cerror | R Documentation |

Calculates partial correlations and partial significance tests.

```
cerror(formula., modelList, data = NULL)
```

`formula.` |
A formula specifying the two correlated variables using |

`modelList` |
A list of structural equations. |

`data` |
A |

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.

Returns a `data.frame`

containing the (partial) correlation and
associated significance test.

Jon Lefcheck <lefcheckj@si.edu>

`%~~%`

```
# 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)
```

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