influence.cdfqr {cdfquantreg}R Documentation

Influence Diagnosis For Fitted Cdfqr Object

Description

Influence Diagnosis (dfbetas) For Fitted Cdfqr Object

Usage

## S3 method for class 'cdfqr'
influence(
  model,
  method = "dfbeta",
  type = c("full", "location", "dispersion"),
  what = "full",
  plot = FALSE,
  id = FALSE,
  ...
)

## S3 method for class 'cdfqr'
dfbeta(model, type = c("full", "location", "dispersion"), what = "full", ...)

## S3 method for class 'cdfqr'
dfbetas(model, type = c("full", "location", "dispersion"), what = "full", ...)

## S3 method for class 'cdfqrH'
influence(
  model,
  method = "dfbeta",
  type = c("full", "location", "dispersion", "zero", "one"),
  what = "full",
  plot = FALSE,
  id = FALSE,
  ...
)

## S3 method for class 'cdfqrH'
dfbeta(
  model,
  type = c("full", "location", "dispersion", "zero", "one"),
  what = "full",
  ...
)

## S3 method for class 'cdfqrH'
dfbetas(
  model,
  type = c("full", "location", "dispersion", "zero", "one"),
  what = "full",
  ...
)

Arguments

model

A cdfqr model object

method

Currently only 'dfbeta' method is available.

type

A string that indicates whether the results for all parameters are to be returned, or only the location/dispersion submodel's parameters returned.

what

for influence statistics based on coefficient values, indicate the predictor variables that needs to be tested.

plot

if plot is needed.

id

for plot only, if TRUE, the case ids will be displayed in the plot.

...

currently ignored.s

Value

A matrix, each row of which contains the estimated influence on parameters when that row's observation is removed from the sample.

See Also

lm.influence, influence.measures

Examples

data(cdfqrExampleData)
fit <- cdfquantreg(crc99 ~ vert | confl, 't2', 't2', data = JurorData)
#It takes some time especially the data is large.
influcne <- influence(fit)
plot(influcne[,2])

## Not run: 
# Same as influence(fit)
dfbetval <- dfbetas(fit)

## End(Not run)


[Package cdfquantreg version 1.2.5 Index]