similar to: discrete variable

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 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]]

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

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-

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

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

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

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

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%

https://bugs.freedesktop.org/show_bug.cgi?id=86503 Bug ID: 86503 Summary: Discrete card seems to be powered on even if reported as Off Product: xorg Version: unspecified Hardware: Other OS: All Status: NEW Severity: normal Priority: medium Component: Driver/nouveau

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

I want to create the mixture formulation of a discrete uniform ie, say f(x) = 1/10, for i = 1,2,3,4,5,6,7,8,9 and 10 and another discrete distribution which has the same values of x, but he probabilities can vary. Can this be done on any package in R? an if so, can the package estimate the 'probabilities' of each of the x value as well as the mixing proportion if I have the data?

What's the reommended way, in R, to determine the strength of association between a discrete variable and a continuous variable? Yes, I have read the manuals, trawled the archives, &c.

Hello, suppose one is forming a probability p(x,y), where the x,y axes are somewhat accidental and rotation is possible. I'm thinking about whether the discrete entropy H(x,y) should change if the probability is rotated in the x,y plane. My current conclusion is that it _does_ change, at least if the entropy is estimated via bins. As a simple example, suppose the probability mass is

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

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

Hi Rolf, I looked at https://docs.microsoft.com/en-us/azure/sql-database/sql-database-public-data-sets One of the first sets in the list is the airline time series (I think it is also used in dplyr examples). https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp You might find other possibilities in that list. HTH, Eric On Tue, Jan 2, 2018 at 12:44 AM, Rolf Turner <r.turner at

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

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: