similar to: Weighted histograms / distributions (retry)

I'm trying to plot and compare weighted histograms and I can't seem find where to start. I have data similar to this: Miles2LAX RADAM2005Pct LAWA2005Pct 35.57 .00123 .00684 24.74 .00118 .00187 27.09 .00965 .00876 16.23 .00587 .00397 { ...

Hi, I found some weird behavior of the function multhist in the plotrix package and I would like to ask you about it: so, it works well to put two histograms next to each other, but I don't understand the breaks… so for example, library(plotrix) mh <- list(rnorm(200, mean=100, sd=10), rnorm(200, mean=100, sd=10)) multhist(mh) #produces a plot with

Thanks in advance for any suggestions: I am using the 'multhist' function in the 'plotrix' package to display histograms of some variable 'x' given some value of another variable 'z' -- for example, separate histograms for some variable according to sex (or another dichotomous) variable. Thus I am using something like the following:

Dear R community members, I am very new to R, so I hope I didn´t oversee the answer to my question in the manuals and faqs. I am trying to create a histogram containing several data sets (I would like to see how the dependent variable is distributed for the different groups). This should be possible using the function multhist contained in the plotrix package. (I am using R 2.2.1, in windows XP).

Hi, I'm plotting a multiple histogram using the function multhist {package plotrix}, something like: library(plotrix) mh <- list(rnorm(200, mean=200, sd=50), rnorm(200, mean=250, sd=50)) multhist(mh) In this graph y-axis represents the frequency of observations.... but I would like it to be scaled into relative frequencies, does anybody know how to do this with multhist or similar

Hi all, Please consider the following code: require(plotrix) l <- list(rnorm(50),rnorm(50,sd=2),rnorm(50,mean=3)) multhist(l) I have two things I need help with: (1) In the output, there are empty spaces on the x-axis. How would I eliminate these spaces? I want a nice, smooth, empty-spaceless x-axis. (2) How could I add tracing lines to each histogram? I am undoubtedly using the wrong

I am creating a side-by-side histogram using "multhist" of PLOTRIX. However, I can't seem to control the X-axis. It yields the following X points in the axis: 1 3 5 7 9 13 17 21 25 29 33 with uneven spacing between x-ticks (see attached file). Especially I want to create a bar for every x-axis (1,2,3,4...36), now not every x-axis has a bar. Is there a way to alter my following

Greetings, I'm having trouble finding a simple way to calculate a weighted histogram where there may be zero raw counts in a given interval. Given equal-length vectors of data 'data' and weights 'w', and breaks (intervals) for the histogram, I calculate a weighted histogram as follows (see MASS's 'truehist' for an unweighted histogram): bin <- cut(data,

dear list, my question concerns the use of `eval' in defining the model formula for `nls' (version 2.6.2.). consider the following simple example, where the same model and data are used to perform unweighted and weighted fits. I intentionally used very uneven weights to guarantee large differences in the results #================================CUT=========================== ln

2 days ago I asked this on r-help, but no luck... since this is actually a programming question, I post it here again: my question concerns the use of `eval' in defining the model formula for `nls' when performing weighted fits. (I use version 2.6.2., but according to NEWS there were no changes to `nls' in 2.7.0, so the problem is still present). in this scenario their

Hi, This is more of a general question than a pure R one, but I hope that is OK. I want to combine one-tailed independent p-values using the weighted version of fisher's inverse chi-square method. The unweighted version is pretty straightforward to implement. If x is a vector with p-values, then I guess that this will do for the unweighted version: statistic <- -2*sum(log(x)) comb.p <-

I have lots of data that needs to be viewed as a histogram, and I've found the multhist package to work quite nicely--for the most part. The only problem is that I can't figure out a way to get the x-axis to make any logical sense. I have a data set with exactly 26 different values, but when I try and plot it with multhist, There are tick marks at 0.5, 3.5...26. When I enlarge

In the HistData package, I have a data frame, PearsonLee, containing observations on heights of parent and child, in weighted form: library(HistData) > str(PearsonLee) 'data.frame': 746 obs. of 6 variables: $ child : num 59.5 59.5 59.5 60.5 60.5 61.5 61.5 61.5 61.5 61.5 ... $ parent : num 62.5 63.5 64.5 62.5 66.5 59.5 60.5 62.5 63.5 64.5 ... $ frequency: num 0.5 0.5

Hello all, I am using the function lm to do my weighted least square regression. model<-lm(Y~X1+X2, weight=w) What I am confused is the r.squared. It does not seem that the r.squared for the weighted case is an ordinary 1-RSS/TSS. What is that precisely? Is the r.squared measure comparable to that obtained by the ordinary least square? <I also notice that model$res is the unweighted

Hello R folks, Reproducible code below - I'm trying to do a weighted mean by a factor and can't figure it out. Thanks in advance for your assistance. Mike data<-data.frame(c(5,5,1,1,1), c(10,8,9,5,3), c("A","A","A","B","B"))

Hi All, I am in the annoying position of having to present some data to someone who seems to be somewhat less than numerate. I need to label the y-axes of a multhist with the y-axis labeled not as counts but as percentage of a population. Plotting the standard histogram is in a way fine, all I need is to: -- have a left-handside y-axis labels for pop 1 and a right-handside y-axis labels for pop2

I?m running some logistic regressions and I?ve been trying to include weights in the equation. However, when I run the model, I get this warning message: Here?s what it says: Warning message: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! I think it is because the weights are non-integer values. What is a good way to run logistic regressions in R when using

Hi, If I used drop1 in a weighted lm fit, it seems to ignore the weights in the AIC calculation of the dropped terms, see the example below. Can this be right? Yan -------------------- library(car) > unweighted.model <- lm(trSex ~ (river+length +depth)^2- length:depth, dno2) > Anova(unweighted.model) Anova Table (Type II tests) Response: trSex Sum Sq Df F value

I would like to apply network measures (such as betweenness centrality, upper boundedness, etc.) to a weighted graph with non-integer weights, defined by a euclidean distance matrix. The package sna provides the measures that I want to use, but seems only to operate on binary graphs. I have read work by Mark Newman (http://aps.arxiv.org/abs/cond-mat/0407503/), who suggests that a weighted graph

Dear Contributors: I have two vectors x and z, and I want to display the histograms of both vectors in the same graph, x in red bars, z in blue bars. If you have any clue on how to do that, I will be very glad to hear it!!!!!! Thanks in advance again, Juan Pablo Fededa [[alternative HTML version deleted]]