*cor*function of the

*stat*package to calculate correlation coefficient between variables. Second, he can use functions such as

*pairs*

*(graphics)*to visually check possible correlated variables. Third, he can combine the first two approach following the example of vinux in stackoverflow

*or using*

*ggpairs*function of

*GGally*package.

*First Approach*

Sepal.Length Sepal.Width Petal.Length Petal.Width

Sepal.Length 1.0000000 -0.1175698 0.8717538 0.8179411

Sepal.Width -0.1175698 1.0000000 -0.4284401 -0.3661259

Petal.Length 0.8717538 -0.4284401 1.0000000 0.9628654

Petal.Width 0.8179411 -0.3661259 0.9628654 1.0000000

*Second Approach*

*Third Approach*
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ReplyDeleteAnother way to look at correlation is with correlograms. An overview is here: http://www.statmethods.net/advgraphs/correlograms.html

ReplyDeleteTry

corrgram(iris, upper.panel=panel.pts, lower.panel=panel.ellipse, diag.panel=panel.density)

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ReplyDeleteHello, you show us three great approaches for correlations, thanks! I wonder about two optional things.

ReplyDelete1) In third approach, is there a possible set up which marks all significant correlations with * / ** / ***, depending on the given significance niveau?

2) (General question) Does it make sense to add a regression line into each correlation diagram, and if yes (specific question), how can this be done best way (e.g. in solution 3)?

Is there is any ways to convert scatterplot into plotly interactive graph.

ReplyDeletei am taliking about pair() function approach for scatter plot