similar to: Help reproducing a contour plot

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'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))*

# Peter B. Mandeville kindly offered to send me code for Hotelling's T^2 # Test. Unfortunately there seems to be no route to his machine. # So i'm trying to reach him via the Mailing List. ------------------------------------------------------------------------ Sir, this morning i recieved your message about the availability of the code for Hotelling's Test. I hurried to find out

R-help, I have the following file I want to import to R (some lines removed) Calibrated CTD data for station:00280001 Calibrated:23/8 2001, Salinity Unsmoothed, Fluorescence Uncalibrated Maximum observed depth: 36 m QUAL has one digit for each of pressure, temp., sal. and fluor. QUAL=1:Uncal., QUAL=2:OK, QUAL=6:Interp., QUAL=9:No data

By accident, I left out the lines defining n1 and n2. Here it is as a function. Peter B. hotelling <- function(d1,d2){ k <- ncol(d1) n1 <- nrow(d1) n2 <- nrow(d2) xbar1 <- apply(d1,2,mean) xbar2 <- apply(d2,2,mean) dbar <- xbar2-xbar1 v <- ((n1-1)*var(d1)+(n2-1)*var(d2))/(n1+n2-2) t2 <- n1*n2*dbar%*%solve(v)%*%dbar/(n1+n2) f <-

Hello there, I have two data sets with 14 variables each, and wish to do the test for equality of their covariance matrices and mean vectors. Normally these tests should be done by chi square test (box provided) and Hotelling's T square test respectively. Which R functions could do this kind of test? I just find some functions could do for one dimension, but no for multidimension. Some one

Dear All, I am running a simulation to obtain coverage probability of Wald type confidence intervals for my parameter d in a function of two parameters (mu,d). I am optimizing it using "optim" method "L-BFGS-B" to obtain MLE. As, I want to invert the Hessian matrix to get Standard errors of the two parameter estimates. However, my Hessian matrix at times becomes

This is a proposal for either adding a new, or updating an existing command line option such that a diagnostic can optionally be produced for unused -D macros. Long-lived large projects with thousands of files and many contributors have a tendency to accumulate build options over time. As time passes some build options like macros become replaced, obsolete, or simply no longer used. At the same

I would like to test the hypothesis that the difference between pairs, for several variables, is zero. This is easily done separately for each variable with: lm(Y ~ rep(0, nrow(Y))) where Y is a matrix whose columns are the differences for each variable between pair members. However, I would like to get an overall probability across all variables from a Wilks or Pillai-Bartlett statistic as in

Dear R-list, I am sorry for my shortage of stat knowlege. I want know how to conduct a hypothesis test : Ho:|E(X)|=|E(Y)|<->H1:otherwise. Actually, in my study, X and Y is two observations of bias, where bias=u^hat-u, u is a parameter I concerned. Given X=(u^hat_xi - u) and Y=(u^hat_yi - u), I want to know which bias is smaller, or the absolute expection of which is smaller. Due to limit

R 1.8.0 on Windows XP Professional. A huge THANK YOU to the R Team for this marvelous software. I am making lattice plots of oceanographic data. The usual layout does not conform to plotting conventions that marine scientists use when depth is the independent variable. Under those conventions, plots are made with the origin at the upper left, depth on the vertical axis (increasing as it

?The idea itself is indeed good. Regarding to escaping: I think we should have it. Imagine the following example (I've took it from D73828). --- !ELF FileHeader: Class: ELFCLASS[[BITS]] Data: ELFDATA2LSB Type: ET_EXEC Machine: EM_386 # RUN: yaml2obj %s --docnum=4 -D BITS=32 -o %t-32bit.o # RUN: yaml2obj %s --docnum=4 -D BITS=64 -o %t-64bit.o Without escaping it would

I am adding -D k=v to yaml2obj, similar to clang -D. This makes it easy to generate {32-bit,64-bit} x {big-endian,little-endian} tests. --- !ELF FileHeader: Class: ELFCLASS[[BITS]] Data: ELFDATA2[[ENCODE]] Type: ET_DYN Machine: EM_X86_64 # RUN: yaml2obj -D BITS=32 -D ENCODE=LSB %s -o %t.32le # RUN: yaml2obj -D BITS=32 -D ENCODE=MSB %s -o %t.32le # RUN: yaml2obj

Hi all, I have a user login form which the browser keeps populating because of its form fill feature. So when anyone goes to log in it shows the previous login''s username and password. How can I stop the browser from populating a text field? Thanks in advance. Cheers, Diego --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to

Hello everybody, I use the sweetpotato database included in R package: data(sweetpotato) This dataset contains two variables: yield(continous variable) and virus(factor variable). Due to Levene test is significant I cannot assume homogeneity of variances and I apply Welch test in R instead of one-way ANOVA followed by Tukey posthoc. Nevertheless, the problems come from when I apply posthoc