Hi.
I'm analysing some fish biological traits with a gam in mgcv. After several
tries, I got this model
log(tle) = sexcolor + s(doy, bs = "cc", by = sexcolor) +log(tl)
sexcolor is a factor with 4 levels
doy is "day of year", which is modeled as a smoother
tl is "total length of the fish"
The summary of this models is (only parametric coefficientes):
Parametric coefficients:
Estimate Std. Error t
value Pr(>|t|)
(Intercept) -1.34237 0.42340 -3.170
0.001733 **
sexcolorSpotted males -0.04405 0.12568 -0.350 0.726309
sexcolorPlain females 0.30812 0.08191 3.762 0.000215 ***
sexcolorSpotted females 0.18642 0.08018 2.325 0.020948 *
log(tl) 3.42331 0.11608
29.490 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Is there any way to do post hoc test for categorical variables in the
parametric part of the model? I mean, levels for sexcolor are being
compared with the reference level ("Plain males"), so I see that "Spotted
males", for example, are not different from "Plain males", whereas females
(both Plain and Spotted) are significantly different from "Plain males".
Now, I'd like to make multiple comparisons between other combination of
levels, i.e., "Plain females" vs. "Spotted males", for example.
Any suggestion?
Thanks,
David
--
View this message in context:
http://r.789695.n4.nabble.com/Post-hoc-tests-in-gam-mgcv-tp4648456.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
R-help@r-project.org mailing list
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.
