I need to interpolate by groups a large dataframe using the nlm function.
I don't have any problems using it on a df with a single group:
#example data
df <- data.frame(var= cumsum(sort(rnorm(100, mean=20, sd=4))),
time= seq(from=0,to=550,length.out=100))
#create function
my_function <- function(Cini, time, theta,var){
fy <- (theta[1]-(theta[1]- Cini)*exp((-theta[2]/100000)*(time-theta[3])))
ssq<-sum((var-fy)^2)
return(ssq)
}
th.start <- c(77, 148, 5) #set starting parameters
#run nlm
my_fitt <- nlm(f=my_function, Cini=400, var = df$var,
time=df$time, p=th.start)
Then, I tried to apply the function in a df with multiple groups using the dlply function:
#data with groups
df.2 <- data.frame(var= cumsum(sort(rnorm(300, mean=20, sd=4))),
time= rep(seq(from=0,to=1200,length.out=100),3),
groups=rep(c(1:3),each=100))
#run nlm
library(plyr)
my_fitt.2 <- dlply(df.2, .(groups),
nlm(f=my_function, Cini=400, var = df.2$var,time=df.2$time, p=th.start))
However I get the message: Error in fs[[i]](x, ...) : attempt to apply non-function.
I also tried to remove the df.2$, obtaining Error in time - theta[3] : non-numeric argument to binary operatorin this example, and Error in f(x, ...) : object 'time.clos' not foundin my original df (time.closis one of the variables).
In addition, I thouth to use the dplyr library
library(dplyr)
df.2 %>%
group_by(groups) %>%
nlm(f=my_function, Cini=400, v= var,
time=time, p=th.start)
obtaining Error in f(x, ...) : unused argument (.). What could be the problem?
I can't help much with the tidyverse environment as I'm more a base R kind of guy. I think the problem in your last call is that you're piping a group data.frame to a function that take a function object as first argument. That cannot work.
Let me propose you a base R way of doing it:
df.2 %>%
split(.$groups) %>%
lapply(function(xx) nlm(f=my_function, Cini=400, var = xx$var, time=xx$time, p=th.start))
This produce a list of length 3 (for three groups) with your three results.