Package 'dynamite'

The dynamite Package

Description

Easy-to-use and efficient interface for Bayesian inference of complex panel data consisting of multiple individuals with multiple measurements over time using dynamic multivariate panel models. Supports several observational distributions, time-varying effects and realistic counterfactual predictions which take into account the dynamic structure of the model.

See Also

• The package vignettes

• dynamiteformula() for information on defining models.

• dynamite() for information on fitting models.

• https://github.com/ropensci/dynamite/issues/ to submit a bug report or a feature request.

Authors

Santtu Tikka (author)	[email protected]
Jouni Helske (author)	[email protected]

Author(s)

Maintainer: Santtu Tikka [email protected] (ORCID)

Authors:

• Jouni Helske [email protected] (ORCID)

Other contributors:

• Nicholas Clark [reviewer]

• Lucy D'Agostino McGowan [reviewer]

See Also

Useful links:

• https://docs.ropensci.org/dynamite/

• https://github.com/ropensci/dynamite/

• Report bugs at https://github.com/ropensci/dynamite/issues/

---

Convert dynamite Output to draws_df Format

Description

Converts the output from a dynamite() call to a draws_df format of the posterior package, enabling the use of diagnostics and plotting methods of posterior and bayesplot packages. Note that this function returns variables in a wide format, whereas as.data.frame.dynamitefit() uses the long format.

Usage

## S3 method for class 'dynamitefit'
as_draws_df(
  x,
  parameters = NULL,
  responses = NULL,
  types = NULL,
  times = NULL,
  groups = NULL,
  ...
)

## S3 method for class 'dynamitefit'
as_draws(x, parameters = NULL, responses = NULL, types = NULL, ...)

Arguments

x	[dynamitefit] The model fit object.
parameters	[character()] Parameter(s) for which the samples should be extracted. Possible options can be found with function get_parameter_names(). Default is all parameters of specific type for all responses. This argument is mutually exclusive with types.
responses	[character()] Response(s) for which the samples should be extracted. Possible options are elements of unique(x$priors$response), and the default is this entire vector. Ignored if the argument parameters is supplied. omega_alpha, and omega_psi. See also get_parameter_types().
types	[character()] Type(s) of the parameters for which the samples should be extracted. See details of possible values. Default is all values listed in details except spline coefficients omega. This argument is mutually exclusive with parameters.
times	[double()] Time point(s) to keep. If NULL (the default), all time points are kept.
groups	[character()] Group name(s) to keep. If NULL (the default), all groups are kept.
...	Ignored.

Details

You can use the arguments parameters, responses and types to extract only a subset of the model parameters (i.e., only certain types of parameters related to a certain response variable).

See potential values for the types argument in as.data.frame.dynamitefit() and get_parameter_names() for potential values for parameters argument.

Value

A draws_df object.

A draws_df object.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
as_draws(gaussian_example_fit, types = c("sigma", "beta"))

# Compute MCMC diagnostics using the posterior package
posterior::summarise_draws(as_draws(gaussian_example_fit))

---

Extract Samples From a dynamitefit Object as a Data Frame

Description

Provides a data.frame representation of the posterior samples of the model parameters.

Usage

## S3 method for class 'dynamitefit'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  types = NULL,
  parameters = NULL,
  responses = NULL,
  times = NULL,
  groups = NULL,
  summary = FALSE,
  probs = c(0.05, 0.95),
  include_fixed = TRUE,
  ...
)

Arguments

x	[dynamitefit] The model fit object.
row.names	Ignored.
optional	Ignored.
types	[character()] Type(s) of the parameters for which the samples should be extracted. See details of possible values. Default is all values listed in details except spline coefficients omega. This argument is mutually exclusive with parameters.
parameters	[character()] Parameter(s) for which the samples should be extracted. Possible options can be found with function get_parameter_names(). Default is all parameters of specific type for all responses. This argument is mutually exclusive with types.
responses	[character()] Response(s) for which the samples should be extracted. Possible options are elements of unique(x$priors$response), and the default is this entire vector. Ignored if the argument parameters is supplied. omega_alpha, and omega_psi. See also get_parameter_types().
times	[double()] Time point(s) to keep. If NULL (the default), all time points are kept.
groups	[character()] Group name(s) to keep. If NULL (the default), all groups are kept.
summary	[logical(1)] If TRUE, returns posterior mean, standard deviation, and posterior quantiles (as defined by the probs argument) for all parameters. If FALSE (default), returns the posterior samples instead.
probs	[numeric()] Quantiles of interest. Default is c(0.05, 0.95).
include_fixed	[logical(1)] If TRUE (default), time-varying parameters for 1:fixed time points are included in the output as NA values. If FALSE, fixed time points are omitted completely from the output.
...	Ignored.

Details

The arguments responses and types can be used to extract only a subset of the model parameters (i.e., only certain types of parameters related to a certain response variable).

Potential values for the types argument are:

• alpha
  Intercept terms (time-invariant or time-varying).

• beta
  Time-invariant regression coefficients.

• cutpoint
  Cutpoints for ordinal regression.

• delta
  Time-varying regression coefficients.

• nu
  Group-level random effects.

• lambda
  Factor loadings.

• kappa
  Contribution of the latent factor loadings in the total variation.

• psi
  Latent factors.

• tau
  Standard deviations of the spline coefficients of delta.

• tau_alpha
  Standard deviations of the spline coefficients of time-varying alpha.

• sigma_nu
  Standard deviations of the random effects nu.

• corr_nu
  Pairwise within-group correlations of random effects nu. Samples of the full correlation matrix can be extracted manually as rstan::extract(fit$stanfit, pars = "corr_matrix_nu") if necessary.

• sigma_lambda
  Standard deviations of the latent factor loadings lambda.

• corr_psi
  Pairwise correlations of the noise terms of the latent factors. Samples of the full correlation matrix can be extracted manually as rstan::extract(fit$stanfit, pars = "corr_matrix_psi") if necessary.

• sigma
  Standard deviations of Gaussian responses.

• corr
  Pairwise correlations of multivariate Gaussian responses.

• phi
  Describes various distributional parameters, such as:

  • Dispersion parameter of the Negative Binomial distribution.

  • Shape parameter of the Gamma distribution.

  • Precision parameter of the Beta distribution.

  • Degrees of freedom of the Student t-distribution.

• omega
  Spline coefficients of the regression coefficients delta.

• omega_alpha
  Spline coefficients of time-varying alpha.

• omega_psi
  Spline coefficients of the latent factors psi. Note that in case of nonzero_lambda = FALSE, mean of these are used to flip the sign of psi to avoid multimodality due to sign-switching, but omega_psi variables are not modified.

• zeta
  Total variation of latent factors, i.e., the sum of sigma_lambda and tau_psi.

Value

A tibble containing either samples or summary statistics of the model parameters in a long format. For a wide format, see as_draws.dynamitefit().

See Also

Model outputs as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
as.data.frame(
  gaussian_example_fit,
  responses = "y",
  types = "beta"
)

# Basic summaries can be obtained automatically with summary = TRUE
as.data.frame(
  gaussian_example_fit,
  responses = "y",
  types = "beta",
  summary = TRUE
)

# Time-varying coefficients "delta"
as.data.frame(
  gaussian_example_fit,
  responses = "y",
  types = "delta",
  summary = TRUE
)

# Obtain summaries for a specific parameters
as.data.frame(
  gaussian_example_fit,
  parameters = c("tau_y_x", "sigma_y"),
  summary = TRUE
)

---

Extract Samples From a dynamitefit Object as a Data Table

Description

Provides a data.table representation of the posterior samples of the model parameters. See as.data.frame.dynamitefit() for details.

Usage

## S3 method for class 'dynamitefit'
as.data.table(
  x,
  keep.rownames = FALSE,
  row.names = NULL,
  optional = FALSE,
  types = NULL,
  parameters = NULL,
  responses = NULL,
  times = NULL,
  groups = NULL,
  summary = FALSE,
  probs = c(0.05, 0.95),
  include_fixed = TRUE,
  ...
)

Arguments

x	[dynamitefit] The model fit object.
keep.rownames	[logical(1)] Not used.
row.names	Ignored.
optional	Ignored.
types	[character()] Type(s) of the parameters for which the samples should be extracted. See details of possible values. Default is all values listed in details except spline coefficients omega. This argument is mutually exclusive with parameters.
parameters	[character()] Parameter(s) for which the samples should be extracted. Possible options can be found with function get_parameter_names(). Default is all parameters of specific type for all responses. This argument is mutually exclusive with types.
responses	[character()] Response(s) for which the samples should be extracted. Possible options are elements of unique(x$priors$response), and the default is this entire vector. Ignored if the argument parameters is supplied. omega_alpha, and omega_psi. See also get_parameter_types().
times	[double()] Time point(s) to keep. If NULL (the default), all time points are kept.
groups	[character()] Group name(s) to keep. If NULL (the default), all groups are kept.
summary	[logical(1)] If TRUE, returns posterior mean, standard deviation, and posterior quantiles (as defined by the probs argument) for all parameters. If FALSE (default), returns the posterior samples instead.
probs	[numeric()] Quantiles of interest. Default is c(0.05, 0.95).
include_fixed	[logical(1)] If TRUE (default), time-varying parameters for 1:fixed time points are included in the output as NA values. If FALSE, fixed time points are omitted completely from the output.
...	Ignored.

Value

A data.table containing either samples or summary statistics of the model parameters.

See Also

Model outputs as.data.frame.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
as.data.table(
  gaussian_example_fit,
  responses = "y",
  types = "beta",
  summary = FALSE
)

---

Simulated Categorical Multivariate Panel Data

Description

A simulated data containing multiple individuals with two categorical response variables.

Usage

categorical_example

Format

A data frame with 2000 rows and 5 variables:

id

Variable defining individuals (1 to 100).

time

Variable defining the time point of the measurement (1 to 20).

x

Categorical variable with three levels, A, B, and C.

y

Categorical variable with three levels, a, b, and c.

z

A continuous covariate.

Source

The data was generated via categorical_example.R in https://github.com/ropensci/dynamite/tree/main/data-raw/

See Also

Example models categorical_example_fit, gaussian_example, gaussian_example_fit, multichannel_example, multichannel_example_fit

---

Model Fit for the Simulated Categorical Multivariate Panel Data

Description

A dynamitefit object obtained by running dynamite on the categorical_example dataset as

set.seed(1)
library(dynamite)
f <- obs(x ~ z + lag(x) + lag(y), family = "categorical") +
  obs(y ~ z + lag(x) + lag(y), family = "categorical")
categorical_example_fit <- dynamite(
  f,
  data = categorical_example,
  time = "time",
  group = "id",
  chains = 1,
  refresh = 0,
  thin = 5,
  save_warmup = FALSE
)

Note the small number of samples due to size restrictions on CRAN.

Usage

categorical_example_fit

Format

A dynamitefit object.

Source

The data was generated via categorical_example_fit.R in https://github.com/ropensci/dynamite/tree/main/data-raw/

See Also

Example models categorical_example, gaussian_example, gaussian_example_fit, multichannel_example, multichannel_example_fit

---

Extract Regression Coefficients of a dynamite Model

Description

Extracts either time-varying or time-invariant parameters of the model.

Usage

## S3 method for class 'dynamitefit'
coef(
  object,
  types = c("alpha", "beta", "delta"),
  parameters = NULL,
  responses = NULL,
  times = NULL,
  groups = NULL,
  summary = TRUE,
  probs = c(0.05, 0.95),
  ...
)

Arguments

object	[dynamitefit] The model fit object.
types	[character()] Type(s) of the parameters for which the samples should be extracted. See details of possible values. Default is all values listed in details except spline coefficients omega. This argument is mutually exclusive with parameters.
parameters	[character()] Parameter(s) for which the samples should be extracted. Possible options can be found with function get_parameter_names(). Default is all parameters of specific type for all responses. This argument is mutually exclusive with types.
responses	[character()] Response(s) for which the samples should be extracted. Possible options are elements of unique(x$priors$response), and the default is this entire vector. Ignored if the argument parameters is supplied. omega_alpha, and omega_psi. See also get_parameter_types().
times	[double()] Time point(s) to keep. If NULL (the default), all time points are kept.
groups	[character()] Group name(s) to keep. If NULL (the default), all groups are kept.
summary	[logical(1)] If TRUE (default), returns posterior mean, standard deviation, and posterior quantiles (as defined by the probs argument) for all parameters. If FALSE, returns the posterior samples instead.
probs	[numeric()] Quantiles of interest. Default is c(0.05, 0.95).
...	Ignored.

Value

A tibble containing either samples or summary statistics of the model parameters in a long format.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
betas <- coef(gaussian_example_fit, type = "beta")
deltas <- coef(gaussian_example_fit, type = "delta")

---

Credible Intervals for dynamite Model Parameters

Description

Extracts credible intervals from dynamitefit object.

Usage

## S3 method for class 'dynamitefit'
confint(object, parm, level = 0.95, ...)

Arguments

object	[dynamitefit] The model fit object.
parm	Ignored.
level	[numeric(1)] Credible interval width.
...	Ignored.

Value

The rows of the resulting matrix will be named using the following logic: ⁠{parameter}_{time}_{category}_{group}⁠ where parameter is the name of the parameter, time is the time index of the parameter, category specifies the level of the response the parameter is related to if the response is categorical, and group determines which group of observations the parameter is related to in the case of random effects and loadings. Non-applicable fields in the this syntax are set to NA.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
confint(gaussian_example_fit, level = 0.9)

---

Estimate a Bayesian Dynamic Multivariate Panel Model With Multiple Imputation

Description

Applies multiple imputation using mice::mice() to the supplied data and fits a dynamic multivariate panel model to each imputed data set using dynamite(). Posterior samples from each imputation run are combined. When using wide format imputation, the long format data is automatically converted to a wide format before imputation to preserve the longitudinal structure, and then converted back to long format for estimation.

Usage

dynamice(
  dformula,
  data,
  time,
  group = NULL,
  priors = NULL,
  backend = "rstan",
  verbose = TRUE,
  verbose_stan = FALSE,
  stanc_options = list("O0"),
  threads_per_chain = 1L,
  grainsize = NULL,
  custom_stan_model = NULL,
  debug = NULL,
  mice_args = list(),
  impute_format = "wide",
  keep_imputed = FALSE,
  stan_csv_dir = tempdir(),
  ...
)

Arguments

dformula	[dynamiteformula] The model formula. See dynamiteformula() and 'Details'.
data	[data.frame, tibble::tibble, or data.table::data.table] The data that contains the variables in the model in long format. Supported column types are integer, logical, double, and factor. Columns of type character will be converted to factors. Unused factor levels will be dropped. The data can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input data is converted to channel specific matrix representations via stats::model.matrix.lm().
time	[character(1)] A column name of data that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.
group	[character(1)] A column name of data that denotes the unique groups or NULL corresponding to a scenario without any groups. If group is NULL, a new column .group is created with constant value 1L is created indicating that all observations belong to the same group. In case of name conflicts with data, see the group_var element of the return object to get the column name of the new variable.
priors	[data.frame] An optional data frame with prior definitions. See get_priors() and 'Details'.
backend	[character(1)] Defines the backend interface to Stan, should be either "rstan" (the default) or "cmdstanr". Note that cmdstanr needs to be installed separately as it is not on CRAN. It also needs the actual CmdStan software. See https://mc-stan.org/cmdstanr/ for details.
verbose	[logical(1)] All warnings and messages are suppressed if set to FALSE. Defaults to TRUE. Setting this to FALSE will also disable checks for perfect collinearity in the model matrix.
verbose_stan	[logical(1)] This is the verbose argument for rstan::sampling(). Defaults to FALSE.
stanc_options	[list()] This is the stanc_options argument passed to the compile method of a CmdStanModel object via cmdstan_model() when backend = "cmdstanr". Defaults to list("O0"). To enable level one compiler optimizations, use list("O1"). See https://mc-stan.org/cmdstanr/reference/cmdstan_model.html for details.
threads_per_chain	[integer(1)] A Positive integer defining the number of parallel threads to use within each chain. Default is 1. See rstan::rstan_options() and https://mc-stan.org/cmdstanr/reference/model-method-sample.html for details.
grainsize	[integer(1)] A positive integer defining the suggested size of the partial sums when using within-chain parallelization. Default is number of time points divided by threads_per_chain. Setting this to 1 leads the workload division entirely to the internal scheduler. The performance of the within-chain parallelization can be sensitive to the choice of grainsize, see Stan manual on reduce-sum for details.
custom_stan_model	[character(1)] An optional character string that either contains a customized Stan model code or a path to a .stan file that contains the code. Using this will override the generated model code. For expert users only.
debug	[list()] A named list of form name = TRUE indicating additional objects in the environment of the dynamite function which are added to the return object. Additionally, values no_compile = TRUE and no_sampling = TRUE can be used to skip the compilation of the Stan code and sampling steps respectively. This can be useful for debugging when combined with model_code = TRUE, which adds the Stan model code to the return object.
mice_args	[list()] Arguments passed to mice::mice() excluding data.
impute_format	[character(1)] Format of the data that will be passed to the imputation method. Should be either "wide" (the default) or "long" corresponding to wide format and long format imputation.
keep_imputed	[logical(1)] Should the imputed datasets be kept in the return object? The default is FALSE. If TRUE, the imputations will be included in the imputed field in the return object that is otherwise NULL.
stan_csv_dir	[character(1)] A directory path to output the Stan .csv files when backend is "cmdstanr". The files are saved here via ⁠$save_output_files()⁠ to avoid garbage collection between sampling runs with different imputed datasets.
...	For dynamite(), additional arguments to rstan::sampling() or the ⁠$sample()⁠ method of the CmdStanModel object (see https://mc-stan.org/cmdstanr/reference/model-method-sample.html), such as chains and cores (chains and parallel_chains in cmdstanr). For summary(), additional arguments to as.data.frame.dynamitefit(). For print(), further arguments to the print method for tibbles (see tibble::formatting). Not used for formula().

See Also

Model fitting dynamite(), get_priors(), update.dynamitefit()

---

Estimate a Bayesian Dynamic Multivariate Panel Model

Description

Fit a Bayesian dynamic multivariate panel model (DMPM) using Stan for Bayesian inference. The dynamite package supports a wide range of distributions and allows the user to flexibly customize the priors for the model parameters. The dynamite model is specified using standard R formula syntax via dynamiteformula(). For more information and examples, see 'Details' and the package vignettes.

The formula method returns the model definition as a quoted expression.

Information on the estimated dynamite model can be obtained via print() including the following: The model formula, the data, the smallest effective sample sizes, largest Rhat and summary statistics of the time-invariant and group-invariant model parameters.

The summary() method provides statistics of the posterior samples of the model; this is an alias of as.data.frame.dynamitefit() with summary = TRUE.

Usage

dynamite(
  dformula,
  data,
  time,
  group = NULL,
  priors = NULL,
  backend = "rstan",
  verbose = TRUE,
  verbose_stan = FALSE,
  stanc_options = list("O0"),
  threads_per_chain = 1L,
  grainsize = NULL,
  custom_stan_model = NULL,
  debug = NULL,
  ...
)

## S3 method for class 'dynamitefit'
formula(x, ...)

## S3 method for class 'dynamitefit'
print(x, full_diagnostics = FALSE, ...)

## S3 method for class 'dynamitefit'
summary(object, ...)

Arguments

dformula	[dynamiteformula] The model formula. See dynamiteformula() and 'Details'.
data	[data.frame, tibble::tibble, or data.table::data.table] The data that contains the variables in the model in long format. Supported column types are integer, logical, double, and factor. Columns of type character will be converted to factors. Unused factor levels will be dropped. The data can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input data is converted to channel specific matrix representations via stats::model.matrix.lm().
time	[character(1)] A column name of data that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.
group	[character(1)] A column name of data that denotes the unique groups or NULL corresponding to a scenario without any groups. If group is NULL, a new column .group is created with constant value 1L is created indicating that all observations belong to the same group. In case of name conflicts with data, see the group_var element of the return object to get the column name of the new variable.
priors	[data.frame] An optional data frame with prior definitions. See get_priors() and 'Details'.
backend	[character(1)] Defines the backend interface to Stan, should be either "rstan" (the default) or "cmdstanr". Note that cmdstanr needs to be installed separately as it is not on CRAN. It also needs the actual CmdStan software. See https://mc-stan.org/cmdstanr/ for details.
verbose	[logical(1)] All warnings and messages are suppressed if set to FALSE. Defaults to TRUE. Setting this to FALSE will also disable checks for perfect collinearity in the model matrix.
verbose_stan	[logical(1)] This is the verbose argument for rstan::sampling(). Defaults to FALSE.
stanc_options	[list()] This is the stanc_options argument passed to the compile method of a CmdStanModel object via cmdstan_model() when backend = "cmdstanr". Defaults to list("O0"). To enable level one compiler optimizations, use list("O1"). See https://mc-stan.org/cmdstanr/reference/cmdstan_model.html for details.
threads_per_chain	[integer(1)] A Positive integer defining the number of parallel threads to use within each chain. Default is 1. See rstan::rstan_options() and https://mc-stan.org/cmdstanr/reference/model-method-sample.html for details.
grainsize	[integer(1)] A positive integer defining the suggested size of the partial sums when using within-chain parallelization. Default is number of time points divided by threads_per_chain. Setting this to 1 leads the workload division entirely to the internal scheduler. The performance of the within-chain parallelization can be sensitive to the choice of grainsize, see Stan manual on reduce-sum for details.
custom_stan_model	[character(1)] An optional character string that either contains a customized Stan model code or a path to a .stan file that contains the code. Using this will override the generated model code. For expert users only.
debug	[list()] A named list of form name = TRUE indicating additional objects in the environment of the dynamite function which are added to the return object. Additionally, values no_compile = TRUE and no_sampling = TRUE can be used to skip the compilation of the Stan code and sampling steps respectively. This can be useful for debugging when combined with model_code = TRUE, which adds the Stan model code to the return object.
...	For dynamite(), additional arguments to rstan::sampling() or the ⁠$sample()⁠ method of the CmdStanModel object (see https://mc-stan.org/cmdstanr/reference/model-method-sample.html), such as chains and cores (chains and parallel_chains in cmdstanr). For summary(), additional arguments to as.data.frame.dynamitefit(). For print(), further arguments to the print method for tibbles (see tibble::formatting). Not used for formula().
x	[dynamitefit] The model fit object.
full_diagnostics	By default, the effective sample size (ESS) and Rhat are computed only for the time- and group-invariant parameters (full_diagnostics = FALSE). Setting this to TRUE computes ESS and Rhat values for all model parameters, which can take some time for complex models.
object	[dynamitefit] The model fit object.

Details

The best-case scalability of dynamite in terms of data size should be approximately linear in terms of number of time points and and number of groups, but as wall-clock time of the MCMC algorithms provided by Stan can depend on the discrepancy of the data and the model (and the subsequent shape of the posterior), this can vary greatly.

Value

dynamite returns a dynamitefit object which is a list containing the following components:

• stanfit
  A stanfit object, see rstan::sampling() for details.

• dformulas
  A list of dynamiteformula objects for internal use.

• data
  A processed version of the input data.

• data_name
  Name of the input data object.

• stan
  A list containing various elements related to Stan model construction and sampling.

• group_var
  Name of the variable defining the groups.

• time_var
  Name of the variable defining the time index.

• priors
  Data frame containing the used priors.

• backend
  Either "rstan" or "cmdstanr" indicating which package was used in sampling.

• permutation
  Randomized permutation of the posterior draws.

• call
  Original function call as an object of class call.

formula returns a quoted expression.

print returns x invisibly.

summary returns a data.frame.

References

Santtu Tikka and Jouni Helske (2024). dynamite: An R Package for Dynamic Multivariate Panel Models. arXiv preprint, doi:10.48550/arXiv.2302.01607.

Jouni Helske and Santtu Tikka (2022). Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models. Advances in Life Course Research, 60, 100617. doi:10.1016/j.alcr.2024.100617.

See Also

Model fitting dynamice(), get_priors(), update.dynamitefit()

Model formula construction dynamiteformula(), lags(), lfactor(), random_spec(), splines()

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN

# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  fit <- dynamite(
    dformula = obs(y ~ -1 + varying(~x), family = "gaussian") +
      lags(type = "varying") +
      splines(df = 20),
    gaussian_example,
    "time",
    "id",
    chains = 1,
    refresh = 0
  )
}


data.table::setDTthreads(1) # For CRAN
formula(gaussian_example_fit)

data.table::setDTthreads(1) # For CRAN
print(gaussian_example_fit)

data.table::setDTthreads(1) # For CRAN
summary(gaussian_example_fit,
  types = "beta",
  probs = c(0.05, 0.1, 0.9, 0.95)
)

---

Deprecated Functions in the dynamite Package

Description

These functions are provided for compatibility with older versions of the package. They will eventually be completely removed.

Usage

plot_betas(x, ...)
plot_deltas(x,  ...)
plot_nus(x, ...)
plot_lambdas(x,  ...)
plot_psis(x, ...)

Arguments

x	[dynamitefit] The model fit object.
...	Not used.

Value

A ggplot object.

Details

• plot_betas is now called via plot(., types = "beta")

• plot_deltas is now called via plot(., types = "delta")

• plot_nus is now called via plot(., types = "nu")

• plot_lambdas is now called via plot(., types = "lambda")

• plot_psis is now called via plot(., types = "psi")

See Also

See plot.dynamitefit() for documentation of the parameters of these functions

---

Model Formula for dynamite

Description

Defines a new observational or a new auxiliary channel for the model using standard R formula syntax. Formulas of individual response variables can be joined together via +. See 'Details' and the package vignettes for more information. The function obs is a shorthand alias for dynamiteformula, and aux is a shorthand alias for dynamiteformula(formula, family = "deterministic").

Usage

dynamiteformula(formula, family, link = NULL)

obs(formula, family, link = NULL)

aux(formula)

## S3 method for class 'dynamiteformula'
e1 + e2

## S3 method for class 'dynamiteformula'
print(x, ...)

Arguments

formula	[formula] An R formula describing the model.
family	[character(1)] The family name. See 'Details' for the supported families.
link	[character(1)] The name of the link function to use or NULL. See details for supported link functions and default values of specific families.
e1	[dynamiteformula] A model formula specification.
e2	[dynamiteformula] A model formula specification.
x	[dynamiteformula] The model formula.
...	Ignored.

Details

Currently the dynamite package supports the following distributions for the observations:

• Categorical: categorical (with a softmax link using the first category as reference). See the documentation of the categorical_logit_glm in the Stan function reference manual https://mc-stan.org/users/documentation/.

• Multinomial: multinomial (softmax link, first category is reference).

• Gaussian: gaussian (identity link, parameterized using mean and standard deviation).

• Multivariate Gaussian: mvgaussian (identity link, parameterized using mean vector, standard deviation vector and the Cholesky decomposition of the correlation matrix).

• Poisson: poisson (log-link, with an optional known offset variable).

• Negative-binomial: negbin (log-link, using mean and dispersion parameterization, with an optional known offset variable). See the documentation on NegBinomial2 in the Stan function reference manual.

• Bernoulli: bernoulli (logit-link).

• Binomial: binomial (logit-link).

• Exponential: exponential (log-link).

• Gamma: gamma (log-link, using mean and shape parameterization).

• Beta: beta (logit-link, using mean and precision parameterization).

• Student t: student (identity link, parameterized using degrees of freedom, location and scale)

The models in the dynamite package are defined by combining the channel-specific formulas defined via R formula syntax. Each channel is defined via the obs function, and the channels are combined with +. For example a formula obs(y ~ lag(x), family = "gaussian") + obs(x ~ z, family = "poisson") defines a model with two channels; first we declare that y is a Gaussian variable depending on a previous value of x (lag(x)), and then we add a second channel declaring x as Poisson distributed depending on some exogenous variable z (for which we do not define any distribution).

Number of trials for binomial channels should be defined via a trials model component, e.g., obs(y ~ x + trials(n), family = "binomial"), where n is a data variable defining the number of trials. For multinomial channels, the number of trials is automatically defined to be the sum of the observations over the categories, but can also be defined using the trials component, for example for prediction.

Multivariate channels are defined by providing a single formula for all components or by providing component-specific formulas separated by a |. The response variables that correspond to the components should be joined by c(). For instance, the following would define c(y1, y2) as multivariate gaussian with x as a predictor for the mean of the first component and x and z as predictors for the mean of the second component: obs(c(y1, y2) ~ x | x + z, family = "mvgaussian"). A multinomial channel should only have a single formula.

In addition to declaring response variables via obs, we can also use the function aux to define auxiliary channels which are deterministic functions of other variables. The values of auxiliary variables are computed dynamically during prediction, making the use of lagged values and other transformations possible. The function aux also does not use the family argument, which is automatically set to deterministic and is a special channel type of obs. Note that lagged values of deterministic aux channels do not imply fixed time points. Instead they must be given starting values using a special function init that directly initializes the lags to specified values, or by past which computes the initial values based on an R expression. Both init and past should appear on the right hand side of the model formula, separated from the primary defining expression via |.

The formula within obs can also contain an additional special function varying, which defines the time-varying part of the model equation, in which case we could write for example obs(x ~ z + varying(~ -1 + w), family = "poisson"), which defines a model equation with a constant intercept and time-invariant effect of z, and a time-varying effect of w. We also remove the duplicate intercept with -1 in order to avoid identifiability issues in the model estimation (we could also define a time varying intercept, in which case we would write ⁠obs(x ~ -1 + z + varying(~ w), family = "poisson)⁠). The part of the formula not wrapped with varying is assumed to correspond to the fixed part of the model, so obs(x ~ z + varying(~ -1 + w), family = "poisson") is actually identical to obs(x ~ -1 + fixed(~ z) + varying(~ -1 + w), family = "poisson") and obs(x ~ fixed(~ z) + varying(~ -1 + w), family = "poisson").

When defining varying effects, we also need to define how the these time-varying regression coefficient behave. For this, a splines component should be added to the model, e.g., ⁠obs(x ~ varying(~ -1 + w), family = "poisson) + splines(df = 10)⁠ defines a cubic B-spline with 10 degrees of freedom for the time-varying coefficient corresponding to the w. If the model contains multiple time-varying coefficients, same spline basis is used for all coefficients, with unique spline coefficients and their standard deviation.

If the desired model contains lagged predictors of each response in each channel, these can be quickly added to the model as either time-invariant or time-varying predictors via lags() instead of writing them manually for each channel.

It is also possible to define group-specific (random) effects term using the special syntax random() similarly as varying(). For example, random(~1) leads to a model where in addition to the common intercept, each individual/group has their own intercept with zero-mean normal prior and unknown standard deviation analogously with the typical mixed models. An additional model component random_spec() can be used to define whether the random effects are allowed to correlate within and across channels and whether to use centered or noncentered parameterization for the random effects.

Value

A dynamiteformula object.

See Also

Model formula construction dynamite(), lags(), lfactor(), random_spec(), splines()

Examples

data.table::setDTthreads(1) # For CRAN
# A single gaussian response channel with a time-varying effect of 'x',
# and a time-varying effect of the lag of 'y' using B-splines with
# 20 degrees of freedom for the coefficients of the time-varying terms.
obs(y ~ -1 + varying(~x), family = "gaussian") +
  lags(type = "varying") +
  splines(df = 20)

# A two-channel categorical model with time-invariant predictors
# here, lag terms are specified manually
obs(x ~ z + lag(x) + lag(y), family = "categorical") +
  obs(y ~ z + lag(x) + lag(y), family = "categorical")

# The same categorical model as above, but with the lag terms
# added using 'lags'
obs(x ~ z, family = "categorical") +
  obs(y ~ z, family = "categorical") +
  lags(type = "fixed")

# A multichannel model with a gaussian, Poisson and a Bernoulli response and
# an auxiliary channel for the logarithm of 'p' plus one
obs(g ~ lag(g) + lag(logp), family = "gaussian") +
  obs(p ~ lag(g) + lag(logp) + lag(b), family = "poisson") +
  obs(b ~ lag(b) * lag(logp) + lag(b) * lag(g), family = "bernoulli") +
  aux(numeric(logp) ~ log(p + 1))

data.table::setDTthreads(1) # For CRAN
obs(y ~ x, family = "gaussian") + obs(z ~ w, family = "exponential")

data.table::setDTthreads(1) # For CRAN
x <- obs(y ~ x + random(~ 1 + lag(d)), family = "gaussian") +
  obs(z ~ varying(~w), family = "exponential") +
  aux(numeric(d) ~ log(y) | init(c(0, 1))) +
  lags(k = 2) +
  splines(df = 5) +
  random_spec(correlated = FALSE)
print(x)

---

Extract Fitted Values of a dynamite Model

Description

Fitted values for a dynamitefit object, i.e., $E(y_t | newdata, \theta)$ where $\theta$ contains all the model parameters. See also predict.dynamitefit() for multi-step predictions.

Usage

## S3 method for class 'dynamitefit'
fitted(
  object,
  newdata = NULL,
  n_draws = NULL,
  thin = 1,
  expand = TRUE,
  df = TRUE,
  ...
)

Arguments

object	[dynamitefit] The model fit object.
newdata	[data.frame] Data used in predictions. If NULL (default), the data used in model estimation is used for predictions as well. There should be no new time points that were not present in the data that were used to fit the model, and no new group levels can be included.
n_draws	[integer(1)] Number of posterior samples to use, default is NULL which uses all samples without permuting (with chains concatenated). If n_drawsis smaller than ndraws(object), a random subset of n_draws posterior samples are used.
thin	[integer(1)] Use only every thin posterior sample. This can be beneficial with when the model object contains large number of samples. Default is 1 meaning that all samples are used.
expand	[logical(1)] If TRUE (the default), the output is a single data.frame containing the original newdata and the predicted values. Otherwise, a list is returned with two components, simulated and observed, where the first contains only the predicted values, and the second contains the original newdata. Setting expand to FALSE can help conserve memory because newdata is not replicated n_draws times in the output. This argument is ignored if funs are provided.
df	[logical(1)] If TRUE (default) the output consists of data.frame objects, and data.table objects otherwise.
...	Ignored.

Value

A data.frame containing the fitted values.

See Also

Obtaining predictions predict.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
fitted(gaussian_example_fit, n_draws = 2L)

set.seed(1)
# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  fit <- dynamite(
    dformula = obs(LakeHuron ~ 1, "gaussian") + lags(),
    data = data.frame(LakeHuron, time = seq_len(length(LakeHuron)), id = 1),
    time = "time",
    group = "id",
    chains = 1,
    refresh = 0
  )

  if (requireNamespace("dplyr") &&
    requireNamespace("tidyr") &&
    base::getRversion() >= "4.1.0") {

    # One-step ahead samples (fitted values) from the posterior
    # (first time point is fixed due to lag in the model):
    fitted(fit) |>
      dplyr::filter(time > 2) |>
      ggplot2::ggplot(ggplot2::aes(time, LakeHuron_fitted, group = .draw)) +
      ggplot2::geom_line(alpha = 0.5) +
      # observed values
      ggplot2::geom_line(ggplot2::aes(y = LakeHuron), colour = "tomato") +
      ggplot2::theme_bw()

    # Posterior predictive distribution given the first time point:
    predict(fit, type = "mean") |>
      dplyr::filter(time > 2) |>
      ggplot2::ggplot(ggplot2::aes(time, LakeHuron_mean, group = .draw)) +
      ggplot2::geom_line(alpha = 0.5) +
      # observed values
      ggplot2::geom_line(ggplot2::aes(y = LakeHuron), colour = "tomato") +
      ggplot2::theme_bw()
  }
}

---

Simulated Data of a Gaussian Response

Description

Simulated data containing a Gaussian response variable y with two covariates. The dataset was generated from a model with time-varying effects of covariate x and the lagged value of the response variable, time-varying intercept, and time-invariant effect of covariate z. The time-varying coefficients vary according to a spline with 20 degrees of freedom.

Usage

gaussian_example

Format

A data frame with 3000 rows and 5 variables:

y

The response variable.

x

A continuous covariate.

z

A binary covariate.

id

Variable defining individuals (1 to 50).

time

Variable defining the time point of the measurement (1 to 30).

Source

The data was generated via gaussian_example.R in https://github.com/ropensci/dynamite/tree/main/data-raw/

See Also

Example models categorical_example, categorical_example_fit, gaussian_example_fit, multichannel_example, multichannel_example_fit

---

Model Fit for the Simulated Data of a Gaussian Response

Description

A dynamitefit object obtained by running dynamite on the gaussian_example dataset as

set.seed(1)
library(dynamite)
gaussian_example_fit <- dynamite(
  obs(y ~ -1 + z + varying(~ x + lag(y)) + random(~1), family = "gaussian") +
    random_spec() + splines(df = 20),
  data = gaussian_example,
  time = "time",
  group = "id",
  iter = 2000,
  warmup = 1000,
  thin = 10,
  chains = 2,
  cores = 2,
  refresh = 0,
  save_warmup = FALSE,
  pars = c("omega_alpha_1_y", "omega_raw_alpha_y", "nu_raw", "nu", "L",
    "sigma_nu", "a_y"),
  include = FALSE
)

Note the very small number of samples due to size restrictions on CRAN.

Usage

gaussian_example_fit

Format

A dynamitefit object.

Source

The data was generated via gaussian_example_fit.R in https://github.com/ropensci/dynamite/tree/main/data-raw/

See Also

Example models categorical_example, categorical_example_fit, gaussian_example, multichannel_example, multichannel_example_fit

---

Extract the Stan Code of the dynamite Model

Description

Returns the Stan code of the model. Mostly useful for debugging or for building a customized version of the model.

Usage

get_code(x, ...)

## S3 method for class 'dynamiteformula'
get_code(x, data, time, group = NULL, blocks = NULL, ...)

## S3 method for class 'dynamitefit'
get_code(x, blocks = NULL, ...)

Arguments

x	[dynamiteformula or dynamitefit] The model formula or an existing dynamitefit object. See dynamiteformula() and dynamite().
...	Ignored.
data	[data.frame, tibble::tibble, or data.table::data.table] The data that contains the variables in the model in long format. Supported column types are integer, logical, double, and factor. Columns of type character will be converted to factors. Unused factor levels will be dropped. The data can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input data is converted to channel specific matrix representations via stats::model.matrix.lm().
time	[character(1)] A column name of data that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.
group	[character(1)] A column name of data that denotes the unique groups or NULL corresponding to a scenario without any groups. If group is NULL, a new column .group is created with constant value 1L is created indicating that all observations belong to the same group. In case of name conflicts with data, see the group_var element of the return object to get the column name of the new variable.
blocks	[character()] Stan block names to extract. If NULL, extracts the full model code.

Value

The Stan model blocks as a character string.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
cat(get_code(obs(y ~ x, family = "gaussian"),
  data = d, time = "time", group = "id"
))
# same as
cat(dynamite(obs(y ~ x, family = "gaussian"),
  data = d, time = "time", group = "id",
  debug = list(model_code = TRUE, no_compile = TRUE)
)$model_code)

---

Extract the Model Data of the dynamite Model

Description

Returns the input data to the Stan model. Mostly useful for debugging.

Usage

get_data(x, ...)

## S3 method for class 'dynamiteformula'
get_data(x, data, time, group = NULL, ...)

## S3 method for class 'dynamitefit'
get_data(x, ...)

Arguments

x	[dynamiteformula or dynamitefit] The model formula or an existing dynamitefit object. See dynamiteformula() and dynamite().
...	Ignored.
data	[data.frame, tibble::tibble, or data.table::data.table] The data that contains the variables in the model in long format. Supported column types are integer, logical, double, and factor. Columns of type character will be converted to factors. Unused factor levels will be dropped. The data can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input data is converted to channel specific matrix representations via stats::model.matrix.lm().
time	[character(1)] A column name of data that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.
group	[character(1)] A column name of data that denotes the unique groups or NULL corresponding to a scenario without any groups. If group is NULL, a new column .group is created with constant value 1L is created indicating that all observations belong to the same group. In case of name conflicts with data, see the group_var element of the return object to get the column name of the new variable.

Value

A list containing the input data to Stan.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
str(get_data(obs(y ~ x, family = "gaussian"),
  data = d, time = "time", group = "id"
))

---

Get Parameter Dimensions of the dynamite Model

Description

Extracts the names and dimensions of all parameters used in the dynamite model. See also get_parameter_types() and get_parameter_names(). The returned dimensions match those of the stanfit element of the dynamitefit object. When applied to dynamiteformula objects, the model is compiled and sampled for 1 iteration to get the parameter dimensions.

Usage

get_parameter_dims(x, ...)

## S3 method for class 'dynamiteformula'
get_parameter_dims(x, data, time, group = NULL, ...)

## S3 method for class 'dynamitefit'
get_parameter_dims(x, ...)

Arguments

x	[dynamiteformula or dynamitefit] The model formula or an existing dynamitefit object. See dynamiteformula() and dynamite().
...	Ignored.
data	[data.frame, tibble::tibble, or data.table::data.table] The data that contains the variables in the model in long format. Supported column types are integer, logical, double, and factor. Columns of type character will be converted to factors. Unused factor levels will be dropped. The data can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input data is converted to channel specific matrix representations via stats::model.matrix.lm().
time	[character(1)] A column name of data that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.
group	[character(1)] A column name of data that denotes the unique groups or NULL corresponding to a scenario without any groups. If group is NULL, a new column .group is created with constant value 1L is created indicating that all observations belong to the same group. In case of name conflicts with data, see the group_var element of the return object to get the column name of the new variable.

Value

A named list with all parameter dimensions of the input model.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
get_parameter_dims(multichannel_example_fit)

---

Get Parameter Names of the dynamite Model

Description

Extracts all parameter names of used in the dynamitefit object.

Usage

get_parameter_names(x, types = NULL, ...)

## S3 method for class 'dynamitefit'
get_parameter_names(x, types = NULL, ...)

Arguments

x	[dynamitefit] The model fit object.
types	[character()] Extract only names of parameter of a certain type. See get_parameter_types().
...	Ignored.

Details

The naming of parameters generally follows style where the name starts with the parameter type (e.g. beta for time-invariant regression coefficient), followed by underscore and the name of the response variable, and in case of time-invariant, time-varying or random effect, the name of the predictor. An exception to this is spline coefficients omega, which also contain the number denoting the knot number.

Value

A character vector with parameter names of the input model.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
get_parameter_names(multichannel_example_fit)

---

Get Parameter Types of the dynamite Model

Description

Extracts all parameter types of used in the dynamitefit object. See as.data.frame.dynamitefit() for explanations of different types.

Usage

get_parameter_types(x, ...)

## S3 method for class 'dynamitefit'
get_parameter_types(x, ...)

Arguments

x	[dynamitefit] The model fit object.
...	Ignored.

Value

A character vector with all parameter types of the input model.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
get_parameter_types(multichannel_example_fit)

---

Get Prior Definitions of a dynamite Model

Description

Extracts the priors used in the dynamite model as a data frame. You can then alter the priors by changing the contents of the prior column and supplying this data frame to dynamite function using the argument priors. See vignettes for details.

Usage

get_priors(x, ...)

## S3 method for class 'dynamiteformula'
get_priors(x, data, time, group = NULL, ...)

## S3 method for class 'dynamitefit'
get_priors(x, ...)

Arguments

x	[dynamiteformula or dynamitefit] The model formula or an existing dynamitefit object. See dynamiteformula() and dynamite().
...	Ignored.
data	[data.frame, tibble::tibble, or data.table::data.table] The data that contains the variables in the model in long format. Supported column types are integer, logical, double, and factor. Columns of type character will be converted to factors. Unused factor levels will be dropped. The data can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input data is converted to channel specific matrix representations via stats::model.matrix.lm().
time	[character(1)] A column name of data that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.
group	[character(1)] A column name of data that denotes the unique groups or NULL corresponding to a scenario without any groups. If group is NULL, a new column .group is created with constant value 1L is created indicating that all observations belong to the same group. In case of name conflicts with data, see the group_var element of the return object to get the column name of the new variable.

Value

A data.frame containing the prior definitions.

Note

Only the prior column of the output should be altered when defining the user-defined priors for dynamite.

See Also

Model fitting dynamice(), dynamite(), update.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
get_priors(obs(y ~ x, family = "gaussian"),
  data = d, time = "time", group = "id"
)

---

HMC Diagnostics for a dynamite Model

Description

Prints the divergences, saturated treedepths, and low E-BFMI warnings.

Usage

hmc_diagnostics(x, ...)

## S3 method for class 'dynamitefit'
hmc_diagnostics(x, ...)

Arguments

x	[dynamitefit] The model fit object.
...	Ignored.

Value

Returns x (invisibly). data.table::setDTthreads(1) # For CRAN hmc_diagnostics(gaussian_example_fit)

See Also

Model diagnostics lfo(), loo.dynamitefit(), mcmc_diagnostics()

---

Add Lagged Responses as Predictors to Each Channel of a dynamite Model

Description

Adds the lagged value of the response of each channel specified via dynamiteformula() as a predictor to each channel. The added predictors can be either time-varying or time-invariant.

Usage

lags(k = 1L, type = c("fixed", "varying", "random"))

Arguments

k	[integer()] Values lagged by k units of time of each observed response variable will be added as a predictor for each channel. Should be a positive (unrestricted) integer.
type	[integer(1)] Either "fixed" or "varying" which indicates whether the coefficients of the added lag terms should vary in time or not.

Value

An object of class lags.

See Also

Model formula construction dynamite(), dynamiteformula(), lfactor(), random_spec(), splines()

Examples

data.table::setDTthreads(1) # For CRAN
obs(y ~ -1 + varying(~x), family = "gaussian") +
  lags(type = "varying") + splines(df = 20)

# A two-channel categorical model with time-invariant predictors
# here, lag terms are specified manually
obs(x ~ z + lag(x) + lag(y), family = "categorical") +
  obs(y ~ z + lag(x) + lag(y), family = "categorical")

# The same categorical model as above, but with the lag terms
# added using 'lags'
obs(x ~ z, family = "categorical") +
  obs(y ~ z, family = "categorical") +
  lags(type = "fixed")

---

Define a Common Latent Factor for the dynamite Model.

Description

This function can be used as part of a dynamiteformula() to define a common latent factor component. The latent factor is modeled as a spline similarly as a time-varying intercept, but instead of having equal effect on each group, there is an additional loading variable for each group so that in the linear predictor we have a term $\lambda_i \psi_t$ for each group $i$.

Usage

lfactor(
  responses = NULL,
  nonzero_lambda = TRUE,
  correlated = TRUE,
  noncentered_psi = FALSE,
  flip_sign = TRUE
)

Arguments

responses	[character()] Names of the responses that the factor should affect. Default is all responses defined with obs except categorical responses, which do not (yet) support the factor component.
nonzero_lambda	[logical()] If TRUE (the default), assumes that the mean of factor loadings is nonzero or not. Should be a logical vector matching the length of responses or a single logical value in case responses is NULL. See details.
correlated	[logical()] If TRUE (the default), the latent factors are assumed to be correlated between channels.
noncentered_psi	[logical(1)] If TRUE, uses a noncentered parametrization for spline coefficients of all the factors. The number of knots is based splines() call. Default is FALSE.
flip_sign	[logical(1)] If TRUE (default), try to avoid multimodality due to sign-switching by defining the sign of $\lambda$ and $\psi$ based on the mean of $\omega_1,\ldots, \omega_D$ coefficients. This only affects channels with nonzero_lambda = FALSE. If the true mean of $\omega$s is close to zero, this might not help, in which case it is better to set flip_sign = FALSE and post-process the samples in other ways (or use only one chain and/or suitable initial values). This argument is common to all factors.

Value

An object of class latent_factor.

See Also

Model formula construction dynamite(), dynamiteformula(), lags(), random_spec(), splines()

Examples

data.table::setDTthreads(1) # For CRAN
# three channel model with common factor affecting for responses x and y
obs(y ~ 1, family = "gaussian") +
  obs(x ~ 1, family = "poisson") +
  obs(z ~ 1, family = "gaussian") +
  lfactor(
    responses = c("y", "x"), nonzero_lambda = c(TRUE, FALSE),
    correlated = TRUE, noncentered_psi = FALSE
  )

---

Approximate Leave-Future-Out (LFO) Cross-validation

Description

Estimates the leave-future-out (LFO) information criterion for dynamite models using Pareto smoothed importance sampling.

Usage

lfo(x, ...)

## S3 method for class 'dynamitefit'
lfo(x, L, verbose = TRUE, k_threshold = 0.7, ...)

Arguments

x	[dynamitefit] The model fit object.
...	Additional arguments passed to rstan::sampling() or the ⁠$sample()⁠ method of the CmdStanModel object, such as chains and cores (parallel_chains in cmdstanr).
L	[integer(1)] Positive integer defining how many time points should be used for the initial fit.
verbose	[logical(1)] If TRUE (default), print the progress of the LFO computations to the console.
k_threshold	[numeric(1)] Threshold for the Pareto k estimate triggering refit. Default is 0.7.

Details

For multichannel models, the log-likelihoods of all channels are combined. For models with groups, expected log predictive densities (ELPDs) are computed independently for each group, but the re-estimation of the model is triggered if Pareto k values of any group exceeds the threshold.

Value

An lfo object which is a list with the following components:

• ELPD
  Expected log predictive density estimate.

• ELPD_SE
  Standard error of ELPD. This is a crude approximation which does not take into account potential serial correlations.

• pareto_k
  Pareto k values.

• refits
  Time points where model was re-estimated.

• L
  L value used in the LFO estimation.

• k_threshold
  Threshold used in the LFO estimation.

References

Paul-Christian Bürkner, Jonah Gabry, and Aki Vehtari (2020). Approximate leave-future-out cross-validation for Bayesian time series models, Journal of Statistical Computation and Simulation, 90:14, 2499-2523.

See Also

Model diagnostics hmc_diagnostics(), loo.dynamitefit(), mcmc_diagnostics()

Examples

data.table::setDTthreads(1) # For CRAN

# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  # this gives warnings due to the small number of iterations
  out <- suppressWarnings(
    lfo(gaussian_example_fit, L = 20, chains = 1, cores = 1)
  )
  out$ELPD
  out$ELPD_SE
  plot(out)
}

---

Approximate Leave-One-Out (LOO) Cross-validation

Description

Estimates the leave-one-out (LOO) information criterion for dynamite models using Pareto smoothed importance sampling with the loo package.

Usage

## S3 method for class 'dynamitefit'
loo(x, separate_channels = FALSE, thin = 1L, ...)

Arguments

x	[dynamitefit] The model fit object.
separate_channels	[logical(1)] If TRUE, computes LOO separately for each channel. This can be useful in diagnosing where the model fails. Default is FALSE, in which case the likelihoods of different channels are combined, i.e., all channels of are left out.
thin	[integer(1)] Use only every thin posterior sample when computing LOO. This can be beneficial with when the model object contains large number of samples. Default is 1 meaning that all samples are used.
...	Ignored.

Value

An output from loo::loo() or a list of such outputs (if separate_channels was TRUE).

References

Aki Vehtari, Andrew, Gelman, and Johah Gabry (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413–1432.

See Also

Model diagnostics hmc_diagnostics(), lfo(), mcmc_diagnostics()

Examples

data.table::setDTthreads(1) # For CRAN

# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  # this gives warnings due to the small number of iterations
  suppressWarnings(loo(gaussian_example_fit))
  suppressWarnings(loo(gaussian_example_fit, separate_channels = TRUE))
}

---

Diagnostic Values of a dynamite Model

Description

Prints HMC diagnostics and lists parameters with smallest effective sample sizes and largest Rhat values. See hmc_diagnostics() and posterior::default_convergence_measures() for details.

Usage

mcmc_diagnostics(x, ...)

## S3 method for class 'dynamitefit'
mcmc_diagnostics(x, n = 3L, ...)

Arguments

x	[dynamitefit] The model fit object.
...	Ignored.
n	[integer(1)] How many rows to print in parameter-specific convergence measures. The default is 3. Should be a positive (unrestricted) integer.

Value

Returns x (invisibly).

See Also

Model diagnostics hmc_diagnostics(), lfo(), loo.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
mcmc_diagnostics(gaussian_example_fit)

---

Simulated Multivariate Panel Data

Description

A simulated multichannel data containing multiple individuals with multiple response variables of different distributions.

Usage

multichannel_example

Format

A data frame with 3000 rows and 5 variables:

id

Variable defining individuals (1 to 50).

time

Variable defining the time point of the measurement (1 to 20).

g

Response variable following gaussian distribution.

p

Response variable following Poisson distribution.

b

Response variable following Bernoulli distribution.

Source

The data was generated via multichannel_example.R in https://github.com/ropensci/dynamite/tree/main/data-raw/

See Also

Example models categorical_example, categorical_example_fit, gaussian_example, gaussian_example_fit, multichannel_example_fit

---

Model Fit for the Simulated Multivariate Panel Data

Description

A dynamitefit object obtained by running dynamite on the multichannel_example dataset as

set.seed(1)
library(dynamite)
f <- obs(g ~ lag(g) + lag(logp), family = "gaussian") +
  obs(p ~ lag(g) + lag(logp) + lag(b), family = "poisson") +
  obs(b ~ lag(b) * lag(logp) + lag(b) * lag(g), family = "bernoulli") +
  aux(numeric(logp) ~ log(p + 1))
multichannel_example_fit <- dynamite(
  f,
  data = multichannel_example,
  time = "time",
  group = "id",
  chains = 1,
  cores = 1,
  iter = 2000,
  warmup = 1000,
  init = 0,
  refresh = 0,
  thin = 5,
  save_warmup = FALSE
)

Note the small number of samples due to size restrictions on CRAN.

Usage

multichannel_example_fit

Format

A dynamitefit object.

Source

THe data was generated via multichannel_example_fit.R in https://github.com/ropensci/dynamite/tree/main/data-raw/

See Also

Example models categorical_example, categorical_example_fit, gaussian_example, gaussian_example_fit, multichannel_example

---

Return the Number of Posterior Draws of a dynamitefit Object

Description

Return the Number of Posterior Draws of a dynamitefit Object

Usage

## S3 method for class 'dynamitefit'
ndraws(x)

Arguments

x	[dynamitefit] The model fit object.

Value

Number of posterior draws as a single integer value.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
ndraws(gaussian_example_fit)

---

Extract the Number of Observations Used to Fit a dynamite Model

Description

Extract the Number of Observations Used to Fit a dynamite Model

Usage

## S3 method for class 'dynamitefit'
nobs(object, ...)

Arguments

object	[dynamitefit] The model fit object.
...	Not used.

Value

Total number of non-missing observations as an integer.

See Also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_data(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
nobs(gaussian_example_fit)

---

Plots for dynamitefit Objects

Description

Produces the traceplots and the density plots of the model parameters. Can also be used to plot the time-varying and time-invariant parameters of the model along with their posterior intervals. See the plot_type argument for details on available plots.

Usage

## S3 method for class 'dynamitefit'
plot(
  x,
  plot_type = c("default", "trace", "dag"),
  types = NULL,
  parameters = NULL,
  responses = NULL,
  groups = NULL,
  times = NULL,
  level = 0.05,
  alpha = 0.5,
  facet = TRUE,
  scales = c("fixed", "free"),
  n_params = NULL,
  ...
)

Arguments

x	[dynamitefit] The model fit object.
plot_type	[character(1)] What type of plot to draw? The default is "default" which draws posterior means and intervals of the parameters selected by types or parameters. If both "types" and parameters are NULL, all parameters are drawn up to the maximum specified by n_params. Option "trace" instead draws posterior densities and traceplots of the parameters. Option "dag" instead plots the directed acyclic graph of the model formula, see plot.dynamiteformula() for the arguments available for this option.
types	[character(1)] Types of the parameter for which the plots should be drawn. Possible options can be found with the function get_parameter_types(). Ignored if the argument parameters is supplied.
parameters	[charecter()] Parameter name(s) for which the plots should be drawn. Possible options can be found with the function get_parameter_names(). The default is all parameters, limited by n_params.
responses	[character()] Response(s) for which the plots should be drawn. Possible options are unique(x$priors$response). Default is all responses. Ignored if the argument parameters is supplied.
groups	[character(1)] Group name(s) for which the plots should be drawn for group-specific parameters.
times	[double()] Time point(s) for which the plots should be drawn for time-varying parameters. By default, all time points are included, up to the maximum number of parameters specified by n_params starting from the first non-fixed time point.
level	[numeric(1)] Level for posterior intervals. Default is 0.05, leading to 90% intervals.
alpha	[numeric(1)] Opacity level for geom_ribbon. Default is 0.5.
facet	[logical(1)] Should the time-invariant parameters be plotted separately (TRUE) or in a single plot (FALSE)?
scales	[character(1)] Should y-axis of the panels be "fixed" (the default) or "free"? See ggplot2::facet_wrap().
n_params	[integer()] A single value or a vector of length 2 specifying the maximum number of parameters to plot. If a single value is provided, the same limit is used for all parameters. If a vector is supplied, the first element defines the maximum number of time-invariant parameters to plot and the second the maximum number of time-varying parameters to plot. The defaults values are 20 for time-invariant parameters and 3 for time-varying parameters. The default value is 5 for plot_type == "trace".
...	Arguments passed to plot.dynamiteformula() when using plot_type = "dag".

Value

A ggplot object.

See Also

Drawing plots plot.dynamiteformula()

Examples

data.table::setDTthreads(1) # For CRAN
plot(gaussian_example_fit, type = "beta")

---

Plot the Model Structure as a Directed Acyclic Graph (DAG)

Description

Plot a snapshot of the model structure at a specific time point with a window of the highest-order lag dependency both into the past and the future as a directed acyclic graph (DAG). Only response variables are shown in the plot. This function can also produce a TikZ code of the DAG to be used in reports and publications.

Usage

## S3 method for class 'dynamiteformula'
plot(
  x,
  show_auxiliary = TRUE,
  show_covariates = FALSE,
  tikz = FALSE,
  vertex_size = 0.25,
  label_size = 18,
  ...
)

Arguments

x	[dynamiteformula] The model formula.
show_auxiliary	[logical(1)] Should deterministic auxiliary responses be shown in the plot? If FALSE, the vertices corresponding to such responses will be projected out. The default is TRUE.
show_covariates	[logical(1)] Should unmodeled covariates be shown in the plot? The defaults is FALSE.
tikz	[logical(1)] Should the DAG be returned in TikZ format? The default is FALSE returning a ggplot object instead.
vertex_size	[double(1)] The size (radius) of the vertex circles used in the ggplot DAG. (The vertical and horizontal distances between vertices in the grid are 1, for reference.)
label_size	[double(1)] Font size (in points) to use for the vertex labels in the ggplot DAG.
...	Not used..

Value

A ggplot object, or a character string if tikz = TRUE.

See Also

Drawing plots plot.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
multichannel_formula <- obs(g ~ lag(g) + lag(logp), family = "gaussian") +
  obs(p ~ lag(g) + lag(logp) + lag(b), family = "poisson") +
  obs(b ~ lag(b) * lag(logp) + lag(b) * lag(g), family = "bernoulli") +
  aux(numeric(logp) ~ log(p + 1))
# A ggplot
plot(multichannel_formula)
# TikZ format
plot(multichannel_formula, tikz = TRUE)

---

Diagnostic Plot for Pareto k Values from LFO

Description

Plots Pareto k values per each time point (with one point per group), together with a horizontal line representing the used threshold.

Usage

## S3 method for class 'lfo'
plot(x, ...)

Arguments

x	[lfo] Output of the lfo method.
...	Ignored.

Value

A ggplot object.

Examples

data.table::setDTthreads(1) # For CRAN

# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  # This gives warnings due to the small number of iterations
  plot(suppressWarnings(
    lfo(gaussian_example_fit, L = 20, chains = 1, cores = 1)
  ))
}

---

Predict Method for a dynamite Model

Description

Obtain counterfactual predictions for a dynamitefit object.

Usage

## S3 method for class 'dynamitefit'
predict(
  object,
  newdata = NULL,
  type = c("response", "mean", "link"),
  funs = list(),
  impute = c("none", "locf", "nocb"),
  new_levels = c("none", "bootstrap", "gaussian", "original"),
  global_fixed = FALSE,
  n_draws = NULL,
  thin = 1,
  expand = TRUE,
  df = TRUE,
  ...
)

Arguments

object	[dynamitefit] The model fit object.
newdata	[data.frame] Data used in predictions. Predictions are computed for missing (NA) values in the response variable columns, and non-missing values are assumed fixed. If NULL (default), the data used in model estimation is used for predictions as well, after all values in the response variable columns after the first fixed time point are converted to NA values. Missing values in predictor columns can be imputed (argument impute). There should be no new time points that were not present in the data that were used to fit the model. New group levels can be included, but if the model contains random effects, an option for the random effects for the new levels must be chosen (argument new_levels). If the grouping variable of the original data is missing, it is assumed that all observations in newdata belong to the first group in the original data. New group levels are not allowed for models using latent factors.
type	[character(1)] Type of prediction, "response" (default), "mean", or "link".
funs	[list()] A named list whose names should correspond to the response variables of the model. Each element of funs should be a a named list of functions that will be applied to the corresponding predicted type of the channel over the individuals for each combination of the posterior draws and time points. In other words, the resulting predictions will be averages over the individuals. The functions should take the corresponding type variable values as their only argument. If funs is empty, the full individual level values are returned instead. Note that this argument can only be used if there are multiple individuals (i.e., group was not NULL in the dynamite call).
impute	[character(1)] Which imputation scheme to use for missing exogenous predictor values. Currently supported options are no imputation: "none" (default), last observation carried forward: "locf", and next observation carried backward: "nocb".
new_levels	[character(1)] Defines if and how to sample the random effects for observations whose group level was not present in the original data. The options are: "none" (the default) which will signal an error if new levels are encountered. "bootstrap" which will randomly draw from the posterior samples of the random effects across all original levels. "gaussian" which will randomly draw from a Gaussian distribution using the posterior samples of the random effects standard deviation (and correlation matrix if applicable). "original" which will randomly match each new level to one of the original levels. The posterior samples of the random effects of the matched levels will then be used for the new levels. This argument is ignored if the model does not contain random effects.
global_fixed	[logical(1)] If FALSE (the default), the first non-fixed time point is counted from the the first non-NA observation for each group member separately. Otherwise, the first non-fixed time point is counted from the first time point globally. If there are no groups, then the options are equivalent.
n_draws	[integer(1)] Number of posterior samples to use, default is NULL which uses all samples without permuting (with chains concatenated). If n_drawsis smaller than ndraws(object), a random subset of n_draws posterior samples are used.
thin	[integer(1)] Use only every thin posterior sample. This can be beneficial with when the model object contains large number of samples. Default is 1 meaning that all samples are used.
expand	[logical(1)] If TRUE (the default), the output is a single data.frame containing the original newdata and the predicted values. Otherwise, a list is returned with two components, simulated and observed, where the first contains only the predicted values, and the second contains the original newdata. Setting expand to FALSE can help conserve memory because newdata is not replicated n_draws times in the output. This argument is ignored if funs are provided.
df	[logical(1)] If TRUE (default) the output consists of data.frame objects, and data.table objects otherwise.
...	Ignored.

Details

Note that forecasting (i.e., predictions for time indices beyond the last time index in the original data) is not supported by the dynamite package. However, such predictions can be obtained by augmenting the original data with NA values before model estimation.

Value

A data.frame containing the predicted values or a list of two data.frames. See the expand argument for details. Note that the .draw column is not the same as .draw from as.data.frame and as_draws methods as predict uses permuted samples. A mapping between these variables can be done using information in object$stanfit@sim$permutation.

See Also

Obtaining predictions fitted.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
out <- predict(gaussian_example_fit, type = "response", n_draws = 2L)
head(out)

# using summary functions
sumr <- predict(multichannel_example_fit, type = "mean",
  funs = list(g = list(m = mean, s = sd), b = list(sum = sum)),
  n_draws = 2L)
head(sumr$simulated)

# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  # Simulate from the prior predictive distribution

  f <- obs(y ~ lag(y) + varying(~ -1 + x), "gaussian") +
    splines(df = 10, noncentered = TRUE)

  # Create data with missing observations
  # Note that due to the lagged term in the model,
  # we need to fix the first time point
  d <- data.frame(y = c(0, rep(NA, 49)), x = rnorm(50), time = 1:50)

  # Suppress warnings due to the lack of data
  suppressWarnings(
    priors <- get_priors(f, data = d, time = "time")
  )

  # Modify default priors which can produce exploding behavior when used
  # without data
  priors$prior <- c(
    "normal(0, 1)",
    "normal(0.6, 0.1)",
    "normal(-0.2, 0.5)",
    "normal(0.2, 0.1)",
    "normal(0.5, 0.1)"
  )

  # Samples from the prior conditional on the first time point and x
  fit <- dynamite(
    dformula = f,
    data = d,
    time = "time",
    verbose = FALSE,
    priors = priors,
    chains = 1
  )

  # Simulate new data
  pp <- predict(fit)

  ggplot2::ggplot(pp, ggplot2::aes(time, y_new, group = .draw)) +
    ggplot2::geom_line(alpha = 0.1) +
    ggplot2::theme_bw()
}

---

Print the results from the LFO

Description

Prints the summary of the leave-future-out cross-validation.

Usage

## S3 method for class 'lfo'
print(x, ...)

Arguments

x	[lfo] Output of the lfo method.
...	Ignored.

Value

Returns x invisibly.

Examples

data.table::setDTthreads(1) # For CRAN

# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  # This gives warnings due to the small number of iterations
  suppressWarnings(lfo(gaussian_example_fit, L = 20))
}

---

Additional Specifications for the Group-level Random Effects of the DMPM

Description

This function can be used as part of dynamiteformula() to define whether the group-level random effects should be modeled as correlated or not.

Usage

random_spec(correlated = TRUE, noncentered = TRUE)

Arguments

correlated	[logical(1)] If TRUE (the default), correlations of random effects are modeled as multivariate normal.
noncentered	[logical(1)] If TRUE (the default), use a noncentered parameterization for random effects. Try changing this if you encounter divergences or other problems in sampling.

Details

With a large number of time points random intercepts can become challenging sample with default priors. This is because with large group sizes the group-level intercepts tend to be behave similarly to fixed group-factor variable so the model becomes overparameterized given these and the common intercept term. Another potential cause for sampling problems is relatively large variation in the intercepts (large sigma_nu) compared to the sampling variation (sigma) in the Gaussian case.

Value

An object of class random_spec.

See Also

Model formula construction dynamite(), dynamiteformula(), lags(), lfactor(), splines()

Examples

data.table::setDTthreads(1) # For CRAN
# two channel model with correlated random effects for responses x and y
obs(y ~ 1 + random(~1), family = "gaussian") +
  obs(x ~ 1 + random(~1 + z), family = "poisson") +
  random_spec(correlated = TRUE)

---

Define the B-splines Used for the Time-varying Coefficients of the Model.

Description

This function can be used as part of dynamiteformula() to define the splines used for the time-varying coefficients $\delta$.

Usage

splines(
  df = NULL,
  degree = 3L,
  lb_tau = 0,
  noncentered = FALSE,
  override = FALSE
)

Arguments

df	[integer(1)] Degrees of freedom, i.e., the total number of spline coefficients. See splines::bs(). Note that the knots are always defined as an equidistant sequence on the interval starting from the first non-fixed time point to the last time point in the data. See dynamiteformula() for more information on fixed time points. Should be an (unrestricted) positive integer.
degree	[integer(1)] See splines::bs(). Should be an (unrestricted) positive integer.
lb_tau	[numeric()] Hard constraint(s) on the lower bound of the standard deviation parameters $\tau$ of the random walk priors. Can be useful in avoiding divergences in some cases. See also the noncentered argument. Can be a single positive value, or vector defining the lower bound separately for each channel, even for channels without varying effects. The ordering is based on the order of channel definitions in the dynamiteformula object.
noncentered	[logical()] If TRUE, use a noncentered parameterization for the spline coefficients. Default is FALSE. Try changing this if you encounter divergences or other problems in sampling for example when simulating from prior predictive distribution. Can be a single logical value, or vector of logical values, defining the parameterization separately for each channel, even for channels without varying effects.
override	[logical(1)] If FALSE (the default), an existing definition for the splines will not be overridden by another call to splines(). If TRUE, any existing definitions will be replaced.

Value

An object of class splines.

See Also

Model formula construction dynamite(), dynamiteformula(), lags(), lfactor(), random_spec()

Examples

data.table::setDTthreads(1) # For CRAN
# Two channel model with varying effects, with explicit lower bounds for the
# random walk prior standard deviations, with noncentered parameterization
# for the first channel and centered for the second channel.
obs(y ~ 1, family = "gaussian") + obs(x ~ 1, family = "gaussian") +
  lags(type = "varying") +
  splines(
    df = 20, degree = 3, lb_tau = c(0, 0.1),
    noncentered = c(TRUE, FALSE)
  )

---

Update a dynamite Model

Description

Note that using a different backend for the original model fit and when updating can lead to an error due to different naming in cmdstanr and rstan sampling arguments.

Usage

## S3 method for class 'dynamitefit'
update(
  object,
  dformula = NULL,
  data = NULL,
  priors = NULL,
  recompile = NULL,
  ...
)

Arguments

object	[dynamitefit] The model fit object.
dformula	[dynamiteformula] Updated model formula. By default the original formula is used.
data	[data.frame, tibble::tibble, or data.table::data.table] Data for the updated model. By default original data is used.
priors	[data.frame] Updated priors. By default the priors of the original model are used.
recompile	[logical(1)] Should the model be recompiled? If NULL (default), tries to avoid recompilation. Recompilation is forced when the model formula or the priors are changed, or if the new data contains missing values in a channel which did not contain missing values in the original data. Recompilation is also forced in case the backend previous or new backend is cmdstanr.
...	Additional parameters to dynamite.

Value

An updated dynamitefit object.

See Also

Model fitting dynamice(), dynamite(), get_priors()

Examples

data.table::setDTthreads(1) # For CRAN
## Not run: 
# re-estimate the example fit without thinning:
# As the model is compiled on Windows, this will fail on other platforms
if (identical(.Platform$OS.type, "windows")) {
  fit <- update(gaussian_example_fit, thin = 1)
}

## End(Not run)

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