Estimate parameters. MLE, IV or user-defined estimator.
# S3 method for lvm
estimate(
x,
data = parent.frame(),
estimator = NULL,
control = list(),
missing = FALSE,
weights,
weightsname,
data2,
id,
fix,
index = !quick,
graph = FALSE,
messages = lava.options()$messages,
quick = FALSE,
method,
param,
cluster,
p,
...
)lvm-object
data.frame
String defining the estimator (see details below)
control/optimization parameters (see details below)
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).
Optional weights to used by the chosen estimator.
Weights names (variable names of the model) in case
weights was given as a vector of column names of data
Optional additional dataset used by the chosen estimator.
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
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.)
For internal use only
For internal use only
Control how much information should be printed during estimation (0: none)
If TRUE the parameter estimates are calculated but all additional information such as standard errors are skipped
Optimization method
set parametrization (see help(lava.options))
Obsolete. Alias for 'id'.
Evaluate model in parameter 'p' (no optimization)
Additional arguments to be passed to lower-level functions
A lvmfit-object.
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:
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.
Starter-function with syntax
function(lvm, S, mu). Three builtin functions are available:
startvalues, startvalues0, startvalues1, ...
String defining which estimator to use (Defaults to
``gaussian'')
Logical variable indicating whether to fit model with meanstructure.
String pointing to
alternative optimizer (e.g. optim to use simulated annealing).
Parameters passed to the optimizer (default
stats::nlminb).
Tolerance of optimization constraints on lower limit of variance parameters.
estimate.default score, information
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
#> Estimate Std. Error Z-value P-value
#> Regressions:
#> y~v1 1.00086 0.01785 56.06986 <1e-12
#> y~v2 0.99394 0.01085 91.59488 <1e-12
#> y~v3 1.00627 0.01103 91.20677 <1e-12
#> y~v4 0.99443 0.01099 90.47824 <1e-12
#> v1~x 0.99205 0.01009 98.33296 <1e-12
#> v2~x 1.00074 0.01012 98.90608 <1e-12
#> v3~x 0.98866 0.01021 96.87999 <1e-12
#> v4~x 1.00562 0.00987 101.88095 <1e-12
#> Intercepts:
#> y 0.00609 0.01001 0.60833 0.543
#> v1 -0.00912 0.01001 -0.91110 0.3622
#> v2 0.00566 0.01004 0.56365 0.573
#> v3 -0.01290 0.01013 -1.27400 0.2027
#> v4 -0.01082 0.00980 -1.10458 0.2693
#> Residual Variances:
#> y 1.00204 0.01417 70.71068
#> v1 1.00257 0.01120 89.48388
#> v1~~v2 0.49732 0.00864 57.55755 <1e-12
#> v1~~v3 0.52040 0.00893 58.25942 <1e-12
#> v1~~v4 0.48340 0.00841 57.48707 <1e-12
#> v2 1.00842 0.01426 70.71068
#> v3 1.02582 0.01451 70.71068
#> v4 0.95968 0.01357 70.71068
## 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
#> Estimate Std. Error Z value Pr(>|z|)
#> Regressions:
#> y1~u 1.08224 0.08055 13.43642 <1e-12
#> y2~u 0.94896 0.06791 13.97290 <1e-12
#> y3~u 1.07444 0.06228 17.25066 <1e-12
#> u~x 1.12905 0.11653 9.68858 <1e-12
#> Intercepts:
#> y1 -0.08777 0.11215 -0.78267 0.4338
#> y2 0.01866 0.10486 0.17799 0.8587
#> y3 0.11980 0.09151 1.30921 0.1905
#> u -0.13072 0.10531 -1.24130 0.2145
#> Residual Variances:
#> y1 1.04880 0.16185 6.48015
#> y2 0.87854 0.13892 6.32385
#> y3 0.83338 0.11787 7.07023
#> u 1.10811 0.15672 7.07044