Dear All
I am quite new to R and would appreciate some help fitting 95% confidence
intervals to a nls function. I have the data
DOY CET
90 5.9
91 8
92 8.4
93 7.7
95 6.6
96 6.8
97 7.1
98 9.7
99 12.3
100 12.8
102 11
103 9.3
104 9.8
105 9.9
107 7.7
110 6.2
111 5.9
112 5.9
113 3.4
114 3.5
116 3.3
117 5.4
118 6.3
119 9.7
120 11.2
121 7.3
124 7.8
etc
I am trying to use some code that has been previously posted on the help
boards but keep getting an error message "dim(X) must have a positive
length"
plot(DOY, CET)
model<-nls(CET~a+(b*sin(((2*pi)/365)*(DOY+t))),start=list(a=9.5, b=-6.5,
t=65))
summary(model)
days<-seq(0,365,1)
predict(model,list(DOY=days))
se.fit <- sqrt(apply(attr(predict(model,list(DOY = days)),"gradient"),1,
function(x) sum(vcov(fm)*outer(x,x))))
matplot(days, predict(model,list(DOY = days))+
outer(se.fit,qnorm(c(.5, .025,.975))),type="l")
Any help would be greatly appreciated
Best wishes
Tom
--
View this message in context:
http://n4.nabble.com/Confidence-intervals-nls-tp1556487p1556487.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
