pscore (version 0.4.0)

predictCS: Internal function to prepare data for prediction

Description

Internal function to prepare data for prediction

Usage

predictCS(object, newdata, groups)

Arguments

object

An object of S4 class “MahalanobisScores”, “SumScores”, or “FactorScores” containing a model and results to be used to get predictions on new data.

newdata

A data frame with identical variable names as was used to build the initial model.

groups

A vector with the same length as the data frame in newdata, has rows, containing the groups each row belongs to. See CompositeData for more details.

Value

An object of S4 class “CompositeReady”

Examples

# NOT RUN {
d <- CompositeData(mtcars[, c("mpg", "hp", "wt", "disp")],
                   thresholds = list(one = with(mtcars, c(
                                     mpg = max(mpg),
                                     hp = max(hp),
                                     wt = min(wt),
                                     disp = min(disp)))),
                   higherisbetter = c(TRUE, TRUE, FALSE, FALSE))
## create the distance scores
## and prepare to create the composite
dres <- prepareComposite(d)

## create composite based on summing the (standardized)
scomp <- sumComposite(dres, "square", "sum")
## use model to generate predictions on new data
predictCS(scomp,
          newdata = mtcars[1, c("mpg", "hp", "wt", "qsec")],
          groups = "one")

## create composite based on mahalanobis distances
mcomp <- mahalanobisComposite(dres)
## use model to generate predictions on new data
predictCS(mcomp,
          newdata = mtcars[1, c("mpg", "hp", "wt", "qsec")],
          groups = "one")
## note in this too simple example, there are negative variance estimates
## create composite based on factor scores
fcomp <- factorComposite(dres, type = "onefactor")
## use model to generate predictions on new data
predictCS(fcomp,
          newdata = mtcars[1:5, c("mpg", "hp", "wt", "disp")],
          groups = rep("one", 5))
# }