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Enumeration Types
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Enumeration Types
This section lists the enumeration types defined in the Extreme
Optimization Numerical Libraries for .NET.
AnovaRowType
Enumerates the possible types of rows in an AnovaTable.
| Member Name |
Description |
| Model |
The data in the row refers to model effects. |
| Error |
The data in the row refers to the residual error in the
model. |
| Total |
The data in the row refers to the total of model effects and
residuals. |
BoundaryIntervalBehavior
Enumerates how segments at the boundaries of subdivided series
are handled.
| Member Name |
Description |
| Exclude |
The entire interval is excluded. |
| Include |
The interval is included. |
| CompleteAndIncludel |
The interval is extended to a full larger interval and the
extended interval is included. |
DateTimeUnit
Enumerates the time units used in the construction of time
scales.
| Member Name |
Description |
| Unknown |
The time unit is unknown. |
| Hour |
The time unit is one hour |
| Day |
The time unit is one day. |
| Week |
The time unit is one week. |
| Month |
The time unit is one month. |
| Quarter |
The time unit is one quarter. |
| Year |
The time unit is one year. |
MissingValueAction
Enumerates the possible actions to be taken when a calculation
encounters a missing value.
| Member Name |
Description |
| Default |
Use the default action: The value or row containing the missing
value is discarded. |
| Discard |
The value or row containing the missing value is
discarded. |
| Ignore |
The value is ignored. Most operations on numerical variables
will give
NaN as a result. |
| ReplaceWithPrevious |
Any missing values are replaced with the value of the previous
observation. If the first observation is missing, it is replaced
with a user-specified value, or 0. |
| ReplaceWithNext |
Any missing values are replaced with the value of the next
observation. If the last observation is missing, it is replaced
with a user-specified value, or 0. |
| ReplaceWithValue |
Any missing values are replaced with a user-specified
value. |
| Fail |
A MissingValueException
is thrown. |
SortOrder
Enumerates the ways data can be sorted.
| Member Name |
Description |
| None |
The data is not sorted. |
| Ascending |
The data is sorted in ascending order. |
| Descending |
The data is sorted in descending order. |
SpecialBins
Enumerates the possible special bins to be included in a
CategoricalScale.
| Member Name |
Description |
| None |
No special bins are included. |
| BelowMinimum |
There is a special bin for values below the scale's minimum
value. |
| AboveMaximum |
There is a special bin for values above the scale's maximum
value. |
| OutOfRange |
There is a special bin for values that are outside the scale's
range. |
| Missing |
There is a special bin for missing values. |
TestOfHomogeneityOfVariances
Enumerates the choices when testing whether a number of samples
have the same variance.
TestOfNormality
Enumerates the choices when testing whether a
sample follows a normal distribution.
RanLuxLuxuryLevel
Enumerates possible values for the luxury
level of a RanLux random number
generator.
| Member Name |
Description |
| Default |
The default (low) level. |
| Better |
A medium value providing better randomness at a reasonable
cost. |
| Best |
Highest possible value, providing best possible randomness at
greatest cost. |
HypothesisTestType
Enumerates the possible values for a hypothesis test.
| Member Name |
Description |
| TwoTailed |
The null hypothesis is rejected if the test statistic lies too
far on either side of the mean of the test distribution. |
| OneTailedLower |
The null hypothesis is rejected if the test statistic lies in
the left (lower) tail of the test distribution. |
| OneTailedUpper |
The null hypothesis is rejected if the test statistic lies in
the right (upper) tail of the test distribution. |
LeveneTestLocationMeasure
Enumerates the ways the central tendency of a sample is
calculated in Levene's
test.
Levene's test for homogeneity of variances assumes that the
underlying populations of the samples have a normal distribution. A
specific choice of measure for central tendency can make the test
more robust when the data is not normal.
| Member Name |
Description |
| Mean |
The mean is used. This works best for normal data. |
| Median |
The median is used. This is the default, and gives better
results when the data is skewed. |
| TrimmedMean |
The 10% trimmed mean is used. This gives better results when
the data is heavy-tailed. |
SamplePairing
Enumerates the possible ways to relate two samples in a two
sample hypothesis test.
| Member Name |
Description |
| Unpaired |
The two samples are independent. |
| Paired |
The two samples are paired. Each observation in the first
sample has a corresponding observation in the second sample. |
VarianceAssumption
Enumerates the possible assumptions made about the variances in
a multi-sample hypothesis test.
| Member Name |
Description |
| None |
No assumption is made about the variances of the samples. |
| AssumeEqual |
The variances of the samples are assumed to be equal. |
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