Package 'STCYP'

Description

Computes the Clayton copula dependence parameter based on Kendall's tau.

Usage

clayton.theta(tau)

Arguments

Value

Numeric, estimated Clayton copula parameter.

Examples

clayton.theta(mean(cor(cbind(u[[1]], u[[2]], u[[3]]), method = "kendall")))

Description

A list containing supported copula types.

Usage

copula_list

Format

A list of copula types.

copulas

"Gaussian" "Clayton" "Frank" "Gumbel" "Joe"

Description

Real crop yield and meteorological data of 24 regions for Ontario, Canada from 1950 to 2022 and anticipated data from 2023 to 2100.

Usage

data

Format

A data frame with 1752 rows and 27 variables:

time

chr: year from 1950-2022

CD

chr: 24 subregions

lat

num: latitude

lon

num: longitude

yield

num: wheat crop yield per census division, in bushel/acre

cdd

num: Annual maximum number of consecutive days with daily precipitation below 1mm (unit = days)

cddcold

num: Annual cooling degree days above 18C (unit = degree_days)

dlyfrzthw

num: Annual number of days with a diurnal freeze-thaw cycle : tmax > 0 degc and tmin <= -1 degc

firstfallfrost

num: First day of year with temperature below 0 degc for at least 1 days

frostdays

num: Annual number of days with minimum daily temperature below 0C

icedays

num: Annual number of days with maximum daily temperature below 0 degC

nrcdd

num: The annual number of dry periods of 6 days and more, during which the maximal precipitation on a window of 6 days is under 1.0 mm

prcptot

num: Annual total precipitation (unit = mm)

r1mm

num: Annual number of days with daily precipitation over 1.0 mm/day

r10mm

num: Annual number of days with daily precipitation over 10.0 mm/day

r20mm

num: Annual number of days with daily precipitation over 20.0 mm/day

rx1day

num: Annual maximum 1-day total precipitation (unit = mm)

rx5day

num: Annual maximum 5-day total precipitation (unit = mm)

tgmean

num: Annual mean of daily mean temperatures (unit = C degrees)

tnmean

num: Annual mean of daily minimum temperatures (unit = C degrees)

tnmin

num: Annual minimum of daily minimum temperatures (unit = C degrees)

tr18

num: Annual number of tropical nights : defined as days with minimum daily temperature above 18 degc

txmax

num: Annual minimum of daily maximum temperature (unit = C degrees)

txmean

num: Annual mean of daily maximum temperature (unit = C degrees)

txgt25

num: Annual number of days where daily maximum temperature exceeds 25 degC

txgt27

num: Annual number of days where daily maximum temperature exceeds 27 degC

txgt29

num: Annual number of days where daily maximum temperature exceeds 29 degC

Source

ClimateData.ca

Description

Selected data from year 1950 to 2022 and covariates including txgt27, tr18, cddcold, txgt29, and tnmean for case study.

Usage

dt

Format

A data frame with 1752 rows and 10 variables:

time

chr: year from 1950-2022

CD

chr: 24 subregions

lat

num: latitude

lon

num: longitude

yield

num: wheat crop yield per census division, in bushel/acre

cddcold

num: Annual cooling degree days above 18C (unit = degree_days)

tnmean

num: Annual mean of daily minimum temperatures (unit = C degrees)

tr18

num: Annual number of tropical nights : defined as days with minimum daily temperature above 18 degc

txgt27

num: Annual number of days where daily maximum temperature exceeds 27 degC

txgt29

num: Annual number of days where daily maximum temperature exceeds 29 degC

Source

ClimateData.ca

Description

Computes the time-varying correlation parameter (rho) for a Gaussian copula.

Usage

dynamic.rho(params, lagged_rho, X_t)

Arguments

Value

Numeric, estimated dynamic Gaussian copula correlation.

Description

Computes the Clayton copula parameter dynamically based on lagged values and covariates.

Usage

dynamic.theta.clayton(params, lagged_theta, X_t)

Arguments

Value

Numeric, estimated dynamic Clayton copula parameter.

Description

Computes the Frank copula parameter dynamically based on lagged values and covariates.

Usage

dynamic.theta.frank(params, lagged_theta, X_t)

Arguments

Value

Numeric, estimated dynamic Frank copula parameter.

Description

Computes the Gumbel copula parameter dynamically based on lagged values and covariates.

Usage

dynamic.theta.gumbel(params, lagged_theta, X_t)

Arguments

Value

Numeric, estimated dynamic Gumbel copula parameter.

Description

Computes the Joe copula parameter dynamically based on lagged values and covariates.

Usage

dynamic.theta.joe(params, lagged_theta, X_t)

Arguments

Value

Numeric, estimated dynamic Joe copula parameter.

Description

Fits a BSTS model for a time series y, given a vector or matrix of covariates z.

Usage

fit_bsts(y, z, lags = 0, MCMC.iter = 5000)

Arguments

Value

A fitted BSTS model.

Description

Computes the Frank copula dependence parameter based on Kendall's tau.

Usage

frank.theta(tau)

Arguments

Value

Numeric, estimated Frank copula parameter.

Description

Computes the Gumbel-Hougaard copula dependence parameter based on Kendall's tau.

Usage

GH.theta(tau)

Arguments

Value

Numeric, estimated Gumbel copula parameter.

Examples

GH.theta(mean(cor(cbind(u[[1]], u[[2]], u[[3]]), method = "kendall")))

Description

Initial Parameters for 3D Pseudo-Loglikelihood Estimation

Usage

init_params_full

Format

A numeric vector of length $(2+M)$ where:

omega

Baseline autoregressive coefficient.

alpha

Parameter controlling variance.

gamma1, gamma2, gamma3

Coefficients related to external factors.

Description

Computes the Joe copula dependence parameter based on Kendall's tau.

Usage

joe.theta(tau)

Arguments

Value

Numeric, estimated Joe copula parameter.

Examples

joe.theta(mean(cor(cbind(u[[1]], u[[2]], u[[3]]), method = "kendall")))

Description

Computes the negative log-likelihood of a 3-dimensional copula model with a time-varying copula structure.

Usage

log_likelihood_noGEV_3d(params, u1, u2, u3, X_t, z1, z2, z3, copula)

Arguments

Value

The negative log-likelihood value for optimization.

Examples

test_ll_3d <- log_likelihood_noGEV_3d(init_params_full,
                                                u[[1]],
                                                u[[2]],
                                                u[[3]],
                                                (z_train[[1]] + z_train[[2]] + z_train[[3]])/3,
                                                z_train[[1]],
                                                z_train[[2]],
                                                z_train[[3]],
                                                "Gaussian")

Description

list containing Chatham-Kent, Lambton, and Wellington

Usage

medoid_names

Format

An object of class character of length 3.

Description

19

Usage

n_test

Format

An object of class integer of length 1.

Description

54

Usage

n_train

Format

An object of class integer of length 1.

Description

Creates a plot of observed data, forecasted values, and confidence intervals.

Usage

plot_forecast(
  forecast,
  data_train,
  data_test,
  time,
  quant_high,
  quant_low,
  observed_col,
  forecast_col,
  title
)

Arguments

Value

A ggplot2 object.

Description

Generates a time series plot comparing the forecasts from two models along with observed data.

Usage

plot_forecast_compare(
  forecast1,
  forecast2,
  data_train,
  data_test,
  time,
  quant_high,
  quant_low,
  col1,
  title
)

Arguments

Value

A ggplot2 object showing the forecast comparison.

Description

Function to optimize the full pseudo-loglikelihood and perform new forecasts

Usage

simul_fun_noGEV_3d(
  nsim = 100,
  n_train,
  n_test,
  copula,
  init_params,
  fn,
  u1,
  u2,
  u3,
  z1_train,
  z2_train,
  z3_train,
  z1_test,
  z2_test,
  z3_test,
  X_t,
  y1_test,
  y2_test,
  y3_test,
  BSTS_1,
  BSTS_2,
  BSTS_3
)

Arguments

Value

A list containing:

Description

1950-2022

Usage

time

Format

An object of class character of length 73.

Description

2004-2022

Usage

time_test

Format

An object of class character of length 19.

Description

1950-2003

Usage

time_train

Format

An object of class character of length 54.

Description

Pseudo-Observations of BSTS Residuals for Crop Yield Forecasting

Usage

u

Format

A matrix with dimensions $(n_train, D)$:

n_train

Number of time points used in the training set.

D

Number of regions analyzed (Chatham-Kent, Lambton,Wellington).

Source

Derived from residuals of BSTS models fitted to crop yield data.

Description

Crop Yield Data for Testing in BSTS Models

Usage

y_test

Format

A matrix with dimensions $(n_test, D)$:

n_train

Number of time points used in the test set.

D

Number of regions analyzed (Chatham-Kent, Lambton,Wellington).

Source

Historical crop yield records from ClimateData.ca.

Description

Training crop-yield data used for BSTS models.

Usage

y_train

Format

A numeric matrix with n_train rows and D columns:

rows (n_train)

Number of time points in the training set.

columns (D)

Regions analyzed (Chatham-Kent, Lambton, Wellington).

Source

ClimateData.ca (processed)

Description

Standardized climate covariates used to forecast with the BSTS models (test).

Usage

z_test

Format

A numeric array with dimensions n_test × D × M:

n_test

Number of test time points.

D

Regions (Chatham-Kent, Lambton, Wellington).

M

Number of covariates (cddcold, tr18, txgt27, tnmean, txgt29).

Source

ClimateData.ca (processed)

Description

Standardized climate covariates used to fit the BSTS models (training).

Usage

z_train

Format

A numeric array with dimensions n_train × D × M:

n_train

Number of training time points.

D

Regions (Chatham-Kent, Lambton, Wellington).

M

Number of covariates (cddcold, tr18, txgt27, tnmean, txgt29).

Source

ClimateData.ca (processed)