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

Author

1. Muhammad Yaseen (myaseen208@gmail.com)

2. Adeela Munawar (adeela.uaf@gmail.com)

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)
summary(Exam2.B.2glm)
#> 
#> Call:
#> glm(formula = y/n ~ x, family = quasibinomial(link = "probit"), 
#>     data = DataExam2.B.2)
#> 
#> 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
#> 
library(parameters)
model_parameters(Exam2.B.2glm)
#> Parameter   | Log-Risk |   SE |         95% CI |  t(9) |      p
#> ---------------------------------------------------------------
#> (Intercept) |    -0.45 | 0.18 | [-0.81, -0.10] | -2.49 | 0.013 
#> x           |     0.25 | 0.04 | [ 0.18,  0.33] |  6.40 | < .001
#> 
#> Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
#>   computed using a Wald t-distribution approximation.