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5-results.Rmd
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5-results.Rmd
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---
title: "5-results"
author: "Bernard"
date: "2021-06-29"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
# Load package
```{r}
# Helper
library (tidyverse)
library (cowplot)
# ML
library (mlr3)
library (mlr3learners)
library (mlr3tuning)
library (mlr3viz)
library (mlr3fselect)
library (mlr3pipelines)
library (mlr3hyperband)
```
# Create tasks
```{r}
dat <- readRDS("output/df.RDS")
# Neck pain
train <- dat$df_list$np$train_imp
test <- dat$df_list$np$test_imp
comb <- bind_rows(train, test)
train_id <- 1: nrow (train)
test_id <- (nrow (train) + 1): nrow (comb)
# Set training task
task_np <- TaskClassif$new (id = "neckpain", backend = comb, target = "outcome")
task_np$set_col_roles("ID", roles = "name")
# Arm pain
train <- dat$df_list$ap$train_imp
test <- dat$df_list$ap$test_imp
comb <- bind_rows(train, test)
train_id <- 1: nrow (train)
test_id <- (nrow (train) + 1): nrow (comb)
# Set training task
task_ap <- TaskClassif$new (id = "armpain", backend = comb, target = "outcome")
task_ap$set_col_roles("ID", roles = "name")
# NDI
train <- dat$df_list$dis$train_imp
test <- dat$df_list$dis$test_imp
comb <- bind_rows(train, test)
train_id <- 1: nrow (train)
test_id <- (nrow (train) + 1): nrow (comb)
# Set training task
task_dis <- TaskClassif$new (id = "disability", backend = comb, target = "outcome")
task_dis$set_col_roles("ID", roles = "name")
measures <- list (msr("classif.auc"),
msr("classif.acc"),
msr("classif.tpr"),
msr("classif.fpr"),
msr("classif.fnr"),
msr("classif.tnr"))
```
# Neck pain
## Internal validation
```{r}
m_list <- readRDS("output/np_result.RDS")
bmr1 <- as_benchmark_result(m_list$rsmp_list$rr_logreg)
bmr2 <- as_benchmark_result(m_list$rsmp_list$rr_kknn)
bmr3 <- as_benchmark_result(m_list$rsmp_list$rr_xgb)
bmr4 <- as_benchmark_result(m_list$rsmp_list$rr_lasso)
bmr5 <- as_benchmark_result(m_list$rsmp_list$rr_rf)
bmr6 <- as_benchmark_result(m_list$rsmp_list$rr_net)
bmr7 <- as_benchmark_result(m_list$rsmp_list$rr_svm)
bmr1$
combine(bmr2)$
combine (bmr3)$
combine (bmr4)$
combine (bmr5)$
combine (bmr6)$
combine (bmr7)
bmr1
```
### Plot results
```{r}
ynames <- c("Area under Curve", "Accuracy", "True positive",
"False positive", "False negative", "True negative")
mod_names <- c("Logistic","KKNN","Xgboost", "Lasso", "RF", "Nnet", "SVM")
plot_list <- vector ("list", length (measures))
for (n in seq_along(measures)) {
plot_list[[n]] <- autoplot(bmr1, measure = measures[[n]]) +
theme_cowplot() +
scale_x_discrete(labels = mod_names) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
ylab (ynames[n])
}
plot_grid(
plotlist = plot_list,
align="hv"
)
```
## External validation
```{r}
pred_list <- vector ("list", length (m_list$model_list))
for (n in seq_along(m_list$model_list)) {
m <- m_list$model_list[[n]]
p <- m$predict(task_np, row_ids = test_id)
pred_list[[n]] <- p$score (measures)
}
names(pred_list) <- mod_names
pred <- pred_list %>%
bind_rows(.id = "models")
names (pred)[-1] <- c("Auc", "Acc", "Tpr",
"Fpr", "Fnr", "Tnr")
```
### Plot results
```{r}
pred_plot <- pred %>%
pivot_longer(cols = -models,
names_to = "Measures",
values_to = "Performance") %>%
ggplot() +
geom_bar(aes (x = Measures, y = Performance, fill = models),
position = "dodge", stat = "identity") +
scale_fill_manual(values = c("#030303", "#0000FF", "#EE0000", "#00CD00", "#EE9A00", "#912CEE", "#8B4726")) +
theme_cowplot()
pred_plot
```
# Arm pain
## Internal validation
```{r}
m_list <- readRDS("output/ap_result.RDS")
bmr1 <- as_benchmark_result(m_list$rsmp_list$rr_logreg)
bmr2 <- as_benchmark_result(m_list$rsmp_list$rr_kknn)
bmr3 <- as_benchmark_result(m_list$rsmp_list$rr_xgb)
bmr4 <- as_benchmark_result(m_list$rsmp_list$rr_lasso)
bmr5 <- as_benchmark_result(m_list$rsmp_list$rr_rf)
bmr6 <- as_benchmark_result(m_list$rsmp_list$rr_net)
bmr7 <- as_benchmark_result(m_list$rsmp_list$rr_svm)
bmr1$
combine(bmr2)$
combine (bmr3)$
combine (bmr4)$
combine (bmr5)$
combine (bmr6)$
combine (bmr7)
bmr1
```
### Plot results
```{r}
ynames <- c("Area under Curve", "Accuracy", "True positive",
"False positive", "False negative", "True negative")
mod_names <- c("Logistic","KKNN","Xgboost", "Lasso", "RF", "Nnet", "SVM")
plot_list <- vector ("list", length (measures))
for (n in seq_along(measures)) {
plot_list[[n]] <- autoplot(bmr1, measure = measures[[n]]) +
theme_cowplot() +
scale_x_discrete(labels = mod_names) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
ylab (ynames[n])
}
plot_grid(
plotlist = plot_list,
align="hv"
)
```
## External validation
```{r}
pred_list <- vector ("list", length (m_list$model_list))
for (n in seq_along(m_list$model_list)) {
m <- m_list$model_list[[n]]
p <- m$predict(task_ap, row_ids = test_id)
pred_list[[n]] <- p$score (measures)
}
names(pred_list) <- mod_names
pred <- pred_list %>%
bind_rows(.id = "models")
names (pred)[-1] <- c("Auc", "Acc", "Tpr",
"Fpr", "Fnr", "Tnr")
```
### Plot results
```{r}
pred_plot <- pred %>%
pivot_longer(cols = -models,
names_to = "Measures",
values_to = "Performance") %>%
ggplot() +
geom_bar(aes (x = Measures, y = Performance, fill = models),
position = "dodge", stat = "identity") +
scale_fill_manual(values = c("#030303", "#0000FF", "#EE0000", "#00CD00", "#EE9A00", "#912CEE", "#8B4726")) +
theme_cowplot()
pred_plot
```
# Disability
## Internal validation
```{r}
m_list <- readRDS("output/dis_result.RDS")
bmr1 <- as_benchmark_result(m_list$rsmp_list$rr_logreg)
bmr2 <- as_benchmark_result(m_list$rsmp_list$rr_kknn)
bmr3 <- as_benchmark_result(m_list$rsmp_list$rr_xgb)
bmr4 <- as_benchmark_result(m_list$rsmp_list$rr_lasso)
bmr5 <- as_benchmark_result(m_list$rsmp_list$rr_rf)
bmr6 <- as_benchmark_result(m_list$rsmp_list$rr_net)
bmr7 <- as_benchmark_result(m_list$rsmp_list$rr_svm)
bmr1$
combine(bmr2)$
combine (bmr3)$
combine (bmr4)$
combine (bmr5)$
combine (bmr6)$
combine (bmr7)
bmr1
```
### Plot results
```{r}
ynames <- c("Area under Curve", "Accuracy", "True positive",
"False positive", "False negative", "True negative")
mod_names <- c("Logistic","KKNN","Xgboost", "Lasso", "RF", "Nnet", "SVM")
plot_list <- vector ("list", length (measures))
for (n in seq_along(measures)) {
plot_list[[n]] <- autoplot(bmr1, measure = measures[[n]]) +
theme_cowplot() +
scale_x_discrete(labels = mod_names) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
ylab (ynames[n])
}
plot_grid(
plotlist = plot_list,
align="hv"
)
```
## External validation
```{r}
pred_list <- vector ("list", length (m_list$model_list))
for (n in seq_along(m_list$model_list)) {
m <- m_list$model_list[[n]]
p <- m$predict(task_dis, row_ids = test_id)
pred_list[[n]] <- p$score (measures)
}
names(pred_list) <- mod_names
pred <- pred_list %>%
bind_rows(.id = "models")
names (pred)[-1] <- c("Auc", "Acc", "Tpr",
"Fpr", "Fnr", "Tnr")
```
### Plot results
```{r}
pred_plot <- pred %>%
pivot_longer(cols = -models,
names_to = "Measures",
values_to = "Performance") %>%
ggplot() +
geom_bar(aes (x = Measures, y = Performance, fill = models),
position = "dodge", stat = "identity") +
scale_fill_manual(values = c("#030303", "#0000FF", "#EE0000", "#00CD00", "#EE9A00", "#912CEE", "#8B4726")) +
theme_cowplot()
pred_plot
```