COVID_Novel_Locations_Lockdown_Moderation_OBS
Rick Reneau
August 27, 2020
Notch Analysis
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 63450.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8328 -0.5475 0.0490 0.6050 4.1275
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 109.6 10.47
## Residual 294.1 17.15
## Number of obs: 7382, groups: subject, 235
##
## Fixed effects:
## Estimate
## (Intercept) 56.1200
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.8433
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -1.4818
## bs(novel_locations, knots = c(1, 26), degree = 1)3 28.9354
## lockdown -8.6569
## dowMonday -1.7550
## dowSaturday 3.2567
## dowSunday 0.9703
## dowThursday -2.3045
## dowTuesday -4.2088
## dowWednesday -2.1026
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.8989
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 4.6083
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -29.8109
## Std. Error
## (Intercept) 1.6783
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6630
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4808
## bs(novel_locations, knots = c(1, 26), degree = 1)3 4.7993
## lockdown 1.5039
## dowMonday 0.7734
## dowSaturday 0.8002
## dowSunday 0.7963
## dowThursday 0.7631
## dowTuesday 0.7587
## dowWednesday 0.7832
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8941
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.6944
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 14.3783
## df t value
## (Intercept) 4072.3370 33.438
## bs(novel_locations, knots = c(1, 26), degree = 1)1 7247.6707 -2.311
## bs(novel_locations, knots = c(1, 26), degree = 1)2 7262.9651 -1.001
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7206.9100 6.029
## lockdown 7222.3152 -5.756
## dowMonday 7148.9312 -2.269
## dowSaturday 7152.1726 4.070
## dowSunday 7151.9158 1.219
## dowThursday 7151.0322 -3.020
## dowTuesday 7149.7617 -5.548
## dowWednesday 7149.6989 -2.685
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 7212.7611 2.586
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 7216.7322 2.720
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 7197.7169 -2.073
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.02086 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.31700
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.00000000173 ***
## lockdown 0.00000000895 ***
## dowMonday 0.02329 *
## dowSaturday 0.00004749561 ***
## dowSunday 0.22307
## dowThursday 0.00254 **
## dowTuesday 0.00000002998 ***
## dowWednesday 0.00728 **
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.00972 **
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.00655 **
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.03818 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 63592.66## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + distance + (1 | subject)
## Data: df
##
## REML criterion at convergence: 63461
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8283 -0.5519 0.0485 0.6065 4.1269
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 109.7 10.47
## Residual 294.0 17.15
## Number of obs: 7382, groups: subject, 235
##
## Fixed effects:
## Estimate
## (Intercept) 56.1385628
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.8204452
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -1.4926529
## bs(novel_locations, knots = c(1, 26), degree = 1)3 26.8601452
## lockdown -8.6596548
## dowMonday -1.7673019
## dowSaturday 3.2478078
## dowSunday 0.9081246
## dowThursday -2.3154719
## dowTuesday -4.2293843
## dowWednesday -2.1171711
## distance 0.0009558
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.8827760
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 4.6050751
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -28.5631507
## Std. Error
## (Intercept) 1.6783769
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6630397
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4807384
## bs(novel_locations, knots = c(1, 26), degree = 1)3 5.0554500
## lockdown 1.5038088
## dowMonday 0.7734596
## dowSaturday 0.8001392
## dowSunday 0.7977146
## dowThursday 0.7631305
## dowTuesday 0.7587760
## dowWednesday 0.7832346
## distance 0.0007321
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8939958
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.6943383
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 14.4092494
## df
## (Intercept) 4070.8744810
## bs(novel_locations, knots = c(1, 26), degree = 1)1 7246.8856536
## bs(novel_locations, knots = c(1, 26), degree = 1)2 7261.8608454
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7211.5825391
## lockdown 7221.2566826
## dowMonday 7147.9419275
## dowSaturday 7151.1286147
## dowSunday 7150.9318312
## dowThursday 7149.9932175
## dowTuesday 7148.7192204
## dowWednesday 7148.7080112
## distance 7194.1323328
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 7211.8431538
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 7215.6986417
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 7197.3168003
## t value
## (Intercept) 33.448
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -2.297
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -1.008
## bs(novel_locations, knots = c(1, 26), degree = 1)3 5.313
## lockdown -5.758
## dowMonday -2.285
## dowSaturday 4.059
## dowSunday 1.138
## dowThursday -3.034
## dowTuesday -5.574
## dowWednesday -2.703
## distance 1.306
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 2.578
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 2.718
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -1.982
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.02163 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.31347
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.00000011100 ***
## lockdown 0.00000000884 ***
## dowMonday 0.02235 *
## dowSaturday 0.00004979724 ***
## dowSunday 0.25499
## dowThursday 0.00242 **
## dowTuesday 0.00000002581 ***
## dowWednesday 0.00689 **
## distance 0.19174
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.00996 **
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.00659 **
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.04749 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## [1] "BIC: "## [1] 63612.46< 200 Novel Locations
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + (1 | subject)
## Data: df[which(df$novel_locations < 200), ]
##
## REML criterion at convergence: 62156.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9764 -0.5550 0.0462 0.6085 4.0999
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 110.2 10.50
## Residual 290.1 17.03
## Number of obs: 7241, groups: subject, 235
##
## Fixed effects:
## Estimate
## (Intercept) 55.8625
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.1899
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -2.9133
## bs(novel_locations, knots = c(1, 26), degree = 1)3 9.3741
## lockdown -8.6058
## dowMonday -1.5849
## dowSaturday 3.1259
## dowSunday 1.1948
## dowThursday -1.7855
## dowTuesday -3.6385
## dowWednesday -1.9768
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.0940
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6.3270
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -8.8408
## Std. Error
## (Intercept) 1.6734
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6557
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4885
## bs(novel_locations, knots = c(1, 26), degree = 1)3 1.8372
## lockdown 1.4942
## dowMonday 0.7759
## dowSaturday 0.8082
## dowSunday 0.8021
## dowThursday 0.7679
## dowTuesday 0.7626
## dowWednesday 0.7874
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8862
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.7140
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 3.2609
## df t value
## (Intercept) 3994.9099 33.382
## bs(novel_locations, knots = c(1, 26), degree = 1)1 7103.9185 -1.927
## bs(novel_locations, knots = c(1, 26), degree = 1)2 7119.0861 -1.957
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7116.8165 5.102
## lockdown 7079.3519 -5.760
## dowMonday 7009.8556 -2.043
## dowSaturday 7012.7342 3.868
## dowSunday 7013.0914 1.490
## dowThursday 7012.0766 -2.325
## dowTuesday 7011.1001 -4.771
## dowWednesday 7010.7446 -2.510
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 7070.4921 2.170
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 7074.7637 3.691
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 7081.6569 -2.711
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.054066 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.050369 .
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.00000034408 ***
## lockdown 0.00000000879 ***
## dowMonday 0.041119 *
## dowSaturday 0.000111 ***
## dowSunday 0.136384
## dowThursday 0.020092 *
## dowTuesday 0.00000186767 ***
## dowWednesday 0.012079 *
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.030003 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.000225 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.006720 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 62298.61## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + distance + (1 | subject)
## Data: df[which(df$novel_locations < 200), ]
##
## REML criterion at convergence: 62168.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9719 -0.5553 0.0468 0.6092 4.0994
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 110.2 10.50
## Residual 290.1 17.03
## Number of obs: 7241, groups: subject, 235
##
## Fixed effects:
## Estimate
## (Intercept) 55.8777510
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.1799493
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -2.9109147
## bs(novel_locations, knots = c(1, 26), degree = 1)3 9.0664711
## lockdown -8.6083378
## dowMonday -1.5955620
## dowSaturday 3.1210858
## dowSunday 1.1481721
## dowThursday -1.7909216
## dowTuesday -3.6561155
## dowWednesday -1.9871029
## distance 0.0006729
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.0891823
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6.3127796
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -8.6372695
## Std. Error
## (Intercept) 1.6735204
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6557421
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4885640
## bs(novel_locations, knots = c(1, 26), degree = 1)3 1.8702897
## lockdown 1.4941974
## dowMonday 0.7759800
## dowSaturday 0.8082634
## dowSunday 0.8038924
## dowThursday 0.7679551
## dowTuesday 0.7628653
## dowWednesday 0.7875212
## distance 0.0007660
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8862345
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.7140642
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 3.2691288
## df
## (Intercept) 3995.4089687
## bs(novel_locations, knots = c(1, 26), degree = 1)1 7103.0746074
## bs(novel_locations, knots = c(1, 26), degree = 1)2 7118.0952033
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7117.3639308
## lockdown 7078.3407482
## dowMonday 7008.8938921
## dowSaturday 7011.7023527
## dowSunday 7012.2069745
## dowThursday 7011.0661048
## dowTuesday 7010.1096929
## dowWednesday 7009.7866839
## distance 7050.3980760
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 7069.5353551
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 7073.8921223
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 7081.2685747
## t value
## (Intercept) 33.389
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -1.921
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -1.956
## bs(novel_locations, knots = c(1, 26), degree = 1)3 4.848
## lockdown -5.761
## dowMonday -2.056
## dowSaturday 3.861
## dowSunday 1.428
## dowThursday -2.332
## dowTuesday -4.793
## dowWednesday -2.523
## distance 0.879
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 2.168
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 3.683
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -2.642
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.054827 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.050561 .
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.0000012759 ***
## lockdown 0.0000000087 ***
## dowMonday 0.039801 *
## dowSaturday 0.000114 ***
## dowSunday 0.153260
## dowThursday 0.019725 *
## dowTuesday 0.0000016802 ***
## dowWednesday 0.011650 *
## distance 0.379692
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.030199 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.000232 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.008258 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## [1] "BIC: "## [1] 62319.24< 100 KM
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + (1 | subject)
## Data: df[which(df$distance < 100), ]
##
## REML criterion at convergence: 60024.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0860 -0.5554 0.0452 0.6093 4.1483
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 113.1 10.63
## Residual 284.4 16.86
## Number of obs: 7007, groups: subject, 234
##
## Fixed effects:
## Estimate
## (Intercept) 55.8078
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.1837
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -3.0114
## bs(novel_locations, knots = c(1, 26), degree = 1)3 18.2851
## lockdown -8.6289
## dowMonday -1.6388
## dowSaturday 3.2729
## dowSunday 1.1888
## dowThursday -1.7975
## dowTuesday -3.4517
## dowWednesday -2.1801
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.1519
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6.6084
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -20.5503
## Std. Error
## (Intercept) 1.6681
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6414
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4780
## bs(novel_locations, knots = c(1, 26), degree = 1)3 2.8641
## lockdown 1.4802
## dowMonday 0.7793
## dowSaturday 0.8186
## dowSunday 0.8124
## dowThursday 0.7709
## dowTuesday 0.7669
## dowWednesday 0.7912
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8694
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.7094
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 6.5159
## df t value
## (Intercept) 3810.6571 33.457
## bs(novel_locations, knots = c(1, 26), degree = 1)1 6866.6438 -1.940
## bs(novel_locations, knots = c(1, 26), degree = 1)2 6881.3954 -2.037
## bs(novel_locations, knots = c(1, 26), degree = 1)3 6874.9383 6.384
## lockdown 6841.5832 -5.830
## dowMonday 6776.2806 -2.103
## dowSaturday 6779.1526 3.998
## dowSunday 6777.9040 1.463
## dowThursday 6777.3188 -2.332
## dowTuesday 6776.6163 -4.501
## dowWednesday 6776.3677 -2.756
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 6833.7065 2.221
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6838.9594 3.866
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 6847.3790 -3.154
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.052466 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.041643 *
## bs(novel_locations, knots = c(1, 26), degree = 1)3 1.83e-10 ***
## lockdown 5.81e-09 ***
## dowMonday 0.035497 *
## dowSaturday 6.45e-05 ***
## dowSunday 0.143447
## dowThursday 0.019747 *
## dowTuesday 6.88e-06 ***
## dowWednesday 0.005875 **
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.026386 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.000112 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.001618 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 60166.35## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ bs(novel_locations, knots = c(1, 26), degree = 1) *
## lockdown + dow + distance + (1 | subject)
## Data: df[which(df$distance < 100), ]
##
## REML criterion at convergence: 60030.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0795 -0.5552 0.0441 0.6104 4.1491
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 113.2 10.64
## Residual 284.4 16.86
## Number of obs: 7007, groups: subject, 234
##
## Fixed effects:
## Estimate
## (Intercept) 55.797732
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.172395
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -2.947063
## bs(novel_locations, knots = c(1, 26), degree = 1)3 19.690438
## lockdown -8.621966
## dowMonday -1.642496
## dowSaturday 3.280757
## dowSunday 1.204468
## dowThursday -1.803341
## dowTuesday -3.462153
## dowWednesday -2.185593
## distance -0.008595
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.138889
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6.656774
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -20.433899
## Std. Error
## (Intercept) 1.668424
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.641746
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.487115
## bs(novel_locations, knots = c(1, 26), degree = 1)3 4.571170
## lockdown 1.480343
## dowMonday 0.779354
## dowSaturday 0.818872
## dowSunday 0.813451
## dowThursday 0.771100
## dowTuesday 0.767366
## dowWednesday 0.791328
## distance 0.021789
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.869834
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.713853
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 6.522840
## df t value
## (Intercept) 3808.736103 33.443
## bs(novel_locations, knots = c(1, 26), degree = 1)1 6866.064509 -1.932
## bs(novel_locations, knots = c(1, 26), degree = 1)2 6883.991713 -1.982
## bs(novel_locations, knots = c(1, 26), degree = 1)3 6887.741374 4.308
## lockdown 6840.705158 -5.824
## dowMonday 6775.276692 -2.108
## dowSaturday 6778.238093 4.006
## dowSunday 6777.061773 1.481
## dowThursday 6776.326452 -2.339
## dowTuesday 6775.641723 -4.512
## dowWednesday 6775.387596 -2.762
## distance 6894.356738 -0.394
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 6832.977372 2.214
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6836.524811 3.884
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 6846.733448 -3.133
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.053360 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.047549 *
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.000016738 ***
## lockdown 0.000000006 ***
## dowMonday 0.035110 *
## dowSaturday 0.000062302 ***
## dowSunday 0.138736
## dowThursday 0.019382 *
## dowTuesday 0.000006538 ***
## dowWednesday 0.005762 **
## distance 0.693229
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.026896 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.000104 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.001740 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 60180.86Z Novel Locations & < 100 KM
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## 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), ]
##
## REML criterion at convergence: 60039.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0819 -0.5554 0.0443 0.6090 4.1304
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 113.1 10.63
## Residual 284.5 16.87
## Number of obs: 7007, groups: subject, 234
##
## Fixed effects:
## Estimate
## (Intercept) 76.05164
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 -22.48642
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 -23.32310
## lockdown -30.07450
## dowMonday -1.62065
## dowSaturday 3.26689
## dowSunday 1.20221
## dowThursday -1.80161
## dowTuesday -3.51572
## dowWednesday -2.15865
## distance -0.00951
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 23.92687
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 28.40375
## Std. Error
## (Intercept) 4.38738
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 4.34269
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 4.38145
## lockdown 6.31087
## dowMonday 0.77933
## dowSaturday 0.81901
## dowSunday 0.81354
## dowThursday 0.77123
## dowTuesday 0.76702
## dowWednesday 0.79139
## distance 0.02179
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 6.29886
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 6.79429
## df
## (Intercept) 6990.47096
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 6888.94394
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 6878.05585
## lockdown 6851.18141
## dowMonday 6777.37282
## dowSaturday 6780.24001
## dowSunday 6779.06651
## dowThursday 6778.36770
## dowTuesday 6777.51145
## dowWednesday 6777.35746
## distance 6896.35729
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 6853.49543
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 6849.34498
## t value
## (Intercept) 17.334
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 -5.178
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 -5.323
## lockdown -4.766
## dowMonday -2.080
## dowSaturday 3.989
## dowSunday 1.478
## dowThursday -2.336
## dowTuesday -4.584
## dowWednesday -2.728
## distance -0.436
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 3.799
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 4.181
## Pr(>|t|)
## (Intercept) < 2e-16
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 0.000000231
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 0.000000105
## lockdown 0.000001923
## dowMonday 0.037606
## dowSaturday 0.000067093
## dowSunday 0.139523
## dowThursday 0.019519
## dowTuesday 0.000004652
## dowWednesday 0.006394
## distance 0.662507
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 0.000147
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 0.000029445
##
## (Intercept) ***
## 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
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
26 Median Knots
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50,
## 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1) *
## lockdown + dow + (1 | subject)
## Data: df[which(df$distance < 100), ]
##
## REML criterion at convergence: 59890
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8179 -0.5533 0.0466 0.6000 4.1430
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 113.2 10.64
## Residual 284.7 16.87
## Number of obs: 7007, groups: subject, 234
##
## Fixed effects:
## Estimate
## (Intercept) 55.7563
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 -3.5510
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 -3.1534
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 -2.4604
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 -3.3248
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 -1.5302
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 -2.6724
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 0.9896
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 -0.1311
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 0.9072
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 1.1248
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 1.6781
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 4.7381
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 -0.7666
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 2.8887
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 4.9455
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 8.1079
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 20.6164
## lockdown -8.6189
## dowMonday -1.6007
## dowSaturday 3.2561
## dowSunday 1.2639
## dowThursday -1.7313
## dowTuesday -3.3967
## dowWednesday -2.1384
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1:lockdown 4.5384
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2:lockdown 4.5664
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3:lockdown 6.5708
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4:lockdown 5.9013
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5:lockdown 5.6398
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6:lockdown 5.0286
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7:lockdown 1.0727
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8:lockdown 4.2416
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9:lockdown -2.3853
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10:lockdown 10.1781
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11:lockdown 4.3716
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12:lockdown -0.3297
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13:lockdown -2.3138
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14:lockdown 3.2772
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15:lockdown -21.5095
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16:lockdown -8.7245
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17:lockdown -39.5627
## Std. Error
## (Intercept) 1.6695
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 1.9154
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 1.7431
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 1.6658
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 1.7216
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 1.7636
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 1.8972
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 1.9694
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 2.0900
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 2.1743
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 2.2636
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 2.4632
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 2.6307
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 2.9363
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 3.4565
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 4.2185
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 2.9991
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 10.8482
## lockdown 1.4809
## dowMonday 0.7818
## dowSaturday 0.8210
## dowSunday 0.8152
## dowThursday 0.7733
## dowTuesday 0.7689
## dowWednesday 0.7932
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1:lockdown 2.1856
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2:lockdown 2.1418
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3:lockdown 2.1891
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4:lockdown 2.2841
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5:lockdown 2.6356
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6:lockdown 2.8822
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7:lockdown 3.1851
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8:lockdown 3.8480
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9:lockdown 4.1932
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10:lockdown 5.1316
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11:lockdown 5.2693
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12:lockdown 6.4585
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13:lockdown 7.6334
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14:lockdown 7.9912
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15:lockdown 9.8912
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16:lockdown 10.5686
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17:lockdown 33.6789
## df
## (Intercept) 3806.7818
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 6813.1612
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 6841.4272
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 6836.5212
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 6841.2170
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 6829.4350
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 6828.3570
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 6822.9304
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 6818.3244
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 6817.9530
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 6814.7711
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 6809.7386
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 6810.6478
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 6792.4016
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 6794.3329
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 6774.9354
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 6798.2209
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 6807.4380
## lockdown 6813.3795
## dowMonday 6748.4066
## dowSaturday 6751.7571
## dowSunday 6749.6817
## dowThursday 6749.7745
## dowTuesday 6749.2806
## dowWednesday 6748.6728
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1:lockdown 6793.7840
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2:lockdown 6803.3897
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3:lockdown 6797.3261
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4:lockdown 6794.0919
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5:lockdown 6787.2906
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6:lockdown 6784.0661
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7:lockdown 6785.5358
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8:lockdown 6778.5337
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9:lockdown 6787.2221
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10:lockdown 6767.6963
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11:lockdown 6768.6050
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12:lockdown 6773.5115
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13:lockdown 6783.0877
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14:lockdown 6795.1440
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15:lockdown 6777.3207
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16:lockdown 6758.5167
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17:lockdown 6775.1514
## t value
## (Intercept) 33.396
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 -1.854
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 -1.809
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 -1.477
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 -1.931
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 -0.868
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 -1.409
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 0.502
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 -0.063
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 0.417
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 0.497
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 0.681
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 1.801
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 -0.261
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 0.836
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 1.172
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 2.703
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 1.900
## lockdown -5.820
## dowMonday -2.048
## dowSaturday 3.966
## dowSunday 1.550
## dowThursday -2.239
## dowTuesday -4.418
## dowWednesday -2.696
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1:lockdown 2.077
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2:lockdown 2.132
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3:lockdown 3.002
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4:lockdown 2.584
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5:lockdown 2.140
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6:lockdown 1.745
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7:lockdown 0.337
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8:lockdown 1.102
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9:lockdown -0.569
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10:lockdown 1.983
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11:lockdown 0.830
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12:lockdown -0.051
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13:lockdown -0.303
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14:lockdown 0.410
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15:lockdown -2.175
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16:lockdown -0.826
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17:lockdown -1.175
## Pr(>|t|)
## (Intercept) < 2e-16
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 0.06379
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 0.07049
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 0.13973
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 0.05349
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 0.38563
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 0.15900
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 0.61534
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 0.94998
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 0.67653
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 0.61926
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 0.49572
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 0.07173
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 0.79404
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 0.40335
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 0.24110
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 0.00688
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 0.05742
## lockdown 0.00000000615
## dowMonday 0.04064
## dowSaturday 0.00007381011
## dowSunday 0.12111
## dowThursday 0.02520
## dowTuesday 0.00001013985
## dowWednesday 0.00704
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1:lockdown 0.03788
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2:lockdown 0.03304
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3:lockdown 0.00269
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4:lockdown 0.00980
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5:lockdown 0.03240
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6:lockdown 0.08109
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7:lockdown 0.73630
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8:lockdown 0.27037
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9:lockdown 0.56948
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10:lockdown 0.04736
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11:lockdown 0.40677
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12:lockdown 0.95928
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13:lockdown 0.76181
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14:lockdown 0.68174
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15:lockdown 0.02969
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16:lockdown 0.40911
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17:lockdown 0.24016
##
## (Intercept) ***
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 .
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 .
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 .
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 .
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 **
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 .
## lockdown ***
## dowMonday *
## dowSaturday ***
## dowSunday
## dowThursday *
## dowTuesday ***
## dowWednesday **
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1:lockdown *
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2:lockdown *
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3:lockdown **
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4:lockdown **
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5:lockdown *
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6:lockdown .
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7:lockdown
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8:lockdown
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9:lockdown
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10:lockdown *
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11:lockdown
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12:lockdown
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13:lockdown
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14:lockdown
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15:lockdown *
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16:lockdown
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17:lockdown
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 60279.64
