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...lation 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) # Load the mvtnorm package library("mvtnorm") # Means mu_x <- 0 mu_y <- 0 # Simulate 1000 observations set.seed(12345) # for reproducibility xy_vals <- rmvnorm(1000, mean = c(mu_x, mu_y), sigma = Sigma_xy) # Have a look at the first observations head(xy_vals) # Create scatterplot plot(xy_vals[, 1], xy_vals[, 2], pch = 16, cex = 2, col = "blue",...

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 fit this model? -Shelly -- View

...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, sigma_x, sigma_y, amp) { exp(-((x - mu_x)^2/(2*sigma_x^2) + (y - mu_y)^2/(2*sigma_y^2)))*amp } #define a function for the sum of squared errors between the data and the Gaussian sse <- function(p) { mu_x <- p mu_y <- p sigma_x <- p sigma_y <- p amp <- p[5] fitted <- gaussia...

Dear list, This is a question about overdispersion and the ML estimates of the parameters returned by the glm.poisson.disp (L. Scrucca) and glm.nb (Venables and Ripley) functions. Both appear to assume a negative binomial distribution for the response variable. Paul and Banerjee (1998) developed C(alpha) tests for "interaction and main effects, in an unbalanced two-way layout of counts

Dear R-help, I am trying to express myself as best as I can here. If you also use Latex to edit math reports or other languages with similar editing method, you'll see what I'm talking about. My sincere appologies if my question is not clear enough to some extend, as also I'm not able to provide my code here because I don`t know which one I can use... When editing the title in R

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

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 &&

Peter Dalgaard writes (in response to a question about 2-way ANOVA with imbalance): > ... There are various > boneheaded ways in which people try to use to assign some kind of > SumSq to main effects in the presence of interaction, and they are all > wrong - although maybe not very wrong if the unbalance is slight. People keep saying this

Hello, I have some parameters from Mclust function. The parameters are in the form *parametersDf * * mu_1 mu_2 var_mc1 var_mc2 c1 c2 * *2 1.357283 2.962736 0.466154 0.1320129 0.5258975 0.4741025 * *21 8.357283 9.962736 0.466154 0.1320129 0.5258975 0.4741025 * Each row in the above data frame

I continue to struggle with mixed models. The square zero version of the problem that I am trying to deal with is as follows: A number (240) of students are measured (tested; for reading comprehension) on 6 separate occasions. Initially (square zero) I want to treat the test time as a factor (with 6 levels). The students are of course ``random effects''. Later I want to look at

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

Thank you so much for your help. The function "dbinom" seems to work very well. However, I'm a bit lost with the "dnorm" function. Apparently, I have to compute the mean "mu" and the standard deviation "sd" but what does it mean exactly? I only have a vector of predicted response and a vector of observed response that I would like to compare! What

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