Fit a linear model for one bootstrap replicate

Fit a linear model for one bootstrap replicate

Usage

fit_linear(data, formula, select)

Arguments

data

A data frame - one bootstrap replicate.

formula

Model formula offering the candidate terms.

select

List with method ("stepwise" or "none"), sle, sls, max_steps. %bootreg equivalents: SELECT=, SLE=, SLS=, MAXSTEP=.

Value

Named numeric vector of kept coefficients, or NULL if the fit failed. NULL tells boot_select() to discard the replicate and draw another, reproducing %bootreg's &regrc check. A warning does not count as failure - the fit is kept, matching the macro, whose &regrc is a return code that warnings do not set. A zero-length result is likewise not a failure: it means selection kept no terms, which is a valid replicate.

Details

Under select = "stepwise", entry and removal both test the partial F, matching PROC REG SELECTION=STEPWISE.

See also

fit_logistic() and fit_cox(), the other two PROC= values %bootreg supports; boot_select(), which calls a fitter once per replicate.

Examples

set.seed(1)
n  <- 200
x1 <- rnorm(n)
df <- data.frame(y = 2 * x1 + rnorm(n), x1 = x1, noise = rnorm(n))

# `method = "stepwise"` is the macro's default; `"none"` is FIXED=1, which
# fits the model as written and bootstraps its coefficients instead.
fit_linear(df, y ~ x1 + noise,
           list(method = "stepwise", sle = 0.10, sls = 0.05, max_steps = 0))
#> (Intercept)          x1 
#>  0.04145787  1.97692216 

fit_linear(df, y ~ x1 + noise,
           list(method = "none", sle = 0.10, sls = 0.05, max_steps = 0))
#> (Intercept)          x1       noise 
#>  0.04040757  1.97877032 -0.02354057