similar to: specify constraints in maximum likelihood

Hi guys again, it seems I haven't been doing the maximum likelihood estimation correctly. I quote below, can someone explain to me please what does it mean that the 2nd and 3rd derivatives of the function equals zero and how to compute that in R. "We have our initial estimated, subjective parameters for the gamma mixture and we have our likelihood that is the mixture of negative

Dear R users, I am new to R. I would like to find *maximum likelihood estimators for psi and alpha* based on the following *log likelihood function*, c is consumption data comprising 148 entries: fn<-function(c,psi,alpha) { s1<-sum(for(i in 1:n){(c[i]-(psi^(-1/alpha)*(lag(c[i],-1))))^2* (lag(c[i],-1)^((-2)*(alpha+1)) )}); s2<- sum(for(m in 1:n){log(lag(c[m],-1)^(((2)*alpha)+2))});

Hi everyone! I am using the mle {stats4} to estimate the parameters of distributions by MLE method. I have a problem with the examples they provided with the mle{stats4} html files. Please check the example and my question below! *Here is the mle html help file * http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html

Hello, After putting together interaction code that worked for a single pair of interactions, when I try to evaluate two pairs of interactions( flowers*gopher, flowers*rockiness) my computer runs out of memory, and the larger desktop I use just doesn't go anywhere after about 20 minutes. Is it really that big a calculation? to start: mle2(minuslogl = Lily_sum$seedlings ~ dnbinom(mu = a,

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Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

Hi all, I have some problems with the mle2 function > RogersIIbinom <- function(N0,attackR3_B,Th3_B) {N0-lambertW(attackR3_B*Th3_B*N0*exp(-attackR3_B*(24-Th3_B*N0)))/(attackR3_B*Th3_B)} > RogersII_B <- mle2(FR~dbinom(size=N0,prob=RogersIIbinom(N0,attackR3_B,Th3_B)/N0),start=list(attackR3_B=1.5,Th3_B=0.04),method="Nelder-Mead",data=dat) Error in dbinom(x, size, prob, log)

I've been programming maximum likelihood estimation models using the function "optim." My current research requires modeling a particular parameter as a categorical variable (what R calls a "factor"), not as a continuous parameter. (The research question is, at what level of X does a subject in our experiment choose Y=1 instead of Y=0? So this is a "light switch"

Hi there, When I do bootstrap on a maximum likelihood estimation, I try the following code, however, I get error: Error in minuslogl(alpha = 0, beta = 0) : object "x" not found It seems that mle() only get data from workspace, other than the boot.fun(). My question is how to pass the data to mle() in my case. I really appreciated to any suggestions. Best wishes, Jinsong

Hi - Classic question that I tried to look up online and couldn't find a clear answer. It seems that I can have R to act as a server. But I never know how this works. Would anyone please provide an example or introduction material where I can learn about this? I'm trying to build an environment where computation are distributed/delegated among different servers requested whenever I need.

Hi, I encounter a situation where I have a list whose element is a column matrix. Says, $'1' [,1] 1 2 3 $'2' [,1] 4 5 6 Is there fast way to collapse the list into a matrix like a cbind operation in this case? Meaning, the result should be a matrix that looks like: [,1] [,2] [1,] 1 4 [2,] 2 5 [3,] 3 6 I can loop through all elements and do

Hello I''d like to know if there''s a way to minimize a non linear function in R subject to some linear restrictions Ax=b I have tried to use optim() but it doesn''t seem to be able to do it. Or, by Lagrange multipliers, solve some non linear equations simultaneously. Anyone can help? Thanks -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Hi, Sorry that I have many questions today. I am using svm function on about 180,000 points of training set. It takes very long time to run. However, I would like it to spit out something to make sure that the run is not dead in between. Would you please suggest anyway to do so? And is there anyway to speed up the performance of this svm function? Thank you. - adschai

Hi there, I have a major problem (major for me that is) with solve.QP and I'm new at this. You see, to solve my quadratic program I need to have the lagrange multipliers after each iteration. Solve.QP gives me the solution, the unconstrained solution aswell as the optimal value. Does anybody have an idea for how I could extract the multipliers? Thanx, Serge "Beatus qui prodest quibus

Hi, I have a question around how to build data structure in R. I have to implement a tree structure of data. I'm wondering if R already has something like a tree where I can extend from or I need to start from scratch. If so, what would be the most effective way to represent parent and child node relationship? I realize that R is not based on pointer so it's quite a different paradigm for

Hi - I tried to get some answer before but there seems to have no one response. My question is that I have a class like below definition: setClass("DBManager", representation(channel="RODBC")) My purpose of the conn slot is to hold a channel to database connection which is returned by a command like: channel <- odbcConnect("DB", uid="user",

Hi,I have a result from polr which I fit a univariate variable (of ordinal data) with probit function. What I would like to do is to overlay the plot of my fitted values with the different intercept for each level in my ordinal data. I can do something like:lines(rep(intercept1, 1000), seq(from=0,to=max(fit),by=max(fit)/1000))where my intercept1 is, for example, the intercept that breaks between

Hi - I am using polr. I can get a result from polr fit by calling result.plr <- polr(formula, data=mydata, method="probit"); However, from the 'result.plr', how can I access standard error of the estimated coefficients as well as the t statistics for each one of them? What I would like to do ultimately is to see which coefficients are not significant and try to refit the

Hi, I am using tsls function from sem package to estimate a model which includes large number of data. Among its predictors, it has a nominal data which has about 10 possible values. So I expand this parameter into 9-binary-value predictors with the coefficient of base value equals 0. I also have another continuous predictor. The problem is that, whenever I run the tsls, I will get 'System

Hi I have the code like I show below. The problem here is that I have a setReplacementMethod to set the value of my class slot. However, this function doesn't work when I call it within another function definition (declared by setMethod) of the same class. I do not understand this behavior that much. I'm wondering how to make this work? Any help would be really appreciated. Thank you.