View source: R/ormPredictionplot.R
plot.orm | R Documentation |
This function plots the model predictions given that all variables that are
not included in the plot are kept constant. Hence it requires at least one
variable to produce a plot.
returns a ggplot
object that can be further customized like any
other ggplot
## S3 method for class 'orm'
plot(
x,
xval,
plot_cols = c(),
plot_rows = c(),
label_with_colname = TRUE,
facet_labels = NULL,
xlab = NULL,
ylab = NULL,
np = 100,
fun = stats::plogis,
boot.type = "bca",
conf.int = 0.95,
...
)
x |
an object created by |
xval |
The model value plotted on the x axis |
plot_cols |
A vector of strings with other model components that should be plotted. These are put on columns. |
plot_rows |
A vector of strings with other model components that should be plotted. These are put on rows. |
label_with_colname |
Should he variable name also be included on plot row and column names |
facet_labels |
A named list of new names for variables on rows and columns |
xlab |
A custom x-axis value (if specified) |
ylab |
A custom y-axis value (if specified) |
np |
the number of equally-spaced points computed for continuous
predictors that vary, i.e., when the specified value is |
fun |
an optional transformation of the linear predictor.
Specify |
boot.type |
set to |
conf.int |
confidence level (highest posterior density interval probability for
Bayesian models). Default is 0.95. Specify |
... |
additional parameters that will be passed to |
a ggplot
plot object
Predict
, datadist
, orm
#load the libraries
library(rms)
library(ormPlot)
#make the datadist
dd<-datadist(educ_data)
options(datadist='dd')
#create the model
cran_model <- orm(educ_3 ~ Rural + sex + max_SEP_3 + cran_rzs, data = educ_data)
#plot the predictions of the model for varying one variable only
plot(cran_model, cran_rzs)
#customize the plotting varying all variables
plot(cran_model, cran_rzs,
plot_cols = max_SEP_3,
plot_rows = c(Rural, sex),
#setting new x-label (optional)
xlab = "Cranial volume (residuals to age an birth date)",
#setting new facet labels (optional)
facet_labels = list(Rural = c("Urban", "Rural"),
sex = c("Boys","Girls"))
)
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