similar to: Empirical Bayes Estimator for Poisson-Gamma Parameters

Dear All, Here is what I am trying to achieve: I would like to plot some data in 3D. Usually, one has a matrix of the kind y_1(x_1) , y_1(x_2).....y_1(x_i) y_2(x_1) , y_2(x_2).....y_2(x_i) ........................................... y_n(x_1) , y_n(x_2)......y_n(x_i) where e.g. y_2(x_1) is the value of y at time 2 at point x_1 (see that the grid in x is the same for the y values at all times).

Well, I could argue that it's not *completely* OT since my question is motivated by an enquiry that I received in respect of a CRAN package "Iso" that I wrote and maintain. The question is this: Given observations y_1, ..., y_n, what is the solution to the problem: minimise \sum_{i=1}^n (y_i - y_i^*)^2 with respect to y_1^*, ..., y_n^* subject to the "isotonic"

Hi everyone, Can anyone tell me if its possibility to extract the coefficients from the lm() command? For instance, imagine that we have the following data set (the number of observations for each company is actually larger than the one showed...): Company Y X1 X2 1 y_1 x1_1 x2_1 1 y_2 x1_2 x2_2 1 y_3 x1_3 x2_3 (...) 2 y_4 x1_4 x2_4 2 y_5 x1_5 x2_5 2 y_6 x1_6 x2_6 (...) n y_n x1_n x2_n n

Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)??

Hi all, I'm not familiar with R programming and I'm trying to reproduce a result from a paper. Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta

Dear R users, I have a response variable in a csv file called "y" and a matrix of predictor variables in a csv file called "mat". I have used the function "nls" I have specified the nonlinear relation between these variable.The code I have witten is called Rprog which begins with the phrase: L.minor.m1<-nls(Y~a ....etc.. The program when I execute the program, I

On Fri, 7 Feb 2014, Timothy B. Terriberry wrote: > Martin Storsjo wrote: >> This is required in order to build using the built-in assembler >> in clang. > > These patches break the gcc build (with "Error: bad instruction"). Ah, right, sorry about that. > Documentation I've seen is contradictory on which order ({cond}{size} or > {size}{cond}) is correct.

Hi, Can anyone tell me how to construct a simple VAR(1) time series with two variables using the dse package? I would like to end up with two time series y_1t = \phi_11 y_1,t-1 + \phi_12 y_2,t-1 + e_1t y_2t = \phi_21 y_1,t-1 + \phi_22 y_2,t-1 + e_2t Best regards, Sam.

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the

Dear R-Users, Dr. Wood replied to a similar topic before where confidence intervals were for a ratio of two treatments ( https://stat.ethz.ch/pipermail/r-help/2011-June/282190.html). But my question is more complicated than that one. In my case, log(E(y)) = s(x) where y is a smooth function of x. What I want is the confidence interval of a ratio of log[(E(y2))/E(y1)] given two fixed x values of

I am trying to test out several mgcv::gam models in a scalar-on-function regression analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i

Hi, I have two (different types of) measurements, say X and Y, resulting from the same set of experiments. So X and Y are paired: (x_1, y_1), (x_2, y_2), ... I am trying to calculate the standard deviation of Y with respect to X. In other words, in terms of the scatter plot of X and Y, I would like to divide it into bins along the X-axis and for each bin calculate the standard deviation along

I did a fourier transform on a function in time domain to get the following functions in frequency domain (in latex): $Y_1[\omega] = \frac{1}{1-\phi_1 e^{-jw}}$ $Y_2[\omega] = \frac{1}{1-(\phi_1 + \phi_2)e^{-jw} +\phi_1\phi_2e^{-2jw}}$ How do I find the spectrum of this function for given $\phi_1$ and $\phi_2$ coefficients and in the discretization interval $w = [-\pi:.1*\pi: \pi]$? Then, how

Dear people, I'm running RedHat 9.0 and R : Version 1.7.1 (2003-06-16) from the help file # Usage: # # filter(x, filter, method = c("convolution", "recursive"), # sides = 2, circular = FALSE, init) # init: for recursive filters only. Specifies the initial values of # the time series just prior to the start value, in reverse # time

Please help, I tried a program on S-plus, and it worked. Also I tried the same program on R but not worked. Here is the programme. I put it in a function form. The model and assumption are at the bottom. where counts<-c(22,2,2,0,5,7,14,0,0,2,36,0,0,1,17,10) which is name.data, i is row size and j is the column size. symmetry function(i, j, name.data) { row <- (c(1:i)) col <-

I am using the lattice packge for its levelplot and contourplot. Is it possible to adjust the line thickness of the 'box' and tickmarks in these plots? Thanks for the attention, Matt Sundling

This is required in order to build using the built-in assembler in clang. --- I squashed the two changes since it would break the normal gcc build otherwise. --- celt/arm/arm2gnu.pl | 2 ++ celt/arm/celt_pitch_xcorr_arm.s | 18 +++++++++--------- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/celt/arm/arm2gnu.pl b/celt/arm/arm2gnu.pl index eab42ef..5c24758 100755 ---

Does anyone know the "standard" commandline to compile and run spec95 benchmarks ? I found some scripts in llvm test suite (External/SPEC/CINT95), but I am not sure if they are the *default ref* configurations. <Some of them seem to use smaller problem sizes.> SPEC95 was retired many years ago, so few people around me know how to compile and run them. I am building from

Hi, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is