Regional Models of Canine Cancer Incidences

<p>The dataset consists of the variables implemented in the regional models of canine cancer incidences (dogCancerModel.txt), the adjacency matrix determining the regions (kNearestNeighbour.txt), and the Swiss municipal boundaries (SwissMunicipalities_2015.shp).</p

Mean, median, lower and upper 95% CI for the effects resulting from the coefficients estimated through the regional models.

<p>Mean, median, lower and upper 95% CI for the effects resulting from the coefficients estimated through the regional models.</p

Effect, lower and upper 95% CI and SQRVIF for the coefficients estimated through the conventional regression model.

<p>Effect, lower and upper 95% CI and SQRVIF for the coefficients estimated through the conventional regression model.</p

The importance of regional models in assessing canine cancer incidences in Switzerland - Fig 3

<p><b>Variations of the R</b>^<b>2</b>_{<b>McFadden</b>}<b>measures across (A) the center and (B) the geographic scale of the regional models.</b> The data is classified according to the quantile classification.</p

Defining regions involving different geographical scales.

<p><b>Example for the regions centered in the municipality of Zurich (A) and Lausanne (B).</b> The center is highlighted in red.</p

Median, interquartile range (IQR), minima, and maxima for the different independent variables perused in this study.

<p>Median, interquartile range (IQR), minima, and maxima for the different independent variables perused in this study.</p

Average canine cancer incidence rates observed in Switzerland for the period 2008–2013.

<p>The data is classified according to the quantile classification.</p