create_merged_classification: Convert data from a Timetable to a timeseries

In KiranLDA/PAMLr: Suite Of Functions For Manipulating Pressure, Activity, Magnetism, Temperature And Light Data In R

Convert data from a Timetable to a timeseries

Description

convert a classification timetable into a classification timeseries

Usage

create_merged_classification(from, to, classification, add_to, missing = NA)

Arguments

from	start of event that was classified (generally timetable output)
to	end of event that was classified (generally timetable output)
classification	classified data
add_to	data which the classifications are to be added to (e.g. PAM_data$pressure)
missing	Missing value replacement. By default NA.

Value

the classification in add_to dataset

Examples

## Not run: 
data(bee_eater)
PAM_data = bee_eater

twl = GeoLight::twilightCalc(PAM_data$light$date, PAM_data$light$obs,
                             LightThreshold = 2, ask = FALSE)


to_classify = create_summary_statistics(PAM_data,
                     method="pressure",
                     twl = twl,
                     Pdiff_thld = 2,
                     light_thld = 2)

classification = classify_summary_statistics((to_classify$total_daily_duration *
                              log(to_classify$night_P_diff+0.001 )
                              * to_classify$total_daily_P_change),
                             states=3, "hmm")$cluster

pressure_classification = create_merged_classification(from = to_classify$start,
                                                       to =to_classify$end,
                                                       classification = classification,
                                                       add_to = PAM_data$pressure,
                                                       missing = NA)

pressure_classification[pressure_classification == NA] = 0

plot(PAM_data$pressure$date, PAM_data$pressure$obs,
     col= viridis::viridis(4)[pressure_classification+1],
     type="o", pch=16, cex=0.6)

## End(Not run)

KiranLDA/PAMLr documentation built on March 6, 2023, 1:40 p.m.