Re: [R] improvement of Ancova analysis
Hi Tobias, If you want to do inferential statistics with groups differing systematically on the covariate, you will need to be extra careful in your interpretation. See, e.g., Miller, G. A. Chapman, J. P. Misunderstanding Analysis of Covariance, Journal of Abnormal Psychology, 2001, 110, 40-48, and a lot of other similar things. That said, with your wide variation in pes you may want to consider restricted cubic splines (natural splines) in Frank Harrell's Hmisc and Design packages. At least, it would be interesting to test whether the influence of pes really is linear, which can be done easily with splines. See also Harrell, F. E. Regression Modeling Strategies, Springer, 2001. Good luck with your small furry creatures! Stephan Tobias Erik Reiners schrieb: Dear Helpers, I just started working with R and I'm a bit overloaded with information. My data is from marsupials reindroduced in a area. I have weight(wt), hind foot lenghts(pes) as continues variables and origin and gender as categorial. condition is just the residuals i took from the model. names(dat1) [1] wt pes origin gender condition my model after model simplification so far: model1-lm(log(wt)~log(pes)+origin+gender+gender:log(pes)) --six intercepts and two slopes the problem is i have some things I can't include in my analysis: 1.Very different sample sizes for each of the treatments tapply(log(wt),origin,length) captivesitewild 119 149 19 2.Substantial differences in the range of values taken by the covariate (leg length) between treatments tapply(pes,origin,var) captive site wild 82.43601 71.2 60.42544 tapply(pes,origin,mean) captive site wild 147.3261 144.8698 148.2895 4.Outliers 5.Poorly behaved residuals thanks for the answer I am open minded to any different kind of analysis. Tobi __ R-help@r-project.org 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. __ R-help@r-project.org 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.
[R] Ancova_non-normality of errors
Hello Helpers, I have some problems with fitting the model for my data... --my Literatur says (crawley testbook)= Non-normality of errors--I get a banana shape Q-Q plot with opening of banana downwards Structure of data: origin wt pes gender 1 wild 5.35 147.0 male 2 wild 5.90 148.0 male 3 wild 6.00 156.0 male 4 wild 7.50 157.0 male 5 wild 5.90 148.0 male 6 wild 5.95 148.0 male 7 wild 8.55 160.5 male 8 wild 5.90 148.0 male 9 wild 8.45 161.0 male 10 wild 4.90 147.0 male 11 wild 6.80 153.0 male 12 wild 5.75 146.0 male 13 wild 8.60 160.0 male 14 captive 6.85 159.0 male 15 captive 7.00 160.0 male 16 captive 6.80 155.0 male .. ... 283site 4.10 130.4 female 284site 3.55 131.1 female 285site 4.20 135.7 female 286site 3.45 128.0 female 287site 3.65 125.3 female The goal of my analysis is to work out what effect the categorial factors(origin, gender) on the relation between log(wt)~log(pes)(--Condition, fett ressource), have. Does the source(origin) of translocated animals have an affect on performance(condition)in the new area? I have already a best fit model and it looks quite good (or not?see below). two slopes(gender difference)and 6 intercepts(3origin levels*2gender levels) lm(formula = log(wt) ~ log(pes) + origin + gender + gender:log(pes)) Residuals: Min 1Q Median 3Q Max -0.54181 -0.07671 0.01520 0.09474 0.28818 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) -7.398791.97605 -3.744 0.000219 *** log(pes) 1.780200.40118 4.437 1.31e-05 *** originsite 0.065720.01935 3.397 0.000781 *** originwild 0.076550.03552 2.155 0.032011 * gendermale -9.324182.37476 -3.926 0.000109 *** log(pes):gendermale 1.903930.47933 3.972 9.06e-05 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1433 on 281 degrees of freedom Multiple R-Squared: 0.7227, Adjusted R-squared: 0.7177 F-statistic: 146.4 on 5 and 281 DF, p-value: 2.2e-16 When plot this model I get a banana-shape in Normal Q-Q Plot(with open site pointing downwards) , indicating non-normality of my datahow to handle this? --Do I have unbalanced data? captivesitewild n-- 119 149 19 My problem is that I see that my data is not as good as the modelsummary tells. Should I include another term in my model formular? I think I have to differenciate more, but I don't know how.(contrasts?, TukeyHSD?,Akaike Information Criterion? or lme())to many different ways out there. Cheers, Tobi __ R-help@r-project.org 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.
[R] (no subject)
Dear R users, I have a question about R installation under Cygwin. Which versionof R should I download ,linux or windows? If linux ,which release should I download? Thanks a lot! Jiansheng Wu PhD Candidate of State Key Laboratory of Bioelectronics Southeast University, Nanjing, 210096, China Tel 86-25-83790881 Email: [EMAIL PROTECTED] [[alternative HTML version deleted]] __ R-help@r-project.org 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: [R] Installing R under Cygwin [was (no subject)]
On Sun, 4 May 2008, lmbewjs wrote: Dear R users, I have a question about R installation under Cygwin. Which versionof R should I download ,linux or windows? If linux ,which release should I download? Thanks a lot! Neither. You need to download the source tarball (R-2.7.0.tar.gz on teh CRAN front page), and build from the sources on Cygwin, following the instructions in the 'R Installation and Administration' manual. Cygwin is not a supported platform, but R does more or less work under it. You could install a binary Windows version, but it will not understand Cygwin file paths. Jiansheng Wu PhD Candidate of State Key Laboratory of Bioelectronics Southeast University, Nanjing, 210096, China Tel 86-25-83790881 Email: [EMAIL PROTECTED] [[alternative HTML version deleted]] __ R-help@r-project.org 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. PLEASE do -- use a helpful subject line, no HTML mail, read the manuals before posting -- Brian D. Ripley, [EMAIL PROTECTED] 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, UKFax: +44 1865 272595 __ R-help@r-project.org 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: [R] Is my understanding of rlnorm correct?
Thank you for your reply. I think I have a poor understanding of this distribution but, if I understand your answer albeit roughly, then to get a mean of 100 I need to select a mu and derive the sd using sqrt(2*(log(100)-mu)). That helps a lot. My application is in modeling/simulating failure/repair processes which I have read are typically log-normal. I should now be able to get the result i expect to get. Thanks again. On Sun, May 4, 2008 at 3:11 PM, Berwin A Turlach [EMAIL PROTECTED] wrote: G'day Phil, On Sun, 4 May 2008 14:05:09 +1000 phil colbourn [EMAIL PROTECTED] wrote: rlnorm takes two 'shaping' parameters: meanlog and sdlog. meanlog would appear from the documentation to be the log of the mean. eg if the desired mean is 1 then meanlog=0. These to parameters are the mean and the sd on the log scale of the variate, i.e. if you take the logarithm of the produced numbers then those values will have the given mean and sd. If X has an N(mu, sd^2) distribution, then Y=exp(X) has a log-normal distribution with parameters mu and sd. R set.seed(1) R y - rlnorm(1, mean=3, sd=2) R summary(log(y)) Min. 1st Qu. MedianMean 3rd Qu.Max. -4.343 1.653 2.968 2.987 4.355 10.620 R mean(log(y)) [1] 2.986926 R sd(log(y)) [1] 2.024713 I noticed on wikipedia lognormal page that the median is exp(mu) and that the mean is exp(mu + sigma^2/2) http://en.wikipedia.org/wiki/Log-normal_distribution Where mu and sigma are the mean and standard deviation of a normal variate which is exponentiated to obtain a log normal variate. And this holds for the above example (upto sampling variation): R mean(y) [1] 143.1624 R exp(3+2^2/2) [1] 148.4132 So, does this mean that if i want a mean of 100 that the meanlog value needs to be log(100) - log(sd)^2/2? A mean of 100 for the log-normal variate? In this case any set of mu and sd for which exp(mu+sd^2/2)=100 (or mu+sd^2/2=log(100)) would do the trick: R mu - 2 R sd - sqrt(2*(log(100)-mu)) R summary(rlnorm(1, mean=mu, sd=sd)) Min. 1st Qu.Median Mean 3rd Qu. Max. 4.010e-04 1.551e+00 7.075e+00 1.006e+02 3.344e+01 3.666e+04 R mu - 4 R sd - sqrt(2*(log(100)-mu)) R summary(rlnorm(1, mean=mu, sd=sd)) Min. 1st Qu.Median Mean 3rd Qu. Max. 0.9965 25.9400 56.0200 101.2000 115.5000 3030. R mu - 1 R sd - sqrt(2*(log(100)-mu)) R summary(rlnorm(1, mean=mu, sd=sd)) Min. 1st Qu.Median Mean 3rd Qu. Max. 9.408e-05 4.218e-01 2.797e+00 8.845e+01 1.591e+01 7.538e+04 Note that given the variation we would expect in the mean in the last example, the mean is actually close enough to the theoretical value of 100: R sqrt((exp(sd^2)-1)*exp(2*mu + sd^2)/1) [1] 36.77435 HTH. Cheers, Berwin === Full address = Berwin A TurlachTel.: +65 6515 4416 (secr) Dept of Statistics and Applied Probability+65 6515 6650 (self) Faculty of Science FAX : +65 6872 3919 National University of Singapore 6 Science Drive 2, Blk S16, Level 7 e-mail: [EMAIL PROTECTED] Singapore 117546 http://www.stat.nus.edu.sg/~statbahttp://www.stat.nus.edu.sg/%7Estatba -- Phil Someone else has solved it and posted it on the internet for free [[alternative HTML version deleted]] __ R-help@r-project.org 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: [R] improvement of Ancova analysis
On Sat, May 3, 2008 at 9:00 PM, Tobias Erik Reiners [EMAIL PROTECTED] wrote: Dear Helpers, I just started working with R and I'm a bit overloaded with information. My data is from marsupials reindroduced in a area. I have weight(wt), hind foot lenghts(pes) as continues variables and origin and gender as categorial. condition is just the residuals i took from the model. names(dat1) [1] wt pes origin gender condition my model after model simplification so far: model1-lm(log(wt)~log(pes)+origin+gender+gender:log(pes)) --six intercepts and two slopes the problem is i have some things I can't include in my analysis: 1.Very different sample sizes for each of the treatments tapply(log(wt),origin,length) captivesitewild 119 149 19 2.Substantial differences in the range of values taken by the covariate (leg length) between treatments tapply(pes,origin,var) captive site wild 82.43601 71.2 60.42544 tapply(pes,origin,mean) captive site wild 147.3261 144.8698 148.2895 4.Outliers 5.Poorly behaved residuals thanks for the answer I am open minded to any different kind of analysis. How about starting with some graphics? e.g. with ggplot2 the following would give you some clues as to whether your models are appropriate or not: qplot(pes, wt, data=dat1, colour=gender, facets = . ~ origin, log=xy) + geom_smooth(method=lm) qplot(pes, wt, data=dat1, facets = gender ~ origin, log=xy) + geom_smooth(method=lm) If you wanted to the see the effect of a robust fit, as suggested by Brian Ripley, replace lm with rlm. Hadley -- http://had.co.nz/ __ R-help@r-project.org 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: [R] Errors bar in barchart
Em Sex 02 Mai 2008, Deepayan Sarkar escreveu: On 5/2/08, Ronaldo Reis Junior [EMAIL PROTECTED] wrote: Hi, I user barplot2 to make a plot bar with errors bars. In old times I needed to use a sequence of segments commands to make this. Now I try to make the same but using lattice. Is possible to use barplot2 in barchart function? If not, what is the simplest way to put errors bar in barchart? I try to find an example in Lattice book, but dont find anythink like this. No there isn't. I don't like the idea of error bars on bar charts, and I would suggest you use them with dot plots instead. There is a demo of this that you can run using demo(intervals, package = lattice) -Deepayan Thanks, I get it. I dont like this idea too, but some people living in the past (Jethro?) Thanks Ronaldo -- If you wait long enough, it will go away... after having done its damage. If it was bad, it will be back. -- Prof. Ronaldo Reis Júnior | .''`. UNIMONTES/Depto. Biologia Geral/Lab. de Biologia Computacional | : :' : Campus Universitário Prof. Darcy Ribeiro, Vila Mauricéia | `. `'` CP: 126, CEP: 39401-089, Montes Claros - MG - Brasil | `- Fone: (38) 3229-8187 | [EMAIL PROTECTED] | [EMAIL PROTECTED] | http://www.ppgcb.unimontes.br/ | ICQ#: 5692561 | LinuxUser#: 205366 __ R-help@r-project.org 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: [R] Ancova_non-normality of errors
Dear Tobias, Your observation that When plot [the residuals from?] this model I get a banana-shape in Normal Q-Q Plot(with open site [side?] pointing downwards), suggests that the residuals are negatively skewed, which in turn suggests that using log(wt) as the response variable may have been ill-advised. Perhaps simply using wt, or a weaker transformation such as sqrt(wt), would produce better-behaved residuals. I hope this helps, John -- John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Tobias Erik Reiners Sent: May-04-08 5:56 AM To: r-help@r-project.org Subject: [R] Ancova_non-normality of errors Hello Helpers, I have some problems with fitting the model for my data... --my Literatur says (crawley testbook)= Non-normality of errors--I get a banana shape Q-Q plot with opening of banana downwards Structure of data: origin wt pes gender 1 wild 5.35 147.0 male 2 wild 5.90 148.0 male 3 wild 6.00 156.0 male 4 wild 7.50 157.0 male 5 wild 5.90 148.0 male 6 wild 5.95 148.0 male 7 wild 8.55 160.5 male 8 wild 5.90 148.0 male 9 wild 8.45 161.0 male 10 wild 4.90 147.0 male 11 wild 6.80 153.0 male 12 wild 5.75 146.0 male 13 wild 8.60 160.0 male 14 captive 6.85 159.0 male 15 captive 7.00 160.0 male 16 captive 6.80 155.0 male .. ... 283site 4.10 130.4 female 284site 3.55 131.1 female 285site 4.20 135.7 female 286site 3.45 128.0 female 287site 3.65 125.3 female The goal of my analysis is to work out what effect the categorial factors(origin, gender) on the relation between log(wt)~log(pes)(--Condition, fett ressource), have. Does the source(origin) of translocated animals have an affect on performance(condition)in the new area? I have already a best fit model and it looks quite good (or not?see below). two slopes(gender difference)and 6 intercepts(3origin levels*2gender levels) lm(formula = log(wt) ~ log(pes) + origin + gender + gender:log(pes)) Residuals: Min 1Q Median 3Q Max -0.54181 -0.07671 0.01520 0.09474 0.28818 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) -7.398791.97605 -3.744 0.000219 *** log(pes) 1.780200.40118 4.437 1.31e-05 *** originsite 0.065720.01935 3.397 0.000781 *** originwild 0.076550.03552 2.155 0.032011 * gendermale -9.324182.37476 -3.926 0.000109 *** log(pes):gendermale 1.903930.47933 3.972 9.06e-05 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1433 on 281 degrees of freedom Multiple R-Squared: 0.7227, Adjusted R-squared: 0.7177 F-statistic: 146.4 on 5 and 281 DF, p-value: 2.2e-16 When plot this model I get a banana-shape in Normal Q-Q Plot(with open site pointing downwards) , indicating non-normality of my datahow to handle this? --Do I have unbalanced data? captivesitewild n-- 119 149 19 My problem is that I see that my data is not as good as the modelsummary tells. Should I include another term in my model formular? I think I have to differenciate more, but I don't know how.(contrasts?, TukeyHSD?,Akaike Information Criterion? or lme())to many different ways out there. Cheers, Tobi __ R-help@r-project.org 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. __ R-help@r-project.org 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.
[R] adaptive optimization of mesh size
DeaR list,
I'm running an external program that computes some electromagnetic
response of a scattering body. The numerical scheme is based on a
discretization with a characteristic mesh size y. The smaller y is,
the better the result (but obviously the computation will take longer).
A convergence study showed the error between the computed values and
the exact solution of the problem to be a quadratic in y, with
standard error increasing as y^3. I wrote the interface to the
program in R, as it is much more user friendly and allows for post-
processing analysis. Currently, it only runs with user-defined
discretization parameter. I would like to implement an adaptive
scheme [1] and provide the following improvements,
1) obtain an estimate of the error by fitting the result against a
series of mesh sizes with the quadratic model, and extrapolate at y =
0. (quite straight forward)
2) adapt dynamically the set of mesh sizes to fulfill a final
accuracy condition, between a starting value (a rule-of thumb
estimate is given by the problem values). The lower limit of y should
also be constrained by the resources (again, an empirical rule
dictates the computation time and memory usage).
I'm looking for advice on this second point (both on the technical
aspect, and whether this is sound statistically):
- I can foresee that I should always start with a few y values before
I can do any extrapolation, but how many of them? 3, 10? How could I
know?
- once I have enough points (say, 10) to use the fitting procedure
and get an estimate of the error, how should I decide the best
location of the next y if the error is too important?
- in a practical implementation, I would use a while loop and append
the successive values to a data.frame(y, value). However, this
procedure will be run for different parameters (wavelengths,
actually), so the set and number of y values may vary between one run
and another. I think I'd be better off using a list with each new run
having its own data.frame. Does this make sense?
Below are a few lines of code to illustrate the problem,
program.result - function(x, p){ # made up function that mimicks
the results of the real program
y - p[3]*x^2 + p[2]*x + p[1]
y * (1 + rnorm(1, mean=0, sd = 0.1 * y^3))
}
p0 - c(0.1, 0.1, 2) # set of parameters
## user defined limits of the y parameter (log scale)
limits - c(0.1, 0.8)
limits.log - (10^limits)
y.log - seq(limits.log[1], limits.log[2], l=10)
y - log10(y.log)
result - sapply(y, function(x) program.result(x, p0)) # results of
the program
fitting and extrapolation procedure
library(gplots) # plot with CI
plotCI(y, result, y^3, xlim=c(0, 1), ylim=c(0, 2)) # the data
with y^3 errors
my.data - data.frame(y = y, value = result)
fm - lm(value ~ poly(y, degree=2, raw=TRUE), data = my.data ,
weights = 1/y^3)
lines(y, predict(fm, data.frame(y=y)), col = 2)
extrap - summary(fm)$coefficients[1,] # intercept and error on it
plotCI(0,extrap[1], 2 * extrap[2], col = 2, add=T)
### my naive take on adaptive runs... ##
objective - 1e-3 # stop when the standard error of the
extrapolated value is smaller than this
err - extrap[2]
my.color - 3
while (err objective){
new.value - min(y)/2 # i don't know how to choose this optimally
y - c(new.value, y)
new.result - program.result(new.value, p0)
result - c(new.result, result)
points(new.value, new.result, col= my.color)
my.data - data.frame(y = y, value = result)
fm - lm(value ~ poly(y, degree=2, raw=TRUE), data = my.data ,
weights = 1/y^3)
lines(y, predict(fm, data.frame(y=y)), col = my.color)
extrap - summary(fm)$coefficients[1,] # intercept and error on it
err - extrap[2]
print(err)
plotCI(0,extrap[1], 2 * err, col = 2, add=T)
my.color - my.color + 1
}
err
Many thanks in advance for your comments,
baptiste
[1]: Yurkin et al., Convergence of the discrete dipole approximation.
II. An extrapolation technique to increase the
accuracy. J. Opt. Soc. Am. A / Vol. 23, No. 10 / October 2006
_
Baptiste Auguié
Physics Department
University of Exeter
Stocker Road,
Exeter, Devon,
EX4 4QL, UK
Phone: +44 1392 264187
http://newton.ex.ac.uk/research/emag
http://projects.ex.ac.uk/atto
__
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[R] help with mars output
I need help in interpreting some output of polymars. The returned model by my command is: m$model pred1knot1 pred2 knot2 coefs SE 1 0 NA 0NA 1.3163 0.0007806758 2 1 NA 0NA -0.10904285 0.0006735827 3 1 1.193575 0NA 1.25396217 0.0205092438 4 1 1.205400 0NA -0.18020096 0.0261931173 5 1 1.230725 0NA 0.04709508 0.0118559207 6 1 1.285400 0NA -0.02593593 0.0097733958 7 1 1.317275 0NA 0.03516571 0.0177043207 I'm trying to confirm the generated output of mars by manually entering the basis functions into my dataset, What I don't understand is that in the knot1 column there are to rows containing NA. In the polymars literature I was reading this means that these basis functions are linear. BF1 = MAX(0, A2 - 1.193575) BF2 =MAX(0, 1.193575 - A2) BF3 = MAX(0, A2 - 1.2054) BF5 = MAX(0, A2 - 1.230725) BF7 = MAX(0, A2 - 1.2854) BF11 = MAX(0, A2 - 1.317275) RESPONSE = 1.3163 - 0.10904285 * C2 + 1.25396217 * D2 - 0.18020096 * E2 + 0.04709508 * F2 - 0.02593593 * G2 + 0.03516571 * H2 On the attached, the CLOSE column is my source data, the ESTIMATE is the column generated by polymars, the PREDICTION column is my formula. Can I have some help with this please. __ R-help@r-project.org 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.
[R] Categorizing Fonts using Statistical Methods
Dear list members, Every modern OS comes with dozens of useless fonts, so that the current font drop-down list in most programs is overcrowded with fonts one never will use. Selecting a useful font becomes a nightmare. In an attempt to ease the selection of useful fonts, I began looking into sorting fonts using some statistical techniques. I summed my ideas on the OpenOffice.org wiki: http://wiki.services.openoffice.org/wiki/User_Experience/ToDo/Product/Font_Categories Of course, there is NO guarantee that something useful will emerge, but at least someone has tried it. I would like to try various statistical methods using R, unfortunately, I got rather stuck in my attempts. I wish to compute: - the length of a standard string for the various fonts - the weight - some variance-type measures for the OX and OY-axis - DCT (possibly analysing separately the low/high-frequencies) - maybe some other measures [I am open to suggestions] 1.) First and foremost, I need the list of fonts installed on my system. [I am using Win2k] Is there any way to get it automatically in R? IF this is not possible, I could create one by hand, though this is cumbersome, but the 2nd problem is more severe. 2.) How do I create/get the 2D-pixel matrix? I need of course the f(x,y)-image representation for a standard text. The following seems a rather ugly hack and I do not actually have the exact text-box size. png(file=mytestfontimage.png) plot.new() title(This is a font) dev.off() strwidth() and strheight() seem to be able to look into the fonts. But how do I get the pixels? And more importantly, can I get also the exact pixel-matrix? [Though, it seems there are no pixel-units in strheight()/width(), but I might be wrong on this.] 3.) The image-analyses capabilities of R are rather limited. I couldn't find any reference to a DCT transform (or other techniques). A search yielded only the following thread: http://tolstoy.newcastle.edu.au/R/help/06/01/19615.html As I do not know (and have access to) mathlab, I am rather confined to R. Which is not bad, but I need a lot of help to accomplish this task. Any help is highly appreciated. Sincerely, Leonard Mada __ R-help@r-project.org 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: [R] Speedups with Ra and jit
A couple of comments on this and the original thread.
As pointed out by several posters, in a vectorized language like R one
can usually create the fastest and cleanest code by using vectorized
operations. This applies to R as well as Matlab.
That said, there are at times reasons for using code consisting of a
set of tight nested loops, such as
it may not be obvious how to rewrite the loops using
vectorization
the code may match a published algorithm and so verifying its
correctness and maintaining it in this form may be easier than
rewriting
the code may not be fast or pretty but it works and gets the right
answer
Given these points it would be nice if R's interpreter could do a
better job. R's interpreter is currently rather slow on these sorts
of computations. For example, a direct translation of the original
poster's grw_permute example runs about 5 times as fast in the
xlispstat interpreter (similar internals in many ways). There are
some things we could do involving relatively little effort to
marginally improve R's interpreter, but significant improvements would
likely require a fairly significant rewrite (which may be worth
considering at some point but probably not soon).
An alternative is to pursue automated tools to transform this sort of
code into faster code. This is in part the motivation for creating a
byte code compiler for R, and for the Ra/jit project. The current
byte code compiler (http://www.stat.uiowa.edu/~luke/R/compiler/)
speeds up this computation by about a factor of 3. This compiler does
not yet optimize variable lookup or indexing of vectors or matrices;
these should be added sometime this summer and should add another
factor of 3 or so for this example.
Steve has done some very interesting things in his Ra/jit project.
Some of the optimizations done there are either already done in the
byre code compiler or have been planned to be added for a while.
Others, in particular specialization for basic data types may be best
done at run time as jit does, and there may be room for merging these
ideas with the static byte code compiler.
The particular specialization approach used in jit means that some
code will produce different results or generate errors; i.e. a user
who requests jit compilation is implicitly agreeing not to try to do
certain things, such as change types or sizes of values stored in
variables used in the compilation. In the long run I would prefer
either a mechanism where such assumptions are declared explicitly by
the user or to arrange for R to automatically switch back to less
optimized code when the assumptions of the optimization are violated.
I believe the main aspect of runtime specialization done in jit now
that may be hard to match in statically compiled byte code is that
a function defined as
f - function(x) {
jit()
s - 0
for (i in seq_along(x)) s - s + x[i]
s
}
will be optimized for integer data when run on integer x and on for
real data when run on real x, and in both cases allocation of
intermediate results is avoided. How valuable this is in the long run
is not yet clear -- it would definitely be very helpful if machine
code was being generated that also allowed intermediate values to stay
in registers (which I believe is what psyco does), but that is messy
and hard to do across many platforms. With fast allocation the
benefits of avoiding allocation alone may not be that substantial.
For example, the byte compiled xlispstat version of the grw_protect
example mentioned above runs about twice as fast at the Ra/jit one
without avoiding intermediate allocation. This isn't conclusive of
course and it will be interesting to do some more careful tests and
see what directions those suggest.
Best,
luke
On Fri, 2 May 2008, [EMAIL PROTECTED] wrote:
The topic of Ra and jit has come up on this list recently
(see http://www.milbo.users.sonic.net/ra/index.html)
so I thought people might be interested in this little demo. For it I
used my machine, a 3-year old laptop with 2Gb memory running Windows
XP, and the good old convolution example, the same one as used on the
web page, (though the code on the web page has a slight glitch in it).
This is using Ra with R-2.7.0.
conv1 - function(a, b) {
### with Ra and jit
require(jit)
jit(1)
ab - numeric(length(a)+length(b)-1)
for(i in 1:length(a))
for(j in 1:length(b))
ab[i+j-1] - ab[i+j-1] + a[i]*b[j]
ab
}
conv2 - function(a, b) {
### with just Ra
ab - numeric(length(a)+length(b)-1)
for(i in 1:length(a))
for(j in 1:length(b))
ab[i+j-1] - ab[i+j-1] + a[i]*b[j]
ab
}
x - 1:2000
y - 1:500
system.time(tst1 - conv1(x, y))
user system elapsed
0.530.000.55
system.time(tst2 - conv2(x, y))
user system elapsed
9.490.009.56
all.equal(tst1, tst2)
[1] TRUE
9.56/0.55
[1] 17.38182
However for this example you can achieve
[R] Text shrinking in pdf graphics
__ R-help@r-project.org 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: [R] Cross Spectrum Analysis
is this a problem? are there error messages? if so could you provide them. Try as.matrix(yourdata). One thing you could do is create a moving average that reduces the signals to the lowest common denominator. Could you provide reproducable code with maybe a toy data set so anybody could have a look at what is going on. good luck Stephen On Sat, May 3, 2008 at 6:11 PM, Maura E Monville [EMAIL PROTECTED] wrote: In the case of muitivariate, from the documentation it looks like I can compare more than two signals at a time. Each column of the input matix seem to accommodate a signal. The problem is that my signals do NOT have the same number of samples (length). They were all collected at 30Hz so the sampling time interval is roughly 0.033[s]. Some signals have about 5000 samples and other ones have more than 8000. The R routine spectrum expect the multivariate to be a matrix ... Any idea how to overcome such an obstacle ? Padding with zeros would alter (I think) the phenomen being studied that is breathing patterns. Is there a way to feed the spectrum function with the signal spectrum (power density) instead of the time domain signal ? Since the sampling interval is equal for all the signal, so is the Nyquist frequency. I can easily get the power spectrum defined over the domain [0, Nyquist-frequency] which does not have the problem of different lengths ... ??? Thank you so much. Maura On Wed, Apr 30, 2008 at 8:56 AM, stephen sefick [EMAIL PROTECTED] wrote: $names [1] freq spec coh phase kerneldf [7] bandwidth n.usedorig.nseriessnames method [13] taper pad detrend demean $freq and $spec are used to plot the power spectrum. freq is the x-axis and spec is the y-axis. $coh is the squared coherency between the two signals in your case and I believe that this is also plotted against frequency. This is your correlation strength. Phase I haven't been able to figure out- I think that it is some sort of estimator for the phase shift. to get either phase or coherency plot add the plot.type argument to your plot command x - spectrum(yourdata, log=no) #this will plot it without a log scale I find it useful to look at both the no log plot and then the logscale plot (just remove the log=no) plot(x, plot.type=marginal) #this is the default type (the powerspectrum) plot(x, plot.type=phase) plot(x, plot.type=coherency) also just look at ?spectrum schumway is a good book - I think it is something like time series analysis with examples in R hope this helps stephen On Tue, Apr 29, 2008 at 8:54 PM, Maura E Monville [EMAIL PROTECTED] wrote: I am reading some documentation about Cross Spectrum Analysis as a technique to compare spectra. My understanding is that it estimates the correlation strength between quasi-periodic structures embedded in two signals. I believe it may be useful for my signals analysis. I was referred to the R functions that implement this type of analysis. I tried all the examples which generated a series of fancy plots. But I need to work on the numerical results. I have read that the following info is available through Cross Spectra analysis: *Cross-periodogram, Cross-Density, Quadrature-density, Cross-amplitude, Squared Coherency, Gain, and Phase Shift* I went through a couple of the two-series (bivariate) cross-spectrum analysis examples with R. I also printed out the attributes of the analysis (see the following). I cannot quite match the above quantities with the attributes/features output of cross-spectra analysis with R. I would greatly appreciate some explanation (which is what) and seeing some more worked out examples. attributes(mfdeaths.spc) $names [1] freq spec coh phase kerneldf [7] bandwidth n.usedorig.nseriessnames method [13] taper pad detrend demean $class [1] spec Thank you so much. Yours Faithfully, -- Maura E.M [[alternative HTML version deleted]] __ R-help@r-project.org 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. -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being mammals. -K. Mullis -- Maura E.M -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals,
[R] Validating a mixed-effects model
Hi I constructed a mixed-effects model from longitudinal repeated measurements of lab values in 22 patients seperated into two groups with the groups as fixed effect using lme. I thought about using the jackknife procedure, i. e., removing any one subject and calculating the fixed effect, to assess the stability of the fixed effect and thereby validate the model. I suppose this has been done in the following study: http://content.nejm.org/cgi/content/full/357/19/1903 (this may be restricted access, sorry) Is such an approach feasible? Also in the article results are confirmed by comparing the mixed model with a fitted least-squares regression. I understand that this can be achieved with lmlist, but only for for models without an additional fixed effect!? Are there any other good approaches to validate a mixed-effects model that will be accepted in medical peer review? -- Armin Goralczyk, M.D. -- Universitätsmedizin Göttingen Abteilung Allgemein- und Viszeralchirurgie Rudolf-Koch-Str. 40 39099 Göttingen -- Dept. of General Surgery University of Göttingen Göttingen, Germany -- http://www.gwdg.de/~agoralc __ R-help@r-project.org 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: [R] Categorizing Fonts using Statistical Methods
Leonard Mada [EMAIL PROTECTED] [Sun, May 04, 2008 at 07:26:04PM CEST]: Dear list members, Every modern OS comes with dozens of useless fonts, so that the current font drop-down list in most programs is overcrowded with fonts one never will use. Selecting a useful font becomes a nightmare. In an attempt to ease the selection of useful fonts, I began looking into sorting fonts using some statistical techniques. I summed my ideas on the OpenOffice.org wiki: http://wiki.services.openoffice.org/wiki/User_Experience/ToDo/Product/Font_Categories Of course, there is NO guarantee that something useful will emerge, but at least someone has tried it. Why is there nothing mentioned with respect to the classical font categorization, Venetian, Aldine, Transitional, Modern, Slab Serif, ... ? [...] - maybe some other measures If you can obtain the *.afm information of the font, you have some useful parameters such as cap height, ascender height, descender height, oblique angle ... -- Johannes Hüsing There is something fascinating about science. One gets such wholesale returns of conjecture mailto:[EMAIL PROTECTED] from such a trifling investment of fact. http://derwisch.wikidot.com (Mark Twain, Life on the Mississippi) __ R-help@r-project.org 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.
[R] plotting pie-charts into a coordinate system
Dear R user group, I wish to plot small pie-charts to specific coordinates in a e.g. scatter-plot: E.g.: plot(rnorm(100),rnorm(100)) points(1,1,col=red,cex=4) - I wish to put pie(c(2,3)) at the position of the red circle... How can I do this efficiently? Thanking you and wishing you a wonderful Sunday! Georg. ** Georg Ehret Johns Hopkins Baltimore USA [[alternative HTML version deleted]] __ R-help@r-project.org 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: [R] plotting pie-charts into a coordinate system
On Sun, May 4, 2008 at 2:30 PM, Georg Ehret [EMAIL PROTECTED] wrote: Dear R user group, I wish to plot small pie-charts to specific coordinates in a e.g. scatter-plot: E.g.: plot(rnorm(100),rnorm(100)) points(1,1,col=red,cex=4) - I wish to put pie(c(2,3)) at the position of the red circle... How can I do this efficiently? For some discussion of the disadvantages of this type of display, and some suggestions for alternatives, you might like to read http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=00018S. Another interesting page is http://www.math.yorku.ca/SCS/Gallery/minard/minard.pdf, which describes the first use of this technique, by Minard. Hadley -- http://had.co.nz/ __ R-help@r-project.org 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: [R] Categorizing Fonts using Statistical Methods
Hello Johannes, Johannes Hüsing wrote: Leonard Mada lmada_at_gmx.net [Sun, May 04, 2008 at 07:26:04PM CEST]: Dear list members, Every modern OS comes with dozens of useless fonts, so that the current font drop-down list in most programs is overcrowded with fonts one never will use. Selecting a useful font becomes a nightmare. In an attempt to ease the selection of useful fonts, I began looking into sorting fonts using some statistical techniques. I summed my ideas on the OpenOffice.org wiki: http://wiki.services.openoffice.org/wiki/User_Experience/ToDo/Product/Font_Categories Of course, there is NO guarantee that something useful will emerge, but at least someone has tried it. Why is there nothing mentioned with respect to the classical font categorization, Venetian, Aldine, Transitional, Modern, Slab Serif, ... ? I played with the idea over and over again, but decided then against it. I had a look both on the Adobe site, and on various other sites (e.g. http://graphicdesign.spokanefalls.edu/tutorials/process/type_basics/type_families.htm#oldstyle).Unfortunately, fonts belonging to different families may look very similar, while fonts within one family are different enough to warrant a distinct classification. Especially this latter aspect makes me think that the font families are not that helpful, and - when choosing the appropriate font - I do NOT want to limit myself to one family. A different font family might look even better. Also, I cannot remember a single time I have used a font based on its family. Rather, a font gets selected based on how it looks within a specific document (well, mostly it gets selected because the person knows it - but lets ignore this and adopt a more scientific approach). Selecting some measures, like font width, height, weight, complexity, compactness, slant, [...] seems a sensible approach. [...] - maybe some other measures If you can obtain the *.afm information of the font, you have some useful parameters such as cap height, ascender height, descender height, oblique angle ... I do have a rather limited understanding of the font-files proper. If I am correct, .afm-files are available only for post-script fonts. Of course, on Windows, most fonts will be TrueType and OpenType. I have no idea, IF such information is available for these fonts. My primary problem is however, that the purpose of this analysis is to let end-users perform this same analysis on their computers on their own font sets. My plan was to do a proof of concept analysis in R, and later (when I have some better idea how to categorise fonts and everything works fine) to post such a feature request in specific programs. At this point, this sorting of fonts is of unproven benefit and of unknown behaviour. So, I wouldn't want to waste developers time into something that might prove useless (though I have high expectations that something useful will emerge - NOT sure however which of the specific measures will bring the most differentiating features). I still hope in completing succefully this task. Many thanks for your advice, I will take another look at afm-files. Sincerely, Leonard __ R-help@r-project.org 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: [R] plotting pie-charts into a coordinate system
Hi Georg Ehret wrote: Dear R user group, I wish to plot small pie-charts to specific coordinates in a e.g. scatter-plot: E.g.: plot(rnorm(100),rnorm(100)) points(1,1,col=red,cex=4) - I wish to put pie(c(2,3)) at the position of the red circle... How can I do this efficiently? For one approach see Integrating grid Graphics Output with Base Graphics Output in R News 3/2 (http://www.r-project.org/doc/Rnews/Rnews_2003-2.pdf) Paul Thanking you and wishing you a wonderful Sunday! Georg. ** Georg Ehret Johns Hopkins Baltimore USA [[alternative HTML version deleted]] __ R-help@r-project.org 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. -- Dr Paul Murrell Department of Statistics The University of Auckland Private Bag 92019 Auckland New Zealand 64 9 3737599 x85392 [EMAIL PROTECTED] http://www.stat.auckland.ac.nz/~paul/ __ R-help@r-project.org 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.
[R] Residual resampling for non linear reg model
I was attempting to use the residual resampling approach to generate 999
bootstrap samples of alpha and beta and find their confidence intervals.
However, I keep getting the error message:Error in nls(resample.mp ~
cases/(alpha + (beta * cases)), start = init.values, : singular
gradientafter R has only produced a few bootstraps.Could anyone suggest where I
am going wrong? Would greatly appreciate it!Mary
manpower-read.table(http://www.creem.st-and.ac.uk/len/classes/mt3607/data/manhours_surgical.dat;,
header=TRUE)#attach dataattach(manpower)B-999#number of data
pointsn-dim(manpower)[1]#alpha level to use for the confidence
limitsalpha-0.05#matrix that's going to contain the bootstrap
coefficientsboot.coef-matrix(NA, B+1, 2)#fit the initial
modelinit.values-c(alpha=20,beta=0)model-nls(manhours~cases/(alpha+(beta*cases)),
start=init.values, trace=TRUE)pred-predict(model)resid-resid(model)#do the
bootstrapfor (i in 1:B){ #resample the residuals resample.ind!
ex-sample(1:n,n,replace=T) resample.mp-pred+resid[resample.index] #refit
the model init.values-c(alpha=20,beta=0)
new.model-nls(resample.mp~cases/(alpha+(beta*cases)),start=init.values,
trace=TRUE) #extract the parameter estimates
boot.coef[i,]-coef(new.model)}#add the original parameter estimates
boot.coef[B+1,]-coef(model)#calculate confidence intervalsfor(i in 1:2){
ci-quantile(boot.coef[,i],probs=c(alpha/2,(1-alpha/2))) cat(residual
bootstrap confidence intervals for parameter,i,are,ci,\n)}
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__
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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: [R] Residual resampling for non linear reg model
Since sending this message I have now solved the problem - needed to alter the
initial values of alpha and beta!
From: [EMAIL PROTECTED] To: r-help@r-project.org Date: Mon, 5 May 2008
00:03:53 +0100 Subject: [R] Residual resampling for non linear reg model
I was attempting to use the residual resampling approach to generate
999 bootstrap samples of alpha and beta and find their confidence intervals.
However, I keep getting the error message:Error in nls(resample.mp ~
cases/(alpha + (beta * cases)), start = init.values, : singular gradientafter
R has only produced a few bootstraps.Could anyone suggest where I am going
wrong? Would greatly appreciate it!Mary
manpower-read.table(http://www.creem.st-and.ac.uk/len/classes/mt3607/data/manhours_surgical.dat;,
header=TRUE)#attach dataattach(manpower)B-999#number of data
pointsn-dim(manpower)[1]#alpha level to use for the confidence
limitsalpha-0.05#matrix that's going to contain the bootstrap
coefficientsboot.coef-matrix(NA, B+1, 2)#fit the initial
modelinit.values-c(alpha=20,beta=0)model-nls(manhours~cases/(alpha+!
(beta*cases)), start=init.values,
trace=TRUE)pred-predict(model)resid-resid(model)#do the bootstrapfor (i in
1:B){ #resample the residuals resample.ind! ex-sample(1:n,n,replace=T)
resample.mp-pred+resid[resample.index] #refit the model
init.values-c(alpha=20,beta=0)
new.model-nls(resample.mp~cases/(alpha+(beta*cases)),start=init.values,
trace=TRUE) #extract the parameter estimates
boot.coef[i,]-coef(new.model)}#add the original parameter estimates
boot.coef[B+1,]-coef(model)#calculate confidence intervalsfor(i in 1:2){
ci-quantile(boot.coef[,i],probs=c(alpha/2,(1-alpha/2))) cat(residual
bootstrap confidence intervals for parameter,i,are,ci,\n)} Miss your
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ide http://www.R-project.org/posting-guide.html and provide commented,
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[R] calling a C program from R
Is it possible to call a program (an .exe wrote in C) from R Gui? I'm interested in interact with inputs for this program and analyze outputs with R. The program itself calls a input.dat file with a number of needed parameters. Based on this input file it produces a number of files resuming the parameterized analysis. I'm actually already able to read these outputs with R, and produce a number of outputs (graphs and tables). Nevertheless, each run of the program is manually done by now. It would be interesting to loop the call of the program, manipulating parameters at each run. Do exist a way of interact with a external exe from R? Thanks in advance. Paulo [[alternative HTML version deleted]] __ R-help@r-project.org 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: [R] calling a C program from R
On 04/05/2008 7:39 PM, Paulo Cardoso wrote: Is it possible to call a program (an .exe wrote in C) from R Gui? I'm interested in interact with inputs for this program and analyze outputs with R. The program itself calls a input.dat file with a number of needed parameters. Based on this input file it produces a number of files resuming the parameterized analysis. I'm actually already able to read these outputs with R, and produce a number of outputs (graphs and tables). Nevertheless, each run of the program is manually done by now. It would be interesting to loop the call of the program, manipulating parameters at each run. Do exist a way of interact with a external exe from R? There are several. You probably want to use system() or shell(). Duncan Murdoch __ R-help@r-project.org 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: [R] Cross Spectrum Analysis
R function spectrum expects a time series as input. I have attached a compressed archive with two detrended and denoised signals (txt format) whose spectra I would like to compare. I start out trying to generate a multivariate time series. Please, notice the different signals length. Moreover, R command cbind forces a matrix with number of rows equal to the longer of the two signals, padding the shorter one by replicating it from its start values What happens in the frequency domain ? The signals are sampled at 30 Hz. s10146 - read.table(10146-Clean-Signal.txt) s45533 - read.table(45533-Clean-Signal.txt) v10146 - as.vector(s10146[,1]) length(v10146) [1] 8133 v45533 - as.vector(s45533[,1]) length(v45533) [1] 6764 xx -cbind(v10146, v45533) dim(xx) [1] 81332 v45533[1:10] [1] -1.7721546 -1.7482835 -1.6964711 -1.6154405 -1.5045701 -1.3747449 [7] -1.2332980 -1.0912172 -0.9585821 -0.8420886 xx[6760:6770,] v10146 v45533 [1,] -0.8585375 -0.6076069 [2,] -0.8060065 -0.5288312 [3,] -0.7541174 -0.4447711 [4,] -0.7028816 -0.3592778 [5,] -0.6524279 -0.2767786 [6,] -0.6027233 -1.7721546# start replicating shorter signal [7,] -0.5536868 -1.7482835 [8,] -0.5052780 -1.6964711 [9,] -0.4574095 -1.6154405 [10,] -0.4097922 -1.5045701 [11,] -0.3623641 -1.3747449 twosig - ts(xx,deltat=0.033,start=0) # time series On Sun, May 4, 2008 at 2:14 PM, stephen sefick [EMAIL PROTECTED] wrote: is this a problem? are there error messages? if so could you provide them. Try as.matrix(yourdata). One thing you could do is create a moving average that reduces the signals to the lowest common denominator. Could you provide reproducable code with maybe a toy data set so anybody could have a look at what is going on. good luck Stephen __ R-help@r-project.org 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: [R] quantitative spectra analysis
look at the spectrums before you do the cbind - I would not suggest letting R wrap the data to fill in a data frame. I would suggest using something that you know how it acts in the frequency domain like zero. You are probably introducing periodicies that are not real, and I would suggest not to go down this path. As for finding commonalities amongst signals- it all depends on what a commonality is in the signal of interest. I am sure if you refine your question an answer can be found. I have used beam forming to look for common peaks among dissolved oxygen signals at different miles along the river- a physicist friend wrote the code in matlab- so I could provide that, but I haven't looked into trying to make it and R specific algorithm- and in reality I am not sure if my programming is to the point of being able to do something like that. hope this helps On Sun, May 4, 2008 at 8:48 PM, Maura E Monville [EMAIL PROTECTED] wrote: The attached picture is what I get passing the time series where the shorter signal is wrapped around. s10146 - read.table(10146-Clean-Signal.txt) s45533 - read.table(45533-Clean-Signal.txt) v10146 - as.vector(s10146[,1]) length(v10146) [1] 8133 v45533 - as.vector(s45533[,1]) length(v45533) [1] 6764 xx -cbind(v10146, v45533) dim(xx) [1] 81332 v45533[1:10] [1] -1.7721546 -1.7482835 -1.6964711 -1.6154405 -1.5045701 -1.3747449 [7] -1.2332980 -1.0912172 -0.9585821 -0.8420886 xx[6760:6770,] v10146 v45533 [1,] -0.8585375 -0.6076069 [2,] -0.8060065 -0.5288312 [3,] -0.7541174 -0.4447711 [4,] -0.7028816 -0.3592778 [5,] -0.6524279 -0.2767786 [6,] -0.6027233 -1.7721546# start replicating shorter signal [7,] -0.5536868 -1.7482835 [8,] -0.5052780 -1.6964711 [9,] -0.4574095 -1.6154405 [10,] -0.4097922 -1.5045701 [11,] -0.3623641 -1.3747449 twosig - ts(xx,deltat=0.033,start=0) # time series spectrum(twosig) I*s there any quantitative analysis, operating in the frequency domain, that can help me identify common pattern features in signals ?* Thank you very much. -- Maura E.M -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being mammals. -K. Mullis [[alternative HTML version deleted]] __ R-help@r-project.org 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: [R] Error in downViewport.vpPath(vpPathDirect(name)
Hi
Andrewjohnclose wrote:
Hi,
I am having trouble plotting a series of dendrograms using lattice and grid
code as found in Paul Murrells book R Graphics.
This is the error message I recieve:
Error in downViewport.vpPath(vpPathDirect(name), strict, recording =
recording) :
Viewport 'plot1.panel.1.1.off.vp' was not found
I have attached the code and also my data file. Should anyone have any
suggestions then your help would be gratefully appreciated.
Your 'height' factor has values 1, 2, 2, 3 so when the second panel is
drawn (for 'height == 2'), there are two dendrograms to draw in the
panel. Specifically, 'dend4b$lower[[subscripts]]' fails because
'subscripts' is c(2, 3). You can see this with some crude debugging as
in ...
dendpanel - function(x, y, subscripts, ...) {
pushViewport(viewport(y = space,
width = 0.90,
height = unit(0.90, npc) - space,
just = bottom))
par(plt = gridPLT(), new = TRUE, ps = 10)
cat(subscripts, \n)
plot(dend4b$lower[[subscripts]], axes = FALSE)
popViewport()
}
... and you can get something to work if you adjust the code like this ...
dendpanel - function(x, y, subscripts, ...) {
pushViewport(viewport(y = space,
width = 0.90,
height = unit(0.90, npc) - space,
just = bottom))
par(plt = gridPLT(), new = TRUE, ps = 10)
plot(dend4b$lower[subscripts][[1]], axes = FALSE)
popViewport()
}
... but that drops one of the dendrograms. You need to set up x, y, and
height differently so that they correspond better to the dendrogram
structure that you have in 'dend4b'.
Hope you can take it from there ...
Paul
Thank you
Andrew
http://www.nabble.com/file/p17017801/dend4c.txt dend4c.txt
http://www.nabble.com/file/p17017801/gL2.csv gL2.csv
--
Dr Paul Murrell
Department of Statistics
The University of Auckland
Private Bag 92019
Auckland
New Zealand
64 9 3737599 x85392
[EMAIL PROTECTED]
http://www.stat.auckland.ac.nz/~paul/
__
R-help@r-project.org 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: [R] Error in downViewport.vpPath(vpPathDirect(name)
Hi
Paul Murrell wrote:
Hi
Andrewjohnclose wrote:
Hi,
I am having trouble plotting a series of dendrograms using lattice and grid
code as found in Paul Murrells book R Graphics.
This is the error message I recieve:
Error in downViewport.vpPath(vpPathDirect(name), strict, recording =
recording) :
Viewport 'plot1.panel.1.1.off.vp' was not found
BTW, you are getting this error because the panel function is failing
(so the popViewport() never happens and lattice gets left inside the
grid viewport that you pushed and so lattice cannot find the viewports
that it is expecting to be able to see).
Paul
I have attached the code and also my data file. Should anyone have any
suggestions then your help would be gratefully appreciated.
Your 'height' factor has values 1, 2, 2, 3 so when the second panel is
drawn (for 'height == 2'), there are two dendrograms to draw in the
panel. Specifically, 'dend4b$lower[[subscripts]]' fails because
'subscripts' is c(2, 3). You can see this with some crude debugging as
in ...
dendpanel - function(x, y, subscripts, ...) {
pushViewport(viewport(y = space,
width = 0.90,
height = unit(0.90, npc) - space,
just = bottom))
par(plt = gridPLT(), new = TRUE, ps = 10)
cat(subscripts, \n)
plot(dend4b$lower[[subscripts]], axes = FALSE)
popViewport()
}
... and you can get something to work if you adjust the code like this ...
dendpanel - function(x, y, subscripts, ...) {
pushViewport(viewport(y = space,
width = 0.90,
height = unit(0.90, npc) - space,
just = bottom))
par(plt = gridPLT(), new = TRUE, ps = 10)
plot(dend4b$lower[subscripts][[1]], axes = FALSE)
popViewport()
}
... but that drops one of the dendrograms. You need to set up x, y, and
height differently so that they correspond better to the dendrogram
structure that you have in 'dend4b'.
Hope you can take it from there ...
Paul
Thank you
Andrew
http://www.nabble.com/file/p17017801/dend4c.txt dend4c.txt
http://www.nabble.com/file/p17017801/gL2.csv gL2.csv
--
Dr Paul Murrell
Department of Statistics
The University of Auckland
Private Bag 92019
Auckland
New Zealand
64 9 3737599 x85392
[EMAIL PROTECTED]
http://www.stat.auckland.ac.nz/~paul/
__
R-help@r-project.org 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.
[R] splitting a vector on comma
Dear R Usergroup, I have the following vector and I would like to split it on ,. How can I do this? u [1] 160798191,160802762,160813395,160816017,160817873,160824082,160825247,160826925,160834272,160836257, Thank you in advance! With my best regards, Georg. Georg Ehret Baltimore USA [[alternative HTML version deleted]] __ R-help@r-project.org 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: [R] splitting a vector on comma
?strsplit On Sun, 4 May 2008, Georg Ehret wrote: Dear R Usergroup, I have the following vector and I would like to split it on ,. How can I do this? u [1] 160798191,160802762,160813395,160816017,160817873,160824082,160825247,160826925,160834272,160836257, Thank you in advance! With my best regards, Georg. Georg Ehret Baltimore USA [[alternative HTML version deleted]] __ R-help@r-project.org 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. _ David Scott Department of Statistics, Tamaki Campus The University of Auckland, PB 92019 Auckland 1142,NEW ZEALAND Phone: +64 9 373 7599 ext 86830 Fax: +64 9 373 7000 Email: [EMAIL PROTECTED] Graduate Officer, Department of Statistics Director of Consulting, Department of Statistics __ R-help@r-project.org 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.
