similar to: working with ordinal predictor variables?

I would consider this is a question for a statistics forum such as stats.stackexchange.com, not R-help, which is about R programming. They do sometimes intersect, as here, but I think you need to *understand what you're doing* before you write the R code to do it. Obviously, IMO. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and

Hi, I have a dataset with a continuous response variable and, among other predictors, an ordinal variable. Here is what it could look like treatment <- factor(rep(c("AA", "AC", "AD","AE", "AB"), each = 10)) length <- c(75, 67, 70, 75, 65, 71, 67, 67, 76, 68, 57, 58, 60, 59, 62, 60, 60, 57, 59, 61, 58,

Here's a more general question following up on the specific question I asked earlier: Can anybody recommend an R command other than mle.aic() (from the wle package) that will give back a ranked list of submodels? It seems like a pretty basic piece of functionality, but the closest I've been able to find is stepAIC(), which as far as I can tell only gives back the best submodel, not a

When I run "lrm" from the Design package, I get a warning about contrasts when I include an ordinal variable: Warning message: Variable ordfac is an ordered factor. You should set options(contrasts=c("contr.treatment","contr.treatment")) or Design will not work properly. in: Design(eval(m, sys.parent())) I don't get this message if I use glm with

Hi, I am working on a regression tree in Rpart that uses a continuous response variable that is ordered. I read a previous response by Pfr. Ripley to a inquiry regarding the ability of rpart to handle ordinal responses in 2003. At that time rpart was unable to implement an algorithm to handle ordinal responses. Has there been any effort to rectify this in recent years? Thanks! Stacey On

I know this a newbie question, but I've only just started using AIC for model comparison and after a bunch of different keyword searches I've failed to find a page laying out what the differences are between the AIC scores assigned by AIC() and mle.aic() using default settings. I started by using mle.aic() to find the best submodels, but then I wanted to also be able to make comparisons

Dear List, I have two questions about how to do predictions using lrm, specifically how to predict the ordinal response for each observation *individually*. I'm very new to cumulative odds models, so my apologies if my questions are too basic. I have a dataset with 4000 observations. Each observation consists of an ordinal outcome y (i.e., rating of a stimulus with four possible

Hi, ?? Are there any packages which prints out p-values for OLR's (like `ologit' from Stata)? I want to run a bunch of OLRs and print the p-value for the first coefficient from each of them. ? I checked polr() under MASS and it doesn't. ?There's a lrm() function under Design which does print out p-values but I couldn't extract p-values from the output. ? Thanks, ? Debs

Hi I would like to analyse an ordinal categorical variable. I know how I can analyse a nominal categorical variable (with multinom or if there are only two levels with glm). Does somebody know which command I need in R to analyse an ordinal categorical variable? I want to describe the variable y with the variables x1,x2,x3 and x4. So my model looks like: y ~ x1+x2+x3+x4. y: ordinal factor

Hello I've been asked to help evaluate a vegetation data set, specifically to examine it for community similarity. The initial problem I see is that the data is ordinal. At best this only captures a relative ranking of abundance and ordinal ranks are assigned after data collection. I've been trying to find a procedure in R that can handle ordinal based classification and so far have

>From the following: basin.map <- readAsciiGrid("c:/temp/area.asc", colname="area") I have a SpatialGridDataFrame which has the x and y cordinate of a cell, and the drainage area of that cell. There are many cells with a low drainage area (in my case, 33000 with an area of 37.16) and one cell with the highest drainage area (again, in my case, a drainage area of of

Dear friends, Being very new to this, I was wondering if I could get some pointers and guidance to interpreting the results of performing a linear regression with ordinal predictors in R. Here is a simple, toy example: y <- c(-0.11, -0.49, -1.10, 0.08, 0.31, -1.21, -0.05, -0.40, -0.01, -0.12, 0.55, 1.34, 1.00, -0.31, -0.73, -1.68, 0.38, 1.22, -1.11, -0.20) x <-

I'm trying to install the ordinal package (http://popgen.unimaas.nl/~plindsey/rlibs.html). I downloaded ordinal03.tgz and untarred it. rmutil was previously installed (and appears to work ok.) Then I installed ordinal: [root at localhost ~]# R CMD INSTALL /home/chippy/Download/ordinal * Installing to library '/usr/lib/R/library' * Installing *source* package 'ordinal' ... **

After working on some more plugins I realized that the naming for flac's ordinal types were not going to cut it. There is too much chance for conflicts with other libraries/programs with names like 'bool' and 'uint32' so I went back and prefixed all flac ordinal types with 'FLAC__'. It makes the code a little harder to read until you get used to it but should make the

I have been asked to analyse some questionnaire data- which is not data I'm that used to dealing with. I'm hoping that I can make use of the nabble expertise (again). The questionnaire has a section which contains a particular issue and then questions which are related to this issue (and potentially to each other): 1) importance of the issue (7 ordinal categories from -3 to +3) 2) impact

Hi there, during my work I have to use kernel smoothing methods for multivariate ordinal data. The R-package "KernSmooth" unfortunately includes only a version for continous scaled variables. Does anybody know whether there exists also a version for ordinal data? Thanks for help! --

Hello,dear all: I want to install the package "ordinal",but I don't see the package listed under package sources. I try to search it by "google",then I found this: http://euridice.tue.nl/~plindsey/rlibs.html but the connect does not work. Where can I found the package "ordinal" ? Is it still available? Thanks in advance. ^^

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There is a difference in the p- value from 0.000 and 0.012 when I am using SPSS. 0.000 when I am using the independent variable as scaled 0.012 if I am using the variable as ordinal. The independent variable is ordinal but it seems that R is using the variable as an scaled, because the P- Value is computed with 4.66e-06 so I am not sure which description I am misunderstanding: SPSS;:

Dear colleagues, i am carrying out an ordinal regression model. I try it on SPSS but I "flirt" with R as well. I have a few questions. 1. What is the most reliable/tested/trusted package for ordinal regression in the R world? 2. Also, I have a statistical question. What is the danger of having to many 'empty cells' in ordinal regression? How many empty cells are too many? Do