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pce_baseline.R
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library(caret)
library(config)
library(ggsci)
library(doParallel)
library(foreach)
library(ggplot2)
# Maybe don't need all of these...
library(survival)
library(pROC)
library(PredictABEL)
source("utils.R")
source("data_loaders.R")
source("nhanes_analysis.R")
source("calculate_stats.R")
source("reclassification_report.R")
source("validation_results.R")
source("expected_over_observed_ratio.R")
source("plots.R")
source("subgroup_model.R")
source("baseline_model.R")
source("logistic_model.R")
source("cox_model.R")
source("original_model.R")
load(file=conf$full_models_file)
load("all_results.rda")
data <- load.training.data(paste(conf$working_dir, conf$train_file, sep="/"))
nofram.data <- data[data$study != "FRAM", ]
pce.results <- nofram.data
for (group in 1:4) {
in.group <- pce.results$grp == group
pce.results[in.group, "risk"] <- original.model(pce.results[in.group,],
group)
pce.results[in.group, "link"] <- original.link(pce.results[in.group,],
group)
}
baseline.results <- baseline.results[row.names(pce.results),]
logistic.2.eq.results <- logistic.2.eq.results[row.names(pce.results),]
pce.stats <- validation.statistics(
pce.results, training.data=pce.results, auc.function=auc_pce)
baseline.stats <- validation.statistics(
baseline.results, pce.results,
training.data=baseline.results, auc.function=cv_auc)
logistic.2.stats <- validation.statistics(
logistic.2.eq.results, pce.results,
training.data=logistic.2.eq.results,
auc.function=cv_auc)
name.of.model <- function(model, prefix="Model Set ") {
if (model == "pce") {
return("Original PCEs")
}
if (model == "baseline") {
return(paste(prefix, "1 - new data", sep=""))
}
if (model == "logistic.2.eq") {
return(paste(prefix, "2 - new data + method", sep=""))
}
return("unknown model")
}
cat("\nTable 1\n")
print(concat.expected_over_observed_table(
expected_over_observed_table(
pce.results, model.name=name.of.model("pce")),
expected_over_observed_table(
baseline.results, pce.results, model.name=name.of.model("baseline")),
expected_over_observed_table(
logistic.2.eq.results, pce.results, model.name=name.of.model("logistic.2.eq"))))
cat("pce survival per group\n")
print.survival_table(survival.per.group(pce.results))
cat("model 1 survival per group\n")
print.survival_table(survival.per.group(
baseline.results, pce.results))
cat("model 2 survival per group\n")
print.survival_table(survival.per.group(
logistic.2.eq.results, pce.results))
exit()
cat("\nTable 4\n")
print.two.by.two.report(
two.by.two.from.cross.validation(
pce.results, name=name.of.model("pce")),
two.by.two.from.cross.validation(
baseline.results, name=name.of.model("baseline")),
two.by.two.from.cross.validation(
logistic.2.eq.results, name=name.of.model("logistic.2.eq")))
print.noquote("Reclassification table")
print.reclassification.report(
baseline=reclassification.from.cross.validation(
pce.results, name=name.of.model("pce")),
reclassification.from.cross.validation(
baseline.results, pce.results, name=name.of.model("baseline")),
reclassification.from.cross.validation(
logistic.2.eq.results, pce.results, name=name.of.model("logistic.2.eq")))
print.noquote("Stats table")
print.validation.report(
concat.validation.report(
report.from.validation(
pce.stats,
name=name.of.model("pce")),
report.from.validation(
baseline.stats,
name=name.of.model("baseline")),
report.from.validation(
logistic.2.stats,
name=name.of.model("logistic.2.eq"))))
for (group in 1:4) {
group.pce.stats <- pce.stats[[group]]
group.baseline.stats <- baseline.stats[[group]]
group.logistic.2.stats <- logistic.2.stats[[group]]
p <- ggplot(group.pce.stats$calibration,
name=name.of.model("pce"))
p <- ggplot(group.baseline.stats$calibration,
name=name.of.model("baseline", prefix=""), guide=F, plot.obj=p)
p <- ggplot(group.logistic.2.stats$calibration,
name=name.of.model("logistic.2.eq", prefix=""), guide=F, plot.obj=p) +
labs(title=paste("Calibration curves for risk models among\n",
group_to_description(group),
sep="")) +
scale_color_jama(name="Model Set") +
scale_shape_discrete(name="Model Set") +
theme(plot.title = element_text(hjust = 0.5, size=conf$title_size),
axis.title.y = element_text(size=conf$title_size),
axis.title.x = element_text(size=conf$title_size),
legend.title = element_text(size=conf$inset_title_size),
legend.text = element_text(size=conf$inset_title_size * 0.95),
legend.margin=margin(0.1, 0.1, 0.1, 0.1, unit='cm'),
legend.key.size = unit(1.0, "lines"),
legend.justification=c(1,0), legend.position=c(0.955,0.045))
p_inset <- ggplot(group.pce.stats$calibration,
name.of.model("pce"), xlim=0.15, ylim=0.15)
p_inset <- ggplot(group.baseline.stats$calibration,
name.of.model("baseline", prefix=""), xlim=0.15, ylim=0.15,
guide=F, plot.obj=p_inset)
p_inset <- ggplot(group.logistic.2.stats$calibration,
name.of.model("logistic.2.eq", prefix=""),
xlim=0.15, ylim=0.15, guide=F, plot.obj=p_inset) +
labs(title="Range [0, 0.15] for clarity") +
scale_color_jama(name="Model Set") +
scale_shape_discrete(name="Model Set") +
theme(
axis.title.x=element_blank(),
axis.title.y=element_blank(),
text = element_text(size=conf$inset_title_size),
plot.title = element_text(size=conf$inset_title_size,
hjust=0.9),
legend.position="none"
)
p <- p + annotation_custom(grob=ggplotGrob(p_inset),
xmin=0.5,
xmax=1.0,
ymin=0.5,
ymax=1.0)
ggsave(plot=p, filename=paste("cv_calibration",group,".svg", sep=""), width=4, height=4)
}