similar to: sampling from a mixture distribution

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 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 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 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

I've been training randomForest models on 7 million rows of data (41 features). Here's an example call: myModel <- randomForest(RESPONSE~., data=mydata, ntree=50, maxnodes=30) I thought surely with only 50 trees and 30 terminal nodes that the memory footprint of "myModel" would be small. But it's 65 megs in a dump file. The object seems to be holding all sorts of

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 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 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-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 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

I've built two RF objects (RF1 and RF2) and have tried to combine them, but I get the following error: Error in rf$votes + ifelse(is.na(rflist[[i]]$votes), 0, rflist[[i]]$votes) : non-conformable arrays In addition: Warning message: In rf$oob.times + rflist[[i]]$oob.times : longer object length is not a multiple of shorter object length Both RF models use the same variables, although

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,

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: Is there any R analogue for the S+ function "na.gam.replace". I would like to make an interaction of a categorical and smooth continuous covariate. Thanks. Vumani Dlamini Central Statistical Office Swaziland

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.

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

Hi all, I appreciate the help this list has given me before. I have a question which has been perplexing me. I have been working on doing a Bayesian calculating inserting studies sequentially after using a non-informative prior to get a meta-analysis type result. I created a function using three iterations of this, my code is below. I insert prior mean and precision (I add precision manually

Hi, I am currently working on fitting a mixture density to financial data. I have the following data: http://s000.tinyupload.com/?file_id=00083355432555420222 I want to fit a mixture density of two normal distributions. I have the formula: f(l)=πϕ(l;μ1,σ21)+(1−π)ϕ(l;μ2,σ22) my R code is: normalmix<-normalmixEM(dat,k=2,fast=TRUE) pi<-normalmix$lambda[1] mu1<-normalmix$mu[1]

Hi, I am trying to fit a function of the form: y = A0 + A1 * exp( -0.5* ( (X - Mu1) / Sigma1 )^2 ) - A2 * exp ( -0.5* ( (X-Mu2)/Sigma2 )^2 ) i.e. a mean term (A0) + a difference between two gaussians. The constraints are A1,A2 >0, Sigma1,Sigma2>0, and usually Sigma2>Sigma1. The plot looks like a "Mexican Hat". I had trouble (poor fits) fitting this function to toy data