Nabble - R help

Windows Only: winMenuAdd() problem

2008-07-06T07:37:48Z

Folks:

I don't know whether the following is a Windows or R problem, nor whether it
is particular to my particular Windows version/setup. So any help would be
appreciated. First the problem, then the info. The following code can be cut
and pasted into your R session

#############################
### start R
### create a plot on device 2 (which can be determined by dev.cur())

plot(1:10)
winMenuAdd("$Graph2Main/newmenu")

## the menu has been added to the graphics window

#### now close all graphics devices
## (can also be done by pointing and clicking on the Windows close icon)

graphics.off()

## Now repeat

plot(1:10)
winMenuAdd("$Graph2Main/newmenu")

## No menu has been added ! -- This is the problem.
## However, note:

winMenuNames()

###### End of Code #####

INFO:

> version
_
platform i386-pc-mingw32
arch i386
os mingw32
system i386, mingw32
status
major 2
minor 7.1
year 2008
month 06
day 23
svn rev 45970
language R
version.string R version 2.7.1 (2008-06-23)

OS Details: Windows XP Professional SP 2

Cheers to all,

Bert Gunter
Genentech, Inc.

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Backgrounds in Multiple Plots made with "fig"

2008-07-06T07:24:06Z

The following code was adapted from an example Vincent Zoonekynd gave on his web site http://zoonek2.free.fr/UNIX/48_R/03.html:

n <- 1000
x <- rnorm(n)
qqnorm(x)
qqline(x, col="red")
op <- par(fig=c(.02,.5,.5,.98), new=TRUE)
hist(x, probability=T,
col="light blue", xlab="", ylab="", main="", axes=F)
lines(density(x), col="red", lwd=2)
box()
par(op)

How can I make the background in the small box in the upper left-hand corner be a different color than the background in the main plot and erase any of the main plot that happens to be behind the box?

Tom

Re: Repeated measures lme or anova

2008-07-06T07:20:30Z

On Sun, Jul 6, 2008 at 7:46 AM, Martin Henry H. Stevens
<HStevens@...> wrote:
> Hi John,
> 1. I do not know why you remove the intercept in the lme model, but keep it
> in the aov model.
> 2. The distributional assumptions are shot --- you can't run any sort of
> normal model with these data. You might consider some sort of binomial
> (metabolite detected vs. not detected).
> Hank

Following along with Hank's suggestion:

names(df) <- tolower(names(df))
library(reshape)
cast(df, drug1 + drug3 + drug2 ~ ., function(x) sum(x > 0.1))

gives:

drug1 drug3 drug2 (all)
1 0 0 0 9
2 0 0 1 9
3 0 1 0 4
4 0 1 1 3
5 1 0 0 0
6 1 0 1 0
7 1 1 0 0
8 1 1 1 0

So drug 3 has the most effect, drug 3 about half as much, and drug 2
appears to have no effect.

Looking at the mean metabolite levels, conditional on the presence of
metabolite, gives a slightly richer story:

cast(df, drug1 + drug3 + drug2 ~ ., function(x) mean(x[x > 0.1]))
drug1 drug3 drug2 (all)
1 0 0 0 471.6033
2 0 0 1 535.9811
3 0 1 0 217.6300
4 0 1 1 393.3667
5 1 0 0 NaN
6 1 0 1 NaN
7 1 1 0 NaN
8 1 1 1 NaN

So under drug 2 doesn't affect the number of people with a detectable
amount of metabolite, but does affect the levels. (Although you do
need to bear in mind that the values will be more variable when there
are a few patients). You'd probably also want to look at this on a
patient by patient basis to ensure that those responders are the same
people.

For this sort of data, I'd encourage you to try Mondrian
(http://rosuda.org/mondrian) for some interactive graphical
exploration.

Hadley

--
http://had.co.nz/

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Re: "lwd" gives different plotted line widths for Vertical and horizontal Lines when lwd= 2?

2008-07-06T07:02:16Z

On Sun, Jul 6, 2008 at 6:33 AM, Pedro de Barros <pbarros@...> wrote:

> Dear All,
>
> I have been trying to add lines to the axis grobs of plots produced with
> ggplot2.
>
> The code I have used is below. It works, although I do not think it is a
> really elegant way of doing what I want....
> However, I am now noticing that when plotted, the width of the lines in the
> axis are not the same as the line widths I get for the ticks (the ticks get
> thicker lines). Also, when I build a function to do the same conversion, I
> get different (thinner) line widths for the x-axis I have been trying to
> find out why this is so, but I seem to have met a dead-end.
>
> #Test
> library(ggplot2)
> #Set the basic overall plot options
> themePublish <-list(background.fill='white', grid.fill='white',
> grid.colour='NA', grid.minor.colour='NA', axis.colour='black',
> border.colour='NA')
> ggtheme(themePublish)
>
> #Generate the data
> plotdata<-data.frame(x=1:10, y=runif(10))
>
> #Build the ggplot object
>
> ggPlotObject<-ggplot()
> ggPlotObject<-ggPlotObject+layer(data=plotdata,
> mapping=aes_string(x='x',y='y'),geom='point', stat='identity', size=4)
> ggPlotObject<-ggPlotObject+ scale_y_continuous(limits=c(0,1), expand=c(0,0))
>
> #################
> #I define the following function
> F.ggPlot.AddAxisLines<-function(ggPlotObject, col='black', lwd=2,
> lty='solid'){
> ###################################################################################################
> # First, capture the ggPlot object as a gTree
> ggPlotTree<-ggplot_plot(ggPlotObject)
>
> #Get the parameters defined for the lines
> LinePars <- gpar(col=col, lwd=lwd, lty=lty)
>
> #Then, create the objects to add to the axes
> YAxis.Line<-linesGrob(x=unit(c(1,1), 'npc'), y=unit(c(0,1), 'npc'),
> vp='left_axis', name='YAxisLine', gp=LinePars)
> XAxis.Line<-linesGrob(x=unit(c(0,1), 'npc'), y=unit(c(1,1), 'npc'),
> vp='bottom_axis', name='XAxisLine', gp=LinePars)
>
> #Add the grobs to this object
> ggPlotTreeMod<-ggPlotTree
> ggPlotTreeMod<-addGrob(ggPlotTreeMod, YAxis.Line, gPath('yaxis'), grep=TRUE)
> ggPlotTreeMod<-addGrob(ggPlotTreeMod, XAxis.Line, gPath('xaxis'), grep=TRUE)
>
> #Edit the properties of the ticks, to match those of the lines
> ggPlotTreeMod<-editGrob(ggPlotTreeMod, gPath('ticks'), gp=LinePars,
> grep=TRUE, global=TRUE)
>
> #Output the new object
>
> ggPlotTreeMod
> }
>
> # and then do
> ggPlotTreeMod2 <- F.ggPlot.AddAxisLines(ggPlotObject)
> grid.draw(ggPlotTreeMod2)
>
> # I get different line thicknesses for the two axes
>
> #However, if I do
> ggPlotTreeMod3 <- F.ggPlot.AddAxisLines(ggPlotObject, lwd=3)
> grid.draw(ggPlotTreeMod3)
>
> # I get the same line thickness on both axes, but thicker ticks

How are you judging thickness? It may be that due to the size of
ticks and the size of the plot surface that thickness of the line is
not a whole number of pixels, and depending on the exact position of
the line, gets rounded up or down. Trying saving as pdf and zooming
in.

Hadley

--
http://had.co.nz/

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Re: Interpreting messages when building packages

2008-07-06T06:42:41Z

Johannes Huesing wrote:

> Dear expRts,
> when I try to build a package by myself, the process fails writing the
> following messages to 00install.out:
>
> * Installing *source* package 'dynalc' ...
> ** libs
> WARNING: no source files found
> chmod: Zugriff auf „/home/hannes/texte/forschung/clot/programme/dynalc.Rcheck/dynalc/libs/*“ nicht möglich: No such file or directory
> ** R
> ** data
> ** inst
> ** help
> Note: removing empty section \value
> Note: removing empty section \details
> Note: removing empty section \description
> Note: removing empty section \note
> Note: removing empty section \references
> Note: removing empty section \title
> Note: removing empty section \arguments
> Note: removing empty section \examples
> Note: removing empty section \seealso
> >>> Building/Updating help pages for package 'dynalc'
> Formats: text html latex example
> allcombos text html latex example
> missing link(s): ~~fun~~
> contribloss text html latex example
> missing link(s): ~~fun~~
> dummy text html latex example
> dynalc text html latex
> hirchalg text html latex example
> missing link(s): ~~fun~~
> hirchloss text html latex example
> missing link(s): ~~fun~~
> setupalc text html latex example
> missing link(s): ~~fun~~
> sumloss text html latex example
> missing link(s): ~~fun~~
> tabble text html latex example
> missing link(s): ~~fun~~
> ** building package indices ...
> Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, :
> line 12 did not have 19 elements
> Calls: <Anonymous> ... <Anonymous> -> switch -> assign -> read.table -> scan
> Execution halted
> ERROR: installing package indices failed
> ** Removing '/home/hannes/texte/forschung/clot/programme/dynalc.Rcheck/dynalc'
>
> Grepping the directory for "switch" or "scan" does not show anything.
> Removing the data set from the "data" directory does not change the error
> message.
> What else can I do to narrow the range of possible targets when looking
> for the error?
>

The earlier messages suggest that you have automatically produced help
files (from package.skeleton) and haven't edited them. I'd suggest
moving them to a temporary directory until you're ready to edit them
properly.

If that doesn't fix it, or it still occurs after you're done with the
editing, then you'll need to post more details: what version of R, what
platform, etc. sessionInfo() will print this.

Duncan Murdoch
> Best wishes
>
>
> Johannes
>

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Interpreting messages when building packages

2008-07-06T06:32:25Z

Dear expRts,
when I try to build a package by myself, the process fails writing the
following messages to 00install.out:

* Installing *source* package 'dynalc' ...
** libs
WARNING: no source files found
chmod: Zugriff auf „/home/hannes/texte/forschung/clot/programme/dynalc.Rcheck/dynalc/libs/*“ nicht möglich: No such file or directory
** R
** data
** inst
** help
Note: removing empty section \value
Note: removing empty section \details
Note: removing empty section \description
Note: removing empty section \note
Note: removing empty section \references
Note: removing empty section \title
Note: removing empty section \arguments
Note: removing empty section \examples
Note: removing empty section \seealso
>>> Building/Updating help pages for package 'dynalc'
Formats: text html latex example
allcombos text html latex example
missing link(s): ~~fun~~
contribloss text html latex example
missing link(s): ~~fun~~
dummy text html latex example
dynalc text html latex
hirchalg text html latex example
missing link(s): ~~fun~~
hirchloss text html latex example
missing link(s): ~~fun~~
setupalc text html latex example
missing link(s): ~~fun~~
sumloss text html latex example
missing link(s): ~~fun~~
tabble text html latex example
missing link(s): ~~fun~~
** building package indices ...
Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, :
line 12 did not have 19 elements
Calls: <Anonymous> ... <Anonymous> -> switch -> assign -> read.table -> scan
Execution halted
ERROR: installing package indices failed
** Removing '/home/hannes/texte/forschung/clot/programme/dynalc.Rcheck/dynalc'

Grepping the directory for "switch" or "scan" does not show anything.
Removing the data set from the "data" directory does not change the error
message.
What else can I do to narrow the range of possible targets when looking
for the error?

Best wishes

Johannes
--
Johannes Hüsing There is something fascinating about science.
One gets such wholesale returns of conjecture
mailto:johannes@... from such a trifling investment of fact.
http://derwisch.wikidot.com (Mark Twain, "Life on the Mississippi")

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Re: Repeated measures lme or anova

2008-07-06T05:46:30Z

Hi John,
1. I do not know why you remove the intercept in the lme model, but
keep it in the aov model.
2. The distributional assumptions are shot --- you can't run any sort
of normal model with these data. You might consider some sort of
binomial (metabolite detected vs. not detected).
Hank

On Jul 4, 2008, at 9:24 AM, John Coulthard wrote:

>
> Hi
>
> As I can't find an example of my data structure I'd like some
> advice on which is the most appropriate test for significant
> effects. If I should be using either lme or anova, is the relevant
> example below the best/correct way to do the test?
>
> The Data...
> 2 groups of patients (5 in GroupA, 7 in GroupB)
> 3 short acting drugs, (I'm not concerned with residual effects from
> the previous test effecting the current test)
> Each patient is given all 8 possible combinations of the 3 drugs (8
> measurements from each subject = repeated measures)
> The dependent effects are metabolite levels in the blood, a
> continuous variable but often skewed towards 0.1 which is the
> minimum detectable level.
> I'm look for individual and combined effects of Group and Drug.
> There are ~400 metabolites which I intend to test independently. (I
> know that will leave me with a multiple testing issue)
>
> What I've worked out for lme is
>> summary(test.lme <- lme(Value ~ Group*Drug1*Drug2*Drug3 - 1,
>> test,random = ~1|Patient))
>> anova(test.lme)
>
>
> and for anova is
>> summary(aov(Value~(Group*Drug1*Drug2*Drug3)+Error(Patient/
>> (Drug1*Drug2*Drug3)),test))
>
> The full structure of the 'test' data table for one metabolite is
> below.
>
> Thanks for your time and any thoughts you may have.
> Ann
>
>> test
> Patient Group Drug1 Drug2 Drug3 Value
> 1 1 A 0 0 0 446.70
> 2 1 A 0 0 1 0.10
> 3 1 A 0 1 1 0.10
> 4 1 A 0 1 0 328.20
> 5 1 A 1 0 0 0.10
> 6 1 A 1 0 1 0.10
> 7 1 A 1 1 1 0.10
> 8 1 A 1 1 0 0.10
> 9 2 B 0 0 0 0.10
> 10 2 B 0 0 1 69.93
> 11 2 B 0 1 0 878.30
> 12 2 B 0 1 1 0.10
> 13 2 B 1 0 1 0.10
> 14 2 B 1 0 0 0.10
> 15 2 B 1 1 1 0.10
> 16 2 B 1 1 0 0.10
> 17 3 A 0 0 0 0.10
> 18 3 A 0 0 1 0.10
> 19 3 A 0 1 0 0.10
> 20 3 A 0 1 1 0.10
> 21 3 A 1 0 1 0.10
> 22 3 A 1 0 0 0.10
> 23 3 A 1 1 0 0.10
> 24 3 A 1 1 1 0.10
> 25 4 B 0 0 1 0.10
> 26 4 B 0 0 0 688.50
> 27 4 B 0 1 0 541.00
> 28 4 B 0 1 1 0.10
> 29 4 B 1 0 0 0.10
> 30 4 B 1 0 1 0.10
> 31 4 B 1 1 1 0.10
> 32 4 B 1 1 0 0.10
> 33 5 B 0 0 0 0.10
> 34 5 B 0 0 1 0.10
> 35 5 B 0 1 0 541.60
> 36 5 B 0 1 1 0.10
> 37 5 B 1 0 0 0.10
> 38 5 B 1 0 1 0.10
> 39 5 B 1 1 0 0.10
> 40 5 B 1 1 1 0.10
> 41 6 B 0 0 1 58.29
> 42 6 B 0 0 0 353.60
> 43 6 B 0 1 0 523.30
> 44 6 B 0 1 1 0.10
> 45 6 B 1 0 0 0.10
> 46 6 B 1 0 1 0.10
> 47 6 B 1 1 0 0.10
> 48 6 B 1 1 1 0.10
> 49 7 A 0 0 0 351.03
> 50 7 A 0 0 1 0.10
> 51 7 A 0 1 1 0.10
> 52 7 A 0 1 0 0.10
> 53 7 A 1 0 0 0.10
> 54 7 A 1 0 1 0.10
> 55 7 A 1 1 1 0.10
> 56 7 A 1 1 0 0.10
> 57 8 A 0 0 0 299.80
> 58 8 A 0 0 1 0.10
> 59 8 A 0 1 0 0.10
> 60 8 A 0 1 1 0.10
> 61 8 A 1 0 1 0.10
> 62 8 A 1 0 0 0.10
> 63 8 A 1 1 0 0.10
> 64 8 A 1 1 1 0.10
> 65 9 B 0 0 0 355.50
> 66 9 B 0 0 1 0.10
> 67 9 B 0 1 0 737.90
> 68 9 B 0 1 1 322.60
> 69 9 B 1 0 0 0.10
> 70 9 B 1 0 1 0.10
> 71 9 B 1 1 0 0.10
> 72 9 B 1 1 1 0.10
> 73 10 A 0 0 1 462.90
> 74 10 A 0 0 0 657.50
> 75 10 A 0 1 0 82.13
> 76 10 A 0 1 1 0.10
> 77 10 A 1 0 1 0.10
> 78 10 A 1 0 0 0.10
> 79 10 A 1 1 1 0.10
> 80 10 A 1 1 0 0.10
> 81 11 B 0 0 0 386.70
> 82 11 B 0 0 1 279.40
> 83 11 B 0 1 1 407.30
> 84 11 B 0 1 0 485.20
> 85 11 B 1 0 1 0.10
> 86 11 B 1 0 0 0.10
> 87 11 B 1 1 0 0.10
> 88 11 B 1 1 1 0.10
> 89 12 B 0 0 1 0.10
> 90 12 B 0 0 0 705.10
> 91 12 B 0 1 0 706.20
> 92 12 B 0 1 1 450.20
> 93 12 B 1 0 1 0.10
> 94 12 B 1 0 0 0.10
> 95 12 B 1 1 0 0.10
> 96 12 B 1 1 1 0.10
>>
>
> _________________________________________________________________
> Discover the new Windows Vista
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-
> guide.html
> and provide commented, minimal, self-contained, reproducible code.

Dr. Hank Stevens, Associate Professor
338 Pearson Hall
Botany Department
Miami University
Oxford, OH 45056

Office: (513) 529-4206
Lab: (513) 529-4262
FAX: (513) 529-4243
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Re: {spam?} Re: Error: cannot use PQL when using lmer

2008-07-06T05:37:32Z

Hi hpdutra,
I do not know what section of which Crawley book you are referring
to, but I assume that Crawley's point is to use a binomial error
distribution (logistic regression) rather than a normal model. It is
generally thought that LaPlace methods are more accurate than PQL
methods.
Hank

On Jul 6, 2008, at 2:55 AM, hpdutra wrote:

>
> In fact I am using Crawley example to fit my data.
> I am running a lmer analysis for binary longitudinal (repeated
> measures)
> data.
> Basically, I have 12 plots, divided in 3 blocks, each block contain
> 4 plots.
> Plots were manipulate for fruits (F) and vegetation (V) that were
> either
> intact(I) or removed(R). Thus, the treatments are
> FIVI
> FIVR
> FRVI
> FRVR
> Within each plot I had 16 track plates. Track plates were checked
> monthly
> for presence or absence of paw prints.
> I am trying to fit lmer model
> track~fruit*vegetation*time*block in which fruit vegetation time
> are fixed
> effects and time is repeated measures and block is a random effect
> here is my code
>> model<-lmer(track~veget*fruit*time*(time|plate)*(1|
>> block),family=binomial)
>> summary(model)
> Generalized linear mixed model fit by the Laplace approximation
> Formula: track ~ veget * fruit * time * (time | plate) * (1 | block)
> AIC BIC logLik deviance
> 933.9 994.5 -454.9 909.9
> Random effects:
> Groups Name Variance Std.Dev. Corr
> plate (Intercept) 0.226747 0.47618
> time 0.054497 0.23345 -1.000
> block (Intercept) 0.615283 0.78440
> Number of obs: 1152, groups: plate, 192; block, 3
>
> Fixed effects:
> Estimate Std.
> Error z value
> Pr(>|z|)
> (Intercept) -1.68645 0.58718
> -2.8721
> 0.00408 **
> vegetremoved -1.39291 0.57742 -2.4123
> 0.01585 *
> fruitremoved -0.54486 0.53765 -1.0134
> 0.31086
> time -0.02091 0.10118
> -0.2067
> 0.83626
> vegetremoved:fruitremoved 0.75130 0.86342 0.8701 0.38422
> vegetremoved:time 0.38229 0.14695 2.6014
> 0.00928 **
> fruitremoved:time 0.17012 0.14227 1.1958
> 0.23178
> vegetremoved:fruitremoved:time -0.47526 0.22134 -2.1473 0.03177 *
>
> According to Crawley PQL is better for fitting binary data like
> this. So
> should I just stick Laplace or try to get the old Lme4? Also, if
> there is an
> interaction of vegetation vs fruit vs time, how can I know which
> months
> fruit had a significant effect?
>
>
>
> =============================
>
> Ben Bolker wrote:
>>
>> <hpdutra <at> yahoo.com> writes:
>>
>>>> library(lme4)
>>>> model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL")
>>> Error in match.arg(method, c("Laplace", "AGQ")) :
>>> 'arg' should be one of “Laplace”, “AGQ”
>>>
>>
>> What is your question?
>> Doug Bates warned a few weeks ago that the newer version
>> of lmer would no longer use PQL for GLMMs (he found that
>> it was unreliable, even as a starting method for Laplace fits).
>> I think you can still get the older version if you want
>> it, or you can use glmmPQL from the MASS package (glmmPQL
>> has some advantages anyway).
>> It might be better to forward further discussion to
>> r-sig-mixed.
>>
>> Ben Bolker
>>
>> ______________________________________________
>> R-help@... mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
> --
> View this message in context: http://www.nabble.com/Error%3A-cannot-
> use-PQL-when-using-lmer-tp18298149p18299437.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help@... mailing list
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> guide.html
> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
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Re: lattice smooth problem?

2008-07-06T05:19:13Z

On Sun, 2008-07-06 at 13:52 +0200, Troels Ring wrote:

> Dear friends - I'm on windows, R 2.7.0
> I try again asking if anyone can explain why a single pig of 16 makes so
> wild swings.
> Warnings are issued, and they are
> 1: pseudoinverse used at 482.1
> 2: neighborhood radius 242.1
> 3: reciprocal condition number 0
> 4: at 360
> 5: radius 14400
> 6: all data on boundary of neighborhood. make span bigger
> 7: There are other near singularities as well. 14400
> 8: pseudoinverse used at 482.1
> 9: neighborhood radius 242.1
> 10: reciprocal condition number 0
> 11: at 360
> 12: radius 14400
> 13: all data on boundary of neighborhood. make span bigger
> 14: There are other near singularities as well. 14400
>

Did you read the warnings? 6 and 13 both say "make span bigger", and if
one does this:

> xyplot(Na+fit~time|ID,hh,type=c("g","p","smooth"), span = 0.75,
+ auto.key=list(lines=TRUE))

The warnings disappear. The default span is 2/3 which is not big enough
for these data.

HTH

G

> Here is the data and code, ready for copying directly to R
> Best wishes
> Troels
>
> Na<-c(135,133,131,129,127,126,124,123)
> ID<-c(13,13,13,13,13,13,13,13)
> fit<-c(134.7945,132.9084,131.0848,129.3372,127.5546,125.8162,
> 124.1836,122.6077)
> time<-c(60,120,180,240,300,360,420,480)
> hh<-data.frame(ID=as.factor(ID),Na=Na,fit=fit,time=time)
> library(lattice)
> xyplot(Na+fit~time|ID,hh,type=c("g","p","smooth"),
> auto.key=list(lines=TRUE))
>

lattice smooth problem?

2008-07-06T04:52:19Z

Dear friends - I'm on windows, R 2.7.0
I try again asking if anyone can explain why a single pig of 16 makes so
wild swings.
Warnings are issued, and they are
1: pseudoinverse used at 482.1
2: neighborhood radius 242.1
3: reciprocal condition number 0
4: at 360
5: radius 14400
6: all data on boundary of neighborhood. make span bigger
7: There are other near singularities as well. 14400
8: pseudoinverse used at 482.1
9: neighborhood radius 242.1
10: reciprocal condition number 0
11: at 360
12: radius 14400
13: all data on boundary of neighborhood. make span bigger
14: There are other near singularities as well. 14400

Here is the data and code, ready for copying directly to R
Best wishes
Troels

Na<-c(135,133,131,129,127,126,124,123)
ID<-c(13,13,13,13,13,13,13,13)
fit<-c(134.7945,132.9084,131.0848,129.3372,127.5546,125.8162,
124.1836,122.6077)
time<-c(60,120,180,240,300,360,420,480)
hh<-data.frame(ID=as.factor(ID),Na=Na,fit=fit,time=time)
library(lattice)
xyplot(Na+fit~time|ID,hh,type=c("g","p","smooth"),
auto.key=list(lines=TRUE))

--

Troels Ring - -
Department of nephrology - -
Aalborg Hospital 9100 Aalborg, Denmark - -
+45 99326629 - -
tring@...

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"lwd" gives different plotted line widths for Vertical and horizontal Lines when lwd= 2?

2008-07-06T04:33:03Z

Dear All,

I have been trying to add lines to the axis grobs of plots produced
with ggplot2.

The code I have used is below. It works, although I do not think it
is a really elegant way of doing what I want....
However, I am now noticing that when plotted, the width of the lines
in the axis are not the same as the line widths I get for the ticks
(the ticks get thicker lines). Also, when I build a function to do
the same conversion, I get different (thinner) line widths for the
x-axis I have been trying to find out why this is so, but I seem to
have met a dead-end.

#Test
library(ggplot2)
#Set the basic overall plot options
themePublish <-list(background.fill='white', grid.fill='white',
grid.colour='NA', grid.minor.colour='NA', axis.colour='black',
border.colour='NA')
ggtheme(themePublish)

#Generate the data
plotdata<-data.frame(x=1:10, y=runif(10))

#Build the ggplot object

ggPlotObject<-ggplot()
ggPlotObject<-ggPlotObject+layer(data=plotdata,
mapping=aes_string(x='x',y='y'),geom='point', stat='identity', size=4)
ggPlotObject<-ggPlotObject+ scale_y_continuous(limits=c(0,1), expand=c(0,0))

#################
#I define the following function
F.ggPlot.AddAxisLines<-function(ggPlotObject, col='black', lwd=2, lty='solid'){
###################################################################################################
# First, capture the ggPlot object as a gTree
ggPlotTree<-ggplot_plot(ggPlotObject)

#Get the parameters defined for the lines
LinePars <- gpar(col=col, lwd=lwd, lty=lty)

#Then, create the objects to add to the axes
YAxis.Line<-linesGrob(x=unit(c(1,1), 'npc'), y=unit(c(0,1), 'npc'),
vp='left_axis', name='YAxisLine', gp=LinePars)
XAxis.Line<-linesGrob(x=unit(c(0,1), 'npc'), y=unit(c(1,1), 'npc'),
vp='bottom_axis', name='XAxisLine', gp=LinePars)

#Add the grobs to this object
ggPlotTreeMod<-ggPlotTree
ggPlotTreeMod<-addGrob(ggPlotTreeMod, YAxis.Line, gPath('yaxis'), grep=TRUE)
ggPlotTreeMod<-addGrob(ggPlotTreeMod, XAxis.Line, gPath('xaxis'), grep=TRUE)

#Edit the properties of the ticks, to match those of the lines
ggPlotTreeMod<-editGrob(ggPlotTreeMod, gPath('ticks'), gp=LinePars,
grep=TRUE, global=TRUE)

#Output the new object

ggPlotTreeMod
}

# and then do
ggPlotTreeMod2 <- F.ggPlot.AddAxisLines(ggPlotObject)
grid.draw(ggPlotTreeMod2)

# I get different line thicknesses for the two axes

#However, if I do
ggPlotTreeMod3 <- F.ggPlot.AddAxisLines(ggPlotObject, lwd=3)
grid.draw(ggPlotTreeMod3)

# I get the same line thickness on both axes, but thicker ticks

I would really appreciate any pointer or hint.
Thanks,
Pedro

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Re: help about random generation of a Normal distribution of

2008-07-06T03:57:33Z

Bill, while your point is a fair one, as far as it goes,
I (and no doubt others) often run R on low-powered hardware,
and therefore like to restrict overload. MASS is quite a big
package.

I've no dispute with your statement that MASS is both recommended
and universally available. And it is valuable and good.I was simply
pointing out that, if all you want from MASS is mvrnorm, then you
don't need to load MASS, once you have extracted mvrnorm's definition
from MASS.

BUT [OVERSIGHT IN MY FIRST POST]: As well as the code for mvrnorm,
you must also define the ginv() function (needed by mvrnorm):

ginv <- function (X, tol = sqrt(.Machine$double.eps))
{
if(length(dim(X)) > 2 || !(is.numeric(X) || is.complex(X)))
stop("'X' must be a numeric or complex matrix")
if (!is.matrix(X)) X <- as.matrix(X)
Xsvd <- svd(X)
if (is.complex(X)) Xsvd$u <- Conj(Xsvd$u)
Positive <- Xsvd$d > max(tol * Xsvd$d[1], 0)
if (all(Positive)) Xsvd$v %*% (1/Xsvd$d * t(Xsvd$u))
else if (!any(Positive)) array(0, dim(X)[2:1])
else Xsvd$v[, Positive, drop = FALSE] %*% ((1/Xsvd$d[Positive]) *
t(Xsvd$u[, Positive, drop = FALSE]))
}

For what it's worth, I use mvrnorm so often (also ginv) that the code
for mvrnorm (as below) and for ginv (as above) is part of my .Rprofile,
so they are always available without having to load anything else.

Best wishes,
Ted.

On 06-Jul-08 05:36:12, Bill.Venables@... wrote:

> What's the problem with loading the MASS library???
>
> The MASS library is one of the recommended packages, so should be
> universally available.
>
> Pinching code like this and freezing it inside your personal scripts
> means that if ever we find bug fixes or improvements you miss out on
> them.
>
>
> Bill Venables
> CSIRO Laboratories
> AUSTRALIA
> http://www.cmis.csiro.au/bill.venables/
>
> -----Original Message-----
> From: r-help-bounces@...
> [mailto:r-help-bounces@...] On Behalf Of Peng Jiang
> Sent: Sunday, 6 July 2008 1:47 PM
> To: Arnau Mir Torres
> Cc: r-help@...
> Subject: Re: [R] help about random generation of a Normal distribution
> ofseveral variables
>
> Hi , Arnau
>
> Did you ever check your mailbox? your question was answered last
> night Beijing time. :)
> Just read the following .
> -------------------------
>
> There is no need to load the MASS library, since the code for
> mvrnorm therein is compact and self-contained:
>
> mvrnorm <- function (n=1, mu, Sigma, tol=1e-06, empirical=FALSE)
> {
> p <- length(mu)
> if(!all(dim(Sigma) == c(p, p)))
> stop("incompatible arguments")
> eS <- eigen(Sigma, symmetric = TRUE, EISPACK = TRUE)
> ev <- eS$values
> if(!all(ev >= -tol * abs(ev[1])))
> stop("'Sigma' is not positive definite")
> X <- matrix(rnorm(p * n), n)
> if(empirical) {
> X <- scale(X, TRUE, FALSE)
> X <- X %*% svd(X, nu = 0)$v
> X <- scale(X, FALSE, TRUE)
> }
> X <- drop(mu) +
> eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X)
> nm <- names(mu)
> if(is.null(nm) && !is.null(dn <- dimnames(Sigma)))
> nm <- dn[[1]]
> dimnames(X) <- list(nm, NULL)
> if(n == 1)
> drop(X)
> else t(X)
> }
>
>
> Define that function as above, then proceed along the lines suggested
> by Gavin Simpson below.
>
> Ted.
>
>
> On 05-Jul-08 16:43:46, Gavin Simpson wrote:
>> On Sat, 2008-07-05 at 18:21 +0200, Arnau Mir wrote:
>>> Hello.
>>>
>>> Somebody knows how can I generate a set of n random vectors of a
>>> normal distribution of several variables?
>>> For example, I want to generate n=100 random vectors of two
>>> dimensions for a normal with mean c(0,1) and variance matrix:
>>> matrix(c(2,1,1,3),2,2).
>>
>> One is mvrnorm() in the MASS package, part of the VR bundle that
>> comes
>> with R.
>>
>>> require(MASS)
>>> mu <- c(0,1)
>>> Sigma <- matrix(c(2,1,1,3),2,2)
>>> res <- mvrnorm(100, mu = mu, Sigma = Sigma)
>>> head(res)
>> [,1] [,2]
>> [1,] 2.7582876 1.04208798
>> [2,] 0.6364184 -0.08043244
>> [3,] -1.8897731 0.04051395
>> [4,] 2.6699881 0.83163661
>> [5,] -1.1942385 -1.17503716
>> [6,] -0.4303459 -0.80880649
>>
>> HTH
>>
>> G
>
> --------------------------------------------------------------------
> E-Mail: (Ted Harding) <Ted.Harding@...>
> Fax-to-email: +44 (0)870 094 0861
> Date: 05-Jul-08 Time: 18:09:23
> ------------------------------ XFMail ------------------------------
>
> ______________________________________________
> R-help@... mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> On 2008-7-6, at Ã¤Â¸Å_Ã¥ÂË12:13, Arnau Mir Torres wrote:
>
>> Hello.
>>
>> Somebody knows how can I generate a set of n random vectors of a
>> normal distribution of several variables?
>> For example, I want to generate n=100 random vectors of two
>> dimensions for a normal with mean c(0,1) and variance matrix:
>> matrix(c(2,1,1,3),2,2).
>>
>> Thanks in advance,
>>
>> Arnau.
>>
>> ______________________________________________
>> R-help@... mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
>
> [[alternative HTML version deleted]]
>
>
>
> ______________________________________________
> R-help@... mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

--------------------------------------------------------------------
E-Mail: (Ted Harding) <Ted.Harding@...>
Fax-to-email: +44 (0)870 094 0861
Date: 06-Jul-08 Time: 11:57:29
------------------------------ XFMail ------------------------------

______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Re: looking for alternative of 'if-else'

2008-07-06T03:08:15Z

Why don't you try "switch" Let me assume that you want to calculate
a=3 and b =5 by using one of "MEAN", "SUM","MIN", and "MAX functions.
Then you could write your code: (If you want to calculate "MEAN")

> example<-function(fun=c("MEAN", "SUM", "MIN", "MAX")){
+ fun<-match.arg(fun);a=3;b=5
+ switch( fun,
+ MEAN=(a+b)/2,
+ SUM=a+b,
+ MIN=min(a,b),
+ MAX=max(a,b))
+ }
> example("MEAN")
[1] 4
>
Hope this is helpful,
Chunhao Tu

Quoting Arun Kumar Saha <arun.kumar.saha@...>:

> There is "if-else" loop if I have to choose 1 item from a 2-item list.
> However if I have a list of 4 items (let say) then how i can choose a single
> item without employing 'if-else' loop? I mean in VBA I can use
> "select-case", is there any equivalent in R as well?
>
> Regards,
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@... mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

Re: looking for alternative of 'if-else'

2008-07-06T03:03:30Z

On 06/07/2008 4:59 AM, Arun Kumar Saha wrote:
> There is "if-else" loop if I have to choose 1 item from a 2-item list.
> However if I have a list of 4 items (let say) then how i can choose a single
> item without employing 'if-else' loop? I mean in VBA I can use
> "select-case", is there any equivalent in R as well?

See ?switch.

Duncan Murdoch

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and provide commented, minimal, self-contained, reproducible code.

Re: lattice question

2008-07-06T02:16:01Z

I think you want
scales = list(x = list(relation="free"))

On Sun, Jul 6, 2008 at 4:13 PM, <markleeds@...> wrote:

> I'm creating a lattice barchart based off a pretty complicated data
> structure. The barchart comes out quite nice ( thanks
> to lattice ) but the problem is that the horizontal axis comes out all
> scrunched because the barchart doesn't know that the intervals
> of Var.1 are really "associated" with the conditioning variable Var.2.
> Therefore, all the intervals of Var.1 are put on the horizontal axis of
> each
> plot whereas the horizontal axis for each plot should only contain the
> intervals ( Var.1 values ) that are on the same row as Var.2.
>
> What I'm saying probably doesn't make that much sense but, if someone ran
> the code below, and looked at the structure of the data, I think it would
> become clearer. Basically, I need a way to tell lattice that subsets of
> Var.1 should be on each horizontal axis, not the whole of Var.1.
>
> Thanks to anyone who could take the time to look at the code below. I think
> I have to somehow use subscripts or somehow reshape the dataset but I don't
> know the lattice package well enough to figure out the former or the latter.
> I spent today trying but with no success. Thanks.
>
> #DATA STRUCTURE CODE
>
> #================================================================================
>
> pdata<-structure(list(Var.1 = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
> 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
> 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
> 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
> 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
> 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
> 11L, 12L, 13L, 14L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 8L, 9L,
> 10L, 11L, 12L, 13L, 14L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 8L,
> 9L, 10L, 11L, 12L, 13L, 14L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
> 8L, 9L, 10L, 11L, 12L, 13L, 14L, 8L, 9L, 10L, 11L, 12L, 13L,
> 14L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 8L, 9L, 10L, 11L, 12L,
> 13L, 14L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 8L, 9L, 10L, 11L,
> 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 15L, 16L, 17L,
> 18L, 19L, 20L, 21L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 15L, 16L,
> 17L, 18L, 19L, 20L, 21L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 15L,
> 16L, 17L, 18L, 19L, 20L, 21L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
> 15L, 16L, 17L, 18L, 19L, 20L, 21L, 15L, 16L, 17L, 18L, 19L, 20L,
> 21L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 15L, 16L, 17L, 18L, 19L,
> 20L, 21L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
> 26L, 27L, 28L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 22L, 23L, 24L,
> 25L, 26L, 27L, 28L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 22L, 23L,
> 24L, 25L, 26L, 27L, 28L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 22L,
> 23L, 24L, 25L, 26L, 27L, 28L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
> 22L, 23L, 24L, 25L, 26L, 27L, 28L, 22L, 23L, 24L, 25L, 26L, 27L,
> 28L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 22L, 23L, 24L, 25L, 26L,
> 27L, 28L), .Label = c("(-0.7,-0.157]", "(-0.157,-0.0798]",
> "(-0.0798,-0.0194]",
> "(-0.0194,0.039]", "(0.039,0.102]", "(0.102,0.184]", "(0.184,0.847]",
> "(-0.656,-0.127]", "(-0.127,-0.0627]", "(-0.0627,-0.0122]",
> "(-0.0122,0.0344]",
> "(0.0344,0.0858]", "(0.0858,0.151]", "(0.151,0.767]", "(-0.56,-0.107]",
> "(-0.107,-0.0511]", "(-0.0511,-0.0103]", "(-0.0103,0.031]", "(0.031,0.078]",
> "(0.078,0.137]", "(0.137,0.702]", "(-0.549,-0.104]", "(-0.104,-0.0489]",
> "(-0.0489,-0.00959]", "(-0.00959,0.0296]", "(0.0296,0.0764]",
> "(0.0764,0.134]", "(0.134,0.679]"), class = "factor"), Var.2 =
> structure(c(1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label =
> c("VNR",
> "NR", "PR", "VPR"), class = "factor"), Var.3 = structure(c(1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label =
> c("Negative",
> "Neutral", "Positive"), class = "factor"), value = c(153L, 157L,
> 151L, 152L, 129L, 129L, 89L, 134L, 129L, 126L, 125L, 106L, 108L,
> 120L, 148L, 109L, 128L, 123L, 132L, 111L, 142L, 139L, 146L, 156L,
> 153L, 167L, 123L, 161L, 177L, 178L, 163L, 175L, 140L, 141L, 143L,
> 217L, 206L, 185L, 192L, 203L, 196L, 190L, 195L, 192L, 217L, 210L,
> 181L, 197L, 222L, 123L, 168L, 156L, 169L, 178L, 178L, 149L, 181L,
> 175L, 177L, 168L, 153L, 179L, 139L, 156L, 142L, 115L, 135L, 143L,
> 164L, 158L, 111L, 119L, 131L, 120L, 160L, 141L, 161L, 123L, 135L,
> 152L, 135L, 164L, 191L, 183L, 81L, 94L, 74L, 90L, 77L, 62L, 57L,
> 74L, 71L, 71L, 77L, 72L, 63L, 77L, 83L, 63L, 68L, 83L, 71L, 81L,
> 73L, 70L, 81L, 91L, 91L, 78L, 93L, 75L, 71L, 64L, 62L, 51L, 56L,
> 47L, 51L, 93L, 66L, 74L, 67L, 70L, 66L, 71L, 60L, 71L, 79L, 73L,
> 96L, 81L, 89L, 54L, 63L, 52L, 48L, 48L, 57L, 69L, 101L, 109L,
> 89L, 74L, 96L, 97L, 96L, 92L, 84L, 88L, 82L, 76L, 78L, 88L, 76L,
> 69L, 83L, 84L, 79L, 76L, 62L, 58L, 79L, 83L, 94L, 95L, 113L,
> 107L, 53L, 45L, 34L, 39L, 42L, 38L, 34L, 41L, 28L, 30L, 36L,
> 48L, 35L, 28L, 31L, 29L, 45L, 45L, 45L, 48L, 39L, 52L, 43L, 41L,
> 42L, 44L, 38L, 44L, 24L, 26L, 22L, 12L, 13L, 17L, 20L, 28L, 28L,
> 32L, 25L, 25L, 35L, 28L, 24L, 24L, 34L, 19L, 28L, 30L, 28L, 17L,
> 18L, 20L, 15L, 17L, 21L, 18L, 54L, 48L, 43L, 46L, 39L, 47L, 52L,
> 38L, 51L, 35L, 57L, 50L, 43L, 29L, 26L, 38L, 44L, 51L, 39L, 31L,
> 52L, 40L, 50L, 49L, 41L, 38L, 45L, 57L, 46L, 21L, 32L, 19L, 26L,
> 27L, 33L, 23L, 27L, 20L, 26L, 35L, 21L, 19L, 24L, 21L, 42L, 37L,
> 23L, 30L, 20L, 29L, 30L, 25L, 37L, 26L, 27L, 35L, 13L, 12L, 6L,
> 8L, 12L, 10L, 11L, 17L, 20L, 17L, 16L, 23L, 19L, 13L, 15L, 14L,
> 21L, 15L, 18L, 19L, 13L, 13L, 17L, 6L, 13L, 7L, 10L, 16L, 33L,
> 28L, 34L, 29L, 28L, 26L, 44L, 23L, 39L, 34L, 23L, 26L, 33L, 26L,
> 18L, 32L, 20L, 36L, 32L, 32L, 18L, 31L, 25L, 28L, 27L, 29L, 31L,
> 38L), cap = c(15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250, 15193749250, 15193749250, 15193749250,
> 15193749250, 15193749250), group = structure(c(1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "ZMH.NYSE", class
> = "factor"),
> wt = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("5.5.5",
> "10.10.10", "20.20.20", "30.30.30"), class = "factor")), .Names =
> c("Var.1",
> "Var.2", "Var.3", "value", "cap", "group", "wt"), row.names = c(NA,
> 336L), class = "data.frame")
>
> print(str(pdata))
> print(pdata)
>
> # PLOTTING CODE
>
> #================================================================================
>
> pdata$value[!is.finite(pdata$value)] <- 0
>
> print(barchart(value ~ Var.1|Var.2*wt, group=Var.3, data=pdata,stack=TRUE,
> layout=c(4,4),
> scales=list(x=list(rot=90),y=list(relation="free")),
> auto.key=list(columns=3)))
>
> ______________________________________________
> R-help@... mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

--
Felix Andrews / 安福立
PhD candidate
Integrated Catchment Assessment and Management Centre
The Fenner School of Environment and Society
The Australian National University (Building 48A), ACT 0200
Beijing Bag, Locked Bag 40, Kingston ACT 2604
http://www.neurofractal.org/felix/
3358 543D AAC6 22C2 D336 80D9 360B 72DD 3E4C F5D8

______________________________________________
R-help@... mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

looking for alternative of 'if-else'

2008-07-06T01:59:09Z

There is "if-else" loop if I have to choose 1 item from a 2-item list.
However if I have a list of 4 items (let say) then how i can choose a single
item without employing 'if-else' loop? I mean in VBA I can use
"select-case", is there any equivalent in R as well?

Regards,

[[alternative HTML version deleted]]

______________________________________________
R-help@... mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Re: Different Autocorrelation using R and other softwares

2008-07-06T01:33:05Z

It is not 'obvious' that SPSS/Minitab/Excel are right -- we quite often
see users who wrongly assume that the autocorrelation is the Pearson
correlation between a series and its lagged values. The correct
definition is given in the reference on the help page for acf.

Please note the footer to this message: you were asked for a reproducible
example (and not to send HTML mail). In the absence of such an example
we cannot investigate if this is user error (which seems the most
plausible explanation.)

On Sun, 6 Jul 2008, Yinny wrote:

> Dear All,
>
> Would like to ask the inconsistency in the autocorrelation from R with
> SPSS/Minitab. I have tried a dataset x with 20 data (1-20) and ask R to give
> the autocorrelation of different lags using the command < acf(x,
> lag.max=100, type = "correlation"), However while SPSS and Minitab give the
> same answers (0.85 for lag1), R gives 0.3688 which is much smaller.
>
> Obviously, the answers from SPSS/Minitab are correct by verifying in
> Excel. Is R using another definition in calculating the traditional
> autocorrelation for a time-series?
>
> Thanks for your attention. Would be very grateful if anyone can help.
>
> Yinny
> School of Mathematics & Statistics
> University of Sydney
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@... mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

--
Brian D. Ripley, ripley@...
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595

______________________________________________
R-help@... mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.