Notch Analysis
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + (1 | subject)
## data: df
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 7382 -14391.94 28829.87 5006(30186) 5.18e-03 1.0e+04
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.397 1.182
## Number of groups: subject 235
##
## Coefficients:
## Estimate Std. Error
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -0.28684 0.16737
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -0.09069 0.14829
## bs(novel_locations, knots = c(1, 26), degree = 1)3 3.37253 0.54039
## lockdown -0.81185 0.15026
## dowMonday -0.19465 0.07945
## dowSaturday 0.33461 0.08259
## dowSunday 0.09388 0.08210
## dowThursday -0.24270 0.07837
## dowTuesday -0.46085 0.07796
## dowWednesday -0.24776 0.08126
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.43648 0.19095
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.43881 0.17029
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -3.69524 1.47291
## z value Pr(>|z|)
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -1.714 0.08657
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -0.612 0.54082
## bs(novel_locations, knots = c(1, 26), degree = 1)3 6.241 4.35e-10
## lockdown -5.403 6.56e-08
## dowMonday -2.450 0.01429
## dowSaturday 4.051 5.09e-05
## dowSunday 1.144 0.25283
## dowThursday -3.097 0.00196
## dowTuesday -5.912 3.39e-09
## dowWednesday -3.049 0.00230
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 2.286 0.02226
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 2.577 0.00997
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -2.509 0.01211
##
## bs(novel_locations, knots = c(1, 26), degree = 1)1 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2
## bs(novel_locations, knots = c(1, 26), degree = 1)3 ***
## lockdown ***
## dowMonday *
## dowSaturday ***
## dowSunday
## dowThursday **
## dowTuesday ***
## dowWednesday **
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown **
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## [0,10]|(10,20] -4.3292 0.1847 -23.442
## (10,20]|(20,30] -3.4662 0.1782 -19.450
## (20,30]|(30,40] -2.6004 0.1750 -14.863
## (30,40]|(40,50] -1.7255 0.1734 -9.953
## (40,50]|(50,60] -0.7625 0.1725 -4.419
## (50,60]|(60,70] 0.4840 0.1725 2.807
## (60,70]|(70,80] 1.4692 0.1735 8.470
## (70,80]|(80,90] 2.5643 0.1765 14.532
## (80,90]|(90,100] 3.9148 0.1870 20.930
## (21028 observations deleted due to missingness)
## [1] "BIC: "
## [1] 28988.73
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + distance + (1 | subject)
## data: df
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 7382 -14567.30 29182.60 1233(7391) 1.77e+02 4.5e+08
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1 1
## Number of groups: subject 235
##
## Coefficients:
## Estimate
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -0.17831022
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.10468885
## bs(novel_locations, knots = c(1, 26), degree = 1)3 2.41204387
## lockdown -0.48938095
## dowMonday -0.16612770
## dowSaturday 0.27945070
## dowSunday 0.08900943
## dowThursday -0.20488716
## dowTuesday -0.38328974
## dowWednesday -0.20464490
## distance 0.00007873
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.22081878
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.18724667
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -3.79154643
## Std. Error z value
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.15976576 -1.116
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.14145970 0.740
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.52975363 4.553
## lockdown 0.14350942 -3.410
## dowMonday 0.07591957 -2.188
## dowSaturday 0.07879961 3.546
## dowSunday 0.07850137 1.134
## dowThursday 0.07488905 -2.736
## dowTuesday 0.07447546 -5.147
## dowWednesday 0.07751360 -2.640
## distance 0.00007247 1.086
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.18246775 1.210
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.16268072 1.151
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 1.42411548 -2.662
## Pr(>|z|)
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.264391
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.459263
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.000005285 ***
## lockdown 0.000649 ***
## dowMonday 0.028655 *
## dowSaturday 0.000391 ***
## dowSunday 0.256854
## dowThursday 0.006221 **
## dowTuesday 0.000000265 ***
## dowWednesday 0.008288 **
## distance 0.277346
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.226210
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.249729
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.007759 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## [0,10]|(10,20] -3.5126 0.1709 -20.550
## (10,20]|(20,30] -2.7310 0.1653 -16.524
## (20,30]|(30,40] -1.9729 0.1626 -12.131
## (30,40]|(40,50] -1.2345 0.1614 -7.646
## (40,50]|(50,60] -0.4481 0.1609 -2.785
## (50,60]|(60,70] 0.5464 0.1609 3.396
## (60,70]|(70,80] 1.3249 0.1615 8.202
## (70,80]|(80,90] 2.2172 0.1636 13.549
## (80,90]|(90,100] 3.3481 0.1714 19.536
## (21028 observations deleted due to missingness)
## [1] "BIC: "
## [1] 29348.36
< 200 Novel Locations
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + (1 | subject)
## data: df[which(df$novel_locations < 200), ]
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 7241 -14083.28 28212.56 5446(33055) 1.05e-02 1.7e+03
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.416 1.19
## Number of groups: subject 235
##
## Coefficients:
## Estimate Std. Error
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -0.22612 0.16815
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -0.22330 0.15032
## bs(novel_locations, knots = c(1, 26), degree = 1)3 1.03635 0.18872
## lockdown -0.81279 0.15066
## dowMonday -0.18185 0.08037
## dowSaturday 0.31917 0.08404
## dowSunday 0.12511 0.08333
## dowThursday -0.19938 0.07946
## dowTuesday -0.41451 0.07892
## dowWednesday -0.23830 0.08236
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.36511 0.19188
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.59945 0.17413
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -0.97119 0.35115
## z value
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -1.345
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -1.486
## bs(novel_locations, knots = c(1, 26), degree = 1)3 5.492
## lockdown -5.395
## dowMonday -2.263
## dowSaturday 3.798
## dowSunday 1.501
## dowThursday -2.509
## dowTuesday -5.253
## dowWednesday -2.894
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.903
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 3.443
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -2.766
## Pr(>|z|)
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.178717
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.137410
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.0000000398 ***
## lockdown 0.0000000686 ***
## dowMonday 0.023656 *
## dowSaturday 0.000146 ***
## dowSunday 0.133270
## dowThursday 0.012097 *
## dowTuesday 0.0000001500 ***
## dowWednesday 0.003810 **
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.057065 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.000576 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.005679 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## [0,10]|(10,20] -4.3333 0.1859 -23.307
## (10,20]|(20,30] -3.4605 0.1793 -19.299
## (20,30]|(30,40] -2.5883 0.1760 -14.704
## (30,40]|(40,50] -1.7121 0.1744 -9.816
## (40,50]|(50,60] -0.7446 0.1736 -4.289
## (50,60]|(60,70] 0.5139 0.1735 2.961
## (60,70]|(70,80] 1.5038 0.1746 8.614
## (70,80]|(80,90] 2.6074 0.1777 14.671
## (80,90]|(90,100] 3.9963 0.1892 21.118
## (12914 observations deleted due to missingness)
## [1] "BIC: "
## [1] 28370.97
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + distance + (1 | subject)
## data: df[which(df$novel_locations < 200), ]
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 7241 -14259.13 28566.26 1258(8800) 2.58e+02 6.6e+07
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1 1
## Number of groups: subject 235
##
## Coefficients:
## Estimate
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -0.12584383
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -0.00382567
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.90940663
## lockdown -0.49251187
## dowMonday -0.15874695
## dowSaturday 0.26880848
## dowSunday 0.11211669
## dowThursday -0.16540066
## dowTuesday -0.34432167
## dowWednesday -0.19356770
## distance 0.00005569
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.15258546
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.35715781
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -1.33103298
## Std. Error z value
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.16025057 -0.785
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.14320954 -0.027
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.18285810 4.973
## lockdown 0.14367589 -3.428
## dowMonday 0.07672128 -2.069
## dowSaturday 0.08012675 3.355
## dowSunday 0.07964204 1.408
## dowThursday 0.07587364 -2.180
## dowTuesday 0.07534858 -4.570
## dowWednesday 0.07851292 -2.465
## distance 0.00007572 0.735
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.18311000 0.833
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.16611235 2.150
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.33580478 -3.964
## Pr(>|z|)
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.432281
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.978688
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.000000658 ***
## lockdown 0.000608 ***
## dowMonday 0.038533 *
## dowSaturday 0.000794 ***
## dowSunday 0.159203
## dowThursday 0.029261 *
## dowTuesday 0.000004884 ***
## dowWednesday 0.013685 *
## distance 0.462066
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.404676
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.031547 *
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.000073794 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## [0,10]|(10,20] -3.5124 0.1716 -20.470
## (10,20]|(20,30] -2.7237 0.1658 -16.425
## (20,30]|(30,40] -1.9618 0.1632 -12.023
## (30,40]|(40,50] -1.2246 0.1620 -7.560
## (40,50]|(50,60] -0.4369 0.1614 -2.707
## (50,60]|(60,70] 0.5654 0.1614 3.503
## (60,70]|(70,80] 1.3475 0.1622 8.309
## (70,80]|(80,90] 2.2467 0.1644 13.666
## (80,90]|(90,100] 3.4081 0.1728 19.720
## (12914 observations deleted due to missingness)
## [1] "BIC: "
## [1] 28731.56
< 100 KM
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + (1 | subject)
## data: df[which(df$distance < 100), ]
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 7007 -13577.13 27200.25 3734(21555) 2.45e+01 NaN
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.484 1.218
## Number of groups: subject 234
##
## Coefficients:
## Estimate Std. Error
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -0.2218 NA
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -0.2370 NA
## bs(novel_locations, knots = c(1, 26), degree = 1)3 2.0545 NA
## lockdown -0.8190 NA
## dowMonday -0.1920 NA
## dowSaturday 0.3332 NA
## dowSunday 0.1283 NA
## dowThursday -0.2074 NA
## dowTuesday -0.3981 NA
## dowWednesday -0.2693 NA
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.3696 NA
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.6319 NA
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -2.2234 NA
## z value Pr(>|z|)
## bs(novel_locations, knots = c(1, 26), degree = 1)1 NA NA
## bs(novel_locations, knots = c(1, 26), degree = 1)2 NA NA
## bs(novel_locations, knots = c(1, 26), degree = 1)3 NA NA
## lockdown NA NA
## dowMonday NA NA
## dowSaturday NA NA
## dowSunday NA NA
## dowThursday NA NA
## dowTuesday NA NA
## dowWednesday NA NA
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown NA NA
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown NA NA
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown NA NA
##
## Threshold coefficients:
## Estimate Std. Error z value
## [0,10]|(10,20] -4.3888 NA NA
## (10,20]|(20,30] -3.4927 NA NA
## (20,30]|(30,40] -2.6041 NA NA
## (30,40]|(40,50] -1.7190 NA NA
## (40,50]|(50,60] -0.7456 NA NA
## (50,60]|(60,70] 0.5279 NA NA
## (60,70]|(70,80] 1.5403 NA NA
## (70,80]|(80,90] 2.6590 NA NA
## (80,90]|(90,100] 4.0529 NA NA
## (13899 observations deleted due to missingness)
## [1] "BIC: "
## [1] 27357.91
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + distance + (1 | subject)
## data: df[which(df$distance < 100), ]
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 7007 -13577.13 27202.26 3995(21252) 9.12e-02 4.3e+05
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.482 1.217
## Number of groups: subject 234
##
## Coefficients:
## Estimate
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -0.2255064
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -0.2412445
## bs(novel_locations, knots = c(1, 26), degree = 1)3 2.0718822
## lockdown -0.8248059
## dowMonday -0.1913897
## dowSaturday 0.3348037
## dowSunday 0.1286502
## dowThursday -0.2075564
## dowTuesday -0.3981668
## dowWednesday -0.2686236
## distance -0.0001218
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.3753480
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.6375293
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -2.1984363
## Std. Error z value
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.1688531 -1.336
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.1521086 -1.586
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.4880736 4.245
## lockdown 0.1511686 -5.456
## dowMonday 0.0816188 -2.345
## dowSaturday 0.0860328 3.892
## dowSunday 0.0854470 1.506
## dowThursday 0.0805565 -2.577
## dowTuesday 0.0802596 -4.961
## dowWednesday 0.0836655 -3.211
## distance 0.0023445 -0.052
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.1926185 1.949
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.1762922 3.616
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.6889188 -3.191
## Pr(>|z|)
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.181707
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.112739
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.0000218574 ***
## lockdown 0.0000000486 ***
## dowMonday 0.019031 *
## dowSaturday 0.0000995931 ***
## dowSunday 0.132166
## dowThursday 0.009980 **
## dowTuesday 0.0000007014 ***
## dowWednesday 0.001324 **
## distance 0.958556
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.051336 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.000299 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.001417 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## [0,10]|(10,20] -4.3931 0.1882 -23.345
## (10,20]|(20,30] -3.4965 0.1812 -19.297
## (20,30]|(30,40] -2.6086 0.1778 -14.674
## (30,40]|(40,50] -1.7233 0.1761 -9.784
## (40,50]|(50,60] -0.7506 0.1753 -4.282
## (50,60]|(60,70] 0.5230 0.1752 2.985
## (60,70]|(70,80] 1.5355 0.1764 8.707
## (70,80]|(80,90] 2.6532 0.1798 14.758
## (80,90]|(90,100] 4.0472 0.1922 21.060
## (13899 observations deleted due to missingness)
## [1] "BIC: "
## [1] 27366.77
Z Novel Locations & < 100 KM
## Cumulative Link Mixed Model fitted with the Laplace approximation
##
## formula: PA_avg ~ bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1) *
## lockdown + dow + distance + (1 | subject)
## data: df[which(df$distance < 100), ]
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 7007 -13579.00 27202.00 3534(19102) 9.55e-01 2.0e+06
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.471 1.213
## Number of groups: subject 234
##
## Coefficients:
## Estimate
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 -2.2741782
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 -2.3715063
## lockdown -3.1557932
## dowMonday -0.1914637
## dowSaturday 0.3326163
## dowSunday 0.1261402
## dowThursday -0.2081317
## dowTuesday -0.4015307
## dowWednesday -0.2660219
## distance -0.0002249
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 2.5271152
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 3.0034922
## Std. Error
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 0.4664708
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 0.4710131
## lockdown 0.6710019
## dowMonday 0.0815877
## dowSaturday 0.0860251
## dowSunday 0.0854347
## dowThursday 0.0805520
## dowTuesday 0.0802057
## dowWednesday 0.0836313
## distance 0.0023459
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 0.6692953
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 0.7213720
## z value
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 -4.875
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 -5.035
## lockdown -4.703
## dowMonday -2.347
## dowSaturday 3.867
## dowSunday 1.476
## dowThursday -2.584
## dowTuesday -5.006
## dowWednesday -3.181
## distance -0.096
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 3.776
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 4.164
## Pr(>|z|)
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 0.000001087
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 0.000000478
## lockdown 0.000002562
## dowMonday 0.01894
## dowSaturday 0.00011
## dowSunday 0.13982
## dowThursday 0.00977
## dowTuesday 0.000000555
## dowWednesday 0.00147
## distance 0.92362
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 0.00016
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 0.000031329
##
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 ***
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 ***
## lockdown ***
## dowMonday *
## dowSaturday ***
## dowSunday
## dowThursday **
## dowTuesday ***
## dowWednesday **
## distance
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown ***
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## [0,10]|(10,20] -6.5122 0.4790 -13.596
## (10,20]|(20,30] -5.6165 0.4763 -11.792
## (20,30]|(30,40] -4.7292 0.4748 -9.961
## (30,40]|(40,50] -3.8442 0.4738 -8.113
## (40,50]|(50,60] -2.8717 0.4732 -6.069
## (50,60]|(60,70] -1.5986 0.4724 -3.384
## (60,70]|(70,80] -0.5867 0.4721 -1.243
## (70,80]|(80,90] 0.5305 0.4729 1.122
## (80,90]|(90,100] 1.9239 0.4769 4.034
## (13899 observations deleted due to missingness)
## [1] "BIC: "
## [1] 27352.8