predict.lrm {Design} | R Documentation

## Predicted Values for Binary and Ordinal Logistic Models

### Description

Computes a variety of types of predicted values for fits from `lrm`, either from the original dataset or for new observations.

### Usage

    ## S3 method for class 'lrm':
    predict(object, ..., type=c("lp", "fitted", "fitted.ind", "mean", "x",
                "data.frame", "terms", "adjto","adjto.data.frame",
                "model.frame"), se.fit=FALSE, codes=FALSE)

### Arguments

`object` | a object created by `lrm`
`...` | arguments passed to `predict.Design`, such as `kint` and `newdata` (which is used if you are predicting `out of data`). See `predict.Design` to see how NAs are handled.
`type` | See `predict.Design` for `"x", "data.frame", "terms", "adjto", "adjto.data.frame"` and `"model.frame"`. `type="lp"` is used to get linear predictors (always using the first intercept). `type="fitted"` is used to get all the probabilities Y>=q j. `type="fitted.ind"` gets all the individual probabilities Y=j. For an ordinal response variable, `type="mean"` computes the estimated mean Y by summing values of Y multiplied by the estimated Prob(Y=j). If Y was a character or `factor` object, the levels are the character values or factor levels, so these must be translatable to numeric, unless `codes=TRUE`. See the Hannah and Quigley reference below for the method of estimating (and presenting) the mean score. If you specify `type="fitted","fitted.ind","mean"` you may not specify `kint`.
`se.fit` | applies only to `type="lp"`, to get standard errors.
`codes` | if `TRUE`, `type="mean"` uses the integer codes 1,2,...,k for the k-level response in computing the predicted mean response.

### Value

a vector (`type="lp"` with `se.fit=FALSE`, or `type="mean"` or only one observation being predicted), a list (with elements `linear.predictors` and `se.fit` if `se.fit=TRUE`), a matrix (`type="fitted"` or `type="fitted.ind"`), a data frame, or a design matrix.

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu

### References

Hannah M, Quigley P: Presentation of ordinal regression analysis on the original scale. Biometrics 52:771–5; 1996.

### See Also

`lrm`, `predict.Design`, `naresid`, `contrast.Design`

### Examples

    # See help for predict.Design for several binary logistic
    # regression examples

    # Examples of predictions from ordinal models
    set.seed(1)
    y <- factor(sample(1:3, 400, TRUE), 1:3, c('good','better','best'))
    x1 <- runif(400)
    x2 <- runif(400)
    f <- lrm(y ~ rcs(x1,4)*x2)
    predict(f, type="fitted.ind")[1:10,]   #gets Prob(better) and all others
    d <- data.frame(x1=.5,x2=.5)
    predict(f, d, type="fitted")        # Prob(Y>=j) for new observation
    predict(f, d, type="fitted.ind")    # Prob(Y=j)
    predict(f, d, type='mean', codes=TRUE) # predicts mean(y) using codes 1,2,3

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