predict.lrm {rms} | R Documentation |
Computes a variety of types of predicted values for fits from
lrm
and orm
, either from the original dataset or for new
observations. The Mean.lrm
and Mean.orm
functions produce
an R function to compute the predicted mean of a numeric ordered
response variable given the linear predictor, which is assumed to use
the first intercept in its computation.
## S3 method for class 'lrm' predict(object, ..., type=c("lp", "fitted", "fitted.ind", "mean", "x", "data.frame", "terms", "cterms", "ccterms", "adjto","adjto.data.frame", "model.frame"), se.fit=FALSE, codes=FALSE) ## S3 method for class 'orm' predict(object, ..., type=c("lp", "fitted", "fitted.ind", "mean", "x", "data.frame", "terms", "cterms", "ccterms", "adjto","adjto.data.frame", "model.frame"), se.fit=FALSE, codes=FALSE) ## S3 method for class 'lrm' Mean(object, codes=FALSE, ...) ## S3 method for class 'orm' Mean(object, codes=FALSE, ...)
object |
a object created by |
... |
arguments passed to |
type |
See |
se.fit |
applies only to |
codes |
if |
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. For
Mean.lrm
and Mean.orm
, the result is an R function.
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
, orm
, predict.rms
,
naresid
, contrast.rms
# See help for predict.rms 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, x=TRUE) #x=TRUE needed for se.fit # Get 0.95 confidence limits for Prob[better or best] L <- predict(f, se.fit=TRUE) #omitted kint= so use 1st intercept plogis(with(L, linear.predictors + 1.96*cbind(-se.fit,se.fit))) predict(f, type="fitted.ind")[1:10,] #gets Prob(better) and all others d <- data.frame(x1=c(.1,.5),x2=c(.5,.15)) 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 m <- Mean(f, codes=TRUE) lp <- predict(f, d) m(lp) # Can use function m as an argument to Predict or nomogram to # get predicted means instead of log odds or probabilities dd <- datadist(x1,x2); options(datadist='dd') m plot(Predict(f, x1, fun=m), ylab='Predicted Mean') # Note: Run f through bootcov with coef.reps=TRUE to get proper confidence # limits for predicted means from the prop. odds model options(datadist=NULL)