similar to: split/decompose lines

Dear R users, I have what I think it is a very simple question concerning plots in R. If you could help me I would be very grateful. How can I include a legend in my plot? Below I give an example of my data and the code I am using. What I wish to accomplish is a legend saying that the black line refers to "A", the red to "B" and the blue to "C". Any suggestions?

Dear R-helper, I wonder if anyone can help me. I am trying to convert a dataset to a format recognizable by a software onboard a research vessel but I am having problems with some steps. I have a data frame as follows: Conc Lat Lon Depth Point0000 56.25 -5.65 70 Point0001 56.55 -5.35 85 Point0002 56.25 -5.65 65 Point0003 56.37

Dear R users, I am trying to build a barplot2 graph however I can't find a way of defining the scale for the x-axis. I would like to show in my x-axis only the numbers 0, 25, 50, 75 etc. (so far R is giving me a random scale hard to interpret and it doens't look nice...). Could anyone advise me on how to do this please, it would be a great help! Thank you. Below I show the code I have

Dear R users, I need to find the coordinates for the point (midpoint) located half way between two pairs of coordinates (lon1,lat1 and lon2,lat2) assuming a straight line between them. What would be the best way? I tried to find an answer in the help archives but without success. I would greatly appreciate any help. df<- data.frame(lon1=c(-4.568,-4.3980), lat1=c(59.235,56.369),

Is there any way to give a "decent" title after I plot something generated by decompose? For example: # generate something with period 12 x <- rnorm(600) + sin(2 * pi * (1:600) / 12) # transform to a monthy time series y <- ts(x, frequency=12, start=c(1950,1)) # decompose z <- decompose(y) # plot plot(z) Now, the title is the ugly "Decomposition of additive time

I just got this error: > library(igraph) > comp <- decompose.graph(gr) Error: protect(): protection stack overflow Error: protect(): protection stack overflow > what can I do? the digraph is, indeed, large (300,000 vertexes), but there are very many very small components (which I would rather not discard). PS. the doc for decompose.graph does not say which mode is the default. --

Greetings, For the following quarterly data I did a classical decomposition by hand in a spreadsheet and got reasonably similar results using Minitab 15. x 1 36 2 44 3 45 4 106 5 38 6 46 7 47 8 112 9 42 10 49 11 48 12 118 13 42 14 50 15

Hello, I'd like to know how R does calculate each component in the decompose() function? More precisely, how is calculated the final trend component in this function? Thanks for your answer Myriam -- View this message in context: http://www.nabble.com/Decompose-function-%3A-calculation-of-each-component-tp24362207p24362207.html Sent from the R help mailing list archive at Nabble.com.

Hi All, I am new to this forum. I have just started learning R. When i use plot(decompose(x)), i am getting the title " Additive time series decomposition". How to make this title off and change to some other title. Any help regarding this is highly appreciated. With sincerer regards, Upananda -- View this message in context:

Is there a simple way to decompose the upper triangle of a correlation matrix to a linear list; For example: X Y Z X 1 2 3 Y 2 1 4 Z 3 4 1 so you get a list like: xy 2 XZ 3 YZ 4 I suspect you can do it with a matrix transformation, but that beyond me at present. Many thanks Mark _________________________ Department of Molecular and Human Genetics, Baylor College of Medicine,

Hi everyone. I'm having some troubles with STL function to decompose some data. My issue is that I have monthly data from September 2005 up to August 2010 i. e. 60 observations. I define it in the following way: *u<-read.csv("C:/CELEBREX.csv",header = TRUE) u.ts<-ts(u, start=c(2005,9), frequency=12) * The issue is that when I try to use stl(u.ts, 'per') Then the

InDATA <-read.table("C:/Data/May 2004/season.txt",header=T) X <- decompose(InDATA) print(X) Period Connections Q1 67519 Q2 69713 Q3 68920 Q4 69452 Q1 70015 Q2 59273 Q3 57063 Q4 65596 Q1 73527 Q2 58586 Q3 69522 Q4 60091 Q1 51686 Q2 63490 Q3 55702 Q4 53200 Q1 51033 Q2 48175 Q3 52709 Q4 50106 Q1 50855 Q2 43466 Q3 48190 Q4 41702 Q1 48747 Q2 51441 Q3 42537

Dear R People: In the function "decompose", the object has the class of "decomposed.ts". (from package stats) I would like to see the class definition and the method for the plotting. However, when I use isClass("decomposed.ts") I get "FALSE". When I check getMethods("plot") there is no method for plot on decomposed.ts Any suggestions,

Hi R-help, I'd like to extract the seasonal component of a short timeseries, and was hoping to use stats::decompose. I don't want to decompose the 'trend' component so I thought I should call decompose(x,filter=0). I think I've either misunderstood the filter argument or come upon a bug/feature in decompose. # EXAMPLE

The decompose() function truncates the seasonal component unnecessarily. I've modified the function to fix this problem, and also added the original data to the object returned (to enable better plotting). I've also modified the plot.decomposed.ts() function so that it plots the original data in the top panel rather than the reconstructed data. The difference between the two is that the

Hi I'm just stepping through the decompose() function, in "stats". Does this contained line of code not work if you have a time series ending "unevenly" (i.e., middle of the year), or am I missing something? season <- na.omit(c(as.numeric(window(season, start(x) + c(1, 0), end(x))), as.numeric(window(season, start(x), start(x) + c(0, f))))) The line seems to

Hi everyone! I would appreciate help with using decompose.graph(), community detection functions from igraph and lapply(). I have an igraph object G with vertex attribute "label" and edge attribute "weight". I want to calculate community memberships using different functions from igraph, for simplicity let it be "walktrap.community". This graph is not connected,

Consider the following analysis of a class experiment done as a Latin Square: > spinner <- gl(4,4,16,label=c("Murray","Angela","Shasha","Stephen")) > order <- gl(4,1,16) > treat <- scan() 1: 1 2 4 3 5: 4 3 1 2 9: 3 4 2 1 13: 2 1 3 4 17: Read 16 items > coin <-

Hello, thanks for the help. Yes And the commands to discovery iscsi results ok Such iscsiadm --mode discovery --type sendtargets And scsiadm -m node -T And the disks appear on pvdisplay 2018-03-14 16:23 GMT-03:00 Marcelo Roccasalva < marcelo-centos at irrigacion.gov.ar>: > On Wed, Mar 14, 2018 at 4:08 PM, marcos sr <msr.mailing at gmail.com> wrote: > > > >

Hi, This is my code (my data is attached): library(languageR) library(rms) library(party) OLDDATA <- read.csv("/Users/Abigail/Documents/OldData250412.csv") OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))? OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))? attach(OLDDATA) defaults <- cbind(YD, ND) set.seed(47) data.controls