similar to: Linear Modeling and Predictions

Hi All I have been fitting regression models and would now like to produce some contour & image plots from the predictors. Is there an easy way to do this? My current (newbie) experience with R would suggest there is but that it's not always easy to find it! f3 <- lm( fc ~ poly( speed, 2 ) + poly( torque, 2 ) + poly( sonl, 2 ) + poly( p_rail, 2 ) + poly( pil_sep, 2 ) + poly( maf, 2

Hey, I am estiamting a partially linear model y=X\beta+f(\theta) where the f(\theta) is estiamted using wavelets. Has anyone heard of methods to test if the betas are significant or to address model fit? Thanks for any thoughts or comments. Elizabeth Lawson __________________________________________________ [[alternative HTML version deleted]]

I'd like to be able to test linear hypotheses after setting up and running a model using optim or perhaps nlm. One hypothesis I need to test are that the average of several coefficients is less than zero, so I don't believe I can use the likelihood ratio test. I can't seem to find a provision anywhere for testing linear combinations of coefficients after max. likelihood. Cheers

I am trying to do a forecasting exercise for a series, x. My forecast model consists of the following I first regress log(x) on time and dummy variables for each month. lm(log(x) ~ time + monthly dummies) I then use predict() to obtain a prediction for the next year. I then fit an AR(6)/AR(12) model on the residuals of the regression. I use predict() here also to obtain the prediction for the

Hi, all, Does anybody got the most general expression of a unitary matrix? I found one in the book, four entries of the matrix are: (cos\theta) exp(j\alpha); -(sin\theta)exp(j(\alpha-\Omega)); (sin\theta)exp(j(\beta+\Omega)); (cos\theta) exp(j\beta); where "j" is for complex. However, since for any two unitary matrices, their product should also be a unitary matrix. When I

Dear R users; Ive got two questions concerning nlme library 3.1-65 (running on R 2.2.0 / Win XP Pro). The first one is related to augPred function. Ive been working with a nonlinear mixed model with no problems so far. However, when the parameters of the model are specified in terms of some other covariates, say treatment (i.e. phi1~trt1+trt2, etc) the augPred function give me the following

Hi, This is not an R related question and I apologize for that, but given the brain power of the R community it is hard for me to resist posting this here. I have a problem where each participant is shown a series of visual cues (displayed on a computer screen in a random order) and asked to respond by pressing a button (from a finite number of buttons) that corresponds to the correct

Hi R-profs, Maybe my question is a little off topic. Could any one tell me how to calculate a linear-circular correlation coefficient and its p-values? I had a quick look at circular and CircStats packages and did not find the related function. Thanks for any kindly help. Xiaohua -- Xiaohua Dai, Dr. Centre for Systems Research, Durban Institute of Technology P.O.Box 953, Durban 4000, South

Thanks to Gabor, Duncan, and Peter. I knew the answer had something to do with solving for a and b in terms of mean and variance. I will build a function using the equations you provided Duncan and will look into using Mathomatic in the future Gabor. Appreciate the help. Peter, this was not homework but I understand your concern. I don't use listserves that often but they do open a whole

Dear all, I have a population with three age-classes, at time t=0 the population is: n.zero <- c(1,0,0) I have a transition matrix A which denotes "fertility" and "survival": A <- matrix(c(0,1,5, 0.3,0,0, 0,0.5,0), ncol=3, byrow=TRUE) To obtain the population at t=1, I calculate: A %*% n.zero To obtain the population t=2, I calculate: A %*% (A %*% n.zero) ... and so

Hi R-Users, I apologize if it is too simple question for all. I have a multivariate dataset having 7 variables as independent and 1 dependent variable. 248 data points are there. I want to do out sample forecast first considering 156 points. So I'll have to start from 157th point and calculate the 157th y_hat value. In this way it will go to 248th data point. Can any one tell me how I can

Hi, I am a user of R computer language.Several days ago,I download R-2.1.0.From the help manual,I saw there is a function "mle" to find the maximum likelihood estimators.But when I try to use this function,there is no this function.Could you help me to solve this problem? I am looking forward to your answer. Thank you. Jun Liu

Hi everybody: I just generate interpolation maps with differents methods, like IDW and kriging, and now i want to compare the predict values versus real, and i don't who is the commant to do it. I want for example, if I pass from console the coordinates of a place, return me the predict value. Could anybody tell me how should do?. Thanks in advance --

Hello, I'm working on the multiple categorical data (5-points scale) using linear mixed effect model and wondering if anyone knows about or works on the linear mixed effect model with ordered logit or probit link. I found that the "lmer" function in R is very flexible and supports various models, but not ordered logit/probit models. I may conduct my analysis by turning my DVs

Suppose I have the following data: y x id 44 0 104 48 58 104 48 55 204 47 105 204 41 275 206 18 67 209 ....... I fit the model >fit=lme(y~x+I(x^2),random=~1|id) Now I want to make a prediction plot: >time=seq(0,300,len=100) >plot(predict(fit,data.frame(x=time),level=0)) Very fine. It gives me the prediction curve based on the model. My further request is to make a confidence bands

Hi, The question is as follows: has anyone coded panel data unit root tests with R? Even the "first generation" tests (see e.g. Levin & Lin 1993; Pesaran, & Smith & Im 1996; Maddala & Wu 1999) would be sufficient for my needs. To my understanding, these are rather easy to code, but as I have taken just my first steps in coding with R, existing code would save me

Dear R-users, Is anyone aware of any function/package for generating a random vector from a joint distribution defined by an independent graph? Or I have to work it out myself? Thanks. Jinfang ------------------------------ Jinfang Wang, Associate Professor Chiba University, Japan

Hi, I am so novice in using R. I have some problems in my R script below which fits time series data and predict it one-step ahead. Here is a brief explanation on what I try to achieve Th16k is time series data (500 data points). The size of window for fitting and predicting is 50 (data points). As you can easily discover from my code, (fixed) window is moving/sliding to get next one-step

I often use simple nested random-effect models for interlaboratory data. The variance components are important things to know. Is there an R function or package that gets variance components from lm objects? Or can someone point me to a method of doing so?

Hello and sorry to disturb. I'm trying to plot the confidence intervals for the fixed effects of a lme. I want to obtain graphically, if it is possible, a bar with Estimate, upper and lower CI for each level of the factors. I know how to do for a lm model but for a lme one, I tried with plot(intervals(...)) and plot(ci(...)) from the gmodels package but it doesn't work well. Thanks