Help for package mlts

Title:

Multilevel Latent Time Series Models with 'R' and 'Stan'

Version:

1.0.0

Description:

Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.

License:

GPL (≥ 3)

Encoding:

UTF-8

RoxygenNote:

7.3.1

URL:

https://github.com/munchfab/mlts

BugReports:

https://github.com/munchfab/mlts/issues

Imports:

cowplot, dplyr (≥ 1.1.3), ggplot2, methods, mvtnorm, pdftools, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rlang, rmarkdown, rstan (≥ 2.32.3), rstantools (≥ 2.4.0), stats

Suggests:

knitr, testthat (≥ 3.0.0)

Config/testthat/edition:

Biarch:

true

Depends:

R (≥ 3.5.0)

LinkingTo:

BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)

SystemRequirements:

GNU make

VignetteBuilder:

knitr

LazyData:

true

NeedsCompilation:

yes

Packaged:

2024-06-26 10:25:36 UTC; fabi

Author:

Kenneth Koslowski

[aut, cre, cph], Fabian Münch

[aut], Tobias Koch

[aut], Jana Holtmann

[aut]

Maintainer:

Kenneth Koslowski <kenneth.koslowski@uni-leipzig.de>

Repository:

CRAN

Date/Publication:

2024-06-27 14:20:02 UTC

mlts: Multilevel Latent Time Series Models with 'R' and 'Stan'

Description

Author(s)

Maintainer: Kenneth Koslowski kenneth.koslowski@uni-leipzig.de (ORCID) [copyright holder]

Authors:

Fabian Münch fabian.felix.muench@uni-jena.de (ORCID)
Tobias Koch tobias.koch@uni-jena.de (ORCID)
Jana Holtmann jana.holtmann@uni-leipzig.de (ORCID)

Simple Time-Series Data

Description

Simulated Data (from mlts_sim) for one time-series variable.

Usage

ar1_data

Format

`ar1_data`

A data frame with 2,500 rows and 3 columns:

ID: Unit identifier
time: Time point
Y1: The time-series variable

Source

mlts_sim

Create Missings for Approximation of Continuous Time Dynamic Models (new version)

Description

Create Missings for Approximation of Continuous Time Dynamic Models (new version)

Usage

create_missings(data, tinterval, id, time, btw_vars = NULL)

Arguments

data

An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model.

tinterval

The step interval for approximation for a continuous time DSEM. The smaller the step interval, the better the approximation.

id

The variable in data that identifies the person or observational unit (as character).

time

The variable in data that contains the (continuous) time (as string).

btw_vars

The names of between-level variables in the data to be added in newly created rows with NAs.

Value

A data.frame with missings imputed for use in mlts_fit.

Examples

# create some data for example
data <- data.frame(
  id = rep(c(1, 2), each = 4),
  time = c(0, 3, 4, 6,
           1, 4, 5, 7)
)

# create missings to approximate continuous time process
create_missings(
  data = data, id = "id", time = "time",
  tinterval = 1 # use time interval of 1 minute
)

Fit Bayesian Multilevel Manifest or Latent Time-Series Models

Description

Fit Bayesian Multilevel Manifest or Latent Time-Series Models

Usage

mlts_fit(
  model,
  data = NULL,
  id,
  ts,
  covariates = NULL,
  outcomes = NULL,
  outcome_pred_btw = NULL,
  center_covs = TRUE,
  time = NULL,
  tinterval = NULL,
  beep = NULL,
  days = NULL,
  n_overnight_NAs,
  na.rm = FALSE,
  iter = 500,
  chains = 2,
  cores = 2,
  monitor_person_pars = FALSE,
  get_SD_latent = FALSE,
  fit_model = TRUE,
  print_message = TRUE,
  print_warning = TRUE,
  ...
)

Arguments

model

data.frame. Output of mlts_model and related functions.

data

An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model. Alternatively, a list object with simulated data created by mlts_sim can be entered directly and allows for comparison of estimates and true population paramter values used in the data generation.

id

Character. The variable in data that identifies the observational cluster unit. Not necessary when data is a list object of simulated data generated with mlts_sim.

ts

Character. The variable(s) in data that contain the time-series construct(s) or their indicator variable(s). If multiple constructs are provided in the model, multiple entries are necessary. Note that the order of variable names provided in ts has to match the specification made in the model. E.g., if multiple constructs (e.g., mlts_model(q = 2)) are provided the order of variables names provided in ts determines which construct is referred to as mu_1, phi(1)_11, etc..

covariates

Named character vector. An optional named vector of characters to refer to predictors of random effects as specified in the model. Note that specifying covariates is only necessary if the respective variable name(s) in data differ from the variables names specified in model.

outcomes

Named character vector. Similar to covariates, an optional named vector of characters to refer to outcome predicted by random effects as specified in the model. Note that specifying outcomes is only necessary if the respective variable name(s) in data differ from the outcome variable name(s) specified in model.

outcome_pred_btw

Named character vector. Similar to covariates, an optional named vector of characters to refer to additional between-level variables entered as outcome predictor(s) as specified in the model. Note that specifying outcome_pred_btw is only necessary if the respective variable name(s) in data differ from the variable name(s) specified in model.

center_covs

Logical. Between-level covariates used as predictors of random effects will be grand-mean centered before model fitting by default. Set center_covs to FALSE when including categorical predictors into the set of covariates. Note that in this case, additional continuous covariates should be grand-mean centered prior to using mlts_fit.

time

Character. The variable in data that contains the (continuous) time of observation.

tinterval

The step interval for approximating equally spaced observations in time by insertion of missing values, to be specified with respect to the time stamp variable provided in time. Procedure for inserting missing values resembles the procedure for time shift transformation as described in Asparouhov, Hamaker, & Muthén (2018).

beep

Character. The variable in data that contains the running beep number starting with 1 for each person.

days

Optional. If a running beep identifier is provided via the beep argument and observations are nested within days (or similar grouping unit), the variable in data that contains the day identifier can be added to correct for overnight lags (see Details).

n_overnight_NAs

Optional. The number of NA rows to add after the last observation of each day (if days is provided).

na.rm

logical. Per default, missing values remain in the data and will be imputed during model estimation. Set to TRUE to remove all rows with missing values in variables given in ts.

iter

A positive integer specifying the number of iterations for each chain (including 50% used as warmup). The default is 500.

chains

A positive integer specifying the number of Markov chains. The default is 2.

cores

The number of cores to use when executing the Markov chains in parallel. The default is 2 (see stan).

monitor_person_pars

Logical. Should person parameters (i.e., values of the latent variables) be stored? Default is FALSE.

get_SD_latent

Logical. Set to TRUE to obtain standardized estimates in multiple-indicator models.

fit_model

Logical. Set to FALSE to avoid fitting the model which may be helpful to inspect prepared data used for model estimation (default = TRUE).

print_message

Logical. Print messages based on defined inputs (default = TRUE).

print_warning

Logical. Print warnings based on defined inputs (default = TRUE).

...

Additional arguments passed to sampling.

Value

An object of class mltsfit. The object is a list containing the following components:

model

the model object passed to mlts_fit

data

the preprocessed data used for fitting the model

param.labels

a data.frame that provides the names of parameters used in the stan model. These parameter names are necessary when running standard post-processing functions using mlts_fit$stanfit

pop.pars.summary

a data.frame that contains summary statistics for all parameter in model

person.pars.summary

if monitor_person_pars = TRUE, a data.frame containing summary statistics for cluster-specific parameters is provided

standata

a list with the data as passed to sampling

stanfit

an object of class stanfit with the raw output created by sampling

posteriors

an array of the MCMC chain results for all parameters in model created by rstan::extract with dimnames adapted to match the parameter names provided in model

References

Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388. doi:10.1080/10705511.2017.1406803

Examples


# build simple vector-autoregressive mlts model for two time-series variables
var_model <- mlts_model(q = 2)

# fit model with (artificial) dataset ts_data
fit <- mlts_fit(
  model = var_model,
  data = ts_data,
  ts = c("Y1", "Y2"), # time-series variables
  id = "ID", # cluster identifier variable
  time = "time", # time variable
  tinterval = 1 # interval for approximation of equidistant measurements,
)

# inspect model summary
summary(fit)

Build a multilevel latent time series model

Description

Build a multilevel latent time series model

Usage

mlts_model(
  class = c("VAR"),
  q,
  p = NULL,
  max_lag = c(1, 2, 3),
  btw_factor = TRUE,
  btw_model = NULL,
  fix_dynamics = FALSE,
  fix_inno_vars = FALSE,
  fix_inno_covs = TRUE,
  inno_covs_zero = FALSE,
  inno_covs_dir = NULL,
  fixef_zero = NULL,
  ranef_zero = NULL,
  ranef_pred = NULL,
  out_pred = NULL,
  out_pred_add_btw = NULL
)

Arguments

class

Character. Indicating the model type to be specified. For now restricted to VAR, the default. Future package releases might include additional model types.

q

Integer. The number of time-varying constructs.

p

Integer. For multiple-indicator models, specify a vector of length q with the number of manifest indicators per construct. If all constructs are measured with the same number of indicators, a single value is sufficient.

max_lag

Integer. The maximum lag of the autoregressive effect to be included in the model. The maximum is 3. Defaults to 1.

btw_factor

Logical. If TRUE (the default), a common between-level factor is modeled across all indicator variables per construct q. If FALSE, instead of a between-level factor, indicator mean levels will be included as individual (random) effects drawn from a joint multivariate normal distribution.

btw_model

A list to indicate for which manifest indicator variables a common between-level factor should be modeled (see Details for detailed instructions). At this point restricted to one factor per latent construct.

fix_dynamics

Logical. Fix all random effect variances of autoregressive and cross-lagged effects to zero (constraining parameters to be equal across clusters).

fix_inno_vars

Logical. Fix all random effect variances of innovation variances to zero (constraining parameters to be equal across clusters).

fix_inno_covs

Logical. Fix all random effect variances of innovation covariances to zero (constraining parameters to be equal across clusters).

inno_covs_zero

Logical. Set to TRUE to treat all innovations as independent.

inno_covs_dir

For bivariate VAR models with person-specific innovation covariances, a latent variable approach is applied (for a detailed description, see Hamaker et al., 2018). by specifying an additional factor that loads onto the contemporaneous innovations of both constructs, capturing the shared variance of innovations, that is not predicted by the previous time points. The loading parameters of this latent factor, however, have to be restricted in accordance with researchers assumptions about the sign of the association between innovations across construct. Hence, if innovations at time $t$ are assumed to be positively correlated across clusters, set the argument to pos, or neg respectively.

fixef_zero

Character. A character vector to index which fixed effects (referring to the parameter labels in model$Param) should be constrained to zero (Note: this also results in removing the random effect variance of the respective parameter).

ranef_zero

Character. A character vector to index which random effect variances (referring to the parameter labels in model$Param) should be constrained to zero.

ranef_pred

A character vector or a named list. Include between-level covariate(s) as predictor(s) of all random effects in model by entering a vector of unique variable names. Alternatively, to include between-level covariates or differing sets of between-level covariates as predictors of specific random effects, a named list (using the labels in model$Param) can be entered (see examples). Note that if a named list is provided, all names that do not match random parameters in model will be ignored. Note that variables entered in ranef_pred will be grand-mean centered by default when fitting the model with mlts_fit.

out_pred

A character vector or a named list. Include between-level outcome(s) to be regressed on all random effects in model by entering a vector of unique variable names. Alternatively, to include multiple between-level outcomes regressed differing sets of specific random effects, a named list (using the labels in model$Param) can be entered (see examples). Note that if a named list is provided, all character strings in the vector of each list (with independent variables) element that do not match random effect parameter names in model$Param will be treated as additional between-level predictors.

out_pred_add_btw

A character vector. If out_pred is a character (vector), all inputs will be treated as between-level covariates to be used as additional predictors of all outcomes specified in out_pred.

Value

An object of class data.frame with the following columns:

Model

Indicates if the parameter in the respective row is part of the structural, or the measurement model (if multiple indicators per construct are provided)

Level

Parameter on the between- or within-level.

Type

Describes the parameter type.

Param

Parameter names to be referred to in arguments of mlts_model.

Param_Label

Parameter labels (additional option to address specific parameters).

isRandom

Indicates which within-level parameters are modeled as random (1) or a constant across clusters (0).

Constraint

Optional. Included if multiple-indicators per construct (p > 1) are provided. Constraints on measurement model parameters can be changed by overwriting the respective value in model. Possible inputs are "free", "= 0" (for SDs of measurement error variances), and "= 1" (for loading parameters).

prior_type

Contains the parameters' prior distribution used in mlts_fit (prior classes can not be changed at this point).

prior_location

Location values of the parameters' prior distribution used in mlts_fit (can be changed to any real value by overwriting the respective value in model).

prior_scale

Scale values of the parameters' prior distribution used in mlts_fit (can be changed to any real value by overwriting the respective value in model).

References

Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate behavioral research, 53(6), 820-841. doi:10.1080/00273171.2018.1446819

Examples


 # To illustrate the general model building procedure, starting with a simple
 # two-level AR(1) model with person-specific individual means, AR effects,
 # and innovation variances (the default option when using mlts_model() and q = 1).
 model <- mlts_model(q = 1)

 # All model parameters (with their labels stored in model$Param) can be inspected by calling:
 model

 # Possible model extensions/restrictions:
 # 1. Introducing additional parameter constraints, such as fixing specific
 #    parameters to a constant value by setting the respective random effect
 #    variances to zero, such as e.g. (log) innovation variances
 model <- mlts_model(q = 1, ranef_zero = "ln.sigma2_1")
 #    Note that setting the argument `fix_inno_vars` to `TRUE` provides
 #    a shortcut to fixing the innovation variances of all constructs
 #    (if q >= 1) to a constant.

 # 2. Including a multiple indicator model, where the construct is measured by
 #    multiple indicators (here, p = 3 indicators)
 model <- mlts_model(
          q = 1, # the number of time-varying constructs
          p = 3, # the number of manifest indicators
          # assuming a common between-level factor (the default)
          btw_factor = TRUE
        )

 # 3. Incorporating between-level variables. For example, inclusion of
 #    an additional between-level variable ("cov1") as predictor of all
 #    (ranef_pred = "cov1") or a specific set of random effects
 #    (ranef_pred = list("phi(1)_11") = "cov1"), an external outcome (e.g., "out1")
 #    to be predicted by all (out_pred = "out1") or specific random effects
 #    (out_pred = list("out1" = c("etaB_1", "phi(1)_11")), using the latent
 #    between-level factor trait scores (etaB_1) and individual first-order
 #    autoregressive effects (phi(1)_11) as joint predictors of outcome "out1".
 model <- mlts_model(
            q = 1,
            p = 3,
            fix_inno_vars = TRUE,
            ranef_pred = "cov1",
            out_pred = list("out1" = c("etaB_1", "phi(1)_11"))
           )
 #    Note that the names of the random effect parameters must match the
 #    parameter labels provided in model$Param, the result of the
 #    mlts_model()-functions.

Create TeX Model Formula from mlts model object

Description

Create TeX Model Formula from mlts model object

Usage

mlts_model_formula(
  model,
  file = NULL,
  keep_tex = FALSE,
  ts = NULL,
  covariates = NULL,
  outcomes = NULL
)

Arguments

model

A model built with mlts_model.

file

An optional string containing the name of the file and file path. Has to end with .pdf file format.

keep_tex

Logical. Should the TeX file be kept (additional to the Rmd file)? Defaults to FALSE.

ts

To be included in future releases. An optional character vector containing the names of the time-series variables or indicators.

covariates

To be included in future releases. An optional character vector containing the names of the between-level covariates.

outcomes

To be included in future releases. An optional character vector containing the names of the between-level outcomes.

Value

An RMarkdown file that is automatically rendered to a pdf document.

Examples


# build a simple vector-autoregressive mlts model with two time-series variables
var_model <- mlts_model(q = 2)

# create formula from the specified model
mlts_model_formula(model = var_model)

Create Path Diagrams from mlts model object

Description

Create Path Diagrams from mlts model object

Usage

mlts_model_paths(
  model,
  file = NULL,
  add_png = FALSE,
  keep_tex = FALSE,
  ts = NULL,
  covariates = NULL,
  outcomes = NULL
)

Arguments

model

A model built with mlts_model.

file

An optional string containing the name of the file and file path. Has to end with .pdf file format.

add_png

Logical. Set to TRUE to transform created PDF to .png file using pdftools::pdf_convert.

keep_tex

Logical. Should the TeX file be kept (additional to the Rmd file)? Defaults to FALSE.

ts

To be included in future releases. An optional character vector containing the names of the time-series variables or indicators.

covariates

To be included in future releases. An optional character vector containing the names of the between-level covariates.

outcomes

To be included in future releases. An optional character vector containing the names of the between-level outcomes.

Value

An RMarkdown file that is automatically rendered to a pdf document.

Examples


# build a simple vector-autoregressive mlts model with two time-series variables
var_model <- mlts_model(q = 2)

# create a pathmodel from the specified model
mlts_model_paths(model = var_model)

Plot results of mlts

Description

Plot results of mlts

Usage

mlts_plot(
  fit,
  type = c("fe", "re", "re.cor"),
  bpe = c("median", "mean"),
  what = c("all", "Fixed effect", "Random effect SD", "RE correlation",
    "Outcome prediction", "RE prediction", "Item intercepts", "Loading",
    "Measurement Error SD"),
  sort_est = NULL,
  xlab = NULL,
  ylab = NULL,
  facet_ncol = 1,
  dot_size = 1,
  dot_color = "black",
  dot_shape = 1,
  errorbar_color = "black",
  errorbar_width = 0.3,
  add_true = FALSE,
  true_color = "red",
  true_shape = 22,
  true_size = 1,
  hide_xaxis_text = TRUE,
  par_labels = NULL,
  labels_as_expressions = FALSE
)

Arguments

fit

An object of class mlts.fit

type

Type of plot. type = "fe" (Default) Forest-plot of model coefficients. type = "re" Plot of individual (random) effects type = "re.cor" Combined plot depicting the distribution of individual parameter estimates (posterior summary statistics as provided by bpe), as well as bivariate scatter plots.

bpe

The Bayesian point estimate is, by default, the median of the posterior distribution (bpe = "median"). Set bpe = "mean" to use the mean of the posterior distribution as point estimates.

what

Character. For type = "fe", indicate which parameters should be included in the plot by setting what to "all" (the default), or one (or multiple) of "Fixed effect", "Random effect SD", "RE correlation", "Outcome prediction", "RE prediction", "Item intercepts", "Loading", or "Measurement Error SD".

sort_est

Add parameter label for sorting of random effects.

xlab

Title for the x axis.

ylab

Title for the y axis.

facet_ncol

Number of facet columns (see ggplot2::facet_grid).

dot_size

numeric, size of the dots that indicate the point estimates.

dot_color

character. indicating the color of the point estimates.

dot_shape

numeric. shape of the dots that indicate the point estimates.

errorbar_color

character. Color of error bars.

errorbar_width

integer. Width of error bars.

add_true

logical. If model was fitted with simulated data using mlts_sim, true population parameter values can be plotted as reference by setting the argument ot TRUE.

true_color

character. Color of points depicting true population parameter used in the data generation.

true_shape

integer. Shape of points depicting true population parameter used in the data generation.

true_size

integer. Size of points depicting true population parameter used in the data generation.

hide_xaxis_text

logical. Hide x-axis text if set to TRUE.

par_labels

character vector. User-specified labels for random effect parameters can be specified.

labels_as_expressions

logical. Should parameter names on plot labels be printed as mathematical expressions? Defaults to FALSE. Still experimental.

Value

Returns a ggplot-object .

Examples


# build simple vector-autoregressive mlts model for two time-series variables
var_model <- mlts_model(q = 2)

# fit model with (artificial) dataset ts_data
fit <- mlts_fit(
  model = var_model,
  data = ts_data,
  ts = c("Y1", "Y2"), # time-series variables
  id = "ID", # identifier variable
  time = "time",
  tinterval = 1 # interval for approximation of continuous-time dynamic model,
)

# inspect model summary
mlts_plot(fit, type = "fe", what = "Fixed effect")

Simulate data from mlts model

Description

Simulate data from mlts model

Usage

mlts_sim(
  model,
  default = FALSE,
  N,
  TP,
  burn.in = 50,
  seed = NULL,
  seed.true = 1,
  btw.var.sds = NULL
)

Arguments

model

data.frame. Output of mlts_model.

default

logical. If set to TRUE, default prior specifications are added.

N

integer Number of observational units.

TP

integer. Number of measurements per observational unit.

burn.in

integer. Length of ‘burn-in’ period.

seed

integer. Seed used for data generation.

seed.true

integer. Separate seed used for sampling of true population parameters values from plausible ranges for stationary time series.

btw.var.sds

named numeric vector. Provide standard deviation(s) for all exogenous between-level variable(s) specified in model, e.g. (btw.var.sds = c("covariate1" = 1), to set the SD of the variable "covariate1" to 1). Mean values of the respective variable(s) will be set to 0 per default.

Details

A function to generate data from an output of mlts_model.

Value

An object of class "mlts_simdata". The object is a list containing the following components:

model

the model object passed to mlts_sim with true parameter values used in the data generation added in the column true.val

data

a long format data.frame of the generated time series data

RE.pars

a matrix of cluster-specific true values used in the data generation

Examples


# build a simple vector-autoregressive mlts model with two time-series variables
var_model <- mlts_model(q = 2)

# simulate data from this model with default true values
# (true values are randomly drawn from normal distribution)
var_data <- mlts_sim(
  model = var_model,
  N = 50, TP = 30, # number of units and number of measurements per unit
  default = TRUE # use default parameter values
)

# the data set is stored in .$data
head(var_data$data)

# individual parameter values are stored in .$RE.pars
head(var_data$RE.pars)

# if the mltssim-object is used in mlts_fit(), true values
# are added to the fitted object
fit <- mlts_fit(
  model = var_model,
  data = var_data,
  id = "ID", ts = c("Y1", "Y2"), time = "time"
)

# inspect model with true values
head(fit$pop.pars.summary)

Get Standardized Estimates for an mlts Model

Description

Get Standardized Estimates for an mlts Model

Usage

mlts_standardized(
  object,
  what = c("between", "within", "both"),
  digits = 3,
  prob = 0.95,
  add_cluster_std = FALSE
)

Arguments

object

mltsfit. Output of mlts_model and related functions.

what

character. Get between-level standardized estimates (what = "between", the default), within-level standardized estimates averaged over clusters (what = "within"), or both (what = "both").

digits

Number of digits. Default is 3.

prob

A value between 0 and 1 to indicate the width of the credible interval. Default is .95.

add_cluster_std

logical. If what = "within", within-level standardized effects for each cluster are included in the output (defaults to FALSE).

Value

A list containing between- and within-level standardized parameters.

Examples


# build simple vector-autoregressive mlts model for two time-series variables
var_model <- mlts_model(q = 2)

# fit model with (artificial) dataset ts_data
fit <- mlts_fit(
  model = var_model,
  data = ts_data,
  ts = c("Y1", "Y2"), # time-series variables
  id = "ID", # identifier variable
  time = "time", # time variable
  tinterval = 1, # interval for approximation of continuous-time dynamic model,
  monitor_person_pars = TRUE # person parameters need to be sampled for standardization
)

# inspect standardized parameter estimates
mlts_standardized(fit)

Create a summary of a fitted model with class `mltsfit`

Description

Create a summary of a fitted model with class mltsfit

Usage

## S3 method for class 'mltsfit'
summary(
  object,
  priors = FALSE,
  se = FALSE,
  prob = 0.95,
  bpe = c("mean", "median"),
  digits = 3,
  flag_signif = FALSE,
  ...
)

Arguments

object

An object of class mltsfit.

priors

Add prior information (default = FALSE).

se

Logical. Should the Monte Carlo Standard Error be included in the summary? Defaults to FALSE.

prob

A value between 0 and 1 to indicate the width of the credible interval. Default is .95.

bpe

Bayesian posterior estimate can be either "mean" (the default) or the "median" of the posterior distribution.

digits

Number of digits.

flag_signif

Add significance flags based on prob (default = FALSE).

...

Additional arguments affecting the summary produced.

Value

A summary of model parameters.

Examples


# build simple vector-autoregressive mlts model for two time-series variables
var_model <- mlts_model(q = 2)

# fit model with (artificial) dataset ts_data
fit <- mlts_fit(
  model = var_model,
  data = ts_data,
  ts = c("Y1", "Y2"), # time-series variables
  id = "ID", # identifier variable
  time = "time",
  tinterval = 1 # interval for approximation of continuous-time dynamic model,
)

# inspect model summary
summary(fit)

Simple Time-Series Data

Description

Simulated Time-Series Data (from mlts_sim) for two time-series variables.

Usage

ts_data

Format

`ts_data`

A data frame with 1,100 rows and 4 columns:

ID: Unit identifier
time: Time point
Y1, Y2: The two time-series variables

Source

mlts_sim

mlts: Multilevel Latent Time Series Models with 'R' and 'Stan'

Description

Author(s)

See Also

Simple Time-Series Data

Description

Usage

Format

ar1_data

Source

Create Missings for Approximation of Continuous Time Dynamic Models (new version)

Description

Usage

Arguments

Value

Examples

Fit Bayesian Multilevel Manifest or Latent Time-Series Models

Description

Usage

Arguments

Value

References

Examples

Build a multilevel latent time series model

Description

Usage

Arguments

Value

References

Examples

Create TeX Model Formula from mlts model object

Description

Usage

Arguments

Value

Examples

Create Path Diagrams from mlts model object

Description

Usage

Arguments

Value

Examples

Plot results of mlts

Description

Usage

Arguments

Value

Examples

Simulate data from mlts model

Description

Usage

Arguments

Details

Value

Examples

Get Standardized Estimates for an mlts Model

Description

Usage

Arguments

Value

Examples

Create a summary of a fitted model with class mltsfit

Description

Usage

Arguments

Value

Examples

Simple Time-Series Data

Description

Usage

Format

ts_data

Source

`ar1_data`

Create a summary of a fitted model with class `mltsfit`

`ts_data`