PPbigdata (version 1.0.0)

plotNugg: Plot of projected 1-dim/2-dim data nuggets

Description

Draw a scatterplot/stripchart/weighted histogram of projected 1-dim/2-dim data nuggets considering the weights of data nuggets.

Usage

plotNugg(nuggproj, weight,
         qt = 0.8,pch = 16,cex = 0.5,
         hist = FALSE, jitter = 0.1,freq = TRUE, breaks = 30,
         main = NULL, xlab = NULL, ylab = NULL,...)

Value

If hist = TRUE, an object of class histogram. See wtd.hist. Otherwise, no returned values.

Arguments

nuggproj

Projected data nugget centers in 1-dim/2-dim space. Must be a data matrix (of class matrix, or data.frame) with two columns or a vector containing only entries of class numeric.

weight

Vector of the weight parameter for each data nugget. Its length should be the same as the number of data nuggets, i.e., nrow(nuggproj) for 2-dim projection/length(nuggproj) for 1-dim projection. Must be of class numeric or integer.

qt

A scalar with value in [0,1] indicating the probability used to obtain a sample quantile of the data nugget weights as the maximal value to transform weights to colors for the plot. Defaults to be 0.8.

pch

Either an integer specifying a symbol or a single character to be used as the default in plotting points. See points for possible values and their interpretation.

cex

A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. See par.

hist

logical; If TRUE, a weighted histogram is plotted for 1-dim projected data nuggets considering the nugget weights. Otherwise, a stripchart of the points jittered is plotted for 1-dim projection with colors indicating nugget weights. Defalut to be FALSE. Ignorable for 2-dim projection.

jitter

If hist = FALSE(default), the amount of jittering applied to the stripchart of 1-dim projected data nuggets. Ignorable for 2-dim projection and 1-dim projection with hist = TRUE.

freq

If hist = TRUE, a logical value indicating whether to plot frequencies. or probability densities for the weighted histogram of 1-dim projected data nuggets. Ignorable for 2-dim projection and 1-dim projection with hist = FALSE.

breaks

If hist = TRUE, the breaks argument for the weighted histogram of 1-dim projected data nuggets. See details in wtd.hist. Ignorable for 2-dim projection and 1-dim projection with hist = FALSE.

main

an overall title for the plot: see title.

xlab

a title for the x axis: see title.

ylab

a title for the y axis: see title.

...

further arguments and graphical parameters passed to plot for 2-dim projection, or stripchart or wtd.hist for 1-dim projection.

Author

Yajie Duan, Javier Cabrera

Details

This function plots a scatterplot/stripchart/weighted histogram of projected 1-dim/2-dim data nuggets considering the weights of data nuggets.

Data nuggets are a representative sample meant to summarize Big Data by reducing a large dataset to a much smaller dataset by eliminating redundant points while also preserving the peripheries of the dataset. Each data nugget is defined by a center (location), weight (importance), and scale (internal variability). Data nuggets for a large dataset could be created and refined by functions create.DN or refine.DN in the package datanugget.

Based on the created and refined data nuggets, the projected 1-dim/2-dim data nuggets are plotted via a scattorplot (2d) or a stripchart with points jittered (1d) of data nugget centers, coloring with data nugget weights where lighter green represents larger weights. If hist = TRUE, a weighted histgram of 1-dim projected data nugget centers is plotted considering the data nugget weights.

References

Cherasia, K. E., Cabrera, J., Fernholz, L. T., & Fernholz, R. (2022). Data Nuggets in Supervised Learning. In Robust and Multivariate Statistical Methods: Festschrift in Honor of David E. Tyler (pp. 429-449). Cham: Springer International Publishing.

Beavers, T. E., Cheng, G., Duan, Y., Cabrera, J., Lubomirski, M., Amaratunga, D., & Teigler, J. E. (2024). Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure. Journal of Computational and Graphical Statistics, (just-accepted), 1-21.

See Also

datanugget-package, create.DN, refine.DN,wtd.hist, stripchart

Examples

  require(datanugget)
  require(rstiefel)

  #2-d small example with visualization
  X = rbind.data.frame(matrix(rnorm(10^4, sd = 0.3), ncol = 2),
            matrix(rnorm(10^4, mean = 1, sd = 0.3), ncol = 2))


  #create data nuggets
  my.DN = create.DN(x = X,
                    R = 500,
                    delete.percent = .1,
                    DN.num1 = 500,
                    DN.num2 = 250,
                    no.cores = 0,
                    make.pbs = FALSE)


  #refine data nuggets
  my.DN2 = refine.DN(x = X,
                     DN = my.DN,
                     EV.tol = .9,
                     min.nugget.size = 2,
                     max.splits = 5,
                     no.cores = 0,
                     make.pbs = FALSE)

  #get nugget centers, weights, and scales
  nugg = my.DN2$`Data Nuggets`[,2:(ncol(X)+1)]
  weight = my.DN2$`Data Nuggets`$Weight
  scale = my.DN2$`Data Nuggets`$Scale

  #plot raw large dataset
  plot(X)

  #plot data nuggets in 2-dim space
  plotNugg(nugg,weight)

  #generate a random projection vector to 1-dim space
  proj_1d = rustiefel(2, 1)

  #project data nugget centers into 1-dim space by the random projection vector
  nuggproj_1d = as.matrix(nugg)%*%proj_1d

  #plot the stripchart for 1-dim projected data nuggets
  plotNugg(nuggproj_1d,weight)
  #plot the weighted histogram for 1-dim projected data nuggets
  plotNugg(nuggproj_1d,weight,hist = TRUE)