similar to: hist.default()$density

For the hist estimate >par(mex=1.3) >dens<-density(q) >options(scipen=4) > ylim<-range(dens$y) > h<-hist(q,breaks="scott",freq=FALSE,probability=TRUE, +? right=FALSE,xlim=c(9000,16000),ylim=ylim,main="Histogram of q(scott)") > lines(dens) >box() ? For the kernel estimate>options(scipen=4) > d <- density(q, bw =

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:

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:

Hi All, I have a for loop inside the function and I cannot get UUU to give me an updated grid.dens object when I run the function (it does update when I run just the for loop). Here's a simplified version of my function: UUU=function(pop, grid.dens) { for (i in 1:10){ Food=grid.dens[pop$yloc[i],pop$xloc[i]] #use initial grid.dens values Consumed=(pop$weight[i]*0.25) Left=Food-Consumed

Hi all, I've got a density graph made with the following commands: win.graph(width=13,height=6) par ( fin=c(13,3) ,mai=c(1,1,0.5,0.5) ,mfrow=c(1,2) ,cex.axis=1.5 ,cex.lab=1.5) dens<-density(DATA1.y[2,]-mean(DATA1.y[2,]),kernel="gaussian") xlimit<-range(dens$x) ylimit<-range(dens$y) hist( DATA1.y[2,]-mean(DATA1.y[2,]) ,xlim=1.1*xlimit ,xlab=expression(q[e])

Hi all, I'm looking to add a "density" smoother on top of a hist when Freq=T. In order to do this I can use the relation between count and density, but I would like to know if there is a way for me to predict it upfront. Here is an example: set.seed(242) z = rnorm(30) hist_z <- hist(z) hist_z$counts / hist_z$density # the relation is 15 # why is this 15 ?? # So I can now do:

Hi list, After a lot of tweaking i have managed to create a histogram with an overlaying density plot. The histogram shows a sample of birth weights of babies and the density plot shows birth weights from a much larger reference populaton. My data is divided in 0.1 Kg bins so in the code below binweigh=0.1. The trouble with the current graph is that it is not very clear since the density plot

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Hi I use the following code and it stores the results of density() in the list dr: dens <- function(run) { density( positions$X[positions$run==run], bw=3, cut=-2 ) } dr <- lapply(1:5, dens) but the results are stored in dr[[i]] and not dr[i], i.e. plot(dr[[1]]) works, but plot([1]) doesn't. Is there any way that I can store them in dr[i]? Thanks a lot, Rainer -- Rainer M. Krug,

Hi all, I'm trying to fit models for data with three levels of nested random effects: site/transect/plot. For example, modelincrBS<-glmer(l.ru.ba.incr~shigo.av+pre.f.crwn.length+bark.thick.bh+Date+slope.pos.num+dens.T+dbh+leaf.area+can.pos.num+(1|site/transect/plot), data=rws30.UL, family=gaussian, na.action=na.omit) but I get the following error: Error: length(f1) == length(f2) is not

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

Hello Everyone,   I'm learning how to perform various statistical analyses in R. I'm checking my understanding by replicating examples from my SAS books. Below is an attempt to replicate a Cox Proportional Hazards model with a time-varying covariate. I think I'm doing this correctly but am not completely sure. I would appreciate it if someone could double-check my results. In case

Hello all, I'm trying to write a function using the gridBase package. I'd like to push several base subplots to a larger plot constructed with grid. However, I'm having trouble getting consistent results when running the function when the plotting window (quartz) is closed, when it is left open and the plot function is repeated to the same window, and when the output is saved to a

Dear r-helpers, I want to show that time is flowing CCW in the following: require(circular) len <- 8 labl <- as.character(c(0, 1, 1, 1, 0, 0, 1, 0)) r <- circular(2*pi* (rep(c(1, 3, 6), each = 200)/len + rnorm(600, 0, 0.025))) r.dens <- density(r, bw = 25, adjust = 4, kernel = 'vonmises') plot(r, shrink = 2.5, axes = FALSE, ticks = FALSE, pch = 1, col =

Hello! Up to now I have been using hist() to display the distributions. Howevere, I noteiced strange numbers on y (vertical) axis, if I used probability = T or freq = F option. I thought it is a bug and launched the R-bug system and found some posts on that matter. Brian Ripley responded to one, that one should look at truehist() for that. Ok I can use truehist() if I want to see the ratios

sorry for cross-posting Dear all, I have tow (several) bivariate distributions with a known mean and variance-covariance structure (hence a known density function) that I would like to compare in order to get an intersect that tells me something about "how different" these distributions are (as t-statistics for univariate distributions). In order to visualize what I mean hear a little

I'm trying to write a function to find the means over factors of the responses in a mlm (something I would do easily in SAS with PROC SUMMARY). The not-working stub of a function to do what I want is below, and my problem is that I don't know how to call aggregate (or some other function) in the context of terms in a linear model extracted from a lm/mlm object. means.mlm <-

Hello R-users, I have the following problem, that I want to solve efficiently: I have a named list, for example: > l <- list(a = 1, b = 3, c = 'asd') > l $a [1] 1 $b [1] 3 $c [1] "asd" I know that I can iterate through it using lapply() function, but I would also like to able to get the list names or some attributes of l in the lapply(). For example if I use

Dear developers, I have come across a (somewhat strange) change in the encoding of Sweave output from R-2.9.2pat to R-2.10.0beta (apparently specific to Rgui) on Windows installations. Of course, the NEWS file contains quite a few changes concerning encoding, but I was not able to locate an entry which explains the observed behaviour. I am not very familiar with encodings/locales/codepages,

Hello, I have a data set of about 300.000 measurements made by an STM which should apporximately fix a normal (Gaussian) distribution. I have imported the data in R and used plot(density()) to get a nice plot of the distribution which in fact looks like a real Gaussian. However, the integral over the surface is not equal to one (I know since some of the plots extend to numbers greater then 1). Is