On Sat, 19 Aug 2006, [EMAIL PROTECTED] wrote:
Dear all,
I have a question about how to get a matrix by combining a large number of
columns from a data file. Suppose I read a file which have 1000 columns
by:
test = read.table(dat.txt, header=F)
I know I could use cbind(). It's easy to
savePlot is just an internal version of dev.copy, part of the support for
the menus on the windows() graphics device.
It is described in `An Introduction to R' (the most basic R manual).
On Sat, 19 Aug 2006, Thomas Harte wrote:
the problem is a little hard to explain; the .Rnw files (below)
The documentation for 'OLS' says that it is a wrapper for 'lm';
if you type 'OLS' at a command prompt, you will see that it does little
more than calling 'lm' and returning the output.
An example of 'dynamic regression' appears in the example section
of the help page for 'arima'.
Dear friends,
After running the lm() model, we can get summary resluts like the
following:
Coefficients:
Estimate Std. Error t value Pr(|t|)
x1 0.115620.10994 1.052 0.2957
x2 -0.138790.09674 -1.435 0.1548
x3 0.010510.09862 0.107 0.9153
x4 0.141830.08471 1.674
Hi there Zhang,
While there might be a better way... an ugly but generic way of
accessing this type of information is to use str() and a little
experimentation... here is a little history() of what I did to find
it...
a
str(a)
str(logr)
a[[1]]
a[[2]]
a[[3]]
a[[4]]
a[[4]][[1]]
a[[4]][1,]
Try constructing the acf plot using the traditional plot tools. Then
you can do what you like with it. Eg if your model is called
model.lme, then something like this should work:
acf.resid - ACF(model.lme, resType = n)
my.lags - acf.resid$lag 0.5
plot(acf.resid$lag[my.lags],
try the following:
data - data.frame(matrix(rnorm(900), ncol = 9))
names(data) - c(y, paste(x, 1:8, sep = ))
logr - lm(y ~ . - 1, data)
a - summary(logr)
coef(a)
coef(a)[, 3:4]
coef(a)[, t value]
coef(a)[, Pr(|t|)]
I hope it helps.
Best,
Dimitris
Dimitris Rizopoulos
Ph.D. Student
I would like a function to strip quotes off character strings. I should
work like this:
A - matrix(1:6, nrow = 2, ncol=3)
AF - as.data.frame(A)
names(AF) - c(First,Second,Third)
AF
First Second Third
1 1 3 5
2 2 4 6
names(AF)[2]
[1] Second
attach(AF)
Try these
get(names(AF)[2])
AF[Second] # this one different than the rest
AF[[Second]]
AF[, Second]
AF$Second
On 8/20/06, Murray Jorgensen [EMAIL PROTECTED] wrote:
I would like a function to strip quotes off character strings. I should
work like this:
A - matrix(1:6, nrow =
Murray Jorgensen [EMAIL PROTECTED] writes:
I would like a function to strip quotes off character strings. I should
work like this:
A - matrix(1:6, nrow = 2, ncol=3)
AF - as.data.frame(A)
names(AF) - c(First,Second,Third)
AF
First Second Third
1 1 3 5
2 2
?get
I really think this has nothing to do with `quoting', rather to do with
evaluating variables from their names. At first I though you were looking
for noquote(), which does unquote in the conventional sense.
noquote(names(AF)[2])
[1] Second
get(names(AF)[2])
[1] 3 4
On Sun, 20 Aug 2006,
Reading Bates' article on R News, I see that random effects require a
grouping variable. As, by convention, all variables in G-studies are
supposed random, what could be a grouping variable in that case? I see that
the model I wrote before (if ever ran...) would take all effects as fixed.
Is it
How do I put grid points (not grid lines) as the base layer of an xyplot?
Is there a way to vary the interval at which x and y grid points are placed?
Is it possible to start a graph so that Y axis begins at 500 and ends at 800? I
am only interested in focusing on the relative distance between
Harold, I have tried to adapt your syntax and got some problems. Some
responses from lmer:
On this one, I have tried to use 1 as a grouping variable. As I understood
from Bates (2005), grouping variables are like nested design, which is not
the case.
fm - lmer(RATING ~ CHAIN*SECTOR*RESP
I think information can be enhanced by using different scaled graphs next to
each other. mfrow() created too much space, there may be no need to again draw
the x-axis. It can be very useful to have different scales of the same data
presented next to each other, in addition to the main graph. So I
Harold,
I have tried the following syntax:
fm - lmer(RATING ~ CHAIN*SECTOR*RESP +(1|CHAIN*SECTOR*RESP), gt)
summary(fm)
Linear mixed-effects model fit by REML
Formula: RATING ~ CHAIN * SECTOR * RESP + (1 | CHAIN * SECTOR * RESP)
Data: gt
AIC BIClogLik MLdeviance REMLdeviance
Try this. gl(2,50) is such that the first 50 points are series 1
and the second 50 points are series 2. The scales= argument
defines the positions of the tick marks and the xlim= argument
defines the x axis limits. The layout puts the panels on top
of each other rather than side by side. strip
Look at oma= and mar= parameters to par for controlling the
space when using mfrow=. e.g.
opar - par(oma = c(6, 0, 5, 0), mar = c(0, 5.1, 0, 2.1), mfrow = c(2,2))
for(i in 1:4) plot(1:10)
par(opar)
On 8/20/06, Anupam Tyagi [EMAIL PROTECTED] wrote:
I think information can be enhanced by using
Dear friends,
suppose my dataset *xy* :
xy
1 5
2 3
5 6
6 8
-generated the data--
x-c(1,2,5,6)
y-c(5,3,6,8)
xy-data.frame(x,y)
---
I want to fit the gap in x with the corresponding y=0, I use the following
programs to generate a
On Sat, 2006-08-19 at 10:25 -0600, Mike Nielsen wrote:
Wow. New respect for parse/eval.
Do you think this is a special case of a more general principle? I
suppose the cost is memory, but from time to time a speedup like this
would be very beneficial.
Any hints about how R programmers
try this:
x-c(1,2,5,6)
y-c(5,3,6,8)
xy-data.frame(x,y)
xy
x y
1 1 5
2 2 3
3 5 6
4 6 8
new.df - data.frame(x=seq(max(xy$x)), y=rep(0, max(xy$x)))
new.df
x y
1 1 0
2 2 0
3 3 0
4 4 0
5 5 0
6 6 0
new.df$y[xy$x] - xy$y
new.df
x y
1 1 5
2 2 3
3 3 0
4 4 0
5 5 6
6 6 8
On 8/20/06, zhijie
Thanks. How do I retain the same scale of grid.points
from one panel to next even if the scale of the data
changes? For example: c(seq(601:700),seq(6510,7000,
by=10)) ~ seq(601:700) | gl(2,50).
--- Gabor Grothendieck [EMAIL PROTECTED]
wrote:
Try this. gl(2,50) is such that the first 50
You've raised a very interesting question about testing a
fixed-effect factor with more than 2 levels using Monte Carlo. Like
you, I don't know how to use 'mcmcsamp' to refine the naive
approximation. If we are lucky, someone else might comment on this for us.
Beyond this, you
Under Ubuntu dapper, after installing packages gcc and g77, under
platform i486-pc-linux-gnu
arch i486
os linux-gnu
system i486, linux-gnu
status
major2
minor2.1
year 2005
month12
day 20
svn rev 36812
language R
I get an error when trying to
Manuel Castejón Limas wrote:
Hello,
I've just compiled Hmisc ok under dapper.
I think you need to further install some packages.
Have you installed libc6-dev?
I would start installing the build-essential package.
Best wishes
Manuel
Thanks Manuel, apt-get install build-essential solved the
Hello.
I'm pretty much new to R and I'm trying to produce some figures.
It seems to me, that R has some asynchronous way of plotting figures.
When I run this code:
#constructs the semivariogram of SC1929
vgm1 - variogram(SC1929~1,~U+V,puerto.map$att.data)
# trying to make new plot
On Sun, 20 Aug 2006, Daniil Ivanov wrote:
Hello.
I'm pretty much new to R and I'm trying to produce some figures.
What have you been reading to get the ideas below? People new to R do not
tend to use dev.next ... indeed experienced users very rarely use it.
It seems to me, that R has
Thank you, Brian, Peter and Gabor
Brian has what want. My heading was a bit misleading. I was looking for
a function that would, in logicians' terms, convert 'mention' into
'use'. (This usually goes along with the story about the importance of
knowing the difference between a lion and lion.)
Hi,
Ok, thanks to all.
Problem was with class of variogram
class(vgm1)
[1] gstatVariogram data.frame
If I fix it manually to
class(vgm1) - gstatVariogram
everything runs as it should.
Thanks, Daniil.
On 8/21/06, Prof Brian Ripley [EMAIL PROTECTED] wrote:
On Sun, 20 Aug 2006, Daniil Ivanov
Dear Harold and others,
I have changed the syntax for lmer() and used this one:
require(lme4)
gt - read.table(gt5.txt)
sink(GT output.txt)
attach(gt)
system.time(fm - lmer(RATING ~ 1
+(1|CHAIN)
+(1|SECTOR)
+(1|RESP)
+(1|ASPECT)
+(1|ITEM)
+(1|SECTOR*RESP)
+(1|SECTOR*ASPECT)
+(1|SECTOR*ITEM)
Ok, what is wrong with a following code:
# remove all the present objects
rm(list = ls())
# load the libraries we need
library(gstat)
data(meuse)
vgm1 - variogram(log(zinc)~1, ~x+y, meuse)
plot(vgm1)
dev.copy2eps(file=fig2.eps,horizontal=T)
dev.off()
it plots nothing
but from the R console
Thanks, David. That worked fabulously!
Here is the R code for the hypercube test example:
## begin R code
library(combinat)
x - rep(3,3) # for partitions of 3 units into the three classes {1,2,3}
hcube(x, scale=1, transl=0)
### end R code
For
That's the default. See the relation subargument to scales
if you want them different.
e.g.
library(lattice)
y - c(601:700, seq(6510,7000, by=10))
x - c(601:700, 601:650)
g - rep(1:2, c(100, 50))
xyplot(y ~ x | g)
On 8/20/06, Anupam Tyagi [EMAIL PROTECTED] wrote:
Thanks. How do I retain the
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