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Multiple linear regression (ordinary least squares)

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

Multivariable OLS regression of a response on two or more predictors: the coefficient table (estimate, standard error, t-statistic, p-value) plus the overall model fit (R-squared, adjusted R-squared and the F-test). The many-predictor generalisation of linearRegression. Computed via the same QR decomposition as base R, so the coefficients, R-squared and F reproduce stats::lm / summary.lm bit-for-bit and statsmodels OLS to machine precision.

Usage

multipleRegression(x)

Arguments

x

A matrix or data frame whose first column is the response and whose remaining columns are the predictors. Incomplete rows are dropped.

Value

A data frame with one row per term (term, estimate, std_error, statistic, p_value), carrying r_squared, adj_r_squared, f_statistic, df1, df2, f_p_value and n as attributes.

See also

linearRegression, partialCorrelation

Examples

x <- matrix(rnorm(120), ncol = 3); colnames(x) <- c("y", "a", "b")
multipleRegression(x)
#>          term     estimate std_error   statistic   p_value
#> 1 (Intercept) -0.107750779 0.1605314 -0.67121305 0.5062539
#> 2           a  0.008988436 0.1431897  0.06277294 0.9502852
#> 3           b -0.004637306 0.1866392 -0.02484637 0.9803110