On 11/15/2012 06:10 AM, Sarah Goslee wrote:
Hi,
Your reproducible example isn't reproducible for me, as I get an error message.
But regardless, the easiest thing to do is write your own wrapper,
something like:
mycenfit <- function(x, y) {
result <- cenfit(x, y)
c(result, sum = result$n * result$mean)
}
No need to change anything in the package itself. Incidentally, using
attach() is generally a really bad idea. with() is much safer.
Sarah
On Thu, Nov 15, 2012 at 7:00 AM, maxbre <mbres...@arpa.veneto.it> wrote:
I want to get access to the code of an S4 method in order to possibly modify
a function to accomplish my particular needs: in my case the function in is
cenfit() from the package NADA
Now, given my reproducible example:
my.ex<-structure(list(TEC = c(0.21, 0.077, 0.06, 0.033, 0.014, 0.0072), LR =
c(0L, 0L, 1L, 0L, 1L, 0L)), .Names = c("TEC", "LR"), class = "data.frame",
row.names = c(NA, -6L))
and the following few lines of code:
library(“NADA)
attach(my.ex)
cenfit(TEC,LR)
giving this output:
n n.cen median mean sd
6.00 2.00 0.033 0.058 0.083
actually, cenfit() returns an object
> xx = cenfit(my.ex$TEC, as.logical(my.ex$LR))
and what you're seeing is the result of the object's 'show' method
> xx
n n.cen median mean sd
6.00000000 2.00000000 0.03300000 0.05836667 0.08350178
You can see the body of the show method with
selectMethod(show, class(xx))
and methods that are available to work on xx with
showMethods(class=class(xx), where=search())
Sarah is right that you'd likely want to write your own function, using a
combination of available methods, e.g.,
censtats <- function(x) {
s = summary(x)
c(n = nrow(s), n.cen = nrow(s) - sum(s$n.event), median = median(x),
mean = mean(x)[["mean"]], sd = sd(x))
}
Martin
I would like to add one more result to the above, namely “sum”, very simply
computed as the product of “n” times “mean”
Do you think that is possible or convenient?
I’ve been reading that editing a S4 methods needs particular attention as by
using showMethods("cenfit") and then getMethod("cenfit"); in fact, I’ve been
trying that but without much success (i.e. understanding of the cumbersome
output)
can anyone give me some help on how is probably better to proceed in order
to get my new result?
different alternative solutions or hints or advices are also more than
welcomed
(I’m pretty new on R and specially on the field of function handling and
customizing)
all the best
max
--
Sarah Goslee
http://www.functionaldiversity.org
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