TableSubgroupMultiGLM: TableSubgroupMultiGLM: Multiple sub-group analysis table for...

In jstable: Create Tables from Different Types of Regression

TableSubgroupMultiGLM: Multiple sub-group analysis table for GLM.

Description

Multiple sub-group analysis table for GLM.

Usage

TableSubgroupMultiGLM(
  formula,
  var_subgroups = NULL,
  var_cov = NULL,
  data,
  family = "binomial",
  decimal.estimate = 2,
  decimal.percent = 1,
  decimal.pvalue = 3,
  line = F,
  labeldata = NULL
)

Arguments

formula	formula with survival analysis.
var_subgroups	Multiple sub-group variables for analysis, Default: NULL
var_cov	Variables for additional adjust, Default: NULL
data	Data or svydesign in survey package.
family	family, "gaussian" or "binomial" or 'poisson' or 'quasipoisson'
decimal.estimate	Decimal for estimate, Default: 2
decimal.percent	Decimal for percent, Default: 1
decimal.pvalue	Decimal for pvalue, Default: 3
line	Include new-line between sub-group variables, Default: F
labeldata	Label info, made by 'mk.lev' function, Default: NULL

Details

This result is used to make forestplot.

Value

Multiple sub-group analysis table.

See Also

map bind

Examples

library(survival)
library(dplyr)
lung %>%
  mutate(
    status = as.integer(status == 1),
    sex = factor(sex),
    kk = factor(as.integer(pat.karno >= 70)),
    kk1 = factor(as.integer(pat.karno >= 60))
  ) -> lung
TableSubgroupMultiGLM(status ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = lung, line = TRUE, family = "binomial"
)

## survey design
library(survey)
data.design <- svydesign(id = ~1, data = lung)
TableSubgroupMultiGLM(status ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = data.design, family = "binomial"
)

jstable documentation built on April 12, 2025, 1:22 a.m.