similar to: Jointly distributed random variables in R

Dear all, I have just downloaded a package 'prob' and also loaded it using library(prob). Whereas function 'tosscoin' works very well the other functions like 'gbinom' for graphing density functions does not work. I get the message: > gbinom(20,0.3) Error: could not find function "gbinom" How do I go about it? Thanks. Kizito.

I have downloaded a package 'combniat' , it now exists in the library. I even loaded it using: library(combinat). But when I call the function tosscoin(1), I instead get an error message: Error: could not find function "tosscoin". Kindly help. Kizito.

Dear All, Suppose Z_i, i=1,...,m are marginally identically distributed as a two normal mixture p0*N(0,1) + (1-p0) *N( miu_i, 1) where miu_i are identically distributed according to a mixture and I have generated Z_i one by one . Now suppose these m random variables are jointly m-dimensional normal with correlation matrix M= (m_ij). How to proceed next or how to start correctly ? Question:

X and Y are independently distributed random variables. I would like to study the contours of the joint density of these two variables. Any function to call? Thank you very much! -- View this message in context: http://www.nabble.com/How-to-plot-contours-for-joint-density-of-2-independently-distributed-r.v.--tp19493126p19493126.html Sent from the R help mailing list archive at Nabble.com.

Hi, I'm new to R and am hoping someone might be able to help with the following lme problem. I am trying to fit an ellipse equation to some spatial human factors data, varying the major and minor axes randomly and specifying an exp~ variogram for errors. Using normally-distributed random effects produces some -ve minor/major axes. I am hoping to be able to specify a gamma distribution to

Hi, I have normally distributed random terms u~N(0,1). I want to get gamma distributed random terms g~(scale,shape) with E(g)=1=shape/scale and var(g)=theta=1/scale=1/shape. How can I reach my goal? The following way doesn't work: use the distribution function of u to get U(0,1)- distributed random terms, then take the quantile function of the gamma distribution with shape and scale. The

I'm a beginner of R. I can use runif() to generate uniformly distributed numbers, but I don't know which function can generate uniformly distributed random integers, or what kind of method do? Thanks! -- View this message in context: http://www.nabble.com/how-to-generate-uniformly-distributed-random-integers-tf4960778.html#a14208376 Sent from the R help mailing list archive at

Hi, I want to get random number which is uniformly distributed on the unit disc. How can I do that with R? Best wishes, WAN WAN [[alternative HTML version deleted]]

Hello, I would be very grateful if somebody more knowledgeable then me could assist me in the following. I have two (three actually but for simplicity I will say two) models which I would like to fit jointly as a state space object. Here are the equations: (1) w = a1 + b1*(p) + e1 a1 = a1[t-1] + g1 g1 = g1[t-1] + e2 b1 = b1[t-1] + e3 (2) d = a2 + b2*(w) + e3 a2 = a2[t-1] + e4 b2 = b2[t-1] + e5

Full_Name: Long Qu Version: 2.3.1 OS: Windows XP Submission from: (NULL) (64.113.93.235) The QQ-plot of two versions of simulating noncentral F-distributed random numbers has quite different scales: > qqplot(rf(1000,2,15,3),qf(runif(1000),2,15,3)) The rf() function reads: > rf function (n, df1, df2, ncp = 0) { if (ncp == 0) .Internal(rf(n, df1, df2)) else rchisq(n, df1,

Dear R-users! I?m faced with following problem: Given is a sample where the sample size is 12, the sample mean is 30, and standard deviation is 4.1. Based on a Student-t distribution i?d like to simulate randomly 500 possible mean values within a two-tailed 95% confidence interval. Calculation of the lower and upper limit of the two-tailed confidence interval is the easy part. m <- 30 #sample

Dear list members, Is there a package somewhere for jointly estimating two poisson processes? I think the closest I've come is using the "SUR" option in the Zelig package (see below), but when I try the "poisson" option instead of the "SUR" optioin I get an error (error given below, and indeed, reading the documentation of the Zelig package, I get the impression

Dear list, I have 603 numbers depicting range sizes of birds in Japan. I would like to learn if the 603 range sizes are randomly distributed or not, in order to check if they meet mid-domain effects. Please kindly advise if any R package or function can check the fit. Also, any more references are highly appreciated. Thank you Elaine [[alternative HTML version deleted]]

Hi all, I am trying to run an lme4 model (logistic regression with mixed effects) in MCMCglmm but am unsure how to implement it properly. Currently, my lme4 model formula looks as follows: "outcome ~ (1 + var1 + var2 | study) + var1 + var2" In English, this means that I am fitting a random effects model, where the intercept, var1 and var2 are jointly distributed according to study.

Dear colleagues, I have a dataset that looks as below. I would like to make a new dataset that excludes the cases which are joint conjunctions of particular state names and years, so Connecticut and 2010, Maryland and 2010 and Vermont and 2010. I'm trying the following subset code: newdata<- subset(bpa, (!State=="Connecticut" & year<"2010")) It appears that

Dear All, I am estimating a Cox proportional hazard model, with several interactions of the type a*z + a*y + a*x + b*z + b*y + b*x. I need to know if the first three (the "a"s) are jointly significantly different from the last three (the "b"s). I have tried several approaches, but have been unsuccessful. Here's the model, and the code I came up with, with the obvious

Dear R help(ers). I'm an R-learner (about 10 hours now) trying to make a ranked dot plot where the symbols are coded by two variables concurrently. I'm trying to use Deepanyan Sarkar's book 'Lattice' as a guide but get the feeling it is a bit advanced for my level of understanding. I have three questions Q1. Right now I like to know how to get the dual coding working

I have a 3xN matrix of parameters obtained from N regressions where the 3 parameters are jointly statistically significant. I would like to reproduce a 3D confidence ellipsoid projecting 2D ellipses onto the XY plane as in Figure 5.2 in this

Dear R-help! I need to draw contour lines in a plot of wave heights (Hs) versus peak periods (Tp) showing the joint probabilities of 1-year wave heights~peak periods, 10-year wave heights~peak periods and 100-year wave heights~peak periods. I've used the contourplot() function in the plot I've added in this mail. You can use the added dataset "Rhelpdata.txt" to reproduce a

Hello, I want to create two random variables (x1,x2) both with uniform distribution bounded by (-1) and (1) that has a correlation of 0.6 between them. Does somebody know how I can do it? For normal random variables I known how to implement it with the rmvnorm command but I don't know how to do it with variables uniformly distributed. Thanks a lot. Alexandra [[alternative HTML