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
lm()
is inverse: the basic use is lm(y ~ x)
(with y
as dependent variable)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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