Define linear constraints on intercept parameters in a lvm-object.

# S3 method for lvm
intercept(object, vars, ...) <- value

Arguments

object

lvm-object

...

Additional arguments

vars

character vector of variable names

value

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).

Value

A lvm-object

Details

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.

Note

Variables will be added to the model if not already present.

See also

covariance<-, regression<-, constrain<-, parameter<-, latent<-, cancel<-, kill<-

Author

Klaus K. Holst

Examples

## 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