search for: binorm

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 formatting I need to due to resolve this error? roc.plot((scicomp5.model1[164]=="High Price"), pre...

...n 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: BINORMAL<-function(varx=1,vary=1,covxy=0,meanx=0,meany=0) { #the following function plots the density of a bi variate normal distribution covXY<-matrix(c(varx,covxy,covxy,vary),2,2) A<-solve(covXY) #up<-max(meanx+4*varx^.5,meanx-4*varx^.5,meany+4*vary^.5,meany-4*vary^.5) #x <- seq(-u...

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,...

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,...

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...

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 get confidence regions. Using ellipse it is plot(ellipse(V, centre=c(a,b))) #it works However there are two issues: I change just one covariance in V, i....

...eled with a standard normal distribution. What I'd like to do is include an additional, ordinal response variable, so that the two (non-independent) response variables are treated as latent traits and modeled with a standard bivariate normal distribution. The VGAM package includes the function binormal(), which seems like it should be of some use here, but if so, I don't know where/how it fits into the model. I've included the results of running the model with the two response variables separately. Any suggestions are welcome. Again, if it helps in your response, I've included a lin...

...uot;fpr") plot(perf,colorize = F) # plot a ROC curve for a single prediction run # with CI by bootstrapping and fitted curve #roc.plot(labels,scores, xlab = "False positive rate", #ylab = "True positive rate", main = NULL, CI = T, n.boot = 100, plot = "both", binormal = TRUE) auc <- as.numeric(performance(pred, measure = "auc", x.measure = "cutoff")@y.values) auc = round(auc,3) text(.9,0,paste(" AUC=", auc, sep=" "), cex=1) names1 = paste (samples, collapse="+") text(.8,.05,names1, cex=0.75)...

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, there will always be a step function, but some will look "more smooth" than others depending upon your data and how the thresholds are defined for...