build_model.coxph | R Documentation |
Models specified terms in model data against an existing model and returns a clean, human readable table of summarizing the effects and statistics for the newly generated model. This functions greatly simplifies fitting a large number of variables against a set of time-to-event data.
## S3 method for class 'coxph' build_model( .object, ..., .mv = FALSE, .test = c("LRT", "Wald"), .col.test = FALSE, .level = 0.95, .stat.pct.sign = TRUE, .digits = 1, .p.digits = 4 )
.object |
An object of class |
... |
One or more unquoted expressions separated by commas representing
columns in the model data.frame. May be specified using
|
.mv |
A logical. Fit all terms into a single multivariable model. If left FALSE, all terms are fit in their own univariate models. |
.test |
A character. The name of a |
.col.test |
A logical. Append a columns for the test and accompanying statistic used to derive the p-value. |
.level |
A double. The confidence level required. |
.stat.pct.sign |
A logical. Paste a percent symbol after all reported frequencies. |
.digits |
An integer. The number of digits to round numbers to. |
.p.digits |
An integer. The number of p-value digits to report. Note
that the p-value still rounded to the number of digits specified in
|
An object of class data.frame summarizing the provided object. If the
tibble
package has been installed, a tibble will be returned.
build_model
library(survival) library(dplyr) data_lung <- lung |> mutate_at(vars(inst, status, sex), as.factor) |> mutate(status = case_when(status == 1 ~ 0, status == 2 ~ 1)) fit <- coxph(Surv(time, status) ~ 1, data = data_lung) # Create a univariate model for each variable fit |> build_model(sex, age)
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