simulate_DepthTS: simulate depth time series data

In RchivalTag: Analyzing and Interactive Visualization of Archival Tagging Data

simulate depth time series data

Description

function to simulate depth series data from a template data set.

Usage

simulate_DepthTS(ts_df, ndays=10, gaps=TRUE, trate=90, 
                 random_Depth=TRUE, ref_Depth_lim=300, random_Depth_lim=c(100, 700))

Arguments

ts_df	data.frame holding the template depth time series data.
ndays	number of days of depth time series data that should be simulated).
gaps, trate	Whether gaps should be introduced into the dataset according to the transmission rate (trate), in addition to any potential gaps in the template data set.
random_Depth, ref_Depth_lim, random_Depth_lim	Whether depth records from the template data set should be additonaly randomized. In this case, depth records >= ref_Depth_lim will be replaced by values between the limits of random_Depth_lim (i.e. in the default settings a record of >= 300 will be replaced by a value between 100 and 700 m).

Author(s)

Robert K. Bauer

See Also

dy_DepthTS, plot_DepthTS, plot_data_coverage

Examples

### load sample depth and temperature time series data from miniPAT:
# ts_file <- system.file("example_files/104659-Series.csv",package="RchivalTag")
# ts_df <- read_TS(ts_file)
# ts_df$Serial <- ts_df$DeployID
# head(ts_df)
# dy_DepthTS(ts_df) # plot original data
# 
# ts_df_sim <- simulate_DepthTS(ts_df) # simulate data
# 
# dy_DepthTS(ts_df_sim) # plot simulated data
# 
# library(dplyr)
# meta <- rbind(ts_df[,names(ts_df_sim)],ts_df_sim) %>% 
#   group_by(DeployID, Serial, Ptt) %>%
#   summarise(dep.date=min(date),pop.date=max(date)) %>% 
#   as.data.frame()
# 
# ts_list <- list(ts_df,ts_df_sim)
# names(ts_list) <- meta$Serial
# 
# # plot data coverage
# plot_data_coverage(x = ts_list,type="ts", meta = meta)

RchivalTag documentation built on Nov. 10, 2023, 5:06 p.m.