Example 2.B.2 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-54)

Exam2.B.2 is used to visualize the effect of glm model statement with binomial data with logit and probit links.

References

1. Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.

See also

DataExam2.B.2

Examples

#-----------------------------------------------------------------------------------
## probitit Model  discussed in Example 2.B.2 using DataExam2.B.2
## Default link is logit
## using fmaily=binomial gives warning message of no-integer successes
#-----------------------------------------------------------------------------------
data(DataExam2.B.2)
Exam2.B.2glm <-
  glm(
      formula    =  y/n~x
    , family     =  quasibinomial(link = "probit")
    , data       =  DataExam2.B.2
    , weights    =  NULL
 #  , subset
 #  , na.action
    , start      =  NULL
 #  , etastart
 #  , mustart
 #  , offset
 #  , control    =  list(...)
 #  , model      =  TRUE
    , method     =  "glm.fit"
 #  , x          =  FALSE
 #  , y          =  TRUE
    , contrasts  =  NULL
    #  , ...
  )
summary(Exam2.B.2glm)
#> 
#> Call:
#> glm(formula = y/n ~ x, family = quasibinomial(link = "probit"), 
#>     data = DataExam2.B.2, weights = NULL, start = NULL, method = "glm.fit", 
#>     contrasts = NULL)
#> 
#> Deviance Residuals: 
#>      Min        1Q    Median        3Q       Max  
#> -0.29424  -0.15211   0.01702   0.15847   0.56045  
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -0.45281    0.18202  -2.488 0.034555 *  
#> x            0.25157    0.03928   6.405 0.000125 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> (Dispersion parameter for quasibinomial family taken to be 0.05522989)
#> 
#>     Null deviance: 3.49679  on 10  degrees of freedom
#> Residual deviance: 0.61969  on  9  degrees of freedom
#> AIC: NA
#> 
#> Number of Fisher Scoring iterations: 5
#>