similar to: S4 classes, some help with the basics

Hi all, I have a difficulty to calculate the PCA scores. The PCA scores I calculated doesn't match with the scores generated by R, mypca<-princomp(mymatrix, cor=T) myscore<-as.matrix(mymatrix)%*%as.matrix(mypca$loadings) Does anybody know how the mypca$scores were calculated? Is my formula not correct? Thanks a lot! Phoebe [[alternative HTML version deleted]]

Dear all, I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis). On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'. However, when I calculate the principal components for the USArrests data set, I don't find this to be the case: Here is how I

> biplot standardGeneric for "biplot" defined from package "stats" function (x, ...) standardGeneric("biplot") <environment: 0x0d4444d8> Methods may be defined for arguments: x Use showMethods("biplot") for currently available ones. > > > showMethods("biplot") Function: biplot (package stats) x="ANY"

Hi, I need to export to LaTex the summary of a PCA. So: myPCA <- prcomp(myDF) mySummary <- summary(myPCA) # print(xtable(mySummary)) How can I export to LaTeX not all the summary but only the first nPCs?? Best Riccardo

I've been using the pcaMethods to develop a scores matrix ======================================= data(iris) pcIr <- pca(iris[,1:4], method="nipals", nPcs=3, cv="q2") test <- scores(pcIr) ======================================== What I'm looking to do is to use lattice's barchart to plot the scores something like below, but expanded to all the scores

Hi, I was trying as well as looking for an answer without success (a bit strange since it should be an easy problem) and therefore I will appreciate you help: My simple script is: # Loadings data of 5 columns and 100 rows of data data1<-read.csv("C:/?/MyPCA.csv") pairs(data1[,1:4]) pca1 <- princomp(data1[,1:4], score=TRUE, cor=TRUE) biplot(pca1) The biplot present the data

Hi, I'm trying to perform a Principal Component Analysis on meteorological data with 10 predictors. I use the library pcaMethods to obtain a lot of information (loadings, scores, mean, standard deviation, etc). How can I obtain the scores (new PC variables) for new values of weather predictors using the results of PCA analysis (loadings, mean value, standard deviation) and without

Hi There, I'm attempting to plot 10 values on a three-dimensional PCA with text labels next to each point. While i have no trouble doing this on 2D plots using the 'text' or 'textxy' function, I cannot find a function to do this on a 3D plot. I am using princomp for my PCA: >PCA<-princomp(eucdata, cor=TRUE) >PCA$scores [,1:3] # the three principal components i

First of all I should say this email is more of a general statistics questions rather than being specific to using R but I'm hoping that this may be of general interest. I have a dataset that I would really like to use PCA on and have been using the package pcaMethods to examine my data. The results using traditional PCA come out really nicely. The dataset is comprised of a set of questions

#using "rollapply" to calculate a moving sum or running sum? #I am tryign to use rollapply to calcualte a moving sum? #I tried rollapply and get the error message #"Error in seq.default(start.at, NROW(data), by = by) : # wrong sign in 'by' argument" #example: mymatrix <- ( matrix(data=1:100, nrow=5, ncol=20) ) mymatrix_cumsum <- ( matrix(data=NA, nrow=5,

Hi, I'm trying to create a new S4 class (myMatrix) which for now just extends dgCMatrix (from package Matrix). Then I want to use "[" which is defined in Matrix. Out of the box with "[" (defined in Matrix) I lose the class information and the result is an object of class dgCMatrix. If I specify a "["-method for myMatrix, it is not used because a signature

Dear R-Helpers, I have a matrix where the first column is known. The second column is the result of multiplying this first column with a constant "const". The third column is the result of multiplying the second column with "const"..... So far, I did it like this (as a simplified example): nr.of.columns <- 4 myconstant <- 27.5 mymatrix <- matrix(numeric(0), nrow=5,

Hello, If a matrix with 5 columns has been defined and the first two columns need to be sorted in ascending order, how can this be achieved whilst ensuring the other 3 columns data are in relative position to the sorted columns? Glen Jones [[alternative HTML version deleted]]

I have two objects, one matrix and one vector. I want to use my vector to subset certain values out of my matrix. For example: I want to tell R, to select out all rows in myMatrix into a new myMatrix2 IF that corresponding row is less than a 0.5 in myVector. So: myMatrix = a matrix of 8000 by 20 myVector = vector of 8000 myMatrix2 = a matrix of < 8000 by 20 (based on selection criteria in

Hi, I have a dataframe (say myData) and want to get a list (say myList) that contains a matrix for each row of the dataframe myData. These matrices are calculated based on the corresponding row of myData. Using a for()-loop to do this is very slow. Thus, I tried to use apply(). However, afaik apply() does only return a list if the matrices have different dimensions, while my matrices have

Hi, Here i have a Matrix MyMatrix <- Name Age --------- ------- ANTONY 27 IMRAN 30 RAJ 22 NAHAS 32 GEO 42 and here i have an array with Minimum and Maximum values. MinMaxArray <- data.frame(MIN = 25,MAX=35) MIN MAX ------ -------- 25 35

Hi, How can I convert the matrices to list. For example I have this snippet: samples<-mymatrix[1,] print(samples) which prints: V1 V2 V3 V4 V5 V6 1 103.9 88.5 242.9 206.6 175.7 164.4 How can I convert the object "samples" such that it prints: [1] 103.9 88.5 242.9 206.6 175.7 164.4 The reason I ask this because I can't use the former "samples"

HI, The code was working perfectly fine yesterday and today, until half an hour ago.? Couldn't find any problems in the code. Still, I am getting error message. myMatrix <- data.matrix(read.table(text=" Name??????????? Age ANTONY??????? 27 IMRAN????????? 30 RAJ????????????????? 22 NAHAS????????? 32 GEO??????????????? 42 ", header=TRUE)) MinMaxArray? <- data.frame(MIN =

I would like to compare every column in my matrix with every other column and get the r-squared. I tried using the following formula and looping through every column: > summary(lm(matrix[,x]~matrix[,y]))$r.squared If I have 10,000 columns, the loops (10,000 * 10,000) take forever even if there is no formula inside. Then, I attempted to vectorize my code: > cor(matrix)^2 With 10,000

I am trying to find out if R can recognize specific criteria for removing rows (i.e. a prexisting function) I have a matrix myMatrix that is 12000 by 20 I would like to remove rows from myMatrix that have: -999 across all columns -999 across all columns but one -999 across all columns but two -999 across all columns but three -999 across all columns but four -999 across all columns but five