Lag-1 dynamic latent variable model family of psychonetrics models for panel data
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.
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 = "diag", 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 = "diag", 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, start = "version2", covtype = c("choose", "ML", "UB"), missing = "listwise", equal = "none", baseline_saturated = TRUE, estimator = "ML", optimizer, storedata = FALSE, verbose = FALSE, sampleStats, baseline = c("stationary_random_intercept", "stationary", "independence", "none"), bootstrap = FALSE, boot_sub, boot_resample) 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(...)
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.baseline: What baseline model should be used? "stationary_random_intercept" includes both within- and between person variances constrained equal across time (default), "stationary" only includes within-person variances constrained equal across time, "independence" (default up to version 0.11) includes a variance for every variable at every time point (not constrained equal across time), and "none" includes no baseline model.bootstrap: Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).boot_sub: Proportion of cases to be subsampled (round(boot_sub * N)).boot_resample: Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)...: Arguments sent to dlvm1.An object of the class psychonetrics (psychonetrics-class )
Sacha Epskamp
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