similar to: significance in difference of proportions

Hi, I have a seemingly simple proportional test. ?here is the question I am trying to answer: ? There is a test running each day in the lab, the test comes out as either positive or negative. So at the end of each month, we can calculate a positive rate in that month as the proportion of positive test results. The data look like: ? Month??? ??# positive?????? # total tests??? positive rate

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. I must fit a logistic regression to data in the form of proportions, but in which some of the proportions are zero. I therefore cannot use the glm function with a binomial link since the link function is not defined for p=0 or 1. What other solutions are available? Any references to this specific problem (i.e. regression using proportions, of which some are zero) would be welcome.

Hi r-users,   I would like to use prop.test code and I also calculate manually to test the proportions for 2 groups.  The problem is the answer for the p-value calculated manually are different from prop.test.  Here are the results:   ## Manually   z value: z= (phat-p)/sqrt(pq/n) = (.084-.081)/sqrt(.081(1-.081)/691)=0.289, pvalue=0.7718   ## Using prop.test code > low <- c(56,58) > tot

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

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

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

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

When one has raw data it is easy to create a table of one variable against another and then calculate proportions For example a.nice.table<-table(a,b) prop.table(a.nice.table,1) However, I looked at several papers and created a data frame of the aggregate data. That means I acually created a table except it is a data frame. The first column lists the name of the first author and the year. I

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

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

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

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

1. You neglected to cc r-help! 2. Word files are **not** text files. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Jun 23, 2017 at 12:23 PM, Jay Zola <jayjay.1988 at hotmail.nl> wrote: > Dear sir, > > >

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

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 everyone, I am trying to compare proportions among groups using the logistic regression approach as follows: 1) Fit the model log(p_i/(1-p_i)) = M + G_i, where p_i is the probability of success in group i and G_i is the effect of group i, i=1,..,I. 2) Test the hypotheses: Ho: G_1 = G_2 = ... = G_I (the probability of success is the same for all groups) versus Ha: at least two