VFP (version 1.4.3)

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

Description

Predictions are made for the variance (type="vc"), standard deviation ("sd") or coefficient of variation ("cv") and their corresponding confidence intervals. The latter are calculated primarily on the variance scale and then transformed to the other scales, if required.

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,
  ...
)

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

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 will be set =1 (should usually not be changed; For the Saddler model, the dispersion is 1.), 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

Examples

# \donttest{
library(VCA)
data(VCAdata1)
lst <- anovaVCA(y~(device+lot)/day/run, VCAdata1, by="sample")
mat <- get_mat(lst)		# automatically selects "total"
res <- fit_vfp(model.no=1:9, Data=mat)
predict(res)
predict(res, dispersion=0.95)
# }