similar to: Generacion de binomiales correlacionadas

R Experts: I am conducting a meta-analysis where the effect measures to be pooled are simple proportions. For example, consider this data from Fleiss/Levin/Paik's Statistical methods for rates and proportions (2003, p189) on smokers: Study N Event P(Event) 1 86 83 0.965 2 93 90 0.968 3 136 129 0.949 4 82 70 0.854 Total

Hello, I have interval censored data, censored between (0, 100). I used the tobit function in the AER package which in turn backs on survreg. Actually I'm struggling with the distribution. Data is asymmetrically distributed, so first choice would be a Weibull distribution. Unfortunately the Weibull doesn't allow for zero values in time data, as it requires x > 0. So I tried the

Hello, The question looks like simple. It's probably even stupid. But I spent several hours searching Internet, downloaded tons of papers, where deviance is mentioned and... And haven't found an answer. Well, it is clear for me the using of entropy when I split some node of a classification tree. The sense is clear, because entropy is an old good measure of how uniform is distribution.

Hi, I have a vector with 4 elements, e.g., tau_i=c(100,200,300,400), but potentially tau_i[0]=0. In a "for" loop, tau_i=c(100,200,300,400) m=4 tau_i[0]=0 # <------- ? P_i=1 for(i in 2:m) { P_i = P_i*(tau_i[i-1]-tau_i[i-2]) } Error in P_i = P_i * (tau_i[k - 1] - tau_i[k - 2]): replacement has length zero Unfortunately, I can add this potential element into

Dear All, Apologies if this is too simple for this list. Let us assume that you have an instrument measuring particle distributions. The output is a set of counts {n_i} corresponding to a set of average sizes {d_i}. The set of {d_i} ranges from d_i_min to d_i_max either linearly of logarithmically. There is no access to further detailed information about the distribution of the measured sizes, but

Hi: I've been reading "Computational Methods for Multilevel Modeling" by Pinheiro and Bates, the idea of embedding the technique in my own c-level code. The basic idea is to rewrite the joint density in a form to mimic a single least squares problem conditional upon the variance parameters. The paper is fairly clear except that some important level of detail is missing. For

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, I am drawing several plots and want to have italics in a main title; this is easy with expression(). However, I want also to add a value to it, say n_i, that depends on an ith plot. For this I am using paste(). An example: n_i = 10, 20, 30; I want to draw a plot for each i with the title: "Relative efficiency for sample size n = n_i", where n should be in italics, and of course n_i

Hello, I'm very new to using R, but I was told it could do what I want. I'm not sure how best to enter the information but here goes... I'm trying to transfer the following integral into R to solve for ln(gamma_1), on the left, for multiple instances of gamma_i and variable N_i. gamma_i is, for example, (0, 0.03012048, 0.05000000, 0.19200000, 0.44000000, 0.62566845) N_i (N_1 or

Hello. I have searched the archives but have not found anything. I need to solve a constrained optimisation problem for a nonlinear function (“maximum entropy formalism”). Specifically, Optimise: -1*SUM(p_ilog(p_i)) for a vector p_i of probabilities, conditional on a series of constraints of the form: SUM(T_i*p_i)=k_i for given values of T_i and k_i (these are constraints on

Suppose I have data organized in the following way: (P_i, M_j, S_k) where i, j and k and indexes for sets. I would like to analyze the data to get for example the following information: what is the average over k for (P_i, M_j) or what is the average over j and k for P_i. My question is what would be the way of doing this in R. Specifically how should I write the data in a csv file and how do I

Dear R community, I have a fairly large file with variables in rows. Every variable (thousands) needs to be regressed on a reference variable. The file is too big to load into R (or R gets too slow having done it) and I do now read in line by line with "scan" (see below) and write the results to out. Although improved, this is still very slow... Can someone please help me and suggest

Hi, I am running simulations that does multiple comparisons to control. For each simulation, I need to model 7 nls functions. I loop over 7 to do the nls using try if try fails, I break out of that loop, and go to next simulation. I get warnings on nls failures, but the simulation continues to run, except when the internal call (internal to nls) of the chol2inv fails.

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 everyone, I'd like a bit of information about the stereo mode settings in psych_44.h of libvorbis. Specifically, about the adj_stereo structure: typedef struct { int pre[PACKETBLOBS]; int post[PACKETBLOBS]; float kHz[PACKETBLOBS]; float lowpasskHz[PACKETBLOBS]; } adj_stereo; What I am attempting to do is bring lossless stereo coupling down to the lower quality levels in

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

Dear helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > Suppose I have the following data set: > > id x y > 023 1 2 > 023 2 5 > 023 4 6 > 023 5 7 > 412 2 5 > 412 3 4 > 412 4 6 > 412 7 9 > 220 5 7 > 220 4 8 > 220 9 8 > ...... > Now I want to compute the

Hi, I would like to know whether there exist algorithms to compute the coefficients or, at least, the degree of the minimal polynomial of a square matrix A (over the field of complex numbers)? I don't know whether this would require symbolic computation. If not, has any of the algorithms been implemented in R? Thanks very much, Ravi. P.S. Just for the sake of completeness, a

Hi, ? I have a relatively big dataset and I want to construct some histograms using the histogram function in lattice. One thing I am interested in is to look at differences between density and percent. I know I can use the hist function but it seems that this function gives sometimes some wrong answers and the density is actually a percent since it is calculated as counts in the bin divided by

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions