View source: R/calculate_subfeatures.R
calculate_subfeatures | R Documentation |
calculate_features
computes several subfeatures associated with a
categorical time series or between a categorical and a real-valued time series
calculate_subfeatures(series, n_series, lag = 1, type = NULL)
series |
An object of type |
n_series |
A real-valued time series. |
lag |
The considered lag (default is 1). |
type |
String indicating the subfeature one wishes to compute. |
Assume we have a CTS of length T
with range \mathcal{V}=\{1, 2, \ldots, r\}
,
\overline{X}_t=\{\overline{X}_1,\ldots, \overline{X}_T\}
, with \widehat{p}_i
being the natural estimate of the marginal probability of the i
th
category, and \widehat{p}_{ij}(l)
being the natural estimate of the joint probability
for categories i
and j
at lag l, i,j=1, \ldots, r
. Assume also that
we have a real-valued time series of length T
, \overline{Z}_t=\{\overline{Z}_1,\ldots, \overline{Z}_T\}
.
The function computes the following subfeatures depending on the argument
type
:
If type=entropy
, the function computes the
subfeatures associated with the estimated entropy, \widehat{p}_i\ln(\widehat{p}_i)
,
i=1,2, \ldots,r
.
If type=gk_tau
, the function computes the
subfeatures associated with the estimated Goodman and Kruskal's tau, \frac{\widehat{p}_{ij}(l)^2}{\widehat{p}_j}
,
i,j=1,2, \ldots,r
.
If type=gk_lambda
, the function computes the
subfeatures associated with the estimated Goodman and Kruskal's lambda, \max_i\widehat{p}_{ij}(l)
,
i=1,2, \ldots,r
.
If type=uncertainty_coefficient
, the function computes the
subfeatures associated with the estimated uncertainty coefficient, \widehat{p}_{ij}(l)\ln\Big(\frac{\widehat{p}_{ij}(l)}{\widehat{p}_i\widehat{p}_j}\Big)
,
i,j=1,2, \ldots,r
.
If type=pearson_measure
, the function computes the
subfeatures associated with the estimated Pearson measure, \frac{(\widehat{p}_{ij}(l)-\widehat{p}_i\widehat{p}_j)^2}{\widehat{p}_i\widehat{p}_j}
,
i,j=1,2, \ldots,r
.
If type=phi2_measure
, the function computes the
subfeatures associated with the estimated Phi2 measure, \frac{(\widehat{p}_{ij}(l)-\widehat{p}_i\widehat{p}_j)^2}{\widehat{p}_i\widehat{p}_j}
,
i,j=1,2, \ldots,r
.
If type=sakoda_measure
, the function computes the
subfeatures associated with the estimated Sakoda measure, \frac{(\widehat{p}_{ij}(l)-\widehat{p}_i\widehat{p}_j)^2}{\widehat{p}_i\widehat{p}_j}
,
i,j=1,2, \ldots,r
.
If type=cramers_vi
, the function computes the
subfeatures associated with the estimated Cramer's vi, \frac{(\widehat{p}_{ij}(l)-\widehat{p}_i\widehat{p}_j)^2}{\widehat{p}_i\widehat{p}_j}
,
i,j=1,2, \ldots,r
.
If type=cohens_kappa
, the function computes the
subfeatures associated with the estimated Cohen's kappa, \widehat{p}_{ii}(l)-\widehat{p}_i^2
,
i=1,2, \ldots,r
.
If type=total_correlation
, the function computes the
subfeatures associated with the total correlation, \widehat{\psi}_{ij}(l)
,
i,j=1,2, \ldots,r
(see type='total_mixed_cor'
in the function calculate_features
).
If type=total_mixed_correlation_1
, the function computes the
subfeatures associated with the total mixed l-correlation, \widehat{\psi}_{i}(l)
,
i=1,2, \ldots,r
(see type='total_mixed_correlation_1'
in the function calculate_features
).
If type=total_mixed_correlation_2
, the function computes the
subfeatures associated with the total mixed q-correlation, \int_{0}^{1}\widehat{\psi}^\rho_{i}(l)^2d\rho
,
i=1,2, \ldots,r
(see type='total_mixed_correlation_2'
in the function calculate_features
).
The corresponding subfeature
Ángel López-Oriona, José A. Vilar
weiss2008measuringctsfeatures
sequence_1 <- GeneticSequences[which(GeneticSequences$Series==1),]
suc <- calculate_subfeatures(series = sequence_1, type = 'uncertainty_coefficient')
# Computing the subfeatures associated with the uncertainty coefficient
# for the first series in dataset GeneticSequences
scv <- calculate_subfeatures(series = sequence_1, type = 'cramers_vi' )
# Computing the subfeatures associated with the cramers vi
# for the first series in dataset GeneticSequences
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