clinpubr (version 1.1.1)

regression_forest: Forest plot of regression results

Description

Generate the forest plot of logistic or Cox regression with different models.

Usage

regression_forest(
  data,
  model_vars,
  y,
  time = NULL,
  time2 = NULL,
  cluster = NULL,
  as_univariate = FALSE,
  est_nsmall = 2,
  p_nsmall = 3,
  show_vars = NULL,
  save_plot = FALSE,
  filename = NULL,
  ...
)

Value

A gtable object.

Arguments

data

A data frame.

model_vars

A character vector or a named list of predictor variables for different models.

y

A character string of the outcome variable.

time

A character string of the time variable. If NULL, logistic regression is used. Otherwise, Cox proportional hazards regression is used.

time2

A character string of the ending time of the interval for interval censored or counting process data only.

cluster

A character string of the cluster variable. If set, correct for heteroscedasticity and for correlated responses from cluster samples using rms::robcov().

as_univariate

A logical value indicating whether to treat the model_vars as univariate.

est_nsmall

An integer specifying the precision for the estimates in the plot.

p_nsmall

An integer specifying the number of decimal places for the p-values.

show_vars

A character vector of variable names to be shown in the plot. If NULL, all variables are shown.

save_plot

A logical value indicating whether to save the plot.

filename

A character string specifying the filename for the plot. If NULL, a default filename is used.

...

Additional arguments passed to the forestploter::forest function.

Examples

data(cancer, package = "survival")
cancer$ph.ecog_cat <- factor(cancer$ph.ecog, levels = c(0:3), labels = c("0", "1", ">=2", ">=2"))
regression_forest(cancer,
  model_vars = c("age", "sex", "wt.loss", "ph.ecog_cat", "meal.cal"), y = "status", time = "time",
  as_univariate = TRUE, save_plot = FALSE
)

regression_forest(cancer,
  model_vars = c("age", "sex", "wt.loss", "ph.ecog_cat", "meal.cal"), y = "status", time = "time",
  show_vars = c("age", "sex", "ph.ecog_cat", "meal.cal"), save_plot = FALSE
)

regression_forest(cancer,
  model_vars = list(
    M0 = c("age"),
    M1 = c("age", "sex", "wt.loss", "ph.ecog_cat", "meal.cal"),
    M2 = c("age", "sex", "wt.loss", "ph.ecog_cat", "meal.cal", "pat.karno")
  ),
  y = "status", time = "time",
  show_vars = c("age", "sex", "ph.ecog_cat", "meal.cal"), save_plot = FALSE
)