baxterj wrote:
I downloaded the package and got it to work with the coding:
model <- function(df) {aov(values ~ WellID, data = twelldata)}
Hmm.. I guess you mean to use 'data = df' instead of 'data = twelldata'
ANOVA1way <- dlply(twelldata, .(Analyte), model)
print(ANOVA1way)
This gives me degrees of freedom and sum of squares for each anova per
analyte. However, I cant get the summary(ANOVA1way) to work so that I can
get p-values, etc... How can I do this?
You need to extend your model function:
- extract the bits you want
- construct a result data frame
- return it
and use a ddply
For example (non-tested):
model <- function(df) {
lmObj <- lm(values ~ WellID, data = df)
summaryLmObj <- summary(lmObj)
res <- data.frame(intercept = coef(lmObj)[1],
adjr2 = summaryLmObj$adj.r.squared) # extract and insert anything
return(res)
}
(ANOVA1way <- ddply(twelldata, .(Analyte), model))
HTH,
Tobias
Tobias Verbeke-2 wrote:
baxterj wrote:
I have a simple 1 way anova coded like
summary(ANOVA1way <- aov(Value ~ WellID, data = welldata))
How can I use the BY function to do this ANOVA for each group using
another
variable in the dataset?? I tried coding it like this, but it doesn't
seem
to work.
summary(ANOVA1way <- by(welldata, Analyte, function(x) aov(Value ~
WellID,
data = welldata)))
In SAS I would code it like this:
Proc sort data=welldata; by analyte; run;
Proc glm data=welldata;
by analyte;
class wellid;
model value = wellid;
run;
Look at the plyr package for a general solution to
this type of problems:
http://cran.r-project.org/web/packages/plyr/index.html
and its introductory guide on the package home page:
http://had.co.nz/plyr/
HTH,
Tobias
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