binomial_glm_plot {model4you} | R Documentation |
Plot for a given logistic regression model (glm with binomial family) with one binary covariate.
Description
Can be used on its own but is also useable as plotfun in
node_pmterminal
.
Usage
binomial_glm_plot(
mod,
data = NULL,
plot_data = FALSE,
theme = theme_classic(),
...
)
Arguments
mod |
A model of class glm with binomial family. |
data |
optional data frame. If NULL the data stored in mod is used. |
plot_data |
should the data in form of a mosaic type plot be plotted? |
theme |
A ggplot2 theme. |
... |
ignored at the moment. |
Examples
set.seed(2017)
# number of observations
n <- 1000
# balanced binary treatment
# trt <- factor(rep(c("C", "A"), each = n/2),
# levels = c("C", "A"))
# unbalanced binary treatment
trt <- factor(c(rep("C", n/4), rep("A", 3*n/4)),
levels = c("C", "A"))
# some continuous variables
x1 <- rnorm(n)
x2 <- rnorm(n)
# linear predictor
lp <- -0.5 + 0.5*I(trt == "A") + 1*I(trt == "A")*I(x1 > 0)
# compute probability with inverse logit function
invlogit <- function(x) 1/(1 + exp(-x))
pr <- invlogit(lp)
# bernoulli response variable
y <- rbinom(n, 1, pr)
dat <- data.frame(y, trt, x1, x2)
# logistic regression model
mod <- glm(y ~ trt, data = dat, family = "binomial")
binomial_glm_plot(mod, plot_data = TRUE)
# logistic regression model tree
ltr <- pmtree(mod)
plot(ltr, terminal_panel = node_pmterminal(ltr,
plotfun = binomial_glm_plot,
confint = TRUE,
plot_data = TRUE))
[Package model4you version 0.9-7 Index]