Displaying 20 results from an estimated 400 matches similar to: "ROCR: auc and logarithm plot"
2008 Jun 12
1
About Mcneil Hanley test for a portion of AUC!
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
2010 Oct 22
2
Random Forest AUC
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
2006 Mar 15
1
How to compare areas under ROC curves calculated with ROCR package
Dear all,
I try to compare the performances of several parameters to diagnose
lameness in dogs.
I have several ROC curves from the same dataset.
I plotted the ROC curves and calculated AUC with the ROCR package.
I would like to compare the AUC.
I used the following program I found on R-help archives :
From: Bernardo Rangel Tura
Date: Thu 16 Dec 2004 - 07:30:37 EST
2014 Jun 20
2
Como construir una curva ROC
Hola!
Tengo que hacer una curva ROC com unos datos que obtuve de hacer una macro
de excel y aplicar unas reglas, y basicamente tengo que a partir de la
variacion del tiempo y la velocidad del barco obtengo diferentes
porcentajes de true positives (TP) y false positives (FP) y con eso deberia
de construir una curva ROC.
Dada mi ignorância en este tema, no se muy bien por donde empezar , estuve
2006 Mar 20
1
How to compare areas under ROC curves calculated with ROC R package
I might be missing something but I thought that AUC was a measure for
comparing ROC curves, so there is nothing else needed to "compare" them. The
larger AUC is the higher correlation of 2 variables compared. No other
measures or calculations are needed.
Jarek Tuszynski
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On
2014 Jun 23
2
Resumen de R-help-es, Vol 64, Envío 33
Hola, Marta,
Por lo que he podido ver tus datos ya tienen calculados las tasas de verdaderos y falsos positivos (TPR y FPR). También parece que los tienes ordenados por la variable FishSpeed y supongo que también por las que parecen marcas de tiempo. No necesitas ROCR porque con un simple plot te sale algo parecido a una curva. Eso sí, veo que son medidas repetidas en el tiempo para cada valor de
2011 Dec 22
0
randomforest and AUC using 10 fold CV - Plotting results
Here is a snippet to show what i'm trying to do.
library(randomForest)
library(ROCR)
library(caret)
data(iris)
iris <- iris[(iris$Species != "setosa"),]
fit <- randomForest(factor(Species) ~ ., data=iris, ntree=50)
train.predict <- predict(fit,iris,type="prob")[,2]
2010 Jan 09
21
[Bug 25966] New: nv25 : rxvt scrolling is very slow
http://bugs.freedesktop.org/show_bug.cgi?id=25966
Summary: nv25 : rxvt scrolling is very slow
Product: xorg
Version: 7.5
Platform: x86 (IA32)
OS/Version: Linux (All)
Status: NEW
Severity: normal
Priority: medium
Component: Driver/nouveau
AssignedTo: nouveau at lists.freedesktop.org
2009 Oct 28
1
need help explain the routine input parameters for seROC and cROC found in the R archive
Please help.
I found the code in the archive.
The author of this script says: "The first function (seROC) calculate
the standard error of ROC curve, the second function (cROC) compare
ROC curves."
Can some one explain to me what are the na, nn and r parameters which
are used as the input to the following two functions?
Thanks much in advance.
> From: Bernardo Rangel Tura
>
2004 Dec 15
3
(no subject)
Dear R-helper,
I would like to compare the AUC of two logistic regression models (same
population). Is it possible with R ?
Thank you
Roman Rouzier
[[alternative HTML version deleted]]
2007 Nov 21
1
Calculating AUC from ROCR
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]]
2008 Jan 05
1
AUC values from LRM and ROCR
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
=
2012 Dec 19
2
pROC and ROCR give different values for AUC
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
2011 Mar 13
1
use of ROCR package (ROC curve / AUC value) in a specific case versus integral calculation
Hello,
I would like to use the ROCR package to draw ROC curves and compute AUC
values.
However, in the specific context of my application, the true positive
rates and false positive rates are already provided by some upstream method.
Of course, I can draw a ROC plot with the following command :
plot(x=FPrate, y=TPrate, "o", xlab="false positive rate", ylab="true
2011 Apr 06
3
ROCR - best sensitivity/specificity tradeoff?
Hi,
My questions concerns the ROCR package and I hope somebody here on the list can help - or point me to some better place.
When evaluating a model's performane, like this:
pred1 <- predict(model, ..., type="response")
pred2 <- prediction(pred1, binary_classifier_vector)
perf <- performance(pred, "sens", "spec")
(Where "prediction" and
2010 Jan 22
2
Computing Confidence Intervals for AUC in ROCR Package
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 <-
2011 Sep 03
2
ROCR package question for evaluating two regression models
Hello All,
I have used logistic regression glm in R and I am evaluating two models both learned with glm but with different predictors. model1 <- glm (Y ~ x4+ x5+ x6+ x7, data = dat, family = binomial(link=logit))model2 <- glm (Y~ x1 + x2 +x3 , data = dat, family = binomial(link=logit))
and I would like to compare these two models based on the prediction that I get from each model:
pred1 =
2007 Oct 17
1
passing arguments to functions within functions
Dear R Users,
I am trying to write a wrapper around summarize and xYplot from Hmisc
and am having trouble understanding how to pass arguments from the
function I am writing to the nested functions.
There must be a way, but I have not been able to figure it out.
An example is below.
Any advice would be greatly appreciated.
Thanks, Dan
# some example data
df=expand.grid(rep=1:4,
2000 Apr 28
1
Using 'by()' in a function
I have a list of dataframes, and want to apply a function to subsets of the rows
of each dataframe. It seemed natural to write a function that takes a dataframe
as an argument, and uses 'by() within it to apply the function to the dataframe
subsets. However, I cannot get it to work. The problem seems to be passing the
data argument of by() as a function argument. Is this bug, or am I
2009 Feb 02
0
Using Information from the Stats4 package in base envir
Hi. Thank you very much in advance for your help.
I have generated data from two simple linear models and used k-means
clustering (stats4) to identify two clusters in the generated data.
Next, I would like to do simple linear regression for each separate
cluster. I can do this if I first use the cluster labels to define
two separate data frames with the subset function.
However, I would