Displaying 20 results from an estimated 600 matches similar to: "How do I compute interactions with anova.mlm ?"
2009 May 22
1
regrouping factor levels
Hi all,
I had some trouble in?regrouping factor levels for a variable. After some experiments, I have figured out how I can recode to modify the factor levels. I would now like some help to understand why some methods work and others don't.
Here's my code :
rm(list=ls())
###some trials in recoding factor levels
char<-letters[1:10]
fac<-factor(char)
levels(fac)
print(fac)
##first
2011 Oct 03
1
function recode within sapply
Dear List,
I am using function recode, from package car, within sapply, as follows:
L3 <- LETTERS[1:3]
(d <- data.frame(cbind(x = 1, y = 1:10), fac1 = sample(L3, 10,
replace=TRUE), fac2 = sample(L3, 10, replace=TRUE), fac3 = sample(L3,
10, replace=TRUE)))
str(d)
d[, c("fac1", "fac2")] <- sapply(d[, c("fac1", "fac2")], recode,
"c('A',
2007 Oct 17
1
passing arguments to functions within functions
Dear R Users,
I am trying to write a wrapper around summarize and xYplot from Hmisc
and am having trouble understanding how to pass arguments from the
function I am writing to the nested functions.
There must be a way, but I have not been able to figure it out.
An example is below.
Any advice would be greatly appreciated.
Thanks, Dan
# some example data
df=expand.grid(rep=1:4,
2002 Jun 04
2
Scaling on a data.frame
Hey,
hopefully there is an easy way to solve my problem.
All that i think off is lengthy and clumsy.
Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3.
Let FAC1 be something like experiment number,
so that there are exactly the same number of rows for each level of FAC1
in the data.frame.
Now i would like to scale all values according to the center of its
experiment.
So i can apply s
2010 Apr 21
5
Bugs? when dealing with contrasts
R version 2.10.1 (2009-12-14)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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.
Natural language support but running in an English locale
R is a collaborative project with
2012 Nov 29
5
bootstrapped cox regression (rms package)
Hi,
I am trying to convert a colleague from using SPSS to R, but am having
trouble generating a result that is similar enough to a bootstrapped cox
regression analysis that was run in SPSS. I tried unsuccessfully with
bootcens, but have had some success with the bootcov function in the rms
package, which at least generates confidence intervals similar to what is
observed in SPSS. However, the
2004 Sep 01
1
obtaining exact p-values in mixed effects model
Hello,
Using a fixed effects linear model (with lm), I can get exact p-values
out of the AVOVA table, even if they are very small, eg. 1.0e-200.
Using lme (linear mixed effects) from the nlme library,
it appears that there is rounding of the p-values to zero, if
the p-value is less than about 1.0e-16. Is there a way we can obtain
the exact p-values from lme without rounding?
used commands:
2005 May 25
2
Weird function call problem
Hi,
I'm encountering a very odd problem with calls to anova.mlm() from within a
function.
Consider the following code (data.n is a matrix of numeric values):
mlmfit <- lm(data.n ~ 1)
mlmfit0 <- lm(data.n ~ 0)
print(mlmfit)
anova(mlmfit,mlmfit0,test="Spherical")
If I run it just like this from the console, it works just fine. If,
however, I call it from within a function,
2012 May 29
1
GAM interactions, by example
Dear all,
I'm using the mgcv library by Simon Wood to fit gam models with interactions and I have been reading (and running) the "factor 'by' variable example" given on the gam.models help page (see below, output from the two first models b, and b1).
The example explains that both b and b1 fits are similar: "note that the preceding fit (here b) is the same as
2005 Nov 15
1
Repeates Measures MANOVA for Time*Treatment Interactions
Dear R folk,
First off I want to thank those of you who responded with comments for
my R quick and dirty stats tutorial. They've been quite helpful, and
I'm in the process of revising them. When it comes to repeated
measures MANOVA, I'm in a bit of a bind, however. I'm beginning to see
that all of the documentation is written for psychologists, who have a
slightly
2010 Nov 29
1
surpressing tickmarks / labels x-as for two sets of boxplot (plotted as stacked boxplots)
Hello,
I am trying to plot two sets of boxplots together. These are estimates of two
experiments and?seven?factors.
The results of the two experiments I want to plot as boxplots stacked to each
other.
Therefore I plot first the results of the first experiment; and next with the
add option the second set of boxplots.
The boxplots are plotted at 'at = 1:7 - 0.15 for the first experiment and
2002 Oct 17
1
manova with Error?
Let's say I have a within-subject experiment with 2 observables, obs1 and ob2 and 2 independent factors, fac1 and fac2.
I can do
summary( aov( obs1~fac1*fac2 + Error(Subject/(fac1*fac2)) ) )
summary( aov( obs2~fac1*fac2 + Error(Subject/(fac1*fac2)) ) )
to test the 2 observables separately.
> summary( fit<-manova( cbind(obs1,obs2)~fac1*fac2 + Error(Subject/(fac1*fac2)) ) )
gives
2010 Oct 13
1
interaction contrasts
hello list,
i'd very much appreciate help with setting up the
contrast for a 2-factorial crossed design.
here is a toy example:
library(multcomp)
dat<-data.frame(fac1=gl(4,8,labels=LETTERS[1:4]),
fac2=rep(c("I","II"),16),y=rnorm(32,1,1))
mod<-lm(y~fac1*fac2,data=dat)
## the contrasts i'm interressted in:
c1<-rbind("fac2-effect in
2009 Oct 06
1
ggplot2: mapping categorical variable to color aesthetic with faceting
Hello Again... I?m making a faceted plot of a response on two categorical
variables using ggplot2 and having troubles with the coloring. Here is a
sample that produces the desired plot:
compareCats <- function(data, res, fac1, fac2, colors) {
require(ggplot2)
p <- ggplot(data, aes(fac1, res)) + facet_grid(. ~ fac2)
jit <- position_jitter(width = 0.1)
p <- p +
2007 Oct 30
2
flexible processing
Hello,
unfortunately, I don't know a better subject. I would like to be very flexible
in how to process my data.
Assume the following dataset:
par1 <- seq(0,1,length.out = 100)
par2 <- seq(1,100)
fac1 <- factor(rep(c("group1", "group2"), each = 50))
fac2 <- factor(rep(c("group3", "group4", "group5", "group6"), each =
2007 May 13
2
Some questions on repeated measures (M)ANOVA & mixed models with lme4
Dear R Masters,
I'm an anesthesiology resident trying to make his way through basic
statistics. Recently I have been confronted with longitudinal data in
a treatment vs. control analysis. My dataframe is in the form of:
subj | group | baseline | time | outcome (long)
or
subj | group | baseline | time1 |...| time6 | (wide)
The measured variable is a continuous one. The null hypothesis in
2011 Jun 17
1
question about split
Dear R-users
I seem to be stumped on something simple. I want to split a data frame
by factor levels given in one or more columns e.g. given
dat <- data.frame(x = runif(100),
fac1 = rep(c("a", "b", "c", "d"), each = 25),
fac2 = rep(c("A", "B"), 50))
I know I can split it by fac1, fac2 by:
2010 Nov 27
1
d.f. in F test of nested glm models
Dear all,
I am fitting a glm to count data using poison errors with the log link. My
goal is to test for the significance of model terms by calling the anova
function on two nested models following the recommendation in Michael
Crawley's guide to Statistical Computing.
Without going into too much detail, essentially, I have a small
overdispersion problem (errors do not fit the poisson
2002 Dec 13
1
Problem with lattice bwplot
I've come across the following error when using free scales with bwplot (I use
a small example data set just to illustrate the problem):
> d <- data.frame(
x=c(34.4, 12.4, NA, 65.3, NA, 12.0, 45.0, 645.0, 644.0,323.0),
fac1=c('a','a','b','a','b','a','a','c','c','c'),
2002 Jun 05
1
[Re: Re: Scaling on a data.frame]
Stefan Roepcke <stefan.roepcke at metagen.de> writes:
> Hey,
>
> hopefully there is an easy way to solve my problem.
> All that i think off is lengthy and clumsy.
>
> Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3.
> Let FAC1 be something like experiment number,
> so that there are exactly the same number of rows for each level of FAC1
> in the