`R/main.R`

`add_factor_scores.Rd`

For each latent variable in a structural model, add an estimated factor score to observed data.

```
add_factor_scores(
d,
m,
mu = 0,
sigma = 1,
CI = FALSE,
p = 0.95,
names_suffix = "_FS",
keep_observed_scores = TRUE,
...
)
```

- d
A data.frame with observed data in standardized form (i.e, z-scores)

- m
A character string with lavaan model

- mu
Population mean of the observed scores. Factor scores will also have this mean. Defaults to 0.

- sigma
Population standard deviation of the observed scores. Factor scores will also have this standard deviation. Defaults to 1.

- CI
Add confidence intervals? Defaults to `FALSE`. If `TRUE`, for each factor score, a lower and upper bound of the confidence interval is created. For example, the lower bound of factor score `X` is `X_LB`, and the upper bound is `X_UB`.

- p
confidence interval proportion. Defaults to 0.95

- names_suffix
A character string added to each factor score name

- keep_observed_scores
The observed scores are returned along with the factor scores.

- ...
parameters passed to simstandardized_matrices

data.frame with observed data and estimated factor scores

```
library(simstandard)
# lavaan model
m = "
X =~ 0.9 * X1 + 0.8 * X2 + 0.7 * X3
"
# Make data.frame for two cases
d <- data.frame(
X1 = c(1.2, -1.2),
X2 = c(1.5, -1.8),
X3 = c(1.8, -1.1))
# Compute factor scores for two cases
add_factor_scores(d, m)
#> X1 X2 X3 X_FS
#> 1 1.2 1.5 1.8 1.435708
#> 2 -1.2 -1.8 -1.1 -1.398951
```