Package 'tsgc'

Return index and value of maximum

Description

Something similar to Python's argmax.

Usage

argmax(x, decreasing = TRUE)

Arguments

x	Object to have its maximum found
decreasing	Logical value indicating whether x should be ordered in decreasing order. Default is TRUE. Setting this to FALSE would find the minimum.

Value

The maximum value and its index.

Examples

library(tsgc)
data(gauteng,package="tsgc")
argmax(gauteng)

---

Compute log growth rate of cumulated dataset

Description

Helper method to compute the log growth rates of cumulated variables. It will compute the log cumulative growth rate for each column in the data frame.

Usage

df2ldl(dt)

Arguments

dt	Cumulated data series.

Value

A data frame of log growth rates of the cumulated variable which has been inputted via the parameter dt.

Examples

library(tsgc)
data(gauteng,package="tsgc")
df2ldl(gauteng)

---

Cumulative cases of Covid-19 in England.

Description

Cumulative cases of Covid-19 in England.

Usage

data(england)

Format

An object of class "xts";

Cases

Cumulative cases of Covid-19

References

Downloaded from https://ukhsa-dashboard.data.gov.uk/topics/covid-19

Examples

data(england)
# plot daily cases
plot(diff(england))

---

FilterResults

Description

Class for estimated Dynamic Gompertz Curve model and contains methods to extract smoothed/filtered estimates of the states, the level of the incidence variable $y$, and forecasts of $y$.

Methods

get_growth_y(smoothed = FALSE, return.components = FALSE)

Returns the growth rate of the incidence ($y$) of the cumulated variable ($Y$). Computed as

$g_t = \exp\{\delta_t\}+\gamma_t.$

Parameters

• smoothed Logical value indicating whether to use the smoothed estimates of $\delta$ and $\gamma$ to compute the growth rate (TRUE), or the contemporaneous filtered estimates (FALSE). Default is FALSE.

• return.components Logical value indicating whether to return the estimates of $\delta$ and $\gamma$ as well as the estimates of the growth rate, or just the growth rate. Default is FALSE.

Return Value

xts object containing smoothed/filtered growth rates and components ($\delta$ and $\gamma$), where applicable.

get_gy_ci(smoothed = FALSE, confidence_level = 0.68)

Returns the growth rate of the incidence ($y$) of the cumulated variable ($Y$). Computed as

$g_t = \exp\{\delta_t\}+\gamma_t.$

Parameters

• smoothed Logical value indicating whether to use the smoothed estimates of $\delta$ and $\gamma$ to compute the growth rate (TRUE), or the contemporaneous filtered estimates (FALSE). Default is FALSE.

• confidence_level Confidence level for the confidence interval. Default is $0.68$, which is one standard deviation for a normally distributed random variable.

Return Value

xts object containing smoothed/filtered growth rates and upper and lower bounds for the confidence intervals.

predict_all(n.ahead, sea.on = FALSE, return.all = FALSE)

Returns forecasts of the incidence variable $y$, the state variables and the conditional covariance matrix for the states.

Parameters

• n.ahead The number of forecasts you wish to create from the end of your sample period.

• sea.on Logical value indicating whether seasonal components should be included in the state-space model or not. Default is TRUE.

• return.all Logical value indicating whether to return all filtered estimates and forecasts (TRUE) or only the forecasts (FALSE). Default is FALSE.

Return Value

xts object containing the forecast (and filtered, where applicable) level of $y$ (y.hat), $\delta$ (level.t.t), $\gamma$ (slope.t.t), vector of states including the seasonals where applicable (a.t.t) and covariance matrix of all states including seasonals where applicable (P.t.t).

predict_level( y.cum, n.ahead, confidence_level, sea.on = FALSE, return.diff = FALSE )

Forecast the cumulated variable or the incidence of it. This function returns the forecast of the cumulated variable $Y$, or the forecast of the incidence of the cumulated variable, $y$. For example, in the case of an epidemic, $y$ might be daily new cases of the disease and $Y$ the cumulative number of recorded infections.

Parameters

• y.cum The cumulated variable.

• n.ahead The number of periods ahead you wish to forecast from the end of the estimation window.

• confidence_level The confidence level for the log growth rate that should be used to compute the forecast intervals of $y$.

• return.diff Logical value indicating whether to return the cumulated variable, $Y$, or the incidence of it, $y$ (i.e., the first difference of the cumulated variable). Default is FALSE.

Return Value

xts object containing the point forecasts and upper and lower bounds of the forecast interval.

print_estimation_results()

Prints a table of estimated parameters in a format ready to paste into LaTeX.

References

Harvey, A. C. and Kattuman, P. (2021). A Farewell to R: Time Series Models for Tracking and Forecasting Epidemics, Journal of the Royal Society Interface, vol 18(182): 20210179

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-20")

# Specify a model
model <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)
# Estimate a specified model
res <- model$estimate()
# Print estimation results
res$print_estimation_results()
# Forecast 7 days ahead from the end of the estimation window
res$predict_level(y.cum = gauteng[idx.est], n.ahead = 7,
  confidence_level = 0.68)
# Forecast 7 days ahead from the model and return filtered states
res$predict_all(n.ahead = 7, return.all = TRUE)
# Return the filtered growth rate and its components
res$get_growth_y(return.components = TRUE)
# Return smoothed growth rate of incidence variable and its confidence
# interval
res$get_gy_ci(smoothed = TRUE, confidence_level = 0.68)

---

Returns forecast of number of periods until peak given KFAS::KFS output.

Description

Since Harvey and Kattuman (2021) show that

$g_{y,t+\ell|T} = \exp\{\delta_{T|T}+\ell \gamma_{T|T}\}+\gamma_{T|T},$

we can compute the $\ell$ for which $g_{y,t}=0$ and then will fall below zero. This $\ell$ is given by

$\ell = \frac{\ln(-\gamma_{T|T})-\delta_{T|T}}{\gamma_{T|T}}.$

This is predicated on $\gamma_{T|T}<0$, else there is super-exponential growth and no peak in sight. Of course, it only makes sense to investigate an upcoming peak for $g_{y,T|T}>0$ (when cases are growing). The estimates of $\delta_{T|T}$ and $\gamma_{T|T}$ are extracted from the KFS object passed to the function.

Usage

forecast_peak(kfs_out)

Arguments

kfs_out

The KFAS::KFS object for which the forecast peak is to be calculated. This would be the output element of a model estimated in the SSModelDynamicGompertz or SSModelDynamic

Value

Forecast of number of periods until peak.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-06")

res <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)$estimate()

forecast_peak(res$output)

---

Returns forecast of number of periods until peak given estimated state variables $\delta$ and $\gamma$.

Description

Since Harvey and Kattuman (2021) show that

$g_{y,t+\ell|T} = \exp\{\delta_{T|T}+\ell \gamma_{T|T}\}+\gamma_{T|T},$

we can compute the $\ell$ for which $g_{y,t}=0$ and then will fall below zero. This $\ell$ is given by

$\ell = \frac{\ln(-\gamma_{T|T})-\delta_{T|T}}{\gamma_{T|T}}.$

This is predicated on $\gamma_{T|T}<0$, else there is super-exponential growth an no peak in sight. Of course, it only makes sense to investigate an upcoming peak for $g_{y,T|T}>0$ (when cases are growing).

Usage

forecast.peak(delta, gamma)

Arguments

delta	The estimate of $\delta$, the level of $\ln g$.
gamma	The estimate of $\gamma$, the slope of $\ln g$.

Value

Forecast of number of periods until peak.

Examples

# Forecasts the peak of an epidemic with gamma < 0 so that a peak is in
# sight.
forecast.peak(-2.87,-0.045)

# Does not return a result (returns an error as gamma > 0)
try(forecast.peak(-2.87,0.045), silent=TRUE)

---

Cumulative cases of Covid-19 in the South African province of Gauteng.

Description

Cumulative cases of Covid-19 in the South African province of Gauteng.

Usage

data(gauteng)

Format

An object of class "xts";

Cases

Cumulative cases of Covid-19 from 10th March 2020

References

Downloaded from https://sacoronavirus.co.za/

Examples

data(gauteng)
# plot daily cases
plot(diff(gauteng))

---

Plots forecast and realised values of the log cumulative growth rate

Description

Plots actual and filtered values of the log cumulative growth rate ($\ln(g_t)$) in the estimation sample and the forecast and realised log cumulative growth rate out of the estimation sample.

Usage

plot_forecast(
  res,
  y.eval,
  n.ahead = 14,
  plt.start.date = NULL,
  title = "",
  caption = ""
)

Arguments

res	Results object estimated using the estimate() method.
y.eval	The out-of-sample realisation of the log growth rate of the cumulated variable (i.e. the actual values to which the forecasts should be compared).
n.ahead	The number of time periods ahead from the end of the sample to be forecast. The default is 14.
plt.start.date	Plot start date. Default is NULL which is the start of the estimation sample.
title	Plot title. Enter as text string.
caption	Plot caption. Enter as text string.

Value

A ggplot2 plot.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-20")
idx.eval <- (zoo::index(gauteng) >= as.Date("2020-07-20")) &
     zoo::index(gauteng) <= as.Date("2020-07-27")

# Specify a model
model <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)
# Estimate a specified model
res <- model$estimate()

# Plot forecast and realised log growth rate of cumulative cases
plot_forecast(res, y.eval = df2ldl(gauteng[idx.eval]), n.ahead = 7,
  title = "Forecast ln(g)", plt.start.date = as.Date("2020-07-13"))

---

Plots the growth rates and slope of the log cumulative growth rate

Description

Plots the smoothed/filtered growth rate of the difference in the cumulated variable ($g_y$) and the associated confidence intervals.

Usage

plot_gy_ci(
  res,
  plt.start.date = NULL,
  smoothed = FALSE,
  title = NULL,
  series.name = NULL,
  pad.right = NULL
)

Arguments

res	Results object estimated using the estimate() method.
plt.start.date	Plot start date. Default is NULL which is the start of the estimation sample.
smoothed	Logical value indicating whether to used the smoothed estimates of $\delta$ and $\gamma$. Default is FALSE, in which case the filtered estimates are returned.
title	Title for plot. Enter as text string. NULL (i.e. no title) by default.
series.name	The name of the series the growth rate is being computed for. E.g. 'New cases'.
pad.right	Numerical value for the amount of time periods of blank space you wish to leave on the right of the graph. Extends the horizontal axis by the given number of time periods.

Value

A ggplot2 plot.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-20")

# Specify a model
model <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)
# Estimate a specified model
res <- model$estimate()

# Plot filtered gy, g and gamma
plot_gy_ci(res, plt.start.date = as.Date("2020-07-13"))

---

Plots the growth rates and slope of the log cumulative growth rate

Description

Plots the smoothed/filtered growth rate of the difference in the cumulated variable ($g_y$), the smoothed/filtered growth rate of the the cumulated variable ($g$), and the smoothed/filtered slope of $\ln(g)$, $\gamma$. Following Harvey and Kattuman (2021), we compute $g_{y,t}$ as

$g_{y,t} = \exp(\delta_t) + \gamma_t.$

Usage

plot_gy_components(res, plt.start.date = NULL, smoothed = FALSE, title = NULL)

Arguments

res	Results object estimated using the estimate() method.
plt.start.date	Plot start date. Default is NULL which is the start of the estimation sample.
smoothed	Logical value indicating whether to used the smoothed estimates of $\delta$ and $\gamma$. Default is FALSE, in which case the filtered estimates are returned.
title	Title for plot. Enter as text string. NULL (i.e. no title) by default.

Value

A ggplot2 plot.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-20")

# Specify a model
model <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)
# Estimate a specified model
res <- model$estimate()

# Plot filtered gy, g and gamma
plot_gy_components(res, plt.start.date = as.Date("2020-07-06"))

---

Plots the forecast of new cases (the difference of the cumulated variable) over a holdout sample.

Description

Plots actual values of the difference in the cumulated variable, the forecasts of the cumulated variable (both including and excluding the seasonal component, where a seasonal is specified) and forecast intervals around the forecasts, plus the actual outcomes from the holdout sample. The forecast intervals are based on the prediction intervals for $\ln(g_t)$. Also reports the mean absolute percentage prediction error over the holdout sample.

Usage

plot_holdout(
  res,
  Y,
  Y.eval,
  confidence.level = 0.68,
  date_format = "%Y-%m-%d",
  series.name = NULL,
  title = NULL,
  caption = NULL
)

Arguments

res	Results object estimated using the estimate() method.
Y	Values of the cumulated variable to be used in the estimation window.
Y.eval	Values of the cumulated variable to be used in the holdout sample (i.e. to which the forecasts should be compared to).
confidence.level	Width of prediction interval for $\ln(g_t)$ to use in forecasts of $y_t = \Delta Y_t$. Default is 0.68, which is approximately one standard deviation for a Normal distribution.
date_format	Date format, e.g. '%Y-%m-%d', which is the default.
series.name	Name of the variable you are forecasting for the purposes of a $y$-axis label. E.g. if series.name = "Cases" the $y$-axis will show "New Cases".
title	Title for forecast plot. Enter as text string. NULL (i.e. no title) by default.
caption	Caption for forecast plot. Enter as text string. NULL (i.e. no caption) by default.

Value

A ggplot2 plot.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-20")
idx.eval <- (zoo::index(gauteng) >= as.Date("2020-07-20")) &
     zoo::index(gauteng) <= as.Date("2020-07-27")

# Specify a model
model <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)
# Estimate a specified model
res <- model$estimate()

# Plot forecasts and outcomes over evaluation period
plot_holdout(res = res, Y = gauteng[idx.est], Y.eval = gauteng[idx.eval])

---

Plots the forecast of new cases (the difference of the cumulated variable)

Description

Plots actual values of the difference in the cumulated variable, the forecasts of the cumulated variable (both including and excluding the seasonal component, where a seasonal is specified) and forecast intervals around the forecasts. The forecast intervals are based on the prediction intervals for $\ln(g_t)$.

Usage

plot_new_cases(
  res,
  Y,
  n.ahead,
  confidence.level = 0.68,
  date_format = "%Y-%m-%d",
  title = NULL,
  plt.start.date = NULL
)

Arguments

res	Results object estimated using the estimate() method.
Y	Cumulated variable.
n.ahead	Number of forecasts (i.e. number of periods ahead to forecast from end of estimation window).
confidence.level	Width of prediction interval for $\ln g_t$ to use in forecasts of $y_t = \Delta Y_t$. Default is 0.68, which is approximately one standard deviation for a Normal distribution.
date_format	Date format. Default is '%Y-%m-%d'.
title	Title for forecast plot. Enter as text string. NULL (i.e. no title) by default.
plt.start.date	First date of actual data (from estimation sample) to plot on graph.NULL (i.e. plots all data in estimation window) by default.

Value

A ggplot2 plot.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-20")

# Specify a model
model <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)
# Estimate a specified model
res <- model$estimate()

# Plot forecast of new cases 7 days ahead
plot_new_cases(res, Y = gauteng[idx.est], n.ahead = 7,
confidence.level = 0.68, date_format = "%Y-%m-%d",
title = "Forecast new cases", plt.start.date = as.Date("2020-07-13"))

---

Reinitialise a data frame by subtracting the reinit.date row from all columns

Description

Reinitialise a data frame by subtracting the reinit.date row from all columns

Usage

reinitialise_dataframe(dt, reinit.date)

Arguments

dt	Cumulated data series.
reinit.date	Reinitialisation date. E.g. ‘⁠'2021-05-12'⁠’.

Value

The reinitialised data frame

Examples

library(tsgc)
data(gauteng,package="tsgc")
reinitialise_dataframe(gauteng,as.Date("2021-01-01"))

---

Base class for estimating time-series growth curve models. Classes SSModelDynamicGompertz and SSModelDynGompertzReinit refer back to this base class.

Description

Base class for estimating time-series growth curve models. Classes SSModelDynamicGompertz and SSModelDynGompertzReinit refer back to this base class.

Methods

estimate(sea.type = "trigonometric", sea.period = 7)

Estimates the dynamic Gompertz curve model when applied to an object of class SSModelDynamicGompertz or SSModelDynGompertzReinit.

Parameters

• sea.type Seasonal type. Options are 'trigonometric' and 'none'. 'trigonometric' will yield a model with a trigonometric seasonal component and 'none' will yield a model with no seasonal component.

• sea.period The period of seasonality. For a day-of-the-week effect with daily data, this would be 7. Not required if sea.type = 'none'.

Return Value

An object of class FilterResults containing the result output for the estimated dynamic Gompertz curve model.

get_dynamic_gompertz_model( y, q = NULL, sea.type = "trigonometric", sea.period = 7, a1 = NULL, P1 = NULL, Q = NULL, H = NULL )

Returns dynamic Gompertz curve model.

Parameters

• y The cumulated variable

• q The signal-to-noise ratio (ratio of slope to irregular variance). Defaults to 'NULL', in which case no signal-to-noise ratio will be imposed. Instead, it will be estimated.

• sea.type Seasonal type. Options are 'trigonometric' and 'none'. 'trigonometric' will yield a model with a trigonometric seasonal component and 'none' will yield a model with no seasonal component.

• sea.period The period of seasonality. For a day-of-the-week effect with daily data, this would be 7. Not required if sea.type = 'none'.

• a1 Optional parameter specifying the prior mean of the states. Defaults to 'NULL'. Leave as 'NULL' for a diffuse prior (no prior information). If a proper prior is to be specified, both a1 and P1 must be given.

• P1 Optional parameter specifying the prior mean of the states. Defaults to 'NULL'. Leave as 'NULL' for a diffuse prior (no prior information). If a proper prior is to be specified, both a1 and P1 must be given.

• Q Optional parameter specifying the state error variances where these are to be imposed rather than estimated. Defaults to 'NULL' which will see the variances estimated.

• H Optional parameter specifying the irregular variance where this is to be imposed rather than estimated. Defaults to 'NULL' which will see the variance estimated.

Description

The dynamic Gompertz with an integrated random walk (IRW) trend is

$\ln g_{t}=\delta_{t}+\varepsilon_{t}, \;\;\;\; \varepsilon_{t}\sim NID(0,\sigma_{\varepsilon }^{2}), \;\;\;\; t=2,...,T,$

where $Y_t$ is the cumulated variable, $y_t = \Delta Y_t$, $\ln g_{t}=\ln y_{t}-\ln Y_{t-1}$ and

$\delta_{t} =\delta_{t-1}+\gamma_{t-1},$

$\gamma_{t} =\gamma_{t-1}+\zeta_{t}, \;\;\;\; \zeta_{t}\sim NID(0,\sigma_{\zeta }^{2}),$

where the observation disturbances $\varepsilon_{t}$ and slope disturbances $\zeta_{t}$, are iid Normal and mutually independent. Note that, the larger the signal-to-noise ratio, $q_{\zeta }=\sigma_{\zeta }^{2}/\sigma_{\varepsilon }^{2}$, the faster the slope changes in response to new observations. Conversely, a lower signal-to-noise ratio induces smoothness.

For the model without seasonal terms (sea.type = 'none') the are priors are

$\begin{pmatrix} \delta_1 \ \gamma_1 \end{pmatrix} \sim N(a_1,P_1)$

. The diffuse prior has $P_1 = \kappa I_{2\times 2}$ with $\kappa \to \infty$. Implementation of the diffuse prior is handled by the package KFAS (Helske, 2017). Where the model has a seasonal component (sea.type = 'trigonometric'), the vector of prior means $a_1$ and the prior covariance matrix $P_1$ are extended accordingly.

See the vignette for details of the variance matrix $Q$. $H = \sigma^2_{\varepsilon}$.

update(pars, model, q, sea.type)

Update method for Kalman filter to implement the dynamic Gompertz curve model. A maximum of 3 parameters are used to set the observation noise (1 parameter), the transition equation slope and seasonal noise. If q (signal to noise ratio) is not null then the slope noise is set using this ratio.

Parameters

• pars Vector of parameters.

• model KFS model object.

• q The signal-to-noise ratio (ratio of slope to irregular variance).

• sea.type Seasonal type. Options are 'trigonometric' and 'none'.

Return Value

KFS model object.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-06")

# Specify a model
model <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)
# Estimate a specified model
res <- model$estimate()

---

Class for dynamic Gompertz curve state space model object.

Description

Class for dynamic Gompertz curve state space model object.

Methods

get_model(y, q = NULL, sea.type = 'trigonometric', sea.period = 7) Retrieves the model object.

Parameters

• y The cumulated variable.

• q The signal-to-noise ratio (ratio of slope to irregular variance). Defaults to 'NULL', in which case no signal-to-noise ratio will be imposed. Instead, it will be estimated.

• sea.type Seasonal type. Options are 'trigonometric' and 'none'. 'trigonometric' will yield a model with a trigonometric seasonal component and 'none' will yield a model with no seasonal component.

• sea.period The period of seasonality. For a day-of-the-week effect with daily data, this would be 7. Not required if sea.type = 'none'.

Return Value

KFS model object.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-06")

# Specify a model
model <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)
# Estimate a specified model
res <- model$estimate()

---

Class for re-initialised dynamic Gompertz curve model

Description

This class allows the implementation of the reinitialisation procedure described in the vignette and summarised below. Let $t=r$ denote the re-initialization date and $r_0$ denote the date at which the cumulative series is set to 0. As the growth rate of cumulative cases is defined as $g_t\equiv \frac{y_t}{Y_{t-1}}$, we have:

$\ln g_t = \ln y_t - \ln Y_{t-1} \;\;\;\; t=1, \ldots, r$

$\ln g_t^r = \ln y_t - \ln Y_{t-1}^r \;\;\;\; t=r+1, \ldots, T$

$Y_{t}^{r}=Y_{t-1}^{r}+y_{t} \;\;\;\; t=r,\ldots,T$

where $Y_{t}^{r}$ is the cumulative cases after re-initialization. We choose to set the cumulative cases to zero at $r_0=r-1, Y_{r-1}^{r}=0$, such that the growth rate of cumulative cases is available from $t=r+1$ onwards. We reinitialise the model by specifying the prior distribution for the initial states appropriately. See the vignette for details.

Methods

• new(Y, q = NULL, reinit.date=NULL, original.results=NULL, use.presample.info=TRUE) Create an instance of the SSModelDynGompertzReinit class.

  Parameters

  • Y The cumulated variable.

  • q The signal-to-noise ratio (ratio of slope to irregular variance). Defaults to 'NULL', in which case no signal-to-noise ratio will be imposed. Instead, it will be estimated.

  • reinit.date The reinitialisation date $r$. Should be specified as an object of class "Date". Must be specified.

  • original.results Rather than re-estimating the model up to the reinit.date, a FilterResults class object can be specified here and the parameters for the reinitialisation will be taken from this object. Default is NULL. This parameter is optional.

  • use.presample.info Logical value denoting whether or not to use information from before the reinitialisation date in the reinitialisation procedure. Default is TRUE. If FALSE, the model is estimated from scratch from the reinitialisation date and no attempt to use information from before the reinitialisation date is made.

• get_model(y, q=NULL, sea.type = NULL, sea.period) Retrieves the model object, which is a dynamic Gompertz curve model reinitialised at self$reinit.date.

  Parameters

  • y The cumulated variable.

  • q The signal-to-noise ratio (ratio of slope to irregular variance). Defaults to 'NULL', in which case no signal-to-noise ratio will be imposed. Instead, it will be estimated.

  • sea.type Seasonal type. Options are 'trigonometric' and 'none'. 'trigonometric' will yield a model with a trigonometric seasonal component and 'none' will yield a model with no seasonal component.

  • sea.period The period of seasonality. For a day-of-the-week effect with daily data, this would be 7. Not required if sea.type = 'none'.

  Return Value

  KFS model object.

Examples

library(tsgc)
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2021-05-20")

# Specify a model
model.reinit <- SSModelDynGompertzReinit$new(Y = gauteng[idx.est], q = 0.005,
  reinit.date = as.Date("2021-04-29"))
# Estimate a specified model
res.reinit <- model.reinit$estimate()

## Alternatively, we could feed in a prior results object rather than a
## reinitialisation date. The results are identical to the above.

# Specify initial model
idx.orig <- zoo::index(gauteng) <= as.Date("2021-04-29")
model.orig <- SSModelDynamicGompertz$new(Y = gauteng[idx.orig], q = 0.005)
res.orig <- model.orig$estimate()
# Estimate a specified model
model.reinit2 <- SSModelDynGompertzReinit$new(Y = gauteng[idx.est],
q = 0.005, reinit.date = as.Date("2021-04-29"), original.results = res.orig)
res.reinit2 <- model.reinit2$estimate()

---

Write a selection of relevant results to disc

Description

Function writes the following results to csv files which get saved in the location specified in res.dir: forecast new cases or incidence variable, $y$; the filtered level and slope of $\ln g$, $\delta$ and $\gamma$; filtered estimates of $g_y$ and the confidence intervals for these estimates.

Usage

write_results(res, res.dir, Y, n.ahead, confidence.level)

Arguments

res	Results object estimated using the ‘⁠estimate()⁠’ method.
res.dir	File path to save the results to.
Y	Cumulated variable.
n.ahead	Number of periods ahead to forecast.
confidence.level	Confidence level to use for the confidence interval on the forecasts $\ln(g_t)$.

Value

A number of csv files saved in the directory specified in res.dir.

Examples

# Not run as do not wish to save to local disc when compiling documentation.
# Below will run if copied and pasted into console.
library(tsgc)
library(here)

res.dir <- tempdir()
data(gauteng,package="tsgc")
idx.est <- zoo::index(gauteng) <= as.Date("2020-07-06")
res <- SSModelDynamicGompertz$new(Y = gauteng[idx.est], q = 0.005)$estimate()

tsgc::write_results(
res=res, res.dir = res.dir, Y = gauteng[idx.est], n.ahead = 14,
confidence.level = 0.68
)

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