Nothing

```
\dontrun{
# read in the example data (generated, not real student data)
sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))
# Use | symbol to separate dependent variables in the left-hand side of formula
mvrlm.fit <- mvrlm.sdf(algebra | geometry ~ dsex + m072801, jrrIMax = 5, data = sdf)
# print method returns coefficients, as does coef method
mvrlm.fit
coef(mvrlm.fit)
# for more detailed results, use summary:
summary(mvrlm.fit)
# Details of model can also be accessed through components of the returned object; for example:
# coefficients (one column per dependent variable)
mvrlm.fit$coef
# coefficient table with standard errors and p-values (1 table per dependent variable)
mvrlm.fit$coefmat
# R-squared values (one per dependent variable)
mvrlm.fit$r.squared
# Residual covariance matrix
mvrlm.fit$residCov
# Model residuals and other details are available as well.
# show the structure of the residuals objects
str(mvrlm.fit$residuals)
str(mvrlm.fit$residPV)
# dependent variables can have plausible values or not (or a combination).
mvrlm.fit <- mvrlm.sdf(composite | mrps22 ~ dsex + m072801, data = sdf, jrrIMax = 5)
summary(mvrlm.fit)
mvrlm.fit <- mvrlm.sdf(algebra | geometry | measurement ~ dsex + m072801, data = sdf, jrrIMax = 5)
summary(mvrlm.fit)
mvrlm.fit <- mvrlm.sdf(mrps51 | mrps22 ~ dsex + m072801, data = sdf, jrrIMax = 5)
summary(mvrlm.fit)
# Hypotheses about coefficient restrictions can also be tested using the Wald test.
mvr <- mvrlm.sdf(algebra | geometry ~ dsex + m072801, data = sdf)
hypothesis <- c("geometry_dsexFemale = 0", "algebra_dsexFemale = 0")
# test statistics based on the F and chi-squared distribution are available.
linearHypothesis(mvr, hypothesis = hypothesis, test = "F")
linearHypothesis(mvr, hypothesis = hypothesis, test = "Chisq")
}
```

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