The lavInspect() and lavTech() functions can be used to
inspect/extract information that is stored inside (or can be computed from) a
fitted lavaan object. Note: the (older) inspect() function is
now simply a shortcut for lavInspect() with default arguments.
lavInspect(object, what = "free", add.labels = TRUE, add.class = TRUE,
list.by.group = TRUE,
drop.list.single.group = TRUE)lavTech(object, what = "free", add.labels = FALSE, add.class = FALSE,
list.by.group = FALSE,
drop.list.single.group = FALSE)
inspect(object, what = "free", ...)
An object of class lavaan.
Character. What needs to be inspected/extracted? See Details for a
full list. Note: the what argument is not case-sensitive
(everything is converted to lower case.)
If TRUE, variable names are added to the vectors
and/or matrices.
If TRUE, vectors are given the `lavaan.vector' class;
matrices are given the `lavaan.matrix' class, and symmetric matrices are
given the `lavaan.matrix.symmetric' class. This only affects the way they
are printed on the screen.
Logical. Only used when the output are model matrices.
If TRUE, the model matrices are nested within groups. If FALSE,
a flattened list is returned containing all model matrices, with repeated
names for multiple groups.
If FALSE, the results are returned as
a list, where each element corresponds to a group (even if there is only
a single group). If TRUE, the list will be unlisted if there is
only a single group.
Additional arguments. Not used by lavaan, but by other packages.
The lavInspect() and lavTech() functions only differ in the way
they return the results. The lavInspect() function will prettify the
output by default, while the lavTech() will not attempt to prettify the
output by default. The (older) inspect() function is a simplified
version of lavInspect() with only the first two arguments.
Below is a list of possible values for the what argument, organized
in several sections:
Model matrices:
"free":A list of model matrices. The non-zero integers
represent the free parameters. The numbers themselves correspond
to the position of the free parameter in the parameter vector.
This determines the order of the model parameters in the output
of for example coef() and vcov().
"partable":A list of model matrices. The non-zero integers
represent both the fixed parameters (for example, factor loadings
fixed at 1.0), and the free parameters if we ignore any equality
constraints. They correspond with all entries (fixed or free)
in the parameter table. See parTable.
"se":A list of model matrices. The non-zero numbers
represent the standard errors for the free parameters in the model.
If two parameters are constrained to be equal, they will have the
same standard error for both parameters.
Aliases: "std.err" and "standard.errors".
"start":A list of model matrices. The values represent
the starting values for all model parameters.
Alias: "starting.values".
"est":A list of model matrices. The values represent
the estimated model parameters. Aliases:
"estimates", and "x".
"dx.free":A list of model matrices. The values represent the gradient (first derivative) values of the model parameters. If two parameters are constrained to be equal, they will have the same gradient value.
"dx.all":A list of model matrices. The values represent
the first derivative with respect to all possible matrix elements.
Currently, this is only available when the estimator is "ML"
or "GLS".
"std":A list of model matrices. The values represent
the (completely) standardized model parameters (the variances of
both the observed and the latent variables are set to unity).
Aliases: "std.all", "standardized".
"std.lv":A list of model matrices. The values represent the standardized model parameters (only the variances of the latent variables are set to unity.)
"std.nox":A list of model matrices. The values represent the (completely) standardized model parameters (the variances of both the observed and the latent variables are set to unity; however, the variances of any observed exogenous variables are not set to unity; hence no-x.)
Information about the data:
"data":A matrix containing the observed variables
that have been used to fit the model. No column/row names are provided.
Column names correspond to the output of lavNames(object),
while the rows correspond to the output of
lavInspect(object, "case.idx".
"ordered":A character vector. The ordered variables.
"nobs":Integer vector. The number of observations in each group that were used in the analysis.
"norig":Integer vector. The original number of observations in each group.
"ntotal":Integer. The total number of observations that
were used in the analysis. If there is just a single group, this
is the same as the "nobs" option; if there are multiple groups,
this is the sum of the "nobs" numbers for each group.
"case.idx":Integer vector. The case/observation numbers that were used in the analysis. In the case of multiple groups: a list of numbers.
"empty.idx":The case/observation numbers of those cases/observations that contained missing values only (at least for the observed variables that were included in the model). In the case of multiple groups: a list of numbers.
"patterns":A binary matrix. The rows of the matrix
are the missing data patterns where 1 and 0 denote non-missing
and missing values for the corresponding observed variables
respectively (or
TRUE and FALSE if lavTech() is used.)
If the data is complete (no missing values), there will be only
a single pattern. In the case of multiple groups: a list of
pattern matrices.
"coverage":A symmetric matrix where each element contains the proportion of observed datapoints for the corresponding pair of observed variables. In the case of multiple groups: a list of coverage matrices.
"group":A character string. The group variable in the data.frame (if any).
"ngroups":Integer. The number of groups.
"group.label":A character vector. The group labels.
"level.label":A character vector. The level labels.
"cluster":A character vector. The cluster variable(s) in the data.frame (if any).
"nlevels":Integer. The number of levels.
"nclusters":Integer. The number of clusters that were used in the analysis.
"ncluster.size":Integer. The number of different cluster sizes.
"cluster.size":Integer vector. The number of observations within each cluster. For multigroup multilevel models, a list of integer vectors, indicating cluster sizes within each group.
"cluster.id":Integer vector. The cluster IDs identifying the clusters. For multigroup multilevel models, a list of integer vectors, indicating cluster IDs within each group.
"cluster.idx":Integer vector. The cluster index for each observation. The cluster index ranges from 1 to the number of clusters. For multigroup multilevel models, a list of integer vectors, indicating cluster indices within each group.
"cluster.label":Integer vector. The cluster ID for each observation. For multigroup multilevel models, a list of integer vectors, indicating the cluster ID for each observation within each group.
"cluster.sizes":Integer vector. The different cluster sizes that were used in the analysis. For multigroup multilevel models, a list of integer vectors, indicating the different cluster sizes within each group.
"average.cluster.size":Integer. The average cluster
size (using the formula
s = (N^2 - sum(cluster.size^2)) / (N*(nclusters - 1L))).
For multigroup multilevel
models, a list containing the average cluster size per group.
Observed sample statistics:
"sampstat":Observed sample statistics. Aliases:
"obs", "observed", "samp", "sample",
"samplestatistics". Since
0.6-3, we always check if an h1 slot is available (the estimates
for the unrestricted model); if present, we extract the sample
statistics from this slot. This implies that if variables are
continuous, and missing = "ml" (or "fiml"), we
return the covariance matrix (and mean vector) as computed by
the EM algorithm under the unrestricted (h1) model. If the h1 is
not present (perhaps, because the model was fitted with
h1 = FALSE), we return the sample statistics from the
SampleStats slot. Here, pairwise deletion is used for the elements
of the covariance matrix (or correlation matrix), and
listwise deletion for all univariate statistics (means, intercepts
and thresholds).
"sampstat.h1":Deprecated. Do not use any longer.
"wls.obs":The observed sample statistics (covariance elements, intercepts/thresholds, etc.) in a single vector.
"wls.v":The weight vector as used in weighted least squares estimation.
"gamma":N times the asymptotic variance matrix of the
sample statistics. Alias: "sampstat.nacov".
Model features:
"meanstructure":Logical. TRUE if a meanstructure
was included in the model.
"categorical":Logical. TRUE if categorical endogenous
variables were part of the model.
"fixed.x":Logical. TRUE if the exogenous x-covariates
are treated as fixed.
"parameterization":Character. Either "delta" or
"theta".
Model-implied sample statistics:
"implied":The model-implied summary statistics.
Alias: "fitted", "expected", "exp".
"resid":The difference between observed and model-implied
summary statistics.
Alias: "residuals", "residual", "res".
"cov.lv":The model-implied variance-covariance matrix
of the latent variables. Alias: "veta" [for V(eta)].
"cor.lv":The model-implied correlation matrix of the latent variables.
"mean.lv":The model-implied mean vector of the latent
variables. Alias: "eeta" [for E(eta)].
"cov.ov":The model-implied variance-covariance matrix
of the observed variables.
Aliases: "sigma", "sigma.hat".
"cor.ov":The model-implied correlation matrix of the observed variables.
"mean.ov":The model-implied mean vector of the observed
variables. Aliases: "mu", "mu.hat".
"cov.all":The model-implied variance-covariance matrix of both the observed and latent variables.
"cor.all":The model-implied correlation matrix of both the observed and latent variables.
"th":The model-implied thresholds.
Alias: "thresholds".
"wls.est":The model-implied sample statistics (covariance elements, intercepts/thresholds, etc.) in a single vector.
"vy":The model-implied unconditional variances of the observed variables.
"rsquare":The R-square value for all endogenous variables.
Aliases: "r-square", "r2".
"fs.determinacy":The factor determinacies (based on regression factor scores). They represent the (estimated) correlation between the factor scores and the latent variables scores.
"fs.reliability":The factor reliabilities (based on regression factor scores). They are the square of the factor determinacies.
"fs.determinacy.Bartlett":The factor determinacies (based on Bartlett factor scores). They represent the (estimated) correlation between the factor scores and the latent variables scores.
"fs.reliability.Bartlett":The factor reliabilities (based on Bartlett factor scores). They are the square of the factor determinacies.
Diagnostics:
"mdist2.fs":The squared Mahalanobis distances for the (Bartlett) factor scores.
"mdist.fs":The Mahalanobis distances for the (Bartlett) factor scores.
"mdist2.resid":The squared Mahalanobis distances for the (Bartlett-based) casewise residuals.
"mdist.fs":The Mahalanobis distances for the (Bartlett-based) casewise residuals.
Optimizer information:
"converged":Logical. TRUE if the optimizer has
converged; FALSE otherwise.
"iteratons":Integer. The number of iterations used by the optimizer.
"optim":List. All available information regarding the optimization results.
"npar":Integer. Number of free parameters (ignoring constraints).
Gradient, Hessian, observed, expected and first.order information matrices:
"gradient":Numeric vector containing the first derivatives of the discrepancy function with respect to the (free) model parameters.
"hessian":Matrix containing the second derivatives of the discrepancy function with respect to the (free) model parameters.
"information":Matrix containing either the observed or the expected information matrix (depending on the information option of the fitted model). This is unit-information, not total-information.
"information.expected":Matrix containing the expected information matrix for the free model parameters.
"information.observed":Matrix containing the observed information matrix for the free model parameters.
"information.first.order":Matrix containing the first.order
information matrix for the free model parameters. This is the
outer product of the gradient elements (the first derivative of
the discrepancy function with respect to the (free) model parameters).
Alias: "first.order".
"augmented.information":Matrix containing either the observed or the expected augmented (or bordered) information matrix (depending on the information option of the fitted model. Only relevant if constraints have been used in the model.
"augmented.information.expected":Matrix containing the expected augmented (or bordered) information matrix. Only relevant if constraints have been used in the model.
"augmented.information.observed":Matrix containing the observed augmented (or bordered) information matrix. Only relevant if constraints have been used in the model.
"augmented.information.first.order":Matrix containing the first.order augmented (or bordered) information matrix. Only relevant if constraints have been used in the model.
"inverted.information":Matrix containing either the observed or the expected inverted information matrix (depending on the information option of the fitted model.
"inverted.information.expected":Matrix containing the inverted expected information matrix for the free model parameters.
"inverted.information.observed":Matrix containing the inverted observed information matrix for the free model parameters.
"inverted.information.first.order":Matrix containing the inverted first.order information matrix for the free model parameters.
"h1.information":Matrix containing either the observed, expected or first.order information matrix (depending on the information option of the fitted model) of the unrestricted h1 model. This is unit-information, not total-information.
"h1.information.expected":Matrix containing the expected information matrix for the unrestricted h1 model.
"h1.information.observed":Matrix containing the observed information matrix for the unrestricted h1 model.
"h1.information.first.order":Matrix containing the
first.order information matrix for the the unrestricted h1 model.
Alias: "h1.first.order".
Variance covariance matrix of the model parameters:
"vcov":Matrix containing the variance covariance matrix of the estimated model parameters.
"vcov.std.all":Matrix containing the variance covariance matrix of the standardized estimated model parameters. Standardization is done with respect to both observed and latent variables.
"vcov.std.lv":Matrix containing the variance covariance matrix of the standardized estimated model parameters. Standardization is done with respect to the latent variables only.
"vcov.std.nox":Matrix containing the variance covariance matrix of the standardized estimated model parameters. Standardization is done with respect to both observed and latent variables, but ignoring any exogenous observed covariates.
"vcov.def":Matrix containing the variance covariance matrix of the user-defined (using the := operator) parameters.
"vcov.def.std.all":Matrix containing the variance covariance matrix of the standardized user-defined parameters. Standardization is done with respect to both observed and latent variables.
"vcov.def.std.lv":Matrix containing the variance covariance matrix of the standardized user-defined parameters. Standardization is done with respect to the latent variables only.
"vcov.def.std.nox":Matrix containing the variance covariance matrix of the standardized user-defined parameters. Standardization is done with respect to both observed and latent variables, but ignoring any exogenous observed covariates.
"vcov.def.joint":Matrix containing the joint variance covariance matrix of both the estimated model parameters and the defined (using the := operator) parameters.
"vcov.def.joint.std.all":Matrix containing the joint variance covariance matrix of both the standardized model parameters and the user-defined parameters. Standardization is done with respect to both observed and latent variables.
"vcov.def.joint.std.lv":Matrix containing the joint variance covariance matrix of both the standardized model parameters and the user-defined parameters. Standardization is done with respect to the latent variables only.
"vcov.def.joint.std.nox":Matrix containing the joint variance covariance matrix of both the standardized model parameters and the user-defined parameters. Standardization is done with respect to both observed and latent variables, but ignoring any exogenous observed covariates.
Miscellaneous:
"coef.boot":Matrix containing estimated model parameters for for each bootstrap sample. Only relevant when bootstrapping was used.
"UGamma":Matrix containing the product of 'U' and 'Gamma' matrices as used by the Satorra-Bentler correction. The trace of this matrix, divided by the degrees of freedom, gives the scaling factor.
"UfromUGamma":Matrix containing the 'U' matrix
as used by the Satorra-Bentler correction. Alias: "U".
"list":The parameter table. The same output as given
by parTable().
"fit":The fit measures. Aliases: "fitmeasures",
"fit.measures", "fit.indices". The same output as
given by fitMeasures().
"mi":The modification indices. Alias: "modindices",
"modification.indices". The same output as given
by modindices().
"loglik.casewise":Vector containing the casewise
loglikelihood contributions. Only available if estimator = "ML".
"options":List. The option list.
"call":List. The call as returned by match.call, coerced to a list.
"timing":List. The timing (in milliseconds) of various lavaan subprocedures.
"test":List. All available information regarding the (goodness-of-fit) test statistic(s).
"baseline.test":List. All available information regarding the (goodness-of-fit) test statistic(s) of the baseline model.
"baseline.partable":Data.frame. The parameter table of the (internal) baseline model.
"post.check":Post-fitting check if the solution is
admissible. A warning is raised if negative variances are found, or if
either lavInspect(fit, "cov.lv") or
lavInspect(fit, "theta") return a non-positive definite matrix.
"zero.cell.tables":List. List of bivariate frequency tables where at least one cell is empty.
"version":The lavaan version number that was used to construct the fitted lavaan object.
lavaan
# fit model
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data = HolzingerSwineford1939, group = "school")
# extract information
lavInspect(fit, "sampstat")
lavTech(fit, "sampstat")
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