Description Usage Arguments Details Value Note Author(s) See Also Examples

Perform canoncial correlation analysis via projection pursuit based on projections through the data points, with a focus on robust and nonparametric methods.

1 2 3 4 5 6 7 | ```
ccaProj(x, y, k = 1, method = c("spearman", "kendall", "quadrant", "M",
"pearson"), control = list(...), standardize = TRUE,
useL1Median = TRUE, fallback = FALSE, ...)
CCAproj(x, y, k = 1, method = c("spearman", "kendall", "quadrant", "M",
"pearson"), standardize = TRUE, useL1Median = TRUE,
fallback = FALSE, ...)
``` |

`x, y` |
each can be a numeric vector, matrix or data frame. |

`k` |
an integer giving the number of canonical variables to compute. |

`method` |
a character string specifying the correlation functional to
maximize. Possible values are |

`control` |
a list of additional arguments to be passed to the specified
correlation functional. If supplied, this takes precedence over additional
arguments supplied via the |

`standardize` |
a logical indicating whether the data should be (robustly) standardized. |

`useL1Median` |
a logical indicating whether the |

`fallback` |
logical indicating whether a fallback mode for robust standardization should be used. If a correlation functional other than the Pearson correlation is maximized, the first attempt for standardizing the data is via median and MAD. In the fallback mode, variables whose MADs are zero (e.g., dummy variables) are standardized via mean and standard deviation. Note that if the Pearson correlation is maximized, standardization is always done via mean and standard deviation. |

`...` |
additional arguments to be passed to the specified correlation functional. Currently, this is only relevant for the M-estimator. For Spearman, Kendall and quadrant correlation, consistency at the normal model is always forced. |

First the candidate projection directions are defined for each data set
from the respective center through each data point. Then the algorithm
scans all *n^2* possible combinations for the maximum correlation,
where *n* is the number of observations.

For higher order canonical correlations, the data are first transformed into suitable subspaces. Then the alternate grid algorithm is applied to the reduced data and the results are back-transformed to the original space.

An object of class `"cca"`

with the following components:

`cor` |
a numeric vector giving the canonical correlation measures. |

`A` |
a numeric matrix in which the columns contain the canonical vectors
for |

`B` |
a numeric matrix in which the columns contain the canonical vectors
for |

`centerX` |
a numeric vector giving the center estimates used in
standardization of |

`centerY` |
a numeric vector giving the center estimates used in
standardization of |

`scaleX` |
a numeric vector giving the scale estimates used in
standardization of |

`scaleY` |
a numeric vector giving the scale estimates used in
standardization of |

`call` |
the matched function call. |

`CCAproj`

is a simple wrapper function for `ccaProj`

for
more compatibility with package pcaPP concerning function names.

Andreas Alfons

`ccaGrid`

, `maxCorProj`

,
`corFunctions`

1 2 3 4 5 6 7 8 9 |

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