similar to: spatially constrained clustering

I don't think Spencer replied to your message. It wasn't addressed to you. He was replying to a specific post on nomograms. However you might try being less specific as a search on "spatial cluster" gave back [R] mclust - clustering by spatial patterns http://finzi.psych.upenn.edu/R/Rhelp02a/archive/13474.html [R] Spatial Clustering

Please could you help me to find a package to apply a depth-constrained cluster analysis on palaeoecological data (in order to zone subfossil diagram)? Thanks, Dr. Emmanuel GANDOUIN Université d'Aix-Marseille 3 Institut Méditerranéen d'Ecologie et de Paléoécologie UMR CNRS 6116 Europôle Méditerranéen de l'Arbois, Bat. Villemin, BP 80 13545 Aix-en-Provence cedex 4 04 42 90 84 40

Having searched CRAN and the mailing list archives, I'm pretty sure there is no function in R that implements constrained clustering. Before I start writing my own, have I overlooked something? Thanks, Tyler

Hi Everyone, I am doing a project based on "Spatially Lagged Predictor Variable Models", I would like to know which package in R would execute this model. Also, I am new to this field of spatial statistics. Any suggestions for a good book on spatial regression analysis would be appreciated. Thanks Again. Cheers Arun -- View this message in context:

Hi, I would like to specify a spherical correlation structure for spatially autocorrelated residuals in a model based upon the logistic function of a response that is a proportion (0 to 1) (so usual binary logistic regression is not an option). There is no need for a g-side random effect with grouping in this model. Am I correct that nlme requires this (meaning a correlated error structure only

Dear R users, I want to explain binomial data by a serie of fixed effects. My problem is that my binomial data are spatially correlated. Naively, I thought I could found something similar to gls to analyze such data. After some reading, I decided that lmer is probably to tool I need. The model I want to fit would look like lmer ( cbind(n.success,n.failure) ~ (x1 + x2 + ... + xn)^2 ,

Hi all The code of trying to write relates to selecting properties (given by x and y co-ordinates) spatially (distance "X" from "infected" properties identified by date) over a certain time period. i.e. what properties are within 3 km from properties infected on "2008-01-01" over the last 14 days. Is any one able to give me some clues on how to write code to solve

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

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'm trying to do some spatial simulation. I have two covariates, Z and C. I want to examine their relationship under different spatial distribution. I have no problem simulating completely spatial random process but I'm totally stuck on poisson (cluster) pattern. I already have a dataset with Z and C (obs=575) and I know the relationship between them. Using these 575

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,

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