Exam2.B.4.RdExam2.B.4 is used to illustrate one way treatment design with Binomial observations.
Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.
#----------------------------------------------------------------------------------- ## logit Model discussed in Example 2.B.2 using DataExam2.B.4 ## Default link is logit ## using fmaily=binomial gives warning message of no-integer successes #----------------------------------------------------------------------------------- data(DataExam2.B.4) DataExam2.B.4$trt <- factor(x = DataExam2.B.4$trt) Exam2.B.4glm <- glm( formula = Yij/Nij~trt , family = quasibinomial(link = "probit") , data = DataExam2.B.4 , 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.4glm)#> #> Call: #> glm(formula = Yij/Nij ~ trt, family = quasibinomial(link = "probit"), #> data = DataExam2.B.4, weights = NULL, start = NULL, method = "glm.fit", #> contrasts = NULL) #> #> Deviance Residuals: #> 1 2 3 4 5 6 #> -0.1660 0.1542 -0.3896 0.3896 0.2044 -0.2007 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) -0.7729 0.3737 -2.068 0.130 #> trt2 0.7729 0.5017 1.541 0.221 #> trt3 0.9408 0.5029 1.871 0.158 #> #> (Dispersion parameter for quasibinomial family taken to be 0.1426701) #> #> Null deviance: 1.01671 on 5 degrees of freedom #> Residual deviance: 0.43701 on 3 degrees of freedom #> AIC: NA #> #> Number of Fisher Scoring iterations: 4 #>