Hey, all. I'm working on a data set with a broken stick linear
regression where I know one of the two slopes. It is a negative
linear function until the line intersects with the x-axis, at which
point it becomes 0. It is not a nonlinear asymptotic function, and,
indeed, using negative
I'm currently using JAGS as my Bayesian program of choice due to
working off of an older mac running OSX. I'd like to utilize some of
the functions from R2WinBUGS, however. As such, I'm attempting to
write something to go from coda output dumped by JAGS into the bugs
object format - I've
A reviewer recently remarked to me that, due to my data being
constrained to not fall below zero, a generalized linear model with a
negative binomial error (or poisson) with a log link would be more
appropriate for fitting my model. I ran it in R with glm.nb() and
got results that matched
Hey, all. I'm looking for packages that are good at two things
1) Drawing directed graphs (i.e nodes and edges), both with single
and double headed arrows, as well as allowing for differences in line
width and solid versus dashed. Note: I've tried Rgraphviz here, but
have run into some
I had a question about scan in R. For better code readability, I
would like to have lines in the block of data to be scanned that are
commented - not just lines that have a comment at the end. For example
#age, weight, height
33,128,65
34,56,155
instead of having to do something like
Right, but what if it is not the first line? For example
#data block 1
34,54,23
23,53,12
35,23,23
#data block 2
64,24,13
354,24,12
324,13,3
On Nov 27, 2006, at 4:39 PM, Duncan Murdoch wrote:
On 11/27/2006 7:25 PM, Jarrett Byrnes wrote:
I had a question about scan in R. For better code
I'm attempting to write a general function to implement Faraway's
bootstrapping algorithm for mixed models with lmer, but have run into
a curious problem. I'm comparing two models
model.1-lmer(Response ~ Treatment + (1|Trial), data=exp.data,
method=ML)
model.2-lmer(Response ~ 1 +
a traceback on this example? It may be related to a
problem that I just fixed in the development version of the lme4
package.
Alternatively if you can make the data available I can generate a
traceback myself.
On 9/12/06, Jarrett Byrnes [EMAIL PROTECTED] wrote:
I'm attempting to write a general
On Mar 1, 2006, at 8:35 PM, Dirk Eddelbuettel wrote:
On 1 March 2006 at 20:06, Jarrett Byrnes wrote:
| Hey, all, I may just be missing something, but I'm trying to
construct
| a temporal autoregression with an independant variable other than
just
| what is happened at a previous point
On Mar 1, 2006, at 8:35 PM, Dirk Eddelbuettel wrote:
On 1 March 2006 at 20:06, Jarrett Byrnes wrote:
| Hey, all, I may just be missing something, but I'm trying to
construct
| a temporal autoregression with an independant variable other than
just
| what is happened at a previous point
Hey, all, I may just be missing something, but I'm trying to construct
a temporal autoregression with an independant variable other than just
what is happened at a previous point in time. So, the model structure
would be something like
y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a*x(t)
I'm even
taken it upon themselves to learn
R, and it's going swimmingly so far!
Jarrett Byrnes
Population Biology Graduate Group, UC Davis
Bodega Marine Lab
707-875-1969
http://www-eve.ucdavis.edu/stachowicz/byrnes.shtml
Jarrett Byrnes
Population Biology Graduate Group, UC Davis
Bodega Marine Lab
707-875-1969
http://www-eve.ucdavis.edu/stachowicz/byrnes.shtml
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
Ah, so, if, say, I sampled on the 1st, 4th, 19th, 20th, and 30th day of
a month, I should use:
idata=data.frame(time=c(1,4,19,20,30))
On Nov 15, 2005, at 11:08 AM, Peter Dalgaard wrote:
Peter Dalgaard [EMAIL PROTECTED] writes:
No... idata is the *intra*-block structure, so it should just be
-stratum effect of a
covariate and the effect of design factors adjusted for the covariate.
On Tue, 8 Nov 2005, Jarrett Byrnes wrote:
I've recently run into the problem of using aov with nested factors,
and wanting to get the type II and III sums of squares. Normally
Anova
from the car
+ slipfactor%in%Dock, data=my.data)
works just fine.
Is there something fundamental that I am missing in the syntax?
On Nov 8, 2005, at 2:06 AM, Dieter Menne wrote:
Jarrett Byrnes redbeard at arrr.net writes:
I'm having syntactical issues, however. When I try
dock.lme-lme(X.open ~ Dock
I've recently run into the problem of using aov with nested factors,
and wanting to get the type II and III sums of squares. Normally Anova
from the car package would do fine, but it doesn't like having an Error
included, so
my.aov -aov(Response ~ Treatment + Error(Treatment:Replicate))
statement in SAS.
5) Better output for post-hocs, and a Ryan's Q implementation.
Thanks in advance for any input, and I hope this can be a resource to a
lot of people!
Jarrett Byrnes
Population Biology Graduate Group, UC Davis
Bodega Marine Lab
707-875-1969
http
I'm attempting to analyze some survey data comparing multiple docks. I
surveyed all of the slips within each dock, but as slips are nested
within docks, getting multiple samples per slip, and don't really
represent any meaningful gradient, slip is a random effect. There are
also an unequal
. break up the x,y
values by block, then color them differently, and plot them on top of
each other as add=TRUE doesn't seem to work for the plot statement -
any pointers?)
On Nov 3, 2005, at 10:31 PM, Jarrett Byrnes wrote:
Hello all,
I'm attempting to plot the functions from a generalized
Hello all,
I'm attempting to plot the functions from a generalized linear model
while iterating over multiple levels of a factor in the model. In
other words, I have a data set
Block, Treatment.Level, Response.Level
So, the glm and code to plot should be
logit.reg-glm(formula =
Hey, all. Quick question. I'm attempting to use some of the great
graphs generated in R for an upcoming talk that I'm writing in
Powerpoint. Copying and pasting (I'm using OSX) yields graphs that
look great in Powerpoint - until I resize them. Then fonts, points,
and lines all become quite
I'm curious, I realize there are methods for Type II and III sums of
squares, and yet, when I've been constructing models with lm, I've
noticed that position of the term of the model has not mattered in
terms of its p-value. Does lm use sequential Type I sums of squares,
or something else?
Quick question, as I attempt to learn R. For post-hoc tests
1) Is there an easy function that will take, say the results of
tukeyHSD and create a grouping table. e.g., if I have treatments 1, 2,
and 3, with 1 and 2 being statistically the same and 3 being different
from both
Group
Indeed, the following works as well
On Oct 26, 2005, at 5:23 PM, P Ehlers wrote:
fm1 - aov(breaks ~ wool*tension, data = warpbreaks)
TukeyHSD(fm1, c(wool,tension, wool:tension))
However, when working with my own dataset, I get the following errors.
I have some inkling this may be due to a
to
make Dock and Slip factors.
dock_2004_data$Dockf-factor(dock_2004_data$Dock)
dock_2004_data$Slipf-factor(dock_2004_data$Slip)
rich.aov - aov(X.open ~ Dockf*Slipf, data=dock_2004_data)
TukeyHSD(rich.aov, c(Dockf, Slipf))
Jarrett Byrnes wrote:
Indeed, the following works as well
On Oct
I'm using lm to run an ANOVA, and would like to use Ryan's Q as my
post-hoc (as recommended by Day and Quinn, 1989, Ecological
Monographs). I can't seem to find any methods in the base stats
package that implement this post-hoc. Is there a good package of
post-hoc methods out there, or has
(sorted_data)
yields
argument is not numeric or logical: returning NA in: mean.default(a)
Nor does something like mean(sorted_data[1]) work - I thought perhaps
breaking it down by treatment might help.
Thanks in advance for any help!
-Jarrett
Jarrett
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