similar to: se.contrast

Displaying 20 results from an estimated 1000 matches similar to: "se.contrast"

2003 May 23
2
predict.smooth.spline
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
2003 Sep 07
3
bug in crossprod? (PR#4092)
# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### # The last line of following code produces a segmentation fault: x <- 1:10 f <- gl(5,2)
2003 May 08
2
natural splines
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),
2002 Aug 22
1
aov bug? (PR#1930)
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,
2005 Feb 10
1
Failure of update.packages()
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
2006 Oct 09
1
split-plot analysis with lme()
Dear R-help, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value
1999 Sep 03
1
pictex device driver
I can't get LaTeX to recognize the output from the pictex device driver. Are these commands for some special latex package which I don't know about? ************************************************ * I.White * * ICAPB, University of Edinburgh * * Ashworth Laboratories, West Mains Road * * Edinburgh EH9 3JT
2000 Sep 04
2
bug in spline()? (PR#653)
BUG IN SPLINE()? Version R-1.0.1, system i486,linux If the spline(x,y,method="natural") function is given values outside the range of the data, it does not give a warning. Moreover, the extrapolated value reported is not the ordinate of the natural spline defined by (x,y). Example. Let x <- c(2,5,8,10) and y <- c(1.2266,-1.7606,-0.5051,1.0390). Then interpolate/extrapolate with
2006 Aug 24
0
syntax for pdDiag (nlme)
At the top of page 283 of Pinheiro and Bates, a covariance structure for the indomethicin example is specified as random = pdDiag(A1 + lrc1 + A2 + lrc2 ~ 1) The argument to pdDiag() looks like a two-sided formula, and I'm struggling to reconcile this with the syntax described in Ch4 of the book and online. Further down page 283 the formula is translated into list(A1 ~ 1, lrc1 ~ 1, A2 ~ 1,
2006 Apr 06
1
polynomial predict with lme
Does lme prediction work correctly with poly() terms? In the following simulated example, the predictions are wildly off. Or am I doing something daft? Milk yield for five cows is measured weekly for 45 weeks. Yield is simulated as cubic function of weekno + random cow effect (on intercept) + residual error. I want to recover an estimate of the fixed curve. ############### library(nlme)
2001 Apr 25
1
manova
I'm running R 1.2.2. The help information for manova says that the result is "A list with components SS: A names list of sums of squares and product matrices. Eigenvalues: A matrix of eigenvalues, stats: A matrix of the statistics, approximate F value and degrees of freedom." However, when I run the example, with fit <- manova(Y ~ rate*additive), I find that fit$SS is NULL.
2003 Jul 22
2
animal models and lme
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:
2005 May 26
1
specifying values in correlation matrix in nlme
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
2006 Jul 08
1
denominator degrees of freedom and F-values in nlme
Hello, I am struggling to understand how denominator degrees of freedom and subsequent significance testing based upon them works in nlme models. I have a data set of 736 measurements (weight), taken within 3 different age groups, on 497 individuals who fall into two morphological catagories (horn types). My model is: Y ~ weight + horn type / age group, random=~1|individual I am modeling
2005 Oct 10
4
plot - no main title and missing abscissa value
Hi all. I have defined a plot thus: par(mar=c(5,5,4,5),las=1, xpd=NA) plot(Day, Ym1Imp, ylim=c(0,100), type="b", bty="l", main="Ym1 Expression", cex=1.3, xaxt="n", yaxt="n") #plot implant data axis(side=1, at=c(0,1,3,5,7,10,14,21), labels=c(0,1,3,5,7,10,14,21)) # label x axis mtext("Day", side =1, at=10, line=3, cex=1.2) # title x
2009 Dec 22
1
trouble with model.tables SE means
Hi, I'm new to R, with some experience with Matlab and SPSS. I've figured out how to run my repeated measures anova and am getting the right numbers for my effects (comparing with results from other software), but am having trouble with the model.tables function. Specifically, using: model.tables(fm,"means",se=TRUE) prints the means, but then won't do the SE values,
2008 Nov 23
1
Help in Programming using Methods
I WROTE THIS FUNCTION BELOW test <- function(x, ...) UseMethod('test', x) test.data.frame = function(x, model, which, error, ...) { av <- aov(formula(model), data = x) res <- test.aovlist(av, which = which, error = error) return(res) } test.aovlist <- function(x, which, error, ...) { mm <- model.tables(x, "means") tabs <- mm$tables[-1]
2007 May 21
2
comparing fit of cubic spline
I want to compare the fit of a quadratic model to continuous data, with that of a cubic spline fit. Is there a way of computing AIC from for e.g. a GAM with a smoothing spine, and comparing this to AIC from a quadratic model? Cheers ****************************************** Tom Reed PhD Student Institute of Evolutionary Biology 102 Ashworth Laboratories Kings Buildings University of
2006 Oct 27
1
(no subject)
Hi, I have generated a profile likelihood for a parameter (x) and am trying to get 95% confidence limits by calculating the two points where the log likelihood (LogL) is 2 units less than the maximum LogL. I would like to do this by linear interpolation and so I have been trying to use the function approxfun which allows me to get a function to calculate LogL for any value of x within
2011 Apr 13
0
ordinal predictor in anova
Hi, I have a dataset with a continuous response variable and, among other predictors, an ordinal variable. Here is what it could look like treatment <- factor(rep(c("AA", "AC", "AD","AE", "AB"), each = 10)) length <- c(75, 67, 70, 75, 65, 71, 67, 67, 76, 68, 57, 58, 60, 59, 62, 60, 60, 57, 59, 61, 58,