ANOVA between objects in two lists
Question
I want to perform a ANOVA between objects that are stored in two separate lists, however rather then performing them one at a time like this
> anova(output.02[[1]], output.03[[1]])
Model df AIC BIC logLik Test L.Ratio p-value
output.02[[1]] 1 9 11221.77 11279.72 -5601.884
output.03[[1]] 2 13 11222.90 11306.60 -5598.450 1 vs 2 6.868822 0.143
> anova(output.02[[2]], output.03[[2]])
Model df AIC BIC logLik Test L.Ratio p-value
output.02[[2]] 1 9 10976.36 11034.31 -5479.182
output.03[[2]] 2 13 10974.90 11058.60 -5474.449 1 vs 2 9.465378 0.0505
I would like to use a loop to perform an ANOVA between the objects in each list. I tried using the mapply function however the output did not produce the results I was expecting.
> mapply(anova, output.02, output.03)
zimmrec zdelrec zdigiback zspotword zsdmt zglobcog zmmse
call factor,2 factor,2 factor,2 factor,2 factor,2 factor,2 factor,2
Model Integer,2 Integer,2 Integer,2 Integer,2 Integer,2 Integer,2 Integer,2
df Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2
AIC Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2
BIC Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2
logLik Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2
Test factor,2 factor,2 factor,2 factor,2 factor,2 factor,2 factor,2
L.Ratio Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2
p-value Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2 Numeric,2
Any suggestions on how I can do this?
Thanks
Edit: Reproducible example
attach(Orthodont)
set.seed(1234)
#example response variables
Orthodont$v1 <- rnorm(n=108, mean=20, sd=1)
Orthodont$v2 <- rnorm(n=108, mean=31, sd=2.8)
Orthodont$v3 <- rnorm(n=108, mean=15, sd=1.5)
head(Orthodont)
#function to loop the response variables through a lme function
#produces first batch of models
myfunc <- function(X){
lapply(X, function(.col){
y <- .col
out <- with(Orthodont, lme(y ~ age, random = ~ age | Subject, method = "ML", na.action = na.exclude, control = lmeControl(opt = "optim")))
out
})
}
output.02 <- myfunc(Orthodont[5:7]) #first list of models
myfunc2 <- function(X){
lapply(X, function(.col){
y <- .col
out <- with(Orthodont, lme(y ~ age + Sex, random = ~ age | Subject, method = "ML", na.action = na.exclude, control = lmeControl(opt = "optim")))
out
})
}
output.03 <- myfunc2(Orthodont[5:7])# second list of models
#anova for each pair of models
anova(output.02[[1]], output.03[[1]])
anova(output.02[[2]], output.03[[2]])
anova(output.02[[3]], output.03[[3]])
#mapply function
mapply(anova, output.02, output.03)
Solution
Use the SIMPLIFY
parameter:
mapply(anova, output.02, output.03, SIMPLIFY=FALSE)
#$v1
# Model df AIC BIC logLik Test L.Ratio p-value
#dots[[1L]][[1L]] 1 6 324.4204 340.5132 -156.2102
#dots[[2L]][[1L]] 2 7 326.2229 344.9978 -156.1115 1 vs 2 0.1974693 0.6568
#
#$v2
# Model df AIC BIC logLik Test L.Ratio p-value
#dots[[1L]][[2L]] 1 6 524.0956 540.1884 -256.0478
#dots[[2L]][[2L]] 2 7 525.7577 544.5326 -255.8788 1 vs 2 0.337934 0.561
#
#$v3
# Model df AIC BIC logLik Test L.Ratio p-value
#dots[[1L]][[3L]] 1 6 387.4002 403.4930 -187.7001
#dots[[2L]][[3L]] 2 7 389.1333 407.9082 -187.5667 1 vs 2 0.266947 0.6054
OTHER TIPS
You could use sapply
MyRes <- sapply(1:length(output.02), function(x) {
anova(output.02[[x]], output.03[[x]])})
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