similar to: Creating two groups of random numbers

Hi all, I want to create two groups of random numbers to calculate proportions. The first group is to represent the number of cases in a study. The second group is to represent the sample size of the study. Apparently, the sample size is going to have to be bigger or equal to the number of cases, but the sample size of a study is not necessarily greater than the number of cases of another study.

I would suggest to post this to the (recently created) R-sig-meta-analysis mailing list. See: https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis Best, Wolfgang >-----Original Message----- >From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Naike Wang >Sent: Monday, June 26, 2017 08:32 >To: R-help at r-project.org >Subject: [R] Classic fail-safe N >

Hi all, I was conducting a meta-analysis of single proportions(i.e. without a control group) using the metafor package. When I performed a classic fail-safe N, I noticed that the result (the number of missing studies that would bring p-value to the alpha, to be exact)was different than that I got in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got different results. *Below is

Hi all, I was conducting a meta-analysis of single proportions(i.e. without a control group) using the metafor package. When I performed a classic fail-safe N, I noticed that the result (the number of missing studies that would bring p-value to the alpha, to be exact)was different than that I got in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got different results. *Below is

Dear Dushanthi, Please keep your e-mails on the R-Help list, where Michael has already given you some excellent advice. As Michael already explained, metafor can handle proportions, but does not have any specific functionality for categorical variables with more than 2 levels (at the moment). So, if it is logical and possible to do so, you could collapse the levels of the categorical outcome to 2

Hi, I have a seemingly common problem but I can't find a proper way to approach it. Let's say we have 5 samples (different size) of IC circuits coming from 5 production lines (A, B, C, D, E). We apply two different non-destructive QA procedures to each sample, producing to sets of binary outcomes (passed: no/yes). So, we have two groups of proportions: QA1 QA2 no/yes no/yes A

Hello, I have been attempting to set up a lme and have looked at numerous posts including 'R's lmer cheat-sheet' as well as reading a number of papers and other resources including R help, but I am still a little confused on how to write my model (I thought I had it). I have asked a number of questions on different forums; most of which have been resolved. My main concern right now

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

R Experts: I am conducting a meta-analysis where the effect measures to be pooled are simple proportions. For example, consider this data from Fleiss/Levin/Paik's Statistical methods for rates and proportions (2003, p189) on smokers: Study N Event P(Event) 1 86 83 0.965 2 93 90 0.968 3 136 129 0.949 4 82 70 0.854 Total

I have the clinical study data. Year 0 Year 3 Retinol (nmol/L) N Mean +-sd Mean +-sd Vitamin A group 73 1.89+-0.36 2.06+-0.53 Trace group 57 1.83+-0.31 1.78+-0.30 where N is the number of male for the clinical study. I want to test if the mean serum retinol has increased over 3 years among subjects in the vitamin A group. > 1.89+0.36

Dear list members, I want to perform in R the analysis "simultaneous confidence interval for multiple proportions", as illustrated in the article of Agresti et al. (2008) "Simultaneous confidence intervals for comparing binomial parameter", Biometrics 64, 1270-1275. If I am not wrong the R function implementing the Agresti et al. method is prop.test(). I ask an help because I

R-users, Background: I took roadside samples of avian species richness and I would like to model the relationship between species richness and habitat around my 500 + sample locations (in a file called ROADSIDE). However, one criticism is that roadside habitats do not represent habitats throughout the study area. I tend to disagree because I detect birds away from the roads and the pixel size

Hi, I am looking for a way to study some phase data with a circular distribution measured in rad.I would like to do a two way ANOVA (if possible mixed, with inter and intrasubject).I haven´t found a package that does that in R?Does sombeody know if there is one or how to do the analysis.Thanks in advance J ToledoCNDR UPenn [[alternative HTML version deleted]]

Version 2.0-0 of meta (an R package for meta-analysis) is now available on CRAN. Changes are described below. Yours, Guido Major revision R package meta linked to R package metafor by Wolfgang Viechtbauer to provide additional statistical methods, e.g. meta-regression and other estimates for tau-squared (REML, ...) New functions: - metareg (meta-regression) - metabias

Version 2.0-0 of meta (an R package for meta-analysis) is now available on CRAN. Changes are described below. Yours, Guido Major revision R package meta linked to R package metafor by Wolfgang Viechtbauer to provide additional statistical methods, e.g. meta-regression and other estimates for tau-squared (REML, ...) New functions: - metareg (meta-regression) - metabias

Dear colleagues, I'm conducting a meta-analysis of studies evaluating adherence of HIV-positive drug users into AIDS treatment, therefore I'm looking for some advice and syntax suggestion for running the meta-regression using proportions, not the usual OR/RR frequently used on RCT studies. Have already searched already several handbooks, R-manuals, mailing lists, professors, but... not

Hi everyone, Suppose I have a count the occurrences of positive results, and the total number of occurrences: pos <- 14 total <- 15 testing that the proportion of positive occurrences is greater than 0.5 gives a p-value and confidence interval: prop.test( pos, total, p=0.5, alternative='greater') 1-sample proportions test with continuity correction data: 14 out of

Dear R Users, Any insights into why the following occurs would be helpful.... Firstly: #Evaluating proportions p<-as.list(rep(0,times=length(n))) #creating object of appropriate size. for(j in 1:length(n)){ for(k in 1:length(n[[j]])){ p[[j]][[k]]<- (s[[j]][[k]]/n[[j]][k])}} # 31 x k x num_samples(dim) where k varies The list object s[[j]][[k]] has 3 levels, j, k, and i. j represents the

Dear Md Kamruzzaman, I've copied this response to the r-help list, where you originally asked your question. That way, other people can follow the conversation, if they're interested and there will be a record of the solution. Please keep r-help in the loop See below: On 2024-01-17 9:47 p.m., Md. Kamruzzaman wrote: > > Caution: External email. > > > Dear John >

Hi all, I have a hypothesis test of proportion question without using R. I hope you could help to answer my question. Here is the statement. ********************* Preliminary data from a feasibility study showed a potential failure level of 1%. A sample size was calculated to detect a potential shift from this 1% level to a 2.5%, at an alpha level of 0.025 and beta of 0.1. The sample size