R Under development (unstable) (2023-11-28 r85645 ucrt) -- "Unsuffered Consequences"
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> 
> library(robustbase)
> ### "intercept only" : "no x"
> 
> 
> set.seed(101)
> k <- rbinom(100, size=3, pr = 0.2)
> y <- cbind(k, n.k = 3 - k)
> 
> gg <- glm(y ~ 1, family = "binomial")
> (cfK <- coef(summary(gg)))
            Estimate Std. Error   z value     Pr(>|z|)
(Intercept) -1.22723  0.1378993 -8.899464 5.611711e-19
> 
> Inf. <- 1e5 # FIXME (note that much larger values *deteriorate* slightly!)
> rg.Inf <- glmrob(y ~ 1, family = "binomial", tcc= Inf.)
> stopifnot(all.equal(unname(cfK[1:2]),
+ 		    unname(unlist(coef(summary(rg.Inf))[1:2])),
+ 		    tolerance = 1e-7))# 4.09e-8
> 
> rg.0 <- glmrob(y ~ 1, family = "binomial")
> summary(rg.0)

Call:  glmrob(formula = y ~ 1, family = "binomial") 


Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -1.2228     0.1408  -8.684   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Robustness weights w.r * w.x: 
 87 weights are ~= 1. The remaining 13 ones are summarized as
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.4213  0.7411  0.7411  0.7165  0.7411  0.7411 

Number of observations: 100 
Fitted by method 'Mqle'  (in 2 iterations)

(Dispersion parameter for binomial family taken to be 1)

No deviance values available 
Algorithmic parameters: 
   acc    tcc 
0.0001 1.3450 
maxit 
   50 
test.acc 
  "coef" 

> str(rg.0, digits= 6)
List of 28
 $ coefficients     : Named num -1.2228
  ..- attr(*, "names")= chr "(Intercept)"
 $ residuals        : Named num [1:100] -0.939795 -0.939795 0.437532 0.437532 -0.939795 ...
  ..- attr(*, "names")= chr [1:100] "1" "2" "3" "4" ...
 $ fitted.values    : Named num [1:100] 0.227444 0.227444 0.227444 0.227444 0.227444 ...
  ..- attr(*, "names")= chr [1:100] "1" "2" "3" "4" ...
 $ w.r              : num [1:100] 1 1 1 1 1 1 1 1 1 1 ...
 $ w.x              : num [1:100] 1 1 1 1 1 1 1 1 1 1 ...
 $ ni               : num [1:100] 3 3 3 3 3 3 3 3 3 3 ...
 $ dispersion       : num 1
 $ cov              : num [1, 1] 0.0198292
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr "(Intercept)"
  .. ..$ : chr "(Intercept)"
 $ matM             : num [1, 1] 0.436674
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr "(Intercept)"
  .. ..$ : chr "(Intercept)"
 $ matQ             : num [1, 1] 0.378112
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr "(Intercept)"
  .. ..$ : chr "(Intercept)"
 $ tcc              : num 1.345
 $ family           :List of 13
  ..$ family    : chr "binomial"
  ..$ link      : chr "logit"
  ..$ linkfun   :function (mu)  
  ..$ linkinv   :function (eta)  
  ..$ variance  :function (mu)  
  ..$ dev.resids:function (y, mu, wt)  
  ..$ aic       :function (y, n, mu, wt, dev)  
  ..$ mu.eta    :function (eta)  
  ..$ initialize: language {     if (NCOL(y) == 1) { ...
  ..$ validmu   :function (mu)  
  ..$ valideta  :function (eta)  
  ..$ simulate  :function (object, nsim)  
  ..$ dispersion: num 1
  ..- attr(*, "class")= chr "family"
 $ linear.predictors: Named num [1:100] -1.2228 -1.2228 -1.2228 -1.2228 -1.2228 ...
  ..- attr(*, "names")= chr [1:100] "1" "2" "3" "4" ...
 $ deviance         : NULL
 $ iter             : int 2
 $ y                : Named num [1:100] 0 0 0.333333 0.333333 0 ...
  ..- attr(*, "names")= chr [1:100] "1" "2" "3" "4" ...
 $ converged        : logi TRUE
 $ model            :'data.frame':	100 obs. of  1 variable:
  ..$ y: num [1:100, 1:2] 0 0 1 1 0 0 1 0 1 1 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : NULL
  .. .. ..$ : chr [1:2] "k" "n.k"
  ..- attr(*, "terms")=Classes 'terms', 'formula'  language y ~ 1
  .. .. ..- attr(*, "variables")= language list(y)
  .. .. ..- attr(*, "factors")= int(0) 
  .. .. ..- attr(*, "term.labels")= chr(0) 
  .. .. ..- attr(*, "order")= int(0) 
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. .. ..- attr(*, "predvars")= language list(y)
  .. .. ..- attr(*, "dataClasses")= Named chr "nmatrix.2"
  .. .. .. ..- attr(*, "names")= chr "y"
 $ call             : language glmrob(formula = y ~ 1, family = "binomial")
 $ formula          :Class 'formula'  language y ~ 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
 $ terms            :Classes 'terms', 'formula'  language y ~ 1
  .. ..- attr(*, "variables")= language list(y)
  .. ..- attr(*, "factors")= int(0) 
  .. ..- attr(*, "term.labels")= chr(0) 
  .. ..- attr(*, "order")= int(0) 
  .. ..- attr(*, "intercept")= int 1
  .. ..- attr(*, "response")= int 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. ..- attr(*, "predvars")= language list(y)
  .. ..- attr(*, "dataClasses")= Named chr "nmatrix.2"
  .. .. ..- attr(*, "names")= chr "y"
 $ data             :<environment: R_GlobalEnv> 
 $ offset           : NULL
 $ control          :List of 4
  ..$ acc     : num 1e-04
  ..$ test.acc: chr "coef"
  ..$ maxit   : num 50
  ..$ tcc     : num 1.345
 $ method           : chr "Mqle"
 $ prior.weights    : num [1:100] 1 1 1 1 1 1 1 1 1 1 ...
 $ contrasts        : NULL
 $ xlevels          : NULL
 - attr(*, "class")= chr [1:2] "glmrob" "glm"
> 
> proc.time()
   user  system elapsed 
   0.34    0.14    0.42