Examine proportional odds and parallelism assumptions of 'orm' and 'lrm' model fits.
Based on codes and strategies from Frank Harrell's canonical 'Regression Modeling Strategies' text
poma(mod.orm, cutval)
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 |
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)
Yong Hao Pua <puayonghao@gmail.com>
Harrell FE. *Regression Modeling Strategies: with applications to linear models, logistic and ordinal regression, and survival analysis.* New York: Springer Science, LLC, 2015.
## 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)
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