Re: [R] Abundance data ordination in R
There are many ways to do this, really. For example if you use constrained (~ canonical) correspondence analysis the distance measure between sites is Chi-square and absences are not informative to the analysis. Or you can use an ecological distance measure (similarity indices like Soerensen, Bray-Curtis, Jaccard, and others) and perform principal coordinates (=multidimensional scaling), etc. Read the documentation and tutorials for the packages vegan, ade4 and labdsv. You might start your search at the page of Jari Oksanen: http://cc.oulu.fi/~jarioksa/softhelp/vegan.html or the one from Dave Roberts http://ecology.msu.montana.edu/labdsv/R/ . The vegan tutorial was useful for me to learn to use vegan: http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf If you need more indeep mathemathical details, you should take a look at Daniel Chessels site: http://pbil.univ-lyon1.fr/R/perso/pagechessel.html There are plenty of pdfs available for download (however, some are suited for beginners, others require more background knowledge) . Be warned: there is a large variety of techniques for multivariate analysis with different properties and weaknesses, sometimes the most popular analysis are not the most appropriate. Be sure of what you want and what you are doing before you perform the analysis, the interpretation will depend on the techniques applied. I personally find ade4 implements many different techniques but is poorly documented and some functionalities are somehow hidden, while vegan provides more information about the functions and is perfect for getting started. I haven't used labdsv yet. hope this help JR El dom, 01-04-2007 a las 09:20 -0700, Milton Cezar Ribeiro escribió: Dear R-gurus I have a data.frame with abundance data for species and sites which looks like: mydf-data.frame( sp1=sample(0:10,5,replace=T), sp2=sample(0:20,5,replace=T), sp3=sample(0:4,5,replace=T), sp4=sample(0:2,5,replace=T)) rownames(mydf)-paste(sites,1:5,sep=) I would like make an ordination analysis of these data and my worries is about the zeros (absence of species) into the matrix. Up to I read (Gotelli - A primir of ecological statistics, 2004), when I have abundance data I cant compute Euclidian Distances because the zeros have the meaning of absence of the species and not as zero counting. Gotelli suggests one make principal coordinates analysis. I would like to here from you what you think about and what is the best packages and functions to I compute my distance matrices and do my ordination analysis. Can I considere zero as NA on my data.frame? Is there a good PDF book available about Multivariate Analysis for abundance data available on the web? Kind regards Miltinho Brazil __ [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch 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@stat.math.ethz.ch 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] cex in xlab, ylab and zlab of persp
I had similar problems, actually it is very difficult to customize persp graphics, you should try wireframe in lattice instead. This reference might help to customize the wireframe plots: http://www.polisci.ohio-state.edu/faculty/lkeele/3dinR.pdf JR El mié, 14-03-2007 a las 20:40 -0700, Joseph Retzer escribió: I've had no success using what I think is the correct code to change the default size of the x, y and z labels in a persp graph (i.e. cex.lab). Is there a particular specification to accomplish this? Many thanks, Joe Retzer [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch 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@stat.math.ethz.ch 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] segfault with correlation structures in nlme
Hi out there,
I am trying to fit a species accumulation curve (increase in number of
species known vs. sampling effort) for multiple regions and several
bootstrap samples. The bootstrap samples represent different
arrangements of the actual sample sequence.
I fitted a series of nlme-models and everything seems OK, but since the
observations are correlated I tried to include some correlation
structure. Since the ARMA classes were not succesful in reducing this
correlation, I tried spatial correlation functions with sampling effort
(measured in time units) as a distance measure. As a result I got
several segfault errors (which I don't know what they exactly mean =/).
I was wondering if it was an effect of the model or the data I used, but
I was able to reproduce the error messages using the Ovary data set and
the example in the Pinheiro Bates book:
library(nlme)
data(ovary)
fm10var.lme - lme(follicles ~ sin(2 * pi * Time) +
cos(2 * pi * Time),data=Ovary,
random=pdDiag(~sin(2*pi*Time)))
fm50var.lme - update(fm10var.lme,correlation=corARMA(p=1,q=1))
fm10var.nlme - nlme(follicles ~ A + B * sin(2 * pi * w * Time) +
C * cos(2 * pi * w * Time),data=Ovary,
fixed= A+B+C+w~1,
random=pdDiag(A+B+w~1),
start = c(fixef(fm50var.lme),1))
plot(ACF(fm10var.nlme,maxLag=10),alpha=.05)
fm20var.nlme - update(fm10var.nlme,corr=corAR1(0.311))
fm30var.nlme - update(fm10var.nlme,corr=corARMA(p=0,q=2))
fm60var.nlme - update(fm10var.nlme,corr=corGaus(form=~Time))
*** caught segfault ***
address 0x1075e501, cause 'memory not mapped'
Traceback:
1: eval(expr, envir, enclos)
2: eval(modelExpression, env)
3: assign(modelValue, eval(modelExpression, env), envir = thisEnv)
4: function (newPars) {if (!missing(newPars)) {for (i in
names(ind)) {assign(i, clearNames(newPars[ind[[i]]]), envir
= env)} assign(modelValue, eval(modelExpression, env),
envir = thisEnv)}modelValue}(c(-4.11936939372863,
0.157676781855328, -0.071492289279548, -3.89562017836122,
3.06079106423361, -0.0260217128029253, -2.83650117790746,
1.62924792896056, 0.0607736472981399, -0.834700672739711,
0.07926271837803, 0.0403415470454326, -0.698687811226831,
-0.121079563050668, 0.032722843419347, 0.0404804003710554,
0.379937934267249, 0.087562808768161, 3.08430247504295,
2.08510442577037, 0.103812382483994, 1.1306260898,
-1.70151546937888, -0.043262231409, 2.37971674786747,
-1.04307471138899, 0.0217885202778396, 2.60583067635322,
-0.638495324795519, -0.137018044847307, 1.70105176178795,
-3.07372462709182, -0.0445309584408364, 12.3518546784787,
-3.22126648052618, -1.69049623029482, 0.907261039321137))
5: .C(fit_nlme, thetaPNLS = as.double(c(as.vector(unlist(sran)),
sfix)), pdFactor = as.double(pdFactor(nlmeSt$reStruct)),
as.integer(unlist(rev(grpShrunk))), as.integer(unlist(Dims)),
as.integer(attr(nlmeSt$reStruct, settings))[-(1:3)], as.double(cF),
as.double(vW), as.integer(unlist(cD)), settings =
as.double(pnlsSettings), additional = double(NReal * (pLen + 1)),
as.integer(!is.null(correlation)), as.integer(!is.null(weights)),
nlModel, NAOK = TRUE)
6: nlme.formula(model = follicles ~ A + B * sin(2 * pi * w * Time) +
C * cos(2 * pi * w * Time), data = Ovary, fixed = A + B + C + w ~ 1,
random = pdDiag(A + B + w ~ 1), start = c(fixef(fm50var.lme), 1),
corr = corGaus(form = ~Time))
7: eval(expr, envir, enclos)
8: eval(call, parent.frame())
9: update.nlme(fm10var.nlme, corr = corGaus(form = ~Time))
10: update(fm10var.nlme, corr = corGaus(form = ~Time))
The above was under:
R version 2.4.1 (2006-12-18)
nlme Version: 3.1-79
Linux 2.6.15-27-386 (Ubuntu 6.06)
Then I tried the same on another computer and got:
*** caught segfault ***
address 0x82e4902a, cause 'memory not mapped'
Violación de segmento
R Version 2.3.1 (2006-06-01)
nlme Version: 3.1-74
Linux 2.6.12-10-386 (Ubuntu 5.10)
I worked out the example of rats body weights, also in the book, and it
gave no error, so the problem seems to be in using the corLin and
corGaus functions with a nlme fit. Apparently is neither my version of R
and nlme, nor the dataset. Can someone try to reproduce this? Or does
someone knows the cause/explanation/fix?
Thanks very much
JR Ferrer-Paris
--
Dipl.-Biol. JR Ferrer Paris
~~~
Laboratorio de Biología de Organismos --- Centro de Ecología
Instituto Venezolano de Investigaciones Científicas (IVIC)
Apdo. 21827, Caracas 1020-A
República Bolivariana de Venezuela
Tel: (+58-212) 504-1452
Fax: (+58-212) 504-1088
email: [EMAIL PROTECTED]
clave-gpg: 2C260A95
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Re: [R] How to open more windows to make more graphs at once!
Dear Monireh,
try using lattice:
library(lattice)
set.seed(1234)
dat - data.frame(months=rep(1:10,80),upper = rnorm(800)+1,
lower = rnorm(800)-1,
observed = rnorm(800), best.sim = rnorm(800),
stname = factor(gl(80, 10)))
jpeg(filename = Rplot%03d.jpeg)
xyplot(best.sim+observed+lower+upper~months|stname,dat,
layout=c(3,4),type=b,auto.key=T)
dev.off()
It should produce almost exactly what you want. Lattice is a very
powerful tool for creating multiple graphics. You can customize the
individual plots within the lattice using panel and prepanel functions,
take a look at the documentation of the library and the documentation of
xyplot and panel.xyplot. Lattice is a little bit more complex than
normal plots in R, so you would have to spend more time in learning
how to use its functionality, but it is worth trying.
have a lot of fun
JR
El mié, 07-03-2007 a las 09:39 +0100, Faramarzi Monireh escribió:
Dear R users,
I have a data frame (test) including five columns of upper (numeric), lower
(numeric), observed (numeric), best_sim (numeric) and stname (factor with 80
levels, each level with different length). Now I would like to write a short
program to draw one graph as follow for each level of stname but I would like
also to draw each time 12 graphs for the 12 levels of stname in the same
graphic windows and save it as jpeg' file . This means at the end I will
have 7 (80 levels/12=7) graphic windows and 7 jpeg files each one with 12
graphs (the last one with 8 graphs) for the 12 levels of stname. I already
wrote the following script to do it each time for 12 levels of stname but I
have to change script each time for the another 12 levels [line 3 in the
script for example: for( i in levels(test$stname)[12:24))] and I do not know
how can I save the obtained graphs (seven graphic windows) as jpeg files
(e.g. plot1.jpeg, plot2.jpeg and so on). As I have 45 dataset like this it
would be gr!
eat if somebody can help me to complete this script to do all together for a
dataset using a script.
Thank you very much in advance for your cooperation,
Monireh
windows(9,9)
par(mfrow = c(3,4))
for( i in levels(test$stname)[1:12])
{
data- test[test$stname==i,]
xx - c(1:length(data$upper), length(data$upper):1)
yy - c(data$upper, rev(data$lower))
zz- data$observed
tt- data$Best_Sim
par(lab =c(10,15,2))
plot.jpeg- plot(xx,yy, type=n, xlim=c(min(xx), max(xx)),
ylim=c(min(zz,yy,tt), max(yy,zz,tt)*1.4),
main= i, xlab=Month (1990-2002), ylab=Discharge(m3/s), font.axis=6)
polygon(xx, yy, col=green, border = NA)
lines(zz, col=blue, lwd=1.5)
lines(tt,col=red, lwd=1.5)
legend(length(zz)-60, max(yy,zz,tt)*1.45, c(Upper Limit, Lower Limit,
Observed,Best etimation)
, lwd=c(10, 1,1.7,1.7), bty=n, col= c(green, white, blue,red))
}
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Re: [R] anova applied to a lme object
The variances of the random effects and the residual variances are given by the summary function. Maybe VarCorr or varcomp gives you the answer you are looking for: library(nlme) library(ape) ?VarCorr ?ape JR El mié, 07-03-2007 a las 13:09 +0100, Berta escribió: Hi R-users, when carrying out a multiple regression, say lm(y~x1+x2), we can use an anova of the regression with summary.aov(lm(y~x1+x2)), and afterwards evaluate the relative contribution of each variable using the global Sum of Sq of the regression and the Sum of Sq of the simple regression y~x1. Now I would like to incorporate a random effect in the model, as some data correspond to the same region and others not: mylme- lme(y~x1+x2, random= ~1|as.factor(region)). I would like to know, if possible, which is the contribution of each variable to the global variability. Using anova(mylme) produce an anova table (without the Sum of Sq column), but I am not sure how can I derive the contribution of each variable from it, or even whether it is nonsense to try, nor can I derive a measure of how much variability is left unexplained. Sorry for the type of question, but I did not find a simple solution and some researchers I work with love to have relative contributions to global variability. Thanks a lot in advance, Berta __ R-help@stat.math.ethz.ch 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. -- Dipl.-Biol. JR Ferrer Paris ~~~ Laboratorio de Biología de Organismos --- Centro de Ecología Instituto Venezolano de Investigaciones Científicas (IVIC) Apdo. 21827, Caracas 1020-A República Bolivariana de Venezuela Tel: (+58-212) 504-1452 Fax: (+58-212) 504-1088 email: [EMAIL PROTECTED] clave-gpg: 2C260A95 __ R-help@stat.math.ethz.ch 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] No years() function?
From the help of weekdays: Note: Other components such as the day of the month or the year are very easy to compute: just use 'as.POSIXlt' and extract the relevant component. Yet another option: help(package=chron) JR El mié, 07-03-2007 a las 15:35 +, Sérgio Nunes escribió: Hi, I'm trying to aggregate date values using the aggregate function. For example: aggregate(data,by=list(weekdays(LM),months(LM)),FUN=length) I would also like to aggregate by year but there seems to be no years() function. Should there be one? Is there any alternative choice? Also, a hours() function would be great. Any tip on this? Thanks in advance! Sérgio Nunes __ R-help@stat.math.ethz.ch 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. -- Dipl.-Biol. JR Ferrer Paris ~~~ Laboratorio de Biología de Organismos --- Centro de Ecología Instituto Venezolano de Investigaciones Científicas (IVIC) Apdo. 21827, Caracas 1020-A República Bolivariana de Venezuela Tel: (+58-212) 504-1452 Fax: (+58-212) 504-1088 email: [EMAIL PROTECTED] clave-gpg: 2C260A95 __ R-help@stat.math.ethz.ch 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 there a quick way to count the number of times each element in a vector appears?
El lun, 05-03-2007 a las 22:16 -0800, Dylan Arena escribió: So here is my question in a nutshell: Does anyone have ideas for how I might efficiently process a matrix like that returned by a call to combinations(n, r, rep=TRUE) to determine the number of repetitions of each element in each row of the matrix? If so, I'd love to hear them! here is an answer in a nutshell: my.table - combinations(3,3,rep=TRUE) ## one possibility is apply(my.table,1,table) ## or better, in plain table(my.table,row(my.table)) look at the help pages of ?table ?apply ?row Thanks very much for your time, Dylan Arena (Statistics M.S. student) that took probably one minute of my time... so never mind -- Dipl.-Biol. JR Ferrer Paris ~~~ Laboratorio de Biología de Organismos --- Centro de Ecología Instituto Venezolano de Investigaciones Científicas (IVIC) Apdo. 21827, Caracas 1020-A República Bolivariana de Venezuela Tel: (+58-212) 504-1452 Fax: (+58-212) 504-1088 email: [EMAIL PROTECTED] clave-gpg: 2C260A95 __ R-help@stat.math.ethz.ch 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] Identifying points in a plot that have duplicate values
A very simple solution is given in: help(sunflowerplot,package=graphics) ##If you want to see it in action: example(sunflowerplot) El mar, 06-03-2007 a las 19:55 +1100, Jim Lemon escribió: David Lloyd wrote: I have code like this: - #--- -- x=scan() 0 0 0 0 0 1 2 3 4 y=scan() 1 1 1 2 2 1 3 4 5 plot(x,y) identify(0,1,3) #Allows me to select manually to identify co-ordinate (0,1) as being duplicated 3 times identify(0,2,2) #Allows me to select manually to identify co-ordinate (0,2) as being duplicated 2 times #--- -- Is there not a way I can automatically display if points are duplicated and by how many times? I thought if I 'jittered' the points ever so slightly I could get an idea of how many duplicates there are but with 100 points the graph looks very messy. Hi David. In the plotrix package there are a few functions that might be helpful. cluster.overplot - moves ovelying points into a small cluster up to 9 count.overplot - displays the number of overlying points sizeplot - displays symbols with size relative to the number of points Jim -- Dipl.-Biol. JR Ferrer Paris ~~~ Laboratorio de Biología de Organismos --- Centro de Ecología Instituto Venezolano de Investigaciones Científicas (IVIC) Apdo. 21827, Caracas 1020-A República Bolivariana de Venezuela Tel: (+58-212) 504-1452 Fax: (+58-212) 504-1088 email: [EMAIL PROTECTED] clave-gpg: 2C260A95 __ R-help@stat.math.ethz.ch 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] The plot of qqmath
qqmath is in the lattice package, which is not compatible with the conventional graphics parameter. Why not using qqnorm and qqline instead? If you want to combine qqmath with other lattice plots you should look at the documentation of the lattice and grid packages. If you read carefully in help(Lattice,package=lattice) Lattice plots are highly customizable via user-modifiable settings. However, these are completely unrelated to base graphics settings; in particular, changing 'par()' settings usually have no effect on lattice plots. help(Grid,package=grid) Grid graphics provides an alternative to the standard R graphics. The user is able to define arbitrary rectangular regions (called _viewports_) on the graphics device and define a number of coordinate systems for each region. Drawing can be specified to occur in any viewport using any of the available coordinate systems. Grid graphics and standard R graphics do not mix! Type 'library(help = grid)' to see a list of (public) Grid graphics functions. El mar, 06-03-2007 a las 13:48 +0100, Serguei Kaniovski escribió: Hello, I would like to inlude the Q-Q plot by qqmath into a panel with other plots, say, using par(mfrow=c(1,2)). How can this be done given that qqmath refreshes the plotting window and there seems to be no series coming out of it? Thanks Serguei hope that helps. JR -- Dipl.-Biol. JR Ferrer Paris ~~~ Laboratorio de Biología de Organismos --- Centro de Ecología Instituto Venezolano de Investigaciones Científicas (IVIC) Apdo. 21827, Caracas 1020-A República Bolivariana de Venezuela Tel: (+58-212) 504-1452 Fax: (+58-212) 504-1088 email: [EMAIL PROTECTED] clave-gpg: 2C260A95 __ R-help@stat.math.ethz.ch 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] different random effects for each level of a factor in lme
I have an interesting lme - problem. The data is part of the Master Thesis of my friend, and she had some problems analysing this data, until one of her Jurors proposed to use linear mixed-effect models. I'm trying to help her since she has no experience with R. I'm very used to R but have very few experience with lme. The group calls of one species of parrot were recorded at many localities in mainland and islands. Within the localities the parrots move in groups, several calls were recorded for each group but the calls can not be separated by individuals. We use the variable s1 to measure one property of the calls (the length of the first part of the call). We are interested in explaining the variability of the calls, one hypothesis is that variability of calls tends to be greater in islands compared with mainland. So we have s1 : as a measure of interest loc : as a grouping variable (locality) grp : as a grouping variable (group) nested in loc isla : is an factor that identify which localities are in island and which are in mainland (it is outer to loc) I began with a simple model with fixed effects in isla (since there are some differences in the length of s1) and nested random effects: f00 - lme(s1~isla,data=s1.ap, random=~1|loc/grp) My final model should have fixed effects in isla, different nested random for both levels of isla, and different error per stratum, something like: f11 - lme(s1~isla,data=s1.ap, random=~isla|loc/grp, weights=varIdent(form=~1|isla)) or perhaps: f11b - lme(s1~isla,s1.ap, random=~isla-1|loc/grp, weights=varIdent(form=~1|isla)) Is this a valid formulation? I have seen that ~x|g1/g2 is usually used for modelling random effects in (the intercept and slope of) covariates and that ~1|g1/f1 is used to model interactions between grouping factors and treatment factors. I fitted the above models (and a few other variants between the simpler and the complex ones) and found f11 to be the best model, f11 and f11b are both identical in terms of AIC or LR-Tests since they are the same model with different parametrization (I guess...). Now, I suppose I did everything right, and I want to compare the variance decomposition in islands and mainland, I use VarCorr(f11) VarianceStdDev Corr loc = pdLogChol(isla) (Intercept) 1643.5904 40.54122 (Intr) islaT962.2991 31.02095 -0.969 grp = pdLogChol(isla) (Intercept) 501.7315 22.39936 (Intr) islaT622.5393 24.95074 -0.818 Residual 547.0888 23.38993 VarCorr(f11b) VarianceStdDev Corr loc =pdLogChol(isla - 1) islaI1643.4821 40.53988 islaI islaT 168.6514 12.98659 0 grp =pdLogChol(isla - 1) islaI 501.7357 22.39946 islaI islaT 209.8698 14.48688 0 Residual 547.0871 23.38989 The variance for islands (islaI) is always greater than the ones for mainland (islaT) as expected, and the estimates of Intercept in f11 are nearly equal to the estimates for islaI in f11b. However, the estimates for islaT are completely different. It seems to me that the estimates in f11b are the correct ones and the ones in f11 are obtained by reparametrization of the variance-covariance matrix. Am I right? I want to say what percentage of the variance is explained by each level, something like this: tmp - data.frame(I=c(1643.48,501.74,(547.09)),T=c(168.65,209.86,(0.7823097*547.09))) rownames(tmp) - c(loc,grp,res) t(t(tmp)/(colSums(tmp))) I T loc 0.6104349 0.2091125 grp 0.1863604 0.2602096 res 0.2032047 0.5306780 (0.7823097 was the result of varIdent for islaT) If I compare the sum of variances in f11b for each level of isla with the variances of the data frame I get similar results: colSums(tmp) I T 2692.3100 806.5038 tapply(s1.ap$s1,s1.ap$isla,var) I T 2417.1361 731.8165 So I guess this is the right way to interpret the variances in the fitted model. Or, is it not? thanks, JR -- Dipl.-Biol. JR Ferrer Paris ~~~ Laboratorio de Biología de Organismos --- Centro de Ecología Instituto Venezolano de Investigaciones Científicas (IVIC) Apdo. 21827, Caracas 1020-A República Bolivariana de Venezuela Tel: (+58-212) 504-1452 Fax: (+58-212) 504-1088 email: [EMAIL PROTECTED] clave-gpg: 2C260A95 __ R-help@stat.math.ethz.ch 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.
