similar to: Dealing with proportions

Hi, I would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for

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, I've looked high and low for a function that provides frequencies, proportions and cumulative proportions side-by-side. Below is the table I need. Is there a function that already does it? Thanks, Bob > # Generate some test scores > myValues <- c(70:95) > Score <- ( sample( myValues, size=1000, replace=TRUE) ) > head(Score) [1] 77 71 81 88 83 93 > >

Hello, is there anyone who has some ideas about the problem I posted? Help! [[alternative HTML version deleted]]

Hi everyone, I've searched the internet and lots of stats books high and low for this one, but nothing seems to be quite what I want. I've got continuous data on four different state activities recorded in seconds, however each continuous session is not equally long, so the data are best expressed as proportions, i.e.: Subject 1: Swimming, 0.5, Hiding, 0.25, Edge, 0.125, Inactive, 0.125

Dear all I am still fishing for help on this theme. In Zar 1999 page 563-565 he describes a "Tukey-type multiple comparison" for testing among proportions. It involves comparisons of ranked proportions transformed to degrees. In the following pages there are a couple of similar comparisons. I cannot find an example of this in R. Is there such a thing? Cheers Chris

> Dear R-users! > > I am using R 1.0.0 and Windows NT 4.0. > Suppose I have a population of N=100 subjects, a binomial variable and a random sample of n=20 subjects from my population, giving 15 "successes". I am interested in obtaining a confidence interval for the proportion of "successes" in my population. In R, I can use > library(ctest) >

what does R have to compare with , say , proc surveymeans, estimate survey means/proportions with standard errors, using Taylor methods? [[alternative HTML version deleted]]

A question about comparing symptom reduction over time and across treatment groups. Each respondent is asked twice if they experience symptoms (coded 1), at baseline and then at 6 months later. They are randomly assigned to either the control or the intervention group. The 2x2x2 table below shows the frequency counts. As can be seen in the margins of the table, 21% of the control group

Hello, I need to get a point estimate and SD for a proportion, but the subjects' data are not binary---they are proportions (of doses received). That is, I have a proportion for each subject. In the past I have analysed these data as a continuous (normal) variable, but I really don't want CIs over 100%. This seems like basic stuff, but I don't remember learning it and it's

I need to do a sample size calculation to determine the number of audits required. The results of an audit will be: 1) OK 2) Minor variances 3) Multiple variances 4) Severe variances We want to know the sample size required to determine the proportions in each category within say 5% with a confidence of say 80% (specific values to be determined). I have used the test of proportions before but

I need to do a sample size calculation to determine the number of audits required. The results of an audit will be: 1) OK 2) Minor variances 3) Multiple variances 4) Severe variances We want to know the sample size required to determine the proportions in each category within say 5% with a confidence of say 80% (specific values to be determined). I have used the test of proportions before but

Just giving the survey package a spin... I'm accustomed to stata, and it seems very similar in many respects. One thing is throwing me, however. I've gotten my data in, and specified the design. Looks like the weighting is right (based on published population estimates from these data), but now I'd like to check my "marginal means" for proportions against those that have

This is partly an R and partly a general statistics question. I'm trying to get confidence intervals of proportions (sometimes for subgroups) estimated from complex survey data. Because a function like prop.test() does not exist for the "survey" package I tried the following: 1) Define a survey object (PSU of clustered sample, population weights); 2) Use svyglm() of the package

Hi All, I have a table based on ordial data and i want to compare proportions and i've seen in the pwr package i can use power.prop.test however i want to find out what the sig. value is based on n1,n2,p1,p2 and this package doesn't contain this.. Does anyone know of a package that does or is it a case of writting a function specifically for this? Many thanks in advance [[alternative

Hello, Is there a combination of par() settings or other commands that will allow me to uniformly reduce the size of graphics outputs? It appears that the png() device outputs 5-inch by 5-inch images, and I am trying to change my whole script to produce 4x4 images with the same proportions. I tried specifying parameters within each png(), but it reduced the size of the file without refitting the

Dear list, I´m using the mgcv package to model the proportion by weight of certain prey on the stomach content of a predator. This proportion is the ratio of two weights (prey weight over stomach weight), and ranges between 0 and 1. The variance is low when proportion is close to 0 and 1, and higher at intermediate values. It seems that the best way to go is to model this using the

Hi, I initially posted this to the general R mailing list, but Bert Gunter thought this may be a mixed model issue, so suggested me to post here. I have a dataset that has 2 groups of subjects. For each subject in each group, the response measured is the number of success (no.success) obatined with the number of trials (no.trials). So a probability of success (prop.success) can be computed as

Hi there, I guess this is an easy one, but still:  I would like to randomly sample 0s and 1s but in a way that I end up having for example 70% of 1s and the rest of 0s and not 50:50 as this function does: sample(c(0,1), 100, replace = TRUE) Any recommendations? Many thanks! Ana [[alternative HTML version deleted]]

Dear all, I am using R version 2.15.0 and 'metafor' package version 1.6-0. Can this version of the package handle proportions from a categorical dependent variable for single studies?If so how do I set up my dataframe for the raw data from different studies? Also how do I give inputs, specially xi, mi (or ni) to the function escalc()? Thanks,Dushanthi [[alternative HTML