similar to: trouble using optim for maximalisation of 2-parameter function

Hi I need help figure out how to fix my code. When I call into R >optimize(llik,init.params=F) I get this error message ####Error in optimize(llik, init.params = F) : element 1 is empty; the part of the args list of 'min' being evaluated was: (interval)#### My data and my code looks like below. R_j R_m 0.002 0.026567296 0.01 0.003194435 . . . . . . . . 0.0006

As part of carrying out a complicated maximum likelihood estimation, I am trying to learn to program likelihoods in R. I started with a simple probit model but am unable to get the code to work. Any help or suggestions are most welcome. I give my code below: ************************************ mlogl <- function(mu, y, X) { n <- nrow(X) zeta <- X%*%mu llik <- 0 for (i in 1:n) { if

Hi I have developed the code below. I am worried that the parameters I want to be estimated are "not being found" when I ran my code. Is there a way I can code them so that R recognize that they should be estimated. This is the error I am getting. > out1=optim(llik,par=start.par) Error in pnorm(au_j, mean = b_j * R_m, sd = sigma_j) : object 'au_j' not found #Yet

Hi May you help me correct my loop function. I want optim to estimates al_j; au_j; sigma_j; b_j by looking at 0 to 20, 21 to 40, 41 to 60 data points. The final result should have 4 columns of each of the estimates AND 4 rows of each of 0 to 20, 21 to 40, 41 to 60. ###MY code is n=20 runs=4 out=matrix(0,nrow=runs) llik = function(x) { al_j=x[1]; au_j=x[2]; sigma_j=x[3]; b_j=x[4]

Hi I have a code that calculate maximisation using optimx and it is working just fine. I want to extend the code to run several colomns of R_j where j runs from 1 to 200. If I am to run the code in its current state, it means I will have to run it 200 times manually. May you help me adjust it to accomodate several rows of R_j and print the 200 results. ***Please do not get intimidated by the

Good Afternoon, I am relatively new to R and have been trying to figure out how to estimate regression coefficients using the MLE method. Some background: I am trying to examine scenarios in which certain estimators might be preferred to others, starting with MLE. I understand that MLE will (should) produce the same results as Ordinary Least Squares if the assumption of normality holds. That

I have had some weird results using the optim() function. I wrote a probit likelihood and wanted to run it with optim() with simulated data. I did not include a gradient at first and found that optim() would not even iterate using BFGS and would only occasionally work using SANN. I programmed in the gradient and it iterates fine but the estimates it returns are wrong. The simulated data work

Hello, the more general thing I'd like to learn here is how to compute Function of Data on the basis of grouping determiend by n variables. In terms of the reason why I am interested in this, I need to compute the average of my data based on the value of the month and day across years. I have come up withy the code below which, as far as I can see, does what I need but getting either a more

Hello, I have a classification tree. I want to obtain a ROC curve for this test. What is the easiest way to obtain one? -Anjali --------------------------------- [[alternative HTML version deleted]]

Hello all, Is AIC calculated incorrectly in lmer? It appears as though it uses AIC = -2*logLik - 2*#parms, instead of -2*LogLik + 2*#parms? Below is output from one of many models I have tried: Generalized linear mixed model fit using PQL Formula: cswa ~ pcov.ess1k + (1 | year) Data: ptct50.5 Family: poisson(log link) AIC BIC logLik deviance 224.8466 219.19 -114.4233 228.8466

Hi , Is there any inbuilt functions to check whether a substring is present in a string and give the result as boolean Thanks -- View this message in context: http://r.789695.n4.nabble.com/String-Manipulation-in-R-tp4633104.html Sent from the R help mailing list archive at Nabble.com.

Hello, I'm using optim to program a set of mle regression procedures for non-normal disturbances. This is for teaching and expository purposes only. I've successfully programmed the normal, generalized gamma, gamma, weibull, exponential, and lognormal regression functions. And optim returns reasonable answers for all of these compared with the identical optimization problems in STATA and

Hello, I would like to run a correlation on values in a table that has 4 rows and 136 columns. However, I am only interested in correlating the values in alternating blocks of 15 columns. For example, I would like to correlate the values in row 1, columns 2-16:31-46:61-76:91-106 with values in row 2, columns 2-16:31-46:61-76:91-106. Is there a way to notate this in the cor command so that the

Hi, I want to convert my MATLAB programs to R studio programs. Kindly guide on the same. [[alternative HTML version deleted]]

Full_Name: Anjali Shitole Version: R 2.5.1 OS: Submission from: (NULL) (202.141.157.91) I want to compute coefficient of variation for each subset of data. Each subset is heterogeneous means number of rows in each subset are different.These all subsets are in one dataset situated one by one. i have used aggregate command to calculate mean for each subset. But same command for calculating sd

Dear R-users, I keep getting an ERROR saying " Error in model.matrix.default(formula, mframe) : model frame and formula mismatch in model.matrix() " when i fit poLCA with more than 63 variables. Below are the details. I am trying to do a Latent Class Analysis using poLCA. My data set contains binary scores of, for instance, 200 students on 100 items. These numbers could even be more

I've got a problem with "mcmcsamp" used with glmer objects produced with "lmer" from the lme4 package. When calling mcmcsamp, I get the error Error in if (var(y) == 0) { : missing value where TRUE/FALSE needed This does not occur with all models, but I can't find anything wrong with the dataset. If the error is in my data, can someone tell me what I am looking

I am attempting to use the lars package with a sparse input feature matrix, but the following fails: library(Matrix) library(lars) data(diabetes) attach(diabetes) x = as(as.matrix(as.data.frame(x)), 'dgCMatrix') lars(x, y, intercept = FALSE) Error in scale.default(x, FALSE, normx) : > > length of 'scale' must equal the number of columns of 'x' > > More

>>>>> Michael Chirico <michaelchirico4 at gmail.com> >>>>> on Tue, 27 Feb 2018 20:18:34 +0800 writes: Slightly amended 'Subject': (unimportant mistake: a dgeMatrix is *not* sparse) MM: modified to commented R code, slightly changed from your post: ## I am attempting to use the lars package with a sparse input feature matrix, ## but the following

Does anyone know where I can download the latest version of Sweave.sty? I have looked all over the site http://www.stat.umn.edu/~charlie/Sweave/ with no luck.