parcorBMany | R Documentation |

This function calls a block version `parcorBijk`

of the function which
uses original data to compute
generalized partial correlations between `X_{idep}`

and `X_j`

where j can be any one of the remaining
variables in the input matrix `mtx`

. Partial correlations remove the effect of
variables `X_k`

other than `X_i`

and `X_j`

. Calculation further
allows for the presence of control variable(s) (if any) to remain always outside
the input matrix and whose effect is also removed in computing partial correlations.

```
parcorBMany(mtx, ctrl = 0, dig = 4, idep = 1, blksiz = 10, verbo = FALSE)
```

`mtx` |
Input data matrix with at least 3 columns. |

`ctrl` |
Input vector or matrix of data for control variable(s), default is ctrl=0 when control variables are absent |

`dig` |
The number of digits for reporting (=4, default) |

`idep` |
The column number of the dependent variable (=1, default) |

`blksiz` |
block size, default=10, if chosen blksiz >n, where n=rows in matrix then blksiz=n. That is, no blocking is done |

`verbo` |
Make this TRUE for detailed printing of computational steps |

A five column ‘out’ matrix containing partials. The first column
has the name of the `idep`

variable. The
second column has the name of the j variable, while the third column
has partial correlation coefficients r*(i,j | k).The last column
reports the absolute difference between two partial correlations.

This function reports all partial correlation coefficients, while avoiding ridge type adjustment.

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

Vinod, H. D. 'Generalized Correlations and Instantaneous Causality for Data Pairs Benchmark,' (March 8, 2015) https://www.ssrn.com/abstract=2574891

Vinod, H. D. (2021) 'Generalized, Partial and Canonical Correlation Coefficients' Computational Economics, 59(1), 1–28.

Vinod, H. D. 'Matrix Algebra Topics in Statistics and Economics Using R', Chapter 4 in Handbook of Statistics: Computational Statistics with R, Vol.32, co-editors: M. B. Rao and C.R. Rao. New York: North Holland, Elsevier Science Publishers, 2014, pp. 143-176.

See Also `parcor_ijk`

, `parcorMany`

.

```
set.seed(234)
z=runif(10,2,11)# z is independently created
x=sample(1:10)+z/10 #x is partly indep and partly affected by z
y=1+2*x+3*z+rnorm(10)# y depends on x and z not vice versa
mtx=cbind(x,y,z)
parcorBMany(mtx, blksiz=10)
## Not run:
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')
parcorBMany(x, idep=1)
## End(Not run)
```

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