estimate.lvm {lava} | R Documentation

## Estimation of parameters in a Latent Variable Model (lvm)

### Description

Estimate parameters. MLE, IV or user-defined estimator.

### Usage

    ## S3 method for class 'lvm'
    estimate(x, data = parent.frame(), estimator = NULL,
      control = list(), missing = FALSE, weights, weightsname, data2, id, fix,
      index = !quick, graph = FALSE, silent = lava.options()$silent,
      quick = FALSE, method, param, cluster, p, ...)

### Arguments

`x` | `lvm`-object
`data` | `data.frame`
`estimator` | String defining the estimator (see details below)
`control` | control/optimization parameters (see details below)
`missing` | Logical variable indiciating how to treat missing data. Setting to FALSE leads to complete case analysis. In the other case likelihood based inference is obtained by integrating out the missing data under assumption the assumption that data is missing at random (MAR).
`weights` | Optional weights to used by the chosen estimator.
`weightsname` | Weights names (variable names of the model) in case `weights` was given as a vector of column names of `data`
`data2` | Optional additional dataset used by the chosen estimator.
`id` | Vector (or name of column in `data`) that identifies correlated groups of observations in the data leading to variance estimates based on a sandwich estimator
`fix` | Logical variable indicating whether parameter restriction automatically should be imposed (e.g. intercepts of latent variables set to 0 and at least one regression parameter of each measurement model fixed to ensure identifiability.)
`index` | For internal use only
`graph` | For internal use only
`silent` | Logical argument indicating whether information should be printed during estimation
`quick` | If TRUE the parameter estimates are calculated but all additional information such as standard errors are skipped
`method` | Optimization method
`param` | set parametrization (see `help(lava.options)`)
`cluster` | Obsolete. Alias for 'id'.
`p` | Evaluate model in parameter 'p' (no optimization)
`...` | Additional arguments to be passed to the low level functions

### Details

A list of parameters controlling the estimation and optimization procedures is parsed via the `control` argument. By default Maximum Likelihood is used assuming multivariate normal distributed measurement errors. A list with one or more of the following elements is expected:

start:

  Starting value. The order of the parameters can be shown by calling `coef` (with `mean=TRUE`) on the `lvm`-object or with `plot(..., labels=TRUE)`. Note that this requires a check that it is actual the model being estimated, as `estimate` might add additional restriction to the model, e.g. through the `fix` and `exo.fix` arguments. The `lvm`-object of a fitted model can be extracted with the `Model`-function.

starterfun:

  Starter-function with syntax `function(lvm, S, mu)`. Three builtin functions are available: `startvalues`, `startvalues0`, `startvalues1`, ...

estimator:

  String defining which estimator to use (Defaults to “`gaussian`”)

meanstructure

  Logical variable indicating whether to fit model with meanstructure.

method:

  String pointing to alternative optimizer (e.g. `optim` to use simulated annealing).

control:

  Parameters passed to the optimizer (default `stats::nlminb`).

tol:

  Tolerance of optimization constraints on lower limit of variance parameters.

### Value

A `lvmfit`-object.

### Author(s)

Klaus K. Holst

### See Also

estimate.default score, information

### Examples

    dd <- read.table(header=TRUE,
    text="x1 x2 x3
     0.0 -0.5 -2.5
    -0.5 -2.0  0.0
     1.0  1.5  1.0
     0.0  0.5  0.0
    -2.5 -1.5 -1.0")
    e <- estimate(lvm(c(x1,x2,x3)~u),dd)

    ## Simulation example
    m <- lvm(list(y~v1+v2+v3+v4,c(v1,v2,v3,v4)~x))
    covariance(m) <- v1~v2+v3+v4
    dd <- sim(m,10000) ## Simulate 10000 observations from model
    e <- estimate(m, dd) ## Estimate parameters
    e

    ## Using just sufficient statistics
    n <- nrow(dd)
    e0 <- estimate(m,data=list(S=cov(dd)*(n-1)/n,mu=colMeans(dd),n=n))
    rm(dd)

    ## Multiple group analysis
    m <- lvm()
    regression(m) <- c(y1,y2,y3)~u
    regression(m) <- u~x
    d1 <- sim(m,100,p=c("u,u"=1,"u~x"=1))
    d2 <- sim(m,100,p=c("u,u"=2,"u~x"=-1))

    mm <- baptize(m)
    regression(mm,u~x) <- NA
    covariance(mm,~u) <- NA
    intercept(mm,~u) <- NA
    ee <- estimate(list(mm,mm),list(d1,d2))

    ## Missing data
    d0 <- makemissing(d1,cols=1:2)
    e0 <- estimate(m,d0,missing=TRUE)
    e0

[Package lava version 1.5.1 Index]