ctlcurvesplot

ctlcurvesplot plots the output of routine ctlcurves

Syntax

  • ctlcurvesplot(outCTL)example
  • ctlcurvesplot(outCTL,Name,Value)example

Description

ctlcurvesplot takes as input the output of function ctlcurves (that is a series of matrices which contain the values of the CTL bands, the set of tentative solutions found using the intersection of the bands, and the likelihood ratio tests for testing k vs k+1 given alpha.

The plot enables interaction in the sense that, if option databrush has been activated, it is possible to click on a point in the plot of the ctlcurves and to see the associated classification in the scatter plot matrix.

example

ctlcurvesplot(outCTL) ctcurves and Portofino plot for the Gyeser data with all default options.

example

ctlcurvesplot(outCTL, Name, Value) Interactive_example Example of the use of option databrush.

Examples

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  • ctcurves and Portofino plot for the Gyeser data with all default options.
  • Y=load('geyser2.txt');
    outCTl=ctlcurves(Y,'plots',false,'nsamp',20)
    ctlcurvesplot(outCTl);
    k=1
    k=2
    k=3
    k=4
    k=5
    Bands k=1
    Bands k=2
    Bands k=3
    Bands k=4
    Bands k=5
    
    outCTl = 
    
      struct with fields:
    
                  Mu: {5×6 cell}
               Sigma: {5×6 cell}
                  Pi: {5×6 cell}
                 IDX: {5×6 cell}
                 CTL: [5×6 double]
            BandsCTL: [5×6×60 double]
               likLB: [5×6 double]
              lik050: [5×6 double]
               likUB: [5×6 double]
                 idx: [271×1 double]
        Optimalalpha: 0.0200
            OptimalK: 3
             TentSol: [2×3 double]
          pvalLRtest: [4×6 table]
                  kk: [1 2 3 4 5]
               alpha: [0 0.0200 0.0400 0.0600 0.0800 0.1000]
         restrfactor: 100
                   Y: [271×2 double]
    
    
    Click here for the graphical output of this example (link to Ro.S.A. website). Graphical output could not be included in the installation file because toolboxes cannot be greater than 20MB. To load locally the image files, download zip file http://rosa.unipr.it/fsda/images.zip and unzip it to <tt>(docroot)/FSDA/images</tt> or simply run routine <tt>downloadGraphicalOutput.m</tt>

  • Interactive example 1. Example of the use of option databrush.
  • %   (brushing is done only once using a rectangular selection tool)
    %  Use the geyser data.
    Y=load('geyser2.txt');
    databrush=struct;
    databrush.selectionmode='Rect';
    outCTl=ctlcurves(Y,'plots',false,'nsamp',20)
    ctlcurvesplot(outCTl,'databrush',databrush);

    Related Examples

    expand all

  • Interactive example 2. Example of the use of option databrush.
  • %   (brushing is persistent)
    %  Use the geyser data.
    Y=load('geyser2.txt');
    databrush=struct;
    databrush.persist='on';
    outCTl=ctlcurves(Y,'plots',false,'nsamp',20)
    ctlcurvesplot(outCTl,'databrush',databrush);

    Input Arguments

    expand all

    outCTL — Information criterion to use. Structure.

    It contains the following fields.

    Value Description
    Mu

    cell of size length(kk)-by-length(alpha) containing the estimate of the centroids for each value of k and each value of alpha. More precisely, suppose kk=1:4 and alpha=[0 0.05 0.1], out.Mu{2,3} is a matrix with two rows and v columns containing the estimates of the centroids obtained when alpha=0.1.

    Sigma

    cell of size length(kk)-by-length(alpha) containing the estimate of the covariance matrices for each value of k and each value of alpha. More precisely, suppose kk=1:4 and alpha=[0 0.05 0.1], out.Sigma{2,3} is a 3D array of size v-by-v-by-2 containing the estimates of the covariance matrices obtained when alpha=0.1.

    Pi

    cell of size length(kk)-by-length(alpha) containing the estimate of the group proportions for each value of k and each value of alpha. More precisely, suppose kk=1:4 and alpha=[0 0.05 0.1], out.Pi{2,3} is a 3D array of size v-by-v-by-2 containing the estimates of the covariance matrices obtained when alpha=0.1.

    out.IDX = cell of size length(kk)-by-length(alpha) containing the final assignment for each value of k and each value of alpha. More precisely, suppose kk=1:4 and alpha=[0 0.05 0.1], out.IDX{2,3} is a vector of length(n) containing the containinig the assignment of each unit obtained when alpha=0.1.

    Elements equal to zero denote unassigned units.

    CTL

    matrix of size length(kk)-by-length(alpha) containing the values of the trimmed likelihood curves for each value of k and each value of alpha.

    BandsCTL

    3D array of size length(kk)-by-length(alpha)-by-nsimul containing the nsimul replicates of out.CTL. This output is present only if input option bands is true or is a struct.

    likLB

    matrix of size length(kk)-by-length(alpha) containing the lower confidence bands of the trimmed likelihood curves for each value of k and each value of alpha. This output is present only if input option bands is true or is a struct.

    likUB

    matrix of size length(kk)-by-length(alpha) containing the upper confidence bands of the trimmed likelihood curves for each value of k and each value of alpha. This output is present only if input option bands is true or is a struct.

    idx

    n-by-1 vector containing assignment of each unit to each of the k groups in correspodence of Optimalalpha and OptimalK. Cluster names are integer numbers from 1 to k. 0 indicates trimmed observations. This output is present only if input option bands is true or is a struct.

    Optimalalpha

    scalar, optimal value of trimming. This output is present only if optional input argument is true.

    OptimalK

    scalar, optimal number of clusters, stored as a positive integer value. This output is present only if optional input argument is true.

    TentSol

    matrix with size m-by 3. Details of the ordered solutions where there was intersection between two consecutive trimmed likelihood curves. First column contains the value of k, second column the value of alpha and third column the index associated to the best value of alpha.

    pvalLRtest

    table with size length(kk)-1-times-length(alpha) which stores the relative frequency in which the Likelihood ratio test is greater than the corresponding bootstrap test.

    kk

    vector containing the values of k (number of components) which have been considered. This vector is equal to input optional argument kk if kk had been specified else it is equal to 1:5.

    alpha

    vector containing the values of the trimming level which have been considered. This vector is equal to input optional argument alpha.

    restractor

    scalar containing the restriction factor which has been used to compute tclust.

    out.Y = Original data matrix Y. The field is present if option Ysave is set to 1.

    Data Types: struct

    Name-Value Pair Arguments

    Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

    Example: 'thresh',0.10 , 'tag','myplot' , 'tag','myplot1' , 'conflev',0.9 , 'datatooltip','' , 'databrush',1 , 'nameY',{'myY1', 'myY2'}

    thresh —threshold which defines where to put NaN in the out.pvalLRtest matrix.

    Scalar in the interval [0 1].

    Tne default value is 0.05. In other words the subsequent values in a particular column of out.pvalLRtest which follow a number breater than 0.05 will be set to NaN.

    Example: 'thresh',0.10

    Data Types: char

    tagCtl —Personalized tag for CTL curves plot.string.

    String which identifies the handle of the plot which is about to be created. The default is to use tag 'pl_Ctl' for the classification likelihood curves plot with bands.

    Note that if the program finds a plot which has a tag equal to the one specified by the user, then the output of the new plot overwrites the existing one in the same window else a new window is created.

    Example: 'tag','myplot'

    Data Types: char

    tagPortofino —Personalized tag for Portofino plot.string.

    String which identifies the handle of the plot which is about to be created. The default is to use tag 'pl_Portofino' for the Portofino plot.

    Note that if the program finds a plot which has a tag equal to the one specified by the user, then the output of the new plot overwrites the existing one in the same window else a new window is created.

    Example: 'tag','myplot1'

    Data Types: char

    conflev —confidence level of the bands.empty value (default) | scalar.

    Scalar in the interval (0 1) which contains the confidence level of the bands.

    The default is to use the confidence level taken from ctlcurves (that is 50 per cent confidence level).

    Example: 'conflev',0.9

    Data Types: double

    datatooltip —interactive clicking.empty value (default) | structure.

    The default is datatooltip=''.

    If datatooltip = 1, the user can select with the mouse a solution in order to have the following information:

    1) value of k which has been selected 2) value of alpha which has been selected 3) frequency distribution of the associated classification If datatooltip is a structure it may contain the following the fields

    Value Description
    DisplayStyle

    Determines how the data cursor displays. datatip | window.

    - datatip displays data cursor information in a small yellow text box attached to a black square marker at a data point you interactively select.

    - window displays data cursor information for the data point you interactively select in a floating window within the figure.

    SnapToDataVertex

    Specifies whether the data cursor snaps to the nearest data value or is located at the actual pointer position. on | off.

    - on data cursor snaps to the nearest data value - off data cursor is located at the actual pointer position.

    (see the MATLAB function datacursormode or the examples below). Default values are datatooltip.DisplayStyle = 'Window' and datatooltip.SnapToDataVertex = 'on'.

    Example: 'datatooltip',''

    Data Types: scalar double or struct

    databrush —interactive mouse brushing.empty value, scalar | structure.

    If databrush is an empty value (default), no brushing is done. The activation of this option (databrush is a scalar or a structure) enables the user to select a set of values of ctl curves in the current plot and to see the corresponding classification highlighted in the scatter plot matrix (spm). If spm does not exist it is automatically created. Please, note that the window style of the other figures is set equal to that which contains the ctl plot. In other words, if the ctl plot is docked all the other figures will be docked too.

    DATABRUSH IS A SCALAR. If databrush is a scalar the default selection tool is a rectangular brush and it is possible to brush only once (that is persist='').

    DATABRUSH IS A STRUCTURE. If databrush is a structure, it is possible to use all optional arguments of function selectdataFS and the following optional arguments: - databrush.persist = repeated brushing enabled. Persist is an empty value or a scalar containing the strings 'on' or 'off'.

    The default value of persist is '', that is brushing is allowed only once.

    If persist is 'on' or 'off' brushing can be done as many time as the user requires.

    If persist='on' then the corresponding spmplot of the solutions currently brushed are added to those previously brushed.

    If persist='off' every time a new brush is performed scatter plot matrices corresponding to previously brushed solutions are removed.

    - dispopt = string which controls how to fill the diagonals in the scatterplot matrix of the brushed solutions. Set dispopt to 'hist' (default) to plot histograms, or 'box' to plot boxplots.

    Example: 'databrush',1

    Data Types: single | double | struct

    nameY —variable labels.cell array.

    Cell array of strings containing the labels of the variables. As default value, the labels which are added are Y1, ..., Yv.

    Example: 'nameY',{'myY1', 'myY2'}

    Data Types: cell

    Output Arguments

    References

    See Also

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