Displaying 20 results from an estimated 10000 matches similar to: "bootstrap subject resampling: resampled subject codes surface as list/vector indices"
2017 Aug 19
0
bootstrap subject resampling: resampled subject codes surface as list/vector indices
I din't have the patience to go through your missive in detail, but do
note that it is not reproducible, as you have not provided a "data"
object. You **are** asked to provide a small reproducible example by
the posting guide.
Of course, others with more patience and/or more smarts may not need
the reprex to figure out what's going on. But if not ...
Cheers,
Bert
Bert Gunter
2017 Dec 26
1
identifying convergence or non-convergence of mixed-effects regression model in lme4 from model output
Hi R community!
I've fitted three mixed-effects regression models to a thousand
bootstrap samples (case-resampling regression) using the lme4 package in
a custom-built for-loop. The only output I saved were the inferential
statistics for my fixed and random effects. I did not save any output
related to the performance to the machine learning algorithm used to fit
the models (REML=FALSE).
2005 Dec 01
1
LME & data with complicated random & correlational structures
Dear List,
This is my first post, and I'm a relatively new R user trying to work out a
mixed effects model using lme() with random effects, and a correlation
structure, and have looked over the archives, & R help on lme, corClasses, &
etc extensively for clues. My programming experience is minimal (1 semester
of C). My mentor, who has much more programming experience, but a comparable
2007 Apr 13
2
replicates in repeated ANOVA
Hi,
I have sort of a newbie question. I've seriously put a lot of effort into how to handle simple replicates in a repeated ANOVA design, but haven't had much luck.
I really liked reading "Notes on the use of R for psychology experiments and questionnaires", by Jonathan Baron and Yuelin Li ( http://www.psych.upenn.edu/~baron/rpsych/rpsych.html ) but still didn't run across
2011 Feb 24
1
parallel bootstrap linear model on multicore mac (re-post)
Hello all,
I am re-posting my previous question with a simpler, more transparent,
commented code.
I have been ramming my head against this problem, and I wondered if
anyone could lend a hand. I want to make parallel a bootstrap of a
linear mixed model on my 8-core mac. Below is the process that I want to
make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot)
command).
2008 May 30
2
inconsistent output when using variable substitution
I am extremely puzzled by this behavior in R. I have a data frame called
Trials in which I have results from an experiment. I am trying to do a
subjects analysis, but getting weird results. Each row has 1 trial in it,
which includes a column for the subject number I get the list of subject
numbers like so:
> Subj=unique(sort(Trials$Subj))
Then I loop over them. But I get strange results. As
2009 Jan 03
1
how specify lme() with multiple within-subject factors?
I have some questions about the use of lme().
Below, I constructed a minimal dataset to explain what difficulties I
experience:
# two participants
subj <- factor(c(1, 1, 1, 1, 2, 2, 2, 2))
# within-subjects factor Word Type
wtype <- factor(c("nw", "w", "nw", "w", "nw", "w", "nw", "w"))
# within-subjects factor
2008 Dec 17
1
repeated measures aov with weights
Dear R-help,
I'm facing a problem with defining a repeated measures anova with
weighted data.
Here's the code to reproduce the problem:
# generate some data
seed=11
rtrep <- data.frame(rt=rnorm(100),ti=rep(1:5,20),subj=gl
(20,5,100),we=runif(100))
# model with within factor for subjects/repeated measurements, no
problem
aov(rt~ti + Error(subj/ti),data=rtrep)
#model with weights
2007 Nov 27
2
rearrange data: one line per subject, one column per condition
Dear R-list,
Is there a way to convert the typical long R data-format to a 1-line per subject format?
I have data formatted as:
Group subj condition variable
1 1 1 746.36625
2 2 1 1076.152857
1 3 1 1076.152857
2 4 1 657.4263636
1 5 1 854.1266667
2 6 1 1191.676154
1 7 1 1028.175385
1 1 2 46.36625
2 2 2 76.152857
1 3 2 76.152857
2 4 2 57.4263636
1 5 2 54.1266667
2 6 2 191.676154
1 7 2 028.175385
2006 Dec 14
2
xyplot: discrete points + continuous curve per panel
I have a number of x, y observations (Time, Conc) for a number of Subjects
(with subject number Subj) and Doses. I can plot the individual points with
xyplot fine:
xyplot(Conc ~ Time | Subj,
Groups=Dose,
data=myData,
panel = function(x,y) {
panel.xyplot(x, y)
panel.superpose(???) # Needs more here
}
)
I also like to plot on
2010 May 06
1
How do I plot geoms in parallel in ggplot
Hello,
I am new to ggplot. Please forgive my ignorance!
I have patient data such that each individual is a row and then the attributes are in columns. So for example:
Subj Time Height Weight WBC Plt
1 1 9 4 4 150
1 2 10 5 6 200
1 3 11 6 5 250
1 4
2008 May 28
2
Tukey HSD (or other post hoc tests) following repeated measures ANOVA
Hi everyone,
I am fairly new to R, and I am aware that others have had this
problem before, but I have failed to solve the problem from previous
replies I found in the archives.
As this is such a standard procedure in psychological science, there
must be an elegant solution to this...I think.
I would much appreciate a solution that even I could understand... ;-)
Now, I want to calculate a
2012 Dec 02
1
Repeated-measures anova with a within-subject covariate (or varying slopes random-effects?)
Dear all,
I am having quite a hard time in trying to figure out how to correctly
spell out a model in R (a repeated-measures anova with a
within-subject covariate, I guess). Even though I have read in the
posting guide that statistical advice may or may not get an answer on
this list, I decided to try it anyway, hoping not to incur in
somebody's ire for misusing the tool.
For the sake of
2009 Mar 23
1
specifying repeated measures model in lmer
Dear Colleagues,
I have what Roger Kirk (Experimental Design: Procedures for the Behavioral
Sciences, 1968) refers to as a randomized block factorial design. The anova
table would look like this:
df
A 3
Subj/A 103 (error term for A)
B 23
A*B 69
B*Subj/A 2369 (error term for B and A*B)
Subjects are nested
2002 Dec 15
2
Interpretation of hypothesis tests for mixed models
My question concerns the logic behind hypothesis tests for fixed-effect
terms in models fitted with lme. Suppose the levels of Subj indicate a
grouping structure (k subjects) and Trt is a two-level factor (two
treatments) for which there are several (n) responses y from each
treatment and subject combination. If one suspects a subject by
treatment interaction, either of the following models seem
2006 Jul 20
2
(robust) mixed-effects model with covariate
Dear all,
I am unsure about how to specify a model in R and I thought of asking
some advice to the list. I have two groups ("Group"= A, B) of
subjects, with each subject undertaking a test before and after a
certain treatment ("Time"= pre, post). Additionally, I want to enter
the age of the subject as a covariate (the performance on the test is
affected by age),
2011 Nov 18
1
[R-sig-ME] account for temporal correlation
[cc'ing back to r-help]
On Fri, Nov 18, 2011 at 4:39 PM, matteo dossena
<matteo.dossena at gmail.com> wrote:
> Thanks a lot,
>
> just to make sure i got it right,
>
> if (using the real dataset) from the LogLikelihood ratio test model1 isn't "better" than model,
> means that temporal auto correlation isn't seriously affecting the model?
yes. (or
2001 Dec 23
1
aov for mixed model (fixed and random)?
I'm starting to understand fixed and random effects, but I'm
puzzled a bit. Here is an example from Hays's textbook (which is
great at explaining fixed vs. random effects, at least to dummies
like me), from the section on mixed models. You need
library(nlme) in order to run it.
------
task <- gl(3,2,36) # Three tasks, a fixed effect.
subj <- gl(6,6,36) # Six subjects, a random
2004 Aug 10
4
Enduring LME confusion… or Psychologists and Mixed-Effects
Dear ExpeRts,
Suppose I have a typical psychological experiment that is a
within-subjects design with multiple crossed variables and a continuous
response variable. Subjects are considered a random effect. So I could model
> aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2))
However, this only holds for orthogonal designs with equal numbers of
observation and no missing values.
2009 Oct 19
1
Reposting various problems with two-way anova, lme, etc.
Hi,
I posted the message below last week, but no answers, so I'm giving it
another attempt in case somebody who would be able to help might have missed
it and it has now dropped off the end of the list of mails.
I am fairly new to R and still trying to figure out how it all works, and I
have run into a few issues. I apologize in advance if my questions are a bit
basic, I'm also no