similar to: partial least regression

Dear developeRs, I maintain a package 'pls', which has a main fit function mvr(), and functions plsr() and pcr() which are meant to take the same arguments as mvr() and do exactly the same, but have different default values for the 'method' argument. The three functions are all exported from the name space. In the 'pre namespace' era, I took inspiration from lm() and

dear all, I am a new comer to R and statistic. Now I have a little confuse about the package pls. I have to use 5 components to form a model. There are strong relationship between some of the components, which leads to the changes of the sign of each coeficeince, of course this is unwanted when using the normal regression way. So I choose the way of PLS, which is good at solve this kind of

Hello! I need help to use the package pls.pcr in R. I installed R in an IRIX 6.5, using the version of R 0.64.1 from sgifreeware(I didn't get to install the newest version using make). I need to use the package pls.pcr and when I give the command: # R R : Copyright 1999, The R Development Core Team Version 0.64.1 (May 8, 1999) R is free software and comes with ABSOLUTELY NO

What version of R, what version of pls.pcr, and on what OS? Have you checked whether your versions of software are up to date? I get: > n <- 1350 > p <- 180 > y <- rnorm(n) > x <- matrix(sample(0:1, n*p, replace=TRUE), n, p) > fit <- mvr(x, y, method="SIMPLS", validat="none", ncomp=2) > xt <- matrix(sample(0:1, 312*p, replace=TRUE), 312,

List users, Using Asterisk in an inbound call center environment has led us to pushing the limits of vertical scaling. In order to treat each caller fairly and to utilize our agents as efficiently as possible, it is desirable to configure each client as a single queue. As far as I know, Asterisk's queues cannot be distributed across servers, so the size of the largest queue we service

Dear R-helpers, I have performed a PLS regression with the mvr function from the pls.pcr package an I have 2 questions : 1- do you know if mvr automatically centers the data ? It seems to me that it does so... 2- why in the situation below does the output say that the optimal number of latent variables is 4 ? In my humble opinion, it is 2 because the RMS increases and the R2 decreases when 3 LVs

Dear list I am using the mvr function of the package pls.pcr to compute PLS resgression using a X matrix of gene expression variables and a Y matrix of medical varaibles. I would like to obtain the R2 (sum of squares captured by the model) and Q2 (proportion of total sum of squares captured in leave-one-out cross validation) of the model. I am not sure if there are specific slots in the

Hi I'm performing a PLS This is my data present in a file Year Y X2 X3 X4 X5 X6 1960 27.8 397.5 42.2 50.7 78.3 65.8 1960 29.9 413.3 38.1 52 79.2 66.9 1961 29.8 439.2 40.3 54 79.2 67.8 1961 30.8 459.7 39.5 55.3 79.2 69.6 1962 31.2 492.9 37.3 54.7 77.4 68.7 My R-code Data <- read.csv("C:/TestData.csv") variable=names(Data)[4:8] dataset=NULL dataset$X=NULL len=length(variable)

Hello, I am trying to understand how to utilize the "mvr" function in the pls Package of R. I am utilizing the R "pls Package" document dated 18 May 2005 as guidance. My data set consists of a 12 x 12 data frame created from reading in a table of values. I have read the data in via the command: volumes <- read.table("THA_vol.txt", header = TRUE) and then

#### I checked through every 3 factor * 3 loading case. #### While, 4 factor * 3 loading failed. #### the data is 6 factor * 3 loading require(sem); cor18<-read.moments(); 1 .68 1 .60 .58 1 .01 .10 .07 1 .12 .04 .06 .29 1 .06 .06 .01 .35 .24 1 .09 .13 .10 .05 .03 .07 1 .04 .08 .16 .10 .12 .06 .25 1 .06 .09 .02 .02 .09 .16 .29 .36 1 .23 .26 .19 .05 .04 .04 .08 .09 .09 1 .11 .13 .12 .03 .05 .03

Does anyone know how to pass a list of parameters into a function? for example: somefun=function(x1,x2,x3,x4,x5,x6,x7,x8,x9){ ans=x1+x2+x3+x4+x5+x6+x7+x8+x9 return(ans) } somefun(1,2,3,4,5,6,7,8,9) # I would like this to work: temp=c(x3=3,x4=4,x5=5,x6=6,x7=7,x8=8,x9=9) somefun(x1=1,x2=2,temp) # OR I would like this to work: temp=list(x3=3,x4=4,x5=5,x6=6,x7=7,x8=8,x9=9)

Error in mvr(Kd_nM ~ qsar, ncomp = 6, data = my, validation = "CV", method = "kernelpls") :   Invalid number of components, ncomp How I can fix this? [[alternative HTML version deleted]]

How to use the segments in the PLS fit1 <- mvr(formula=Y~X1+X2+X3+X4+x5+....+x27, data=Dataset, comp=5,segment =7 ) here when i use segments,the error was like this rror in mvrCv(X, Y, ncomp, method = method, scale = sdscale, ...) : argument 7 matches multiple formal arguments Please help -- View this message in context:

hi R help group, I have installed PLS package in R and use it for princomp & prcomp commands for calculating PCA using its example file(USArrests example). But How I can use PLS for Partial least square, R square, mvrCv one more think how i can import external file in R. When I use plsr, R2, RMSEP it show error could not find function plsr, RMSEP etc. How I can calculate PLS, R2, RMSEP, PCR,

Hi, You might need to check library(boot).? I have never used that before.? So, I can't comment much.? It is better to post on R-help list.? I had seen your postings on Nabble in the past.? Unfortunately those postings were not accepted in R-help.? You have to directly post at ? r-help at r-project.org after registering at: https://stat.ethz.ch/mailman/listinfo/r-help ?

Dear R users, I have a data with 1000 variables named "x1", "x2", ..., "x1000", and I want to construct a formula like this format: ~x1+x2+...+x1000+x1:x2+x1:x3+x999:x1000+log(x1)+...+log(x1000) That is: the base variables followed by all interaction terms and all base feature log-transformations. I know I can use several paste functions to construct it. But is

R-help, I have a list whose elements are data frames. I want to change the colnames attribute in each element of this list but an error message comes up: > lapply(LD_strataNew,function(x) dimnames(x)[[2]][-1]) <- as.roman(1:9)[-6] Error in lapply(LD_strataNew, function(x) dimnames(x)[[2]][-1]) <- as.roman(1:9)[-6] : could not find function "lapply<-" >

Hi, Using S=1000 and simdata <- replicate(S, generate(3000)) #If you want both "m1" and "m0" #here the missing values are 0 res1<-sapply(seq_len(ncol(simdata.psm1)),function(i) {x1<-merge(simdata.psm0[,i],simdata.psm1[,i],all=TRUE); x1[is.na(x1)]<-0; x1}) res1[,997:1000] #????? [,1]???????? [,2]???????? [,3]???????? [,4]??????? #x1??? Numeric,3000 Numeric,3000

All, I have been using a data set to build pls models for three different soil properties. Two of the three models run fine; however I receive the following error for the final model. > libs.IC.cal <- mvr(libs.IC.fmla, data = libsdata.cond.cal, ncomp=20,validation = "LOO", method = "oscorespls") Error in colMeans(x, n, prod(dn), na.rm) : 'x' must

ChangeLog: ========= v2 => v3: a) Patch 1 data structure cleanups, header file include cleanups, IDA interface reuse and switching to device_create_with_groups(..) as per feedback from Greg Kroah-Hartman. b) Patch 7 signal documentation, sleep workaround removal and sysfs access API cleanups as per feedback from Michael S. Tsirkin. v1 => v2: @ http://lwn.net/Articles/563131/ a)