similar to: how to deal with vector[0]?

Dear R-users, I am a new user for R. I am eager to lean about it. I wanted to read and summary of the a simple data file I used the following, rel <- read.table("C:/Documents and Settings/ashta/My Documents/R_data/rel.dat", quote="",header=FALSE,sep="",col.names= c("id","orel","nrel")) summary(rel) Below is the

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

Buenas tardes, Estoy interesado en generar observaciones de una distribucion binomial bivariada en la que hay _cierto_ grado de correlacion (denotemoslo rho). Podria por favor alguien indicarme como hacerlo en R? Este es el contexto. Supongamos que se tienen dos experimentos en los que la variable respuesta _sigue_ una distribucion binomial, i.e., X_i ~Binomial(n_i, p_i), i=1,2 y que, por ahora,

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the

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 all, I'm trying to define and log-likelihood function to work with MLE. There will be parameters like mu_i, sigma_i, tau_i, ro_i, for i between 1 to 24. Instead of listing all the parameters, one by one in the function definition, is there a neat way to do it in R ? The example is as follows: ll<- function(mu1=-0.5,b=1.2,tau_1=0.5,sigma_1=0.5,ro_1=0.7) { if (tau1>0 &&

I am trying to download the tar files on the website below filename<-" http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2010/SolarAnywhere/x.tar" where x is one those tar files I downloaded x using download.file(). But, the file was corrupt. Can someone help me how to download and untar these files using R. Thanks, Alemu [[alternative HTML version deleted]]

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

Dear all, I have define a discrete distribution P(y_i=x_i)=p_i, which I want to plot a CDF plot. However, I can not find a function in R to draw it for me after searching R and R-archive. I only find the one for the sample CDF instead my theoretical one. I find stepfun can do it for me, however, I want to plot some different CDF with same support x in one plot. I can not manage how to do it with

Dear UseRs: I have simulated data from a zero-inflated Poisson model, and would like to use a package like zicounts to test my code of fitting the model. My question is: can I use zicounts directly with the following simulated data? Create a sample of n=1000 observations from a ZIP model with no intercept and a single covariate x_{i} which is N(0,1). The logit part is logit(p_{i})=x_{i}*beta

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.

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the

I've come across a difference I can't explain between the way Samba behaves on Fedora 20 (4.1.17-1.fc20) and Centos 7 (4.1.12-21.el7). I have a test server of each system (Fedora 20 and Centos 7), each newly built, fully updated, and with the same config file. Each is joined to our AD domain (Windows DCs). Some of our client systems are joined to the domain and use Kerberos tickets

I'm trying to fit a nested mixed model using lmer and have some questions about the output and my model formulations. I have replicate measures on Lines which are strictly nested within Populations. (a) So if I want to fit a model where Line is a random effect and Populations are fixed and the random Line effect is constant across Populations, I have: measure_ijk = mu + P_i + L_ij +

Hi everyone, I am trying to compare proportions among groups using the logistic regression approach as follows: 1) Fit the model log(p_i/(1-p_i)) = M + G_i, where p_i is the probability of success in group i and G_i is the effect of group i, i=1,..,I. 2) Test the hypotheses: Ho: G_1 = G_2 = ... = G_I (the probability of success is the same for all groups) versus Ha: at least two

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:

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 All, I am trying to code an R script which gives me the time varying parameters of the NIG and GH distributions. Further, becasue I think these these time varying parameters should be more responsive to more recent observations, I would like to include a weighted likelihood estimation proceedure where the observations have an exponentially decaying weighting rather than the equal weighting