A function to detect participants that consistently respond exceptionally.
exceptionalScores(
dat,
items = NULL,
exception = 0.025,
totalOnly = TRUE,
append = TRUE,
both = TRUE,
silent = FALSE,
suffix = "_isExceptional",
totalVarName = "exceptionalScores"
)The dataframe containing the variables to inspect, or the vector
to inspect (but for vectors, exceptionalScore() might be more
useful).
The names of the variables to inspect.
When an item will be considered exceptional, passed on as
prob to exceptionalScore().
Whether to return only the number of exceptional scores for each row in the dataframe, or for each inspected item, which values are exceptional.
Whether to return the supplied dataframe with the new variable(s) appended (if TRUE), or whether to only return the new variable(s) (if FALSE).
Whether to look for both low and high exceptional scores (TRUE)
or not (FALSE; see exceptionalScore()).
Can be used to suppress messages.
If not returning the total number of exceptional values, for each inspected variable, a new variable is returned indicating which values are exceptional. The text string is appended to each original variable name to create the new variable names.
If returning only the total number of exceptional values, and appending these to the provided dataset, this text string is used as variable name.
Either a vector containing the number of exceptional values, a dataset containing, for each inspected variable, which values are exceptional, or the provided dataset where either the total or the exceptional values for each variable are appended.
exceptionalScores(mtcars);
#> No items specified: extracting all variable names in dataframe.
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> exceptionalScores
#> Mazda RX4 0
#> Mazda RX4 Wag 0
#> Datsun 710 0
#> Hornet 4 Drive 0
#> Hornet Sportabout 0
#> Valiant 0
#> Duster 360 0
#> Merc 240D 0
#> Merc 230 1
#> Merc 280 0
#> Merc 280C 0
#> Merc 450SE 0
#> Merc 450SL 0
#> Merc 450SLC 0
#> Cadillac Fleetwood 1
#> Lincoln Continental 1
#> Chrysler Imperial 0
#> Fiat 128 0
#> Honda Civic 2
#> Toyota Corolla 2
#> Toyota Corona 0
#> Dodge Challenger 0
#> AMC Javelin 0
#> Camaro Z28 0
#> Pontiac Firebird 0
#> Fiat X1-9 0
#> Porsche 914-2 0
#> Lotus Europa 1
#> Ford Pantera L 1
#> Ferrari Dino 0
#> Maserati Bora 2
#> Volvo 142E 0