ANOVA on R with different dependent variables

I know so far how to run a ANOVA on R, but I allways have to duplicate the code to run de ANOVA for another variable, I was wondering if I could pass it somehow to the aov() in a loop the names of the variables and store the result of the ANOVA in variables so I don't have to manually change them by copying the code block.

E.G.:

Variables I want to test: Z, Y, X

Categorical Variable: Treatment

VectorVariables = c(Z, Y, X)

for (i in Vector)
AnovaZ <- aov(Z ~ Treatment) #then
AnovaY <- aov(Y ~ Treatment) # and so on..




It is possible in some way??

TukeyHSD

3 Answers
3

There is no need for a for loop! You can simply cbind different response variables together.

for

cbind

Here is an example:

Since you don't provide a sample dataset, I generate some sample data based on the npk dataset, where I add a second response variable yield2 which is the same as yield with some added white noise.

npk

yield2

yield

set.seed(2018)
df <- npk
df$yield2 <- df$yield + rnorm(nrow(df), mean = 0, sd = 5)


Perform ANOVAs based on the two response variables yield and yield2

yield

yield2

res <- aov(cbind(yield, yield2) ~ block, df)
#Call:
# aov(formula = cbind(yield, yield2) ~ block, data = df)
#
#Terms:
# block Residuals
#resp 1 343.295 533.070
#resp 2 905.0327 847.2597
#Deg. of Freedom 5 18
#
#Residual standard errors: 5.441967 6.860757
#Estimated effects may be unbalanced


resp 1 and resp 2 give the sum of squares that you get if you had run aov(yield ~ block, df) and aov(yield2 ~ block, df) individually.

resp 1

resp 2

aov(yield ~ block, df)

aov(yield2 ~ block, df)

So in your case, the command would be something like

res <- aov(cbind(Y, Z) ~ Treatment)


Or if you want to run and store results from separate ANOVAs, store the response variables in a list and use lapply:

list

lapply

lapply(list(Y = "Y", Z = "Z"), function(x)
aov(as.formula(sprintf("%s ~ Treatment", x)), df))


This produces a list of ANOVA results, where every list element corresponds to a response variable.

list

list

If you want to do a loop, the trick is to use as.formula(paste()).

as.formula(paste())

Create a list (we'll call it result) to store each aov output. Then loop through dependent variable names stored in Vector:

result

aov

Vector

n <- length(Vector)
result <- vector(mode="list", length=n)
for(i in 1:n)
result[[i]] <- aov(as.formula(paste(Vector[i], "~ Treament")))



Another solution is to use list columns and purrr::map. This can be useful when working with many models (e.g. see http://r4ds.had.co.nz/many-models.html).

library(tidyverse)

aov_f <- function(df) aov(value ~ carb, data = df)

mtcars_n <- gather(mtcars, obs, value, mpg:gear) %>%
group_by(obs) %>%
nest() %>%
mutate(aov = map(data, aov_f))


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