# svycor: Calculate Pearson correlations with complex survey data In jtools: Analysis and Presentation of Social Scientific Data

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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()`.

## 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.

## 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

## 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.

## Author(s)

Jacob Long <long.1377@osu.edu>

`wtd.cor`, `svymean()`

Other survey package extensions: `svysd()`

Other survey tools: `pf_sv_test()`, `svysd()`, `weights_tests()`, `wgttest()`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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 } ```

jtools documentation built on Sept. 5, 2021, 5:51 p.m.