# Sampling-weighted principal component analysis

### Description

Computes principal components using the sampling weights.

### Usage

1 2 3 4 5 6 | ```
svyprcomp(formula, design, center = TRUE, scale. = FALSE, tol = NULL, scores = FALSE, ...)
## S3 method for class 'svyprcomp'
biplot(x, cols=c("black","darkred"),xlabs=NULL,
weight=c("transparent","scaled","none"),
max.alpha=0.5,max.cex=0.5,xlim=NULL,ylim=NULL,pc.biplot=FALSE,
expand=1,xlab=NULL,ylab=NULL, arrow.len=0.1, ...)
``` |

### Arguments

`formula` |
model formula describing variables to be used |

`design` |
survey design object. |

`center` |
Center data before analysis? |

`scale.` |
Scale to unit variance before analysis? |

`tol` |
Tolerance for omitting components from the results; a proportion of the standard deviation of the first component. The default is to keep all components. |

`scores` |
Return scores on each component? These are needed for |

`x` |
A |

`cols` |
Base colors for observations and variables respectively |

`xlabs` |
Formula, or character vector, giving labels for each observation |

`weight` |
How to display the sampling weights: |

`max.alpha` |
Opacity for the largest sampling weight, or for all points if |

`max.cex` |
Character size (as a multiple of |

`xlim,ylim,xlab,ylab` |
Graphical parameters |

`expand,arrow.len` |
See |

`pc.biplot` |
See |

`...` |
Other arguments to |

### Value

`svyprcomp`

returns an object of class `svyprcomp`

, similar to
class `prcomp`

but including design information

### See Also

`prcomp`

, `biplot.prcomp`

### Examples

1 2 3 4 5 6 7 8 9 10 |

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