ols_pure_error_anova: Lack of fit F test

In olsrr: Tools for Building OLS Regression Models

Lack of fit F test

Description

Assess how much of the error in prediction is due to lack of model fit.

Usage

ols_pure_error_anova(model, ...)

Arguments

model	An object of class lm.
...	Other parameters.

Details

The residual sum of squares resulting from a regression can be decomposed into 2 components:

• Due to lack of fit

• Due to random variation

If most of the error is due to lack of fit and not just random error, the model should be discarded and a new model must be built.

Value

ols_pure_error_anova returns an object of class "ols_pure_error_anova". An object of class "ols_pure_error_anova" is a list containing the following components:

lackoffit	lack of fit sum of squares
pure_error	pure error sum of squares
rss	regression sum of squares
ess	error sum of squares
total	total sum of squares
rms	regression mean square
ems	error mean square
lms	lack of fit mean square
pms	pure error mean square
rf	f statistic
lf	lack of fit f statistic
pr	p-value of f statistic
pl	p-value pf lack of fit f statistic
mpred	data.frame containing data for the response and predictor of the model
df_rss	regression sum of squares degrees of freedom
df_ess	error sum of squares degrees of freedom
df_lof	lack of fit degrees of freedom
df_error	pure error degrees of freedom
final	data.frame; contains computed values used for the lack of fit f test
resp	character vector; name of response variable
preds	character vector; name of predictor variable

Note

The lack of fit F test works only with simple linear regression. Moreover, it is important that the data contains repeat observations i.e. replicates for at least one of the values of the predictor x. This test generally only applies to datasets with plenty of replicates.

References

Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

Examples

model <- lm(mpg ~ disp, data = mtcars)
ols_pure_error_anova(model)

olsrr documentation built on May 29, 2024, 12:35 p.m.