Define linear constraints on intercept parameters in a lvm-object.
# S3 method for lvm
intercept(object, vars, ...) <- valuelvm-object
Additional arguments
character vector of variable names
Vector (or list) of parameter values or labels (numeric or
character) or a formula defining the linear constraints (see also the
regression or covariance methods).
A lvm-object
The intercept function is used to specify linear constraints on the
intercept parameters of a latent variable model. As an example we look at
the multivariate regression model
$$ E(Y_1|X) = \alpha_1 + \beta_1 X$$ $$ E(Y_2|X) = \alpha_2 + \beta_2 X$$
defined by the call
m <- lvm(c(y1,y2) ~ x)
To fix \(\alpha_1=\alpha_2\) we call
intercept(m) <- c(y1,y2) ~ f(mu)
Fixed parameters can be reset by fixing them to NA. For instance to
free the parameter restriction of \(Y_1\) and at the same time fixing
\(\alpha_2=2\), we call
intercept(m, ~y1+y2) <- list(NA,2)
Calling intercept with no additional arguments will return the
current intercept restrictions of the lvm-object.
Variables will be added to the model if not already present.
covariance<-, regression<-,
constrain<-, parameter<-,
latent<-, cancel<-, kill<-
## A multivariate model
m <- lvm(c(y1,y2) ~ f(x1,beta)+x2)
regression(m) <- y3 ~ f(x1,beta)
intercept(m) <- y1 ~ f(mu)
intercept(m, ~y2+y3) <- list(2,"mu")
intercept(m) ## Examine intercepts of model (NA translates to free/unique paramete##r)
#> Intercept parameters:
#> y1 y2 y3
#> * 2 mu