wt_dist {configural} | R Documentation |

## Weighted descriptive statistics for a vector of numbers

### Description

Compute the weighted mean and variance of a vector of numeric values. If no weights are supplied, defaults to computing the unweighted mean and the unweighted maximum-likelihood variance.

### Usage

```
wt_dist(
x,
wt = rep(1, length(x)),
unbiased = TRUE,
df_type = c("count", "sum_wts")
)
wt_mean(x, wt = rep(1, length(x)))
wt_var(
x,
wt = rep(1, length(x)),
unbiased = TRUE,
df_type = c("count", "sum_wts")
)
```

### Arguments

`x` |
Vector of values to be analyzed. |

`wt` |
Weights associated with the values in x. |

`unbiased` |
Logical scalar determining whether variance should be unbiased (TRUE) or maximum-likelihood (FALSE). |

`df_type` |
Character scalar determining whether the degrees of freedom for unbiased estimates should be based on numbers of cases ("count"; default) or sums of weights ("sum_wts"). |

### Details

The weighted mean is computed as

`\bar{x}_{w}=\frac{\Sigma_{i=1}^{k}x_{i}w_{i}}{\Sigma_{i=1}^{k}w_{i}}`

where *x* is a numeric vector and *w* is a vector of weights.

The weighted variance is computed as

`var_{w}(x)=\frac{\Sigma_{i=1}^{k}\left(x_{i}-\bar{x}_{w}\right)^{2}w_{i}}{\Sigma_{i=1}^{k}w_{i}}`

and the unbiased weighted variance is estimated by multiplying `var_{w}(x)`

by `\frac{k}{k-1}`

.

### Value

A weighted mean and variance if weights are supplied or an unweighted mean and variance if weights are not supplied.

### Author(s)

Jeffrey A. Dahlke

### Examples

```
wt_dist(x = c(.1, .3, .5), wt = c(100, 200, 300))
wt_mean(x = c(.1, .3, .5), wt = c(100, 200, 300))
wt_var(x = c(.1, .3, .5), wt = c(100, 200, 300))
```

*configural*version 0.1.5 Index]