rms_po_assume: Create figures to visually examine proportional odds...

In jenniferthompson/JTHelpers: Personal Helper Functions, Some Related to rms Package

Description

Based on code and strategy outlined in Frank Harrell's Regression Modeling Strategies (see citation below).

Usage

rms_po_assume(lrmObj = NULL, ...)

## S3 method for class 'fit.mult.impute'
rms_po_assume(lrmObj, cuts, plotVars = NULL,
  mfrowAuto = FALSE, modelData, impObj, plotType = c("ggplot", "base"))

## Default S3 method:
rms_po_assume(lrmObj, cuts, plotVars = NULL,
  mfrowAuto = FALSE, modelData, plotType = c("ggplot", "base"))

Arguments

cuts	Numeric vector; sequence of points to cut outcome. Should not include lowest outcome level.
plotVars	Character vector; which variables to plot. Defaults to all.
mfrowAuto	Logical; whether to determine par(mfrow = ...) automatically. Defaults to FALSE.
modelData	Data.frame; data set used to fit original model. Used to fit logistic models with outcome dichotomized at each cut point.
impObj	aregImpute or mice object used to fit original model in fit.mult.impute.
rmsObj	Model fit of class 'lrm'.

Methods (by class)

• fit.mult.impute: Method for lrm() models fit with fit.mult.impute.

• default: Method for lrm() model fits without imputation.

See Also

Harrell FE. *Regression Modeling Strategies: with applications to linear models, logistic regression, and survival analysis.* New York: Springer Science + Business Media, LLC, 2001.

Examples

## Regular lrm() model, no imputation
df <- data.frame(ptclass = sample(1:4, size = 20, replace = TRUE),
                 v1 = rnorm(n = 20),
                 v2 = rnorm(mean = 5, sd = 1, n = 20))

mymod <- lrm(ptclass ~ v1 + v2, data = df)
rms_po_assume(mymod, cuts = 2:4, modelData = df)

## Model using imputation
df$v1[sample(1:nrow(df), size = 5)] <- NA

aregdf <- aregImpute(~ ptclass + v1 + v2, nk = 0, data = df)
mymodImp <- fit.mult.impute(ptclass ~ v1 + v2, fitter = lrm, xtrans = aregdf, data = df)
rms_po_assume(mymodImp, cuts = 2:4, impObj = aregdf, modelData = df)

jenniferthompson/JTHelpers documentation built on May 19, 2019, 4:04 a.m.