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I have two data frame A and B adn want to cross them. A has format as: a1 a2 a3 1 2 3 2 3 1 1 3 2 ... B: b1 b2 1 2 2 1 ... the combine result shall be something like a1 a2 a3 b1 b2 1 2 3 1 2 1 2 3 2 1 2 3 1 1 2 2 3 1 2 1 1 3 2 1 2 1 3 2 2 1 .... is there a function able of doing this instead of loops? Thanks, Sun

Hi, I am trying to convert a variable a = sample(1:3,100,rep = T) represents choices into a 3X100 dummy varible b with corresponding element set to 1 otherwise 0. eg. a: 1 3 2 1 2 3 1 1.... b: 1 0 0 1 0 0 1 1.. 0 0 1 0 1 0 0 0... 0 1 0 0 0 1 0 0... Is there something like b[a] =1 existing? I could not figure this out myself. ---------------------------------

Hi, I have a data frame df with column names a,b and c. Now I want to get a subset of df that satified the condition a == 1 and b == 2, how do I specify this. For one condition like a == 1 I can use subdf = df[,df$a==1] to get the right answer, but subdf = df[,df$a==1 && df$b ==2] does not return the right answer. Some quick tips please.

Hi I am trying to generate a design using Algdesign and came across terms of "exact design" and "approxiamte theory design", I did not find a reference to explain what they are, could some one shed some light about this on me? Another question is, I want to measure the main effects and at least two interactions, variables are factors, how do I ensure this in formula,

I have strings contain postcode and letters, some seperated with blank, some with comma, and some hasn't seperated. eg, "2324gz" "2567 HK" "3741,BF" I want to seperate the number and letters into two new variables. I know this should be quite basic question, but searched on regex syntax and that seems a bit scarey to me, any one can shot me a quick solution

Hi, I have some problems when using AlgDesign->optFederov() generating designs. I have 6 variables, all factors. 3^2 and 4^4, I want to have a design that can take care of main effects and two interactions within 2 pair of variables v3-v4 and v5-v6, the following is the code ################ require(AlgDesign) set.seed(1) levels = c(v1=3,v2=3, v3=4,v4=4,v5=4,v6=4)

Bear me if this is a naive question. I have a dataframe, all lists inside it are factors. When I use as.matrix(df) to convert, I get a character matrix while my intention is to get numeric matrix. I can make the convertion iteratively by each list but I do think there is some more elegant way of doing this. please let me know if I missed somthing there. Thanks in advance. Sun

The other day, CNN had a story on working at Google. Out of curiosity, I went to the Google employment web site (I'm not looking, but just curious). In perusing their job posts for statisticians, preference is given to those who use R and python. Other languages, S-Plus and something called SAS were listed as lower priorities. When I started using Python, I noted they have a portion of the

I got errors when I tried to compile my code into dll on XP box. "C:/R/R-2.6.2/src/gnuwin32/MkRules:235: warning: overriding commands for target `.c.d' C:/R/R-2.6.2/src/gnuwin32/MkRules:223: warning: ignoring old commands for target `.c.d' C:/R/R-2.6.2/src/gnuwin32/MkRules:255: warning: overriding commands for target `.c.o' C:/R/R-2.6.2/src/gnuwin32/MkRules:243: warning:

Hi, R users I am trying to use EM algorithmto estimate a latent class model of discrete choice. The basic model is a logit model which has two variables X=(X1,X2) and the utility is defined as v = Beta*X. There are 3 classes of individuals each has its own Beta values, so Beta is a 3*2 matrix. For each individual, (there are 1000), he make a choice between two randomly generated choice

I am converting some matlab code into R that use inverse of the complementary error function, erfcinv and did not find an equivalent in R, is there such a function in some contributed modules? Thanks.

HI, I am testing several models about three latent constructs that measure risk attitudes. Two models with different structure obtained identical of fit measures from chisqure to BIC. Model1 assumes three factors are correlated with each other and model two assumes a higher order factor exist and three factors related to this higher factor instead of to each other. Model1: model.one