princmp | R Documentation |

Enhanced Output for Principal and Sparse Principal Components

```
princmp(
formula,
data = environment(formula),
method = c("regular", "sparse"),
k = min(5, p - 1),
kapprox = min(5, k),
cor = TRUE,
sw = FALSE,
nvmax = 5
)
```

`formula` |
a formula with no left hand side, or a numeric matrix |

`data` |
a data frame or table. By default variables come from the calling environment. |

`method` |
specifies whether to use regular or sparse principal components are computed |

`k` |
the number of components to plot, display, and return |

`kapprox` |
the number of components to approximate with stepwise regression when |

`cor` |
set to |

`sw` |
set to |

`nvmax` |
maximum number of predictors to allow in stepwise regression PC approximations |

Expands any categorical predictors into indicator variables, and calls `princomp`

(if `method='regular'`

(the default)) or `sPCAgrid`

in the `pcaPP`

package (`method='sparse'`

) to compute lasso-penalized sparse principal components. By default all variables are first scaled by their standard deviation after observations with any `NA`

s on any variables in `formula`

are removed. Loadings of standardized variables, and if `orig=TRUE`

loadings on the original data scale are printed. If `pl=TRUE`

a scree plot is drawn with text added to indicate cumulative proportions of variance explained. If `sw=TRUE`

, the `leaps`

package `regsubsets`

function is used to approximate the PCs using forward stepwise regression with the original variables as individual predictors.

A `print`

method prints the results and a `plot`

method plots the scree plot of variance explained.

a list of class `princmp`

with elements `scores`

, a k-column matrix with principal component scores, with `NA`

s when the input data had an `NA`

, and other components useful for printing and plotting. If `k=1`

`scores`

is a vector. Other components include `vars`

(vector of variances explained), `method`

, `k`

.

Frank Harrell

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