similar to: Principle component analysis function

Thanks to Mr.Liviu Androvic and Mr.Richard Rowe helped me in PCA. Because I have just learn R language in a few day so I have many problem. 1) I don't know why PCA rotation function not run although I try many times. Would you please hepl me and explain how to read the PCA map (both of rotated and unrotated) in a concrete example. 2) Where I can find document relate: Plan S(A), S(A*B),

I have an exercise. With 3 kinds of yohourt a,b,c. There are 25 participatients estimate 3 norms: taste (va,vb,vc), structure (ca,cb,cc) and price (ga,gb,gc) and give the mark from 1 to 5. I don't know how to PCA this data. Please help me! I attached the data file follow: Va Vb Vc Ca Cb Cc Ga Gb Gc 4 2 4 5 5 5 4 4 2 2 2 4 3 2 5 4 5 1 2 2 1 2 3 3 3 1 4 1 1 2 2 3 3 4 3 2 3 4 4 4 3 1 2 1 2 1 1 1

I run the following code: library(rpart) data(kyphosis) fit <- rpart(Kyphosis ~ ., data=kyphosis) plot(fit) text(fit, use.n=TRUE) The text labels represent the count of each class at the leaf node. Unfortunately, the numbers are rounded and in scientific notation rather than the exact number of examples sorted by that node in each class. The plot is supposed to look like

Hello all, I would like to compare the similarity of two cluster solutions using a validation criteria (such as Hubert's gamma coefficient, the Dunn index the corrected rand index and so on) I see (from here:http://www.statmethods.net/advstats/cluster.html) that the function cluster.stats() in the fpc package provides a mechanism for comparing 2 cluster solutions - *BUT* - it requires me to

I ran this code (several times) from the Quick-R web page ( http://www.statmethods.net/advstats/cart.html) but my cross-validation errors increase instead of decrease (same thing happens with an unrelated data set). Why does this happen? Am I doing something wrong? # Classification Tree with rpart library(rpart) # grow tree fit <- rpart(Kyphosis ~ Age + Number + Start,

I am interested in applying a "classification tree" analysis where the response variable is a censored variable (survival data). I've discovered the package 'party' through this page: http://www.statmethods.net/advstats/cart.html. However, as my sample is not very big I would like to apply 'bootstrap' and use 'random forests', but with my censored response

Dear All, It might be a simple question. But I could not find the answer from function “prcomp” or “princomp”. Does anyone know what are the codes to get coefficient and scores of Principal component analysis in R? Your reply will be appreciated! Best Zunqiu [[alternative HTML version deleted]]

I am trying to create a classification tree using either tree or rpart functions but when it comes to plotting the results the formatting I get is different than what I see in all the tutorials (like http://www.youtube.com/watch?v=9XNhqO1bu0A or http://www.youtube.com/watch?v=m3mLNpeke0I&feature=related or http://www.statmethods.net/advstats/cart.html "tree for kyphosis"). I am

Hola! No he podido consultar la doc. del paquete ade4, algo debe estar caído en CRAN ahora mismo. Dos cosas sobre la metodología -aun desconociendo los detalles de cómo lo hace ade4: El output de un PCA, los "pesos" de cada variable en las dimensiones de los componentes se interpretan como correlaciones, a mayor valor absoluto mayor asociación variable-componente. Ahora, como tales

Hi folks, I'm trying to estimate bias-corrected percentile (BCP) confidence intervals on a vector from a simple for loop used for resampling. I am attempting to follow steps in Manly, B. 1998. Randomization, bootstrap and monte carlo methods in biology. 2nd edition., p. 48. PDF of the approach/steps should be available here: https://wyocoopunit.box.com/s/9vm4vgmbx5h7um809bvg6u7wr392v6i9 If

Tjats great that you are familiar and thanks for responding. Have you ever done what I am referring to? I have alteady spent time going through links and tutorials about decision trees and random forrests and have even used them both before. Mike On Apr 13, 2016 5:32 PM, "Sarah Goslee" <sarah.goslee at gmail.com> wrote: It sounds like you want classification or regression trees.

Hi, I am trying to do a Principal component analysis on histogram data. Basically, I have a group of subjects and for each of them, I have a column of bin-counts (vis-a-vis intervals) and a corresponding column of frequencies (or normalized frequencies). The bin counts are the same for all the subjects. I also have a group-averaged histogram (with the same bin counts and a column of frequencies)

Buen día, no sé si estoy utilizando bien la lista, es la primera vez. Si lo hago mal me corrigen por favor. Sobre tu comentario Pedro, muchas gracias. Lo qeu entiendo con tu sugerencia de set.seed es qeu de esa forma fijas los resultados, pero no estoy seguro si otra agrupación funcione mejor. Es decir me interesa un método de agrupación que genere la "mejor" agrupación y como los

I need the output to have groups and the probability any given record in that group then has of being in the response class. Just like my email in the beginning i need the output that looks like if A and if B and if C then %77 it will be D. The examples you provided are just simply not similar. They are different and would take interpretation to get what i need. On Apr 14, 2016 1:26 AM,

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of

I still need the output to match my requiremnt in my original post. With decision rules "clusters" and probability attached to them. The examples are sort of similar. You just provided links to general info about trees. Sent from my Verizon, Samsung Galaxy smartphone<div> </div><div> </div><!-- originalMessage --><div>-------- Original message

Hi, I am trying to calculate Principal Component Scores per id per year using the psych package. The following lines provide the scores per obeservation pca = data.frame(read.table(textConnection(" id year A B C D 1001 1972 64 56 14 23 1003 1972 60 55 62 111 1005 1972 57 51 10 47 1007 1972 59 49 7 10 1009 1972 65 50 9 32 1011 1972 52 58 3 5 1013

I am running a PCA, but would like to rotate my data and limit the number of factors that are analyzed. I can do this using the "principal" command from the psych package [principal(my.data, nfactors=3,rotate="varimax")], but the issue is that this does not report scores for the Principal Components the way "princomp" does. My question is: Can you get an

Haj i try to perform a principal component analysis by using a tetrachoric correlation matrix as data input tetra <- tetrachoric (image_na, correct=TRUE) t_matrix <- tetra$rho pca.tetra <- principal(t_matrix, nfactors = 10, n.obs = nrow(image_na), rotate="varimax", scores=TRUE) the problem i have is to compute the individual factor scores from the pca. the code runs perfect

Hi All, I have got matrix 'data' of dimension 22000x600. I want to make 50 independent samples of dimension 22000x300 from the original matrix 'data'. And then want to calculate pearsons CC for each of the obtained 50 matrices. It seems it is possible to do this using 'boot' function from library boot but I am not able to figure out how? I am really stuck. Please help!