similar to: Problem building package for R 2.10.0 on Mac OS X

Hi All, I am looking for help in rotating species titles produced using the strat.plot( ) function in the rioja package. This function produces a stratigraphic plot of paleoenvironmental data. Currently the titles of each species are plotted vertically while they are typically plotted at a 45 degree angle in other programs. Does anyone have any idea of how to rotates these titles? Below is an

Hello, I would like to know if there is a function in an R library that allows to do cluster analysis under contiguity constraints ? Thank you very much for your answer ! Lise Bellanger -- Lise Bellanger, Universit? de Nantes D?partement de Math?matiques, Laboratoire Jean Leray UMR CNRS 6629 2, Rue de la Houssini?re BP 92208 - F-44322 Nantes Cedex 03 T?l. : (33|0) 2 51 12

Hi all, I'm looking for an efficient solution (speed and memory) for the following problem: Given - a data.frame x containing numbers of type double with nrow(x)>ncol(x) and unique row lables and - a character vector y containing a sorted order labels Now, I'd like to sort the rows of the data.frame x w.r.t. the order of labels in y. example: x <- data.frame(c(1:4),c(5:8))

Hello can anyone help, I have been running the following script to obtain a PCA plot but the end result is rather disappointing as the points are very very small and there are no titles etc geochemdata<-read.csv(file.choose(),header=TRUE) names(geochemdata) library(vegan) bstick<-function(n, tot.var=1) rev(cumsum(tot.var/n:1)/n) geopca<-rda(geochemdata, scale=TRUE) geopca

Hi, I need to build ROC curve in R, can you please provide data steps / code or guide me through it. Thanks and Regards Rithesh M Mohan [[alternative HTML version deleted]]

Hi, Can someone please guide me towards how to produce "heatmap" output from the output of "hclust" run prior to the actual "heatmap" call? I have some rather lengthy clustering going on and tweeking the visual output with "heatmap" recalculating the clustering every time is not feasible. Thanks, Joh

Dear all: I am comparing the PLS outputs of R and SAS for the following data set: Y x1 x2 x3 3 6 2 2 3 1 5 5 4 7 4 1 5 6 5 6 2 4 3 2 8 5 0 9 where Y is the dependent variable and x1, x2, x3 are the independent variables. I found several PLS algorithms in R (NIPALS,SIMPLS,KERNEL PLS). SAS has SIMPLS and NIPALS. The following are the NIPALS calculations of

Hello, is it possible in R to calculate a Weighted averaging partial least squares regression? I'm not firm in statistics and didn't found anything about weighted averaging in combination with PLS in the help archives. Or is it possible to develop a workaround with the pls-package? thanks for help in advance Andreas Plank -- _____________________________________________ Dipl. Biol.

Having searched CRAN and the mailing list archives, I'm pretty sure there is no function in R that implements constrained clustering. Before I start writing my own, have I overlooked something? Thanks, Tyler

Dear list, yet another question on S4 method behaviour or rather its side effects: Consider the following example (see below) where a new S4 method 'predict' is defined which by doing so makes 'predict' a S4 standardGeneric. It works and simple test examples run but I'm cautious whether this is a 'silly' thing to do. Is this a dangerous thing to do, already known to

Dear all, I am sending a patch for extending pdf device to embed popup text and web links. I implemented this feature by using annotation object, a interactive feature of pdf. You can see a sample code for using this feature in the following, and sample pdf is in

Hi, I am a new R user and am trying to construct a palaeoenvironmental transfer function (weighted averaging method) using the package rioja. I've managed to insert the two matrices (the species abundance and the environmental data) and have assigned them to the y and x values respectively. When I try and enter the 'WA' function though, I get an 'Error in FUN' message (see

I have a plot created with strat.plot() from package rioja. When the plot is created with scale.percent=FALSE, each x axes is labeled at 0 and its maximum. However, when scale.percent=TRUE, the x axes are not labeled. I need to use scale.percent=TRUE and I need labels for the x axes. I have been able to add labels to the x axes with mtext but it is very tedious to find the correct

Buenos días No sé qué función tengo que utilizar para cortar una variable cadena. Veamos a ver, teno la variable MUNICIPIO que recoge la siguiente información: 26001-Ábalos y necesito crear la variable COD_MUNICIPIO cuyo valor ha de ser 26001. Gracias BB Belén Cillero Jiménez Técnico de Estadística Instituto de Estadística de La Rioja bcillero en larioja.org ________________________________

Dear R users, I'd like to ask u whether you know how to sort out the following: I'm trying to reproduce a dataset of clusters, and for that I need to build up a cluster index (inside a function) using the command "replicate" as follows: dataset<- function( clusters=100, cluster.size=50, outcome.mean=list(mean=3, sd=1), outcome.var=list(mean=0, sd=1), treat=1, unbal =

If it's anything like the rooms I saw last year...some rooms have "wardrobes" meaning that you have a unit with a shelf, a "closet" cabinet type thing, and about 5 drawers. If you have lots of clothes, bring extra storage containers because thos drawers can't really hold much. Or don't bring so many clothes:) I think some of the two room doubles have huge-ass floor

Hello everyone I tried to use clust function using "hclustWard" clustering method The distance mesaured I have used is "rf" The data used is in the form of data frame (it is not a distance matrix) - sd.df The piece of code I have used is: c1 <- clust(sd.df,k=6,method="hclustWard",distMethod="rf") But I get the following error message, Error in if (n

Hi R-helpers I have a dataframe DF (lets say with the variables, y, x1, x2, x3, ..., clust) containing relatively many NAs. When I fit an ordinal regression model with the function lrm from the Design library: model.lrm <- lrm(y ~ x1 + x2, data=DF, x=TRUE, y=TRUE) it will by default delete missing values in the variables y, x1, x2. Based on model.lrm, I want to apply the robust covariance

Buenos días quiero definir situación_2 <-tapply(ICE_3$FACTOR_CORR, list(X.1.1Eneltrimestrequeestáacabando=ICE_3$X.1.1Eneltrimestrequeestáacabando, CNAE_1=ICE_3$CNAE_1), f) donde f sea el porcentaje por filas (y/o el porcentaje por columnas) y las funciones rowPercent y colPercent me dan el siguiente error Error en tapply(ICE_3$FACTOR_CORR, list(X.1.1Eneltrimestrequeestáacabando =

Hello, I would like to cut a hclust tree into several groups at a specific similarity. I assume this can be achieved by specifying the "h" argument with the specified similarity, e.g.: clust<-hclust(dist,"average") cut<-cutree(clust,h=0.65) Now, I would like to draw rectangles around the branches of the dendrogram highlighting the corresponding clusters, as is done by