Specifies correlated errors among predictors

e1 

Details

For use in psem to identify correlated sets of variables.

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)
#> 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 is.data.frame(data): 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 is.data.frame(data): object 'dat' not found