similar to: Turn categorical array into matrix with dummy variables

Hi, I have built two R packages. One of them (PKG1) needs to use the functions of the other package (PKG2). So I need to link these two packages together, so that the functions of PKG2 can be available to PKG1. And when I load one package using 'library("PKG1")', PKG2 can be loaded at the same. Any ideas welcome. -- View this message in context:

Dear R-Users, I have a data frame with several categorical variables. I want to create a new data frame with dummy variables (with all levels). I know the function model.matrix (see below) but it works only for one categorical variable : > tt <- c("a","a","b","a","c") > tt <- factor(tt) > model.matrix(~ tt - 1) is there anyone who

Colleagues, I have generated several dummy variables: n$native0 <- 1 * (n$re=="white" & n$usborn=="yes") n$native1 <- 1 * (n$re=="afam" & n$usborn=="yes") n$native2 <- 1 * (n$re=="carib" & n$usborn=="yes") n$native3 <- 1 * (n$re=="carib" & n$usborn=="no") I would now like to combine these

Hello, I have a problem with categorical variables and dummy encoding. I've a factor and for each pair (i,j) with i != j, I'd like to fit res ~ a*x[i] - b*x[j]. A brief example with 3 variables: a - b = 2 b - c = -1 c - a = 0 Thus I fitted the following model: fit <- lm(result ~ X + Y) where Y is just the negative of X, means y[i] = -x[i]. But I don't want to double the

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

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

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 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

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).

Dear metafor users, I am working on a meta-analysis of reliability and the correlation associations. I need some help about conducting categorical moderators variables. Questions 1: How to conduct the weighted ANOVAs assuming a mixed-effects model on the tranformed alpha coefficients/the tranformes correlation coefficients for the categorical moderator variables? Questions 2: How to

Hi, Suppose a binomial GLM with both continuous as well as categorical predictors (sometimes referred to as GLM-ANCOVA, if I remember correctly). For the categorical predictors = indicator variables, is then there a suggested minimum frequency of each level ? Would such a rule/ recommendation be dependent on the y-side too ? Example: N is quite large, a bit > 100. Observed however are

Dear all, I have the following data set. V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 alpha beta 1 11 1 11 1 11 1 11 1 11 alpha beta1 2 12 2 12 2 12 2 12 2 12 alpha beta1 3 13 3 13 3 13 3 13 3 13 alpha beta1 4 14 4 14 4 14 4 14 4 14 alpha beta1 5 15 5 15 5 15 5 15 5

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 list people Suppose we have a data.frame where variables are categorical and the response is categorical eg: my.df=NULL for(i in LETTERS[1:3]){my.df[[i]]=sample(letters, size=10)} my.df=data.frame(my.df) my.df$class=factor(rep(c("pos", "neg"), times=5)) my.glm=glm(class ~ ., data=my.df, family=binomial) summary(my.glm) .... Estimate Std. Error z

I am doing a logistic regression, and by accident I included a field which has the 2digit abbreviation for all 50 states labeled "st". I was surprised to see that the glm did not come up with an error message but instead appears to have automatically broken down this field into individual fields (stAK and stAL). Does R really know to turn all categorical variables in binary dummy

Hi, I am doing multiple regression, and have several X variables that are categorical. I read that I can use dummy or contrast codes for that, but are there any special rules when there're unequal #observations in each groups (4 females vs 7 males in a "gender" variable)? Also, can R generate these codes for me? THanks.

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

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

Dear all, I would like to run a simple regression model y~x1+x2+x3+... The problem is that I have a lot of independent variables (xi) -- around one hundred -- and that some of them are categorical with a lot of categories (like, for example, ZIP code). One straightforward way would be to (a) transform all categorical variables into 1/0 dummies and (b) enter all the variables into an lm model.

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