| predict.lrm {Design} | R Documentation |
Computes a variety of types of predicted values for fits from
lrm, either from the original dataset or for new observations.
## 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)
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.
|
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.
Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu
Hannah M, Quigley P: Presentation of ordinal regression analysis on the original scale. Biometrics 52:771–5; 1996.
lrm, predict.Design, naresid, contrast.Design
# 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