I've got a vector of data (hours to drive from a to b) y.
After a qqplot I know, that they don't fit the normal probability.
I would like to transform these data with the boxcox transformation
(MASS), that they fit the model.
When I try
ybx-boxcox(y~1,0)
qqnorm(ybx)
the plot is
Dear R-Helpers,
I have a large data matrix (9707 rows, 60 columns), which contains missing
data. The matrix looks something like this:
1) X X X X X X NA X X X X X X X X X
2) NA NA NA NA X NA NA NA X NA NA
3) NA NA X NA NA NA NA NA NA NA
5) NA X NA X X X NA X X X X NA X
..
9708) X NA
A new version of odfWeave is on CRAN. Changes include:
- handling of locales. Errors were being produced when locales were set
to anything but C. The fix changes the locale to C and changes back
to the original locale when the user's code is executed.
- a bug fix for default plot device units
On Thu, 27 Jul 2006, [EMAIL PROTECTED] wrote:
I am new to R, so please forgive me if there is an obvious answer to
this question. I have done fairly extensive searching through R docs,
google and a few R users and have not found an answer to my question.
Is there a way to create a
Hi
thank you for talking the time to help me with this.
I have a sequence of numbers in a file and an equal sequence of various
character, say(a b c d) each occurs more than once. I need to plot the numbers
so that numbers corresponding to a in the other sequence would have green dots,
those
Hi,
On Friday 28 July 2006 20:21, Horace Tso wrote:
Unless there is another level of complexity that i didn't see here,
wouldn't it be a simply application of sapply as follow,
sapply( 1:dim(M2)[[1]], function(x) M1[M2[x,1], M2[x,2]] )
Andy's previous answer involving matrix indexing
Wasn't exactly sure what you wanted to do. Is this close?
mypch - c(a=19, b=19, c=19, d=22) #point type
mycol - c(a='green', b='red', c='black', d='blue') #color
mydf - data.frame(x=c('a','b', 'b','c','d'), y=c(2, 4, 8, 6, 2))
plot(mydf$y, type='p', pch=mypch[mydf$x], col=mycol[mydf$x])
On
Is this what you want?
set.seed(1)
x - matrix(sample(c(1, NA), 100, TRUE), nrow=10) # creat some data
x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]11 NA1 NA1 NA11 1
[2,]111 NA NA NA1 NA NA 1
[3,] NA NA
Hello again,
The reason why I thought the order at which rows are passed to randomForest
affect the error rate is because I get different results for different ways of
splitting my positive/negative data.
First get the data (attached with this email)
pos.df=read.table(C:/Program
Alexandre Bonnet bonnet at gmail.com writes:
*hi,*
*using articial data, i'm supposed to estimate model*
*y(t) = beta(1) + beta(2)*x(t) + u(t), u(t) = gamma*u(t-1) + v(t), t =
1,...,100*
*which is correctly specified in two ways: ML ommiting the first
observation, and ML using all
On 7/29/06, jim holtman [EMAIL PROTECTED] wrote:
Is this what you want?
set.seed(1)
x - matrix(sample(c(1, NA), 100, TRUE), nrow=10) # creat some data
x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]11 NA1 NA1 NA11 1
[2,]111
On 7/28/06, Gregor Gorjanc [EMAIL PROTECTED] wrote:
Ben Bolker bolker at ufl.edu writes:
...
I haven't tried it, but you could also consider using
a Poisson-lognormal (rather than neg binomial, which is Poisson-gamma)
distribution, which might make this all work rather well
in lmer:
On 7/28/06, Xianqun (Wilson) Wang [EMAIL PROTECTED] wrote:
Hi, all,
I have a question about random effects model. I am dealing with a
three-factor experiment dataset. The response variable y is modeled
against three factors: Samples, Operators, and Runs. The experimental
design is as follow:
Dear All,
I am practicing with the image and wireframe (the latter in the lattice
package) plotting tools.
I am a bit puzzled by the colors I observe in some test plots I have
been generating.
Consider:
rm(list=ls())
library(lattice)
x - seq(-2*pi, 2*pi, len = 100)
y - seq(-2*pi, 2*pi, len =
I am trying to create a lattice plot and would like to later, i.e. after
the plot is drawn, add a grey rectangle behind a portion of it.
The following works except that the rectrangle is on top of and
obscures a portion of the chart. I also tried adding col = transparent
to the gpar list but that
Hello,
I am struggling to find the root of a exponent
function.
uniroot is complaining about a values at end points
not of opposite sign?
s- sapply(1:length(w),function(i)
+ {
+
+ +
+
+
uniroot(saeqn,lower=-5000,upper=0.01036597923,l=list(t=w[i],gp=gp))$root
+ })
Error in uniroot(saeqn,
Hello All,
I have a device that spews out experimental data as a series of text
files each of which contains one column with several rows of numeric
data. My problem is that for each trial it gives me one text file
(and I run between 30 to 50 trials at a time) and I would ideally like
to merge
This will read in all the data files in a directory. I am assuming that
your file names are the same as the column names you want.
# use file names as column headers
setwd(...where ever you want it)
result - list()
for (i in list.files()){
result[[i]] - scan(i, what=0) # assume single
Hi all,
In response to a previous post about plotting a numeric square matrix
as a colored matrix, I was referred to both image and the
color2D.matplot function in the plotrix package. Both have worked for
me thanks!!
However I need to plot my data in a log transformed color scale. Is
this
Hi Gabor,
On Sat, 29 Jul 2006 17:20:29 -0400,
Gabor Grothendieck [EMAIL PROTECTED] wrote:
I am trying to create a lattice plot and would like to later, i.e. after
the plot is drawn, add a grey rectangle behind a portion of it. The
following works except that the rectrangle is on top of and
The reason I explicitly specified in the problem that the rectangle should
not be drawn first is that the xyplot is issued as part of a
larger routine that I don't want to modify.
On 7/29/06, Sebastian P. Luque [EMAIL PROTECTED] wrote:
Hi Gabor,
On Sat, 29 Jul 2006 17:20:29 -0400,
Gabor
For nested, repeated measures of normally distributed data, the best
capability available in R (and perhaps anywhere) is in the 'nlme'
package. Excellent documentation is available in Pinheiro and Bates
(2000) Mixed-Effects Models in S and S-Plus (Springer). This book is
also a
Hi everyone,
I would like to ask a question regarding to the data used to fit the mixed
model.
I wonder that, for the response variable data used to fit the mixed model
(either via spm or lme), we must have several observations per subject
(i.e. Yij, i = 1,..,M, j = 1,.., ni) or it can be
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