corr_test: Correlation Coefficient Test

In mbir: Magnitude-Based Inferences

Description

Provides magnitude-based inferences for the association between given data vectors. Evaluates normality assumption, performs either Pearson or Spearman correlation and subsequently estimates magnitude-based inferences.

Usage

corr_test(x, y, conf.int = 0.9, auto = TRUE, method = "pearson",
  swc = 0.1, plot = FALSE)

Arguments

x, y	numeric vectors of data values
conf.int	(optional) confidence level of the interval. Defaults to 0.90
auto	(character) logical indicator specifying if user wants function to programmatically detect statistical procedures. Defaults to TRUE
method	(character) if auto = F, logical indicator specifying which correlation to execute (pearson, spearman, kendall). Defaults to "pearson".
swc	(optional) number indicating smallest worthwhile change. Defaults to 0.1
plot	(optional) logical indicator specifying to print associated plot. Defaults to FALSE

Details

Refer to vignette for further information.

Value

Associated effect size measure, r, and respective confidence intervals.

Examples

a <- rnorm(25, 80, 35)
b <- rnorm(25, 100, 35)

corr_test(a, b, 0.95)

Example output

   Normality Observed, No Outliers Detected 
   Method:  Pearson 

   r = 0.28
   95% CI [-0.13, 0.61]

   Magnitude-Based Inference 

        Unlikely   Possibly  Possibly  
        "Negative" "Trivial" "Positive"
MBI (%) "10.3"     "26.9"    "62.8"    

Inference: Unclear Association.

mbir documentation built on May 1, 2019, 10:11 p.m.