cor.pearson.r.twosample.independent.simple: Two Independent Sample Test of Pearson's Correlation...

In burrm/lolcat: Miscellaneous Useful Functions

Two Independent Sample Test of Pearson's Correlation Coefficient

Description

Calculate test of significance difference for Pearson's Correlation Coefficient between four samples. Null hypothesis: No significant difference between correlation coefficient between x1 and x2 vs. correlation coefficient between x3 and x4. Significant result: Low p value indicates that a statistically significant difference exists between correlation coefficient between x1 and x2 vs. correlation coefficient between x3 and x4.

Usage

cor.pearson.r.twosample.independent(
  x1,
  x2,
  x3,
  x4,
  alternative = c("two.sided", "less", "greater"),
  conf.level = 0.95
)

cor.pearson.r.twosample.independent.simple(
  sample.r.g1.g2,
  sample.size.g1.g2,
  sample.r.g3.g4,
  sample.size.g3.g4,
  alternative = c("two.sided", "less", "greater"),
  conf.level = 0.95
)

Arguments

x1	Vector - Variable 1 values
x2	Vector - Variable 2 values
x3	Vector - Variable 4 values
alternative	The alternative hypothesis to use for the test computation.
conf.level	The confidence level for this test, between 0 and 1.
sample.r.g1.g2	Scalar - Sample correlation coefficient between x1 and x2.
sample.size.g1.g2	Scalar - Sample size for correlation between x1 and x2.
sample.r.g3.g4	Scalar - Sample correlation coefficient between x3 and x4.
sample.size.g3.g4	Scalar - Sample size for correlation between x3 and x4.

Value

Hypothesis test result showing results of test.

burrm/lolcat documentation built on Sept. 15, 2023, 11:35 a.m.