Data selection

Purpose

The goal is to define the variable under consideration on the dataset. One can look at observations, covariates and categories. This is specified when the user defines each column type in the data set as in the following picture.

choiceOfData

Notice that Datxplore often provides an initial guess of the type of the column depending on the name.

Possible types for Datxplore

The user can choose in the following list for the column’s type

  • ignore: corresponds to a column in the data set the user wants to ignore.
  • id: corresponds to the id of the subject.
  • time: corresponds to the time.
  • y: corresponds to the observations.
  • yType: corresponds to the type of the observations. This is useful only when several types of observations are present in the data set. In the example of a PKPD data set, two observations are in the data set, the PK and the PD respectively. To differentiate the observations, yType equals 1 for the first observation, and 2 else wise.
  • cov: corresponds to a continuous covariate, the weight for example.
  • cat: corresponds to a categorical covariate, the gender for example.
  • reg: corresponds to a regressor of the data set, the outdoor temperature for example.
  • date: corresponds to the date. This can be combined with the time to have another definition of the time.
  • amt: corresponds to the amount of drug.

Labeling

The name proposed in the figure and in the data choice is the one defined in the label. The user can modify it. By default, the label used is the one defined in the data set.

Observation type

There are three types of observations

  • continuous: The observation is continuous with respect to time. For example, a concentration is a continuous observation.
  • categorical: The observation values takes place in a finite categorical space. For example, the observation can be a categorical observation (an effect can be observed as low, medium, high) or a count observation over a defined time (the number of epileptic crisis in a defined time).
  • event: The observation is an event, for example the occurring of an epileptic crisis.

To specify it, the user can do it in the interface as the following figure

observation

Notice that for multiple outputs, the user shall define all the names. By default, these are names y_1, y_2, …