Description Usage Arguments Details Value References See Also Examples
comp_accu_freq
computes a list of current accuracy metrics
from the 4 essential frequencies (hi
,
mi
, fa
, cr
)
that constitute the current confusion matrix and
are contained in freq
.
1 2 
hi 
The number of hits 
mi 
The number of misses 
fa 
The number of false alarms 
cr 
The number of correct rejections 
w 
The weighting parameter 
Currently computed accuracy metrics include:
acc
: Overall accuracy as the proportion (or probability)
of correctly classifying cases or of dec_cor
cases:
acc = dec_cor/N = (hi + cr)/(hi + mi + fa + cr)
Values range from 0 (no correct prediction) to 1 (perfect prediction).
wacc
: Weighted accuracy, as a weighted average of the
sensitivity sens
(aka. hit rate HR
, TPR
,
power
or recall
)
and the the specificity spec
(aka. TNR
)
in which sens
is multiplied by a weighting parameter w
(ranging from 0 to 1) and spec
is multiplied by
w
's complement (1  w)
:
wacc = (w * sens) + ((1  w) * spec)
If w = .50
, wacc
becomes balanced accuracy bacc
.
mcc
: The Matthews correlation coefficient (with values ranging from 1 to +1):
mcc = ((hi * cr)  (fa * mi)) / sqrt((hi + fa) * (hi + mi) * (cr + fa) * (cr + mi))
A value of mcc = 0
implies random performance; mcc = 1
implies perfect performance.
See Wikipedia: Matthews correlation coefficient for additional information.
f1s
: The harmonic mean of the positive predictive value PPV
(aka. precision
)
and the sensitivity sens
(aka. hit rate HR
,
TPR
, power
or recall
):
f1s = 2 * (PPV * sens) / (PPV + sens)
See Wikipedia: F1 score for additional information.
Notes:
Accuracy metrics describe the correspondence of decisions (or predictions) to actual conditions (or truth).
There are several possible interpretations of accuracy:
as probabilities (i.e., acc
being the proportion of correct classifications,
or the ratio dec_cor
/N
),
as frequencies (e.g., as classifying a population of N
individuals into cases of dec_cor
vs. dec_err
),
as correlations (e.g., see mcc
in accu
).
Computing exact accuracy values based on probabilities (by comp_accu_prob
) may differ from
accuracy values computed from (possibly rounded) frequencies (by comp_accu_freq
).
When frequencies are rounded to integers (see the default of round = TRUE
in comp_freq
and comp_freq_prob
) the accuracy metrics computed by
comp_accu_freq
correspond to these rounded values.
Use comp_accu_prob
to obtain exact accuracy metrics from probabilities.
A list accu
containing current accuracy metrics.
Consult Wikipedia: Confusion matrix for additional information.
accu
for all accuracy metrics;
comp_accu_prob
computes exact accuracy metrics from probabilities;
num
for basic numeric parameters;
freq
for current frequency information;
txt
for current text settings;
pal
for current color settings;
popu
for a table of the current population.
Other metrics: accu
, acc
,
comp_accu_prob
, comp_acc
,
comp_err
, err
Other functions computing probabilities: comp_FDR
,
comp_FOR
, comp_NPV
,
comp_PPV
, comp_accu_prob
,
comp_acc
, comp_comp_pair
,
comp_complement
,
comp_complete_prob_set
,
comp_err
, comp_fart
,
comp_mirt
, comp_ppod
,
comp_prob_freq
, comp_prob
,
comp_sens
, comp_spec
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  comp_accu_freq() # => accuracy metrics for freq of current scenario
comp_accu_freq(hi = 1, mi = 2, fa = 3, cr = 4) # medium accuracy, but cr > hi
# Extreme cases:
comp_accu_freq(hi = 1, mi = 1, fa = 1, cr = 1) # random performance
comp_accu_freq(hi = 0, mi = 0, fa = 1, cr = 1) # random performance: wacc and f1s are NaN
comp_accu_freq(hi = 1, mi = 0, fa = 0, cr = 1) # perfect accuracy/optimal performance
comp_accu_freq(hi = 0, mi = 1, fa = 1, cr = 0) # zero accuracy/worst performance, but see f1s
comp_accu_freq(hi = 1, mi = 0, fa = 0, cr = 0) # perfect accuracy, but see wacc and mcc
# Effects of w:
comp_accu_freq(hi = 3, mi = 2, fa = 1, cr = 4, w = 1/2) # equal weights to sens and spec
comp_accu_freq(hi = 3, mi = 2, fa = 1, cr = 4, w = 2/3) # more weight to sens
comp_accu_freq(hi = 3, mi = 2, fa = 1, cr = 4, w = 1/3) # more weight to spec
## Contrasting comp_accu_freq and comp_accu_prob:
# (a) comp_accu_freq (based on rounded frequencies):
freq1 < comp_freq(N = 10, prev = 1/3, sens = 2/3, spec = 3/4) # => hi = 2, mi = 1, fa = 2, cr = 5
accu1 < comp_accu_freq(freq1$hi, freq1$mi, freq1$fa, freq1$cr) # => accu1 (based on rounded freq).
# accu1
#
# (b) comp_accu_prob (based on probabilities):
accu2 < comp_accu_prob(prev = 1/3, sens = 2/3, spec = 3/4) # => exact accu (based on prob).
# accu2
all.equal(accu1, accu2) # => 4 differences!
#
# (c) comp_accu_freq (exact values, i.e., without rounding):
freq3 < comp_freq(N = 10, prev = 1/3, sens = 2/3, spec = 3/4, round = FALSE)
accu3 < comp_accu_freq(freq3$hi, freq3$mi, freq3$fa, freq3$cr) # => accu3 (based on EXACT freq).
# accu3
all.equal(accu2, accu3) # => TRUE (qed).

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