demo/test.spatial.data2.R
In DanRunfola/SimTests: MatchIt: Nonparametric Preprocessing for Parametric Casual Inference
rm(list = ls())
library(sp)
library(ncf)
library(gstat)
library(devtools)
library(rpart)
library(rpart.plot)
library(Rcpp)
Sys.setenv("PKG_CXXFLAGS"="-fopenmp")
Sys.setenv("PKG_LIBS"="-fopenmp")
sourceCpp("/home/aiddata/Desktop/Github/MatchIt/demo/splitc.cpp")
#detach("package:MatchIt", unload=TRUE)
#load_all("~/Desktop/Github/MatchIt/R")
library(devtools)
install_github("itpir/matchit")
library(MatchIt)
iterations <- 25
results <- data.frame(
id=c(1:iterations)
)
results_out <- data.frame(
id=c(1:iterations)
)
results_nospill <- data.frame(
id=c(1:iterations)
)
ptm <- proc.time()
for(p in 1:iterations)
{
results["id"] <- p
results_out["id"] <- p
# -----------------------------------------------------------------------------
# Simulation Settings
# -----------------------------------------------------------------------------
#General Options
# set dataframe size (number of points)
nrandom <- 2000
#General psill
#Note: setting this to 0 will result in no data randomization.
#Larger values indicate more autocorrelation.
psill <- 1 + runif(1, -.95, 20)
# define bounding box
minx <- -45
maxx <- 45
miny <- -22.5
maxy <- 22.5
#Covariate Spatial Correlation
var1.vrange <- 1 + runif(1, -.95, 20)
#Degree to which the covariate
#explains the propensity to receive
#treatment (1.0 = perfect correlation, or no error)
prop_acc = runif(1, 0.05, .95)
#Spatial pattern in the PSM error, if any
#(vrange = 1 approximates random noise)
var1_error.vrange <- runif(1, 0.1, 20)
#Define the spatial pattern of any model error.
#Magnitue of error is 0-1 (0 = no error)
mod_error.vrange <- 1.0
mod_error.magnitude <- runif(1, 0.1, 2)
#Percent of locations which are to be defined as
#treated
trt_prc = runif(1, 0.45, 0.5)
#Theta coefficient for treatment effect
#used for defining the outcome
theta <- 1#runif(1, -.95, 10)
#Beta coefficient for ancillary data
#in defining the treatment
beta <- 1#runif(1,1,5) * theta
#Spillover Range
spill.vrange <- 1.0 + runif(1, -.95, 2)
#Spillover Magnitude (relative to theta)
spill.magnitude <- 1 * runif(1, 0, 3)
#Caliper for Matching
cal = .5
sample_size = runif(1,0.05,1.0)
tree_split_lim = sample(2:50,1)
# -----------------------------------------------------------------------------
# Data Simulation
# -----------------------------------------------------------------------------
#Given your settings, outputs:
#spdf@data$modelVar - an ancillary variable with error (defined by var1_error)
#spdf@data$treatment.status - Binary 1/0 treated
#spdf@data$trueOutcome - the measured outcome
#spdf@data$modelOutcome - measured outcome including measurement error (model.error)
source("/home/aiddata/Desktop/Github/MatchIt/demo/simulation_spatial_data.R")
# -----------------------------------------------------------------------------
# Data Generation Visualizations (Per-iteration; currently not saved)
# Disable for a large speed boost.
# Note visualizations use MatchIt for PSM calculations (nearest/logit/caliper=0.25)
# -----------------------------------------------------------------------------
#Creates a figure describing your iteration (PSM vs. observed and parameterized spillovers
#and maps).
# source("/home/aiddata/Desktop/Github/MatchIt/demo/test.spatial.figures.R")
# -----------------------------------------------------------------------------
# Model Tests
# Save all Model Predictions into a SPDF for comparison.
# -----------------------------------------------------------------------------
outcome.predictions <- spdf
outcome.predictions@data <- outcome.predictions@data[c(1,8)]
treatment.predictions <- spdf
treatment.predictions@data <- treatment.predictions@data[c(1,6,7)]
treatment.predictions@data$trueTreatment <- (treatment.predictions@data$treatment.status)*
theta + treatment.predictions$trueSpill
nospill.t.pred <- spdf
nospill.t.pred@data <- nospill.t.pred@data[1]
nospill.t.pred@data$trueTreatment <- theta
model_dta <- spdf[sample(nrow(spdf), (nrandom * sample_size)), ]
#No Matching
baseline <- lm(modelOutcome ~ treatment.status + modelVar, data=model_dta@data)
outcome.predictions@data$baseline <- predict(baseline, newdata=spdf@data)
treatment.predictions@data$baseline <- summary(baseline)$coefficients[2]
nospill.t.pred@data$baseline <- summary(baseline)$coefficients[2]
#Baseline for Comparison
baseline.matchit <- matchit(treatment.status ~ modelVar, data= model_dta@data,
method="nearest", distance="logit",
caliper=cal, calclosest=FALSE, calrandom=FALSE)
baseline.model <- lm(modelOutcome ~ treatment.status + modelVar,
data=match.data(baseline.matchit))
outcome.predictions@data$baseline.matchit <- predict(baseline.model, newdata=spdf@data)
treatment.predictions@data$baseline.matchit <- summary(baseline.model)$coefficients[2]
nospill.t.pred@data$baseline.matchit <- summary(baseline.model)$coefficients[2]
#Cheating Spatial PSM - we give the accurate vrange, and use it as a threshold.
spatial.opts <- list(decay.model = "threshold",
threshold = spill.vrange)
spatial.trueThreshold <- matchit(treatment.status ~ modelVar, data= model_dta,
method = "nearest", distance = "logit",
caliper=cal, calclosest=FALSE, calrandom=FALSE,
spatial.options=spatial.opts)
spatial.trueThreshold.model <- lm(modelOutcome ~ treatment.status + modelVar,
data=match.data(spatial.trueThreshold))
outcome.predictions@data$spatial.trueThreshold <- predict(spatial.trueThreshold.model,
newdata=spdf@data)
treatment.predictions@data$spatial.trueThreshold <-
summary(spatial.trueThreshold.model)$coefficients[2]
nospill.t.pred@data$spatial.trueThreshold <- summary(spatial.trueThreshold.model)$coefficients[2]
#PSM-approximating Traditional and Spatial PSMs
#Propensity Correlogram
p_cor_spdf <- model_dta
p_cor_spdf$m1.pscore <- baseline.matchit$distance
correlog.pscore.spillover <- correlog(x=p_cor_spdf@coords[, 1],
y=p_cor_spdf@coords[, 2],
z=p_cor_spdf$treatment.status,
increment=500,
latlon=TRUE, na.rm=TRUE, resamp=5,
quiet=FALSE)
pscore.spillover.model.dta <- data.frame(
mean.of.class = c(correlog.pscore.spillover$mean.of.class),
correlation = c(correlog.pscore.spillover$correlation)
)
correlog.m1.polynomial <- lm(correlation ~ poly(mean.of.class, degree=5, raw=TRUE),
data=pscore.spillover.model.dta)
estimated.spillover.weights <- c()
for (k in 1:length(p_cor_spdf)) {
correlog.dist <- spDists(p_cor_spdf[k, ]@coords, p_cor_spdf@coords, longlat=TRUE)
correlog.neighbors <- correlog.dist > 0
correlog.treated <- p_cor_spdf$treatment.status
correlog.newdata <- data.frame(
mean.of.class = c(correlog.dist)
)
#Limit to the min (after the final predicted distance all values are 0)
correlog.newdata$weight_dist <- predict(correlog.m1.polynomial, newdata=correlog.newdata)
correlog.newdata$weight_dist[correlog.newdata$weight_dist < 0] <- 0
estimated.spillover.weights[k] <- sum(correlog.newdata$weight_dist * correlog.neighbors *
correlog.treated)
}
p_cor_spdf$spillover.est <- estimated.spillover.weights
#Spatial Matchit
spatial.opts <- list(decay.model = "threshold",
threshold = correlog.pscore.spillover$x.intercept+1)#(correlog.pscore.spillover$x.intercept+1))
#print(correlog.pscore.spillover$x.intercept)
spatial.matchit.spill <- matchit(treatment.status ~ modelVar, data=p_cor_spdf@data,
method="nearest", distance="logit",
caliper=cal, calclosest=FALSE, calrandom=FALSE)
spatial.matchit.spill.model <- lm(modelOutcome ~ treatment.status + modelVar,
data=match.data(spatial.matchit.spill))
outcome.predictions@data$spatial.matchit.spill <- predict(spatial.matchit.spill.model,
newdata=spdf@data)
treatment.predictions@data$spatial.matchit.spill <-
((summary(spatial.matchit.spill.model)$coefficients[2] * nrandom)) /
nrandom
nospill.t.pred@data$spatial.matchit.spill <- summary(spatial.matchit.spill.model)$coefficients[2]
p_cor_spdf@data["coord1"] <- coordinates(p_cor_spdf)[,1]
p_cor_spdf@data["coord2"] <- coordinates(p_cor_spdf)[,2]
#TOT - Non Random Forest
trans_dta <- p_cor_spdf
trans_dta <- trans_dta[(trans_dta@data$m1.pscore != 0 &
trans_dta@data$m1.pscore != 1),]
transOutcome <- list(rep(0,nrow(trans_dta)))
for(i in 1:nrow(p_cor_spdf))
{
if(trans_dta$treatment.status[i] == 1)
{
transOutcome[i] = trans_dta@data$modelOutcome[i] / trans_dta@data$m1.pscore[i]
}
else
{
transOutcome[i] = trans_dta@data$modelOutcome[i] / (1-trans_dta@data$m1.pscore[i])
}
}
trans_dta@data$transOutcome <- unlist(transOutcome)
tot.fit.spill <- rpart(transOutcome ~ modelVar + coord1 + coord2,
data = trans_dta@data,
control=rpart.control(cp=0, minsplit=tree_split_lim),
method="anova")
cpbest <- tot.fit.spill$cptable[which.min(tot.fit.spill$cptable[,"xerror"]),"CP"]
tot.fit.spillB <- rpart(transOutcome ~ modelVar + coord1 + coord2,
data = trans_dta@data,
control=rpart.control(cp=cpbest, minsplit=tree_split_lim),
method="anova")
spdf@data["coord1"] <- coordinates(spdf)[,1]
spdf@data["coord2"] <- coordinates(spdf)[,2]
res <- predict(tot.fit.spillB, newdata=spdf@data)
# Add by Jianing
db = spdf@data
tree2 = tot.fit.spillB
tree2$frame$yval = as.numeric(rownames(tree2$frame))
res2 = predict(tree2,newdata=spdf@data)
leaf = unique(tot.fit.spillB$where)
res3 = 0
for(i in 1:length(leaf)){
treatcount = 0
untreatcount = 0
count = 0
temp = 0
for(j in 1:nrow(db)){
if(res2[j] == leaf[i]){
temp = c(temp,j)
count = count + 1
if(db$treatment.status[j] == 1){
treatcount = treatcount + 1
}
if(db$treatment.status[j] == 0){
untreatcount = untreatcount + 1
}
}
}
if(count == treatcount | count == untreatcount ){
temp = temp[-1]
res3 = c(res3,temp)
}
}
res_leaf2prune = res3[-1]
#res[res_leaf2prune] = NA
## end
#Total Outcome
outcome.predictions@data$tot.spill <- NA
treatment.predictions@data$tot.spill <- res * treatment.predictions@data$treatment.status
nospill.t.pred@data$tot.spill <- NA
#CT
source("/home/aiddata/Desktop/Github/MatchIt/demo/CT_functions.R")
alist <- list(eval=ctev, split=ctsplit, init=ctinit)
#trans_dta
dbb = trans_dta@data
k = 10
n = dim(dbb)[1]
#sample_size = floor(n)
#ridx = sample(1:n,sample_size,replace=FALSE)
#crxvdata= dbb[ridx,]
crxvdata = dbb
crxvdata$id <- sample(1:k, nrow(crxvdata), replace = TRUE)
list = 1:k
fit1 = rpart(cbind(modelOutcome,treatment.status,m1.pscore,transOutcome) ~ modelVar + coord1 + coord2,
crxvdata,
control = rpart.control(cp = 0,minsplit = tree_split_lim),
method=alist)
fit = data.matrix(fit1$frame)
index = as.numeric(rownames(fit1$frame))
tsize = dim(fit1$frame[which(fit1$frame$var=="<leaf>"),])[1]
alpha = 0
alphalist = 0
alphalist = cross_validate(fit, index,alphalist)
res = rep(0,length(alphalist)-1)
for(j in 2:(length(alphalist)-1)){
res[j] = sqrt(alphalist[j]*alphalist[j+1])
}
alphacandidate = res
alphaset = rep(0,length(alphacandidate))
errset = rep(0,length(alphacandidate))
tsize = 0
for(l in 1:length(alphacandidate)){
alpha = alphacandidate[l]
error = 0
treesize = 0
for (i in 1:k){
trainingset <- subset(crxvdata, id %in% list[-i])
testset <- subset(crxvdata, id %in% c(i))
fit1 = rpart (cbind(modelOutcome,treatment.status,m1.pscore,transOutcome) ~ modelVar + coord1 + coord2,
trainingset,
control = rpart.control(cp = alpha,minsplit = tree_split_lim),
method=alist)
if(dim(fit1$frame)[1] == 1){
error = 0
break
}
else{
treesize = treesize + dim(fit1$frame[which(fit1$frame$var=="<leaf>"),])[1]
pt = predict(fit1,testset,type = "matrix")
y = data.frame(pt)
val = data.matrix(y)
idx = as.numeric(rownames(y))
dbidx = as.numeric(rownames(dbb))
for(pid in 1:(dim(y)[1])){
id = match(idx[pid],dbidx)
error = error + (crxvdata$transOutcome[id] - val[pid])^2
#print(error)
}
}
}
tsize = c(tsize,treesize/k)
if(error == 0){
errset[l] = 1000000
}
else{
errset[l] = error/k
}
msg = paste(l,": ",errset[l]*k,sep="")
#print(msg)
}
tsize = tsize[-1]
alpha_res = alphacandidate[which.min(errset)]
fit_ctpred <- rpart(cbind(modelOutcome,treatment.status,m1.pscore,transOutcome) ~ modelVar + coord1 + coord2,
crxvdata, control=rpart.control(minsplit=tree_split_lim,cp=alpha_res),
method=alist)
#prp(fit_ctpred)
#res = rep(0,length(alphalist)-1)
#for(j in 2:(length(alphalist)-1)){
# res[j] = sqrt(alphalist[j]*alphalist[j+1])
#}
#Total Outcome - CT
outcome.predictions@data$ct.spill <- NA
treatment.predictions@data$ct.spill <- predict(fit_ctpred,newdata=spdf@data) * treatment.predictions@data$treatment.status
print("CT Nodes:")
print(length(unique(fit_ctpred$where)))
nospill.t.pred@data$ct.spill <- NA
#Save Summary Results
for(i in 3:length(nospill.t.pred@data))
{
if(p == 1)
{
results_nospill[names(nospill.t.pred@data)[i]] <- NA
results_nospill["trueTreatment"] <- NA
}
results_nospill[names(nospill.t.pred@data)[i]][p,] <- sum(nospill.t.pred@data[,i]) / (nrandom*trt_prc)
results_nospill["trueTreatment"][p,] <- theta
}
for(i in 4:length(treatment.predictions@data))
{
if(p == 1)
{
results[names(treatment.predictions@data)[i]] <- NA
results["trueTreatment"] <- NA
}
results[names(treatment.predictions@data)[i]][p,] <- sum(treatment.predictions@data[,i],na.rm=T) / (nrandom*trt_prc)
results["trueTreatment"][p,] <- sum(treatment.predictions@data$trueTreatment) / (nrandom*trt_prc)
}
for(i in 3:length(outcome.predictions@data))
{
if(p == 1)
{
results_out[names(outcome.predictions@data)[i]] <- NA
results_out["trueTreatment"] <- NA
}
results_out[names(outcome.predictions@data)[i]][p,] <- sum(outcome.predictions@data[,i]) / (nrandom*trt_prc)
results_out["trueTreatment"][p,] <- sum(treatment.predictions@data$trueTreatment) / (nrandom*trt_prc)
}
#Save relevant parameters
if(p == 1)
{
results["spill.magnitude"] <- NA
results["psill"] <- NA
results["var1.vrange"] <- NA
results["prop_acc"] <- NA
results["mod_error.vrange"] <- NA
results["mod_error.magnitude"] <- NA
results["spill.vrange"] <- NA
results["beta"] <- NA
results["var1_error.vrange"] <- NA
results["theta"] <- NA
results["caliper"] <- NA
results["sample_size"] <- NA
results["tree_split_lim"] <- NA
results_out["spill.magnitude"] <- NA
results_out["psill"] <- NA
results_out["var1.vrange"] <- NA
results_out["prop_acc"] <- NA
results_out["mod_error.vrange"] <- NA
results_out["mod_error.magnitude"] <- NA
results_out["spill.vrange"] <- NA
results_out["beta"] <- NA
results_out["var1_error.vrange"] <- NA
results_out["theta"] <- NA
results_out["caliper"] <- NA
results_out["sample_size"] <- NA
results_out["tree_split_lim"] <- NA
results_nospill["spill.magnitude"] <- NA
results_nospill["psill"] <- NA
results_nospill["var1.vrange"] <- NA
results_nospill["prop_acc"] <- NA
results_nospill["mod_error.vrange"] <- NA
results_nospill["mod_error.magnitude"] <- NA
results_nospill["spill.vrange"] <- NA
results_nospill["beta"] <- NA
results_nospill["var1_error.vrange"] <- NA
results_nospill["theta"] <- NA
results_nospill["caliper"] <- NA
results_nospill["sample_size"] <- NA
results_nospill["tree_split_lim"] <- NA
}
results["spill.magnitude"][p,] <- spill.magnitude
results["psill"][p,] <- psill
results["var1.vrange"][p,] <- var1.vrange
results["prop_acc"][p,] <- prop_acc
results["mod_error.vrange"][p,] <- mod_error.vrange
results["mod_error.magnitude"][p,] <- mod_error.magnitude
results["spill.vrange"][p,] <- spill.vrange
results["beta"][p,] <- beta
results["theta"][p,] <- theta
results["var1_error.vrange"][p,] <- var1_error.vrange
results["caliper"][p,] <- cal
results["sample_size"][p,] <- sample_size
results["tree_split_lim"][p,] <- tree_split_lim
results_out["spill.magnitude"][p,] <- spill.magnitude
results_out["psill"][p,] <- psill
results_out["var1.vrange"][p,] <- var1.vrange
results_out["prop_acc"][p,] <- prop_acc
results_out["mod_error.vrange"][p,] <- mod_error.vrange
results_out["mod_error.magnitude"][p,] <- mod_error.magnitude
results_out["spill.vrange"][p,] <- spill.vrange
results_out["beta"][p,] <- beta
results_out["theta"][p,] <- theta
results_out["var1_error.vrange"][p,] <- var1_error.vrange
results_out["caliper"][p,] <- cal
results_out["sample_size"][p,] <- sample_size
results_out["tree_split_lim"][p,] <- tree_split_lim
results_nospill["spill.magnitude"][p,] <- spill.magnitude
results_nospill["psill"][p,] <- psill
results_nospill["var1.vrange"][p,] <- var1.vrange
results_nospill["prop_acc"][p,] <- prop_acc
results_nospill["mod_error.vrange"][p,] <- mod_error.vrange
results_nospill["mod_error.magnitude"][p,] <- mod_error.magnitude
results_nospill["spill.vrange"][p,] <- spill.vrange
results_nospill["beta"][p,] <- beta
results_nospill["theta"][p,] <- theta
results_nospill["var1_error.vrange"][p,] <- var1_error.vrange
results_nospill["caliper"][p,] <- cal
results_nospill["sample_size"][p,] <- sample_size
results_nospill["tree_split_lim"][p,] <- tree_split_lim
#Compare Maps
#spplot(outcome.predictions, zcol=names(outcome.predictions)
# [names(outcome.predictions) != "id"])
#spplot(treatment.predictions, zcol=names(treatment.predictions)
# [names(treatment.predictions) != "id"])
print("Iteration Complete")
print(p)
print("----------------")
}
print(proc.time() - ptm)
DanRunfola/SimTests documentation built on May 6, 2019, 1:23 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
rm(list = ls())
library(sp)
library(ncf)
library(gstat)
library(devtools)
library(rpart)
library(rpart.plot)
library(Rcpp)
Sys.setenv("PKG_CXXFLAGS"="-fopenmp")
Sys.setenv("PKG_LIBS"="-fopenmp")
sourceCpp("/home/aiddata/Desktop/Github/MatchIt/demo/splitc.cpp")
#detach("package:MatchIt", unload=TRUE)
#load_all("~/Desktop/Github/MatchIt/R")
library(devtools)
install_github("itpir/matchit")
library(MatchIt)
iterations <- 25
results <- data.frame(
id=c(1:iterations)
)
results_out <- data.frame(
id=c(1:iterations)
)
results_nospill <- data.frame(
id=c(1:iterations)
)
ptm <- proc.time()
for(p in 1:iterations)
{
results["id"] <- p
results_out["id"] <- p
# -----------------------------------------------------------------------------
# Simulation Settings
# -----------------------------------------------------------------------------
#General Options
# set dataframe size (number of points)
nrandom <- 2000
#General psill
#Note: setting this to 0 will result in no data randomization.
#Larger values indicate more autocorrelation.
psill <- 1 + runif(1, -.95, 20)
# define bounding box
minx <- -45
maxx <- 45
miny <- -22.5
maxy <- 22.5
#Covariate Spatial Correlation
var1.vrange <- 1 + runif(1, -.95, 20)
#Degree to which the covariate
#explains the propensity to receive
#treatment (1.0 = perfect correlation, or no error)
prop_acc = runif(1, 0.05, .95)
#Spatial pattern in the PSM error, if any
#(vrange = 1 approximates random noise)
var1_error.vrange <- runif(1, 0.1, 20)
#Define the spatial pattern of any model error.
#Magnitue of error is 0-1 (0 = no error)
mod_error.vrange <- 1.0
mod_error.magnitude <- runif(1, 0.1, 2)
#Percent of locations which are to be defined as
#treated
trt_prc = runif(1, 0.45, 0.5)
#Theta coefficient for treatment effect
#used for defining the outcome
theta <- 1#runif(1, -.95, 10)
#Beta coefficient for ancillary data
#in defining the treatment
beta <- 1#runif(1,1,5) * theta
#Spillover Range
spill.vrange <- 1.0 + runif(1, -.95, 2)
#Spillover Magnitude (relative to theta)
spill.magnitude <- 1 * runif(1, 0, 3)
#Caliper for Matching
cal = .5
sample_size = runif(1,0.05,1.0)
tree_split_lim = sample(2:50,1)
# -----------------------------------------------------------------------------
# Data Simulation
# -----------------------------------------------------------------------------
#Given your settings, outputs:
#spdf@data$modelVar - an ancillary variable with error (defined by var1_error)
#spdf@data$treatment.status - Binary 1/0 treated
#spdf@data$trueOutcome - the measured outcome
#spdf@data$modelOutcome - measured outcome including measurement error (model.error)
source("/home/aiddata/Desktop/Github/MatchIt/demo/simulation_spatial_data.R")
# -----------------------------------------------------------------------------
# Data Generation Visualizations (Per-iteration; currently not saved)
# Disable for a large speed boost.
# Note visualizations use MatchIt for PSM calculations (nearest/logit/caliper=0.25)
# -----------------------------------------------------------------------------
#Creates a figure describing your iteration (PSM vs. observed and parameterized spillovers
#and maps).
# source("/home/aiddata/Desktop/Github/MatchIt/demo/test.spatial.figures.R")
# -----------------------------------------------------------------------------
# Model Tests
# Save all Model Predictions into a SPDF for comparison.
# -----------------------------------------------------------------------------
outcome.predictions <- spdf
outcome.predictions@data <- outcome.predictions@data[c(1,8)]
treatment.predictions <- spdf
treatment.predictions@data <- treatment.predictions@data[c(1,6,7)]
treatment.predictions@data$trueTreatment <- (treatment.predictions@data$treatment.status)*
theta + treatment.predictions$trueSpill
nospill.t.pred <- spdf
nospill.t.pred@data <- nospill.t.pred@data[1]
nospill.t.pred@data$trueTreatment <- theta
model_dta <- spdf[sample(nrow(spdf), (nrandom * sample_size)), ]
#No Matching
baseline <- lm(modelOutcome ~ treatment.status + modelVar, data=model_dta@data)
outcome.predictions@data$baseline <- predict(baseline, newdata=spdf@data)
treatment.predictions@data$baseline <- summary(baseline)$coefficients[2]
nospill.t.pred@data$baseline <- summary(baseline)$coefficients[2]
#Baseline for Comparison
baseline.matchit <- matchit(treatment.status ~ modelVar, data= model_dta@data,
method="nearest", distance="logit",
caliper=cal, calclosest=FALSE, calrandom=FALSE)
baseline.model <- lm(modelOutcome ~ treatment.status + modelVar,
data=match.data(baseline.matchit))
outcome.predictions@data$baseline.matchit <- predict(baseline.model, newdata=spdf@data)
treatment.predictions@data$baseline.matchit <- summary(baseline.model)$coefficients[2]
nospill.t.pred@data$baseline.matchit <- summary(baseline.model)$coefficients[2]
#Cheating Spatial PSM - we give the accurate vrange, and use it as a threshold.
spatial.opts <- list(decay.model = "threshold",
threshold = spill.vrange)
spatial.trueThreshold <- matchit(treatment.status ~ modelVar, data= model_dta,
method = "nearest", distance = "logit",
caliper=cal, calclosest=FALSE, calrandom=FALSE,
spatial.options=spatial.opts)
spatial.trueThreshold.model <- lm(modelOutcome ~ treatment.status + modelVar,
data=match.data(spatial.trueThreshold))
outcome.predictions@data$spatial.trueThreshold <- predict(spatial.trueThreshold.model,
newdata=spdf@data)
treatment.predictions@data$spatial.trueThreshold <-
summary(spatial.trueThreshold.model)$coefficients[2]
nospill.t.pred@data$spatial.trueThreshold <- summary(spatial.trueThreshold.model)$coefficients[2]
#PSM-approximating Traditional and Spatial PSMs
#Propensity Correlogram
p_cor_spdf <- model_dta
p_cor_spdf$m1.pscore <- baseline.matchit$distance
correlog.pscore.spillover <- correlog(x=p_cor_spdf@coords[, 1],
y=p_cor_spdf@coords[, 2],
z=p_cor_spdf$treatment.status,
increment=500,
latlon=TRUE, na.rm=TRUE, resamp=5,
quiet=FALSE)
pscore.spillover.model.dta <- data.frame(
mean.of.class = c(correlog.pscore.spillover$mean.of.class),
correlation = c(correlog.pscore.spillover$correlation)
)
correlog.m1.polynomial <- lm(correlation ~ poly(mean.of.class, degree=5, raw=TRUE),
data=pscore.spillover.model.dta)
estimated.spillover.weights <- c()
for (k in 1:length(p_cor_spdf)) {
correlog.dist <- spDists(p_cor_spdf[k, ]@coords, p_cor_spdf@coords, longlat=TRUE)
correlog.neighbors <- correlog.dist > 0
correlog.treated <- p_cor_spdf$treatment.status
correlog.newdata <- data.frame(
mean.of.class = c(correlog.dist)
)
#Limit to the min (after the final predicted distance all values are 0)
correlog.newdata$weight_dist <- predict(correlog.m1.polynomial, newdata=correlog.newdata)
correlog.newdata$weight_dist[correlog.newdata$weight_dist < 0] <- 0
estimated.spillover.weights[k] <- sum(correlog.newdata$weight_dist * correlog.neighbors *
correlog.treated)
}
p_cor_spdf$spillover.est <- estimated.spillover.weights
#Spatial Matchit
spatial.opts <- list(decay.model = "threshold",
threshold = correlog.pscore.spillover$x.intercept+1)#(correlog.pscore.spillover$x.intercept+1))
#print(correlog.pscore.spillover$x.intercept)
spatial.matchit.spill <- matchit(treatment.status ~ modelVar, data=p_cor_spdf@data,
method="nearest", distance="logit",
caliper=cal, calclosest=FALSE, calrandom=FALSE)
spatial.matchit.spill.model <- lm(modelOutcome ~ treatment.status + modelVar,
data=match.data(spatial.matchit.spill))
outcome.predictions@data$spatial.matchit.spill <- predict(spatial.matchit.spill.model,
newdata=spdf@data)
treatment.predictions@data$spatial.matchit.spill <-
((summary(spatial.matchit.spill.model)$coefficients[2] * nrandom)) /
nrandom
nospill.t.pred@data$spatial.matchit.spill <- summary(spatial.matchit.spill.model)$coefficients[2]
p_cor_spdf@data["coord1"] <- coordinates(p_cor_spdf)[,1]
p_cor_spdf@data["coord2"] <- coordinates(p_cor_spdf)[,2]
#TOT - Non Random Forest
trans_dta <- p_cor_spdf
trans_dta <- trans_dta[(trans_dta@data$m1.pscore != 0 &
trans_dta@data$m1.pscore != 1),]
transOutcome <- list(rep(0,nrow(trans_dta)))
for(i in 1:nrow(p_cor_spdf))
{
if(trans_dta$treatment.status[i] == 1)
{
transOutcome[i] = trans_dta@data$modelOutcome[i] / trans_dta@data$m1.pscore[i]
}
else
{
transOutcome[i] = trans_dta@data$modelOutcome[i] / (1-trans_dta@data$m1.pscore[i])
}
}
trans_dta@data$transOutcome <- unlist(transOutcome)
tot.fit.spill <- rpart(transOutcome ~ modelVar + coord1 + coord2,
data = trans_dta@data,
control=rpart.control(cp=0, minsplit=tree_split_lim),
method="anova")
cpbest <- tot.fit.spill$cptable[which.min(tot.fit.spill$cptable[,"xerror"]),"CP"]
tot.fit.spillB <- rpart(transOutcome ~ modelVar + coord1 + coord2,
data = trans_dta@data,
control=rpart.control(cp=cpbest, minsplit=tree_split_lim),
method="anova")
spdf@data["coord1"] <- coordinates(spdf)[,1]
spdf@data["coord2"] <- coordinates(spdf)[,2]
res <- predict(tot.fit.spillB, newdata=spdf@data)
# Add by Jianing
db = spdf@data
tree2 = tot.fit.spillB
tree2$frame$yval = as.numeric(rownames(tree2$frame))
res2 = predict(tree2,newdata=spdf@data)
leaf = unique(tot.fit.spillB$where)
res3 = 0
for(i in 1:length(leaf)){
treatcount = 0
untreatcount = 0
count = 0
temp = 0
for(j in 1:nrow(db)){
if(res2[j] == leaf[i]){
temp = c(temp,j)
count = count + 1
if(db$treatment.status[j] == 1){
treatcount = treatcount + 1
}
if(db$treatment.status[j] == 0){
untreatcount = untreatcount + 1
}
}
}
if(count == treatcount | count == untreatcount ){
temp = temp[-1]
res3 = c(res3,temp)
}
}
res_leaf2prune = res3[-1]
#res[res_leaf2prune] = NA
## end
#Total Outcome
outcome.predictions@data$tot.spill <- NA
treatment.predictions@data$tot.spill <- res * treatment.predictions@data$treatment.status
nospill.t.pred@data$tot.spill <- NA
#CT
source("/home/aiddata/Desktop/Github/MatchIt/demo/CT_functions.R")
alist <- list(eval=ctev, split=ctsplit, init=ctinit)
#trans_dta
dbb = trans_dta@data
k = 10
n = dim(dbb)[1]
#sample_size = floor(n)
#ridx = sample(1:n,sample_size,replace=FALSE)
#crxvdata= dbb[ridx,]
crxvdata = dbb
crxvdata$id <- sample(1:k, nrow(crxvdata), replace = TRUE)
list = 1:k
fit1 = rpart(cbind(modelOutcome,treatment.status,m1.pscore,transOutcome) ~ modelVar + coord1 + coord2,
crxvdata,
control = rpart.control(cp = 0,minsplit = tree_split_lim),
method=alist)
fit = data.matrix(fit1$frame)
index = as.numeric(rownames(fit1$frame))
tsize = dim(fit1$frame[which(fit1$frame$var=="<leaf>"),])[1]
alpha = 0
alphalist = 0
alphalist = cross_validate(fit, index,alphalist)
res = rep(0,length(alphalist)-1)
for(j in 2:(length(alphalist)-1)){
res[j] = sqrt(alphalist[j]*alphalist[j+1])
}
alphacandidate = res
alphaset = rep(0,length(alphacandidate))
errset = rep(0,length(alphacandidate))
tsize = 0
for(l in 1:length(alphacandidate)){
alpha = alphacandidate[l]
error = 0
treesize = 0
for (i in 1:k){
trainingset <- subset(crxvdata, id %in% list[-i])
testset <- subset(crxvdata, id %in% c(i))
fit1 = rpart (cbind(modelOutcome,treatment.status,m1.pscore,transOutcome) ~ modelVar + coord1 + coord2,
trainingset,
control = rpart.control(cp = alpha,minsplit = tree_split_lim),
method=alist)
if(dim(fit1$frame)[1] == 1){
error = 0
break
}
else{
treesize = treesize + dim(fit1$frame[which(fit1$frame$var=="<leaf>"),])[1]
pt = predict(fit1,testset,type = "matrix")
y = data.frame(pt)
val = data.matrix(y)
idx = as.numeric(rownames(y))
dbidx = as.numeric(rownames(dbb))
for(pid in 1:(dim(y)[1])){
id = match(idx[pid],dbidx)
error = error + (crxvdata$transOutcome[id] - val[pid])^2
#print(error)
}
}
}
tsize = c(tsize,treesize/k)
if(error == 0){
errset[l] = 1000000
}
else{
errset[l] = error/k
}
msg = paste(l,": ",errset[l]*k,sep="")
#print(msg)
}
tsize = tsize[-1]
alpha_res = alphacandidate[which.min(errset)]
fit_ctpred <- rpart(cbind(modelOutcome,treatment.status,m1.pscore,transOutcome) ~ modelVar + coord1 + coord2,
crxvdata, control=rpart.control(minsplit=tree_split_lim,cp=alpha_res),
method=alist)
#prp(fit_ctpred)
#res = rep(0,length(alphalist)-1)
#for(j in 2:(length(alphalist)-1)){
# res[j] = sqrt(alphalist[j]*alphalist[j+1])
#}
#Total Outcome - CT
outcome.predictions@data$ct.spill <- NA
treatment.predictions@data$ct.spill <- predict(fit_ctpred,newdata=spdf@data) * treatment.predictions@data$treatment.status
print("CT Nodes:")
print(length(unique(fit_ctpred$where)))
nospill.t.pred@data$ct.spill <- NA
#Save Summary Results
for(i in 3:length(nospill.t.pred@data))
{
if(p == 1)
{
results_nospill[names(nospill.t.pred@data)[i]] <- NA
results_nospill["trueTreatment"] <- NA
}
results_nospill[names(nospill.t.pred@data)[i]][p,] <- sum(nospill.t.pred@data[,i]) / (nrandom*trt_prc)
results_nospill["trueTreatment"][p,] <- theta
}
for(i in 4:length(treatment.predictions@data))
{
if(p == 1)
{
results[names(treatment.predictions@data)[i]] <- NA
results["trueTreatment"] <- NA
}
results[names(treatment.predictions@data)[i]][p,] <- sum(treatment.predictions@data[,i],na.rm=T) / (nrandom*trt_prc)
results["trueTreatment"][p,] <- sum(treatment.predictions@data$trueTreatment) / (nrandom*trt_prc)
}
for(i in 3:length(outcome.predictions@data))
{
if(p == 1)
{
results_out[names(outcome.predictions@data)[i]] <- NA
results_out["trueTreatment"] <- NA
}
results_out[names(outcome.predictions@data)[i]][p,] <- sum(outcome.predictions@data[,i]) / (nrandom*trt_prc)
results_out["trueTreatment"][p,] <- sum(treatment.predictions@data$trueTreatment) / (nrandom*trt_prc)
}
#Save relevant parameters
if(p == 1)
{
results["spill.magnitude"] <- NA
results["psill"] <- NA
results["var1.vrange"] <- NA
results["prop_acc"] <- NA
results["mod_error.vrange"] <- NA
results["mod_error.magnitude"] <- NA
results["spill.vrange"] <- NA
results["beta"] <- NA
results["var1_error.vrange"] <- NA
results["theta"] <- NA
results["caliper"] <- NA
results["sample_size"] <- NA
results["tree_split_lim"] <- NA
results_out["spill.magnitude"] <- NA
results_out["psill"] <- NA
results_out["var1.vrange"] <- NA
results_out["prop_acc"] <- NA
results_out["mod_error.vrange"] <- NA
results_out["mod_error.magnitude"] <- NA
results_out["spill.vrange"] <- NA
results_out["beta"] <- NA
results_out["var1_error.vrange"] <- NA
results_out["theta"] <- NA
results_out["caliper"] <- NA
results_out["sample_size"] <- NA
results_out["tree_split_lim"] <- NA
results_nospill["spill.magnitude"] <- NA
results_nospill["psill"] <- NA
results_nospill["var1.vrange"] <- NA
results_nospill["prop_acc"] <- NA
results_nospill["mod_error.vrange"] <- NA
results_nospill["mod_error.magnitude"] <- NA
results_nospill["spill.vrange"] <- NA
results_nospill["beta"] <- NA
results_nospill["var1_error.vrange"] <- NA
results_nospill["theta"] <- NA
results_nospill["caliper"] <- NA
results_nospill["sample_size"] <- NA
results_nospill["tree_split_lim"] <- NA
}
results["spill.magnitude"][p,] <- spill.magnitude
results["psill"][p,] <- psill
results["var1.vrange"][p,] <- var1.vrange
results["prop_acc"][p,] <- prop_acc
results["mod_error.vrange"][p,] <- mod_error.vrange
results["mod_error.magnitude"][p,] <- mod_error.magnitude
results["spill.vrange"][p,] <- spill.vrange
results["beta"][p,] <- beta
results["theta"][p,] <- theta
results["var1_error.vrange"][p,] <- var1_error.vrange
results["caliper"][p,] <- cal
results["sample_size"][p,] <- sample_size
results["tree_split_lim"][p,] <- tree_split_lim
results_out["spill.magnitude"][p,] <- spill.magnitude
results_out["psill"][p,] <- psill
results_out["var1.vrange"][p,] <- var1.vrange
results_out["prop_acc"][p,] <- prop_acc
results_out["mod_error.vrange"][p,] <- mod_error.vrange
results_out["mod_error.magnitude"][p,] <- mod_error.magnitude
results_out["spill.vrange"][p,] <- spill.vrange
results_out["beta"][p,] <- beta
results_out["theta"][p,] <- theta
results_out["var1_error.vrange"][p,] <- var1_error.vrange
results_out["caliper"][p,] <- cal
results_out["sample_size"][p,] <- sample_size
results_out["tree_split_lim"][p,] <- tree_split_lim
results_nospill["spill.magnitude"][p,] <- spill.magnitude
results_nospill["psill"][p,] <- psill
results_nospill["var1.vrange"][p,] <- var1.vrange
results_nospill["prop_acc"][p,] <- prop_acc
results_nospill["mod_error.vrange"][p,] <- mod_error.vrange
results_nospill["mod_error.magnitude"][p,] <- mod_error.magnitude
results_nospill["spill.vrange"][p,] <- spill.vrange
results_nospill["beta"][p,] <- beta
results_nospill["theta"][p,] <- theta
results_nospill["var1_error.vrange"][p,] <- var1_error.vrange
results_nospill["caliper"][p,] <- cal
results_nospill["sample_size"][p,] <- sample_size
results_nospill["tree_split_lim"][p,] <- tree_split_lim
#Compare Maps
#spplot(outcome.predictions, zcol=names(outcome.predictions)
# [names(outcome.predictions) != "id"])
#spplot(treatment.predictions, zcol=names(treatment.predictions)
# [names(treatment.predictions) != "id"])
print("Iteration Complete")
print(p)
print("----------------")
}
print(proc.time() - ptm)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.