Description Usage Arguments Details Value Examples

`transform_to_elogit`

transforms the proportion of looks for
each interest area to empirical logits. Proportions are inherently bound
between 0 and 1 and are therefore not suitable for some types of analysis.
Logits provide an unbounded measure, though range from negative infinity to
infinity, so it is important to know that this logit function adds a constant
(hence, empirical logit). Additionally this calculates weights which estimate
the variance in each bin (because the variance of the logit depends on the
mean). This is important for regression analyses. N.B.: This function will
work for data with a maximum of 8 interest areas.

1 2 | ```
transform_to_elogit(data, NoIA = NULL, ObsPerBin = NULL,
Constant = 0.5, ObsOverride = FALSE)
``` |

`data` |
A data table object output by |

`NoIA` |
A positive integer indicating the number of interest areas defined when creating the study. |

`ObsPerBin` |
A positive integer indicating the number of observations to
use in the calculation. Typically, this will be the number of samples per
bin, which can be determined with |

`Constant` |
A positive number used for the empirical logit and weights calculation; by default, 0.5 as in Barr (2008). |

`ObsOverride` |
A logical value controlling restrictions on the value provided to ObsPerBin. Default value is FALSE. |

These calculations were adapted from:
Barr, D. J., (2008) Analyzing 'visual world' eyetracking data using
multilevel logistic regression, *Journal of Memory and Language*,
*59*(4), 457–474.

A data table with additional columns (the number of which depends on
the number of interest areas specified) added to `data`

.

1 2 3 4 5 6 | ```
## Not run:
library(VWPre)
# Convert proportions to empirical logits and calculate weights...
df <- transform_to_elogit(dat, NoIA = 4, ObsPerBin = 20, Constant = 0.5)
## End(Not run)
``` |

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