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
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
f statistic
pure_error
pure error
rss
regression sum of squares
ess
error sum of squares
total
total sum of squares
rms
ems
p-value of fstat
lms
degrees of freedom
pms
name(s) of variable
rf
name of group_var
lf
f statistic
pr
p-value of fstat
pl
degrees of freedom
mpred
name(s) of variable
df_rss
name of group_var
df_ess
f statistic
df_lof
p-value of fstat
df_error
degrees of freedom
final
name(s) of variable
resp
name of group_var
preds
name of group_var
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.
References
Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition).
Chicago, IL., McGraw Hill/Irwin.