`svycor`

extends the `survey`

package by calculating correlations
with syntax similar to the original package, which for reasons unknown lacks
such a function.

## Usage

```
svycor(
formula,
design,
na.rm = FALSE,
digits = getOption("jtools-digits", default = 2),
sig.stats = FALSE,
bootn = 1000,
mean1 = TRUE,
...
)
```

## Arguments

- formula
A formula (e.g., ~var1+var2) specifying the terms to correlate.

- design
The

`survey.design`

or`svyrep.design`

object.- na.rm
Logical. Should cases with missing values be dropped?

- digits
An integer specifying the number of digits past the decimal to report in the output. Default is 2. You can change the default number of digits for all jtools functions with

`options("jtools-digits" = digits)`

where digits is the desired number.- sig.stats
Logical. Perform non-parametric bootstrapping (using

`wtd.cor`

) to generate standard errors and associated t- and p-values. See details for some considerations when doing null hypothesis testing with complex survey correlations.- bootn
If

`sig.stats`

is TRUE, this defines the number of bootstraps to be run to generate the standard errors and p-values. For large values and large datasets, this can contribute considerably to processing time.- mean1
If

`sig.stats`

is TRUE, it is important to know whether the sampling weights should have a mean of 1. That is, should the standard errors be calculated as if the number of rows in your dataset is the total number of observations (TRUE) or as if the sum of the weights in your dataset is the total number of observations (FALSE)?- ...
Additional arguments passed to

`svyvar()`

.

## Value

If significance tests are not requested, there is one returned value:

- cors
The correlation matrix (without rounding)

If significance tests are requested, the following are also returned:

- p.values
A matrix of p values

- t.values
A matrix of t values

- std.err
A matrix of standard errors

## Details

This function extends the `survey`

package by calculating the
correlations for user-specified variables in survey design and returning a
correlation matrix.

Using the `wtd.cor`

function, this function also
returns standard errors and p-values for the correlation terms using a
sample-weighted bootstrapping procedure. While correlations do not require
distributional assumptions, hypothesis testing (i.e., \(r > 0\)) does.
The appropriate way to calculate standard errors and use them to define a
probability is not straightforward in this scenario since the weighting
causes heteroskedasticity, thereby violating
an assumption inherent in the commonly used methods for converting Pearson's
correlations into t-values. The method provided here is defensible, but if
reporting in scientific publications the method should be spelled out.

## Note

This function was designed in part on the procedure recommended by Thomas Lumley, the author of the survey package, on Stack Overflow. However, he has not reviewed or endorsed this implementation. All defects are attributed to the author.

## See also

Other survey package extensions:
`svysd()`

Other survey tools:
`pf_sv_test()`

,
`svysd()`

,
`weights_tests()`

,
`wgttest()`

## Author

Jacob Long jacob.long@sc.edu

## Examples

```
if (requireNamespace("survey")) {
library(survey)
data(api)
# Create survey design object
dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw,
data = apistrat, fpc = ~fpc)
# Print correlation matrix
svycor(~api00 + api99 + dnum, design = dstrat)
# Save the results, extract correlation matrix
out <- svycor(~api00 + api99 + dnum, design = dstrat)
out$cors
}
#> api00 api99 dnum
#> api00 1.0000000 0.9759047 0.2543484
#> api99 0.9759047 1.0000000 0.2441910
#> dnum 0.2543484 0.2441910 1.0000000
```