pillar3D

Give data on x, y, z coordinate values of a 3D surface, this function
plots them after making pillars near each z value by adding and
subtracting small amounts dz. Instead of pins of the height z this
creates pillars which better resemble a surface. It uses the
wireframe() function of `lattice' package to do the plotting.

Since causal paths from data are important for all sciences, the
package provides many sophisticated functions. causeSummBlk() and causeSum2Blk()
give easy-to-interpret causal paths. Let Z denote control variables and compare
two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X,Z)+e2. Our criterion Cr1
says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent"
than in Y and causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These
inequalities between many absolute values are quantified by four orders of
stochastic dominance. Our third criterion Cr3 for the causal path X to Y
requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|.
The function parcorVec() reports generalized partials between the first
variable and all others. The package provides several R functions including
get0outliers() for outlier detection, bigfp() for numerical integration by the
trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
canonRho() for generalized canonical correlations, depMeas() measures nonlinear
dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns
is easiest to use. Portfolio selection: decileVote(), momentVote(), dif4mtx(),
exactSdMtx() can rank several stocks. Several functions whose names begin with 'boot' provide bootstrap
statistical inference including a new bootGcRsq() test for "Granger-causality"
allowing nonlinear relations. A new tool for evaluation of out-of-sample
portfolio performance is outOFsamp(). Panel data implementation is now included.
See six vignettes of the package for theory
and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.

Hrishikesh Vinod

generalCorr

Generalized Correlations, Causal Paths and Portfolio Selection

pillar3D function

<dl><dt>z</dt>
<dd>z-coordinate values</dd>
<dt>x</dt>
<dd>x-coordinate values</dd>
<dt>y</dt>
<dd>y-coordinate values</dd>
<dt>drape</dt>
<dd>logical value, default drape=TRUE to give color to heights</dd>
<dt>xlab</dt>
<dd>default "x" label on the x axis</dd>
<dt>ylab</dt>
<dd>default "y" label on the y axis</dd>
<dt>zlab</dt>
<dd>default "z" label on the z axis</dd>
<dt>mymain</dt>
<dd>default "Pillar Chart" main label on the plot</dd></dl>

Arguments

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

Author

Create a 3D pillar chart to display (x, y, z) data coordinate surface. — pillar3D

<dl>

<dt>z</dt>
<dd>z-coordinate values</dd>


<dt>x</dt>
<dd>x-coordinate values</dd>


<dt>y</dt>
<dd>y-coordinate values</dd>


<dt>drape</dt>
<dd>logical value, default drape=TRUE to give color to heights</dd>


<dt>xlab</dt>
<dd>default "x" label on the x axis</dd>


<dt>ylab</dt>
<dd>default "y" label on the y axis</dd>


<dt>zlab</dt>
<dd>default "z" label on the z axis</dd>


<dt>mymain</dt>
<dd>default "Pillar Chart" main label on the plot</dd>

</dl>

pillar3D: Create a 3D pillar chart to display (x, y, z) data coordinate surface.

Description

Usage

Value

Arguments

Author

Details