How can I compare nls parameters across groups in R - r

Question

I have a data set like this
iu
sample
obs
1.5625
s
0.312
1.5625
s
0.302
3.125
s
0.335
3.125
s
0.333
6.25
s
0.423
6.25
s
0.391
12.5
s
0.562
12.5
s
0.56
25
s
0.84
25
s
0.843
50
s
1.202
50
s
1.185
100
s
1.408
100
s
1.338
200
s
1.42
200
s
1.37
1.5625
t
0.317
1.5625
t
0.313
3.125
t
0.345
3.125
t
0.343
6.25
t
0.413
6.25
t
0.404
12.5
t
0.577
12.5
t
0.557
25
t
0.863
25
t
0.862
50
t
1.22
50
t
1.197
100
t
1.395
100
t
1.364
200
t
1.425
200
t
1.415
I want to use R to recreate SAS code below. I believe this SAS code means a nonlinear fit is performed for each subsets, where three parameters are the same and one parameter is different.
proc nlin data＝assay;
　model obs＝D＋(A－D)/(1＋(iu/((cs∗(sample＝“S”)
＋Ct∗(sample＝“T”))))∗∗(B));
　parms　D=1　B=1　Cs=1　Ct=1 A=1;
run;
So I write something like this then get
nlm_1 <- nls(obs ~ (a - d) / (1 + (iu / c[sample]) ^ b) + d, data = csf_1, start = list(a = 0.3, b = 1.8, c = c(25, 25), d = 1.4))
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
But without[sample], the model can be calculated
nlm_1 <- nls(obs ~ (a - d) / (1 + (iu / c) ^ b) + d, data = csf_1, start = list(a = 0.3, b = 1.8, c = c(25), d = 1.4))
summary(nlm_1)
Formula: obs ~ (a - d)/(1 + (iu/c)^b) + d
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 0.31590 0.00824 38.34 <2e-16 ***
b 1.83368 0.06962 26.34 <2e-16 ***
c 25.58422 0.55494 46.10 <2e-16 ***
d 1.44777 0.01171 123.63 <2e-16 ***
---
Signif. codes:
0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02049 on 28 degrees of freedom
Number of iterations to convergence: 4
Achieved convergence tolerance: 6.721e-06
I don't get it, could some one tell me what's wrong with my code, and how can I achieve my goal with R? Thanks!

Answer 1

Thanks to #akrun. After I converting csf_1$sample to factor, I finally get what I wanted.
csf_1[, 2] <- as.factor(c(rep("s", 16), rep("t", 16)))
nlm_1 <- nls(obs ~ (a - d) / (1 + (iu / c[sample]) ^ b) + d, data = csf_1, start = list(a = 0.3, b = 1.8, c = c(25, 25), d = 1.4))
summary(nlm_1)
Formula: obs ~ (a - d)/(1 + (iu/c[sample])^b) + d
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 0.315874 0.008102 38.99 <2e-16 ***
b 1.833303 0.068432 26.79 <2e-16 ***
c1 26.075317 0.656779 39.70 <2e-16 ***
c2 25.114050 0.632787 39.69 <2e-16 ***
d 1.447901 0.011518 125.71 <2e-16 ***
---
Signif. codes:
0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02015 on 27 degrees of freedom
Number of iterations to convergence: 4
Achieved convergence tolerance: 6.225e-06