similar to: superimpose histogram on biplot

Dear list, Here is a small example code that use optim and optimize in order to fit two functions. Is it possible to fit two functions (like those two for example) at the same time using optim ... or another function in R ? Thanks Arnaud ###################################################################### ## function 1 x1 <- 1:100 y1 <- 5.468 * x + 3 # + rnorm(100,0, 10) dfxy <-

Hi all: How can I calculate the cumulative variance (or variance for each component) in correspondence analysis? If were possible in ADE4 package Thank you -- Antonio Punzón Merino O__---- Instituto Español de Oceanografía c/ /'_ --- Centro Oceanográfico de Santander (*) \(*) -- Promontorio de San Martín S/N ~~~~~~~~~~ 39004-Santander; Spain PO BOX: 240 Tlf: +34 942 29 10 60 Fax: +34

I am trying to drop a column of a data frame. The code below attempts to drop a numeric column (which does not work but gives no error or warning) and a factor column (which does not work but gives an error). I would appreciate someone telling me why my code does not work, and suggesting code that will work. Thanks, John rm(dfxyz,dfxz,dfxy) # create the data frame. dfxyz <-

Hello i need some precision about nipals in the chemometrics package in R . When i use nipals in chemometrics i obtain T and P matrix. I really don't understand what to do with these two matrix to obtain the scores for every the component (like in spss fo example) Comp1 Comp2 Comp3 quest1 0,8434 0,54333 0,3466 quest2 0,665 0,7655 0,433 Thank you very

Dear list members; The code given below corresponds to the PCA-NIPALS (principal component analysis) algorithm adapted from the nipals function in the package chemometrics. The reason for using NIPALS instead of SVD is the ability of this algorithm to handle missing values, but that's a different story. I've been trying to find a way to improve (if possible) the efficiency of the code,

Hello, after calculating a multinomial logit regression on my data, I compared the output to an output retrieved with SPSS 18 (Mac). The coefficients appear to be the same, but the logLik (and therefore fit) values differ widely. Why? The regression in R: set.seed(1234) df <- data.frame( "y"=factor(sample(LETTERS[1:3], 143, repl=T, prob=c(4, 1, 10))), "a"=sample(1:5,

V; Sent via BlackBerry from T-Mobile -----Original Message----- From: r-help-request at r-project.org Date: Tue, 30 Sep 2008 12:00:06 To: <r-help at r-project.org> Subject: R-help Digest, Vol 67, Issue 31 Send R-help mailing list submissions to r-help at r-project.org To subscribe or unsubscribe via the World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/r-help or, via email,

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

I would like to announce the availability of the 'nipals' package on CRAN, https://cran.r-project.org/web/packages/nipals/. The nipals package tries to do just one thing really well...find principal components of a matrix using Nonlinear Partial Least Squares. Missing values are allowed, the principal components are orthogonal, and the code has been heavily optimized. Details of the

I would like to announce the availability of the 'nipals' package on CRAN, https://cran.r-project.org/web/packages/nipals/. The nipals package tries to do just one thing really well...find principal components of a matrix using Nonlinear Partial Least Squares. Missing values are allowed, the principal components are orthogonal, and the code has been heavily optimized. Details of the

This problem is not limited to R, but R is one of the packages in which it arises. princomp is a nice function which creates an object for which inspection methods have been written. Unfortunately, princomp does not admit cases in which the x matrix is wider than high (i. e. more variables than observations). Such cases are typical in spectroscopy and related disciplines. It would be nice if the

I have a covariance matrix that is not positive semi-definite matrix and I need it to be via some sort of adjustment. Is there any R routine or package to help me do this? Thanks, Roger [[alternative HTML version deleted]]

The following code in clusplot.default (package cluster) is in error: x1 <- cmdscale(x, k = 2, eig = TRUE) var.dec <- sum(x1$eig)/sum(diag(x1$x)) if (var.dec < 0) var.dec <- 0 if (var.dec > 1) var.dec <- 1 x1 <- x1$points x1 has components with names "points" and "eig", not "x", so

Hello I am using the following command but not able to text the values on the graph can someone please make suggestions for improvement #here is the command loc_mds <- cmdscale(dist.r, k = 7, eig = TRUE) loc_mds$eig sum(abs(loc_mds$eig[1:2]))/sum(abs(loc_mds$eig)) sum((loc_mds$eig[1:2])^2)/sum((loc_mds$eig)^2) x <-loc_mds$points[,1] y <-loc_mds$points[,2] plot(x, y,

Dear all: I am comparing the PLS outputs of R and SAS for the following data set: Y x1 x2 x3 3 6 2 2 3 1 5 5 4 7 4 1 5 6 5 6 2 4 3 2 8 5 0 9 where Y is the dependent variable and x1, x2, x3 are the independent variables. I found several PLS algorithms in R (NIPALS,SIMPLS,KERNEL PLS). SAS has SIMPLS and NIPALS. The following are the NIPALS calculations of

Dear All, I was wondering whether any one knows of a matrix bending function in R that can turn non-positive definite matrices into the nearest positive definite matrix. I was hoping there would be something akin to John Henshall's flbend program (http://agbu.une.edu.au/~kmeyer/pdmatrix.html), which allows the standard errors of the estimated matrix elements to be considered in the

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Dear R-listers Is there anyone who knows why I get different eigenvectors when I run MatLab and R? I run both programs in Windows Me. Can I make R to produce the same vectors as MatLab? #R Matrix PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43 ,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24 ,58/53 ,26/244 ,0/1 ,5/43) #R-syntax

Hi, Maybe I have not been looking in the right spot, but, I have not been able to fine a command to automatically calculate the running cumulative sum of a vector. Is there such a command? Example of current code: > eig$values [1] 678.365651 6.769697 2.853783 > prop<-eig$values/sum(eig$values) > prop [1] 0.986012163 0.009839832 0.004148005 >

Hello, We are trying to find a function to compute "stress" in our multidimensional scaling analysis of a dissimilarity matrix. We've used "dist()" to create the matrix and "cmdscale()" for the scaling. In order to determine the number of dimensions we would like to plot stress vs. dimensions. However, we cannot find a pre-made command. It seems that other