score: Computes the score test for transformation in regression

In fsdaR: Robust Data Analysis Through Monitoring and Dynamic Visualization

Computes the score test for transformation in regression

Description

Computes the score test for transformation in regression

Usage

score(x, ...)

## S3 method for class 'formula'
score(
  formula,
  data,
  subset,
  weights,
  na.action,
  model = TRUE,
  contrasts = NULL,
  offset,
  ...
)

## Default S3 method:
score(
  x,
  y,
  intercept = TRUE,
  la = c(-1, -0.5, 0, 0.5, 1),
  lik = FALSE,
  nocheck = FALSE,
  trace = FALSE,
  ...
)

Arguments

x	An n x p data matrix (n observations and p variables). Rows of x represent observations, and columns represent variables. Missing values (NA's) and infinite values (Inf's) are allowed, since observations (rows) with missing or infinite values will automatically be excluded from the computations.
...	potential further arguments passed to lower level functions.
formula	a formula of the form y ~ x1 + x2 + ....
data	data frame from which variables specified in formula are to be taken.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
weights	an optional vector of weights to be used NOT USED YET.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The “factory-fresh” default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
model	logical indicating if the model frame, is to be returned.
contrasts	an optional list. See the contrasts.arg of model.matrix.default.
offset	this can be used to specify an a priori known component to be included in the linear predictor during fitting. An offset term can be included in the
y	Response variable. A vector with n elements that contains the response variable.
intercept	wheather to use constant term (default is intercept=TRUE
la	values of the transformation parameter for which it is necessary to compute the score test. Default value of lambda is la=c(-1, -0.5, 0, 0.5, 1), i.e., the five most common values of lambda.
lik	likelihood for the augmented model. If true the value of the likelihood for the augmented model will be calculated and returend otherwise (default) only the value of the score test will be given
nocheck	Whether to check input arguments. If nocheck=TRUE no check is performed on matrix y and matrix X. Notice that y and X are left unchanged. In other words the additional column of ones for the intercept is not added. The default is nocheck=FALSE.
trace	Whether to print intermediate results. Default is trace=FALSE.

Value

An S3 object of class score.object will be returned which is basically a list containing the following elements:

1. la: vector containing the values of lambda for which fan plot is constructed

2. Score: a vector containing the values of the score test for each value of the transformation parameter.

3. Lik: value of the likelihood. This output is produced only if lik=TRUE.

Author(s)

FSDA team, [e-mail]

References

Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis Springer Verlag, New York.

Examples

 ## Not run: 
   data(wool)
   XX <- wool
   y <- XX[, ncol(XX)]
   X <- XX[, 1:(ncol(XX)-1), drop=FALSE]

   (out <- score(X, y))                          # call 'score' with all default parameters
   (out <- score(cycles~., data=wool))           # use the formula interface
   (out <- score(cycles~., data=wool, lik=TRUE)) # return the likelihood

   data(loyalty)
   head(loyalty)
   ##    la is a vector containing the values of \lambda which have to be tested
   (out <- score(amount_spent~., data=loyalty, la=c(0.25, 1/3, 0.4, 0.5)))
   (out <- score(amount_spent~., data=loyalty, la=c(0.25, 1/3, 0.4, 0.5), lik=TRUE))
 
## End(Not run)

fsdaR documentation built on March 31, 2023, 8:18 p.m.