similar to: Continous variables with implausible transformation?

Dear R users: Recently, I tried to write a program to calculate cross-validated predicted value. My sources are as follows. However, the R reported an error. Could you please check the sources? Thanks. set.seed(100) x<-rnorm(100) y<-sample(rep(0:1,50),replace=T) dat<-data.frame(x,y) library(rms) fito<-lrm(y~x) preo<-predict(fito) pre<-matrix(NA,nrow=100,ncol=200) for (i in

Hi, everyone: I ask for help about translating a stata program into R. The program perform cross validation as it stated. #1. Randomly divide the data set into 10 sets of equal size, ensuring equal numbers of events in each set #2. Fit the model leaving out the 1st set #3. Apply the fitted model in (2) to the 1st set to obtain the predicted probability of a prostate cancer diagnosis. #4. Repeat

Hi R helpers, I'm preparing dataset to fir logistic regression model with lrm(). I have various cointinous and discrete variables and I would like to: 1. Optimaly discretize continous variables (Optimaly means, maximizing information value - IV for example) 2. Regroup discrete variables to achieve perhaps smaller number of level and better information value... Please suggest if there is

After upgrading R to 2.9.2, I can't use the anova() fuction. It says "could not find function "Varcov" ". What's wrong with my computer? Help needed, thanks! Yao Zhu Department of Urology Fudan University Shanghai Cancer Center No. 270 Dongan Road, Shanghai, China [[alternative HTML version deleted]]

Hi, everyone. I know it may be a basic statistical question. But I can't find a good answer. I have a question raised by one of the reviewers. Factor A expression was strongly correlated with B expression (chi-square) in this series. Prior reports by the same authors showed that B expression strongly correlated with survival (Log-rank). Please provide an explanation why then were the results

1) How does one capture the plots from the plsmo procedure? Simply inserting a routing call to a graphical device (such as jpeg, png, etc) and then running the plsmo procedure (and then dev.off()) does not route the output to the file system. 1b) Related to above, has anyone thought of revising the plsmo procedure to use ggplot? I'd like to capture several such graphs into a faceted

Dear useRs: I use pROC package to compute the bootstrap C.I. of AUC. The command was as follows: roc1<-roc(all$D,all$pre,ci=TRUE,boot.n=200) However, the result was: Area under the curve: 0.5903 95% CI: 0.479-0.7016 (DeLong) Why the C.I. was computed by the Delong Method? Yao Zhu Department of Urology Fudan University Shanghai Cancer Center Shanghai, China [[alternative HTML version

--On Tuesday, November 10, 2015 12:53:20 PM -0800 Gordon Messmer <gordon.messmer at gmail.com> wrote: > That depends on what you mean by "support." > > It's almost certainly possible to run the binaries on CentOS, but if you > need any technical support from the vendor of that application, they > might not provide it. Your first step should be to talk to them

Dear R-users, I have used the nomogram function from the rms package for a logistic regresison model made with lrm(). Everything works perfectly (r version 2.15.1 on a mac). My question is this: if my final model is not the one created by lrm, but I internally validated the model and 'shrunk' the regression coefficients and computed a new intercept, how can I build a nomogram using that

I'm trying to solve a system of 3 equations as part of a sub-routine in R, ie first eqn a/x-b*sqrtx+c=log(1/dx+1/e(sqrtx); snd eqn (f*y)/z-g/y-h=-log(2/x+(z/y)/(i*x) and third eqn is of the form zz=x/(j-k(z/y) where a..k inclusive are constants, x,y,z and zz are inputs. How can this be done in R? [[alternate HTML version deleted]]

For those of you wanting to salvage your Cisco ATA-186 after inadvertent locking, or after recovering your devices from a vendor who has locked them, here is a rainy-day project for you: http://www.sst.com/downloads/datasheet/S71077.pdf The above document gives exact specifications on the 4mb flash EEPROM that stores all program and configuration data on the ATA-186 (aka Komodo.) If you

I have the following code (which I did not write) that generates data based on a logistic model.? I'm only getting a single record with y=1.? It seems implausible that in 50k cases that have a single y=1.? Does that ring alarm bells for anyone else? ? beta<-c(-1.585600,-0.246900) betasize<-length(beta) meanpred<-c(0,35.900000) varpred<-c(0,1.000000) #loop code

Dear all, I have some growth curve data from an experiment that I try to fit using lm and lmer. The curves describe the growth of classification accuracy with the amount of training data t, so basically y ~ 0 + t (there is no intercept because y=0 at t0) Since the growth is somewhat nonlinear *and* in order to estimate the treatment effect on the growth curve, the final model is y ~ 0 + t +

I'm having trouble with the ordisurf function in the vegan package. I have created an ordination plot (cmdscale) of 60 samples based on Bray-Curtis dissimilarities, and would like to overlay various soil edaphic characteristics as possible clues to the clustering I observe in my plot. However, I find that ordisurf creates a surface on the plot that is a perfect, even gradient - and

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On 11/11/15 15:17, Edward Ned Harvey (centos) wrote: >> From: centos-bounces at centos.org >> [mailto:centos-bounces at centos.org] On Behalf Of Devin Reade >> >> The above answer is right-on. From a technical perspective, you >> can probably expect the 3rd party software to work exactly the >> same on RHEL and

Hello, I have the data set of 1 + 49 variables. One of them is binary other are continous. I would like to be able to fit the model with all 49 variables and then run stepwise model selction. I'd appriciate some code snippets...

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <-

Hi, I am running a logistic regression model using lrm library and I get the following error when I run the command: mod1 <- lrm(death ~ factor(score), x=T, y=T, data = env1) Unable to fit model using ?lrm.fit? where score is a numeric variable from 0 to 6. LRM executes fine for the following commands: mod1 <- lrm(death ~ score, x=T, y=T, data = env1) mod1<- lrm(death ~

Hi R-helpers I have a dataframe DF (lets say with the variables, y, x1, x2, x3, ..., clust) containing relatively many NAs. When I fit an ordinal regression model with the function lrm from the Design library: model.lrm <- lrm(y ~ x1 + x2, data=DF, x=TRUE, y=TRUE) it will by default delete missing values in the variables y, x1, x2. Based on model.lrm, I want to apply the robust covariance