# cor.multcomp: Comparison of several Pearson's linear correlation... In RVAideMemoire: Testing and Plotting Procedures for Biostatistics

## Description

Performs comparisons of several Pearson's linear correlation coefficients. If no difference, the function returns the common correlation coefficient, its confidence interval and test for its equality to a given value. If difference is significative, the functions performs pairwise comparisons between coefficients.

## Usage

 ```1 2``` ```cor.multcomp(var1, var2, fact, alpha = 0.05, conf.level = 0.95, theo = 0, p.method = "fdr") ```

## Arguments

 `var1` numeric vector (first variable). `var2` numeric vector (second variable). `fact` factor (groups). `alpha` significance level. `conf.level` confidence level. `theo` theoretical coefficient. `p.method` method for p-values correction. See help of`p.adjust`.

## Value

 `method.test` a character string giving the name of the global test computed. `data.name` a character string giving the name(s) of the data. `statistic` test statistics. `parameter` test degrees of freedom. `p.value` p-value for comparison of the coefficients. `null.value` the value of the difference in coefficients under the null hypothesis, always 0. `alternative` a character string describing the alternative hypothesis. `estimate` the estimated correlation coefficients. `alpha` significance level. `conf.level` confidence level. `p.adjust.method` method for p-values correction. `p.value.multcomp` data frame of pairwise comparisons result. `common.name` a character string explaining the elements of the table below. `common` data frame of results if the coefficients are not significantly different (common coefficient).

## Author(s)

Maxime Herv<e9> <[email protected]>

`cor.test`

## Examples

 ```1 2 3 4 5 6 7``` ```var1 <- c(1:15+rnorm(15,0,4),1:15+rnorm(15,0,1),1:15+rnorm(15,0,8)) var2 <- c(-1:-15+rnorm(15,0,4),1:15+rnorm(15,0,1),1:15+rnorm(15,0,8)) fact <- gl(3,15,labels=LETTERS[1:3]) cor.multcomp(var1,var2,fact) var3 <- c(1:15+rnorm(15,0,1),1:15+rnorm(15,0,3),1:15+rnorm(15,0,2)) cor.multcomp(var1,var3,fact) ```

### Example output

```*** Package RVAideMemoire v 0.9-68 ***

Comparison of 3 Pearson's linear correlation coefficients

data:  var1 and var2 by fact
X-squared = 52.654, df = 2, p-value = 3.684e-12
alternative hypothesis: true difference in coefficients is not equal to 0
sample estimates:
coeff in group A coeff in group B coeff in group C
-0.5716642        0.9794360        0.4296422

Pairwise comparisons

A         B
B 2.008e-12         -
C 6.576e-03 1.184e-05

Comparison of 3 Pearson's linear correlation coefficients

data:  var1 and var3 by fact
X-squared = 0.1056, df = 2, p-value = 0.9486
alternative hypothesis: true difference in coefficients is not equal to 0
sample estimates:
coeff in group A coeff in group B coeff in group C
0.7499207        0.6927853        0.7456012

Common correlation coefficient, 95% confidence interval
and equality to given value 0

inf      r    sup theoretical     U  Pr(>|U|)
0.5392 0.7304 0.8501           0 5.578 2.432e-08 ***
```

RVAideMemoire documentation built on May 14, 2018, 5:07 p.m.