similar to: Question on estimating standard errors with noisy signals using the quantreg package

I will have data in the following form: Time resp type stim type 300 a A 200 b A 155 a B 250 b B 80 c A 1000 d B ... c is left censored observation; d is right censored This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of Survival Data under the name

Hi Folks, I am dealing with data which have been presented as at each x_i, mean m_i of the y-values at x_i, sd s_i of the y-values at x_i number n_i of the y-values at x_i and I want to linearly regress y on x. There does not seem to be an option to 'lm' which can deal with such data directly, though the regression problem could be algebraically

Hi all, I have this data set: ## Empirical copula ## dt1 = ranking ## dt2 = observed uniform data associated with the ranking   Sample data, > dt1         S_i   R_i  [1,]   7.0  10.0  [2,] 232.5 440.5  [3,] 143.0 141.5  [4,] 272.5 222.0  [5,]  46.0  34.0  [6,] 527.0 483.0  [7,] 420.5 563.5  [8,]  23.5  16.5  [9,]  56.5  68.5 [10,] 341.5 382.5   > dt2       unisk1 unisk2  [1,]  0.008  0.010

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my

Hi, I am confused about how to select elements from a list. I'm trying to select all rows of a table 'crossRsorted' such that the mean of a related vector is > 0. The related vector is accessible as a list element l[[i]] where i is the row index. I thought this would work: > crossRsorted[mean(q[[ crossRsorted[,1] ]], na.rm = TRUE) > 0, ] Error in q[[crossRsorted[, 1]]] :

Hello, I am trying to get a time derivative on a plot title. I prefer to have it in the form \dot{s_i}, but \partial s_i/\partial t would be O.K. In the graphics demo I cannot find either a dot or a partial equivalent. Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia University, Wolfville, NS, B0P 1X0 (902)585-1461 All opinions stated

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

Hello Dear, I have a function, like z=f(x,y), and try a surface plot with this function. But, on the reference of "wireframe" requires data option, so I generated x and y, and computed z with them. But, still I have a problem to draw a surface plot. The code and errors are ################################################## mle_beta0=64.43707; mle_beta1=-24365.16; # generating for

Hello, I am trying to implement a formula aij= transition from state S_i to S_j/no of transition at state S_i Code I have written is working with three state {1,2,3 }, but if the number of states become={1,2,3,4,......n} then the code will not work, so can some help me with this. For and some rows of my data frame look like

Dear All, I will be confronted (relatively soon) with the following problem: given a set of known statistical indicators {s_i} , i=1,2...N for a N countries I would like to be able to do some data clustering i.e. determining the best way to partition the N countries according to their known properties, encoded by the {s_i} set of indicators for those countries. Some properties of these

Hi, I'm trying to find the maximum (likelihood) of a function. Therefore, I'm trying to minimize the negative likelihood function: # params: vector containing values of mu and sigma # params[1] - mu, params[2]- sigma # dat: matrix of data pairs y_i and s_i # dat[,1] - column of y_i , dat[,2] column of s_i negll <- function(params,dat,constant=0) { for(i in 1:length(dat[,1])) {

Dear All, I am recycling a previous email of mine where I asked some questions about clustering mixed numerical/categorical data. This time I am more into data mining. I am given a set of known statistical indexes {s_i}, i=1,2...N for a N countries. These indexes in general are a both numerical and categorical variables. For each country, I also have a property x_i whose value is known, but

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

Hi, I think you overthought this one a little bit, I don't know if this is the kind of code you are expecting but I came up with something like that: generate_transition_matrix <- function(data, n_states) { #To be sure I imagine you should check n_states is right at this point transitions <- matrix(0, n_states, n_states) #we could improve a little bit here because at

Parsed string lists are allocated by malloc, but were never freed. --- src/generator.ml | 16 +++++++++++++++- 1 files changed, 15 insertions(+), 1 deletions(-) diff --git a/src/generator.ml b/src/generator.ml index 7571f95..c72c329 100755 --- a/src/generator.ml +++ b/src/generator.ml @@ -6320,7 +6320,7 @@ and generate_fish_cmds () = | OptString n | FileIn n |

Hi, is there a R function that order a matrix according to some criteria based on the rows(or cols) of that matrix? For example, let's say that my matrix S is composed by n rows S_1, S_2,.., S_n and that I compute some real value g_i=g(S_i) for each row. Then I want to order this set of g_i (from smaller to bigger) and order the correspondent row to the new position. Is it possible (apart

Hi, I'm not used to thinking along these lines, and wanted to ask your advice: Suppose you have a sample of around 100, consisting of patients according to doctors, in which patients and doctors are given a questionnaire with categorical responses. Each patient somehow has roughly 3 doctors, or 3 rows of data. The goal is to assess by category of each question or DV the agreement between

Dear All, In the past I have often used minpack (http://bit.ly/zXVls3) relying on the Levenberg-Marquardt algorithm to perform non-linear fittings. However, I have always dealt with a function of a single variable. Is there any difference if the function depends on two variables? To fix the ideas, please consider the function f(R,N)=(a/(log(2*N))+b)*R+c*N^d, where a,b,c,d are fit parameters. For

hi! i want to change the "robustness weights" used by loess. these are described on page 316 of chambers and hastie's "statistical models in S" book as r_i = B(e_i,6m) where B is tukey's biweight function, e_i are the residulas, and m is the median average distance from 0 of the residuals. i want to change 6m to, say, 3m. is there a way to do this? i cant

I'm looking for a function in R that extends kappa to multiple raters when there is more than one response per subject. For example, say a group of doctors have to assign diseases to patients. Each patient will be assigned one to many diseases, and the number of doctors assigning diseases to any one patient will be two to many. Here's an extremely simple example of the type of data I