Description Author(s) References See Also Examples
Exam4.1 REML vs ML criterion is used keeping block effects random
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 | DataSet4.1$trt <- factor(x = DataSet4.1$trt)
DataSet4.1$block <- factor(x = DataSet4.1$block)
##---REML estimates on page 138(article 4.4.3.3)
library(lme4)
Exam4.1REML <-
lmer(
formula = y~ trt +( 1|block )
, data = DataSet4.1
, REML = TRUE
# , control = lmerControl()
, start = NULL
# , verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = NULL
, devFunOnly = FALSE
# , ...
)
VarCompREML4.1 <-
VarCorr(x = Exam4.1REML
# , sigma = 1
# , ...
)
print(VarCompREML4.1, comp=c("Variance"))
##---ML estimates on page 138(article 4.4.3.3)
Exam4.1ML <-
lmer(
formula = y ~ trt + (1|block)
, data = DataSet4.1
, REML = FALSE
# , control = lmerControl()
, start = NULL
# , verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = NULL
, devFunOnly = FALSE
# , ...
)
VarCompML4.1 <-
VarCorr(x = Exam4.1ML
# , sigma = 1
# , ...
)
print(VarCompML4.1,comp=c("Variance"))
Exam4.1.lm <-
lm(
formula = y~ trt + block
, data = DataSet4.1
# , subset
# , weights
# , na.action
, method = "qr"
, model = TRUE
# , x = FALSE
# , y = FALSE
, qr = TRUE
, singular.ok = TRUE
, contrasts = NULL
# , offset
# , ...
)
summary(anova(object = Exam4.1.lm))
|
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