similar to: Power w/ unequal sample sizes

How i can perform a Pearson's Chi-squared Test in this data set: | Outcome -----------------+-----------+----------------------------------+ Treatment | Sex | None |Some | Marked | Total -----------------+------------+--------+--------+-------------+ Active | Female | 6 | 5 | 16 | 27

I am using levelplot() from lattice with grids that have unequal cell sizes. This means that the boundary between two cells is not always half-way between nodes, as levelplot() assumes. The result is that some cell sizes are rendered incorrectly, which can be painfully obvious if using relatively large cells. Is there any work-around? I am using the conditioning capability of lattice and

I ask for a histogram of counts with slightly uneven breaks but histogram() ignores the 'type' argument and provides density scaling instead. x = sample(1:3, 100, replace=TRUE) histogram( ~ x, breaks=c(0,1.5,2.5,3.5), type='count') My real application has time data with month boundaries for the breaks and I DO want counts on the y-axis. A work-around is not difficult but

Dear R folks - Sorry to be so dense, but can you help me with two programming problems? 1. Randomizing within factors. I have factored the values in an array according to a series of ranked breakpoints. Now I want to randomize the values that fall within each of the breakpoint boundaries, without replacement. I want to end up with an array in which the values within each factored category

Hi I am trying to run an ANOVA on data with unequal variance. I am new to nlme, but to my understanding I need to use the gls function. I have single response variable (distance which is continuous) and the explanatory variable is individual ID (class variable: individuals differ in the variance in their distance values hence the need to using nlme). So I would create a model

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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

Dear R helpers   Suppose e.g. I have a csv file having three variables defined and each of these variables have data items of say 40, 50, 45 length. When I open this csv file in 'R', I get 10 trailing 'NA's under first column and 5 'NA' s in case of 3rd column.   How do I get rid of these NA's s.t. when I read the first column, there should be only 40 data items, 2nd

Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley

Hello All, Would anyone be able to help me understand how R computes a quantile-quantile plot for comparing two data samples with unequal sample sizes? Normally, the procedure should be to rearrange the larger data sample into n equally-spaced parts using interpolation, where n is the sample size of the smaller sample, and then plot the matching data pairs. I tried using different plotting

Hi I have a data frame with rows specifying companies (codes are assigned to companies) and columns specify months (monthly data). The data is based on male (M) and female (F) information for each month. Following is an example of how my data looks like: 01 02 03 04 001 M M M na 001 F M M M 002 M na F F 003 F F F M 003 F F M na 003 M

This is in followup to a thread on R-help with subject "Sampling". I claim that R does the wrong thing by default when sampling with unequal probabilities without replacement - the selection probabilities are not proportional to 'prob', for any draw after the first: I suggest that R do what S-PLUS now does (though you're free to choose a better implementation). What S-PLUS

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 **********************************************************************

Hi, I am doing multiple regression, and have several X variables that are categorical. I read that I can use dummy or contrast codes for that, but are there any special rules when there're unequal #observations in each groups (4 females vs 7 males in a "gender" variable)? Also, can R generate these codes for me? THanks.

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) :

Hi, I am having problems getting cohen's kappa to work. I have been using the function: ><-ckappa(x,y) from the psy package. I am trying to test for inter-observer reliability, I have 2 observers and 26 categories, however, the two observers might not necessarily have the same range of categories (I have unequal score ranges). However, I thought R could cope with this. Each time I

Dear users I have received an output from one of the functions of igraph. The output is in the following form [[1]] [1] 1 196 94 89 92 197 0 [[2]] [1] 1 [[3]] [1] 1 196 97 193 4 [[4]] [1] 1 194 97 193 4 [[5]] [1] 1 194 97 193 4 6 [[6]] [1] 1 196 97 193 4 6 [[7]] [1] 1 196 97 193 4 185 7 [[8]] [1] 1 194 97 193 4 185 7 [[9]] [1] 1 196 97 193

Dear r-help mailing list, Is there a way to incorporate weights into the minsplit criteria in rpart, when the weights are uneven? I could not find a way for the minsplit threshold to take the weights into account, and when the weights are uneven it becomes an issue, as the following example shows. My current workaround is to expand the data into one in which each row is an observation, but that

Hi, vec1<-factor(rep(c("A","B","C"),c(19,8,11)),levels=c("A","B","C")) sapply(split(vec1,vec1),length) # A? B? C #19? 8 11 #or you could change gl() function gl.new<-function (n, k, labels = 1:n, ordered = FALSE) { ? out<- numeric() ?? for(i in 1:n){ ?? out<- append(out,rep(i,length.out=k[i])) ?? } ?? out<-factor(out) ??

I am getting a repeated error when I try to run a logistic regression in R 2.8.1 >(glm(prop1~x1,data=glm1,family=binomial("logit"),weights=nt1)) Error in model.frame.default(formula = prop1 ~ x1, data = glm1, weights = nt1, : invalid type (list) for variable 'x1' x1 is multistate categorical (3 categories). 2 of the categories have 12 observation, one has 9. Is this what