R Under development (unstable) (2023-11-28 r85645 ucrt) -- "Unsuffered Consequences"
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> library(robustbase)
> 
> ## Model without coefficients [ print.glmrob() tests for this ..]
> 
> ### very simple model [with outliers]
> set.seed(1)
> y <- rpois(1000, lambda = 4)
> 
> ## without outliers
> m0o <- glm(y ~ 0, family = poisson, epsilon = 1e-12)
> m1o <- glm(y ~ 1, family = poisson, epsilon = 1e-12)
> 
> y[1:3] <- 99:101 # outliers
> 
> m0 <- glm(y ~ 0, family = poisson, epsilon = 1e-12)
> m1 <- glm(y ~ 1, family = poisson, epsilon = 1e-12)
> 
> ## these both failed in version 0.1-2:
> rm0 <- glmrob(y ~ 0, family = poisson, acc = 1e-12)
> rm1 <- glmrob(y ~ 1, family = poisson, acc = 1e-12)
> 
> rm0

Call:  glmrob(formula = y ~ 0, family = poisson, acc = 1e-12) 

No coefficients


Number of observations: 1000 
Fitted by method  'Mqle' 
> rm1

Call:  glmrob(formula = y ~ 1, family = poisson, acc = 1e-12) 

Coefficients:
(Intercept)  
      1.391  

Number of observations: 1000 
Fitted by method  'Mqle' 
> (s0 <- summary(rm0))

Call:  glmrob(formula = y ~ 0, family = poisson, acc = 1e-12) 

No coefficients


Number of observations: 1000 
Fitted by method 'Mqle'  (in 0 iterations)

(Dispersion parameter for poisson family taken to be 1)

No deviance values available 
Algorithmic parameters: 
      acc       tcc 
1.000e-12 1.345e+00 
maxit 
   50 
test.acc 
  "coef" 

> (s1 <- summary(rm1))

Call:  glmrob(formula = y ~ 1, family = poisson, acc = 1e-12) 


Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  1.39067    0.01621   85.77   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Robustness weights w.r * w.x: 
 791 weights are ~= 1. The remaining 209 ones are summarized as
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0278  0.6769  0.8934  0.7906  0.9039  0.9039 

Number of observations: 1000 
Fitted by method 'Mqle'  (in 7 iterations)

(Dispersion parameter for poisson family taken to be 1)

No deviance values available 
Algorithmic parameters: 
      acc       tcc 
1.000e-12 1.345e+00 
maxit 
   50 
test.acc 
  "coef" 

> str(s1)
List of 19
 $ call         : language glmrob(formula = y ~ 1, family = poisson, acc = 1e-12)
 $ 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 "numeric"
  .. .. ..- attr(*, "names")= chr "y"
 $ family       :List of 13
  ..$ family    : chr "poisson"
  ..$ link      : chr "log"
  ..$ 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:  expression({  if (any(y < 0))  stop("negative values not allowed for the 'Poisson' family")  n <- rep.int(1, nobs| __truncated__
  ..$ validmu   :function (mu)  
  ..$ valideta  :function (eta)  
  ..$ simulate  :function (object, nsim)  
  ..$ dispersion: num 1
  ..- attr(*, "class")= chr "family"
 $ iter         : int 7
 $ control      :List of 4
  ..$ acc     : num 1e-12
  ..$ test.acc: chr "coef"
  ..$ maxit   : num 50
  ..$ tcc     : num 1.34
 $ method       : chr "Mqle"
 $ residuals    : Named num [1:1000] 47.39 47.89 48.39 1.49 -1.01 ...
  ..- attr(*, "names")= chr [1:1000] "1" "2" "3" "4" ...
 $ fitted.values: Named num [1:1000] 4.02 4.02 4.02 4.02 4.02 ...
  ..- attr(*, "names")= chr [1:1000] "1" "2" "3" "4" ...
 $ w.r          : num [1:1000] 0.0284 0.0281 0.0278 0.9039 1 ...
 $ w.x          : num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
 $ deviance     : NULL
 $ df.residual  : NULL
 $ null.deviance: NULL
 $ df.null      : NULL
 $ df           : NULL
 $ aliased      : Named logi FALSE
  ..- attr(*, "names")= chr "(Intercept)"
 $ coefficients : num [1, 1:4] 1.3907 0.0162 85.7719 0
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr "(Intercept)"
  .. ..$ : chr [1:4] "Estimate" "Std. Error" "z value" "Pr(>|z|)"
 $ dispersion   : num 1
 $ cov.scaled   : num [1, 1] 0.000263
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr "(Intercept)"
  .. ..$ : chr "(Intercept)"
 - attr(*, "class")= chr "summary.glmrob"
> stopifnot(all.equal(c(coef(s1)),
+ 		    c(1.390672035557, 0.016213613600955,
+ 		      85.77187478275, 0), tolerance = 1e-13))# 32-b: 4.7e-15
> 
> cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons''
Time elapsed:  0.31 0.15 0.45 NA NA 
> 
> proc.time()
   user  system elapsed 
   0.31    0.15    0.45