similar to: Logistic Regression: variable selection based on p value?

Hi, I am using logistic regression model named lrm(Design) Rite now I was using Area Under Curve (AUC) for testing my model. But, now I have to calculate precision/recall of the model on test cases. For lrm, precision and recal would be simply defined with the help of 2 terms below: True Positive (TP) - Number of test cases where class 1 is given probability >= 0.5. False Negative (FP) -

Greetings, I have generated several ROC curves and would like to compare the AUCs. The data are cross sectional and the outcomes are binary. I am testing which of several models provide the best discrimination. Would it be most appropriate to report AUC with 95% CI's? I have been looking in to the "net reclassification improvement" (see below for reference) but thus far I can only

Hi, Is there a way when doing logistic regression for the output to spit out accuracy, sensitivity, and specificity? I would like to know these basic measures for my model. Thanks! [[alternative HTML version deleted]]

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

I know how to compute the ROC curve and the empirical AUC from the logistic regression after fitting the model. But here is my question, how can I compute the standard error for the AUC estimator resulting form logistic regression? The variance should be more complicated than AUC based on known test results. Does anybody know a reference on this problem? [[alternative HTML version deleted]]

Dear R users, I am a new R user and something stops me when I try to write a academic article. I want to make a nomogram to predict the risk of prostate cancer (PCa) using several factors which have been selected from the Logistic regression run under the SPSS. Always, a calibration plot is needed to validate the prediction accuracy of the nomogram. However, I tried many times and read a lot of

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

Thank you Frank and all for your advices. Here I attach the raw data from the Pawinski's paper. I have obtained permission from the corresponding Author to post it here for everyone. The only condition of use is that the Authors retain ownership of the data, and any publication resulting from these data must be managed by them. The dataset is composed as follows: patient number / MMF dose in

I am new to R and I am trying to do a monte carlo simulation where I generate data and interject error then test various cut points; however, my output was garbage (at x equal zero, I did not get .50) I am basically testing the performance of classifiers. Here is the code: n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta

Hi all, I am using the glmnet R package to run LASSO with binary logistic regression. I have over 290 samples with outcome data (0 for alive, 1 for dead) and over 230 predictor variables. I currently using LASSO to reduce the number of predictor variables. I am using the cv.glmnet function to do 10-fold cross validation on a sequence of lambda values which I let glmnet determine. I then take

I am using logistic regression model (lrm) of package Design. Can some one please tell me how to calculate the average Area Under Curve (AUC) for n-fold cross-validation The help for lrm function says to do cross validation like this f <- lrm( cy ~ x1 + x2, x=TRUE, y=TRUE) val <- validate.lrm(f, method="cross", B=5) Now I dont know what to do with variable "val" to

I think the argument supporting the use of bootstrap to determine coefficients, as opposed to just running linear regression on the whole dataset, is the comparison of Rsq and prediction errors between these two approaches - page 1502. There's a substantial difference in favor of the bootstrap approach. -- Michal J. Figurski Gustaf Rydevik wrote: > The url for the mentioned paper is

Hello all, I've been having trouble with assessing a breakpoint in a logistic GLM with two explanatory variables. For this analysis I've been using the 'segmented' package version 0.2-9.1. But I keep getting an error and I don't see where I would be going awry. The situation is the following: Two explanatory variables: bedekking - a variable with possible values between 0 and

I was hoping for some advice regarding indexing, From a dataframe there are 27 variables of interest, with the prefix of "pre". [7] "Decision" "MHCDate" "pre01" "pre01111" "pre012" "pre013" [13] "pre02" "pre02111" "pre02114" "pre0211" "pre0212"

Dear Sir or Madam? I am a doctor of urology,and I am engaged in developing a nomogram of bladder cancer. May I ask for your help on below issue? I set up a dataset which include 317 cases. I got the Binary Logistic Regression model by SPSS.And then I try to reconstruct the model ?lrm(RECU~Complication+T.Num+T.Grade+Year+TS)? by R-Project,and try to internal validate the model through

Does anyone know how to get the C-index from a logistic model - not using the dataset that was used to train the model, but instead using a fresh dataset on the same model? I have a dataset of 400 points that I've split into two halves, one for training the logistic model, and the other for evaluating it. The structure is as follows: column headers are "got a loan" (dichotomous),

Hi, I am trying to cross-validate a logistic regression model. I am using logistic regression model (lrm) of package Design. f <- lrm( cy ~ x1 + x2, x=TRUE, y=TRUE) val <- validate.lrm(f, method="cross", B=5) My class cy has values 0 and 1. "val" variable will give me indicators like slope and AUC. But, I also need the vector of predicted values of class variable

Hi, all, I am new to R or even to statistics. Not sure if the question has a answer. But I couldn't find a straight forward answer in the help mailing list. I need use MicroArray data to select several diagnostic genes between Normal samples and Tumor samples and use these genes to predict unknow samples. Since the sample size is so small and data doesn't follow normal distribution, I am

I am trying to develop a prognostic model using logistic regression. I built a full , approximate models with the use of penalization - design package. Also, I tried Chi-square criteria, step-down techniques. Used BS for model validation. The main purpose is to develop a predictive model for future patient population. One of the strong predictor pertains to the study design and would not

Hi, I'm trying to set up a prediction software, now i testing the performance of my method, so i need to calculate a ROC curve, specially auc, cut-off, sens and spec, i just looking at ROCH package, but it's a mass for me, i'm not a math guy and I'm getting lost Could any of you recommend me an easy-to-use package to do this task? i just have a list of positive/negative samples