similar to: problem on pg32 of ISwR

Hello I am trying to find an automated way of fitting a mixture of normal and log-normal distributions to data which is clearly bimodal. Here's a simulated example: x.1<-rnorm(6000, 2.4, 0.6)x.2<-rlnorm(10000, 1.3,0.1)X<-c(x.1, x.2) hist(X,100,freq=FALSE, ylim=c(0,1.5))lines(density(x.1), lty=2, lwd=2)lines(density(x.2), lty=2, lwd=2)lines(density(X), lty=4) Currently i am using

I am learning to use R using a book that uses examples from the ISwR library so I need to have that library. When I type library (ISwR), R says: Error in library(ISwR) : there is no package called 'ISwR' Do I need to install ISwR separately? How do I do this? Thanks! -- View this message in context: http://www.nabble.com/ISwR-library-tf3548959.html#a9907582 Sent from the R help mailing

Dear All: good morning I am trying to install the "" package, but I am getting this error message. *> utils:::menuInstallPkgs()* *Warning in install.packages(NULL, .libPaths()[1L], dependencies = NA, type = type) :* * 'lib = "C:/Program Files/R/R-3.4.1/library"' is not writable* *Error in install.packages(NULL, .libPaths()[1L], dependencies = NA, type = type) : *

I wanted to install the ISwR package onto my Linux system and used the install.packages command but it returns the message that the package was not found at the repositories. So I tried to install locally adding the argument repos=NULL but it gave a non-zero exit status report and the package is not present on the system. I am a novice UNIX user and also new to R but I have followed the

What if you answer 'Yes' ? On Wed, Sep 20, 2017 at 1:03 PM, AbouEl-Makarim Aboueissa < abouelmakarim1962 at gmail.com> wrote: > Dear All: good morning > > I am trying to install the "" package, but I am getting this error message. > > > *> utils:::menuInstallPkgs()* > *Warning in install.packages(NULL, .libPaths()[1L], dependencies = NA, type >

Hi, This is a very basic question, but I would like to undestand hist(). I thought that the hist( , freq=FALSE) should provide the relative frequencies (probabilities), and so they should sum 1, however: set.seed(2) ah <- hist(rnorm(100), freq=F) sum(ah$intensities) [1] 2 set.seed(2) bh <- hist(rlnorm(100), freq=F) sum(bh$intensities) [1] 0.4999996 I'm getting similar figures with

Hi, I thought that I had read somewhere that there was a really simple way to overlay the probability density function of a normal distribution over a histogram, after the histogram has already been plotted. Possibly a one word command. I've found this email from the archives, but I don't think this is what I'm looking for. I thought there was something more simple than

Dear All I would like to draw a picture with the density curve of a normal distribution over a histogram of a set of random numbers extracted from the same normal distribution. Is that possible? Thanks in advance, Paul

Dear R-users Idea: Plot a dnorm line using specific mean/sd to complete a histogram (skewed). xs:range of y-values, ys: dnorm function Problem: I expected to multiply the ys function with the sample size (n=250-300). I was wondering about a factor between 12'000 and 30'000 to match the size of the dnorm line with the specific histogram. Thanks Sibylle hist(Biotree[Ld,]$Height2008,

Dear All, I would like to be able to plot histograms/densities on a semi-log or log-log scale. I found several suggestions online http://tolstoy.newcastle.edu.au/R/help/05/09/12044.html https://stat.ethz.ch/pipermail/r-help/2002-June/022295.html http://www.harding.edu/fmccown/R/#histograms Now, consider the code snippet taken from http://www.harding.edu/fmccown/R/#histograms # Get a random

Dear People, I have a data set of data x from a probability distribution, and I have a function, mydensity, of the pdf of that distribution. I'm asking for help in superimposing the histogram of x and the plot of mydensity. In the function below, I call truehist and curve, but these are plotted in different figures. I'd like them to be plotted on the same figure, and to use common

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))

Hi, I am facing the problem that I want to plot a histogram chart set freq to true and overlay with normal or weibull or exponential distribution curve. The sample code is shown as below: >samp<-c(-8.2262,-8.2262,-8.2262,-8.20209,-8.09294,-8.07321,-8.07321, -8.07321,-8.07175,-8.04948,-8.04948,-8.04948,-8.03848,-8.03848,

I am trying to plot a lognormal density curve on top of an existing histogram. Can anybody suggest a simple way to do this? Even if someone could just explain how to plot a regular normal density curve on top of an existing histogram, it would be a big help. Also, is there some way to search through the R-help archives other than simple browsing? Thank you so much. Your help and time is greatly

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",

Hello, everybody: I recently encountered an example with in which the graph was placed in a way that did not leave room for a legend. Maybe you would describe it as "legend too big", I'm not sure. I found myself wishing I could force in some space after the title. Here's working example where I'd like to make room for a legend. x <- rnorm(100) hist(x, freq=F,

Dear All: One more thing. I want to add the normal curve to the histogram. Is there away to stretch the peak of the curve to the top of the histogram or at least near to the top of the histogram. Please see the code below. Lizard.tail.lengths <- c(6.2, 6.6, 7.1, 7.4, 7.6, 7.9, 8, 8.3, 8.4, 8.5, 8.6,8.8, 8.8, 9.1, 9.2, 9.4, 9.4, 9.7, 9.9, 10.2, 10.4, 10.8,11.3, 11.9) x<-seq(5,12, 0.001)

Hi, I have a couple of observations, a question or two, and perhaps a suggestion related to the plotting of density on the y-axis within the hist() function when freq=FALSE. I was using the function and trying to develop an intuitive understanding of what the density is telling me. After reading through this fairly helpful post:

Hello All,* *I want to draw a histogram with density curve. * *For that simply i created a data called*"x" *and i used the function called * hist(x, col = "blue", freq = FALSE),** *from this function i got a histogram*. *After that , i tried this function* ** lines(density(x), col = "red", lwd = 2 ) *but i could not get the density curve. So, Again i used

There were some questions about hist() a couple of days ago which triggered this post. My question/suggestion is about the y-axis in hist. There are reasons to prefer making the y-axis density=relative frequency/bin width. One reason is that the height of the plot does not depend on the bin width; another is that if your histogram is in density then you can easily superimpose a smooth theoretical