[R-sig-ME] Graphical validation of residuals of two models or their AICc?

Andrew Robinson @pro @end|ng |rom un|me|b@edu@@u
Sun Jan 15 21:31:57 CET 2023


Neither residual plot really floats my boat.  Presumably you have reasons to compare these model specifications that are unrelated to the data as we see them.  Ultimately it depends on the use to which the model will be put. Are you trying to estimate parameters with uncertainty or to make a prediction of some future state or something else?

I'd note that AIC is computed from LL at its maximum conditional on the model.  If the model is wrong then the MLE is unknowably wrong and the AIC is unknowably wrong. So absent other context I focus on the information that is available about the model, namely the residual plots.

I'd add that making time a factor is a curious decision.

Cheers,

Andrew

--
Andrew Robinson
Chief Executive Officer, CEBRA and Professor of Biosecurity,
School/s of BioSciences and Mathematics & Statistics
University of Melbourne, VIC 3010 Australia
Tel: (+61) 0403 138 955
Email: apro using unimelb.edu.au
Website: https://researchers.ms.unimelb.edu.au/~apro@unimelb/

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On 15 Jan 2023 at 6:45 AM +1100, Timothy MacKenzie <fswfswt using gmail.com>, wrote:
Dear All,

I'm deciding between two candidate models (m1 and m2). AICc shows that m2
is to be preferred over m1.

However, when I plot the residuals of m1 and m2, residuals of m1 look more
reasonable than those of m2.

I wonder which criteria to rely on to choose my model: graphical validation
of residuals of the two models or their AICc?

Thank you,
Tim M
#-----------------------------------------#
dat <- read.csv("https://raw.githubusercontent.com/fpqq/w/main/c.csv")
dat <- transform(dat, time = factor(time))

library(nlme)
m1 <- lme(score ~ time*cal,
random = list(id = pdDiag(~cal +0)),
data = dat,
weights = varIdent(form = ~ cal:time),
control = lmeControl(msMaxIter = 2e2))

m2 <- lme(score ~ time*cal,
random = list(id = pdDiag(~cal:time +0)),
data = dat,
weights = varIdent(form = ~ cal:time),
control = lmeControl(msMaxIter = 2e2))

library(bbmle)

AICctab(m1, m2)

plot(m1)
plot(m2)
#-----------------------------------------#

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