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Simple linear regression (ordinary least squares)

Source: R/ops-timefreq.R
linearRegression.Rd

Univariate OLS regression of y on x: the slope, intercept, R-squared, and the two-sided t-test of the slope. The slope, R-squared and p-value reproduce base R stats::lm / summary.lm and scipy.stats.linregress bit-for-bit (to machine precision against lm's QR path).

Usage

linearRegression(x, y = NULL)

Arguments

x

Either the numeric predictor vector (with y the response), or a two-column matrix/data frame (column 1 = x, column 2 = y), or a list of two numeric vectors. Incomplete (x, y) pairs are dropped.

y

The numeric response vector when x is a single predictor vector.

Value

A list with slope, intercept, r_squared, statistic (the slope t-statistic), p_value (two-sided), df (n - 2) and n.

See also

correlationTest

Examples

linearRegression(1:20, 2 * (1:20) + rnorm(20))
#> $slope
#> [1] 1.97987
#> 
#> $intercept
#> [1] 0.2700651
#> 
#> $r_squared
#> [1] 0.9854246
#> 
#> $statistic
#> [1] 34.88495
#> 
#> $p_value
#> [1] 5.542676e-18
#> 
#> $df
#> [1] 18
#> 
#> $n
#> [1] 20
#>