The name is absent

Stata Technical Bulletin

. mcompr2 schpl, default(rl) cutoff(0.8) effects

. mcompr2 schpl, default(Poor) cutoff(0.5) effects label
Group       Vs. Diff. P-value

Excellent   Average  2711.313     0.0927

Excellent      Fair  2909.323     0.1977

Excellent      Poor  3816.672     0.3466

An additional program for displaying pairwise differences is included on the distribution diskette. This program, mcompr3,
was developed for the special application of a client, but it may be of interest to other Stata users as well. The syntax of mcompr3
is

mcompr3 varname , greater (macro list) less (macro list) [ cutoff (#) def ault (name) label ]

There must be as many macro names in each list as there are categories that are compared with mcompp. These macros receive
the names or descriptions of the variables that this category is definitely greater than or less than. All other options work the
same as in mcompr2.

mcompr3 can be a little tricky, but the following example should give you the general idea of its operation and of its
flexibility.

. mcompr3 schpl_t label great(Gl G2 G3 G4 G5) less(Ll L2 L3 L4 L5) cutoff(.3) default(Poor)

. capture program drop doit

. program define doit

1. di "Category              Clearly Greater Than        Clearly Less Than"

2. di "Poor" .col(26) "$G1" .col(54) "$L1"

3. di "Fair" .col(26) "$G2" .col(54) "$L2"

4. di "Average" .col(26) "$G3" .col(54) "$L3"

5. di "Good" .col(26) "$G4" .col(54) "$L4"

6. di "Excellent" .col(26) "$G5" .col(54) "$L5"

7. end

. doit

Category              Clearly Greater Than        Clearly Less Than

Poor

Fair                                                  Excellent

Average                                               Excellent

Good

Excellent               Fair_jAverage

There is yet another program, ehcvsrc, on the distribution diskette. The adventurous reader can examine ehcvsrc for a
further elaboration of the use of mcompr3.

Formulas

The formulas are virtually identical to those described in [5s] oneway and, hence, are not repeated here.

snp7 Natural cubic splines

Peter Sasieni, Imperial Cancer Research Fund, London, FAX (011)-44-171-269-3429

This entry consists of three related programs for smoothing by regression onto the truncated power base for a natural cubic
spline: spline, sp_adj and spbase.

spline may be regarded as an alternative to ksm. It smooths a ^-variable against an ж-variable and displays a graph of
the original data with the smooth superimposed. The smooth is calculated by regression onto a cubic spline basis. The user may
specify the type of regression used to fit the smooth, e.g., logistic, poisson,.... By default the program uses regress (least
squares).

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