Features
Features are measurable attributes shared by artifacts (or assemblages). It is often assumed in seriation that the evolution of a feature's measure over time is gradual or even unimodal.
If you have chosen a seriation Technique other than Custom, you will not be able to modify some of the feature parameters.
Features Menu
Show/Hide Table allows you to open/close the Features table and create, delete, select and edit the features and their parameters.
Include All allows you to include all excluded features in the seriation.
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Features Table
In the Features table you may add, edit, select and delete features. There are a number of attributes associated with each feature. For many of these attributes you can set the values for all rows at the same time by right clicking on the column.
Exclude allows you to exclude a feature temporarily from your seriation. The alternative is to delete the feature in which case all information and data related to the feature will be lost to this seriation permanently. In contrast, using Exclude allows you to resurrect the feature and its data by clearing the Exclude field. Excluded features are greyed out and cannot be edited. Right clicking on the column brings up a popup menu that allows you to include all features with one click.
Graph is a toggle that allows you to select features to be included in the features graph. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
Index is an integer value assigned to a feature that allows you to sort features in this table.
Feature is a name that uniquely identifies the feature. Feature names are restricted to 50 characters. The feature name is required for each feature. The following names are reserved for use by OptiPath and cannot be used as feature names: Index, Name, Assemblage, Type, Description, Earliest, Latest, Exclude, Order, Date, Distance, Rate.
Description is an optional description that can be entered for each feature. There is no limit to the length of a description.
Weight indicates a weight to be given to a feature. Weights are relative. Weights of 1, 2 and 3 on three features are equivalent to weights of 3, 6 and 9. For weights to be considered, the seriation parameter Weights must be selected (this allows you to turn weights on and off without having to edit the weight for every feature). If the seriation parameter Weights is not selected, a weight of 1 is used for each feature rather than the values set in this feature parameter. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
Normalize allows the user to specify that the values (and hence the distances) for this feature should be normalized. Right clicking on the column brings up a popup menu that allows you to set all features with one click. Normalizing is done by converting values to standard deviations. If all features are normalized, then each contributes equally to the total distance between artifacts (or assemblages). Otherwise, a feature whose values range from zero to a thousand would generally have a much greater contribution to distance than a feature that ranges from zero to ten. Normalization is particularly useful if different features are expressed in very different units (for example, weight and length, or tons and ounces) or if different distance functions (Euclidean, Manhattan or Hamming) are used for different features, particularly for features using Euclidean or Manhattan distance if another feature uses Hamming distance. The intention of normalizing data is to make one feature as comparable as possible to another as far as its contribution to the total distance between artifacts is concerned. It is probably not a very good idea to normalize features using Hamming distance. If Normalize is checked for any feature and Use Frequencies is also checked in the Seriations table (which we suggest you think out very carefully before doing), then OptiPath will compute the frequencies first and normalize the values second.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Normalize parameter.
Transition is a penalty to be applied to this feature each time the feature transitions from absent to present or vice versa in a seriation. Many practitioners believe that a stylistic feature is likely to appear only once in the archaeological record - once it disappears it is unlikely to reappear. The implication is that a seriation with a feature present for a number of consecutive artifacts and then absent for a number of subsequent artifacts and then present again for even later artifacts is less realistic than a seriation where the artifacts having a feature are not interrupted by some that do not. The transition penalty is a means of enforcing this (unimodality is another). The larger the penalty, the less likely OptiPath is to create a seriation with interrupted occurrences of a feature. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Transition parameter. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
Ranks indicates the limit on the number of ranks or classes allowed for ranked or classed data (see Data above). If Ranks is equal to zero, there is no limit on the number of ranks. For Measured data Ranks must be 0. For Classed data Ranks must be 0 or 1. For Ranked data Ranks may be any non-negative integer. Setting Ranks equal to 1 implies binary data - an artifact either possesses or does not possess a feature - all non-blank entries are considered to indicate the presence of the feature, while all blank entries indicate the absence. Right clicking on the column brings up a popup menu that allows you to set all features with one click. For more information see Ranks.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Ranks parameter.
Data allows the user to specify the format of the data. The options are Measured, Ranked and Classed. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Data parameter.
Metric allows the user to specify the metric (or distance function) to be used in computing distances (in "feature space") between items (artifacts or assemblages). The options are Euclidean distance, Manhattan distance and Hamming distance. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
Euclidean distance is the normal distance that we deal with in everyday life.
Manhattan distance is computed by taking the sum of the distances for each feature taken one at a time. While Euclidean gives you the shortest distance between two points "as the crow flies", Manhattan distance is like walking along city blocks in New York - the distance walked is the sum of the distances walked along streets and avenues separately. With Manhattan distance you don't have the option of cutting diagonally through city blocks.
Hamming distance, like Manhattan distance, is the sum of the distances computed feature by feature, where the distance for each feature is restricted to be either zero or one. The feature distance between two artifacts (or assemblages) is zero if the two have the same feature value, one otherwise (regardless of how different they are). The effect of Hamming distance is simply to count in how many features two artifacts (or assemblages) differ. One difference between Euclidean distance and Manhattan distance is that Euclidean distance penalizes large distances disproportionately more than small distances. That is, using Euclidean distance, the distance between two artifacts which differ by one unit in each of two features is less than the distance between two artifacts which differ by two units in only one feature.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Metric parameter.
Earlier indicates how this feature should be considered for artifacts earlier than the earliest artifact in the data set. The options are Absent, Zero and Unknown, or combinations of these. Earlier artifacts could be considered to be absent or their feature values could be assumed to be zero or unknown. Each assumption can lead to different results in seriation. For more information see Earlier and Setting the Earlier, Later, Blanks and Zeroes Parameters. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Earlier parameter.
Later - indicates how this feature should be considered for artifacts later than the latest artifact in the data set. The options are Unknown, Absent and Zero, or combinations of these. Later artifacts could be considered to be absent or their feature values could be assumed to be zero or unknown. Each assumption can lead to different results in seriation. For more information see Later and Setting the Earlier, Later, Blanks and Zeroes Parameters. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Later parameter.
Blanks - indicates how blank values in the data set should be considered for this feature. The options are Unknown, Absent and Zero, or combinations of these. Blanks could be considered to indicate the feature is absent or that it has a value of zero or unknown. Each assumption can lead to different results in seriation. For more information see Blanks and Zeroes and Setting the Earlier, Later, Blanks and Zeroes Parameters. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Blanks parameter.
Zeroes indicates how zero values in the data set should be considered for this feature. The options are Unknown, Absent and Zero, or combinations of these. A zero could be considered to indicate the value of the measure of a feature, or it could indicate the feature is absent, or that it both has a value of zero and is absent, or it is unknown. Each assumption can lead to different results in seriation. For more information see Blanks and Zeroes and Setting the Earlier, Later, Blanks and Zeroes Parameters. Right clicking on the column brings up a popup menu that allows you to set all features with one click.
If you have chosen a seriation Technique other than Custom, you will not be able to modify the Zeroes parameter.