Nothing
test_that("Test whether plot_ggparty works (ctns)", {
skip_on_cran()
library(ggparty)
library(partykit)
library(rpart)
dat_ctns = generate_subgrp_data(family="gaussian")
Y = dat_ctns$Y
X = dat_ctns$X
A = dat_ctns$A
family <- "gaussian"
# Fit #
res0 <- PRISM(Y, A, X, param="lm")
Subgrps <- res0$out.train$Subgrps
p0_est <- res0$param.dat
p0_est <- p0_est[p0_est$Subgrps!="ovrl",]
p0_est$label <- with(p0_est, paste( sprintf("%.2f", round(est,2)),
" [",
sprintf("%.2f", round(LCL,2)), ",",
sprintf("%.2f", round(UCL,2)), "]", sep=""))
p0_est$prob.est <- with(p0_est, sprintf("%.2f", round(`Prob(>0)`,2)))
p0 <- plot(res0, type="tree")
# Make sure the node estimates match up #
pdat0_est <- data.frame(Subgrps = p0$data$id,
estimand = p0$data$estimand,
prob.est = p0$data$prob.est,
label = p0$data$label)
pdat0_est$Subgrps <- as.character(pdat0_est$Subgrps)
p0_check <- dplyr::left_join(p0_est, pdat0_est, by=c("Subgrps"))
p0_label_check <- all.equal(p0_check$label.x, p0_check$label.y)
p0_prob_check <- all.equal(p0_check$prob.est.x, p0_check$prob.est.y)
p0_estimand_check <- ifelse(unique(na.omit(pdat0_est$estimand))=="E(Y|A=1)-E(Y|A=0)",
TRUE, FALSE)
# Make sure the outcome plots are correct #
p0_outdat <- NULL
for (s in unique(p0_check$Subgrps)) {
hold <- p0$data$info_list[[as.numeric(s)]]$plot.dat
hold <- data.frame(Subgrps = s, est_Y = hold$est,
Y = Y[Subgrps==s])
p0_outdat <- rbind(hold, p0_outdat)
}
p0_outplot_check <- all.equal(p0_outdat$est_Y, p0_outdat$Y)
### Run with resampling: Check Density ###
# Fit #
res1 <- PRISM(Y, A, X, param="lm", ple="None",
resample = "Bootstrap", filter.resamp = "None",
R = 50)
Subgrps <- res1$out.train$Subgrps
p1_est <- res1$param.dat
p1_est <- p1_est[p1_est$Subgrps!="ovrl",]
p1_est$label <- with(p1_est, paste( sprintf("%.2f", round(est,2)),
" [",
sprintf("%.2f", round(LCL,2)), ",",
sprintf("%.2f", round(UCL,2)), "]", sep=""))
p1_est$prob.est <- with(p1_est, sprintf("%.2f", round(`Prob(>0)`,2)))
p1 <- plot(res1, type="tree", tree.plots = "density")
# Make sure the node estimates match up #
pdat1_est <- data.frame(Subgrps = p1$data$id,
estimand = p1$data$estimand,
prob.est = p1$data$prob.est,
label = p1$data$label)
pdat1_est$Subgrps <- as.character(pdat1_est$Subgrps)
p1_check <- dplyr::left_join(p1_est, pdat1_est, by=c("Subgrps"))
p1_label_check <- all.equal(p1_check$label.x, p1_check$label.y)
p1_prob_check <- all.equal(p1_check$prob.est.x, p1_check$prob.est.y)
p1_estimand_check <- ifelse(unique(na.omit(pdat1_est$estimand))=="E(Y|A=1)-E(Y|A=0)",
TRUE, FALSE)
# Make sure the density plots are correct #
p1_densdat <- NULL
for (s in unique(p1_check$Subgrps)) {
plot.s <- p1$data$info_list[[as.numeric(s)]]$dat.dens
hold.s = res1$resamp_dist[res1$resamp_dist$Subgrps==s,]
dat.s = with(density(hold.s$est, na.rm=T), data.frame(x, y))
hold <- data.frame(plot.s, dat.s)
p1_densdat <- rbind(hold, p1_densdat)
}
p1_densx_check <- all.equal(p1_densdat$x, p1_densdat$x.1)
p1_densy_check <- all.equal(p1_densdat$y, p1_densdat$y.1)
# Check #
expect_equal(p0_prob_check, TRUE)
expect_equal(p0_label_check, TRUE)
expect_equal(p0_outplot_check, TRUE)
expect_equal(p0_estimand_check, TRUE)
expect_equal(p1_prob_check, TRUE)
expect_equal(p1_label_check, TRUE)
expect_equal(p1_densx_check, TRUE)
expect_equal(p1_densy_check, TRUE)
expect_equal(p1_estimand_check, TRUE)
})
test_that("Test whether plot_ggparty works (binomial)", {
skip_on_cran()
library(ggparty)
library(partykit)
library(rpart)
dat_bin = generate_subgrp_data(family="binomial")
Y = dat_bin$Y
X = dat_bin$X
A = dat_bin$A
family <- "binomial"
# Fit #
res0 <- PRISM(Y, A, X, param="lm")
Subgrps <- res0$out.train$Subgrps
p0_est <- res0$param.dat
p0_est <- p0_est[p0_est$Subgrps!="ovrl",]
p0_est$label <- with(p0_est, paste( sprintf("%.2f", round(est,2)),
" [",
sprintf("%.2f", round(LCL,2)), ",",
sprintf("%.2f", round(UCL,2)), "]", sep=""))
p0_est$prob.est <- with(p0_est, sprintf("%.2f", round(`Prob(>0)`,2)))
p0 <- plot(res0, type="tree")
# Make sure the node estimates match up #
pdat0_est <- data.frame(Subgrps = p0$data$id,
estimand = p0$data$estimand,
prob.est = p0$data$prob.est,
label = p0$data$label)
pdat0_est$Subgrps <- as.character(pdat0_est$Subgrps)
p0_check <- dplyr::left_join(p0_est, pdat0_est, by=c("Subgrps"))
p0_label_check <- all.equal(p0_check$label.x, p0_check$label.y)
p0_prob_check <- all.equal(p0_check$prob.est.x, p0_check$prob.est.y)
p0_estimand_check <- ifelse(unique(na.omit(pdat0_est$estimand))=="P(Y=1|A=1)-P(Y=1|A=0)",
TRUE, FALSE)
# Make sure the outcome plots are correct #
p0_outdat <- NULL
for (s in unique(p0_check$Subgrps)) {
hold <- p0$data$info_list[[as.numeric(s)]]$plot.dat
hold <- data.frame(Subgrps = s, est_Y = hold$est,
Y = Y[Subgrps==s])
p0_outdat <- rbind(hold, p0_outdat)
}
p0_outplot_check <- all.equal(p0_outdat$est_Y, p0_outdat$Y)
### Run with resampling: Check Density ###
# Fit #
res1 <- PRISM(Y, A, X, param="lm", ple="None",
resample = "Bootstrap", filter.resamp = "None",
R = 50)
Subgrps <- res1$out.train$Subgrps
p1_est <- res1$param.dat
p1_est <- p1_est[p1_est$Subgrps!="ovrl",]
p1_est$label <- with(p1_est, paste( sprintf("%.2f", round(est,2)),
" [",
sprintf("%.2f", round(LCL,2)), ",",
sprintf("%.2f", round(UCL,2)), "]", sep=""))
p1_est$prob.est <- with(p1_est, sprintf("%.2f", round(`Prob(>0)`,2)))
p1 <- plot(res1, type="tree", tree.plots = "density")
# Make sure the node estimates match up #
pdat1_est <- data.frame(Subgrps = p1$data$id,
estimand = p1$data$estimand,
prob.est = p1$data$prob.est,
label = p1$data$label)
pdat1_est$Subgrps <- as.character(pdat1_est$Subgrps)
p1_check <- dplyr::left_join(p1_est, pdat1_est, by=c("Subgrps"))
p1_label_check <- all.equal(p1_check$label.x, p1_check$label.y)
p1_prob_check <- all.equal(p1_check$prob.est.x, p1_check$prob.est.y)
p1_estimand_check <- ifelse(unique(na.omit(pdat1_est$estimand))=="P(Y=1|A=1)-P(Y=1|A=0)",
TRUE, FALSE)
# Make sure the density plots are correct #
p1_densdat <- NULL
for (s in unique(p1_check$Subgrps)) {
plot.s <- p1$data$info_list[[as.numeric(s)]]$dat.dens
hold.s = res1$resamp_dist[res1$resamp_dist$Subgrps==s,]
dat.s = with(density(hold.s$est, na.rm=T), data.frame(x, y))
hold <- data.frame(plot.s, dat.s)
p1_densdat <- rbind(hold, p1_densdat)
}
p1_densx_check <- all.equal(p1_densdat$x, p1_densdat$x.1)
p1_densy_check <- all.equal(p1_densdat$y, p1_densdat$y.1)
# Check #
expect_equal(p0_prob_check, TRUE)
expect_equal(p0_label_check, TRUE)
expect_equal(p0_outplot_check, TRUE)
expect_equal(p0_estimand_check, TRUE)
expect_equal(p1_prob_check, TRUE)
expect_equal(p1_label_check, TRUE)
expect_equal(p1_densx_check, TRUE)
expect_equal(p1_densy_check, TRUE)
expect_equal(p1_estimand_check, TRUE)
})
test_that("Test whether plot_ggparty works (survival)", {
skip_on_cran()
library(ggparty)
library(partykit)
library(rpart)
library(survival)
require(TH.data); require(coin)
data("GBSG2", package = "TH.data")
surv.dat = GBSG2
# Design Matrices ###
Y = with(surv.dat, Surv(time, cens))
X = surv.dat[,!(colnames(surv.dat) %in% c("time", "cens")) ]
set.seed(513)
A = rbinom(n = dim(X)[1], size=1, prob=0.5)
family <- "survival"
# Fit #
res0 <- PRISM(Y, A, X, family = family, param="cox", ple="None")
Subgrps <- res0$out.train$Subgrps
p0_est <- res0$param.dat
p0_est <- p0_est[p0_est$Subgrps!="ovrl",]
p0_est$label <- with(p0_est, paste( sprintf("%.2f", round(exp(est),2)),
" [",
sprintf("%.2f", round(exp(LCL),2)), ",",
sprintf("%.2f", round(exp(UCL),2)), "]", sep=""))
p0_est$prob.est <- with(p0_est, sprintf("%.2f", round(1-`Prob(>0)`,2)))
p0 <- plot(res0, type="tree")
# Make sure the node estimates match up #
pdat0_est <- data.frame(Subgrps = p0$data$id,
estimand = p0$data$estimand,
prob.est = p0$data$prob.est,
label = p0$data$label)
pdat0_est$Subgrps <- as.character(pdat0_est$Subgrps)
p0_check <- dplyr::left_join(p0_est, pdat0_est, by=c("Subgrps"))
p0_label_check <- all.equal(p0_check$label.x, p0_check$label.y)
p0_prob_check <- all.equal(p0_check$prob.est.x, p0_check$prob.est.y)
p0_estimand_check <- ifelse(unique(na.omit(pdat0_est$estimand))=="HR_1-HR_0",
TRUE, FALSE)
# Make sure the outcome plots are correct #
p0_outdat <- NULL
for (s in unique(p0_check$Subgrps)) {
hold <- p0$data$info_list[[as.numeric(s)]]$plot.dat
hold.dat <- data.frame(Y, A)[Subgrps==as.numeric(s),]
pred.s <- NULL
for (a in c(0,1) ) {
km_a <- survfit(Y ~ 1, data=hold.dat[hold.dat$A==a,])
surv_a <- data.frame(A=a, time=c(0,km_a$time),
surv=c(1, km_a$surv))
pred.s <- rbind(pred.s, surv_a)
}
pred.s$A <- factor(pred.s$A, levels = c("0", "1"))
pred.s <- data.frame(id=s, pred.s)
hold <- data.frame(hold, pred.s)
p0_outdat <- rbind(hold, p0_outdat)
}
p0_outplot_check <- all.equal(p0_outdat$surv, p0_outdat$surv.1)
### Run with resampling: Check Density ###
# Fit #
res1 <- PRISM(Y, A, X, param="cox", ple="None",
resample = "Bootstrap", filter.resamp = "None",
R = 50)
Subgrps <- res1$out.train$Subgrps
p1_est <- res1$param.dat
p1_est <- p1_est[p1_est$Subgrps!="ovrl",]
p1_est$label <- with(p1_est, paste( sprintf("%.2f", round(exp(est),2)),
" [",
sprintf("%.2f", round(exp(LCL),2)), ",",
sprintf("%.2f", round(exp(UCL),2)), "]", sep=""))
p1_est$prob.est <- with(p1_est, sprintf("%.2f", round(1-`Prob(>0)`,2)))
p1 <- plot(res1, type="tree", tree.plots = "density")
# Make sure the node estimates match up #
pdat1_est <- data.frame(Subgrps = p1$data$id,
estimand = p1$data$estimand,
prob.est = p1$data$prob.est,
label = p1$data$label)
pdat1_est$Subgrps <- as.character(pdat1_est$Subgrps)
p1_check <- dplyr::left_join(p1_est, pdat1_est, by=c("Subgrps"))
p1_label_check <- all.equal(p1_check$label.x, p1_check$label.y)
p1_prob_check <- all.equal(p1_check$prob.est.x, p1_check$prob.est.y)
p1_estimand_check <- ifelse(unique(na.omit(pdat1_est$estimand))=="HR_1-HR_0",
TRUE, FALSE)
# Make sure the density plots are correct #
p1_densdat <- NULL
for (s in unique(p1_check$Subgrps)) {
plot.s <- p1$data$info_list[[as.numeric(s)]]$dat.dens
hold.s = res1$resamp_dist[res1$resamp_dist$Subgrps==s,]
dat.s = with(density(exp(hold.s$est), na.rm=T), data.frame(x, y))
hold <- data.frame(plot.s, dat.s)
p1_densdat <- rbind(hold, p1_densdat)
}
p1_densx_check <- all.equal(p1_densdat$x, p1_densdat$x.1)
p1_densy_check <- all.equal(p1_densdat$y, p1_densdat$y.1)
# Check #
expect_equal(p0_prob_check, TRUE)
expect_equal(p0_label_check, TRUE)
expect_equal(p0_outplot_check, TRUE)
expect_equal(p0_estimand_check, TRUE)
expect_equal(p1_prob_check, TRUE)
expect_equal(p1_label_check, TRUE)
expect_equal(p1_densx_check, TRUE)
expect_equal(p1_densy_check, TRUE)
expect_equal(p1_estimand_check, TRUE)
})
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