residuals function

Residual Covariances for a Structural Equation Model

These functions compute residual covariances, variance-standardized residual covariances, and normalized residual covariances for the observed variables in a structural-equation model fit by sem.

## S3 method for class 'sem'
residuals(object, ...)
## S3 method for class 'msem'
residuals(object, ...)

## S3 method for class 'sem'
standardizedResiduals(object, ...)
## S3 method for class 'msem'
standardizedResiduals(object, ...)

## S3 method for class 'objectiveML'
normalizedResiduals(object, ...)
## S3 method for class 'objectiveGLS'
normalizedResiduals(object, ...)
## S3 method for class 'msemObjectiveML'
normalizedResiduals(object, ...)

Arguments

• object: an object inheriting from class sem or msem returned by the sem function.
• ...: not for the user.

Details

Residuals are defined as $S - C$, where $S$ is the sample covariance matrix of the observed variables and $C$ is the model-reproduced covariance matrix. The standardized residual covariance for a pair of variables divides the residual covariance by the product of the sample standard deviations of the two variables, $(s_{ij} - c_{ij})/(s_{ii}s_{jj})^{1/2}$. The normalized residual is given by

where $N^{*}$ is the number of observations minus one if the model is fit to a covariance matrix, or the number of observations if it is fit to a raw moment matrix.

Returns

Each function returns a matrix of residuals.

References

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

Author(s)

John Fox jfox@mcmaster.ca

See Also

sem

Examples

# In the first example, readMoments() and specifyModel() read from the
# input stream. This example cannot be executed via example() but can be entered
# at the command prompt. The example is repeated using file input;
# this example can be executed via example(). 
    ## Not run:

# Duncan, Haller, and Portes peer-influences model

R.DHP <- readMoments(diag=FALSE, names=c("ROccAsp", "REdAsp", "FOccAsp", 
                "FEdAsp", "RParAsp", "RIQ", "RSES", "FSES", "FIQ", "FParAsp"))
    .6247     
    .3269  .3669       
    .4216  .3275  .6404
    .2137  .2742  .1124  .0839
    .4105  .4043  .2903  .2598  .1839
    .3240  .4047  .3054  .2786  .0489  .2220
    .2930  .2407  .4105  .3607  .0186  .1861  .2707
    .2995  .2863  .5191  .5007  .0782  .3355  .2302  .2950
    .0760  .0702  .2784  .1988  .1147  .1021  .0931 -.0438  .2087
            
model.dhp <- specifyModel()
    RParAsp  -> RGenAsp, gam11,  NA
    RIQ      -> RGenAsp, gam12,  NA
    RSES     -> RGenAsp, gam13,  NA
    FSES     -> RGenAsp, gam14,  NA
    RSES     -> FGenAsp, gam23,  NA
    FSES     -> FGenAsp, gam24,  NA
    FIQ      -> FGenAsp, gam25,  NA
    FParAsp  -> FGenAsp, gam26,  NA
    FGenAsp  -> RGenAsp, beta12, NA
    RGenAsp  -> FGenAsp, beta21, NA
    RGenAsp  -> ROccAsp,  NA,     1
    RGenAsp  -> REdAsp,  lam21,  NA
    FGenAsp  -> FOccAsp,  NA,     1
    FGenAsp  -> FEdAsp,  lam42,  NA
    RGenAsp <-> RGenAsp, ps11,   NA
    FGenAsp <-> FGenAsp, ps22,   NA
    RGenAsp <-> FGenAsp, ps12,   NA
    ROccAsp <-> ROccAsp, theta1, NA
    REdAsp  <-> REdAsp,  theta2, NA
    FOccAsp <-> FOccAsp, theta3, NA
    FEdAsp  <-> FEdAsp,  theta4, NA

sem.dhp <- sem(model.dhp, R.DHP, 329,
    fixed.x=c('RParAsp', 'RIQ', 'RSES', 'FSES', 'FIQ', 'FParAsp'))
residuals(sem.dhp)
normalizedResiduals(sem.dhp) 
standardizedResiduals(sem.dhp) # same as residuals because model is fit to correlations
## End(Not run)

# The following example can be executed via example():

etc <- system.file(package="sem", "etc") # path to data and model files
   
(R.DHP <- readMoments(file=file.path(etc, "R-DHP.txt"),
                                diag=FALSE, names=c("ROccAsp", "REdAsp", "FOccAsp", 
                "FEdAsp", "RParAsp", "RIQ", "RSES", "FSES", "FIQ", "FParAsp")))
(model.dhp <- specifyModel(file=file.path(etc, "model-DHP.txt")))
(sem.dhp <- sem(model.dhp, R.DHP, 329,
    fixed.x=c('RParAsp', 'RIQ', 'RSES', 'FSES', 'FIQ', 'FParAsp')))

residuals(sem.dhp)

normalizedResiduals(sem.dhp) 

standardizedResiduals(sem.dhp)  # same as residuals because model is fit to correlations