similar to: factors and mca

Is it possible to make disappear the id numbers from scatter.dudi (mc analysis) ? a <- as.factor(c(1, 2, 3, 2, 1)) b <- as.factor(c(3, 2, 3, 1, 1)) x <- as.factor(c(1, 2, 2, 1, 3)) y <- as.factor(c(2, 2, 3, 1, 1)) dat <- data.frame(a=a, b=b,x=x,y=y) summary(dat) dat require(ade4) dat.acm <- dudi.acm(dat, scann = FALSE, nf = 2) scatter.dudi(dat.acm) Thank you very much !

Thank you for help with the first part. The second part I do not understand. All I get is "3" "3". Presumably this is the factor that you refer to. Is there no simple way of extracting the text alone ----- Forwarded Message ---- From: Peter Alspach <PAlspach@hortresearch.co.nz> To: Jabez Wilson <jabezwuk@yahoo.co.uk> Sent: Tuesday, 7 November, 2006 7:46:02 PM

Kia ora I'm a using lme (from nlme package) with data similar to the Orthodont dataset and am getting positive log-likelihoods (>100). This seems usual and I wondered if someone could offer a possible explanation. I can supply a sample dataset if requested, but I feel almost certain that this question has been asked and answered recently. However, I can find no trace of it in the mail

Please, how to fill the area under the curve? x <- c(1:10) y <- c(rnorm(10)) plot(x,y) lines(x,y) Thanks, Mauricio Cardeal

Hi Does anyone know where is the package: npmc (Nonparametric Multiple Comparisons). I found the reference on R Site Search, but not the package itself on CRAN as suggested. Thanks Mauricio

Hi everyone ! Could you please help me with this problem ? I??ve trying to write a code that assign to a variable the content from another, but all I??ve got is a message error. For example: if (age <=10) {group == 1} else if (age > 10 & age <= 20) {group == 2} else {group == 3} Syntax error Or if (age <=10) {group == 1} else (age > 10 & age <= 20) {group == 2}

Hi, I would like to do a multiple correspondence analysis with the mca function in the MASS library on data that I have as a contingency table (which I've tried converting to a data frame). For example, ========= > data(HairEyeColor) > hair.df <- as.data.frame(HairEyeColor) > hair.df Hair Eye Sex Freq 1 Black Brown Male 32 2 Brown Brown Male 38 3 Red

Kia ora list members: I'm having a little difficulty getting the correct standard errors from a glm.object (R 1.9.0 under Windows XP 5.1). predict() will gives standard errors of the predicted values, but I am wanting the standard errors of the mean. To clarify: Assume I have a 4x3x2 factorial with 2 complete replications (i.e. 48 observations, I've appended a dummy set of data at the

Please, I??d like to store this sample matrix as a new object. How can I do this ? pulse <- c(67, 67, 68, 68, 68, 69, 69, 69, 69, 69, 70, 70, 70, 70, 71, 71, 72, 72, 73, 74) m <- NULL x <- 0 for (i in 1:5) { x <- sample(pulse,3) m <- mean(x) cat(x,m,"\n") } Thanks, Mauricio

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

Dear list: I have tried MASS's mca function and SAS's PROC corresp on the farms data (included in MASS, also used as mca's example), the results are different: R: mca(farms)$rs: 1 2 1 0.059296637 0.0455871427 2 0.043077902 -0.0354728795 3 0.059834286 0.0730485572 4 0.059834286 0.0730485572 5 0.012900181 -0.0503121890 6

(on the correct mailing list) Hi all, tl;dr: We'd like to propose moving tools/llvm-mca/lib into lib/MCA and create a new MCA library in LLVM. llvm-mca has recently been split <https://bugs.llvm.org/show_bug.cgi?id=37696> into its core part and the tool part. - The core part simulates the execution of a basic block of machine instructions as modeled by the llvm SchedModel.

Greetings. I'm about to embark on my first big (to me at least!) R project, which will be to write a function to plot three-dimensional multiple correspondence analysis (mca) plots in a manner similar to scatterplot3d(). (plot.mca() plots only two dimensions, even though mca() will calculate more.) Before I do so, however, I would love to know that I'm not reinventing the wheel or any

Dear list, Working the example in Stats complements to V&R 3rd ed., I found this : > library(MASS) > library(mva) > data(farms) > plot(mca(farms,abbrev=TRUE),cex=rep(0.7,2)) # ... Works OK # Sheer curiosity ... > plot(mca(farms,abbrev=TRUE,nf=4),cex=rep(0.7,2)) Error in rep(p * X.svd$d[sec], c(n, n)) : invalid number of copies in "rep" A bit of exploration in the

Hello. I asked before and it was great, cause as a beginner I learned a lot. But, if I have this in R (1 and 2 are codes for sex): > sex<-c(1,2,2,1,1,2,2,2) > sex [1] 1 2 2 1 1 2 2 2 I´d like to obtain the proportion according to sex.So I type: > prop.table(sex) [1] 0.07692308 0.15384615 0.15384615 0.07692308 0.07692308 0.15384615 0.15384615 [8] 0.15384615 The result is OK, but I

Introduction ----------------- Currently llvm-mca only accepts assembly code as input. We would like to extend llvm-mca to support object files, allowing users to analyze the performance of binaries. The proposed changes (which involve both clang and llvm) optionally introduce an object file section, but this can be stripped-out if desired. For the llvm-mca binary support feature to be useful, a

Hi. Please, how can I put together 2 or more lines at the same scatterplot ? Example: measures of protein intake (quantitative) of 4 children over 30 days, by day. How to plot all children at same graphic: Protein X Time ? Is there any command like "overlay" ? Thank you, Mauricio

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

Hi :-) I'm new to R and I started using it for a project (I'm the CS guy in a group of statisticians helping them find out how to solve issues as they come out). This is my first post to the list and I am starting to learn R. Well, they were used to doing MCA analysis in other programs where the data seems to be preprocessed automatically before running MCA. So, they need to process a

Hi All, I would like to use llvm-mca to estimate the IPC of a given code region. I am interested in the library version (https://reviews.llvm.org/D50929?id=162210 <https://reviews.llvm.org/D50929?id=162210>) but I have troubles understanding how to use it. Could you please point me to some documentations or (better) some code examples if any? Reading the documentation