**[P] matrix score** -- Score data from coefficient vectors

__Syntax__

__mat__**rix** __sco__**re** [*type*] *newvar* **=** *b* [*if*] [*in*] [**,** __eq__**uation(#***#*|*eqname***)**
__m__**issval(***#***)** **replace** **forcezero**]

where *b* is a 1 x p matrix.

__Description__

**matrix score** creates *newvar* = *x_jb*' (*b* being a row vector), where *x_j* is
the row vector of values of the variables specified by the column names
of *b*. The name **_cons** is treated as a variable equal to 1.

__Options__

**equation(#***#*|*eqname***)** specifies the equation -- by either number or name --
for selecting coefficients from *b* to use in scoring. See **[P] matrix**
**rownames** for more on equation labels with matrices.

**missval(***#***)** specifies the value to be assumed if any values are missing
from the variables referred to by the coefficient vector. By
default, this value is taken to be missing (.), and any missing value
among the variables produces a missing score.

**replace** specifies that *newvar* already exists. Here observations not
included by **if** *exp* and **in** *range* are left unchanged; that is, they are
not changed to missing. Be warned that **replace** does not promote the
storage type of the existing variable; if the variable was stored as
an **int**, the calculated scores would be truncated to integers when
stored.

**forcezero** specifies that, should a variable described by the column names
of *b* not exist, the calculation treat the missing variable as if it
did exist and was equal to zero for all observations. It contributes
nothing to the summation. By default, a missing variable would
produce an error message.

__Example__

Setup
**. sysuse auto**
**. regress price weight mpg**

Define matrix **coefs** equal to **e(b)**, the coefficient vector
**. matrix coefs = e(b)**

List the contents of **coefs**
**. mat list coefs**

Create variable **lc** containing the linear predictions
**. matrix score lc = coefs**

Summarize **lc**
**. summarize lc**

Setup
**. sureg (price weight mpg) (displacement weight)**

Define matrix **coefs** equal to **e(b)**, the coefficient vector
**. matrix coefs = e(b)**

List the contents of **coefs**
**. mat list coefs**

Create variable **lca** containing the linear predictions for equation **price**
**. matrix score lca = coefs, eq(price)**

Same as above command
**. matrix score lc1 = coefs, eq(#1)**

Same as above command
**. matrix score lcnoeq = coefs**

Create variable **lcb** containing the linear predictions for equation
**displacement**
**. matrix score lcb = coefs, eq(displacement)**

Same as above command
**. matrix score lc2 = coefs, eq(#2)**

Summarize newly created variables
**. summarize lc***