Correlate every pair of several numeric variables

Runs stats::cor.test() on every pair of the named columns, each pair on the participants who have both values, and returns one row per pair with the coefficient, its interval and the number of pairs it rests on.

Usage

Analyze_CorrelationMatrix(
  dfData,
  chrCols,
  strMethod = "pearson",
  nConfLevel = 0.95,
  nMinPairs = nMinGroupDefault
)

Arguments

dfData

data.frame One row per participant.

chrCols

character Names of two or more numeric columns.

strMethod

character The coefficient: "pearson" or "spearman". Default: "pearson".

nConfLevel

numeric Confidence level of the intervals. Default: 0.95.

nMinPairs

numeric The smallest number of complete pairs a cell is computed for. A pair with fewer has status "too_small" and no numbers in its row. Default: nMinGroupDefault, which is 5. See StatisticsResult.

Value

The fixed result described in StatisticsResult. Here p_value is NA and estimates and statistic have no rows; counts is a named list of column to participants with a value; and rows is the matrix in long form, one row per pair of columns, each pair once, with the columns x, y, counts (the complete pairs), estimate, lower, upper, level, status, reason and warning. The result's own status is "too_small" when every pair is, and "error" when no pair could be computed for any other reason, such as a column that does not vary.

Details

No p-values are returned. A matrix of many coefficients is for seeing which variables move together, and a p-value on every cell would be many unadjusted tests. To test one pair, use Analyze_Correlation().

See also

Other statistics: Analyze_Contingency(), Analyze_Correlation(), Analyze_Fit(), Analyze_GroupDifference(), Analyze_Screen(), Analyze_Survival()

Examples

# Four biomarkers at Baseline, one column each
dfBaseline <- Synthetic_Results[Synthetic_Results$VISIT == "Baseline", ]
dfFrame <- Synthetic_Participants["USUBJID"]
for (strBiomarker in c("TNF-alpha", "IL-10", "IL-6", "CRP")) {
  dfOne <- dfBaseline[dfBaseline$TEST == strBiomarker, ]
  dfFrame[[strBiomarker]] <- dfOne$STRESN[match(dfFrame$USUBJID, dfOne$USUBJID)]
}

lResult <- Analyze_CorrelationMatrix(dfFrame, c("TNF-alpha", "IL-10", "IL-6", "CRP"))
lResult$rows[c("x", "y", "counts", "estimate", "lower", "upper")]
#>           x     y counts    estimate       lower      upper
#> 1 TNF-alpha IL-10    200  0.63838226  0.54819474 0.71389384
#> 2 TNF-alpha  IL-6    200  0.15152706  0.01306062 0.28429141
#> 3 TNF-alpha   CRP    200 -0.05072134 -0.18813840 0.08864347
#> 4     IL-10  IL-6    200  0.07962745 -0.05977393 0.21598238
#> 5     IL-10   CRP    200 -0.06857137 -0.20535868 0.07084362
#> 6      IL-6   CRP    200  0.01975789 -0.11931019 0.15806561