cor.pearson.r.twosample.dependent.simple: Two Dependent Sample Test of Pearson's Correlation...

In burrm/lolcat: Miscellaneous Useful Functions

Two Dependent Sample Test of Pearson's Correlation Coefficient

Description

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

Usage

cor.pearson.r.twosample.dependent(
  x1,
  x2,
  x3,
  alternative = c("two.sided", "less", "greater"),
  method = c("Steiger", "Hotelling"),
  conf.level = 0.95
)

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

Arguments

x1	Vector - Variable 1 values
x2	Vector - Variable 2 values
x3	Vector - Variable 3 values
alternative	The alternative hypothesis to use for the test computation.
method	Scalar - character string - method used in calculation.
conf.level	The confidence level for this test, between 0 and 1.
sample.r.g1.g3	Scalar - Sample correlation coefficient between x1 and x3.
sample.r.g2.g3	Scalar - Sample correlation coefficient between x2 and x3.
sample.r.g1.g2	Scalar - Sample correlation coefficient between x1 and x2.
sample.size	Scalar - Sample size to use for the calculation.

Value

Hypothesis test result showing results of test.

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