similar to: weird breaks in hist (PR#2431)

Hi Folks, I'd like to know how hist() decides how many cells to use when it ignores my "suggestion" to use say 'hist(...,breaks=50)'. More specifically, I have the results of 10000 simulations, each returning an 8-vector, therefore 8 variables each with 10000 values. Some of these 8 have somewhat skew distributions. Say one of these 8 variables is X. I ask for H <-

Buenas. Sé que en R hay multitud de datasets y me haría falta alguno que trataran de variables relacionadas con salud, sobre todo para aprender más acerca de cómo realizar una regresión logística binomial o multinomial. Gracias..

Hi Group, I've a vector of 1000 numeric values for which I want to draw a histogram. I've read this vector into R with no variable name.I mean only the 1000 values, which makes V1 the name of the variable by default?? Then I tried > hist(V1, breaks = "Sturges", +      freq = NULL, probability = !freq, +      include.lowest = TRUE, right = TRUE, +      density = NULL, angle =

Dear all, I am a newbie in R and could not find help on this problem. I am trying to plot an histogram with probabilities in the y axis. This is the code I am using: #TLC uniform n=30 mi=1; mx=6 nrep=1000 xbar=rep(0,nrep) for (i in 1:nrep) {xbar[i]=mean(runif(n,min=mi,max=mx))} hist(xbar,prob=TRUE,breaks="Sturges",xlim=c(1,6),main=paste("n =",n), xlab="Média",

Hi all: As to hist,the help file says:" R's default with equi-spaced breaks (also the default) is to plot the counts in the cells defined by breaks." I wanna know how the break is calculated by R? In other words: break = (max - min)/(number of group) but how the "number of group" is calculated by R? Thanks!

Hello everybody, This is a bug involving functions in core R package: graphics::hist.default, grDevices::nclass.FD, and base::pretty.default. It is not yet on Bugzilla. I cannot submit it myself, as I do not have an account. Could somebody else add it for me, perhaps? That would be much appreciated. Kind regards, Sietse Sietse Brouwer Summary ------- Floating point errors can cause a data

Hey everybody, I try to make a graph with two different plots. First I make a boxplot of my data. It is a collection off correlation values of different pictures. For example: 0.23445 pica 0.34456 pica 0.45663 pica 0.98822 picb 0.12223 picc 0.34443 picc etc. Ok, I make this boxplot and I get for every picture the boxes. After this I want to know, how many correlations per picture exist. So I

> x = rnorm(100) > b = seq(min(x) - 1, max(x) + 1, length = 11) > b [1] -3.4038769 -2.7451072 -2.0863375 -1.4275678 -0.7687980 -0.1100283 [7] 0.5487414 1.2075111 1.8662808 2.5250506 3.1838203 > > invisible(hist(x, breaks = b, include.lowest = TRUE, plot = FALSE)) Warning message: argument 'include.lowest' is not made use of in: hist.default(x, breaks = b,

Dear list, I'm trying to do a set of generic functions do make contingency tables from data.frames. It is just running "nice" (I'm learning R), but I think it can be better. I would like to filter the data.frame, i.e, eliminate all not numeric variables. And I don't know how to make it: please, help me. Below one of the my functions ('er' is a mention to EasieR,

Dear All, According to the Sturges rule, the number of classes of a histogram is the closest integer to 1 + logb(n,base=2) where n is the number of observations. The function hist(), by default, uses the Sturges rule. However, the code x <- 1:200 hist(x) produces a histogram with 10 classes and not 9 classes as determined by the Sturges rule. What am I missing? Thanks in advance, Paul

Hi All, I would like to make an invitation to make a collective package with all functions related to TABLES. I know that there are many packages with these functions, the original idea is collect all this functions and to make a single package, because is arduous for the user know all this functions broadcast in many packages. So, I think that the original packages can continue with its

Hi, Standard correlations (Pearson's, Spearman's, Kendall's Tau) do not accurately reflect how closely the model (GAM) fits the data. I was told that the accuracy of the correlation can be improved using a root mean square deviation (RMSD) calculation on binned data. For example, let 'o' be the real, observed data and 'm' be the model data. I believe I can calculate

Hi I have recently been analyzing birthweight data from a clinic. The data has obvious defects in that there is digit preference on certain weights making them overrepresented. This shows as spikes in the histogram on certain well rounded weights like 2, 2.5, 3, etc. I would like to show this to government officials but can't figure out how I should present the finding in an easy to

Using R-2.12.1 and latticeExtra-0.6-14, I would like to understand why a rootogram displaying samples from the Poisson distribution looks like I expected it, whereas a rootogram using the normal distribution does not: library(latticeExtra) rootogram(~rpois(1000, lambda = 50), dfun = function(x) dpois(x, lambda = 50)) rootogram(~rnorm(1000), dfun = function(x) dnorm(x,mean(x),sd(x))) I

In R0.61, In hist(), should the line rel.freqs <- counts/(sum(x) * diff(breaks)) computing the relative frequencies or height of the rectangles in a histogram not be rel.freqs <- counts/(sum(counts) * diff(breaks)) instead, or do I misunderstand something? Thanks, Philippe -- -------------------------------------------------------- Philippe Lambert Tel:

The command hist(c(2,2,2,4,5,6)) returns a histogram that looks incorrect -- 3 in the bin labeled 2 on the left, but 1 each in the bins labeled 3,4,5 on the left. Thanks! Pam Surko -------------------- > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor

Can someone help me with a misunderstanding I'm having with hist? I expected, from the example below, that the number of bins would always be 10 and the length of the counts array the same. According to the help section 'breaks' can be a integer indicating the number of bins. From the example below, the number of bins (length of the counts array) varies. Am I wrong in expecting the

Hi, Is this a bug in truehist()? > library(MASS) > x <- rep(1, 10) > truehist(x) Error in pretty(data, nbins) : invalid 'n' value Thanks, Gad > R.version platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 4.1 year 2006 month 12 day 18 svn

Hi, Is this a bug in truehist()? > library(MASS) > x <- rep(1, 10) > truehist(x) Error in pretty(data, nbins) : invalid 'n' value Thanks, Gad > R.version platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 4.1 year 2006 month 12 day 18 svn

Full_Name: Gerald Guglielmo Version: 2.8.1 (2008-12-22) OS: OSX Leopard Submission from: (NULL) (131.225.103.35) When I attempt to use the hist() function to bin integers the behavior seems very odd as the bin boundary seems inconsistent across the various bins. For some bins the upper boundary includes the next integer value, while in others it does not. If I add 0.1 to every value, then the