search for: closenness

Hi lily, It's not too hard to do it using dataframes. Getting the indexing right is usually that hardest part: # these values are the centers of the black cells lat<-rep(28:38,11) lon<-rep(98:108,each=11) pop<-sample(80:200,121) # just use the data.frame function blackcells<-data.frame(lat=lat,lon=lon,pop=pop) plot(0,type="n",xlim=c(97.5,108.5),ylim=c(27.5,38.5),

Hi lily, There are one or two assumptions to be made here. First is that the latitude and longitude values of the "black" cells are equally spaced as in your illustration. Second, that all latitude and longitude values for the "red" cells fall at the corners of four "black" cells. You can get the four "black" cells by finding the lat/lon values that are

Hi R users, I have a question about data processing. I have such a dataset, while each black grid cell has a few attributes and the corresponding attribute values. The latitude and longitude of the center of each grid cell are given also. Then I want to average the attribute values from four adjacent grid cells to get the average value for the center of each red grid cell. Thus, there are the

Hi Jim, Thanks. It works. I now have more complex problems. If at each blackcell, there are two variables such as pop and mood. For each variable, there are daily records in one year, so 365 records for pop and 365 records for mood. The averaged values for the redcells should be daily records too. What kind of format do you recommend for this problem? Right now, I just get the latitudes and

Hi lily, You could also create "blackcells" as a dataframe (which is itself a type of list). I used a list as I thought it would be a more general solution if there were different numbers of values for different grid cells. The use of 1 for the comparison was due to the grid increments being 1. If you had larger or smaller grid increments, you would use the grid increment size for the

Hi, I am having some confusion.It has been said that we can only estimate the future values using meanForecast +/- 2*standardDeviation. with 95% confidence.This means using this garch model we can only have a upper and lower limit of the values within which the next actual value is expected to lie.Then how come in research papers they plot the actual and predicted value so neatly.The simple

Hello R Help Team, I have created graph from weighted adjecency matrix .Is there a way I can find highly connected nodes in Igraph like the Package RBGL does. nathan -- View this message in context: http://www.nabble.com/Highly-Connected-Nodes-in-Igraph-tp22377522p22377522.html Sent from the R help mailing list archive at Nabble.com.