similar to: learning about panel functions in lattice

Hi Group, Here is some data. p <- runif(1000) # sample data groups <- rep(c(1,2),each=500) #conditioning variable mydata <- cbind(p,groups) n <- length(p) u <- (1:n)/(n + 1) # uniform distribution reference for qqplot logp <- -log(p,base=10) logu <- -log(u,base=10) qqplot(logp,logu) How can I make the above qqplot in lattice and/or ggplot2. The sample is uniform, and I take

Hi, I'm having a problem getting the panel.average function to work as I expect it to in a lattice plot. I wish to draw lines between the averages of groups of y-values at specific x-values. I have created a dataset below which is similar to my real data. I also show an example of using panel.loess in place of panel.average; it performs in a manner similar to what I want panel.average to do

With this data x <- c(0,0,1,1,2,2) y <- c(5,6,4,3,2,6) lwr <- y-1 upr <- y+1 xlab <- c("Low","Low","Med","Med","High","High") mydata <- data.frame(x,xlab,y,lwr,upr) I would like to make a dot plot and use lwr and upr as error bars. Above 0=Low. I would like there to be some space between the 5 and the 6 corresponding

Hi, for the data below: time<-c(rep(1:10,5)) y<-time+rnorm(50,5,2) subject<-c(rep('a',10),rep('b',10),rep('c',10),rep('d',10),rep('e',10)) group<-c(rep('A',30),rep('B',20)) df<-data.frame(subject,group,time,y) I'd like to produce a plot with a single pannel with two loess curves one for each group. the code below does

Hi All, I had posted a question on a similar topic, but I think it was not focused. I am posting a modification that I think better accomplishes this. I hope this is ok, and I apologize if it is not. :) I am looping through variables and running several regressions. I have reason to believe that the data is being duplicated because I have been monitoring the memory use on unix. How can I avoid

Hi Group, Consider a data frame like this: mylabel1 <- rep(c("A","B","C"),each=3) mylabel2 <- rep(c("A","B","C"),3) corrs <- c(1,.8,.7,.8,1,.7,.7,.7,1) myData <- data.frame(mylabel1,mylabel2,corrs) myData mylabel1 mylabel2 corrs 1 A A 1.0 2 A B 0.8 3 A C 0.7 4 B

Here is some sample data: mydata <- read.table(textConnection("Est Group Tri 0 0 4.639644 1 0 4.579189 2 0 4.590714 0 1 4.443696 1 1 4.588243 2 1 4.650505 0 2 4.296608 1 2 4.826036 2 2 4.765386"),header=TRUE); closeAllConnections(); I can form two plots,

Here is some data (dput output below) > myData id group 1 D599 A 2 002-0004 B 3 F01932 A 18 F16 B 19

Hi Group, I have a data frame below. Within this data frame there are samples (columns) that are measured more than once. Samples are indicated by "idx". So "id1" is present in columns 1, 3, and 5. Not every id is repeated. I would like to create a new data frame so that the repeated ids are averaged. For example, in the new data frame, columns 1, 3, and 5 of the original

Dear R gurus.. Is it possible to control span settings for different values of a grouping variable, when using xyplot? an example code shown below d=data.frame(x=rep(sample(1:5,rep=F),10),y=rnorm(50),z=rep(sample(LETTERS[1:2],rep=F),25)) xyplot(y~x,data=d,groups=z,panel=panel.superpose,panel.groups=panel.loess(span=c(2/3, 3/4,1/2)) or something like..

Dear R Programmers, I am trying to run a Poisson regression on all pairs of variables in a data set and obtain the permutation distribution. The number of pairs is around 100000. It seems my code will take weeks to run, unless I try something else. Could you give me any suggestions on how to improve the speed of the code below, or any general suggestions on how I may accomplish this task. Thanks

I am calculating the mean of each column grouped by the variable 'id'. I do this using aggregate, data.table, and plyr. My aggregate results do not match the other two, and I am trying to figure out what is incorrect with my syntax. Any suggestions? Thanks. Here is the data. myData <- structure(list(var1 = c(31.59, 32.21, 31.78, 31.34, 31.61, 31.61, 30.59, 30.84, 30.98, 30.79, 30.79,

Hi ilai Thank you for your suggestions. I do not know what happened yesterday I must have omitted a few changes out in going from R to email and apologies for the double posting - I had troubles sending it as my ISP gave a message of not being connected for email but was for the web I was trying to get panel.Locfit to work in a number of situations. 1. Conditioned by Farm (3 panels) with 2

Hi, I'd like to plot 4 groups of data using xyplot and panel.superpose so that the points are overlayed on a single plot. For each group of data I'd also like a loess smoothed function (using panel.loess). I have tried the following: xyplot(series ~ time | gr, data=etable, panel = function(x,y, ...) { panel.superpose(x,y, ...) panel.loess(x,y,span=.15)

I have a file in stata format, which I have read in, and I am trying to create a text file. I have exported the data using various delimiters, but I'm unable to read it back in. I originally read in the file with: library(foreign) myData <- read.dta("mydata.dta") I then exported it with write.table using comma, tab, and exclamation marks as a delimiter. When I was unable to

Hi Group, I posted this question on the ggplot list and was advised to try here also. The code below produces a plot as a png and pdf. The pdf looks great, and I cannot make the png look this way. I've tried various combinations of height, width, and dpi, but it has not worked out so far. Any suggestions to make the png look like the pdf? I received a response that the problem does not occur

I have a data set with about 6 million rows and 50 columns. It is a mixture of dates, factors, and numerics. What I am trying to accomplish can be seen with the following simplified data, which is given as dput output below. > head(myData) mydate gender mygroup id 1 2012-03-25 F A 1 2 2005-05-23 F B 2 3 2005-09-08 F B 2 4 2005-12-07 F B 2

Hi R community, I'm running R 2.7.2 on Windows XP SP2. I'm trying to (1) plot loess lines for each of my groupings using the same color for each group; (2) plot loess predicted values. The first part is easy: data1 <- data.frame(Names=c(rep("Jon",9),rep("Karl",9)),Measurements=c(2,4,16,25,36,49,64,81,100,1,2,5,12,17,21,45,54,67),PlotAt=c(1:9,1:9)) data2 <-

I am recoding some data. Many values that should be 1.5 are recorded as 1-2. Some example data and my solution is below. I am curious about better approaches or any other suggestions. Thanks! # example input data myData <- read.table(textConnection("id, v1, v2, v3 a,1,2,3 b,1-2,,3-4 c,,3,4"),header=TRUE,sep=",") closeAllConnections() # the first column is IDs so remove

Hi, I've been working with R for a couple of years, and I've been able to get most of the things done that I needed (sometimes in a roundabout way). A few experienced statisticians told me that R is best for interactive data analysis, but for large-scale computations, one needs something else. I understand that this all depends on what you are trying to accomplish, and R offers many ways