Define regression association between variables in a lvm-object and define linear constraints between model equations.

# S3 method for lvm
regression(object = lvm(), to, from, fn = NA,
messages = lava.options()\$messages, additive=TRUE, y, x, value, ...)
# S3 method for lvm
regression(object, to=NULL, quick=FALSE, ...) <- value

## Arguments

object

lvm-object.

...

Additional arguments to be passed to the low level functions

value

A formula specifying the linear constraints or if to=NULL a list of parameter values.

to

Character vector of outcome(s) or formula object.

from

Character vector of predictor(s).

fn

Real function defining the functional form of predictors (for simulation only).

messages

Controls which messages are turned on/off (0: all off)

If FALSE and predictor is categorical a non-additive effect is assumed

y

Alias for 'to'

x

Alias for 'from'

quick

Faster implementation without parameter constraints

## Value

A lvm-object

## Details

The regression function is used to specify linear associations between variables of a latent variable model, and offers formula syntax resembling the model specification of e.g. lm.

For instance, to add the following linear regression model, to the lvm-object, m: $$E(Y|X_1,X_2) = \beta_1 X_1 + \beta_2 X_2$$ We can write

regression(m) <- y ~ x1 + x2

Multivariate models can be specified by successive calls with regression, but multivariate formulas are also supported, e.g.

regression(m) <- c(y1,y2) ~ x1 + x2

defines $$E(Y_i|X_1,X_2) = \beta_{1i} X_1 + \beta_{2i} X_2$$

The special function, f, can be used in the model specification to specify linear constraints. E.g. to fix $$\beta_1=\beta_2$$ , we could write

regression(m) <- y ~ f(x1,beta) + f(x2,beta)

The second argument of f can also be a number (e.g. defining an offset) or be set to NA in order to clear any previously defined linear constraints.

Alternatively, a more straight forward notation can be used:

regression(m) <- y ~ beta*x1 + beta*x2

All the parameter values of the linear constraints can be given as the right handside expression of the assigment function regression<- (or regfix<-) if the first (and possibly second) argument is defined as well. E.g:

regression(m,y1~x1+x2) <- list("a1","b1")

defines $$E(Y_1|X_1,X_2) = a1 X_1 + b1 X_2$$. The rhs argument can be a mixture of character and numeric values (and NA's to remove constraints).

The function regression (called without additional arguments) can be used to inspect the linear constraints of a lvm-object.

## Note

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

Klaus K. Holst

## Examples


m <- lvm() ## Initialize empty lvm-object
### E(y1|z,v) = beta1*z + beta2*v
regression(m) <- y1 ~ z + v
### E(y2|x,z,v) = beta*x + beta*z + 2*v + beta3*u
regression(m) <- y2 ~ f(x,beta) + f(z,beta)  + f(v,2) + u
### Clear restriction on association between y and
### fix slope coefficient of u to beta
regression(m, y2 ~ v+u) <- list(NA,"beta")

regression(m) ## Examine current linear parameter constraints
#> Regression parameters:
#>       y1 z    v y2 x    u
#>    y1    *    *
#>    y2    beta *    beta beta

## ## A multivariate model, E(yi|x1,x2) = beta[1i]*x1 + beta[2i]*x2:
m2 <- lvm(c(y1,y2) ~ x1+x2)