similar to: Date Time Objects

Dear all, I wish to create a POSIXct variable from date and time variables using the ymd_hm function in package lubridate. In some cases data for time is missing, which causes a problem for ymd_hm. I wish to find a smooth way to handle this. # Some example data: x <- data.frame(date = c("2011-09-22", "2011-07-28"), time = c("15:00", NA)) x # paste date and

Hi all, I have multiple datasets of time series data taken from GPS collars. The collars are supposed to take a fix every hour on the half hour, i.e., 0:30, 1:30, 2:30...23:30, (because it sometimes takes longer for the collars to acquire a location the minute of these locations vary from 30-34) but because of a software glitch in the collars, at random times the collars start taking multiple

Hi, For this example: O <- c(0 0 0 2 0 0 2 0) I want to create an array every time O[i] > 0. The array should be in the form; R[j] <- array(-1, dim=c(2,O[i])) i.e. if O[i] > 0 4 times I want 4 R arrays. Does anyone have any suggestions? Thanks, Doug -- View this message in context: http://r.789695.n4.nabble.com/Create-Arrays-tp2996706p2996706.html Sent from the R help

Hi, I am having some problems using the if statement correctly. I have used it many times previously so I dona't know what is different with this case. Here is my problem: I have a 1X10 matrix of values as follows: > H.MC [,1] [1,] 4.257669 [2,] 7.023242 [3,] 4.949857 [4,] 5.107000 [5,] 4.257669 [6,] 4.257669 [7,] 4.257669 [8,] 4.257669 [9,] 4.257669 [10,] 4.257669

R community, I am trying to write a code that fills in data gaps in a time series. I have no R or statistics background at all but the use of R is proving to be a large portion of my PhD research. So far my code identifies where and the number of new entries required but I do not know how to add additional rows or columns into an array. Any advice on how this can be done? Here is an example:

R community, I am creating a bivariate return level plot by adding calculated return period values as lines onto an existing plot using the following code with the points representing the return periods. plot(H2,D2,pch="+",axes=TRUE) points(H.10,D.10, type="l",col="blue") points(H.20,D.20, type="l",col="green") points(H.50,D.50,

Is there a way to estimate a nominal response model? To be more specific let's say I want to calibrate: \pi_{v}(\theta_j)=\frac{e^{\xi_{v}+\lambda_{v}\theta_j}}{\sum_{h=1}^m e^{\xi_{h}+\lambda_{h}\theta_j}} Where $\theta_j$ is a the dependent variable and I need to estimate $\xi_{h}$ and $\lambda_{h}$ for $h \in {1...,m}$. Thank you, Mauricio Romero Quantil S.A.S. Cel: 3112231150

R community, I am trying to create an array of the time differences between datapoints for a very large set. For some reason for 4 of the values the difference has been calculated as NA. Looking at the individual points two of them are "1981-03-29 01:40:00" and "1981-03-29 02:00:00" This is the exact same format as the other points that return values of 20 mins as expected.

Hi, I am using the uniroot function in order to carry out a bivariate Monte Carlo simulation using the logistics model. I have defined the function as: BV.FV <- function(x,y,a,A) (((x^(-a^-1)+y^(-a^-1))^(a-1))*(y^(a-1/a))*(exp(-((1^(-a^-1)+y^(-a^-1))^a)+y^-1)))-A and the procedure is as follows: Randomly generate values of A~(0,1), y0 = -(lnA)^-1 Where: A=Pr{X<xi|Y=yi-1} and a is the

(Apologies to those Toronto Asterisk Users' Group folks who have seen this message... I figured I'd have more success with a wider audience) I'm trying to boot a Cisco 7960 from an ISC DHCPD server (3.0.3 on FreeBSD 4.11), so far unsuccessful, and getting some odd behaviour on the wire. I wonder if anyone has done this before and therefore can validate whether or not the traffic I am

Im having a trouble understanding the config files setup even with some documentation ive read such as the handbook, maybe im just illiterate. Anyway do you think some one can point me to some examples of real config files. Such as IAX, Extensions, and Sip. I just cant grasp the concept for some reason. If someone would like to help me out, maybe even explain one on one? Thanks a lot

Hi, and sorry for asking such an unspecific question. Does anybody know of statistical / data mining methods that are available in R that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS Enterprise Miner. I don't expect a complete list, just two or three examples or hints where and what to look for. I found some older comparisons, and the R methods mentioned there