Prepares the data for dfbetas plot.
Examples
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
dfb <- dfbetas(model)
n <- nrow(dfb)
threshold <- 2 / sqrt(n)
dbetas <- dfb[, 1]
df_data <- data.frame(obs = seq_len(n), dbetas = dbetas)
ols_prep_dfbeta_data(df_data, threshold)
#> obs dbetas color fct_color txt
#> Mazda RX4 1 -0.2408625040 normal normal NA
#> Mazda RX4 Wag 2 -0.0893854682 normal normal NA
#> Datsun 710 3 -0.0226714112 normal normal NA
#> Hornet 4 Drive 4 0.0165461439 normal normal NA
#> Hornet Sportabout 5 -0.0001791988 normal normal NA
#> Valiant 6 0.2161392861 normal normal NA
#> Duster 360 7 0.0559934442 normal normal NA
#> Merc 240D 8 0.0339246041 normal normal NA
#> Merc 230 9 0.4484991305 outlier outlier 9
#> Merc 280 10 -0.0178054777 normal normal NA
#> Merc 280C 11 -0.0032897727 normal normal NA
#> Merc 450SE 12 0.0270335275 normal normal NA
#> Merc 450SL 13 -0.0022994410 normal normal NA
#> Merc 450SLC 14 0.0663413205 normal normal NA
#> Cadillac Fleetwood 15 -0.0010867064 normal normal NA
#> Lincoln Continental 16 -0.0129594354 normal normal NA
#> Chrysler Imperial 17 -0.0672243472 normal normal NA
#> Fiat 128 18 -0.0353581676 normal normal NA
#> Honda Civic 19 0.0591178524 normal normal NA
#> Toyota Corolla 20 -0.3583658816 outlier outlier 20
#> Toyota Corona 21 0.3763120390 outlier outlier 21
#> Dodge Challenger 22 -0.2073631012 normal normal NA
#> AMC Javelin 23 -0.1393390661 normal normal NA
#> Camaro Z28 24 -0.0209471589 normal normal NA
#> Pontiac Firebird 25 0.0503356537 normal normal NA
#> Fiat X1-9 26 -0.0059464066 normal normal NA
#> Porsche 914-2 27 0.1174959855 normal normal NA
#> Lotus Europa 28 0.1206617098 normal normal NA
#> Ford Pantera L 29 -0.0016098012 normal normal NA
#> Ferrari Dino 30 -0.0519535108 normal normal NA
#> Maserati Bora 31 -0.2994696671 normal normal NA
#> Volvo 142E 32 -0.0168421538 normal normal NA