similar to: Using function 'boot on a bi-polar sample'

Greetings Everybody: I generated a 1.2MB dta file based on the general social survey with Stata8 for linux. The file can be re-opened with Stata, but when I bring it into R, it says all the values are missing for most of the variables. This dataset is called "morgen.dta" and I dropped a copy online in case you are interested http://www.ku.edu/~pauljohn/R/morgen.dta looks like this

Any help with this problem would be greatly appreciated: I need to produce a custom plot i haven't come across in R. Basically, I want to show means, 1st standard deviation and 5th and 95th percentiles visually, using something resembling a boxplot. Is it possible to completely customize a boxplot so that it shows means as the bar (instead of, not as well as medians), standard deviations at

Dear R-users, I am trying to use the grid.text and expression functions to display several character strings and plotmath text on a viewport. Some strings can include a variable portion (PI.limits in the following example), which I thought could be implemented by combining the bquote and the expression functions. Unfortunately, my expressions do not seem to be evaluated. I would greatly

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

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

When the binary search returns 0 (exact match), the target key will necessarily be at slot 0 of all nodes below the current one, so in this case the binary search is not needed because it will always return 0, and we waste time doing it, holding node locks for longer than necessary, etc. Below follow histograms with the times spent on the current approach of doing a binary search when the

Hi All, I am looking at applications of percentiles to time sequenced data. I had just been using the quantile function to get percentiles over various periods, but am more interested in if there is an accepted (and/or R-implemented) method to apply weighting to the data so as to weigh recent data more heavily. I wrote the following function, but it seems quite inefficient, and not really very

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

Hello everybody, I need to know which samples (S1-S6) contain a value that is bigger than the median + five standard deviations of the column he is in. This is just an example. Command should be applied to a data frame wich is a lot bigger (over 100 columns). Any solutions? Thank you very much for your help!!! > s Samples A B C E 1 S1 1 2 3 7 2

Hello, I have a doubt, but it is more statistic than just about R: How the people deal usually with negative percentile and quartile? In my concrete case, I want to know the distribution of an error, so the nearest it is to 0 the better (I think it would be optimun to have the nearest values to 0 in the lower percentiles). The best result would be making the percentile of the absolute value

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

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

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.

I'd like to do a simple scatter plot but instead of using the variable values on the X axis I would like to plot the percentiles. I searched in the manual for percentiles but did not find what I was looking for. I've been using SAS for several years but I new to R. -- Dean Sonneborn Programmer Analyst Department of Public Health Sciences University of California, Davis (916) 734-6656

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

Good Afternoon- I was wanting to alter the boxplot to remove the lower whisker, both the whisker line and staple just on the lower end. Is there a way to do this? As my code is currently: boxplot(log_loads~ind,data=nfmaum, horizontal=TRUE, notch=T, outline=FALSE, whisker=0, main="Maumee River Near Future Climate Scenarios", ylab="Log Load",xlab="Climate Scenarios")

Hello, I have two random variables with their percentiles which correspond to their probability distribution function. My objective is to sum these two random variables. There exists any algorithm or procedure in R capable of converting the percentiles to a probability density function? is the fast Fourier transform function of R(fft) capable of doing the sum with a convolution? I'm just

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, Two questions: 1 - Say I have average speed and directions for tide and I would like to plot them on a polar plot, but with different colors so I can indicate the two directions. I'm using polar.plot from the plotrix library. How can I add a second "b" and "dir.b" series to a polar.plot? library(plotrix) a = 3 dir.a = 85 b = 4 dir.b = 250 polar.plot(a, dir.a, start =

Folks, I have this code fragment: set.seed(1001) x <- c(0.79211363702017, 0.940536712079832, 0.859757602692931, 0.82529998629531, 0.973451006822, 0.92378802164835, 0.996679563355802, 0.943347739494445, 0.992873542980045, 0.870624707845108, 0.935917364493788) range(x) # from 0.79 to 0.996 e <- function(x,d) { median(x[d]) } b <- boot(x, e, R=1000)