gaussian_weight {lavaSearch2} | R Documentation |
Estimate LVM With Weights
Description
Estimate LVM with weights.
Usage
gaussian_weight.estimate.hook(x, data, estimator, ...)
gaussian_weight_method.lvm
gaussian_weight_logLik.lvm(object, type = "cond", p, data, weights, ...)
gaussian_weight_objective.lvm(x, ...)
gaussian_weight_score.lvm(
x,
data,
p,
S,
n,
mu = NULL,
weights = NULL,
debug = FALSE,
reindex = FALSE,
mean = TRUE,
constrain = TRUE,
indiv = FALSE,
...
)
gaussian_weight_gradient.lvm(...)
gaussian_weight_hessian.lvm(x, p, n, weights = NULL, ...)
Arguments
x , object |
A latent variable model |
data |
dataset |
estimator |
name of the estimator to be used |
... |
passed to lower level functions. |
type |
must be "cond" |
p |
parameter value |
weights |
weight associated to each iid replicate. |
S |
empirical variance-covariance matrix between variable |
n |
number of iid replicates |
mu |
empirical mean |
debug , reindex , mean , constrain , indiv |
additional arguments not used |
Format
An object of class character
of length 1.
Examples
#### linear regression with weights ####
## data
df <- data.frame(Y = c(1,2,2,1,2),
X = c(1,1,2,2,2),
missing = c(0,0,0,0,1),
weights = c(1,1,2,1,NA))
## using lm
e.lm.GS <- lm(Y~X, data = df)
e.lm.test <- lm(Y~X, data = df[df$missing==0,], weights = df[df$missing==0,"weights"])
## using lvm
m <- lvm(Y~X)
e.GS <- estimate(m, df)
## e.lava.test <- estimate(m, df[df$missing==0,], weights = df[df$missing==0,"weights"])
## warnings!!
e.test <- estimate(m, data = df[df$missing==0,],
weights = df[df$missing==0,"weights"],
estimator = "gaussian_weight")
[Package lavaSearch2 version 2.0.3 Index]