search for: ygrid

...d.csv("~/Desktop/Files_for_Package/data.csv"); bcidata Modelsfunc<- function(bcidata){ occupancymean.data.frame <- NULL for (k in seq(2.5,250,by=2.5)){ i <- 1000/k j <- 500/k bcidata$Xgrid <- cut(bcidata$PX, breaks = i, include.lowest = T) bcidata$Ygrid <- cut(bcidata$PY, breaks = j, include.lowest = T) bcidata$IDgrid <- with(bcidata, interaction(Xgrid,Ygrid)) bcidata$IDNgrid <- factor(bcidata$IDgrid) levels(bcidata$IDgrid) <- seq_along(levels(bcidata$IDgrid)) bcidata$count <- ave(bcidata$PX, bcidata$IDgrid, FUN...

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...set=us-ascii Content-Description: plot.mefista.Rd Content-Disposition: attachment; filename="plot.mefista.Rd" \name{plot.mefista} \alias{plot.mefista} \title{Plot an object of class mefista.} \usage{ plot.mefista(mef,draw.style=NA,Space=mef$param$space,xgrid=mef$param$xgrid, ygrid=mef$param$ygrid,nlevels=15,ncolors=0,\dots) } %- maybe also `usage' for other objects documented here. \arguments{ \item{mef}{An object of class \code{mefista}.} \item{draw.style}{Values: 0 - linear, 1 - \code{\link{image}}, 2 - \code{\link{contour}}, 3 - \code{image()} \emph{and} \code...

I'm running into some problems with the spacing of some faceted ggplot plots. I have a number of time series faceted to be one above another, but the scale labels of the y axes all clobber each other at the bottom/top of each. for example, try: qplot(x, y, data = data.frame(x = 1:10, y = 1:10, size = 1:10), facets = size ~ ., size = size) + scale_x_continuous(breaks = 1:10) +

Dear All, I am trying to produce log-log plots in R and I was wondering if any of you have a 'template' for generating these with 'nice' labels and log-log grids? I know I can set up axes individually and use the intervals I want, however, I will be producing a large number of these plots and would not like to do this manually for each of them + I am very new to R and at the

I want to create a grid of function values for use in `contour' or `persp'. The function is the log-likelihood for the gamma. The sample is stored as vector of length 20 called `Survival'. A single evaluation of the log-likelihood at, say, scale = 9 and shape = 10 would be obtained by sum(dgamma(Survival, scale = 9, shape = 10, log = TRUE)) (This may work only 0.99.0, I'm not