similar to: Hessian Matrix Issue

I am having issues with the following: (muhat = 1/n^2(sum of all the xi's) ) essentially if xbar = the sample mean, muhat = sample mean but square the n. Question: Use R to run a Monte Carlo simulation which compares the finite-sample performance of xbar and muhat. Specifically generate 1000 samples n=30 from a standard normal distribution. For each sample calculate xbar and muhat. I have

Hello everyone, I'm trying to estimate the parameters of the returns series attached using the GARCH code below, but I get the following error message: Error in solve.default(Hessian) : system is computationally singular: reciprocal condition number = 0 Error in diag(solve(Hessian)) : error in evaluating the argument 'x' in selecting a method for function 'diag' Can

Full_Name: Nuno Monteiro Version: 2.3.1 OS: Windows XP HE Submission from: (NULL) (84.9.38.207) I'm using the FEAR library to perform Data Envelopment analysis with a 36,000 obs dataset. The function dea is working fine but then when I try to use the boot.sw98 to come up with some sensitivity analysis I get the following error:

Hello there, I have two data sets with 14 variables each, and wish to do the test for equality of their covariance matrices and mean vectors. Normally these tests should be done by chi square test (box provided) and Hotelling's T square test respectively. Which R functions could do this kind of test? I just find some functions could do for one dimension, but no for multidimension. Some one

I would like to test the hypothesis that the difference between pairs, for several variables, is zero. This is easily done separately for each variable with: lm(Y ~ rep(0, nrow(Y))) where Y is a matrix whose columns are the differences for each variable between pair members. However, I would like to get an overall probability across all variables from a Wilks or Pillai-Bartlett statistic as in

Hello R-list-members, I'm trying to model ARMA(0,2) & GARCH(1,1) process using the code below, but according to my textbook, the estimated parameters are wrong. The MA-parameters should be negative. (I've got the same problem using garchFit()). Can anyone tell me what I'm doing wrong? And how can I calculate the hessian matrix? Many thanks, Desislava Kavrakova Code:

Here's one possibility, assuming muhat and sigmahat are estimtes of mu and sigma from N iid draws of N(mu, sigma^2): tStat <- abs(x - muhat) / sigmahat pValue <- pt(tStat, df=N, lower=TRUE) I'm not quite sure what df tStat should have (exercise for math stat), but given fairly large N, that should make little difference. Andy > From: Ross Clement > > Hi. I have a

Hi there, I am trying to write a function to perform GOF test of data to a zero-truncated Poisson distribution. I am facing 2 problems. 1) How can I obtain a frequency table for all values within the range of observed values? For instance if the observations are obs <- c("A", "A", "A", "A", "B", "C", "C", "D",

I'm getting unexpected results from the coxph function when using weights from counter-matching. For example, the following code produces a parameter estimate of -1.59 where I expect 0.63: d2 = structure(list(x = c(1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1), wt = c(5, 42, 40, 4, 43, 4, 42, 4, 44, 5, 38, 4, 39, 4, 4, 37, 40, 4, 44, 5, 45, 5, 44, 5), riskset =

I have been trying to create code to calculate the power for an adaptive design with a survival endpoint according to the method of Schafer and Muller ('Modification of the sample size and the schedule of interim analyses in survival trials based on interim inspections,' Stats in Med, 2001). This design allows for the sample size to be increased (if necessary) based on an interim look at

Hi, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform

Hi All, I am trying to reproduce this using R instead. [image: Full-size image (38 K)] I tried using the following code *SChla <- read.csv("SM_Chla_data.csv")* *Atlantis <- SChla[16:66,]* *head(Atlantis)* * * Seamount Station Depth Pico Nano Micro Total_Ch dbar Latitude Longitud 16 Atlantis 1217 Surface 0.0639 0.1560 0.0398 0.2597 2.082 -32.71450 57.29733

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

Good morning, I am using R to try to model the proportion of burned area in Portugal. The dependent variable is the proportion. The family used is binomial and the epsilon would be binary. I am not able to find the package to be used when the proportion (%) has to be used in glm. Could someone help me? I am using normal commands of glm.. for example: glm_5<- glm(formula=p~Precipitation,

# Peter B. Mandeville kindly offered to send me code for Hotelling's T^2 # Test. Unfortunately there seems to be no route to his machine. # So i'm trying to reach him via the Mailing List. ------------------------------------------------------------------------ Sir, this morning i recieved your message about the availability of the code for Hotelling's Test. I hurried to find out

Hello, I have written a function that demonstrates the CLT by generating samples following the exponential distribution, calculating the means, plotting the histogram, and drawing the limiting normal curve as an overlay. I have the title of each histogram state the sample size and rate (1/theta) for the exponential (the output is actually 4 histograms), but I can't get the greek letter theta

R-help, I have the following file I want to import to R (some lines removed) Calibrated CTD data for station:00280001 Calibrated:23/8 2001, Salinity Unsmoothed, Fluorescence Uncalibrated Maximum observed depth: 36 m QUAL has one digit for each of pressure, temp., sal. and fluor. QUAL=1:Uncal., QUAL=2:OK, QUAL=6:Interp., QUAL=9:No data

I am new to R. I want to graph group data using a "Traditional Bar Chart with Standard Error Bar", like the kind shown here: http://samiam.colorado.edu/~mcclella/ftep/twoGroups/twoGroupGraphs.html Is there a simple way to do this? So far, I have only figured out how to plot the bars using barplot. testdata <- scan(, list(group=0,xbar=0,se=0)) 400 0.36038 0.02154 200 0.35927

Hi, there I'm trying to plot what is returned from a call to tapply, and can't figure out how to do it. My guess is that it has something to do with the inclusion of row names when you ask for the values you're interested in, but if anyone has any ideas on how to get it to work, that would be stellar. Here's some example code: y1<-rnorm(40, 2) x1<-rep(1:2, each=20)

Has anyone written utilities to do rescaled range analysis in R? Jeff -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch