similar to: Plot y ~ x under condition of variable a and b [Broadcast ]

Hello, I was wondering if I can plot two curves I get from "density(data)" into one plot. I want to compare both. With the following commad, I just get one curve plotted: plot( density(mydata) ) Sorry for this stupid question but I could not find a solution until now... Antje

Hi List, I've got a count matrix like this: [-11.9,-11] (-11,-10.2] (-10.2,-9.3] (-9.3,-8.44] [-11.9,-10.6] 0 0 0 0 (-10.6,-9.3] 0 0 0 0 (-9.3,-8.01] 0 0 0 0 (-8.01,-6.73] 0 0 0 0 I want to

Hi List, I was not able to make this work. I know it is a simple one, sorry to bother. Give me some hints pls. Thanks! Jen if(length(real.d)>=30 && length(real.b)>=30 && beta1*beta2*theta1*theta2>0 ) { r <- 1; corr <- 1; } real.d and real.b are two vectors, beta1,beta2,theta1,and theta2 are constants. The error occurred like this: Error in if

My data frame looks like: SightingID PA1 PA2 PlotID InOverlap Area1 2001 1 -99 392 Y 0.22 2002 1 -99 388 Y 0.253 2008 1 NA 104 N 0.344 2010 1 NA 71 N 0.185 2012 1 NA 61 N 0.166 2013 1 NA 61 N 0.227 2014 1 NA 62

Dear List, I'm an [R] novice starting analysis of an ecological dataset containing the basal areas of different tree species in a number of research plots. Example data follow: > Trees<-data.frame(SppID=as.factor(c(rep('QUEELL',2), rep('QUEALB',3), 'CORAME', 'ACENEG', 'TILAME')), BA=c(907.9, 1104.4, 113.0, 143.1, 452.3, 638.7, 791.7, 804.3),

Dear List, I'm stuck on what seems like a simple indexing problem, I'd be very grateful to anyone willing to help me out. I queried a dataframe which returns a character vector called "plot". I have another dataframe from which I want to subset or select only those rows that match "plot". I've tried subset, and also the "which" command. plot

Hi everyone. I am trying to add a formula to my data using the groupedData function. My experiment consists of randomized block design using fruits, vegetation and time as factors. The idea is to see if fruits, vegetation and time explain the abundance of mice. I am using tree density as a covariate. So I tried to fit the following structure to my data. >

Dear all, Is it possible to incorporate a nested design in GEE? I have measurements on trees that where measured in two years. The trees are nested in plots. Each plot contains 24 trees. The number of plots is 72. Hence we would expect 2 * 24 * 72 = 3456 data points. A few are missing, so we end up wih 3431 data points. This is what I have tried until now. #assuming independence between trees

Greetings, I?ve fit the following mixed effects model using the NLME package: hd.impute.lme <- lme(I(log(HEIGHT_M - 1.37)) ~ SPECIES + SPECIES:I(1/(DBH_CM + 2.54)), random = ~ I(1/(DBH_CM + 2.54)) | PLOTID, data = trees, na.action = na.exclude) I would now like to extract a jackknife estimate of model error. I tried the following code, however, the estimate produced seems too

Hello, I would appreciate if somebody could help me clear my mind about the below issues. I have a factorial experiment to study the effects of Grazing and Fire on Forest biomass production. The experimental unit (to which the treatment combinations are applied) are PLOTs. The measures were made repeatedly for 13 years. I am planning to use the linear mixed effect model function lme in R for this.

"Jenny Stadt" <jennystadt at yahoo.ca> asked: > > I am struggling in a bivariate Weibull distribution although I > searched R-Site-Help and found suggestion with Copula. Seems the > maximum likelihood estimate is beyond what I can understand. > > My case is: given two known marginal distribution (both are Weibull), > and the correlation between them. How can I

Hi; I am working with the similarity matrix below and I would like to plot a two-dimensional MDS solution such as each point in the plot has a label. This is what I did: data <- read.table('c:/multivariate/mds/colour.txt',header=FALSE) similarity <- as.dist(data) distance <- 1-similarity result.nmds <- nmds(distance) plot(result.nmds) (nmds and plot.nmds as defined at

I have a text file that I have imported into R. It contains 3 columns and 316940 rows. The first column is vegetation plot ID, the second species names and the third is a cover value (numeric). I imported using the read.table function. My problem is this. I need to reformat the information as a matrix, with the first column becoming the row labels and the second the column labels and the

Hi List, Is there anyway to estimate the parameters with integral forms? Here is the function. The idea is to calculate the real mean from the censored mean given normal distribution. I have the function as trun.mean<- function(mu,sigma,t) # t is the threshold { f <- function(x) (1/(sigma*sqrt(2*pi)))*exp(-(x-mu)^2/(2*sigma^2)); pdf.fun <- function(x) x*f(x); mut <-

Hi all, Does anybody have the experience of using optim to estimate variables with integral forms? here the code: trun.mean<- function(x) # t is the threshold { mu=x[1]; sigma=x[2]; t=x[3]; f <- function(x) (1/(sigma*sqrt(2*pi)))*exp(-(x-mu)^2/(2*sigma^2)); pdf.fun <- function(x) x*f(x); integrate(f,thre,upper=Inf)$value/integrate(pdf.fun,thre,upper=Inf)$value ; } when I