R Under development (unstable) (2024-06-18 r86781 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> if (MuMIn:::testStart("nnet", "MASS")) {
+ 
+     # Trimmed-down model from example(birthwt)
+     data(birthwt)
+ 
+     bwt <- with(birthwt, data.frame(
+             low = low,
+             race = factor(race, labels = c("white", "black", "other")),
+             ptd = factor(ptl > 0),
+             smoke = (smoke > 0)
+             ))
+ 
+     options(contrasts = c("contr.treatment", "contr.poly"))
+     bwt.mu <- multinom(low ~ ., data = bwt)
+     dd <- dredge(bwt.mu, trace=T)
+ 
+     summary(model.avg(dd[1:5]))
+     gm <- get.models(dd, subset = 1:5)
+     ma <- model.avg(gm)
+ 
+     summary(ma)
+ 
+     # predict(ma) // Cannot average factors!
+ }
Loading required package: MuMIn
# weights:  6 (5 variable)
initial  value 131.004817 
iter  10 value 105.451155
final  value 105.451149 
converged
Fixed term is "(Intercept)"
0 : multinom(formula = low ~ 1, data = bwt)
# weights:  2 (1 variable)
initial  value 131.004817 
final  value 117.335998 
converged
1 : multinom(formula = low ~ ptd + 1, data = bwt)
# weights:  3 (2 variable)
initial  value 131.004817 
final  value 110.948878 
converged
2 : multinom(formula = low ~ race + 1, data = bwt)
# weights:  4 (3 variable)
initial  value 131.004817 
iter  10 value 114.830822
final  value 114.830815 
converged
3 : multinom(formula = low ~ ptd + race + 1, data = bwt)
# weights:  5 (4 variable)
initial  value 131.004817 
iter  10 value 108.511317
iter  10 value 108.511317
final  value 108.511317 
converged
4 : multinom(formula = low ~ smoke + 1, data = bwt)
# weights:  3 (2 variable)
initial  value 131.004817 
final  value 114.902303 
converged
5 : multinom(formula = low ~ ptd + smoke + 1, data = bwt)
# weights:  4 (3 variable)
initial  value 131.004817 
final  value 109.664449 
converged
6 : multinom(formula = low ~ race + smoke + 1, data = bwt)
# weights:  5 (4 variable)
initial  value 131.004817 
final  value 109.987355 
converged
7 : multinom(formula = low ~ ptd + race + smoke + 1, data = bwt)
# weights:  6 (5 variable)
initial  value 131.004817 
iter  10 value 105.451155
final  value 105.451149 
converged
# weights:  6 (5 variable)
initial  value 131.004817 
iter  10 value 105.451155
final  value 105.451149 
converged
# weights:  5 (4 variable)
initial  value 131.004817 
iter  10 value 108.511317
iter  10 value 108.511317
final  value 108.511317 
converged
# weights:  4 (3 variable)
initial  value 131.004817 
final  value 109.664449 
converged
# weights:  3 (2 variable)
initial  value 131.004817 
final  value 110.948878 
converged
# weights:  5 (4 variable)
initial  value 131.004817 
final  value 109.987355 
converged

Call:
model.avg(object = gm)

Component model call: 
multinom(formula = low ~ <5 unique rhs>, data = bwt)

Component models: 
    df  logLik   AICc delta weight
123  5 -105.45 221.23  0.00   0.72
12   4 -108.51 225.24  4.01   0.10
13   3 -109.66 225.46  4.23   0.09
1    2 -110.95 225.96  4.73   0.07
23   4 -109.99 228.19  6.96   0.02

Term codes: 
  ptd  race smoke 
    1     2     3 

Model-averaged coefficients:  
(full average) 
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -1.7911     0.4598   3.896 9.79e-05 ***
ptdTRUE       1.2927     0.4709   2.745  0.00605 ** 
raceblack     0.9028     0.6023   1.499  0.13390    
raceother     0.8333     0.5307   1.570  0.11634    
smokeTRUE     0.7477     0.4934   1.515  0.12966    
 
(conditional average) 
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -1.7911     0.4598   3.896 9.79e-05 ***
ptdTRUE       1.3222     0.4334   3.051  0.00228 ** 
raceblack     1.0690     0.5019   2.130  0.03317 *  
raceother     0.9867     0.4267   2.313  0.02075 *  
smokeTRUE     0.8960     0.3984   2.249  0.02452 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> 
> 
> proc.time()
   user  system elapsed 
   1.76    0.21    1.96