# Variance helper functions

`var_mean_indep.Rd`

These functions help calculate the variance matrix of different
kinds of samples. `var_mean_indep`

creates an asymptotic
covariance matrix for the sample means of a list of independent
samples. `var_prop_indep`

creates an asymptotic covariance
matrix for the sample proportions of a list of independent
samples. `var_mean_onesample`

creates an asymptotic covariance
matrix for the sample means of several variables from the same
sample.

## Usage

```
var_mean_indep(x_vectors)
var_mean_onesample(df, vars = names(df))
var_prop_indep(pi_hat, nobs)
```

## Examples

```
# list of independent samples
x_vectors <- list(
rnorm(1000, mean = 1, sd = 2),
rnorm(10, mean = 4, sd = 0.5),
rnorm(1000000, mean = 0, sd = 1)
)
var_mean_indep(x_vectors)
#> [,1] [,2] [,3]
#> [1,] 0.004000005 0.00000000 0.000000e+00
#> [2,] 0.000000000 0.01760705 0.000000e+00
#> [3,] 0.000000000 0.00000000 9.996391e-07
# sample proportions
pi_hat <- c(0.1, 0.6, 0.3)
nobs <- 1000
var_prop_indep(pi_hat, nobs)
#> [,1] [,2] [,3]
#> [1,] 9e-05 0.00000 0.00000
#> [2,] 0e+00 0.00024 0.00000
#> [3,] 0e+00 0.00000 0.00021
# covariance of educ and age in cps dataset
var_mean_onesample(cps, c("educ", "age"))
#> educ age
#> educ 0.0015103246 -0.0001510177
#> age -0.0001510177 0.0201794244
```