Description Usage Arguments Value Author(s) Examples

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
tsdlvm1(data, lambda, contemporaneous = c("cov", "chol",
"prec", "ggm"), residual = c("cov", "chol", "prec",
"ggm"), beta = "full", omega_zeta = "full", delta_zeta
= "full", kappa_zeta = "full", sigma_zeta = "full",
lowertri_zeta = "full", omega_epsilon = "empty",
delta_epsilon = "empty", kappa_epsilon = "empty",
sigma_epsilon = "empty", lowertri_epsilon = "empty",
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)
ts_lvgvar(...)
``` |

`data` |
A data frame encoding the data used in the analysis. Can be missing if |

`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 |

`omega_zeta` |
Only used when |

`delta_zeta` |
Only used when |

`kappa_zeta` |
Only used when |

`sigma_zeta` |
Only used when |

`lowertri_zeta` |
Only used when |

`omega_epsilon` |
Only used when |

`delta_epsilon` |
Only used when |

`kappa_epsilon` |
Only used when |

`sigma_epsilon` |
Only used when |

`lowertri_epsilon` |
Only used when |

`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. |

`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 ID |

`vars` |
An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in |

`groups` |
An optional string indicating the name of the group variable in |

`covs` |
A sample varianceâ€“covariance matrix, or a list/array of such matrices for multiple groups. Make sure |

`means` |
A vector of sample means, or a list/matrix containing such vectors for multiple groups. |

`nobs` |
The number of observations used in |

`missing` |
How should missingness be handled in computing the sample covariances and number of observations when |

`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 |

`optimizer` |
The optimizer to be used. Can be one of |

`storedata` |
Logical, should the raw data be stored? Needed for bootstrapping (see |

`standardize` |
Which standardization method should be used? |

`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 |

`verbose` |
Logical, should messages be printed? |

`...` |
Arguments sent to |

An object of the class psychonetrics (psychonetrics-class)

Sacha Epskamp

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ```
# 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)
``` |

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