# [R] Fixed effects in negative binomial mixed model mgcv::gam

Smith, Desmond DSmith @ending from mednet@ucl@@edu
Fri Oct 19 23:57:50 CEST 2018

```I am using gam from the mgcv package to analyze a dataset with 24 entries :

ran  f1     f2   y
1   3000    5   545
1   3000    10  1045
1   10000   5   536
1   10000   10  770
2   3000    5   842
2   3000    10  2042
2   10000   5   615
2   10000   10  1361
3   3000    5   328
3   3000    10  1028
3   10000   5   262
3   10000   10  722
4   3000    5   349
4   3000    10  665
4   10000   5   255
4   10000   10  470
5   3000    5   680
5   3000    10  1510
5   10000   5   499
5   10000   10  1422
6   3000    5   628
6   3000    10  2062
6   10000   5   499
6   10000   10  2158

The data has two fixed effects (f1 and f2) and one random effect (ran). The dependent data is y. Because the dependent data y represents counts and is overdispersed, I am using a negative binomial model.

The gam model and its summary output is as follows:

library(mgcv)
summary(gam(y ~ f1 * f2 + s(ran, bs = "re"), data = df2, family = nb, method = "REML"))

Family: Negative Binomial(27.376)

Formula:
y ~ f1 * f2 + s(ran, bs = "re")

Parametric coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)  5.500e+00  3.137e-01  17.533  < 2e-16 ***
f1          -3.421e-05  3.619e-05  -0.945    0.345
f2           1.760e-01  3.355e-02   5.247 1.55e-07 ***
f1:f2        2.665e-07  4.554e-06   0.059    0.953
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
edf Ref.df Chi.sq p-value
s(ran) 4.726      5  85.66  <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.866   Deviance explained = 93.6%
-REML = 185.96  Scale est. = 1         n = 24

The Wald test from summary gives very high significance for f2 (P = 1.55e-07). However, when I test the significance of f2 by comparing two different models using anova, I get dramatically different results:

anova(gam(y ~ f1 * f2 + s(ran, bs = "re"), data = df2, family = nb, method = "ML"),
gam(y ~ f1 + s(ran, bs = "re"), data = df2, family = nb, method = "ML"),
test="Chisq")

Analysis of Deviance Table

Model 1: y ~ f1 * f2 + s(ran, bs = "re")
Model 2: y ~ f1 + s(ran, bs = "re")
Resid. Df Resid. Dev      Df Deviance Pr(>Chi)
1    14.843     18.340
2    16.652     21.529 -1.8091   -3.188   0.1752

f2 is no longer significant. The models were changed from REML to ML, as recommended for evaluation of fixed effects.

If the interaction is preserved, f2 still remains insignificant using anova:

anova(gam(y ~ f1 + f2 + f1:f2 + s(ran, bs = "re"), data = df2, family = nb, method = "ML"),
gam(y ~ f1 + f1:f2 + s(ran, bs = "re"), data = df2, family = nb, method = "ML"),
test="Chisq")
Analysis of Deviance Table

Model 1: y ~ f1 + f2 + f1:f2 + s(ran, bs = "re")
Model 2: y ~ f1 + f1:f2 + s(ran, bs = "re")
Resid. Df Resid. Dev       Df Deviance Pr(>Chi)
1    14.843     18.340
2    15.645     19.194 -0.80159 -0.85391   0.2855

I would be very grateful for advice on which of these approaches is most appropriate. Many thanks!

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