View source: R/forestplotCombineRegrObj.R
forestplotCombineRegrObj | R Documentation |
Creates a composite from different regression objects into one forestplot where you can choose the variables of interest to get an overview and easier comparison.
forestplotCombineRegrObj( regr.obj, variablesOfInterest.regexp = NULL, estimate.txt = NULL, add_first_as_ref = FALSE, ref_txt = "ref.", digits = 1, post_process_data = function(x) x, is.summary = NULL, xlab = NULL, zero = NULL, xlog = NULL, exp = xlog, ... )
regr.obj |
A list with all the fits that have variables that are to be identified through the regular expression |
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. |
estimate.txt |
The text of the estimate, usually HR for hazard ratio, OR for odds ratio |
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. |
ref_txt |
Text instead of estimate number |
digits |
Number of digits to use for the estimate output |
post_process_data |
A function that takes the data frame just prior to calling 'forestplot' and allows you to manipulate it. Primarily used for changing the 'column_label' that has the names shown in the final plot. |
is.summary |
A vector indicating by |
xlab |
x-axis label |
zero |
Indicates what is zero effect. For survival/logistic fits the zero is 1 while in most other cases it's 0. |
xlog |
If TRUE, x-axis tick marks are to follow a logarithmic scale, e.g. for
logistic regression (OR), survival estimates (HR), Poisson regression etc.
Note: This is an intentional break with the original |
exp |
Report in exponential form. Default true since the function was built for use with survival models. |
... |
Passed to |
Other forestplot wrappers:
forestplotRegrObj()
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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