Sorts rows of x by missingness patterns, and centers/scales
columns of x. Calculates various bookkeeping quantities needed
for input to other functions, such as `em.norm`

and `da.norm`

.

1 | ```
prelim.norm(x)
``` |

`x` |
data matrix containing missing values. The rows of x
correspond to observational units, and the columns to variables.
Missing values are denoted by |

a list of thirteen components that summarize various features of x after the data have been centered, scaled, and sorted by missingness patterns. Components that might be of interest to the user include:

`nmis` |
a vector of length ncol(x) containing the number of missing values for each variable in x. This vector has names that correspond to the column names of x, if any. |

`r` |
matrix of response indicators showing the missing data patterns in x. Dimension is (S,p) where S is the number of distinct missingness patterns in the rows of x, and p is the number of columns in x. Observed values are indicated by 1 and missing values by 0. The row names give the number of observations in each pattern, and the column names correspond to the column names of x. |

See Section 5.3.1 of Schafer (1996).

1 2 3 4 | ```
data(mdata)
s <- prelim.norm(mdata) #do preliminary manipulations
s$nmis[s$co] #look at nmis
s$r #look at missing data patterns
``` |

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