similar to: Fiting a beta distribution in R

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Hello! I hope, i'm doing right, writing here. I have a question about the R statistical formulas: were can I look, what are they? E. g. I'd like to know which exactly mathematical formula is used in Wilcoxon test, and others. Is there somewhere on the Internet information about that? Thanks a lot! Ilya. PS Sorry for any mistakes in my English, it is not my mother-tongue.

There is a code from SPSS Syntax do if sub(ATVK,2,2)="01". comp strata=1. else if sub(ATVK,2,2)>="05" and sub(ATVK,2,2)<="27". comp strata=3. else if sub(ATVK,4,2)>"20" or sub(ATVK,6,2)>"20". comp strata=4. else if sub(ATVK,4,2)>"00". comp strata=2. end if. value labels strata 1 "R 2 "Li" 3 "M" 4

Hi how can I find, in a vector of characters, which is the most frequent one? Thanks Gabriele Zoppoli, MD Ph.D. Fellow, Experimental and Clinical Oncology and Hematology, University of Genova, Genova, Italy Guest Researcher, LMP, NCI, NIH, Bethesda MD Work: 301-451-8575 Mobile: 301-204-5642 Email: zoppolig at mail.nih.gov

Hello there, I have a problem concerning bootstrapping in R - especially focusing on the resampling part of it. I try to sum it up in a simplified way so that I would not confuse anybody. I have a small database consisting of 20 observations (basically numbers from 1 to 20, I mean: 1, 2, 3, 4, 5, ... 18, 19, 20). I would like to resample this database many times for the bootstrap process with

Hi again, I'd like to ask you a question again. I have a matrix like this: a <-matrix(c(1,2,3,4,5,6,7,8,9,10,11,12)) a [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 [5,] 5 [6,] 6 [7,] 7 [8,] 8 [9,] 9 [10,] 10 [11,] 11 [12,] 12 Is there a proper way to change the dimensions of this matrix so that I'll get this as a result: [,1] [,2] [,3] [1,]

Hello everybody, I have a little problem in good old R. It is basically the following. I have this small database with 3 rows and the following columns: d1, d2, d3 and Highest d value - which selects the highest value from d1, d2, d3 in each row. d1 d2 d3 Highest d value 1 51.398426 39.111721 11.6086220 51.398426 2 4.057801

Dear Members, I work on a simulaton experiment but it has an bottleneck. It's quite fast because of R and vectorizing, but it has a very slow for loop. The adjacent element of a vector (in terms of index number) depends conditionally on the former value of itself. Like a simple cumulating function (eg. cumsum) but with condition. Let's show me an example: a_vec = rnorm(100) b_vec = rep(0,

I want to fit some p-values to a beta distribution. But the problem is some of the values have 0s and 1's. I am getting an error if I use the MASS function to do this. Is there anyway to get around this? -- Thanks, Jim. [[alternative HTML version deleted]]

Hello everyone, I'd like to ask you a question again, basically focusing on referring to different objects. Let's suppose we create the following databases this way: id <-c(1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3) a <-c(3,1,3,3,1,3,3,3,3,1,3,2,1,2,1,3,3,2,1,1,1,3,1,3,3,3,2,1,1,3) b <-c(3,2,1,1,1,1,1,1,1,1,1,2,1,3,2,1,1,1,2,1,3,1,2,2,1,3,3,2,3,2) c

Hello everyone, I'd like to ask you a question again, basically focusing on referring to different objects. Let's suppose we create the following databases this way: id <-c(1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3) a <-c(3,1,3,3,1,3,3,3,3,1,3,2,1,2,1,3,3,2,1,1,1,3,1,3,3,3,2,1,1,3) b <-c(3,2,1,1,1,1,1,1,1,1,1,2,1,3,2,1,1,1,2,1,3,1,2,2,1,3,3,2,3,2) c

Hi there, I have a huge dataframe containing 70,000 observations. I have filtered this dataframe (let it's name be "transformed_dataframe") as I wanted to select only those observations which are greater than or equal to 60,001 regarding the very first identity column. So I have a transformed dataframe now including 10,000 obeservations (from 60,001 - to 70,000) and if you send

Hello there, I don't know if I'm addressing my question to the right e-mail address, I hope I do. Actually I have a little problem concerning writing a code in R. I try to briefly sum up my problem. As you can see below, I created the functions "Equation1" and "Equation2" with some conditions. Equation1 <-function(x){ if

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Hello everybody, I have a data frame in R which is similar to the follows. Actually my real 'df' dataframe is much bigger than this one here but I really do not want to confuse anybody so that is why I try to simplify things as much as possible. So here's the data frame. id <-c(1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3) a

Hi, I'd like to ask you a question again. It is basically about data frames, NAs and tabulate function. I have this data frame. I already used this in one of the previous questions of mine. It intentionally looks this simple, my real 'df' dataframe is much bigger actually and again, I am not willing to annoy anyone with huge databases... So, my database: id

Dear 'R' Users, I have CO2 timeseries which I want to fit with fourth harmonic function and then would like to compute residuals. I tried with two option but not sure which one is correct, kindly any one can help correcting me: # #I would like to fit a model with Fourth harmonic function as:

Dear R Users, I have a CO2 time series. I want to fit this series seasonal cycle and trend with fourth harmonic function, and then compute residuals. I am doing something like: file<-read.csv("co2data.csv") names(file) attach(file) fit<-lm(co2~1+time+I(time^2)+sin(2*pi*time)+cos(2*pi*time)+sin(4*pi*time)+cos(4*pi*time)+

Hello, I tried to install package ?TailRank? using the command install.packages (RankTail) and library (TailRank) but I got the following errors. So, how can I install TaiRank in Rstudio to have se beta-binomial distribution, CDF and inverse CDG of beta-binomal? The commands I used are: > install.packages("TailRank") Installing package into