diggingnumbers:correlation

In R, basic correlation tests are executed with two commands: `cor()`

and `lm()`

(where `lm`

stands for linear model).

To calculate product moment correlation coefficient between `Maxle`

and `Maxwi`

for bronze spears:

> Bronze = subset(spearhead, subset=Mat=="1") > cor(Bronze$Maxle, Bronze$Maxwi) [1] 0.6892216

To calculate Spearman's rank correlation coefficient between `Date`

and `Weight`

for bronze spears:

> cor(Bronze$Date, Bronze$Weight, method="spearman") [1] 0.1269293

To draw a scatterplot for `Maxle`

and `Maxwi`

:

> plot(Bronze$Maxle, Bronze$Maxwi)

The scatterplot by itself is already interesting, but R gives us another interesting function with the `lm()`

command (where `lm`

stands for *linear model*).

> result <- lm(Bronze$Maxwi ~ Bronze$Maxle) > result Call: lm(formula = Maxwi ~ Maxle) Coefficients: (Intercept) Maxle 1.5053 0.1277

- note that the order of arguments to
`lm()`

is inverse: the basic use is`lm(y ~ x)`

(with`y`

as*dependent*variable) - the result of
`lm()`

is… a rect. You can see by yourself*plotting*it over the scatterplot

> abline(result$coefficients, col="blue")

Plotting the `lm()`

result by itself like

> plot(result)

gives you more informative graphs about the linear model, but their content is beyond the scope of this tutorial.

Start · Data description · Transforming variables · Tables · Pictorial displays · Measures of position and variability · Sampling · Tests of difference · Tests of distribution · **Correlation** · Tests of association

diggingnumbers/correlation.txt · Last modified: 2018/08/04 00:01 (external edit)