Package 'waterlevel' reference manual

Title:	Well Water Level Analysis Tools
Description:	Well water level analysis tools for R. The current focus is on barometric efficiency.
Authors:	Jonathan Kennel [aut, cre], Beth Parker [ths]
Maintainer:	Jonathan Kennel <[email protected]>
License:	GPL-3
Version:	0.0.1
Built:	2026-05-28 14:38:10 UTC
Source:	https://github.com/rpkgs/waterlevel

A short title line describing what the package does

Description

A more detailed description of what the package does. A length of about one to five lines is recommended.

Details

This section should provide a more detailed overview of how to use the package, including the most important functions.

Author(s)

Your Name, email optional.

Maintainer: Your Name <[email protected]>

References

This optional section can contain literature or other references for background information.

Examples

  ## Not run: 
     ## Optional simple examples of the most important functions
     ## These can be in \dontrun{} and \donttest{} blocks.   
  
## End(Not run)
## Not run: 
     ## Optional simple examples of the most important functions
     ## These can be in \dontrun{} and \donttest{} blocks.   
  
## End(Not run)

add_level_shifts

Description

add_level_shifts

Usage

add_level_shifts(x, y)
add_level_shifts(x, y)

Arguments

x

datetimes

y

shift vector

Value

vector of shifts

Computation of AIC for mlm objects

Description

Extends the extractAIC method from the stats package to handle multi-predictand linear models (objects of class mlm). FROM paleocar

Usage

AIC_mlm(fit, scale = 0, k = 2)
AIC_mlm(fit, scale = 0, k = 2)

Arguments

fit

An object of class mlm.

scale

The estimate of the error variance. scale = 0 indicates that it is to be estimated by maximum likelihood.

k

Numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

Value

A list of length 2 giving

df The 'equivalent degrees of freedom' for the fitted model fit.
AIC A vector of the (generalized) Akaike Information Criterion for the fits.

baro_elev

Description

Calculate the effect of elevation on barometric pressure.

Usage

baro_elev(
  elevation = 0,
  pressure_sea = 101325,
  temperature = 20,
  gravity = 9.80665
)
baro_elev(
  elevation = 0,
  pressure_sea = 101325,
  temperature = 20,
  gravity = 9.80665
)

Arguments

elevation

vector elevations (numeric)

pressure_sea

standard pressure (numeric)

temperature

temperature in celcius (numeric)

gravity

gravity (numeric)

Value

barometric pressure dependence on elevation

Examples

plot(baro_elev(0:10000), type='l', xlab = "elev (m)", ylab = 'pressure')

plot(baro_elev(0:10000), type='l', xlab = "elev (m)", ylab = 'pressure')

be_acworth_eq_4

Description

Implementation of Acworth et. al. 2016. This is basically the transfer function value for the semi-diurnal band.

Usage

be_acworth(
  dat,
  wl = "wl",
  ba = "ba",
  et = "et",
  method = "spec_pgram",
  inverse = TRUE,
  ...
)
be_acworth(
  dat,
  wl = "wl",
  ba = "ba",
  et = "et",
  method = "spec_pgram",
  inverse = TRUE,
  ...
)

Arguments

dat

data that has the independent and dependent variables (data.table)

wl

name of the water level column (character).

ba

name of the barometric pressure column (character).

et

name of the Earth tide column (character).

method

either spec_pgram or spec_welch

inverse

logical whether the barometric relationship is inverse (TRUE means that when the barometric pressure goes up the measured water level goes down (vented transducer, depth to water), FALSE means that when the barometric pressure goes up so does the measured pressure (non-vented transducer)).

...

arguments to pass to transfer_acworth, spec_welch, spec_pgram

Value

barometric efficiency

References

Acworth, R. I., Halloran, L. J. S., Rau, G. C., Cuthbert, M. O., & Bernardi, T. L. (2016). An objective frequency-domain method for quantifying confined aquifer compressible storage using Earth and atmospheric tides. Geophysical Research Letters, 43 (November). doi:10.1002/2016GL071328

Acworth, R. I., G. C. Rau, L. J. S. Halloran, and W. A. Timms (2017), Vertical groundwater stor1077 age properties and changes in confinement determined using hydraulic head response to atmospheric tides, Water Resources Research, 53(4), 2983–2997, doi:10.1002/2016WR020311.

be_acworth_eq_4

Description

Implementation of Acworth et. al. 2016 equation 4.

Usage

be_acworth_eq_4(s2_gw, s2_et, s2_at, m2_gw, m2_et, d_phase, inverse = TRUE)
be_acworth_eq_4(s2_gw, s2_et, s2_at, m2_gw, m2_et, d_phase, inverse = TRUE)

Arguments

s2_gw

numeric s2 component in the groundwater levels

s2_et

numeric s2 component in the earth tides

s2_at

numeric s2 component for atmospheric pressure

m2_gw

numeric m2 component in the groundwater levels

m2_et

numeric m2 component in the earth tides

d_phase

numeric phase difference between Earth tide and atmospheric drivers s2_et and s2_at

inverse

Value

barometric efficiency

References

Examples


be_acworth_eq_4(s2_at = 7.461,
           s2_et=224.640,
           s2_gw=4.086,
           m2_gw = 0.471,
           m2_et = 492.526,
           d_phase=-56.709)
be_acworth_eq_4(s2_at = 6.164,
           s2_et=270.463,
           s2_gw=0.329,
           m2_gw = 0.225,
           m2_et = 551.572,
           d_phase=-71.726)
be_acworth_eq_4(s2_at = 5.897,
           s2_et=234.478,
           s2_gw=5.536,
           m2_gw = 0.773,
           m2_et = 558.075,
           d_phase=-70.393)

be_acworth_eq_4(s2_at = 7.461,
           s2_et=224.640,
           s2_gw=4.086,
           m2_gw = 0.471,
           m2_et = 492.526,
           d_phase=-56.709)
be_acworth_eq_4(s2_at = 6.164,
           s2_et=270.463,
           s2_gw=0.329,
           m2_gw = 0.225,
           m2_et = 551.572,
           d_phase=-71.726)
be_acworth_eq_4(s2_at = 5.897,
           s2_et=234.478,
           s2_gw=5.536,
           m2_gw = 0.773,
           m2_et = 558.075,
           d_phase=-70.393)

be_clark

Description

Clark 1967 solution for calculating barometric efficiency.

Usage

be_clark(
  dat,
  dep = "wl",
  ind = "baro",
  lag_space = 1,
  inverse = TRUE,
  return_model = FALSE
)
be_clark(
  dat,
  dep = "wl",
  ind = "baro",
  lag_space = 1,
  inverse = TRUE,
  return_model = FALSE
)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

lag_space

space between difference calculation in number of observations

inverse

whether the barometric relationship is inverse (TRUE means that when the barometric pressure goes up the measured water level goes down (vented transducer, depth to water), FALSE means that when the barometric pressure goes up so does the measured pressure (non-vented transducer)) (logical).

return_model

whether to return the lm model or just the barometric/loading efficiency (logical).#'

Value

lm linear model for Clark's method. The coefficient is the BE/LE

References

Clark, W., 1967. Computing the Barometric Efficiency of a well. Proc. Am. Soc. Civ. Eng. J. Hydraul. Div. V. 93, 93–98.

Examples

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='sec' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl   <- -0.4 * baro + rnorm(length(datetime), sd = 0.02)
dat  <- data.table(baro, wl, datetime)
be_clark(dat, dep='wl', ind='baro', lag_space=1, inverse=TRUE)

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='sec' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl   <- -0.4 * baro + rnorm(length(datetime), sd = 0.02)
dat  <- data.table(baro, wl, datetime)
be_clark(dat, dep='wl', ind='baro', lag_space=1, inverse=TRUE)

be_correct

Description

Adjust values based on the barometric efficiency

Usage

be_correct(
  dat,
  dep = "wl",
  ind = "baro",
  be = 0,
  inverse = TRUE,
  known_mean = NULL
)
be_correct(
  dat,
  dep = "wl",
  ind = "baro",
  be = 0,
  inverse = TRUE,
  known_mean = NULL
)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

be

numeric value of the barometric efficiency

inverse

known_mean

numeric explicitly enter the mean if known. Otherwise estimate from data.

Value

numeric vector of corrected values

Examples

library(data.table)
baro = rnorm(1000, sd = 0.01) + 9
wl <- baro * 0.4 + 18
dat <- data.table(baro, wl)
dat$wl + be_correct(dat, be=0.4, inverse = FALSE)
dat$wl + be_correct(dat, be=0.4, inverse = FALSE, known_mean = 9) 
# should return ~21.6 = 18 + 0.4 * 9

library(data.table)
baro = rnorm(1000, sd = 0.01) + 9
wl <- baro * 0.4 + 18
dat <- data.table(baro, wl)
dat$wl + be_correct(dat, be=0.4, inverse = FALSE)
dat$wl + be_correct(dat, be=0.4, inverse = FALSE, known_mean = 9) 
# should return ~21.6 = 18 + 0.4 * 9

be_high_low

Description

Uses the maximum and minimum values for a time period to calculate be.

Usage

be_high_low(
  dat,
  dep = "wl",
  ind = "baro",
  time = "datetime",
  time_group = "%Y-%m-%d"
)
be_high_low(
  dat,
  dep = "wl",
  ind = "baro",
  time = "datetime",
  time_group = "%Y-%m-%d"
)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

time

name of the column containing the time (character)

time_group

format for grouping time (ie: "%Y-%m-%d") (character)

Value

barometric efficiency based on high and low values

Examples

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 00:00:00", tz = 'UTC'),
                       as.POSIXct("2016-01-06 00:00:00", tz = 'UTC'), by='hour')
baro  <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl    <- 0.4 * baro + rnorm(length(datetime), sd = 0.01)
dat   <- data.table(baro, wl, datetime)
be_hl <- be_high_low(dat)
be_hl <- be_high_low(dat, time_group = 86400)

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 00:00:00", tz = 'UTC'),
                       as.POSIXct("2016-01-06 00:00:00", tz = 'UTC'), by='hour')
baro  <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl    <- 0.4 * baro + rnorm(length(datetime), sd = 0.01)
dat   <- data.table(baro, wl, datetime)
be_hl <- be_high_low(dat)
be_hl <- be_high_low(dat, time_group = 86400)

be_least_squares

Description

Calculate the barometric efficiency by using least squares.

Usage

be_least_squares(
  dat,
  dep = "wl",
  ind = "baro",
  inverse = TRUE,
  return_model = FALSE
)
be_least_squares(
  dat,
  dep = "wl",
  ind = "baro",
  inverse = TRUE,
  return_model = FALSE
)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

inverse

return_model

whether to return the lm model or just the barometric/loading efficiency (logical).#'

Value

barometric efficiency calculated by least squares

Examples

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl <- -0.4 * baro
dat <- data.table(baro, wl, datetime)

be_least_squares(dat)

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl <- -0.4 * baro
dat <- data.table(baro, wl, datetime)

be_least_squares(dat)

be_least_squares_diff

Description

Calculate the barometric efficiency by using the least squares with differences

Usage

be_least_squares_diff(
  dat,
  dep = "wl",
  ind = "baro",
  lag_space = 1,
  inverse = TRUE,
  return_model = FALSE
)
be_least_squares_diff(
  dat,
  dep = "wl",
  ind = "baro",
  lag_space = 1,
  inverse = TRUE,
  return_model = FALSE
)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

lag_space

space between difference calculation in number of observations

inverse

return_model

whether to return the lm model or just the barometric/loading efficiency (logical).#'

Value

barometric efficiency calculated by least squares

Examples

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl <- -0.4 * baro
dat <- data.table(baro, wl, datetime)
be_least_squares_diff(dat, lag_space = 1)

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl <- -0.4 * baro
dat <- data.table(baro, wl, datetime)
be_least_squares_diff(dat, lag_space = 1)

be_rahi ## need to check for correctness

Description

Rahi 2010 solution for calculating barometric efficiency.

Usage

be_rahi(dat, dep = "wl", ind = "baro", lag_space = 1, inverse = TRUE)
be_rahi(dat, dep = "wl", ind = "baro", lag_space = 1, inverse = TRUE)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

lag_space

space between difference calculation in number of observations

inverse

Value

barometric efficiency using Rahi's method

References

Rahi, K. A. (2010). Estimating the hydraulic parameters of the Arbuckle-Simpson aquifer by analysis of naturally-induced stresses (Doctoral dissertation, Oklahoma State University).

Examples

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2 * pi, length.out = length(datetime)))
noise <- rnorm(length(datetime), sd = 0.01)
wl <- -0.4 * baro + noise
dat <- data.table(baro, wl, datetime)
be_rahi(dat, dep = 'wl', ind = 'baro', lag_space = 1)

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2 * pi, length.out = length(datetime)))
noise <- rnorm(length(datetime), sd = 0.01)
wl <- -0.4 * baro + noise
dat <- data.table(baro, wl, datetime)
be_rahi(dat, dep = 'wl', ind = 'baro', lag_space = 1)

be_ratio

Description

Calculate the barometric efficiency using the mean (or other statistic) of the water level change divided by the barometric pressure change . There is the option to only include responses that are large.

Usage

be_ratio(
  dat,
  dep = "wl",
  ind = "baro",
  lag_space = 1,
  inverse = TRUE,
  quant = 0.9,
  stat = mean,
  ...
)
be_ratio(
  dat,
  dep = "wl",
  ind = "baro",
  lag_space = 1,
  inverse = TRUE,
  quant = 0.9,
  stat = mean,
  ...
)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

lag_space

space between difference calculation in number of observations

inverse

quant

quantile cutoff value that differences need to be this large (numeric)

stat

the result to return (mean, median, quantile) (function)

...

other arguments to pass to "stat"

Value

barometric efficiency calculated by using ratio method

Examples

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
noise <- rnorm(length(datetime), sd = 0.01)
wl <- -0.4 * baro + noise
dat <- data.table(baro, wl, datetime)
be_ratio(dat, quant = 0.5, stat=median)
be_ratio(dat, quant = 0.9, stat=mean)

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
noise <- rnorm(length(datetime), sd = 0.01)
wl <- -0.4 * baro + noise
dat <- data.table(baro, wl, datetime)
be_ratio(dat, quant = 0.5, stat=median)
be_ratio(dat, quant = 0.9, stat=mean)

be_storage

Description

This function estimates the storage coefficient from physical properties and the barometric efficiency The equation used is from Batu 1998, eq 2-101, pg. 72

Usage

be_storage(be, n, b, gamma = 9799.74, beta = 4.786e-10)
be_storage(be, n, b, gamma = 9799.74, beta = 4.786e-10)

Arguments

be

the barometric efficiency (numeric)

n

the porosity 0 to 1 (numeric)

b

aquifer thickness (m) (numeric)

gamma

the specific weight of water (N/m^3) (numeric)

beta

the compressibility of water (4.786e-10 m^2/N) (numeric)

Value

storage calculated from barometric efficiency

References

Batu, V. (1998). Aquifer hydraulics: a comprehensive guide to hydrogeologic data analysis. John Wiley & Sons.

Examples

be_storage(0.5, 0.32, 45)

be_storage(0.5, 0.32, 45)

be_visual

Description

Generate dataset for comparing barometric efficiency

Usage

be_visual(
  dat,
  dep = "wl",
  ind = "baro",
  time = "datetime",
  be_tests = seq(0, 1, 0.1),
  inverse = TRUE,
  subsample = TRUE
)
be_visual(
  dat,
  dep = "wl",
  ind = "baro",
  time = "datetime",
  be_tests = seq(0, 1, 0.1),
  inverse = TRUE,
  subsample = TRUE
)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

time

name of the column containing the time (character)

be_tests

vector of barometric efficiencies to test (between 0 and 1) (numeric)

inverse

subsample

should the data be subsampled for plotting? (logical)

Value

data.table of barometric efficiency compensated datasets

References

Smith, L. A., van der Kamp, G., & Hendry, M. J. (2013). A new technique for obtaining high‐resolution pore pressure records in thick claystone aquitards and its use to determine in situ compressibility. Water Resources Research, 49(2), 732-743. doi:10.1002/wrcr.20084

Examples

library(data.table)
be <- 0.43
x <- seq(0, 28*pi, pi / (12*12))

baro <- sin(x) + rnorm(length(x), sd = 0.04)
wl <- -sin(x) * be + rnorm(length(x), sd = 0.04)
dat <- data.table(datetime = as.POSIXct(x * 86400 / (2 * pi),
                                        origin = '1970-01-01', tz = 'UTC'),
                  wl = wl, baro = baro)
be_visual(dat)

library(data.table)
be <- 0.43
x <- seq(0, 28*pi, pi / (12*12))

baro <- sin(x) + rnorm(length(x), sd = 0.04)
wl <- -sin(x) * be + rnorm(length(x), sd = 0.04)
dat <- data.table(datetime = as.POSIXct(x * 86400 / (2 * pi),
                                        origin = '1970-01-01', tz = 'UTC'),
                  wl = wl, baro = baro)
be_visual(dat)

be_visual_data

Description

Generate dataset for comparing barometric efficiency

Usage

be_visual_data(
  dat,
  dep = "wl",
  ind = "baro",
  be_tests = seq(0, 1, 0.1),
  inverse = TRUE
)
be_visual_data(
  dat,
  dep = "wl",
  ind = "baro",
  be_tests = seq(0, 1, 0.1),
  inverse = TRUE
)

Arguments

dat

data that has the independent and dependent variables (data.table)

dep

name of the dependent variable column (character). This is typically the name for the column holding your water level data.

ind

name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data.

be_tests

vector of barometric efficiencies to test (between 0 and 1) (numeric)

inverse

Value

data.table of barometric efficiency compensated datasets

Examples

library(data.table)
be <- 0.43
x <- seq(0, 28*pi, pi / (12*12))

baro <- sin(x) + rnorm(length(x), sd = 0.04)
wl <- -sin(x) * be + rnorm(length(x), sd = 0.04)
dat <- data.table(datetime = as.POSIXct(x * 86400 / (2 * pi),
                                        origin = '1970-01-01', tz = 'UTC'),
                  wl = wl, baro = baro)
be_visual_data(dat)

library(data.table)
be <- 0.43
x <- seq(0, 28*pi, pi / (12*12))

baro <- sin(x) + rnorm(length(x), sd = 0.04)
wl <- -sin(x) * be + rnorm(length(x), sd = 0.04)
dat <- data.table(datetime = as.POSIXct(x * 86400 / (2 * pi),
                                        origin = '1970-01-01', tz = 'UTC'),
                  wl = wl, baro = baro)
be_visual_data(dat)

be_visual_plot

Description

Plot to compare barometric efficiency. Large datasets may take a long time to plot. Subsample should be set to TRUE

Usage

be_visual_plot(dat, time = "datetime", subsample = TRUE)
be_visual_plot(dat, time = "datetime", subsample = TRUE)

Arguments

dat

data that has the independent and dependent variables (data.table)

time

name of the column containing the time (character)

subsample

should the data be subsampled for plotting? (logical)

Value

plotly graph for barometric efficiency estimation with Smith method

Examples

library(plotly)
library(data.table)
be <- 0.43
x <- seq(0, 28 * pi, pi / (12 * 12))

baro <- sin(x) + rnorm(length(x), sd = 0.04)
wl <- -sin(x) * be + rnorm(length(x), sd = 0.04)
dat <- data.table(datetime = as.POSIXct(x * 86400 / (2 * pi),
                                        origin = '1970-01-01', tz = 'UTC'),
                  wl = wl, baro = baro)
dat_be <- be_visual_data(dat)
#be_visual_plot(dat_be) #not run

library(plotly)
library(data.table)
be <- 0.43
x <- seq(0, 28 * pi, pi / (12 * 12))

baro <- sin(x) + rnorm(length(x), sd = 0.04)
wl <- -sin(x) * be + rnorm(length(x), sd = 0.04)
dat <- data.table(datetime = as.POSIXct(x * 86400 / (2 * pi),
                                        origin = '1970-01-01', tz = 'UTC'),
                  wl = wl, baro = baro)
dat_be <- be_visual_data(dat)
#be_visual_plot(dat_be) #not run

brf_from_frf

Description

Calculate the barometric response function from the transfer function

Usage

brf_from_frf(x)
brf_from_frf(x)

Arguments

x

the complex transfer function vector

Value

vector of the barometric response function

coh_phase

Description

calculate phase and coherency from the spectra and cross-spectra

Usage

coh_phase(pgram)
coh_phase(pgram)

Arguments

pgram

numeric array must be multivariate

Value

list of coherency and phase

convolve_fft

Description

convolve_fft

Usage

convolve_fft(x, y)
convolve_fft(x, y)

Arguments

x

numeric vector

y

convolution kernel

Value

numeric vector result of convolution

cross_basis_fft

Description

cross_basis_fft

Usage

cross_basis_fft(basisvar, basislag)
cross_basis_fft(basisvar, basislag)

Arguments

basisvar

numeric vector

basislag

lagging matrix

Value

numeric vector result of convolution

det_parallel

Description

Determinant for an array in parallel

Usage

det_parallel(a)
det_parallel(a)

Arguments

a

numeric array values to evaluate

Value

vector of determinants

diff_shift

Description

Calculate lagged differences padded with NA values

Usage

diff_shift(x, lag_space = 1)
diff_shift(x, lag_space = 1)

Arguments

x

vector to difference (numeric)

lag_space

spacing for lags, useful for higher frequency monitoring (integer).

Value

lagged differences

Examples

diff_shift(1:100, 2)

diff_shift(1:100, 2)

distributed_lag

Description

Depending on the vector to lag and the maximum knot, distributed_lag will either use an FFT (no NA and large maximum knot), or a parallel method (NA, or small maximum knot).

Usage

distributed_lag(
  x,
  knots,
  spline_fun = ns,
  lag_name = "",
  n_subset = 1,
  n_shift = 0
)
distributed_lag(
  x,
  knots,
  spline_fun = ns,
  lag_name = "",
  n_subset = 1,
  n_shift = 0
)

Arguments

x

numeric vector to lag

knots

specific knots for the lagging process

spline_fun

spline function to use i.e. splines::ns, splines::bs

lag_name

name of variable to lag

n_subset

for distributed_lag parallel

n_shift

for distributed_lag parallel

Value

matrix with distributed lag terms

distributed_lag_parallel

Description

This method calculates the basis for a distributed lag in parallel. It is currently slow.

Usage

distributed_lag_parallel(x, bl, lag_max, n_subset = 1L, n_shift = 0L)
distributed_lag_parallel(x, bl, lag_max, n_subset = 1L, n_shift = 0L)

Arguments

x

matrix value of lag

bl

matrix the basis lags

lag_max

integer maximum number of lags

n_subset

integer subset the data

n_shift

integer to shift

Value

distributed lag basis

energy_density_wang

Description

energy_density_wang

Usage

energy_density_wang(magnitude, distance)
energy_density_wang(magnitude, distance)

Arguments

magnitude

Richter magnitude

distance

epicentral distance

Value

value of the energy density

Examples

energy_density_wang(5, 9)
energy_density_wang(5, 9)

fftw_convolve

Description

Do convolution for a data series and matrix of equal length filters. For long data series this function will be much faster than non-fft methods.

Usage

fftw_convolve(x, y, normalize = TRUE, align = "center")
fftw_convolve(x, y, normalize = TRUE, align = "center")

Arguments

x

numeric vector or single column matrix the data series

y

numeric matrix of filter(s)

normalize

logical do you want the values normalized

align

character what alignment for the convolution

Value

convolution of data series and filter

Examples


library(splines)

knots <- round(10^seq(0.5, 4.635484, length.out = 9))
n_lags <- 86400 * 2

bla <- ns(0:(n_lags),
  knots = knots,
  Boundary.knots = c(0, n_lags)
)
bv <- rnorm(86400 * 2 + 3600 * 24 * 4)
system.time({
  dl <- fftw_convolve(bv, bla, normalize = FALSE)
})

library(splines)

knots <- round(10^seq(0.5, 4.635484, length.out = 9))
n_lags <- 86400 * 2

bla <- ns(0:(n_lags),
  knots = knots,
  Boundary.knots = c(0, n_lags)
)
bv <- rnorm(86400 * 2 + 3600 * 24 * 4)
system.time({
  dl <- fftw_convolve(bv, bla, normalize = FALSE)
})

find_gaps

Description

find_gaps

Usage

find_gaps(x, dep_var = "val", time_var = "datetime", time_interval = 1)
find_gaps(x, dep_var = "val", time_var = "datetime", time_interval = 1)

Arguments

x

the data set (data.table)

dep_var

the dependent variable name (character)

time_var

the time variable name (character)

time_interval

the delta t (numeric)

Value

data.table of gaps

find_level_shift

Description

find_level_shift

Usage

find_level_shift(x, dep_var = "val", time_var = "datetime", time_interval = 1)
find_level_shift(x, dep_var = "val", time_var = "datetime", time_interval = 1)

Arguments

x

the data set (data.table)

dep_var

the dependent variable name (character)

time_var

the time variable name (character)

time_interval

the delta t (numeric)

Value

data.table of gaps

fit_gaps

Description

fit_gaps

Usage

fit_gaps(x, recipe, time_var = "datetime")
fit_gaps(x, recipe, time_var = "datetime")

Arguments

x

the data set (data.table)

recipe

the recipe to apply

time_var

the time variable name (character)

Value

data.table of fit results

formula_from_recipe guess formula from recipe

Description

formula_from_recipe guess formula from recipe

Usage

formula_from_recipe(recipe)
formula_from_recipe(recipe)

Arguments

recipe

recipe to apply

Value

formula

frf_predict_terms

Description

frf_predict_terms

Usage

frf_predict_terms(
  frf,
  x,
  time_col = "datetime",
  vars = c("wl", "baro"),
  coherency_cut = 0.3,
  limit = 2
)
frf_predict_terms(
  frf,
  x,
  time_col = "datetime",
  vars = c("wl", "baro"),
  coherency_cut = 0.3,
  limit = 2
)

Arguments

frf

frequency response function

x

input variable dataset

time_col

datetime

vars

variables used in the transfer function call

coherency_cut

values must have at least this coherency

limit

gain must be lower than this value. Often useful for Earth tides.

Value

data.table of results

gap_fill

Description

gap_fill

Usage

gap_fill(
  x,
  gaps,
  recipe,
  dep_var = "wl",
  time_var = "datetime",
  time_interval = 1L,
  buffer_start = 86400 * 4,
  buffer_end = 86400 * 4,
  max_interp = 86400 * 7,
  include_level_shift = TRUE
)
gap_fill(
  x,
  gaps,
  recipe,
  dep_var = "wl",
  time_var = "datetime",
  time_interval = 1L,
  buffer_start = 86400 * 4,
  buffer_end = 86400 * 4,
  max_interp = 86400 * 7,
  include_level_shift = TRUE
)

Arguments

x

data.table of water levels

gaps

data.table of gaps

recipe

recipe to apply

dep_var

the time variable name (character)

time_var

the time variable name (character)

time_interval

the delta t (numeric)

buffer_start

how much buffer on each side of gap

buffer_end

how much buffer on each side of gap

max_interp

largest gap to interpolate

include_level_shift

include level shift in adjustment

Value

data.table of predictions

gap_fill2

Description

gap_fill2

Usage

gap_fill2(x, y)
gap_fill2(x, y)

Arguments

x

gap data.table

y

interpolated values

Value

data.table with gap values adjusted for range

get_fit_summary

Description

get_fit_summary

Usage

get_fit_summary(x, recipe, start_reg, end_reg, start_gap, end_gap)
get_fit_summary(x, recipe, start_reg, end_reg, start_gap, end_gap)

Arguments

x

data.table of water levels

recipe

recipe to apply

start_reg

start subset time

end_reg

end subset time

start_gap

start subset time

end_gap

end subset time

Value

data.table summary

get_intercept_stats

Description

get_intercept_stats

Usage

get_intercept_stats(x)
get_intercept_stats(x)

Arguments

x

data.table of level shifts from regression

Value

data.table of shifts

get_level_shift_coef

Description

get_level_shift_coef

Usage

get_level_shift_coef(x)
get_level_shift_coef(x)

Arguments

x

data.table of level shifts from regression

Value

data.table of shifts

get_shift

Description

get_shift

Usage

get_shift(x, recipe, start, end)
get_shift(x, recipe, start, end)

Arguments

x

data.table of water levels

recipe

recipe to apply

start

start subset time

end

end subset time

Value

data.table of predictions

harmonic

Description

calculate sin and cos curves from POSIXct times (serial)

Usage

harmonic(times, freq)
harmonic(times, freq)

Arguments

times

numeric vector times to calculate sin and cos at

freq

numeric vector frequencies for sin and cos

Value

sin and cos curves

konno_ohmachi_parallel

Description

This method does konno ohmachi smoothing

Usage

konno_ohmachi_parallel(x, f, b = 10L)
konno_ohmachi_parallel(x, f, b = 10L)

Arguments

x

vector to smooth

f

vector of frequencies

b

integer even magnitude to smooth

Value

konno ohmachi smoothed results

konno_ohmachi_serial

Description

This method does konno ohmachi smoothing

Usage

konno_ohmachi_serial(x, f, b = 10L)
konno_ohmachi_serial(x, f, b = 10L)

Arguments

x

numeric vector to smooth

f

numeric vector of frequencies

b

integer smoothing coefficient

Value

vector of konno ohmachi smoothed results

lag_difference

Description

Calculate differences for the dependent and independent variables and remove any NA values. This modifies the data.table in place.

Usage

lag_difference(
  dat,
  var_name = "wl",
  lag_space = 1,
  inverse = FALSE,
  remove_na = TRUE
)
lag_difference(
  dat,
  var_name = "wl",
  lag_space = 1,
  inverse = FALSE,
  remove_na = TRUE
)

Arguments

dat

data that has the independent and dependent variables (data.table)

var_name

name of the column to lag (character)

lag_space

space between difference calculation in number of observations

inverse

remove_na

remove NA values (logical)

Value

data.table with lagged differences

Examples

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl <- 0.4 * baro
dat <- data.table(baro, wl, datetime)
lag_difference(dat, lag_space = 1, remove_na = FALSE)

library(data.table)
datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"),
                       as.POSIXct("2016-01-05 12:00:00"), by='hour' )
baro <- sin(seq(0, 2*pi, length.out = length(datetime)))
wl <- 0.4 * baro
dat <- data.table(baro, wl, datetime)
lag_difference(dat, lag_space = 1, remove_na = FALSE)

lag_matrix

Description

lag data and subset the results

Usage

lag_matrix(x, lags, n_subset = 1L, n_shift = 0L, var_name = "lag")
lag_matrix(x, lags, n_subset = 1L, n_shift = 0L, var_name = "lag")

Arguments

x

numeric vector to lag

lags

integer vector with the lags

n_subset

integer subset every n_subset values

n_shift

integer amount to shift results

var_name

character name for the generated matrix columns

Value

matrix with lagged values

level_shift_to_datetime

Description

level_shift_to_datetime

Usage

level_shift_to_datetime(x)
level_shift_to_datetime(x)

Arguments

x

character string that includes the date

Value

datetime

log_lags

Description

Generate logarithmically spacing for lags. Note: the number of lags will not exactly equal n unless max_time_lag is large or n is very small

Usage

log_lags(n, max_time_lag)
log_lags(n, max_time_lag)

Arguments

n

number of lags (integer)

max_time_lag

maximum lag (integer)

Value

integer vector of lags

Examples

log_lags(12, 86401)
log_lags(12, 86401)

pad_kernel

Description

Pad a smoothing kernel

Usage

pad_kernel(k, n)
pad_kernel(k, n)

Arguments

k

tskernel smoothing "tskernel" object.

n

integer the total length

Value

vector of the padded kernel

Extract Finalized Training Set

Description

As steps are estimated by 'prep', these operations are applied to the training set. Rather than running 'bake' to duplicate this processing, this function will return variables from the processed training set.

Usage

portion(object, ...)
portion(object, ...)

Arguments

object

A 'recipe' object that has been prepared with the option 'retain = TRUE'.

...

One or more selector functions to choose which variables will be returned by the function. See selections() for more details. If no selectors are given, the default is to use dplyr::everything().

Details

When preparing a recipe, if the training data set is retained using 'retain = TRUE', there is no need to 'bake' the recipe to get the preprocessed training set.

'portion' will return the results of a recipes where _all steps_ have been applied to the data, irrespective of the value of the step's 'skip' argument.

predict_gaps

Description

predict_gaps

Usage

predict_gaps(x, fits, term_labels = NULL)
predict_gaps(x, fits, term_labels = NULL)

Arguments

x

the data set (data.table)

fits

data.table of fits

term_labels

names of the group (character)

Value

data.table of predictions

predict_terms

Description

predict by term for 'mlm' or 'lm' object

Usage

predict_terms(object, ...)
predict_terms(object, ...)

Arguments

object

model fit from lm object

...

currently not used

Value

return matrix of predictions

Resample an fft using varying numbers of sine tapers

Description

Produce an un-normalized psd based on an fft and a vector of optimal sine tapers

Usage

resample_fft_parallel(
  fftz,
  tapers,
  verbose = TRUE,
  dbl = TRUE,
  tapcap = 10000L
)
resample_fft_parallel(
  fftz,
  tapers,
  verbose = TRUE,
  dbl = TRUE,
  tapcap = 10000L
)

Arguments

fftz

complex; a matrix representing the dual-length fft; see also the dbl argument

tapers

integer; a vector of tapers

verbose

logical; should messages be given?

dbl

logical; should the code assume fftz is dual-length or single-length?

tapcap

integer; the maximum number of tapers which can be applied; note that the length is automatically limited by the length of the series.

Details

To produce a psd estimate with our adaptive spectrum estimation method, we need only make one fft calculation initially and then apply the weighting factors given by parabolic_weights_rcpp, which this function does.

Examples

fftz <- complex(real=1:8, imaginary = 1:8)
taps <- 1:4
try(resample_fft_rcpp2(fftz, taps))

fftz <- complex(real=1:8, imaginary = 1:8)
taps <- 1:4
try(resample_fft_rcpp2(fftz, taps))

Resample an fft using varying numbers of sine tapers

Description

Produce an un-normalized psd based on an fft and a vector of optimal sine tapers

Usage

resample_mvfft(fftz, tapers, verbose = TRUE, dbl = TRUE, tapcap = 10000L)
resample_mvfft(fftz, tapers, verbose = TRUE, dbl = TRUE, tapcap = 10000L)

Arguments

fftz

complex; a matrix representing the dual-length fft; see also the dbl argument

tapers

integer; a vector of tapers

verbose

logical; should messages be given?

dbl

logical; should the code assume fftz is dual-length or single-length?

tapcap

integer; the maximum number of tapers which can be applied; note that the length is automatically limited by the length of the series.

Details

Examples

fftz <- complex(real=1:8, imaginary = 1:8)
taps <- 1:4
try(resample_fft_rcpp2(fftz, taps))

fftz <- complex(real=1:8, imaginary = 1:8)
taps <- 1:4
try(resample_fft_rcpp2(fftz, taps))

response_from_fit

Description

response_from_fit

Usage

response_from_fit(x, ...)
response_from_fit(x, ...)

Arguments

x

model fit object, commonly lm

...

additional arguments

Value

tibble of model fit

set_level_shift

Description

Create a factor column with the data subseted according to break points.

Usage

set_level_shift(x, level_breaks = NULL)
set_level_shift(x, level_breaks = NULL)

Arguments

x

vector of numeric

level_breaks

break points in the data

Value

vector of type factor

Examples

set_level_shift(1:100, c(2, 40, 86))
set_level_shift(1:100, c(2, 40, 86))

shift_subset

Description

lag data and subset the results

Usage

shift_subset(x, lag = 0L, n_subset = 1L, n_shift = 0L)
shift_subset(x, lag = 0L, n_subset = 1L, n_shift = 0L)

Arguments

x

numeric vector to lag

lag

integer vector with the lags

n_subset

integer subset every n_subset values

n_shift

integer amount to shift results

Value

vector with lagged values

solve_tf_parallel

Description

Calculate the transfer functions for an array in parallel

Usage

solve_tf_parallel(a)
solve_tf_parallel(a)

Arguments

a

numeric array values to evaluate

Value

vector of transfer functions

spec_from_pgram

Description

spec_from_pgram

Usage

spec_from_pgram(pgram, method = "spec_pgram", ...)
spec_from_pgram(pgram, method = "spec_pgram", ...)

Arguments

pgram

the periodogram

method

either spec_pgram or spec_welch

...

additional arguments

Value

spectrum from the pgram

spec_pgram

Description

calculate the spectra and cross-spectra

Usage

spec_pgram(
  x,
  spans = NULL,
  kernel = NULL,
  taper = 0.1,
  pad = 0,
  fast = TRUE,
  demean = FALSE,
  detrend = TRUE,
  plot = TRUE,
  na.action = na.fail,
  ...
)
spec_pgram(
  x,
  spans = NULL,
  kernel = NULL,
  taper = 0.1,
  pad = 0,
  fast = TRUE,
  demean = FALSE,
  detrend = TRUE,
  plot = TRUE,
  na.action = na.fail,
  ...
)

Arguments

x

univariate or multivariate time series.

spans

vector of odd integers giving the widths of modified Daniell smoothers to be used to smooth the periodogram.

kernel

alternatively, a kernel smoother of class "tskernel".

taper

specifies the proportion of data to taper. A split cosine bell taper is applied to this proportion of the data at the beginning and end of the series.

pad

proportion of data to pad. Zeros are added to the end of the series to increase its length by the proportion pad.

fast

logical; if TRUE, pad the series to a highly composite length.

demean

logical. If TRUE, subtract the mean of the series.

detrend

logical. If TRUE, remove a linear trend from the series. This will also remove the mean.

plot

plot the periodogram?

na.action

NA action function.

...

graphical arguments passed to plot.spec.

Value

array of spectra

spec_welch

Description

calculate the spectra and cross-spectra. The scaling of individual series requires a closer look.

Usage

spec_welch(
  x,
  n_subsets = 10,
  overlap = 0.5,
  window = window_hann,
  demean = TRUE,
  detrend = TRUE,
  ...
)
spec_welch(
  x,
  n_subsets = 10,
  overlap = 0.5,
  window = window_hann,
  demean = TRUE,
  detrend = TRUE,
  ...
)

Arguments

x

numeric matrix univariate or multivariate time series

n_subsets

number of subsets (integer)

overlap

amount of overlap 0.0 < overlap < 1.0 (numeric)

window

window function to apply (function). The options are window_hann, window_hamming, window_blackman, window_nuttall, window_blackman_nuttall, window_blackman_harris

demean

should the mean be removed from x prior to calculation (logical)

detrend

should the x be detrended to calculation (logical)

...

not used

Value

array of spectra

spec_welch_tf_error

Description

spec_welch_tf_error

Usage

spec_welch_tf_error(coh, amp, overlap = 0.5, n_subsets = 10, return = "amp")
spec_welch_tf_error(coh, amp, overlap = 0.5, n_subsets = 10, return = "amp")

Arguments

coh

coherency

amp

amplitude

overlap

overlap fraction from spec_welch

n_subsets

number of subsets in spec welch

return

either 'amp', or 'phase'

Value

error

Distributed lag response

Description

'step_distributed_lag' creates a *specification* of a recipe step that are distributed lag versions of a particular variable. Uses FFT for fast calculation with a large maximum lag and many observations

Usage

step_distributed_lag(
  recipe,
  ...,
  role = "distributed_lag",
  trained = FALSE,
  knots = 1,
  spline_fun = splines::ns,
  n_subset = 1,
  n_shift = 0,
  prefix = "distributed_lag_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("distributed_lag")
)
step_distributed_lag(
  recipe,
  ...,
  role = "distributed_lag",
  trained = FALSE,
  knots = 1,
  spline_fun = splines::ns,
  n_subset = 1,
  n_shift = 0,
  prefix = "distributed_lag_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("distributed_lag")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables are affected by the step. See [selections()] for more details. For the 'tidy' method, these are not currently used.

role

Defaults to "distributed_lag"

trained

A logical to indicate if the quantities for preprocessing have been estimated.

knots

specific knots for the lagging process

spline_fun

spline function to use i.e. splines::ns, splines::bs

n_subset

for distributed_lag parallel

n_shift

for distributed_lag parallel

prefix

A prefix for generated column names, default to "distributed_lag_".

default

Passed to dplyr::lag(), determines what fills empty rows left by lagging (defaults to NA).

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

Details

'step_distributed_lag' calculates the earthtide response and then lags (leads) the terms.

Value

An updated version of 'recipe' with the new step added to the sequence of existing steps (if any). For the 'tidy' method, a tibble with columns 'terms' which is the columns that will be affected and 'holiday'.

Examples

data(transducer)

rec <- recipe(wl ~ .,
  data = transducer[1:1000, list(datetime, wl, baro)]
)

with_et <- rec %>%
  step_distributed_lag(baro, knots = c(0, 10, 100)) %>%
  step_naomit(everything()) %>%
  prep() %>%
  juice()

data(transducer)

rec <- recipe(wl ~ .,
  data = transducer[1:1000, list(datetime, wl, baro)]
)

with_et <- rec %>%
  step_distributed_lag(baro, knots = c(0, 10, 100)) %>%
  step_naomit(everything()) %>%
  prep() %>%
  juice()

Lagged Earth tide response

Description

'step_earthtide' creates a *specification* of a recipe step that are the Earth tide harmonics for a particular location. Requires the *earthtide* package

Usage

step_earthtide(
  recipe,
  ...,
  role = "earthtide",
  trained = FALSE,
  method = "gravity",
  astro_update = 1L,
  latitude = 0,
  longitude = 0,
  elevation = 0,
  azimuth = 0,
  gravity = 0,
  earth_radius = 6378136.3,
  earth_eccen = 0.0066943979514,
  cutoff = 1e-06,
  wave_groups = NULL,
  catalog = "ksm04",
  eop = NULL,
  scale = TRUE,
  prefix = "earthtide_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("earthtide")
)
step_earthtide(
  recipe,
  ...,
  role = "earthtide",
  trained = FALSE,
  method = "gravity",
  astro_update = 1L,
  latitude = 0,
  longitude = 0,
  elevation = 0,
  azimuth = 0,
  gravity = 0,
  earth_radius = 6378136.3,
  earth_eccen = 0.0066943979514,
  cutoff = 1e-06,
  wave_groups = NULL,
  catalog = "ksm04",
  eop = NULL,
  scale = TRUE,
  prefix = "earthtide_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("earthtide")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables are affected by the step. See [selections()] for more details. For the 'tidy' method, these are not currently used.

role

Defaults to "earthtide"

trained

A logical to indicate if the quantities for preprocessing have been estimated.

method

One or more of "gravity", "tidal_potential", "tidal_tilt", "vertical_displacement", "horizontal_displacement", "n_s_displacement", "e_w_displacement", "vertical_strain", "areal_strain", "volume_strain", "horizontal_strain", or "ocean_tides", "pole_tide", "lod_tide". The pole tide and lod_tide are used in predict mode even if do_predict is FALSE. More than one value can only be used if do_predict == TRUE.

astro_update

How often to update astro parameters in number of samples. This speeds up code but may make it slightly less accurate.

latitude

The station latitude (numeric) defaults to 0.

longitude

The station longitude (numeric) defaults to 0.

elevation

The station elevation (m) (numeric) defaults to 0.

azimuth

Earth azimuth (numeric) defaults to 0.

gravity

Gravity at the station (m/s^2) (numeric) 0 to estimate gravity from elevation and latitude.

earth_radius

Radius of earth (m) (numeric) defaults to 6378136.3

earth_eccen

Eccentricity of earth (numeric) defaults to 6.69439795140e-3

cutoff

Cutoff amplitude for constituents (numeric) defaults to 1e-6.

wave_groups

Two column data.frame having start and end of frequency groups (data.frame). This data.frame must have two columns with the names 'start', and 'end' signifying the start and end of the wave groupings. An optional third column 'multiplier' can be provided to scale the particular wave group. If column names do no match, the inferred column positions are start, end, multiplier.

catalog

Use the "hw95s" catalog or "ksm04" catalog (character).

eop

User defined Earth Orientation Parameter (EOP) data.frame with the following columns: datetime, ddt, ut1_utc, lod, x, y, dx, dy

scale

Scale results when do_predict is FALSE

prefix

A prefix for generated column names, default to "earthtide_".

default

Passed to dplyr::lag(), determines what fills empty rows left by lagging (defaults to NA).

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

skip

id

A character string that is unique to this step to identify it.

Details

'step_earthtide' calculates the Earth tide harmonics

Value

Examples

library(earthtide)
data(transducer)
data(eterna_wavegroups)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl)])

wg <- na.omit(eterna_wavegroups[eterna_wavegroups$time == '1 month',])
with_et <- rec %>%
  step_earthtide(datetime, 
                 latitude = 34, 
                 longitude = -118.5, 
                 wave_groups = wg) %>% 
  prep() %>%
  juice()

library(earthtide)
data(transducer)
data(eterna_wavegroups)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl)])

wg <- na.omit(eterna_wavegroups[eterna_wavegroups$time == '1 month',])
with_et <- rec %>%
  step_earthtide(datetime, 
                 latitude = 34, 
                 longitude = -118.5, 
                 wave_groups = wg) %>% 
  prep() %>%
  juice()

Generate harmonics

Description

'step_harmonic2' creates a *specification* of a recipe step for harmonics (sin and cos terms).

Usage

step_harmonic2(
  recipe,
  ...,
  role = "harmonic",
  trained = FALSE,
  freq = 1,
  prefix = "harmonic_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("harmonic")
)
step_harmonic2(
  recipe,
  ...,
  role = "harmonic",
  trained = FALSE,
  freq = 1,
  prefix = "harmonic_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("harmonic")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables are affected by the step. See [selections()] for more details. For the 'tidy' method, these are not currently used.

role

Defaults to "harmonic"

trained

A logical to indicate if the quantities for preprocessing have been estimated.

freq

numeric vector frequencies for sin and cos

prefix

A prefix for generated column names, default to "harmonic_".

default

Passed to dplyr::lag(), determines what fills empty rows left by lagging (defaults to NA).

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

skip

id

A character string that is unique to this step to identify it.

Details

'step_harmonic2' calculates the Earth tide harmonics

Value

Examples

data(transducer)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl)])

with_et <- rec %>%
  step_harmonic2(datetime, freq = c(1, 1.93, 2)) %>% 
  prep() %>%
  juice()

data(transducer)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl)])

with_et <- rec %>%
  step_harmonic2(datetime, freq = c(1, 1.93, 2)) %>% 
  prep() %>%
  juice()

Lagged Earth tide response

Description

'step_lag_earthtide' creates a *specification* of a recipe step that are lagged versions of the Earth tide response at a particular location. Requires the *earthtide* package

Usage

step_lag_earthtide(
  recipe,
  ...,
  role = "lag_earthtide",
  trained = FALSE,
  method = "gravity",
  lag = 0,
  astro_update = 1L,
  latitude = 0,
  longitude = 0,
  elevation = 0,
  azimuth = 0,
  gravity = 0,
  earth_radius = 6378136.3,
  earth_eccen = 0.0066943979514,
  cutoff = 1e-06,
  wave_groups = NULL,
  catalog = "ksm04",
  eop = NULL,
  prefix = "lag_earthtide_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("lag_earthtide")
)
step_lag_earthtide(
  recipe,
  ...,
  role = "lag_earthtide",
  trained = FALSE,
  method = "gravity",
  lag = 0,
  astro_update = 1L,
  latitude = 0,
  longitude = 0,
  elevation = 0,
  azimuth = 0,
  gravity = 0,
  earth_radius = 6378136.3,
  earth_eccen = 0.0066943979514,
  cutoff = 1e-06,
  wave_groups = NULL,
  catalog = "ksm04",
  eop = NULL,
  prefix = "lag_earthtide_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("lag_earthtide")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables are affected by the step. See [selections()] for more details. For the 'tidy' method, these are not currently used.

role

Defaults to "lag_earthtide"

trained

A logical to indicate if the quantities for preprocessing have been estimated.

method

lag

A vector of positive integers. Each specified column will be lagged for each value in the vector.

astro_update

How often to update astro parameters in number of samples. This speeds up code but may make it slightly less accurate.

latitude

The station latitude (numeric) defaults to 0.

longitude

The station longitude (numeric) defaults to 0.

elevation

The station elevation (m) (numeric) defaults to 0.

azimuth

Earth azimuth (numeric) defaults to 0.

gravity

Gravity at the station (m/s^2) (numeric) 0 to estimate gravity from elevation and latitude.

earth_radius

Radius of earth (m) (numeric) defaults to 6378136.3

earth_eccen

Eccentricity of earth (numeric) defaults to 6.69439795140e-3

cutoff

Cutoff amplitude for constituents (numeric) defaults to 1e-6.

wave_groups

catalog

Use the "hw95s" catalog or "ksm04" catalog (character).

eop

User defined Earth Orientation Parameter (EOP) data.frame with the following columns: datetime, ddt, ut1_utc, lod, x, y, dx, dy

prefix

A prefix for generated column names, default to "lag_earthtide_".

default

Passed to dplyr::lag(), determines what fills empty rows left by lagging (defaults to NA).

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

skip

id

A character string that is unique to this step to identify it.

Details

'step_lag_earthtide' calculates the earthtide response and then lags (leads) the terms.

Value

Examples

data(transducer)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl)])

with_et <- rec %>%
  step_lag_earthtide(datetime, latitude = 34, longitude = -118.5, lag = -1:1) %>% 
  prep() %>%
  juice()

data(transducer)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl)])

with_et <- rec %>%
  step_lag_earthtide(datetime, latitude = 34, longitude = -118.5, lag = -1:1) %>% 
  prep() %>%
  juice()

Create a lagged (or lead) predictor

Description

'step_lag_matrix' creates a *specification* of a recipe step that will add new columns of lagged data. Lagged data will by default include NA values where the lag was induced. These can be removed with [step_naomit()], or you may specify an alternative filler value with the 'default' argument. This method is faster than [step_lag()] and allows for negative values.

Usage

step_lag_matrix(
  recipe,
  ...,
  role = "lag_matrix",
  trained = FALSE,
  lag = 1,
  n_subset = 1,
  n_shift = 0,
  prefix = "lag_matrix_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("lag_matrix")
)
step_lag_matrix(
  recipe,
  ...,
  role = "lag_matrix",
  trained = FALSE,
  lag = 1,
  n_subset = 1,
  n_shift = 0,
  prefix = "lag_matrix_",
  default = NA,
  columns = NULL,
  skip = FALSE,
  id = rand_id("lag_matrix")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose which variables are affected by the step. See [selections()] for more details.

role

Defaults to "predictor"

trained

A logical to indicate if the quantities for preprocessing have been estimated.

lag

A vector of integers. They can be positive, negative or zero. Each specified column will be lagged for each value in the vector.

n_subset

subset every n_subset values

n_shift

shift the data n_shift values

prefix

A prefix for generated column names, default to "lag_".

default

Passed to dplyr::lag(), determines what fills empty rows left by lagging (defaults to NA).

columns

A character string of variable names that will be populated (eventually) by the 'terms' argument.

skip

A logical. Should the step be skipped when the recipe is baked by [bake.recipe()]? While all operations are baked when [prep.recipe()] is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using 'skip = TRUE' as it may affect the computations for subsequent operations

id

A character string that is unique to this step to identify it.

Details

The step assumes that the data are already _in the proper sequential order_ for lagging.

Value

An updated version of 'recipe' with the new step added to the sequence of existing steps (if any).

Examples

data(transducer)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl, baro)])

with_et <- rec %>%
  step_lag_matrix(baro, lag = -1:1) %>%
  step_naomit(everything()) %>% 
  prep() %>%
  juice()

data(transducer)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl, baro)])

with_et <- rec %>%
  step_lag_matrix(baro, lag = -1:1) %>%
  step_naomit(everything()) %>% 
  prep() %>%
  juice()

stretch_interp

Description

stretch_interp

Usage

stretch_interp(start_val = NA, end_val = NA, values)
stretch_interp(start_val = NA, end_val = NA, values)

Arguments

start_val

starting value to match

end_val

end value to match

values

set of values

Value

shifted vector of values

summarize_coef

Description

summarize_coef

Usage

summarize_coef(fit)
summarize_coef(fit)

Arguments

fit

model fit from lm object

Value

data.table with coefficient results

summarize_lm

Description

summarize_lm

Usage

summarize_lm(fit, ...)
summarize_lm(fit, ...)

Arguments

fit

model fit from lm object

...

additional arguments

Value

data.table with fit results

synthetic

Description

This function is used for testing purposes

Usage

synthetic(
  sd_noise = 2e-04,
  sd_noise_trend = 3e-05,
  linear_trend = 1e-07,
  n = 14 * 86400,
  seed = NULL,
  scale = 0.5,
  baro_kernel = NULL
)
synthetic(
  sd_noise = 2e-04,
  sd_noise_trend = 3e-05,
  linear_trend = 1e-07,
  n = 14 * 86400,
  seed = NULL,
  scale = 0.5,
  baro_kernel = NULL
)

Arguments

sd_noise

standard deviation of random noise to add (numeric)

sd_noise_trend

standard deviation of noise to add to generate a trend (numeric)

linear_trend

magnitude of linear trend in time (numeric)

n

length of time series in seconds (integer)

seed

random number seed for reproducibility (numeric)

scale

multiplier for barometric pressure (numeric)

baro_kernel

vector values to convolve with barometric pressure

Value

data.table of synthetic water levels and barometric pressure

synthetic_wl

Description

This function is used for testing purposes

Usage

synthetic_wl(
  baro,
  datetime,
  sd_noise = 0,
  linear_trend = 0,
  intercept = 0,
  seed = NULL,
  kernel = NULL
)
synthetic_wl(
  baro,
  datetime,
  sd_noise = 0,
  linear_trend = 0,
  intercept = 0,
  seed = NULL,
  kernel = NULL
)

Arguments

baro

barometric pressure (numeric vector)

datetime

POSIXct dates

sd_noise

standard deviation of random noise to add (numeric)

linear_trend

magnitude of linear trend in time (numeric)

intercept

seed

random number seed for reproducibility (numeric)

kernel

Value

data.table of synthetic water levels and barometric pressure

transducer

Description

This data.frame contains the water levels, barometric pressure, and synthtetic earthtides from 2016-08-25 to 2016-10-15 (subsampled to every 2 minutes).

Usage

transducer
transducer

Format

A data.frame The columns are:

datetime: POSIXct date and time
wl: transducer pressure of water column(dbar)
baro: barometric pressure (dbar)
et: synthetic gravity

Examples

utils::data(transducer)
utils::data(transducer)

transfer_fun

Description

transfer_fun

Usage

transfer_fun(dat, vars, time = "datetime", method = "spec_pgram", ...)
transfer_fun(dat, vars, time = "datetime", method = "spec_pgram", ...)

Arguments

dat

data that has the independent and dependent variables (data.table)

vars

variables to include for transfer function calculation

time

the name of the column that contains the POSIXct date and time

method

either spec_pgram, spec_welch, or spec_multitaper

...

arguments to pass to method

Value

transfer function, gain and phase, coherency

window_blackman

Description

Blackman window for FFT

Usage

window_blackman(n)
window_blackman(n)

Arguments

n

length of signal (integer)

Value

window

Examples

window_blackman(100)

window_blackman(100)

window_blackman_harris

Description

Blackman-Harris window for FFT

Usage

window_blackman_harris(n)
window_blackman_harris(n)

Arguments

n

length of signal (integer)

Value

window

Examples

window_blackman_harris(100)

window_blackman_harris(100)

window_blackman_nuttall

Description

Blackman-Nuttall window for FFT

Usage

window_blackman_nuttall(n)
window_blackman_nuttall(n)

Arguments

n

length of signal (integer)

Value

window

Examples

window_blackman_nuttall(100)

window_blackman_nuttall(100)

window_first_deriv

Description

First derivative window for FFT

Usage

window_first_deriv(n, a0, a1, a2, a3)
window_first_deriv(n, a0, a1, a2, a3)

Arguments

n

length of signal (integer)

a0

numeric coefficient

a1

numeric coefficient

a2

numeric coefficient

a3

numeric coefficient

Value

window

Examples

# nuttall window
window_first_deriv(100, 0.355768, 0.487396, 0.144232, 0.012604)

# nuttall window
window_first_deriv(100, 0.355768, 0.487396, 0.144232, 0.012604)

window_gaussian

Description

Gaussian window for FFT https://en.wikipedia.org/wiki/Window_function

Usage

window_gaussian(n, sigma)
window_gaussian(n, sigma)

Arguments

n

length of signal (integer)

sigma

the standard deviation in periods (numeric)

Value

window

Examples

window_gaussian(100, 0.3)
window_gaussian(100, 0.3)

window_hamming

Description

Hamming window for FFT

Usage

window_hamming(n)
window_hamming(n)

Arguments

n

length of signal (integer)

Value

window

Examples

window_hamming(100)

window_hamming(100)

window_hann

Description

Hann window for FFT

Usage

window_hann(n)
window_hann(n)

Arguments

n

length of signal (integer)

Value

window

Examples

window_hann(100)

window_hann(100)

window_nuttall

Description

Nuttall window for FFT

Usage

window_nuttall(n)
window_nuttall(n)

Arguments

n

length of signal (integer)

Value

window

Examples

window_nuttall(100)

window_nuttall(100)

window_rectangular

Description

Rectangular window for FFT

Usage

window_rectangular(n)
window_rectangular(n)

Arguments

n

length of signal (integer)

Value

window

Examples

window_rectangular(100)

window_rectangular(100)

wipp30

Description

This data.frame contains the water levels, barometric pressure, and earthtides for wipp30. This is the dataset included in BETCO.

Usage

wipp30
wipp30

Format

A data.frame The columns are:

time: time in hours
wl: transducer pressure of water column
baro: barometric pressure
et: synthetic gravity

Examples

utils::data(wipp30)
utils::data(wipp30)

Package 'waterlevel'

Help Index

A short title line describing what the package does

Description

Details

Author(s)

References

See Also

Examples

add_level_shifts

Description

Usage

Arguments

Value

Computation of AIC for mlm objects

Description

Usage

Arguments

Value

baro_elev

Description

Usage

Arguments

Value

Examples

be_acworth_eq_4

Description

Usage

Arguments

Value

References

be_acworth_eq_4

Description

Usage

Arguments

Value

References

Examples

be_clark

Description

Usage

Arguments

Value

References

Examples

be_correct

Description

Usage

Arguments

Value

Examples

be_high_low

Description

Usage

Arguments

Value

Examples

be_least_squares

Description

Usage

Arguments

Value

Examples

be_least_squares_diff

Description

Usage

Arguments

Value

Examples

be_rahi ## need to check for correctness

Description

Usage

Arguments

Value

References

Examples

be_ratio

Description

Usage

Arguments