similar to: help with table

I've been groping my way through a classification/discrimination problem, from a consulting client. There are 26 observations, with 4 possible categories and 24 (!!!) potential predictor variables. I tried using lda() on the first 7 predictor variables and got 24 of the 26 observations correctly classified. (Training and testing both on the complete data set --- just to get started.) I

I would like to do a SS type III analysis on a proportional odds logistic regression model. I use drop1(), but dropterm() shows the same behaviour. It works as expected for regular main effects models, however when the model includes an interaction effect it seems to have problems with matching the parameters to the predictor terms. An example: library("MASS"); options(contrasts =

Hi, I am trying to test the proportional-odds model using the "polr" function in the MASS library with the dataset of "housing" contained in the MASS book ("Sat" (factor: low, medium, high) is the dependent variable, "Infl" (low, medium, high), "Type" (tower, apartment, atrium, terrace) and "Cont" (low, high) are the predictor variables

Hi, do you guys know what function in R handles the multiple regression on categorical predictor data. i.e, 'lm' is used to handle continuous predictor data. thanks, karena -- View this message in context: http://r.789695.n4.nabble.com/regression-function-for-categorical-predictor-data-tp2532045p2532045.html Sent from the R help mailing list archive at Nabble.com.

Dear R-users, Is there a function in R that classifies data in more than 2 groups using logistic regression/classification? I want to compare the c-indices of earlier research (lrm, binary response variables) with new c-indices obtained from 'multiple' (more response variables) logistic regression. Best regards, Stephanie Delalieux Department Biosystems M?-BIORES Group of Geomatics

My data is 50,000 instances of about 200 predictor values, and for all 50,000 examples I have the actual class labels (binary). The data is quite unbalanced with about 10% or less of the examples having a positive outcome and the remainder, of course, negative. Nothing suggests the data has any order, and it doesn't appear to have any, so I've pulled the first 30,000 examples to use as

Dear list I'm testing a predictor and I produced nice performance plots with ROCR package utilizing the 3 standard command pred <- prediction(predictions, labels) perf <- performance(pred, measure = "tpr", x.measure = "fpr") plot(perf, col=rainbow(10)) The pred object and the perfo object are S4 with the following slots An object of class "performance"

Full_Name: Daniel Frey Version: 1.7.1 OS: Windows 2000 Submission from: (NULL) (80.254.164.242) Generating a data frame out of a xtabs result acts unusual. Take the following sample to reproduce it: > a.a <- c("a","a","a","b","b") > a.b <- c("c","c","d","e","f") >

Hello, Currently it's possible to convert an object of class table to a data frame with as.data.frame.table(), but there's no ready-made function, AFAIK, to do the reverse operation, i.e. conversion of a data frame to a table. Do you think it would be a good idea to add a data.frame method to as.table(), to allow such conversions? The idea is that if `x' is a table and `y <-

Dear all, I would like to use a tree regression method to analyze my dataset. I am interested in the fact that random forests creates in-bag and out-of-bag datasets, but I also need an estimate of support for each split. That seems hard to do in random forests since each tree is grown using a subset of the predictor variables. I was thinking of setting mtry = number of predictor variables,

Reporting a possible inconsistency or bug in handling stringsAsFactors in as.data.frame.table() Here is a simple test > options()$stringsAsFactors [1] TRUE > x<-c("a","b","c","a","b") > d<-as.data.frame(table(x)) > d x Freq 1 a 2 2 b 2 3 c 1 > class(d$x) [1] "factor" >

I'm fitting a lme growth curve model with two predictors and their interaction as predictors. The multilevel model is nested so that level 1 is time within the individual, and level 2 is the individual. I would like to plot the mean group-level trajectories at plus and minus 1 SD from the mean of the main effects composing the interaction term. Thus, the plot should have 4 lines (mean

Good Morning I want to perform a synthetic control method with R. For this purpose, I created the following code: # Re-load packages library(Synth) library(readxl) # Pfadeinstellung Excel-Blatt excel_file_path <- ("C:\\Users\\xxxxx\\Desktop\\DATA_INVESTMENTVOLUMEN_FOR_R_WITHOUT_NA.xlsx") # Load the Excel file INVESTMENTVOLUME <- read_excel(excel_file_path) #

Would anyone help me solve my problem with R, please? I am very new to R. I am doing logistic regression analysis on the presence/absence of salamanders using several predictor variables, as shown below. I have checked my data, but I didn't find any 'NA' or empty cells. When I used step() or stepAIC to select significant predictor variables, <none>s appear to places where

I would like to preface this by saying that I am new to R, so I would ask that you be patient and thorough, so that I'm not completely clueless. I am trying to convert a list to numeric so that I can perform computations on it (specifically mean-center the variable), but I am running into problems. I have imported the data set into "task" (data frame). The data frame is made of

Hello! I think I am relatively clear on how predictor importance (the first one) is calculated by Random Forests for a Classification tree: Importance of predictor P1 when the response variable is categorical: 1. For out-of-bag (oob) cases, randomly permute their values on predictor P1 and then put them down the tree 2. For a given tree, subtract the number of votes for the correct class in the

In SAS, for a two-way (or 3-way, stratified) table, the CMH option in SAS PROC FREQ gives 3 tests that take ordinality of the factors into account, for both variables, just the column variable or neither. Is there an equivalent in R? The mantelhaen.test in stats gives something quite different (a test of conditional independence for *nominal* factors in a 3-way table). e.g. I'd like to

I applied bootpred for multinomial logistic reg. (with nnet package). I used same as theta.fit and theta.predict of R for my data. but give me error. Can I do this with response vriable;7 levels predictor variables:5 (1 classifier, 4 continuous)?   Thanks alot Azam   [[alternative HTML version deleted]]

Hello everyone. This is a question regarding generation of the concordance index (c index) in R using the function rcorr.cens. In particular about interpretation of its direction and form of the 'predictor'. One of the arguments is a "numeric predictor variable" ( presumably this is just a *single* predictor variable). Say this variable takes numeric values.... Am I

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or