mlVAR0: Multilevel VAR Estimation for Multiple Time Series

In mlVAR: Multi-Level Vector Autoregression

Multilevel VAR Estimation for Multiple Time Series

Description

The function mlVAR0 computes estimates of the multivariate vector autoregression model as introduced by Bringmann et al. (2013) which can be extended through treatment effects, covariates and pre- and post assessment effects.

FUNCTION IS DEPRECATED AND WILL BE REMOVED SOON.

Usage

mlVAR0(data, vars, idvar, lags = 1, dayvar, beepvar,
                 periodvar, treatmentvar, covariates, timevar,
                 maxTimeDiff, control = list(optimizer = "bobyqa"),
                 verbose = TRUE, orthogonal, estimator = c("lmer",
                 "lmmlasso"), method = c("default", "stepwise",
                 "movingWindow"), laginteractions = c("none", "mains",
                 "interactions"), critFun = BIC, lambda = 0,
                 center = c("inSubject","general","none"))

Arguments

data	Data frame
vars	Vectors of variables to include in the analysis
idvar	String indicating the subject ID
lags	Vector indicating the lags to include
dayvar	String indicating assessment day (if missing, every assessment is set to one day)
beepvar	String indicating assessment beep per day (if missing, is added)
periodvar	String indicating the period (baseline, treatment period, etc.) of assessment (if missing, every assessment is set to one period)
treatmentvar	Character vector indicating treatment
covariates	Character indicating covariates independent of assessment.
timevar	Character indicating the time variable
maxTimeDiff	Maximum time differece to include observation pairs
control	A list of arguments sent to lmerControl
verbose	Logical to print progress to the console
orthogonal	Logical to indicate if orthogonal estimation (no correlated random effects) should be used. Defaults to FALSE if the number of nodes is less than 6 and TRUE otherwise
estimator	Estimator to use. Note: lmmlasso implementation is very experimental
method	Method to use. Experimental
laginteractions	Experimental, do not use.
critFun	Experimental, do not use.
lambda	lmmlasso lambda parameter
center	Centering to be used. "inSubject" uses within-person centering, "general" uses grand-mean centering and "none" does not use centering. IMPORTANT NOTE: "inSubject" leads to coefficients to resemble within-person slopes, the other centering option leads to coefficients to be a blend of within and between person slopes.

Details

mlVAR0 has been built to extract individual network dynamics by estimating a multilevel vector autoregression model that models the time dynamics of selected variables both within an individual and on group level. For example, in a lag-1-model each variable at time point t is regressed to a lagged version of itself at time point t-1 and all other variables at time point t-1. In psychological research, for example, this analysis can be used to relate the dynamics of symptoms on one day (as assessed by experience sampling methods) to the dynamics of these symptoms on the consecutive day.

Value

mlVAR0 returns a 'mlVAR0' object containing

fixedEffects	A matrix that contains all fixed effects coefficients with dependent variables as rows and the lagged independent variables as columns.
se.fixedEffects	A matrix that contains all standard errors of the fixed effects.
randomEffects	A list of matrices that contain the random effects coefficients.
randomEffectsVariance	A matrix containing the estimated variances between the random-effects terms
pvals	A matrix that contains p-values for all fixed effects.
pseudologlik	The pseudo log-likelihood.
BIC	Bayesian Information Criterion, i.e. the sum of all univariate models' BICs
input	List containing the names of variables used in the analysis

Author(s)

Sacha Epskamp ([e-mail]), Marie K. Deserno ([e-mail]) and Laura F. Bringmann ([e-mail])

References

Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., ... & Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PloS one, 8(4), e60188.

See Also

fixedEffects, fixedEffects

Examples

## Not run: 
### Small network ###
nVar <- 3
nPerson <- 25
nTime <- 25

# Simulate model and data:
Model <- mlVARsim0(nPerson,nVar,nTime,sparsity = 0.5)

# Run mlVAR0:
Res <- mlVAR0(Model)

# Compare true fixed model with significant edges of estimated fixed model:
layout(t(1:2))
plot(Model,"fixed", title = "True model",layout="circle", edge.labels = TRUE)
plot(Res,"fixed", title = "Estimated model", layout = "circle", onlySig = TRUE, 
        alpha = 0.05, edge.labels = TRUE)

# Compare true and estimated individual differences in parameters:
layout(t(1:2))
plot(Model,"fixed", title = "True model",layout="circle", edge.color = "blue", 
        edge.labels = TRUE)
plot(Res,"fixed", title = "Estimated model", layout = "circle", edge.color = "blue", 
        edge.labels = TRUE)

# Compare networks of subject 1:
layout(t(1:2))
plot(Model,"subject",subject = 1, title = "True model",layout="circle", 
        edge.labels = TRUE)
plot(Res,"subject",subject = 1,title = "Estimated model", layout = "circle", 
        edge.labels = TRUE)



### Large network ###
nVar <- 10
nPerson <- 50
nTime <- 50

# Simulate model and data:
Model <- mlVARsim0(nPerson,nVar,nTime, sparsity = 0.5)

# Run orthogonal mlVAR:
Res <- mlVAR0(Model, orthogonal = TRUE)

# Compare true fixed model with significant edges of estimated fixed model:
layout(t(1:2))
plot(Model,"fixed", title = "True model",layout="circle")
plot(Res,"fixed", title = "Estimated model", layout = "circle", onlySig = TRUE, 
        alpha = 0.05)

# Compare true and estimated individual differences in parameters:
layout(t(1:2))
plot(Model,"fixed", title = "True model",layout="circle", edge.color = "blue")
plot(Res,"fixed", title = "Estimated model", layout = "circle", edge.color = "blue")

# Compare networks of subject 1:
layout(t(1:2))
plot(Model,"subject",subject = 1, title = "True model",layout="circle")
plot(Res,"subject",subject = 1,title = "Estimated model", layout = "circle")

## End(Not run)

mlVAR documentation built on May 29, 2024, 2:15 a.m.