predict_with_ciR Documentation

Create a Prediction data.frame with confidence intervals

Description

returns a data.frame object similar to the Predict however it adds a column dependent that lists all factor levels with appropriate confidence intervals calculated for each level. It is similar to predict.lrm with type="fitted.ind" but also generates selected confidence intervals.

Usage

predict_with_ci(
  x,
  ...,
  np = 100,
  fun = stats::plogis,
  conf.int = 0.95,
  boot.type = "bca"
)

Arguments

x

an object created by Predict

...

One or more variables to vary, or single-valued adjustment values. Specify a variable name without an equal sign to use the default display range, or any range you choose (e.g. seq(0,100,by=2),c(2,3,7,14)). The default list of values for which predictions are made is taken as the list of unique values of the variable if they number fewer than 11. For variables with >10 unique values, np equally spaced values in the range are used for plotting if the range is not specified. Variables not specified are set to the default adjustment value limits[2], i.e. the median for continuous variables and a reference category for non-continuous ones. Later variables define adjustment settings. For categorical variables, specify the class labels in quotes when specifying variable values. If the levels of a categorical variable are numeric, you may omit the quotes. For variables not described using datadist, you must specify explicit ranges and adjustment settings for predictors that were in the model. If no variables are specified in ..., predictions will be made by separately varying all predictors in the model over their default range, holding the other predictors at their adjustment values. This has the same effect as specifying name as a vector containing all the predictors. For rbind, ... represents a series of results from Predict. If you name the results, these names will be taken as the values of the new .set. variable added to the concatenated data frames. See an example below.

np

the number of equally-spaced points computed for continuous predictors that vary, i.e., when the specified value is . or NA

fun

an optional transformation of the linear predictor. Specify fun='mean' if the fit is a proportional odds model fit and you ran bootcov with coef.reps=TRUE. This will let the mean function be re-estimated for each bootstrap rep to properly account for all sources of uncertainty in estimating the mean response. fun can be a general function and can compute confidence limits (stored as a list in the limits attribute) of the transformed parameters such as means.

conf.int

confidence level (highest posterior density interval probability for Bayesian models). Default is 0.95. Specify FALSE to suppress.

boot.type

set to 'bca' to compute BCa confidence limits or 'basic' to use the basic bootstrap. The default is to compute percentile intervals

Value

a data.frame

See Also

Predict,orm, predict.lrm

Examples

set.seed(123)
#load the libraries
library(rms)
library(ormPlot)

#make the datadist
dd<-rms::datadist(educ_data)
options(datadist="dd")

#create the model
cran_model <- orm(educ_3 ~ Rural + sex + max_SEP_3 + cran_rzs, data = educ_data)

#get the predictions of the orm model with confidence intervals for all levels
predictiondf<-predict_with_ci(cran_model, cran_rzs, Rural, sex, max_SEP_3)
#show the predictions head
head(predictiondf)

#get the predictions of the orm model with confidence intervals for sex only
predictiondf_sex<-predict_with_ci(cran_model,  sex)
#show the predictions head
head(predictiondf_sex)