Hi,
I make an ajust using NLS.
The ajust is:
> library(nls)
> modelo <- nls(y~a-b*exp(-c*x),start=list(a=100,b=90,c=0.1609))
> summary(modelo)
Formula: y ~ a - b * exp(-c * x)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 115.2527 2.9139 39.55 < 2e-16 ***
b 118.6876 7.8925 15.04 < 2e-16 ***
c 0.1235 0.0171 7.22 2.44e-09 ***
---
Signif. codes: 0 `***'' 0.001 `**'' 0.01 `*'' 0.05
`.'' 0.1 ` '' 1
Residual standard error: 13.21 on 51 degrees of freedom
Correlation of Parameter Estimates:
a b
b 0.0784
c -0.6587 0.4612
My doubt is about the model significance, I read in list some questions about
the nonsense of this, but I need of the model significance and the R^2.
I make this:
modelonulo <- lm(y~1)
> anova(modelo,modelonulo,test="F")
Analysis of Variance Table
Model 1: y ~ a - b * exp(-c * x)
Model 2: y ~ 1
Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
1 51 8897
2 53 59008 -2 -50111 143.62 < 2.2e-16 ***
---
Signif. codes: 0 `***'' 0.001 `**'' 0.01 `*'' 0.05
`.'' 0.1 ` '' 1
And the R^2 is:
> 50111/59008
[1] 0.8492238
It is """correct"""?
Thanks
Ronaldo
--
Sutil como gato que vai pegar passarinho.
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