similar to: Mixtures of Discrete Uniforms

Hello, I have some discrete pvalues and I would like to sort them. Then add random noise so that they are ordered the same way as the original pvalues. Off course I don't want any pvalues less than 0 or greater than 1. Any ideas on how to do this in R? -- Thanks, Jim. [[alternative HTML version deleted]]

Hi all, I am REALLY confused with the variance right now. for a discrete uniform distribution on [1,12] the mean is (1+12)/2=6.5 which is ok. y=1:12 mean(y) then var(y) gives me 13 1- on http://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29 wiki the variance is (12^2-1)/12=143/12 2-

Dear all, I am trying to simulate from discrete uniform distribution. But I could not find any in-built code in R. Could anyone help me please? Thanks in advance for the time and help. Cassie [[alternative HTML version deleted]]

Hello, I am new to R and I am trying to estimate a discrete model with three choices. I am stuck at a point and cannot find a solution. I have probability functions for occurrence of these choices, and then I build the likelihood functions associated to these choices and finally I build the general log-likelihood function. There are four parameters in the model, three of them are associated to

Dear all, I have a continuous variable that can take on values between 0 and 100, for example: x<-runif(100,0,100) I also have a second variable that defines a series of thresholds, for example: y<-c(3, 4.5, 6, 8) I would like to convert my continuous variable into a discrete one using the threshold variables: If x is between 0 and 3 the discrete variable should be 1 If x is between 3

Can anyone send me the R code for fitting a mixture of normal distributions. Or in fact any'combination' of mixtures of distributions. I looke dta mix but it is still not clear to me. Jim [[alternative HTML version deleted]]

Is there a package that will allow me to fit Brownian motion and Ornstein?Uhlenbeck models of evolution for discrete traits? I know that geiger and ouch have commands for fitting these models for continuous traits, but these aren't suitable for discrete trait evolution, correct? -- View this message in context:

Dear R users, I have a problem for which my objective function depends on both discrete and continuous arguments. The problem is that the number of combinations for the (multivariate) discrete arguments can become overwhelming (when it is univariate this is not an issue) hence search over the continuous arguments for each possible combination of the discrete arguments may not be feasible. Guided

Hey I want to generate a discrete uniform distribution as follows: For example: I want to get 278734 records each with a numbers between 7-10. And the sum of numbers in 278734 records to be equal to 2253712. Once this is done, I want to get that printed to an excel file such that Record Value 1 9 2 7 so on 278734 8 The

Hello, I am using the mda package and in particular the fda routine to classify in term of gear a set of 20 trips. I preformed a flexible discriminant analysis (FDA) using a set of 151 trips. FDAT1 <- fda(as.factor(gear) ~ . , data =matrizR) A total of 22 predictors were considered. 20 of the predictors are "numeric" and 2 are "factors/discrete". The resulting FDA

Hi r-help, I have been using your RHmm package for some time and have recently had to try using the package for a new dataset. Basically I have a dataset with a number of discrete observation variables that change over time, and I would love to try modeling them using a HMM. Basically I was wondering if RHmm can be used to model a multivariate discrete HMM, i.e., the observations are a vector

Dear Rgurus, To my knowledge the best way to visualize the distribution of a discrete variable X is plot(table(X)) The problem which I have is the following. I have to discrete variables X and Y which distribution I would like to compare. To overlay the distribution of Y with lines(table(Y)) gives not satisfying results. This is the same in case of using density or histogram. Hence, I am

I have not followed the history of this thread, but I am quite flummoxed as to why the OP is rewriting code to estimate parameters from an univariate Gaussian mixture model when alternatives such as EMCluster (which generally appears to handle initialization better than MClust) exist. Or perhaps there is more to it in which case I apologize. But I thought that I would make the OP aware of the

Hi Group, I have been working with the code below. Everything seems to work okay, except that the discrete values on the x-axis are far from each end of the graph. I've tried several things including changing the discrete values and playing with the limits, but can't get it to work. I tested this on simulated data and do not have the same problem, so I guessing it is how I'm

Hello list: I generate by simulation (using different procedures) two sample vectors of size N, each corresponding to a discrete variable and I want to text if these samples can be considered as having the same probability distribution (which is unknown). What is the best test for that? I've read that Kolmogorov-Smirnov and Anderson-Darling tests are restricted to continuous data

Dear all, I have a discrete distribution showing how age is distributed across a population using a certain set of bands: Age <- matrix(c(74045062, 71978405, 122718362, 40489415), ncol=1, dimnames=list(c("<18", "18-34", "35-64", "65+"),c())) Age_dist <- Age/sum(Age) For example I know that 23.94% of all people are between 0-18 years, 23.28%

I am looking for (publicly available) examples of discrete valued time series data sets. I have googled around a bit and have found lots of articles and books on discrete valued time series, but have had no success in locating sites at which data are available. Can anyone make any useful suggestions? Thanks. cheers, Rolf Turner -- Technical Editor ANZJS Department of Statistics

Hello all, First of all, thanks so those of you who helped me a week or so ago managing a time series with varying gaps between the data series in 'R'. (My final preferred solution was to use "its" function & then forecast(Arima( ) ). ) My next question is a general statistical question where I'd like some advice, for those willing / able to proffer any wisdom:

Hello all, I'd like to announce the availability of a mailing list for a newly-formed SIG (Special Interest Group) dedicated to using R for Discrete Choice Modelling. This list is intended for discussion of issues revolving around the design and analysis of Discrete Choice (aka Stated Choice, Stated Preference or Choice-Based Conjoint) experiments. While R has good infrastructure for

I want to investigate possible relationships between two discrete variables. I have tried a few things but figured you guys might be able to point me at some purpose built functions. Our scientists score results of tests which are performed in lets say, 8 positions. The scores are assigned a value of 1,2,3 or 4. I want to know if there is a correlation between the test results and the