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Dear R community,and especially Giovanni Millo, For my master's thesis i need to simulate a panel data with the fixed effects correlated with the predicor, so i run the the following code: set.seed(1970) #######################Panel data simulation with alphai correlated with xi##################################### n <- 5 t <- 4 nt <- n*t pData <- data.frame(id =

Dear R community,and especially Giovanni Millo, For my master's thesis i need to simulate a panel data with the fixed effects correlated with the predicor, so i run the the following code: set.seed(1970) #######################Panel data simulation with alphai correlated with xi##################################### n <- 5 t <- 4 nt <- n*t pData <- data.frame(id =

Dear R community,and especially Giovanni Millo, For my master's thesis i need to simulate a panel data with the fixed effects correlated with the predicor, so i run the the following code: set.seed(1970) #######################Panel data simulation with alphai correlated with xi##################################### n <- 5 t <- 4 nt <- n*t pData <- data.frame(id =

Dear Carlos, please refrain from posting the same question umpteen times. Please consider that code is hard to read and people might not have the time to run your simulation etc. etc.. As I told you privately in response to your message on 18/1, > Re: generating correlated effects, I tried this only once, but I > didn't get it right. Simulations using this are, e.g., Hansen (2007)

Hi! I have a difficulty in the use of function optimize(). Could you help me? I want to maximize this function: ##### Logaritmo da distribui??o condicional de alpha[i] lp_alphai <- function(alphai, i, beta, tau, N){ t1 <- (N[i+1] - N[i])*log(alphai) t2 <- - (N[i+1] - N[i])*alphai*log(beta[i]) t3 <- (alphai - 1)*sum(log(times[(N[i] + 1):N[i+1]])) t4 <- -

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Hi, I'm trying to obtain confidence intervals for auto and cross correlation estimates. I've adapted code made available by Stock and Watson that uses the Bartlett Kernel and the delta method. In R it runs really, really slow because of the loops it uses and I have 9 series that I'd like to examine (81 total combinations). It was easy enough to replace one of the while loops with a

Hello, I'm attempting to create a data frame with correlations between every pair of variables in a data frame, so that I can then sort by the value of the correlation coefficient and see which pairs of variables are most strongly correlated. The sm2vec function in the corpcor library works very nicely as shown here: library(Hmisc) library(corpcor) # Create example data x1 = runif(50) x2 =

Hi I'm trying to write a generic script for processing some data which finishes off with some plots. Given Im never sure how many columns will be in my dataframe I wanted to using the following plot(spectra.wavelength, cormat, type = "l", ylim=c(-1,1), xlab="Wavelength (nm)", ylab="Correlation") however even if I specify as type="l" it appears plot

Dear R users, I am trying to print heatmaps in a loop (with a pause). Idea is to visualize changing correlations over time and for testing I wrote this simple (reproducible) code below. My problem is that levelplot() does not produce any output when I run the code (though heatmap does). Ideally I would like to use levelplot() as it produces a neat index on the side indicating the color and the

Dear all, Sorry to post my query once again in the list, since I did not get attention from anyone in my previous mail to this list. Now I make it simple here that please give me a code for find out the columns of a dataframe whose correlation coefficient is below a pre-determined threshold. (For detailed query please see my previous message to this list, pasted hereunder) Thanks and regards,

Hi for the loop section runif needs curved brackets Try IAP <-NA for (i in 1:Sample.Size){ if (DataSet$SES[i]>0) { IAP[i] <- ifelse(runif(1)>0.75, 1, 0) # High SES, higher chance to be in Treatment # } else { IAP[i] <- ifelse(runif(1)<=0.25, 1, 0) # Low SES, lower chance to be in Treatment # } } # End loop # IAP IAP zjiaqi19880219 wrote > Hi,

Newsgroup members, Does anyone have a clever way to simulate a correlation matrix such that each column contains dichotomous variables (0,1) and where each column has different prevalence rates. For instance, I would like to simulate the following correlation matrix: > CORMAT[1:4,1:4] PUREPT PTCUT2 PHQCUT2T ALCCUTT2 PUREPT 1.0000000 0.5141552 0.1913139 0.1917923 PTCUT2

Hello R-User! I appologise in advance if this should also go into statistics but I am presently puzzled. I have a data.frame (about 300 rows and about 80 variables) and my variables are dichotomous factors, continuous (numerical) and ordered factors. I would like to calculate the linear correlation between every pair of my variables, because I would like to perform a logistic regression (glm())

Hi to all R users, I have just uploaded in CRAN R2HTML 1.3, latest update of the R2HTML package, so that it should soon be available for your favorite platform. CHANGELOG: - Fixed some user-contributed bugs. - More CSS styles are defined, so that it's possible to change the whole look-and-feel of outputs / Add some CSS sample files. - Added HTMLstem to produce a stem-and-leaf plot (as

Hi to all R users, I have just uploaded in CRAN R2HTML 1.3, latest update of the R2HTML package, so that it should soon be available for your favorite platform. CHANGELOG: - Fixed some user-contributed bugs. - More CSS styles are defined, so that it's possible to change the whole look-and-feel of outputs / Add some CSS sample files. - Added HTMLstem to produce a stem-and-leaf plot (as

Hi all, Thanks for your help in generating the matrix of distance vs correlation. I did it using plot(as.vector(as.matrix(cormat)), as.vector(as.matrix(distmat))) Now I want to quantitate the same. May be on linear regression or some other statistical functions. I have tried using linmod for linear regression. But as I have two matrices in the form of the dataframes, I'm wondering if it

Hello, I try to set up namespaces for packages. It is fine for several of them, except one whose compilation fails (under Windows XP & R 2.0.0): ---------- Making package svViews ------------ adding build stamp to DESCRIPTION installing NAMESPACE file and metadata Error in parse(file, n, text, prompt) : syntax error on line 21 Execution halted make[2]: *** [nmspace] Error 1 make[1]: ***

--- celt/arm/arm_celt_map.c | 24 +++++++++++- celt/arm/pitch_arm.h | 97 +++++++++++++++++++++++++++++++++---------------- 2 files changed, 88 insertions(+), 33 deletions(-) diff --git a/celt/arm/arm_celt_map.c b/celt/arm/arm_celt_map.c index 0c9acff..cc6b706 100644 --- a/celt/arm/arm_celt_map.c +++ b/celt/arm/arm_celt_map.c @@ -94,9 +94,14 @@ void (*const

Hello Timothy, Appreciate the thorough review. Have a few questions before I re-spin the patch in-line. On 28 November 2014 at 15:52, Timothy B. Terriberry <tterribe at xiph.org> wrote: > Review comments inline. > >> +if OPUS_ARM_NEON_INTR >> +noinst_LTLIBRARIES = libarmneon.la >> +libarmneon_la_SOURCES = $(CELT_SOURCES_ARM_NEON_INTR) >>