effects function

Total, Direct, and Indirect Effects for Structural Equation Models

The sem method for the standard generic function effects computes total, direct, and indirect effects for a fitted structural equation model according to the method described in Fox (1980).

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

## S3 method for class 'semeffects'
print(x, digits = getOption("digits"), ...)
## S3 method for class 'semeffectsList'
print(x, digits = getOption("digits"), ...)

Arguments

• object: a fitted structural-equation model object produced by the sem function.
• x: an object of class semeffects or semeffectsList, produced by effects.
• digits: digits to print.
• ...: not used.

Returns

effect.sem returns an object of class semeffects with Total, Direct, and Indirect elements.

References

Fox, J. (1980) Effect analysis in structural equation models: Extensions and simplified methods of computation. Sociological Methods and Research

9 , 3--28.

Author(s)

John Fox jfox@mcmaster.ca

See Also

sem

Examples

## Not run:

# These examples are from Fox (1980)

# In the first pair of examples, readMoments() and specifyModel() read from the
# input stream. These examples cannot be executed via example() but can be entered
# at the command prompt. The Blau and Duncan example is repeated using file input;
# this example can be executed via example(). 

# The recursive Blau and Duncan basic stratification model:
#  x1 is father's education, x2 father's SES, y3 respondent's education,
#  y4 SES of respondent's first job, y5 respondent's SES in 1962

R.bd <- readMoments(names=c("x1", "x2", "y3", "y4", "y5"))
1
.516 1
.453 .438 1
.332 .417 .538 1
.322 .405 .596 .541 1

mod.bd <- specifyModel()
y3 <- x1, gam31
y3 <- x2, gam32
y4 <- x2, gam42
y4 <- y3, beta43
y5 <- x2, gam52
y5 <- y3, beta53
y5 <- y4, beta54

sem.bd <- sem(mod.bd, R.bd, N=20700, fixed.x=c("x1", "x2"))
summary(sem.bd)
effects(sem.bd)

# The nonrecursive Duncan, Haller, and Portes peer-influences model for observed variables:

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()
RIQ      -> ROccAsp, gam51,  NA
RSES     -> ROccAsp, gam52,  NA
FSES     -> FOccAsp, gam63,  NA
FIQ      -> FOccAsp, gam64,  NA
FOccAsp  -> ROccAsp, beta56, NA
ROccAsp  -> FOccAsp, beta65, NA
ROccAsp <-> ROccAsp, ps55,   NA
FOccAsp <-> FOccAsp, ps66,   NA
ROccAsp <-> FOccAsp, ps56,   NA

# Note: The following generates a warning because not all of the variables
#       in the correlation matrix are used
sem.dhp <- sem(model.dhp, R.DHP, 329,
                fixed.x=c('RIQ', 'RSES', 'FSES', 'FIQ'))
summary(sem.dhp)
effects(sem.dhp)
## End(Not run)

    
## the following example may be executed via example()

etc <- system.file(package="sem", "etc") # path to data and model files

# The recursive Blau and Duncan basic stratification model:
#  x1 is father's education, x2 father's SES, y3 respondent's education,
#  y4 SES of respondent's first job, y5 respondent's SES in 1962

(R.bd <- readMoments(file=file.path(etc, "R-Blau-Duncan.txt"),
                                        names=c("x1", "x2", "y3", "y4", "y5")))
(mod.bd <- specifyModel(file=file.path(etc, "model-Blau-Duncan.txt")))
sem.bd <- sem(mod.bd, R.bd, N=20700, fixed.x=c("x1", "x2"))
summary(sem.bd)
effects(sem.bd)