```
<- ' visual =~ x1 + x2 + x3
HS.model textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
<- cfa(HS.model,
fit.HS.ortho data = HolzingerSwineford1939,
orthogonal = TRUE)
```

# Model syntax 2

## Fixing parameters

Consider a simple one-factor model with 4 indicators. By default, lavaan will always fix the factor loading of the first indicator to 1. The other three factor loadings are free, and their values are estimated by the model. But suppose that you have good reasons to fix all the factor loadings to 1. The syntax below illustrates how this can be done:

`f =~ y1 + 1*y2 + 1*y3 + 1*y4`

In general, to fix a parameter in a lavaan formula, you need to pre-multiply the corresponding variable in the formula by a numerical value. This is called the pre-multiplication mechanism and will be used for many purposes. As another example, consider again the three-factor Holzinger and Swineford CFA model. Recall that, by default, all exogenous latent variables in a CFA model are correlated. But if you wish to fix the correlation (or covariance) between a pair of latent variables to zero, you need to explicitly add a covariance-formula for this pair, and fix the parameter to zero. In the syntax below, we allow the covariance between the latent variables `visual`

and `textual`

to be free, but the two other covariances are fixed to zero. In addition, we fix the variance of the factor `speed`

to unity. Therefore, there is no need anymore to set the factor loading of its first indicator (`x7`

) equal to one. To force this factor loading to be free, we pre-multiply it with `NA`

, as a hint to lavaan that the value of this parameter is ‘missing’ and therefore still unknown.

```
# three-factor model
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ NA*x7 + x8 + x9
# orthogonal factors
visual ~~ 0*speed
textual ~~ 0*speed
# fix variance of speed factor
speed ~~ 1*speed
```

If you need to constrain all covariances of the latent variables in a CFA model to be orthogonal, there is a shortcut. You can omit the covariance formulas in the model syntax and simply add an argument `orthogonal = TRUE`

to the function call:

Similarly, if you want to fix the variances of *all* the latent variables in a CFA model to unity, there is again a shortcut. Simply add the argument `std.lv = TRUE`

to the function call:

```
<- ' visual =~ x1 + x2 + x3
HS.model textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
<- cfa(HS.model,
fit data = HolzingerSwineford1939,
std.lv = TRUE)
```

If the argument `std.lv = TRUE`

is used, the factor loadings of the first indicator of each latent variable will no longer be fixed to 1.

## Starting Values

The lavaan package automatically generates starting values for all free parameters. Normally, this works fine. But if you prefer to provide your own starting values, you are free to do so. The way it works is based on the pre-multiplication mechanism that we discussed before. But the numeric constant is now the argument of a special function `start()`

. An example will make this clear:

```
visual =~ x1 + start(0.8)*x2 + start(1.2)*x3
textual =~ x4 + start(0.5)*x5 + start(1.0)*x6
speed =~ x7 + start(0.7)*x8 + start(1.8)*x9
```

## Parameter labels

A nice property of the lavaan package is that all free parameters are automatically named according to a simple set of rules. This is convenient, for example, if equality constraints are needed (see the next subsection). To see how the naming mechanism works, we will use the model that we used for the Politcal Democracy data.

```
<- '
model # latent variable definitions
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual (co)variances
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
<- sem(model,
fit data = PoliticalDemocracy)
coef(fit)
```

```
ind60=~x2 ind60=~x3 dem60=~y2 dem60=~y3 dem60=~y4 dem65=~y6
2.180 1.819 1.257 1.058 1.265 1.186
dem65=~y7 dem65=~y8 dem60~ind60 dem65~ind60 dem65~dem60 y1~~y5
1.280 1.266 1.483 0.572 0.837 0.624
y2~~y4 y2~~y6 y3~~y7 y4~~y8 y6~~y8 x1~~x1
1.313 2.153 0.795 0.348 1.356 0.082
x2~~x2 x3~~x3 y1~~y1 y2~~y2 y3~~y3 y4~~y4
0.120 0.467 1.891 7.373 5.067 3.148
y5~~y5 y6~~y6 y7~~y7 y8~~y8 ind60~~ind60 dem60~~dem60
2.351 4.954 3.431 3.254 0.448 3.956
dem65~~dem65
0.172
```

The function `coef()`

extracts the estimated values of the free parameters in the model, together with their names. Each name consists of three parts and reflects the part of the formula where the parameter was involved. The first part is the variable name that appears on the left-hand side (lhs) of the formula. The middle part is the operator type (op) of the formula, and the third part is the variable in the right-hand side (rhs) of the formula that corresponds with the parameter.

Often, it is convenient to choose your own labels for specific parameters. The way this works is similar to fixing a parameter. But instead of pre-multiplying with a numerical constant, we use a character string (the label) instead. In the example below, we ‘label’ the factor loading of the `x3`

indicator with the label `myLabel`

:

```
<- '
model # latent variable definitions
ind60 =~ x1 + x2 + myLabel*x3
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual (co)variances
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
```

It is important that labels start with a letter (a-zA-Z), and certainly not with a digit. For example ‘13bis’ is not a valid label, and will confuse the lavaan syntax parser. (Note: before version 0.4-8, it was necessary to use the modifier `label()`

to specify a custom label. Although it is still supported, it is not recommended anymore. The only reason why it should be used in new syntax is if the label contains an operator like “`=~`

”.)

## Modifiers

We have seen the use of the pre-multiplication mechanism (using the `*`

operator) a number of times: to fix a parameter, to provide a starting value, and to label a parameter. We refer to these operations as *modifiers*, because they modify some properties of certain model parameters. More modifiers will be introduced later.

Each term on the right-hand side in a formula can have one modifier only. If you want to specify more modifiers for the same parameter, you need to list the term multiple times in the same formula. For example:

`f =~ y1 + y2 + myLabel*y3 + start(0.5)*y3 + y4`

The indicator `y3`

was listed twice, each time with a different modifier. The parser will accumulate all the different modifiers, but still treat `y3`

as a single indicator.

## Simple equality constraints

In some applications, it is useful to impose equality constraints on one or more otherwise free parameters. Consider again the three-factor H&S CFA model. Suppose a user has a priori reasons to believe that the factor loadings of the `x2`

and `x3`

indicators are equal to each other. Instead of estimating two free parameters, lavaan should only estimate a single free parameter, and use that value for both factor loadings. The main mechanism to specify this type of (simple) equality constraint is by using labels: if two parameters have the same label, they will be considered to be the same, and only one value will be computed for them. This is illustrated in the following syntax:

```
visual =~ x1 + v2*x2 + v2*x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
```

Remember: all parameters having the same label will be constrained to be equal.

An alternative approach is to use the `equal()`

modifier. This is useful if no custom label has been specified, and one needs to refer to the automatically generated label. For example:

```
visual =~ x1 + x2 + equal("visual=~x2")*x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
```

## Nonlinear equality and inequality constraints

Consider the following regression:

`y ~ b1*x1 + b2*x2 + b3*x3`

where we have explicitly labeled the regression coefficients as `b1`

, `b2`

and `b3`

. We create a toy dataset containing these four variables and fit the regression model:

```
set.seed(1234)
<- data.frame(y = rnorm(100),
Data x1 = rnorm(100),
x2 = rnorm(100),
x3 = rnorm(100))
<- ' y ~ b1*x1 + b2*x2 + b3*x3 '
model <- sem(model, data = Data)
fit coef(fit)
```

```
b1 b2 b3 y~~y
-0.052 0.084 0.139 0.970
```

Suppose that we need to impose the following two (nonlinear) constraints on \(b_1\): \(b_1 = (b_2+b_3)^2\) and \(b_1 \geq \exp(b_2 + b_3)\). The first constraint is an equality constraint. The second is an inequality constraint. To specify these constraints, you can use the following syntax:

```
<- ' # model with labeled parameters
model.constr y ~ b1*x1 + b2*x2 + b3*x3
# constraints
b1 == (b2 + b3)^2
b1 > exp(b2 + b3) '
```

To see the effect of the constraints, we refit the model:

```
<- ' # model with labeled parameters
model.constr y ~ b1*x1 + b2*x2 + b3*x3
# constraints
b1 == (b2 + b3)^2
b1 > exp(b2 + b3) '
<- sem(model.constr, data = Data)
fit coef(fit)
```

```
b1 b2 b3 y~~y
0.495 -0.405 -0.299 1.610
```

The reader can verify that the constraints are indeed respected. The equality constraint holds exactly. The inequality constraint has resulted in an equality between the left-hand side (\(b_1\)) and the right-hand side (\(\exp(b_2 + b_3)\)).