The dailyDCOffsetMetric() function identifies days with a jump in the signal mean.
dailyDCOffsetMetric(df,
offsetDays=5,
outlierWindow=7,
outlierThreshold=3.0,
outputType=1)A list is returned with a SingleValueMetric object for the last day-floor(outlierWindow/2) (default 3rd from last day) in the incoming dataframe if outputType=1 (one list element), otherwise the first+offsetDays to last day-floor(outlierWindow/2) (multiple list elements, one per day) if outputType=0.
a dataframe containing sample_mean values obtained with getSingleValueMetrics()
number of days used in calculating weighting factors
window size passed to findOutliers() function in the seismicRoll package
detection threshold passed to findOutliers() function in the seismicRoll package
if 1, return last day of valid values (index= length(index)-floor(outlierWindow/2)); if 0, return all valid values (indices= max(offsetDays, floor(outlierWindow/2): length(index)-floor(outlierWindow/2))
Jonathan Callahan jonathan@mazamascience.com
This algorithm calculates lagged differences of the daily mean timeseries over a window of offsetDays days.
Shifts in the mean that are persistent and larger than the typical standard deviation of daily means will
generate higher metric values.
Details of the algorithm are as follows
# data0 = download requested daily means (in the 'df' dataframe), must be greater than max(offsetDays,outlierWindow)+floor(outlierWindow/2)
# data1 = remove outliers using MAD outlier detection with the 'outlier' arguments specified
# data2 = replace outliers with rolling median values using a default 7 day window, remove last floor(outlierWindow/2) number of samples.
# weights = calculate absolute lagged differences with 1-N day lags, default N=5
# metric0 = multiply the lagged differences together and take the N'th root
# stddev0 = calculate the rolling standard deviation of data2 with a N-day window
# METRIC = divide metric0 by the median value of stddev0
getSingleValueMetrics