search for: emdbook

I am using curve3d in the emdbook package to graph a gaussian copula density function generated via the copula package. Unfortunately, it appears that emdbook masks dmvnorm from the package mvtnorm in a way that prohibits copula from generating the gaussian copula. (Sounds very confusing!) For example, > library(copula) > f&l...

Hello, I am using rbetabinom ( to generate beta binomial random variables) function available in the "emdbook"package written by Professor. Ben Bolker for my research study. I have no questions with this function. However, I am looking for the theoretical method/algorithm of the function "rbetabinom " . Morris (1997), American Naturalist 150:299-327 is given as the reference in the package...

Dear all, I want to use the deltavar() function from emdbook. I can use it directly from the command terminal but within a function it behaves weird. Working example: ---------------------- library("emdbook") fn <- function() { browser() y <- 2 print(deltavar(y*b2, meanval=c(b2=3), Sigma=1) ) } x <- 2 print(deltavar(x*b1, meanval=c(b1=...

If I want to find out the soltion of X1,X2 that min(3X1+2X2+X1X2) subject to 20<=X1+3X2<=50 10<=X1 which function or package can I use? Thanks.

I have data that looks like O.lengthO.age 176 1 179 1 182 1 ... 493 5 494 5 514 5 606 5 462 6 491 6 537 6 553 6 432 7 522 7 625 8 661 8 687 10 704 10 615 12 (truncated) with a simple VonB growth model from within nls(): plot(O.length~O.age, data=OS) Oto = nls(O.length~Linf*(1-exp(-k*(O.age-t0))), data=OS, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) mod <- seq(0, 12)

I can take the results of a simulation with one random variable and generate an empirical interval that contains 95% of the observations, e.g., x <- rnorm(10000) quantile(x,probs=c(0.025,0.975)) Is there an R function that can take the results from two random variables and generate an empirical ellipse that contains 95% of the observations, e.g., x <- rnorm(10000) y <- rnorm(10000) ?

...argument to mathematical function Can somenone explain met what this error means? All my parameters and data are defined, so I don't understand what the non-numeric argument is??? With different equations the script does work........... For this function to run you need packages: bbmle and emdbook. Testdata I pasted here N0 FR 5 4 3 2 5 3 5 4 6 4 5 4 4 2 5 4 6 5 5 3 10 5 14 7 12 6 17 9 10 4 16 8 15 8 14 5 15 6 15 5 19 10 20 11 22 10 21 12 25 12 26...

...the model parameter estimates differ between years, using likelihood ratio tests and AIC. Can anyone give advice on how to do this? My likelihood functions are long so I'll use the tadpole predation example from Ben Bolker's book, Ecological Data and Models in R (p. 268-270). library(emdbook) data(ReedfrogFuncresp) attach(ReedfrogFuncresp) # Holling Type II Equation holling2.pred = function(N0, a, h, P, T) { a * N0 * P * T/(1 + a * h * N0) } # Negative log likelihood function NLL.holling2 = function(a, h, P = 1, T = 1) { -sum(dbinom(Killed, prob = a * T * P/(1 + a * h * Initial),...

Dear all, I have strong interest on Ecological Niche Model, which in general use a set of environmental variables (continuous, categorical etc) and Presence (or Presense/Absence) records for species. I think that "grasp" and "adehabitat" packages could help me on these tasks. My input layers are on ASC format, and the record of species is a data-frame with X, Y, name of

Dear all, Is there a way to retrieve standard errors from nls models? The help page tells me that arguments such as se.fit are ignored... Many thanks and best wishes Christoph -- Dr. rer.nat. Christoph Scherber University of Goettingen DNPW, Agroecology Waldweg 26 D-37073 Goettingen Germany phone +49 (0)551 39 8807 fax +49 (0)551 39 8806 Homepage http://www.gwdg.de/~cscherb1

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Dear all, I have a very simple question about how I can include HPD confidence lines in a Marginal posterior distribution scatterplot I have the following code to draw the scatterplot(s) from an output table; par(mfrow=c(2,2)) plot(temp1[,5], temp1[,6], xlab="log10(r)", ylab="log10(tf)", pch=46) plot(temp2[,5], temp2[,6],

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Hi,I know only mle function but it seems that in mle one can only specify the bound of the unknowns forming the likelihood function. But I would like to specify something like, a = 2b or a <= 2b where 'a' and 'b' could be my parameters in the likelihood function. Any help would be really appreciated. Thank you!- adschai [[alternative HTML version deleted]]

Dear All, The function curve() draws the graph of functions from R to R. Is there some homologous function to curve() to draw functions from R^2 to R? Thanks in advance, Paul

Are there any R packages that could be used to generate random data given a set of parameters? Or, if not a package, how would one generate such data? What I would like to do is simulate some sample data for a regression model given a set of population covariances and distribution parameters to be used in a MC simulation. Thanks for any tips. [[alternative HTML version deleted]]

I am trying to calculate Bayesian Credible Intervals for a proportion (disease prevalence values to be more specific) and am having trouble using R to do this. I am working with ncredint() function but have not had success with it. Please help! Example: Positive samples = 3 Total sampled = 10 Prevalence = 0.3 pvec <- seq(1,10,by=1) npost = dbinom(pvec,10,prob=0.3, log=FALSE) ncredint(pvec,

Hi I want to do simulation on quasi-binomial distribution with some covariates. Does anyone have an idea how to do that? [[alternative HTML version deleted]]

Hi guys again, it seems I haven't been doing the maximum likelihood estimation correctly. I quote below, can someone explain to me please what does it mean that the 2nd and 3rd derivatives of the function equals zero and how to compute that in R. "We have our initial estimated, subjective parameters for the gamma mixture and we have our likelihood that is the mixture of negative

Dear all, Is there a fairly general R implementation of the delta method that uses numerical derivatives? I realise that the delta method has been implemented using symbolic derivatives (e.g. alr3::delta.method, emdbook::deltamethod, msm::deltamethod and survey:::nlcon), however possibly non-linear estimators using the delta method with numerical derivatives can be quite useful (e.g. predictnl in Stata and the estimate and predict statements for proc nlmixed in SAS). I would like something akin to the following p...