Bivariate correlation matrix with p values and pairwise N

Computes pairwise correlations and prints a formatted lower-triangle correlation matrix showing r, p values, and pairwise N for each pair. Supports Pearson (default), Spearman, and Kendall methods. Handles haven-labelled and factor variables with numeric levels. Warns when variables may be categorical rather than continuous.

Usage

jcorr(
  data,
  ...,
  method = "pearson",
  subset = NULL,
  variable.id = NULL,
  numeric = NULL,
  categorical = NULL,
  count = NULL,
  value.id = NULL,
  layout = NULL,
  case.processing.detail = NULL,
  digits = NULL
)

Arguments

data: A data frame.
...: Unquoted variable names within data.
method: Character. Correlation method: "pearson" (default), "spearman", or "kendall".
subset: An optional unquoted logical expression (e.g. Group == 1) to subset cases for this call only. Applied after jcomplete and jsubset. Does not affect other function calls.
variable.id: Character or NULL. Variable label display mode: one of "both", "names", "labels", "legend", or "legend.bottom". "names" shows variable names only; "both" shows "name: label"; "labels" shows variable labels as the matrix row/column headers (honored even if the matrix grows wide – best for short labels; rerun with a legend mode otherwise); "legend"/"legend.bottom" keep names and print a label legend after the table. NULL (default) defers to joutput()'s variable.id setting. Not a logical.
numeric: Optional character vector of variable names to treat as continuous for this call (the per-call counterpart of jnumeric()). Its only effect in jcorr() is to suppress the structural "seems categorical" caution for those variables; correlations are computed the same way regardless (labelled variables are coerced to numeric either way).
categorical: Not supported by jcorr() yet. Correlation requires numeric variables; supplying categorical raises an error pointing to jcrosstab() for association between categorical variables. (How jcorr() should handle an asserted-categorical variable is a parked design decision.)
count: Optional character vector of variable names to treat as counts for this call (the per-call counterpart of jcount()). A count is numeric-like here, so it behaves like numeric: it suppresses the "seems categorical" caution for those variables.
value.id: Not supported by jcorr(). The function does not display value labels, so passing this argument is an error. It exists only to return a clear message rather than misreporting the token as a missing variable. Leave at NULL (default).
layout: Character or NULL. How each correlation cell is laid out when three or more variables are given: "wide" (default) puts r and its p-value on one line with N on a second line beneath; "stacked" places r, p, and N on three separate lines, giving a narrower table that fits more variables before wrapping. Ignored for a single pair (two variables), which always prints a one-line summary. NULL (default) defers to the corr.layout setting in joptions() (itself defaulting to "wide").
case.processing.detail: Per-call override of the Case Processing Summary detail tier: one of "none", "totals", or "per_code". NULL (default) uses the active joutput() level default.
digits: Integer or NULL. Number of decimal places for continuous statistics in the output tables (range 0-7; digits = 0 prints whole numbers with no trailing decimal point). Does not affect p-values, percentages, or integer quantities (counts, N, degrees of freedom), which keep their own fixed conventions. NULL (default) defers to joutput()'s digits setting (default 3).

Value

Invisibly returns a list of class jst_corr containing: r (correlation matrix), p (p-value matrix), n (pairwise N matrix), method, model_frame (the analysis data frame used for plotting), and sample_info (pipeline and missing data counts).

Details

A red title identifying the correlation method is printed first, followed by variable labels (if present), then the matrix.

Examples

# With explicit data frame
jcorr(community, Income, Age, WellbeingScore)
#> Pearson Bivariate Correlations
#> 
#> Case Processing  Excluded  Remaining
#>     Original            —        100
#>     Remaining N         —        100
#> 
#> Missing-data breakdown  From 100    %
#>     Income
#>       Missing              6      6.0
#> 
#> ─────────────────────────────────────
#> 
#> Bivariate Correlations (Pearson)
#>                 Income          Age             WellbeingScore
#> --------------  --------------  --------------  --------------
#> Income           1                                            
#>                                                               
#> Age              .282 (p=.006)   1                            
#>                 N=94                                          
#>                                                               
#> WellbeingScore   .599 (p<.001)   .352 (p<.001)   1            
#>                 N=94            N=100                         
#> 
jcorr(community, Income, Age, WellbeingScore, method = "spearman")
#> Spearman Bivariate Correlations
#> 
#> Case Processing  Excluded  Remaining
#>     Original            —        100
#>     Remaining N         —        100
#> 
#> Missing-data breakdown  From 100    %
#>     Income
#>       Missing              6      6.0
#> 
#> ─────────────────────────────────────
#> 
#> Bivariate Correlations (Spearman)
#>                 Income          Age             WellbeingScore
#> --------------  --------------  --------------  --------------
#> Income           1                                            
#>                                                               
#> Age              .301 (p=.003)   1                            
#>                 N=94                                          
#>                                                               
#> WellbeingScore   .597 (p<.001)   .366 (p<.001)   1            
#>                 N=94            N=100                         
#> 
#> Note: Spearman p-values are approximate due to tied values in the data.
#> 

# Using juse() default
juse(community)
#> Default data frame set to: community
jcorr(Income, Age, WellbeingScore)
#> Pearson Bivariate Correlations
#> Using default data frame: community
#> 
#> Case Processing  Excluded  Remaining
#>     Original            —        100
#>     Remaining N         —        100
#> 
#> Missing-data breakdown  From 100    %
#>     Income
#>       Missing              6      6.0
#> 
#> ─────────────────────────────────────
#> 
#> Bivariate Correlations (Pearson)
#>                 Income          Age             WellbeingScore
#> --------------  --------------  --------------  --------------
#> Income           1                                            
#>                                                               
#> Age              .282 (p=.006)   1                            
#>                 N=94                                          
#>                                                               
#> WellbeingScore   .599 (p<.001)   .352 (p<.001)   1            
#>                 N=94            N=100                         
#>

Bivariate correlation matrix with p values and pairwise N

Usage

Arguments

Value

Details

See also

Examples