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,