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# class definitions
#
# initial version: YR 25/03/2009
# added ModelSyntax: YR 02/08/2010
# deleted ModelSyntax: YR 01/11/2010 (using flattened model syntax now)
setClass("lavData",
representation(
data.type="character", # "full", "moment" or "none"
group="character", # group variable
ngroups="integer", # number of groups
group.label="character", # group labels
block.label="character", # block labels
cluster="character", # cluster variable(s)
nlevels="integer", # number of levels
level.label="character", # level labels
std.ov="logical", # standardize observed variables?
nobs="list", # effective number of observations
norig="list", # original number of observations
ov.names="list", # variable names (per group)
ov.names.x="list", # exo variable names (per group)
ov.names.l="list", # names per level
#ov.types="list", # variable types (per group)
#ov.idx="list", # column indices (all observed variables)
ordered="character", # ordered variables
weights="list", # sampling weights (per group)
sampling.weights="character", # sampling weights variable
ov="list", # variable table
case.idx="list", # case indices per group
missing="character", # "listwise" or not?
Mp="list", # if not complete, missing patterns
# we need this here, to get nobs right!
Rp="list", # response patterns (categorical only)
Lp="list", # level patterns
eXo="list", # local copy exo only
X="list" # local copy
)
)
setClass("lavSampleStats", # sample moments
representation(
var="list", # observed variances (per group)
cov="list", # observed var/cov matrix (per group)
mean="list", # observed mean vector (per group)
th="list", # thresholds for non-numeric var (per group)
th.idx="list", # th index (0 for numeric)
th.names="list", # threshold names
res.cov="list", # residual var/cov matrix (if conditional.x)
res.var="list", # residual variances
res.th="list", # residual thresholds
res.th.nox="list", # residual thresholds ignoring x
res.slopes="list", # slopes exo (if conditional.x)
res.int="list", # intercepts (if conditional.x)
mean.x="list", # mean exo
cov.x="list", # variance/covariance exo
bifreq="list", # bivariate frequency tables
group.w="list", # group weight
nobs="list", # effective number of obs (per group)
ntotal="numeric", # total number of obs (all groups)
ngroups="integer", # number of groups
x.idx="list", # x.idx if fixed.x = TRUE
icov="list", # inverse of observed cov (per group)
cov.log.det="list", # log det of observed cov (per group)
res.icov="list",
res.cov.log.det="list",
#ridge.constant="numeric", # ridge constant (per group)
#ridge.constant.x="numeric",# ridge constant (per group) for eXo
ridge="numeric",
WLS.obs="list", # all relevant observed stats in a vector
WLS.V="list", # weight matrix for GLS/WLS
WLS.VD="list", # diagonal of weight matrix only
NACOV="list", # N times the asymptotic covariance matrix
NACOV.user="logical", # user-specified NACOV?
missing.flag="logical", # missing patterns?
missing="list", # missingness information
missing.h1="list", # h1 model
YLp = "list", # cluster/level information
zero.cell.tables="list" # bivariate tables with empty cells
)
)
setClass("lavModel", # MATRIX representation of the sem model
representation(
GLIST="list", # list of all model matrices (for all groups)
dimNames="list", # dim names for the model matrices
isSymmetric="logical", # model matrix symmetric?
mmSize="integer", # model matrix size (unique only)
representation="character", # stub, until we define more classes
modprop="list", # model properties
meanstructure="logical",
correlation="logical",
categorical="logical",
multilevel="logical",
group.w.free="logical",
link="character",
nblocks="integer",
ngroups="integer", # only for rsem!! (which uses rsem:::computeDelta)
nefa="integer",
nmat="integer",
nvar="integer",
num.idx="list",
th.idx="list",
nx.free="integer",
nx.unco="integer",
nx.user="integer",
m.free.idx="list",
x.free.idx="list",
#m.unco.idx="list", # always the same as m.free.idx
x.unco.idx="list",
m.user.idx="list",
x.user.idx="list",
x.def.idx="integer",
x.ceq.idx="integer",
x.cin.idx="integer",
x.free.var.idx="integer",
ceq.simple.only="logical",
ceq.simple.K="matrix",
eq.constraints="logical",
eq.constraints.K="matrix",
eq.constraints.k0="numeric",
def.function="function",
ceq.function="function",
ceq.jacobian="function",
ceq.JAC="matrix",
ceq.rhs="numeric",
ceq.linear.idx="integer",
ceq.nonlinear.idx="integer",
cin.function="function",
cin.jacobian="function",
cin.JAC="matrix",
cin.rhs="numeric",
cin.linear.idx="integer",
cin.nonlinear.idx="integer",
ceq.efa.JAC="matrix",
con.jac="matrix",
con.lambda="numeric",
nexo="integer",
conditional.x="logical",
fixed.x="logical",
parameterization="character",
ov.x.dummy.ov.idx="list",
ov.x.dummy.lv.idx="list",
ov.y.dummy.ov.idx="list",
ov.y.dummy.lv.idx="list",
ov.efa.idx="list",
lv.efa.idx="list",
rv.ov="list",
rv.lv="list",
H="list",
lv.order="list",
estimator="character",
estimator.args="list"
)
)
setClass("Fit",
representation(
npar="integer", # number of free parameters
#ndat="integer",
#df="integer",
x="numeric", # x
partrace="matrix", # parameter trace
start="numeric", # starting values (only for other packages)
est="numeric", # estimated values (only for other packages)
se="numeric", # standard errors
fx="numeric",
fx.group="numeric",
logl="numeric",
logl.group="numeric",
iterations="integer", # number of iterations
converged="logical",
control="list",
Sigma.hat="list",
Mu.hat="list",
TH="list",
test="list"
)
)
setClass("lavaan",
representation(
version = "character", # lavaan version
call = "call", # matched call
timing = "list", # timing information
Options = "list", # lavOptions
ParTable = "list", # parameter table user-specified model
pta = "list", # parameter table attributes
Data = "lavData", # full data
SampleStats = "lavSampleStats", # sample statistics
Model = "lavModel", # internal matrix representation
Cache = "list", # housekeeping stuff
Fit = "Fit", # fitted results
boot = "list", # bootstrap results
optim = "list", # optimizer results
loglik = "list", # loglik values and info
implied = "list", # model implied moments
vcov = "list", # vcov
test = "list", # test
h1 = "list", # unrestricted model results
baseline = "list", # baseline model results
internal = "list", # optional slot, for internal use
external = "list" # optional slot, for add-on packages
)
)
setClass("lavaanList",
representation(
call = "call", # matched call
Options = "list", # lavOptions
ParTable = "list",
pta = "list",
Data = "lavData", # from first dataset (ngroups!)
Model = "lavModel", # based on first dataset
meta = "list",
timingList = "list",
ParTableList = "list",
DataList = "list",
SampleStatsList = "list",
CacheList = "list",
vcovList = "list",
testList = "list",
optimList = "list",
impliedList = "list",
h1List = "list",
loglikList = "list",
baselineList = "list",
internalList = "list",
funList = "list",
external = "list" # optional slot, for add-on packages
)
)
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