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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