search for: binormally

I am trying to create the roc plot bootstrap method from library(verification), and when I set the plot =both or emp then I get the following error. The roc.plot works fine when the plot is set to binorm. This is my first time using this function in R and am not sure what this error means or how to resolve the issue. It seems to work ok with the example dataset. Is there an option or data

hi all why does the following not work??? this was someone elses code and i couldnt explain why it doesn't work. m=matrix(c(0,0),2,1) v=matrix(c(1,0,0,1),2,2) Y=function(X1,X2,mu=m,V=v) { X=matrix(c(X1,X2),2,1) a=(1/((2*pi)*sqrt(det(V))))*exp((-0.5)*(t(X-mu)%*%solve(V)%*%(X-mu))) a[1] } x1=seq(-1,1) x2=x1 Z=outer(x1,x2,FUN="Y",mu=m,V=v) persp(x1,x2,Z) my code:

Dear users, does any one have a code (S or R) to compute the binormal distribution (or the upper its quadrant area) other than the pmvnorm. Thanks -- Nabil Channouf etudiant en Ph.D. Bureau 3733 Departement d'Informatique et de Recherche Operationnelle (D.I.R.O.) Universite de Montreal, C.P. 6128, succ. Centre-Ville, Montreal, H3C 3J7 Tel.: (514) 343-6111, poste 1796 Fax.: (514) 343-5834

Hello everyone, I'm trying to use mle from package stats4 to fit a bi/multi-modal distribution to some data, but I have some problems with it. Here's what I'm doing (for a bimodal distribution): # Build some fake binormally distributed data, the procedure fails also with real data, so the problem isn't here data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3)) # Just to check it's bimodal plot(density(data)) f = function(m, s, m2, s2, w) { -log( sum(w*dnorm(data, mean=m, sd=s)) + sum((1-w)*dnorm(data, mean=m2, sd=...

Hi, Suppose I want to test a set of random numbers (using the Binormal random number generator), input the following command to generate a set of random numbers: test <- rbinom( 10000000, 1, 0.5 ) How can I, after obtaining the result, export the vector "test" into an text file? Then, suppose I want to do something like: > x <- 1e10 > x [1] 1e+10

Hi all, I'm intersted in draw confidence regions for two estimates that *are not* in the same GLM, but I know their point estimates c(a,b) and their covariance matrix V, say. Of course > V[1,2]==V[2,1] [1] TRUE Their joint distribution is not binormal (at least i don't know it), but I know (or I assume) their limiting distribution is Normal, so I can use the bivariate Wald statistics to

Hello. I have yet to receive a response to my previous post, so I may have done a poor job asking the question. So, here is the general question: how can I run a run a multivariate (more than one non-independent, response variables) ordered probit regression model? I've had success doing this in the univariate case using the vglm() function in the VGAM package. For example:

Hello R gurus, I have the code below for which i need help and pointers to make it run in parallel on a dual core win7 computer with R 2.13.x, using foreach, iterators,doMC. library(scatterplot3d) # Loads 3D library. library(fields) library(MASS) library(ROCR) library(verification) library(caret) library(gregmisc) ##simulated data d=replicate(9, rnorm(40)+10)

Hi Brian, Your underlying dataset for the ROC curve only has 4 unique values for specificity, even though there are 23 elements in the vector, hence the step function nature of the first plot. The default smoothing in the smooth() function is "binormal". You might try one of the other smoothing options to see the result and whether they make visual sense. In the absence of smoothing,