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---
title: "Elastic Net Predictions"
author: "Catherine Walsh"
date: "9/11/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libraries and data}
library(tidyverse)
library(caret)
load("data/PCs.RData")
load("data/behav.RData")
```
```{r set up data}
PC_list <- list(univ=univ_PCs, sim = sim_PCs, MVPA = MVPA_PCs, EEG = EEG_PCs, RS = RS_PCs, struct = struc_PCs, FA = FA_PCs)
reg_data <- data.frame(PTID = constructs_fMRI$PTID,
span = constructs_fMRI$omnibus_span_no_DFR_MRI)
reg_data <- Reduce(function(x,y) merge(x=x, y=y, by = "PTID", all.x = TRUE),
list(reg_data, p200_demographics, p200_data))
reg_data <- dplyr::select(reg_data,c("PTID", "span", "BPRS_TOT", "XDFR_MRI_ACC_L3","AGE", "GENDER", "SCANNER"))
colnames(reg_data) <- c("PTID", "span", "BPRS", "high_acc","AGE", "GENDER", "SCANNER")
reg_data[,6:7] <- reg_data[,6:7]-1
set.seed(123)
```
```{r run for span}
best_models_span <- list()
RMSE_cv_span <- data.frame(matrix(ncol=10, nrow=7))
pred_list <- list()
compare_span <- data.frame(matrix(nrow = 2, ncol=7))
rownames(compare_span) <- c("RMSE", "Rsquared")
for (data_class in seq.int(1,7)){
reg_data_span <- merge(reg_data_span, PC_list[[data_class]], by ="PTID", all.x=TRUE)
reg_data_span <- reg_data_span[complete.cases(reg_data_span),]
split <- vfold_cv(reg_data_span,v=10)
best_models <- list()
for (fold in seq.int(1,10)){
# set up training and test set for fold
train_data <- analysis(split$splits[[fold]])
train_data <- train_data[,c(2, 5:ncol(reg_data_span))]
test_data <- assessment(split$splits[[fold]])
test_data <- test_data[,c(2, 5:ncol(reg_data_span))]
train_data[,c(2, 5:ncol(train_data))] <- sapply(train_data[,c(2, 5:ncol(train_data))],scale)
train_data <- data.frame(train_data)
test_data[,c(2, 5:ncol(train_data))] <- sapply(test_data[,c(2, 5:ncol(train_data))], scale)
test_data <- data.frame(test_data)
elastic <- train(
span ~., data = train_data, method = "glmnet",
trControl = trainControl("cv", number = 10),
tuneLength = 10
)
best_models[[fold]] <- elastic$finalModel
preds <- elastic %>% predict(test_data)
# Make predictions
if (fold == 1){
cv_preds_span <- data.frame(true = test_data$span, pred = preds)
}else{
cv_preds_span <- rbind(cv_preds_span, data.frame(true = test_data$span, pred = preds))}
RMSE_cv_span[data_class,fold] <- RMSE(preds, test_data$span)
print(paste0("finished fold ", fold, sep=""))
}
compare_span[1, data_class] <- rowMeans(RMSE_cv_span[data_class,])
compare_span[2, data_class] <- cor(cv_preds_span$true, cv_preds_span$pred)^2
pred_list[[data_class]] <- cv_preds_span
best_models_span[[data_class]] <- best_models
print(paste0("finished data addition ", data_class, sep=""))
}
```
```{r run for BPRS}
best_models_BPRS <- list()
RMSE_cv_BPRS <- data.frame(matrix(ncol=10, nrow=7))
pred_list_BPRS <- list()
compare_BPRS <- data.frame(matrix(nrow = 2, ncol=7))
rownames(compare_BPRS) <- c("RMSE", "Rsquared")
for (data_class in seq.int(1,7)){
reg_data_BPRS <- merge(reg_data, PC_list[[data_class]], by ="PTID", all.x=TRUE)
reg_data_BPRS <- reg_data_BPRS[complete.cases(reg_data_BPRS),]
split <- vfold_cv(reg_data_BPRS,v=10)
best_models <- list()
for (fold in seq.int(1,10)){
# set up training and test set for fold
train_data <- analysis(split$splits[[fold]])
train_data <- train_data[,c(3, 5:ncol(reg_data_BPRS))]
test_data <- assessment(split$splits[[fold]])
test_data <- test_data[,c(3, 5:ncol(reg_data_BPRS))]
train_data[,c(1:2, 5:ncol(train_data))] <- sapply(train_data[,c(1:2, 5:ncol(train_data))],scale)
train_data <- data.frame(train_data)
test_data[,c(1:2, 5:ncol(train_data))] <- sapply(test_data[,c(1:2, 5:ncol(train_data))], scale)
test_data <- data.frame(test_data)
elastic <- train(
BPRS ~., data = train_data, method = "glmnet",
trControl = trainControl("cv", number = 10),
tuneLength = 10
)
best_models[[fold]] <- elastic$finalModel
preds <- elastic %>% predict(test_data)
# Make predictions
if (fold == 1){
cv_preds_BPRS <- data.frame(true = test_data$BPRS, pred = preds)
}else{
cv_preds_BPRS <- rbind(cv_preds_BPRS, data.frame(true = test_data$BPRS, pred = preds))}
RMSE_cv_BPRS[data_class,fold] <- RMSE(preds, test_data$BPRS)
print(paste0("finished fold ", fold, sep=""))
}
compare_BPRS[1, data_class] <- rowMeans(RMSE_cv_BPRS[data_class,])
compare_BPRS[2, data_class] <- cor(cv_preds_BPRS$true, cv_preds_BPRS$pred)^2
pred_list_BPRS[[data_class]] <- cv_preds_BPRS
best_models_BPRS[[data_class]] <- best_models
print(paste0("finished data addition ", data_class, sep=""))
}
```
`
```{r run for high_acc}
best_models_high_acc <- list()
RMSE_cv_high_acc <- data.frame(matrix(ncol=10, nrow=7))
pred_list_high_acc <- list()
compare_high_acc <- data.frame(matrix(nrow = 2, ncol=7))
rownames(compare_high_acc) <- c("RMSE", "Rsquared")
for (data_class in seq.int(1,7)){
reg_data_high_acc <- merge(reg_data, PC_list[[data_class]], by ="PTID", all.x=TRUE)
reg_data_high_acc <- reg_data_high_acc[complete.cases(reg_data_high_acc),]
split <- vfold_cv(reg_data_high_acc,v=10)
best_models <- list()
for (fold in seq.int(1,10)){
# set up training and test set for fold
train_data <- analysis(split$splits[[fold]])
train_data <- train_data[,c(4, 5:ncol(reg_data_high_acc))]
test_data <- assessment(split$splits[[fold]])
test_data <- test_data[,c(4, 5:ncol(reg_data_high_acc))]
train_data[,c(1:2, 5:ncol(train_data))] <- sapply(train_data[,c(1:2, 5:ncol(train_data))],scale)
train_data <- data.frame(train_data)
test_data[,c(1:2, 5:ncol(train_data))] <- sapply(test_data[,c(1:2, 5:ncol(train_data))], scale)
test_data <- data.frame(test_data)
elastic <- train(
high_acc ~., data = train_data, method = "glmnet",
trControl = trainControl("cv", number = 10),
tuneLength = 10
)
best_models[[fold]] <- elastic$finalModel
preds <- elastic %>% predict(test_data)
# Make predictions
if (fold == 1){
cv_preds_high_acc <- data.frame(true = test_data$high_acc, pred = preds)
}else{
cv_preds_high_acc <- rbind(cv_preds_high_acc, data.frame(true = test_data$high_acc, pred = preds))}
RMSE_cv_high_acc[data_class,fold] <- RMSE(preds, test_data$high_acc)
print(paste0("finished fold ", fold, sep=""))
}
compare_high_acc[1, data_class] <- rowMeans(RMSE_cv_high_acc[data_class,])
compare_high_acc[2, data_class] <- cor(cv_preds_high_acc$true, cv_preds_high_acc$pred)^2
pred_list_high_acc[[data_class]] <- cv_preds_high_acc
best_models_high_acc[[data_class]] <- best_models
print(paste0("finished data addition ", data_class, sep=""))
}
```