Many critical questions in medicine require the analysis of complex multivariate data, often from large data sets describing numerous variables. By addressing these issues, CoPlot facilitates rich interpretation of multivariate data. We present an example using CoPlot on a recently. Purpose: To describe CoPlot, a publicly available, novel tool for visualizing multivariate data. Methods: CoPlot simultaneously evaluates associations between.

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Plotting O18 as a function of Ageand color coding the symbols by Insol levels, reveals the nature of the control of ice volume by insolation:. For the given example, the obtained non-metric MDS embedding of the dataset is shown in Figure 2.

Journal of Software, 7, Although Figure 2 and Figure 5 seem similar for the given example, as the percentage of outliers in the data.

Embedding field returns the coordinates of the data points found by the selected MDS method.

Finally, here are some multi- and single-panel plots of climate-station data, the interpretation of which is straightforward. At high elevations, there is more variability but a general tendency for winter precipitation to dominate.

Multivariate Plots

Coplkt Section 4, two examples are provided for the application of the package. Alder can be used to plot points and surfaces and lines in a 3-D space. Austrian Journal of Statistics, 40, As was the case when examining relationships among pairs of variables, there are several basic characteristics of the relationship among sets of variables that are of interest.


Robust CoPlot analysis of ChineseCities.

OutlierRatio field should also be defined. However, this method is very sensitive to outliers.

The spplot function in the sp package is a Lattice-plot type method, and can be thought of as either extending the capabilities of Lattice plots to maps, or extending the ability of R to produce multi-panel maps.

This paper makes an important. Information from four variables at a time can also be displayed. Although given example uses city-block distance, various distance metrics can be selected to create distance matrix in the RobCoP package. The main advantage of this method is that it enables the simultaneous investigation of the relations between the observations and between the variables for a set of data.

Solid lines indicate required fields, while dashed lines indicate optional ones. The color column is also omitted from the analysis. ColorColumn, is used for colorizing the data points on the obtained MDS graph.

What is going on here is that proximity to the Pacific is a much more important control than elevation, and low elevation coastal and inland stations are quite wet.

RobCoP: A Matlab Package for Robust CoPlot Analysis

All of the examples given in this section use the same dataset to make comparisons between classical and Robust CoPlots. To produce non-metric MDS results, following code snippet can be used.

Classical CoPlot analysis of ChineseCities. The top panel shows unglaciated cirques in pink and glaciated ones in turquoise, while the bottom panel shows the reverse, multivvariate cirques in pink, unglaciated in turquoise. This subset can be either a those observations that fall in a particular group, or b they may represent a the values that fall within a particular range of the values of a variable.


In other words, the input file should not contain any unnamed columns. The user needs to know that the data matrix standardization type and computation method of the vector correlation coefficients, InStrct.

It has also been used as a supplemental tool to cluster analysis, data envelopment analysis DEA and outlier detection methods in the literature. Robust CoPlot method considers all the variables as well as the observations simultaneously to obtain two dimensional map.

Multivariate Plots

We believe that this package will be used in various areas, especially in applied statistics. CoPlot is used for geome- trical representation of multi-criteria decision problems [2] [3] [4]has been utilized in econometric studies [5]in energy and environmental modeling [6]in exploratory data analysis [7]as an outlier detection tool [8] [9] and for presenting DEA graphically [10] [11] [12].

X field of the input structure should take the data file name. The general idea is that precipitation should increase with increasing elevation, but at least for the western part of the state the reverse seems to be true! In addition, the output structure also contains an OutStrct.

A Basis for a Differential Development Policy.