plotSampleFeatures: 2D-features scatter-plot

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

2D-features scatter-plot

Description

Plot 2D-features scatter-plot of all particles. Grey color (label=0) is for data to cleaned or to remove in classification process.

Usage

plotSampleFeatures(
  data,
  label,
  parH = NULL,
  parV = NULL,
  figure.title = "Scatter plot",
  logscale = "",
  cex = 0.8,
  point.param = expand.grid(col = c("grey", "black", "red", "blue", "green", "cyan",
    "yellow", "orange", "rosybrown", "palevioletred", "darkblue", "deeppink",
    "blueviolet", "darkgoldenrod1", "chartreuse", "darkorchid1", "deeppink", "coral",
    "darkolivegreen1", "#66C2A5", "#9DAE8C", "#D49A73", "#F08F6D", "#C79693", "#9E9DBA",
    "#9F9BC9", "#C193C6", "#E28BC3", "#D2A29F", "#BABF77", "#AAD852", "#CBD844",
    "#ECD836", "#FAD53E", "#F1CD64", "#E7C689", "#D7BF9C", "#C5B9A7", "#B3B3B3",
    "#D53E4F", "#E04F4A", "#EB6046", "#F47346", 
     "#F88B51", "#FBA35C", "#FDB869",
    "#FDCA79", "#FDDD88", "#F6E68F", "#EDEE93", "#E2F398", "#CDEA9D", "#B7E2A1",
    "#A0D8A4", "#86CEA4", "#6DC4A4", "#58B2AB", "#459DB4", "#3288BD"), pch = c(20, 0:18),
    stringsAsFactors = FALSE),
  env.plot = NULL
)

Arguments

data	matrix or data.frame of raw data (points by line).
label	vector of labels.
parH	character vector specifying the name of the feature to use as x-axis.
parV	character vector specifying the name of the feature to use as y-axis.
figure.title	character vector specifying the title of the scatter-plot.
logscale	character vector containing "x" if the x-axis is to be logarithmic, "y" if the y-axis is to be logarithmic and "xy" or "yx" if both axes are to be logarithmic.
cex	numeric value specifying the size of the graphical labels.
point.param	data.frame specifying the colors and the symbols to use for clusters display.
env.plot	environment where to store graphical parameters used by function PlotScatter to select points.

Details

plotSampleFeatures plots 2D-features scatter-plot of all particles

Value

None

See Also

plotProfile, visualizeSampleClustering

Examples

dat <- rbind(matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 6, sd = 0.3), ncol = 2))
colnames(dat) <- c("x","y")
tf1 <- tempfile()
write.table(dat, tf1, sep=",", dec=".")

sig <- data.frame(ID=rep(1:150, each=30), SIGNAL=rep(dnorm(seq(-2,2,length=30)),150))
tf2 <- tempfile()
write.table(sig, tf2, sep=",", dec=".")

x <- importSample(file.features=tf1, file.profiles=tf2)

res <- KmeansQuick(x$features$initial$x, K=3)
new.labels <- formatLabelSample(res$cluster, x)

plotSampleFeatures(x$features$initial$x, label = new.labels, parH="x", parV="y")

RclusTool documentation built on Aug. 29, 2022, 9:07 a.m.