Lag-1 dynamic latent variable model family of psychonetrics models for time-series data
This is the family of models that models a dynamic factor model on time-series. There are two covariance structures that can be modeled in different ways: contemporaneous for the contemporaneous model and residual for the residual model. These can be set to "cov" for covariances, "prec" for a precision matrix, "ggm" for a Gaussian graphical model and "chol" for a Cholesky decomposition. The ts_lvgvar wrapper function sets contemporaneous = "ggm" for the graphical VAR model.
tsdlvm1(data, lambda, contemporaneous = c("cov", "chol", "prec", "ggm"), residual = c("cov", "chol", "prec", "ggm"), beta = "full", omega_zeta = "full", delta_zeta = "diag", kappa_zeta = "full", sigma_zeta = "full", lowertri_zeta = "full", omega_epsilon = "zero", delta_epsilon = "diag", kappa_epsilon = "diag", sigma_epsilon = "diag", lowertri_epsilon = "diag", nu, mu_eta, identify = TRUE, identification = c("loadings", "variance"), latents, beepvar, dayvar, idvar, vars, groups, covs, means, nobs, missing = "listwise", equal = "none", baseline_saturated = TRUE, estimator = "ML", optimizer, storedata = FALSE, sampleStats, covtype = c("choose", "ML", "UB"), centerWithin = FALSE, standardize = c("none", "z", "quantile"), verbose = FALSE, bootstrap = FALSE, boot_sub, boot_resample) ts_lvgvar(...)
data: A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.lambda: 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.contemporaneous: The type of contemporaneous model used. See description.residual: The type of residual model used. See description.beta: A model matrix encoding the temporal relationships (transpose of temporal network) between latent variables. 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: Only used when contemporaneous = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.delta_zeta: Only used when contemporaneous = "ggm". Either "diag" or "zero" (not recommended), or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.kappa_zeta: Only used when contemporaneous = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.sigma_zeta: Only used when contemporaneous = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.lowertri_zeta: Only used when contemporaneous = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.omega_epsilon: Only used when residual = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.delta_epsilon: Only used when residual = "ggm". Either "diag" or "zero" (not recommended), or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.kappa_epsilon: Only used when residual = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.sigma_epsilon: Only used when residual = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.lowertri_epsilon: Only used when residual = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 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.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.beepvar: Optional string indicating assessment beep per day. Adding this argument will cause non-consecutive beeps to be treated as missing!dayvar: Optional string indicating assessment day. Adding this argument makes sure that the first measurement of a day is not regressed on the last measurement of the previous day. IMPORTANT: only add this if the data has multiple observations per day.idvar: Optional string indicating the subject IDvars: An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in data.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. Make sure covtype argument is set correctly to the type of covariances used.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.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).standardize: Which standardization method should be used? "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution.sampleStats: An optional sample statistics object. Mostly used internally.centerWithin: Logical, should data be within-person centered?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.verbose: Logical, should messages be printed?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 tsdlvm1An object of the class psychonetrics (psychonetrics-class )
Sacha Epskamp
# Note: this example is wrapped in a dontrun environment because the data is not # available locally. ## Not run: # Obtain the data from: # # Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A. M., # Riese, H., & Cramer, A. O. (2018). Personalized network modeling in psychopathology: # The importance of contemporaneous and temporal connections. Clinical Psychological # Science, 6(3), 416-427. # # Available here: https://osf.io/c8wjz/ tsdata <- read.csv("Supplementary2_data.csv") # Encode time variable in a way R understands: tsdata$time <- as.POSIXct(tsdata$time, tz = "Europe/Amsterdam") # Extract days: tsdata$Day <- as.Date(tsdata$time, tz = "Europe/Amsterdam") # Variables to use: vars <- c("relaxed", "sad", "nervous", "concentration", "tired", "rumination", "bodily.discomfort") # Create lambda matrix (in this case: one factor): Lambda <- matrix(1,7,1) # Estimate dynamical factor model: model <- tsdlvm1( tsdata, lambda = Lambda, vars = vars, dayvar = "Day", estimator = "FIML" ) # Run model: model <- model %>% runmodel # Look at fit: model %>% print model %>% fit # Pretty bad fit ## End(Not run)