similar to: PCA legend outside of PCA plot

Hi All, I'm using the following code to produce some stacked bar graphs. *setwd("C:\\Users\\Tinus\\Documents\\NMMU\\R\\Seamounts")* *SChla <- read.csv("SM_Chla_data.csv")* * * *#Extract mean values from data file* * * *Coral <- SChla[185:223,] #Reduce SChla to Coral only* *coral <- with(Coral , aggregate(cbind(Pico, Nano, Micro), list(Depth),FUN=mean))*

Hi All, I have a data set wit three size classes (pico, nano and micro) and 12 different sites (Seamounts). I want to produce a table with the mean and standard deviation values for each site. Seamount Pico Nano Micro Total_Ch 1 Off_Mount 1 0.0691 0.24200 0.00100 0.31210 2 Off_Mount 1 0.0938 0.00521 0.02060 0.11961 3 Off_Mount 1 0.1130 0.20000 0.06620 0.37920 4 Off_Mount 1

Hi All, I am trying to reproduce this using R instead. [image: Full-size image (38 K)] I tried using the following code *SChla <- read.csv("SM_Chla_data.csv")* *Atlantis <- SChla[16:66,]* *head(Atlantis)* * * Seamount Station Depth Pico Nano Micro Total_Ch dbar Latitude Longitud 16 Atlantis 1217 Surface 0.0639 0.1560 0.0398 0.2597 2.082 -32.71450 57.29733

[Re-directing to R-devel, as I think this needs changes to the code.] Can I suggest a modification to stats:predict.princomp so that it will check for column (variable) names? In src/library/stats/R/princomp-add.R, insert the following after line 4: if (!is.null(cn <- names(object$center))) newdata <- newdata[, cn] Now Dana's example looks like: > predict(pca1, frz) Error in

Hallo everybody! I am trying to perform a TiTAN (Baker & King 2010) analysis with R 2.14.1. I have come that far: h89Abund <- read.csv("Fish89Abund.csv") > names (Fish89Abund) [1] "StationCode" "Abramisbrama" "Alburnoidesbipunctatus" "Alburnusalburnus" [5] "Ameiurusmelas" >

Hi, I was trying as well as looking for an answer without success (a bit strange since it should be an easy problem) and therefore I will appreciate you help: My simple script is: # Loadings data of 5 columns and 100 rows of data data1<-read.csv("C:/?/MyPCA.csv") pairs(data1[,1:4]) pca1 <- princomp(data1[,1:4], score=TRUE, cor=TRUE) biplot(pca1) The biplot present the data

Dear Forum, I have a class variable 1 (populations A-E), and two other class variables, variable 2 and variable 3. What I want is to see if the combination of var 2 and var 3, will give me a pattern that allows to distinguish populations. I found several packages like ade4, with pcaiv function and factoMineR. but there are not working. Using the ade4 package, when I try to build the pca: pca1

Hi!! I'm having trouble selecting 10 out of 41 attributes of the KDD data set. In order to identify the components with the higher variance I'm using princomp. the result i get for summary(pca1) is: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9

I am examining the following nlme model. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) mod1<-nlme(fa20~(ah*habdiv+ad*log(d)+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2), fixed=ah+ad+ads+ads2+at+th1+th2~1, random=th1+th2~1, start=c(ah=.9124,ad=.9252,ads=.5,ads2=-.1,at=-1,th1=2.842,th2=-6.917), data=pca1.grouped) However, the two random effects (th1 and th2)

Is there an option to draw concentration ellipses in biplots ? It seems really nice to summarize large number of points of each group. Cheers../ Murli

I am working with data sets in which the number and order of columns may vary, but each column is uniquely identified by its name. E.g., one data set might have columns MW logP Num_Rings Num_H_Donors while another has columns Num_Rings Num_Atoms Num_H_Donors logP MW I would like to be able to perform a principal component analysis (PCA) on one data set and save the PCA object to

Hola compañeros de la lista, qué tal. Estoy haciendo un análisis de componentes principales utilizando las funciones "dudi.pca" (paquete "ade4") y "PCA" (paquete "FactoMineR"). Sucede que al comparar las coordenadas de cada individuo que obtiene cada función, las que corresponden al segundo componente principal tienen idéntica magnitud pero con

I am working on a nlme model that has multiple fixed effects (linear and nonlinear) with a nonlinear (asymptotic) random effect. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) asymporigb<-function(x,th1b,th2b)th1b*(1-exp(-exp(th2b)*x)) mod.vol.nlme<-nlme(fa20~(ah*habdiv+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2)+ asymporigb(vol,th1b,th2b),

hello all R experts, how do I calculate the reliability between the two groups using the ICCs? I'll appreciate your reply, Thanks Sincerely, Supreet kaur, Biomedical research engineer, Nationwide Childrens Hospital, Columbus, OH (614)355-3509 [[alternative HTML version deleted]]

Instalo ade4 correctamente y no me abren los datos como por ejemplo data(bsetal97) ¿Qué piensan de eso? De: r-help-es-bounces en r-project.org [mailto:r-help-es-bounces en r-project.org] En nombre de Francesc Carmona Enviado el: Tuesday, October 01, 2013 7:30 AM Para: r-help-es en r-project.org Asunto: Re: [R-es] Análisis de componentes principales con ade4 y FactoMineR Por definición

Por definición la primera componente principal es una combinación lineal que maximiza la varianza de modo que si la componente 1 es el vector de coeficientes a, entonces, el vector -a también puede ser dicha componente. Las otras componentes, por ejemplo la segunda, es incorrelacionada con la primera y también maximiza la varianza, luego el signo no importa. Así pues, el signo de cada

Efectivamente. Puedes cambiar el signo de todos los valores de la segunda componente, por ejemplo al hacer un gráfico de dispersión. No creo que se pueda hacer directamente en la función. Saludos Francesc El 01/10/13 17:53, Argel Gastélum Arellánez ha escrit: > Hola Francesc, muchas gracias por tu respuesta. > > Entonces, si quisiera que las gráficas de los resultados de

Hi, This is just to report about the "alignment check: 0000 [#1] SMP" kernel messages in one of my build system (domU) running Scientific Linux 6.1. I am compiling/rpm package for sbcl (http://sbcl.sourceforge.net/) in my build system (domU) and hit the following kernel messages although the domU in question still running and compilation still in progress. I have done some searching

Hi, 1. How do I construct 95% prediction interval for new x values, for example - x = 30000? 2. How do I construct 95% confidence interval? my dataframe is as follows : >dt structure(list(y = c(26100000, 60500000, 16200000, 30700000, 70100000, 57700000, 46700000, 8600000, 10000000, 61800000, 30200000, 52200000, 71900000, 55000000, 12700000 ), x = c(108000, 136000,

Hello R community, I am attempting to run multiple logistic regressions (multinomial, via package 'nnet'), with Automated Model Selection (dredge, package 'MuMIn'). The aim is to reduce the number of predictor variables by assessing relative performance of each variable, which can be done in a coarse fashion using the Automated Model Selection option in package 'MuMIn'