poma: Examine proportional odds and parallelism assumptions of...

In rms: Regression Modeling Strategies

Examine proportional odds and parallelism assumptions of 'orm' and 'lrm' model fits.

Description

Based on codes and strategies from Frank Harrell's canonical 'Regression Modeling Strategies' text

Usage

poma(mod.orm, cutval, minfreq = 15, ...)

Arguments

mod.orm	Model fit of class 'orm' or 'lrm'. For 'fit.mult.impute' objects, 'poma' will refit model on a singly-imputed data-set
cutval	Numeric vector; sequence of observed values to cut outcome
minfreq	Numeric vector; an 'impactPO' argument which specifies the minimum sample size to allow for the least frequent category of the dependent variable.
...	parameters to pass to 'impactPO' function such as 'newdata', 'nonpo', and 'B'.

Details

Strategy 1: Compare PO model fit with models that relax the PO assumption (for discrete response variable)
Strategy 2: Apply different link functions to Prob of Binary Ys (defined by cutval). Regress transformed outcome on combined X and assess constancy of slopes (betas) across cut-points
Strategy 3: Generate score residual plot for each predictor (for response variable with <10 unique levels)
Strategy 4: Assess parallelism of link function transformed inverse CDFs curves for different XBeta levels (for response variables with >=10 unique levels)

Author(s)

Yong Hao Pua <[e-mail]>

See Also

Harrell FE. *Regression Modeling Strategies: with applications to linear models, logistic and ordinal regression, and survival analysis.* New York: Springer Science, LLC, 2015.
Harrell FE. Statistical Thinking - Assessing the Proportional Odds Assumption and Its Impact. https://www.fharrell.com/post/impactpo/. Published March 9, 2022. Accessed January 13, 2023. [rms::impactPO()]

Examples

## Not run: 
## orm model (response variable has fewer than 10 unique levels)
mod.orm <- orm(carb ~ cyl + hp , x = TRUE, y = TRUE, data = mtcars)
poma(mod.orm)


## runs rms::impactPO when its args are supplied
## More examples: (https://yhpua.github.io/poma/)
d <- expand.grid(hp = c(90, 180), vs = c(0, 1))
mod.orm <- orm(cyl ~ vs + hp , x = TRUE, y = TRUE, data = mtcars)
poma(mod.orm, newdata = d)


## orm model (response variable has >=10 unique levels)
mod.orm <- orm(mpg ~ cyl + hp , x=TRUE, y=TRUE, data = mtcars)
poma(mod.orm)


## orm model using imputation
dat <- mtcars
## introduce NAs
dat[sample(rownames(dat), 10), "cyl"] <- NA
im <- aregImpute(~ cyl + wt + mpg + am, data = dat)
aa <- fit.mult.impute(mpg ~ cyl + wt , xtrans = im, data = dat, fitter = orm)
poma(aa)

## End(Not run)

rms documentation built on Sept. 11, 2024, 7:51 p.m.