Description Usage Arguments Details Value Author(s) References Examples

This is a wrapper that implements `getFeatures`

for each group in a data
frame using `plyr::ddply`

.

1 2 3 |

`y` |
Data frame, each row containing a vector of measurements for a particular point in time, with columns indicating the discrete and/or continuous measured variables (and possibly other descriptive variables). The data processed presuming the rows are orderd chronologically. |

`.variables` |
character vector with variable names in |

`cont` |
Vector of integers or a character vector indicating the columns
of |

`disc` |
Vector of integers or character vector indicating the columns
of |

`centerScale` |
Logical indicating whether the continuous variables (indicate by |

`stats` |
This argument defines the summary statistics that will be calculated
for each of the regression parameters. It can be a character vector of summary statistics,
which are passed to |

`fitQargs` |
Named list of arguments for |

`nJobs` |
The number of parallel jobs to run when extracting the features. |

A least one of `cont`

or `disc`

must be specified.

Instead of a data frame, the `y`

argument can be a
`valid_getFeatures_args`

object (returned by `check_getFeatures_args`

), in which
case all the subsequent arguments to `getFeature`

are ignored
(because the `valid_getFeatures_args`

object contains all those arguments).

Parallel processing,
if requested via `nJobs > 1`

, is facilitated via `Smisc::pddply`

,
a wrapper for parallelized calls to `plyr::ddply`

.

A dataframe with one row for each grouping defined by `.variables`

.
The features computed by `getFeatures`

is presented across the columns.

Landon Sego

Amidan BG, Ferryman TA. 2005. "Atypical Event and Typical Pattern Detection within Complex Systems." IEEE Aerospace Conference Proceedings, March 2005.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Load the data
data(demoData)
str(demoData)
# Calculate features for each subset defined by the unique combinations of
# "subject" and "phase", calculate the mean and standard deviation summary
# statistics to summarize the coefficients of the quadratic model fits
f <- ddply_getFeatures(demoData, c("subject", "phase"),
cont = 3:4, disc = 8:9, stats = c("mean", "skew"),
fitQargs = list(x1 = -5:5), nJobs = 2)
str(f)
head(f)
``` |

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