similar to: boxplot

Dear All, I have two simple data samples (no groups or factors, etc.) and would just like to compute the two-sample Wilcoxon Rank Sum test using the wilcox_test function contained in the coin package, which is reportedly better than the regular wilcox.test function because it performs some adjustment for ties. Would anyone know how to craft a script to perform this task? Much appreciated. Janh

Greetings! We are trying to obtain confidence and prediction intervals for a predicted Y value from bootstrapped linear regression using the boot function. Does anyone know how to code it? Greatly appreciated. Janh [[alternative HTML version deleted]]

Hello Rui, Thanks for your helpful suggestions. Just for illustration, let's use the well known Duncan dataset of prestige vs education + income that is contained in the "car" package. Suppose I wish to use boot function to bootstrap a linear regression of prestige ~ education + income and use the following script: duncan.function <- function(data, indices) {data =

Dear Users Regarding the NADA package, would anyone be able to help me understand what values are actually plotted on the Y axis of the plot obtained by using the *ros* function on the data and plotting the result with the plot() function? The Y axis is labeled "Values". According to the NADA user manual, ros performs a log transformation of the data by default, but the user can specify

R-help is not a free coding service. We expect users to make the effort to learn R and *may* provide help when they get stuck. Pay a local R programmer if you do not wish to make such an effort. Cheers, Bert On Oct 14, 2017 7:58 AM, "Janh Anni" <annijanh at gmail.com> wrote: Greetings! We are trying to obtain confidence and prediction intervals for a predicted Y value from

Hello, Much clearer now, thanks. It's a matter of changing the function boot calls to return the predicted values at the point of interess, education = 50, income = 75. I have changed the way the function uses the indices a bit, the result is the same, it's just the way I usually do it. pred.duncan.function <- function(data, indices) { mod <- lm(prestige ~ education +

Hello All, Would anyone be able to help me understand how R computes a quantile-quantile plot for comparing two data samples with unequal sample sizes? Normally, the procedure should be to rearrange the larger data sample into n equally-spaced parts using interpolation, where n is the sample size of the smaller sample, and then plot the matching data pairs. I tried using different plotting

I don't find how to do what I need to do in Dalgaard or 'R Cookbook', so I'm asking here. I have a data frame with water chemistry data and I want to start exploring these data. There are three factors (site, date, chemical) associated with each measurement. The data frame looks like this: > summary(chemdata) site_id.sample_date.param.quant

Dear list, I have a data frame (238304 rows and 6 columns). I want the data frame sorted by two columns in ascending order. I am showing the first 5 rows of the data frame > clones.info[1:5,1:6] USER_CLONE_ID CHROMOSOME Expr1002 KB_POSITION Allele_A WELL_ID 1 SNP_A-1855402 17 41419603 41419603 C rs17572851 2 SNP_A-4249904 17 41420045 41420045 A rs17572893 3 SNP_A-2174835 18 41407760

Hi all, I am looking for a clever way to create the following graph using R: I got information on the shares of some subgroups over time (summing up to 1 in each year). The graph I want to create should display the development of the individual shares over time by shading rectangulars for each share in a different color. Is there a clever of doing this? Thanks for your help Stefan

Dear all, After a thorough research, I still find myself unable to find a function that does linear regression of 2 vectors of data using the "total least squares", also called "orthogonal regression" (see : http://en.wikipedia.org/wiki/Total_least_squares) instead of the "ordinary least squares" method. Indeed, the "lm" function has a

Hi All: I was recently working with a dataset on arsenic poisoning. Among the variables in the dataset, I used the following three variables to produce crosstabulations (variable names: FOLSTAT, GENDER, ASBIN; all three were categorical variables, FOLSTAT denoted follow up status for the subjects and had seven levels, GENDER denoted sex (two levels: male,female), and ASBIN denoted binarized

Hi All: I am working with a dataset on Arsenic toxicity, and I am trying to calculate the 20th, 40th, 60th, 80th, and highest percentiles for a variable, dietary Moisture (variable name dMoist). The inbuilt function quantile(dMoist) would print 0, 25th, 50th, 75th, and 100th percentile. Does there exist a function that can calculate xth percentile (where x = 10th, 20th, ... etc) values?

Hi All: Greetings, and best wishes from the festive times here at Kolkata, India -- the time of Durga Puja celebrations. I seek your advice as I try plotting lines for my data. The problem: I have created a dataframe that looks like this (name: myFrame): lowest second third fourth highest significance INAS 0.107 0.115 0.123 0.115 0.166 0.000 MMA 0.091 0.107 0.115

Hi,all: I want to perform multiple correspondance analysis via MCA{FactoMineR}. The data is in the attachment. My code: dat<-read.delim("e:\\mydata.txt",header=T) MCA(dat,quanti.sup=7,quali.sup=1:6) Error in `[.data.frame`(tab, , i) : undefined columns selected My question: Why does the error happen? Many thanks. Best. -------------- next part -------------- An embedded and

R Users: My question is probably more about elementary statistics than the mechanics of using R, but I've been dabbling in R (version 2.2.0) and used it recently to test some data . I have a relatively small set of observations (n = 12) of arsenic concentrations in background groundwater and wanted to test my assumption of normality. I used the Shapiro-Wilk test (by calling shapiro.test()

https://bugs.freedesktop.org/show_bug.cgi?id=69180 Priority: medium Bug ID: 69180 Assignee: nouveau at lists.freedesktop.org Summary: Delays when using nouveau and switcheroo QA Contact: xorg-team at lists.x.org Severity: normal Classification: Unclassified OS: Linux (All) Reporter: a0vhv at t-online.de

> On 1 Jun 2017, at 22:42, Roy Mendelssohn - NOAA Federal <roy.mendelssohn at noaa.gov> wrote: > > Thanks to all for responses/. There was a question of exactly what was wanted. It is the generalization of the obvious example I gave, > >>>> junk1 <- junk[, rev(seq_len(10), ] > > > so that > > junk[1,1,1 ] = junk1[1,10,1] > junk[1,2,1] =

Hello all, I have data in the form of a table: X Y1 Y2 0.1 3 2 0.2 2 1 And I would like to transform in the form: X Y 0.1 Y1 0.1 Y1 0.1 Y1 0.1 Y2 0.1 Y2 0.2 Y1 0.2 Y1 0.2 Y2 Any ideas how? Thanks in advance, IOanna [[alternative HTML version deleted]]

On the off chance that anyone is still interested, here is the corrected function using aperm(): z <- array(1:120,dim=2:5) f2 <- function(a, wh) { idx <- seq_len(length(dim(a))) dims <- setdiff(idx, wh) idx <- append(idx[-1], idx[1], wh-1) aperm(apply(a, dims, rev), idx) } all.equal(f(z, 1), f2(z, 1)) # [1] TRUE all.equal(f(z, 2), f2(z, 2)) # [1] TRUE