search for: albyn

I am trying to do a power analysis to get the number of replicas per treatment. If I try to get the power it works just fine: setn=c(2,3) sdx=c(1.19,4.35) power.t.test(n = setn, delta = 13.5, sd = sdx, sig.level = 0.05,power = NULL) If I go the other way to obtain the "n" I have problems. sdx=c(1.19,4.35) pow=c(.8,.8) power.t.test(n = NULL, delta = 13.5, sd = sdx, sig.level = 0.05,

Everyone, I'm very new to R, especially when it comes to loops and functions, so please bear with me if this is an elementary question. I cannot seem to figure out how to construct a loop which runs a function until a certain value is computed. For example, say I have the following: num = numeric (10) num [1] = 0 for (i in 2:10) { num [i] = num [i-1] + 5 } This adds 5 to the

What are some effective ways to leverage the strengths of R and Mathematica for the analysis of a single data set? More specifically, are there any functions that can assist with any of the following? 1. Calling an R function from Mathematica. 2. Calling a Mathematica function from R. 3. Using XML or another reliable data format to pass vectors, matrices, and/or lists from one environment to

Hello all, I had a very strange looking problem that turned out to be due to unexpected (by me at least) format changes to one of my data files. We have a small lab study in which each run is represented by a row in a tab-delimited file; each row identifies a repetition of the experiment and associates it with some subjective measurements and times from our notes that get used to index another

Dear R developers, I have noticed a discrepancy between the coefficients returned by R's glm() for logistic regression and SAS's PROC LOGISTIC. I am using dist = binomial and link = logit for both R and SAS. I believe R uses IRLS whereas SAS uses Fisher's scoring, but the difference is something like 100 SE on the intercept. What accounts for such a huge difference? Thank you for

Dear List Members, I am in the process of designing a study examining pay and performance in Major League Baseball across several seasons, and before I get too deeply into it, I'd like to ask whether the group members think that performance across seasons is independent, or if it needs to be treated like a time-series variable so that lack of independence can be controlled. Any ideas or

When I try to apply mean to a list, I get the answer : argument is not numeric or logical: returning NA Could you help me? (I am a very beginner) -- View this message in context: http://r.789695.n4.nabble.com/mean-tp4674999.html Sent from the R help mailing list archive at Nabble.com.

I'm having trouble to do an infinite sum in R I want to do the infinite sum of 1/(1+n) how would I do this in R? Thank You -- View this message in context: http://r.789695.n4.nabble.com/How-to-do-an-infinite-sum-in-R-tp4649770.html Sent from the R help mailing list archive at Nabble.com.

Hi all, What's the equivalent to length(unique(x)) == 1 if want to ignore small floating point differences? Should I look at diff(range(x)) or sd(x) or something else? What cut off should I use? If it helps to be explicit, I'm interested in detecting when a vector is constant for the purpose of visual display. In other words, if I rescale x to [0, 1] do I have enough precision to get

Hi, Are there any special statistical tests, or functions in R I could use to measure "cone-like" distributions? I have several data-sets, which I've been plotting parts of as 2D plots, where I get a "cone-like" distribution of the data points. That is, the data appears to be bounded by two non-parallel lines, starting at the origin, giving rise to a

Hi there, I need to do the same thing as cumsum but with the variance and skewness. I have tried to do a loop for like this: var.value <- vector(mode = "numeric", length = length(daily)) for (i in (1:length(daily))) { var.value[i] <- var(daily[1:i]) } But because my dataset is so huge, I run out of memory..... Any ideas?!?! Much appreciate

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 am a student of Materials Engineering and I want to minister an introductory course of R at the university I study here in Brazil. I know R is a free software, but I just want to know if I do need a special authorization for doing it. The course will be totaly free and I also will not receive any money for doing it. The idea is just to show the program. -- Atenciosamente, Pedro

As usual, the R newsgroup set me straight (thanks to Douglas Bates, Robert Balshaw and Albyn Jones). There is really no difference between using orthogonal polynomials of the form: Linear -3 -1 1 3 Quadratic 1 -1 -1 1 Cubic -1 3 -3 1 Versus > poly(c(1:4),3) 1 2 3 [1,] -0.6708204 0.5 -0.2236068 [2,] -0.2236068 -0.5 0.6708204 [3,] 0....

Sometimes the p.value returned by t.test() is the same that I calculate using pt() and sometimes it's not. I don't understand the difference. I'm sure there is a simple explanation but I haven't been able to find it, even after looking at the code for t.test.default. I apologize if this is a basic and obvious question. For example: > data(sleep) >

Dear R users I'd like to generate two sets of random numbers with a fixed correlation coefficient, say .4, using R. Any suggestion will be greatly appreciated. Regards, Kathryn Lord -- View this message in context: http://r.789695.n4.nabble.com/generate-two-sets-of-random-numbers-that-are-correlated-tp3735695p3735695.html Sent from the R help mailing list archive at Nabble.com.

Hi! I have a question that is probably very basic, but I cannot figure out how to do it. I simply need to compare the significance of a regression slope against a slope of 1, instead of the default of zero. I know this topic has been posted before, and I have tried to use the advice given to others to fix my problem. I tried the offset command based on one of these advice threads as follows:

All, How come both of these are the same. Both say "1-sample proportions test without continuity correction." I would suspect one would say "without" and one would say "with." > prop.test(118,236,.5,correct=FALSE,conf.level=0.95) 1-sample proportions test without continuity correction data: 118 out of 236, null probability 0.5 X-squared = 0, df = 1,

Hi, I have a vector 'data' of 58 probability values (bounded between 0 and 1) and want to draw a probability density function of these values. For this, I used the commands: data <- runif(58) a <- density(data, from=0, to=1) plot(a, type="l",lwd=3) But then, when I try to approximate the area under the plotted curve with the command: area <- sum(a$y)*(a$x[1]-a$y[2])

Hello all, There is currently a (very !) lively discussions happening around the web, surrounding the following topics: 1) Is R efficient? (scripting wise, and performance wise) 2) Should R be written from scratch? 3) What should be the license of R (if it was made a new)? Very serious people have taken part in the debates so far. I hope to let you know of the places I came by, so you might be