Interpreting Results from LOF.test() from qpcR package
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 served by statistics discussion/help sites rather than R-Help. Though with this small a data set and this complex a model, I would be surprised if there could be LOF unless it were glaringly obvious from simple plots. Cheers, Bert -- Bert
On Sun, Aug 20, 2023 at 6:02?PM Paul Bernal <paulbernal07 at gmail.com> wrote:
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 as follows:
nlregmod3 <- nlsr(formula=y ~ theta1 - theta2*exp(-theta3*x), data =
mod14data2_random,
start = list(theta1 = 0.37,
theta2 = -exp(-1.8),
theta3 = 0.05538))
And the data used to fit this model is the following:
dput(mod14data2_random)
structure(list(index = c(14L, 27L, 37L, 33L, 34L, 16L, 7L, 1L,
39L, 36L, 40L, 19L, 28L, 38L, 32L), y = c(0.44, 0.4, 0.4, 0.4,
0.4, 0.43, 0.46, 0.49, 0.41, 0.41, 0.38, 0.42, 0.41, 0.4, 0.4
), x = c(16, 24, 32, 30, 30, 16, 12, 8, 36, 32, 36, 20, 26, 34,
28)), row.names = c(NA, -15L), class = "data.frame")
Cheers,
Paul
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