similar to: help about package "reldist" (Relative Distribution)

Dear all users, I read all R programs and texts in "Relative Distribution Methods" website, but I encounter two problems when I used it. then I used "Google" to try to find any solutions about the two problems,but I got no useful hints. 1、 In package "reldist" y mean sample from comparison distribution yo mean sample from reference distribution but I

Dear R users, I'm using the "reldist" add-on package to calculate relative distribution in R as part of my research project. The subject is a general mental health score ranging from 0 to 12 (integer values only) with 0 indicating no mental health problem and positive values meaning some or sever mental health problem. When I run the programme to compare the scores of population

Dear R community, I have one problem with figures. I draw the Relative Distribution graph, it looks like barchart(X,Y plot), but I have ten(year) Relative Distribution grapgs, have any command am can combine ten barcharts(X,Y plot) to become a three dimensions barchart(X,Y,Z plot)? All help highly appreciated Best, Yichih One Relative Distribution graph:

Dear all, I have 1980~1990 eleven datas, every year have three variables, wage gender(1=female, 2=male) race(1=black, 2=white) My original commands is: fig2b<-reldist(y=mu1990$wage,yo=mu1980$wage,.......) I have three questions: 1. If I want to appoint y=women's wage in 1990 yo=women's wage in 1980 2. If I want to appoint y=women's wage in

Hi, I am using the reldist package and having problems determining the p-value for the entropy value from the reldist function. I am able to properly determine the entropy value, but cannot figure out what function to use to find the p-value. I have tried using rpy, rpluy (which provides p-values for the polarization values) and investing the results from reldist(). Thus, far I cannot find the

Hello, Thank you very much for your help. How can I draw a Lorenz curve with several replications ? Here is an example with 4 replications: hosts=c(23,31,19,10,7,7,3, 39,40,8,3,6,2,2, 47,17,8,10,6,11,1, 30,30,10,0,15,15,0) parasites=rep(seq(from=0,to=6,by=1),4) replications=c(rep(1,7),rep(2,7),rep(3,7),rep(4,7)) test <- cbind(parasites,hosts,replications) Should I

Hello, I would like to build a Lorenz curve and calculate a Gini coefficient in order to find how much parasites does the top 20% most infected hosts support. Here is my data set: Number of parasites per host: parasites = c(0,1,2,3,4,5,6,7,8,9,10) Number of hosts associated with each number of parasites given above: hosts = c(18,20,28,19,16,10,3,1,0,0,0) To represent the Lorenz curve: I

Hola Ivan gracias por tu ayuda, justo ayer a ultima hora hice una modificacion en el programa q hizo q el programita ande, aunq no entendi porque lo hizo. La modificacion fue agregar "*w" al final de cada parametro en la definicion de la funcion "grini" q habia creado, cosa q deduje de mirar la funcion q le habia robado al ejemplo del manual de boot ratio <- function(d, w)

Hi listers, Does it necessary to install any package in order to use the GINI or INEQ functions. If I use the following command the R tells me that didn't find the GINI function. x<-c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) G<-gini(x) Thanks in advance, Marcio -- View this message in context:

Hi All Please see the Rprofile file which i have modified as follows and after that when I start R then I see that R says to me "TRUE" for all the packages implying that all loaded at once. But when i try to use commands as simple as help("lm"), it doesnt work nor any of the menu "Packages" is not working. Although the regression using lm ( Y ~ X ) is working

Hi, I have a data.frame in wide format which I would like to reshape into a long format: example (nonsense) data: > dput(perus2) structure(list(id = c(30L, 38L, 21L, 12L, 22L, 28L, 31L, 44L, 8L, 47L, 23L, 20L, 41L, 42L, 29L, 50L, 5L, 33L, 4L, 17L, 11L, 1L, 18L, 6L, 9L, 32L, 16L, 14L, 39L, 48L, 37L, 43L, 25L, 27L, 35L, 40L, 45L, 49L, 2L, 34L, 10L, 15L, 24L, 26L, 46L, 7L, 13L, 3L, 19L, 36L),

Dear All, Comparing linear mixed effect models in SAS and R, I found the following discrepancy: SAS R random statement random subj(program); random = ~ 1 | Subj -2*loglik 1420.8 1439.363 random effects variance(Intercept) 9.6033 9.604662

Hola, paso el mini programita q estoy viendo, lo q me llama la atencion es una parte donde se definen las funciones. Probe primero meter adentro del boots la estadistica a estimar usando directamente gini(varible, pesos) pero no me dejo. Vi q en el ej del manual de boots, siempre define antes la funcion, entonces probe definir antes una funcion haciendo grini<-function(x) {gini(variable,

Guys Oppologies for the cross post. I''ve been scouring the docs coming with the wrr patches and for the life of me I can''t figure out why there are 8 parameters for each class. <snip> weight1, min1, decr1, incr1, weight2, min2, decr2, incr2 </snip> According to the docs these parameters are handled the same way, but looking at the example scripts there are

I have set up wrr successfully on my bridge/shaper machine. That much works fine. I originally used sfq in the inner classes. However, there was a problem with high-bandwidth connections (web downloads, bittorrent) starving low-bandwidth low-latency connections like ssh. I would like to use prio or similar to prioritize the interactive traffic, but it does not seem to work. I have tested with

Hi: How do I find average across three columns such as weight1, weight2, weight3 (20 people, each person has three different measures)? They have NA's also. Thanks Tom, Univ of Pittsburgh, ttrut at yahoo.com

I'm trying to plot a series of densities using/comparing differing weights. I see the reference to weights and subscripts, but I don't understand how to implement that. My data are of the form: I, J, Actual, Distance, Subset, Weight1, Weight2, ... I'm trying to see the effect of the distance distribution (Actual by Distance) compared to the various weighted distributions

Hi all, I have a data set with missing value. I would like to estimate those missing value by using normal ratio method. Below is part of my data: AS BL Serdang Jhr Phg Target station 0 0.0 12.8 0.0 23.7 0.0 6 0.0 81.7 0.2 0.0 NA 0 1.5 60.9 0.0 0.0 15.5 1 13.0 56.8 17.5 32.8 6.4 4 3.0 66.4

Hi, I am an R newbie and one thing I am having trouble with binding variables that I have created within one data frame into a new data frame when using na.omit(). To illustrate this problem I will give the example I am working on and the approah I have been using:- data.frame1<-filepath.... attach(data.frame1) #create a new variable using a function new.variable<-rep(1,length(weight3))

I frequently want to test for differences between animal size frequency distributions. The obvious test (I think) to use is the Kolmogorov-Smirnov two sample test (provided in R as the function ks.test in package ctest). The KS test is for continuous variables and this obviously includes length, weight etc. However, limitations in measuring (e.g length to the nearest cm/mm, weight to the nearest