similar to: Contingency table: logistic regression

Dear list, I'm trying to do a set of generic functions do make contingency tables from data.frames. It is just running "nice" (I'm learning R), but I think it can be better. I would like to filter the data.frame, i.e, eliminate all not numeric variables. And I don't know how to make it: please, help me. Below one of the my functions ('er' is a mention to EasieR,

mingw, llvm 2.6 (buid with llvm-gcc) Example source code: http://npcontemplation.blogspot.com/2008/06/secret-of-llvm-c-bindings.html I change LLVMCreateJITCompiler(&engine, provider, &error); to LLVMCreateJITCompiler(&engine, provider, 3, &error); $ llvm-gcc `llvm-config --cflags` -c fac.c $ g++ `llvm-config --libs --cflags --ldflags core analysis executionengine jit

Hi, I need to resample rows in a data frame by subsets L3 <- LETTERS[1:3] d <- data.frame(cbind(x=1, y=1:10), fac=sample(L3, 10, repl=TRUE)) x y fac 1 1 1 A 2 1 2 A 3 1 3 A 4 1 4 A 5 1 5 C 6 1 6 C 7 1 7 B 8 1 8 A 9 1 9 C 10 1 10 A I have seen this used to sample rows with replacement d[sample(nrow(d), replace=T), ] x y fac 7 1 7 B 2

Hi all, I noticed something strange when I ran aov and anova. vtot=c(7.29917, 7.29917, 7.29917) #identical values fac=as.factor(c(1,1,2)) #group 1 has first two elements, group 2 has the 3rd element When I run: > anova(lm(vtot~fac)) Analysis of Variance Table Response: vtot Df Sum Sq Mean Sq F value Pr(>F) fac 1 1.6818e-30 1.6818e-30 0.3333 0.6667 Residuals 1

Dear all, I'm using the mgcv library by Simon Wood to fit gam models with interactions and I have been reading (and running) the "factor 'by' variable example" given on the gam.models help page (see below, output from the two first models b, and b1). The example explains that both b and b1 fits are similar: "note that the preceding fit (here b) is the same as

Suppose I have a dataframe 'd' defined as L3 <- LETTERS[1:3] d0 <- data.frame(cbind(x = 1, y = 1:10), fac = sample(L3, 10, replace = TRUE)) (d <- d0[d0$fac %in% c('A', 'B'),]) x y fac 2 1 2 B 3 1 3 A 4 1 4 A 5 1 5 A 6 1 6 B 8 1 8 A Even though factor 'fac' in 'd' only has 2 levels, but it seems to bear the birthmark

Hi, I am trying to plot an interaction.plot with different color for each level of a factor. It has an erratic behavior. For example, it works for the first interaction.plot below, with the example from the ALDA book, but not with the other plots, from the NPK dataset: # from http://www.ats.ucla.edu/stat/r/examples/alda/ch2.htm tolerance <-

[I originally posted this on the R-help mailing list, and it was suggested that R-devel would be a better place to dicuss it.] Running ?table? on a factor with levels containing non-ASCII characters seems to result in extremely bad performance on Windows. Here?s a simple example with benchmark results (I?ve reduced the number of replications to make the function finish within reasonable time):

Hi I'm doing a glm analysis and I have two doubts (at least :) 1) When I run the function it gives a lot of warnings (see below) what they mean ? (may be I'm ignorant about this analysis ...) glm.poisson<-glm(log(Jkij+1)~fac.ano+fac.tri+fac.icesr+fac.mat+fac.ano:fac.icesr+fac.ano:fac.tri,family=poisson()) warnings() 40: non-integer x = 1.252763 41: non-integer x = 1.864785 42:

R version 2.10.1 (2009-12-14) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with

Hi all, I had some trouble in?regrouping factor levels for a variable. After some experiments, I have figured out how I can recode to modify the factor levels. I would now like some help to understand why some methods work and others don't. Here's my code : rm(list=ls()) ###some trials in recoding factor levels char<-letters[1:10] fac<-factor(char) levels(fac) print(fac) ##first

hi ,everyone I want subtract some dataset by subset. >From the help running help(subset), ths information is "*subset(airquality, Day == 1, select = -Temp)* " while I running my script written as "*g1data<-subset(errdata, fac>12) *" ,it is wrong with the error information "*subset.default(newerrdata, fac>12),can not find fac*" and g1 in read

I''m using collection.select lists and I would love to sort them by one or two of the columns as the lists are getting long enough to make a difference. <%= options = [[''Select a Facility'', '''']] + @facility.collect { |fac| [fac.name, fac.id] } select ''placement'', ''facility_id'', options %>

Hi All, I have a doubt regarding join tables I''m having 2 models 1)Fac 2)Cont and both models have " has and belong to many" relationships so there are 3 tables 1)facs 2)conts 3)conts_facs then i''m fetching the data in controller as @conts=Cont.find(:all]) @cfacs=Fac.all(:joins=>:conts, :select=>"facs.name") but i dont know how to get the

All, I added a third peer for a arbiter brick host to replica 2 cluster. Then I realized I can't use it since it has no infiniband like the other two hosts (infiniband and ethernet for clients). So I removed the new arbiter bricks from all of the volumes. However, I can't detach the peer as it keeps saying there are bricks it hosts. Nothing in volume status or info shows that host to be

Hi, I would like to add confidence intervales to an ANCOVA with 2 covariates when using xyplot. What would be a good way of accomplishing this? --8<---------------cut here---------------start------------->8--- rm(list = ls(all = TRUE)) rm(list = c(ls())) library(lattice) ## 1. generate data random <- rnorm(200) y <- abs(random) x1.cont <- abs(random) x2.fac <-

Hello, I'm using SPSS at work but really would like to switch to R. Right now I'm trying to learn R in reproducing calculations I did with SPSS but am stuck with something that is quite simple and comprehensible in SPSS-Syntax: IF (variable1.fac = 0 AND variable2.num = 0) variable3=1. IF (variable1.fac = 0 AND variable2.num >= 1) variable3=2. IF (variable1.fac = 1 AND variable2.num =

Dear all Could someone explain me why lapply(split(x,fac),mean) returns means per levels of fac for each column of x whereas lapply(split(x,fac),sum) does not return sums per level of fac and columns of x, but adds all columns together? Hence, how can I get sum to behave as mean in this instance? Thank you very much in advance E. Castella

Hi, Excuse me for this simple question. How to convert as.data.frame to as.character? ?data.frame > L3 <- LETTERS[1:3] > L10 <- LETTERS[1:10] > d <- data.frame(cbind(x=c("XYZ"), y=L10), fac=sample(L3, 10, repl=TRUE)) > d x y fac 1 XYZ A A 2 XYZ B A 3 XYZ C A 4 XYZ D A 5 XYZ E B 6 XYZ F C 7 XYZ G A 8 XYZ H C 9 XYZ I B 10 XYZ

I can't quite seem to solve a problem subsetting a data frame. Here's a reproducible example. Given a data frame: dat <- data.frame(fac = rep(c("a", "b"), each = 100), value = c(rnorm(130), rep(NA, 70)), other = rnorm(200)) What I want is a new data frame (with the same columns as dat) excluding the top 5% of "value"