1. There should have been warning or error when using "_" as it is
depreceated. Use "<-" instead.
2. There is an extra "]"
3. Could it be that after removing all the cases with NA, you do not
have sufficient observations. Example :
> j <- c(NA, 2, NA, 4, NA)
> k <- c(1, NA, 3, NA, 5)
> lm(j~k,na.action=na.exclude)
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
0 (non-NA) cases
4. I would suggest you put a 'cat(i, "before", "\n") or something
similar before and after the problematic syntax to find out which values
of i is causing the problem.
On Tue, 2004-05-04 at 14:55, Christoph Scherber wrote:
> actually, the situation is much more complicated. I am producing
> multiple graphs within a "for" loop. For some strange reason, the
> plotting routine always stops once lm(y~x) encounters more than one
> missing value (I have marked the important bit with "***********"):
>
> par(mfrow=c(5,5))
> p_seq(3,122,2)
> i_0
> k_0
> number_0
> for (i in p) {
> j_foranalysis[93:174,i+1]
> k_foranalysis[93:174,i]
> df_data.frame(j,k)
> mainlab1_substring(names(foranalysis[i]),2,8)
> mainlab2_"; corr.:"
> mainlab3_round(cor(j,k,na.method="available"),4)
> mainlab4_"; excl.Mono:"
> mainlab5_round(cor(j[j<0.9],k[j<0.9],na.method="available"),4)
> mainlab_paste(mainlab1,mainlab2,mainlab3,mainlab4,mainlab5)
> plot(k,j,main=mainlab,xlab="% of total biomass",ylab="% of total
> cover",pch="n")
> for (k in 1:length(foranalysis[93:174,i]))
> number[k]_substring(plotcode[foranalysis[k,1]],1,5)
> text(foranalysis[93:174,i],foranalysis[93:174,i+1],number)
> **********************************
> model_lm(j~k,na.action=na.exclude])
> **********************************
> abline(model)
> abline(0,1,lty=2)
> }
>
> Does anyone have any suggestions on this?
>
> Best regards
> Chris.,
>
>
>
>
> Liaw, Andy wrote:
>
> >By (`factory') default that's done for you automagically, because
> >options("na.action") is `na.omit'.
> >
> >If you really want to do it `by hand', and have the data in a data frame,
> >you can use something like:
> >
> >lm(y ~ x, df[complete.cases(df),])
> >
> >HTH,
> >Andy
> >
> >
> >
> >>From: Christoph Scherber
> >>
> >>Dear all,
> >>
> >>I have a data frame with different numbers of NAÂs in each
> >>column, e.g.:
> >>
> >>x y
> >>1 2
> >>NA 3
> >>NA 4
> >>4 NA
> >>1 5
> >>NA NA
> >>
> >>
> >>I now want to do a linear regression on y~x with all the NAÂs
> >>removed.
> >>The problem now is that is.na(x) (and is.na(y) obviously
> >>gives vectors
> >>with different lengths. How could I solve this problem?
> >>
> >>Thank you very much for any help.
> >>
> >>Best regards
> >>Chris
> >>
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> >>
> >>
> >>
> >
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