[R] nls() and nls2() behavior?

[Walmes Zeviani]

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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..(....)... 0ooo...                              Walmes Zeviani
...\..(.....(.....)...     Master in Statistics and Agricultural
Experimentation
....\_)..... )../....       walmeszeviani at hotmail.com, Lavras - MG, Brasil
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(_/............................................................................................
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