gvlma (version 1.0.0.3)

summary.gvlmaDel: 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 gvlmaDel
summary(object, allstats = TRUE, …)
# S3 method for 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.

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.

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.

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

# NOT RUN {
data(CarMileageData)
CarModelAssess <- gvlma(NumGallons ~ MilesLastFill, data = CarMileageData)
CarModelAssess
CarModelDel <- deletion.gvlma(CarModelAssess)
CarModelDel
summary(CarModelDel)
summary(CarModelDel, allstats = FALSE)
# }