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 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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