search for: categorization

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

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

Hello, I wanted to do the multiple regression on categorical predictor data there's variable x1,x2,x3 and x3 is categorical one. so i just used as.factor(x3) and then ran multiple regression is it a good way to do the multiple regression on categorical predictor data? and how can I interpret the estimates? also if using as.factor is a good way, is there any difference with doing dummy coding

It is unclear to me how aov() handles non-categorical variables. I mean it works and produces results that I would expect, but I was under impression that ANOVA is only defined for categorical variables. In addition, help(aov) says that it "call to 'lm' for each stratum", which I presume means that it calls to lm() for every group of the categorical variable, however I

Dear users, In my psychometric test i have applied logistic regression on my data. My data consists of 50 predictors (22 continuous and 28 categorical) plus a binary response. Using glm(), stepAIC() i didn't get satisfactory result as misclassification rate is too high. I think categorical variables are responsible for this debacle. Some of them have more than 6 level (one has 10 level).

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-----

i think there ara two ways of relate products and categories , basically i want to fix one product(e.g hp dv7....) to some categories (notebook,17"notebooks...) i made a table named categorization(incuding category_id,product_id fields) then in models i write these codes below class Product < ActiveRecord::Base has_many :categories, :through => :categorizations has_many :categorizations end class Categorization < ActiveRecord::Base belongs_to :product belongs_to...

Hello all, It seems that the next improvement to the R Graph Gallery is categorization of the graphics, that way each graph will be easier to find. That step should be done *carefully* if we want to avoid the opposite side-effect : graph not reachable through the categories. That's why the wisdom of the R community is required. Graphics will be classified in : - categories...

Are there any suitable routines that perform factor analysis of categorical or mixed categorical/continuous data in R? Stuart Dr Stuart Leask MA MB BChir (Cantab) MRCPsych Clinical Lecturer in Psychiatry, University of Nottingham Dept Psychiatry Duncan Macmillan House, Porchester Road, Nottingham. NG3 6AA. UK tel. 00 44 115 969 1300 xtn 40784 fax 0115 955 5352

Hi all, I' m trying to figure out if it is appropriate to do a PCA having only categorical data (not ordinal). I have only find the following quote: One method to find such relationships is to select appropriate variables and to view the data using a method like Principle Components Analysis (PCA) [4]. This approach gives us a clear picture of the data using KL-plot of the PCA. However, the

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

It appears to be legitimate to include multi-level categorical and continuous variables in defining the model for earth (e.g. y ~ cat + cont1 + cont2) but is it also then possible use categoricals in the predict method using the earth result? I tried but it returns an error which is not very informative. Thanks Chris

...had forgotten about and many functions that I never knew existed to begin with. (who knew there were bibtex facilities in R? obviously, everyone except me.) I wonder whether there is a complete list of all R commands (incl the standard packages) somewhere, preferably each with its one-liner AND categorization(s). the one-liner can be generated from the documentation. I am thinking one categorization for function area (e.g., "programming related" for, say, deparse; and "statistical model related" for lm; and another categorization for importance (e.g., like "common" for lm...

Dear all, I have a question about using categorical predictors for SVM, using "svm" from library(e1071). If I have multiple categorical predictors, should they just be included as factors? Take a simple artificial data example: x1<-rnorm(500) x2<-rnorm(500) #Categorical Predictor 1, with 5 levels x3<-as.factor(rep(c(1,2,3,4,5),c(50,150,130,70,100))) #Catgegorical Predictor

Hello, I am working in a longitudinal study, with a categorical variable as a dependent variable (alcohol consumption: no use, use, abuse and dependence) with repeated measures (baseline, 1 year, 2 years). Besides I have another variable with two groups: control and experimental. I would like to analyze the evolution in each group, I have though in lme, but I am not sure if I can do an lme with

Dear helpers I constructed a data frame with this structure > str(dados1) `data.frame': 485 obs. of 16 variables: $ Emissor : int 1 1 1 1 1 1 1 1 1 1 ... $ Marisca.Rio : int 1 1 1 1 1 1 1 1 1 1 ... $ Per?odo : int 1 1 1 1 1 1 1 1 1 1 ... $ Reproducao : int 3 3 3 3 3 3 3 3 3 3 ... $ Estacao : int 2 2 2 2 2 2 2 2 2 2 ... $ X30cm : int

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

Hello R folks, I am encountering a problem with the following scatterplot function from the car package: > scatterplot(y~x|z) where y and x are continuous (interval) random variables and z is a categorical variable. When z is a categorical variable coded 1 or 2, I (appropriately) get a scatterplot of y by x, coded by z. Similarly, when z is a categorical variable coded 1, 2, or 3, there is

I am running the ctree function in R. My data has about 10 variables, many of which are categorical. 2 of the categorical variables have many levels (one has 900 levels, another has 1,000 levels). As an example, 1 of these variables is disease code and is structured as A, B, C, ...., AA, AB, AC.... Each time i've tried to run the ctree function, including these 2

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 -