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...ata, -1)), 1)[states,] checkdf=data.frame(clusterNum=c(3,2,3,1,1,3,1,3,2,1,1,3,2,1,3,2)) states=c(2,3) transition_matrix= generate_transition_matrix(checkdf$clusterNum, states) transition_matrix 2017-08-28 18:09 GMT+02:00 niharika singhal <niharikasinghal1990 at gmail.com>: > yes or S_i= number of state > > 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 > For example 1 row of 3 states will be > x= s1->s1/no of transition in s1 > y= s1->s2/no of transition in s1 >...

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 expressed in these terms. One way of fud...

..._i in the data but, instead, in the data I only have a fraction p of these r_i patients. In fact, with (known) probability p a patient is included in the data. Thus, for each doctor i the number of patients IN THE DATA follows a binomial distribution with parameters r_i and p. For each i I now have s_i patients in the data where s_i is a draw from this binomial distribution. That is, the problem with the data is that I don't observe r_i but s_i. Simple montecarlo experiments confirm my intuition that standard errors should be larger when using the "noisy" information s_i/p instead...

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 17:50 GMT+02:00 niharika singhal <niharikasinghal1990 at gmail.com>: > Sorry I have typed wrong value for output > (2/6, 1/6, 3/6) > > On Mon, Aug 28, 20...

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 are...

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 checkdf=data.frame(clusterNum=c(3,2,3,...

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  [2,]  0.298  0.615  [3,]  0.194  0.187  [4,]  0.357  0.297  [...

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)))

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 countries may be categorical or...

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])) { llsum <- log( params[2]^2 + dat[i,2]^2) + (( dat[i,1] - params[1])^2/ (params[2]^2 + dat[i,2]^2)) } ll <- -0.5 * llsum + constant return(-ll) } Using (find...

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 that I also would like to be able to predict correctly using a model. This is needed in order to assess the importance...

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

...s () = generate_c_call_args ~handle:"g" style; pr ";\n"; + (* XXX: There must be a cleaner way to write this *) + iteri ( + fun i -> + function + | StringList name | DeviceList name -> + pr " char **%s_i = %s;\n" name name; + pr " while(*%s_i) {\n" name; + pr " free(*%s_i);\n" name; + pr " %s_i++;\n" name; + pr " }\n"; + pr " free(%s);\n" name; + | _ -> ();...

...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 "competing risks". Observations are obtained from n independent individuals in the form (t_i,r_i;s_i) where t_i is the time of the event (failure), r_i is the response type (failure type), and s_i is the stimulus type (explanatory variable). I am wondering if it is possible to use survfit5 to fit parametric and nonparametric models to data like these, and if so how to do it. I read the documentat...

...)) no_of_state=4 transition_matrix= generate_transition_matrix(checkdf$clusterNum, no_of_state) transition_matrix 2017-08-28 16:37 GMT+02:00 niharika singhal <niharikasinghal1990 at gmail.com>: > 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 &gt...

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 from looping on the index) to do this with some predefined R function? Thanks, Francesco

...usters # Data set set.seed(123) mydata <- matrix(rnorm(100*10), ncol=10) colnames(mydata) <- paste(''X'', 1:10, sep="") rownames(mydata) <- paste(''s'', 1:100, sep="") # Función para determinar el cluster al que # pertenece cada muestra s_i foo <- function(X, k = 7){ x <- X[sample(nrow(X)),] rx <- rownames(x) cluster <- kmeans(x, k)$cluster names(cluster) <- rx cluster[rownames(X)] } # Repitiendo foo 1000 veces N <- 1000 res <- replicate(N, foo(mydata)) # Dandole un nuevo f...

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 'S_IRUSR\|S_IRGRP\|S_IROTH' incl...

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