mlVAR (version 0.5.2)

mlVAR0: 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"))

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

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.

Author

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

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.

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

if (FALSE) {
### 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")
}