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integer and is the primary key for the
data table.
Its initial value is zero, and it increments by one for each row.
text and has a different value for every row;
i.e., the names are unique and can act as substitutes for the primary key.
The names are intended to be easier for a human to remember than the ids.
integer and is the
integrand_id
that identifies
the integrand for this measurement.
integer and is the
density_id
that identifies
the density function for the measurement nose.
The density_name
corresponding to
density_id
cannot be uniform.
(Use hold_out
to ignore data during fitting.)
This density may be replaced using the data_density_command
.
integer and is the
node_id
that identifies
the node for this measurement.
node_id
is the
parent_node_id
,
this data will be associated with the parent node and not
have any random effects in its model.
node_id
is a
child
of the parent node,
or a descendant
of a child,
the data will be associated with the random effects for that child.
In this case
density_id
cannot correspond to
laplace
or
log_laplace
.
The corresponding densities would not be differentiable at zero
and the Laplace approximation would not make sense in this case.
integer and is the
subgroup_id
that this data point corresponds to.
subgroup_id
even though the
group_id
does not
appear in the data file (if it does appear, it will not be used).
subgroup_id
must be null.
integer and is the
weight_id
that identifies
the weighting used for this measurement.
If
weight_id
is nu
weight_id
is null,
the constant weighting is used for this data point.
integer and has value zero or one.
Only the rows where hold_out is zero are included
in the objective optimized during a fit_command
.
See the fit command hold_out
documentation.
real and is the measured value
for each row of the data table;
i.e., the measurement of the integrand, node, etc.
real,
has same units at the data,
and must be a positive number.
This is not the only contribution to the standard deviation
used in the data likelihood; see
minimum cv standard deviation
@(@
\Delta
@)@,
transformed standard deviation
@(@
\sigma
@)@, and
adjusted standard deviation
@(@
\delta ( \theta )
@)@.
real.
If
density_id
corresponds to a
log scaled density
,
eta
must be greater than or equal zero and is
the offset in the log transformation for this data point; see
log scaled case definition of the
weighted residual function
.
This offset may be replaced using the data_density_command
.
density_id
does not correspond to
log_gaussian, log_laplace, or log_students,
eta
can be null.
real.
If
density_id
corresponds to
students or log_students,
nu
must be greater than two and is
number of degrees of freedom in the distribution for this point; see
the definition of the log-density for
Student's-t
and
Log-Student's-t
.
The degrees of freedom may be replaced using the
data_density_command
.
density_id
does not correspond to
students or log_students,
nu
can be null.
real and is the lower age limit
for this measurement.
It must be greater than or equal the minimum age_table
value.
real and is the upper age limit
for this measurement.
It must be greater than or equal the corresponding
age_lower
and less than or equal the maximum age_table
value.
real and is the lower time limit
for this measurement.
It must be greater than or equal the minimum time_table
value.
real and is the upper time limit
for this measurement.
It must be greater than or equal the corresponding
time_lower
and less than or equal the maximum time_table
value.
real and column names
that begin with the two characters x_.
For each valid covariate_id
,
column
x_covariate_id
contains
the value, for this measurement, of the covariate specified by
covariate_id
.
null is interpreted as the
reference
value for
the corresponding covariate.
data tables.