similar to: problem with ML estimation

Hi, I have a bivariate longitudinal dataset. As an example say, i have the data frame with column names var1 var2 Unit time trt (trt represents the treatment) Now suppose I want to fit a joint model of the form for the *i* th unit var1jk = alpha1 + beta1*timejk + gamma1* trtjk + delta1* timejk:trtjk + error1jk var2 = alpha2 + beta2*timejk + gamma2* trtjk + delta2* timejk:trtjk +

Does anybody know of a function that implements the derivative (gradient) of the multivariate normal density with respect to the *parameters*? It?s easy enough to implement myself, but I?d like to avoid reinventing the wheel (with some bugs) if possible. Here?s a simple example of the result I?d like, using numerical differentiation: library(mvtnorm) library(numDeriv) f=function(pars, xx, yy)

Dear R-help, I'm using the 'optim' functions to minimise functions, and have read the documentation, but I'm still not sure how to determine sensible values to use for the 'fnscale' and 'parscale' options. If I have understood everything correctly, 'fnscale' should be used to scale the objective function, so that for example if the default is

Hi, I??m trying to use the Win2BUGS package from R and I have a similar problem that reurns with the message: Error in FUN(X[[1]], ...) : symbol print-name too long But, there is no stray ` character in the file ( Sugestions given by: Duncan Temple Lang <duncan> Date: Mon, 26 Sep 2005 07:31:08 -0700 ) The progam in R is: library(R2WinBUGS) library(rbugs) dat <-

Dear All- I am trying to merge two data files - they have different date formats and different times zones. I need to match up the date/time of the datasets and then invoke a conditional statement, such as: if dataC$mph is >= 12 then keep dataM$co23 for the corresponding time/date stamp. snippets of data files: *dataC.txt* LST in mph Deg DegF DegF2 % volts Deg

Dear knowing people, Dennis Murphy helped me a lot with my first loop last week. Thanks again - I could have made more than 10 "Thank-You cakes" in the time it saved me! But now I want to complicate the thing. My ideas didn't work. Let's see if anyone is smarter ;-) The following packages are needed: library(adehabitatHR) library(rgdal) library(plyr) # My dataframe looks

Dear all, I'm trying to get a Brownian bridge kernel (kernelbb) for each combination of two consecutive animal locations (see commands below) and put them, with a loop, inside a list. It works well at the beginning but after 42 runs, it appears the following warning : >Error in seq.default(yli[1], yli[2], by = diff(xg[1:2])) :  >  invalid (to - from)/by in seq(.) I looked at the

Hi, Using indicator variables I have been able to fit and run the code for fitting a bivariate mixed model using unstructured covariance matrix The code is lme.fit1<- lme(one.var~-1+indic1+indic2+I(indic1*d.time)+I(indic2*d.time), random =~ -1+indic1+indic2|m.unit, weights = varIdent(~1|indic1) ,data = new.data) My variables are one.var :- the two response variables stacked one after

libswfdec/swfdec_bits.c | 27 +- libswfdec/swfdec_swf_decoder.c | 46 ---- test/Makefile.am | 19 + test/swfedit.c | 81 +++++++ test/swfedit_file.c | 209 ++++++++++++++++++ test/swfedit_file.h | 59 +++++ test/swfedit_tag.c | 61 +++++ test/swfedit_tag.h | 56 ++++ test/swfedit_token.c |

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

I'm having trouble with the standard deviation distribution as shown on http://mathworld.wolfram.com/StandardDeviationDistribution.html . (Eric Weisstein references Kenney and Keeping 1951, which I can't check.) I believe the graphs they show, but when I code the function in R, according to the listed formula, I get very different graphs. Would someone please point out my error or tell

Hello I'm new to R (one week) so please excuse any obvious mistakes in my code or posting. I am attempting to fit a non linear function defining the relationship between dependent variable A and the variables PAR and T grouped by the condition Di. The following steps are taken in the Rcode below: 1) load the data (not shown) 2) define the function to be fit 3) define the starting values

R-users E-mail: r-help@r-project.org Hi! R-users. I am just wondering what the definition of "dffits" in R language is. Let me show you an simple example. function() { library(MASS) xx <- c(1,2,3,4,5) yy <- c(1,3,4,2,4) data1 <- data.frame(x=xx, y=yy) lm.out <- lm(y~., data=data1, x=T) lev1 <- lm.influence(lm.out)$hat sig1 <-

Hello all, I wanted to get some feedback on this patch for ScalarEvolution. It addresses a performance problem I am seeing for simple benchmark. Starting with this C code: 01: signed char foo(void) 02: { 03: const int count = 8000; 04: signed char result = 0; 05: int j; 06: 07: for (j = 0; j < count; ++j) { 08: result += (result_t)(3); 09: } 10: 11: return result; 12: } I

Hi all, I have the output of summary() of an lme object called "lme.exp1", for example ############################################# > summary(lme.exp1) Linear mixed-effects model fit by REML Data: DATA Log-restricted-likelihood: -430.8981 Fixed: fixed.exp1 .... Random effects: Formula: ~-1 + mu1 + log.sig1 | animID Structure: Diagonal mu1 log.sig1

I'm using R 2.0.1 under Windows XP. I get the following message when I run the code listed below. I don't seem to have any problems using the function "slice" outside "integrate". Error in integrate(slice, 0, Inf, , , , , , , a, b) * delta : non-numeric argument to binary operator [ By the way, I'm trying to evaluate a two-dimensional integral by

Dear R guru, I am Saikat Sarkar working as a researcher of Economics in Tampere University, Finland. I am trying to estimate some Garch related tests with Bayesian analysis by R programme. I am not good in R but trying to survive. Anyway I have the coding but not working properly. I have tried to find the problem but failed. I am writing to all R gurus to help me out. Could you please look at

Would anyone be able to provide insight for the following question, please? Setting: estimation of prediction intervals for age-period-cohort models using GAMs (rate ~ s(age,period)) Method: bootstrap (Davison and Hinkley, 1997) Issue: standardisation of the residuals for resampling requires an adjustment using the diagonals of the hat matrix. Is there a simple way to get the hat values out of a