---
title: "optimum-mbp"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{optimum-mbp}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup}
library(telemetR)
```
```{r, load some data from the package, echo=TRUE}
fish <- telemetR::fish
receivers <- telemetR::receivers
detects <- telemetR::filtered_detections
```
```{r, format the data to make it easier to use the package, echo = TRUE}
local_tz <- "America/Los_Angeles"
fish <- format_org(fish,
var_Id = "TagCode",
var_release = "Release_Date",
var_tag_life = "TagLife",
var_ping_rate = "PRI",
local_time_zone = local_tz,
time_format = "%Y-%m-%d %H:%M:%S")
receivers <- format_receivers(receivers,
var_receiver_serial = "receiver_serial_number",
var_receiver_make = "receiver_make",
var_receiver_deploy = "receiver_start",
var_receiver_retrieve = "receiver_end",
local_time_zone = local_tz,
time_format = "%m/%d/%Y %H:%M:%S")
detects <- format_detects(detects,
var_Id = "Tag_Code",
var_datetime_local = "DateTime_Local",
var_receiver_serial = "ReceiverSN",
local_time_zone = local_tz,
time_format = "%Y-%m-%d %H:%M:%S")
```
```{r, Begin the blanking period analysis, echo = TRUE}
# Build a new df which uses a range of blanking times to reprocess the original
# detection data (this is a slow process, especially for large datasets or
# large sequences of n)
blanked <- blanking_event(detects,
#Each general location is distinct enough to be a site
var_site = "receiver_general_location",
#Group the data by species, run, and origin (optional)
var_groups = "fish_type",
var_Id = "Tag_Code",
var_datetime = "DateTime_Local",
var_ping_rate = "tag_pulse_rate_interval_nominal",
n_val = seq(1,1000,5),
#Pulse rates for these tags are in seconds
time_unit = "secs"
)
head(blanked)
```
```{r, Compare the results of the blanking analysis to a set of times, echo = TRUE}
# This function compares the durations of the events created above. The function
# collects all the events for each given blanking period and compares each
# event duration to a sequence of given times. It then calculates the proportion
# of events which are longer than each given time for each blanking period for
# each organism group
compared <- duration_compare(blanked,
var_groups = "fish_type",
time_seq = seq(1,10000,10))
head(compared)
```
```{r plot the proportion of events with durations greater than t, echo = TRUE}
residence_plot(compared, var_groups = "fish_type", time_unit = "secs")
```
```{r, echo = TRUE}
# NOTE: The curves last longer than the longest time! This should indicate we
# should add more times to test, and may need shorter intervals of n.
```
```{r next calculate the renormalized sum of squares, echo = TRUE}
# Renormalized Sum of Squares
rSSR <- renorm_SSR(compared, var_groups = "fish_type")
head(rSSR)
```
```{r next calculate the convergence thresholds, echo = TRUE}
# Thresholds for 99% and 99.5% of total range
thresh <- telemetR::conv_thresholds(rSSR, var_groups = "fish_type",
thresh_levels = c(0.01,0.005))
```
```{r finally determine optimum mbps and plot rSSR results, echo = TRUE}
optimums <- opt_mbp(rSSR, thresh)
rSSR_plot(rSSR, optimums, var_groups = "fish_type")
# Note: This would be a bad choice of maximum blanking period. We have not yet
# reached convergence
```