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mode 100644 docs/reference/ti-4.png create mode 100644 docs/reference/ti-5.png create mode 100644 docs/reference/ti-6.png diff --git a/R/forecast.mvgam.R b/R/forecast.mvgam.R index bfe7984e..6b1ac9c0 100644 --- a/R/forecast.mvgam.R +++ b/R/forecast.mvgam.R @@ -28,13 +28,12 @@ forecast <- function(object, ...){ #'@rdname forecast.mvgam #'@method forecast mvgam #' @examples -#' \dontrun{ -#' simdat <- sim_mvgam(n_series = 3, trend_model = 'AR1') +#' \donttest{ +#' simdat <- sim_mvgam(n_series = 3, trend_model = AR()) #' mod <- mvgam(y ~ s(season, bs = 'cc', k = 6), #' trend_model = AR(), +#' noncentred = TRUE, #' data = simdat$data_train, -#' burnin = 300, -#' samples = 300, #' chains = 2) #' #' # Hindcasts on response scale diff --git a/R/hindcast.mvgam.R b/R/hindcast.mvgam.R index 0a8e49b2..7715fd78 100644 --- a/R/hindcast.mvgam.R +++ b/R/hindcast.mvgam.R @@ -7,7 +7,8 @@ #'organized into a convenient format #'@return An object of class \code{mvgam_forecast} containing hindcast distributions. #'See \code{\link{mvgam_forecast-class}} for details. -#'#'@seealso \code{\link{forecast.mvgam}} +#' +#'@seealso \code{\link{forecast.mvgam}} #'@export hindcast <- function(object, ...){ UseMethod("hindcast", object) @@ -16,13 +17,12 @@ hindcast <- function(object, ...){ #'@rdname hindcast.mvgam #'@method hindcast mvgam #' @examples -#' \dontrun{ -#' simdat <- sim_mvgam(n_series = 3, trend_model = 'AR1') +#' \donttest{ +#' simdat <- sim_mvgam(n_series = 3, trend_model = AR()) #' mod <- mvgam(y ~ s(season, bs = 'cc'), #' trend_model = AR(), +#' noncentred = TRUE, #' data = simdat$data_train, -#' burnin = 300, -#' samples = 300, #' chains = 2) #' #' # Hindcasts on response scale diff --git a/R/loo.mvgam.R b/R/loo.mvgam.R index 318eb1d8..799f3106 100644 --- a/R/loo.mvgam.R +++ b/R/loo.mvgam.R @@ -39,7 +39,7 @@ #' forecast evaluations for further scrutiny of models (see for example \code{\link{forecast.mvgam}}, #' \code{\link{score.mvgam_forecast}} and \code{\link{lfo_cv}}) #'@examples -#'\dontrun{ +#'\donttest{ #'# Simulate 4 time series with hierarchical seasonality #'# and independent AR1 dynamic processes #'set.seed(111) diff --git a/R/mcmc_plot.mvgam.R b/R/mcmc_plot.mvgam.R index 67a9b0db..66432bad 100644 --- a/R/mcmc_plot.mvgam.R +++ b/R/mcmc_plot.mvgam.R @@ -23,12 +23,11 @@ #' that can be used for argument `variable` #' @examples #' \dontrun{ -#' simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1') +#' simdat <- sim_mvgam(n_series = 1, trend_model = AR()) #' mod <- mvgam(y ~ s(season, bs = 'cc', k = 6), #' trend_model = AR(), +#' noncentred = TRUE, #' data = simdat$data_train, -#' burnin = 300, -#' samples = 300, #' chains = 2) #' mcmc_plot(mod) #' mcmc_plot(mod, type = 'neff_hist') diff --git a/R/monotonic.R b/R/monotonic.R index 4da7cca8..ab9dc8ed 100644 --- a/R/monotonic.R +++ b/R/monotonic.R @@ -44,7 +44,7 @@ #' @author Nicholas J Clark #' @name monotonic #' @examples -#' \dontrun{ +#' \donttest{ #' # Simulate data from a monotonically increasing function #' set.seed(123123) #' x <- runif(80) * 4 - 1 @@ -72,8 +72,6 @@ #' mod2 <- mvgam(y ~ s(x, bs = 'moi', k = 18), #' data = mod_data, #' family = gaussian(), -#' burnin = 300, -#' samples = 300, #' chains = 2) #' #' plot_predictions(mod2, @@ -108,8 +106,6 @@ #' mod <- mvgam(y ~ s(x, bs = 'moi', by = fac, k = 8), #' data = mod_data, #' family = gaussian(), -#' burnin = 300, -#' samples = 300, #' chains = 2) #' #' # Visualise the different monotonic functions diff --git a/R/ti.R b/R/ti.R index 846e452c..086cb643 100644 --- a/R/ti.R +++ b/R/ti.R @@ -18,7 +18,7 @@ #' #' #' @examples -#' \dontrun{ +#' \donttest{ #' # Simulate some data #' dat <- mgcv::gamSim(1, n = 200, scale = 2) #' diff --git a/docs/index.html b/docs/index.html index 23a67632..fff8e221 100644 --- a/docs/index.html +++ b/docs/index.html @@ -114,14 +114,14 @@

Getting started Gamma() for non-negative real-valued data
  • -betar() for proportional data on (0,1) +betar() for proportional data on (0,1)
  • bernoulli() for binary data
  • poisson() for count data
  • -nb() for overdispersed count data
  • +nb() for overdispersed count data
  • binomial() for count data with known number of trials
  • @@ -131,9 +131,9 @@

    Getting started tweedie() for overdispersed count data

  • -

    Note that only poisson(), nb(), and tweedie() are available if using JAGS. All families, apart from tweedie(), are supported if using Stan. See ??mvgam_families for more information. Below is a simple example for simulating and modelling proportional data with Beta observations over a set of seasonal series with independent Gaussian Process dynamic trends:

    +

    Note that only poisson(), nb(), and tweedie() are available if using JAGS. All families, apart from tweedie(), are supported if using Stan. See ??mvgam_families for more information. Below is a simple example for simulating and modelling proportional data with Beta observations over a set of seasonal series with independent Gaussian Process dynamic trends:

    -data <- sim_mvgam(family = betar(),
    +data <- sim_mvgam(family = betar(),
                       T = 80,
                       trend_model = GP(),
                       trend_rel = 0.5, 
    @@ -150,7 +150,7 @@ 

    Getting started trend_model = GP(), data = data$data_train, newdata = data$data_test, - family = betar())

    + family = betar())

    Plot the estimated posterior hindcast and forecast distributions for each series

     layout(matrix(1:4, nrow = 2, byrow = TRUE))
    diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png
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    @@ -148,69 +148,174 @@

    References

    Examples

    -
    if (FALSE) {
    -# Example of logistic growth with possible changepoints
    -# Simple logistic growth model
    -dNt = function(r, N, k){
    -   r * N * (k - N)
    -}
    -
    -# Iterate growth through time
    -Nt = function(r, N, t, k) {
    -for (i in 1:(t - 1)) {
    -
    - # population at next time step is current population + growth,
    - # but we introduce several 'shocks' as changepoints
    - if(i %in% c(5, 15, 25, 41, 45, 60, 80)){
    -   N[i + 1] <- max(1, N[i] + dNt(r + runif(1, -0.1, 0.1),
    -                                 N[i], k))
    -   } else {
    -   N[i + 1] <- max(1, N[i] + dNt(r, N[i], k))
    -   }
    -  }
    - N
    -}
    -
    -# Simulate expected values
    -set.seed(11)
    -expected <- Nt(0.004, 2, 100, 30)
    -plot(expected, xlab = 'Time')
    -
    -# Take Poisson draws
    -y <- rpois(100, expected)
    -plot(y, xlab = 'Time')
    -
    -# Assemble data into dataframe and model. We set a
    -# fixed carrying capacity of 35 for this example, but note that
    -# this value is not required to be fixed at each timepoint
    -mod_data <- data.frame(y = y,
    -                       time = 1:100,
    -                       cap = 35,
    -                       series = as.factor('series_1'))
    -plot_mvgam_series(data = mod_data)
    -
    -# The intercept is nonidentifiable when using piecewise
    -# trends because the trend functions have their own offset
    -# parameters 'm'; it is recommended to always drop intercepts
    -# when using these trend models
    -mod <- mvgam(y ~ 0,
    -             trend_model = PW(growth = 'logistic'),
    -             family = poisson(),
    -             data = mod_data,
    -             chains = 2)
    -summary(mod)
    -
    -# Plot the posterior hindcast
    -plot(mod, type = 'forecast')
    -
    -# View the changepoints with ggplot2 utilities
    -library(ggplot2)
    -mcmc_plot(mod, variable = 'delta_trend',
    -          regex = TRUE) +
    -scale_y_discrete(labels = mod$trend_model$changepoints) +
    -labs(y = 'Potential changepoint',
    -     x = 'Rate change')
    -}
    +
    # \donttest{
    +# Example of logistic growth with possible changepoints
    +# Simple logistic growth model
    +dNt = function(r, N, k){
    +   r * N * (k - N)
    +}
    +
    +# Iterate growth through time
    +Nt = function(r, N, t, k) {
    +for (i in 1:(t - 1)) {
    +
    + # population at next time step is current population + growth,
    + # but we introduce several 'shocks' as changepoints
    + if(i %in% c(5, 15, 25, 41, 45, 60, 80)){
    +   N[i + 1] <- max(1, N[i] + dNt(r + runif(1, -0.1, 0.1),
    +                                 N[i], k))
    +   } else {
    +   N[i + 1] <- max(1, N[i] + dNt(r, N[i], k))
    +   }
    +  }
    + N
    +}
    +
    +# Simulate expected values
    +set.seed(11)
    +expected <- Nt(0.004, 2, 100, 30)
    +plot(expected, xlab = 'Time')
    +
    +
    +# Take Poisson draws
    +y <- rpois(100, expected)
    +plot(y, xlab = 'Time')
    +
    +
    +# Assemble data into dataframe and model. We set a
    +# fixed carrying capacity of 35 for this example, but note that
    +# this value is not required to be fixed at each timepoint
    +mod_data <- data.frame(y = y,
    +                       time = 1:100,
    +                       cap = 35,
    +                       series = as.factor('series_1'))
    +plot_mvgam_series(data = mod_data)
    +
    +
    +# The intercept is nonidentifiable when using piecewise
    +# trends because the trend functions have their own offset
    +# parameters 'm'; it is recommended to always drop intercepts
    +# when using these trend models
    +mod <- mvgam(y ~ 0,
    +             trend_model = PW(growth = 'logistic'),
    +             family = poisson(),
    +             data = mod_data,
    +             chains = 2)
    +#> Compiling Stan program using cmdstanr
    +#> 
    +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5,
    +#>                  from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4,
    +#>                  from stan/lib/stan_math/stan/math/prim/prob.hpp:359,
    +#>                  from stan/lib/stan_math/stan/math/prim.hpp:16,
    +#>                  from stan/lib/stan_math/stan/math/rev.hpp:16,
    +#>                  from stan/lib/stan_math/stan/math.hpp:19,
    +#>                  from stan/src/stan/model/model_header.hpp:4,
    +#>                  from C:/Users/uqnclar2/AppData/Local/Temp/RtmpU5rG2n/model-3fc0236235ea.hpp:2:
    +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)':
    +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers
    +#>   194 |       if (cdf_n < 0.0)
    +#>       | 
    +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory
    +#> Start sampling
    +#> Running MCMC with 2 parallel chains...
    +#> 
    +#> Chain 1 Iteration:   1 / 1000 [  0%]  (Warmup) 
    +#> Chain 2 Iteration:   1 / 1000 [  0%]  (Warmup) 
    +#> Chain 1 Iteration: 100 / 1000 [ 10%]  (Warmup) 
    +#> Chain 2 Iteration: 100 / 1000 [ 10%]  (Warmup) 
    +#> Chain 1 Iteration: 200 / 1000 [ 20%]  (Warmup) 
    +#> Chain 2 Iteration: 200 / 1000 [ 20%]  (Warmup) 
    +#> Chain 1 Iteration: 300 / 1000 [ 30%]  (Warmup) 
    +#> Chain 2 Iteration: 300 / 1000 [ 30%]  (Warmup) 
    +#> Chain 1 Iteration: 400 / 1000 [ 40%]  (Warmup) 
    +#> Chain 2 Iteration: 400 / 1000 [ 40%]  (Warmup) 
    +#> Chain 1 Iteration: 500 / 1000 [ 50%]  (Warmup) 
    +#> Chain 1 Iteration: 501 / 1000 [ 50%]  (Sampling) 
    +#> Chain 2 Iteration: 500 / 1000 [ 50%]  (Warmup) 
    +#> Chain 2 Iteration: 501 / 1000 [ 50%]  (Sampling) 
    +#> Chain 1 Iteration: 600 / 1000 [ 60%]  (Sampling) 
    +#> Chain 2 Iteration: 600 / 1000 [ 60%]  (Sampling) 
    +#> Chain 1 Iteration: 700 / 1000 [ 70%]  (Sampling) 
    +#> Chain 2 Iteration: 700 / 1000 [ 70%]  (Sampling) 
    +#> Chain 1 Iteration: 800 / 1000 [ 80%]  (Sampling) 
    +#> Chain 2 Iteration: 800 / 1000 [ 80%]  (Sampling) 
    +#> Chain 2 Iteration: 900 / 1000 [ 90%]  (Sampling) 
    +#> Chain 1 Iteration: 900 / 1000 [ 90%]  (Sampling) 
    +#> Chain 2 Iteration: 1000 / 1000 [100%]  (Sampling) 
    +#> Chain 2 finished in 7.1 seconds.
    +#> Chain 1 Iteration: 1000 / 1000 [100%]  (Sampling) 
    +#> Chain 1 finished in 7.3 seconds.
    +#> 
    +#> Both chains finished successfully.
    +#> Mean chain execution time: 7.2 seconds.
    +#> Total execution time: 7.4 seconds.
    +#> 
    +summary(mod)
    +#> GAM formula:
    +#> y ~ 1
    +#> <environment: 0x000001ad823a6de8>
    +#> 
    +#> Family:
    +#> poisson
    +#> 
    +#> Link function:
    +#> log
    +#> 
    +#> Trend model:
    +#> PW(growth = "logistic")
    +#> 
    +#> N series:
    +#> 1 
    +#> 
    +#> N timepoints:
    +#> 100 
    +#> 
    +#> Status:
    +#> Fitted using Stan 
    +#> 2 chains, each with iter = 1000; warmup = 500; thin = 1 
    +#> Total post-warmup draws = 1000
    +#> 
    +#> 
    +#> GAM coefficient (beta) estimates:
    +#>             2.5% 50% 97.5% Rhat n_eff
    +#> (Intercept)    0   0     0  NaN   NaN
    +#> 
    +#> Latent trend growth rate estimates:
    +#>             2.5%   50%  97.5% Rhat n_eff
    +#> k_trend[1] -0.26 -0.15 -0.071 1.01   348
    +#> 
    +#> Latent trend offset estimates:
    +#>            2.5%  50% 97.5% Rhat n_eff
    +#> m_trend[1]  -14 -4.3 -0.36 1.01   347
    +#> 
    +#> Stan MCMC diagnostics:
    +#> n_eff / iter looks reasonable for all parameters
    +#> Rhat looks reasonable for all parameters
    +#> 0 of 1000 iterations ended with a divergence (0%)
    +#> 0 of 1000 iterations saturated the maximum tree depth of 12 (0%)
    +#> E-FMI indicated no pathological behavior
    +#> 
    +#> Samples were drawn using NUTS(diag_e) at Mon Jul 01 8:47:11 AM 2024.
    +#> For each parameter, n_eff is a crude measure of effective sample size,
    +#> and Rhat is the potential scale reduction factor on split MCMC chains
    +#> (at convergence, Rhat = 1)
    +
    +# Plot the posterior hindcast
    +plot(mod, type = 'forecast')
    +
    +
    +# View the changepoints with ggplot2 utilities
    +library(ggplot2)
    +mcmc_plot(mod, variable = 'delta_trend',
    +          regex = TRUE) +
    +scale_y_discrete(labels = mod$trend_model$changepoints) +
    +labs(y = 'Potential changepoint',
    +     x = 'Rate change')
    +#> Scale for y is already present.
    +#> Adding another scale for y, which will replace the existing scale.
    +
    +# }
    +
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