Kristi,
The easiest way to do what you are looking for is the aovSufficient function
in the HH package.
## install.packages("HH") ## if you don't have it yet
library(HH)
B.aov <- aovSufficient(mean ~ site, data=B, sd=B$SE, weights=c(3,3,3,3))
summary(B.aov)
You must have the sample size for each of the groups. Your example did
not include sample sizes, so there is no justification for the degrees
of freedom in the residual.
I invented sizes of 3 observations per group.
See the pulmonary example in ?aovSufficient for a complete example.
There are several style issues that need to be commented on.
It is usually a bad idea to attach a data.frame. In this example, you attached
B and then never used it, and also never detached it. That has the potential
to mask objects farther down the search() list.
When you use aov(), or any other function that takes a data= argument,
it is best to use the data= argument.
Rich
On Fri, Dec 26, 2014 at 9:38 AM, Kristi Glover
<kristi.glo...@hotmail.com> wrote:
> Hi R user,
> I am wondering whether I can perform a simple ANOVA analysis in the data in
> which I have mean + SE (+- Standard Error) for several groups.
> For this one, I calculated upper and lower confidence interval and made
> three classes for each group (mean, upper and lower values). After that, I
> did ANOVA (simple Anova). I am wondering whether this is a wrong approach? I
> have given an example
>
>
> library(reshape)
> B<-structure(list(mean = c(0.0241262, 0.0433538, 0.2204764, 0.7830054
> ), SE = c(0.0209097, 0.0329281, 0.1003248, 0.3019256), site = structure(1:4,
> .Label = c("A",
> "B", "C", "D"), class = "factor")), .Names = c("mean", "SE",
> "site"), class = "data.frame", row.names = c(NA, -4L))
> attach(B)
> B1<-data.frame(B, Upper=mean+1.96*SE, Lower=mean-1.96*SE)
> B2<-subset(B1, select=c(-2))
> B2
> B3<-melt(B2, id=c("site"))
> B3
> Anova<-aov(B3$value~B3$site)
> summary(Anova)
>
> Thanks
>
>
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