priors_for: Specify priors for a SEM

In INLAvaan: Approximate Bayesian Latent Variable Analysis

Specify priors for a SEM

Description

Specify priors for a SEM, similar to how blavaan::dpriors() works.

Usage

priors_for(...)

Arguments

...	Named arguments specifying prior distributions for lavaan parameter types.

Details

This function provides a convenient way to specify prior distributions for different types of parameters in a structural equation model (SEM). It uses a registry of default priors for common lavaan parameter types (e.g., loadings, regressions, residuals, etc.) and allows users to override these defaults by passing named arguments.

The parameter names, and default settings, are:

• nu = "normal(0,32)": Observed variable intercepts

• alpha = "normal(0,10)": Latent variable intercepts

• lambda = "normal(0,10)": Factor loadings

• beta = "normal(0,10)": Regression coefficients

• theta = "gamma(1,.5)[sd]": Residual precisions

• psi = "gamma(1,.5)[sd]": Latent variable precisions

• rho = "beta(1,1)": Correlations (both latent and observed)

• tau = "normal(0,1.5)": Thresholds for ordinal variables

Note that the normal distributions are parameterised using standard deviations, and not variances. For example, normal(0,10) means a normal distribution with mean 0 and standard deviation 10 (not variance 10).

Value

A named character vector of prior specifications, where names correspond to lavaan parameter types (e.g., "lambda", "beta", "theta", etc.) and values are character strings specifying the prior distribution (e.g., "normal(0,10)", "gamma(1,0.5)[sd]", "gamma(1,1)[prec]", etc.).

Scale qualifiers

For variance parameters (theta, psi), the prior distribution can be placed on a transformed scale by appending a qualifier:

• [sd]: Prior is on the standard deviation \sigma. Example: "gamma(1,0.5)[sd]" places a Gamma(1, 0.5) prior on \sigma = \sqrt{\text{variance}}.

• [prec]: Prior is on the precision \tau = 1/\sigma^2. Example: "gamma(1,1)[prec]" places a Gamma(1, 1) prior on \tau = 1/\text{variance}. This is the parameterisation used by blavaan and corresponds to an Inverse-Gamma prior on the variance.

The necessary Jacobian adjustment is applied automatically in both cases.

See Also

inlavaan(), acfa(), asem(), agrowth()

Examples

priors_for(nu = "normal(0,10)", lambda = "normal(0,1)", rho = "beta(3,3)")

# Precision-scale prior for residual variances (blavaan-style)
priors_for(theta = "gamma(1,1)[prec]")

INLAvaan documentation built on Oct. 2, 2026, 1:07 a.m.