rms (version 6.1-0)

poma: 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)

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

Details

Strategy 1: 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 2: Generate score residual plot for each predictor (for response variable with <10 unique levels) Strategy 3: Assess parallelism of link function transformed inverse CDFs curves for different XBeta levels (for response variables with >=10 unique levels)

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.

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)


## 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)
# }