dlvm1_family: Lag-1 dynamic latent variable model family of psychonetrics...

In SachaEpskamp/psychonetrics: Structural Equation Modeling and Confirmatory Network Analysis

Lag-1 dynamic latent variable model family of psychonetrics models for panel data

Description

This is the family of models that models a dynamic factor model on panel data. There are four covariance structures that can be modeled in different ways: within_latent, between_latent for the within-person and between-person latent (contemporaneous) models respectively, and within_residual, between_residual for the within-person and between-person residual models respectively. The panelgvar wrapper function sets the lambda to an identity matrix, all residual variances to zero, and models within-person and between-person latent (contemporaneous) models as GGMs. The panelvar wrapper does the same but models contemporaneous relations as a variance-covariance matrix. Finally, the panel_lvgvar wrapper automatically models all latent networks as GGMs.

Usage

dlvm1(data, vars, lambda, within_latent = c("cov", "chol",
                    "prec", "ggm"), within_residual = c("cov", "chol",
                    "prec", "ggm"), between_latent = c("cov", "chol",
                    "prec", "ggm"), between_residual = c("cov", "chol",
                    "prec", "ggm"), beta = "full", omega_zeta_within =
                    "full", delta_zeta_within = "full", kappa_zeta_within
                    = "full", sigma_zeta_within = "full",
                    lowertri_zeta_within = "full", omega_epsilon_within =
                    "zero", delta_epsilon_within = "diag",
                    kappa_epsilon_within = "diag", sigma_epsilon_within =
                    "diag", lowertri_epsilon_within = "diag",
                    omega_zeta_between = "full", delta_zeta_between =
                    "full", kappa_zeta_between = "full",
                    sigma_zeta_between = "full", lowertri_zeta_between =
                    "full", omega_epsilon_between = "zero",
                    delta_epsilon_between = "diag", kappa_epsilon_between
                    = "diag", sigma_epsilon_between = "diag",
                    lowertri_epsilon_between = "diag", nu, mu_eta,
                    identify = TRUE, identification = c("loadings",
                    "variance"), latents, groups, covs, means, nobs,
                    covtype = c("choose", "ML", "UB"), missing =
                    "listwise", equal = "none", baseline_saturated = TRUE,
                    estimator = "ML", optimizer, storedata = FALSE,
                    verbose = FALSE, sampleStats)

panelgvar(data, vars, within_latent = c("ggm","chol","cov","prec"), 
          between_latent = c("ggm","chol","cov","prec"), ...)

panelvar(data, vars, within_latent = c("cov","chol","prec","ggm"), 
          between_latent = c("cov","chol","prec","ggm"), ...)

panel_lvgvar(...)

Arguments

data	A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.
vars	Required argument. Different from in other psychonetrics models, this must be a *matrix* with each row indicating a variable and each column indicating a measurement. The matrix must be filled with names of the variables in the dataset corresponding to variable i at wave j. NAs can be used to indicate missing waves. The rownames of this matrix will be used as variable names.
lambda	Required argument. A model matrix encoding the factor loading structure. Each row indicates an indicator and each column a latent. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
within_latent	The type of within-person latent contemporaneous model to be used.
within_residual	The type of within-person residual model to be used.
between_latent	The type of between-person latent model to be used.
between_residual	The type of between-person residual model to be used.
beta	A model matrix encoding the temporal relationships (transpose of temporal network). A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Can also be "full" for a full temporal network or "zero" for an empty temporal network.
omega_zeta_within	Only used when within_latent = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
delta_zeta_within	Only used when within_latent = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
kappa_zeta_within	Only used when within_latent = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
sigma_zeta_within	Only used when within_latent = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
lowertri_zeta_within	Only used when within_latent = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
omega_epsilon_within	Only used when within_residual = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
delta_epsilon_within	Only used when within_residual = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
kappa_epsilon_within	Only used when within_residual = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
sigma_epsilon_within	Only used when within_residual = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
lowertri_epsilon_within	Only used when within_residual = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
omega_zeta_between	Only used when between_latent = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
delta_zeta_between	Only used when between_latent = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
kappa_zeta_between	Only used when between_latent = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
sigma_zeta_between	Only used when between_latent = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
lowertri_zeta_between	Only used when between_latent = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
omega_epsilon_between	Only used when between_residual = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
delta_epsilon_between	Only used when between_residual = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
kappa_epsilon_between	Only used when between_residual = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
sigma_epsilon_between	Only used when between_residual = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
lowertri_epsilon_between	Only used when between_residual = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
nu	Optional vector encoding the intercepts of the observed variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.
mu_eta	Optional vector encoding the means of the latent variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.
identify	Logical, should the model be automatically identified?
identification	Type of identification used. "loadings" to fix the first factor loadings to 1, and "variance" to fix the diagonal of the latent variable model matrix (sigma_zeta, lowertri_zeta, delta_zeta or kappa_zeta) to 1.
latents	An optional character vector with names of the latent variables.
groups	An optional string indicating the name of the group variable in data.
covs	A sample variance–covariance matrix, or a list/array of such matrices for multiple groups. IMPORTANT NOTE: psychonetrics expects the maximum likelihood (ML) covariance matrix, which is NOT obtained from cov directly. Manually rescale the result of cov with (nobs - 1)/nobs to obtain the ML covariance matrix.
means	A vector of sample means, or a list/matrix containing such vectors for multiple groups.
nobs	The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.
start	Start value specification. Can be either a string or a psychonetrics model. If it is a string, "version2" indicates the latest version of start value computation, "version1" indicates start values as they were computed up to version 0.11, and "simple" indicate simple starting values. If this is a psychonetrics model the starting values will be based on the ouptut. This can be useful, for example, if you first estimate a model with matrices set to a Cholesky decomposition, then use those values as start values for estimating Gaussian graphical models.
missing	How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "listwise" for listwise deletion, or "pairwise" for pairwise deletion.
equal	A character vector indicating which matrices should be constrained equal across groups.
baseline_saturated	A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.
estimator	The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation, "FIML" for full-information maximum likelihood estimation, "ULS" for unweighted least squares estimation, "WLS" for weighted least squares estimation, and "DWLS" for diagonally weighted least squares estimation.
optimizer	The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), and C++ based optimizers "cpp_L-BFGS-B", "cpp_BFGS", "cpp_CG", "cpp_SANN", and "cpp_Nelder-Mead". The C++ optimizers are faster but slightly less stable. Defaults to "nlminb".
storedata	Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).
verbose	Logical, should progress be printed to the console?
sampleStats	An optional sample statistics object. Mostly used internally.
covtype	If 'covs' is used, this is the type of covariance (maximum likelihood or unbiased) the input covariance matrix represents. Set to "ML" for maximum likelihood estimates (denominator n) and "UB" to unbiased estimates (denominator n-1). The default will try to find the type used, by investigating which is most likely to result from integer valued datasets.
...	Arguments sent to dlvm1.

Value

An object of the class psychonetrics (psychonetrics-class)

Author(s)

Sacha Epskamp

Examples

library("dplyr")

# Smoke data cov matrix, based on LISS data panel https://www.dataarchive.lissdata.nl
smoke <- structure(c(47.2361758611759, 43.5366809116809, 41.0057465682466, 
                     43.5366809116809, 57.9789886039886, 47.6992521367521, 
                     41.0057465682466, 
                     47.6992521367521, 53.0669434731935), .Dim = c(3L, 3L), 
                   .Dimnames = list(
                       c("smoke2008", "smoke2009", "smoke2010"), c("smoke2008", 
                   "smoke2009", "smoke2010")))

# Design matrix:
design <- matrix(rownames(smoke),1,3)

# Form model:
mod <- panelvar(vars = design, 
                covs = smoke, nobs = 352
)


# Run model:
mod <- mod %>% runmodel

# Evaluate fit:
mod %>% fit

SachaEpskamp/psychonetrics documentation built on Sept. 1, 2023, 3:40 a.m.