similar to: Tricky vectorization problem

Hi all, I'm looking for an analytic method to obtain the PDF & CDF of the sampling distribution of a given correlation (rho) at a given sample size (N). I've attached code describing a monte carlo method of achieving this, and while it is relatively fast, an analytic solution would obviously be optimal. get.cors <- function(i, x, y, N){ end=i*N

Hi all, I understand that it is simple to create data with a specific correlation (say, .5) using mvrnorm from the MASS library: > library(MASS) > set.seed(1) > > a=mvrnorm( + n=10 + ,mu=rep(0,2) + ,Sigma=matrix(c(1,.5,.5,1),2,2) + ,empirical=T + ) > a [,1] [,2] [1,] -1.0008380 -1.233467875 [2,] -0.1588633 -0.003410001 [3,] 1.2054727 -0.620558768

Hi all, Hopefully this will be quick, I'm looking for pointers to packages/ functions that would allow me to calculate the power of a t.test when the DV has measurement error. That is, I understand that, ceteris paribus, experiments using measure with more error (lower reliability) will have lower power. Mike -- Mike Lawrence Graduate Student, Department of Psychology, Dalhousie

Hi all, I have collected response time data from 178 participants ('sub') for each combination of 4 within-Ss factors ('con','int','tone','cue'). Additionally, I have recorded the gender of each participant, so this forms a between-Ss factor ('gender'). Normally this would be analyzed using aov:

I have hit a problem with the design of the mcmc package I can't figure out, possibly because I don't really understand the R function call mechanism. The function metrop in the mcmc package has a ... argument that it passes to one or two user-supplied functions, which are other arguments to metrop. When the two functions don't have the same arguments, this doesn't work.

You could calculate the confidence interval of the correlation at your desired df: http://davidmlane.com/hyperstat/B8544.html The below code takes as arguments the observed correlation, N, and alpha, calculates the confidence interval and checks whether this includes 0. cor.test2=function(r,n,a=.05){ phi=function(x){ log((1+x)/(1-x))/2 } inv.phi=function(x){

Hi all, A suggestion derived from discussions amongst a number of R users in my research group: set the default column names produced by aggregate () equal to the names of the objects in the list passed to the 'by' object. ex. it is annoying to type with( my.data ,aggregate( my.dv ,list( one.iv = one.iv ,another.iv = another.iv ,yet.another.iv = yet.another.iv )

Hi all, Tinkering with a wrapper for xyplot that will help me plot a bunch of plots in a data analysis I'm doing and I ran into an odd error that I'm guessing is a scope issue. Here's a very simple version of the code: ############### #load lattice library(lattice) #create the wrapper function do.xyplot = function( plot.formula, data.frame, plot.groups){ print(plot.groups)

Hi all, This is my first post to the developers list. As I understand it, aggregate() currently repeats a function across cells in a dataframe but is only able to handle functions with single value returns. Aggregate() also lacks the ability to retain the names given to the returned value. I've created an agg() function (pasted below) that is apparently backwards compatible (i.e.

Hi all, Just thought I'd share something I discovered last night. I was interested in creating animations consisting of a series of plots and after finding very little in the usual sources regarding animation in R directly, and disliking the imagemagick method described here (http://tolstoy.newcastle.edu.au/R/help/05/10/13297.html), I discovered that if one exports the plots to a

Hi all, Consider the following function: #### my.func = function(x){ y=ifelse(x>-.5,0,ifelse(x< -.8,abs(x)/2,abs(x))) print(c(x,y)) #print what was tested and what the result is return(y) } curve(my.func,from=-1,1) #### When I attempt to find the maximum of this function, which should be -.8, I find that optimize gets stuck in the plateau area and doesn't bother testing the

Hi all, Looking to calculate the expected mean and variance of a ratio distribution where the source distributions are gaussian with known parameters and sample values are correlated. I see (from wikipedia: http://en.wikipedia.org/wiki/ Ratio_distribution#Gaussian_ratio_distribution) that this calculation is quite involved, so I'm hoping that someone has already coded a function to

Hello, I would like to simulate randomly from a multivariate normal distribution using a correlation matrix, rho. I do not have sigma. I have searched the help archive and the R documentation as well as doing a standard google search. What I have seen is that one can either use rmvnorm in the package: mvtnorm or mvrnorm in the package: MASS. I believe I read somewhere that the latter was

Hi all, I want to draw a histgram for each row of a matrix and compare them. However the plot I got does not have the same y range and x range, which makes it difficult to make the comparison. Is there a easy way to fix the x range and y range in a xy plot for several plots, instead of specifying them for each plot. The following is my code for generalizing the matrix and draw the histogram.

Dear all, I need to generate numbers from multivariate normal with large dimensions (5,000,000). Below is my code and the error I got from R. Sigma in the code is the covariance matrix. Can anyone give some idea on how to take care of this error. Thank you. Hannah > m <- 5000000 > m1 <- 0.5*m > rho <- 0.5 > Sigma <- rho* matrix(1, m, m)+diag(1-rho, m)

Dear list members, I want to perform an MM-regression. This seems an easy task using the function lmrob(), however, this function provides me with NA coefficients. My data generating process is as follows: rho <- 0.15 # low interdependency Sigma <- matrix(rho, d, d); diag(Sigma) <- 1 x.clean <- mvrnorm(n, rep(0,d), Sigma) beta <- c(1.0, 2.0, 3.0, 4.0) error <- rnorm(n = n,

Thanks for your reply. I use mvrnorm from the *MASS* package and lmrob from the *robustbase* package. To further explain my data generating process, the idea is as follows. The explanatory variables are generated my a multivariate normal distribution where the covariance matrix of the variables is defined by Sigma in my code, with ones on the diagonal and rho = 0.15 on the non-diagonal. Then y

What is 'd'? What is 'n'? On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < christienkerbert at gmail.com> wrote: > Thanks for your reply. > > I use mvrnorm from the *MASS* package and lmrob from the *robustbase* > package. > > To further explain my data generating process, the idea is as follows. The > explanatory variables are generated my a

> On Mar 3, 2018, at 3:04 PM, Christien Kerbert <christienkerbert at gmail.com> wrote: > > Dear list members, > > I want to perform an MM-regression. This seems an easy task using the > function lmrob(), however, this function provides me with NA coefficients. > My data generating process is as follows: > > rho <- 0.15 # low interdependency > Sigma <-

d is the number of observed variables (d = 3 in this example). n is the number of observations. 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>: > What is 'd'? What is 'n'? > > > On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < > christienkerbert at gmail.com> wrote: > >> Thanks for your reply. >> >> I use