similar to: propogation of rounding error for t.tests with unequal sample sizes (PR#7225)

My apologies for not including the transcript in the original message: > xx <- rep(2.10,80) > groups.balanced <- as.factor(c(rep("one",40),rep("two",40))) > groups.unbalanced <- as.factor(c(rep("one",44),rep("two",36))) > t.test(xx ~ groups.balanced) Welch Two Sample t-test data: xx by groups.balanced t = 0, df = 78, p-value = 1

Hi, i'm looking for an R function to fit a one-way ANOVA with one factor containing 10 levels. The factor levels have different numbers of observations (varying between 20 to 40). For most of the dependent variables i'm testing there are unequal variances among the factor levels. I see the function oneway.test: oneway.test(variable ~ factor, data=dataset) which by default does not

Hi, all I have a rather un-ideal dataset that I am trying to work with, and would appreciate any advice you have on the matter. I have 4 years worth of data taken at 3 depth-zones from which samples have been taken at random. I am looking at the abundance of organism A between depth zones and across years, and am interested in the possible interaction of organism A distributions shifting

Hi, I am trying a two way anova with unequal sample sizes but results are not as expected: I take the example from Applied Linear Statistical Models (Neter et al. pp889-897, 1996) growth rate gender bone development 1.4 1 1 2.4 1 1 2.2 1 1 2.4 1 2 2.1 2 1 1.7 2 1 2.5 2 2 1.8 2 2 2 2 2 0.7 3 1 1.1 3 1 0.5 3 2 0.9 3 2 1.3 3 2 expected results are

I am using R for the first time in one of my classes. My students have alerted me to a problem for which we have not found an answer. We find that some means returned by the model.tables function are not correct when missing data is present in analysis of variance problems. We have duplicated the problem using R 1.2.0, 1.2.1, and 1.2.2 under Windows 98 and several distributions of Linux (Redhat

Hello- I have run an ANOVA on 4 treatments with unequal sample sizes (n=9,7,10 and 10). I want to determine where my sig. differences are between treatments using a Bonferroni test, and have run the code: pairwise.t.test(Wk16, Treatment, p.adf="bonf") I receive an error message stating that my arguments are of unequal length: Error in tapply(x, g, mean, na.rm = TRUE) :

Dear R community, I tried to optimized my R code by using Rprof. In my R code I'm using MySQL database connections intensively. After a bunch of queries R fails with the following error message: Error in .Call("RS_MySQL_newConnection", drvId, con.params, groups, PACKAGE = .MySQLPkgName) : RS-DBI driver: (could not connect mylogin@mydatabase on dbname "myDB"

Hi All, I was looking into MachineCopyPropagationPass.ccp file in llvm, which in first look, looks likes to me doing only redundant copy elimination in same basic block. I am interested in any pass which is doing copy propogation across the basic block in a function. Let me know if any such pass exist ? Thanks, Deepali -------------- next part -------------- An HTML attachment was scrubbed...

Hello, I am hoping someone could shed some light on power calculations for me. I have two small data sets of unequal sample size after NA removal (m = 5, f = 7). m <- c(2.0863, 2.1340, 2.1008, 1.9565, 2.0413, NA, NA) f <- c(1.8938, 1.9709, 1.8613, 2.0836, 1.9485, 2.0630, 1.9143) In a R help message/reply from Sep 30, 2001, it was noted that the "power.t.test" function

Hi, i need to test the equality of proportions in a paired case, like here: after before + - + 10 14 - 5 53 So usually i use the mcnemar.test(stats). Due to mortality, I now got the problem that my sample sizes in both factors are not equal any more -- there are less cases in the "after" condition. I already learned that Ekbohm (1982) and Marascuilo adressed the problem

Power calculations two sample test for proportions is very useful. Is there a way however, to get away from the two samples being of the same size. What would happen if one had n=15 in the one sample and n=45 in the other sample. Farrel Buchinsky, MD Pediatric Otolaryngologist Allegheny General Hospital Pittsburgh, PA **********************************************************************

After loading the RODBC package version 1.1-7, Ctrl-C changes its behaviour and is quitting R and returning to the (unix-)command prompt on the solaris2.8 platform here. Here's what happened before and after loading RODBC > for (i in 1:10^5) rnorm(10) ^C > library(RODBC) > for (i in 1:10^5) rnorm(10) ^C bash-3.00$ platform sparc-sun-solaris2.8 arch

Conversion of a data frame to a matrix using as.matrix() when a column of the data frame is POSIXt and all other columns are numeric has changed in R 1.9.0 from R 1.8.1. The new behavior issues a warning message and converts to a character matrix. In R 1.8.1, such an object was converted to a numeric matrix. Here is an example. #### R 1.9.0 #### > foo <- data.frame(

Hi! I am using R 1.8.1 and the svm of the e1071 package for classification. The problem is that I have unbalanced classes e.g. the first one is much bigger than the second one and therfore the svm is biased to the first class. If I manually adjust the class size the bias disappears. The question is then how to include this unequal class distribution to the svm (e.g. via wheights or costs)?

Dear R people: I wish to switch from SPSS to R, but there is one particular type of ANOVA design that cannot be done in R. Or more likely, it can be done, but it is nowhere documented. The problem is typical for psychologists: You have a repeated measures design with different groups of subjects. Now, this can be done with the aov command, but the number of subjects in both groups must be

Greetings from Rio de Janeiro, Brazil. I am looking for advice / references on binary logistic regression with weighted least squares (using lrm & weights), on the following context: 1) unbalanced sample (n0=10000, n1=700); 2) sampling weights used to rebalance the sample (w0=1, w1=14.29); e 3) after modelling, adjust the intercept in order to reflect the expected % of 1?s in the population

I have a three-way unbalanced ANOVA that I need to calculate (fixed effects plus interactions, no random effects). But word has it that aov() is good only for balanced designs. I have seen a number of different recommendations for working with unbalanced designs, but they seem to differ widely (car, nlme, lme4, etc.). So I would like to know what is the best or most usual way to go about working

Hi there, I am looking to fit a two-way random effects model to an *unblalanced* layout, y_ijk = mu + a_i + b_j + eps_ijk, i=1,...,R, j=1,...,C, k=1,...,K_ij. I am interested first of all in estimates for the variance components, sigsq_a, sigsq_b and sigsq_error. In the balanced case, there are simple (MM, MLE) estimates for these; In the unbalanced setup,

Hello all, This may be a simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas with unbalanced design in R. In my case, I have 11 vegetation transects (with 1000 10cmx10cm squares), where we estimated shrub cover. We have two different treatments: wildfire (4 transects) and prescribed burning (7 transects) and we want to

Hi everyone! I need to perform a Wilcoxon rank sum test, but I have some ties and the groups have different size also. When I deal with ties I use the wilcox.exact function, how can I solve the different size problem using this function? thanks net -- View this message in context: http://r.789695.n4.nabble.com/Wilcoxon-rank-sum-in-unbalanced-design-tp3447400p3447400.html Sent from the R help