piecewiseSEM (version 2.3.0)

cerror: Correlated errors

Description

Calculates partial correlations and partial significance tests.

Usage

cerror(formula., modelList, data = NULL)

Value

Returns a data.frame containing the (partial) correlation and associated significance test.

Arguments

formula.

A formula specifying the two correlated variables using %~~%.

modelList

A list of structural equations.

data

A data.frame containing the data used in the list of equations.

Author

Jon Lefcheck <[e-mail]>

Details

If the variables are exogenous, then the correlated error is the raw bivariate correlation.

If the variables are endogenous, then the correlated error is the partial correlation, accounting for the influence of any predictors.

The significance of the correlated error is conducted using cor.test if the variables are exogenous. Otherwise, a t-statistic is constructed and compared to a t-distribution with N - k - 2 degrees of freedom (where N is the total number of replicates, and k is the total number of variables informing the relationship) to derive a P-value.

See Also

%~~%

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)

# Same as cor.test
with(dat, cor.test(x1, x2))

# Look at correlatde error between x1 and y1
# (endogenous)
cerror(y1 %~~% x1, sem, dat)

# Not the same as cor.test
# (accounts for influence of x1 and x2 on y1)
with(dat, cor.test(y1, x1))

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

coefs(sem)