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R-Help I am attempting to create a series of bivariate normal distributions. So using the mvtnorm library I have created the following code ... # Standard deviations and correlation sig_x <- 1 sig_y <- 1 rho_xy <- 0.0 # Covariance between X and Y sig_xy <- rho_xy * sig_x *sig_y # Covariance matrix Sigma_xy <- matrix(c(sig_x ^ 2, sig_xy, sig_xy, sig_y ^ 2), nrow = 2, ncol = 2)

Hi, I have a dataset with the following properties: Y_i ~ N(mu_i, theta * (mu_i)^2) ln(mu_i) = B'Xi theta and beta's are the parameters here. I want to come up with a model to fit the data with the above property and test that model on the built in R dataset quine. Does nls() make sense in this case? Or is there any existing R package which can f...

Good afternoon, I have been working on my thesis project on the topic "Urban Heat Island Pattern in India". To achieve the results I am applying a* two-dimensional Gaussian fit* on an LST raster of 1 km spatial resolution but I am facing two errors in the following code. library(raster) LST <- raster("D:/Celsius_Day/MOD_01.tif") gaussian2d <- function(x, y, mu_x, mu_y,

...for TNBI and T_a. This made me happy. Now I thought to try the ML estimates from glm.nb to see if the results would be the same but I am having difficulty relating the dispersion phi from glm.poisson.disp to theta estimated by glm.nb. According to the R help for glm.poisson.disp " Var(y_i) = mu_i(1+mu_i*phi) ". The help for glm.nb lead me to a book by V&R (1994) which indicates that Var(y)=mu+mu^2/theta. From this I gathered that phi=1/theta but the estimates do not relate to each other in this way unless one is in error. In a document by L.P. Ammann he says a "negative binomi...

...ow which one I can use... When editing the title in R plots, such as using 'plot', or 'xyplot' in 'lattic', what method do you use to write greek letters and make use of superscript and subscript, e.g. to write mathematical expressions like using Latex: \sigma^2 \tau^{2s} \mu_i \pi_{2s} Also I would like to learn how to make two lines in the main title or sub title if the text I need it too long for putting in a single line, e.g. are there some R code/syntax allowing me to do something like in Latex to make two lines in the title, for example using '//' or '\...

...)=x_{i}*gamma with gamma=1. beta.true<-1.0 gamma.true<-1.0 n<-1000 x<-matrix(rnorm(n),n,1) pi<-expit(x*beta.true) mu<-exp(x*gamma.true) y<-numeric(n) # blank vector z<-(runif(n)<pi) # logical: T with prob p_i, F otherwise y[z]<-rpois(sum(z),mu[z]) # draw y_i ~ Poisson(mu_i) where z_i = T y[!z]<-0 # set y_i = 0 where z_i = F Thanks for your time! Jacob Jacob L van Wyk Department of Statistics University of Johannesburg, APK P O Box 524 Auckland Park 2006 South Africa Tel: +27 11 489 3080 Fax: +27 11 489 2832

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 && ro<1 &&...

...her soul as to whether the hypothesis which is being tested is of any actual interest. This would go much further toward bringing the desciple to true enlightenment. Point 3 --- what hypothesis is being tested by SSA? Let factor A correspond to index i, and B to index j. Let the cell means be mu_ij. (In the overparameterized notation, mu_ij = mu + alpha_i + beta_j + gamma_ij.) The hypothesis being tested is H_0: mu_1.-bar = mu_2.-bar = ... = mu_a.-bar where factor A has a levels, and ``mu_i.-bar'' means the average (arithmetic mean) of mu_i1, mu_i2, ..., mu_ib. (Note --- factor...

...w in the above data frame represent the segment and each segment contain two-mixture component. (Latter I will increase the segment and the mixture component) So for every segment with n mixture component I have to find the *max f(x) with ?infinity < x < infinity* *f(x)=sum_i c_i N(O,mu_i,sigma_i). * *Since i need to calculate the derivate f? and set f?(x)=0 I thought of using Newton method * *In R the function I selected for my problem is optim.* *Below is my code * *l = 2 # represent the number of mixture component* *i=1# represent the segment 1 if i=2 represent...

...0.08461 9.90 tstnum5 0.47083 0.08461 5.56 tstnum6 0.97500 0.08461 11.52 The mean of (the columns of) the data matrix is 3.229167 3.695833 3.729167 4.066667 3.700000 4.204167 which is in exact agreement with the lmer() results when converted to the same parameterization (mu_i = mu + alpha_i, with alpha_1 = 0). (Notice the surprizing, depressing, and so far unexplained *drop* in the response over the second summer.) What I *don't* understand is the correlation structure of the estimates produced by lmer(), which is: Correlation of Fixed Effects: (Intr) ts...

Hello! I am using for logistic regression in survey data the svyglm procedure. I wondered how does the strata effect estimates SE (in addition to the weights given proportional to population size). I know that for simple regression measurements of each strata is assumed to have different variance. But in a logistic model this is not the case. Can anyone help me here? Thank you Ron [[alternative

Hi, I've two fitted models, one binomial model with presence-absence data that predicts probability of presence and one gaussian model (normal or log-normal abundances). I would like to evaluate these models not on their capability of adjustment but on their capability of prediction by calculating the (log)likelihood between predicted and observed values for each type of model. I found

...is the observed values (0 or 1). > > 1) Is anybody can tell me if this formula is statistically true? This looks correct. > 2) Can someone tell me what is the formula of the likelihood between > observed and predicted values for a gaussian model ? > -2 L = sum( (x_i - mu_i)^2)/sigma^2 - 2*n*log(sigma) + C assuming independence and equal variances: but don't trust my algebra, see ?dnorm and take the log of the likelihood shown there for yourself. You're reinventing the wheel a bit here: -2*sum(dbinom(y,prob=phat,size=1,log=TRUE)) and -2*sum(dnorm(x,mean=mu...

I am trying to understand the meaning of the eps parameter of the equiv.test parameter of the equiv.test function (package equivUMP) The help file for equiv.test states that the parameter eps is "a single strictly positive number giving the equivalence limits" What is the scale of measurement of eps? It does not appear to be the same scale as the scale used for the two vectors that are

This is a question about removing the intercept in a binomial glm() model with categorical predictors. V&R (3rd Ed. Ch7) and Chambers & Hastie (1993) were very helpful but I wasn't sure I got all the answers. In a simplistic example suppose I want to explore how disability (3 levels, profound, severe, and mild) affects the dichotomized outcome. The glm1 model (see below) is

Full_Name: Carmen Fernandez Version: 1.3.1 OS: Submission from: (NULL) (138.251.202.115) I think there is a problem when computing Pearson residuals, in that they seem to be computed at the raw residuals divided by the square root of the corresponding diagonal element of the weight matrix W evaluated at the last step of the iterative model fitting procedure (IWLS), instead of dividing by the

Hello list. I'm trying to rebuild the 2.6.9.67.0.20.EL kernel, but it fails even without modifications. How did I try it? Created a (non-root) build environment (not a mock ) Installed the kernel.scr.rpm and did a rpmbuild -ba --target=`uname -m` kernel-2.6.spec 2> prep-err.log | tee prep-out.log The build failed at the end: Processing files: kernel-xenU-devel-2.6.9-67.0.20.EL Checking