make.grid {rflowcyt} | R Documentation |
A two-dimensional plot can be subdivided via grid
marks and lines. Each component of the resulting
grid is called a cell. The function make.grid
determines a matrix of values corresponding
to the number of observations that lie
within each cell of the grid. The function make.density
estimates the values allocated to each grid cell by a 'status' binary
variable. The values are estimated to be either a difference in
counts between the two status categories, a proportion, a normalized
proportion, and a z statistic for each cell such that an
image or ContourScatterPlot plot can be implemented.
make.grid(x, y, x.grid = seq(0, 1025, by = 25), y.grid = seq(0, 1025, by = 25)) make.density(x, y, status = NULL, x.grid = seq(0, 1025, by = 25), y.grid = seq(0, 1025, by = 25), type.CSP = c("count.diff", "p.hat", "p.hat.norm", "z.stat"))
x |
a vector of data values for the x-axis |
y |
a vector of data values for the y-axis |
status |
a vector of 0, 1 values denoting two categories |
x.grid |
a vector of grid marks to allocate x |
y.grid |
a vector of grid marks to allocate y |
type.CSP |
character string denoting the type.CSP of value to be estimated using the 'status' for each cell grid |
The following details the options for 'type.CSP':
(ie, (p.hat - 0.05)/sqrt( (0.05 * (1-0.05)) /n)
p.hat is the proportion in 'status'==1
where n is the number of cells in the grid with information. The default is to set the z statistic to zero for the cells with no information in either status. The value 0.5 is considered to be the case of no difference when the counts of both categories of 'status' are the same in the grid cell.
(ie, (p.hat - p.bar)/se(p.bar))
p.hat is the proportion in 'status'==1
p.bar is the average of p.hat over the whole grid
se(p.bar)=sqrt((1-p.bar)(p.bar)/n), where n is the number of cells in the grid with information.
z |
matrix of values corresponding to the counts in an x-y grid |
n.cells |
(only output for 'make.grid'); number of total observations in z |
type.CSP |
(only output for 'make.density'); the type.CSP of value in each cell. |
In the base package, the function image
could make a
plot with the resulting matrix of values.
Zoe Moodie, A.J. Rossini, J.Y. Wan
image
, ContourScatterPlot
,
pairs.CSP
, legend.CSP
, heat.colors
if (require(rfcdmin)){ data.there<-is.element(c("st.1829", "unst.1829", "st.DRT", "unst.DRT"),objects()) if ( ( sum(data.there) != length(data.there) )){ ## obtaining the FCS objects from VRC data data(VRCmin) } var1<-st.DRT@data[,4] var2<-st.DRT@data[,5] var1.2<-unst.DRT@data[,4] var2.2<-unst.DRT@data[,5] col.nm<-colnames(st.DRT@data) ## The status where 1=stimulated ## 0 = unstimulated status<-c(rep(1, dim(st.DRT@data)[1]), rep(0, dim(unst.DRT@data)[1])) x <- c(var1, var1.2) y <-c(var2, var2.2) count.output1<-make.grid(var1, var2) count.output0<-make.grid(var1.2, var2.2) ## matrix of counts mat.counts1<-count.output1$z mat.counts0<-count.output0$z ##total observations total.stimulated<-count.output1$n.cells total.unstimulated<-count.output0$n.cells count.diff.output <-make.density(x, y, status=status, type.CSP="count.diff") ## matrix of cont differences between the status categories mat.count.diff <-count.diff.output$z p.hat.output <-make.density(x, y, status=status, type.CSP="p.hat") ## matrix of cont differences between the status categories mat.p.hat <-p.hat.output$z p.hat.norm.output <-make.density(x, y, status=status, type.CSP="p.hat.norm") ## matrix of cont differences between the status categories mat.p.hat.norm <-p.hat.norm.output$z z.stat.output <-make.density(x, y, status=status, type.CSP="z.stat") ## matrix of cont differences between the status categories mat.z.stat <-z.stat.output$z if (interactive()){ par(mfrow=c(3,2)) image(mat.counts1,yaxt="n", xaxt="n", main="make.grid: Counts for stimulated", xlab=col.nm[4], ylab=col.nm[5], col=heat.colors(20)) image( mat.counts0,yaxt="n", xaxt="n", main="make.grid: Counts for unstimulated", xlab=col.nm[4], ylab=col.nm[5], col=heat.colors(20)) image( mat.count.diff,yaxt="n", xaxt="n", main="make.density: Count Difference (Stimulated-Unstimulated)", xlab=col.nm[4], ylab=col.nm[5], col=heat.colors(20)) image( mat.p.hat,yaxt="n", xaxt="n", main="make.density: Proportion of Stimulated", xlab=col.nm[4], ylab=col.nm[5], col=heat.colors(20)) image( mat.p.hat.norm,main="make.density: Normalized proportion of Stimulated", xlab=col.nm[4],yaxt="n", xaxt="n", ylab=col.nm[5], col=heat.colors(20)) image( mat.z.stat, main="make.density: z statistic", xlab=col.nm[4],yaxt="n", xaxt="n", ylab=col.nm[5], col=heat.colors(20)) } }