# 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 ```r 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) ## Examples ```r ## 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) ``` ## 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()] ## Author(s) Yong Hao Pua

poma function