I would suggest that a simple plot of residuals vs. fitted values and
perhaps plots of residuals vs. the independent variables are almost always
more useful than omnibus LOF tests. (many would disagree!) However,as Ben
noted, this is wandering outside R-Help's strict remit, and you would be
better
The p-values are non-significant by any standard cutoff (e.g.
p<=0.05, p<=0.1) but note that this is a *lack-of-fit* test -- i.e.,
"does my function fit the data well enough?", **not** a "significant
pattern" test (e.g., "does my function fit the data better than a
reasonable null model?").
I am using LOF.test() function from the qpcR package and got the following
result:
> LOF.test(nlregmod3)
$pF
[1] 0.97686
$pLR
[1] 0.77025
Can I conclude from the LOF.test() results that my nonlinear regression
model is significant/statistically significant?
Where my nonlinear model was fitted a
3 matches
Mail list logo
