Exam3.3: Example 3.3 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
Author(s)
References
See Also
Examples
Description
Exam3.3 use RCBD data with fixed location effect and different forms of estimable functions are shown in this example.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Adeela Munawar (adeela.uaf@gmail.com)
References
Stroup, W. W. (2012).
Generalized Linear Mixed Models: Modern Concepts, Methods and Applications.
CRC Press.
See Also
Examples
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#-----------------------------------------------------------------------------------
## linear model for Gaussian data
#-----------------------------------------------------------------------------------
data(DataSet3.2)
DataSet3.2$trt <- factor(x = DataSet3.2$trt, level = c(3,0,1,2))
DataSet3.2$loc <- factor(x = DataSet3.2$loc, level = c(8, 1, 2, 3, 4, 5, 6, 7))
Exam3.3.lm1 <-
lm(
formula = Y~ trt+loc
, data = DataSet3.2
# , subset
# , weights
# , na.action
, method = "qr"
, model = TRUE
# , x = FALSE
# , y = FALSE
, qr = TRUE
, singular.ok = TRUE
, contrasts = NULL
# , offset
# , ...
)
summary( Exam3.3.lm1 )
#-------------------------------------------------------------
## Individula least squares treatment means
#-------------------------------------------------------------
library(lsmeans)
(Lsm3.3 <-
lsmeans::lsmeans(
object = Exam3.3.lm1
, specs = "trt"
# , ...
)
)
#---------------------------------------------------
## Pairwise treatment means estimate
#---------------------------------------------------
contrast( object = Lsm3.3 , method = "pairwise")
#---------------------------------------------------
## Repairwise treatment means estimate
#---------------------------------------------------
## contrast( object = Lsm3.3 , method = "repairwise")
#-------------------------------------------------------
## LSM Trt0 (This term is used in Walter Stroups' book)
#-------------------------------------------------------
library(phia)
list3.3.1 <- list(trt=c("0" = 1 ) )
Test3.3.1 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.1)
)
#-------------------------------------------------------
## LSM Trt0 alt(This term is used in Walter Stroups' book)
#-------------------------------------------------------
list3.3.2 <-
list(trt=c("0" = 1 )
, loc=c("1" = 0,"2" = 0,"3" = 0,"4" = 0,"5" = 0,"6" = 0,"7" = 0)
)
Test3.3.2 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.2)
)
#-------------------------------------------------------
## Trt0 Vs Trt1
#-------------------------------------------------------
list3.3.3 <- list(trt=c("0" = 1,"1" = -1))
Test3.3.3 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.3)
)
#-------------------------------------------------------
## average Trt0+1
#-------------------------------------------------------
list3.3.4 <- list(trt=c("0" = 0.5 , "1" = 0.5))
Test3.3.4 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.4)
)
#-------------------------------------------------------
## average Trt0+2+3
#-------------------------------------------------------
list3.3.5 <- list(trt=c("0" = 0.33333,"2" = 0.33333,"3" = 0.33333))
Test3.3.5 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.5)
)
#-------------------------------------------------------
## Trt 2 Vs 3 difference
#-------------------------------------------------------
list3.3.6 <- list(trt=c("2" = 1,"3" = -1))
Test3.3.6 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.6)
)
#-------------------------------------------------------
## Trt 1 Vs 2 difference
#-------------------------------------------------------
list3.3.7 <- list(trt=c("1" = 1,"2" = -1))
Test3.3.7 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.7)
)
#-------------------------------------------------------
## Trt 1 Vs 3 difference
#-------------------------------------------------------
list3.3.8 <- list(trt=c("1" = 1,"3" = -1))
Test3.3.8 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.8)
)
#-------------------------------------------------------
## Average trt0+1 vs Average Trt2+3
#-------------------------------------------------------
list3.3.9 <- list(trt=c("0" = 0.5,"1" = 0.5,"2" = -0.5,"3" = -0.5))
Test3.3.9 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.9)
)
#-------------------------------------------------------
## Trt1 vs Average Trt0+1+2
#-------------------------------------------------------
list3.3.10 <- list(trt=c("0" = 0.33333,"1" = -1,"2" = 0.33333,"3" = 0.33333))
Test3.3.10 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.10)
)
#-------------------------------------------------------
## Sidak Multiplicity adjustment for p-values
#-------------------------------------------------------
library(mutoss)
PValues3.3 <-
c(
Test3.3.3[[7]][1, 4]
, Test3.3.6[[7]][1, 4]
, Test3.3.7[[7]][1, 4]
, Test3.3.8[[7]][1, 4]
, Test3.3.9[[7]][1, 4]
, Test3.3.10[[7]][1, 4]
)
AdjPValues3.3 <- sidak(PValues3.3)
Example output
Call:
lm(formula = Y ~ trt + loc, data = DataSet3.2, method = "qr",
model = TRUE, qr = TRUE, singular.ok = TRUE, contrasts = NULL)
Residuals:
Min 1Q Median 3Q Max
-2.7750 -0.7875 0.3625 1.0813 2.3000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.2500 0.9945 28.407 < 2e-16 ***
trt0 -2.1375 0.8481 -2.520 0.01988 *
trt1 -2.9500 0.8481 -3.478 0.00224 **
trt2 0.2875 0.8481 0.339 0.73798
loc1 -0.9750 1.1994 -0.813 0.42538
loc2 -3.2500 1.1994 -2.710 0.01312 *
loc3 -1.6750 1.1994 -1.397 0.17713
loc4 -0.3500 1.1994 -0.292 0.77329
loc5 0.8250 1.1994 0.688 0.49907
loc6 -0.3000 1.1994 -0.250 0.80492
loc7 -3.5750 1.1994 -2.981 0.00713 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.696 on 21 degrees of freedom
Multiple R-squared: 0.6818, Adjusted R-squared: 0.5303
F-statistic: 4.5 on 10 and 21 DF, p-value: 0.001795
Loading required package: estimability
trt lsmean SE df lower.CL upper.CL
3 27.0875 0.5996899 21 25.84038 28.33462
0 24.9500 0.5996899 21 23.70288 26.19712
1 24.1375 0.5996899 21 22.89038 25.38462
2 27.3750 0.5996899 21 26.12788 28.62212
Results are averaged over the levels of: loc
Confidence level used: 0.95
contrast estimate SE df t.ratio p.value
3 - 0 2.1375 0.8480896 21 2.520 0.0856
3 - 1 2.9500 0.8480896 21 3.478 0.0111
3 - 2 -0.2875 0.8480896 21 -0.339 0.9862
0 - 1 0.8125 0.8480896 21 0.958 0.7742
0 - 2 -2.4250 0.8480896 21 -2.859 0.0430
1 - 2 -3.2375 0.8480896 21 -3.817 0.0051
Results are averaged over the levels of: loc
P value adjustment: tukey method for comparing a family of 4 estimates
Loading required package: car
Loading required package: mvtnorm
StroupGLMM documentation built on May 2, 2019, 9:42 a.m.
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Description Author(s) References See Also Examples
Description
Exam3.3 use RCBD data with fixed location effect and different forms of estimable functions are shown in this example.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Adeela Munawar (adeela.uaf@gmail.com)
References
Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.
See Also
Examples
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | #-----------------------------------------------------------------------------------
## linear model for Gaussian data
#-----------------------------------------------------------------------------------
data(DataSet3.2)
DataSet3.2$trt <- factor(x = DataSet3.2$trt, level = c(3,0,1,2))
DataSet3.2$loc <- factor(x = DataSet3.2$loc, level = c(8, 1, 2, 3, 4, 5, 6, 7))
Exam3.3.lm1 <-
lm(
formula = Y~ trt+loc
, data = DataSet3.2
# , subset
# , weights
# , na.action
, method = "qr"
, model = TRUE
# , x = FALSE
# , y = FALSE
, qr = TRUE
, singular.ok = TRUE
, contrasts = NULL
# , offset
# , ...
)
summary( Exam3.3.lm1 )
#-------------------------------------------------------------
## Individula least squares treatment means
#-------------------------------------------------------------
library(lsmeans)
(Lsm3.3 <-
lsmeans::lsmeans(
object = Exam3.3.lm1
, specs = "trt"
# , ...
)
)
#---------------------------------------------------
## Pairwise treatment means estimate
#---------------------------------------------------
contrast( object = Lsm3.3 , method = "pairwise")
#---------------------------------------------------
## Repairwise treatment means estimate
#---------------------------------------------------
## contrast( object = Lsm3.3 , method = "repairwise")
#-------------------------------------------------------
## LSM Trt0 (This term is used in Walter Stroups' book)
#-------------------------------------------------------
library(phia)
list3.3.1 <- list(trt=c("0" = 1 ) )
Test3.3.1 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.1)
)
#-------------------------------------------------------
## LSM Trt0 alt(This term is used in Walter Stroups' book)
#-------------------------------------------------------
list3.3.2 <-
list(trt=c("0" = 1 )
, loc=c("1" = 0,"2" = 0,"3" = 0,"4" = 0,"5" = 0,"6" = 0,"7" = 0)
)
Test3.3.2 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.2)
)
#-------------------------------------------------------
## Trt0 Vs Trt1
#-------------------------------------------------------
list3.3.3 <- list(trt=c("0" = 1,"1" = -1))
Test3.3.3 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.3)
)
#-------------------------------------------------------
## average Trt0+1
#-------------------------------------------------------
list3.3.4 <- list(trt=c("0" = 0.5 , "1" = 0.5))
Test3.3.4 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.4)
)
#-------------------------------------------------------
## average Trt0+2+3
#-------------------------------------------------------
list3.3.5 <- list(trt=c("0" = 0.33333,"2" = 0.33333,"3" = 0.33333))
Test3.3.5 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.5)
)
#-------------------------------------------------------
## Trt 2 Vs 3 difference
#-------------------------------------------------------
list3.3.6 <- list(trt=c("2" = 1,"3" = -1))
Test3.3.6 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.6)
)
#-------------------------------------------------------
## Trt 1 Vs 2 difference
#-------------------------------------------------------
list3.3.7 <- list(trt=c("1" = 1,"2" = -1))
Test3.3.7 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.7)
)
#-------------------------------------------------------
## Trt 1 Vs 3 difference
#-------------------------------------------------------
list3.3.8 <- list(trt=c("1" = 1,"3" = -1))
Test3.3.8 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.8)
)
#-------------------------------------------------------
## Average trt0+1 vs Average Trt2+3
#-------------------------------------------------------
list3.3.9 <- list(trt=c("0" = 0.5,"1" = 0.5,"2" = -0.5,"3" = -0.5))
Test3.3.9 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.9)
)
#-------------------------------------------------------
## Trt1 vs Average Trt0+1+2
#-------------------------------------------------------
list3.3.10 <- list(trt=c("0" = 0.33333,"1" = -1,"2" = 0.33333,"3" = 0.33333))
Test3.3.10 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.10)
)
#-------------------------------------------------------
## Sidak Multiplicity adjustment for p-values
#-------------------------------------------------------
library(mutoss)
PValues3.3 <-
c(
Test3.3.3[[7]][1, 4]
, Test3.3.6[[7]][1, 4]
, Test3.3.7[[7]][1, 4]
, Test3.3.8[[7]][1, 4]
, Test3.3.9[[7]][1, 4]
, Test3.3.10[[7]][1, 4]
)
AdjPValues3.3 <- sidak(PValues3.3)
|
Example output
Call:
lm(formula = Y ~ trt + loc, data = DataSet3.2, method = "qr",
model = TRUE, qr = TRUE, singular.ok = TRUE, contrasts = NULL)
Residuals:
Min 1Q Median 3Q Max
-2.7750 -0.7875 0.3625 1.0813 2.3000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.2500 0.9945 28.407 < 2e-16 ***
trt0 -2.1375 0.8481 -2.520 0.01988 *
trt1 -2.9500 0.8481 -3.478 0.00224 **
trt2 0.2875 0.8481 0.339 0.73798
loc1 -0.9750 1.1994 -0.813 0.42538
loc2 -3.2500 1.1994 -2.710 0.01312 *
loc3 -1.6750 1.1994 -1.397 0.17713
loc4 -0.3500 1.1994 -0.292 0.77329
loc5 0.8250 1.1994 0.688 0.49907
loc6 -0.3000 1.1994 -0.250 0.80492
loc7 -3.5750 1.1994 -2.981 0.00713 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.696 on 21 degrees of freedom
Multiple R-squared: 0.6818, Adjusted R-squared: 0.5303
F-statistic: 4.5 on 10 and 21 DF, p-value: 0.001795
Loading required package: estimability
trt lsmean SE df lower.CL upper.CL
3 27.0875 0.5996899 21 25.84038 28.33462
0 24.9500 0.5996899 21 23.70288 26.19712
1 24.1375 0.5996899 21 22.89038 25.38462
2 27.3750 0.5996899 21 26.12788 28.62212
Results are averaged over the levels of: loc
Confidence level used: 0.95
contrast estimate SE df t.ratio p.value
3 - 0 2.1375 0.8480896 21 2.520 0.0856
3 - 1 2.9500 0.8480896 21 3.478 0.0111
3 - 2 -0.2875 0.8480896 21 -0.339 0.9862
0 - 1 0.8125 0.8480896 21 0.958 0.7742
0 - 2 -2.4250 0.8480896 21 -2.859 0.0430
1 - 2 -3.2375 0.8480896 21 -3.817 0.0051
Results are averaged over the levels of: loc
P value adjustment: tukey method for comparing a family of 4 estimates
Loading required package: car
Loading required package: mvtnorm
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