Dear helpers,
In an attempt to use a loop to generate graphs in a for loop in run into
a problem. The plan is to fill each page with eight graphs (mfrow c(4,2)) in to
two columns. Only the buttom graphs ( meaning every fourth
graph) have tick labels on the x axis to preserve space. I used an if
.... Else statement to achieve that.
The problem is that the first eight graphs are skipped when I run the
loop, the other graphs are fine. However, all graphs can be generated
individually. This indicates that the data sets are fine.
Pulling the results from a nonlinear regression to more than 90 data
sets from a list (resultslist[[i]])
Generating the first derivative and multiply it by 100 to adjust for the
scale.
Here is the code.
pdf('F:/diffnormal_16001a.pdf')
par(mfcol = c(4,2))
for(i in 1: 92){
fit <- NULL
der <- NULL
Zeit <- NULL
Zeit <- seq(0,300)
try(guess <- predict(resultslist_1600[[i]]) )
if(class(guess) == "try-error") {next}
fit <- smooth.spline(Zeit, guess)
der <- 100*(predict(fit, Zeit, deriv = 1))$y
if((i/4)%%1 ==0){par(mar =c(4,4,0, 0) + 0.1)}
else{par(mar =c(0,4,0, 0) + 0.1)}
leg = paste("Data_", i,sep = "")
plot(resultslist_1600[[i]], type = "all", pch = ".", ylab
= "Signal",
log = "", axes = F)
lines(Zeit, der)
if((i/4)%%1 ==0){axis(1, at = seq(0, 360, length = 6), label = c(),
font = 2)}
axis(2, at
pretty(na.omit(eval(parse(text=paste("bleeder1600[,",i,"]",sep
=""))))),
label = c())
mtext(side = 3, leg, line = -2)}
I understand, that I am supposed to submit working code. However, I deal
with a fairly comprehensive data set and I have to generate a large list
using another R package. Thus I explain what it does:
Generate time points(Zeit <- seq(0,300)
Pulling the results from a nonlinear regression to more than 90 data
sets from a list (resultslist_1600[[i]])
Test if prediction can be done
try(guess <- predict(resultslist_1600[[i]]) )
if(class(guess) == "try-error") {next}
(it turned out it can be done for all data set, as I am able to generate
the graphs for each data set from the command line individually.
Generating the first derivative and multiply it by 100 to adjust for the
scale.
fit <- smooth.spline(Zeit, guess)
der <- 100*(predict(fit, Zeit, deriv = 1))$y
The problem appears to be hidden in the mfrow statement.
I spent quite a bit of time on this problem, thus any help or new ideas
would be very much appreciated.
Thank you
Andreas Betz
Scientist
andreasbeyz@earthlink.net
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