summary.gvlmaDel {gvlma} | R Documentation |
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 SlateEH@musc.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.
data(CarMileageData) CarModelAssess <- gvlma(NumGallons ~ MilesLastFill, data = CarMileageData) CarModelAssess CarModelDel <- deletion.gvlma(CarModelAssess) CarModelDel summary(CarModelDel) summary(CarModelDel, allstats = FALSE)