similar to: testing coeficients of glm

Hello, I am trying to construct two matrices, F and V, composed of partial derivatives and then find the eigenvalues of F*Inverse(V). I have the following equations in ryacas notation: > library(Ryacas) > FIh <- Expr("betah*Sh*Iv") > FIv <- Expr("betav*Sv*Ih") > VIh <- Expr("(muh + gamma)*Ih") > VIv <- Expr("muv*Iv") I

Dear all: Assume I have 3 distributions, x1, x2, and x3. x1 ~ normal(mu1, sd1) x2 ~ normal(mu2, sd2) x3 ~ normal(mu3, sd3) y1 = x1 + x2 y2 = x1 + x3 Now that the data I can observed is only y1 and y2. It is easy to estimate (mu1+m2), (mu1+mu3), (sd1^2+sd2^2) and (sd1^2+sd3^2) by EM algorithm since y1 ~ normal(mu1+mu2, sqrt(sd1^2+sd2^2)) and y2 ~ normal(mu1+mu3, sqrt(sd1^2+sd3^2)) However, I want

Please help me in writing the R code for this problem. I've been solving this for 4 days. It was hard for me to solve it. It's a simulation problem in R. The problem is My true model is a normal mixture which is given as 0.5 N(-0.8,1) + 0.5 N(0.8,1). This model has two components. I will get a random sample of size 100 from this model. I will do this 300 times. That means, I will have

Dear All, I may look ridiculous, but I am puzzled at the behavior of the nls with a fitting I am currently dealing with. My data are: x N 1 346.4102 145.428256 2 447.2136 169.530634 3 570.0877 144.081627 4 721.1103 106.363316 5 894.4272 130.390552 6 1264.9111 36.727069 7 1788.8544 52.848587 8 2449.4897 25.128742 9 3464.1016 7.531766 10 4472.1360 8.827367 11

Dear R users, I used to "OPTIM" to minimize the obj. function below. Even though I used the true parameter values as initial values, the results are not very good. How could I improve my results? Any suggestion will be greatly appreciated. Regards, Kathryn Lord #------------------------------------------------------------------------------------------ x = c(0.35938587,

Dear R users, I used to "OPTIM" to minimize the obj. function below. Even though I used the true parameter values as initial values, the results are not very good. How could I improve my results? Any suggestion will be greatly appreciated. Regards, Kathryn Lord #------------------------------------------------------------------------------------------ x = c(0.35938587,

I am wanting to plot a 95% confidence band using segplot, yet I am wanting to have groups. For example if I have males and females, and then I have them in different races, I want the racial groups in different panels. I have this minor code, completely made up but gets at what I am wanting, 4 random samples and 4 samples of confidence, I know how to get A & B into one panel and C&D in to

I am trying to write a function that will run a linear model and plot the regression coeficients with their corresponding means. I am having two problems. I can get the plot with the function below, but I am having trouble labeling the points. function(y,x1,x2,x3,x4){ outlm<-lm(y~x1+x2+x3+x4) imp<-as.data.frame(outlm$coef[-1]) meanvec<-c(mean(x1),mean(x2),mean(x3),mean(x4))

Hi, I am new to R. My problem is with the ordered logistic model. Here is my question: Generate an order discrete variable using the variable wrwage1 = wages in first full calendar quarter after benefit application in the following way: * wage*1*Ordered *= 1 *if*0 *· wrwage*1 *< *1000 2 *if*1000 *· wrwage*1 *< *2000 3 *if*2000 *· wrwage*1 *< *3000 4 *if*3000 *· wrwage*1 *<

Hi! I am trying to calculate HPD for the coeficients of regression models fitted with lm or lmrob in R, pretty much in the same way that can be accomplished by the association of mcmcsamp and HPDinterval functions for multilevel models fitted with lmer. Can anyone point me in the right direction on which packages/how to implement this? Thanks for your time! R. [[alternative HTML version

I am still fairly new to R and have a fairly rudimentary question. I am trying to name a vector of coefficients retrieved from a multilevel model using the coef function. I guess the default name is "Intercept" and I cannot figure out how to rename it. I have tried the using the code below to name the column of coefficients ind.y derived from an lme model. Unfortunately, the

Dear Users, I am trying to understand the inner workings of a repeated measures linear model. Take for example a situation with 6 individuals sampled twice for two conditions (control and treated). set.seed(12) ctrl <- rnorm(n = 6, mean = 2) ttd <- rnorm(n = 6, mean = 10) dat <- data.frame(vals = c(ctrl, ttd), group = c(rep("ctrl", 6), rep("ttd",

Dear listers, I can't find the variance or se of the coefficients in a multilevel model using lme. I want to calculate a Chi square test statistics for the variability of the coefficients across levels. I have a simple 2-level problem, where I want to check weather a certain covariate varies across level 2 units. Pinheiro Bates suggest just looking at the intervals or doing a rather

Hi all, I appreciate the help this list has given me before. I have a question which has been perplexing me. I have been working on doing a Bayesian calculating inserting studies sequentially after using a non-informative prior to get a meta-analysis type result. I created a function using three iterations of this, my code is below. I insert prior mean and precision (I add precision manually

Hi, is there an easy way to get the calculated weights in a regression equation? for e.g. if my model has 2 variables 1 and 2 with coefficient .05 and .6 how can I get the computed values for a test dataset for each coefficient? data var1,var2 10,100 so I want to get .5, 60 back in a vector. This is a one row example but I would want to get a matrix of multiplied out coefficients

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

hi there, i'm about to install wine on my debian system. is it better to use libwine for the dll's or is it better to use MS's dll's? thanks, pete -- "You may not use the Software in connection with any site that disparages Microsoft, MSN, MSNBC, Expedia, or their products or services ..." -- Clause from license for FrontPage 2002

Hello, I was reading the docs and have a few questions: 1. I compile wine from the daily debian repository at gluck.debian.org. Do I still need to first remove the old wine before dpkg'ing a newly compiled wine source? 2. I plan on installing wine WITH Windows. Can I still use winesetuptk? I can ignore installation on wine-lib, right? 3. Copyright issues aside, would the most

Most books on R I come across describe running statistical procedures in R. Any suggestions on a good book that teaches *programming* in R? Thanks, Anjan -- ============================= anjan purkayastha, phd bioinformatics analyst whitehead institute for biomedical research nine cambridge center cambridge, ma 02142 purkayas [at] wi [dot] mit [dot] edu 703.740.6939 [[alternative HTML version

I would like to estimate a 95% highest density area for a multivariate parameter space (In the context of anova). Unfortunately I have only experience with univariate kernel density estimation, which is remarkebly easier :) Using Gibbs, i have sampled from a posterior distirbution of an Anova model with k means (mu) and 1 common residual variance (s2). The means are independent of eachother, but