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

Description Usage Arguments Details Value Author(s) References See Also Examples

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

fscores(model, ...)

## S3 method for class 'sem'
fscores(model, data, center = TRUE, scale = FALSE, ...)

`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.

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

	   ## 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)