Thank you so much Dr. Nash, I truly appreciate your kind and valuable
contribution.
Cheers,
Paul
El El sáb, 19 de ago. de 2023 a la(s) 3:35 p. m., J C Nash <profjcn...@gmail.com
<mailto:profjcn...@gmail.com>> escribió:
Why bother. nlsr can find a solution from very crude start.
Mixture <- c(17, 14, 5, 1, 11, 2, 16, 7, 19, 23, 20, 6, 13, 21, 3, 18, 15,
26, 8, 22)
x1 <- c(69.98, 72.5, 77.6, 79.98, 74.98, 80.06, 69.98, 77.34, 69.99, 67.49,
67.51, 77.63,
72.5, 67.5, 80.1, 69.99, 72.49, 64.99, 75.02, 67.48)
x2 <- c(29, 25.48, 21.38, 19.85, 22, 18.91, 29.99, 19.65, 26.99, 29.49,
32.47,
20.35, 26.48, 31.47, 16.87, 27.99, 24.49, 31.99, 24.96, 30.5)
x3 <- c(1, 2, 1, 0, 3, 1, 0, 2.99, 3, 3, 0, 2, 1, 1, 3, 2,
3, 3, 0, 2)
y <- c(1.4287, 1.4426, 1.4677, 1.4774, 1.4565,
1.4807, 1.4279, 1.4684, 1.4301, 1.4188, 1.4157, 1.4686, 1.4414,
1.4172, 1.4829, 1.4291, 1.4438, 1.4068, 1.4524, 1.4183)
mydata<-data.frame(Mixture, x1, x2, x3, y)
mydata
mymod <- y ~ 1/(Beta1*x1 + Beta2*x2 + Beta3*x3)
library(nlsr)
strt<-c(Beta1=1, Beta2=2, Beta3=3)
trysol<-nlxb(formula=mymod, data=mydata, start=strt, trace=TRUE)
trysol
# or pshort(trysol)
Output is
residual sumsquares = 1.5412e-05 on 20 observations
after 29 Jacobian and 43 function evaluations
name coeff SE tstat pval gradient
JSingval
Beta1 0.00629212 5.997e-06 1049 2.425e-42 4.049e-08
721.8
Beta2 0.00867741 1.608e-05 539.7 1.963e-37 -2.715e-08
56.05
Beta3 0.00801948 8.809e-05 91.03 2.664e-24 1.497e-08
10.81
J Nash
On 2023-08-19 16:19, Paul Bernal wrote:
> Dear friends,
>
> Hope you are all doing well and having a great weekend. I have data that
> was collected on specific gravity and spectrophotometer analysis for 26
> mixtures of NG (nitroglycerine), TA (triacetin), and 2 NDPA (2 -
> nitrodiphenylamine).
>
> In the dataset, x1 = %NG, x2 = %TA, and x3 = %2 NDPA.
>
> The response variable is the specific gravity, and the rest of the
> variables are the predictors.
>
> This is the dataset:
> dput(mod14data_random)
> structure(list(Mixture = c(17, 14, 5, 1, 11, 2, 16, 7, 19, 23,
> 20, 6, 13, 21, 3, 18, 15, 26, 8, 22), x1 = c(69.98, 72.5, 77.6,
> 79.98, 74.98, 80.06, 69.98, 77.34, 69.99, 67.49, 67.51, 77.63,
> 72.5, 67.5, 80.1, 69.99, 72.49, 64.99, 75.02, 67.48), x2 = c(29,
> 25.48, 21.38, 19.85, 22, 18.91, 29.99, 19.65, 26.99, 29.49, 32.47,
> 20.35, 26.48, 31.47, 16.87, 27.99, 24.49, 31.99, 24.96, 30.5),
> x3 = c(1, 2, 1, 0, 3, 1, 0, 2.99, 3, 3, 0, 2, 1, 1, 3, 2,
> 3, 3, 0, 2), y = c(1.4287, 1.4426, 1.4677, 1.4774, 1.4565,
> 1.4807, 1.4279, 1.4684, 1.4301, 1.4188, 1.4157, 1.4686, 1.4414,
> 1.4172, 1.4829, 1.4291, 1.4438, 1.4068, 1.4524, 1.4183)), row.names
=
> c(NA,
> -20L), class = "data.frame")
>
> The model is the following:
> y = 1/(Beta1x1 + Beta2x2 + Beta3x3)
>
> I need to determine starting (initial) values for the model parameters
for
> this nonlinear regression model, any ideas on how to accomplish this
using
> R?
>
> Cheers,
> Paul
>
> [[alternative HTML version deleted]]
>
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