similar to: Interpreting GLM coefficients

I am having a hard time putting the below into a loop, where it pulls out ppt from all he stations I have versus having to go through and hard code the data to the specific stations. I tried stnID <- stnid[which(duplicated(stnid)==FALSE)] for(i in 1:length(stnID)) { ppt[i] <- ppt[which(stnid==[i])] } but it doesn't like to use the which function inside a for loop? Any idea's here is

Hi folks, Forgive me if this question is a trivial issue. I was doing a series of Fishers' exact test using the fisher.test function in stats package. Since the counts I have were quite large (c(64, 3070, 2868, 4961135)), R suggested me to use *other algorithms* for the test which can be specified through the 'control' argument of the fisher.test function as I understood. But where

Hello, I'm using R to apply Fishers exact test to a whole pile of contingency tables, and I've run into the bug shown below. regards, Francis -- > dat1 = matrix(c(0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0, 1,1,0,0,1,0,0,1,0,0,1,0,0,3,0,0,2,0,0,1,0,0,0,1,0,0,2,0,0, 2,0,0,1,0,1,0,0,3,0,0,2,0,0,0,1,0,5,0,0,0,1,0,1,0,0), nrow=3) >

I have a matrix with 2 columns and I want to do fishers exact test for these with the totals for each row being 100 say. The data has the form: 23 12 32 21 12 2 and these represents the tables: 23 12 77 88 32 21 78 79 12 2 88 98 How do I use apply to speed up aclculation of the fisher.exact test? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear R-help members. Maybe this is not the right platform to ask this, but I'm looking desperately for a test which is calculating confidence intervals from diversity measurements (non-normaly distributed) (fishers alpha diversity). I was checking the package "vegan" but there seems to be nothing useful. Does anyone of you know with what package I easily could calculate such a

Dear R Users I am maximizing a user defined log likelihood function. It includes variance parameter (sigma). I used R function optim with BFGS maximization method. However, it stops before the solution saying ?sqrt(sigma): NaNs produced? Could anybody know a proper transformation for sigma which can be passed in the function? For the correlation parameter I used Fishers? transformation so it

Hi all, When I write out some values and then use 'plot' and 'lines' respectively I can get R to plot me two lines. However when I get the data from a csv file and run it I only manage to get one line running (whichever was invoked first). The sample files are attached below and I've reproduced the code below with the suspicious part marked as #??? (not even sure if thats

A simple question - using the following fishers test it appears that the P value is significant, but the CI includes 1. Is this result correct? > data.50p10min <- matrix(c(16,15, 8, 24),nrow=2) > fisher.test(data.50p10min) Fisher's Exact Test for Count Data data: data.50p10min p-value = 0.03941 alternative hypothesis: true odds ratio is not equal to 1 95

Dear R-Users, I am currently looking for a way to test the equality of two correlations that are related in a very special way. Let me describe the situation with an example. - There are 100 respondents, and there are 2 points in time, t=1 and t=2. - For each of the respondents and at each of the time points, I have information on 10 X-variables and on 10 Y-variables. - Based on this

Dear R-experts, I am struggling with the following problem, and I am looking for advice from more experienced R-users: I have a data frame with 2 identifying variables (comn and mi), and an output variable (x). comn is a variable for a company and mi is a variable for a month. comn<-c("abc", "abc", "abc", "abc", "abc", "abc",

Hi,everyone. I use the following program calculates Fisher's alpha from counts of individuals and species. The program is wrote by Prof. Kyle Harm. However, when I run the program, it can work very quickly sometimes, but it can not work very well sometimes. It depends on the counts of individuals and species. For example, > calc.alpha(1000,70) [1] 17.14375 > calc.alpha(10000,70) [1]

Hello R help members. I came across today with an article on Barnard's exact test (http://www.cytel.com/Papers/twobinomials.pdf), that is supposed to give a more powerful fisher.test - Because it doesn't assume that we know the row and column totals are in advance. Any pointers to such a function ? Thanks, Tal -- ---------------------------------------------- My contact information:

Hi, I''m attempting to loop through an array in a method in a model, Product.rb, and return a value if a certain conditional is met. I''m having some trouble however, as it doesn''t seem to be working as expected. The method from my model is as follows: def product_availability_label QCC.config(:product_availability).each do |av| "#{av.first}" if

I wan't a test for the 'association' between two events, lets say the color of balls picked and the pickers (this is quite a good analogy to my data). I have 200 different pickers P I have 1,000 colors of balls C I have 1,000,000 picks in total I am totally confused about what test to apply and when and why. This is what I *think* I know how many balls each picker

Hello, Does the "lda" function (package MASS) perform or can it perform classic two-group Fisher discriminant analysis? R-version: 1.9.1, MASS package (latest available) Thank you, Borut Rajer

I am contemplating improving my ctest package as follows: * Add exact p,q,r,s functions for the Wilcoxon distribution, and change the test accordingly (make `exact' work). * Make Fisher's test work for tables larger than 2 by 2. * Perhaps add an exact unconditional test for 2 by 2 tables? * Perhaps add something on estimating/testing relative risk and odds? As clearly I'd like to

Hi! I have some data that looks like this up down percentaje uew_21 20 14 58.82 uew_20_5 27 40 40.29 uew_20 8 13 38.09 uew_19_5 17 42 28.81 So I have 4 experimental conditions and I am counting number of animals in the up and down compartment and the calculating the percentage, I want to know which one of the conditions is different from each other. If the data wouldn't be percentage

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Dear All, R keeps crashing when I use fisher.test . I'm using it on 2 x c (where c varies from 2 to 7) table, the total number of counts is 918. It does work with some of the categorisations of the data, but it's failing on those categorisations where the Chi-squared approximation breaks down due to small expected counts. A conglomeration of categories is not feasible. Is my memory too

In an effort to select the most appropriate number of clusters in a mixture analysis I am comparing the expected and actual membership of individuals in various clusters using the Fisher?s exact test. I aim for the model with the lowest possible p-value, but I frequently get p-values below 2.2e-16 and therefore does not get exact p-values with standard Fisher?s exact tests in R. Does anybody know