similar to: Problem with data type recognition and conversion

library(StreamMetabolism) day <- (structure(c(10.08, 10.08, 10.05, 10.03, 10, 9.98, 9.96, 9.95, 9.95, 9.96, 9.96, 9.98, 10.01, 10.05, 10.06, 10.09, 10.11, 10.11, 10.13, 10.13, 10.15, 10.15, 10.13, 10.14, 10.11, 10.13, 10.14, 10.13, 10.12, 10.13, 10.14, 10.16, 10.18, 10.19, 10.23, 10.27, 10.33, 10.37, 10.45, 10.57, 10.6, 10.66, 10.73, 10.77, 10.84, 10.86, 10.87, 10.94, 10.98, 11.01, 11.05,

Hello, Using the following lines of code, I created the following graph:

Hello, Using the following lines of code, I created the following graph:

Hi, I am trying to create Bid/Ask for each second from a high volume stock and the only way I have been able to solve this is using loops to create the target matrix from the source tick data matrix. Looping is too slow and not practical to use on multiple stocks. For example: Bids Matrix (a real one is 400,000++ length): Bid Time 10.03 11:05:03.124 10.04 11:05:03.348 10.05

Hi r-users,   I have 2 sets of data and I would like to superimpose this cumulative density in one graph.   I know how to put the 2 graphs in one same graph but my problem is the data are different.   > z[1:20]  [1]  2.02347388  3.19514379  0.05188875  1.41333812  3.50249892  4.34272676  6.65639581  5.01533819  4.18207505 [10]  2.87615551  2.28202152  0.49431541  0.06570579  5.68352438

Hi, pnorm(-1.53,0,1) under version 3.0.2 gives 0.05155075. I am pretty sure it should be 0.063. Is there something wrong with this version of R? I am using: R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) -- Tom [[alternative HTML version deleted]]

Hi, I need to correct for ar(1) behavior of my residuals of my model. I noticed that there are multiple gls models in R. I am wondering if anyone has experience in choosing between gls models. For example, how should one decide whether to use lm.gls in MASS, or gls in nlme for correcting ar(1)? Does anyone have a preference? Any advice is appreciated! Thanks, -- Tom [[alternative HTML

Hi, I would like to use a weighted lm model to reduce heteroscendasticity. I am wondering if the only way to generate the weights in R is through the laborious process of trial and error by hand. Does anyone know if R has a function that would automatically generate the weights need for lm? Thanks, -- Tom [[alternative HTML version deleted]]

Hi, I tried to stack two charts on top of each other using the following R functions: par(mfrow=c(2,1)) plot(rnorm(1:3),xaxt="n",xlab="") plot(rnorm(1:3)) This created two charts, one on top of the other, but there is too much space between them. Does anyone know how to elimiate the space in between the charts? Thanks, -- Tom [[alternative HTML version deleted]]

Hi, I noticed that max(x) returns the maximum value of a vector, but the function doesn't give the user the option of retrieving the row index number instead. If I used max(x) to find the maximum value of vector x, then is there a function I can use to find the index number, or row name, of the maximum value? Thanks, Tom [[alternative HTML version deleted]]

Hi, Does anyone know if R has a built-in function that is equvalent to Excel's percentrank, i.e., returns the rank of a value in a data set as a percentage of the data set? Thanks, -- Tom [[alternative HTML version deleted]]

Hi, I am currently using the following to formate numbers into percentages: x=0.00112 paste(round(x*100,2),"%",sep="") I am wondering if there is a built in R function that does the same. Does anyone know? Thanks, -- Tom [[alternative HTML version deleted]]

Hi, The documentation for scale() states:"If center is TRUE then centering is done by subtracting the column means (omitting NAs) of x from their corresponding columns". But it seems that R is subtracting something else instead of the column mean: > x=c(2,4,3,4,5) > mean(x) [1] 3.6 > x-mean(x) [1] -1.6 0.4 -0.6 0.4 1.4 > scale(x) [,1] [1,] -1.4032928 [2,]

Hej, i need the which() funktion to find the positions of an entry in a matrix. the entries i'm looking for are : seq(begin,end,0.01) and there are no empty spaces i'm searching in the right range. so i was looking for the results R can find and i recieved this answer. for (l in

Hi, I have the following data in two columns. The first column is the date, the second is data. Date Data 3-Jan-95 459.21 4-Jan-95 459.13 5-Jan-95 460.73 6-Jan-95 460.38 9-Jan-95 460.67 10-Jan-95 460.9 11-Jan-95 461.68 12-Jan-95 461.64 13-Jan-95 461.64 16-Jan-95 465.97 17-Jan-95 469.38 18-Jan-95 470.05 19-Jan-95 469.72 20-Jan-95 466.95 23-Jan-95 464.78 24-Jan-95 465.81 25-Jan-95 465.86 I would

Hi Your statement about attaching data is problematic. We cannot do much with it. Instead use output from dput(yourdata) to show us what exactly your data look like. We also do not know how do you want to split your data. It would be nice if you can show also what should be the bins with respective data. Unless you provide this information you probably would not get any sensible answer. Cheers

Hi, Does anyone know if R has a function that is similar to lag.plot but instead of auto-correlation, it plots cross-correlation with lags? Thanks, -- Tom [[alternative HTML version deleted]]

Hi, I have a 2D chart that is divided into four quadrants, I, II, III, IV: plot(1:10,ylim=c(0,10),xlim=c(0,10),type="n") abline(v=5,h=5) text(x=c(7.5,7.5,2.5,2.5),y=c(2.5,7.5,7.5,2.5),labels=c("I","II","III","IV")) I would like to fill each quadrant with a background color unique to the quadrant. Does anyone know how to do this in R? Thanks, --

Hi, I am trying to reproduce some functionalities of Excel pivot table in R, sadly, I couldn't figure out how to do it. I am wondering if this is even possible in R. Does anyone know? Here is an example: year=rep(2003,16) quarter=rep(1:4,each=4) sales=1:16 company=rep(c("a","b","c","d"),4) df=data.frame(year,quarter,sales,company) #this is the

Hi, I have time series data stored in csv files (see below for an example of the data). I understand that in order to analyze my data in R, I need to first transform it into a ts object. Howeve, I could not find an example on how exactly to do that. Is ts the only function I need? What are the steps that I need to go through to build a time series object from raw data like this? Thanks, Tom