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13_alpha_kappa.Rmd
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13_alpha_kappa.Rmd
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---
title: "13_kappa_alpha"
author: "randy"
date: "`r Sys.Date()`"
output: pdf_document
---
```{r setup, include=FALSE}
# knitr::opts_chunk$set(echo = TRUE,
# warnings = FALSE,
# message = FALSE,
# comment = "#>",
# results = "hide",
# error = FALSE)
## clean the R environment
## load packages
library(here, quietly = TRUE)
library(tidyverse, quietly = TRUE)
library(splines)
library(tibble)
library(nlme)
library(gamlss)
library(JMbayes)
## check the directory for the file
# here::dr_here()
here::set_here()
## the figure or results should be saved
# paste0("foldername/Sfilename_workingresult_",
#. Sys.Date(), ".filetype")
```
```{r}
people_like_us <- function(train_data,
test_data,
outcome_var = "ht",
time_var = "time",
id_var = "id",
tmin = 0,
tmax = 17,
brokenstick_knots,
anchor_time,
linear_formula = "ht ~ as.factor(time) * sex + ethnic + genotype + baseline",
gamlss_formula = "ht ~ cs(time, df = 3)",
gamlss_sigma = "~ cs(time, df = 1)",
match_methods = c("euclidean", "mahalanobis", "single"),
weight = FALSE,
match_alpha = NULL,
match_number = NULL,
match_plot = TRUE,
predict_plot = TRUE,
...) {
## user defined variables
outcome_var <- ensym(outcome_var)
time_var <- ensym(time_var)
id_var <- ensym(id_var)
# id_train <- dplyr::select(train_data, !!id_var) %>% unique() %>% unlist()
id_test <- dplyr::select(test_data, !!id_var) %>% unique() %>% unlist()
## extract the test baseline information
test_baseline <- test_data %>%
group_by(!!id_var) %>%
arrange(!!time_var) %>%
slice(1L) %>%
## change the baseline outcome_vars as new variable
# dplyr::select(baseline = !!outcome_var, everything()) %>%
## move the original time_var as all ZEROs
dplyr::select(- !!time_var)
# Tue Jul 25 22:09:42 2023 ------------------------------
## will add other methods probably will add ifelse
## currently just the brokenstick model
brokenstick <- impute_brokenstick(outcome_var = !!outcome_var,
time_var = !!time_var,
id_var = !!id_var,
bs_knots = brokenstick_knots,
anchor_time = anchor_time,
data = train_data)
## linear regression is the necessary one will be kept
lm_bks <- lm(as.formula(linear_formula),
data = brokenstick)
# Tue Jul 25 22:30:34 2023 ------------------------------
# test_baseline[paste0("anchor_", anchor_time)] <- NA
data_test1 <- test_baseline %>%
# dplyr::select(-!!time_var) %>%
## this is the unnest way of doing testing dataset
mutate(time = list(anchor_time)) %>%
unnest(cols = !!time_var) %>%
## original way of augment the testing dataset
# group_by(!!id_var) %>%
# pivot_longer(cols = contains("anchor_"),
# names_to = "time0",
# names_prefix = "anchor_",
# values_to = "lm_bks_target") %>%
rename(baseline = !!outcome_var)
lp_test <- data_test1 %>%
ungroup() %>%
mutate(lm_bks_target = predict(lm_bks, newdata = .)) %>%
dplyr::select(!!id_var, !!time_var, contains("lm_bks_target")) %>%
as.matrix() %>%
as.data.frame() %>%
rename(!!outcome_var := lm_bks_target)
lp_train <- brokenstick %>%
ungroup() %>%
mutate(lm_bks_target = predict(lm_bks)) %>%
dplyr::select(!!id_var, !!time_var, contains("lm_bks_target")) %>%
as.matrix() %>%
as.data.frame() %>%
rename(!!outcome_var := lm_bks_target)
# browser()
# browser()
## end of 01_impute.R file ------------------------
subset <- lp_test %>%
group_by(!!id_var) %>%
group_map(~dis_match(lb_train = lp_train,
lb_test_ind = .,
train = train_data,
match_methods = match_methods,
id_var = !!id_var,
outcome_var = !!outcome_var,
time_var = !!time_var,
match_alpha = match_alpha,
match_number = match_number,
match_time = match_time,
match_plot = match_plot) %>%
dplyr::select("id") %>%
unique() %>%
nrow())
results <- quantile(unlist(subset), probs = seq(0, 1, 0.25))
# ?quantile
return(nrow = results)
}
```
```{r include=FALSE}
# load("R/train_test.rda")
outcome3q <- map(c(0.6, 0.65, 0.70, 0.75, 0.8, 0.85, 0.9, 0.95, 0.97, 0.99, 0.995),
~ people_like_us(train_data = train,
test_data = test,
anchor_time = c(4, 8, 12),
brokenstick_knots = c(10, 12, 15),
match_methods = "mahalanobis",
match_alpha = .,
weight = FALSE,
match_plot = FALSE,
predict_plot = FALSE))
outcome41q <- map(c(0.6, 0.65, 0.70, 0.75, 0.8, 0.85, 0.9, 0.95, 0.97, 0.99, 0.995),
~ people_like_us(train_data = train,
test_data = test,
anchor_time = c(3, 6, 9, 12),
brokenstick_knots = c(10, 12, 15),
match_methods = "mahalanobis",
match_alpha = .,
weight = FALSE,
match_plot = FALSE,
predict_plot = FALSE))
outcome42q <- map(c(0.6, 0.65, 0.70, 0.75, 0.8, 0.85, 0.9, 0.95, 0.97, 0.99, 0.995),
~ people_like_us(train_data = train,
test_data = test,
anchor_time = c(3,6,9,11,12,15),
brokenstick_knots = c(10, 12, 15),
match_methods = "mahalanobis",
match_alpha = .,
weight = FALSE,
match_plot = FALSE,
predict_plot = FALSE))
outcome3q <- map(outcome3q, cbind) %>% as.data.frame() %>%
dplyr::select(p60 = 1, p65 = 2, p70 = 3, p75 = 4, p80 = 5, p85 = 6, p90 = 7, p95 = 8, p97 = 9, p99 = 10, p100 = 11)
outcome41q <- map(outcome41q, cbind) %>% as.data.frame() %>%
dplyr::select(p60 = 1, p65 = 2, p70 = 3, p75 = 4, p80 = 5, p85 = 6, p90 = 7, p95 = 8, p97 = 9, p99 = 10, p100 = 11)
outcome42q <- map(outcome42q, cbind) %>% as.data.frame() %>%
dplyr::select(p60 = 1, p65 = 2, p70 = 3, p75 = 4, p80 = 5, p85 = 6, p90 = 7, p95 = 8, p97 = 9, p99 = 10, p100 = 11)
result3q <- rbind(outcome3q, outcome41q, outcome42q) %>%
as.data.frame()
```
```{r}
library(xtable)
print(xtable(result3q, type = "latex"),
file = paste0("figure/S10_alpha_kappa", Sys.Date(), ".tex"))
xtable(result3q, type = "latex")
```
% latex table generated in R 4.2.2 by xtable 1.8-4 package
% Wed Dec 20 09:26:57 2023
\begin{table}[ht]
\centering
\begin{tabular}{rrrrrrrrrrrr}
\hline
& p65 & p70 & p75 & p80 & p85 & p90 & p95 & p97 & p99\\
\hline
1 & 41.00 & 38.00 & 29.00 & 26.00 & 22.00 & 17.00 & 11.00 & 7.00 & 6.00 & 1.00 & 0.00 \\
2 & 277.00 & 261.00 & 238.00 & 221.00 & 201.00 & 173.00 & 151.00 & 115.00 & 98.00 & 68.00 & 54.00 \\
3 & 338.00 & 326.00 & 308.00 & 296.00 & 275.00 & 246.00 & 219.00 & 173.00 & 146.00 & 96.00 & 74.00 \\
4 & 368.00 & 355.00 & 340.00 & 323.00 & 302.00 & 277.00 & 247.00 & 199.00 & 169.00 & 113.00 & 91.00 \\
5 & 388.00 & 372.00 & 358.00 & 346.00 & 320.00 & 297.00 & 260.00 & 215.00 & 186.00 & 138.00 & 113.00 \\
6 & 77.00 & 71.00 & 57.00 & 43.00 & 39.00 & 29.00 & 24.00 & 13.00 & 11.00 & 6.00 & 4.00 \\
7 & 323.00 & 314.00 & 302.00 & 280.00 & 264.00 & 231.00 & 206.00 & 166.00 & 145.00 & 105.00 & 91.00 \\
8 & 381.00 & 370.00 & 357.00 & 341.00 & 326.00 & 305.00 & 282.00 & 237.00 & 212.00 & 160.00 & 127.00 \\
9 & 405.00 & 395.00 & 384.00 & 371.00 & 355.00 & 337.00 & 307.00 & 266.00 & 239.00 & 184.00 & 152.00 \\
10 & 418.00 & 412.00 & 402.00 & 391.00 & 373.00 & 356.00 & 331.00 & 283.00 & 255.00 & 198.00 & 173.00 \\
\hline
\end{tabular}
\end{table}
\begin{table}[ht]
\centering
\begin{tabular}{rrrrrrrrrrrr}
\hline
& p65 & p70 & p75 & p80 & p85 & p90 & p95 & p97 & p99\\
\hline
1 & 41.00 & 38.00 & 29.00 & 26.00 & 22.00 & 17.00 & 11.00 & 7.00 & 6.00 & 1.00 & 0.00 \\
2 & 277.00 & 261.00 & 238.00 & 221.00 & 201.00 & 173.00 & 151.00 & 115.00 & 98.00 & 68.00 & 54.00 \\
3 & 338.00 & 326.00 & 308.00 & 296.00 & 275.00 & 246.00 & 219.00 & 173.00 & 146.00 & 96.00 & 74.00 \\
4 & 368.00 & 355.00 & 340.00 & 323.00 & 302.00 & 277.00 & 247.00 & 199.00 & 169.00 & 113.00 & 91.00 \\
5 & 388.00 & 372.00 & 358.00 & 346.00 & 320.00 & 297.00 & 260.00 & 215.00 & 186.00 & 138.00 & 113.00 \\
6 & 77.00 & 71.00 & 57.00 & 43.00 & 39.00 & 29.00 & 24.00 & 13.00 & 11.00 & 6.00 & 4.00 \\
7 & 323.00 & 314.00 & 302.00 & 280.00 & 264.00 & 231.00 & 206.00 & 166.00 & 145.00 & 105.00 & 91.00 \\
8 & 381.00 & 370.00 & 357.00 & 341.00 & 326.00 & 305.00 & 282.00 & 237.00 & 212.00 & 160.00 & 127.00 \\
9 & 405.00 & 395.00 & 384.00 & 371.00 & 355.00 & 337.00 & 307.00 & 266.00 & 239.00 & 184.00 & 152.00 \\
10 & 418.00 & 412.00 & 402.00 & 391.00 & 373.00 & 356.00 & 331.00 & 283.00 & 255.00 & 198.00 & 173.00 \\
11 & 160.00 & 138.00 & 120.00 & 103.00 & 86.00 & 75.00 & 57.00 & 39.00 & 27.00 & 18.00 & 12.00 \\
12 & 533.00 & 469.00 & 361.00 & 350.00 & 338.00 & 322.00 & 302.00 & 260.00 & 229.00 & 186.00 & 162.00 \\
13 & 645.00 & 550.00 & 413.00 & 403.00 & 393.00 & 379.00 & 360.00 & 325.00 & 303.00 & 256.00 & 235.00 \\
14 & 684.00 & 584.00 & 426.00 & 419.00 & 413.00 & 403.00 & 386.00 & 355.00 & 334.00 & 288.00 & 260.00 \\
15 & 700.00 & 610.00 & 437.00 & 431.00 & 423.00 & 417.00 & 402.00 & 375.00 & 353.00 & 305.00 & 274.00 \\
\hline
\end{tabular}
\end{table}