similar to: lme or lmer for unbalance data

Is there any package to balance the unbalance panel data? -- Akhil Dua Mo:+91-7827-662-202 Consultant National Institute of Public Finance and Policy [[alternative HTML version deleted]]

Dear community, I've plotted data and coloured depending on the factor variable v3. In the legend, I'd like to assign properly the same colors than in the factor (the factor has 5 levels). I've been trying this but it doesn't work. plot(var1, var2, xlab = "var1", ylab = "var2", col =var3 , bty='L') legend(locator(1),c("level 1 var3",

Dear Community, I want to identify outliers in my data. I don't know how to use identify command in the plots obtained. I've gone through help files and use mahalanobis example for my purpose: NormalMultivarianteComparefunc <- function(x) { Sx <- cov(x) D2 <- mahalanobis(x, colMeans(x), Sx) plot(density(D2, bw=.5), main="Squared Mahalanobis distances, n=nrow(x),

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

windows Vista R 2.10.1 What is the difference (or differences) between lme and lmer? Both appear to perform mixed effects regression analyses. Thanks John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone)

Hi everyone, I am trying to analyse my data from a small plant experiment (for a meeting tomorrow afternoon) and am a beginner to R so I apologise if this is a very basic question. I carried out a plant experiment examining plant interactions between two species (A and B) under different watering treatments. I had: - 7 watering treatments (7 different watering frequencies labelled 1-7) - 3

Dear all, I've tried a multiple regression, and now I want to try a cross-validation. I obtain this error (it must be sth related to df) that I don't understand, any help would be appreciated. cv.lm(df= dat, lm2.52f, m=3) Error en `[.data.frame`(df, , ynam) : undefined columns selected lm2.52f is my lm object, dat is a dataframe where the variables involved in .lm are I tried CVlm

Dear community, I've a tree which included at first 23 variables. Then I've pruned this tree, and there are only 8 variables involved. I'd like to predict and only introduce in newdata the values of these 8 variables involved. However, as the tree was built with the 23, it asked me for 15 values, even if it doesn't need them. Is there a way to introduce only this 8 values?

My data have heterogeneity of variance (in a categorical variable), do I need to specify a variance structure accounting for this in my model or do GLMMs by their nature account for such heterogeneity (as a result of using deviances rather than variances)? And if I do need to do this, how do I do it (e.g. using something like the VarIdent function in nlme) and in what package? This is my first

Dear Community, I'd like to plot an rq object the same way I do with a lm one, is it possible? Something like this plot(rqmodel , 1:4, id.labels=rownames(pga1)); where rqmodel <- rq(log(vd) ~ v1 + log(v2) +log(v3) + v4 + v5 ,data =dat) Thanks in advance and apologies, I'm pretty newbie with this, user at host.com -- View this message in context:

Before loading sql packages, i was wondering, once all packages required are installed, if it's possible to do from R console sth like this: SQL> select v1 from sql_table where v2 in ("R_variable") # being "R_variable" a vector Thanks in advance, user at host.com -- View this message in context:

Dear community, Apologies, I'm still pretty newbie. Anyway, I am performing linar regression analysis. As a common cause of non-normally distributed residuals is non-normally predictor variables, i'm interested in achieving the best transformation of the predictors. I've seen some commands at R, but I would like to know if it exists a command equivalent to gladder (graphic display)

Dear Community, I've a Work -Shift Scheduling Problem I'd like to solve via constraint linear programming. Maybe something similar to http://support.sas.com/documentation/cdl/en/orcpug/63349/HTML/default/viewer.htm#orcpug_clp_sect037.htm Can anybody suggest me any package/R examples to solve this? If it's needed more details of my little problemm I can provide. Thanks in

Hi; I am running generalized linear mixed models (GLMMs) with the lmer function from the lme4 package in R 2.6.2. My response variable is overdispersed, and I would like (if possible) to run a negative binomial GLMM with lmer if possible. I saw a posting from November 15, 2007 which indicated that there was a way to get lmer to work with negative binomial by assigning: family =

Dear community, I'm working with the data.frame attached ( http://r.789695.n4.nabble.com/file/n4122926/df1.xls df1.xls ), let's call it df1. I typed: df1<- read.xls("C:/... dir .../df1.xls",colNames= TRUE, rowNames= TRUE) Then I splited randomly df1 using splitdf function (http://gettinggeneticsdone.blogspot.com/2011/03/splitting- dataset-revisited-keeping.html)

Dear lmer users, The experiment includes 15 groups of (3 males and 1 female). The female is characterized by its quality Q1 and Q2. Each male of a group is characterized by the number of MatingAttempts (with Poisson distribution). I want to examine if male mating attempts depend on female quality. I can see from graphic exploration that the within-group heterogeneity of male attempts increases

Hi ALL I want to know how to calculate estimated Variance Components for unbalance ANOVA. Can anybody please provide me the formula? Thanks in advance! WeiQiang [[alternative HTML version deleted]]

Dear altogether, is it possible to integrate "weights" arguments within lmer to incorporate statements to handle heteroscedasticity as it is possible with lme? I searched the R-archive but found nothing, insofer I assume it is not possible, but as lmer is under heavy develpoment, maybe something changed or is solved differently. Thus my question: While encountering heavy

Dear R users, I'm hoping that more experienced users will be able to assist me in examining the model fit of a mixed generalised linear model. The example using the data 'bacteria' within the MASS package will hopefully illustrate what I would like to acheive; library(MASS) library(nlme) attach(bacteria) # y being output and the trt - treatment group being an explanatory variable.

Fixes a regression introduced by d5c1e84b3a130f0 "drm/nouveau: hold mutex while syncing to kernel channel". Cc: stable at vger.kernel.org #3.13 Reported-by: Fengguang Wu <fengguang.wu at intel.com> Signed-off-by: Maarten Lankhorst <maarten.lankhorst at canonical.com> --- diff --git a/drivers/gpu/drm/nouveau/nouveau_display.c b/drivers/gpu/drm/nouveau/nouveau_display.c index