COVID_Novel_Locations_Lockdown_Moderation
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: 64690.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8297 -0.5532 0.0468 0.6016 4.1258
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 120.9 11.00
## Residual 295.3 17.18
## Number of obs: 7519, groups: subject, 249
##
## Fixed effects:
## Estimate
## (Intercept) 55.7335
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.8737
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -1.6572
## bs(novel_locations, knots = c(1, 26), degree = 1)3 29.4089
## lockdown -8.8954
## dowMonday -1.7918
## dowSaturday 3.3314
## dowSunday 0.7781
## dowThursday -2.1945
## dowTuesday -4.1084
## dowWednesday -2.1109
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.9280
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 4.9171
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -30.3410
## Std. Error
## (Intercept) 1.6800
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6541
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4744
## bs(novel_locations, knots = c(1, 26), degree = 1)3 4.7938
## lockdown 1.4979
## dowMonday 0.7685
## dowSaturday 0.7962
## dowSunday 0.7921
## dowThursday 0.7589
## dowTuesday 0.7536
## dowWednesday 0.7778
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8847
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.6882
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 14.3926
## df t value
## (Intercept) 4031.0414 33.176
## bs(novel_locations, knots = c(1, 26), degree = 1)1 7364.3940 -2.342
## bs(novel_locations, knots = c(1, 26), degree = 1)2 7379.6091 -1.124
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7330.3034 6.135
## lockdown 7338.4248 -5.939
## dowMonday 7269.2723 -2.332
## dowSaturday 7272.1576 4.184
## dowSunday 7272.5381 0.982
## dowThursday 7269.6114 -2.892
## dowTuesday 7270.5816 -5.452
## dowWednesday 7268.2996 -2.714
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 7329.9540 2.615
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 7334.3065 2.913
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 7314.8365 -2.108
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.01921 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.26106
## bs(novel_locations, knots = c(1, 26), degree = 1)3 8.97e-10 ***
## lockdown 3.01e-09 ***
## dowMonday 0.01975 *
## dowSaturday 2.89e-05 ***
## dowSunday 0.32597
## dowThursday 0.00384 **
## dowTuesday 5.15e-08 ***
## dowWednesday 0.00667 **
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.00895 **
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.00360 **
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.03506 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 64833.51## 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: 64701.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8252 -0.5544 0.0478 0.6027 4.1251
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 120.9 11.00
## Residual 295.3 17.18
## Number of obs: 7519, groups: subject, 249
##
## Fixed effects:
## Estimate
## (Intercept) 55.7526168
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.8508438
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -1.6673547
## bs(novel_locations, knots = c(1, 26), degree = 1)3 27.3109669
## lockdown -8.8977797
## dowMonday -1.8038112
## dowSaturday 3.3229714
## dowSunday 0.7159411
## dowThursday -2.2051463
## dowTuesday -4.1286609
## dowWednesday -2.1249660
## distance 0.0009696
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.9116086
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 4.9131425
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -29.0816146
## Std. Error
## (Intercept) 1.6799685
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6540623
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4743830
## bs(novel_locations, knots = c(1, 26), degree = 1)3 5.0493894
## lockdown 1.4978218
## dowMonday 0.7685165
## dowSaturday 0.7961427
## dowSunday 0.7934094
## dowThursday 0.7588913
## dowTuesday 0.7537053
## dowWednesday 0.7778798
## distance 0.0007335
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8846311
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.6881181
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 14.4232832
## df
## (Intercept) 4030.3223808
## bs(novel_locations, knots = c(1, 26), degree = 1)1 7363.6352693
## bs(novel_locations, knots = c(1, 26), degree = 1)2 7378.5430781
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7334.4544159
## lockdown 7337.3952863
## dowMonday 7268.3293973
## dowSaturday 7271.1498738
## dowSunday 7271.7111991
## dowThursday 7268.6051832
## dowTuesday 7269.6170519
## dowWednesday 7267.3521557
## distance 7311.4786302
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 7329.0639908
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 7333.3003627
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 7314.4687570
## t value
## (Intercept) 33.187
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -2.328
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -1.131
## bs(novel_locations, knots = c(1, 26), degree = 1)3 5.409
## lockdown -5.940
## dowMonday -2.347
## dowSaturday 4.174
## dowSunday 0.902
## dowThursday -2.906
## dowTuesday -5.478
## dowWednesday -2.732
## distance 1.322
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 2.606
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 2.910
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -2.016
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.01993 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.25814
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.00000006546 ***
## lockdown 0.00000000297 ***
## dowMonday 0.01895 *
## dowSaturday 0.00003030011 ***
## dowSunday 0.36690
## dowThursday 0.00367 **
## dowTuesday 0.00000004450 ***
## dowWednesday 0.00632 **
## distance 0.18625
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.00918 **
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.00362 **
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.04381 *
## ---
## 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] 64853.28< 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: 63395.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9735 -0.5641 0.0446 0.6081 4.0978
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 121.5 11.02
## Residual 291.4 17.07
## Number of obs: 7378, groups: subject, 249
##
## Fixed effects:
## Estimate
## (Intercept) 55.4845
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.2244
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -3.0892
## bs(novel_locations, knots = c(1, 26), degree = 1)3 9.3404
## lockdown -8.8417
## dowMonday -1.6274
## dowSaturday 3.1957
## dowSunday 0.9916
## dowThursday -1.6817
## dowTuesday -3.5429
## dowWednesday -1.9915
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.1323
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6.6292
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -8.6961
## Std. Error
## (Intercept) 1.6751
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6468
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4820
## bs(novel_locations, knots = c(1, 26), degree = 1)3 1.8281
## lockdown 1.4883
## dowMonday 0.7708
## dowSaturday 0.8041
## dowSunday 0.7977
## dowThursday 0.7635
## dowTuesday 0.7574
## dowWednesday 0.7819
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8769
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.7074
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 3.2598
## df t value
## (Intercept) 3958.4603 33.124
## bs(novel_locations, knots = c(1, 26), degree = 1)1 7220.5714 -1.958
## bs(novel_locations, knots = c(1, 26), degree = 1)2 7236.1725 -2.084
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7239.1171 5.109
## lockdown 7195.5480 -5.941
## dowMonday 7130.3418 -2.111
## dowSaturday 7132.6400 3.974
## dowSunday 7133.7933 1.243
## dowThursday 7130.7769 -2.202
## dowTuesday 7132.2719 -4.678
## dowWednesday 7129.4148 -2.547
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 7187.4829 2.202
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 7192.5953 3.883
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 7199.8941 -2.668
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.050265 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.037154 *
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.00000033146 ***
## lockdown 0.00000000297 ***
## dowMonday 0.034786 *
## dowSaturday 0.00007127603 ***
## dowSunday 0.213868
## dowThursday 0.027666 *
## dowTuesday 0.00000295674 ***
## dowWednesday 0.010885 *
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.027717 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.000104 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.007655 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 63538.31## 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: 63407.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9690 -0.5647 0.0463 0.6094 4.0973
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 121.5 11.02
## Residual 291.4 17.07
## Number of obs: 7378, groups: subject, 249
##
## Fixed effects:
## Estimate
## (Intercept) 55.5002558
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.2139900
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -3.0867328
## bs(novel_locations, knots = c(1, 26), degree = 1)3 9.0271143
## lockdown -8.8440939
## dowMonday -1.6379640
## dowSaturday 3.1911737
## dowSunday 0.9443726
## dowThursday -1.6867981
## dowTuesday -3.5604067
## dowWednesday -2.0014943
## distance 0.0006941
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.1269698
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6.6147252
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -8.4898158
## Std. Error
## (Intercept) 1.6751353
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6468300
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4820171
## bs(novel_locations, knots = c(1, 26), degree = 1)3 1.8606340
## lockdown 1.4883583
## dowMonday 0.7709387
## dowSaturday 0.8041277
## dowSunday 0.7994173
## dowThursday 0.7635698
## dowTuesday 0.7576794
## dowWednesday 0.7819916
## distance 0.0007674
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8769050
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.7075069
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 3.2678316
## df
## (Intercept) 3959.3370113
## bs(novel_locations, knots = c(1, 26), degree = 1)1 7219.7459526
## bs(novel_locations, knots = c(1, 26), degree = 1)2 7235.1967365
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7240.0842804
## lockdown 7194.5528778
## dowMonday 7129.4314667
## dowSaturday 7131.6327901
## dowSunday 7133.0919503
## dowThursday 7129.7873100
## dowTuesday 7131.3771293
## dowWednesday 7128.4928187
## distance 7168.0217096
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 7186.5454107
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 7191.7242834
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 7199.6509251
## t value
## (Intercept) 33.132
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -1.952
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -2.083
## bs(novel_locations, knots = c(1, 26), degree = 1)3 4.852
## lockdown -5.942
## dowMonday -2.125
## dowSaturday 3.968
## dowSunday 1.181
## dowThursday -2.209
## dowTuesday -4.699
## dowWednesday -2.559
## distance 0.904
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 2.199
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 3.874
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -2.598
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.051022 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.037305 *
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.00000125008 ***
## lockdown 0.00000000294 ***
## dowMonday 0.033651 *
## dowSaturday 0.00007303859 ***
## dowSunday 0.237513
## dowThursday 0.027200 *
## dowTuesday 0.00000266219 ***
## dowWednesday 0.010503 *
## distance 0.365761
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.027923 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.000108 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.009396 **
## ---
## 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] 63558.91< 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: 61230.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0843 -0.5601 0.0450 0.6097 4.1440
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 124.3 11.15
## Residual 285.8 16.91
## Number of obs: 7140, groups: subject, 248
##
## Fixed effects:
## Estimate
## (Intercept) 55.4380
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.2100
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -3.2036
## bs(novel_locations, knots = c(1, 26), degree = 1)3 18.5458
## lockdown -8.8683
## dowMonday -1.6647
## dowSaturday 3.3443
## dowSunday 0.9715
## dowThursday -1.6893
## dowTuesday -3.3428
## dowWednesday -2.1954
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.1735
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6.9327
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -20.6701
## Std. Error
## (Intercept) 1.6700
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.6331
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.4720
## bs(novel_locations, knots = c(1, 26), degree = 1)3 2.8502
## lockdown 1.4749
## dowMonday 0.7745
## dowSaturday 0.8144
## dowSunday 0.8079
## dowThursday 0.7666
## dowTuesday 0.7619
## dowWednesday 0.7859
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.8609
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.7035
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 6.5188
## df t value
## (Intercept) 3786.9314 33.197
## bs(novel_locations, knots = c(1, 26), degree = 1)1 6979.7538 -1.966
## bs(novel_locations, knots = c(1, 26), degree = 1)2 6994.9174 -2.176
## bs(novel_locations, knots = c(1, 26), degree = 1)3 6992.8091 6.507
## lockdown 6954.3956 -6.013
## dowMonday 6892.0631 -2.149
## dowSaturday 6895.5933 4.106
## dowSunday 6895.6097 1.202
## dowThursday 6892.5125 -2.204
## dowTuesday 6893.8974 -4.388
## dowWednesday 6891.3587 -2.794
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 6947.5264 2.243
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6953.5592 4.070
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 6961.4092 -3.171
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.04938 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.02956 *
## bs(novel_locations, knots = c(1, 26), degree = 1)3 8.21e-11 ***
## lockdown 1.91e-09 ***
## dowMonday 0.03165 *
## dowSaturday 4.07e-05 ***
## dowSunday 0.22925
## dowThursday 0.02759 *
## dowTuesday 1.16e-05 ***
## dowWednesday 0.00523 **
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.02495 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 4.76e-05 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.00153 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 61372.6## 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: 61236.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0794 -0.5603 0.0453 0.6093 4.1444
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 124.4 11.15
## Residual 285.9 16.91
## Number of obs: 7140, groups: subject, 248
##
## Fixed effects:
## Estimate
## (Intercept) 55.430264
## bs(novel_locations, knots = c(1, 26), degree = 1)1 -3.201216
## bs(novel_locations, knots = c(1, 26), degree = 1)2 -3.155746
## bs(novel_locations, knots = c(1, 26), degree = 1)3 19.594379
## lockdown -8.863022
## dowMonday -1.667925
## dowSaturday 3.349554
## dowSunday 0.983222
## dowThursday -1.693930
## dowTuesday -3.350845
## dowWednesday -2.200191
## distance -0.006407
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 4.163086
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6.968758
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown -20.583978
## Std. Error
## (Intercept) 1.670314
## bs(novel_locations, knots = c(1, 26), degree = 1)1 1.633460
## bs(novel_locations, knots = c(1, 26), degree = 1)2 1.481036
## bs(novel_locations, knots = c(1, 26), degree = 1)3 4.549760
## lockdown 1.475051
## dowMonday 0.774643
## dowSaturday 0.814648
## dowSunday 0.808954
## dowThursday 0.766822
## dowTuesday 0.762399
## dowWednesday 0.786082
## distance 0.021668
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 1.861316
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 1.707958
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 6.525685
## df t value
## (Intercept) 3785.317653 33.186
## bs(novel_locations, knots = c(1, 26), degree = 1)1 6979.272046 -1.960
## bs(novel_locations, knots = c(1, 26), degree = 1)2 6998.046821 -2.131
## bs(novel_locations, knots = c(1, 26), degree = 1)3 7008.395783 4.307
## lockdown 6953.528874 -6.009
## dowMonday 6891.128417 -2.153
## dowSaturday 6894.677108 4.112
## dowSunday 6894.585952 1.215
## dowThursday 6891.580948 -2.209
## dowTuesday 6892.909001 -4.395
## dowWednesday 6890.350011 -2.799
## distance 7017.231111 -0.296
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 6946.947016 2.237
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 6951.096883 4.080
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 6960.718275 -3.154
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)1 0.05006 .
## bs(novel_locations, knots = c(1, 26), degree = 1)2 0.03314 *
## bs(novel_locations, knots = c(1, 26), degree = 1)3 0.00001679805 ***
## lockdown 0.00000000197 ***
## dowMonday 0.03134 *
## dowSaturday 0.00003973927 ***
## dowSunday 0.22425
## dowThursday 0.02721 *
## dowTuesday 0.00001123659 ***
## dowWednesday 0.00514 **
## distance 0.76746
## bs(novel_locations, knots = c(1, 26), degree = 1)1:lockdown 0.02534 *
## bs(novel_locations, knots = c(1, 26), degree = 1)2:lockdown 0.00004550644 ***
## bs(novel_locations, knots = c(1, 26), degree = 1)3:lockdown 0.00162 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "BIC: "## [1] 61387.21Z 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: 61236.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0794 -0.5603 0.0453 0.6093 4.1445
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 124.4 11.15
## Residual 285.9 16.91
## Number of obs: 7140, groups: subject, 248
##
## Fixed effects:
## Estimate
## (Intercept) 55.430307
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 -3.202324
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 -3.153349
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3 19.604698
## lockdown -8.863034
## dowMonday -1.667950
## dowSaturday 3.349495
## dowSunday 0.983132
## dowThursday -1.693885
## dowTuesday -3.351004
## dowWednesday -2.200187
## distance -0.006421
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 4.059114
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 6.968536
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3:lockdown -20.605229
## Std. Error
## (Intercept) 1.670313
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 1.652159
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 1.481172
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3 4.550797
## lockdown 1.475050
## dowMonday 0.774642
## dowSaturday 0.814648
## dowSunday 0.808950
## dowThursday 0.766821
## dowTuesday 0.762398
## dowWednesday 0.786081
## distance 0.021668
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 1.892474
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 1.708202
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3:lockdown 6.530120
## df
## (Intercept) 3785.312645
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 6978.687049
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 6998.062608
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3 7008.355880
## lockdown 6953.528711
## dowMonday 6891.128363
## dowSaturday 6894.677287
## dowSunday 6894.586140
## dowThursday 6891.580929
## dowTuesday 6892.909389
## dowWednesday 6890.349899
## distance 7017.234069
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 6946.938492
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 6951.085699
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3:lockdown 6960.678914
## t value
## (Intercept) 33.186
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 -1.938
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 -2.129
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3 4.308
## lockdown -6.009
## dowMonday -2.153
## dowSaturday 4.112
## dowSunday 1.215
## dowThursday -2.209
## dowTuesday -4.395
## dowWednesday -2.799
## distance -0.296
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 2.145
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 4.079
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3:lockdown -3.155
## Pr(>|t|)
## (Intercept) < 2e-16
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1 0.05263
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2 0.03329
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3 0.00001670089
## lockdown 0.00000000197
## dowMonday 0.03134
## dowSaturday 0.00003975101
## dowSunday 0.22429
## dowThursday 0.02721
## dowTuesday 0.00001122551
## dowWednesday 0.00514
## distance 0.76698
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)1:lockdown 0.03200
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)2:lockdown 0.00004564547
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3:lockdown 0.00161
##
## (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 *
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3 ***
## 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 ***
## bs(znovel_locations, knots = c(-0.73, -0.3), degree = 1)3:lockdown **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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: 61096.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8350 -0.5572 0.0471 0.6025 4.1396
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 124.3 11.15
## Residual 286.1 16.92
## Number of obs: 7140, groups: subject, 248
##
## Fixed effects:
## Estimate
## (Intercept) 55.3877
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 -3.6415
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 -3.2230
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 -2.5339
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 -3.7092
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 -1.6932
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 -2.5092
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 0.7200
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 -0.4711
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 0.7135
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 1.2355
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 1.4399
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 4.9141
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 -0.4897
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 2.4623
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 4.9453
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 8.0388
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 20.4783
## lockdown -8.8617
## dowMonday -1.6330
## dowSaturday 3.3198
## dowSunday 1.0410
## dowThursday -1.6149
## dowTuesday -3.2906
## dowWednesday -2.1535
## 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 4.5694
## 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 4.8158
## 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 6.6535
## 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 6.4183
## 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 5.9754
## 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 5.0019
## 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 1.4759
## 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 4.7191
## 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 -2.0913
## 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 10.1980
## 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 4.7799
## 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 -0.4319
## 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 -2.3886
## 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 3.7986
## 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 -21.3048
## 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 -8.4824
## 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 -39.5062
## Std. Error
## (Intercept) 1.6714
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 1.9052
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 1.7314
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 1.6546
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 1.7124
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 1.7519
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 1.8864
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 1.9612
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 2.0780
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 2.1605
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 2.2587
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 2.4595
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 2.5977
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 2.9290
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 3.4437
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 4.2245
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 2.9981
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 10.8648
## lockdown 1.4757
## dowMonday 0.7769
## dowSaturday 0.8168
## dowSunday 0.8107
## dowThursday 0.7691
## dowTuesday 0.7639
## dowWednesday 0.7879
## 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 2.1749
## 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 2.1320
## 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 2.1777
## 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 2.2747
## 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 2.6260
## 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 2.8790
## 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 3.8485
## 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 4.1945
## 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 5.1405
## 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 5.2782
## 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 6.4599
## 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 7.6483
## 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 8.0036
## 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 9.9154
## 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 10.5937
## 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 33.7653
## df
## (Intercept) 3786.2341
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 6927.4824
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 6954.2527
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 6954.1597
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 6954.3986
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 6943.2967
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 6944.3827
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 6936.6273
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 6938.3925
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 6941.1727
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 6927.1325
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 6923.7142
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 6930.9172
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 6907.7075
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 6911.8309
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 6888.6553
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 6910.7882
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 6919.9435
## lockdown 6926.2953
## dowMonday 6864.3321
## dowSaturday 6868.4340
## dowSunday 6867.3391
## dowThursday 6864.9645
## dowTuesday 6866.8590
## dowWednesday 6863.7241
## 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 6908.5729
## 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 6916.6775
## 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 6914.2564
## 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 6907.9260
## 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 6901.1184
## 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 6899.1961
## 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 6899.1857
## 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 6894.0398
## 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 6903.4594
## 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 6881.3170
## 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 6882.3640
## 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 6888.1995
## 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 6896.7661
## 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 6908.7352
## 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 6890.6839
## 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 6872.2326
## 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 6888.4145
## t value
## (Intercept) 33.139
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)1 -1.911
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 -1.862
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 -1.531
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 -2.166
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 -0.967
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 -1.330
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 0.367
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 -0.227
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 0.330
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 0.547
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 0.585
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 1.892
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 -0.167
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 0.715
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 1.171
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 2.681
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 1.885
## lockdown -6.005
## dowMonday -2.102
## dowSaturday 4.064
## dowSunday 1.284
## dowThursday -2.100
## dowTuesday -4.308
## dowWednesday -2.733
## 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 2.101
## 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 2.259
## 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 3.055
## 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 2.822
## 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 2.275
## 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 1.737
## 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 0.463
## 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 1.226
## 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 -0.499
## 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 1.984
## 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 0.906
## 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 -0.067
## 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 -0.312
## 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 0.475
## 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 -2.149
## 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 -0.801
## 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 -1.170
## 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 0.05600
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)2 0.06271
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)3 0.12572
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)4 0.03034
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)5 0.33382
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)6 0.18351
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)7 0.71352
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)8 0.82067
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)9 0.74123
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)10 0.58439
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)11 0.55827
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)12 0.05857
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)13 0.86724
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)14 0.47462
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)15 0.24179
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)16 0.00735
## bs(novel_locations, knots = c(1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150), degree = 1)17 0.05949
## lockdown 0.00000000201
## dowMonday 0.03560
## dowSaturday 0.00004870143
## dowSunday 0.19920
## dowThursday 0.03579
## dowTuesday 0.00001671647
## dowWednesday 0.00629
## 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 0.03568
## 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 0.02392
## 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 0.00226
## 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 0.00479
## 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 0.02291
## 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 0.08237
## 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 0.64310
## 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 0.22016
## 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 0.61810
## 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 0.04731
## 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 0.36518
## 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 0.94670
## 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 0.75482
## 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 0.63508
## 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 0.03170
## 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 0.42333
## 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 0.24203
##
## (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] 61487.05
