cut_off_point_method {HMMpa} | R Documentation |
Cut-Off Point Method for Assigning Physical Activity Patterns
Description
This function assigns an activity range to each observation of a time-series, such as for a sequence of impulse counts recorded by an accelerometer. The activity ranges are defined by thresholds called “cut-off points”. Furthermore, bout periods are analysed (see Details for further informations).
Usage
cut_off_point_method(
x,
cut_points,
names_activity_ranges = NA,
hidden_PA_levels = NA,
bout_lengths = NULL,
plotting = 0
)
Arguments
x |
a vector object of length |
cut_points |
a vector object containing cut-off points to separate activity ranges. For instance, the vector c(7,15,23) separates the four activity ranges [0,7);[7,15);[15,23);[23,Inf). |
names_activity_ranges |
an optional character string vector to name the activity
ranges induced by the cut-points. This vector must contain one element more
than the vector |
an optional vector object of length | |
bout_lengths |
a vector object (with even number of elemets) to define the
range of the bout intervals (see Details for the definition of bouts). For instance,
|
plotting |
a numeric value between |
Details
A bout is defined as a period of time spending a defined intensity of physical activities in a specified physical activity range, without switching to activity intensities in a different activity range.
Value
cut_off_point_method
returns a list containing the extracted sequence
of activity ranges and plots key figures.
- activity_ranges
an array object containing the cut-off intervals that indicate the activity ranges.
- classification
an integer vector containing the sequence of activity ranges that were assigned to the observed time-series of accelerometer counts. If
hidden_PA_levels=NA
, thenclassification
is the output of the traditional cut-point method, meaning that an activity range has been assigned to each accelerometer count over its observed value actual position. In case whenhidden_PA_levels
is available,classification
is the output of the extendend cut-point method using hidden Markov models (seeHMM_based_method
for further details).- classification_per_activity_range
a pairlist object containing the classification of the observed counts by the assigned activity range.
- freq_acitvity_range
table object containing the absolute frequencies of classifications into activity ranges.
- rel_freq_acitvity_range
table object containing the relative frequencies of classifications into activity ranges.
- quantity_of_bouts
overall number of bouts.
- bout_periods
an array including the bout length assigned to acitiy ranges.
- abs_freq_bouts_el
a pairlist object containing the absolute frequency of bout length per epoch length (aggregated).
Author(s)
Vitali Witowski (2013)
See Also
Examples
x <- c(1,16,19,34,22,6,3,5,6,3,4,1,4,3,5,7,9,8,11,11,
14,16,13,11,11,10,12,19,23,25,24,23,20,21,22,22,18,7,
5,3,4,3,2,3,4,5,4,2,1,3,4,5,4,5,3,5,6,4,3,6,4,8,9,12,
9,14,17,15,25,23,25,35,29,36,34,36,29,41,42,39,40,43,
37,36,20,20,21,22,23,26,27,28,25,28,24,21,25,21,20,21,
11,18,19,20,21,13,19,18,20,7,18,8,15,17,16,13,10,4,9,
7,8,10,9,11,9,11,10,12,12,5,13,4,6,6,13,8,9,10,13,13,
11,10,5,3,3,4,9,6,8,3,5,3,2,2,1,3,5,11,2,3,5,6,9,8,5,
2,5,3,4,6,4,8,15,12,16,20,18,23,18,19,24,23,24,21,26,
36,38,37,39,45,42,41,37,38,38,35,37,35,31,32,30,20,39,
40,33,32,35,34,36,34,32,33,27,28,25,22,17,18,16,10,9,
5,12,7,8,8,9,19,21,24,20,23,19,17,18,17,22,11,12,3,9,
10,4,5,13,3,5,6,3,5,4,2,5,1,2,4,4,3,2,1)
# 1) Traditional cut point method -----------------------
# Assigning activity ranges to activity counts using
# fictitious cut-off points that produce the four activity
# ranges "sedentary"", "light"", "moderate"", and "vigorous".
solution_of_traditional_cut_off_point_method <-
cut_off_point_method(x = x,
cut_points = c(5,15,23),
names_activity_ranges = c("SED","LIG","MOD","VIG"),
bout_lengths = c(1,1,2,2,3,3,4,4,5,5,6,12,13,40,41,265,1,265),
plotting = 0)
print(solution_of_traditional_cut_off_point_method)
# 2) Extension of the traditional cut_point method
# using HMMs
# The following three steps define an extension of the
# traditional cut-off method by first extracting the hidden
# physical activity pattern behind the accelerometer counts
# using a HMM (those three steps are basically combined in
# the function HMM_based_method, see HMM_based_method for
# further details and references):
# Step 1 ---
# Train hidden Markov model for different number of
# states m=2,...,6 and select the model with the most
# plausible m
m_trained_HMM <-
HMM_training(x = x,
min_m = 2,
max_m = 6, BW_print=FALSE,
distribution_class = "pois")$trained_HMM_with_selected_m
# Step 2 ---
# Decode the trained HMM (by using the
# Viterbi algorithm (global decoding)) to get the estimated
# sequence of hidden physical activity levels
# underlying the the accelerometer counts
# You have to compute 'm_trained_HMM' first (see Step 1)
global_decoding <-
HMM_decoding(x = x,
m = m_trained_HMM$m,
delta = m_trained_HMM$delta,
gamma = m_trained_HMM$gamma,
distribution_class = m_trained_HMM$distribution_class,
distribution_theta = m_trained_HMM$distribution_theta,
decoding_method = "global")
hidden_PA_levels <- global_decoding$decoding_distr_means
# Step 3 ---
# Assigning activity ranges to activity counts using the
# information extracted by decoding the HMM for the counts
# (PA-levels) and fictitious cut-off points that produce
# four so-called activity ranges:"sedentary", "light",
# "moderate" and "vigorous":
# You have to compute 'm_trained_HMM' and 'hidden_PA_levels' first (see above)
solution_of_HMM_based_cut_off_point_method <-
cut_off_point_method(x = x,
hidden_PA_levels = hidden_PA_levels,
cut_points = c(5,15,23),
names_activity_ranges = c("SED","LIG","MOD","VIG"),
bout_lengths = c(1,1,2,2,3,3,4,4,5,5,6,12,13,40,41,265,1,265),
plotting=1)