Create a univariable forest plot using ggplot2

This function creates forest plots from univariable regression models. For new code, consider using forestplotMV() which can handle both adjusted and unadjusted estimates.

forestplotUV(
  response,
  covs,
  data,
  model = "glm",
  id = NULL,
  corstr = NULL,
  family = NULL,
  digits = getOption("reportRmd.digits", 2),
  conf.level = 0.95,
  colours = "default",
  showEst = TRUE,
  showRef = TRUE,
  logScale = getOption("reportRmd.logScale", TRUE),
  nxTicks = 5,
  showN = TRUE,
  showEvent = TRUE,
  xlim = NULL
)

Arguments

response

character vector with names of columns to use for response

covs

character vector with names of columns to use for covariates

data

dataframe containing your data

model

fitted model object (default "glm")

id

character vector which identifies clusters. Only used for geeglm

corstr

character string specifying the correlation structure. Only used for geeglm

family

description of the error distribution and link function to be used in the model

digits

number of digits to round to (default 2)

conf.level

controls the width of the confidence interval (default 0.95)

colours

can specify colours for risks less than, equal to, and greater than 1.0. Default is green, black, red

showEst

logical, should the risks be displayed on the plot in text? Default is TRUE

showRef

logical, should reference levels be shown? Default is TRUE

logScale

logical, should OR/RR be shown on log scale? Defaults to TRUE

nxTicks

Number of tick marks for x-axis (default 5)

showN

Show number of observations per variable and category (default TRUE)

showEvent

Show number of events per variable and category (default TRUE)

xlim

numeric vector of length 2 specifying x-axis limits (ex c(0.2, 5)) Confidence intervals extending beyond these limits will be shown with arrows.

Value

a ggplot object

Examples

data("pembrolizumab")
forestplotUV(response = "orr",
             covs = c("change_ctdna_group", "sex", "age", "l_size"),
             data = pembrolizumab, family = 'binomial')
#> Warning: Vectorized input to `element_text()` is not officially supported.
#> ℹ Results may be unexpected or may change in future versions of ggplot2.
#> Note: Very wide confidence intervals detected. X-axis capped for readability.
#> `height` was translated to `width`.