summary.gvlmaDel: Basic Information for the Leave-One-Out Global and...
In gvlma: Global Validation of Linear Models Assumptions

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

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

`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.

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.

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.

Slate, EH slate@stat.fsu.edu and Pena, EA pena@stat.sc.edu.

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

gvlma, deletion.gvlma

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