generic.HAC: Generic Method for 'cSFM' Objects

In cSFM: Covariate-adjusted Skewed Functional Model (cSFM)

Description

Print, extract fitted values and predict for cSFM object.

Methods of the generic function print, fitted and predict for objects inheriting from class cSFM.

Usage

## S3 method for class 'cSFM'
print(x, ...)
## S3 method for class 'cSFM'
fitted(object, quantile = TRUE, 
                     quantile.level = c(0.5, 0.8, 0.9, 0.95, 0.99), ...)
## S3 method for class 'cSFM'
predict(object, newdata, cp.valid, tp.valid = NULL, ...)

Arguments

x	A cSFM object; returned by the function cSFM.est
object	A cSFM object; returned by the function cSFM.est
quantile	logical; if TRUE(default), estimates of quantiles are returned
quantile.level	quantile vector; quantile levels to be estimated when quantile = TRUE
newdata	the partically observed new data set (missing data allowed) to be predicated
cp.valid	observed covariate vector for newdata with length nrow{newdata}
tp.valid	observed timepoint vector for newdata with length ncol{newdata}; See "Details".
...	other arguments passed to the generic functions

Details

When use the function predict, each row of newdata corresponds to covariate information cp.valid, while the column is for the time points tp.valid. When tp.valid is null, then we assume the validation data set has the same time points as the training data set, which is used to obtain object.

See Also

cSFM.est, print, fitted, predict

cSFM documentation built on May 29, 2017, 6:10 p.m.