Generalized Correlations and Various Causal Paths


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Documentation for package ‘generalCorr’ version 1.1.8

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A B C D E G H I J K M N O P R S W

-- A --

absBstdres Block version of abs-stdres Absolute values of residuals of kernel regressions of standardized x on standardized y, no control variables.
absBstdresC Block version of Absolute values of residuals of kernel regressions of standardized x on standardized y and control variables.
absBstdrhserC Block version abs_stdrhser Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(Resid*RHS).
abs_res Absolute residuals of kernel regression of x on y.
abs_stdapd Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized.
abs_stdapdC 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.
abs_stdres Absolute values of residuals of kernel regressions of x on y when both x and y are standardized.
abs_stdresC Absolute values of residuals of kernel regressions of x on y when both x and y are standardized and control variables are present.
abs_stdrhserC Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(RHS*y) not gradients.
abs_stdrhserr Absolute values of Hausman-Wu null in kernel regressions of x on y when both x and y are standardized.
allPairs 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.

-- B --

badCol internal badCol
bigfp Compute the numerical integration by the trapezoidal rule.
bootGcLC Compute vector of n999 nonlinear Granger causality paths
bootGcRsq Compute vector of n999 nonlinear Granger causality paths
bootPairs Compute matrix of n999 rows and p-1 columns of bootstrap 'sum' (strength from Cr1 to Cr3).
bootPairs0 Compute matrix of n999 rows and p-1 columns of bootstrap 'sum' index (strength from older criterion Cr1, with newer Cr2 and Cr3).
bootQuantile Compute confidence intervals [quantile(s)] of indexes from bootPairs output
bootSign Probability of unambiguously correct (+ or -) sign from bootPairs output
bootSignPcent Probability of unambiguously correct (+ or -) sign from bootPairs output transformed to percentages.
bootSummary Compute usual summary stats of 'sum' indexes from bootPairs output

-- C --

canonRho Generalized canonical correlation, estimating alpha, beta, rho.
causeSummary Kernel causality summary of evidence for causal paths from three criteria
causeSummary0 Older Kernel causality summary of evidence for causal paths from three criteria
causeSummBlk Block Version Kernel causality summary causal paths from three criteria
cofactor Compute cofactor of a matrix based on row r and column c.
comp_portfo2 Compares two vectors (portfolios) using stochastic dominance of orders 1 to 4.

-- D --

da internal da
da2Lag internal da2Lag
depMeas depMeas Measure dependence between two vectors.
diff.e0 Internal diff.e0
dig Internal dig

-- E --

e0 internal e0
EuroCrime European Crime Data

-- G --

GcRsqX12 Granger nonlinear causality R^2 for x1=f(x1,x2), R^2 for flipped x1 and x2 and the difference between the two R^2 values
GcRsqX12c Granger nonlinear causality R^2 for x1=f(x1,x2) minus R^2 for flipped 1 and 2
GcRsqYX Nonlinear Granger causality between two time series workhorse function.
GcRsqYXc Nonlinear Granger causality between two time series workhorse function.(local constant version)
generalCorrInfo generalCorr package description:
get0outliers Function to compute outliers and their count using Tukey method using 1.5 times interquartile range (IQR) to define boundaries.
getSeq Two sequences: starting+ending values from n and blocksize (internal use)
gmc0 internal gmc0
gmc1 internal gmc1
gmcmtx0 Matrix R* of generalized correlation coefficients captures nonlinearities.
gmcmtxBlk Matrix R* of generalized correlation coefficients captures nonlinearities using blocks.
gmcmtxZ compute the matrix R* of generalized correlation coefficients.
gmcxy_np Function to compute generalized correlation coefficients r*(x|y) and r*(y|x) from two vectors (not matrices)
goodCol internal goodCol

-- H --

heurist Heuristic t test of the difference between two generalized correlations.

-- I --

i internal i
ibad internal object
ii internal ii

-- J --

j internal j

-- K --

kern Kernel regression with options for residuals and gradients.
kern2 Kernel regression version 2 with optional residuals and gradients with regtype="ll" for local linear, bwmethod="cv.aic" for AIC-based bandwidth selection.
kern2ctrl 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.
kern_ctrl Kernel regression with control variables and optional residuals and gradients.

-- M --

mag Approximate overall magnitudes of kernel regression partials dx/dy and dy/dx.
mag_ctrl After removing control variables, magnitude of effect of x on y, and of y on x.
min.e0 internal min.e0
minor Function to do compute the minor of a matrix defined by row r and column c.
mtx internal mtx
mtx0 internal mtx0
mtx2 internal mtx2

-- N --

n internal n
nall internal nall
nam.badCol internal nam.badCol
nam.goodCol internal nam.goodCol
nam.mtx0 internal nam.mtx0
napair Function to do pairwise deletion of missing rows.
naTriplet Function to do matched deletion of missing rows from x, y and control variable(s).
NLhat Compute fitted values from kernel regression of x on y and y on x

-- O --

out1 internal out1

-- P --

p1 internal p1
Panel2Lag Function to compute a vector of 2 lagged values of a variable from panel data.
PanelLag Function for computing a vector of one-lagged values of xj, a variable from panel data.
parcorBijk Block version of generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.
parcorBMany Block version reports many generalized partial correlation coefficients allowing control variables.
parcorMany Report many generalized partial correlation coefficients allowing control variables.
parcorMtx Matrix of generalized partial correlation coefficients, always leaving out control variables, if any.
parcorSilent Silently compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*.
parcor_ijk Generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.
parcor_ijkOLD Generalized partial correlation coefficient between Xi and Xj after removing the effect of all others. (older version, deprecated)
parcor_linear Partial correlation coefficient between Xi and Xj after removing the linear effect of all others.
parcor_ridg Compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*. (deprecated)
pcause Compute the bootstrap probability of correct causal direction.
pillar3D Create a 3D pillar chart to display (x, y, z) data coordinate surface.
prelec2 Intermediate weighting function giving Non-Expected Utility theory weights.
probSign Compute probability of positive or negative sign from bootPairs output

-- R --

rhs.lag2 internal rhs.lag2
rhs1 internal rhs1
ridgek internal ridgek
rij internal rij
rijMrji internal rijMrji
rji internal rji
rrij internal rrij
rrji internal rrji
rstar Function to compute generalized correlation coefficients r*(x,y).

-- S --

sales2Lag internal sales2Lag
salesLag internal salesLag
seed internal seed
sgn.e0 internal sgn.e0
silentMtx No-print kernel-causality unanimity score matrix with optional control variables
silentMtx0 Older kernel-causality unanimity score matrix with optional control variables
silentPairs No-print kernel causality scores with control variables Hausman-Wu Criterion 1
silentPairs0 Older version, kernel causality weighted sum allowing control variables
siPairsBlk Block Version of silentPairs for causality scores with control variables
some0Pairs 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)
someCPairs Kernel causality computations admitting control variables reporting a 7-column matrix (has older Cr1)
someCPairs2 Kernel causality computations admitting control variables reporting a 7-column matrix, version 2.
someMagPairs Summary magnitudes after removing control variables in several pairs where dependent variable is fixed.
somePairs Function reporting kernel causality results as a 7-column matrix.(deprecated)
somePairs2 Function reporting kernel causality results as a 7-column matrix, version 2.
sort.abse0 internal sort.abse0
sort.e0 internal sort.e0
sort_matrix Sort all columns of matrix x with respect to the j-th column.
stdres Residuals of kernel regressions of x on y when both x and y are standardized.
stdz_xy Standardize x and y vectors to achieve zero mean and unit variance.
stochdom2 Compute vectors measuring stochastic dominance of four orders.
symmze Replace asymmetric matrix by max of abs values of [ij] or [ji] elements useful in symmetrizing gmcmtx0 general correlation matrix

-- W --

wtdpapb Creates input for the stochastic dominance function stochdom2