Graphical Data Analysis - Maple Help

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Graphical Data Analysis

 

Description

Available Commands

Live Data Plots Palette

Examples

Description

  

The Statistics package supports a variety of tools for visualizing univariate and multivariate data. These include bar charts, line charts, histograms, scatter plots, etc. The following is a list of available commands.

Available Commands

AgglomeratedPlot

generate agglomerated plots

AreaChart

create area charts from data

BarChart

create bar charts from data

Biplot

generate biplots

BoxPlot

create box plots from data

BubblePlot

generate bubble plots

ColumnGraph

create column graphs from data

CumulativeSumChart

generate cumulative sum charts

DensityPlot

plot the density of a random variable

ErrorPlot

generate error plots

FrequencyPlot

generate frequency plots

GridPlot

generate a grid of plots

HeatMap

generate heat maps

Histogram

generate histograms

KernelDensityPlot

plot the kernel density estimate of a data set

LineChart

generate line charts

NormalPlot

generate normal plots

PieChart

generate pie charts

PointPlot

generate point plots

ProbabilityPlot

generate probability plots

ProfileLikelihood

plot a profile of the likelihood function

ProfileLogLikelihood

plot a profile of the log likelihood function

QuantilePlot

generate quantile-quantile plots

ScatterPlot

generate scatter plots

ScreePlot

generate scree plots for variance

SunflowerPlot

generate sunflower plots

SurfacePlot

generate surface plots

SymmetryPlot

generate symmetry plots

TreeMap

generate tree maps

  

These commands can be used with tools available in plots and plottools packages to create multi-component plots.

Live Data Plots Palette

  

The Live Data Plots palette makes it easy to create and customize statistical plots, including area charts, histograms, pie charts, and scatter plots. For more details on the Live Data Plots palette, see Live Data Plots.

Examples

withStatistics:

A5,2,5,8,4,5,10,5:

Bmapt→t0.5,A:

Generate bar chart and line chart

PColumnGraphA,legend=Data Set A:

QLineChartB,color=red,thickness=3,symbol=circle,symbolsize=10,legend=Data Set B:

Superimpose the two plots and display the result.

plots[display]P,Q,title=Bar Chart and Line Chart,axis2=gridlines=7,thickness=2,linestyle=dash,color=white

Generate a pie chart

Ta,b,c,a,a,b,a,a,a,b,b,c,c,1,2,3,a,1:

PieChartT,sector=0..180,color=red..yellow

Generate a random sample drawn from the non-central Beta distribution. Generate a box plot and a histogram and display them in a single plot.

XRandomVariableNonCentralBeta1,2,3:

SSampleX,104:

PBoxPlotS,orientation=horizontal,offset=0.6,width=0.1,deciles=false:

QHistogramS:

RDensityPlotX,range=0..1,thickness=3:

plots[display]P,Q,R,gridlines=true

Use probability plot to compare a sample distribution and the original distribution

TSampleX,102:

ProbabilityPlotT,X

The Explore command can be used to interactively explore characteristics of datasets, such as point density using the ScatterPlot and SunflowerPlot commands:

YVectorSampleRandomVariableNormal0,1,200,SampleRandomVariableNormal2.6,1,200:

ZVectorSampleRandomVariableNormal0,1,200,SampleRandomVariableNormal2.6,1,200:

ExcisePoints := proc( fractionexcised, dataset1, dataset2, plottype )
  uses Statistics;
   local plotopts;
   plotopts := view = [min(dataset1)..max(dataset1), min(dataset2)..max(dataset2)];

   `if`( plottype = SunflowerPlot,
          SunflowerPlot( Excise(evalf[3](fractionexcised), dataset1, dataset2 ), plotopts ),
          ScatterPlot( Excise(evalf[3](fractionexcised), dataset1, dataset2 ), plotopts  ) );

end proc:

Explore( ExcisePoints( fractionexcised, Y, Z, plottype ),
        parameters = [[plottype = [ScatterPlot,SunflowerPlot], label = ` `, placement = bottom],
        [ fractionexcised = -1.0 .. 1.0, label = `Fraction Excised` ] ],
        initialvalues = [fractionexcised = 0],
        title = "Excising Data" );

 

 

See Also

Live Data Plots

plots

plottools

Statistics

Statistics[Commands]

Statistics[DescriptiveStatistics]

 


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