iid2 {lavaSearch2}

Influence Function With Small Sample Correction.

Description

Extract the influence function from a latent variable model. It is similar to lava::iid but with small sample correction.

Usage

iid2(object, ...)

## S3 method for class 'lvmfit'
iid2(
  object,
  robust = TRUE,
  cluster = NULL,
  as.lava = TRUE,
  ssc = lava.options()$ssc,
  ...
)

## S3 method for class 'lvmfit2'
iid2(object, robust = TRUE, cluster = NULL, as.lava = TRUE, ...)

## S3 method for class 'lvmfit2'
iid(x, robust = TRUE, cluster = NULL, as.lava = TRUE, ...)

Arguments

object, x

a lvmfit or lvmfit2 object (i.e. output of lava::estimate or lavaSearch2::estimate2).

...

additional argument passed to estimate2 when using a lvmfit object.

robust

[logical] if FALSE, the influence function is rescaled such its the squared sum equals the model-based standard error (instead of the robust standard error). Do not match the model-based correlation though.

cluster

[integer vector] the grouping variable relative to which the observations are iid.

as.lava

[logical] if TRUE, uses the same names as when using stats::coef.

ssc

[character] method used to correct the small sample bias of the variance coefficients ("none", "residual", "cox"). Only relevant when using a lvmfit object.

Details

When argument object is a lvmfit object, the method first calls estimate2 and then extract the variance-covariance matrix.

Value

A matrix containing the 1st order influence function relative to each sample (in rows) and each model coefficient (in columns).

See Also

estimate2 to obtain lvmfit2 objects.

Examples

#### simulate data ####
n <- 5e1
p <- 3
X.name <- paste0("X",1:p)
link.lvm <- paste0("Y~",X.name)
formula.lvm <- as.formula(paste0("Y~",paste0(X.name,collapse="+")))

m <- lvm(formula.lvm)
distribution(m,~Id) <- Sequence.lvm(0)
set.seed(10)
d <- sim(m,n)

#### latent variable model ####
e.lvm <- estimate(lvm(formula.lvm),data=d)
iid.tempo <- iid2(e.lvm)