped_db_ode_desolve) ++#> [1] "Mean relative difference: 5.252514e-08"eval_ode_desolve <- evaluate_design(poped_db_ode_desolve) all.equal(eval_std$ofv,eval_ode_desolve$ofv) -#> [1] "Mean relative difference: 2.493735e-08" +#> [1] "Mean relative difference: 5.104789e-08" eval_ode_desolve_c <- evaluate_design(poped_db_ode_desolve_c) all.equal(eval_std$ofv,eval_ode_desolve_c$ofv) -#> [1] "Mean relative difference: 2.493735e-08" +#> [1] "Mean relative difference: 5.172292e-08" eval_ode_desolve_rccp <- evaluate_design(poped_db_ode_desolve_rcpp) all.equal(eval_std$ofv,eval_ode_desolve_rccp$ofv) -#> [1] "Mean relative difference: 2.493735e-08" +#> [1] "Mean relative difference: 5.172292e-08" eval_ode_pkpdsim <- evaluate_design(poped_db_ode_pkpdsim) all.equal(eval_std$ofv,eval_ode_pkpdsim$ofv) @@ -691,13 +681,13 @@eval_ode_mrg <- evaluate_design(poped_db_ode_mrg) all.equal(eval_std$ofv,eval_ode_mrg$ofv) -#> [1] "Mean relative difference: 2.361616e-08"
diff --git a/docs/articles/model_def_other_pkgs_files/figure-html/unnamed-chunk-14-1.png b/docs/articles/model_def_other_pkgs_files/figure-html/unnamed-chunk-14-1.png index 18b08ec8..597d2144 100644 Binary files a/docs/articles/model_def_other_pkgs_files/figure-html/unnamed-chunk-14-1.png and b/docs/articles/model_def_other_pkgs_files/figure-html/unnamed-chunk-14-1.png differ diff --git a/docs/authors.html b/docs/authors.html index 6f390dab..b91bcd20 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -141,10 +141,7 @@Speed of FIM computation
We can compare the speed of the computations. Analytic solutions are fast, as expected, in this case more than 20 times faster than any of the ODE methods. mrgsolve is the fastest of the ODE solvers in this example. Note that, in previous work (http://pkpdsim.ronkeizer.com/speed.html), much of the speed difference between mrgsolve, RxODE and PKPDsim has been found to be due to the overhead from pre- and post-processing of the simulation from ODE systems. Other ways of handling the pre- and post-processing may speed up these computations.
-@@ -790,7 +893,7 @@library(microbenchmark) +-library(microbenchmark) compare <- microbenchmark( evaluate_design(poped_db_analytic), evaluate_design(poped_db_ode_desolve), @@ -707,70 +697,183 @@evaluate_design(poped_db_ode_mrg), evaluate_design(poped_db_ode_rx), times = 100L)
Version information
-sessionInfo() -#> R version 3.6.3 (2020-02-29) -#> Platform: x86_64-apple-darwin15.6.0 (64-bit) -#> Running under: macOS Catalina 10.15.4 -#> -#> Matrix products: default -#> BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib -#> LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib -#> -#> locale: -#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 -#> -#> attached base packages: -#> [1] stats graphics grDevices utils datasets methods base ++#> [1] /tmp/RtmpYLQNhi/temp_libpath4b8c409fc0cf +#> [2] /home/matt/R/x86_64-pc-linux-gnu-library/3.6 +#> [3] /usr/lib64/R/librarydevtools::session_info() +#> ─ Session info ─────────────────────────────────────────────────────────────── +#> setting value +#> version R version 3.6.0 (2019-04-26) +#> os Gentoo/Linux +#> system x86_64, linux-gnu +#> ui X11 +#> language (EN) +#> collate en_US.utf8 +#> ctype en_US.utf8 +#> tz US/Central +#> date 2020-04-23 #> -#> other attached packages: -#> [1] RxODE_0.9.2-0 Rcpp_1.0.4.4 deSolve_1.28 mrgsolve_0.10.1 -#> [5] PKPDsim_1.0.7 kableExtra_1.1.0 knitr_1.28 PopED_0.4.0.9002 -#> [9] testthat_2.3.2 +#> ─ Packages ─────────────────────────────────────────────────────────────────── +#> package * version date lib +#> assertthat 0.2.1 2019-03-21 [2] +#> backports 1.1.6 2020-04-05 [2] +#> bookdown 0.18 2020-03-05 [2] +#> callr 3.4.3 2020-03-28 [2] +#> cli 2.0.2 2020-02-28 [2] +#> colorspace 1.4-1 2019-03-18 [2] +#> crayon 1.3.4 2017-09-16 [2] +#> curl 4.3 2019-12-02 [2] +#> data.table * 1.12.8 2019-12-09 [2] +#> desc 1.2.0 2018-05-01 [2] +#> deSolve * 1.28 2020-03-08 [2] +#> devtools 2.3.0 2020-04-10 [2] +#> digest 0.6.25 2020-02-23 [2] +#> dplyr 0.8.5 2020-03-07 [2] +#> ellipsis 0.3.0 2019-09-20 [2] +#> evaluate 0.14 2019-05-28 [2] +#> fansi 0.4.1 2020-01-08 [2] +#> fs 1.4.1 2020-04-04 [2] +#> ggplot2 3.3.0 2020-03-05 [2] +#> glue 1.4.0 2020-04-03 [2] +#> gtable 0.3.0 2019-03-25 [2] +#> hms 0.5.3 2020-01-08 [2] +#> htmltools 0.4.0 2019-10-04 [2] +#> httr 1.4.1 2019-08-05 [2] +#> kableExtra * 1.1.0 2019-03-16 [2] +#> knitr * 1.28 2020-02-06 [2] +#> lifecycle 0.2.0 2020-03-06 [2] +#> lotri 0.2.1 2020-02-12 [2] +#> magrittr 1.5 2014-11-22 [2] +#> MASS 7.3-51.4 2019-03-31 [3] +#> memoise 1.1.0 2017-04-21 [2] +#> microbenchmark * 1.4-7 2019-09-24 [2] +#> mrgsolve * 0.10.1 2020-04-24 [2] +#> munsell 0.5.0 2018-06-12 [2] +#> mvnfast 0.2.5 2018-01-31 [2] +#> pillar 1.4.3 2019-12-20 [2] +#> pkgbuild 1.0.6 2019-10-09 [2] +#> pkgconfig 2.0.3 2019-09-22 [2] +#> pkgdown 1.5.1 2020-04-09 [2] +#> pkgload 1.0.2 2018-10-29 [2] +#> PKPDsim * 1.0.7 2020-04-24 [2] +#> PopED * 0.4.0.9002 2020-04-24 [1] +#> PreciseSums 0.3 2018-04-12 [2] +#> prettyunits 1.1.1 2020-01-24 [2] +#> processx 3.4.2 2020-02-09 [2] +#> ps 1.3.2 2020-02-13 [2] +#> purrr 0.3.4 2020-04-17 [2] +#> R6 2.4.1 2019-11-12 [2] +#> Rcpp * 1.0.4.6 2020-04-09 [2] +#> RcppArmadillo 0.9.860.2.0 2020-04-14 [2] +#> readr 1.3.1 2018-12-21 [2] +#> remotes 2.1.1 2020-02-15 [2] +#> rlang 0.4.5 2020-03-01 [2] +#> rmarkdown 2.1 2020-01-20 [2] +#> rprojroot 1.3-2 2018-01-03 [2] +#> rstudioapi 0.11 2020-02-07 [2] +#> rvest 0.3.5 2019-11-08 [2] +#> RxODE * 0.9.2-0 2020-03-13 [2] +#> scales 1.1.0 2019-11-18 [2] +#> sessioninfo 1.1.1 2018-11-05 [2] +#> stringi 1.4.6 2020-02-17 [2] +#> stringr 1.4.0 2019-02-10 [2] +#> testthat 2.3.2 2020-03-02 [2] +#> tibble 3.0.1 2020-04-20 [2] +#> tidyselect 1.0.0 2020-01-27 [2] +#> units 0.6-6 2020-03-16 [2] +#> usethis 1.6.0 2020-04-09 [2] +#> vctrs 0.2.4 2020-03-10 [2] +#> viridisLite 0.3.0 2018-02-01 [2] +#> webshot 0.5.2 2019-11-22 [2] +#> withr 2.2.0 2020-04-20 [2] +#> xfun 0.13 2020-04-13 [2] +#> xml2 1.3.1 2020-04-09 [2] +#> yaml 2.2.1 2020-02-01 [2] +#> source +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> Github (metrumresearchgroup/mrgsolve@dafc88d) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> Github (InsightRX/PKPDsim@bd9b9e4) +#> local +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) +#> CRAN (R 3.6.0) #> -#> loaded via a namespace (and not attached): -#> [1] httr_1.4.1 pkgload_1.0.2 -#> [3] tidyr_1.0.2 viridisLite_0.3.0 -#> [5] gtools_3.8.1 assertthat_0.2.1 -#> [7] yaml_2.2.1 remotes_2.1.1 -#> [9] sessioninfo_1.1.1 pillar_1.4.3 -#> [11] backports_1.1.5 glue_1.3.2 -#> [13] digest_0.6.25 rvest_0.3.5 -#> [15] colorspace_1.4-1 htmltools_0.4.0 -#> [17] pkgconfig_2.0.3 devtools_2.2.2 -#> [19] bookdown_0.18 purrr_0.3.3 -#> [21] mvtnorm_1.1-0 scales_1.1.0 -#> [23] webshot_0.5.2 processx_3.4.2 -#> [25] tibble_2.1.3 ggplot2_3.3.0 -#> [27] usethis_1.5.1 ellipsis_0.3.0 -#> [29] withr_2.1.2 cli_2.0.2 -#> [31] magrittr_1.5 crayon_1.3.4 -#> [33] mvnfast_0.2.5 memoise_1.1.0 -#> [35] evaluate_0.14 ps_1.3.2 -#> [37] fs_1.3.2 fansi_0.4.1 -#> [39] MASS_7.3-51.5 xml2_1.2.5 -#> [41] RcppArmadillo_0.9.850.1.0 pkgbuild_1.0.6 -#> [43] tools_3.6.3 data.table_1.12.8 -#> [45] prettyunits_1.1.1 hms_0.5.3 -#> [47] lifecycle_0.2.0 stringr_1.4.0 -#> [49] munsell_0.5.0 callr_3.4.2 -#> [51] compiler_3.6.3 pkgdown_1.5.0 -#> [53] rlang_0.4.5 units_0.6-6 -#> [55] grid_3.6.3 rstudioapi_0.11 -#> [57] lotri_0.2.1 PreciseSums_0.3 -#> [59] rmarkdown_2.1 boot_1.3-24 -#> [61] gtable_0.3.0 codetools_0.2-16 -#> [63] curl_4.3 R6_2.4.1 -#> [65] dplyr_0.8.5 rprojroot_1.3-2 -#> [67] readr_1.3.1 desc_1.2.0 -#> [69] stringi_1.4.6 vctrs_0.2.4 -#> [71] tidyselect_1.0.0 xfun_0.12
Citation
Source:inst/CITATIONNyberg J, Ueckert S, Stroemberg EA, Hennig S, Karlsson MO, Hooker AC (2012). -“PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool.” -Computer Methods and Programs in Biomedicine, 108. -
+@Article{, title = {PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool}, author = {Joakim Nyberg and Sebastian Ueckert and Eric A Stroemberg and Stefanie Hennig and Mats O Karlsson and Andrew C Hooker}, @@ -152,10 +149,7 @@-Citation
year = {2012}, volume = {108}, }Foracchia M, Hooker AC, Vicini P, Ruggeri A (2004). -“POPED, a software for optimal experiment design in population kinetics.” -Computer Methods and Programs in Biomedicine, 74. -
+@Article{, title = {POPED, a software for optimal experiment design in population kinetics}, author = {Marco Foracchia and Andrew C Hooker and Paolo Vicini and A Ruggeri}, @@ -211,7 +205,7 @@Authors
-+-diff --git a/docs/news/index.html b/docs/news/index.html index d1c5bc4f..32de8249 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -141,9 +141,9 @@PopED computes optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix (FIM).
@@ -111,9 +111,9 @@Installation
You need to have R installed. Download the latest version of R from www.r-project.org. You can install the released version of PopED from CRAN with:
-+install.packages("PopED")install.packages("PopED")And the development version from GitHub with:
-# install.packages("devtools") +# install.packages("devtools") devtools::install_github("andrewhooker/PopED")@@ -136,7 +136,7 @@@@ -203,7 +203,7 @@
Dev status
Changelog
Source:NEWS.md+--PopED 0.4.0.9000 Unreleased +PopED 0.4.0.9000 Unreleased
+diff --git a/docs/reference/PopED_output_summary_RS.txt b/docs/reference/PopED_output_summary_RS.txt index d9c99bd9..d10e1ebb 100644 --- a/docs/reference/PopED_output_summary_RS.txt +++ b/docs/reference/PopED_output_summary_RS.txt @@ -1,6 +1,6 @@ PopED Optimization Results for the Adaptive Random Search Algorithm - 2020-03-27 17:30:52 + 2020-04-23 23:57:14 ============================================================================== Model description : PopED model @@ -187,4 +187,4 @@ Expected relative standard error SIGMA[1,1] 0.01 33 33 SIGMA[2,2] 0.25 26 26 -Total running time: 0.053 seconds +Total running time: 0.04 seconds diff --git a/docs/reference/RS_opt.html b/docs/reference/RS_opt.html index 52bdd3d2..aea0521f 100644 --- a/docs/reference/RS_opt.html +++ b/docs/reference/RS_opt.html @@ -421,7 +421,7 @@--PopED 0.4.0 2018-09-10 +PopED 0.4.0 2018-09-10
+--PopED 0.3.2 2016-12-12 +PopED 0.3.2 2016-12-12
+--PopED 0.3.1 2016-10-19 +PopED 0.3.1 2016-10-19
+--PopED 0.3.0 2015-12-29 +PopED 0.3.0 2015-12-29
+diff --git a/docs/reference/LinMatrixL.html b/docs/reference/LinMatrixL.html index 3ddc4ad9..0f883634 100644 --- a/docs/reference/LinMatrixL.html +++ b/docs/reference/LinMatrixL.html @@ -221,7 +221,7 @@--PopED 0.2.0 2015-03-20 +PopED 0.2.0 2015-03-20
+--PopED 0.1.2 2014-11-19 +PopED 0.1.2 2014-11-19
+--PopED 0.1.1 2014-05-27 +PopED 0.1.1 2014-05-27
+diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 9c545281..eb174ff1 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,9 +1,9 @@ -pandoc: 2.9.2 -pkgdown: 1.5.0 +pandoc: 1.19.2.1 +pkgdown: 1.5.1 pkgdown_sha: ~ articles: examples: examples.html intro-poped: intro-poped.html model_def_other_pkgs: model_def_other_pkgs.html -last_built: 2020-03-27T16:30Z +last_built: 2020-04-24T04:57Z diff --git a/docs/reference/Doptim.html b/docs/reference/Doptim.html index 528aa006..1e954c8d 100644 --- a/docs/reference/Doptim.html +++ b/docs/reference/Doptim.html @@ -415,7 +415,7 @@-PopED 0.1.0 2014-04-28 +PopED 0.1.0 2014-04-28
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -525,7 +525,7 @@diff --git a/docs/reference/Dtrace.html b/docs/reference/Dtrace.html index 5152cce5..7de3c5fe 100644 --- a/docs/reference/Dtrace.html +++ b/docs/reference/Dtrace.html @@ -293,7 +293,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -453,7 +453,7 @@diff --git a/docs/reference/LEDoptim.html b/docs/reference/LEDoptim.html index cc0b1d59..57a073fc 100644 --- a/docs/reference/LEDoptim.html +++ b/docs/reference/LEDoptim.html @@ -362,7 +362,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -463,7 +463,7 @@diff --git a/docs/reference/LinMatrixH.html b/docs/reference/LinMatrixH.html index 71cfde5a..90b40fcb 100644 --- a/docs/reference/LinMatrixH.html +++ b/docs/reference/LinMatrixH.html @@ -224,7 +224,7 @@Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -292,7 +292,7 @@diff --git a/docs/reference/LinMatrixLH.html b/docs/reference/LinMatrixLH.html index b91d337e..992bb9f7 100644 --- a/docs/reference/LinMatrixLH.html +++ b/docs/reference/LinMatrixLH.html @@ -251,7 +251,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -326,7 +326,7 @@diff --git a/docs/reference/LinMatrixL_occ.html b/docs/reference/LinMatrixL_occ.html index 9a4aaff0..f5ff1a6e 100644 --- a/docs/reference/LinMatrixL_occ.html +++ b/docs/reference/LinMatrixL_occ.html @@ -249,7 +249,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -315,7 +315,7 @@diff --git a/docs/reference/PopED-1.png b/docs/reference/PopED-1.png index 3db3babd..b2a0bda7 100644 Binary files a/docs/reference/PopED-1.png and b/docs/reference/PopED-1.png differ diff --git a/docs/reference/PopED.html b/docs/reference/PopED.html index cf1b70de..073430a4 100644 --- a/docs/reference/PopED.html +++ b/docs/reference/PopED.html @@ -357,7 +357,7 @@Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -516,7 +516,7 @@Examp #> SIGMA[1,1] 0.01 33 33 #> SIGMA[2,2] 0.25 26 26 #> -#> Total running time: 0.053 seconds
+#> Total running time: 0.04 secondsif (FALSE) { RS_opt(poped.db) @@ -552,7 +552,7 @@diff --git a/docs/reference/a_line_search.html b/docs/reference/a_line_search.html index d34e4540..693bd95e 100644 --- a/docs/reference/a_line_search.html +++ b/docs/reference/a_line_search.html @@ -275,7 +275,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -357,7 +357,7 @@Examp #> Group 1: 100 #> #> -#> Line search run time: 0.296 seconds +#> Line search run time: 0.275 seconds #> *************************** #>
if (FALSE) { @@ -384,7 +384,7 @@diff --git a/docs/reference/bfgsb_min.html b/docs/reference/bfgsb_min.html index eea0eaf9..475ef2b1 100644 --- a/docs/reference/bfgsb_min.html +++ b/docs/reference/bfgsb_min.html @@ -232,7 +232,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -320,7 +320,7 @@diff --git a/docs/reference/blockexp.html b/docs/reference/blockexp.html index a4a1d3d8..fbfeed91 100644 --- a/docs/reference/blockexp.html +++ b/docs/reference/blockexp.html @@ -230,7 +230,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -336,7 +336,7 @@diff --git a/docs/reference/blockfinal.html b/docs/reference/blockfinal.html index 43606d4c..ec59de91 100644 --- a/docs/reference/blockfinal.html +++ b/docs/reference/blockfinal.html @@ -324,7 +324,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -400,7 +400,7 @@Examp #> sig_prop 0.01 32 32 #> sig_add 0.25 26 26 #> -#> Total running time: 0.916 seconds
+#> Total running time: 0.777 secondsContents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -465,7 +465,7 @@diff --git a/docs/reference/blockopt.html b/docs/reference/blockopt.html index d13c124e..a3d03bd1 100644 --- a/docs/reference/blockopt.html +++ b/docs/reference/blockopt.html @@ -201,7 +201,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -274,7 +274,7 @@diff --git a/docs/reference/calc_autofocus.html b/docs/reference/calc_autofocus.html index f759a15d..46edf541 100644 --- a/docs/reference/calc_autofocus.html +++ b/docs/reference/calc_autofocus.html @@ -318,7 +318,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -379,7 +379,7 @@diff --git a/docs/reference/calc_ofv_and_fim.html b/docs/reference/calc_ofv_and_fim.html index b725aff9..b2adde3a 100644 --- a/docs/reference/calc_ofv_and_fim.html +++ b/docs/reference/calc_ofv_and_fim.html @@ -352,7 +352,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -444,7 +444,7 @@diff --git a/docs/reference/calc_ofv_and_grad.html b/docs/reference/calc_ofv_and_grad.html index 0e5656c2..2e9e1736 100644 --- a/docs/reference/calc_ofv_and_grad.html +++ b/docs/reference/calc_ofv_and_grad.html @@ -302,7 +302,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -365,15 +365,15 @@Examp #> #> $g #> [,1] -#> [1,] 0.079631592 -#> [2,] -0.025697632 -#> [3,] -0.312973640 -#> [4,] 0.009484126 -#> [5,] 0.019485344 -#> [6,] -0.001675732 -#> [7,] -0.009543726 -#> [8,] -0.001929126 -#> [9,] -0.035843401 +#> [1,] 0.079631184 +#> [2,] -0.025697327 +#> [3,] -0.312973786 +#> [4,] 0.009484130 +#> [5,] 0.019485306 +#> [6,] -0.001675713 +#> [7,] -0.009544063 +#> [8,] -0.001929802 +#> [9,] -0.035844547 #>
if (FALSE) { @@ -400,7 +400,7 @@diff --git a/docs/reference/cell.html b/docs/reference/cell.html index 7391136d..963f5bdd 100644 --- a/docs/reference/cell.html +++ b/docs/reference/cell.html @@ -213,7 +213,7 @@Contents
Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc1bcdeb0> +#> <bytecode: 0x55605775bd98> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -1085,7 +1085,7 @@diff --git a/docs/reference/create_design.html b/docs/reference/create_design.html index c3518132..4fc24247 100644 --- a/docs/reference/create_design.html +++ b/docs/reference/create_design.html @@ -271,7 +271,7 @@Contents
Contents
Contents
Contents
Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -357,7 +357,7 @@Examp ## ED evaluate using LaPlace approximation tic() -output <- evaluate.e.ofv.fim(poped.db.n,use_laplace=TRUE)
#> Error in poped.db$settings$iEDCalculationType <- use_laplace: object 'poped.db.n' not foundtoc()#> Elapsed time: 0.001 seconds.output$E_ofv#> Error in eval(expr, envir, enclos): object 'output' not found#> Error in poped.db$settings$iEDCalculationType <- use_laplace: object 'poped.db.n' not foundtoc()#> Elapsed time: 0.002 seconds.output$E_ofv#> Error in eval(expr, envir, enclos): object 'output' not foundif (FALSE) { @@ -458,7 +458,7 @@diff --git a/docs/reference/ed_mftot.html b/docs/reference/ed_mftot.html index fda2987c..ceac59db 100644 --- a/docs/reference/ed_mftot.html +++ b/docs/reference/ed_mftot.html @@ -290,7 +290,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -370,7 +370,7 @@diff --git a/docs/reference/efficiency.html b/docs/reference/efficiency.html index 601e7d71..67d8ddea 100644 --- a/docs/reference/efficiency.html +++ b/docs/reference/efficiency.html @@ -234,7 +234,7 @@Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -409,7 +409,7 @@Examp ## ED evaluate using Laplace approximation tic() output <- evaluate.e.ofv.fim(poped.db,use_laplace=TRUE) -toc()
#> Elapsed time: 0.984 seconds.output$E_ofv#> [1] 1.302806e+24+toc()#> Elapsed time: 0.795 seconds.output$E_ofv#> [1] 1.302806e+24if (FALSE) { ## ED expected value with more precision. @@ -443,7 +443,7 @@diff --git a/docs/reference/evaluate.fim.html b/docs/reference/evaluate.fim.html index 2625ca92..d9b743c0 100644 --- a/docs/reference/evaluate.fim.html +++ b/docs/reference/evaluate.fim.html @@ -311,7 +311,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc1bcdeb0> +#> <bytecode: 0x55605775bd98> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -370,7 +370,7 @@diff --git a/docs/reference/evaluate_design.html b/docs/reference/evaluate_design.html index 3c6c1c7e..622558c2 100644 --- a/docs/reference/evaluate_design.html +++ b/docs/reference/evaluate_design.html @@ -198,7 +198,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -239,7 +239,7 @@diff --git a/docs/reference/evaluate_power-1.png b/docs/reference/evaluate_power-1.png index ff9df3c4..4e11c33c 100644 Binary files a/docs/reference/evaluate_power-1.png and b/docs/reference/evaluate_power-1.png differ diff --git a/docs/reference/evaluate_power-2.png b/docs/reference/evaluate_power-2.png index 7a33459f..0e2fb58c 100644 Binary files a/docs/reference/evaluate_power-2.png and b/docs/reference/evaluate_power-2.png differ diff --git a/docs/reference/evaluate_power.html b/docs/reference/evaluate_power.html index 72578fc0..6cf69525 100644 --- a/docs/reference/evaluate_power.html +++ b/docs/reference/evaluate_power.html @@ -291,13 +291,13 @@Examp #> d_CL 0.00000 0.000000 0.000000 3.107768e+03 10.728786 0.02613561 #> d_V 0.00000 0.000000 0.000000 1.072879e+01 27307.089308 3.26560786 #> d_KA 0.00000 0.000000 0.000000 2.613561e-02 3.265608 41.81083599 -#> sig_prop 0.00000 0.000000 0.000000 5.215403e+02 11214.210707 71.08763896 +#> sig_prop 0.00000 0.000000 0.000000 5.215403e+02 11214.210707 71.08763902 #> sig_prop #> CL 0.00000 #> V 0.00000 @@ -270,7 +270,7 @@
Contents
Examp #> P1 P2 lam1 beta lam2 d_P1 #> P1 611.712173 -12.723468 -24.210864 -12.105432 -5.109809 0.000000e+00 #> P2 -12.723468 580.838635 -16.202945 -8.101473 -39.342222 0.000000e+00 -#> lam1 -24.210864 -16.202945 648.887537 324.443769 -6.742703 0.000000e+00 +#> lam1 -24.210864 -16.202945 648.887538 324.443769 -6.742703 0.000000e+00 #> beta -12.105432 -8.101473 324.443769 324.443769 -3.371351 0.000000e+00 #> lam2 -5.109809 -39.342222 -6.742703 -3.371351 643.716238 0.000000e+00 #> d_P1 0.000000 0.000000 0.000000 0.000000 0.000000 9.354418e+02 #> d_P2 0.000000 0.000000 0.000000 0.000000 0.000000 4.051226e-01 #> d_lam1 0.000000 0.000000 0.000000 0.000000 0.000000 1.465796e+00 -#> d_lam2 0.000000 0.000000 0.000000 0.000000 0.000000 6.533665e-02 +#> d_lam2 0.000000 0.000000 0.000000 0.000000 0.000000 6.533664e-02 #> SIGMA[1,1] 0.000000 0.000000 0.000000 0.000000 0.000000 5.831126e+03 #> d_P2 d_lam1 d_lam2 SIGMA[1,1] #> P1 0.0000000 0.0000000 0.000000e+00 0.000 @@ -305,11 +305,11 @@
Examp #> lam1 0.0000000 0.0000000 0.000000e+00 0.000 #> beta 0.0000000 0.0000000 0.000000e+00 0.000 #> lam2 0.0000000 0.0000000 0.000000e+00 0.000 -#> d_P1 0.4051226 1.4657963 6.533665e-02 5831.126 +#> d_P1 0.4051226 1.4657963 6.533664e-02 5831.126 #> d_P2 843.4338997 0.6563403 3.869526e+00 8499.377 #> d_lam1 0.6563403 1052.6375905 1.136601e-01 1889.172 #> d_lam2 3.8695261 0.1136601 1.035926e+03 2335.057 -#> SIGMA[1,1] 8499.3770635 1889.1721996 2.335057e+03 11411954.974 +#> SIGMA[1,1] 8499.3770552 1889.1722062 2.335057e+03 11411954.974 #> #> $rse #> P1 P2 lam1 beta lam2 d_P1 @@ -324,71 +324,71 @@
Examp #> [1] 74.54742 #> #> $fim -#> P1 P2 lam1 beta lam2 d_P1 -#> P1 606.082950 -12.698834 -26.35693 -14.251496 -5.275302 0.000000e+00 -#> P2 -12.698834 590.575894 -15.71391 -7.612439 -35.363104 0.000000e+00 -#> lam1 -26.356928 -15.713911 647.69637 323.252599 -6.706090 0.000000e+00 -#> beta -14.251496 -7.612439 323.25260 323.252599 -3.334739 0.000000e+00 -#> lam2 -5.275302 -35.363104 -6.70609 -3.334739 645.343252 0.000000e+00 -#> d_P1 0.000000 0.000000 0.00000 0.000000 0.000000 9.184102e+02 -#> d_P2 0.000000 0.000000 0.00000 0.000000 0.000000 4.034200e-01 -#> d_lam1 0.000000 0.000000 0.00000 0.000000 0.000000 1.748309e+00 -#> d_lam2 0.000000 0.000000 0.00000 0.000000 0.000000 6.967871e-02 -#> SIGMA[1,1] 0.000000 0.000000 0.00000 0.000000 0.000000 6.355107e+03 +#> P1 P2 lam1 beta lam2 d_P1 +#> P1 606.085238 -12.699077 -26.355895 -14.250463 -5.275413 0.000000e+00 +#> P2 -12.699077 590.575904 -15.713924 -7.612451 -35.363100 0.000000e+00 +#> lam1 -26.355895 -15.713924 647.696264 323.252495 -6.706096 0.000000e+00 +#> beta -14.250463 -7.612451 323.252495 323.252495 -3.334744 0.000000e+00 +#> lam2 -5.275413 -35.363100 -6.706096 -3.334744 645.343254 0.000000e+00 +#> d_P1 0.000000 0.000000 0.000000 0.000000 0.000000 9.184094e+02 +#> d_P2 0.000000 0.000000 0.000000 0.000000 0.000000 4.034200e-01 +#> d_lam1 0.000000 0.000000 0.000000 0.000000 0.000000 1.748333e+00 +#> d_lam2 0.000000 0.000000 0.000000 0.000000 0.000000 6.967869e-02 +#> SIGMA[1,1] 0.000000 0.000000 0.000000 0.000000 0.000000 6.355130e+03 #> d_P2 d_lam1 d_lam2 SIGMA[1,1] #> P1 0.0000000 0.0000000 0.000000e+00 0.000 #> P2 0.0000000 0.0000000 0.000000e+00 0.000 #> lam1 0.0000000 0.0000000 0.000000e+00 0.000 #> beta 0.0000000 0.0000000 0.000000e+00 0.000 #> lam2 0.0000000 0.0000000 0.000000e+00 0.000 -#> d_P1 0.4034200 1.7483086 6.967871e-02 6355.107 -#> d_P2 872.1867153 0.6179178 3.165957e+00 7662.897 -#> d_lam1 0.6179178 1048.7800081 1.124325e-01 2004.872 -#> d_lam2 3.1659572 0.1124325 1.041176e+03 2204.565 -#> SIGMA[1,1] 7662.8965834 2004.8718636 2.204565e+03 11396937.363 +#> d_P1 0.4034200 1.7483334 6.967869e-02 6355.130 +#> d_P2 872.1867430 0.6179188 3.165957e+00 7662.896 +#> d_lam1 0.6179188 1048.7796745 1.124327e-01 2004.881 +#> d_lam2 3.1659566 0.1124327 1.041176e+03 2204.564 +#> SIGMA[1,1] 7662.8956868 2004.8814970 2.204564e+03 11396938.075 #> #> $rse #> P1 P2 lam1 beta lam2 d_P1 d_P2 -#> 0.3388993 0.5155216 7.9357907 29.9948348 1.3144681 11.0205784 11.3204964 +#> 0.3388987 0.5155216 7.9357907 29.9948370 1.3144681 11.0205831 11.3204962 #> d_lam1 d_lam2 SIGMA[1,1] -#> 10.2946079 10.3325269 7.0480558 +#> 10.2946096 10.3325268 7.0480557 #>
evaluate_power(poped.db_2,bpop_idx = 4)#> $ofv #> [1] 74.54742 #> #> $fim -#> [,1] [,2] [,3] [,4] [,5] [,6] -#> [1,] 606.082950 -12.698834 -26.35693 -14.251496 -5.275302 0.000000e+00 -#> [2,] -12.698834 590.575894 -15.71391 -7.612439 -35.363104 0.000000e+00 -#> [3,] -26.356928 -15.713911 647.69637 323.252599 -6.706090 0.000000e+00 -#> [4,] -14.251496 -7.612439 323.25260 323.252599 -3.334739 0.000000e+00 -#> [5,] -5.275302 -35.363104 -6.70609 -3.334739 645.343252 0.000000e+00 -#> [6,] 0.000000 0.000000 0.00000 0.000000 0.000000 9.184102e+02 -#> [7,] 0.000000 0.000000 0.00000 0.000000 0.000000 4.034200e-01 -#> [8,] 0.000000 0.000000 0.00000 0.000000 0.000000 1.748309e+00 -#> [9,] 0.000000 0.000000 0.00000 0.000000 0.000000 6.967871e-02 -#> [10,] 0.000000 0.000000 0.00000 0.000000 0.000000 6.355107e+03 +#> [,1] [,2] [,3] [,4] [,5] [,6] +#> [1,] 606.085238 -12.699077 -26.355895 -14.250463 -5.275413 0.000000e+00 +#> [2,] -12.699077 590.575904 -15.713924 -7.612451 -35.363100 0.000000e+00 +#> [3,] -26.355895 -15.713924 647.696264 323.252495 -6.706096 0.000000e+00 +#> [4,] -14.250463 -7.612451 323.252495 323.252495 -3.334744 0.000000e+00 +#> [5,] -5.275413 -35.363100 -6.706096 -3.334744 645.343254 0.000000e+00 +#> [6,] 0.000000 0.000000 0.000000 0.000000 0.000000 9.184094e+02 +#> [7,] 0.000000 0.000000 0.000000 0.000000 0.000000 4.034200e-01 +#> [8,] 0.000000 0.000000 0.000000 0.000000 0.000000 1.748333e+00 +#> [9,] 0.000000 0.000000 0.000000 0.000000 0.000000 6.967869e-02 +#> [10,] 0.000000 0.000000 0.000000 0.000000 0.000000 6.355130e+03 #> [,7] [,8] [,9] [,10] #> [1,] 0.0000000 0.0000000 0.000000e+00 0.000 #> [2,] 0.0000000 0.0000000 0.000000e+00 0.000 #> [3,] 0.0000000 0.0000000 0.000000e+00 0.000 #> [4,] 0.0000000 0.0000000 0.000000e+00 0.000 #> [5,] 0.0000000 0.0000000 0.000000e+00 0.000 -#> [6,] 0.4034200 1.7483086 6.967871e-02 6355.107 -#> [7,] 872.1867153 0.6179178 3.165957e+00 7662.897 -#> [8,] 0.6179178 1048.7800081 1.124325e-01 2004.872 -#> [9,] 3.1659572 0.1124325 1.041176e+03 2204.565 -#> [10,] 7662.8965834 2004.8718636 2.204565e+03 11396937.363 +#> [6,] 0.4034200 1.7483334 6.967869e-02 6355.130 +#> [7,] 872.1867430 0.6179188 3.165957e+00 7662.896 +#> [8,] 0.6179188 1048.7796745 1.124327e-01 2004.881 +#> [9,] 3.1659566 0.1124327 1.041176e+03 2204.564 +#> [10,] 7662.8956868 2004.8814970 2.204564e+03 11396938.075 #> #> $rse #> P1 P2 lam1 beta lam2 d_P1 d_P2 -#> 0.3388993 0.5155216 7.9357907 29.9948348 1.3144681 11.0205784 11.3204964 +#> 0.3388987 0.5155216 7.9357907 29.9948370 1.3144681 11.0205831 11.3204962 #> d_lam1 d_lam2 SIGMA[1,1] -#> 10.2946079 10.3325269 7.0480558 +#> 10.2946096 10.3325268 7.0480557 #> #> $power #> Value RSE power_pred power_want need_rse min_N_tot -#> beta 0.262 29.99483 91.52704 80 35.69408 142 +#> beta 0.262 29.99484 91.52704 80 35.69408 142 #>diff --git a/docs/reference/extract_norm_group_fim.html b/docs/reference/extract_norm_group_fim.html index 9bb4e3b7..c6ed3c66 100644 --- a/docs/reference/extract_norm_group_fim.html +++ b/docs/reference/extract_norm_group_fim.html @@ -181,7 +181,7 @@Contents
diff --git a/docs/reference/feps.add-1.png b/docs/reference/feps.add-1.png index c29a80a9..809fd2bb 100644 Binary files a/docs/reference/feps.add-1.png and b/docs/reference/feps.add-1.png differ diff --git a/docs/reference/feps.add.html b/docs/reference/feps.add.html index 6d31043b..9bb194f2 100644 --- a/docs/reference/feps.add.html +++ b/docs/reference/feps.add.html @@ -220,7 +220,7 @@Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbbc8fdcc0> +#> <bytecode: 0x556056301520> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -284,7 +284,7 @@diff --git a/docs/reference/feps.add.prop-1.png b/docs/reference/feps.add.prop-1.png index 58a496b1..638449f0 100644 Binary files a/docs/reference/feps.add.prop-1.png and b/docs/reference/feps.add.prop-1.png differ diff --git a/docs/reference/feps.add.prop.html b/docs/reference/feps.add.prop.html index 0006649b..1971a54a 100644 --- a/docs/reference/feps.add.prop.html +++ b/docs/reference/feps.add.prop.html @@ -222,7 +222,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc1bcdeb0> +#> <bytecode: 0x55605775bd98> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -283,7 +283,7 @@diff --git a/docs/reference/feps.prop-1.png b/docs/reference/feps.prop-1.png index 13c054a0..eb4008b3 100644 Binary files a/docs/reference/feps.prop-1.png and b/docs/reference/feps.prop-1.png differ diff --git a/docs/reference/feps.prop.html b/docs/reference/feps.prop.html index 2748b785..c5068f29 100644 --- a/docs/reference/feps.prop.html +++ b/docs/reference/feps.prop.html @@ -229,7 +229,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -271,7 +271,7 @@diff --git a/docs/reference/feval.html b/docs/reference/feval.html index 50ceb988..e9bc627e 100644 --- a/docs/reference/feval.html +++ b/docs/reference/feval.html @@ -199,7 +199,7 @@Examp #> [4,] 0.00000 0.000000 0.000000 3.107768e+03 10.728786 0.02613561 #> [5,] 0.00000 0.000000 0.000000 1.072879e+01 27307.089308 3.26560786 #> [6,] 0.00000 0.000000 0.000000 2.613561e-02 3.265608 41.81083599 -#> [7,] 0.00000 0.000000 0.000000 5.215403e+02 11214.210707 71.08763896 +#> [7,] 0.00000 0.000000 0.000000 5.215403e+02 11214.210707 71.08763902 #> [,7] #> [1,] 0.00000 #> [2,] 0.00000 @@ -297,7 +297,7 @@
Contents
Contents
diff --git a/docs/reference/ff.PK.1.comp.oral.md.CL-1.png b/docs/reference/ff.PK.1.comp.oral.md.CL-1.png index 58a496b1..638449f0 100644 Binary files a/docs/reference/ff.PK.1.comp.oral.md.CL-1.png and b/docs/reference/ff.PK.1.comp.oral.md.CL-1.png differ diff --git a/docs/reference/ff.PK.1.comp.oral.md.CL.html b/docs/reference/ff.PK.1.comp.oral.md.CL.html index 317ee9a3..c2018a9e 100644 --- a/docs/reference/ff.PK.1.comp.oral.md.CL.html +++ b/docs/reference/ff.PK.1.comp.oral.md.CL.html @@ -219,7 +219,7 @@Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc1bcdeb0> +#> <bytecode: 0x55605775bd98> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -280,7 +280,7 @@diff --git a/docs/reference/ff.PK.1.comp.oral.md.KE-1.png b/docs/reference/ff.PK.1.comp.oral.md.KE-1.png index 58a496b1..638449f0 100644 Binary files a/docs/reference/ff.PK.1.comp.oral.md.KE-1.png and b/docs/reference/ff.PK.1.comp.oral.md.KE-1.png differ diff --git a/docs/reference/ff.PK.1.comp.oral.md.KE.html b/docs/reference/ff.PK.1.comp.oral.md.KE.html index 0b25fd5d..0cd7b727 100644 --- a/docs/reference/ff.PK.1.comp.oral.md.KE.html +++ b/docs/reference/ff.PK.1.comp.oral.md.KE.html @@ -219,7 +219,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbbe45fc20> +#> <bytecode: 0x55605474d448> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -281,7 +281,7 @@diff --git a/docs/reference/ff.PK.1.comp.oral.sd.CL-1.png b/docs/reference/ff.PK.1.comp.oral.sd.CL-1.png index 13c054a0..eb4008b3 100644 Binary files a/docs/reference/ff.PK.1.comp.oral.sd.CL-1.png and b/docs/reference/ff.PK.1.comp.oral.sd.CL-1.png differ diff --git a/docs/reference/ff.PK.1.comp.oral.sd.CL.html b/docs/reference/ff.PK.1.comp.oral.sd.CL.html index 0a0e3267..539ced0a 100644 --- a/docs/reference/ff.PK.1.comp.oral.sd.CL.html +++ b/docs/reference/ff.PK.1.comp.oral.sd.CL.html @@ -226,7 +226,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -268,7 +268,7 @@diff --git a/docs/reference/ff.PK.1.comp.oral.sd.KE-1.png b/docs/reference/ff.PK.1.comp.oral.sd.KE-1.png index c29a80a9..809fd2bb 100644 Binary files a/docs/reference/ff.PK.1.comp.oral.sd.KE-1.png and b/docs/reference/ff.PK.1.comp.oral.sd.KE-1.png differ diff --git a/docs/reference/ff.PK.1.comp.oral.sd.KE.html b/docs/reference/ff.PK.1.comp.oral.sd.KE.html index f6c41cdf..68909207 100644 --- a/docs/reference/ff.PK.1.comp.oral.sd.KE.html +++ b/docs/reference/ff.PK.1.comp.oral.sd.KE.html @@ -217,7 +217,7 @@Examp #> [4,] 0.00000 0.000000 0.000000 3.107768e+03 10.728786 0.02613561 #> [5,] 0.00000 0.000000 0.000000 1.072879e+01 27307.089308 3.26560786 #> [6,] 0.00000 0.000000 0.000000 2.613561e-02 3.265608 41.81083599 -#> [7,] 0.00000 0.000000 0.000000 5.215403e+02 11214.210707 71.08763896 +#> [7,] 0.00000 0.000000 0.000000 5.215403e+02 11214.210707 71.08763902 #> [,7] #> [1,] 0.00000 #> [2,] 0.00000 @@ -294,7 +294,7 @@
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbbc8fdcc0> +#> <bytecode: 0x556056301520> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -255,7 +255,7 @@diff --git a/docs/reference/ff.PKPD.1.comp.oral.md.CL.imax-1.png b/docs/reference/ff.PKPD.1.comp.oral.md.CL.imax-1.png index 857f4e02..bc4d71da 100644 Binary files a/docs/reference/ff.PKPD.1.comp.oral.md.CL.imax-1.png and b/docs/reference/ff.PKPD.1.comp.oral.md.CL.imax-1.png differ diff --git a/docs/reference/ff.PKPD.1.comp.oral.md.CL.imax.html b/docs/reference/ff.PKPD.1.comp.oral.md.CL.imax.html index e42ca6f2..2c5f6f7c 100644 --- a/docs/reference/ff.PKPD.1.comp.oral.md.CL.imax.html +++ b/docs/reference/ff.PKPD.1.comp.oral.md.CL.imax.html @@ -223,7 +223,7 @@Examp #> [4,] 0.0000 0.000000 0.000000 3010.9773834 40.490260 0.1151092 #> [5,] 0.0000 0.000000 0.000000 40.4902598 27487.656006 3.1586320 #> [6,] 0.0000 0.000000 0.000000 0.1151092 3.158632 41.8319046 -#> [7,] 0.0000 0.000000 0.000000 784.2206816 10869.344969 70.0662365 +#> [7,] 0.0000 0.000000 0.000000 784.2206814 10869.344969 70.0662365 #> [,7] #> [1,] 0.00000 #> [2,] 0.00000 @@ -281,7 +281,7 @@
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc3b4a7e0> +#> <bytecode: 0x55605a576200> #> <environment: namespace:PopED>
ff.PK.1.comp.oral.md.CL#> function (model_switch, xt, parameters, poped.db) #> { #> with(as.list(parameters), { @@ -236,7 +236,7 @@Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc1bcdeb0> +#> <bytecode: 0x55605775bd98> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -343,7 +343,7 @@diff --git a/docs/reference/ff.PKPD.1.comp.sd.CL.emax-1.png b/docs/reference/ff.PKPD.1.comp.sd.CL.emax-1.png index 901e8de5..fa168084 100644 Binary files a/docs/reference/ff.PKPD.1.comp.sd.CL.emax-1.png and b/docs/reference/ff.PKPD.1.comp.sd.CL.emax-1.png differ diff --git a/docs/reference/ff.PKPD.1.comp.sd.CL.emax.html b/docs/reference/ff.PKPD.1.comp.sd.CL.emax.html index c3ac83f8..c8f170f7 100644 --- a/docs/reference/ff.PKPD.1.comp.sd.CL.emax.html +++ b/docs/reference/ff.PKPD.1.comp.sd.CL.emax.html @@ -218,7 +218,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbbe5f84d8> +#> <bytecode: 0x5560557ee998> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -334,7 +334,7 @@diff --git a/docs/reference/fileparts.html b/docs/reference/fileparts.html index d59f3da3..e5aac9bd 100644 --- a/docs/reference/fileparts.html +++ b/docs/reference/fileparts.html @@ -210,7 +210,7 @@Contents
Contents
diff --git a/docs/reference/getTruncatedNormal.html b/docs/reference/getTruncatedNormal.html index c82205e1..bf1c544d 100644 --- a/docs/reference/getTruncatedNormal.html +++ b/docs/reference/getTruncatedNormal.html @@ -187,7 +187,7 @@Contents
diff --git a/docs/reference/get_all_params.html b/docs/reference/get_all_params.html index 7da0bd0e..fd977acb 100644 --- a/docs/reference/get_all_params.html +++ b/docs/reference/get_all_params.html @@ -194,7 +194,7 @@Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -287,7 +287,7 @@diff --git a/docs/reference/get_rse.html b/docs/reference/get_rse.html index ed1dbc69..51ecdd2b 100644 --- a/docs/reference/get_rse.html +++ b/docs/reference/get_rse.html @@ -238,7 +238,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc1bcdeb0> +#> <bytecode: 0x55605775bd98> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -297,7 +297,7 @@diff --git a/docs/reference/get_unfixed_params.html b/docs/reference/get_unfixed_params.html index 19991ed1..49aa3083 100644 --- a/docs/reference/get_unfixed_params.html +++ b/docs/reference/get_unfixed_params.html @@ -191,7 +191,7 @@Contents
Contents
diff --git a/docs/reference/getfulld.html b/docs/reference/getfulld.html index ffb733cf..e1d09a0d 100644 --- a/docs/reference/getfulld.html +++ b/docs/reference/getfulld.html @@ -198,7 +198,7 @@Contents
diff --git a/docs/reference/gradf_eps.html b/docs/reference/gradf_eps.html index bc8b3226..03ce7c0e 100644 --- a/docs/reference/gradf_eps.html +++ b/docs/reference/gradf_eps.html @@ -239,7 +239,7 @@Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -310,7 +310,7 @@diff --git a/docs/reference/index.html b/docs/reference/index.html index 39e2ddd0..3bdd3820 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -556,7 +556,7 @@Contents
Contents
diff --git a/docs/reference/inv.html b/docs/reference/inv.html index 34c3661c..c5b5e166 100644 --- a/docs/reference/inv.html +++ b/docs/reference/inv.html @@ -194,7 +194,7 @@Contents
diff --git a/docs/reference/isempty.html b/docs/reference/isempty.html index fc0b8b2a..cf896d49 100644 --- a/docs/reference/isempty.html +++ b/docs/reference/isempty.html @@ -197,7 +197,7 @@Contents
diff --git a/docs/reference/log_prior_pdf.html b/docs/reference/log_prior_pdf.html index f50cfad1..5cff36f8 100644 --- a/docs/reference/log_prior_pdf.html +++ b/docs/reference/log_prior_pdf.html @@ -205,7 +205,7 @@Contents
diff --git a/docs/reference/mc_mean.html b/docs/reference/mc_mean.html index 77f4eeb4..4e4f3a0a 100644 --- a/docs/reference/mc_mean.html +++ b/docs/reference/mc_mean.html @@ -234,7 +234,7 @@Contents
diff --git a/docs/reference/median_hilow_poped.html b/docs/reference/median_hilow_poped.html index d36d45f9..15018c80 100644 --- a/docs/reference/median_hilow_poped.html +++ b/docs/reference/median_hilow_poped.html @@ -160,7 +160,7 @@Arg
... -Additional arguments passed to
smedian.hilowor +Additional arguments passed to
smedian.hilowormedian_hilow, depending on your version of ggplot.Contents
Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -426,7 +426,7 @@Examp #> sig_prop 0.01 32 23 #> sig_add 0.25 26 30 #> -#> Total running time: 0.029 seconds
+#> Total running time: 0.044 secondsif (FALSE) { @@ -478,7 +478,7 @@diff --git a/docs/reference/mftot.html b/docs/reference/mftot.html index 4f63d4ea..94232d64 100644 --- a/docs/reference/mftot.html +++ b/docs/reference/mftot.html @@ -262,7 +262,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -343,7 +343,7 @@diff --git a/docs/reference/model_prediction.html b/docs/reference/model_prediction.html index 34e423c2..8b2b7dec 100644 --- a/docs/reference/model_prediction.html +++ b/docs/reference/model_prediction.html @@ -315,7 +315,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc1bcdeb0> +#> <bytecode: 0x55605775bd98> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -1856,7 +1856,7 @@diff --git a/docs/reference/ofv_criterion.html b/docs/reference/ofv_criterion.html index 746fb43e..a1649f86 100644 --- a/docs/reference/ofv_criterion.html +++ b/docs/reference/ofv_criterion.html @@ -232,7 +232,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -303,7 +303,7 @@Examp ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=6), length(get_unfixed_params(poped.db)[["all"]]), poped.db, - ofv_calc_type=6)
#> [1] 27.06949+ ofv_calc_type=6)#> [1] 1.75168ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=7), length(get_unfixed_params(poped.db)[["all"]]), poped.db, @@ -324,7 +324,7 @@diff --git a/docs/reference/ofv_fim.html b/docs/reference/ofv_fim.html index 22e1c55d..3e0025c9 100644 --- a/docs/reference/ofv_fim.html +++ b/docs/reference/ofv_fim.html @@ -244,7 +244,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -300,8 +300,8 @@Examp #> [7,] 192840.20092 6659.569867 #> [8,] 6659.56987 475.500111
#> CL V KA d_CL d_V d_KA sig_prop sig_add #> 5.096246 3.031164 14.260384 29.761226 36.681388 26.748640 32.011719 25.637971-det(FIM)#> [1] 1.143859e+24ofv_fim(FIM,poped.db,ofv_calc_type=1) # det(FIM)#> [1] 1.143859e+24ofv_fim(FIM,poped.db,ofv_calc_type=2) # 1/trace_matrix(inv(FIM))#> [1] 9.127328ofv_fim(FIM,poped.db,ofv_calc_type=4) # log(det(FIM))#> [1] 55.39645ofv_fim(FIM,poped.db,ofv_calc_type=6) # Ds with fixed effects as "important"#> [1] 14534520ofv_fim(FIM,poped.db,ofv_calc_type=6, - ds_index=c(1,1,1,0,0,0,1,1)) # Ds with random effects as "important"#> [1] 1662231933ofv_fim(FIM,poped.db,ofv_calc_type=7) # 1/sum(get_rse(FIM,poped.db,use_percent=FALSE))#> [1] 0.5772714+det(FIM)#> [1] 1.143859e+24ofv_fim(FIM,poped.db,ofv_calc_type=1) # det(FIM)#> [1] 1.143859e+24ofv_fim(FIM,poped.db,ofv_calc_type=2) # 1/trace_matrix(inv(FIM))#> [1] 9.127328ofv_fim(FIM,poped.db,ofv_calc_type=4) # log(det(FIM))#> [1] 55.39645ofv_fim(FIM,poped.db,ofv_calc_type=6) # Ds with fixed effects as "important"#> [1] 16.49204ofv_fim(FIM,poped.db,ofv_calc_type=6, + ds_index=c(1,1,1,0,0,0,1,1)) # Ds with random effects as "important"#> [1] 21.23143ofv_fim(FIM,poped.db,ofv_calc_type=7) # 1/sum(get_rse(FIM,poped.db,use_percent=FALSE))#> [1] 0.5772714Contents
Examp #> It. 400 | OFV = 67.5762 #> #> Total iterations: 400 -#> Elapsed time: 0.157 seconds. +#> Elapsed time: 0.141 seconds. #> #> Final OFV = 67.57618 #> Parameters: -15.35198 @@ -493,7 +493,7 @@
Examp #> Maximum number of identical optimal values reached (max_run=200), optimization stopped. #> #> Total iterations: 392 -#> Elapsed time: 0.139 seconds. +#> Elapsed time: 0.126 seconds. #> #> Final OFV = 67.6696 #> Parameters: -15.97183 @@ -626,7 +626,7 @@
Examp #> It. 400 | OFV = 503.399 #> #> Total iterations: 400 -#> Elapsed time: 0.16 seconds. +#> Elapsed time: 0.143 seconds. #> #> Final OFV = 503.399 #> Parameters: 2.130566 2 2 2.059427 2 2 2.167358 2 2 2.142739 2 2 2.030765 2.254329 2 2 2.56842 2.045564 2.276299 2 2 2 2 2 3.690364 @@ -733,7 +733,7 @@
Examp #> It. 400 | OFV = 15264 #> #> Total iterations: 400 -#> Elapsed time: 0.27 seconds. +#> Elapsed time: 0.181 seconds. #> #> Final OFV = 15264 #> Parameters: 1 19 8 5 4 3 18 2 14 9 12 15 13 16 17 6 11 7 20 10 21 1 @@ -871,7 +871,7 @@
Examp #> It. 400 | OFV = 47.7055 #> #> Total iterations: 400 -#> Elapsed time: 0.202 seconds. +#> Elapsed time: 0.143 seconds. #> #> Final OFV = 47.7055 #> Parameters: 6.562641 @@ -943,7 +943,7 @@
Contents
Examp #> Searching parameter 1 #> Changed from 50 to -15.8166 ; OFV = 67.485 #> -#> Elapsed time: 0.036 seconds. +#> Elapsed time: 0.03 seconds. #> #> Final OFV = 67.48502 #> Parameters: -15.81658 @@ -310,7 +310,7 @@
Examp #> Searching parameter 1 #> Changed from 50 to -5.0055 ; OFV = 69.8766 #> -#> Elapsed time: 0.039 seconds. +#> Elapsed time: 0.027 seconds. #> #> Final OFV = 69.87659 #> Parameters: -5.005501 @@ -425,7 +425,7 @@
Examp #> Searching parameter 14 #> Changed from 3 to 2 ; OFV = 446.962 #> -#> Elapsed time: 0.146 seconds. +#> Elapsed time: 0.111 seconds. #> #> Final OFV = 446.9622 #> Parameters: 2 2.144144 2 2.144144 2 2 2.144144 2 2.144144 2 2.144144 2 2.144144 2 2 2.144144 2 2.144144 2 2.144144 2 2 2 2.108108 4 @@ -522,7 +522,7 @@
Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -321,7 +321,7 @@diff --git a/docs/reference/plot_efficiency_of_windows-1.png b/docs/reference/plot_efficiency_of_windows-1.png index a732390f..594270d3 100644 Binary files a/docs/reference/plot_efficiency_of_windows-1.png and b/docs/reference/plot_efficiency_of_windows-1.png differ diff --git a/docs/reference/plot_efficiency_of_windows.html b/docs/reference/plot_efficiency_of_windows.html index 26b4589d..4a981c42 100644 --- a/docs/reference/plot_efficiency_of_windows.html +++ b/docs/reference/plot_efficiency_of_windows.html @@ -285,7 +285,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -353,7 +353,7 @@diff --git a/docs/reference/plot_model_prediction.html b/docs/reference/plot_model_prediction.html index 3f978ba1..db12ea5b 100644 --- a/docs/reference/plot_model_prediction.html +++ b/docs/reference/plot_model_prediction.html @@ -366,7 +366,7 @@Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbc1bcdeb0> +#> <bytecode: 0x55605775bd98> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -513,7 +513,7 @@diff --git a/docs/reference/poped.choose.html b/docs/reference/poped.choose.html index 5a49de2f..d1de259e 100644 --- a/docs/reference/poped.choose.html +++ b/docs/reference/poped.choose.html @@ -191,7 +191,7 @@Contents
Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -522,7 +522,7 @@Examp #> Searching parameter 1 #> Changed from 100 to 100 ; OFV = 56.032 #> -#> Elapsed time: 0.026 seconds. +#> Elapsed time: 0.023 seconds. #> #> Final OFV = 56.03204 #> Parameters: 100 @@ -565,7 +565,7 @@
Examp #> sig_prop 0.01 32 23 #> sig_add 0.25 26 30 #> -#> Total running time: 0.181 seconds
+#> Total running time: 0.195 seconds# cost function # PRED at 120 hours crit_fcn <- function(poped.db,...){ @@ -610,7 +610,7 @@Examp #> Initial OFV = 0.939866 #> #> Total iterations: 2 -#> Elapsed time: 0.006 seconds. +#> Elapsed time: 0.005 seconds. #> #> Final OFV = 0.9398657 #> Parameters: 70 @@ -632,7 +632,7 @@
Examp #> Searching parameter 1 #> Changed from 75.5698 to 100 ; OFV = 1.34267 #> -#> Elapsed time: 0.009 seconds. +#> Elapsed time: 0.008 seconds. #> #> Final OFV = 1.342665 #> Parameters: 100 @@ -682,7 +682,7 @@
Examp #> Searching parameter 1 #> Changed from 100 to 100 ; OFV = 1.34267 #> -#> Elapsed time: 0.009 seconds. +#> Elapsed time: 0.008 seconds. #> #> Final OFV = 1.342665 #> Parameters: 100 @@ -725,7 +725,7 @@
Examp #> sig_prop 0.01 32 23 #> sig_add 0.25 26 30 #> -#> Total running time: 0.204 seconds
+#> Total running time: 0.306 seconds# minimize the cost function out_3 <- poped_optim(poped.db,opt_a =TRUE, ofv_fun=crit_fcn, @@ -751,7 +751,7 @@Examp #> Initial OFV = 0.939866 #> #> Total iterations: 2 -#> Elapsed time: 0.005 seconds. +#> Elapsed time: 0.007 seconds. #> #> Final OFV = 0.7854542 #> Parameters: 58.49963 @@ -773,7 +773,7 @@
Examp #> Searching parameter 1 #> Changed from 50.435 to 0.01 ; OFV = 0.000134267 #> -#> Elapsed time: 0.018 seconds. +#> Elapsed time: 0.012 seconds. #> #> Final OFV = 0.0001342665 #> Parameters: 0.01 @@ -801,7 +801,7 @@
Examp #> Initial OFV = 0.000134267 #> #> Total iterations: 2 -#> Elapsed time: 0.009 seconds. +#> Elapsed time: 0.006 seconds. #> #> Final OFV = 0.0001342665 #> Parameters: 0.01 @@ -823,7 +823,7 @@
Examp #> Searching parameter 1 #> Changed from 0.01 to 0.01 ; OFV = 0.000134267 #> -#> Elapsed time: 0.017 seconds. +#> Elapsed time: 0.022 seconds. #> #> Final OFV = 0.0001342665 #> Parameters: 0.01 @@ -854,7 +854,7 @@
Examp #> Efficiency: #> (ofv_final / ofv_init) = 0.00014286 #> -#> Total running time: 0.26 seconds
+#> Total running time: 0.311 secondsif (FALSE) { @@ -980,7 +980,7 @@diff --git a/docs/reference/poped_optim_1.html b/docs/reference/poped_optim_1.html index 8ec7e361..7dc2dcf5 100644 --- a/docs/reference/poped_optim_1.html +++ b/docs/reference/poped_optim_1.html @@ -421,7 +421,7 @@Contents
Contents
Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -490,11 +490,11 @@Examp #> sig_add 0.25 26 #> #> RS - It. : 2 OFV : 55.3964 -#> Run time for random search: 0.016 seconds +#> Run time for random search: 0.017 seconds #> #> SG - It. : 1 OFV : 55.45 Diff. : 1 #> SG - It. : 2 OFV : 55.61 Diff. : 0.002799 -#> Stochastic gradient run time: 0.36 seconds +#> Stochastic gradient run time: 0.325 seconds #> #> Searching xt8 on group 1 #> Searching xt5 on group 1 @@ -507,7 +507,7 @@
Examp #> OFV(MF): 55.8146 #> Searching a1 on individual/group 1 #> OFV(MF): 56.5866 -#> Line search run time: 0.199 seconds +#> Line search run time: 0.188 seconds #> #> =============================================================================== #> FINAL RESULTS @@ -535,7 +535,7 @@
Examp #> sig_prop 0.01 32 25 #> sig_add 0.25 26 16 #> -#> Total running time: 0.579 seconds
+#> Total running time: 0.534 secondsif (FALSE) { # RS+SG+LS optimization of sample times @@ -641,7 +641,7 @@diff --git a/docs/reference/rand.html b/docs/reference/rand.html index e47ad894..f6d81f30 100644 --- a/docs/reference/rand.html +++ b/docs/reference/rand.html @@ -206,7 +206,7 @@Contents
Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -248,7 +248,7 @@Examp ##################################### shrinkage(poped.db)
#> # A tibble: 3 x 5 -#> `d[1]` `d[2]` `d[3]` type group +#> d_CL d_V d_KA type group #> <dbl> <dbl> <dbl> <chr> <chr> #> 1 0.0244 0.174 0.0301 shrink_var grp_1 #> 2 0.0123 0.0910 0.0152 shrink_sd grp_1 @@ -270,7 +270,7 @@diff --git a/docs/reference/size.html b/docs/reference/size.html index 69b4c2f1..7288b5c6 100644 --- a/docs/reference/size.html +++ b/docs/reference/size.html @@ -201,7 +201,7 @@Contents
Contents
Contents
Examp #> return(list(y = y, poped.db = poped.db)) #> }) #> } -#> <bytecode: 0x7ffbb9461e08> +#> <bytecode: 0x5560532ef6a8> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff @@ -267,7 +267,7 @@Examp #> Initial OFV = 55.3964 #> #> Total iterations: 2 -#> Elapsed time: 0.017 seconds. +#> Elapsed time: 0.012 seconds. #> #> Final OFV = 55.39645 #> Parameters: 70 @@ -289,7 +289,7 @@
Examp #> Searching parameter 1 #> Changed from 83.2011 to 100 ; OFV = 56.032 #> -#> Elapsed time: 0.02 seconds. +#> Elapsed time: 0.017 seconds. #> #> Final OFV = 56.03204 #> Parameters: 100 @@ -332,7 +332,7 @@
Examp #> sig_prop 0.01 32 23 #> sig_add 0.25 26 30 #> -#> Total running time: 0.176 seconds
+#> Total running time: 0.267 secondssummary(out_1)+#> Total running time: 0.267 secondsContents
Examp toc()
#> Elapsed time: 0 seconds.#> Elapsed time: 0.001 seconds.tic() -toc()#> Elapsed time: 0 seconds.toc()#> Elapsed time: 0.001 seconds.#> Elapsed time: 0.003 seconds.+toc()#> Elapsed time: 0 seconds.toc()#> Elapsed time: 0 seconds.#> Elapsed time: 0.002 seconds.See a
Examples
+toc()tic() -toc()#> Elapsed time: 0.001 seconds.+toc()#> Elapsed time: 0 seconds.#> Elapsed time: 0.012 seconds.tic() -toc()#> Elapsed time: 0.001 seconds.toc()#> Elapsed time: 0.002 seconds.#> Elapsed time: 0.014 seconds.#> Elapsed time: 0.001 seconds.tic() +toc()#> Elapsed time: 0.001 seconds.toc()#> Elapsed time: 0.001 seconds.#> Elapsed time: 0.003 seconds.Contents
Contents
+