cdfquantreg (version 1.3.1-2)

influence.cdfqr: Influence Diagnosis For Fitted Cdfqr Object

Description

Influence Diagnosis (dfbetas) For Fitted Cdfqr Object

Usage

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

Value

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

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 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.

...

Pass onto other functions or currently ignored

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])

if (FALSE) {
# Same as influence(fit)
dfbetval <- dfbetas(fit)
}