# predict.blrm: Make predictions from a 'blrm()' fit In rmsb: Bayesian Regression Modeling Strategies

## Description

Predict method for `blrm()` objects

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```## S3 method for class 'blrm' predict( object, ..., kint = NULL, ycut = NULL, zcppo = TRUE, fun = NULL, funint = TRUE, type = c("lp", "fitted", "fitted.ind", "mean", "x", "data.frame", "terms", "cterms", "ccterms", "adjto", "adjto.data.frame", "model.frame"), se.fit = FALSE, codes = FALSE, posterior.summary = c("mean", "median", "all"), cint = 0.95 ) ```

## Arguments

 `object, ..., type, se.fit, codes` see `predict.lrm()` `kint` This is only useful in a multiple intercept model such as the ordinal logistic model. There to use to second of three intercepts, for example, specify `kint=2`. The default is the middle intercept corresponding to the median `y`. You can specify `ycut` instead, and the intercept corresponding to Y >= `ycut` will be used for `kint`. `ycut` for an ordinal model specifies the Y cutoff to use in evaluating departures from proportional odds, when the constrained partial proportional odds model is used. When omitted, `ycut` is implied by `kint`. The only time it is absolutely mandatory to specify `ycut` is when computing an effect (e.g., odds ratio) at a level of the response variable that did not occur in the data. This would only occur when the `cppo` function given to `blrm` is a continuous function. If `type='x'` and neither `kint` nor `ycut` are given, the partial PO part of the design matrix is not multiplied by the `cppo` function. If `type='x'`, the number of predicted observations is 1, `ycut` is longer than 1, and `zcppo` is `TRUE`, predictions are duplicated to the length of `ycut` as it is assumed that the user wants to see the effect of varying `ycut`, e.g., to see cutoff-specific odds ratios. `zcppo` applies only to `type='x'` for a constrained partial PO model. Set to `FALSE` to prevent multiplication of Z matrix by `cppo(ycut)`. `fun` a function to evaluate on the linear predictor, e.g. a function created by `Mean()` or `Quantile()` `funint` set to `FALSE` if `fun` is not a function such as the result of `Mean()`, `Quantile()`, or `ExProb()` that contains an `intercepts` argument `posterior.summary` set to `'median'` or `'mode'` to use posterior median/mode instead of mean. For some `type`s set to `'all'` to compute the needed quantity for all posterior draws, and return one more dimension in the array. `cint` probability for highest posterior density interval. Set to `FALSE` to suppress calculation of the interval.

## Value

a data frame, matrix, or vector with posterior summaries for the requested quantity, plus an attribute `'draws'` that has all the posterior draws for that quantity. For `type='fitted'` and `type='fitted.ind'` this attribute is a 3-dimensional array representing draws x observations generating predictions x levels of Y.

## Author(s)

Frank Harrell

`predict.lrm()`
 ```1 2 3 4 5``` ```## Not run: f <- blrm(...) predict(f, newdata, type='...', posterior.summary='median') ## End(Not run) ```