similar to: CLuster analysis with only nominal variables

Dear R users and developers, I have a dataset containing 34 variables measured in a survey, which has some missing items. I would like to conduct a factor analysis of this data. I tested mi, Amelia, and MissForest as alternative packages in order to impute the missing data. I now have 5 separate datasets with the variables I am interested in factor analysing. In my reading of the package

Hi, The answers to my previous question about nominal variables has lead me to a more important question. What is the "best practice" way to feed nominal variable to an SVM. For example: color = ("red, "blue", "green") I could translate that into an index so I wind up with color= (1,2,3) But my concern is that the SVM will now think that the values are

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

Hi, are there readily available R packages that are able to perform FA on ordinal and/or nominal data? If not, what other approaches and helpful packages would you suggest? BR, Jay

Dear R-help listers, I am a new convert to R. I am trying to use a r package to conduct latent class analysis as a triangulation check of my cluster analysis using the cluster package in R. I have about 30 cases and 6 indicators, some of which are binary indicators and others are ratio-level variables (percentages). I looked around for information in flexmix, lca, and poLCA, and couldn't find

I am trying to perform cluster analysis on survey data where each respondent has answered several questions, some of which have categorical answers ("blue" "pink" "green" etc) and some of which have scale answers (rating from 1 to 10 etc).My problem is that certain age groups were over-sampled and I need to weight the data collected in order to accurately reflect the

Dear colleagues, In checking a function I am adding to an R package, I get the following warning pair: ... Bad \usage lines found in documentation object 'nominal': "\\method{print}{nominal}"(x, max.print = 10, posthoc = "std.pearson.residuals.sign", assoc = ifelse("univariate" list(c("N", "alpha.X2",

I have a consulting client with data containing a mixture of continuous, nominal, and ordinal variables, who wishes to do some correlational analyses. Ignoring for the moment whether this is wise or useful, is there some way to calculate a matrix using, for example, polychoric correlations for pairs of nominal variables, or correlations based on normal scores for ordinal variables? [The PRELIS

Dear useRs, A new package 'drm', version 0.5-4, is available on CRAN. The drm package provides functions for marginal regression analysis of repeated (or otherwise clustered) binary, ordinal and nominal responses. This package can be considered as a likelihood-based alternative to GEE approach for marginal regression. In addition to regression modelling, several temporal and latent

Dear useRs, A new package 'drm', version 0.5-4, is available on CRAN. The drm package provides functions for marginal regression analysis of repeated (or otherwise clustered) binary, ordinal and nominal responses. This package can be considered as a likelihood-based alternative to GEE approach for marginal regression. In addition to regression modelling, several temporal and latent

Hi, I would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for

Hi, I am trying to run randomForests on a datasets of size 500000X650 and R pops up memory allocation error. Are there any better ways to deal with large datasets in R, for example, Splus had something like bigData library. Thank you, Nagu

Hi guyz I have problems running the gwr when I include the categorical/nominal variables with 2 to 3 levels. Could it be that gwr does not cater for such variables? I tried indicating that these variables are factors but this is not helping. Firstly, the cross-validation bandwidth (using gwr.sel) cannot be computed (gives warnings) and the gwr does not give the parameter estimates (Error in abs

Hi, I have a 1.000 observations with 10 attributes (of different types: numeric, dicotomic, categorical ecc..) and a measure M. I need to cluster these observations in order to assign a new observation (with the same 10 attributes but not the measure) to a cluster. I want to calculate for the new observation a measure as the average of the meausures M of the observations in the cluster

Hi All, I have a question regarding the nominal jitter buffer configuration: The call was setup as G.729A (annexb=no), ptime=20ms and nominal jitter buffer size = 50ms, and round trip delay is 200ms, the TDM side will experience intermittent one way voice during the call, but IP side can always heard the voice from TDM side. My question is, should this possible caused by the nominal jitter

Hi, <p>apparently, I didn't delete my test samples ... I found them. :) Here's a small table of what nominal bitrate fits to which quality at what sample rate. Quality levels inbetween -1 and 0 are possible, but I didn't test them. Nominal bitrates of those quality settings will be somewhere between those of -1 and 0 and therefore can be guessed. q/channels || -1/mono |

Hi! This is my first time posting. I've read the general rules and guidelines, but please bear with me if I make some fatal error in posting. Anyway, I have a continuous response and 29 predictors made up of continuous variables and nominal and ordinal categorical variables. I'd like to do lasso on these, but I get an error. The way I am using "lars" doesn't allow for the

Hello All: I know the function loglin() and loglm() from librarary(MASS) performs analysis on nominal categorical data. Are there any libraries, functions or examples available for analysis of ordinal categorical data? I have in mind procedures that can replicate results in Alan Agresti (1984) "Analysis of Ordinal Categorical Data." Thanks, ANDREW

Hi, I am a new user of R. I have 2 series of nominal data (2 series of answers for the same question) and I want to calculate the correlation between these 2 series. I've tried to use the correlation function (corr, ...) but all are for numeric data... Does anyone know what function should I use for the nominal data? Thanks a lot, Bianca

Hello everybody! I have a question. I am working with R and I am loading a .txt from my computer. This file contains a set of features from patients with numerical values and the score, that determines the gravity of the disease( from 0 to 3). Due to I want to obtain a classification of my patients using SVM,neural networks or C.4.5, I think I need to "convert" the numeric class to