sem: General Structural Equation Models

In sem1: Structural Equation Models

Description

sem fits general structural equation models (with both observed and unobserved variables) by the method of maximum likelihood, assuming multinormal errors. Observed variables are also called indicators or manifest variables; unobserved variables are also called factors or latent variables. Normally, the generic function (sem) would be used.

Usage

sem(ram, ...)

## S3 method for class 'mod'
sem(ram, S, N, obs.variables=rownames(S), fixed.x=NULL, debug=FALSE, ...)
    
## Default S3 method:
sem(ram, S, N, param.names = paste("Param", 1:t, sep = ""), 
    var.names = paste("V", 1:m, sep = ""), fixed.x = NULL, raw=FALSE, 
    debug = FALSE, analytic.gradient = TRUE, warn = FALSE, maxiter = 500, 
    par.size=c('ones', 'startvalues'), refit=TRUE, start.tol=1E-6, ...) 
    
startvalues(S, ram, debug = FALSE, tol=1E-6)

## S3 method for class 'sem'
print(x, ...)

## S3 method for class 'sem'
summary(object, digits=5, conf.level=0.9, ...)

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

## S3 method for class 'sem'
df.residual(object, ...)

## S3 method for class 'sem'
anova(object, model.2, ...)

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

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

Arguments

ram	RAM specification, which is a simple encoding of the path diagram for the model. The ram matrix may be given either in symbolic form (as a mod object, as returned by the specify.model function, or as a character matrix), invoking sem.mod, which calls sem.default after setting up the model, or (less conveniently) in numeric form, invoking sem.default directly (see Details below).
S	covariance matrix among observed variables; may be input as a symmetric matrix, or as a lower- or upper-triangular matrix. S may also be a raw (i.e., “uncorrected”) moment matrix — that is, a sum-of-squares-and-products matrix divided by N. This form of input is useful for fitting models with intercepts, in which case the moment matrix should include the mean square and cross-products for a unit variable all of whose entries are 1; of course, the raw mean square for the unit variable is 1. Raw-moment matrices may be computed by raw.moments. If the ram argument is given in symbolic form, then the observed-variable covariance or raw-moment matrix may contain variables that do not appear in the model, in which case a warning is printed.
N	number of observations on which the covariance matrix is based.
obs.variables	names of observed variables, by default taken from the row names of the covariance matrix S.
fixed.x	names (if the ram matrix is given in symbolic form) or indices (if it is in numeric form) of fixed exogenous variables. Specifying these obviates the necessity of having to fix the variances and covariances among these variables (and produces correct degrees of freedom for the model chisquare).
raw	TRUE if S is a raw moment matrix, as opposed to a covariance matrix; the default is FALSE.
debug	if TRUE, some information is printed to help you debug the symbolic model specification; for example, if a variable name is misspelled, sem will assume that the variable is a (new) latent variable. The default is FALSE.
...	arguments to be passed down, including from sem.default to the nlm optimizer.
param.names	names of the t free parameters, given in their numerical order; default names are Param1, ..., Paramt. Note: Should not be specified when the ram matrix is given in symbolic form.
var.names	names of the m entries of the v vector (typically the observed and latent variables — see below), given in their numerical order; default names are Var1, ..., Varm. Note: Should not be specified when the ram matrix is given in symbolic form.
analytic.gradient	if TRUE (the default), then analytic first derivatives are used in the maximization of the likelihood; otherwise numeric derivatives are used.
warn	if TRUE, warnings produced by the optimization function will be printed. This should generally not be necessary, since sem prints its own warning, and saves information about convergence. The default is FALSE.
maxiter	the maximum number of iterations for the optimization performed by the nlm function, to be passed to it via its iterlim argument.
par.size	the anticipated size of the free parameters; if "ones", a vector of ones is used; if "startvalues", taken from the start values. You can try changing this argument if you encounter convergence problems. The default is "startvalues" if the largest input variance is at least 100 times the smallest, and "ones" otherwise.
refit	if TRUE (the default), attempt to refit the model eliminating apparently aliased parameters if under-identification is detected.
start.tol, tol	if the magnitude of an automatic start value is less than start.tol, then it is set to start.tol; defaults to 1E-6.
object, x	an object of class sem returned by the sem function.
digits	number of digits for printed output.
conf.level	level for confidence interval for the RMSEA index (default is .9).
model.2	an object of class sem returned by the sem function, to be compared by a likelihood-ratio test to object; the two models must be fit to the same data.

Details

The model is set up using RAM (“reticular action model” – don't ask!) notation – a simple format for specifying general structural equation models by coding the “arrows” in the path diagram for the model (see, e.g., McArdle and McDonald, 1984).

The variables in the v vector in the model (typically, the observed and unobserved variables, but not error variables) are numbered from 1 to m. the RAM matrix contains one row for each (free or constrained) parameter of the model, and may be specified either in symbolic format or in numeric format.

A symbolic ram matrix consists of three columns, as follows:

1. Arrow specification:

This is a simple formula, of the form "A -> B" or, equivalently, "B <- A" for a regression coefficient (i.e., a single-headed or directional arrow); "A <-> A" for a variance or "A <-> B" for a covariance (i.e., a double-headed or bidirectional arrow). Here, A and B are variable names in the model. If a name does not correspond to an observed variable, then it is assumed to be a latent variable. Spaces can appear freely in an arrow specification, and there can be any number of hyphens in the arrows, including zero: Thus, e.g., "A->B", "A --> B", and "A>B" are all legitimate and equivalent.

2. Parameter name:

The name of the regression coefficient, variance, or covariance specified by the arrow. Assigning the same name to two or more arrows results in an equality constraint. Specifying the parameter name as NA produces a fixed parameter.

3. Value:

start value for a free parameter or value of a fixed parameter. If given as NA, sem will compute the start value.

It is simplest to construct the RAM matrix with the specify.model function, which returns an object of class mod. This process is illustrated in the examples below.

A numeric ram matrix consists of five columns, as follows:

1. Number of arrow heads:

1 (directed arrow) or 2 (covariance).

2. Arrow to:

index of the variable at the head of a directional arrow, or at one end of a bidirectional arrow. Observed variables should be assigned the numbers 1 to n, where n is the number of rows/columns in the covariance matrix S, with the indices corresponding to the variables' positions in S. Variable indices above n represent latent variables.

3. Arrow from:

the index of the variable at the tail of a directional arrow, or at the other end of a bidirectional arrow.

4. Parameter number:

free parameters are numbered from 1 to t, but do not necessarily appear in consecutive order. Fixed parameters are given the number 0. Equality contraints are specified by assigning two or more parameters the same number.

5. Value:

start value for a free parameter, or value of a fixed parameter. If given as NA, the program will compute a start value, by a slight modification of the method described by McDonald and Hartmann (1992). Note: In some circumstances, some start values are selected randomly; this might produce small differences in the parameter estimates when the program is rerun.

sem fits the model by calling the nlm optimizer to minimize the negative log-likelihood for the model. If nlm fails to converge, a warning message is printed.

The RAM formulation of the general structural equation model is given by the basic equation

v = Av + u

where v and u are vectors of random variables (observed or unobserved), and the parameter matrix A contains regression coefficients, symbolized by single-headed arrows in a path diagram. Another parameter matrix,

P = E(uu')

contains covariances among the elements of u (assuming that the elements of u have zero means). Usually v contains endogenous and exogenous observed and unobserved variables, but not error variables (see the examples below).

The startvalues function may be called directly, but is usually called by sem.default.

The sem methods for the generic functions deviance and df.residual functions return, respectively, minus twice the difference in the log-likelihoods for the fitted model and a saturated model, and the degrees of freedom associated with the deviance.

Value

sem returns an object of class sem, with the following elements:

ram	RAM matrix, including any rows generated for covariances among fixed exogenous variables; column 5 includes computed start values.
coeff	estimates of free parameters.
criterion	fitting criterion — minus twice the difference in the log-liklihood between the fitted model and a saturated model, divided by N - 1 (or N if the model is fit to a raw moment matrix).
cov	estimated asymptotic covariance matrix of parameter estimates.
S	observed covariance matrix.
J	RAM selection matrix, J, which picks out observed variables.
C	model-reproduced covariance matrix.
A	RAM A matrix.
P	RAM P matrix.
n.fix	number of fixed exogenous variables.
n	number of observed variables.
N	number of observations.
m	number of variables (observed plus unobserved).
t	number of free parameters.
par.posn	indices of free parameters.
var.names	vector of variable names.
observed	indices of observed variables.
convergence	convergence code returned by nlm (a code > 2 indicates a problem).
iterations	number of iterations performed.
raw	TRUE if the model is fit to a raw moment matrix, FALSE otherwise.
chisqNull	Unless the model is fit to a raw moment matrix, the chisquare value associated with a null model in which all of the observed variables are uncorrelated.

Warning

A common error is to fail to specify variance or covariance terms in the model, which are denoted by double-headed arrows, <->.

In general, every observed or latent variable in the model should be associated with a variance or error variance. This may be a free parameter to estimate or a fixed constant (as in the case of a latent exogenous variable for which you wish to fix the variance, e.g., to 1). Again in general, there will be an error variance associated with each endogenous variable in the model (i.e., each variable to which at least one single-headed arrow points — including observed indicators of latent variables), and a variance associated with each exogenous variable (i.e., each variable that appears only at the tail of single-headed arrows, never at the head).

To my knowledge, the only apparent exception to this rule is for observed variables that are declared to be fixed exogenous variables. In this case, the program generates the necessary (fixed-constant) variances and covariances automatically.

If there are missing variances, a warning message will be printed, and estimation will almost surely fail in some manner. Missing variances might well indicate that there are missing covariances too, but it is not possible to deduce this in a mechanical manner.

Author(s)

John Fox [e-mail]

References

Fox, J. (2006) Structural equation modeling with the sem package in R. Structural Equation Modeling 13:465–486.

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

Bollen, K. A. and Long, J. S. (eds.) Testing Structural Equation Models, Sage.

McArdle, J. J. and Epstein, D. (1987) Latent growth curves within developmental structural equation models. Child Development 58, 110–133.

McArdle, J. J. and McDonald, R. P. (1984) Some algebraic properties of the reticular action model. British Journal of Mathematical and Statistical Psychology 37, 234–251.

McDonald, R. P. and Hartmann, W. M. (1992) A procedure for obtaining initial values of parameters in the RAM model. Multivariate Behavioral Research 27, 57–76.

Raftery, A. E. (1993) Bayesian model selection in structural equation models. In Bollen, K. A. and Long, J. S. (eds.) Testing Structural Equation Models, Sage.

Raftery, A. E. (1995) Bayesian model selection in social research (with discussion). Sociological Methodology 25, 111–196,

See Also

raw.moments, nlm

Examples

# Note: These examples can't be run via example() because the default file
#  argument of specify.model() and read.moments() requires that the model 
#  specification and covariances, correlations, or raw moments be entered
#  at the command prompt. The examples can be copied and run in the R console,
#  however. See ?specify.model and ?read.moments for further information.

    ## Not run: 

# ------------- Duncan, Haller and Portes peer-influences model ----------------------
# A nonrecursive SEM with unobserved endogenous variables and fixed exogenous variables

R.DHP <- read.moments(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
            
# Fit the model using a symbolic ram specification

model.dhp <- specify.model()
    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.1 <- sem(model.dhp, R.DHP, 329,
    fixed.x=c('RParAsp', 'RIQ', 'RSES', 'FSES', 'FIQ', 'FParAsp'))

summary(sem.dhp.1)

##     Model Chisquare =  26.697   Df =  15 Pr(>Chisq) = 0.031302
##     Chisquare (null model) =  872   Df =  45
##     Goodness-of-fit index =  0.98439
##     Adjusted goodness-of-fit index =  0.94275
##     RMSEA index =  0.048759   90 % CI: (0.014517, 0.07831)
##     Bentler-Bonnett NFI =  0.96938
##     Tucker-Lewis NNFI =  0.95757
##     Bentler CFI =  0.98586
##     SRMR =  0.020204
##     BIC =  -60.244 
##    
##     Normalized Residuals
##       Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    -0.7990 -0.1180  0.0000 -0.0120  0.0397  1.5700 
##    
##     Parameter Estimates
##           Estimate  Std Error z value Pr(>|z|)                       
##    gam11   0.161224 0.038487   4.1890 2.8019e-05 RGenAsp <--- RParAsp
##    gam12   0.249653 0.044580   5.6001 2.1428e-08 RGenAsp <--- RIQ    
##    gam13   0.218404 0.043476   5.0235 5.0730e-07 RGenAsp <--- RSES   
##    gam14   0.071843 0.050335   1.4273 1.5350e-01 RGenAsp <--- FSES   
##    gam23   0.061894 0.051738   1.1963 2.3158e-01 FGenAsp <--- RSES   
##    gam24   0.228868 0.044495   5.1437 2.6938e-07 FGenAsp <--- FSES   
##    gam25   0.349039 0.044551   7.8346 4.6629e-15 FGenAsp <--- FIQ    
##    gam26   0.159535 0.040129   3.9755 7.0224e-05 FGenAsp <--- FParAsp
##    beta12  0.184226 0.096207   1.9149 5.5506e-02 RGenAsp <--- FGenAsp
##    beta21  0.235458 0.119742   1.9664 4.9255e-02 FGenAsp <--- RGenAsp
##    lam21   1.062674 0.091967  11.5549 0.0000e+00 REdAsp <--- RGenAsp 
##    lam42   0.929727 0.071152  13.0668 0.0000e+00 FEdAsp <--- FGenAsp 
##    ps11    0.280987 0.046311   6.0674 1.2999e-09 RGenAsp <--> RGenAsp
##    ps22    0.263836 0.044902   5.8759 4.2067e-09 FGenAsp <--> FGenAsp
##    ps12   -0.022601 0.051649  -0.4376 6.6168e-01 FGenAsp <--> RGenAsp
##    theta1  0.412145 0.052211   7.8939 2.8866e-15 ROccAsp <--> ROccAsp
##    theta2  0.336148 0.053323   6.3040 2.9003e-10 REdAsp <--> REdAsp  
##    theta3  0.311194 0.046665   6.6687 2.5800e-11 FOccAsp <--> FOccAsp
##    theta4  0.404604 0.046733   8.6578 0.0000e+00 FEdAsp <--> FEdAsp  
##    
##     Iterations =  28 


# Fit the model using a numerical ram specification

ram.dhp <- matrix(c(
#               heads   to      from    param  start
                1,       1,     11,      0,     1,
                1,       2,     11,      1,     NA, # lam21
                1,       3,     12,      0,     1,
                1,       4,     12,      2,     NA, # lam42
                1,      11,      5,      3,     NA, # gam11
                1,      11,      6,      4,     NA, # gam12
                1,      11,      7,      5,     NA, # gam13
                1,      11,      8,      6,     NA, # gam14
                1,      12,      7,      7,     NA, # gam23
                1,      12,      8,      8,     NA, # gam24
                1,      12,      9,      9,     NA, # gam25
                1,      12,     10,     10,     NA, # gam26
                1,      11,     12,     11,     NA, # beta12
                1,      12,     11,     12,     NA, # beta21
                2,       1,      1,     13,     NA, # theta1
                2,       2,      2,     14,     NA, # theta2
                2,       3,      3,     15,     NA, # theta3
                2,       4,      4,     16,     NA, # theta4
                2,      11,     11,     17,     NA, # psi11
                2,      12,     12,     18,     NA, # psi22
                2,      11,     12,     19,     NA  # psi12
                ), ncol=5, byrow=TRUE)

params.dhp <- c('lam21', 'lam42', 'gam11', 'gam12', 'gam13', 'gam14',
                 'gam23',  'gam24',  'gam25',  'gam26',
                 'beta12', 'beta21', 'theta1', 'theta2', 'theta3', 'theta4',
                 'psi11', 'psi22', 'psi12')
                 
vars.dhp <- c('ROccAsp', 'REdAsp', 'FOccAsp', 'FEdAsp', 'RParAsp', 'RIQ',
                'RSES', 'FSES', 'FIQ', 'FParAsp', 'RGenAsp', 'FGenAsp')
                
sem.dhp.2 <- sem(ram.dhp, R.DHP, 329, params.dhp, vars.dhp, fixed.x=5:10)

summary(sem.dhp.2)
    
##     Model Chisquare =  26.697   Df =  15 Pr(>Chisq) = 0.031302
##     Chisquare (null model) =  872   Df =  45
##     Goodness-of-fit index =  0.98439
##     Adjusted goodness-of-fit index =  0.94275
##     RMSEA index =  0.048759   90 % CI: (0.014517, 0.07831)
##     Bentler-Bonnett NFI =  0.96938
##     Tucker-Lewis NNFI =  0.95757
##     Bentler CFI =  0.98586
##     SRMR =  0.020204
##     BIC =  -60.244 
##    
##     Normalized Residuals
##       Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    -0.7990 -0.1180  0.0000 -0.0120  0.0397  1.5700 
##    
##     Parameter Estimates
##           Estimate  Std Error z value Pr(>|z|)                       
##    lam21   1.062674 0.091967  11.5549 0.0000e+00 REdAsp <--- RGenAsp 
##    lam42   0.929727 0.071152  13.0668 0.0000e+00 FEdAsp <--- FGenAsp 
##    gam11   0.161224 0.038487   4.1890 2.8019e-05 RGenAsp <--- RParAsp
##    gam12   0.249653 0.044580   5.6001 2.1428e-08 RGenAsp <--- RIQ    
##    gam13   0.218404 0.043476   5.0235 5.0730e-07 RGenAsp <--- RSES   
##    gam14   0.071843 0.050335   1.4273 1.5350e-01 RGenAsp <--- FSES   
##    gam23   0.061894 0.051738   1.1963 2.3158e-01 FGenAsp <--- RSES   
##    gam24   0.228868 0.044495   5.1437 2.6938e-07 FGenAsp <--- FSES   
##    gam25   0.349039 0.044551   7.8346 4.6629e-15 FGenAsp <--- FIQ    
##    gam26   0.159535 0.040129   3.9755 7.0224e-05 FGenAsp <--- FParAsp
##    beta12  0.184226 0.096207   1.9149 5.5506e-02 RGenAsp <--- FGenAsp
##    beta21  0.235458 0.119742   1.9664 4.9255e-02 FGenAsp <--- RGenAsp
##    theta1  0.412145 0.052211   7.8939 2.8866e-15 ROccAsp <--> ROccAsp
##    theta2  0.336148 0.053323   6.3040 2.9003e-10 REdAsp <--> REdAsp  
##    theta3  0.311194 0.046665   6.6687 2.5800e-11 FOccAsp <--> FOccAsp
##    theta4  0.404604 0.046733   8.6578 0.0000e+00 FEdAsp <--> FEdAsp  
##    psi11   0.280987 0.046311   6.0674 1.2999e-09 RGenAsp <--> RGenAsp
##    psi22   0.263836 0.044902   5.8759 4.2067e-09 FGenAsp <--> FGenAsp
##    psi12  -0.022601 0.051649  -0.4376 6.6168e-01 RGenAsp <--> FGenAsp
    
##   Iterations =  28 


# -------------------- Wheaton et al. alienation data ----------------------
    

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 is the model in the SAS manual for PROC CALIS: A Recursive SEM with
# latent endogenous and exogenous variables.
# Curiously, both factor loadings for two of the latent variables are fixed.

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)

summary(sem.wh.1)

##     Model Chisquare =  13.485   Df =  9 Pr(>Chisq) = 0.14186
##     Chisquare (null model) =  2131.4   Df =  15
##     Goodness-of-fit index =  0.99527
##     Adjusted goodness-of-fit index =  0.98896
##     RMSEA index =  0.023136   90 % CI: (NA, 0.046997)
##     Bentler-Bonnett NFI =  0.99367
##     Tucker-Lewis NNFI =  0.99647
##     Bentler CFI =  0.99788
##     SRMR =  0.014998
##     BIC =  -48.051 
##    
##     Normalized Residuals
##        Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    -1.26000 -0.13100  0.00014 -0.02870  0.11400  0.87400 
##    
##     Parameter Estimates
##         Estimate  Std Error z value  Pr(>|z|)                                 
##    lamb   5.36880  0.433982  12.3710 0.0000e+00 SEI <--- SES                  
##    gam1  -0.62994  0.056128 -11.2233 0.0000e+00 Alienation67 <--- SES         
##    beta   0.59312  0.046820  12.6680 0.0000e+00 Alienation71 <--- Alienation67
##    gam2  -0.24086  0.055202  -4.3632 1.2817e-05 Alienation71 <--- SES         
##    the1   3.60787  0.200589  17.9864 0.0000e+00 Anomia67 <--> Anomia67        
##    the2   3.59494  0.165234  21.7567 0.0000e+00 Powerless67 <--> Powerless67  
##    the3   2.99366  0.498972   5.9996 1.9774e-09 Education <--> Education      
##    the4 259.57583 18.321121  14.1681 0.0000e+00 SEI <--> SEI                  
##    the5   0.90579  0.121710   7.4422 9.9032e-14 Anomia71 <--> Anomia67        
##    psi1   5.67050  0.422906  13.4084 0.0000e+00 Alienation67 <--> Alienation67
##    psi2   4.51481  0.334993  13.4773 0.0000e+00 Alienation71 <--> Alienation71
##    phi    6.61632  0.639506  10.3460 0.0000e+00 SES <--> SES                  
##    
##     Iterations =  78 


# The same model, but treating one loading for each latent variable as free
# (and equal to each other).

model.wh.2 <- specify.model()
    Alienation67   ->  Anomia67,      NA,        1
    Alienation67   ->  Powerless67,   lamby,    NA
    Alienation71   ->  Anomia71,      NA,        1
    Alienation71   ->  Powerless71,   lamby,    NA 
    SES            ->  Education,     NA,        1     
    SES            ->  SEI,           lambx,    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.2 <- sem(model.wh.2, S.wh, 932)

summary(sem.wh.2)

##     Model Chisquare =  12.673   Df =  8 Pr(>Chisq) = 0.12360
##     Chisquare (null model) =  2131.4   Df =  15
##     Goodness-of-fit index =  0.99553
##     Adjusted goodness-of-fit index =  0.98828
##     RMSEA index =  0.025049   90 % CI: (NA, 0.04985)
##     Bentler-Bonnett NFI =  0.99405
##     Tucker-Lewis NNFI =  0.99586
##     Bentler CFI =  0.9978
##     SRMR =  0.012981
##     BIC =  -42.026 
##    
##     Normalized Residuals
##         Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##    -0.997000 -0.140000  0.000295 -0.028800  0.100000  0.759000 
##    
##     Parameter Estimates
##          Estimate  Std Error z value  Pr(>|z|)                                 
##    lamby   0.86261  0.033383  25.8402 0.0000e+00 Powerless67 <--- Alienation67 
##    lambx   5.35302  0.432591  12.3743 0.0000e+00 SEI <--- SES                  
##    gam1   -0.62129  0.056142 -11.0663 0.0000e+00 Alienation67 <--- SES         
##    beta    0.59428  0.047040  12.6335 0.0000e+00 Alienation71 <--- Alienation67
##    gam2   -0.23580  0.054684  -4.3121 1.6173e-05 Alienation71 <--- SES         
##    the1    3.74499  0.249823  14.9906 0.0000e+00 Anomia67 <--> Anomia67        
##    the2    3.49378  0.200754  17.4033 0.0000e+00 Powerless67 <--> Powerless67  
##    the3    2.97409  0.499661   5.9522 2.6456e-09 Education <--> Education      
##    the4  260.13252 18.298141  14.2163 0.0000e+00 SEI <--> SEI                  
##    the5    0.90377  0.121818   7.4190 1.1791e-13 Anomia71 <--> Anomia67        
##    psi1    5.47380  0.464073  11.7951 0.0000e+00 Alienation67 <--> Alienation67
##    psi2    4.36410  0.362722  12.0315 0.0000e+00 Alienation71 <--> Alienation71
##    phi     6.63576  0.640425  10.3615 0.0000e+00 SES <--> SES                  
##    
##     Iterations =  79 
##

# Compare the two models by a likelihood-ratio test:

anova(sem.wh.1, sem.wh.2)

##    LR Test for Difference Between Models
##    
##            Model Df Model Chisq Df LR Chisq Pr(>Chisq)
##    Model 1        9     13.4851                       
##    Model 2        8     12.6731  1   0.8119     0.3676


# ----------------------- Thurstone data ---------------------------------------
#  Second-order confirmatory factor analysis, from the SAS manual for PROC CALIS

R.thur <- read.moments(diag=FALSE, names=c('Sentences','Vocabulary',
        'Sent.Completion','First.Letters','4.Letter.Words','Suffixes',
        'Letter.Series','Pedigrees', 'Letter.Group'))
    .828                                              
    .776   .779                                        
    .439   .493    .46                                 
    .432   .464    .425   .674                           
    .447   .489    .443   .59    .541                    
    .447   .432    .401   .381    .402   .288              
    .541   .537    .534   .35    .367   .32   .555        
    .38   .358    .359   .424    .446   .325   .598   .452  
            
model.thur <- specify.model()
    F1 -> Sentences,                      lam11, NA
    F1 -> Vocabulary,                     lam21, NA
    F1 -> Sent.Completion,                lam31, NA
    F2 -> First.Letters,                  lam41, NA
    F2 -> 4.Letter.Words,                 lam52, NA
    F2 -> Suffixes,                       lam62, NA
    F3 -> Letter.Series,                  lam73, NA
    F3 -> Pedigrees,                      lam83, NA
    F3 -> Letter.Group,                   lam93, NA
    F4 -> F1,                             gam1,  NA
    F4 -> F2,                             gam2,  NA
    F4 -> F3,                             gam3,  NA 
    Sentences <-> Sentences,              th1,   NA
    Vocabulary <-> Vocabulary,            th2,   NA
    Sent.Completion <-> Sent.Completion,  th3,   NA
    First.Letters <-> First.Letters,      th4,   NA
    4.Letter.Words <-> 4.Letter.Words,    th5,   NA
    Suffixes <-> Suffixes,                th6,   NA
    Letter.Series <-> Letter.Series,      th7,   NA
    Pedigrees <-> Pedigrees,              th8,   NA
    Letter.Group <-> Letter.Group,        th9,   NA
    F1 <-> F1,                            NA,     1
    F2 <-> F2,                            NA,     1
    F3 <-> F3,                            NA,     1
    F4 <-> F4,                            NA,     1

sem.thur <- sem(model.thur, R.thur, 213)

summary(sem.thur)

##     Model Chisquare =  38.196   Df =  24 Pr(>Chisq) = 0.033101
##     Chisquare (null model) =  1101.9   Df =  36
##     Goodness-of-fit index =  0.95957
##     Adjusted goodness-of-fit index =  0.9242
##     RMSEA index =  0.052822   90 % CI: (0.015262, 0.083067)
##     Bentler-Bonnett NFI =  0.96534
##     Tucker-Lewis NNFI =  0.98002
##     Bentler CFI =  0.98668
##     SRMR =  0.043595
##     BIC =  -90.475 
##    
##     Normalized Residuals
##         Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##    -9.72e-01 -4.16e-01 -4.20e-07  4.01e-02  9.39e-02  1.63e+00 
##    
##     Parameter Estimates
##          Estimate Std Error z value Pr(>|z|)                                       
##    lam11 0.51512  0.064964  7.9293  2.2204e-15 Sentences <--- F1                   
##    lam21 0.52031  0.065162  7.9849  1.3323e-15 Vocabulary <--- F1                  
##    lam31 0.48743  0.062422  7.8087  5.7732e-15 Sent.Completion <--- F1             
##    lam41 0.52112  0.063137  8.2538  2.2204e-16 First.Letters <--- F2               
##    lam52 0.49707  0.059673  8.3298  0.0000e+00 4.Letter.Words <--- F2              
##    lam62 0.43806  0.056479  7.7562  8.6597e-15 Suffixes <--- F2                    
##    lam73 0.45244  0.071371  6.3392  2.3100e-10 Letter.Series <--- F3               
##    lam83 0.41729  0.061037  6.8367  8.1020e-12 Pedigrees <--- F3                   
##    lam93 0.40763  0.064524  6.3175  2.6584e-10 Letter.Group <--- F3                
##    gam1  1.44381  0.264173  5.4654  4.6184e-08 F1 <--- F4                          
##    gam2  1.25383  0.216597  5.7888  7.0907e-09 F2 <--- F4                          
##    gam3  1.40655  0.279331  5.0354  4.7681e-07 F3 <--- F4                          
##    th1   0.18150  0.028400  6.3907  1.6517e-10 Sentences <--> Sentences            
##    th2   0.16493  0.027797  5.9334  2.9678e-09 Vocabulary <--> Vocabulary          
##    th3   0.26713  0.033468  7.9816  1.5543e-15 Sent.Completion <--> Sent.Completion
##    th4   0.30150  0.050686  5.9484  2.7073e-09 First.Letters <--> First.Letters    
##    th5   0.36450  0.052358  6.9617  3.3618e-12 4.Letter.Words <--> 4.Letter.Words  
##    th6   0.50642  0.059963  8.4455  0.0000e+00 Suffixes <--> Suffixes              
##    th7   0.39033  0.061599  6.3367  2.3474e-10 Letter.Series <--> Letter.Series    
##    th8   0.48137  0.065388  7.3618  1.8141e-13 Pedigrees <--> Pedigrees            
##    th9   0.50510  0.065227  7.7437  9.5479e-15 Letter.Group <--> Letter.Group      
##
##    Iterations =  54 
##

#------------------------- Kerchoff/Kenney path analysis ---------------------
# An observed-variable recursive SEM from the LISREL manual

R.kerch <- read.moments(diag=FALSE, names=c('Intelligence','Siblings',
                        'FatherEd','FatherOcc','Grades','EducExp','OccupAsp'))
    -.100                                
     .277  -.152                          
     .250  -.108  .611                     
     .572  -.105  .294   .248               
     .489  -.213  .446   .410   .597         
     .335  -.153  .303   .331   .478   .651   
    
model.kerch <- specify.model()
    Intelligence -> Grades,       gam51,    NA
    Siblings -> Grades,           gam52,    NA
    FatherEd -> Grades,           gam53,    NA
    FatherOcc -> Grades,          gam54,    NA
    Intelligence -> EducExp,      gam61,    NA
    Siblings -> EducExp,          gam62,    NA
    FatherEd -> EducExp,          gam63,    NA
    FatherOcc -> EducExp,         gam64,    NA
    Grades -> EducExp,            beta65,   NA
    Intelligence -> OccupAsp,     gam71,    NA
    Siblings -> OccupAsp,         gam72,    NA
    FatherEd -> OccupAsp,         gam73,    NA
    FatherOcc -> OccupAsp,        gam74,    NA
    Grades -> OccupAsp,           beta75,   NA
    EducExp -> OccupAsp,          beta76,   NA
    Grades <-> Grades,            psi5,     NA
    EducExp <-> EducExp,          psi6,     NA
    OccupAsp <-> OccupAsp,        psi7,     NA

                       
sem.kerch <- sem(model.kerch, R.kerch, 737, fixed.x=c('Intelligence','Siblings',
    'FatherEd','FatherOcc'))
    
summary(sem.kerch)

##     Model Chisquare =  3.2685e-13   Df =  0 Pr(>Chisq) = NA
##     Chisquare (null model) =  1664.3   Df =  21
##     Goodness-of-fit index =  1
##     BIC =  3.2685e-13 
##    
##     Normalized Residuals
##         Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##    -4.28e-15  0.00e+00  0.00e+00  7.62e-16  1.47e-15  5.17e-15 
##    
##     Parameter Estimates
##           Estimate  Std Error z value  Pr(>|z|)                             
##    gam51   0.525902 0.031182  16.86530 0.0000e+00 Grades <--- Intelligence  
##    gam52  -0.029942 0.030149  -0.99314 3.2064e-01 Grades <--- Siblings      
##    gam53   0.118966 0.038259   3.10951 1.8740e-03 Grades <--- FatherEd      
##    gam54   0.040603 0.037785   1.07456 2.8257e-01 Grades <--- FatherOcc     
##    gam61   0.160270 0.032710   4.89979 9.5940e-07 EducExp <--- Intelligence 
##    gam62  -0.111779 0.026876  -4.15899 3.1966e-05 EducExp <--- Siblings     
##    gam63   0.172719 0.034306   5.03461 4.7882e-07 EducExp <--- FatherEd     
##    gam64   0.151852 0.033688   4.50758 6.5571e-06 EducExp <--- FatherOcc    
##    beta65  0.405150 0.032838  12.33799 0.0000e+00 EducExp <--- Grades       
##    gam71  -0.039405 0.034500  -1.14215 2.5339e-01 OccupAsp <--- Intelligence
##    gam72  -0.018825 0.028222  -0.66700 5.0477e-01 OccupAsp <--- Siblings    
##    gam73  -0.041333 0.036216  -1.14126 2.5376e-01 OccupAsp <--- FatherEd    
##    gam74   0.099577 0.035446   2.80924 4.9658e-03 OccupAsp <--- FatherOcc   
##    beta75  0.157912 0.037443   4.21738 2.4716e-05 OccupAsp <--- Grades      
##    beta76  0.549593 0.038260  14.36486 0.0000e+00 OccupAsp <--- EducExp     
##    psi5    0.650995 0.033946  19.17743 0.0000e+00 Grades <--> Grades        
##    psi6    0.516652 0.026943  19.17590 0.0000e+00 EducExp <--> EducExp      
##    psi7    0.556617 0.029026  19.17644 0.0000e+00 OccupAsp <--> OccupAsp    
##    
##     Iterations =  0 


#------------------- McArdle/Epstein latent-growth-curve model -----------------
# This model, from McArdle and Epstein (1987, p.118), illustrates the use of a 
# raw moment matrix to fit a model with an intercept. (The example was suggested
# by Mike Stoolmiller.)

M.McArdle <- read.moments(names=c('WISC1', 'WISC2', 'WISC3', 'WISC4', 'UNIT'))
    365.661                                      
    503.175     719.905                           
    675.656     958.479    1303.392                
    890.680    1265.846    1712.475    2278.257     
     18.034      25.819      35.255      46.593     1.000
 
mod.McArdle <- specify.model()
    C -> WISC1, NA, 6.07
    C -> WISC2, B2, NA
    C -> WISC3, B3, NA
    C -> WISC4, B4, NA
    UNIT -> C, Mc, NA
    C <-> C, Vc, NA,
    WISC1 <-> WISC1, Vd, NA
    WISC2 <-> WISC2, Vd, NA
    WISC3 <-> WISC3, Vd, NA
    WISC4 <-> WISC4, Vd, NA

sem.McArdle <- sem(mod.McArdle, M.McArdle, 204, fixed.x="UNIT", raw=TRUE)
summary(sem.McArdle)

##    Model fit to raw moment matrix.
##    
##     Model Chisquare =  83.791   Df =  8 Pr(>Chisq) = 8.4377e-15
##     BIC =  41.246 
##    
##     Normalized Residuals
##        Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    -0.15300 -0.01840  0.00132 -0.00576  0.02400  0.07760 
##    
##     Parameter Estimates
##       Estimate Std Error z value Pr(>|z|)                 
##    B2  8.61354 0.135438  63.5976 0        WISC2 <--- C    
##    B3 11.64054 0.168854  68.9387 0        WISC3 <--- C    
##    B4 15.40323 0.213071  72.2916 0        WISC4 <--- C    
##    Mc  3.01763 0.060690  49.7219 0        C <--- UNIT     
##    Vc  0.44343 0.047704   9.2955 0        C <--> C        
##    Vd 11.78832 0.674060  17.4885 0        WISC1 <--> WISC1
##    
##     Iterations =  37 

    
## End(Not run)

sem1 documentation built on May 2, 2019, 6:38 p.m.