# Correlation

In R, basic correlation tests are executed with two commands: `cor()` and `lm()` (where `lm` stands for linear model).

## Calculating correlation

To calculate product moment correlation coefficient between `Maxle` and `Maxwi` for bronze spears:

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

To calculate Spearman's rank correlation coefficient between `Date` and `Weight` for bronze spears:

``````> cor(Bronze\$Date, Bronze\$Weight, method="spearman")
 0.1269293
``````

## Plotting correlation

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

``````
1. note that the order of arguments to `lm()` is inverse: the basic use is `lm(y ~ x)` (with `y` as dependent variable)
2. 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.

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