Table 2 Equations for biologically relevant candidate models containing only significant variables in ordinary least squares regression and the corresponding AICC and R2 when these models were fitted using a Geographically Weighted Regression model.
From: Correlations between meteorological parameters and prostate cancer
Model Description | OLS regression equation for model | GWR AICC [AICmodel- AICbest model]* | GWR R2(adj R2) |
|---|---|---|---|
radiation only | Y = 240 - 6.50 RAD | 18943.51 [172.91] | 30.9% (29.9) |
radiation with quadratic term | Y = 1101 - 122 RAD + 3.83 RAD2 | 18913.39 [142.79] | 32.5%(31.1) |
Pollutant and confounders | Y = 179 - .364 HRT_DS -1.477 UNEMPLOY + .00003CROP | 18905.71 [135.11] | 34.9%(32.4) |
Radiation, confounders, and pollutant | Prst = 823.62 - .275 HRT_DS -2 UNEMPLOY -2.6RAD+.00003CROP+2.6 RAD2 | 18876.93 [106.33] | 37.1% (34.0) |
radiation and confounders | Y = 840.54 - .293HRT_DS -2.36 UNEMPLOY - 83.54RAD + 2.63 RAD2 | 18868.70 [98.10] | 36.0% (33.4) |
HDD and confounders | Y = 170 - 0.234 HRT_DS - 1.84 UNEMPLOY - 0.00550 HDD + 0.000002 HDD2 | 18843.07 [72.47] | 36.4%(33.9) |
Meteorological parameters without radiation but with confounders | Y = 178.3 - .21 HRT_DS -1.77 UNEMPLOY - .006HDD + .027SNOW -0.085RAIN + 0.000002 HDD2 | 18812.07 [41.47] | 39.0% (35.8) |
Meteorological parameters including radiation and confounders | Y = 467 - .193 HRT_DS - 1.83 UNEMPLOY -31.7RAD - .0094HDD - .16RAIN+.000002 HDD2 +.9 RAD2 | 18777.54 [6.94] | 40.3% (36.8) |
Meteorological parameters including radiation and pollutant and confounders | Y = 470 - .193 HRT_DS -1.78 UNEMPLOY -34RAD - .009HDD + .00002CROP +.03SNOW - .116RAIN + .000002 HDD2 + 1 RAD2 | 18776.77 [6.17] | 42.5%(38.1) |
Meteorological parameters including radiation and pollutant and confounders and interactions | Y = 460 - 0.198 HRT DS - 1.50 UNEMPLOY - 0.000010 CROP - 33.1 RAD - 0.00834 HDD + 0.0079 SNOW - 0.117 RAIN + 0.000002 HDD2 + 0.985 RAD2 + 0.00000046 CROP X SNOW | 18770.60 | 43.3% (38.7) |