similar to: R analogue

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

Dear R users, I would like to sample from a mixture distribution p1*f(x1)+p2*f(x2). I usually sample variates from both distributions and weight them with their respective probabilities, but someone told me that was wrong. What is the correct way? Vumani

Dear R users; I am trying to write a small program which reads in a data set, and selects observations from certain years before the analysis. I have a problem including the selection criteria in the header of the program. Here is the problem; dataFIT<-function(MODEL, MARGINS, yearConsidered){ library(foreign CovPaper<-read.spss("C:/Data/data.sav") NewData <-

Dear R users: I have the following matrix. 0 1 1 0 1 0 2 1 0 3 0 0 I would like to spread the matrix such that whenever the row sum is greater than 1 the row is repeated the number of times given by the row sum. Furthermore I would like to split the following cases: 0 1 1 such that it map to the following matrix 0 1 0 0 0 1 such that each row adds up to 1. I have no problems with cases

it is easy to fit a distribution using fitdistr poisdata <- rpois(n = 100, lambda = 2) poismle <- fitdistr(poisdata, "Poisson") poismle but i would like to know whether its possible to get an identical result using glm. I use poistab <- data.frame(table(poisdata)) colnames(poistab) <- c("width","freq"); poistab[,"width"] <-

Dear all; I am trying to use HMISC imputation function to perform multiple imputations on my data and I keep on getting errors for the code given in the help files. When using "aregImpute" the error is; >f <- aregImpute(~y + x1 + x2 + x3, n.impute=100) Loading required package: acepack Iteration:1 Error in .Fortran("wclosepw", as.double(w), as.double(x),

Dear R users, I have a matrix with 2 columns with the variables: "daughter index", "mother index". I would like to draw a network digraph using this data, where each daughter is connected to a mother and between the connections inlcude a circle with the information on the indices ("daughter index", "mother index"): i.e. something similar to graphs

Dear R users; I would like to convert a series of vectors to matrices in the following way; (2,1,1) to a matrix 1 0 0 1 0 0 0 1 0 0 0 1 The idea is that the column sum of the matrix should be equal to the elements of the vector. Thanks. Vumani

Dear R-Help, I'm hoping to find a Multinomial Nested Logit package in R. It would be great to find something analogous to "PROC MDC" in SAS: > The MDC (Multinomial Discrete Choice) procedure analyzes models > where the > choice set consists of multiple alternatives. This procedure > supports conditional logit, > mixed logit, heteroscedastic extreme value,

Is there any library for fitting multinomial probit using either likelihood or the "method of simulated moments" or both. I presume it would be possible to write a family function in VGAM for the multinomial probit, but was hoping that someone has done it already. Thanking you as always. Vumani Dlamini CSO-Swaziland

Dear useRs, Am trying to use xtable on the following data.frame and I don't get what I expect: example.table <- data.frame(rbind( c("Gender"," "," "," "), cbind(rep(" ",2),c("Male","Female"),c(3.0,4.0),c(3/7,4/7)) )) colnames(example.table) <- c(" "," ","number of

Dear R users, I am trying to convert a dataset from one format to several rectangular datasets. A consultant helped design the data entry program for our survey using Delphi/Pascal and for each household the information is stored in a file called "EA-HM-HH.TXT" where EA is the enumeration area number, HM is the homestead number and HH is the household number. Within this file the

Dear R users, Is it possible to force a hard return in an expression. I tried including one for a legend using an expression and it didn't work. Here is my code, legend(12, 0.10, c("Fitted density", as.expression(substitute(paste("Weibull (", alpha==shapeU," , ", beta==scaleU,") Mean = ",meanU," days"), list(shapeU =

Dear R-users: I asked a question on how I can have a universal legend in a plot and received the following result. I tried using "layout" but I can't seem to work on the "empty" plot (where I have to have the legend). I tried "oma" but I couldn't improve the quality of the plot, and that I didn't know how to specify all the line types using the

Dear Listers, I am trying to use "rbinom" in my C code, but i always get zeros as output no matter the probability. Am not sure what I am doing wrong because the function has worked before. Attached in an example. Noticed that "rbinom" expects 'n' to be REAL. Regards, Vumani R 2.3.1 (2006-06-01) Windows XP Gcc /* Called this file binom.c and then ran rcmd shlib on it

Dear listers, Am currently using MCMC approaches to estimate some parameters of my model. One parameter has to be updated using a tuned gamma distribution. So at each iteration I estimate the mean and variance of the density of the gamma approximation using "vmmin" (i also supply the gradient argument). For moderate replications the procedure works, but if I increase them R crashes.

Is your data such that it can be restructured into a form amenable to a lattice plot, such as xyplot()? In that case, the legend (key in lattice) can be placed pretty much anywhere. Regards, Andrew C. Ward CAPE Centre Department of Chemical Engineering The University of Queensland Brisbane Qld 4072 Australia andreww at cheque.uq.edu.au On Friday, January 17, 2003 10:22 PM, Vumani Dlamini

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

Dear all, I have a question on the use of GAM with repeated measures. My dataset is as follows: - a number of study areas where bird abundance has been determined. Counts have been performed in 3 consecutive years and there were 2 counts per year (i.e. in total 6 counts). - a number of environmental predictors that do not change over year Xi). When using a GLM, a repeated measures design would

Dear List, I'm using GAMs in a multiple imputation project, and I want to be able to combine the parameter estimates and covariance matrices from each completed dataset's fitted model in the end. In order to do this, I need the knots to be uniform for each model with partially-imputed data. I want to specify these knots based on the quantiles of the unique values of the non-missing