similar to: How to calculate the AUC in R

Dear all, I am trying to apply the logistic regression to determine the limit of detection (LOD) of a molecular biology assay, the polymerase chain reaction (PCR). The aim of the procedure is to identify the value (variable "dilution") that determine a 95% probability of success, that is "positive"/"total"=0.95. The procedure I have implemented seemed to work looking

I am having a similar problem understanding the data structure of the "yarn" dataset described in the "[R] data structure for plsr" posts. I have spectroscopic data I'd like to run through a PLSR and have read the tutorial series, but still do not understand the data format required for the code to process my data. My current data structure consists of a .csv file read into

Here it is again, hope this is more clear I am using the following data (only a small subset is given): Habitat Fungus.yield Birch 20.83829053 Birch 22.9718181 Birch 22.28216829 Birch 24.23136797 Birch 22.32147961 Birch 20.30783598 Oak 27.24047258 Oak 29.7730014 Oak 30.12608508 Oak 25.76088669 Oak 30.14750974 Hornbeam 17.05307949 Hornbeam 15.32805111 Hornbeam 18.26920177 Hornbeam 21.30987049

Hi I'm running the latest R on a presumably up to date Linux server. 'Doing something silly I'm sure, but can't see why my saved PLMset objects come out all wrong. To use an example: Setting up an example PLMset (I have the same problem no matter what example I use) > library(affyPLM) > data(Dilution) # affybatch object > Dilution = updateObject(Dilution)

Hi all! My question is why, and what I can do about that I sometimes, but not always, get warning-messages like nlminb returned message singular convergence (7) in: LMEopt(x = mer, value = cv) or IRLS iterations for PQL did not converge when trying to fit a model looking like this: lmer<-(cbind(Diseased,Healthy)~Fungus+(1|Family)+(1|Fungus:Family), family="binomial") to four

Dear all, I am using the ROCR library to compute the AUC and also the Hmisc library to compute the C-index of a predictor and a group variable. The results of AUC and C-index are similar and give a value of about 0.57. The Wilcoxon p-value is <0.001! Why the AUC is showing small value and the p-value is high significant? The AUC is based on Wilcoxon calculation? Many thanks, Lina

Hi, there. Suppose I already have sensitivities and specificities. What is the quick R-function to calculate AUC for the ROC plot? There seem to be many R functions to calculate AUC. Thanks. Yulei [[alternative HTML version deleted]]

Hi, I upgraded to R-1.6.0 and R CMD check is behaving a bit weird. The package I am check cannot make it throught because of errors like > ##___ Examples ___: > > data(Dilution) > hist(Dilution[,1]) Error in if (log == T) { : missing value where logical needed Execution halted while the function called in the example defined in the .Rd fiel as a signature in which

I am doing a simulation with a relatively large data set (20,000 observations) for which I want to calculate the area under the Receiver Operator Curve (AUC) for many parameter combinations. I am using the ROC library and the following commands to generate each AUC: rocobj=rocdemo.sca(truth = ymis, data = model$fitted.values, rule = dxrule.sca) #generation of observed ROC object

Dear List, I am trying to assess the prediction accuracy of an ordinal model fit with LRM in the Design package. I used predict.lrm to predict on an independent dataset and am now attempting to assess the accuracy of these predictions. >From what I have read, the AUC is good for this because it is threshold independent. I obtained the AUC for the fit model output from the c score (c =

Dear R-philes, I am plotting ROC curves for several cross-validation runs of a classifier (using the function below). In addition to the average AUC, I am interested in obtaining a confidence interval for the average AUC. Is there a straightforward way to do this via the ROCR package? plot_roc_curve <- function(roc.dat, plt.title) { #print(str(vowel.ROC)) pred <-

Packages pROC and ROCR both calculate/approximate the Area Under (Receiver Operator) Curve. However the results are different. I am computing a new variable as a predictor for a label. The new variable is a (non-linear) function of a set of input values, and I'm checking how different parameter settings contribute to prediction. All my settings are predictive, but some are better. The AUC i

Hi, I would like to compare differences in AUC from 2 different models, glm and gam for predicting presence / absence. I know that in theory the model with a higher AUC is better, but what I am interested in is if statistically the increase in AUC from the glm model to the gam model is significant. I also read quite extensive discussions on the list about ROC and AUC but I still didn't find

Hello Is there an easy way, i.e. a function in a package, to calculate the area under the curve (AUC) for drug serum levels? Thanks for any advice -- Armin Goralczyk, M.D. -- Universit?tsmedizin G?ttingen Abteilung Allgemein- und Viszeralchirurgie Rudolf-Koch-Str. 40 39099 G?ttingen -- Dept. of General Surgery University of G?ttingen G?ttingen, Germany -- http://www.chirurgie-goettingen.de

Dear all, I was wondering if someone can help me. I am learning SVM for classification in my research with kernlab package. I want to know about classification performance using Area Under Curve (AUC). I know ROCR package can do this job but I found all example in ROCR package have include prediction, for example, ROCR.hiv {ROCR}. My problem is how to produce prediction in SVM and to find

I am running my code in a loop and it does not work but when I run it outside the loop I get the values I want. n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta <- 0 ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit))

Hallo! Does anybody know a way to calculate the sample size for comparing AUC of ROC curves against 'by chance' with AUC=0.5 (and/or against anothe AUC)? Thanks! Karl

Dear all I am trying to compare the performances of several methods using the AUC0.1 and not the whole AUC. (meaning I wanted to compare to AUC's whose x axis only goes to 0.1 not 1) I came to know about the Mcneil Hanley test from Bernardo Rangel Tura and I referred to the original paper for the calculation of "r" which is an argument of the function cROC. I can only find the

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be

Hi all, does anyone know why the area under the curve (AUC) is negative? I'm using ROC function with a logistic regression, package Epi. First time it happens... Thanks a lot! Bruno -- View this message in context: http://r.789695.n4.nabble.com/ROC-Curve-negative-AUC-tp4650469.html Sent from the R help mailing list archive at Nabble.com.