similar to: optimization

below is a code to compute hessian matrix , which i need to generate 29 number of different matrices for example first element in x1 and x2 is use to generate let say matrix (M1) and second element in x1 and x2 give matrix (M2) upto  matrix (M29) corresponding to the total number of observations and b1 and b2 are constant.  can some one guide me or help to implement this please. I did not

[[1]]              [,1]      [,2] [1,]  0.003632559 0.2190629 [2,] -2.090772847 0.2190629 [[2]]             [,1]       [,2] [1,] 0.004278991 0.04337005 [2,] 0.190723602 0.04337005 [[3]]            [,1]        [,2] [1,] 0.01237827 -0.01544811 [2,] 0.06452200 -0.01544811 g<-c(1,2,3) function(g,hessianList){    for(i in 1:3){          ft1<-g[2]*hessianList[[2]]          ft2<-sum(ft1)   

Hello everyone! I am currently working with a time series panel data set measuring six dependent variables: 4 of which are binary and 2 of which are count data. I am interested in constructing a model to measure if the dependent variables influence one another. For example: DV1~ DV2 + IV1+IV2+ Controls and DV2~ DV1 + IV1+ IV2+ Controls (where IV stands for independent variable, not

Hi, Sorry to bother you again. I would like to estimate the effect of several categorical factors (two between subjects and one within subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i

# I've tried to make this easy to paste into R, though it's probably so simple you won't need to. # I have some data (there are many more variables, but this is a reasonable approximation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1",

Does anyone know if there is a way to run multivariate linear regression in R? I tried using the lm function (e.g., lm(dv1, dv2~iv1+iv2+iv3), but got error messages. Is my syntax wrong, or do I need a particular package? Thanks, Jeff-- ________________________________________________________ Jeffrey J. Lusk, Ph.D. Postdoctoral Research Associate Department of Forestry &

Hi, I am trying to compile the version on rhel 5.x box. Nasm version 0.98.39 I extracted the tar.gz and ran make in the extracted directory. Here is where it stops. gcc -Wp,-MT,rawcon.o,-MD,./.rawcon.o.d -m32 -ffreestanding -fno-stack-protector -fwrapv -freg-struct-return -march=i386 -Os -fomit-frame-pointer -mregparm=3 -DREGPARM=3 -msoft-float -fno-exceptions -fno-asynchronous-unwind-tables

Hi R users, I have a test dataframe ("file1," shown below) for which I am trying to create a flag for the first and last ID record (equivalent to SAS first.id and last.id variables. Dump of file1: > file1 id rx week dv1 1 1 1 1 1 2 1 1 2 1 3 1 1 3 2 4 2 1 1 3 5 2 1 2 4 6 2 1 3 1 7 3 1 1 2 8 3 1 2 3 9 3 1 3

Hello all, Is there an r function that exists that will perform repeated measures MANOVAs? For example, let's say I have 3 DVs, one between-subjects IV, and one within-subjects IV. Based on the documentation for the manova command, a function like that below is not appropriate because it cannot take Error arguments. manova(cbind(DV1,DV2,DV3) ~ BetweenSubjectsIV * WithinSubjectsIV +

Hi, I have been using R to do multiple analyses of variance with two covariates, but recently found that the results in SPSS were very different. I have check several books and web resources and I think that both methods are correct, but I am less familiar with R, so I was hoping someone could offer some suggestions. Oddly simple ANOVA is the same in SPSS and R. Including covariates improves the

make[1]: Entering directory `/syslinux-4.02-6-ge841d69/dos' gcc -Wp,-MT,syslxopt.o,-MD,./.syslxopt.o.d -m32 -ffreestanding -fno-stack-protector -fwrapv -freg-struct-return -march=i386 -Os -fomit-frame-pointer -mregparm=3 -DREGPARM=3 -msoft-float -fno-exceptions -fno-asynchronous-unwind-tables -fno-strict-aliasing -falign-functions=0 -falign-jumps=0 -falign-labels=0 -falign-loops=0

Dear R users I'm new in R management and maybe It's a silly question. I'm working with GLM to obtain predictive models. I have some problesm with the prediction instruction: > DatosTotal <- read.csv("Var_perdizcsv.csv", sep =";") > edvariable <- edit(DatosTotal) > pre <- predict(rlfinal, DatosTotal, type = 'probs') Erro en

I am working on a script that takes numeric performance indicators and runs them against a series of regressors (dummy regressors, yes\no stuff via 0 and 1, e.g. Was is Christmas this week 0=no, 1=yes). The script is as follows (Written as a function): -- Begin Script -- doEnv <- function(HOUR,ENVNAME,REPORTNAME) { library(RODBC) library(forecast) library("geneplotter")

Dear everyone: I am a new user of R. I have a dataset with a dependent variable (DV) censored at different values. The dataset looks like, conditions .....IDV1 IDV2 DV 1 2 4 89 1 6 6 75 1 4 5 0 ( DV<=70) ...... 2 3 5 15 2 5 5 0

Are there any implicit assumptions about where alloca instructions can appear. I've got a failing test where the only difference between a passing test and a failing test is one application of this code in instcombine: // Convert: malloc Ty, C - where C is a constant != 1 into: malloc [C x Ty], 1 Seems pretty harmless to me. Later on the instcombine code does this: // Scan to the end of

Hi, r-users I got a problem when I try to call a *gls* function in loop structure. The gls function seems not able to recognize the parameters that I pass into the loop function! (But, if I use lm function, it works.) The code looks like this: ================================================= gls.lm <- function(Data, iv1, dv1) { gls.model <- gls(Data[ , dv1] ~ Data[ , iv1], correlation =

to clean up some code I would like to make a list of arbitrary length to store?various objects for use in a loop sample code: ############ BEGIN SAMPLE ############## # You can see the need for a loop already linearModel1=lm(modelSource ~ .,mcReg) linearModel2=step(linearModel1) linearModel3=lm(modelSource ~ .-1,mcReg) linearModel4=step(linearModel3) #custom linearModel5=lm(modelSource ~ .

Dear R-ers, I feel I am close, but can't get it quite right. Thanks a lot for your help! Dimitri # I have 2 data frames: x<-data.frame(a=c("aa","aa","ab","ab","ba","ba","bb","bb"),b=c(1:2,1:2,1:2,1:2),d=c(10,20,30,40,50,60,70,80))

dear list, my question concerns the use of `eval' in defining the model formula for `nls' (version 2.6.2.). consider the following simple example, where the same model and data are used to perform unweighted and weighted fits. I intentionally used very uneven weights to guarantee large differences in the results #================================CUT=========================== ln

Hello R Users and Developers, I have a basic question about how R works. Over the past few years I have struggled when I try to generate a new data frame that I believe should contain numeric data in some columns and character data in others only to find everything converted to character data. Is there a general method to create data frames that contain the data in the desired format: numbers