# fscores: Factor Scores for Latent Variables In sem1: Structural Equation Models

## Description

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

## Usage

 ```1 2 3 4``` ```fscores(model, ...) ## S3 method for class 'sem' fscores(model, data, center = TRUE, scale = FALSE, ...) ```

## Arguments

 `model` an object of class `"sem"`, produced by the `sem` function. `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 coefficients are returned. `center` if `TRUE`, the default, the means of the observed variables are subtracted prior to computing factor scores. One would normally use this option if the model is estimated from a covariance or correlation matrix among the observed variables. `scale` if `TRUE`, the possibly centered variables are divided by their room-mean-squares; the default is `FALSE`. One would normally use this option if the model is estimated from a correlation matrix among the observed variables. Centering and scaling are performed by the `scale` function. `...` arguments to pass down.

## Details

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.

## Value

Either a matrix of estimated factor scores (if the `data` argument is supplied) or a matrix of factor-score coefficients (otherwise).

## Author(s)

John Fox [email protected]

## References

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) ```