similar to: help in plotting

Dear R-helper, I am working with ROCR of Tobias Sing et. al. to compare the performances of logistic and nnet models on a binary response. I had the performance plots, but I have problem finding out other performance statistics (eg. MSE/ASE, AUC). Any help on this? Thanks Ilham [[alternative HTML version deleted]]

Hi, Ia m working in discretized data. Here my data: x <- c(2,1,3, 5), and I want to make (0,1) data based on the length of each component in x. So the new data should like: y = (0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1). I spent too much time with "seq", "rep". Still didn't get it. Any help? Thanks Ilham [[alternative HTML version deleted]]

Dear R-list, I want to combine several plots in one graph. I did this: plot(a1); plot(a2, add=TRUE); ...plot(a5, add=TRUE) The problem is the more plot we put, the more complex the graph. Is there any way to label each line; or other way just to make sure I know which one which? Thanks, Ilham [[alternative HTML version deleted]]

Hi R help, I run my data in nnet with skip layer, factor response (with 0 & 1 values) and explicitly put softmax=T to compare the result of the default nnet with no softmax specification. I assume this should give me the same result. I got the result the default one, but not the softmax version and I got the error message that I did not quite understand. test6.nn.skipT.softm.Yfac <-

hi, is there a way of calculating of measuring dependence between two categorical variables. i tried using the chi square test to test for independence but i got error saying that the lengths of the two vectors don't match. Suppose X and Y are two factors. X has 5 levels and Y has 7 levels. This is what i tried doing >temp<-chisq.test(x,y) but got error "the lengths of the two

Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know

Dear helper, After some cleaning, I found out that I cannot open my R2.6.0 console with message: "Fatal error: unable to restore saved data in .Rdata", and every time I retry it just kick me off. I am wondering if I still be able to retrieve the data that I've been working for a month. I have installed the new R2.6.1 and it has no problem to open, only there is nothing in it I also

Hello everyone, I was trying to do some distribution fitting with a numerical field called Tolls. The sample size = 999 rows. Basically I assigned the Toll data to a new variable K by doing: k<-dtest$Toll After that, tried to fit a gamma distribution by doing: fitG<-fitdist(k, "gamma") Then the following messages showed (oh and I checked for empty rows before doing this):

Hi, All, I am getting some funny results trying to use R's built in distribution functions. In R: > dgamma(4.775972,1.37697964405418, 0.106516604930466) [1] 0.05585295 > dgamma(4.775972,1.37697964405418, 0.106516604930466,TRUE) ### THIS IS JUST WRONG! [1] 0.01710129 > log(dgamma(4.775972,1.37697964405418, 0.106516604930466)) [1] -2.885033 > In C:

Hi All, Here 's what I got using dgamma function : > nu<-.2 > nu*log(nu)-log(gamma(nu))+(nu-1)*log(1)-nu*(1) [1] -2.045951 > dgamma(1,nu,nu,1) [1] 0.0801333 > dgamma(1,nu,nu,0) [1] NaN Warning message: NaNs produced in: dgamma(x, shape, scale, log) Could anyone tell me what is wrong here ? I am using R-2.4.1 on windows XP. Thanks a lot.

I define priors in jags within r using a gamma distribution. I would like to control the shape but I have problems. Any help will be usefull. From help of dgamma ___________________ The Gamma distribution with parameters shape = a and scale = s has density f(x)= 1/(s^a Gamma(a)) x^(a-1) e^-(x/s) and rate=1/scale From jags user manual ____________________ dgamma(r, mu) has a density of

I want to create a grid of function values for use in `contour' or `persp'. The function is the log-likelihood for the gamma. The sample is stored as vector of length 20 called `Survival'. A single evaluation of the log-likelihood at, say, scale = 9 and shape = 10 would be obtained by sum(dgamma(Survival, scale = 9, shape = 10, log = TRUE)) (This may work only 0.99.0, I'm not

Hello everyone, With th bar histogram (number of occurrences) hist<-c(24,7,4,1,2,1,1) of seven equally spaces classes ]1-4], ]5-8], ]9-12], ]13-16], ]17-20], ]21-24], ]25-28], I obtained shape=0.8276 and rate=0.1448. I would like to know how to build the continuous pdf of a this gamma distribution knowing these two estimated parameters such that I will be able to predict the pdf of any

Hi, How can I convert the matrices to list. For example I have this snippet: samples<-mymatrix[1,] print(samples) which prints: V1 V2 V3 V4 V5 V6 1 103.9 88.5 242.9 206.6 175.7 164.4 How can I convert the object "samples" such that it prints: [1] 103.9 88.5 242.9 206.6 175.7 164.4 The reason I ask this because I can't use the former "samples"

Dear R-Devel group, My name is Alexey, a data scientist from Moscow, currently working for Align Technology Inc. We have recently had a discussion of the results that the dgamma function (stats) returns for an extreme point (x == 0). <dgamma(0,1,1,log = FALSE) [1] 1 and <dgamma(0,0.5,1,log = FALSE) [1] Inf Density appears to be defined in point zero for the distribution with the

Dear R Users, I am trying to obtain a best-fit analytic distribution for a dataset with 11535459 entries. The data range in value from 1 to 300000000. I use: fitdistr(data, "gamma") to obtain mle's for the parameters. I get the following error: Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : non-finite finite-difference value [1] And the following warnings:

Hy all, Is there a direct way to build the complete function call of an arbitrary function? Here's what I want to do. A function will build a function which will itself call a probability density function for some law given in argument to the first function: > f("gamma", 1000) will return, say, function(x, shape, rate, scale = 1/rate) dgamma(x + 1000, shape, rate,

dgamma and friends in S are documented as dgamma(x, shape, rate=1) pgamma(q, shape, rate=1) qgamma(p, shape, rate=1) rgamma(n, shape, rate=1) whereas R has dgamma(x, shape, scale=1, log = FALSE) pgamma(q, shape, scale=1, lower.tail = TRUE, log.p = FALSE) qgamma(p, shape, scale=1, lower.tail = TRUE, log.p = FALSE) rgamma(n, shape, scale=1) Note the use of rate vs scale. Indeed, as both S and

Hello, In WinBUGS 1.4 manual (http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/manual14.pdf), the gamma density is presented as dgamma(r,mu) where r and mu are the shape and rate parameters, respectively. In JAGS (rjags) manual version 1.0.2, May 9, 2008 (http://www-fis.iarc.fr/~martyn/software/jags/jags_user_manual.pdf), on page 26 the gamma density is presented as dgamma(mu,r) instead of dgamma(r,mu).

Hi, I??m trying to use the Win2BUGS package from R and I have a similar problem that reurns with the message: Error in FUN(X[[1]], ...) : symbol print-name too long But, there is no stray ` character in the file ( Sugestions given by: Duncan Temple Lang <duncan> Date: Mon, 26 Sep 2005 07:31:08 -0700 ) The progam in R is: library(R2WinBUGS) library(rbugs) dat <-