Description Usage Arguments Value Examples

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

1 2 3 4 5 6 7 8 9 10 11 | ```
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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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)
``` |

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