similar to: Pseudodataset text file

Hi all, I am trying to bootstrap a small data set into 1000 "pseudodatasets" and then run an ANOVA on each one. Can anyone provide guidance on how I could do this? Thank you. -Dan Janes ************************************************ Dan Janes, Ph.D. Harvard University/OEB 26 Oxford St. Cambridge, MA 02138 Office: 617-496-2375 Fax: 617-495-5667 Email: djanes at oeb.harvard.edu

Hello James, Parth, Following our discussion on IRC and on code review, the way FeatureVector class works needs some discussion. Presently, the FeatureVector class is defined as follows, with a fixed number of feature count (19): class FeatureVector::Internal : public Xapian::Internal::intrusive_base{ friend class FeatureVector; double label; double score;

I am using R 2.1.1 under Mac OS 10.3.9. Two related problems (see notes 1. and 2. below) are illustrated by results of the following: y <- rnorm(10) x <- z <- 1:10 is.na(x[9]) <- TRUE lm0 <- lm(y ~ 1) lm1 <- lm(y ~ 1, weights = rep(1, 10)) add1(lm0, scope = ~ x) ## works ok add1(lm1, scope = ~ x) ## error lm2 <- lm(y ~ 1, offset = 1:10) add1(lm0, scope = ~ z) ##

David, Thanks. The reason add1.lm (and drop1.lm) do not support offsets is that lm did not when they were written, and the person who added offsets to lm did not change them. (I do wish they had not added an offset arg and just used the formula as in S's glm.) That is easy to add. For the other point, some care is needed if 'x' is supplied and the upper scope reduces the number

Dear R-users: How does one calculate in R the odds ratios for a CATEGORICAL predictor variable that has 4 levels. I see r-help inquiries regarding odds ratios for what looked like a continuous predictor variable. I was wondering how to get the pairwise odds ratios for comparisons of levels of a categorical predictor variable. I can't seem to get the correct output using: >

Hi, I would like to know how to capture the console output from running an algorithm for further analysis. I can capture this using capture.output() but that yields a character vector. I would like to extract the actual numeric values. Here is an example of what I am trying to do. fr <- function(x) { ## Rosenbrock Banana function on.exit(print(f)) x1 <- x[1] x2 <- x[2]

> > > > The approach I was thinking would look something like this: > > * instead of Features, which is really a namespace implemented as a > class, we separate out the calculation of the different features > into distinct subclasses of Feature, whose only job is to calculate > a single feature. Currently the FeatureManager calls these (via >

I'm sorry, maybe the question was bad posed. Ista has well described my problem. Thanks Massimo >----Messaggio originale---- >Da: izahn at psych.rochester.edu >Data: 28/07/2011 17.52 >A: "David Winsemius"<dwinsemius at comcast.net> >Cc: "m.fenati at libero.it"<m.fenati at libero.it>, <r-help at r-project.org> >Ogg: Re: [R]

Hello, Is there an easy function for switching list to matrix. My list is of genetic distances between species pairs: A A 0 A B .5 A C .25 B C .5 and I want a distance matrix such as: A B C A 0 .5 .25 B .5 0 .5 C .25 .5 0 for use in a mantel test. Thank you for the help! cheers, charlie -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Charles G. Willis Department of Organismic

Hello, How can one write the output of a GLM/GEE model to a text file, such that the results appear in text file in the same (or similar) format they appear in the R console? For instance, I have the following model: Traitresult <- compar.gee(Y~X, data = data, family = "binomial", phy = tree, scale.fix = TRUE, scale.value = 1) How would I write "Traitresult" to a text

Hello, I am trying to test for non-linearity in a set of non-parametric data. Furthermore, I would like to do so in a multi-variate model. Frankly, I don't even know if this is possible, and if it is possible, I don't know if it is implemented in an R package. Any advice would greatly appreciated. Thank you! Charlie -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Charles G.

Hello, I am trying to preform a comparative analysis using the partial mantel method. However, i am having trouble creating compatible matrices. I can create matrices from my numerical data using the 'distance(x, method)' function from ecodist, but the row/column names are out put as numbers. While when i create a phyo-distance matrix using 'cophenetic(x)' from APE, the row/column

Hello, I am running the program COMPAR.GEE within the package APE. My dependent variable is binomial, while my independent variable is a multi-state categorical variable. The output reports an estimate for each state of the independent variable except the first one. For example, for the variable X with 3 states, the output is: intercept (estimate) X2 (estimate) X3 (estimate)

Hello, I am trying to run the APE program COMPAR.GEE with a model containing a categorical response variable and a mixture of continuous and categorical independent variables. The model runs when I have categorical (binary) response and two continuous independent variables (VAR1 and VAR2), but when I include a categorical (binary) independent variable (VAR3), I receive the following output with

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@Don your solution does not solve Vijayan's scenario 2. I used spread and gather for that. An alternative solution to insert mising Fval - picking up with Don's newtst - is newtst <- c("FName: fname1", "Fval: Fval1.name1", "FName: fname2", "Fval: Fval2.name2", "FName: fname3", "FName: fname4", "Fval: fval4.fname4")

Hi! When I'm mounting a client with fstab and a server-supplied client-spec file I get the following in the server-log: 2008-09-15 15:04:42 D [tcp-server.c:145:tcp_server_notify] server: Registering socket (63) for new transport object of 192.168.10.12 2008-09-15 15:04:42 E [server-protocol.c:5212:mop_getspec] server: Unable to open /etc/glusterfs/glusterfs-client.vol.192.168.10.12 (No such

Using Ulrik?s example data (and assuming I understand what is wanted), here is what I would do: ex.dat <- c("FName: fname1", "Fval: Fval1.name1", "Fval: ", "FName: fname2", "Fval: Fval2.name2", "FName: fname3") tst <- data.frame(x = ex.dat, stringsAsFactors=FALSE) sp <- strsplit(tst$x, ':', fixed=TRUE) chk <-

Hi Vijayan, one way going about it *could* be this: library(dplyr) library(tidyr) library(purrr) ex_dat <- c("FName: fname1", "Fval: Fval1.name1", "Fval: ", "FName: fname2", "Fval: Fval2.name2", "FName: fname3") data.frame(x = ex_dat) %>% separate(x, c("F1", "F2"), sep = ": ") %>% filter(F2

I am attempting to lower the selectCC instruction to the instruction set of the backend I'm working on and I cannot seem to find a way to correctly implement this instruction. I know how this instruction should get implemented; I just have yet to find a way to do it. I want the select_cc instruction to be lowered into a comparison followed by a conditional move. I've attempted to use a