similar to: Binomial Power/Sample Size

Hallo! I found a question exactly as mine, but I did not found an answer. Therefore?I post this again - hopefully there will be an answer! Thanks in advance! karl From: Berta <ibanez> Date: Tue, 27 Feb 2007 18:58:48 +0100 Hi R-users, I want to calculate the sample size needed to carry out a 2-sample proprotion test. I have the hypotesized treatment probability of success (0.80), the

Hi, I'm try to compute the minimum sample size needed to have at least an 80% of power, with alpha=0.05. The problem is that empirical proportions are really small: 0.00154 in one case and 0.00234. These are the estimated failure proportion of two medical treatments. Thomas and Conlon (1992) suggested Fisher's exact test and proposed a computational method, which according to their table

Dear friends, I am not sure that this is the right place?to ask,? but please feel?free to suggest an alternative discussion group. My question is that I?want to do a comparative study in order to compare the rate of incidence in two populations. I know that a pilot study was conducted a few weeks ago and found?8/140 (around 6%) incidence in population A. Population B was not sampled. Assuming

RUsers, I am trying (with little success) to determine how to combine unique and min to do some data frame manipulations and I could use a little advice. I have tried various combinations of 'unique', 'min' and 'apply' with no luck. Given this simple example data (x, y) x<-c(10, 10, 12, 13, 15, 16 ,17, 17, 17) y<-c(112, 117, 111, 117, 114, 113, 119, 121, 130)

Hi folks, I am running a very simple regression using mylm <- lm(mass ~ tarsus, na.action=na.exclude) I would like the use the residuals from this analysis for more regression but I'm running into a snag when I try cbind(mylm$residuals, mydata) # where my data is the original data set The error tells me that it cannot use cbind because the length of mylm$residuals is

The only references to this I can find searching the archives are to a student who asked in relation to his course work on a stats course. Promise I'm not doing that! I have a situation in which we want to test proportions against an expected proportion, binom.test() is great. I'd like to do some post hoc power tests (the x and n were beyond our control in the survey as all we could set

Hello, I am looking for a sample size function for samples sizes, to test proportions that are not binomial proportions. The proportions represent a ratio of (final measure) / (baseline measure) on the same experimental unit. Searches using RSeek and such bring multiple hits for binomial proportions, but that doesn't seem to fit my situation. Perhaps there's some standard terminology

UseRs, I am working on a dataset (see small example below) where individuals were followed on a specific date-time combo and multiple repeated measurements were taken (e.g., height in meters, behavior class in 2 letter code). Observation numbers varied between individual (ranging from 1 observation for each date-time combo to >50) I am trying to summarize the data into 1 row per

Is there a way I can have R automatically execute the commands in a source file without ever having to use R interactively? If so, what arguments should I pass to the UNIX call to R? I need to do this to run several R jobs in parallel. An alternative may be to have R and an R package behave as a stand-alone application that can be called from the UNIX command line. Is there any documentation on

R-Users, I hope this is not a uniformed question, but I am a little lost. I ran into a problem this morning and I was wondering if anyone had seen it before. I was trying to summarize each column of a data set (150,000 rows, ~50mb, so it was a relatively big file) imported from a text file using the below code; data.summary <- read.csv("c:/summary.txt", sep="")

Hi, We released individual birds into a room with 2 trees. We counted the number of visits to each of the 2 tree. One of the trees is always a control tree and the other tree is either treatment 1, treatment 2 or treatment3 or treatment 4. Ind Treat ContrTree ExpTree Total visits 1 1 11 16 27 1 2 6 9 15 1 3 5 13 18 1 4 11 25 36 2 1 2 3 5 4 1 6 7 13 4 3 4 4 8 4 4 2 5 7 6 1 1 1 2 6 4 5 16 21

I'm trying to use the inflated binomial distribution of zeros (since 75% of the values are zeros) in a randomized block experiment with four quantitative treatments (0, 0.5, 1, 1.5), but I'm finding it difficult, since the examples available in VGAM packages like for example, leave us unsure of how it should be the data.frame for such analysis. Unfortunately the function glm does not have

All, I constructed a fairly simple plot using the below and scatterplot3d. However, the axis locations in scatterplot3d seem to 'hide' quite a bit of the points in my graphic based on the viewing angle, hence not adhering to Tufte's rules. I tried to determine if I could rotate the graph and relocate all three axis (origin (0, 0, 0))so they would be at the back left so the

Greetings, I would like to plot three lines on the same figure, and I am lost. There is an answer to a similar thread… but I tried matplot and it is beyond me. An example of the data follows: Year EM IM BM 1983 9.1 16.8 -7.7 1984 12.0 18.0 -6.0 1985 13.6 19.1 -5.5 1986 12.4 17.3 -4.9 1987 14.6 20.3 -5.7 1988 20.6 23.3 -2.6 1989 25.0 27.2 -2.2 1990 28.4 30.2 -1.8 1991 33.3 31.2 2.1 1992 40.6

I wanted to know if there is a way to perform a two sample binomial test in R. I know you can use the proportion test i.e.: prop.test(c(19,5),c(53,39),p=NULL,alternative="two.sided"). But I was looking to use the exact binomial test, binom.test, however when I have tried replacing prop.test with binom.test I get an error. Is there any way to do this? -- View this message in context:

Hi, I've got one silly question for evening. I don't know is this reasonable, but can test with two the most extreme proportions from the samples could be good enough evidence for testing equivalence, or should I have to look for something else...? -- Mi³ego dnia [[alternative HTML version deleted]]

Hi everyone. I need to do a sample size calculation for a one sample based on a CI interval of +- 5% of the proportion, we expect to have a rate of 0.07 based on similar studies. I have looked at the pwr-package as well as the power.prop.test function, but I can't see how to make either do what I need. Could anyone point me to a function, or explain how to do this will the functions in pwr?

Greetings.? My wife is teaching an introductory stat class at UC Davis.? The class emphasizes the use of simulations, rather than mathematics, to get insight into statistics, and R is the mandated tool.?? A student in the class recently inquired about different approaches to sampling from a binomial distribution.? I've appended some code that exhibits the idea, the gist of which is that using

hello, I asked about this simulation a few days ago, but still i can't get what i need. I have 2 units: icu and regular. from icu I want to take 200 observations from binomial distribution, when probability for disease is: p=0.6. from regular I want to take 300 observation with the same probability: p=0.6 . the distribution to detect disease when disease occurred- *for someone from icu* - is:

try ?power.prop.test > -----Messaggio originale----- > Da: Tim Wilson [mailto:wilson at visi.com] > Inviato: sabato 6 luglio 2002 6.05 > A: R-help > Oggetto: [R] one-sample binomial test > > > Hi everyone, > > Here's how I solved a problem for my stats class. I'm pretty sure I > understand what's going on, but I wonder if there's a more >