Description Author(s) References See Also Examples
Exam7.1 explains multifactor models with all factors qualitative
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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | library(lsmeans)
library(car)
data(DataSet7.1)
DataSet7.1$a <- factor(x = DataSet7.1$a)
DataSet7.1$b <- factor(x = DataSet7.1$b)
Exam7.1.lm1 <-
lm(
formula = y ~ a + b + a*b
, data = DataSet7.1
# , subset
# , weights
# , na.action
, method = "qr"
, model = TRUE
# , x = FALSE
# , y = FALSE
, qr = TRUE
, singular.ok = TRUE
, contrasts = NULL
# , offset
# , ...
)
summary( Exam7.1.lm1 )
anova(Exam7.1.lm1)
##---Result obtained as in SLICE statement in SAS for a0 & a1
library(phia)
a0 <- list(a=c("0"=1))
phia::testInteractions(Exam7.1.lm1, custom=a0, across="b")
a1 <- list(a=c("1"=1))
phia::testInteractions(Exam7.1.lm1, custom=a1, across="b")
##---Interaction plot
lsmip(
object = Exam7.1.lm1
, formula = a~b
, ylab = "y Lsmeans"
, main = "Lsmeans for a*b"
)
#-------------------------------------------------------------
## Individula least squares treatment means
#-------------------------------------------------------------
Lsm7.1 <-
lsmeans::lsmeans(
object = Exam7.1.lm1
, specs = ~a*b
# , ...
)
Lsm7.1
##---Simpe effects comparison of interaction by a
## (but it doesn't give the same p-value as in article 7.4.2 page#215)
SimpleEff7.1 <-
lsmeans::lsmeans(
object = Exam7.1.lm1
, specs = pairwise~b|a
# , ...
)$contrasts
SimpleEff7.1
##---Alternative method of pairwise comparisons by applying contrast
## coefficient (gives the same p-value as in 7.4.2)
ContrastLsm7.1 <-
lsmeans::contrast(
Lsm7.1
, list (
c1 = c(1,0,-1,0,0,0)
, c2 = c(1,0,0,0,-1,0)
, c3 = c(0,0,1,0,-1,0)
, c4 = c(0,1,0,-1,0,0)
, c5 = c(0,1,0,0,0,-1)
, c6 = c(0,1,0,0,-1,0)
)
)
ContrastLsm7.1
##---Nested Model (page 216)----
Exam7.1.lm2 <-
lm(
formula = y ~ a + a %in% b
, data = DataSet7.1
# , subset
# , weights
# , na.action
, method = "qr"
, model = TRUE
# , x = FALSE
# , y = FALSE
, qr = TRUE
, singular.ok = TRUE
, contrasts = NULL
# , offset
# , ...
)
summary( Exam7.1.lm2 )
anova(Exam7.1.lm2)
ContrastA0lm2 <- car::linearHypothesis(Exam7.1.lm2, c("a0:b1=a0:b2"))
ContrastA0lm2
ContrastA1lm2 <- car::linearHypothesis(Exam7.1.lm2,c("a1:b1=a1:b2"))
ContrastA1lm2
##---Bonferroni's adjusted p-values
SimpleEff7.1B <-
lsmeans::lsmeans(
object = Exam7.1.lm2
, specs = pairwise~b|a
, adjust = "bonferroni"
)$contrasts
SimpleEff7.1B
##---Alternative method of pairwise comparisons by applying contrast coefficient with Bonferroni adjustment
Bonferroni7.1 <-
lsmeans::contrast(
Lsm7.1
, list(
c1 = c(1,0,-1,0,0,0)
, c2 = c(1,0,0,0,-1,0)
, c3 = c(0,0,1,0,-1,0)
, c4 = c(0,1,0,-1,0,0)
, c5 = c(0,1,0,0,0,-1)
, c6 = c(0,1,0,0,-1,0)
)
, adjust="bonferroni"
)
Bonferroni7.1
|
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