phylolm results do not match binary.PGLMM results. How to explain that?

[Jigyasa3]

Hey all

I wanted to ask if -
a) its possible for two statistical tests not to give similar results?
b) if yes, then how can I explain the differences?

my data-
sample diet_type bacterial_category
sample1 1 0
sample2 2 1
sample3 1 1
sample3 2 0
sample4 3 0

the r code-

library(phylolm)
summary(phyloglm(bactero ~ as.factor(diet_type_number2), phy=pruned.trees, data=bacterial_groups))

#output-
Call:
phyloglm(formula = bactero ~ as.factor(diet_type_number2), data = bacterial_groups,
phy = pruned.trees)
AIC logLik Pen.logLik
179.13 -85.57 -82.81

Method: logistic_MPLE
Mean tip height: 116.187
Parameter estimate(s):
alpha: 0.04745155

Coefficients:
Estimate StdErr z.value p.value
(Intercept) -0.35924 0.33408 -1.0753 0.282234
as.factor(diet_type_number2)2 1.50004 0.58005 2.5861 0.009707 **
as.factor(diet_type_number2)3 1.11070 0.53438 2.0785 0.037666 *

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Note: Wald-type p-values for coefficients, conditional on alpha=0.04745155

#second_method-

library(ape)
binaryPGLMM(bactero ~ as.factor(diet_type_number2), phy=pruned.trees, data=bacterial_groups)

#output-
Call:bactero ~ as.factor(diet_type_number2)

Random effect (phylogenetic signal s2):
s2 Pr
1 1.346 0.01215

Fixed effects:
Value Std.Error Zscore Pvalue
(Intercept) -0.29269 0.62758 -0.4664 0.6409
as.factor(diet_type_number2)2 1.10401 0.80099 1.3783 0.1681
as.factor(diet_type_number2)3 0.71368 0.76539 0.9324 0.3511

[lamho86]

Hello:

It looks like binaryPGLMM fits a different model than phyloglm. So, the results can be different.

Lam

[cecileane]

It looks like it's a different model, and a different method too. The difference between generalized mixed models (GLMM) versus generalized estimating equations (GEE) might be at play here. See this recent r-sig-phylo question for a large difference in statistical significance, also.

[Jigyasa3]

Thanks @lamho86 and @cecileane for the advice! I will check the methods themselves.