Concise example of a basic linear regression in Julia, using GLM and RDatasets.

Reference:

In [1]:

using RDatasets, GLM

In [8]:

# List Datasets in the RDatasets julia library, in the "ISLR" package (there's lots more)
RDatasets.datasets("ISLR")

Out[8]:

11 rows × 5 columns

	Package	Dataset	Title	Rows	Columns
	String15	String31	String	Int64	Int64
1	ISLR	Auto	Auto Data Set	392	9
2	ISLR	Caravan	The Insurance Company (TIC) Benchmark	5822	86
3	ISLR	Carseats	Sales of Child Car Seats	400	11
4	ISLR	College	U.S. News and World Report's College Data	777	19
5	ISLR	Default	Credit Card Default Data	10000	4
6	ISLR	Hitters	Baseball Data	322	20
7	ISLR	OJ	Orange Juice Data	1070	18
8	ISLR	Portfolio	Portfolio Data	100	2
9	ISLR	Smarket	S&P Stock Market Data	1250	9
10	ISLR	Wage	Mid-Atlantic Wage Data	3000	12
11	ISLR	Weekly	Weekly S&P Stock Market Data	1089	9

In [11]:

# RDatasets has a dataset() function to reads in a dataset from the catalog
auto = dataset("ISLR", "Auto");
first(auto, 5)

Out[11]:

5 rows × 9 columns (omitted printing of 1 columns)

	MPG	Cylinders	Displacement	Horsepower	Weight	Acceleration	Year	Origin
	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64
1	18.0	8.0	307.0	130.0	3504.0	12.0	70.0	1.0
2	15.0	8.0	350.0	165.0	3693.0	11.5	70.0	1.0
3	18.0	8.0	318.0	150.0	3436.0	11.0	70.0	1.0
4	16.0	8.0	304.0	150.0	3433.0	12.0	70.0	1.0
5	17.0	8.0	302.0	140.0	3449.0	10.5	70.0	1.0

In [22]:

# Call GLM, note you just use the column names without any Str of Symbol stuff
ols = lm(@formula(MPG ~ Cylinders + Displacement), auto)

Out[22]:

StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}, Matrix{Float64}}

MPG ~ 1 + Cylinders + Displacement

Coefficients:
─────────────────────────────────────────────────────────────────────────────
                   Coef.  Std. Error      t  Pr(>|t|)   Lower 95%   Upper 95%
─────────────────────────────────────────────────────────────────────────────
(Intercept)   36.5377     1.19661     30.53    <1e-99  34.1851     38.8903
Cylinders     -0.576348   0.443276    -1.30    0.1943  -1.44786     0.295169
Displacement  -0.0511185  0.00722576  -7.07    <1e-11  -0.0653249  -0.0369121
─────────────────────────────────────────────────────────────────────────────

In [ ]: