R Under development (unstable) (2024-06-18 r86781 ucrt) -- "Unsuffered Consequences"
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> # Test varia
> 
> require(MuMIn)
Loading required package: MuMIn
> packageVersion("MuMIn")
[1] '1.48.2'
> options(na.action = "na.fail")
> 
> #print(packageDescription("MuMIn", fields = "Version"))
> # TEST binary response ---------------------------------------------------------
> ldose <- rep(0:5, 2)
> numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
> sex <- factor(rep(c("M", "F"), c(6, 6)))
> SF <- cbind(numdead, numalive=20-numdead)
> budworm.lg <- glm(SF ~ sex*ldose, family = binomial)
> dd <- dredge(budworm.lg, trace=FALSE)
Fixed term is "(Intercept)"
> gm <- get.models(dd, 1:4)
> model.avg(gm)

Call:
model.avg(object = gm)

Component models: 
'12'  '123' '1'   '2'  

Coefficients: 
       (Intercept)    ldose     sexM ldose:sexM
full     -3.365538 1.033944 0.900864 0.06372988
subset   -3.365538 1.033944 0.928449 0.35291299
> 
> # The same, but use cbind directly in the formula
> budworm.lg <- glm(cbind(numdead, numalive=20-numdead) ~ sex*ldose, family=binomial)
> dd <- dredge(budworm.lg, trace=TRUE)
Fixed term is "(Intercept)"
0 : glm(formula = cbind(numdead, numalive = 20 - numdead) ~ 1, family = binomial)
1 : glm(formula = cbind(numdead, numalive = 20 - numdead) ~ ldose + 
    1, family = binomial)
2 : glm(formula = cbind(numdead, numalive = 20 - numdead) ~ sex + 
    1, family = binomial)
3 : glm(formula = cbind(numdead, numalive = 20 - numdead) ~ ldose + 
    sex + 1, family = binomial)
7 : glm(formula = cbind(numdead, numalive = 20 - numdead) ~ ldose + 
    sex + ldose:sex + 1, family = binomial)
> avgmod <- model.avg(get.models(dd, 1:4))
> 
> # TEST for consistency of vcov and se calculation ------------------------------
> if(!isTRUE(all.equal(coefTable(avgmod, adjust.se = FALSE)[,2],
+ 	sqrt(diag(vcov(avgmod))), tolerance = .001)))
+ 	stop("'vcov' has a problem")
> 
> # TEST evaluation from within function -----------------------------------------
> 
> budworm <- data.frame(ldose = rep(0:5, 2), numdead = c(1, 4, 9, 12, 18, 20, 0,
+ 	2, 6, 10, 12, 16), sex = factor(rep(c("M", "F"), c(6, 6))))
> budworm$SF <- cbind(numdead = budworm$numdead, numalive = 20 - budworm$numdead)
> 
> # evaluate within an exotic environment
> (function(dat) (function(dat2) {
+ 	#mod <- glm(SF ~ sex*ldose, data = dat2, family = "quasibinomial", trace=T)
+ 	mod <- glm(SF ~ sex*ldose, data = dat2, family = "binomial")
+ 	#mod <- glm(SF ~ sex*ldose, data = budworm, family = "binomial", trace=F)
+ 	print(dd <- dredge(mod, rank = "QAIC", chat = summary(budworm.lg)$dispersion))
+ 	gm <- get.models(dd, subset = NA, family = "binomial")
+ 	#print(sys.frames())
+ 	summary(model.avg(gm))
+ })(dat))(budworm)
Fixed term is "(Intercept)"
Global model call: glm(formula = SF ~ sex * ldose, family = "binomial", data = dat2)
---
Model selection table 
    (Int)   lds sex lds:sex df  logLik  QAIC  delta weight
4 -3.4380 1.053   +          3 -18.736  43.5   0.00  0.552
8 -2.9940 0.906   +       +  4 -17.981  44.0   0.49  0.432
2 -2.7810 1.002              2 -23.301  50.6   7.13  0.016
3 -0.4754         +          2 -74.119 152.2 108.77  0.000
1 -0.1671                    1 -76.849 155.7 112.23  0.000
Models ranked by QAIC(x, chat = 1) 

Call:
model.avg(object = gm)

Component model call: 
glm(formula = SF ~ <5 unique rhs>, family = binomial, data = dat2)

Component models: 
       df logLik   QAIC  delta weight
12      3 -18.74  43.47   0.00   0.55
123     4 -17.98  43.96   0.49   0.43
1       2 -23.30  50.60   7.13   0.02
2       2 -74.12 152.24 108.77   0.00
(Null)  1 -76.85 155.70 112.23   0.00

Term codes: 
    ldose       sex ldose:sex 
        1         2         3 

Model-averaged coefficients:  
(full average) 
            Estimate Std. Error Adjusted SE z value Pr(>|z|)    
(Intercept)  -3.2356     0.5517      0.6287   5.146    3e-07 ***
ldose         0.9885     0.1638      0.1861   5.313    1e-07 ***
sexM          0.6493     0.7159      0.7971   0.815    0.415    
ldose:sexM    0.1393     0.2368      0.2604   0.535    0.593    
 
(conditional average) 
            Estimate Std. Error Adjusted SE z value Pr(>|z|)    
(Intercept)  -3.2356     0.5517      0.6287   5.146    3e-07 ***
ldose         0.9885     0.1638      0.1861   5.313    1e-07 ***
sexM          0.6596     0.7169      0.7991   0.825    0.409    
ldose:sexM    0.3225     0.2659      0.3129   1.031    0.303    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> 
> 
> 
> rm(list=ls())
> 
> # END TESTS
> 
> proc.time()
   user  system elapsed 
   1.64    0.23    1.85