search for: sasmixed

I assigned a class the first problem in Pinheiro & Bates, which uses the data set PBIB from the SASmixed package. I have recently downloaded 2.0.1 and its associated packages. On trying library(SASmixed) data(PBIB) library(nlme) plot(PBIB) I get a warning message Warning message: replacing previous import: coef in: namespaceImportFrom(self, asNamespace(ns)) after library(nlme) and a "pairs&q...

...amiliar with the specification of random effects with proc mixed or lme. Very useful are the examples of Ramon Littell's book: "SAS System for Mixed Models (1996)" (http://ftp.sas.com/samples/A55235) The same data set's are kindly made available by Douglas Bates in the library(SASmixed). In the help file are examples of the lme statements equivalent to the proc mixed ones. To explain the different estimates, Hein and Brian suppose to check whether both analyses with SAS and R uses ML estimates or REML estimates. However, this was not the problem, the default in proc mixed an...

...lmer() function. I have a problem with the output anova table when using the anova() function on the lmer output object: I only get the numerator d.f., the sum of squares and the mean squares, but not the denominator d.f., F statistics and P values. Below is a sample output, following D. Bates' SASmixed example in his paper "Fitting linear mixed models in R" (R-News 5: 27-30). By reading the R-help archive, I see that this problem has come up before (e.g., http://tolstoy.newcastle.edu.au/R/help/06/04/25013.html). What I understand from the replies to this message is that this incomplete...

I'm looking at Pinheiro & Bates "Mixed-Effects Models in S and S-PLUS" at the moment. Several datasets are used, one of which is called "PBIB" (a partially balanced incomplete block design). All the other datasets can be found somewhere or other in R. However, I cannot locate PBIB, and it does not seem to be mentioned in the latest edition of the R Full Reference

.... . . program*time WI 5 0 . . . . program*time WI 6 0 . . . . program*time WI 7 0 . . . . #The R code: library(nlme) library(SASmixed) options( contrasts = c(unordered = "contr.SAS", ordered = contr.poly")) data(Weights) fm1Weight <- lme( strength ~ Program * Time, data = Weights, random = ~ 1 | Subj) summary( fm1Weight ) VarCorr( fm1Weight ) #The R results: > summary( fm1Weight ) Linea...

...See file README for details. URL: http://www.r-project.org Index of Contents (Text) Reference Manual (PDF) Package Sources AnalyzeFMRI CoCoAn Devore5 EMV GLMMGibbs GenKern GeneSOM KernSmooth Matrix NISTnls Oarray PHYLOGR PTAk RArcInfo RMySQL RODBC RPgSQL RandomFields RmSQL Rstreams Rwave SASmixed SuppDists VR XML acepack adapt akima ash bindata blighty boot bootstrap bqtl car cclust cfa chron cluster cmprsk coda conf.design cramer date diamonds dse e1071 ellipse event.chart exactRankTests fastICA fdim fields foreign fracdiff gafit gee geoR gld gregmisc gss ineq leaps lgtdl lmtest locfit log...

...0 4 Re: [R] stepAIC/lme problem (1.7.0 only) Prof Brian Ripley 04:19 p.m. 28/04/03 +0100 6 Re: [R] stepAIC/lme problem (1.7.0 only) Prof Brian Ripley 06:09 p.m. 29/04/03 +0100 6 Re: [R] stepAIC/lme problem (1.7.0 only) I am using windowsXP and have experimented with the data set HR from the SASmixed library without success with rw1.6.2. I would greatly appreciate the correct syntax for version rw1.6.2. Thank you very much, Peter B.

...he following correspondence on s-news sally.rodriguez at philips.com 12:57 p.m. 24/04/03 -0400 7 [S] LME summary and multicomp.default() Is it possible to use multicomp with lme in R and if so what is the syntax from a simple readily available example such as the data frame HR in the library SASmixed?. Thank you very much. Peter B.

Dear all, In the SASmixed package there is an example of an analysis of a split-plot experiment. The model is fm1Semi <- lme( resistance ~ ET * position, data = Semiconductor, random = ~ 1 | Grp) where Grp in the Semiconductor dataset is defined as ET*Wafer. Is it possible to specify the grouping directly some way, e...

...earching but have not been able to find an answer to my problem. I apologize in advance if this has been asked before. I am applying a mixed model to my data using lmer. I will use sample data to illustrate my question: >library(lme4) >library(arm) >data("HR", package = "SASmixed") > str(HR) 'data.frame': 120 obs. of 5 variables: $ Patient: Factor w/ 24 levels "201","202","203",..: 1 1 1 1 1 2 2 2 2 2 ... $ Drug : Factor w/ 3 levels "a","b","p": 3 3 3 3 3 2 2 2 2 2 ... $ baseHR : num 92 92 92...

Dear list, I ran a mixed effect model using R 1.4.1 and SAS 8.0 on the SIMS data found in the SASmixed package and found that the degrees of freedoms for fixed effects are very different. From R, df = n - v -1 where n is total # of observations, v is the # of levels for the grouping factor. From SAS df = v -1. Am I wrong about this or can somebody explain which is correct and why? Thanks a lot!...

Dear list members, I try to simulate an incomplete block design in which every participants receives 3 out of 4 possible treatment. The outcome in binary. Assigning a binary outcome to the BIB or PBIB dataset of the package SASmixed gives the appropriate output. With the code below, fixed treatment estimates are not given for each of the 4 possible treatments, instead a kind of summary measure(?) for 'treatment' is given. block<-rep(1:24,each=3) treatment<-c(1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3, 4, 2, 4, 3...

...failed, cause I believe I haven't installed all neccessary includes and libaries: checking for package ipred ...failed! checking for package cramer ...failed! checking for package hdf5 ...failed! checking for package pcurve ...failed! checking for package Bhat ...failed! checking for package SASmixed ...failed! checking for package netCDF ...failed! checking for package pear ...failed! checking for package Rmpi ...failed! checking for package tripack ...failed! checking for package spdep ...failed! checking for package vegan ...failed! checking for package rsprng ...failed! checking for package...

Hi, I'm analyzing a longitudinal data set with 387 cows were observed in 63 days divided into 6 groups, and every 30 days was found to produce milk. Does not aim to model the time using regression. Only compare the groups differ in terms of milk production. There are many missing observations. Because the data are correlated I used the SAS program: proc mixed data=univar

...g and Local Methods mva Classical Multivariate Analysis nlme Linear and nonlinear mixed effects models nnet Feed-forward neural networks and multinomial log-linear models rpart Recursive partitioning SASmixed Data sets from "SAS System for Mixed Models" spatial functions for kriging and point pattern analysis splines Regression Spline Functions and Classes stepfun Step Functions, including Empirical...

Dear all, I'd appreciate some advice on the following problem. I'm attempting to analyse a nested cross-sectional design in which an intervention was offered to a series of randomly selected (small) communities, so the unit of randomisation is the community. All available individuals in each community were interviewed before the intervention and again at follow-up

Dear R-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

Hello, I want to know if there are some functions or packages to solve differential and integral equation using R. Thanks. Shao chunxuan. [[alternative HTML version deleted]]

...stream","RSvgDevice","rtiff","RUnit","rv","Rwave","RWeka","RWinEdt","rwt","RXshrink","sac","SAGx","sampfling","sampling","samr","sandwich","SASmixed","sca","scape","scapeMCMC","scatterplot3d","scope","scuba","sde","seacarb","seas","seewave","sem","SemiPar","SensoMineR","SenSrivastava","seqinr&quot...

Dear all, I'm working with Cox-regression, because data could be censored. But in this particular case not. Now I have a simple example: PRO and PRE are (0,1) coded. The response is not normal distributed. We are interested in a model which could describe interaction. But my results are depending strongly in the choose of the contrast option. It is clear that there is some dependence in