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KFWGrids_panelResults_Max_Pre2001_HighPressure.R
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KFWGrids_panelResults_Max_Pre2001_HighPressure.R
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#-------------------------------------------------
#-------------------------------------------------
#Panel Models - KFW Grid
#Testing in Panel the impact of being treated with demarcation
#On the Max Level of NDVI, measured as the yearly max NDVI value (LTDR)
#-------------------------------------------------
#-------------------------------------------------
library(devtools)
devtools::install_github("itpir/SCI@master")
library(SCI)
library(stargazer)
library(lmtest)
library(multiwayvcov)
library(SDMTools)
loadLibs()
#-------------------------------------------------
#-------------------------------------------------
#Load in Processed Data - produced from script build_panel_ALL151_highpressure.R
#-------------------------------------------------
#-------------------------------------------------
psm_Long<-read.csv("/Users/rbtrichler/Documents/AidData/KFW Brazil Eval/GridDataProcessed/psm_Long_151_highpressure.csv")
#Create post-2004 and post-2008 dummy
psm_Long$Post2004 <- 0
psm_Long$Post2004[psm_Long$Year > 2004] <- 1
psm_Long$Post2008<- 0
psm_Long$Post2008[psm_Long$Year>2008]<-1
## Run Models
pModelMax_A <- "MaxL ~ trtdem + factor(reu_id)"
pModelMax_B <- "MaxL ~ trtdem + Pop+ MeanT + MeanP +MaxT + MaxP + MinT + MinP + factor(reu_id) "
pModelMax_C <- "MaxL ~ trtdem + Pop +MeanT + MeanP +MaxT + MaxP + MinT + MinP + Year + factor(reu_id)"
pModelMax_C2004 <- "MaxL ~ trtdem + Pop + MeanT + MeanP +MaxT + MaxP + MinT + MinP + Year + Post2004*trtdem + factor(reu_id)"
pModelMax_C1_2004 <- "MaxL ~ trtdem + trtenf + Pop + MeanT + MeanP +MaxT + MaxP + MinT + MinP + Year + Post2004*trtenf + factor(reu_id)"
pModelMax_D <- "MaxL ~ trtdem + Pop + MeanT + MeanP +MaxT + MaxP + MinT + MinP + factor(Year) + factor(reu_id)"
pModelMax_D1 <- "MaxL ~ trtdem + trtenf +Pop +MeanT + MeanP +MaxT + MaxP + MinT + MinP + factor(Year) + factor(reu_id)"
pModelMax_D2004 <- "MaxL ~ trtdem + Pop +MeanT + MeanP +MaxT + MaxP + MinT + MinP + Post2004*trtdem + factor(Year) + factor(reu_id)"
pModelMax_D1_2004 <- "MaxL ~ trtdem + trtenf + Pop +MeanT + MeanP +MaxT + MaxP + MinT + MinP + Post2004*trtenf + factor(Year) + factor(reu_id)"
pModelMax_A_fit <- Stage2PSM(pModelMax_A ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_B_fit <- Stage2PSM(pModelMax_B ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C_fit <- Stage2PSM(pModelMax_C ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C2004_fit <- Stage2PSM(pModelMax_C2004 ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C1_2004_fit <- Stage2PSM(pModelMax_C1_2004 ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_D_fit <- Stage2PSM(pModelMax_D ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_D1_fit <- Stage2PSM(pModelMax_D1 ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_D2004_fit <- Stage2PSM(pModelMax_D2004 ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_D1_2004_fit <- Stage2PSM(pModelMax_D1_2004 ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
#Create new interaction terms to assist in stargazer formatting later
psm_Long$predict_NDVI_max_pre_cat_int<-psm_Long$predict_NDVI_max_pre_cat*psm_Long$trtdem
psm_Long$predict_NDVI_max_pre_int <- psm_Long$predict_NDVI_max_pre*psm_Long$trtdem
psm_Long$pre_trend_NDVI_max_cat_int <- psm_Long$pre_trend_NDVI_max_cat*psm_Long$trtdem
psm_Long$pre_trend_NDVI_max_int <- psm_Long$pre_trend_NDVI_max*psm_Long$trtdem
pModelMax_E <- "MaxL ~ trtdem + Pop + MeanT + MeanP + MaxT + MaxP + MinT + MinP +
predict_NDVI_max_pre_cat + predict_NDVI_max_pre_cat_int + factor(reu_id) + factor(Year)"
pModelMax_F <- "MaxL ~ trtdem +Pop +MeanT + MeanP + MaxT + MaxP + MinT + MinP +
predict_NDVI_max_pre + predict_NDVI_max_pre_int + factor(reu_id) + factor(Year)"
pModelMax_G <- "MaxL ~ trtdem + Pop +MeanT + MeanP + MaxT + MaxP + MinT + MinP +
pre_trend_NDVI_max_cat + pre_trend_NDVI_max_cat_int + factor(reu_id) + factor(Year)"
pModelMax_H <- "MaxL ~ trtdem +Pop +MeanT + MeanP + MaxT + MaxP + MinT + MinP +
pre_trend_NDVI_max + pre_trend_NDVI_max_int + factor(reu_id) + factor(Year)"
pModelMax_E_fit <- Stage2PSM(pModelMax_E,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_F_fit <- Stage2PSM(pModelMax_F,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_G_fit <- Stage2PSM(pModelMax_G,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_H_fit <- Stage2PSM(pModelMax_H ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
## -------
## Stargazer Output
## -------
#Used for JEEM resubmission
stargazer(pModelMax_A_fit $cmreg,pModelMax_B_fit $cmreg,pModelMax_C_fit $cmreg,pModelMax_D_fit $cmreg,
pModelMax_D1_fit $cmreg,
pModelMax_E_fit $cmreg,pModelMax_F_fit $cmreg,pModelMax_G_fit$cmreg, pModelMax_H_fit$cmreg,
type="html",align=TRUE,
keep=c("trt","Pop", "MeanT","MeanP","MaxT","MaxP","MinT","MinP",
"predict_NDVI_max_pre_cat","predict_NDVI_max_pre_cat_int",
"predict_NDVI_max_pre","predict_NDVI_max_pre_int",
"pre_trend_NDVI_max_cat","pre_trend_NDVI_max_cat_int",
"pre_trend_NDVI_max","pre_trend_NDVI_max_int","Year"),
covariate.labels=c("Treatment (Demarcation)","Treatment (Demarcation + Enforcement Support)", "Population", "Mean Temp","Mean Precip","Max Temp","Max Precip","Min Temp","Min Precip",
"Predicted NDVI Pre-Trend (Cat)","Predicted NDVI Pre-Trend(Cat)*Treatment","Predicted NDVI Pre-Trend",
"Predicted NDVI Pre-Trend * Treatment", "NDVI Pre-Trend (Cat)", "NDVI Pre-Trend(Cat)*Treatment",
"NDVI Pre-Trend","NDVI Pre-Trend*Treatment", "Year"),
order=c("trt","Pop","MeanT","MeanP","MaxT","MaxP","MinT","MinP","NDVI","Year"),
keep.stat=c("n"),
add.lines=list(c("Observations","246,007","246,007","246,007","246,007","246,007","246,007","246,007","246,007","246,007"),
c("Community Fixed Effects?","Yes","Yes","Yes","Yes","Yes","Yes","Yes","Yes","Yes"),
c("Year Fixed Effects?","No","No","No","Yes","Yes","Yes","Yes","Yes","Yes")),
title="Regression Results",
dep.var.labels=c("Max NDVI")
)
#Used for JEEM resubmission
stargazer(pModelMax_C2004_fit $cmreg,pModelMax_D2004_fit $cmreg,pModelMax_C1_2004_fit $cmreg,
pModelMax_D1_2004_fit $cmreg,
type="html",align=TRUE,
keep=c("trt","Pop", "MeanT","MeanP","MaxT","MaxP","MinT","MinP","Year"),
covariate.labels=c("Treatment (Demarcation)","Treatment (Demarcation + Enforcement Support)",
"Treatment (Demarcation)*Post2004", "Treatment (Dem + Enf)*Post2004",
"Population", "Mean Temp","Mean Precip","Max Temp","Max Precip",
"Min Temp","Min Precip", "Year"),
order=c("trt","Pop","MeanT","MeanP","MaxT","MaxP","MinT","MinP","NDVI","Year"),
keep.stat=c("n"),
add.lines=list(c("Observations","246,007","246,007","246,007","246,007"),
c("Community Fixed Effects?","Yes","Yes","Yes","Yes"),
c("Year Fixed Effects?","No","No","Yes","Yes")),
title="Regression Results",
dep.var.labels=c("Max NDVI")
)
## -------
#STATS TABLE, weighted
#---------
# for JEEM 2nd submission, used code to output summary stats table
# but then had to manually add (to html or in Excel where formatted) the weighted mean and sd (and correlation coefficient)
# for below, only the var names and min/max values are correct if want to weight by community size
stargazer(psm_Long, type="html",
keep=c("MaxL","Slope","Road","Riv","Elevation","terrai_are","Pop","Mean","Min","MaxT",
"MaxP","pre_trend_NDVI_max","predict_NDVI_max_pre"),
# covariate.labels=c("NDVI","Slope (degree)","Distance to Road (m)","Distance to River (m)","Elevation (m)",
# "Area (hectares)","Population Density","Mean Temperature","Mean Precipitation",
# "Min Temperature","Min Precipitation","Max Temperature","Max Precipitation",
# "NDVI Pre Trend","Predicted NDVI Pre Trend"),
omit.summary.stat=c("n"))
## Manually produce weighted mean and sd
# weight by size of community for summary stats
#create weight
psm_Long$commwt <- 1/psm_Long$terrai_are
summary(psm_Long$commwt)
#manually change the var as needed
wt.mean(psm_Long$terrai_are,psm_Long$commwt)
wt.sd(psm_Long$terrai_are,psm_Long$commwt)
summary(psm_Long$terrai_are)
## Manually create correlation coefficients with outcome/control vars and year of demarcation
#produced for JEEM second revision summary stats table
#need to subset to only communities that were demarcated
dem<-psm_Long[!is.na(psm_Long$demend_y),]
cor(dem$Slope,dem$demend_y)
# !actually need to produce weighted correlation coefficients for JEEM second revision
# manually change var as needed in excel formatting file
wtd.cors(dem$Slope,dem$demend_y)
wtd.cors(dem$Slope, dem$demend_y,dem$commwt)
#---------------------
#Workspace
#---------------------
## -- trying to implement lag function
psm_Long_lag <- TimeSeriesLag(psm_Long,"Year","GridID",1,"MaxL_","MaxL_lag",1983,2010)
psm_Long_lag_test <- psm_Long_lag[psm_Long_lag["GridID"]==319588,]
psm_Long_85 <- psm_Long[psm_Long["reu_id"]==85,]
psm_Long_143 <- psm_Long[psm_Long["reu_id"]==143,]
pModelMax_I <- "MaxL_ ~ MaxL_lag"
pModelMax_J <- "MaxL_ ~ MaxL_lag + factor(reu_id)"
pModelMax_K <- "MaxL_ ~ pre_trend_NDVI_max + factor(reu_id)"
pModelMax_I_fit <- Stage2PSM(pModelMax_I,psm_Long_lag,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_J_fit <- Stage2PSM(pModelMax_J,psm_Long_lag,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_K_fit <- Stage2PSM(pModelMax_K,psm_Long_lag,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
## -- weights with gridid
try<-psm_Long
test<-table(psm_Long$reu_id)
test<-as.data.frame(table(psm_Long$reu_id))
try<-merge(try,test,by.x="reu_id",by.y="Var1")
try$gridwt<-1/try$Freq
## -- for loop for summary stats (didn't get it to work)
#subset
stat_vars<-c("Id","demend_y","Year","MaxL","Slope","Riv_Dist","Road_dist","Elevation","terrai_are","Pop",
"MeanT","MeanP","MinT","MinP","MaxT","MaxP","pre_trend_NDVI_max","predict_NDVI_max_pre","commwt")
stats<-psm_Long[,(names(psm_Long) %in% stat_vars)]
stats<-stats[,order(names(stats))]
names(stats)
statstest<-stats[stats$Id==155,]
#get summary stats and corr coefficient
result <- zeros
for(i in stats)
{
print(wt.mean((i),stats$commwt))
}
for(i in stats)
{
print(wt.sd((i),stats$commwt))
}
for(i in stats)
{
print(summary(i))
}
for(i in stats)
{
print(summary(i))
}
#the below didn't work, don't know how to save results output
n<-length(statstest)
v2<-rep(NA,n)
for (i in statstest)
{
v2<-wt.mean((i),statstest$commwt)
}
## -- learning how to output tables
test<-function(mean) {
tabl<-mean(psm_Long$terrai_are)
return(tabl)
}
apply(psmenf,2,function(x) length (x[x<0]))
y<-psm_Long$demend_y
test<-psm_Long[,1:4]
tbl<-sapply(test,function(x) cor(x,y))
tbl<-sapply(test,function(x) mean(x))
xtable(tbl)
design.matrix <- model.matrix(mean(x), data = test)
xtable(design.matrix, digits = 0)
dat <- psm_Long[1:3, 1:4]
z <- xtable(bubba)
ztab<-xtable(z)
print.xtable(ztab,type="html")
bubba<-data.frame(row.names<-names(tbl),matrix(unlist(tbl)))
bub<-data.frame(row.names<-names(blah),matrix(unlist(blah)))
blah<-sapply(dat,function(x) mean(x))
bubba_large<-cbind(bubba,bub)
bubba_large<-merge(bubba,bub,by.x=bubba$row.names....names.tbl.,by.y=bub$row.names....names.blah.)
sapply(psmenf, function(x) cor(psmenf$demend_y,psmenf$terrai_are))
dtf <- sapply(psmenf, each(min, max, mean, sd, var, median, IQR))
dtf
#------
#Archived
#------
pModelMax_C_reuid <- "MaxL_ ~ trtdem + Pop_ + MeanT_ + MeanP_ +MaxT_ + MaxP_ + MinT_ + MinP_ + Year*factor(reu_id) + factor(reu_id)"
pModelMax_C_reuidenf <- "MaxL_ ~ trtdem + trtenf+ Pop_ + MeanT_ + MeanP_ +MaxT_ + MaxP_ + MinT_ + MinP_ + Year*factor(reu_id) + factor(reu_id)"
pModelMax_C_reuid_fe <- "MaxL_ ~ trtdem + Pop_ + MeanT_ + MeanP_ +MaxT_ + MaxP_ + MinT_ + MinP_ + Year*factor(reu_id) + factor(Year) + factor(reu_id)"
pModelMax_C_reuidenf_fe <- "MaxL_ ~ trtdem + trtenf+ Pop_ + MeanT_ + MeanP_ +MaxT_ + MaxP_ + MinT_ + MinP_ + Year*factor(reu_id) + factor(Year) + factor(reu_id)"
pModelMax_C_reuid_fit <- Stage2PSM(pModelMax_C_reuid ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C_reuidenf_fit <- Stage2PSM(pModelMax_C_reuidenf ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C_reuid_fe_fit <- Stage2PSM(pModelMax_C_reuid_fe ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
pModelMax_C_reuidenf_fe_fit <- Stage2PSM(pModelMax_C_reuidenf_fe ,psm_Long,type="cmreg", table_out=TRUE, opts=c("reu_id","Year"))
#####Making weighted summary statistics more automatically####
#
##make a subset of the whole dataframe with only the variables that we want to end up in the table
#psm_Long_sub <- psm_Long[, c("MaxL","Slope","Road_dist","Riv_Dist","Elevation","terrai_are","Pop","MeanT","MinT","MaxT",
# "MinP","MeanP","MaxP","pre_trend_NDVI_max","predict_NDVI_max_pre")]
#
##make the weights, put them in the original dataframe
#psm_Long$commwt <- 1/psm_Long$terrai_are
#summary(psm_Long$commwt)
#
##make a new subset of the original dataframe for making the correlations,
##in this case it must be subset to only demarcated communities
#dem <- psm_Long[!is.na(psm_Long$demend_y),]
##again, subset to the variables that we want to end up in the table, PLUS the variable that we are finding correlations with (in this case demend_y)
#dem <- dem[, c("MaxL","Slope","Road_dist","Riv_Dist","Elevation","terrai_are","Pop","MeanT","MinT","MaxT",
# "MinP","MeanP","MaxP","pre_trend_NDVI_max","predict_NDVI_max_pre", "demend_y")]
#
##must make weights again and put them in this dataframe
#dem$commwt <- 1/dem$terrai_are
#
##create the dataframe which will be displayed in stargazer, starting with the column of variable names
#summ_df <- data.frame(Statistic = c("MaxL","Slope","Road_dist","Riv_Dist","Elevation","terrai_are","Pop","MeanT","MinT","MaxT",
# "MinP","MeanP","MaxP","pre_trend_NDVI_max","predict_NDVI_max_pre"))
#
##using lapply, calculate weighted variables
#summ_df$Mean <- lapply(psm_Long_sub[,], wt.mean, wt=psm_Long$commwt)
#summ_df$St.Dev. <- lapply(psm_Long_sub[,], wt.sd, wt=psm_Long$commwt)
#summ_df$Min <- lapply(psm_Long_sub[,], min)
#summ_df$Max <- lapply(psm_Long_sub[,], max)
#
##using lapply, calculate weighted correlations, then drop the variables outside of 1:15 (demend_y and commwt) which we don't want in the stargazer table
#summ_df$Corr <- as.list(lapply(dem[,], wtd.cors, y=dem$demend_y, weight=dem$commwt))[1:15]
#
##finally, make the stargazer table, with summary statistics omitted so that the dataframe itself is displayed
#stargazer(summ_df, type = "html", summary = FALSE, rownames = FALSE)