The package has been renamed LinearRegressionKit.jl because the name LinearRegression.jl was already registered. The package LinearRegression.jl won't be updated anymore, and you should use LinearRegressionKit.jl from now on. If there is any question please ask them on LinearRegressionKit.jl
LinearRegression.jl implements linear regression using the least-squares algorithm (relying on the sweep operator). This package is in the alpha stage. Hence it is likely that some bugs exist. Furthermore, the API might change in future versions. User's or prospective users' feedback is welcome.
Enter the Pkg REPL by pressing ] from the Julia REPL. Then install the package with: pkg> add https://github.com/ericqu/LinearRegression.jl.git.
To uninstall use pkg> rm LinearRegression
The following is a simple usage:
using LinearRegression, DataFrames, StatsModels
x = [0.68, 0.631, 0.348, 0.413, 0.698, 0.368, 0.571, 0.433, 0.252, 0.387, 0.409, 0.456, 0.375, 0.495, 0.55, 0.576, 0.265, 0.299, 0.612, 0.631]
y = [15.72, 14.86, 6.14, 8.21, 17.07, 9.07, 14.68, 10.37, 5.18, 9.36, 7.61, 10.43, 8.93, 10.33, 14.46, 12.39, 4.06, 4.67, 13.73, 14.75]
df = DataFrame(y=y, x=x)
lr = regress(@formula(y ~ 1 + x), df)
which outputs the following information:
Model definition: y ~ 1 + x
Used observations: 20
Model statistics:
R²: 0.938467 Adjusted R²: 0.935049
MSE: 1.01417 RMSE: 1.00706
σ̂²: 1.01417
Confidence interval: 95%
Coefficients statistics:
Terms ╲ Stats │ Coefs Std err t Pr(>|t|) low ci high ci
──────────────┼─────────────────────────────────────────────────────────────────────────────
(Intercept) │ -2.44811 0.819131 -2.98867 0.007877 -4.16904 -0.727184
x │ 27.6201 1.66699 16.5688 2.41337e-12 24.1179 31.1223
First, the GLM package provides more than linear regression with Ordinary Least-Squares through the Generalized Linear Model with Maximum Likelihood Estimation.
LinearRegression now accept model without intercept. Like models made with GLM the intercept is implicit, and to enable the no intercept the user must specify it in the formula (for instance y ~ 0 + x).
LinearRegression now supports analytical weights; GLM supports frequency weights.
Both LinearRegression and GLM rely on StatsModels.jl for the model's description (@formula); hence it is easy to move between the two packages. Similarly, contrasts and categorical variables are defined in the same way facilitating moving from one to the other when needed.
LinearRegression relies on the Sweep operator to estimate the coefficients, and GLM depends on Cholesky and QR factorizations.
The Akaike information criterion (AIC) is calculated with the formula relevant only for Linear Regression hence enabling comparison between linear regressions (AIC=n log(SSE / n) + 2p; where SSE is the Sum of Squared Errors and p is the number of predictors). On the other hand, the AIC calculated with GLM is more general (based on log-likelihood), enabling comparison between a broader range of models.
LinearRegression package provides access to some robust covariance estimators (for Heteroscedasticity: White, HC0, HC1, HC2 and HC3 and for HAC: Newey-West)
- AIC: Akaike information criterion with the formula AIC=n log(SSE / n) + 2p; where SSE is the Sum of Squared Errors and p is the number of predictors.
- SSE Sum of Squared Errors as the output from the sweep operator.
- SST as the Total Sum of Squares as the sum over all squared differences between the observations and their overall mean.
- R² as 1 - SSE/SST.
- Adjusted R².
- σ̂² (sigma) Estimate of the error variance.
- Variance Inflation Factor.
- CI the confidence interval based the \alpha default value of 0.05 giving the 95% confidence interval.
- The t-statistic.
- The mean squared error.
- The root of the mean squared error.
- The standard errors and their equivalent with a Heteroscedasticity or HAC covariance estimator
- The t values and their equivalent with a Heteroscedasticity or HAC covariance estimator
- P values for each predictor and their equivalent with a Heteroscedasticity or HAC covariance estimator
- Type 1 & 2 Sum of squares
- Squared partial correlation coefficient, squared semi-partial correlation coefficient.
- PRESS as the sum of square of predicted residuals errors
- The predicted values
- The residuals values (as the actual values minus the predicted ones)
- The Leverage or the i-th diagonal element of the projection matrix.
- STDI is the standard error of the individual predicted value.
- STDP is the standard error of the mean predicted value
- STDR is the standard error of the residual
- Student as the studentized residuals also knows as the Standardized residuals or internally studentized residuals.
- Rstudent is the studentized residual with the current observation deleted.
- LCLI is the lower bound of the confidence interval for the individual prediction.
- UCLI is the upper bound of the confidence interval for the individual prediction.
- LCLP is the lower bound of the confidence interval for the expected (mean) value.
- UCLP is the upper bound of the confidence interval for the expected (mean) value.
- Cook's Distance
- PRESS as predicted residual errors
Please post your questions, feedabck or issues in the Issues tabs. As much as possible, please provide relevant contextual information.
- Goodnight, J. (1979). "A Tutorial on the SWEEP Operator." The American Statistician.
- Gordon, R. A. (2015). Regression Analysis for the Social Sciences. New York and London: Routledge.
- https://blogs.sas.com/content/iml/2021/07/14/performance-ls-regression.html
- https://github.com/joshday/SweepOperator.jl
- http://hua-zhou.github.io/teaching/biostatm280-2019spring/slides/12-sweep/sweep.html
- https://github.com/mcreel/Econometrics for the Newey-West implementation
The following is a short example illustrating some statistics about the predicted data. First, a simulation of some data with a polynomial function.
using LinearRegression, DataFrames, StatsModels
using Distributions # for the data generation with Normal() and Uniform()
using VegaLite
# Data simulation
f(x) = @. (x^3 + 2.2345x - 1.2345 + rand(Normal(0, 20)))
xs = [x for x in -2:0.1:8]
ys = f(xs)
vdf = DataFrame(y=ys, x=xs)Then we can make the first model and look at the results:
lr, ps = regress(@formula(y ~ 1 + x^3 ), vdf, "all",
req_stats=["default", "vif", "AIC"],
plot_args=Dict("plot_width" => 200 ))
lrModel definition: y ~ 1 + x
Used observations: 101
Model statistics:
R²: 0.750957 Adjusted R²: 0.748441
MSE: 5693.68 RMSE: 75.4565
σ̂²: 5693.68 AIC: 875.338
Confidence interval: 95%
Coefficients statistics:
Terms ╲ Stats │ Coefs Std err t Pr(>|t|) low ci high ci VIF
──────────────┼──────────────────────────────────────────────────────────────────────────────────────────
(Intercept) │ -24.5318 10.7732 -2.27711 0.0249316 -45.9082 -3.15535 0.0
x │ 44.4953 2.57529 17.2778 1.20063e-31 39.3854 49.6052 1.0
This is pretty good, so let's further review some diagnostic plots.
[[ps["fit"] ps["residuals"]]
[ps["histogram density"] ps["qq plot"]]]
Please note that for the fit plot, the orange line shows the regression line, in dark grey the confidence interval for the mean, and in light grey the interval for the individuals predictions.
Plots are indicating the potential presence of a polynomial component. Hence one might try to add one by doing the following:
lr, ps = regress(@formula(y ~ 1 + x^3 ), vdf, "all",
req_stats=["default", "vif", "AIC"],
plot_args=Dict("plot_width" => 200 ))Giving:
Model definition: y ~ 1 + :(x ^ 3)
Used observations: 101
Model statistics:
R²: 0.979585 Adjusted R²: 0.979379
MSE: 466.724 RMSE: 21.6038
σ̂²: 466.724 AIC: 622.699
Confidence interval: 95%
Coefficients statistics:
Terms ╲ Stats │ Coefs Std err t Pr(>|t|) low ci high ci VIF
──────────────┼──────────────────────────────────────────────────────────────────────────────────────────
(Intercept) │ 1.23626 2.65774 0.465157 0.642841 -4.03726 6.50979 0.0
x ^ 3 │ 1.04075 0.0151001 68.9236 1.77641e-85 1.01079 1.07071 1.0
Further, in addition to the diagnostic plots helping confirm if the residuals are normally distributed, a few tests can be requested:
# Data simulation
f(x) = @. (x^3 + 2.2345x - 1.2345 + rand(Uniform(0, 20)))
xs = [x for x in -2:0.001:8]
ys = f(xs)
vdf = DataFrame(y=ys, x=xs)
lr = regress(@formula(y ~ 1 + x^3 ), vdf,
req_stats=["default", "vif", "AIC", "diag_normality"])Giving:
Model definition: y ~ 1 + :(x ^ 3)
Used observations: 10001
Model statistics:
R²: 0.997951 Adjusted R²: 0.997951
MSE: 43.4392 RMSE: 6.59084
σ̂²: 43.4392 AIC: 37719.4
Confidence interval: 95%
Coefficients statistics:
Terms ╲ Stats │ Coefs Std err t Pr(>|t|) low ci high ci VIF
──────────────┼──────────────────────────────────────────────────────────────────────────────────────────
(Intercept) │ 11.3151 0.0815719 138.714 0.0 11.1552 11.475 0.0
x ^ 3 │ 1.03984 0.000471181 2206.87 0.0 1.03892 1.04076 1.0
Diagnostic Tests:
Kolmogorov-Smirnov test (Normality of residuals):
KS statistic: 3.47709 observations: 10001 p-value: 0.0
with 95.0% confidence: reject null hyposthesis.
Anderson–Darling test (Normality of residuals):
A² statistic: 24.924901 observations: 10001 p-value: 0.0
with 95.0% confidence: reject null hyposthesis.
Jarque-Bera test (Normality of residuals):
JB statistic: 241.764504 observations: 10001 p-value: 0.0
with 95.0% confidence: reject null hyposthesis.
Here is how to request the robust covariance estimators:
lr = regress(@formula(y ~ 1 + x^3 ), vdf, cov=["white", "nw"])Giving:
Model definition: y ~ 1 + :(x ^ 3)
Used observations: 101
Model statistics:
R²: 0.979585 Adjusted R²: 0.979379
MSE: 466.724 RMSE: 21.6038
Confidence interval: 95%
White's covariance estimator (HC3):
Terms ╲ Stats │ Coefs Std err t Pr(>|t|) low ci high ci
──────────────┼─────────────────────────────────────────────────────────────────────────────
(Intercept) │ 1.23626 2.66559 0.463785 0.64382 -4.05285 6.52538
x ^ 3 │ 1.04075 0.0145322 71.6169 4.30034e-87 1.01192 1.06959
Newey-West's covariance estimator:
Terms ╲ Stats │ Coefs Std err t Pr(>|t|) low ci high ci
──────────────┼─────────────────────────────────────────────────────────────────────────────
(Intercept) │ 1.23626 2.4218 0.510472 0.610857 -3.56912 6.04165
x ^ 3 │ 1.04075 0.0129463 80.3897 5.60424e-92 1.01506 1.06644
Finally if you would like more examples I encourage you to go to the documentation as it gives a few more examples.
- Addition of cross-validation features: K-fold cross-validation and the PRESS statistic at the model level.
- Addition of several model statistics: Type 1 and 2 SS, squared partial correlation coefficients based on Type 1 SS and Type 2 SS, squared semi-partial correlation coefficients based on Type 1 and 2 SS.
- additional diagnostic using the frequency tables to identify a set of categories with no observation.