similar to: Multiple Correspondence Analysis

Hi all, I have a simple question: I have a character vector like this [1] "10_1" "1_1" "11_1" "2_1" "3_1" "4_1" "5_1" "6_1" "7_1" "8_1" [11] "9_1" "1_3" "2_3" "3_3" "4_3" "1_4" "2_4" "3_4"

Dear all, I tried out the example in the help document for mca (the multiple correspondence analysis of the MASS package): farms.mca <- mca(farms, abbrev=TRUE) farms.mca plot(farms.mca) But the graphic that I get seems unfeasible to me: I cannot recognize the numbers (printed in black) because they are all overlapping and concealing each other. I don ?t dare using my own data, which

Enio Jelihovschi" eniojelihovs@gmail.com Date: Fri, 20 Oct 2006 11:28:12 -0200 Subject: CORRESPONCE ANALYSIS Dear All I am new R user, trying to do correspondence analysis using the function mca of the package MASS. My question is: In the following example farms.mca <- mca(farms, abbrev = T) # Use levels as names plot(farms.mca, cex = rep(0.7, 2), axes = F) How can I change the

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

Hi, I would like to extract x,y from mjca function in ca package to be able to draw other graph types. For example for simple mca function from MASS I can do the following: ------------- anydata.mca <- mca(anydata, nf = 2, abbrev = TRUE) x <- anydata.mca$rs[,1] #that is what I can 'extract' y <- anydata.mca$rs[,2] plot(anydata.mca) #standard plot library(lattice)

Hi, I would like to extract x,y from mjca function in ca package to be able to draw other graph types. For example for simple mca function from MASS I can do the following: ------------- mydata.mca <- mca(anydata, nf = 2, abbrev = TRUE) x <- anydata.mca$rs[,1] #that is what I can 'extract' y <- anydata.mca$rs[,2] plot(anydata.mca) #standard plot library(lattice)

Hi, New to both R and SEM, so this may be a very simple question. I am trying to run a very simple path analysis using the sem package. There are 2 exogenous (FARSCH, LOCUS10) and 2 endogenous (T_ATTENT, RMTEST) observed variables in the model. The idea is that T_ATTENT mediates the effect of FARSCH and LOCUS10 on RMTEST. The RAM specification I used is FARSCH -> T_ATTENT, y1x1, NA

Hello, I have a question concerning the R-function chisq.test. For example, I have some count data which can be categorized as follows class1: 15 observations class2: 0 observations class3: 3 observations class4: 4 observations I would like to test the hypothesis whether the population probabilities are all equal (=> Test for discrete uniform distribution) If you have a small sample size

Greetings, again. This is not strictly an R question, so please feel free to ignore it if you like. My question is about the substance of correspondence analysis. Specifically, is it appropriate to use ca on a matrix of values such that the columns and/or rows are not mutually exclusive? To be more detailed: - The standard use of ca is illustrated in the example of corresp() (from MASS):

Is there a way to get the coordinates from a plot of an MCA object? [[alternative HTML version deleted]]

Dear list, I have the following data set id 1 2 3 4 5 6 7 8 9 10 disease a b c d e f g h i j age 23 40 32 34 25 32 22 35 29 21 city NY LD NY SG NY LD VG SA LD SG sex 1 1 2 2 2 2 1 1 1 2 treat_a y y y y treat_b n n n n n n ques1_1 2 4 5 6 8 3 1 2 4 5 ques1_2 6 4 5 12 10 9 8 4 5 7

In this years biostat teaching I will include Rcommander (it indeed simplifies syntax problems that makes students frequently miss the core statistical problems). But I could not find how to make a simple chisquare comparison between observed frequencies and expected frequencies (eg in genetics where you expect phenotypic frequencies corresponding to 3:1 in standard dominant/recessif

hi, I have following data and code; cov <- c (1.670028 ,-1.197685 ,-2.931445,-1.197685,1.765646,3.883839,-2.931445,3.883839,12.050816) cov.matrix <- matrix(cov, 3, 3, dimnames=list(c("y1","x1","x2"), c("y1","x1","x2"))) path.model <- specify.model() x1 -> y1, x1-y1 x2 <-> x1, x2-x1 x2 <->

we just started to use R and having some problems that no one in our school could solve. I hope someone here can help me out. the first problem is with the chisquare test. we want to exclude the missing values from the data. we used na.omit and made two new variables. now we want to use the chi square method but get the error x and y must have the same length. how do i use the chisquare method

Hi all, I'm getting contradictory results from bptest and ncvTest on a model calculated by GLS as: olslm = lm(log(rr)~log(aloi)*reg*inv, data) varlm = lm(I(residuals(olslm)^2)~log(aloi)*reg*inv, data) glslm = lm(log(rr)~log(aloi)*reg*inv, data, weights=1/fitted(varlm)) Testing both olslm and glslm with both ncvTest and bptest gives: > ncvTest(olslm) Non-constant Variance Score Test

Hi, I am trying to test for pairwise associations between genotypes ( Rows=individuals, Columns =genes, data are up to 4 genotypes per gene, some with 2,3 or 4) where each chisquare comparison is different depending on the genes tested. The test is the observed multilocus (across columns for each individual) genotypes vs the expectation, which is the product of the individual frequency for each

Mil gracias, José Antonio! Creo que, entre tu recomendación y el apelo al ingenio de Javier, voy a terminar generando la tabla que necesito. Mi problema, al contrario de lo que entiendo que les pasa a los que necesitan condensar, es que necesito extender... mi ya extensa data.frame. Pero poder pensar en el problema de otros me ayuda a entender el mío. Hau idatzi du José Antonio Palazón Ferrando

Hi Lorenzo, I’ll answer the first question you have: > I would like to use llvm-mca to estimate the IPC of a given code region. I am interested in the library version

Thanks for clarifying it Matt. In general, I quite like your suggested design. My only concern is about the semantic of the two new intrinsics. You design doesn't allow mca ranges to span through multiple basic blocks. That constraint is acceptable for now, since llvm-mca doesn't know how to deal with control flow. However, I am a bit concerned about what might happen in future if we

Andrea, thanks for the advice. On Jan 2, 2020, at 8:09 AM, Andrea Di Biagio <andrea.dibiagio at gmail.com<mailto:andrea.dibiagio at gmail.com>> wrote: Hi Lewis, Basically - if I understand correctly - you want to design a pass that uses llvm-mca as a library to compute throughput indicators for your outlined functions. You would then use those indicators to classify outlined