Linear regression models a linear relationship of a scalar dependent variable \( y \) to one or more explanatory independent variables \( x \) and builds a model of coefficients.
The linear regression training function has the following syntax.
linregr_train( source_table, out_table, dependent_varname, independent_varname, grouping_cols, heteroskedasticity_option )
Arguments
TEXT. Name of the table containing the training data.
TEXT. Name of the generated table containing the output model.
The output table contains the following columns:
<...> | Any grouping columns provided during training. Present only if the grouping option is used. |
---|---|
coef | FLOAT8[]. Vector of the coefficients of the regression. |
r2 | FLOAT8. R-squared coefficient of determination of the model. |
std_err | FLOAT8[]. Vector of the standard error of the coefficients. |
t_stats | FLOAT8[]. Vector of the t-statistics of the coefficients. |
p_values | FLOAT8[]. Vector of the p-values of the coefficients. |
condition_no | FLOAT8 array. The condition number of the \(X^{*}X\) matrix. A high condition number is usually an indication that there may be some numeric instability in the result yielding a less reliable model. A high condition number often results when there is a significant amount of colinearity in the underlying design matrix, in which case other regression techniques, such as elastic net regression, may be more appropriate. |
bp_stats | FLOAT8. The Breush-Pagan statistic of heteroskedacity. Present only if the heteroskedacity argument was set to True when the model was trained. |
bp_p_value | FLOAT8. The Breush-Pagan calculated p-value. Present only if the heteroskedacity parameter was set to True when the model was trained. |
num_rows_processed | BIGINT. The number of rows that are actually used in each group. |
num_missing_rows_skipped | INTEGER. The number of rows that have NULL values in the dependent and independent variables, and were skipped in the computation for each group. |
variance_covariance | FLOAT[]. Variance/covariance matrix. |
A summary table named <out_table>_summary is created together with the output table. It has the following columns:
method | 'linregr' for linear regression. |
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source_table | The data source table name |
out_table | The output table name |
dependent_varname | The dependent variable |
independent_varname | The independent variables |
num_rows_processed | The total number of rows that were used in the computation. |
num_missing_rows_skipped | The total number of rows that were skipped because of NULL values in them. |
grouping_cols | Names of the grouping columns. |
TEXT. Expression to evaluate for the dependent variable.
TEXT. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1
term in the independent variable list.
TEXT, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL GROUP BY
clause. When this value is null, no grouping is used and a single result model is generated for the whole data set.
CREATE TABLE <output table> AS SELECT (r).* FROM ( SELECT linregr(<dependent variable>, <independent variable>) as r FROM <source table> ) q;
linregr_predict(coef, col_ind)Arguments
DROP TABLE IF EXISTS houses; CREATE TABLE houses (id INT, tax INT, bedroom INT, bath FLOAT, price INT, size INT, lot INT); INSERT INTO houses VALUES (1 , 590 , 2 , 1 , 50000 , 770 , 22100), (2 , 1050 , 3 , 2 , 85000 , 1410 , 12000), (3 , 20 , 3 , 1 , 22500 , 1060 , 3500), (4 , 870 , 2 , 2 , 90000 , 1300 , 17500), (5 , 1320 , 3 , 2 , 133000 , 1500 , 30000), (6 , 1350 , 2 , 1 , 90500 , 820 , 25700), (7 , 2790 , 3 , 2.5 , 260000 , 2130 , 25000), (8 , 680 , 2 , 1 , 142500 , 1170 , 22000), (9 , 1840 , 3 , 2 , 160000 , 1500 , 19000), (10 , 3680 , 4 , 2 , 240000 , 2790 , 20000), (11 , 1660 , 3 , 1 , 87000 , 1030 , 17500), (12 , 1620 , 3 , 2 , 118600 , 1250 , 20000), (13 , 3100 , 3 , 2 , 140000 , 1760 , 38000), (14 , 2070 , 2 , 3 , 148000 , 1550 , 14000), (15 , 650 , 3 , 1.5 , 65000 , 1450 , 12000);
DROP TABLE IF EXISTS houses_linregr, houses_linregr_summary; SELECT madlib.linregr_train( 'houses', 'houses_linregr', 'price', 'ARRAY[1, tax, bath, size]' );(Note that in this example we are dynamically creating the array of independent variables from column names. If you have large numbers of independent variables beyond the PostgreSQL limit of maximum columns per table, you would pre-build the arrays and store them in a single column.)
DROP TABLE IF EXISTS houses_linregr_bedroom, houses_linregr_bedroom_summary; SELECT madlib.linregr_train( 'houses', 'houses_linregr_bedroom', 'price', 'ARRAY[1, tax, bath, size]', 'bedroom' );
-- Set extended display on for easier reading of output \x ON SELECT * FROM houses_linregr;Result:
-[ RECORD 1 ]+--------------------------------------------------------------------------- coef | {-12849.4168959872,28.9613922651765,10181.6290712648,50.516894915354} r2 | 0.768577580597443 std_err | {33453.0344331391,15.8992104963997,19437.7710925923,32.928023174087} t_stats | {-0.38410317968819,1.82156166004184,0.523806408809133,1.53416118083605} p_values | {0.708223134615422,0.0958005827189772,0.610804093526536,0.153235085548186} condition_no | 9002.50457085737 num_rows_processed | 15 num_missing_rows_skipped | 0 variance_covariance | {{1119105512.78479,217782.067878023,-283344228.394562,-616679.69319088}, ...Alternatively you can unnest the results for easier reading of output.
\x OFF SELECT unnest(ARRAY['intercept','tax','bath','size']) as attribute, unnest(coef) as coefficient, unnest(std_err) as standard_error, unnest(t_stats) as t_stat, unnest(p_values) as pvalue FROM houses_linregr;Result:
attribute | coefficient | standard_error | t_stat | pvalue -----------+-------------------+------------------+-------------------+-------------------- intercept | -12849.4168959872 | 33453.0344331391 | -0.38410317968819 | 0.708223134615422 tax | 28.9613922651765 | 15.8992104963997 | 1.82156166004184 | 0.0958005827189772 bath | 10181.6290712648 | 19437.7710925923 | 0.523806408809133 | 0.610804093526536 size | 50.516894915354 | 32.928023174087 | 1.53416118083605 | 0.153235085548186 (4 rows)
\x ON SELECT * FROM houses_linregr_bedroom ORDER BY bedroom;Result:
-[ RECORD 1 ]------------+---------------------------------------------------------------- bedroom | 4 coef | {0.0112536020318378,41.4132554771633,0.0225072040636757,31.3975496688276} r2 | 1 std_err | {0,0,0,0} t_stats | {Infinity,Infinity,Infinity,Infinity} p_values | condition_no | Infinity num_rows_processed | 1 num_missing_rows_skipped | 0 variance_covariance | {{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0}} -[ RECORD 2 ]------------+---------------------------------------------------------------- bedroom | 3 coef | {-88155.8292501601,27.1966436294429,41404.0293363612,62.637521075324} r2 | 0.841699901311252 std_err | {57867.9999702625,17.8272309154689,43643.1321511114,70.8506824863954} t_stats | {-1.52339512849005,1.52556747362508,0.948695185143966,0.884077878676067} p_values | {0.188161432894871,0.187636685729869,0.386340032374927,0.417132778705789} condition_no | 11722.6225642147 num_rows_processed | 9 num_missing_rows_skipped | 0 variance_covariance | {{3348705420.5583,433697.545104226,-70253017.45773,-2593488.13800193}, ... -[ RECORD 3 ]------------+---------------------------------------------------------------- bedroom | 2 coef | {-84242.0345406597,55.4430144648696,-78966.9753675319,225.611910021192} r2 | 0.968809546465313 std_err | {35018.9991665742,19.5731125320686,23036.8071292552,49.0448678148784} t_stats | {-2.40560942761235,2.83261103077151,-3.42786111480046,4.60011251070697} p_values | {0.250804617665239,0.21605133377602,0.180704400437373,0.136272031474122} condition_no | 10086.1048721726 num_rows_processed | 5 num_missing_rows_skipped | 0 variance_covariance | {{1226330302.62852,-300921.595596804,551696673.397849,-1544160.63236119}, ...
\x OFF SELECT houses.*, madlib.linregr_predict( m.coef, ARRAY[1,tax,bath,size] ) as predict, price - madlib.linregr_predict( m.coef, ARRAY[1,tax,bath,size] ) as residual FROM houses, houses_linregr m ORDER BY id;Result:
id | tax | bedroom | bath | price | size | lot | predict | residual ----+------+---------+------+--------+------+-------+------------------+------------------- 1 | 590 | 2 | 1 | 50000 | 770 | 22100 | 53317.4426965542 | -3317.44269655424 2 | 1050 | 3 | 2 | 85000 | 1410 | 12000 | 109152.124955627 | -24152.1249556268 3 | 20 | 3 | 1 | 22500 | 1060 | 3500 | 51459.3486308563 | -28959.3486308563 4 | 870 | 2 | 2 | 90000 | 1300 | 17500 | 98382.215907206 | -8382.21590720605 5 | 1320 | 3 | 2 | 133000 | 1500 | 30000 | 121518.221409606 | 11481.7785903937 6 | 1350 | 2 | 1 | 90500 | 820 | 25700 | 77853.9455638561 | 12646.0544361439 7 | 2790 | 3 | 2.5 | 260000 | 2130 | 25000 | 201007.926371721 | 58992.0736282788 8 | 680 | 2 | 1 | 142500 | 1170 | 22000 | 76130.7259665617 | 66369.2740334383 9 | 1840 | 3 | 2 | 160000 | 1500 | 19000 | 136578.145387498 | 23421.8546125019 10 | 3680 | 4 | 2 | 240000 | 2790 | 20000 | 255033.90159623 | -15033.9015962295 11 | 1660 | 3 | 1 | 87000 | 1030 | 17500 | 97440.5250982852 | -10440.5250982852 12 | 1620 | 3 | 2 | 118600 | 1250 | 20000 | 117577.415360321 | 1022.58463967926 13 | 3100 | 3 | 2 | 140000 | 1760 | 38000 | 186203.892319613 | -46203.8923196126 14 | 2070 | 2 | 3 | 148000 | 1550 | 14000 | 155946.739425521 | -7946.73942552117 15 | 650 | 3 | 1.5 | 65000 | 1450 | 12000 | 94497.4293105379 | -29497.4293105379 (15 rows)
\x OFF SELECT houses.*, madlib.linregr_predict( m.coef, ARRAY[1,tax,bath,size] ) as predict, price - madlib.linregr_predict( m.coef, ARRAY[1,tax,bath,size] ) as residual FROM houses, houses_linregr_bedroom m WHERE houses.bedroom = m.bedroom ORDER BY id;Result:
id | tax | bedroom | bath | price | size | lot | predict | residual ----+------+---------+------+--------+------+-------+------------------+------------------- 1 | 590 | 2 | 1 | 50000 | 770 | 22100 | 43223.5393423978 | 6776.46065760222 2 | 1050 | 3 | 2 | 85000 | 1410 | 12000 | 111527.609949684 | -26527.609949684 3 | 20 | 3 | 1 | 22500 | 1060 | 3500 | 20187.9052986341 | 2312.09470136587 4 | 870 | 2 | 2 | 90000 | 1300 | 17500 | 99354.9203362612 | -9354.92033626116 5 | 1320 | 3 | 2 | 133000 | 1500 | 30000 | 124508.080626412 | 8491.91937358756 6 | 1350 | 2 | 1 | 90500 | 820 | 25700 | 96640.8258367579 | -6140.8258367579 7 | 2790 | 3 | 2.5 | 260000 | 2130 | 25000 | 224650.799707327 | 35349.2002926733 8 | 680 | 2 | 1 | 142500 | 1170 | 22000 | 138458.174652714 | 4041.82534728572 9 | 1840 | 3 | 2 | 160000 | 1500 | 19000 | 138650.335313722 | 21349.6646862777 10 | 3680 | 4 | 2 | 240000 | 2790 | 20000 | 240000 | 0 11 | 1660 | 3 | 1 | 87000 | 1030 | 17500 | 62911.2752186594 | 24088.7247813406 12 | 1620 | 3 | 2 | 118600 | 1250 | 20000 | 117007.693446414 | 1592.30655358579 13 | 3100 | 3 | 2 | 140000 | 1760 | 38000 | 189203.861766403 | -49203.8617664034 14 | 2070 | 2 | 3 | 148000 | 1550 | 14000 | 143322.539831869 | 4677.46016813093 15 | 650 | 3 | 1.5 | 65000 | 1450 | 12000 | 82452.4386727394 | -17452.4386727394 (15 rows)
Ordinary least-squares (OLS) linear regression refers to a stochastic model in which the conditional mean of the dependent variable (usually denoted \( Y \)) is an affine function of the vector of independent variables (usually denoted \( \boldsymbol x \)). That is,
\[ E[Y \mid \boldsymbol x] = \boldsymbol c^T \boldsymbol x \]
for some unknown vector of coefficients \( \boldsymbol c \). The assumption is that the residuals are i.i.d. distributed Gaussians. That is, the (conditional) probability density of \( Y \) is given by
\[ f(y \mid \boldsymbol x) = \frac{1}{\sqrt{2 \pi \sigma^2}} \cdot \exp\left(-\frac{1}{2 \sigma^2} \cdot (y - \boldsymbol x^T \boldsymbol c)^2 \right) \,. \]
OLS linear regression finds the vector of coefficients \( \boldsymbol c \) that maximizes the likelihood of the observations.
Let
Maximizing the likelihood is equivalent to maximizing the log-likelihood \( \sum_{i=1}^n \log f(y_i \mid \boldsymbol x_i) \), which simplifies to minimizing the residual sum of squares \( RSS \) (also called sum of squared residuals or sum of squared errors of prediction),
\[ RSS = \sum_{i=1}^n ( y_i - \boldsymbol c^T \boldsymbol x_i )^2 = (\boldsymbol y - X \boldsymbol c)^T (\boldsymbol y - X \boldsymbol c) \,. \]
The first-order conditions yield that the \( RSS \) is minimized at
\[ \boldsymbol c = (X^T X)^+ X^T \boldsymbol y \,. \]
Computing the total sum of squares \( TSS \), the explained sum of squares \( ESS \) (also called the regression sum of squares), and the coefficient of determination \( R^2 \) is done according to the following formulas:
\begin{align*} ESS & = \boldsymbol y^T X \boldsymbol c - \frac{ \| y \|_1^2 }{n} \\ TSS & = \sum_{i=1}^n y_i^2 - \frac{ \| y \|_1^2 }{n} \\ R^2 & = \frac{ESS}{TSS} \end{align*}
Note: The last equality follows from the definition \( R^2 = 1 - \frac{RSS}{TSS} \) and the fact that for linear regression \( TSS = RSS + ESS \). A proof of the latter can be found, e.g., at: http://en.wikipedia.org/wiki/Sum_of_squares
We estimate the variance \( Var[Y - \boldsymbol c^T \boldsymbol x \mid \boldsymbol x] \) as
\[ \sigma^2 = \frac{RSS}{n - k} \]
and compute the t-statistic for coefficient \( i \) as
\[ t_i = \frac{c_i}{\sqrt{\sigma^2 \cdot \left( (X^T X)^{-1} \right)_{ii} }} \,. \]
The \( p \)-value for coefficient \( i \) gives the probability of seeing a value at least as extreme as the one observed, provided that the null hypothesis ( \( c_i = 0 \)) is true. Letting \( F_\nu \) denote the cumulative density function of student-t with \( \nu \) degrees of freedom, the \( p \)-value for coefficient \( i \) is therefore
\[ p_i = \Pr(|T| \geq |t_i|) = 2 \cdot (1 - F_{n - k}( |t_i| )) \]
where \( T \) is a student-t distributed random variable with mean 0.
The condition number [2] \( \kappa(X) = \|X\|_2\cdot\|X^{-1}\|_2\) is computed as the product of two spectral norms [3]. The spectral norm of a matrix \(X\) is the largest singular value of \(X\) i.e. the square root of the largest eigenvalue of the positive-semidefinite matrix \(X^{*}X\):
\[ \|X\|_2 = \sqrt{\lambda_{\max}\left(X^{*}X\right)}\ , \]
where \(X^{*}\) is the conjugate transpose of \(X\). The condition number of a linear regression problem is a worst-case measure of how sensitive the result is to small perturbations of the input. A large condition number (say, more than 1000) indicates the presence of significant multicollinearity.
[1] Cosma Shalizi: Statistics 36-350: Data Mining, Lecture Notes, 21 October 2009, http://www.stat.cmu.edu/~cshalizi/350/lectures/17/lecture-17.pdf
[2] Wikipedia: Condition Number, http://en.wikipedia.org/wiki/Condition_number.
[3] Wikipedia: Spectral Norm, http://en.wikipedia.org/wiki/Spectral_norm#Spectral_norm
[4] Wikipedia: Breusch–Pagan test, http://en.wikipedia.org/wiki/Breusch%E2%80%93Pagan_test
[5] Wikipedia: Heteroscedasticity-consistent standard errors, http://en.wikipedia.org/wiki/Heteroscedasticity-consistent_standard_errors
File linear.sql_in, source file for the SQL functions