plotSpikeRG(spike,parameter,RG,array = 1, bc = FALSE, area = TRUE, xlab = "log(Rintensity)", ylab = "log(Gintensity)", main = colnames(spike$R)[array], onlycalib = FALSE, xlim = NULL, ylim = NULL, pch = 19, cex = 0.3, col = "black", allpch = 19, allcex = 0.05, allcol = "lightgrey", diag = TRUE, diagcol = "grey", diaglwd = 1, curvecol = "blue", curvelwd = 1.5, calibtype = 1, adjusttype = 4, ...)SpikeList object.ParameterList object..
If parameter argument is sepcified, model curves are shown on the plot. RGList_CALIB
object. If parameter argument is specified, this argument
is obligated. More description in Detail section.TRUE means background corrected measured
intensities are used. Default is FALSE.TRUE means spot area is used to calculate
measured intensities. Namly, measured intensities are calculated by
foreground intensities(or background corrected intensities, if bc is
TRUE) multiply spot area. FALSE means spot area is not used.
Default is TRUE.TRUE means only the calibration controls are on
the plot. FALSE means to plot all the spikesTRUE.
Default is TRUE.plot. array gives the array index to be plotted. If parameter estimation is done, the model curves can be plotted by giving
the argument parameter. And if the argument parameter is specified,
the argument RG is obligated. The combination of these two arguments is
used to compare how the model fits to the spike and to the whole data set. More
details about the usage of this function refers to the readme file of this package.
It supports different ways to calculate the measured intensities.
Arguments bc and area are logical and their combinations are used
for specifying four differents ways. bc indicates using background correction
or not. area indicates multipling spot area or not. The default value
of these two arguments are bc = FALSE and area = TRUE.
see other graphic functions for the other arguments.
# load data: spike
data(spike)
# specify the array to be plotted.
array <- 1
# use the default values for other arguments
plotSpikeRG(spike,array=array)
# after parameter estimation, the model curves can be shown on the plot.
data(parameter)
data(RG)
plotSpikeRG(spike,parameter,RG)
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