A wrapper for lme4::lmer for use in nadir::super_learner().
Arguments
- data
A dataframe to train a learner / learners on.
- formula
A regression formula to use inside this learner.
- weights
Observation weights; see
?lm- ...
Any extra arguments that should be passed to the internal model for model fitting purposes.
Value
A prediction function that accepts newdata,
which returns predictions (a numeric vector of values, one for each row
of newdata).
Examples
# random intercepts for each level of cyl column:
lnr_lmer(mtcars, mpg ~ (1 | cyl) + disp + am + wt)(mtcars)
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 21.65855 20.86347 26.33460 19.04361
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 16.90665 18.48289 16.50132 23.22732
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 23.38836 18.89864 18.89864 15.46074
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 16.52085 16.36495 10.57359 10.10495
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 10.47440 26.88915 28.73162 28.07399
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 25.65161 16.91580 17.26702 15.72104
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 15.39761 27.71356 26.82010 28.93021
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 17.96030 21.28321 17.02096 24.82030