similar to: Data Mining Packages in R for categorical and numerical values

Dear R users, if all my numerical variables in my datasets having the same units, may I leave them unnormalized, just do cv.glmnet directly(cv.glmnet(data,standardize=FALSE))? i know normally if there is a mixture of numerical and categorical , one has to standardize the numerical part before applying cv.glmnet with standardize=fase, but that's due to the different units in the numerical

What ways are there to plot categorical vs numerical data in R. I have two columns: one with categorical data in 5 categories a,b,c,d,e, and a numerical column with integers between 1 and 100. I have used a boxplot with a,b,c,d,e on the x-axis and an increasing numerical scale on the y-axis. This look fine but im looking for other ways to present the data. What other ways can i do this??? --

I am looking to fit one or more latent categorical variables to data that is a mixture of categorical and continuous variables. Factor analysis would work for continuous data, latent class analysis for categorical data. I understand that in a package such as MPlus I could perform a single analysis of both data types. Are there similar routines available in R? Stuart -----Original Message-----

Dear all, I have the following problem: I have been using the routine "optim" in order to maximize a joint likelihood (basically a mixture with modeled weights) with quantitative variables..so far so good. Now I need to plug into the model a categorical variable (namely, age classes). Obviously, given the way optim works, it won't allow me to treat it "directly" (i.e.

Dear All, I am recycling a previous email of mine where I asked some questions about clustering mixed numerical/categorical data. This time I am more into data mining. I am given a set of known statistical indexes {s_i}, i=1,2...N for a N countries. These indexes in general are a both numerical and categorical variables. For each country, I also have a property x_i whose value is known, but

Hello everyone, Can anyone suggest a decently documented (with good examples in the documentation) R package/function that performs segmentation (cluster, mixture modeling) of a population using both continuous and categorical input variables? Thank you, Dan [[alternative HTML version deleted]]

Hello there. Is there any function in R that can do cluster on a set of data that has both categorical and numerical variables? thanks. siangli

Hi R help, Hi R help, Which is the easiest (most elegant) way to force "aov" to treat numerical variables as categorical ? Sincerely, Andrea Bernasconi DG PROBLEM EXAMPLE I consider the latin squares example described at page 157 of the book: Statistics for Experimenters: Design, Innovation, and Discovery by George E. P. Box, J. Stuart Hunter, William G. Hunter. This example use

Hi, I need a help to know whether I can perform the following in R: I have a set of observations (Ns) and each observation is drawn from a poisson distribution with an unkown mean, lambda. The set of lambdas in their turn are drawn from a common prior distribution which is supposed to be a a mixture of two gamma distributions. Is there a way to determine the poisson means in R, given the Ns and

Hi All: I am working on a dataset of a study on healthcare workers. One of the variables I am studying is a categorical variable (variable name:EDUC, indicates educational achievement, with 6 levels: "illiterate", "primary", "junior high school", "high school completed", "undergraduate", and "postgraduate"). I want to collapse the

Dear R-list, I have two variables (numerical and categorical) and would like to have the sum (and maybe some other statistics) of the numerical variable by the categorical one. Can you help me, Thank you, Fabrizio --------------------------------------------------------------- Fabrizio De Amicis IT Department Generali Information Technologies - (GIT) Centro Galleria 2, Via Cantonale CH -

I have two questions: 1. If I want to do a binomial logit, how to handle the categorical response variable? Data for the response variables are not numerical, but text. 2. What if I want to do a multinomial logit, still with categorical response variable? The variable has 5 non-numerical response levels, I have to do it with a multinomial logit. Any input is highly appreciated! Thanks! Ed

Hi, I am a new R user. I am using it develop regression models with categorical variables. Is there a way to force some regression coefficients to be zero for some of the values in a categorical variable (with 12 factor levels)? I am recoding the values to the default value (1st in the order of dummy's). But I am not sure if this is the correct approach if I want to force coefficients to be

Hi all, In a data-frame, I have two columns of data that are categorical. How do I form some sort of measure of correlation between these two columns? For numerical data, I just need to regress one to the other, or do some pairs plot. But for categorical data, how do I find and/or visualize correlation between the two columns of data? Thanks!

Hello there: I got 13 variables in my training/target set, the first 12 variables are mixture of numerical and categorical variables. The last one is the one I need to predict, and it is a numerical variable. >train<-read.table("train.txt") >test<-read.table("test.txt") >cl<-factor(train[,13]) >pred<-knn(train, test, clk=3, prob=TRUE) >pred I got

Hello, I am trying to run the APE program COMPAR.GEE with a model containing a categorical response variable and a mixture of continuous and categorical independent variables. The model runs when I have categorical (binary) response and two continuous independent variables (VAR1 and VAR2), but when I include a categorical (binary) independent variable (VAR3), I receive the following output with

Dear All, Can the x matrix in the glmnet() function of glmnet package be a data.frame with numeric columns and factor columns? I am asking this because I have a model with both numeric and categorical predictors, which I would like to study with glmnet. I have already tried to use a data.frame, but with no success -- as far as I know, the matrix object can only have data of a single type. Is

To whom it may concern: I recently used lmer (for non-normally distributed data and mixed effects, using the Laplace method). All 3 of my fixed effects were categorical, including two ordered factors and one unordered factor. In my tables, I currently report the number of observations for the response variable, and both the degrees of freedom and Chi Square values from tests of reduced

Hello, I am trying to do a logistic regression and have one predictor variable (x) that is ratio and two predictor variables (y and z) that are categorical. These have three levels each which I have called "High", "Medium" and "Low". My question: do I need to use a numerical coding scheme for the categorical variables as required by some statistical software

Dear Metafor users, I''d like to test a model with 2 continuous and 2 categorical moderators in a meta regression. One categorical parameter has 2 levels and the other has 4 levels. If I understand correctly, when I include all moderators in the model, Metafor returns main effects of the continuous parameters and contrasts of each level of categorical moderators with the intercept (which