Correlated error operator

Specifies correlated errors among predictors

e1 %~~% e2

Arguments

e1

first variable involved in correlated error

e2

second variable involved in correlated error

Details

For use in psem to identify correlated sets of variables.

See also

cerror

Author

Jon Lefcheck <LefcheckJ@si.edu>, Jarrett Byrnes

Examples

# Generate example data
dat <- data.frame(x1 = runif(50),
  x2 = runif(50), y1 = runif(50),
    y2 = runif(50))

# Create list of structural equations
sem <- psem(
  lm(y1 ~ x1 + x2, dat),
  lm(y2 ~ y1 + x1, dat)
)

# Look at correlated error between x1 and x2
# (exogenous)
cerror(x1 %~~% x2, sem, dat)
#>    Response Predictor   Estimate Std.Error DF Crit.Value   P.Value 
#> df     ~~x1      ~~x2 0.06590461        NA 48  0.4575954 0.6493078 

# Same as cor.test
with(dat, cor.test(x1, x2))
#> 
#> 	Pearson's product-moment correlation
#> 
#> data:  x1 and x2
#> t = 0.4576, df = 48, p-value = 0.6493
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  -0.2164131  0.3380510
#> sample estimates:
#>        cor 
#> 0.06590461 
#> 

# Look at correlatde error between x1 and y1
# (endogenous)
cerror(y1 %~~% x1, sem, dat)
#> Error in eval(mf, parent.frame()): object 'dat' not found

# Not the same as cor.test
# (accounts for influence of x1 and x2 on y1)
with(dat, cor.test(y1, x1))
#> 
#> 	Pearson's product-moment correlation
#> 
#> data:  y1 and x1
#> t = -0.58041, df = 48, p-value = 0.5644
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#>  -0.3536131  0.1995013
#> sample estimates:
#>         cor 
#> -0.08348228 
#> 

# Specify in psem
sem <- update(sem, x1 %~~% y1)

coefs(sem)
#> Error in eval(mf, parent.frame()): object 'dat' not found