问题
I have a dataset where I want to apply non linear least squares by group. This is a continuation to my previous question: NLS Function - Number of Iterations Exceeds max
The dataset looks like this:
df
x y GRP
0 0 1
426 9.28 1
853 18.5 1
1279 27.8 1
1705 37.0 1
2131 46.2 1
0 0 2
450 7.28 2
800 16.5 2
1300 30.0 2
2000 40.0 2
2200 48.0 2
If I were to do this with one group it would be like this:
df1<-filter(df, GRP==1)
a.start <- max(df1$y)
b.start <- 1e-06
control1 <- nls.control(maxiter= 10000,tol=1e-02, warnOnly=TRUE)
nl.reg <- nls(y ~ a * (1-exp(-b * x)),data=df1,start=
list(a=a.start,b=b.start),
control= control1)
coef(nl.reg)[1]
coef(nl.reg)[2]
> coef(nl.reg)[1]
a
5599.075
> coef(nl.reg)[2]
b
3.891744e-06
I would then do the same thing for GRP2. I want my final output to look like this:
x y GRP a b
0 0 1 5599.075 3.891744e-06
426 9.28 1 5599.075 3.891744e-06
853 18.5 1 5599.075 3.891744e-06
1279 27.8 1 5599.075 3.891744e-06
1705 37.0 1 5599.075 3.891744e-06
2131 46.2 1 5599.075 3.891744e-06
0 0 2 New Value for a GRP2 New Value for b GRP2
450 7.28 2 New Value for a GRP2 New Value for b GRP2
800 16.5 2 New Value for a GRP2 New Value for b GRP2
1300 30.0 2 New Value for a GRP2 New Value for b GRP2
2000 40.0 2 New Value for a GRP2 New Value for b GRP2
2200 48.0 2 New Value for a GRP2 New Value for b GRP2
Ideally I think dplyr would be the best way but I can't figure out how to do it. This is what I think it will probably look like:
control1 <- nls.control(maxiter= 10000,tol=1e-02, warnOnly=TRUE)
b.start <- 1e-06
df %>%
group_by(GRP) %>%
do(nlsfit = nls( form = y ~ a * (1-exp(-b * x)), data=.,
start= list( a=max(.$y), b=b.start),
control= control1) ) %>%
list(a = coef(nlsfit)[1], b = coef(nlsfit)[2])
Error:
in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates
Not really sure how to do this though and any help would be great. Thanks!
回答1:
I initially got the same error message (re: not finding object 'y' in nls
) as I did with a tidyverse
stab when initially attempting to use the lapply-split-function
paradigm and went searching for: "[r] using nls inside function". I've changed my original use of attach
to list2env
:
sapply( split( df , df$GRP), function(d){ dat <- list2env(d)
nlsfit <- nls( form = y ~ a * (1-exp(-b * x)), data=dat, start= list( a=max(y), b=b.start),
control= control1)
list(a = coef(nlsfit)[1], b = coef(nlsfit)[2])} )
#---
1 2
a 14.51827 441.5489
b 2.139378e-06 -6.775562e-06
You also get warnings that you were expecting. These could be suppressed with suppressWarnings( ... )
One of the suggestions was to use attach
. Which I then did with extreme reluctance, since I have often warned newbies not to ever use attach
. But here it seemed to force a local environment to be constructed. I'm more comforatable with list2env as a mechaism to satisfy nls. The top of the code for nls
was what led me to that choice:
if (!is.list(data) && !is.environment(data))
stop("'data' must be a list or an environment")
来源:https://stackoverflow.com/questions/52710549/nls-function-by-group