What suggestions would you have for this? Or, more precisely, how would you create multiple graphs from subsequent columns of a data.frame?
Thomas Lumley wrote:
On Tue, 4 May 2004, Liaw, Andy wrote:
1. If your code actually runs, you should upgrade R, and quit using `_' for assignment... 8-)
2. You seem to have an extraneous `]' after the na.exclude. Could that be
the problem?
More seriously, the for() loop over k will mess up the value of k that you want to use for lm.
-thomas
Andy
From: Christoph Scherber
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:
a data frame,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
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 NA´s 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 NA´s 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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Thomas Lumley Assoc. Professor, Biostatistics [EMAIL PROTECTED] University of Washington, Seattle
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