I think the problem with nls() is the model specification. Look:
> y <- c(0.4334,0.3200,0.5848,0.6214,0.3890,0.5233,0.4753,
+ 0.2104,0.3240,0.2827,0.3847,0.5571,0.5432,0.1326,0.3481)
> x <- c(0.3521,0.4334,0.3200,0.5848,0.6214,0.3890,0.5233,
+ 0.1379,0.2104,0.3240,0.3404,0.3847,0.5571,0.5432,0.1326)
> dummy <- rep(c("m1","m2","m3"), c(7,3,5))
>
> d <- data.frame(y=y, x=x, dummy=dummy)
>
> n0 <- nls(y~b0[dummy]+b1*x, data=d,
+ start=list(b0=c(1,1,1), b1=1))
> summary(n0)
Formula: y ~ b0[dummy] + b1 * x
Parameters:
Estimate Std. Error t value Pr(>|t|)
b01 0.48616 0.13993 3.474 0.00520 **
b02 0.27626 0.09970 2.771 0.01820 *
b03 0.39994 0.12597 3.175 0.00884 **
b1 -0.01735 0.28352 -0.061 0.95230
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1331 on 11 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 2.713e-07
I don't know abou nls2().
Walmes.
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..ooo0
...................................................................................................
..(....)... 0ooo... Walmes Zeviani
...\..(.....(.....)... Master in Statistics and Agricultural
Experimentation
....\_)..... )../.... walmeszevi...@hotmail.com, Lavras - MG, Brasil
............
(_/............................................................................................
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