similar to: Sorting NA's and Graphing a histogram

I have three matrices with the same row and column names, but different data. e.g. Data Alpha Beta Gamma Delta A .1 .2 .3 .4 B .2 .3 .4 .5 C .8 .9 .43 .13 D .13 .34 .34 .3 For each column, I would like to create a separate plot on a single window. I currently am using the cmd-line: windows() par(mfrow=c(2,2))

Hello, I'm trying to compare the fit of two distributions, normal and gamma, to a histogram of my response variable. rate<-mean(na.omit(rwb$post.f.crwn.length))/var(na.omit(rwb$post.f.crwn.length)) shape<-rate*mean(na.omit(rwb$post.f.crwn.length)) hist((rwb$post.f.crwn.length), main="rwb$post.f.crwn.length")

For a real data column X value ranged between (56.4521,32317.9) with missing values, I need to overlay 2 plots: histogram & fitted gamma density. I use following to generate histogram. xbk_seq(50,33000,by=100) hist(x,breaks=xbk) But I don't know how to get "fitted gamma density"? In SAS proc capability, I got Shape=2.59, Scale=3481). But when I do plot(dgamma(x,

Hi r-users,   I try to draw histogram (in terms of probabilty) and superimpose with the gamma pdf. Using this code below I can get the plots BUT the y scale for the density is out of scale.  How do I change the y-axis scale to max 1?  Another thing, how I do draw smooth line rather that points?   Nota that my observed data is hume_pos and the fitted data is rgam1.   hist(hume_pos,prob=TRUE)

Hi r-users,   I would like to overlap a smooth line on the histogram.  I tried using spline but it does not work. sq     <- seq(0,900,by=50) sq.50  <- as.character(sq) datobs <- sum_pos ## first, plot histogram histo <- hist(datobs,breaks=sq,freq=F) ## extract counts from histogram and calculate the probability in ## each specified interval. Also check the length of the interval is

I was wondering if anyone could help me with a problem. I need to randomly select, say 500 subjects from the 5000 cases I have and then need to run a test to create 500 sample means and graph the means in a histogram. Does anyone know how to do this. I'm not that familiar with R so please be patient with me. Thanks a lot! Mike Hollingsworth -- View this message in context:

Hello list! I would like to fit a distribution to each of the peaks in a histogram, such as this: http://photos1.blogger.com/blogger/7029/2724/1600/DU145-Bax3-Bcl-xL.png . The peaks are identified using Petr Pikal peaks function ( http://finzi.psych.upenn.edu/R/Rhelp02a/archive/33097.html), but after that I am quite stuck. Any idea as to how I can: Fit a distribution to each peak Integrate the

Hello, I am very new to R and have hit my first main problem that I hope someone can easily resolve. I have some data that looks like this (there are 20,000 rows): > qdata day month year flow [1,] 2 10 1945 NA [2,] 3 10 1945 NA [3,] 4 10 1945 NA [4,] 5 10 1945 NA [5,] 6 10 1945 2.973 [6,] 7 10 1945 NA

I think this may be a histogram, but I'm not sure. Can anyone tell me how to replicate this graph in R? http://www.ginworks.com/images/multitot.gif Thanks, Joshua Gramlich

I can't quite seem to solve a problem subsetting a data frame. Here's a reproducible example. Given a data frame: dat <- data.frame(fac = rep(c("a", "b"), each = 100), value = c(rnorm(130), rep(NA, 70)), other = rnorm(200)) What I want is a new data frame (with the same columns as dat) excluding the top 5% of "value"

Hey, I am having trouble graphing the following function √2Γ(n/2)/[√n − 1Γ((n − 1)/2 for the values of n between 2 and 50. i know that Γ(n) = (n-1)!, which in R is factorial(n-1) When i type that into R, using y <- function(n)..... and then plot(y,2,50), it doesnt give me anything meaningful, in fact, it comes up with a message saying something like "in gamma(n+1) ploted" or

Hello, I am new to R exploratory data analysis and plotting. Is anyone aware of a way to overlay a set of conditional histograms with conditional PDFs? Below, I generate a lattice plot of precipitation histograms based on different months and stations, given a subset of the dataset: histogram(~ data | month * station, data = sta.stack[sta.stack[,"type"]=="precip" &

Hi Guy, I am having trouble graphing the following function √2Γ(n/2)/[√n − 1Γ((n − 1)/2 for the values of n between 2 and 50. i know that Γ(n) = (n-1)!, which in R is factorial(n-1) When i type that into R, using y <- function(n)..... and then plot(y,2,50), it doesnt give me anything meaningful, in fact, it comes up with a message saying something like "in gamma(n+1) ploted" or

Hi! I have bazillions of incoming (rejected) attempts to connect to my SMTP server, and I'm interested in separating out those that seem to come in huge bunches (e.g., the one from yesterday that ran for about 10 hours and sent over 4100 attempts), and graphing them so I can see the spacing and/or distribution in time. I can figure out some simple scripting to turn the maillog entries into

Hi, how can I treat data organised in classes and frequencies? Ex. class frequency 20-23 9 23-25 7 26-28 5 29-31 5 32-34 3 Thanks Angelo Scozzarella

Hello everyone, I''m looking to produce some graphs broadly like the ones shown here: http://www.dotnetcharting.com/gallery/List.aspx?gal=6 No prize for guessing what type of app it is... This particular graphing library is .NET-specific, but as you can see the results it produces are really nice. More than likely, they''re several notches above what I''ll be able to

Hello everyone, With th bar histogram (number of occurrences) hist<-c(24,7,4,1,2,1,1) of seven equally spaces classes ]1-4], ]5-8], ]9-12], ]13-16], ]17-20], ]21-24], ]25-28], I obtained shape=0.8276 and rate=0.1448. I would like to know how to build the continuous pdf of a this gamma distribution knowing these two estimated parameters such that I will be able to predict the pdf of any

I'm looking for goodness of fit tests for gamma distributions with large data sizes. I have a matrix with around 10,000 data values in it and i have fitted a gamma distribution over a histogram of the data. The problem is testing how well that distribution fits. Chi-squared seems to be used more for discrete distributions and kolmogorov-smirnov seems that large sample sizes make it had to

I am trying to graphically represent a large set of data who's result is not strictly uniform. http://n4.nabble.com/file/n961629/egraph_rules_list_2.png The scatter plot to the left has all of the data rising in steps however I know that there are cases within my data that do not fit the dotted line. temp<-edat[edat$R1SC>0,,]

I have a data with 236 observations. After plotting the histogram, I found that it looks like non-central t distribution. I would like to get MLE for mu and df. I found an example to find MLE for gamma distribution from "fitting distributions with R": library(stats4) ## loading package stats4 ll<-function(lambda,alfa) {n<-200 x<-x.gam