summary.gvlmaDel {gvlma}

Basic Information for the Leave-One-Out Global and Directional Tests for Linear Model Assumptions

Description

Summarize the test statistic values and p-values for assessing unusual observations using the global and directional test statistics that were computed in a gvlmaDel object resulting from a call to deletion.gvlma.

Usage

## S3 method for class 'gvlmaDel'
summary(object, allstats = TRUE, ...)
## S3 method for class 'gvlmaDel'
print(x, ...)

Arguments

object, x

Object resulting from a call to deletion.gvlma, which takes a gvlma object and performes the leave-one-out analyses for assessment of the influence of each observation on the global and directional tests for linear model assumptions.

allstats

For summary.gvlmaDel, if allstats = TRUE (the default), then the summary statistics are provided for global test and all 4 directional test statistics. If summary.gvlmaDel is FALSE, then the summary is provided for the deletion global test statistics only.

...

Additional arguments that are ignored.

Details

The summary values are the min, first quartile, median, average, 3rd quartile and maximum of the deletion test statistic values and p-values. Additionally, observations and the corresponding deletion test statistic values and p-values for which the deletion test statistic value or its p-value is outside the outer fences (Q1 - 3*IQR, Q3 + 3*IQR) of the set of deletion statistics are reported.

print.gvlmaDel simply invokes summary.gvlmaDel with allstats = TRUE.

Value

A dataframe of dimension nobs x 5 is returned invisibly, where nobs is the number of observations in the linear model fit. The 5 columns are named DeltaGlobalStat, DeltaStat1, DeltaStat2, DeltaStat3, and DeltaStat4, indicating the deletion global test and the four deletion directional test statistics. Each entry in the dataframe is TRUE/FALSE, indicating whether the corresponding test statistic was unusual (i.e. beyond the outer fences) with respect to either its value or its p-value.

Author(s)

Slate, EH [e-mail] and Pena, EA [e-mail].

References

Pena, EA and Slate, EH (2006). “Global validation of linear model assumptions,” J.\ Amer.\ Statist.\ Assoc., 101(473):341-354.

See Also

gvlma, deletion.gvlma

Examples

data(CarMileageData)
CarModelAssess <- gvlma(NumGallons ~ MilesLastFill, data = CarMileageData)
CarModelAssess
CarModelDel <- deletion.gvlma(CarModelAssess)
CarModelDel
summary(CarModelDel)
summary(CarModelDel, allstats = FALSE)