Re: [R] 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 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 > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Interpreting Results from LOF.test() from qpcR package
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?"). In other words, this test tells you that you *can't* reject the null hypothesis that the model is "good enough" in some sense. To test against a constant null model, you could do nullmod <- nlsr(y ~ const, data = mod14data2_random, start = list(const = 0.45)) anova(nlregmod3, nullmod) (This question seems to be verging on "general question about statistics" rather than "question about R", so maybe better for a venue like https://stats.stackexchange.com ??) On 2023-08-20 9:01 p.m., Paul Bernal 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 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Interpreting Results from LOF.test() from qpcR package
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 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
