China
Africa
Explore chapters and articles related to this topic
Data Tours
Published in Wendy L. Martinez, Angel R. Martinez, Jeffrey L. Solka, Exploratory Data Analysis with MATLAB®, 2017
Wendy L. Martinez, Angel R. Martinez, Jeffrey L. Solka
To summarize, the grand tour provides an overview or tour of a highdimensional space by visualizing a sequence of 2-D scatterplots, in such a way that the tour is representative of all projections of the data. The tour continues until the analyst sees some interesting structure, at which time it is halted. The output of the grand tour method is a movie or animation with information encoded in the smooth motion of the 2-D scatterplots. A benefit from looking at a moving sequence of scatterplots is that two additional dimensions of information are available in the speed vectors of the data points (Buja and Asimov, 1986). For example, the further away a point is from the computer screen, the faster the point rotates.
Analysing a cycling grand tour: Can we monitor fatigue with intensity or load ratios?
Published in Journal of Sports Sciences, 2018
Dajo Sanders, Mathieu Heijboer, Matthijs K. C. Hesselink, Tony Myers, Ibrahim Akubat
The increases in the slope of linear relationship between sRPE and TSS and sRPE and iTRIMP are presented in Figure 1. The relationship between sRPE and TSS was nearly perfect during baseline (r = 0.91, [95% CI: 0.84 to 0.95], n = 48) and very strong for week 1 (r = 0.62, [95% CI: 0.42 to 0.74], n = 77), week 2 (r = 0.80, [95% CI: 0.71 to 0.87], n = 85) and week 3 (r = 0.87, [95% CI: 0.78 to 0.92], n = 51) of the Grand Tour. The relationship between sRPE and iTRIMP was very strong during baseline (r = 0.72, [95% CI: 0.53 to 0.84], n = 40), week 2 (r = 0.86, [95% CI: 0.70 to 0.94], n = 24) and week 3 (r = 0.88, [95% CI: 0.67 to 0.96], n = 15) and moderate during week 1 (r = 0.43, [95% CI: 0.08 to 0.69], n = 29).