similar to: multinomial conditional logit models

Inthe code below I was trying to to obtain the GMM estimates for CAPM (REGRESSION) for 36 stocks each have 180 observations,however it only gives me one output rather than 36. In SAS i would just put in a *By statement*. I have a variable TICKER that categorize them into 36 groups. *How can I obtain all 36 output instead of just one.* **

Dear all, I know there have been various questions posted over the years about loops but I'm afraid that I'm still stuck. I am using Windows XP and R 2.9.2. I am generating some data using the multivariate normal distribution (within the 'mnormt' package). [The numerical values of sanad and covmat are not important.] > datamat <-

Dear R-helpers, I am using a vrtest on time series data. My commands are as follows; read.table("B.txt",sep="\t",fill=TRUE, na.strings = "NA") require(vrtest) rm(list=ls(all=TRUE)) datamat <- read.table("B.txt",sep="\t",fill=TRUE, na.strings = "NA") column <- 1 nob <- nrow(datamat) y <-

Hello, Does anyone knows how to deal with zero subscript in R. I have this code: for (i in 1:nitems){ + for (j in 1:ncat-1) { + draw<-matrix(rnorm(nitems*(ncat-1),seed1,seed2),nitems,(ncat-1)) + d<-( sigma_d*draw ) + mu_d + draw<-matrix(rtnorm((nitems*(ncat-1)),mean = seed1, sd = seed2, lower = .1, upper = 1.5),nitems,(ncat-1)) +

Hello, I am trying to simulate 10 relicates of 100-tables. Each table is a 2 x 3 and 80% pf the tables are true nulls and 20% are non-nulls. The nulls follow the Hardy Weinberg distribution (ratio) 1:2:1. I have the code below but the p-values are not what I am expecting. I want to use the Cochran Armitage trend test to get the p-values. num.reps=10 num.vars=1000 pi0 = 80 num.subjects = 100

Hello, I want to draw a histogram of the mean of sample observations drawn from multivariate t distribution. I am getting the following error corresponding to the code I used. Though I am getting the graph, but I am curious to know the warning message. Warning messages: 1: In if (freq) x$counts else { : the condition has length > 1 and only the first element will be used 2: In if (!freq)

Hello, I'm working with R version 1.5.0 in Windows. I've written a function (SummaryMat, segment below) which uses a loop to repeatedly call another function (PercentsMat, segment below). PercentsMat creates a matrix and adds rows to it each time it is called. I use deparse(substitute(...)) to get the names of the lists sent to PercentsMat to use them as row names in the generated

Hello all, Here goes one of my first functions. I want to make a nonparametric multiple sample comparison with unequal sample sizes (see Zar?s Biostatistical Analysis, 3rd. Ed., pg. 201 Example 10.11, pg. 288 Example 11.10). In the real world, I want to compare samples of fish length captured with different fishing gears. After using the Kruskal-Wallis test I want to check the differences

Dear R-devel, I am trying to alter rpart so that it makes additional calculations when growing the tree. In the "rpart.s" there is a call to the C routine: rp <- .C("s_to_rp2", as.integer(nobs), as.integer(nsplit), as.integer(nodes), as.integer(ncat),

Hi, I am reading the source code of rpart. I have problems understand the following code and would appreciate for any helps. In rpart.s, there is a line: rpfit &lt;- .C(C_s_to_rp, &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; n = as.integer(nobs), &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; nvarx

I am trying to run a partialPlot with Random Forest (as I have done many times before). First I run my forest... Cell is a 6 level factor that is the dependent variable - all other variables are predictors, most of these are factors as well. predCell<-randomForest(x=tempdata[-match("Cell",names(tempdata))],y=tempdata$Cell,importance=T) Then I try my partial plot to look at the

Dear all, I have written a function for calculating the volume of a tree (=trad) or snag (=h?gst). The included volume regreesion model includes ten parameter values, which are tree species specific. bj?rk.formh?jd.pars is an object which includes the parameter values (parameter set) for birch (=bj?rk). There is one row per tree in the data object. > relev.kols[1:5,

Hi, I got a computational problem with lme (nlme library R 1.9.1) using corSymm(). Here is the data: [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.19639793 0.09127954 0.11733288 0.07598273 0.06545106 0.06211532 [2,] 0.22773467 0.10981912 0.16052847 0.38101187 0.18353474 0.24072918 [3,] 0.46743388 0.45733836 0.32191178 0.43356107 0.39159746 0.53984221 [4,]

I've uploaded a new version of "catspec" to CRAN. Catspec is for estimating certain "special categorical" models. It also contains "ctab", a function for creating one-way, two-way, and multi-way percentage tables (nothing special there really). Ctab can now print more than one percentage type, as well as table marginals. The first special model in catspec is

I've uploaded a new version of "catspec" to CRAN. Catspec is for estimating certain "special categorical" models. It also contains "ctab", a function for creating one-way, two-way, and multi-way percentage tables (nothing special there really). Ctab can now print more than one percentage type, as well as table marginals. The first special model in catspec is

I have used R a lot in the past, but never for simulation. I have a code in SAS for the Graded Response Model (GRM), also known as Samejima's model. This code simulates an ordinal response, provided item characteristics (A=item discrimination, BB(G) are thresholds between various categorical responses). It is a macro file. I am thinking that I can write this as a function, and call it up

Dear all, I am trying to make a barplot with clustered pairs of bars, using class=numeric data and the following command: barplot(c(bline_precip[10,9], bline_runoff[10,9], cccma_precip[10,9], cccma_runoff[10,9], csiro_precip[10,9], csiro_runoff[10,9], ipsl_precip[10,9], ipsl_runoff[10,9], mpi_precip[10,9], mpi_runoff[10,9], ncar_precip[10,9], ncar_runoff[10,9], ukmo_precip[10,9],

Hi Mark: I had also used sample and tabulate for generating multinomial and found it to be quite slow. So I had written a multinomial random numbers generator based on the GENMUL subroutine from "ranlib", which in turn is based on the algorithm from Luc Devroye's book on "Non-Uniform Random Variate Generation" You may want to compare this with your hybrid algorithm and

Hi All, I have a doubt. I used macros and i try to pass a value to a macro by concatenating a bunch of strings. But it does not seem to work. Please help. I have written down my code and the error message please tell me how to pass the value that a string points to. Thanks in advance #macro defined

Hi guys, I need some help to analyzing my data. I start to describe my data: I have 21 matrices, every matrix on the rows has users and on columns has items, in my case films. Element of index (i, j) represent the rating expressed by user i about item j. I have a matrix for each of professions. An example of a this type of matrix is: item 1 item 2 item 3 item4 id