Calculate factor scores or factor-score coefficients for the latent variables in a structural-equation model.

1 2 3 4 |

`model` |
an object of class |

`data` |
an optional numeric data frame or matrix containing the observed variables
in the model; if present, the estimated factor scores are returned; if absent, the
factor-score |

`center` |
if |

`scale` |
if |

`...` |
arguments to pass down. |

Factor-score coefficients are computed by the “regression” method as
*C^-1 C**, where *C* is the model-implied covariance or
moment matrix among the observed variables and *C** is the matrix
of model-implied covariances or moments between the observed and latent variables.

Either a matrix of estimated factor scores (if the `data`

argument is
supplied) or a matrix of factor-score coefficients (otherwise).

John Fox jfox@mcmaster.ca

Bollen, K. A. (1989)
*Structural Equations With Latent Variables.* Wiley.

`sem`

, `scale`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ```
## Not run:
S.wh <- read.moments(names=c('Anomia67','Powerless67','Anomia71',
'Powerless71','Education','SEI'))
11.834
6.947 9.364
6.819 5.091 12.532
4.783 5.028 7.495 9.986
-3.839 -3.889 -3.841 -3.625 9.610
-21.899 -18.831 -21.748 -18.775 35.522 450.288
# This model in the SAS manual for PROC CALIS
model.wh.1 <- specify.model()
Alienation67 -> Anomia67, NA, 1
Alienation67 -> Powerless67, NA, 0.833
Alienation71 -> Anomia71, NA, 1
Alienation71 -> Powerless71, NA, 0.833
SES -> Education, NA, 1
SES -> SEI, lamb, NA
SES -> Alienation67, gam1, NA
Alienation67 -> Alienation71, beta, NA
SES -> Alienation71, gam2, NA
Anomia67 <-> Anomia67, the1, NA
Anomia71 <-> Anomia71, the1, NA
Powerless67 <-> Powerless67, the2, NA
Powerless71 <-> Powerless71, the2, NA
Education <-> Education, the3, NA
SEI <-> SEI, the4, NA
Anomia67 <-> Anomia71, the5, NA
Powerless67 <-> Powerless71, the5, NA
Alienation67 <-> Alienation67, psi1, NA
Alienation71 <-> Alienation71, psi2, NA
SES <-> SES, phi, NA
sem.wh.1 <- sem(model.wh.1, S.wh, 932)
fscores(sem.wh.1)
## Alienation67 Alienation71 SES
## Anomia67 0.413112363 0.048268330 -0.05212632
## Powerless67 0.345402079 0.040014780 -0.04355578
## Anomia71 0.052663484 0.430618716 -0.03999218
## Powerless71 0.043704122 0.360044434 -0.03339943
## Education -0.074921670 -0.063969383 0.50571037
## SEI -0.004638977 -0.003960837 0.03131242
## End(Not run)
``` |

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