Dear list,
I am currently working on a project looking at the predictors of spinyness in
palms
Spinyness is coded binary. I have a number of potential predictors and want to
identify the best set of predictors. I am using "phyloglm" from the "phylolm"
package.
I am currently using the method=�logistic_MPLE� method which as I describe
below gives odd results. method=�logistic_IG10� fails to converge.
AIC and the estimated significance of predictors from Walds test seems oddly
inconsistent, one predictor produced a much lower AIC but is found to be a
non-significant predictor. Another is found to be highly significant but
produces a much higher AIC. I am aware that Walds test are only expected to be
approximately correct but the degree of difference surprise me. What is the
cause of this inconsistent behavior and more importantly, how can I under these
circumstances see what is the best predictor(s) in my data?
My problem is illustrated by four potential models
M1= phyloglm(Spines~1, dat=DATA, phy=TREE, method="logistic_MPLE")
M2= phyloglm(Spines~ Climbing, dat=DATA, phy=TREE, method="logistic_MPLE")
M3= phyloglm(Spines~ Rainforest, dat=DATA, phy=TREE, method="logistic_MPLE")
M4= phyloglm(Spines~ Climbing + Rainforest, dat=DATA, phy=TREE,
method="logistic_MPLE")
AIC(M1)= 568.17; AIC(M2)= 546.94; AIC(M3)= 528.68; AIC(M4)= 531.98
Based on AIC it is thus clear that the best model is M3, but I get surprised
when I compare the results of the models
summary(M2)
Estimate StdErr z.value p.value
(Intercept) 0.90537 1.43428 0.6312 0.5278839
Climbing 0.39713 0.11884 3.3417 0.0008327 ***
Note: Wald-type p-values for coefficients, conditional on alpha=0.004616504
summary(M3)
Estimate StdErr z.value p.value
(Intercept) 1.065304 1.493856 0.7131 0.47577
Rainforest -0.172741 0.090467 -1.9094 0.05621 .
Note: Wald-type p-values for coefficients, conditional on alpha=0.004046622
Summary(M4)
Estimate StdErr z.value p.value
(Intercept) 0.66313 0.87768 0.7556 0.449916
Climbing 0.29763 0.10801 2.7555 0.005861 **
Rainforest 0.27519 0.18470 1.4899 0.136257
Note: Wald-type p-values for coefficients, conditional on alpha= 0.004563691
Hope someone can help me, S�ren
-
S�ren Faurby
http://antonelli-lab.net<http://antonelli-lab.net/>
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