similar to: Re : CDF of a Multivariate Normal

To whom it may concern, I try to simulate a non-normal multivariate distribution. The MASS package allows by mean of "mvrnorm" command to perform a multivariate normal simulation. Is there an equivalent command for an arbitrary multivariate distribution available in the R-language? Thank you in advance. Bernard Colin Colin Bernard Professeur titulaire D?partement de Math?matiques et

To whom it may concern, For a sample (xi,yi) i=1,2,...,n of a bivariate normal vector (X,Y), I have to evaluate ( for some simulation) the cumulative distribution function of (X,Y) at each point (xi,yi) and I have not find in R a package that evaluate this function. Is there a such package in R? If no, how can I do to make my simulation succesful? Thank you very much for your assistance.

Is there a way to apply a function, say cor(), to each combination of some number of variables, and this, without using loops? For example, I have day, hour, var1 and var2. How could I compute cor(var1,var2) for each day*hour combination and obtain a matrix with day, hour and the cor value for each combination? Thanks for your time, Marc =================== Marc Bélisle Professeur adjoint

Dear All, I am using the pmst function from the sn package (version 0.4-0). After inserting the example from the help page, I get non-trivial answers, so everything is fine. However, when I try to extend it to higher dimension: xi <- alpha <- x <- rep(0,27) Omega <- diag(0,27) p1 <- pmst(x, xi, Omega, alpha, df = 5) I get the following result: >p1 [1] 0 attr(,"error")

How do one dertermine the optimal number of rescaling cycles to use when performing DCA using the decorana function (library vegan)? Thanks for your time, Marc =================== Marc Bélisle Professeur adjoint Chaire de recherche du Canada en écologie spatiale et en écologie du paysage Département de biologie Université de Sherbrooke 2500 Boul. de l'Université Sherbrooke, Québec J1K 2R1

Howdee, One of my student spotted something I can't explain: a probability >1 vs a normal probability density function. > dnorm(x=1, mean=1, sd=0.4) [1] 0.9973557 > dnorm(x=1, mean=1, sd=0.39) [1] 1.022929 > dnorm(x=1, mean=1, sd=0.3) [1] 1.329808 > dnorm(x=1, mean=1, sd=0.1) [1] 3.989423 > dnorm(x=1, mean=1, sd=0.01) [1] 39.89423 > dnorm(x=1, mean=1, sd=0.001) [1]

Howdee, My question appears at #6 below: 1. I want to model the growth of each of a large number of individuals using a 4-parameter logistic growth curve. 2. nlme does not converge with the random structure that I want to use. 3. nlsList does not converge for some individuals. 4. I decided to go around nlsList using: t(sapply(split(data, list(data$id)), function(subd){coef(nls(mass ~

Howdee, *** I know that the lmList() function exists, yet I don't want to use it. *** Would anyone be kind enough to tell how I can apply the function lm() to each level of a given factor so to obtain the intercept and slope for each factor level within a matrix? For instance, suppose a dataframe containing 3 variables: id, x and y. I want to compute the function lm() for each level

In trying to change the driver used by Sweave on the command line using R CMD Sweave --driver=foo I consistently get the "directory 'foo' does not exist' error. (For any value of 'foo', even the default 'RweaveLatex'.) Looking up the source code for function .Sweave that is called by 'R CMD Sweave', I notice that the argument 'driver', if

Howdee, I am able to fit a 4-parameter logistic growth curve to a dataset which comprise many individuals (using R v. 2.3.1). Yet, if I want to obtain the parameters for each individual (i.e., for each 'id') using nlsList, then I obtain an Error message which I have trouble interpreting. Any advice as to how I can solve this problem? Thanks for your time, Marc > reg <-nls(mass ~

Hi there, I have 3 numeric variables: day (e.g., 05), month (e.g., 06), year (e.g., 2009). I would like to create a (string) variable of the following form: month/day/year (e.g., 06/05/2009). I would be grateful to anyone who could point me toward a solution. Sincerely, Marc =================== Marc B?lisle Professeur agr?g? Chaire de recherche du Canada en ?cologie spatiale et en ?cologie

Hello. I have to fit a mixed model from a repeated measures split-plot experiment in which the response variable is binary. This requires a generalised linear mixed model in which I can specify a binomial distribution. I can’t find the appropriate package in R. I have looked at glmmML, but it doesn’t seem to allow any mixed structure beyond a simple 2-level one. Can anyone point me to the

Hello. I want to plot the distribution of a continuous variable (y) in each of two groups on the same graph as histograms. I suppose one could call this a 2-d histogram? Can this be done in R? Here is a typical data.set: y group 1.2 1 3.3 1 2.4 2 5.7 1 0.2 2 etc. Bill Shipley Subject Matter Editor, Ecology North American Editor, Annals of

Hello. I cannot find out how to extract the residuals from a mixed model using the lmer function. Can someone help? Bill Shipley North American Editor, Annals of Botany Editor, "Population and Community Biology" series, Springer Publishing Département de biologie, Université de Sherbrooke, Sherbrooke (Québec) J1K 2R1 CANADA Bill.Shipley@USherbrooke.ca

Dear useRs, I'm happy to announce a substantial update of package actuar that bumps the version number to 2.0-0. This release focuses on additional support for continuous and discrete distributions, new functions to simulate data from compound models and mixtures, and revised and improved documentation. A slightly shortened version of the NEWS file follows: NEW FEATURES ? Support for the

Dear useRs, I'm happy to announce a substantial update of package actuar that bumps the version number to 2.0-0. This release focuses on additional support for continuous and discrete distributions, new functions to simulate data from compound models and mixtures, and revised and improved documentation. A slightly shortened version of the NEWS file follows: NEW FEATURES ? Support for the

Hello. I am having trouble downloading the lme4 package from the CRAN site. The error is: > local({a <- CRAN.packages() + install.packages(select.list(a[,1],,TRUE), .libPaths()[1], available=a, dependencies=TRUE)}) trying URL `http://cran.r-project.org/bin/windows/contrib/2.0/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 26129 bytes opened URL downloaded 25Kb

Hello. I am about to do a rather complicated analysis and am not sure how to do it. The experiment has a split-plot design and also repeated measures. Both of these complications require one to define an error term and it seems that one cannot specify two such terms. The split-plot command is: aov(y~covariates +A*B+Error(C), data=) where A and B are the fixed effects and C is the

Hello. I am trying to fit a non-rectangular hyperbola function to data of photosynthetic rate vs. light intensity. There are 4 parameters that have to be estimated. I find the nls function very difficult to use because it often fails to converge and then gives out cryptic error messages. I have tried playing with the control parameters but this does not always help. Is there another

Hello. I want to estimate the predicted values and standard errors of Y=f(t) and its first derivative at each unique value of t using the smooth.spline function. However, the data (plant growth as a function of time) show substantial heterogeneity of variance since the variance of plant mass increases over time. What is the consequence of such heterogeneity of variance in terms of bias in the