# Solved – R – Model selection in Glmer

Having troubles to perform a model selection for glmer in R. I'm using the package lme4 with the following structure:

``    glo_mo <- glmer(aban ~ year + hab + wlv + gra + cov + (1|lodge),                data = aban, family='binomial',               na.action = na.omit)      ``` str(aban) Classes ‘spec_tbl_df’, ‘tbl_df’, ‘tbl’ and 'data.frame':    67 obs. of  9 variables:  \$ lodge  : chr  "2" "52" "34" "39" ...  \$ year   : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...  \$ hab    : chr  "for" "for" "for" "for" ...  \$ wlv    : num  7 1 NA NA 4 NA NA -4 44 NA ...  \$ dlv    : num  5 NA NA NA 7 NA NA 2 4 NA ...  \$ gra    : num  3 0 0 0 3 NA 0 8 5 4 ...  \$ cov    : num  3.92 16.46 1.78 1.25 2.48 ...  \$ for_str: num  4.4 4.06 3.65 5.54 4.14 5.69 8.61 5.84 6.23 4.36 ...  \$ aban   : Factor w/ 2 levels "0","1": 1 2 1 2 1 2 2 2 1 2 ... ``

When I run the model:

``    summary(glo_mo)     Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod]  Family: binomial  ( logit ) Formula: aban ~ year + hab + wlv + gra + cov + (1 | lodge)    Data: aban       AIC      BIC   logLik deviance df.resid      76.4     89.7    -31.2     62.4       42   Scaled residuals:      Min      1Q  Median      3Q     Max  -1.7283 -1.1100  0.5375  0.7449  1.4179   Random effects:  Groups Name        Variance Std.Dev.  lodge  (Intercept) 0.09585  0.3096   Number of obs: 49, groups:  lodge, 32  Fixed effects:              Estimate Std. Error z value Pr(>|z|) (Intercept) -0.360995   0.824027  -0.438    0.661 year2        0.605911   0.650404   0.932    0.352 habstep     -0.340842   0.926110  -0.368    0.713 wlv          0.005414   0.009677   0.559    0.576 gra          0.032089   0.086737   0.370    0.711 cov          0.023428   0.022942   1.021    0.307  Correlation of Fixed Effects:         (Intr) year2  habstp wlv    gra    year2   -0.239                             habstep -0.470  0.033                      wlv     -0.127 -0.051 -0.155               gra     -0.666 -0.130  0.411  0.313        cov     -0.130 -0.074 -0.647  0.185 -0.170  ``

Then, I tried to standarize and use the function dredge to automatically select best models, but this last one did not work. The following error mistake

``stad <- standardize(glo_mo, standardize.y=F) options(na.action = "na.fail") mset <- dredge(stad)  Error in dredge(glo_mo) : 'global.model' uses 'na.action' = "na.omit" ``

So that blocks me to continue to the selection model. Based on my previous steps and with the aim to select best models,
1. What is wrong in my script?

1. Also, Is AIC the only parameter to select the best models? Do I have to run each of the model combinations to select the best one, or can I apply function dredge or steps to do that?

2. What are the other options to select best models in glmer with lme4(or other recommend it packages)?

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