Learn R Programming

⚠️There's a newer version (1.2.6) of this package.Take me there.

generalCorr (version 1.2.3)

Generalized Correlations, Causal Paths and Portfolio Selection

Description

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}.

Copy Link

Version

Install

install.packages('generalCorr')

Monthly Downloads

443

Version

1.2.3

License

GPL (>= 2)

Maintainer

Hrishikesh Vinod

Last Published

May 1st, 2023

Functions in generalCorr (1.2.3)

European Crime Data

Nonlinear Granger causality between two time series workhorse function.(local constant version)

Block version of abs-stdres Absolute values of residuals of kernel regressions of standardized x on standardized y, no control variables.

Nonlinear Granger causality between two time series workhorse function.

Function for computing a vector of one-lagged values of xj, a variable from panel data.

Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.

Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.

Block version of Absolute values of residuals of kernel regressions of standardized x on standardized y and control variables.

Compute fitted values from kernel regression of x on y and y on x

Function to compute a vector of 2 lagged values of a variable from panel data.

Absolute residuals of kernel regression of x on y.

Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized and control variables are present.

Absolute values of residuals of kernel regressions of x on y when both x and y are standardized.

Absolute values of Hausman-Wu null in kernel regressions of x on y when both x and y are standardized.

Absolute values of residuals of kernel regressions of x on y when both x and y are standardized and control variables are present (C for control presence).

internal badCol

Block version abs_stdrhser Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(Resid*RHS).

Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized.

Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(RHS*y) not gradients.

Report causal identification for all pairs of variables in a matrix (deprecated function). It is better to choose a target variable and pair it with all others, instead of considering all possible targets.

Compute the numerical integration by the trapezoidal rule.

Compute matrix of n999 rows and p-1 columns of bootstrap `sum' index (strength from older criterion Cr1, with newer Cr2 and Cr3).

Compute vector of n999 nonlinear Granger causality paths

Compute vector of n999 nonlinear Granger causality paths

bootstrap confidence intervals for (x2-x1) exact SD1 to SD4 stochastic dominance .

Probability of unambiguously correct (+ or -) sign from bootPairs output transformed to percentages.

Probability of unambiguously correct (+ or -) sign from bootPairs output

Compute matrix of n999 rows and p-1 columns of bootstrap `sum' (scores from Cr1 to Cr3).

Compute matrix of n999 rows and p-1 columns of bootstrap `sum' (strength from Cr1 to Cr3).

Compute confidence intervals [quantile(s)] of indexes from bootPairs output

Generalized canonical correlation, estimating alpha, beta, rho.

No print (NoP) version of causeSummBlk summary causal paths from three criteria

Older Kernel causality summary of evidence for causal paths from three criteria

Kernel causality summary of evidence for causal paths from three criteria

All Pair Version Kernel (block) causality summary paths from three criteria

Compute usual summary stats of 'sum' indexes from bootPairs output

Block Version 2: Kernel causality summary of causal paths from three criteria

Compute usual summary stats of 'sum' index in (-100, 100) from bootPair2

Block Version 2: Kernel causality summary of causal paths from three criteria

Compares two vectors (portfolios) using momentVote, DecileVote and exactSdMtx functions.

Kernel regressions based causal paths in Panel Data.

order 4 differencing of a time series vector

Compute cofactor of a matrix based on row r and column c.

Compares two vectors (portfolios) using stochastic dominance of orders 1 to 4.

Kernel causality summary of evidence for causal paths from three criteria using new exact stochastic dominance. The function develops a unanimity index regarding the which flip (y on xi) or (xi on y) is best. Relevant signs determine the causal direction and unanimity index among three criteria. While allowing the researcher to keep some variables as controls, or outside the scope of causal path determination (e.g., age or latitude) this function produces detailed causal path information in a 5 column matrix identifying the names of variables, causal path directions, path strengths re-scaled to be in the range [--100, 100], (table reports absolute values of the strength) plus Pearson correlation and its p-value. The `2' in the name of the function suggests a second implementation where exact stochastic dominance, decileVote and momentVote are used and where we avoid Anderson's trapezoidal approximation.

Function compares nine deciles of stock return distributions.

internal da2Lag

order four differencing of a matrix of time series

Exact stochastic dominance computation from areas above ECDF pillars.

Two sequences: starting+ending values from n and blocksize (internal use)

Internal diff.e0

generalCorrInfo

generalCorr package description:

depMeas Signed measure of nonlinear nonparametric dependence between two vectors.

Function to compute outliers and their count using Tukey method using 1.5 times interquartile range (IQR) to define boundaries.

Function to compute generalized correlation coefficients r*(x|y) and r*(y|x) from two vectors (not matrices)

compute the matrix R* of generalized correlation coefficients.

Matrix R* of generalized correlation coefficients captures nonlinearities using blocks.

Kernel regression with options for residuals and gradients.

Matrix R* of generalized correlation coefficients captures nonlinearities.

Function compares Pearson Stats and Sharpe Ratio for a matrix of stock returns

Heuristic t test of the difference between two generalized correlations.

Function to do compute the minor of a matrix defined by row r and column c.

internal min.e0

After removing control variables, magnitude of effect of x on y, and of y on x.

Kernel regression version 2 with optional residuals and gradients with regtype="ll" for local linear, bwmethod="cv.aic" for AIC-based bandwidth selection.

internal object

Compare out-of-sample portfolio choice algorithms by a leave-percent-out method.

internal goodCol

Function to do pairwise deletion of missing rows.

Kernel regression with control variables and optional residuals and gradients. version 2 regtype="ll" for local linear, bwmethod="cv.aic" for AIC-based bandwidth selection. It admits control variables.

Function to do matched deletion of missing rows from x, y and z variable(s).

Generalized partial correlation coefficients between Xi and Xj, after removing the effect of Xk, via OLS regression residuals.

internal nam.goodCol

Approximate overall magnitudes of kernel regression partials dx/dy and dy/dx.

Kernel regression with control variables and optional residuals and gradients.

Compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*. (deprecated)

Function to do matched deletion of missing rows from x, y and control variable(s).

Generalized partial correlation coefficients between Xi and Xj,

Partial correlation coefficient between Xi and Xj after removing the linear effect of all others.

Block version reports many generalized partial correlation coefficients allowing control variables.

Block version of generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.

Compute the portfolio return knowing the rank of a stock in the input `mtx'.

Compute probability of positive or negative sign from bootPairs output

Intermediate weighting function giving Non-Expected Utility theory weights.

internal ridgek

Vector of hybrid generalized partial correlation coefficients.

Vector of hybrid generalized partial correlation coefficients.

Generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.

internal nam.badCol

Matrix of generalized partial correlation coefficients, always leaving out control variables, if any.

Report many generalized partial correlation coefficients allowing control variables.

Generalized partial correlation coefficient between Xi and Xj after removing the effect of all others. (older version, deprecated)

Vector of generalized partial correlation coefficients (GPCC), always leaving out control variables, if any.

Silently compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*.

internal nam.mtx0

internal salesLag

internal sales2Lag

internal rhs.lag2

Block Version of silentPair2 for causality scores with control variables

Block Version of silentPairs for causality scores with control variables

kernel causality (version 2) scores with control variables

No-print kernel causality scores with control variables Hausman-Wu Criterion 1

Kernel causality computations admitting control variables.

No-print kernel-causality unanimity score matrix with optional control variables

Older kernel-causality unanimity score matrix with optional control variables

Summary magnitudes after removing control variables in several pairs where dependent variable is fixed.

Function reporting kernel causality results as a 7-column matrix.(deprecated)

Kernel causality computations admitting control variables reporting a 7-column matrix, version 2.

Function to compute generalized correlation coefficients r*(x,y).

Compute the bootstrap probability of correct causal direction.

Function reporting kernel causality results as a 7-column matrix, version 2.

internal rijMrji

internal sort.e0

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

internal sgn.e0

Older version, kernel causality weighted sum allowing control variables

Sort all columns of matrix x with respect to the j-th column.

internal sort.abse0

Replace asymmetric matrix by max of abs values of [i,j] or [j,i] elements.

Compute vectors measuring stochastic dominance of four orders.

Function reporting detailed kernel causality results in a 7-column matrix (uses deprecated criterion 1, no longer recommended but may be useful for second and third criterion typ=2,3)

Pseudo regression coefficients from generalized partial correlation coefficients, (GPCC).

sudoCoefParcorH

Peudo regression coefficients from hybrid generalized partial correlation coefficients (HGPCC).

Residuals of kernel regressions of x on y when both x and y are standardized.

Standardize x and y vectors to achieve zero mean and unit variance.

Creates input for the stochastic dominance function stochdom2

Compute ranks of rows of matrix and summarize them into a choice suggestion.