Creates a summary object that produces detailed output when printed: the pairwise partial-correlation table (with zero-order comparison, df, t, and p) and — for three or more variables — the partial-correlation matrix.
Usage
# S3 method for class 'partial_cor'
summary(object, pairwise = TRUE, matrix = TRUE, digits = 3, ...)Arguments
- object
A
partial_corresult object.- pairwise
Logical. Show the pairwise results table? (Default: TRUE)
- matrix
Logical. Show the partial-correlation matrix (three or more analysis variables)? (Default: TRUE)
- digits
Number of decimal places for formatting (Default: 3).
- ...
Additional arguments (not used).
See also
partial_cor for the main analysis function.
Examples
result <- partial_cor(survey_data, trust_government, trust_media,
trust_science, controls = age)
summary(result)
#>
#> Partial Correlation Results
#> ---------------------------
#> - Variables: trust_government, trust_media, trust_science
#> - Controlling for: age
#> - Missing: Listwise deletion
#>
#> Partial Correlation Matrix:
#> ---------------------------
#> trust_government trust_media trust_science
#> trust_government 1.000 0.014 0.020
#> trust_media 0.014 1.000 0.015
#> trust_science 0.020 0.015 1.000
#> ---------------------------
#>
#> Pairwise Results:
#> --------------------------------------------------------------------------------------
#> Variable 1 Variable 2 Partial r Zero-order r df t p n sig
#> --------------------------------------------------------------------------------------
#> trust_government trust_media 0.014 0.014 2132 0.645 .519 2135
#> trust_government trust_science 0.020 0.020 2132 0.938 .348 2135
#> trust_media trust_science 0.015 0.015 2132 0.687 .492 2135
#> --------------------------------------------------------------------------------------
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
summary(result, matrix = FALSE)
#>
#> Partial Correlation Results
#> ---------------------------
#> - Variables: trust_government, trust_media, trust_science
#> - Controlling for: age
#> - Missing: Listwise deletion
#>
#> Pairwise Results:
#> --------------------------------------------------------------------------------------
#> Variable 1 Variable 2 Partial r Zero-order r df t p n sig
#> --------------------------------------------------------------------------------------
#> trust_government trust_media 0.014 0.014 2132 0.645 .519 2135
#> trust_government trust_science 0.020 0.020 2132 0.938 .348 2135
#> trust_media trust_science 0.015 0.015 2132 0.687 .492 2135
#> --------------------------------------------------------------------------------------
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05