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Chuck (Is it fine to call you Chuck?) has far more R jutsu than I do obviously. I don't know much about pmin and factor but it might worth looking into if you want to manipulate states by names (I assume this is why one might want to use it?) generate_transition_matrix <- function(data, states) prop.table(table(head(data, -1), tail(data, -1)), 1)[states,]

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

Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

Ok, I assumed you wanted to compute a matrix M for all states such that M[i][j]= transition from state i to state j / number of transition from state i but from what you just answered it looks like you want to compute a matrix M for a set of states S such that: M[S_i][S_j]= transition from state S_i to state S_j / number of transition from state S_i where S_i is in the set S ? 2017-08-28

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

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

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

Is this how a t hypothesis test is done when I don't have the actual data, but just the summarized statistics: > #Homework 9.2.6 [1] > n<-31 > xbar<-3.10 > s_x<-1.469 > m<-57 > ybar<-2.43 > s_y<-1.35 > s_pooled<- (((n-1)*s_x^2) + ((m-1)*s_y^2)) / (n + m - 2) > s_pooled [1] 1.939521 > t_obs <- (xbar - ybar) / (s_pooled * (sqrt(1/n + 1/m))) > t_obs [1] 1.547951 > qt(c(.025, .975), n+m-2) [1] -1.987934 1.987934 > # Therefore, fail to reject H0 at the 0.05 level o...

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

I am new to generalized linear models and studying McCullagh & Nelder (1989). Especially, I have a problem resembling the \"cheese taste\" example (5.3.1. p. 109) of the book. I tried to analyse the cheese example with R but failed to do so because R allowed me to use logit link function only with binary family that supposes 0 <= y <= 1. Do I need to scale the y\'s or

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 |

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, 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

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

Buenos dias para todos, Estoy trabajando en una aplicación que involucra análisis de clusters. Básicamente el objetivo es determinar a qué cluster pertenece cada observación de una matriz de datos "mydata" y luego generar muestras aleatorias de los mismos datos para determinar la proporción de veces que cada observación es clasificada en el cluster k. Este tipo de análisis es muy común

On Thu, 12 Apr 2007, Fernando Magno Quintao Pereira wrote: >> I'm definitely interested in improving coalescing and it sounds like >> this would fall under that work. Do you have references to papers >> that talk about the various algorithms? > > Some suggestions: > > @InProceedings{Budimlic02, > AUTHOR = {Zoran Budimlic and Keith D. Cooper and Timothy

For the purposes of the module parameters, specifies the permissions of the corresponding files in sysfs in predefined S_I* form rather than in octal notation. Withal it makes the source code more consistent. Moreover, because all parameters are readable to everyone, it is more user-friendly. $ grep S_IRUGO include/linux/stat.h #define S_IRUGO (S_IRUSR|S_IRGRP|S_IROTH) $ grep

Dear all, I'm struggling with weighted least squares, where something that I had assumed to be true appears not to be the case. Take the following data set as an example: df <- data.frame(x = runif(100, 0, 100)) df$y <- df$x + 1 + rnorm(100, sd=15) I had expected that: summary(lm(y ~ x, data=df, weights=rep(2, 100))) summary(lm(y ~ x, data=rbind(df,df))) would be equivalent, but