model.syntax: The Lavaan Model Syntax

In lavaan: Latent Variable Analysis

The Lavaan Model Syntax

Description

The lavaan model syntax describes a latent variable model. The function lavaanify turns it into a table that represents the full model as specified by the user. We refer to this table as the parameter table.

Usage

lavaanify(model = NULL, meanstructure = FALSE, int.ov.free = FALSE, 
    int.lv.free = FALSE, marker.int.zero = FALSE,
    orthogonal = FALSE, orthogonal.y = FALSE,
    orthogonal.x = FALSE, orthogonal.efa = FALSE, std.lv = FALSE,
    correlation = FALSE, effect.coding = "", conditional.x = FALSE, 
    fixed.x = FALSE, parameterization = "delta", constraints = NULL, 
    ceq.simple = FALSE, auto = FALSE, model.type = "sem", 
    auto.fix.first = FALSE, auto.fix.single = FALSE, auto.var = FALSE, 
    auto.cov.lv.x = FALSE, auto.cov.y = FALSE, auto.th = FALSE, 
    auto.delta = FALSE, auto.efa = FALSE, 
    varTable = NULL, ngroups = 1L, nthresholds = NULL, 
    group.equal = NULL, group.partial = NULL, group.w.free = FALSE,
    debug = FALSE, warn = TRUE, as.data.frame. = TRUE)

lavParTable(model = NULL, meanstructure = FALSE, int.ov.free = FALSE,
    int.lv.free = FALSE, marker.int.zero = FALSE,
    orthogonal = FALSE, orthogonal.y = FALSE, 
    orthogonal.x = FALSE, orthogonal.efa = FALSE, std.lv = FALSE,
    correlation = FALSE, effect.coding = "", conditional.x = FALSE, 
    fixed.x = FALSE, parameterization = "delta", constraints = NULL, 
    ceq.simple = FALSE, auto = FALSE, model.type = "sem",
    auto.fix.first = FALSE, auto.fix.single = FALSE, auto.var = FALSE,
    auto.cov.lv.x = FALSE, auto.cov.y = FALSE, auto.th = FALSE,
    auto.delta = FALSE, auto.efa = FALSE,
    varTable = NULL, ngroups = 1L, nthresholds = NULL,
    group.equal = NULL, group.partial = NULL, group.w.free = FALSE,
    debug = FALSE, warn = TRUE, as.data.frame. = TRUE)

lavParseModelString(model.syntax = '', as.data.frame. = FALSE,
                    parser = "new", warn = TRUE, debug = FALSE)

Arguments

model	A description of the user-specified model. Typically, the model is described using the lavaan model syntax; see details for more information. Alternatively, a parameter table (e.g., the output of lavParseModelString is also accepted.
model.syntax	The model syntax specifying the model. Must be a literal string.
meanstructure	If TRUE, intercepts/means will be added to the model both for both observed and latent variables.
int.ov.free	If FALSE, the intercepts of the observed variables are fixed to zero.
int.lv.free	If FALSE, the intercepts of the latent variables are fixed to zero.
marker.int.zero	Logical. Only relevant if the metric of each latent variable is set by fixing the first factor loading to unity. If TRUE, it implies meanstructure = TRUE and std.lv = FALSE, and it fixes the intercepts of the marker indicators to zero, while freeing the means/intercepts of the latent variables. Only works correcly for single group, single level models.
orthogonal	If TRUE, all covariances among latent variables are set to zero.
orthogonal.y	If TRUE, all covariances among endogenous latent variables only are set to zero.
orthogonal.x	If TRUE, all covariances among exogenous latent variables only are set to zero.
orthogonal.efa	If TRUE, all covariances among latent variables involved in rotation only are set to zero.
std.lv	If TRUE, the metric of each latent variable is determined by fixing their variances to 1.0. If FALSE, the metric of each latent variable is determined by fixing the factor loading of the first indicator to 1.0. If there are multiple groups, std.lv = TRUE and "loadings" is included in the group.label argument, then only the latent variances i of the first group will be fixed to 1.0, while the latent variances of other groups are set free.
correlation	If TRUE, a correlation structure is fitted. For continuous data, this implies that the (residual) variances are no longer parameters of the model.
effect.coding	Can be logical or character string. If logical and TRUE, this implies effect.coding = c("loadings", "intercepts"). If logical and FALSE, it is set equal to the empty string. If "loadings" is included, equality constraints are used so that the average of the factor loadings (per latent variable) equals 1. Note that this should not be used together with std.lv = TRUE. If "intercepts" is included, equality constraints are used so that the sum of the intercepts (belonging to the indicators of a single latent variable) equals zero. As a result, the latent mean will be freely estimated and usually equal the average of the means of the involved indicators.
conditional.x	If TRUE, we set up the model conditional on the exogenous ‘x’ covariates; the model-implied sample statistics only include the non-x variables. If FALSE, the exogenous ‘x’ variables are modeled jointly with the other variables, and the model-implied statistics refect both sets of variables.
fixed.x	If TRUE, the exogenous ‘x’ covariates are considered fixed variables and the means, variances and covariances of these variables are fixed to their sample values. If FALSE, they are considered random, and the means, variances and covariances are free parameters.
parameterization	Currently only used if data is categorical. If "delta", the delta parameterization is used. If "theta", the theta parameterization is used.
constraints	Additional (in)equality constraints. See details for more information.
ceq.simple	If TRUE, and no other general constraints are used in the model, simple equality constraints are represented in the parameter table as duplicated free parameters (instead of extra rows with op = "==").
auto	If TRUE, the default values are used for the auto.* arguments, depending on the value of model.type.
model.type	Either "sem" or "growth"; only used if auto=TRUE.
auto.fix.first	If TRUE, the factor loading of the first indicator is set to 1.0 for every latent variable.
auto.fix.single	If TRUE, the residual variance (if included) of an observed indicator is set to zero if it is the only indicator of a latent variable.
auto.var	If TRUE, the (residual) variances of both observed and latent variables are set free.
auto.cov.lv.x	If TRUE, the covariances of exogenous latent variables are included in the model and set free.
auto.cov.y	If TRUE, the covariances of dependent variables (both observed and latent) are included in the model and set free.
auto.th	If TRUE, thresholds for limited dependent variables are included in the model and set free.
auto.delta	If TRUE, response scaling parameters for limited dependent variables are included in the model and set free.
auto.efa	If TRUE, the necessary constraints are imposed to make the (unrotated) exploratory factor analysis blocks identifiable: for each block, factor variances are set to 1, factor covariances are constrained to be zero, and factor loadings are constrained to follow an echelon pattern.
varTable	The variable table containing information about the observed variables in the model.
ngroups	The number of (independent) groups.
nthresholds	Either a single integer or a named vector of integers. If nthresholds is a single integer, all endogenous variables are assumed to be ordered with nthresholds indicating the number of thresholds needed in the model. If nthresholds is a named vector, it indicates the number of thresholds for these ordered variables only. This argument should not be used in combination with varTable.
group.equal	A vector of character strings. Only used in a multiple group analysis. Can be one or more of the following: "loadings", "intercepts", "means", "regressions", "residuals" or "covariances", specifying the pattern of equality constraints across multiple groups. When (in the model syntax) a vector of labels is used as a modifier for a certain parameter, this will override the group.equal setting if it applies to this parameter. See also the Multiple groups section below for using modifiers in multiple groups.
group.partial	A vector of character strings containing the labels of the parameters which should be free in all groups (thereby overriding the group.equal argument for some specific parameters).
group.w.free	Logical. If TRUE, the group frequencies are considered to be free parameters in the model. In this case, a Poisson model is fitted to estimate the group frequencies. If FALSE (the default), the group frequencies are fixed to their observed values.
as.data.frame.	If TRUE, return the list of model parameters as a data.frame.
parser	Character. If "old", use the original/classic parser. If "new", use the new/ldw parser. The default is "new".
warn	If TRUE, some (possibly harmless) warnings are printed out.
debug	If TRUE, debugging information is printed out.

Details

The model syntax consists of one or more formula-like expressions, each one describing a specific part of the model. The model syntax can be read from a file (using readLines), or can be specified as a literal string enclosed by single quotes as in the example below.

myModel <- '
  # 1. latent variable definitions
    f1 =~ y1 + y2 + y3
    f2 =~ y4 + y5 + y6
    f3 =~ y7 + y8 + 
          y9 + y10 
    f4 =~ y11 + y12 + y13

  ! this is also a comment
 
  # 2. regressions
    f1 ~ f3 + f4
    f2 ~ f4
    y1 + y2 ~ x1 + x2 + x3

  # 3. (co)variances
    y1 ~~ y1
    y2 ~~ y4 + y5 
    f1 ~~ f2

  # 4. intercepts
    f1 ~ 1; y5 ~ 1

  # 5. thresholds
    y11 | t1 + t2 + t3
    y12 | t1
    y13 | t1 + t2

  # 6. scaling factors
    y11 ~*~ y11
    y12 ~*~ y12
    y13 ~*~ y13

  # 7. formative factors
    f5 <~ z1 + z2 + z3 + z4
'

Blank lines and comments can be used in between the formulas, and formulas can be split over multiple lines. Both the sharp (#) and the exclamation (!) characters can be used to start a comment. Multiple formulas can be placed on a single line if they are separated by a semicolon (;).

There can be seven types of formula-like expressions in the model syntax:

1. Latent variable definitions: The "=~" operator can be used to define (continuous) latent variables. The name of the latent variable is on the left of the "=~" operator, while the terms on the right, separated by "+" operators, are the indicators of the latent variable.

   The operator "=~" can be read as “is manifested by”.

2. Regressions: The "~" operator specifies a regression. The dependent variable is on the left of a "~" operator and the independent variables, separated by "+" operators, are on the right. These regression formulas are similar to the way ordinary linear regression formulas are used in R, but they may include latent variables. Interaction terms are currently not supported.

3. Variance-covariances: The "~~" (‘double tilde’) operator specifies (residual) variances of an observed or latent variable, or a set of covariances between one variable, and several other variables (either observed or latent). Several variables, separated by "+" operators can appear on the right. This way, several pairwise (co)variances involving the same left-hand variable can be expressed in a single expression. The distinction between variances and residual variances is made automatically.

4. Intercepts: A special case of a regression formula can be used to specify an intercept (or a mean) of either an observed or a latent variable. The variable name is on the left of a "~" operator. On the right is only the number "1" representing the intercept. Including an intercept formula in the model automatically implies meanstructure = TRUE. The distinction between intercepts and means is made automatically.

5. Thresholds: The "|" operator can be used to define the thresholds of categorical endogenous variables (on the left hand side of the operator). By convention, the thresholds (on the right hand sided, separated by the "+" operator, are named "t1", "t2", etcetera.

6. Scaling factors: The "~*~" operator defines a scale factor. The variable name on the left hand side must be the same as the variable name on the right hand side. Scale factors are used in the Delta parameterization, in a multiple group analysis when factor indicators are categorical.

7. Formative factors: The "<~" operator can be used to define a formative factor (on the right hand side of the operator), in a similar way to how a reflexive factor is defined (using the "=~" operator). This is just syntax sugar to define a phantom latent variable (equivalent to using "f =~ 0"). And in addition, the (residual) variance of the formative factor is fixed to zero.

There are 4 additional operators, also with left- and right-hand sides, that can be included in model syntax. Three of them are used to specify (in)equality constraints on estimated parameters (==, >, and <), and those are demonstrated in a later section about (In)equality constraints. The final additional operator (:=) can be used to define “new” parameters that are functions of one or more other estimated parameters. The := operator is demonstrated in a section about User-defined parameters.

Usually, only a single variable name appears on the left side of an operator. However, if multiple variable names are specified, separated by the "+" operator, the formula is repeated for each element on the left side (as for example in the third regression formula in the example above). The only exception are scaling factors, where only a single element is allowed on the left hand side.

In the right-hand side of these formula-like expressions, each element can be modified (using the "*" operator) by either a numeric constant, an expression resulting in a numeric constant, an expression resulting in a character vector, or one of three special functions: start(), label() and equal(). This provides the user with a mechanism to fix parameters, to provide alternative starting values, to label the parameters, and to define equality constraints among model parameters. All "*" expressions are referred to as modifiers. They are explained in more detail in the following sections.

Fixing parameters

It is often desirable to fix a model parameter that is otherwise (by default) free. Any parameter in a model can be fixed by using a modifier resulting in a numerical constaint. Here are some examples:

• Fixing the regression coefficient of the predictor x2:

  y ~ x1 + 2.4*x2 + x3

• Specifying an orthogonal (zero) covariance between two latent variables:

  f1 ~~ 0*f2

• Specifying an intercept and a linear slope in a growth model:

  i =~ 1*y11 + 1*y12 + 1*y13 + 1*y14
  s =~ 0*y11 + 1*y12 + 2*y13 + 3*y14

Instead of a numeric constant, one can use a mathematical function that returns a numeric constant, for example sqrt(10). Multiplying with NA will force the corresponding parameter to be free.

Additionally, the == operator can be used to set a labeled parameter equal to a specific numeric value. This will be demonstrated in the section below about (In)equality constraints.

Starting values

User-provided starting values can be given by using the special function start(), containing a numeric constant. For example:

y ~ x1 + start(1.0)*x2 + x3

Note that if a starting value is provided, the parameter is not automatically considered to be free.

Parameter labels and equality constraints

Each free parameter in a model is automatically given a name (or label). The name given to a model parameter consists of three parts, coerced to a single character vector. The first part is the name of the variable in the left-hand side of the formula where the parameter was implied. The middle part is based on the special ‘operator’ used in the formula. This can be either one of "=~", "~" or "~~". The third part is the name of the variable in the right-hand side of the formula where the parameter was implied, or "1" if it is an intercept. The three parts are pasted together in a single string. For example, the name of the fixed regression coefficient in the regression formula y ~ x1 + 2.4*x2 + x3 is the string "y~x2". The name of the parameter corresponding to the covariance between two latent variables in the formula f1 ~~ f2 is the string "f1~~f2".

Although this automatic labeling of parameters is convenient, the user may specify its own labels for specific parameters simply by pre-multiplying the corresponding term (on the right hand side of the operator only) by a character string (starting with a letter). For example, in the formula f1 =~ x1 + x2 + mylabel*x3, the parameter corresponding with the factor loading of x3 will be named "mylabel". An alternative way to specify the label is as follows: f1 =~ x1 + x2 + label("mylabel")*x3, where the label is the argument of special function label(); this can be useful if the label contains a space, or an operator (like "~").

To constrain a parameter to be equal to another target parameter, there are two ways. If you have specified your own labels, you can use the fact that equal labels imply equal parameter values. If you rely on automatic parameter labels, you can use the special function equal(). The argument of equal() is the (automatic or user-specified) name of the target parameter. For example, in the confirmatory factor analysis example below, the intercepts of the three indicators of each latent variable are constrained to be equal to each other. For the first three, we have used the default names. For the last three, we have provided a custom label for the y2a intercept.

model <- '
  # two latent variables with fixed loadings
    f1 =~ 1*y1a + 1*y1b + 1*y1c
    f2 =~ 1*y2a + 1*y2b + 1*y2c

  # intercepts constrained to be equal
  # using the default names
    y1a ~ 1
    y1b ~ equal("y1a~1") * 1
    y1c ~ equal("y1a~1") * 1

  # intercepts constrained to be equal
  # using a custom label
    y2a ~ int2*1
    y2b ~ int2*1
    y2c ~ int2*1
'

Multiple groups

In a multiple group analysis, modifiers that contain a single element should be replaced by a vector, having the same length as the number of groups. If you provide a single element, it will be recycled for all the groups. This may be dangerous, in particular when the modifier is a label. In that case, the (same) label is copied across all groups, and this would imply an equality constraint across groups. Therefore, when using modifiers in a multiple group setting, it is always safer (and cleaner) to specify the same number of elements as the number of groups. Consider this example with two groups:

HS.model <- ' visual  =~ x1 + 0.5*x2 + c(0.6, 0.8)*x3
              textual =~ x4 + start(c(1.2, 0.6))*x5 + x6
              speed   =~ x7 + x8 + c(x9.group1, x9.group2)*x9 '

In this example, the factor loading of the ‘x2’ indicator is fixed to the value 0.5 for both groups. However, the factor loadings of the ‘x3’ indicator are fixed to 0.6 and 0.8 for group 1 and group 2 respectively. The same logic is used for all modifiers. Note that character vectors can contain unquoted strings.

Multiple modifiers

In the model syntax, you can specify a variable more than once on the right hand side of an operator; therefore, several ‘modifiers’ can be applied simultaneously; for example, if you want to fix the value of a parameter and also label that parameter, you can use something like:

 f1 =~ x1 + x2 + 4*x3 + x3.loading*x3

(In)equality constraints

The == operator can be used either to fix a parameter to a specific value, or to set an estimated parameter equal to another parameter. Adapting the example in the Parameter labels and equality constraints section, we could have used different labels for the second factor's intercepts:

    y2a ~ int1*1
    y2b ~ int2*1
    y2c ~ int3*1

Then, we could fix the first intercept to zero by including in the syntax an operation that indicates the parameter's label equals that value:

    int1 == 0

Whereas we could still estimate the other two intercepts under an equality constraint by setting their different labels equal to each other:

    int2 == int3

Optimization can be less efficient when constraining parameters this way (see the documentation linked under See also for more information). But the flexibility might be advantageous. For example, the constraints could be specified in a separate character-string object, which can be passed to the lavaan(..., constraints=) argument, enabling users to compare results with(out) the constraints.

Inequality constraints work much the same way, using the < or > operator indicate which estimated parameter is hypothesized to be greater/less than either a specific value or another estimated parameter. For example, a variance can be constrained to be nonnegative:

    y1a ~~ var1a*y1a
    ## hypothesized constraint:
    var1a > 0

Or the factor loading of a particular indicator might be expected to exceed other indicators' loadings:

    f1 =~ L1*y1a + L2*y1b + L3*y1c
    ## hypothesized constraints:
    L1 > L2
    L3 < L1

User-defined parameters

Functions of parameters can be useful to test particular hypotheses. Following from the Multiple groups example, we might be interested in which group's factor loading is larger (i.e., an estimate of differential item functioning (DIF) when the latent scales are linked by anchor items with equal loadings).

  speed =~ c(L7, L7)*x7 + c(L8, L8)*x8 + c(L9.group1, L9.group2)*x9 '
  ## user-defined parameter:
  DIF_L9 := L9.group1 - L9.group2

Note that this hypothesis is easily tested without a user-defined parameter by using the lavTestWald() function. However, a user-defined parameter additionally provides an estimate of the parameter being tested.

User-defined parameters are particularly useful for specifying indirect effects in models of mediation. For example:

model <- ' # direct effect
             Y ~ c*X
           # mediator
             M ~ a*X
             Y ~ b*M
             
# user defined parameters:

    # indirect effect (a*b)
             ab := a*b
    # total effect (defined using another user-defined parameter)
             total := ab + c
'

References

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.18637/jss.v048.i02")}

lavaan documentation built on Sept. 27, 2024, 9:07 a.m.