Description Author(s) References See Also Examples
Exam2.B.7 is related to multi batch regression data assuming different forms of linear models with factorial experiment.
Muhammad Yaseen (myaseen208@gmail.com)
Adeela Munawar (adeela.uaf@gmail.com)
Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.
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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.