cocor {cocor} | R Documentation

## Compare two correlations based on either dependent or independent groups

### Description

Performs a test of significance for the difference between two correlations based on either dependent or independent groups. Dependent correlations can be either overlapping (they share a variable) or nonoverlapping (they have no variable in common). The function expects raw data input from which the correlations are calculated.

### Usage

    cocor(formula, data, alternative = "two.sided", test = "all",
      na.action = getOption("na.action"), alpha = 0.05, conf.level = 0.95,
      null.value = 0, return.htest = FALSE)

### Arguments

`formula` | A formula specifying the correlations and their underlying variables (See details).
`data` | A list holding two data.frames/matrices for independent groups or a single data.frame/matrix for dependent groups that contain the variables specified in `formula` as columns.
`alternative` | A character string specifying whether the alternative hypothesis is two-sided ("`two.sided`"; default) or one-sided ( "`greater`" or "`less`", depending on the direction). Optionally, the initial letter of the character strings ("`t`", "`g`", and "`l`)" can be used.
`test` | For the tests available, see cocor.indep.groups, cocor.dep.groups.overlap, and cocor.dep.groups.nonoverlap. Use `all` to apply all tests (default).
`na.action` | A function which handles missing data. Defaults to `getOption("na.action")`. See na.omit for more options.
`alpha` | A number defining the alpha level for the hypothesis test. The default value is .05.
`conf.level` | A number defining the level of confidence for the confidence interval (if a test is used that calculates confidence intervals). The default value is .95.
`null.value` | A number defining the hypothesized difference between the two correlations used for testing the null hypothesis. The default value is 0. If the value is other than 0, only the test `zou2007` that uses a confidence interval is available.
`return.htest` | A logical indicating whether the result should be returned as a list containing a list of class 'htest' for each test. The default value is `FALSE`.

### Details

Comparison of two correlations based on independent groups

  The `formula` parameter for the comparison of two correlations based on independent groups can either be `~a + b | a + b`, `~a + b | a + c`, or `~a + b | c + d`. The variables of the first correlation – `a` and `b` before the "`|`" character – must refer to columns in the data.frame/matrix of the first element in the list of the `data` object, whereas the variables of the second correlation – `a`, `b`, `c`, and `d` after the "`|`" character – must refer to columns in the data.frame/matrix of the second element in the list.

Comparison of two overlapping correlations based on dependent groups

  The `formula` parameterfor correlations based on dependent groups with overlapping variables must follow the pattern `~a + b | a + c`. The variables of the two correlation – `a`, `b`, and `c` – must refer to columns in the data.frame/matrix of the `data` object.

Comparison of two nonoverlapping correlations based on dependent groups

  The `formula` for correlations based on dependent groups with nonoverlapping variables must have the form `~a + b | c + d`. The variables of the two correlation – `a`, `b`, `c`, and `d` – must refer to columns in the data.frame/matrix of the `data` object.

### Value

Returns an object of class 'cocor.indep.groups', 'cocor.dep.groups.overlap', or 'cocor.dep.groups.nonoverlap' depending on the invoked comparison function.

### See Also

cocor.indep.groups, cocor.dep.groups.overlap, cocor.dep.groups.nonoverlap, as.htest

### Examples

    data("aptitude")

    # Compare two correlations based on two independet groups
    cocor(~logic + intelligence.a | logic + intelligence.a, aptitude)

    # Compare two correlations based on two depenendent groups
    # The correlations are overlapping
    cocor(~knowledge + intelligence.a | logic + intelligence.a, aptitude$sample1)
    cocor(~knowledge + intelligence.a | logic + intelligence.a, aptitude$sample2)
    # The correlations are nonoverlapping
    cocor(~logic + intelligence.b | knowledge + intelligence.a, aptitude$sample1)
    cocor(~logic + intelligence.b | knowledge + intelligence.a, aptitude$sample2)

    # Return result as a list of class 'htest'
    cocor(~knowledge + intelligence.b | logic + intelligence.a, aptitude$sample1,
          return.htest=TRUE)

[Package cocor version 1.1-3 Index]