# Exam2.B.7: Example 2.B.7 from Generalized Linear Mixed Models: Modern... In StroupGLMM: R Codes and Datasets for Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup

## Description

Exam2.B.7 is related to multi batch regression data assuming different forms of linear models with factorial experiment.

## References

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

`DataExam2.B.7`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97``` ```#----------------------------------------------------------------------------------- ## Classical main effects and Interaction Model #----------------------------------------------------------------------------------- data(DataExam2.B.7) DataExam2.B.7\$a <- factor(x = DataExam2.B.7\$a) DataExam2.B.7\$b <- factor(x = DataExam2.B.7\$b) Exam2.B.7.lm1 <- lm( formula = y~ a + b + a*b , data = DataExam2.B.7 # , subset # , weights # , na.action , method = "qr" , model = TRUE # , x = FALSE # , y = FALSE , qr = TRUE , singular.ok = TRUE , contrasts = NULL # , offset # , ... ) #----------------------------------------------------------------------------------- ## One way treatment effects model #----------------------------------------------------------------------------------- DesignMatrix.lm1 <- model.matrix (object = Exam2.B.7.lm1) DesignMatrix2.B.7.2 <- DesignMatrix.lm1[,!colnames(DesignMatrix.lm1) %in% c("a2","b")] lmfit2 <- lm.fit( x = DesignMatrix2.B.7.2 , y = DataExam2.B.7\$y , offset = NULL , method = "qr" , tol = 1e-07 , singular.ok = TRUE # , ... ) Coefficientslmfit2 <- coef( object = lmfit2) #----------------------------------------------------------------------------------- ## One way treatment effects model without intercept #----------------------------------------------------------------------------------- DesignMatrix2.B.7.3 <- as.matrix(DesignMatrix.lm1[,!colnames(DesignMatrix.lm1) %in% c("(Intercept)","a2","b")]) lmfit3 <- lm.fit( x = DesignMatrix2.B.7.3 , y = DataExam2.B.7\$y , offset = NULL , method = "qr" , tol = 1e-07 , singular.ok = TRUE # , ... ) Coefficientslmfit3 <- coef( object = lmfit3) #----------------------------------------------------------------------------------- ## Nested Model (both models give the same result) #----------------------------------------------------------------------------------- Exam2.B.7.lm4 <- lm( formula = y~ a + a/b , data = DataExam2.B.7 # , subset # , weights # , na.action , method = "qr" , model = TRUE # , x = FALSE # , y = FALSE , qr = TRUE , singular.ok = TRUE , contrasts = NULL # , offset # , ... ) summary(Exam2.B.7.lm4) Exam2.B.7.lm4 <- lm( formula = y~ a + a*b , data = DataExam2.B.7 # , subset # , weights # , na.action , method = "qr" , model = TRUE # , x = FALSE # , y = FALSE , qr = TRUE , singular.ok = TRUE , contrasts = NULL # , offset # , ... ) summary(Exam2.B.7.lm4) ```