Get the values from model weights — get

Returns weighting variable of a model.

Usage

get_weights(x, ...)

# Default S3 method
get_weights(x, remove_na = FALSE, null_as_ones = FALSE, ...)

Arguments

x: A fitted model.
...: Currently not used.
remove_na: Logical, if TRUE, removes possible missing values.
null_as_ones: Logical, if TRUE, will return a vector of 1 if no weights were specified in the model (as if the weights were all set to 1).

Value

The weighting variable, or NULL if no weights were specified. If the weighting variable should also be returned (instead of NULL) when all weights are set to 1 (i.e. no weighting), set null_as_ones = TRUE.

Examples

data(mtcars)
set.seed(123)
mtcars$weight <- rnorm(nrow(mtcars), 1, .3)

# LMs
m <- lm(mpg ~ wt + cyl + vs, data = mtcars, weights = weight)
get_weights(m)
#>  [1] 0.8318573 0.9309468 1.4676125 1.0211525 1.0387863 1.5145195 1.1382749
#>  [8] 0.6204816 0.7939441 0.8663014 1.3672245 1.1079441 1.1202314 1.0332048
#> [15] 0.8332477 1.5360739 1.1493551 0.4100149 1.2104068 0.8581626 0.6796529
#> [22] 0.9346075 0.6921987 0.7813326 0.8124882 0.4939920 1.2513361 1.0460119
#> [29] 0.6585589 1.3761445 1.1279393 0.9114786

get_weights(lm(mpg ~ wt, data = mtcars), null_as_ones = TRUE)
#>  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

# GLMs
m <- glm(vs ~ disp + mpg, data = mtcars, weights = weight, family = quasibinomial)
get_weights(m)
#>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
#>           0.8318573           0.9309468           1.4676125           1.0211525 
#>   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
#>           1.0387863           1.5145195           1.1382749           0.6204816 
#>            Merc 230            Merc 280           Merc 280C          Merc 450SE 
#>           0.7939441           0.8663014           1.3672245           1.1079441 
#>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
#>           1.1202314           1.0332048           0.8332477           1.5360739 
#>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
#>           1.1493551           0.4100149           1.2104068           0.8581626 
#>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
#>           0.6796529           0.9346075           0.6921987           0.7813326 
#>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
#>           0.8124882           0.4939920           1.2513361           1.0460119 
#>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
#>           0.6585589           1.3761445           1.1279393           0.9114786 
m <- glm(cbind(cyl, gear) ~ mpg, data = mtcars, weights = weight, family = binomial)
get_weights(m)
#>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
#>            8.318573            9.309468           11.740900            9.190373 
#>   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
#>           11.426650           13.630675           12.521023            4.963853 
#>            Merc 230            Merc 280           Merc 280C          Merc 450SE 
#>            6.351553            8.663014           13.672245           12.187386 
#>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
#>           12.322546           11.365253            9.165724           16.896813 
#>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
#>           12.642907            3.280119            9.683254            6.865301 
#>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
#>            4.757570           10.280683            7.614185            8.594659 
#>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
#>            8.937370            3.951936           11.262025            9.414107 
#>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
#>            8.561266           15.137589           14.663210            7.291828