[R] Help MLE
Dear,
I'm starting on R language. I would like some help to implement a MLE
function.
I wish to obtain the variables values (alpha12, w_g12, w_u12) that maximize
the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12).
Following the code:
rm(list=ls())
ls()
library(stats4)
Model = function(alpha12,w_g12,w_u12)
{
Y = 1
u = 0.5
g = -1
Y*log(alpha12 + g*w_g12 + u*w_u12)
}
res = mle(minuslog=Model,start=list(alpha12=0.1,w_u12=0.1,w_g12=0.1),
method = "BFGS")
res
Error message:
> res = mle(minuslog=Model,start=list(alpha12=0.1, w_u12=0.1, w_g12=0.1),
method = "BFGS")
Error in optim(start, f, method = method, hessian = TRUE, ...) :
non-finite finite-difference value [1]
Thanks,
LF
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Re: [R] Help MLE
Dear Ben Bolker,
Thanks a lot for your help.
I have two more questions:
1) My goal is maximize the function (> r = Y*log(comb)) but the
parameters found, minimized the function (r = Y*log(comb)).
2) What this function do?
> model2 <- function(p) {
> do.call("Model",as.list(p))
> }
Best regards,
LFRC
Ben Bolker wrote:
>
> LFRC yahoo.com.br> writes:
>
>>
>>
>> Dear,
>>
>> I'm starting on R language. I would like some help to implement a MLE
>> function.
>>
>> I wish to obtain the variables values (alpha12, w_g12, w_u12) that
>> maximize
>> the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12).
>>
>
>
> You're running into a problem because your linear combination
> goes negative in the course of the optimization, which makes
> the logarithm NaN, which crashes the optimizer. The quick
> hack would be to calculate the linear combination and then
> set it to max(value,some_small_positive_number) -- this will
> (probably) work if the solution is not on the boundary.
> Alternatively you can use constrOptim (see below),
> because you need to impose a linear inequality constraint.
>
> rm(list=ls())
> ls()
> library(stats4)
>
> Model = function(alpha12,w_g12,w_u12)
> {
> Y = 1
> u = 0.5
> g = -1
> comb = alpha12 + g*w_g12 + u*w_u12
> r = Y*log(comb)
> cat(alpha12,w_g12,w_u12,comb,log(comb),r,"\n") ## debug
> r
> }
>
> res = mle(minuslog=Model,start=list(alpha12=0.1,w_u12=0.1,w_g12=0.1),
> method = "BFGS")
>
> model2 <- function(p) {
> do.call("Model",as.list(p))
> }
>
> constrOptim(theta=c(0.1,0.1,0.1),f=model2,
> grad=NULL,
> ui = c(1,-1,0.5),
> ci = rep(0,3))
>
>
> Ben Bolker
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
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Re: [R] Help MLE
Thanks again.
Now I want to complicate the model. Supose that Y is a vector with 30 data.
Y = c(0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1, 0)
Then the r = sum(Y*log(comb))
Is it possible? I want to discover the variables values
(alpha12,w_g12,w_u12) for each Y that optimize the function r.
The new code is:
Model = function(alpha12,w_g12,w_u12)
{
Y = c(0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0)
u = 0.5
g = -1
comb = alpha12 + g*w_g12 + u*w_u12
r = sum(Y*log(comb))
cat(alpha12,w_g12,w_u12,comb,log(comb),r,"\n") ## debug
r
}
res = mle(minuslog=Model,start=list(alpha12=0.1,w_u12=0.1,w_g12=0.1),
method = "BFGS")
model2 <- function(p) {
do.call("Model",as.list(p))
}
constrOptim(theta=c(0.1,0.1,0.1),f=model2,
grad=NULL,
ui = c(1,-1,0.5),
ci = rep(0,3))
ERROR:
Error in optim(start, f, method = method, hessian = TRUE, ...) :
objective function in optim evaluates to length 30 not 1
Best regards,
LFRC
Ben Bolker wrote:
>
> LFRC yahoo.com.br> writes:
>
>>
>>
>> Dear Ben Bolker,
>>
>> Thanks a lot for your help.
>>
>> I have two more questions:
>>
>> 1) My goal is maximize the function (> r = Y*log(comb)) but the
>> parameters found, minimized the function (r = Y*log(comb)).
>>
>
> Oh. Oops. Just change the sign ( r = -Y*log(comb)) or
> set fnscale=-1 (see ?optim)
>
>> 2) What this function do?
>> > model2 <- function(p) {
>> > do.call("Model",as.list(p))
>> > }
>>
>
> I got a little carried away, this is R Magic.
> mle() expects the function to be written out with
> a list of parameters ( func(param1,param2,param3) ),
> while optim and constrOptim want the function to
> have a single vector argument ( func(paramvec) ).
> model2() is a wrapper that converts a vector p
> into a list and passes it to the Model function.
>
> Ben Bolker
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
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[R] MLE Constraints
Dears,
I'm trying to find the parameters (a,b, ... l) that optimize the function
(Model)
described below.
1) How can I set some constraints with MLE2 function? I want to set p1>0,
p2>0,
p3>0, p1>p3.
2) The code is giving the following warning.
Warning: optimization did not converge (code 1)
How can I solve this problem?
Can someone help me?
M <- 14
Y = c(0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1)
x1 = c(0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.25,
0.25, 0.25, 0.25)
x2 = c(-1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
x3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
states = c(1, 1, 2, 3, 1, 2, 3, 1, 1, 2, 2, 3, 1, 1)
prob_fn = rep(0,M)
Model=function(a, b, c, d, e, f, g, h, i, j, k, l)
{
p1 = exp(-(a g*x1 d*x2 j*x3))
p2 = exp(-(b h*x1 e*x2 k*x3))
p3 = exp(-(c i*x1 f*x2 l*x3))
### Set P
t5 = 0
while(t5http://www.nabble.com/MLE-Constraints-tp19994553p19994553.html
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Re: [R] MLE Constraints
Dears,
Any help?
Thanks,
LFRC
LFRC wrote:
>
> Dears,
>
> I'm trying to find the parameters (a,b, ... l) that optimize the function
> (Model)
> described below.
>
> 1) How can I set some constraints with MLE2 function? I want to set p1>0,
> p2>0,
> p3>0, p1>p3.
>
> 2) The code is giving the following warning.
> Warning: optimization did not converge (code 1)
> How can I solve this problem?
>
> Can someone help me?
>
> M <- 14
> Y = c(0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1)
> x1 = c(0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.25,
> 0.25, 0.25, 0.25)
> x2 = c(-1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
> x3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
> states = c(1, 1, 2, 3, 1, 2, 3, 1, 1, 2, 2, 3, 1, 1)
> prob_fn = rep(0,M)
>
> Model=function(a, b, c, d, e, f, g, h, i, j, k, l)
> {
> p1 = exp(-(a g*x1 d*x2 j*x3))
> p2 = exp(-(b h*x1 e*x2 k*x3))
> p3 = exp(-(c i*x1 f*x2 l*x3))
>
> ### Set P
> t5 = 0
> while(t5 {
> t5 = t5 1
>
> if(states[t5]==1) {prob_ok = p1[1]}
> if(states[t5]==2) {prob_ok = p2[1]}
> if(states[t5]==3) {prob_ok = p3[1]}
> prob_fn[t5] = c(prob_ok)
> }
>
> prob_fn[prob_fn==0] = 0.1
>
> ### LL
> ll_calc = -(sum(Y*log(prob_fn)))
> return(ll_calc)
> }
>
> res = mle2(Model, start=list(a=1, b=1, c=1, d=0.15, e=0.15,
> f=0.15, g=0.9, h=0.9, i=0.9, j=0.1, k=0.1, l=0.1), method = "Nelder-
> Mead")
> res
>
> Best regards,
> LFRC
>
>
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