similar to: Percentiles/Quantiles with Weighting

Hi, I'm faced with the following problem and would appreciate some advice. I could have a data frame x that looks like this: aa bb a 1 "A" b 2 "B" The advantage of this is that I could access all the individual components easily. Also I could access all the rows and columns easily. Alternatively, I could have a list of

For example, the correlation matrix is 3x3 and looks like 1 0.75 0 0 0 0.75 1 0 0 0 0 0 0 0 0 Can I write the code like this? p<- 3 # number of variables per observation N<- 10 # number of samples # define population correlation matrix sigma sigma<-matrix(0,p,p) #creates a px p matrix of 0 rank<-2 for (i in 1:rank){ for (j in 1:rank){ rho<-0.75

I'm fairly new to R, and I'm trying to write out a population model that satisfies the following; the system consists of s species, i= 1, 2,...,s network of interactions between species is specified by a (s x s) real matrix, C[i,j] x[i] being the relative population of the "ith" species (0 =< x[i] =< 1, sum(x[i]=1) the evolution rule being considered is as follows;

Dear List, I have some discrete data and want to calculate the percentiles and the percentile ranks for each of the unique scores. I can calculate the percentiles with quantile(). I know that "ecdf" can be used to calculate the empirical cumulative distribution. However, I don't know how to exact the cumulative probabilities for each unique element. The requirement is similar

I was hoping to calculate the percentile for each date. So group all of one date together, calculating the value of the 2.5 and 97.5 percentile. -- View this message in context: http://r.789695.n4.nabble.com/Calculating-percentiles-for-multiple-dates-tp4638183p4638378.html Sent from the R help mailing list archive at Nabble.com.

G'day, All. I have been trying to trackdown a problem in my R analysis script. I perform a scale() operation on a matrix then do further work. Is there any way of inverting the scale() such that sX <- scale(X) Xprime <- inv.scale(x); # does inv.scale exist? resulting in Xprime_{ij} == X_{ij} where Xprime_{ij} \in R There must be some way of doing it but I'm such a newb

Dear R users, I came up to a problem dealing with percentiles in R. >From my previous questions: I do have a big data.frame, with lots of columns and rows. The following command enables me to calculate means for all data frame. dat1$newID<-rep(1:(nrow(dat1)/12),each=12) #if nrow(dat1)/12 is integer dat2<-with(dat1,aggregate(cbind(dat1[,1:71]),by=list(newID),mean)) What I need is to

Hi there! Still struggling to translate Matlab code into R's tsDyn package. Here is my question: Is there in R an equivalent function to Matlab's prctile()? To the moment I thought it was quantile(), but I just realized I was wrong. The definition of the Matlab function: prctile Percentiles of a sample SyntaxY = prctile(X,p) Description Y = prctile(X,p) calculates a value that is

Hello, I'm trying to get percentiles (PERCENTRANK for excel users) by factor in the following data.frame: myExample <- data.frame(Ret=seq(-2, 2.5, by=0.5),PE=seq(10,19),Sectors=rep(c("Financial","Industrial"),5)) myExample <- na.omit(myExample) Thanks to Patrick I I managed to put together the following lines which does it for the "Ret" column: myecdf

Hi All: I am working with a dataset on Arsenic toxicity, and I am trying to calculate the 20th, 40th, 60th, 80th, and highest percentiles for a variable, dietary Moisture (variable name dMoist). The inbuilt function quantile(dMoist) would print 0, 25th, 50th, 75th, and 100th percentile. Does there exist a function that can calculate xth percentile (where x = 10th, 20th, ... etc) values?

Hi, I am using the package "demography" from Rob Hyndman for the Lee-Carter-Model. It is an amazing powerful tool but I am struggling with one issue: I want to compute different percentiles of the survival probability distribution derived from the Lee-Carter-Forecast (e.g. the 50%tile, 60%tile, 75%tile and 99%tile) for each of the next 10 years. Is there any possibility to retrieve

Hi all, I'm just getting started with R and I would appreciate some help. I'm having trouble creating a boxplot with whiskers at the 95th and 5th percentiles instead of at 1.5 * IQR. I have read the relevant documentation, and checked existing mails on this topic. I found a small modification that should work : https://stat.ethz.ch/pipermail/r-help/2001-November/016817.html and tried to

The standard R boxplot appears to use quartiles to determine the height of the rectangles and a range parameter - RNG - (default=1.5 I think) that determines the length of the whiskers as <= RNG x Interquartile Range. Is it possible to instead define the range as extending to the 95th percentile? If so, how would this be done? nb, I'm plotting multiple boxplots on a single chart so a

I have a problem where I need to calculate the corresponding cohort percentile ranks for each of several variables. Essentially, what I need is a function that will calculate the distribution-free percentiles from each variable's data vector, returning a corresponding vector of percentiles: e.g.: percentile.my.data<-/function/(my.data) I tried to make ecdf() perform this task but

How do I calculate the 95th percentile when I know the 25th, the median and the 75th?? Thanks, Harmony Tenney [[alternative HTML version deleted]]

Hi, Is there a function that does percentile-percentile plot. I do not mean the qqplot. I need to plot the percentiles rather than points themselves. I am hoping for a plot that tells me that the x percentile of one data set corresponds to the y percentile of the other. for example a point on the plot of (.5, .2) will tell me that the 50th percentile of the first data and the 20th percentile of

Dear R-list, I try to calculate the 10- and 90-Percentile from grouped data. Using 'lappy' it works fine but I have no success with 'gapply': df <- data.frame(test = runif(100)) df <- df[order(df[,1]),] z <- rep(1:10, each = 10) lapply(split(df, z), function(x) quantile(x, probs=c(10,90)/100)) library(nlme) gapply(as.data.frame(df), which=1, function(x)

In modelling functions some people like to use a weight of 0 to drop an observation instead of using a subset value of FALSE. E.g., weights=c(0,1,1,...) instead of subset=c(FALSE, TRUE, TRUE, ...) to drop the first observation. lm() and summary.lm() appear to treat these in the same way, decrementing the number of degrees of freedom for each dropped observation. However, predict.lm() does

Dear list, I am calculating the 95th percentile of a set of values with R and with SPSS In R: > normal200<-rnorm(200,0,1) > qnorm(0.95,mean=mean(normal200),sd=sd(normal200),lower.tail =TRUE) [1] 1.84191 In SPSS, if I use the same 200 values and select Analyze -> Descriptive Statistics -> Frequencies and under "Statistics", I type in '95' under Percentiles,

Hi all, I have a problem on how R calculates Percentiles : Suppose I have following data set: > data1 [1] -16648185 -14463457 -14449400 -13905309 -13443436 -13234755 -12956282 -11660896 [9] -10061040 -9805005 -9789583 -9754642 -9562164 -9391709 -9212182 -9151073 [17] -9092732 -9068214 -8978151 -8943912 -8761890 -8632106 -8541580 -8501249 [25] -8234466 -8219015