similar to: Linear Trend in Residiuals From lme

I'm using R 1.7.0 on linux. With this version of R the package modreg is automatically loaded at start of session. However attempting to use predict.smooth.spline() produces Error: couldn't find function predict.smooth.spline. The function smooth.spline() is OK. What am I missing? ====================================== I.White ICAPB, University of Edinburgh Ashworth Laboratories, West

When I try to run the example from Variogram with an lme object, I get an error (although summary works): R : Copyright 2005, The R Foundation for Statistical Computing Version 2.2.1 (2005-12-20 r36812) ISBN 3-900051-07-0 ... > fm1 <- lme(weight ~ Time * Diet, BodyWeight, ~ Time | Rat) Error: couldn't find function "lme" > Variogram(fm1, form = ~ Time | Rat, nint =

Hi, You should look at Pinheiro and Bates (2000) Mixed-effects models in S and S-Plus. It describes how to format the correlation matrix to pass to functions lme and gls. Basically, the correlation matrix has to be one of the corStruct classes, probably corSymm for your example. So in the call to lme (or gls if you really have no random effects), use something like:

Apologies if this is this too obscure for R-help. In package splines, ns(x,,knots,intercept=TRUE) produces an n by K+2 matrix N, the values of K+2 basis functions for the natural splines with K (internal) knots, evaluated at x. It does this by first generating an n by K+4 matrix B of unconstrained splines, then postmultiplying B by H, a K+4 by K+2 representation of the nullspace of C (2 by K+4),

Can anyone explain why with latest version of R (2.0.1) on FC3, installed from R-2.0.1-0.fdr.2.fc3.i386.rpm, update.packages() produces the message /usr/lib/R/bin/Rcmd exec: INSTALL: not found. Indeed /usr/lib/R/bin seems to lack various shell scripts (INSTALL, REMOVE, etc). ====================================== I.White University of Edinburgh Ashworth Laboratories, West Mains Road Edinburgh

I have used lme() on data from a between-within subjects experiment. The correct ANOVA table is known because this is a textbook example (Experimental Design by Roger Kirk Chapter 12: Split-Plot Factorial Design). The lme() F-values differ from the known results. Please help me understand why. d<-read.table("kirkspf2.dat",header=TRUE) for(j in 1:4) d[,j] <- factor(d[,j]) ### Make

Could anyone help with a linear mixed model fitting problem ? The model is : Y= Xp + Zu + e where X, Z are known design matrix, p is fixed effect factor, u is random effect, u~ (0, G) , e~(0,R) The main problem is , I want to fix the covariance matrix G to be a constant times a known covariance matrix A, G = c*A (c is positive constant, A is a predefined matrix with values manually set by

Hi, If I set df=2 in my smooth.spline function, is that equivalent to running a linear regression through my data? It appears that df=# of data points gives the interpolating spline and that df = 2 gives the linear regression, but I just want to confirm this. Thank you, Steven

I'm using groupedData from nlme. I set up a groupedData data.frame with outer=~group1. When I try to plot with outer=TRUE, I get "subscript out of bounds." This happens most of the time. When it works, I get spaghetti-type plots for comparing groups. But I don't understand why it doesn't usually work. > longa.mod.1.gd <- groupedData(mod1.logit~time|

I'm estimating models using lme in a for loop. Sometimes the model doesn't converge or there are other problems. This causes the evaluation to stop prematurely. I can't remember the function name that I need to use to allow the loop to continue until the end. Could someone remind me the name of the function? I've tried searching but haven't hit upon the function. Rick B.

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

R : Copyright 2001, The R Development Core Team Version 1.4.0 (2001-12-19) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type `license()' or `licence()' for distribution details. R is a collaborative project with many contributors. Type `contributors()' for more information. Type `demo()' for some demos,

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line

Hi all -- I'm trying to use R for my "Analysis of Categorical Data" class, but I can't figure out how to do a weighted linear trend analysis. I have a table of categorical data, to which I've assigned weights to the rows and columns. I need to calculate r and M^2, which is apparently done in SAS using "PROC FREQ" and in STATA using "correlate var1 var2

hej! can anyone help me with a hint how to find a function to fit a linear trend line into a scatter plot. as well as testing it for significance. thanx/thisse

Hello, I have a netcdf file for summer monsoon rainfall gridded data over Indian region. How can I find the linear trend in R? regards Sourabh Bal Dr. Sourabh Bal Assistant Professor Department of Physics Swami Vivekananda Institute of Science and Technology Kolkata 700145 [[alternative HTML version deleted]]

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

Dear people, I've a problem in doing a power calculation. In Fryer and Nicholson (1993), ICES J. mar. Sci. 50: 161-168 page 164 an example is given with the following characteristics T=5, points in time R=5, replicates Var.within=0.1 q=10, a 10% increase per year The degrees of freedom for the test are calculated as Vl=T*R-2=23 and the non-centrality parameter Dl=4.54. Using this they get a

Using the weight argument with a variance function in lme (nlme), you can allow for heteroscedasticity of the within-group error. Is there a way to do this for the other variance components? For example, suppose you had subjects, days nested within subjects, and visits nested within days within subjects (a fully nested two-way design) and you had, say men and women subjects. Could you allow for

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance