summary.gvlma: Print Basic Information for a Gvlma Object

In gvlma: Global Validation of Linear Models Assumptions

Description

Prints the basic information for a gvlma object, which is the output object from the function gvlma.

Usage

## S3 method for class 'gvlma'
summary(object, ...)
## S3 method for class 'gvlma'
print(x, ...)
display.gvlmatests(gvlmaobj)

Arguments

x, object, gvlmaobj	An object resulting from a call to gvlma. It is a list containing the components of a call to lm plus an item with the name GlobalTest.
...	Additional arguments that are passed to summary.lm.

Details

print.gvlma invokes print on the lm object and then calls display.gvlmatests.

summary.gvlma invokes summary on the lm object with the additional ... arguments and then calls display.gvlmatests.

display.gvlmatests provides the test statistics, p-values and decision (whether linear models assumptions are satisfied) for the global and directional tests associated with the gvlma object. The decision is reported at the level of significance used when the gvlma object was created. See the argument alphalevel to gvlma.

Value

The value returned invisibly is a dataframe with row names indicating the global test and the 4 directional tests. Variables are

Value	Value of the test statistic.
p-value	p-value associated with the test.
Decision	Text string indicating whether the test statistic is significant at the significance level specified in the original call to gvlma.

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, display.gvlmatests, summary.lm

Examples

data(CarMileageData)
CarModelAssess <- gvlma(NumGallons ~ MilesLastFill, data = CarMileageData)
CarModelAssess
summary(CarModelAssess)

Example output

Call:
lm(formula = NumGallons ~ MilesLastFill, data = CarMileageData)

Coefficients:
  (Intercept)  MilesLastFill  
      6.80821        0.01602  


ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance =  0.05 

Call:
 gvlma(x = NumGallons ~ MilesLastFill, data = CarMileageData) 

                     Value   p-value                   Decision
Global Stat        27.4560 1.607e-05 Assumptions NOT satisfied!
Skewness            0.2355 6.275e-01    Assumptions acceptable.
Kurtosis           25.1085 5.419e-07 Assumptions NOT satisfied!
Link Function       1.6298 2.017e-01    Assumptions acceptable.
Heteroscedasticity  0.4822 4.874e-01    Assumptions acceptable.

Call:
lm(formula = NumGallons ~ MilesLastFill, data = CarMileageData)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.99345 -0.32612  0.00885  0.36902  2.46317 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)   6.808214   0.491732   13.85   <2e-16 ***
MilesLastFill 0.016017   0.001305   12.27   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.591 on 203 degrees of freedom
Multiple R-squared:  0.426,	Adjusted R-squared:  0.4232 
F-statistic: 150.7 on 1 and 203 DF,  p-value: < 2.2e-16


ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance =  0.05 

Call:
 gvlma(x = NumGallons ~ MilesLastFill, data = CarMileageData) 

                     Value   p-value                   Decision
Global Stat        27.4560 1.607e-05 Assumptions NOT satisfied!
Skewness            0.2355 6.275e-01    Assumptions acceptable.
Kurtosis           25.1085 5.419e-07 Assumptions NOT satisfied!
Link Function       1.6298 2.017e-01    Assumptions acceptable.
Heteroscedasticity  0.4822 4.874e-01    Assumptions acceptable.

gvlma documentation built on May 2, 2019, 9:20 a.m.