This is a bit ugly but I think it works.
================================================
myf <- function(x) 1-pnorm(x,mean(x), sd(x))
results <- apply(test, 2, myf)
mymeans <- apply(test, 2, mean); mymeans
for (i in 1:length(test)){
test[,i][test[,1]>=mymeans[i]] <- NA
}
results[is.na(test)] <- NA
====================================================
--- Jabez Wilson <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I've read in a csv file (test.csv) which gives me
> the following table:
>
> Hin1 Hin2 Hin3 Hin4 Hin5
> Hin6
> HAI1 9534.83 4001.74 157.16 3736.93 484.60 59.25
> HAI2 13272.48 1519.88 36.35 33.64 46.68 82.11
> HAI3 12587.71 5686.94 656.62 572.29 351.60 136.91
> HAI4 15240.81 10031.57 426.73 275.29 561.30 302.38
> HAI5 15878.32 10517.14 18.93 22.00 16.91 21.17
>
> I would like to find a way of finding the 1-pnorm
> of each value in the table based on the mean and sd
> of the data only in the column in which the value
> lies. I can do it using a for loop, but would like
> to know if it can be done using e.g. apply or
> something similar, so that the whole table is
> printed out with the 1-pnorm values.
> 1-pnorm(test[,1],mean([,1]), sd([,1])) gives me
> the values for col1 only, but that's as far as I've
> got.
>
> tia
>
> p.s. I know I'm asking a lot, but ideally, I'd
> like to print out the table with those 1-pnorm
> values only if they are in the right hand tail (i.e.
> >= mean) and if not nothing or NA be written.
>
>
>
> ---------------------------------
>
> [[alternative HTML version deleted]]
>
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>
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