similar to: cov2cor exp

Dear all, I tried running the following syntax but it keeps running for about 4 hours and then i got the following errors: Error in if (is.na(f0$objective)) { : argument is of length zero In addition: Warning message: In is.na(f0$objective) : is.na() applied to non-(list or vector) of type 'NULL' Here is the syntax itself: library('nloptr') library('pracma') #

Hi R People: I am looking for some objects: >objects(pat="f") [1] "dufus" "f" "f1" "fake.df" "ff" "fm1" "one.df" "one1.df" "x.df" "xf" >mode(objects(pat="f")) [1] "character" > I would like to determine the mode of these objects.

Dear All, I am wondering if there is an R function to convert a covariance matrix to a correlation matrix. I have a covariance matrix sigma and I want to compute the corresponding correlation matrix R from sigma. Thank you very much, Bernard --------------------------------- [[alternative HTML version deleted]]

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

Suppose I have a Variance-covariance matrix A. Is there any fast way to calculate correlation matrix from 'A' and vice-versa without emplying any 'for' loop? [[alternative HTML version deleted]]

List members, Via searches I've seen similar discussion of this topic but have not seen resolution of the particular issue I am experiencing. If my search on this topic failed, I apologize for the redundancy. I am attempting to generate random covariance matrices but would like the corresponding correlations to be uniformly distributed between -1 and 1. The approach I have been using is:

Dear R users, I'm trying to fit a set an ODE to an experimental time series. In the attachment you find the R code I wrote using modFit and modCost of FME package and the file of the time series. When I run summary(Fit) I obtain this error message, and the values of the parameters are equal to the initial guesses I gave to them. The problem is not due to the fact that I have only one

Hi everyone. Based on a dependent variable (y), I'm trying to generate some independent variables with a specified correlation. For this there's no problems. However, I would like that have all my "regressors" to be orthogonal (i.e. no correlation among them. For example, y = x1 + x2 + x3 where the correlation between y x1 = 0.7, x2 = 0.4 and x3 = 0.8. However, x1, x2 and x3

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

Greetings, I have a seemingly simple task which I have not been able to solve today and I checked all of the help archives on this and have been unable to find anything useful. I want to construct a symmetric matrix of arbtriray size w/o using loops. The following I thought would do it: p <- 6 Rmat <- diag(p) dat.cor <- rnorm(p*(p-1)/2) Rmat[outer(1:p, 1:p, "<")] <-

Dear all, I used modFit of the package FME to fit a set of ODE to a ste of eperiemntal data. The summary of this fit give me the following error > summary(Fit) Residual standard error: 984.1 on 452 degrees of freedom Error in cov2cor(x$cov.unscaled) : 'V' is not a square numeric matrix In addition: Warning message: In summary.modFit(Fit) : Cannot estimate covariance; system is

Hi UseRs, I don't want to die beeing idiot... I dont understand the different results between: cor() and cov2cov(cov()). See this little example: > x=matrix(c(0.5,0.2,0.3,0.1,0.4,NA,0.7,0.2,0.6,0.1,0.4,0.9),ncol=3) > cov2cor(cov(x,use="pairwise.complete.obs")) [,1] [,2] [,3] [1,] 1.0000000 0.4653400 -0.1159542 [2,] 0.4653400 1.0000000

hi, I have following data and code; cov <- c (1.670028 ,-1.197685 ,-2.931445,-1.197685,1.765646,3.883839,-2.931445,3.883839,12.050816) cov.matrix <- matrix(cov, 3, 3, dimnames=list(c("y1","x1","x2"), c("y1","x1","x2"))) path.model <- specify.model() x1 -> y1, x1-y1 x2 <-> x1, x2-x1 x2 <->

Dear R-list member, I'm using the function pmnorm() (-->library(mnormt)) within a for-loop. Certain parameter values leads to an error message: "(In sqrt(diag(S)) : NaNs produced, In sqrt(1/diag(V)) : NaNs produced, In cov2cor(S) : diag(.) had 0 or NA entries; non-finite result is doubtful)" obviously because "NaNs" were produced. Is it possible to tell R that it

I have followed the instructions on how to build a Windows package and everything seems to work EXCEPT that I can't see the data files that I have loaded into the data directory. I have placed the appropriate data in the data directory (as in the instructions). There are 3 data sets, which I can see when I use the command in R. Details below. I have searched everywhere to try to fix this, but

Dear list, I have a data file which includes, alongside various variables representing questionnaire scores, a variable for survey weights computed as the number of observations in the sample drawn from that group divided by the number of observations in the population in the group. I need to calculate a covariance matrix of the questionnaire scores for use in sem. How do I apply the weights?

Hello everyone., I am looking for a covariance function this not the first time I have this type of problem (to find which function does something). I try in google with "R cran covariance function" but usually this ends with different results that do not help me that much. Could you please try to advice me how to search for what function implements the functionality you want.

Hola! Para simular matrices de datos normales multivariados con la sentencia rmvnorm (dentro del paquete mvtnorm) se necesita, entre otras cosas, el número de vectores a simular, el vector de parámetros-medias correspondiente a cada variable y su respectiva matriz de Varianza-Covarianza. En este último punto, tengo problemas. En lugar de ingresar una matriz sigma creada por mi, necesito simular

Dear R-users; I'm working with a a dataset that was previously used to fit a nonlinear model of the form: Y ~ a * (1 + b * log(1 - c * X^d)) The parameters published elsewhere are: a = 1.758863, b = .217217, c = .99031, and d = .054589 However, there is no way I can replicate this result. I've tried several options (including SAS) w/o success. The data is: X <-

Hi everyone, I am fairly new to R, and I am aware that others have had this problem before, but I have failed to solve the problem from previous replies I found in the archives. As this is such a standard procedure in psychological science, there must be an elegant solution to this...I think. I would much appreciate a solution that even I could understand... ;-) Now, I want to calculate a