similar to: Multiple dependant proportions and sample size

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 was preparing an e-mail for the help list and ran across a quandary. When asking for help it is useful to include the code/data so others can run your code and test it. I was running code on a data frame and wanted to include a small version of the data frame. The data frame was based on experimental data. What is the best way to do this? I didn't want to send an attachment so a wrote code

I am debugging some code and found a function that returns and error most of the time. I have issolated the problem to when it raises an argument to the 1/3 power but for the life of me I can not figure out why it is not working. i have gone through the FAQs and have found nothing (not to say I might have missed something). I am highly embarrased with my inability find the problem (my face is

I have searched the archives and instructions high and low but have not found what I need. I have a dataframe named Data with columns: AdjResND0 - the numeric result Parameter - the classification (chemical name) Sort - the order I want the chemical names to appear, with leading zeros so all are the same length Client.Name - the sample name I am trying to generate a series of 1 page barcharts,

I am trying to get the same text printed on each page of a multi-page series of bar charts. The text need to appear in the upper left-hand corner of the page, outside of the plot area. A watermark might be the closest analogy to what I am after This is what I have so far: PData <- na.omit(subset(TData,Matrix == "Product")) barchart(AdjResND0 ~ reorder(Compound, Sort) | Label ,

Greetings, We are performing a meta-analysis of mink pup survival data versus chemical concentration. We have modeled percent survival successfully using nls as shown below and the plot. What we need to do is construct a confidence interval on the concentration at which we get 50% survival (aka the EC50, although we may want other percent survivals in the future). My first question is, what seems

I am using lattice to make figures as pdfs: trellis.device(device = "pdf",file = "Figure6.pdf",color = FALSE) I need to specify some blank space on the left-hand margins (the pages will be bound so we need about 0.5 inch)). I have tried a number of solutions but none seems to work (e.g. par.set). Can this be done when initiating the plotting device? Or is the some other way

I have been using the ECDF function in the Hmisc package to produce cumulative distribution function plots. The problem is that for small datasets the steps "look bad" (not my characterization but from the client). Is there a way to get the same information but smoothed? I have tried the densityplot (lattice), which gives a smoothed line, but this does not give the cumulative density.

I am looking to create what I would call a "simple variation" on the boxplot. What I would like to do is to be able to plot the upper and lower confidence limits as the "box" and the 10th and 90th percentile as the whiskers. What I have done is write the code to create a dataframe, the columns of which are the mean, sd, 10th percentile, 90th percentile, lower confidence limit

I am looking for procedure that allow one to fit multiple distributions to a variable. For example, based on analysis of the data we suppose that the data can be represented by 3-5 normal distributions added together. I would like to be able to determine the mean, sd, and weight associated with each distribution and examine the improvement of the fit when 3,4 or 5 normal distributions are used as

Greetings, I am running a buch of wilcox tests and need to be able to rapidly export the results into a csv file. I have attached example code as well as my attempts to get what I need. I have tried unlist,cbind,rbind etc but I am obvously missing something simple. FYI I am actually running about 50 WRS tests per dataset, this is just an example. Thanks 10^6 Mike AKCCR <-

I have written a few different summary functions. I want to calculate the statistics by groups and I am having trouble getting the output as a dataframe. I have attached one example with a small dataset that calculates summary stats and percentiles, I have others that calculate upper confidence limits etc. I would like the output to be converted to a dataframe with one of the columns as the

I am trying to create some plots of concentration versus depth. I want Depth on the Y axis and the concentrations on the X axis. I also need to plot error bars. The xYplot function in Hmisc is very nearly ideal but I have to put depth on the x-axis to get it to work. When I transpose the X and Y axes in the xYplot command, the error bars disappear (no surprise given the help file). It there a

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 >

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

I have data set with binary responses. I would like to conduct polychoric principal component analysis (pPCA). I know there are several packages used in PCA but I could not find one that directly estimate pPCA and graph the individuals and variables maps. I will appreciate any help that expand these reproducible scripts. #How to conduct polychoric principal component analysis pPCA using #either

Dear Md Kamruzzaman, To answer your second question first, you could just use the svychisq() function. The difference-of-proportion test is equivalent to a chisquare test for the 2-by-2 table. You don't say how you computed the confidence intervals for the two separate proportions, but if you have their standard errors (and if not, you should be able to infer them from the confidence

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 > >

{Note the DATE ! this message bounced ... because it was sent to R-announce which was definitely the wrong list anyway. MM - your list maintainer } Dear R-Team I have installed R version 0.990 and the foreign package under Windows NT.. Whilst lookup.xport() works as it should, read.xport() crashes reporting an "access error" as well for larger datasets as also for small

Dear all, Does someone know an R function implementing the method of Sison and Glaz (1995) (see full ref below) for computing Simultaneous confidence intervals for multinomial proportions? As alternative method, I think to boostrap the mean of each proportion and get in that way confidence interval of the mean. I observed 21 times a response that could be one out of 8 categories