search for: totalarea

...X, bcidata$IDgrid, FUN = length) aggregate <- aggregate(bcidata$PX,bcidata[,c("Xgrid","Ygrid","IDNgrid")], FUN = length) Totalgrids <- length(levels(bcidata$IDgrid)) Occupiedgrids <- length(aggregate$IDNgrid) sum <- sum(aggregate$x) TotalArea <- 500000 Area <- (1000/i*500/j) Occupancy <- (Occupiedgrids/Totalgrids) Mean <- length(bcidata$Latin)/(Occupiedgrids) Variance <- var(aggregate$x) occupancymean.data.frame <- rbind(occupancymean.data.frame, data.frame(Area, Totalgrids, Occupiedgrids, Occu...

Visual inspection of the plot of a density() function vs a normal with the same mean and variance suggests the area under the density curve is bigger than under the normal curve. The two curves are very close over most of the domain. Assuming the normal curve does integrate to 1, this implies the area under density() is > 1. Is there any assurance that the density kernel smoother produces

Hi Guys Thanks for this! As I am a beginner, I bet I'm running into some really basic problems. Using the example from the Map2poly function in maptools: try <- read.shape("euadmll.shp") mappolys <- Map2poly(try) # this produces 14 warnings() of the type: # 1: level 2 circularity at 62 in: .mtlbuild(.mtafters(r1), rD) # etc # 14: From next release, default hole handling