Dear R users,
Can I fit nls.lm to a non-continuous date data. looked at previous
examples but still not able to fit the model to my data. There are 25
rows of observations as below;
df <- data.frame(Date=as.Date(rownames(df),'%m/%d/%Y'),Y=df$height)
df$days <- as.numeric(df$Date - df[1,]$Date)
head(df)
Date Y days
1 2009-12-01 0.2631250 0
2 2010-01-08 0.4436012 38
3 2010-02-04 0.7151786 65
4 2010-03-03 1.1379762 92
5 2010-04-05 1.7986866 125
6 2010-05-04 2.2982635 154
The following code, tried different values of the list, did not work.
f <- function(parS, xx) {parS$a + parS$b*log(parS$tc - xx)}
resids <- function(p, observed, xx) {df$Y - f(p,xx)}
nls.out <- nls.lm(par=list(a=1,b=-0.001,tc=25), fn = resids,
observed = df$Y, xx = df$days)
and the following error is always produced.
Warning messages:
1: In log(parS$c - xx) : NaNs produced
2: In log(parS$c - xx) : NaNs produced
3: In log(parS$c - xx) : NaNs produced
4: In log(parS$c - xx) : NaNs produced
5: In log(parS$c - xx) : NaNs produced
then the nls.out should integrated in nls.final as proposed by jlhoward
# use output of L-M algorithm as starting estimates in nls(...)
par <- nls.out$par
nls.final <- nls(Y~a+b*log(tc-days),data=df,
start=c(a=par$a, b=par$b, tc=par$tc))
summary(nls.final) # display statistics of the fit
# append fitted values to df
df$pred <- predict(nls.final)
Also attempting to fit a selfstart model
a_start<-8
b_start<-2*log(2)/a_start
m<-nls(df$Y~a*exp(-b*df$days),start=list(a=a_start,b=b_start))
Error in nls(df$Y ~ a * exp(-b * df$days), start = list(a = a_start, b
= b_start)) :
parameters without starting value in 'data': Y, days
Could you please help
Thank you
Ahmed Attia, Ph.D.
Agronomist & Soil Scientist
