The scale.fix doesn't seem to work or is displayed incorrectly

[ghost]

For the following data:

d <- structure(list(ID = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 
4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 
9, 9, 10, 10, 10, 10), TimeOrd = structure(c(1L, 2L, 3L, 4L, 
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 
1L, 2L, 3L, 4L), .Label = c("M0", "M1", "M2", "M3"), class = c("ordered", 
"factor")), Result = c(1, 1.1, 1.5, 2, 1.1, 1.1, 1.4, 3, 1.1, 
1.2, 1.7, 5, 1.5, 3, 4.6, 6, 2.3, 4.4, 5.5, 8, 2, 2.2, 2.3, 7, 
4.5, 1, 3, 7, 3.3, 4.4, 5.5, 5, 3, 4, 5, 8, 1.1, 2.2, 3.3, 5), 
    SucN = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 
    0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 
    1, 1, 1, 1, 0), Time = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 
    3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
    2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 
    1L, 2L, 3L, 4L), .Label = c("M0", "M1", "M2", "M3"), class = "factor")), row.names = c(NA, 
40L), class = "data.frame")

For any model, when I want to estimate the scale (apart from the sense in this example), it shows me 1, while I know, from other packages, that it should be different.

> summary(geeglm(SucN ~ Time, family=binomial(link = logit), data=d, id=ID, corstr = "unstructured", std.err="san.se", scale.fix = F))

Call:
geeglm(formula = SucN ~ Time, family = binomial(link = logit), 
    data = d, id = ID, corstr = "unstructured", scale.fix = F, 
    std.err = "san.se")

 Coefficients:
            Estimate Std.err Wald Pr(>|W|)  
(Intercept)    -2.20    1.05 4.35    0.037 *
TimeM1          1.35    0.92 2.15    0.142  
TimeM2          2.60    1.08 5.85    0.016 *
TimeM3          3.04    1.53 3.98    0.046 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation structure = unstructured 
Estimated Scale Parameters:

            Estimate Std.err
(Intercept)        1   0.577
  Link = identity 

Estimated Correlation Parameters:
          Estimate Std.err
alpha.1:2   0.5092   0.482
alpha.1:3   0.2722   0.288
alpha.1:4  -0.5092   0.479
alpha.2:3   0.5345   0.409
alpha.2:4  -0.5238   0.492
alpha.3:4  -0.0891   0.332
Number of clusters:   10  Maximum cluster size: 4 

Please note, the link is incorrect ("identity") and the scale is 1. While it should be 1.11. I guess it's unnecessary rounded in the output. Below confirmed by gee::gee and geeM::geem

> summary(gee::gee(SucN ~ Time, family=binomial(link = logit), data=d, id=ID, corstr = "unstructured", scale.fix = F))
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept)      TimeM1      TimeM2      TimeM3 
      -2.20        1.35        2.60        3.04 

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Logit 
 Variance to Mean Relation: Binomial 
 Correlation Structure:     Unstructured 

Call:
gee::gee(formula = SucN ~ Time, id = ID, data = d, family = binomial(link = logit), 
    corstr = "unstructured", scale.fix = F)

Summary of Residuals:
   Min     1Q Median     3Q    Max 
-0.700 -0.300 -0.100  0.325  0.900 


Coefficients:
            Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept)    -2.20       1.11   -1.98        1.05    -2.08
TimeM1          1.35       0.97    1.39        0.92     1.47
TimeM2          2.60       1.13    2.30        1.08     2.42
TimeM3          3.04       1.61    1.89        1.53     2.00

Estimated Scale Parameter:  1.11
Number of Iterations:  1

Working Correlation
       [,1]   [,2]    [,3]    [,4]
[1,]  1.000  0.509  0.2722 -0.5092
[2,]  0.509  1.000  0.5345 -0.5238
[3,]  0.272  0.535  1.0000 -0.0891
[4,] -0.509 -0.524 -0.0891  1.0000

or

> summary(geeM::geem(SucN ~ Time, family=binomial(link = logit), data=d, id=ID, corstr = "unstructured", scale.fix = F))
            Estimates Model SE Robust SE  wald      p
(Intercept)     -2.20    1.111      1.05 -2.08 0.0371
TimeM1           1.35    0.727      0.92  1.47 0.1423
TimeM2           2.60    1.039      1.08  2.42 0.0155
TimeM3           3.04    1.732      1.53  2.00 0.0460

 Working Correlation: 
       [,1]   [,2]   [,3]   [,4]
[1,]  1.000  0.764  0.408 -0.764
[2,]  0.764  1.000  0.802 -0.786
[3,]  0.408  0.802  1.000 -0.134
[4,] -0.764 -0.786 -0.134  1.000
 Correlation Structure:  unstructured 
 Est. Scale Parameter:  1.11 

 Number of GEE iterations: 2 
 Number of Clusters:  10    Maximum Cluster Size:  4 
 Number of observations with nonzero weight:  40