help parmcip_opts(Roger Newson)

Options for metaparm and parmcip

Syntax

notdist Use Normal or t-distribution

eform Estimates and confidence limits exponentiated

float Numeric output variables of type float or less

nozerop Reset zero P-values to smallest positive double-precision number

nullvalue(#) Value of estimated parameters under null hypotheses

fast Calculate confidence limits without precautions

plower Create variable containing lower one-tailed P-values

pupper Create variable containing upper one-tailed P-values

estimate(varname) Name of input estimate variable

stderr(varname) Name of input standard error variable

dof(varname) Name of input degrees of freedom variable

zstat(newvarname) Name of output z-statistic variable

tstat(newvarname) Name of output t-statistic variable

pvalue(newvarname) Name of output P-value variable

plovalue(newvarname) Name of output lower one-sided P-value variable

pupvalue(newvarname) Name of output upper one-sided P-value variable

stars(numlist) List of P-value thresholds for stars

nstars(newvarname) Name of output stars variable for P-value

nstarslower(newvarname) Name of output stars variable for lower one-sided P-value

nstarsupper(newvarname) Name of output stars variable for upper one-sided P-value

level(numlist) Confidence level(s) for calculating confidence limits

clnumber(numbering_rule) Numbering rule for naming confidence limit variables

minprefix(prefix) Prefix for lower confidence limits

maxprefix(prefix) Prefix for upper confidence limits

mcompare(method) Multiple-comparison method

mcomci(method) Multiple-comparison method for confidence limits only

replace Replace variables with same names as output variables

where numbering_rule is

level | rank

and method is

noadjust | bonferroni | sidak

Description

These options are available for metaparm and for parmcip.
They control the calculation of confidence limits and P-values.
They are not often used, as metaparm and parmcip have defaults for these options,
which are usually sensible.

Options

notdist specifies whether or not a t-distribution is used
to calculate confidence limits. If tdist is specified, then a t-distribution is used.
If notdist is specified, then a standard Normal distribution is used.
If neither tdist nor notdist is specified by the user,
then the option is set to tdist if the dof() option
is set to the name of an existing variable, and is set to notdist otherwise.
If tdist is specified with metaparm, then the degrees of freedom in the output dataset
are calculated from the degrees of freedom and standard errors in the input dataset,
using the Satterthwaite formula (Satterthwaite, 1946),
or using the Welch formula (Welch, 1947) if the user specifies dfcombine(welch),
or using the common constant degrees of freedom if the user specifies dfcombine(constant).

eform indicates that the input estimates are exponentiated,
and that the input standard errors are multiplied by the exponentiated estimate,
and that the output confidence limits are to be exponentiated.
If eform is used with metaparm, then the estimate variable in the output dataset is exponentiated,
and the standard error variable in the output dataset is multiplied
by the exponentiated estimate variable.

float specifies that the numeric output variables will be created as type float or below.
If float is unset, then the numeric output variables are created as type double.
Note that all generated variables are compressed as much as possible without loss of information,
whether or not float is specified.

nozerop specifies that P-values less than c(smallestdouble),
the smallest full-precision double value greater than zero,
will be reset to c(smallestdouble).
This can be useful if the user wants to compute or plot their logs.
Note that these P-values will be reset back to zero if float is specified.

nullvalue(#) specifies a value for the estimated parameters
under the null hypotheses tested using the z-statistics or t-statistics
and their P-values.
If not specified, then it is set to 1 if eform is specified,
and set to 0 otherwise.

fast is an option for programmers, and specifies that no action will be taken
to restore the original data if the user presses Break.
If used with metaparm, the fast option implies the norestore option (see metaparm_outdest_opts).

plower specifies that a new variable will be created, with default name plower,
containing the lower one-sided P-values for the parameter estimates.

pupper specifies that a new variable will be created, with default name pupper,
containing the upper one-sided P-values for the parameter estimates.

estimate(varname) specifies the name of the input variable containing estimates.
It is set to estimate if not specified.

stderr(varname) specifies the name of the input variable containing standard errors.
It is set to stderr if not specified.

dof(varname) specifies the name of the input variable containing degrees of freedom.
It is set to dof if not specified.

zstat(newvarname) specifies the name of the output variable containing the z-statistics.
It is set to z if not specified.

tstat(newvarname) specifies the name of the output variable containing the t-statistics.
It is set to t if not specified.

pvalue(newvarname) specifies the name of the output variable containing the P-values.
It is set to p if not specified.

stars(numlist) is used to generate a string variable with default name stars,
containing the stars for the P-values.
It works in the same way as the stars() option of parmest.

nstars(newvarname) specifies the name of the output variable
containing the stars for the P-value,
if stars() is specified. If nstars() is not specified,
then the name is set to stars.

nstarslower(newvarname) and nstarsupper(newvarname)
specify the names of output variables containing the stars for the lower and upper one-sided P-values,
if the user specifies the options plower and pupper, respectively,
and if stars() is also specified.
The default names for these output variables are starslower and starsupper, respectively.

level(numlist) specifies the confidence levels, in percent, for
the confidence limit variables created in the output dataset.
It works in the same way as the level() option of parmest.

clnumber(numbering_rule) specifies the rule used to number the names of the
confidence limit variables created in the output dataset.
It works similarly to the clnumber() option of parmest.
However, with parmcip and metaparm, the user may specify
prefixes other than min and max for the confidence limits, using the
minprefix() and maxprefix() options.

minprefix(prefix) specifies the prefix used for naming the lower confidence limit variables.
It is set to min if not specified. For instance, if the user specifies minprefix(inf), then
the lower 95% confidence limit variable will be named inf95.

maxprefix(prefix) specifies the prefix used for naming the upper confidence limit variables.
It is set to max if not specified. For instance, if the user specifies maxprefix(sup), then
the upper 95% confidence limit variable will be named sup95.

mcompare(method) specifies a multiple-comparison method
used to adjust the generated confidence limits and P-values.
This method may be noadjust (the default, indicating no adjustment),
bonferroni (indicating the Bonferroni adjustment),
and sidak (indicating the Sidak adjustment).
The adjustments, if requested, are calculated for the total number of parameters estimated,
or for the number of parameters estimated in the by-group,
if a by varlist: prefix is specified.
Note that, if a by varlist: prefix is specified for metaparm,
then a by() option must also be specified,
and must begin with the variables in the by varlist: prefix.

mcomci(method) specifies a multiple-comparison method
used to adjust the generated confidence limits only, and not the generated P-values.
If the user wants to generate adjusted P-values without adjusting the confidence limits,
or to generate adjusted P-values using a different method from the one used for adjusting the confidence limits,
then the user is advised to use the qqvalue package,
which can be downloaded from SSC.
Note that the mcompci() and mcompare() options do not affect the names
of the generated variables containing the confidence limits,
only their values.

replace is a parmcip option, ignored by metaparm.
It specifies that, if there are existing variables in the input dataset with the same names as the generated variables
specified by the zstat(), tstat(), pvalue(), clnumber(), minprefix() or maxprefix() options,
then those variables will be replaced.
Whether or not replace is specified, the names of the output variables are not allowed to clash
with each other, with the input variables,
or with the variables in the by() and sumvar() options of metaparm.

Author

Roger Newson, King's College London, UK.
Email: roger.newson@kcl.ac.uk

References

Satterthwaite, F. E. 1946. An approximate distribution of estimates of variance components.
Biometrics Bulletin 2(6): 110-114.

Welch, B. L. 1947. The generalization of `Student's' problem when several different population variances are involved.
Biometrika 34(1/2): 28-35.

Also see

Manual:

Help: [U] 20 Estimation and postestimation commands

parmest, parmby, parmcip, metaparm,
metaparm_outdest_opts, metaparm_content_opts,
metaparm_resultssets, parmest_resultssets

qqvalue if installed