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

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

## Arguments

object lvm-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).

## 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

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

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