similar to: partial residuals

Dear list, I would like to know how the function residuals.lm calculates the partial residuals from an lm object with more than one predictor variable. In other words what is residuals.lm(...,type="partial") doing behind the scenes? According to the help file for residuals.lm (?residuals.lm), "The partial residuals are a matrix with each column formed by omitting a term from

Dear list, I would like to produce a plot of variables where the size of the points will be indicative of their standard errors. How is that possible? Thank you! [[alternative HTML version deleted]]

Dear list, does anybody know whether there is a way to easily retrieve the so called "partial residuals" of a gam fit with package mgcv? The partial residuals are the residuals you would get if you would "leave out" a particular predictor and are the dots in the plots created by plot(gam.object,residuals=TRUE) residuals.gam() gives me whole model residuals and

Hi guys! I have a problem regarding a binary logistic hierarchical model I am trying to use. The model contains various covariates that depend on the location the response was measured at but do not depend on time (year). I also have a spatial covariate that depends both on location and time. I have been trying to use the lme4 pack but the package only allows me to model variance covariance

Hi all! I try to estimate a statistic of the form: (x1-x2)/(y1-y2), where x1,x2,y1,y2 represent variable means, so each has an estimate and standard error associated with it. How is it possible to estimate the mean and the variance of this ratio? Thank you! [[alternative HTML version deleted]]

Hi all! I am trying to run a regression where the predictor values are not real data but each is estimated from a different model. So, for each value I have a mean and variance. Which package/function should I use in this case? Thank you! Irene [[alternative HTML version deleted]]

Hi all I want to construct a generalised nonlinear model (binomial family) using gnm, of the form: Response = a + b variable1 + c variable2 + d variable3 - d b variable4 - d c variable5, with the parameters b, c, and d appearing more than once. Hence, I think I need to use a custom nonlin function with gnm. One of my predictor variables is categorical, so I have created a dummy variable for

Hello R Community, I am using the Lavaan package in R 2.15.0 to analyze data collected from 1200 lakes across North America. My dataset includes 3 continuous independent variables (LOG_NTL, LOG_PTL, and LOG_SR_A_D) and 1 continuous dependent variable (BIOVOL) . I have successfully constructed structural equation models using the Lavaan package (example included below with code), but I have not

Dear all, I would like to fit a non-linear model of the form: y=g*x/(a+b*x) with nls(). However this model is somehow overparameterized and I get the error message about singular gradient matrix at initial parameter estimates. What I am interested in is to make inference about parameters b and g, so this has to be taken into account in the model formulation. What options do I have? Also, how is

Hi R-helpers, My real data is a panel (unbalanced and with gaps in years) of thousands of firms, by year and industry, and with financial information (variables X, Y, Z, for example), the number of firms by year and industry is not always equal, the number of years by industry is not always equal. #reproducible example firm1<-sort(rep(1:10,5),decreasing=F) year1<-rep(2000:2004,10)

R experts - I'm using John Fox's sem package to analyze a simple path model (two correlated predictor variables directly influencing a single criterion variable): Predictor1 -> Criterion Predictor2 -> Criterion Predictor1 <-> Predictor2 I'm giving a presentation on this material next week, and I'd like to use component-residual plots (i.e., partial residual plots)

Dear all, I have a for loop which includes nls model estimation. The loop breaks after the first non-convergence error. How can I make the loop continue and try to estimate all models? I suppose it should be sth like: if(...) { next } but I have no idea how to setup the arguements... Thank you! Irene

I would like to combine time-series data to test for correlations and interactions using random and fixed effects meta-analysis. So, I am looking for the right packages and documentation. I know about meta and rmeta packages of R. Are there any more? What are the diffrences in brief? Can you please suggest some references that could be used as a guide for meta-analysis in R (or S-plus)?

Dear list members, I would like to know the difference in outputs and calculation processes between residuals.glm(object, type="response") and residuals.lm(object). For above-ground biomass estimation of trees, I estimated parameters of an allometric equation (ln y = b0 + b1*ln x) using glm as follows: fm <- glm(Ws~log(Wb), family=quasi(link="log",

Hello, I am trying to simulate binary outcome data for a logistic regression Monte Carlo study. I need to eventually be able to manipulate the structure of the error term to give groups of observations a random effect. Right now I am just doing a very basic set up to make sure I can recover the parameters properly. I am running into trouble with the code below. It works if you take out the object

I am a new R user and so I thought I could start with "Using R for Introductory statistics" by Verzani. In order to use some of the functions and datasets I have to install the simpleR package which is is now inside the UsingR package. I did so using >install.packages("UsingR"). However, the functions such as "simple.freqpoly.R" do not work. I also tried to

Hello everyone, Recently, I faced a problem on explanatory of *Interaction variable* in Linear Regression, could anyone give me some help on how to explain that? the response variable Y is significantly correlated with *Interaction variable X* which is consisted of Continuous predictor A and Categorical predictor B. The Categorical predictor B has two factors B1 (value=1) and B2 (value=0). The

Hi folks, I've a basic question concerning missing data. I'm running mixed effects analyses using nlme. I've a sizable chunk of missing data on the outcome being modeled, and am using "na.action=na.omit" when running the models. After fitting the models, I'm then trying to extract and use the fitted values and/or residuals for additional analysis, but keep hitting

Dear R users, I read your methods of extracting the variance explained by each predictor in different places. My question is: using the method you suggested, the sum of the deviance explained by all terms is not equal to the deviance explained by the full model. Could you tell me what caused such problem? > set.seed(0) > n<-400 > x1 <- runif(n, 0, 1) > ## to see problem

--- begin inclusion -- After I simulate Time and Censor data vectors denoting the censoring time and status respectively, I can call the following function to fit the data into the Cox model (a is a data.frame containing 4 columns X1, X2, Time and Censor): b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); Now the purpose of me doing simulation is that I have