VFP (version 1.0)

predict.VFP: Predict Method for Objects of Class 'VFP'.

Description

Predict Method for Objects of Class 'VFP'.

Usage

# S3 method for VFP
predict(object, model.no = NULL, newdata = NULL,
  alpha = 0.05, dispersion = NULL, type = c("vc", "sd", "cv"),
  CI.method = c("chisq", "t", "normal"), use.log = FALSE, ...)

Arguments

object

(object) of class "VFP"

model.no

(integer) specifying a fitted model stored in 'object'

newdata

(numeric) optionally, a vector specifying mean-values for which predictions on the user-defined scale ('type') are requested. If omitted, fitted values will be returned.

alpha

(numeric) value specifying the 100 x (1-alpha)% confidence interval of predicted values

dispersion

(numeric) NULL = the dispersion parameter will be estimated, numeric value = the dispersion parameter will be used as specified

type

(character) specifying on which scale the predicted values shall be returned, possible are "vc" = variance, "sd"=standard deviation, "cv"=coefficient of variation

CI.method

(character) one of "t", "normal", "chisq" specifying which CI-method to use

use.log

(logical) TRUE = X- and Y-axis will be log-transformed

...

additional parameters passed forward to function predict.gnm predict.gnm

Value

(data.frame) with numeric variables:

Mean

value at which predictions were requested

Fitted

prediction at 'Mean'

SE

standard error of prediction

Scale

residual scale

LCL

lower confidence limit of the 100x(1-'alpha')% CI

UCL

upper confidence limit of the 100x(1-'alpha')% CI

Examples

# NOT RUN {
library(VCA)
data(VCAdata1)
lst <- anovaVCA(y~(device+lot)/day/run, VCAdata1, by="sample")
mat <- getMat.VCA(lst)		# automatically selects "total"
res <- fit.vfp(model.no=1:10, Data=mat)
predict(res)
predict(res, dispersion=0.95)
# }