There probably is a better way to do this but I'd
suggest getting an overall total and then splitting
the data.frame into it's 3 component parts then apply
the equations below to get the sums and the number of
missing values for each subject over all four
data.frames.
================================================apply(dd, 1, sum, na.rm=TRUE)#
sum of responses
count.NAS=function(x) length(which(is.na(x))) #from
J. Valbuena
apply(dd,1,count.NAS) # number of NA's per subject
=================================
Then prorate for each of the four data.frames.
I hope this makes sense.
--- Bob Green <bgreen at dyson.brisnet.org.au> wrote:
> Hello,
>
> I am hoping for some advice as to how I can prorate
> a number of scale
> items that comprise a score. At least 69 of 159
> cases have at least 1
> value missing (65 cases have H7 missing). The
> maximum number of missing is
> 5.
>
> I want to compute a total score, a score for the H
> items, the R items and
> C items.
>
> H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, C1, C2, C3,
> C4, C5, R1, R2, R3,
> R4, R5
>
> I am uncertain as to whether a series of IF
> statements are required or
> some other strategy. Prorating is the method a
> researcher using this scale
> has recommended.
>
> Any advice is appreciated,
>
> Bob Green
>
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