table3 {tableeasy} | R Documentation |
Table 3
Description
Creates 'Table 3' which is about stratified analysis. The three regression methods include general linear regression, logistic regression and cox proportional hazards regression.
Usage
table3(
x,
y,
data,
split_var,
y_time = NULL,
adj = c(),
split_div = list(),
outformat = 4,
method = "general"
)
Arguments
x |
A string. The independent variable to be summarized given as a string. |
y |
A string. The dependent variable to be summarized given as a string. |
data |
A data frame in which these variables exist. |
split_var |
A vector of strings. Strata variables to be summarized given as a character vector. |
y_time |
A string. The survival time variable to be summarized given as a string. It only works when |
adj |
A vector of strings, default = |
split_div |
A list containing numeric vectors or a vector of integers that are summarized given as a string, default |
outformat |
|
method |
( |
Value
An object about stratified analysis.
Examples
## Load Mayo Clinic Primary Biliary Cirrhosis Data
library(survival)
library(tableeasy)
data(pbc)
## Check variables
head(pbc)
##The censored data is not discussed here
pbc_full <- subset(pbc,status!=0)
pbc_full$status <- pbc_full$status-1
## Make categorical variables factors
varsToFactor <- c('status','trt','ascites','hepato','spiders','edema','stage','sex')
pbc_full[varsToFactor] <- lapply(pbc_full[varsToFactor], factor)
## Moderator variables
adj_pbc <- c('age','alk.phos','ast')
## Converts the continuous variables named 'albumin' to a categorical variable named 'albumin_2'.
albumin_2 <- div_quantile('albumin',div = c(2),pbc_full)
pbc_full <- data.frame(pbc_full,'albumin_2' = albumin_2)
## General linear regression:
table3(x = 'albumin_2', y = 'bili',
adj = c(), data = pbc_full,
split_var = c('age','alk.phos','ast','trt'), split_div = list(),
outformat = 1)
## Logistic regression:
table3(x = 'albumin_2', y = 'status',
adj = adj_pbc, data = pbc_full,
split_var = c('age','alk.phos','ast','trt'), split_div = list(c('2','3'),c('3')),
outformat = 2,method = 'logistic')
## Cox proportional hazards regression:
table3(x = 'albumin_2',y = 'status',y_time = 'time',
adj = adj_pbc,data = pbc_full,
split_var = c('age','alk.phos','ast','trt'), split_div = list(c(45),c(1500,1700),c(),c()),
outformat = 3,method = 'cox')