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functions.R
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functions.R
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## function below runs the mediation analysis for each mediator
## mediator = name of mediator, e.g. "BMI_cat"
## dataframe = df to use for mediation
## sim.df = simulated dataframe to use for predictions
## mc_iteration = iteration for monte carlo error reduction loop
## reference cohort = default is 1945
## strata = default is FALSE, options: "sex", "ethnicity"
#output will be c(nc results, cf results)
#output stratified by sex will be c(FEMALES (c(nc results, cf results)), MALES (c(nc results, cf results)))
#output stratified by ethn will be c(white (c(nc results, cf results)), black (c(nc results, cf results))), hisp (c(nc results, cf results))), other (c(nc results, cf results)))
mediation_analysis <- function(mediator, dataframe, sim.df, mc_iteration, reference_cohort = 1945, strata = FALSE){
## check to make sure the correct dataset is being used
if (nrow(dataframe) != 163760) {return(paste0("CHECK IF DATASET IS CORRECT! Current dataset has ", nrow(dataframe)," rows"))}
if (strata =="FALSE"){
## sample from dataframe
bootstrap_sample <- cluster.resample(dataframe, cluster.name = "HHIDPN",
size = length(unique(dataframe[,"HHIDPN"])))
## fit outcome model with APC specifications (Carstensen approach)
## check the paper for more information on the APC approach
outcome.model <- glm(paste("depress ~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) +", mediator, "+ RAGENDER + RAEDUC + ethn",sep=" "),
family ="binomial",
data = bootstrap_sample)
## copy simulated df
sim.df.nc <- sim.df.cf <- data.frame(1:nrow(sim.df))
## fit mediator model
if (class(dataframe[,mediator]) == "factor" & length(unique(dataframe[,mediator])) == 2){
mediator.model <- glm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC + ethn",sep=""),
family ="binomial",
data = bootstrap_sample)
probs <- predict(mediator.model, type = "response", newdata = sim.df)
## start of Monte Carlo loop
for (m in 1:mc_iteration){
## natural course
## predict mediator values
sim.df[,mediator] <- as.factor(rbinom(n = nrow(sim.df), size = 1,
prob = probs))
levels(sim.df[,mediator]) <- levels(dataframe[,mediator])
sim.df.nc[,m] <- predict(outcome.model, type="response", newdata = sim.df)
## counterfactual
## our counterfactual is the health behavior factor distribution of the 1945 cohort
## so we predict just with that cohort
sim.df[,mediator] <- sim.df[,mediator][sim.df$birthcohort2==reference_cohort]
sim.df.cf[,m] <- predict(outcome.model, type="response", newdata = sim.df)
}
} else if (class(dataframe[,mediator]) == "factor" & length(unique(dataframe[,mediator])) > 2){
mediator.model <- multinom(bootstrap_sample[,mediator] ~ Ns(AGEY_M, df=6) +
Ns(period, df=5,detrend=TRUE) +
Ns(birthcohort2, df=10) + RAGENDER + RAEDUC + ethn,
data = bootstrap_sample)
simulated_mediator <- predict(mediator.model, type="probs",newdata = sim.df)
for (m in 1:mc_iteration){
#natural course
#predict mediator values
sim.df[,mediator] <- rMultinom(m = 1, probs = simulated_mediator)
sim.df.nc[,m] <- predict(outcome.model, type="response", newdata = sim.df)
#counterfactual
sim.df[,mediator] <- sim.df[,mediator][sim.df$birthcohort2==reference_cohort]
sim.df.cf[,m] <- predict(outcome.model, type="response", newdata = sim.df)
}
} else if (class(dataframe[,mediator]) == "numeric"){
mediator.model <- lm(bootstrap_sample[,mediator] ~ Ns(AGEY_M, df=6) +
Ns(period, df=5,detrend=TRUE) +
Ns(birthcohort2, df=10) + RAGENDER + RAEDUC + ethn,
data = bootstrap_sample)
simulated_mediator <- predict(mediator.model, type = "response", newdata = sim.df)
for (m in 1:mc_iteration){
#natural course
#predict mediator values
sim.df[,mediator] <- rnorm(n=nrow(sim.df), mean = mean(simulated_mediator), sd=sd(mediator.model$residuals))
sim.df.nc[,m] <- predict(outcome.model, type="response", newdata = sim.df)
#counterfactual
sim.df[,mediator] <- sim.df[,mediator][sim.df$birthcohort2==reference_cohort]
sim.df.cf[,m] <- predict(outcome.model, type="response", newdata = sim.df)
}
} else {
return("Mediator has the wrong class")
}
## aggregate MC results for natural course and counterfactual
df.BS.nc <- rowMeans(sim.df.nc)
df.BS.cf <- rowMeans(sim.df.cf)
c(df.BS.nc,df.BS.cf)
}
## strata for sex
else if(strata == "sex"){
df.F <- subset(dataframe, dataframe[,"RAGENDER"]=="2.Female")
df.M <- subset(dataframe, dataframe[,"RAGENDER"]=="1.Male")
bootstrap_sample.F <- sample_n(df.F, size = nrow(dataframe), replace = TRUE)
bootstrap_sample.M <- sample_n(df.M, size = nrow(dataframe), replace = TRUE)
outcome.model.F <- glm(paste("depress ~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) +", mediator, " + RAEDUC + ethn",sep=" "),
family ="binomial",
data = bootstrap_sample.F)
outcome.model.M <- glm(paste("depress ~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) +", mediator, " + RAEDUC + ethn",sep=" "),
family ="binomial",
data = bootstrap_sample.M)
sim.df.F.nc <- sim.df.F.cf <- sim.df.M.nc <- sim.df.M.cf <- data.frame(1:nrow(sim.df))
sim.df.F <- sim.df.M <- sim.df
if (class(dataframe[,mediator]) == "factor" & length(unique(dataframe[,mediator])) == 2){
mediator.model.F <- glm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) + RAEDUC + ethn",sep=""),
family ="binomial",
data = bootstrap_sample.F)
mediator.model.M <- glm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) + RAEDUC + ethn",sep=""),
family ="binomial",
data = bootstrap_sample.M)
probs.F <- predict(mediator.model.F, type = "response", newdata = sim.df.F)
probs.M <- predict(mediator.model.M, type = "response", newdata = sim.df.M)
for (m in 1:mc_iteration){
#FEMALES
#natural course
#predict mediator values
sim.df.F[,mediator] <- as.factor(rbinom(n = nrow(sim.df.F), size = 1,
prob = probs.F))
levels(sim.df.F[,mediator]) <- levels(dataframe[,mediator])
sim.df.F.nc[,m] <- predict(outcome.model.F, type="response", newdata = sim.df.F)
#counterfactual
sim.df.F[,mediator] <- sim.df.F[,mediator][sim.df.F$birthcohort2==reference_cohort]
sim.df.F.cf[,m] <- predict(outcome.model.F, type="response", newdata = sim.df.F)
#MALES
#natural course
#predict mediator values
sim.df.M[,mediator] <- as.factor(rbinom(n = nrow(sim.df.M), size = 1,
prob = probs.M))
levels(sim.df.M[,mediator]) <- levels(dataframe[,mediator])
sim.df.M.nc[,m] <- predict(outcome.model.M, type="response", newdata = sim.df.M)
#counterfactual
sim.df.M[,mediator] <- sim.df.M[,mediator][sim.df.M$birthcohort2==reference_cohort]
sim.df.M.cf[,m] <- predict(outcome.model.M, type="response", newdata = sim.df.M)
}
} else if (class(dataframe[,mediator]) == "factor" & length(unique(dataframe[,mediator])) > 2){
mediator.model.F <- multinom(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) + RAEDUC + ethn",sep=""),
data = bootstrap_sample.F)
mediator.model.M <- multinom(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) + RAEDUC + ethn",sep=""),
data = bootstrap_sample.M)
simulated_mediator.F <- predict(mediator.model.F, type="probs",newdata = sim.df.F)
simulated_mediator.M <- predict(mediator.model.M, type="probs",newdata = sim.df.M)
for (m in 1:mc_iteration){
#FEMALES
#natural course
#predict mediator values
sim.df.F[,mediator] <- rMultinom(m = 1, probs = simulated_mediator.F)
sim.df.F.nc[,m] <- predict(outcome.model.F, type="response", newdata = sim.df.F)
#counterfactual
sim.df.F[,mediator] <- sim.df.F[,mediator][sim.df.F$birthcohort2==reference_cohort]
sim.df.F.cf[,m] <- predict(outcome.model.F, type="response", newdata = sim.df.F)
#MALES
#natural course
#predict mediator values
sim.df.M[,mediator] <- rMultinom(m = 1, probs = simulated_mediator.M)
sim.df.M.nc[,m] <- predict(outcome.model.M, type="response", newdata = sim.df.M)
#counterfactual
sim.df.M[,mediator] <- sim.df.M[,mediator][sim.df.M$birthcohort2==reference_cohort]
sim.df.M.cf[,m] <- predict(outcome.model.M, type="response", newdata = sim.df.M)
}
} else if (class(dataframe[,mediator]) == "numeric"){
mediator.model.F <- lm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) + RAEDUC + ethn",sep=""),
family ="binomial",
data = bootstrap_sample.F)
mediator.model.M <- lm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=5,detrend=TRUE) + Ns(birthcohort2, df=10) + RAEDUC + ethn",sep=""),
family ="binomial",
data = bootstrap_sample.M)
simulated_mediator.F <- predict(mediator.model.F, type = "response", newdata = sim.df.F)
simulated_mediator.M <- predict(mediator.model.M, type = "response", newdata = sim.df.M)
for (m in 1:mc_iteration){
#FEMALES
#natural course
#predict mediator values
sim.df.F[,mediator] <- rnorm(n=nrow(sim.df.F), mean = mean(simulated_mediator.F), sd=sd(mediator.model.F$residuals))
sim.df.F.nc[,m] <- predict(outcome.model.F, type="response", newdata = sim.df.F)
#counterfactual
sim.df.F[,mediator] <- sim.df.F[,mediator][sim.df.F$birthcohort2==reference_cohort]
sim.df.F.cf[,m] <- predict(outcome.model.F, type="response", newdata = sim.df.F)
#MALES
#natural course
#predict mediator values
sim.df.M[,mediator] <- rnorm(n=nrow(sim.df.M), mean = mean(simulated_mediator.M), sd=sd(mediator.model.M$residuals))
sim.df.M.nc[,m] <- predict(outcome.model.M, type="response", newdata = sim.df.M)
#counterfactual
sim.df.M[,mediator] <- sim.df.M[,mediator][sim.df.M$birthcohort2==reference_cohort]
sim.df.M.cf[,m] <- predict(outcome.model.M, type="response", newdata = sim.df.M)
}
} else {
return("Mediator has the wrong class")
}
df.BS.F.nc <- rowMeans(sim.df.F.nc)
df.BS.F.cf <- rowMeans(sim.df.F.cf)
results_FEMALES <- c(df.BS.F.nc, df.BS.F.cf)
df.BS.M.nc <- rowMeans(sim.df.M.nc)
df.BS.M.cf <- rowMeans(sim.df.M.cf)
results_MALES <- c(df.BS.M.nc, df.BS.M.cf)
c(results_FEMALES,results_MALES)
}
## strate for race/ethnicity
else if(strata == "ethnicity"){
df.1 <- subset(dataframe, dataframe[,"ethn"]=="1.White/Caucasian")
df.2 <- subset(dataframe, dataframe[,"ethn"]=="2.Black/African American")
df.3 <- subset(dataframe, dataframe[,"ethn"]=="3.Hispanic")
df.4 <- subset(dataframe, dataframe[,"ethn"]=="4.Other")
bootstrap_sample.1 <- sample_n(df.1, size = nrow(dataframe), replace = TRUE)
bootstrap_sample.2 <- sample_n(df.2, size = nrow(dataframe), replace = TRUE)
bootstrap_sample.3 <- sample_n(df.3, size = nrow(dataframe), replace = TRUE)
bootstrap_sample.4 <- sample_n(df.4, size = nrow(dataframe), replace = TRUE)
outcome.model.1 <- glm(paste0("depress ~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) +", mediator, " + RAGENDER + RAEDUC"),
family ="binomial",
data = bootstrap_sample.1)
outcome.model.2 <- glm(paste0("depress ~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) +", mediator, " + RAGENDER + RAEDUC"),
family ="binomial",
data = bootstrap_sample.2)
outcome.model.3 <- glm(paste0("depress ~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) +", mediator, " + RAGENDER + RAEDUC"),
family ="binomial",
data = bootstrap_sample.3)
outcome.model.4 <- glm(paste0("depress ~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) +", mediator, " + RAGENDER + RAEDUC"),
family ="binomial",
data = bootstrap_sample.4)
sim.df.1.nc <- sim.df.1.cf <- sim.df.2.nc <- sim.df.2.cf <- sim.df.3.nc <- sim.df.3.cf <- sim.df.4.nc <- sim.df.4.cf <- data.frame(1:nrow(sim.df))
sim.df.1 <- sim.df.2 <- sim.df.3 <- sim.df.4 <- sim.df
if (class(dataframe[,mediator]) == "factor" & length(unique(dataframe[,mediator])) == 2){
mediator.model.1 <- glm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
family ="binomial",
data = bootstrap_sample.1)
mediator.model.2 <- glm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
family ="binomial",
data = bootstrap_sample.2)
mediator.model.3 <- glm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
family ="binomial",
data = bootstrap_sample.3)
mediator.model.4 <- glm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
family ="binomial",
data = bootstrap_sample.4)
probs.1 <- predict(mediator.model.1, type = "response", newdata = sim.df.1)
probs.2 <- predict(mediator.model.2, type = "response", newdata = sim.df.2)
probs.3 <- predict(mediator.model.3, type = "response", newdata = sim.df.3)
probs.4 <- predict(mediator.model.4, type = "response", newdata = sim.df.4)
for (m in 1:mc_iteration){
#1
#natural course
#predict mediator values
sim.df.1[,mediator] <- as.factor(rbinom(n = nrow(sim.df.1), size = 1,
prob = probs.1))
levels(sim.df.1[,mediator]) <- levels(dataframe[,mediator])
sim.df.1.nc[,m] <- predict(outcome.model.1, type="response", newdata = sim.df.1)
#counterfactual
sim.df.1[,mediator] <- sim.df.1[,mediator][sim.df.1$birthcohort2==reference_cohort]
sim.df.1.cf[,m] <- predict(outcome.model.1, type="response", newdata = sim.df.1)
#2
#natural course
#predict mediator values
sim.df.2[,mediator] <- as.factor(rbinom(n = nrow(sim.df.2), size = 1,
prob = probs.2))
levels(sim.df.2[,mediator]) <- levels(dataframe[,mediator])
sim.df.2.nc[,m] <- predict(outcome.model.2, type="response", newdata = sim.df.2)
#counterfactual
sim.df.2[,mediator] <- sim.df.2[,mediator][sim.df.2$birthcohort2==reference_cohort]
sim.df.2.cf[,m] <- predict(outcome.model.2, type="response", newdata = sim.df.2)
#3
#natural course
#predict mediator values
sim.df.3[,mediator] <- as.factor(rbinom(n = nrow(sim.df.3), size = 1,
prob = probs.3))
levels(sim.df.3[,mediator]) <- levels(dataframe[,mediator])
sim.df.3.nc[,m] <- predict(outcome.model.3, type="response", newdata = sim.df.3)
#counterfactual
sim.df.3[,mediator] <- sim.df.3[,mediator][sim.df.3$birthcohort2==reference_cohort]
sim.df.3.cf[,m] <- predict(outcome.model.3, type="response", newdata = sim.df.3)
#4
#natural course
#predict mediator values
sim.df.4[,mediator] <- as.factor(rbinom(n = nrow(sim.df.4), size = 1,
prob = probs.4))
levels(sim.df.4[,mediator]) <- levels(dataframe[,mediator])
sim.df.4.nc[,m] <- predict(outcome.model.4, type="response", newdata = sim.df.4)
#counterfactual
sim.df.4[,mediator] <- sim.df.4[,mediator][sim.df.4$birthcohort2==reference_cohort]
sim.df.4.cf[,m] <- predict(outcome.model.4, type="response", newdata = sim.df.4)
}
} else if (class(dataframe[,mediator]) == "factor" & length(unique(dataframe[,mediator])) > 2){
mediator.model.1 <- multinom(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
data = bootstrap_sample.1)
mediator.model.2 <- multinom(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
data = bootstrap_sample.2)
mediator.model.3 <- multinom(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
data = bootstrap_sample.3)
mediator.model.4 <- multinom(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
data = bootstrap_sample.4)
simulated_mediator.1 <- predict(mediator.model.1, type="probs",newdata = sim.df.1)
simulated_mediator.2 <- predict(mediator.model.2, type="probs",newdata = sim.df.2)
simulated_mediator.3 <- predict(mediator.model.3, type="probs",newdata = sim.df.3)
simulated_mediator.4 <- predict(mediator.model.4, type="probs",newdata = sim.df.4)
for (m in 1:mc_iteration){
#1
#natural course
#predict mediator values
sim.df.1[,mediator] <- rMultinom(m = 1, probs = simulated_mediator.1)
sim.df.1.nc[,m] <- predict(outcome.model.1, type="response", newdata = sim.df.1)
#counterfactual
sim.df.1[,mediator] <- sim.df.1[,mediator][sim.df.1$birthcohort2==reference_cohort]
sim.df.1.cf[,m] <- predict(outcome.model.1, type="response", newdata = sim.df.1)
#2
#natural course
#predict mediator values
sim.df.2[,mediator] <- rMultinom(m = 1, probs = simulated_mediator.2)
sim.df.2.nc[,m] <- predict(outcome.model.2, type="response", newdata = sim.df.2)
#counterfactual
sim.df.2[,mediator] <- sim.df.2[,mediator][sim.df.2$birthcohort2==reference_cohort]
sim.df.2.cf[,m] <- predict(outcome.model.2, type="response", newdata = sim.df.2)
#3
#natural course
#predict mediator values
sim.df.3[,mediator] <- rMultinom(m = 1, probs = simulated_mediator.3)
sim.df.3.nc[,m] <- predict(outcome.model.3, type="response", newdata = sim.df.3)
#counterfactual
sim.df.3[,mediator] <- sim.df.3[,mediator][sim.df.3$birthcohort2==reference_cohort]
sim.df.3.cf[,m] <- predict(outcome.model.3, type="response", newdata = sim.df.3)
#4
#natural course
#predict mediator values
sim.df.4[,mediator] <- rMultinom(m = 1, probs = simulated_mediator.4)
sim.df.4.nc[,m] <- predict(outcome.model.4, type="response", newdata = sim.df.4)
#counterfactual
sim.df.4[,mediator] <- sim.df.4[,mediator][sim.df.4$birthcohort2==reference_cohort]
sim.df.4.cf[,m] <- predict(outcome.model.4, type="response", newdata = sim.df.4)
}
} else if (class(dataframe[,mediator]) == "numeric"){
mediator.model.1 <- lm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
family ="binomial",
data = bootstrap_sample.1)
mediator.model.2 <- lm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
family ="binomial",
data = bootstrap_sample.2)
mediator.model.3 <- lm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
family ="binomial",
data = bootstrap_sample.3)
mediator.model.4 <- lm(paste(mediator, "~ Ns(AGEY_M, df=6) + Ns(period, df=4,detrend=TRUE) + Ns(birthcohort2, df=10) + RAGENDER + RAEDUC",sep=""),
family ="binomial",
data = bootstrap_sample.4)
simulated_mediator.1 <- predict(mediator.model.1, type = "response", newdata = sim.df.1)
simulated_mediator.2 <- predict(mediator.model.2, type = "response", newdata = sim.df.2)
simulated_mediator.3 <- predict(mediator.model.3, type = "response", newdata = sim.df.3)
simulated_mediator.4 <- predict(mediator.model.4, type = "response", newdata = sim.df.4)
for (m in 1:mc_iteration){
#1
#natural course
#predict mediator values
sim.df.1[,mediator] <- rnorm(n=nrow(sim.df.1), mean = mean(simulated_mediator.1), sd=sd(mediator.model.1$residuals))
sim.df.1.nc[,m] <- predict(outcome.model.1, type="response", newdata = sim.df.1)
#counterfactual
sim.df.1[,mediator] <- sim.df.1[,mediator][sim.df.1$birthcohort2==reference_cohort]
sim.df.1.cf[,m] <- predict(outcome.model.1, type="response", newdata = sim.df.1)
#2
#natural course
#predict mediator values
sim.df.2[,mediator] <- rnorm(n=nrow(sim.df.2), mean = mean(simulated_mediator.2), sd=sd(mediator.model.2$residuals))
sim.df.2.nc[,m] <- predict(outcome.model.2, type="response", newdata = sim.df.2)
#counterfactual
sim.df.2[,mediator] <- sim.df.2[,mediator][sim.df.2$birthcohort2==reference_cohort]
sim.df.2.cf[,m] <- predict(outcome.model.2, type="response", newdata = sim.df.2)
#4
#natural course
#predict mediator values
sim.df.3[,mediator] <- rnorm(n=nrow(sim.df.3), mean = mean(simulated_mediator.3), sd=sd(mediator.model.3$residuals))
sim.df.3.nc[,m] <- predict(outcome.model.3, type="response", newdata = sim.df.3)
#counterfactual
sim.df.3[,mediator] <- sim.df.3[,mediator][sim.df.3$birthcohort2==reference_cohort]
sim.df.3.cf[,m] <- predict(outcome.model.3, type="response", newdata = sim.df.3)
#4
#natural course
#predict mediator values
sim.df.4[,mediator] <- rnorm(n=nrow(sim.df.4), mean = mean(simulated_mediator.4), sd=sd(mediator.model.4$residuals))
sim.df.4.nc[,m] <- predict(outcome.model.4, type="response", newdata = sim.df.4)
#counterfactual
sim.df.4[,mediator] <- sim.df.4[,mediator][sim.df.4$birthcohort2==reference_cohort]
sim.df.4.cf[,m] <- predict(outcome.model.4, type="response", newdata = sim.df.4)
}
} else {
return("Mediator has the wrong class")
}
df.BS.1.nc <- rowMeans(sim.df.1.nc)
df.BS.1.cf <- rowMeans(sim.df.1.cf)
results_white <- c(df.BS.1.nc, df.BS.1.cf)
df.BS.2.nc <- rowMeans(sim.df.2.nc)
df.BS.2.cf <- rowMeans(sim.df.2.cf)
results_black <- c(df.BS.2.nc, df.BS.2.cf)
df.BS.3.nc <- rowMeans(sim.df.3.nc)
df.BS.3.cf <- rowMeans(sim.df.3.cf)
results_hisp <- c(df.BS.3.nc, df.BS.3.cf)
df.BS.4.nc <- rowMeans(sim.df.4.nc)
df.BS.4.cf <- rowMeans(sim.df.4.cf)
results_other <- c(df.BS.4.nc, df.BS.4.cf)
c(results_white,results_black,results_hisp, results_other)
} else {
return("Specify strata as FALSE, sex or ethnicity")
}
}
## decomposition table function
## needs results_nc and results_cf income
## strata=FALSE -> main analysis, can also accommodate strata="sex" and strata="ethnicity"
## bs_it are the bootstrap iteration. If not specified 499 iterations are assumed.
decomp_table <- function(results_nc, results_cf, bs_it=499, strata=FALSE){
x <- ncol(results_nc)
if(strata=="FALSE"){
results_nc$lb <- round(apply(results_nc[,4:x], 1, quantile, probs = c(0.025))*100,2)
results_nc$ub <- round(apply(results_nc[,4:x], 1, quantile, probs = c(0.975))*100,2)
results_nc$prob.depress <- round(rowMeans(results_nc[,4:x])*100,2)
results_nc$scenario <- "natural course"
#remove unaggregated columns
results_nc <- results_nc[,c(1:3, (bs_it+4):(bs_it+7))]
results_cf$lb <- round(apply(results_cf[,4:x], 1, quantile, probs = c(0.025))*100,2)
results_cf$ub <- round(apply(results_cf[,4:x], 1, quantile, probs = c(0.975))*100,2)
results_cf$prob.depress <- round(rowMeans(results_cf[,4:x])*100,2)
results_cf$scenario <- "counterfactual"
#remove unaggregated columns
results_cf <- results_cf[,c(1:3, (bs_it+4):(bs_it+7))]
plot.results <- merge(results_nc, results_cf, by = c("birthcohort2", "period", "AGEY_M"))
plot.results.c <- subset(plot.results, plot.results$AGEY_M == 50 &
plot.results$period == 1996)
#remove age and period columns
plot.results.c <- plot.results.c[,c(1,4:11)]
plot.results.c <- as.data.frame(plot.results.c)
plot.results.c$naturalcourse <- paste0(plot.results.c$prob.depress.x,"% (",plot.results.c$lb.x,"-",plot.results.c$ub.x,")")
plot.results.c$counterfactual <- paste0(plot.results.c$prob.depress.y,"% (",plot.results.c$lb.y,"-",plot.results.c$ub.y,")")
table <- plot.results.c[,c(1,10,11)]
}
else if (strata=="sex"){
results_nc$lb <- round(apply(results_nc[,5:x], 1, quantile, probs = c(0.025))*100,2)
results_nc$ub <- round(apply(results_nc[,5:x], 1, quantile, probs = c(0.975))*100,2)
results_nc$prob.depress <- round(rowMeans(results_nc[,5:x])*100,2)
results_nc$scenario <- "natural course"
#remove unaggregated columns
results_nc <- results_nc[,c(1:4, (bs_it+5):(bs_it+8))]
results_cf$lb <- round(apply(results_cf[,5:x], 1, quantile, probs = c(0.025))*100,2)
results_cf$ub <- round(apply(results_cf[,5:x], 1, quantile, probs = c(0.975))*100,2)
results_cf$prob.depress <- round(rowMeans(results_cf[,5:x])*100,2)
results_cf$scenario <- "counterfactual"
#remove unaggregated columns
results_cf <- results_cf[,c(1:4, (bs_it+5):(bs_it+8))]
plot.results <- merge(results_nc, results_cf, by = c("birthcohort2", "period", "AGEY_M","sex"))
plot.results.c <- subset(plot.results, plot.results$AGEY_M == 50 &
plot.results$period == 1996)
plot.results.c <- plot.results.c[,c(1,4,5:12)]
plot.results.c <- as.data.frame(plot.results.c[order(plot.results.c$sex),])
plot.results.c$naturalcourse <- paste0(plot.results.c$prob.depress.x,"% (",plot.results.c$lb.x,"-",plot.results.c$ub.x,")")
plot.results.c$counterfactual <- paste0(plot.results.c$prob.depress.y,"% (",plot.results.c$lb.y,"-",plot.results.c$ub.y,")")
table <- plot.results.c[,c(1,2,11,12)]
}
else if (strata=="ethn"){
results_nc$lb <- round(apply(results_nc[,5:x], 1, quantile, probs = c(0.025))*100,2)
results_nc$ub <- round(apply(results_nc[,5:x], 1, quantile, probs = c(0.975))*100,2)
results_nc$prob.depress <- round(rowMeans(results_nc[,5:x])*100,2)
results_nc$scenario <- "natural course"
results_nc <- results_nc[,c(1:4, (bs_it+5):(bs_it+8))]
results_cf$lb <- round(apply(results_cf[,5:x], 1, quantile, probs = c(0.025))*100,2)
results_cf$ub <- round(apply(results_cf[,5:x], 1, quantile, probs = c(0.975))*100,2)
results_cf$prob.depress <- round(rowMeans(results_cf[,5:x])*100,2)
results_cf$scenario <- "counterfactual"
results_cf <- results_cf[,c(1:4, (bs_it+5):(bs_it+8))]
plot.results <- merge(results_nc, results_cf, by = c("birthcohort2", "period", "AGEY_M","ethn"))
plot.results.c <- subset(plot.results, plot.results$AGEY_M == 50 &
plot.results$period == 1996)
plot.results.c <- plot.results.c[,c(1,4,5:12)]
plot.results.c <- as.data.frame(plot.results.c[order(plot.results.c$ethn),])
plot.results.c$naturalcourse <- paste0(plot.results.c$prob.depress.x,"% (",plot.results.c$lb.x,"-",plot.results.c$ub.x,")")
plot.results.c$counterfactual <- paste0(plot.results.c$prob.depress.y,"% (",plot.results.c$lb.y,"-",plot.results.c$ub.y,")")
table <- plot.results.c[,c(1,2,11,12)]
}
return(table)
}
## make relative difference and contribution function
## needs results_nc and results_cf income
## strata=FALSE -> main analysis, can also accommodate strata="sex" and strata="ethnicity"
## bs_it are the bootstrap iteration. If not specified 499 iterations are assumed.
rel.diff_contr_table <- create_mediationplot_abs.rel.diff <- function(results_nc, results_cf, bs_it=499, strata =FALSE){
results_nc <- subset(results_nc, results_nc$AGEY_M == 50 &
results_nc$period == 1996)
results_cf <- subset(results_cf, results_cf$AGEY_M == 50 &
results_cf$period == 1996)
x <- ncol(results_nc)
if (strata=="FALSE"){
#relative difference
BS.diff <- (results_cf[,4:x] / results_nc[,4:x])-1
BS.diff2 <- BS.diff
BS.diff2$lb <- round(apply(BS.diff, 1, quantile, probs = c(0.025))*100,2)
BS.diff2$ub <- round(apply(BS.diff, 1, quantile, probs = c(0.975))*100,2)
BS.diff2$diff <- round(rowMeans(BS.diff)*100,2)
#contribution
#ref.cf prob.
#ref cohort prob same for cf and nc
p.depr.refcohort <- rowMeans(results_nc[results_nc$birthcohort2==1945,4:x])
perc.med <- 1-((results_cf[,4:x] - p.depr.refcohort)/((results_nc[,4:x]) - p.depr.refcohort))
perc.med2 <- perc.med
perc.med2$contr <- round(apply(perc.med, 1, median)*100,2)
perc.med2$lb_c <- round(apply(perc.med, 1, quantile, probs = c(0.025))*100,2)
perc.med2$ub_c <- round(apply(perc.med, 1, quantile, probs = c(0.975))*100,2)
make.table <- cbind(results_nc[,1:3], BS.diff2[,(ncol(BS.diff2)-2):ncol(BS.diff2)], perc.med2[,(ncol(perc.med2)-2):ncol(perc.med2)])
make.table <- as.data.frame(make.table)
make.table$reldiff <- paste0(make.table$diff,"% (",make.table$lb,"-",make.table$ub,")")
make.table$contr <- paste0(make.table$contr,"% (",make.table$lb_c,"-",make.table$ub_c,")")
return(make.table[,c(1:3,10,7)])
}
else if (strata=="sex"){
#relative difference
BS.diff <- (results_cf[,5:x] / results_nc[,5:x])-1
BS.diff2 <- BS.diff
BS.diff2$lb <- round(apply(BS.diff, 1, quantile, probs = c(0.025))*100,2)
BS.diff2$ub <- round(apply(BS.diff, 1, quantile, probs = c(0.975))*100,2)
BS.diff2$diff <- round(rowMeans(BS.diff)*100,2)
#contribution
p.depr.refcohort <- ifelse(results_nc$sex == "female",
rowMeans(results_nc[results_nc$birthcohort2==1945 & results_nc$sex=="female",5:x]),
ifelse(results_nc$sex == "male",
rowMeans(results_nc[results_nc$birthcohort2==1945 & results_nc$sex=="male",5:x]), NA))
perc.med <- 1-((results_cf[,5:x] - p.depr.refcohort)/((results_nc[,5:x]) - p.depr.refcohort))
perc.med2 <- perc.med
perc.med2$contr <- round(apply(perc.med, 1, median)*100,2)
perc.med2$lb_c <- round(apply(perc.med, 1, quantile, probs = c(0.025))*100,2)
perc.med2$ub_c <- round(apply(perc.med, 1, quantile, probs = c(0.975))*100,2)
make.table <- cbind(results_nc[,1:4], BS.diff2[,(ncol(BS.diff2)-2):ncol(BS.diff2)], perc.med2[,(ncol(perc.med2)-2):ncol(perc.med2)])
make.table <- as.data.frame(make.table[order(make.table$sex),])
make.table$reldiff <- paste0(make.table$diff,"% (",make.table$lb,"-",make.table$ub,")")
make.table$contr <- paste0(make.table$contr,"% (",make.table$lb_c,"-",make.table$ub_c,")")
return(make.table[,c(1:4,11,8)])
}
else if (strata=="ethn"){
#relative difference
BS.diff <- (results_cf[,5:x] / results_nc[,5:x])-1
BS.diff2 <- BS.diff
BS.diff2$lb <- round(apply(BS.diff, 1, quantile, probs = c(0.025))*100,2)
BS.diff2$ub <- round(apply(BS.diff, 1, quantile, probs = c(0.975))*100,2)
BS.diff2$diff <- round(rowMeans(BS.diff)*100,2)
#contribution
p.depr.refcohort <- ifelse(results_nc$ethn == "White/Caucasian",
rowMeans(results_nc[results_nc$birthcohort2==1945 & results_nc$ethn=="White/Caucasian",5:x]),
ifelse(results_nc$ethn == "Black/African American",
rowMeans(results_nc[results_nc$birthcohort2==1945 & results_nc$ethn=="Black/African American",5:x]),
ifelse(results_nc$ethn == "Hispanic",
rowMeans(results_nc[results_nc$birthcohort2==1945 & results_nc$ethn=="Hispanic",5:x]), NA)))
perc.med <- 1-((results_cf[,5:x] - p.depr.refcohort)/((results_nc[,5:x]) - p.depr.refcohort))
perc.med2 <- perc.med
perc.med2$contr <- round(apply(perc.med, 1, median)*100,2)
perc.med2$lb_c <- round(apply(perc.med, 1, quantile, probs = c(0.025))*100,2)
perc.med2$ub_c <- round(apply(perc.med, 1, quantile, probs = c(0.975))*100,2)
make.table <- cbind(results_nc[,1:4], BS.diff2[,(ncol(BS.diff2)-2):ncol(BS.diff2)], perc.med2[,(ncol(perc.med2)-2):ncol(perc.med2)])
make.table <- as.data.frame(make.table[order(make.table$ethn),])
make.table$reldiff <- paste0(make.table$diff,"% (",make.table$lb,"-",make.table$ub,")")
make.table$contr <- paste0(make.table$contr,"% (",make.table$lb_c,"-",make.table$ub_c,")")
return(make.table[,c(1:4,11,8)])
}
}