Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data ... Research Note
Research Note  |   September 01, 1989
Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus
 
Author Affiliations & Notes
  • C. Formby
    University of Florida
  • B. Albritton
    Department of Computer and Information Sciences, University of Florida
  • I. M. Rivera
    University of Florida
Article Information
Research Notes
Research Note   |   September 01, 1989
Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus
Journal of Speech, Language, and Hearing Research, September 1989, Vol. 32, 681-687. doi:10.1044/jshr.3203.681
History: Received September 6, 1988 , Accepted November 28, 1988
 
Journal of Speech, Language, and Hearing Research, September 1989, Vol. 32, 681-687. doi:10.1044/jshr.3203.681
History: Received September 6, 1988; Accepted November 28, 1988

We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data and fitted curves to show how adjustments in the parameters of the model affect the fitted curve. The best fitting parameters are presented for curves fit to 120 warm caloric responses. The fitting parameters and the efficacy of the fitted curves are compared before and after the SCV data were smoothed to reduce response variability. We also consider a more flexible four-parameter Weibull equation that, for 98% of the smoothed caloric responses, yields fits that describe the data more precisely than a line through the mean. Finally, we consider advantages and problems in fitting the Weibull function to caloric data.

Order a Subscription
Pay Per View
Entire Journal of Speech, Language, and Hearing Research content & archive
24-hour access
This Article
24-hour access