similar to: hist and normal curve

. I am looking for a histogram or box plot with the adding normal distribution curve I think that must be possible, but I am not able to find out how to do. Regards Knut

Currently, I am doing it this way. x <- mtcars$mpg h<-hist(x, breaks=10, col="red", xlab="Miles Per Gallon", main="Histogram with Normal Curve") xfit<-seq(min(x),max(x),length=40) yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) yfit <- yfit*diff(h$mids[1:2])*length(x) lines(xfit, yfit, col="blue", lwd=2) But since, ggplot2 has more appealing

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, in order to save space for a publication, it would be nice to have a combined scatter and density plot similar to what is shows on http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=78 I wonder if anybody perhaps has already developed code for this and is willing to share. This is the reproducible code for the histogram version obtained from the site: def.par <-

Hi all, I defined the following ############################# myhist=function(x){ hist(x,xlab="",main="") h=hist(x) xfit=seq(min(x),max(x),length=100) yfit=dnorm(xfit,mean(x),sd=sd(x)) yfit=yfit*diff(h$mids[1:2])*length(x) lines(xfit, yfit, col="blue", lwd=2) } ############################# individually, it worked fine however, if I used par(mfrow=c(2,2))

Dear lattice wizards, I am trying to figure out how to plot predicted values in xyplot, where the intercept, but not the slope, varies among conditioning factor levels. I am sure it involves the groups, but I have been unsuccessful in my search in Pinhiero and Bate, in the help files, or in the archive, or in my attempts on my own. My example follows: FACT is a factor with levels a,b,c

Hi, I want to get a histogram with the legend for my data. I drew a normal density curve and kernel density curve in the histogram, and I also label mean and median in the X axis. From the code, I got two legend: One shows "Normal Density" and "Kernel Density" and their corresponding lines, the other shows "Mean = value" and "Median = value" and their

Dear All, I am trying to do some simple mixture analyses. For instance, I have a sample of n observations and I suspect they come from two different exponential distributions with parameters rate1 and rate2, respectively. So, I want to estimate rate1, rate2, and the proportions of both kinds of individuals in the sample. I had a look at the packages mda and mclust, but they do not seem to do this

Hello I followed the example in page 59, chapter 11 of the 'Introduction to R' manual. I entered my own x,y data. I used the least squares. My function has 5 parameters: p[1], p[2], p[3], p[4], p[5]. I plotted the x-y data. Then I used lines(spline(xfit,yfit)) to overlay best curves on the data while changing the parameters. My question is how do I calculate the residual sum of squares.

Dear all, For two sets of random variables, say, x <- rnorm(1000, 10, 10) and y <- rnorm(1000. 3, 20). Is there any way to overlay the histograms (and density curves) of x and y on the plot of y vs. x? The histogram of x is on the x axis and that of y is on the y axis. The density curve here is to approximate the shape of the distribution and does not have to have area 1. Thank you

# Dear R list, # # I'm needing help with lattice, regression and respective lines. # My data is below: bra = gl(2, 24, label = c('c', 's')) em = rep(gl(3, 8, label = c('po', 'pov', 'ce')), 2) tem = rep(c(0, 0, 30, 30, 60, 60, 90, 90), 6) tem2 = tem^2 r = rep(1:2, 24) y = c(40.58, 44.85, 32.55, 35.68, 64.86, 51.95, 42.52, 52.21,

Hello!!! I have a folder (C:/R/) with matrix files, named by number, i.e. 0.mat, 1.mat,...,1250.mat. In this case, they are 5x5 simetric matrices. I would like to compute a property for each matrix and put calculated values into a data frame for posterior ploting and printing. Below there is an example for 7 matrices (0.mat..6.mat) #define data frame L <- data.frame(frame=numeric(7),

Dear R People: Here is yet another histogram question (yahq), please: When I use the hist() command with freq=F, I get density on the side. I would really like to have relative frequency; that is, Rf = density/(sum(density)) Is there a step that I'm leaving out, please? thanks! Sincerely, Erin hodgess at uhddx01.dt.uh.edu

Hi all, I have this figure: http://docs.google.com/Doc?id=df5zfsj4_103rjt2v4d5 created with the following steps: > x [1] 90.4 57.8 77.0 103.7 55.4 217.5 68.1 85.3 152.0 113.0 97.1 89.9 [13] 68.1 83.7 77.4 34.5 104.9 170.3 88.6 88.1 108.8 77.4 85.6 82.7 [25] 81.3 108.0 49.5 71.0 85.7 99.3 203.5 275.9 51.1 84.8 16.5 72.6 [37] 160.5 158.3 136.7 140.0 98.4 116.1

Dear r-help, I am trying to plot several scatter plots with marginal histograms on one page. Ideally, a page is equally divided into 4 figure regions. Within each figure region, a scatter plot with marginal histograms will be plotted. I followed Dr. Paul Murrell's code released online to successfully plot the scatter plot with marginal histograms. The code applies "layout()" to

Dear R People: Is there a library for Multivariate time series, please? For some reason, I'm thinking that Dr. Paul Gilbert may have one? R Version 1.6.2 (i've updated!) for Windows Thanks so much! Sincerely, Erin Hodgess University of Houston - Downtown mailto: hodgess at uhddx01.dt.uh.edu

Dear all, Here I come with another stupid question. Suppose I want to use nls() to fit a series of data (here modelled by generated points), then plot the points and the fitting curve. I figured out some way of doing it: x <- runif(1:20, 0, 10) y <- 0.1*x^2 - rep(3, length(x)) + rnorm(length(x), sd = 0.5) yfit <- nls(y ~ a*x^2 + b*x + c, start = list(a = 1, b = 1, c = 1),

I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2)

Dear R People: Here is an interesting question(I think) Suppose I want to calculate Centered Moving Averages; i.e. x[1] <- ( sum(y[1:12]) )/12 x[2] <- ( sum(y[2:13]) )/12 and so on. Of course, this is easily done through loops. However, I have been trying to do this more elegantly, but have failed. I have tried things like j1 <- 1:109 j2 <- 12:120 x[1:109] <- ( sum( y[j1:j2])

Dear R People: I have a weekly time series. How do I put this into the ts command, please? That is, what do I use for frequency, please? R version 1.5.1 for Windows. Thanks in advance. Sincerely, Erin mailto: hodgess at uhddx01.dt.uh.edu -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html