View source: R/getModelData4Forestplot.R
getModelData4Forestplot | R Documentation |
A helper function for forestplotCombineRegrObj()
. Extracts
the data from the regression model fits and returns a list
with model data gathered by the function [broom::tidy()]
getModelData4Forestplot(
regr.obj,
exp = TRUE,
variablesOfInterest.regexp = NULL,
add_first_as_ref = FALSE
)
regr.obj |
A list with all the fits that have variables that are to be identified through the regular expression |
exp |
Report in exponential form. Default true since the function was built for use with survival models. |
variablesOfInterest.regexp |
A regular expression identifying the variables that are of interest of comparing. For instance it can be "(score|index|measure)" that finds scores in different models that should be compared. |
add_first_as_ref |
If you want that the first variable should be reference for that group of variables. The ref is a variable with the estimate 1 or 0 depending if exp() and the confidence interval 0. |
org.par <- par("ask" = TRUE)
# simulated data to test
library(tidyverse)
set.seed(10)
cov <- tibble(ftime = rexp(200),
fstatus = sample(0:1, 200, replace = TRUE),
x1 = runif(200),
x2 = runif(200),
x3 = runif(200)) |>
# Add some column labels
Gmisc::set_column_labels(x1 = "First variable",
x2 = "Second variable")
library(rms)
ddist <- datadist(cov)
options(datadist = "ddist")
fit1 <- cph(Surv(ftime, fstatus) ~ x1 + x2, data = cov)
fit2 <- cph(Surv(ftime, fstatus) ~ x1 + x3, data = cov)
list(`First model` = fit1,
`Second model` = fit2) |>
forestplotCombineRegrObj(variablesOfInterest.regexp = "(x2|x3)") |>
fp_set_style(lines = "steelblue",
box = "darkblue")
# How to add expressions to the plot label
list(fit1, fit2) |>
forestplotCombineRegrObj(variablesOfInterest.regexp = "(x2|x3)",
reference.names = c("First model", "Second model"),
post_process_data = \(data) {
data$column_label[4] <- c(rlang::expr(expression(Fever >= 38.5)))
return(data)
})
par(org.par)
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