similar to: Visualization of coefficients

Hi all R users I did a logistic regression with my binary variable Y (0/1) and 2 explanatory variables. Now I try to draw my nomogram with predictive value. I visited the help of R but I have problem to understand well the example. When I use glm fonction, I have a problem, thus I use lrm. My code is: modele<-lrm(Y~L+P,data=donnee) fun<- function(x) plogis(x-modele$coef[1]+modele$coef[2])

The rms package includes the nomogram function, which generates a list object that can be passed to plot for graphical production of nomograms. I would like to remove the "linear predictor" line in the graph, which means (I suspect) removing it from the nomogram output object. I've looked at the nomogram output object, but it is not clear to me if or how it might be edited to

Dear volunteer: I am a graduate student of medcine in china.And now,I am devoting myself to constructing a nomograms of bladder cancer.I want to do it with R-project.However, I do not know how to construct a nomograms with R-project.I want to get yours help,thank you! I wish you can tell me the operating procedure of the R-project. And I apologize for my english,it is poor,sorry!

Hello, I am using Dr. Harrell's design package to make a nomogram. I was able to make a beautiful one without stratifying, however, I will need to stratify to meet PH assumptions. This is where I go wrong, but I'm not sure where. Non-Stratified Nomogram:

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

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

Hello, I am using Dr. Harrell's design package to make a nomogram. I was able to make a beautiful one. However, I want to change 5-year survival probability to 5-year failure probability. I couldn?t get hazard rate from Hazard(f1) because I used cph for the model. Here is my code: f1 <- cph(Surv(retime,dfs) ~ age+her2+t_stage+n_stage+er+grade+cytcyt+Cyt_PCDK2 , data=data11, surv=T,

Hi List, does anyone know of an implementation of spatially constrained clustering in R? This is where there is a vector of measurements for points on a plane and only neighbors can be clustered together. I have tried implementint in myself -- but if someone has alkready done it ! I have searched on the obvios terms "spatially constrained clustering" without any luck.

Dear R-users, I am a novice R-user with some experience in using the RMS package for taking nomograms after various survival models. This time, I am trying to plot a nomogram after a Random Effects Cox, implemented by the "coxme" package. My questions are: 1. Is it possible to take a nomogram directly after the coxme survival function? 2. If not is there a way to take the linear

I am a new user of R for Windows, enthusiast about the many functions of the Design and Hmisc libraries. I combined the results of a Cox regression model after multiple imputation (of missing values in some covariates). Now I got my vector of coefficients (and of standard errors). My question is: How could I use directly that vector to run programs such as 'nomogram', 'calibrate',

Dear list, I'm facing the following problem : A cox model with my sex variable interacting with several continuous variables : cph(S~sex*(x1+x2+x3)) And I'd like to make a nomogram. I know it's a bit tricky and one mights argue that nomogram is not a good a choice... I could use the parameter interact=list(sex=("male","female"),x1=c(a,b,c))... but with rcs or pol

Hello, I am constructing a nomogram using cph and nomogram commands in Dr. Harrell's Design/RMS package. The HR that I obtain for dichotomous and categorical variables are identical to those that I obtain using STATA stcox. However, the inter-quartile HR I obtain for continuous variables is obviously different, since STATA gives me HR for each unit (year, centimeter, etc) like coxph would

Hi, I would like to emphasize ("zoom") the zone of a nomogram where the probability are > 0.01 (nomogram built with nomogram, Design). As a consequence, I don't need to draw the part of the "Total points" axis with score < 60 equivalent in my case to a linear predictor < 4.5 - As far as I know, this is not possible with the arguments of the function. - Changing

Hello, I am using Dr. Harrell's rms package to make a nomogram. I was able to make a beautiful one. However, I want to change 5-year survival probability to 5-year failure probability. I couldn?t get hazard rate from Hazard(f1) because I used cph for the model. Here is my code: library(rms) f1 <- cph(Surv(retime,dfs) ~ age+her2+t_stage+n_stage+er+grade+cytcyt+Cyt_PCDK2 , data=data11,

dear professor: I have a problem about the nomogram.I have got the result through analysing the dataset "exp2.sav" through multinominal logistic regression by SPSS 17.0. and I want to deveop the nomogram through R-Projject,just like this : > n<-100 > set.seed(10) > T.Grade<-factor(0:3,labels=c("G0", "G1", "G2","G3")) >

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

dear£º in the example£¨nomogram£©£¬I don't understand the meanings of the program which have been marked by red line.And how to compile the program(L <- .4*(sex=='male') + .045*(age-50) + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))). n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10)

Hi R users, I'd like to draw a nomogram using a competing-risks regression (crr function in R), rather than a cox regression. However, the nomogram function provided in the Design package is not good for this purpose. Do you have any suggestion. I really appreciate your help Many thanks F.Abdollah, MD San-Raffele hospital Milan, Italy -- View this message in context:

Dear listers I am a very newbie with graphs in R. I have a pulmonary function prediction equation in the form of PVC = 1.1 - 0.45*age in years + 0.011*height in cm. How can I draw the corresponding nomogram?. I read the help for the design.nomogram function but it is too difficult for me. Excuse my ignorance. Any direct help will be appreciated. Thanks in advance.

Dear R-Help, I am using 'rms' package to draw nomogram. I wonder how is the "Points" determined for each predictor in the model? Is it by the coefficient estimate (beta) relative to the highest effect in the model or? Thanks Lin -- View this message in context: http://r.789695.n4.nabble.com/points-tp4638552.html Sent from the R help mailing list archive at Nabble.com.