## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ roaming_entropy + time_of_day + precipi + mean_temp +
## distance + dow + (1 | subject)
## Data: df[which(df$lockdown == 0), ]
##
## REML criterion at convergence: 41650.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9243 -0.5331 0.0465 0.6093 3.4187
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 111.7 10.57
## Residual 294.4 17.16
## Number of obs: 4826, groups: subject, 251
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 63.557370 2.613406 4670.431042 24.320
## roaming_entropy 0.622125 0.303622 4743.966238 2.049
## time_of_day 0.040150 0.014628 4621.874974 2.745
## precipi 1.933098 1.770036 4610.655066 1.092
## mean_temp -0.147603 0.031175 4692.538551 -4.735
## distance 0.002097 0.000716 4624.696719 2.930
## dowMonday -3.517568 1.027297 4588.652966 -3.424
## dowSaturday 5.211242 1.044068 4590.837985 4.991
## dowSunday -1.102697 1.029303 4595.748201 -1.071
## dowThursday -3.505592 0.972934 4582.232944 -3.603
## dowTuesday -7.917715 0.931007 4589.355944 -8.504
## dowWednesday -4.325523 0.973888 4580.325197 -4.441
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## roaming_entropy 0.040516 *
## time_of_day 0.006081 **
## precipi 0.274836
## mean_temp 0.000002259 ***
## distance 0.003411 **
## dowMonday 0.000622 ***
## dowSaturday 0.000000622 ***
## dowSunday 0.284089
## dowThursday 0.000318 ***
## dowTuesday < 0.0000000000000002 ***
## dowWednesday 0.000009144 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rmng_n tm_f_d precip mn_tmp distnc dwMndy dwStrd dwSndy
## romng_ntrpy -0.241
## time_of_day -0.166 0.019
## precipi -0.079 0.040 0.022
## mean_temp -0.855 -0.070 -0.008 -0.054
## distance -0.030 -0.224 0.005 -0.014 0.088
## dowMonday -0.463 0.049 0.031 0.201 0.272 0.026
## dowSaturday -0.170 0.119 -0.004 0.157 -0.068 -0.047 0.475
## dowSunday -0.408 0.208 -0.006 0.163 0.170 -0.086 0.553 0.491
## dowThursday -0.261 0.041 -0.002 0.218 0.038 0.003 0.546 0.515 0.536
## dowTuesday -0.413 0.026 -0.011 0.093 0.223 0.022 0.593 0.506 0.569
## dowWednesdy -0.278 0.039 0.002 0.223 0.057 0.003 0.550 0.512 0.538
## dwThrs dwTsdy
## romng_ntrpy
## time_of_day
## precipi
## mean_temp
## distance
## dowMonday
## dowSaturday
## dowSunday
## dowThursday
## dowTuesday 0.570
## dowWednesdy 0.563 0.571
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ novel_locations + time_of_day + precipi + mean_temp +
## distance + dow + (1 | subject)
## Data: df[which(df$lockdown == 0), ]
##
## REML criterion at convergence: 39616.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8798 -0.5304 0.0507 0.6156 3.4694
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 108.5 10.42
## Residual 291.4 17.07
## Number of obs: 4594, groups: subject, 249
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 61.8175174 2.6014142 4454.4355737 23.763
## novel_locations 0.0286850 0.0047144 4481.7217276 6.085
## time_of_day 0.0479237 0.0149239 4394.4844406 3.211
## precipi 1.8103321 1.7820815 4384.6142583 1.016
## mean_temp -0.1283149 0.0313610 4469.8918309 -4.092
## distance 0.0007938 0.0007410 4409.0524697 1.071
## dowMonday -2.5607596 1.0732482 4361.4498928 -2.386
## dowSaturday 4.7744618 1.0597375 4357.3001669 4.505
## dowSunday -0.9926783 1.0383992 4360.0896535 -0.956
## dowThursday -3.2934031 1.0056530 4354.7604672 -3.275
## dowTuesday -7.2862356 0.9655927 4365.9010052 -7.546
## dowWednesday -3.7903010 1.0211630 4353.4114705 -3.712
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## novel_locations 0.0000000012655839 ***
## time_of_day 0.001331 **
## precipi 0.309756
## mean_temp 0.0000436050894111 ***
## distance 0.284140
## dowMonday 0.017076 *
## dowSaturday 0.0000068006792172 ***
## dowSunday 0.339141
## dowThursday 0.001065 **
## dowTuesday 0.0000000000000544 ***
## dowWednesday 0.000208 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nvl_lc tm_f_d precip mn_tmp distnc dwMndy dwStrd dwSndy
## novel_lctns -0.199
## time_of_day -0.167 0.003
## precipi -0.081 0.023 0.021
## mean_temp -0.893 0.089 -0.003 -0.054
## distance -0.014 -0.340 0.008 -0.011 0.039
## dowMonday -0.474 0.110 0.036 0.211 0.270 -0.004
## dowSaturday -0.150 -0.018 -0.008 0.173 -0.067 -0.012 0.483
## dowSunday -0.392 0.100 -0.013 0.181 0.186 -0.071 0.570 0.500
## dowThursday -0.272 0.079 -0.004 0.235 0.036 -0.015 0.559 0.533 0.562
## dowTuesday -0.435 0.136 -0.009 0.115 0.219 -0.019 0.606 0.522 0.600
## dowWednesdy -0.293 0.080 -0.001 0.233 0.063 -0.019 0.556 0.522 0.558
## dwThrs dwTsdy
## novel_lctns
## time_of_day
## precipi
## mean_temp
## distance
## dowMonday
## dowSaturday
## dowSunday
## dowThursday
## dowTuesday 0.591
## dowWednesdy 0.575 0.584
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: roaming_entropy ~ lockdown + time_of_day + precipi + mean_temp +
## distance + dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 18641.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8348 -0.6179 0.0793 0.6454 5.1186
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.08582 0.2929
## Residual 0.60872 0.7802
## Number of obs: 7753, groups: subject, 253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.21786652 0.07546790 5399.99653015 29.388
## lockdown -1.37954466 0.02011854 7711.86125867 -68.571
## time_of_day -0.00087166 0.00052184 7582.93613988 -1.670
## precipi -0.03893910 0.04380624 7553.54393056 -0.889
## mean_temp 0.00355873 0.00091914 6969.35694737 3.872
## distance 0.00054505 0.00003115 7590.00753956 17.500
## dowMonday -0.14759023 0.03433725 7551.59721678 -4.298
## dowSaturday -0.11893190 0.03550144 7529.99681236 -3.350
## dowSunday -0.45553383 0.03530311 7532.20017698 -12.904
## dowThursday -0.05585909 0.03386655 7524.40620754 -1.649
## dowTuesday -0.05041249 0.03356106 7535.92899443 -1.502
## dowWednesday -0.08757940 0.03425624 7520.25490578 -2.557
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## lockdown < 0.0000000000000002 ***
## time_of_day 0.094891 .
## precipi 0.374088
## mean_temp 0.000109 ***
## distance < 0.0000000000000002 ***
## dowMonday 0.0000174 ***
## dowSaturday 0.000812 ***
## dowSunday < 0.0000000000000002 ***
## dowThursday 0.099110 .
## dowTuesday 0.133110
## dowWednesday 0.010590 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lckdwn tm_f_d precip mn_tmp distnc dwMndy dwStrd dwSndy
## lockdown -0.295
## time_of_day -0.210 0.006
## precipi -0.018 -0.200 0.021
## mean_temp -0.879 0.249 0.004 -0.049
## distance -0.067 0.112 0.009 0.000 0.039
## dowMonday -0.385 0.001 0.035 0.053 0.153 0.019
## dowSaturday -0.227 -0.036 -0.001 -0.042 0.001 -0.017 0.513
## dowSunday -0.315 0.019 -0.010 0.016 0.093 -0.041 0.531 0.500
## dowThursday -0.265 -0.029 -0.003 0.100 0.018 0.008 0.548 0.518 0.527
## dowTuesday -0.351 0.079 -0.004 -0.032 0.116 0.016 0.558 0.525 0.538
## dowWednesdy -0.270 0.006 0.002 0.046 0.028 0.008 0.539 0.512 0.520
## dwThrs dwTsdy
## lockdown
## time_of_day
## precipi
## mean_temp
## distance
## dowMonday
## dowSaturday
## dowSunday
## dowThursday
## dowTuesday 0.550
## dowWednesdy 0.544 0.545
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_locations ~ lockdown + time_of_day + precipi + mean_temp +
## distance + dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 79500.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.6285 -0.5126 -0.1831 0.2609 13.2830
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 198.8 14.10
## Residual 2198.6 46.89
## Number of obs: 7519, groups: subject, 249
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 78.611456 4.459834 4847.185721 17.627 < 0.0000000000000002
## lockdown -34.021748 1.215104 7505.242271 -27.999 < 0.0000000000000002
## time_of_day -0.003031 0.031810 7374.672773 -0.095 0.924096
## precipi -2.180774 2.637353 7343.532036 -0.827 0.408332
## mean_temp -0.270487 0.054684 5645.313114 -4.946 0.000000778062306382
## distance 0.056120 0.001876 7391.240283 29.921 < 0.0000000000000002
## dowMonday -13.781707 2.115396 7332.440214 -6.515 0.000000000077521404
## dowSaturday 7.183744 2.164039 7304.876243 3.320 0.000906
## dowSunday -12.051352 2.157280 7310.531725 -5.586 0.000000024020657398
## dowThursday -8.204777 2.075927 7300.711055 -3.952 0.000078119820402815
## dowTuesday -16.635046 2.055771 7313.381439 -8.092 0.000000000000000683
## dowWednesday -9.916710 2.120360 7295.825704 -4.677 0.000002964852330136
##
## (Intercept) ***
## lockdown ***
## time_of_day
## precipi
## mean_temp ***
## distance ***
## dowMonday ***
## dowSaturday ***
## dowSunday ***
## dowThursday ***
## dowTuesday ***
## dowWednesday ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lckdwn tm_f_d precip mn_tmp distnc dwMndy dwStrd dwSndy
## lockdown -0.300
## time_of_day -0.215 0.004
## precipi -0.021 -0.195 0.021
## mean_temp -0.882 0.246 0.004 -0.050
## distance -0.069 0.115 0.008 0.001 0.040
## dowMonday -0.388 -0.001 0.038 0.055 0.141 0.017
## dowSaturday -0.239 -0.028 -0.002 -0.037 -0.004 -0.017 0.521
## dowSunday -0.324 0.024 -0.013 0.021 0.086 -0.040 0.537 0.512
## dowThursday -0.273 -0.023 -0.005 0.103 0.011 0.008 0.554 0.530 0.538
## dowTuesday -0.358 0.083 -0.003 -0.027 0.105 0.016 0.562 0.537 0.548
## dowWednesdy -0.279 0.005 0.000 0.047 0.025 0.006 0.540 0.519 0.526
## dwThrs dwTsdy
## lockdown
## time_of_day
## precipi
## mean_temp
## distance
## dowMonday
## dowSaturday
## dowSunday
## dowThursday
## dowTuesday 0.560
## dowWednesdy 0.549 0.550
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ lockdown + time_of_day + precipi + mean_temp + distance +
## dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 66812.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7505 -0.5570 0.0514 0.6011 4.0606
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 123.1 11.09
## Residual 297.9 17.26
## Number of obs: 7753, groups: subject, 253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 55.2533166 1.7993688 4202.6423583 30.707
## lockdown -6.1871107 0.4480669 7610.6826562 -13.808
## time_of_day 0.0241138 0.0115688 7531.1857722 2.084
## precipi -1.8720810 0.9704045 7515.1716223 -1.929
## mean_temp -0.0130543 0.0209011 7736.6886756 -0.625
## distance 0.0027902 0.0006905 7531.5766827 4.041
## dowMonday -2.5388763 0.7606206 7514.6427501 -3.338
## dowSaturday 3.5035114 0.7860270 7505.2806861 4.457
## dowSunday 0.2051902 0.7817024 7508.4750933 0.262
## dowThursday -2.6067522 0.7497389 7503.2754527 -3.477
## dowTuesday -4.6947823 0.7431834 7509.5074100 -6.317
## dowWednesday -2.7102946 0.7582951 7501.2961739 -3.574
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## lockdown < 0.0000000000000002 ***
## time_of_day 0.037159 *
## precipi 0.053747 .
## mean_temp 0.532269
## distance 0.000053846533 ***
## dowMonday 0.000848 ***
## dowSaturday 0.000008422748 ***
## dowSunday 0.792950
## dowThursday 0.000510 ***
## dowTuesday 0.000000000282 ***
## dowWednesday 0.000354 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lckdwn tm_f_d precip mn_tmp distnc dwMndy dwStrd dwSndy
## lockdown -0.280
## time_of_day -0.196 0.007
## precipi -0.015 -0.200 0.020
## mean_temp -0.839 0.251 0.005 -0.049
## distance -0.061 0.112 0.009 0.000 0.037
## dowMonday -0.364 0.003 0.035 0.053 0.157 0.019
## dowSaturday -0.210 -0.037 -0.002 -0.043 0.000 -0.017 0.512
## dowSunday -0.297 0.019 -0.010 0.015 0.096 -0.041 0.531 0.500
## dowThursday -0.246 -0.029 -0.004 0.099 0.018 0.008 0.548 0.518 0.527
## dowTuesday -0.331 0.079 -0.004 -0.033 0.119 0.016 0.558 0.525 0.538
## dowWednesdy -0.252 0.006 0.002 0.046 0.028 0.008 0.538 0.512 0.520
## dwThrs dwTsdy
## lockdown
## time_of_day
## precipi
## mean_temp
## distance
## dowMonday
## dowSaturday
## dowSunday
## dowThursday
## dowTuesday 0.550
## dowWednesdy 0.544 0.545
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NA_avg ~ lockdown + time_of_day + precipi + mean_temp + distance +
## dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 67166.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5853 -0.6257 -0.0664 0.5594 4.4828
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 208.3 14.43
## Residual 307.3 17.53
## Number of obs: 7753, groups: subject, 253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 40.3598640 1.9227581 2973.7340300 20.991
## lockdown 3.8025421 0.4558335 7570.7580217 8.342
## time_of_day -0.0238897 0.0117555 7515.1508516 -2.032
## precipi 1.9196759 0.9858470 7503.6677665 1.947
## mean_temp 0.0119724 0.0213693 7719.1565000 0.560
## distance -0.0022198 0.0007017 7514.2214403 -3.163
## dowMonday 1.2524059 0.7727188 7503.1967918 1.621
## dowSaturday -3.1316546 0.7984350 7497.1913075 -3.922
## dowSunday -0.8155057 0.7940847 7500.8703770 -1.027
## dowThursday 1.9764571 0.7615585 7496.4907083 2.595
## dowTuesday 3.7478867 0.7549628 7501.1071802 4.964
## dowWednesday 2.0234321 0.7702299 7495.1191918 2.627
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## lockdown < 0.0000000000000002 ***
## time_of_day 0.04217 *
## precipi 0.05154 .
## mean_temp 0.57532
## distance 0.00157 **
## dowMonday 0.10511
## dowSaturday 0.000088512 ***
## dowSunday 0.30447
## dowThursday 0.00947 **
## dowTuesday 0.000000705 ***
## dowWednesday 0.00863 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lckdwn tm_f_d precip mn_tmp distnc dwMndy dwStrd dwSndy
## lockdown -0.267
## time_of_day -0.186 0.007
## precipi -0.014 -0.200 0.020
## mean_temp -0.803 0.251 0.005 -0.049
## distance -0.058 0.112 0.009 0.000 0.037
## dowMonday -0.347 0.003 0.036 0.053 0.158 0.019
## dowSaturday -0.200 -0.037 -0.002 -0.043 0.000 -0.018 0.512
## dowSunday -0.283 0.019 -0.010 0.015 0.096 -0.041 0.531 0.500
## dowThursday -0.234 -0.029 -0.004 0.099 0.018 0.008 0.548 0.519 0.527
## dowTuesday -0.316 0.079 -0.004 -0.033 0.119 0.016 0.558 0.525 0.538
## dowWednesdy -0.240 0.006 0.002 0.046 0.028 0.008 0.538 0.512 0.520
## dwThrs dwTsdy
## lockdown
## time_of_day
## precipi
## mean_temp
## distance
## dowMonday
## dowSaturday
## dowSunday
## dowThursday
## dowTuesday 0.550
## dowWednesdy 0.544 0.545
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ roaming_entropy * lockdown + time_of_day + precipi +
## mean_temp + distance + dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 66802.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7583 -0.5562 0.0505 0.6093 4.0440
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 122.9 11.09
## Residual 297.6 17.25
## Number of obs: 7753, groups: subject, 253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 54.483369 1.899466 4733.185663 28.684
## roaming_entropy 0.293447 0.292981 7619.289419 1.002
## lockdown -6.831692 0.895424 7580.356521 -7.630
## time_of_day 0.024595 0.011565 7529.445607 2.127
## precipi -1.839107 0.969954 7513.400047 -1.896
## mean_temp -0.012451 0.020965 7733.803832 -0.594
## distance 0.002582 0.000706 7531.671010 3.657
## dowMonday -2.440498 0.761113 7512.047215 -3.206
## dowSaturday 3.417232 0.789697 7504.457338 4.327
## dowSunday 0.358199 0.791788 7508.655234 0.452
## dowThursday -2.609492 0.749655 7501.237973 -3.481
## dowTuesday -4.668881 0.742889 7507.337599 -6.285
## dowWednesday -2.655735 0.758212 7499.387445 -3.503
## roaming_entropy:lockdown 1.158606 0.542039 7586.998947 2.137
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## roaming_entropy 0.316573
## lockdown 0.0000000000000264 ***
## time_of_day 0.033480 *
## precipi 0.057988 .
## mean_temp 0.552583
## distance 0.000257 ***
## dowMonday 0.001349 **
## dowSaturday 0.0000152926307521 ***
## dowSunday 0.650999
## dowThursday 0.000503 ***
## dowTuesday 0.0000000003467098 ***
## dowWednesday 0.000463 ***
## roaming_entropy:lockdown 0.032590 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ novel_locations * lockdown + time_of_day + precipi +
## mean_temp + distance + dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 64753.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8414 -0.5483 0.0553 0.6007 4.0800
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 121.2 11.01
## Residual 295.4 17.19
## Number of obs: 7519, groups: subject, 249
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 52.9672016 1.8447225 4278.7362131 28.713
## novel_locations 0.0302826 0.0046105 7363.3261734 6.568
## lockdown -5.0629320 0.5503620 7390.5125056 -9.199
## time_of_day 0.0290147 0.0117003 7298.3556906 2.480
## precipi -1.9355484 0.9690284 7283.9255619 -1.997
## mean_temp -0.0061121 0.0210864 7493.5500071 -0.290
## distance 0.0009840 0.0007336 7311.8712455 1.341
## dowMonday -1.9176134 0.7792697 7282.6287622 -2.461
## dowSaturday 3.3282634 0.7965258 7272.6261081 4.178
## dowSunday 0.5668278 0.7936949 7276.5970184 0.714
## dowThursday -2.4404664 0.7630201 7269.3322930 -3.198
## dowTuesday -4.2246989 0.7584080 7278.5027727 -5.570
## dowWednesday -2.2226676 0.7791500 7268.4595329 -2.853
## novel_locations:lockdown -0.0082004 0.0109194 7352.5164368 -0.751
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## novel_locations 0.0000000000544 ***
## lockdown < 0.0000000000000002 ***
## time_of_day 0.01317 *
## precipi 0.04582 *
## mean_temp 0.77193
## distance 0.17983
## dowMonday 0.01389 *
## dowSaturday 0.0000296905151 ***
## dowSunday 0.47515
## dowThursday 0.00139 **
## dowTuesday 0.0000000263104 ***
## dowWednesday 0.00435 **
## novel_locations:lockdown 0.45268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ roaming_entropy * pre_covid_re + time_of_day + precipi +
## mean_temp + distance + dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 72995.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7943 -0.5481 0.0489 0.5932 3.8334
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 119.5 10.93
## Residual 300.2 17.33
## Number of obs: 8468, groups: subject, 252
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 46.9038596 4.9202552 419.8494541 9.533
## roaming_entropy 1.6395787 1.1888960 8452.1697381 1.379
## pre_covid_re 0.0129182 1.9645302 338.0654407 0.007
## time_of_day 0.0262979 0.0110921 8238.7030006 2.371
## precipi -2.7593189 0.9080948 8227.9736762 -3.039
## mean_temp 0.0092203 0.0191255 8449.6944879 0.482
## distance 0.0019008 0.0006646 8237.0819432 2.860
## dowMonday -2.2181717 0.7229884 8223.7042723 -3.068
## dowSaturday 3.5015540 0.7490010 8212.0415578 4.675
## dowSunday 1.4407572 0.7501042 8215.0639921 1.921
## dowThursday -2.7710379 0.7179295 8212.2193408 -3.860
## dowTuesday -4.0912996 0.7106837 8216.0169907 -5.757
## dowWednesday -1.9735186 0.7224857 8210.0249769 -2.732
## roaming_entropy:pre_covid_re 0.2929299 0.4917214 8451.7495289 0.596
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## roaming_entropy 0.167908
## pre_covid_re 0.994757
## time_of_day 0.017770 *
## precipi 0.002384 **
## mean_temp 0.629752
## distance 0.004246 **
## dowMonday 0.002162 **
## dowSaturday 0.00000298691 ***
## dowSunday 0.054799 .
## dowThursday 0.000114 ***
## dowTuesday 0.00000000888 ***
## dowWednesday 0.006317 **
## roaming_entropy:pre_covid_re 0.551376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PA_avg ~ novel_locations * pre_covid_re + time_of_day + precipi +
## mean_temp + distance + dow + (1 | subject)
## Data: df
##
## REML criterion at convergence: 70969.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0141 -0.5497 0.0532 0.5923 3.8027
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 116.7 10.80
## Residual 299.7 17.31
## Number of obs: 8232, groups: subject, 250
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.3203621 4.8276830 321.6584502 8.559
## novel_locations 0.0835881 0.0269578 8151.7253918 3.101
## pre_covid_re 1.9642816 1.9223534 257.6763999 1.022
## time_of_day 0.0293139 0.0112430 8005.4576006 2.607
## precipi -3.4484216 0.9053191 7997.8322567 -3.809
## mean_temp 0.0555501 0.0189812 8208.4845074 2.927
## distance 0.0008124 0.0006920 8010.3179949 1.174
## dowMonday -1.7420563 0.7411553 7992.0099471 -2.350
## dowSaturday 2.9094195 0.7586113 7977.8367791 3.835
## dowSunday 1.1280905 0.7593241 7981.5836300 1.486
## dowThursday -2.6464478 0.7313762 7978.0103443 -3.618
## dowTuesday -3.1715720 0.7240793 7981.6891586 -4.380
## dowWednesday -1.5062050 0.7427685 7977.4968381 -2.028
## novel_locations:pre_covid_re -0.0150575 0.0108297 8151.2860600 -1.390
## Pr(>|t|)
## (Intercept) 0.000000000000000475 ***
## novel_locations 0.001937 **
## pre_covid_re 0.307829
## time_of_day 0.009143 **
## precipi 0.000141 ***
## mean_temp 0.003436 **
## distance 0.240442
## dowMonday 0.018774 *
## dowSaturday 0.000126 ***
## dowSunday 0.137411
## dowThursday 0.000298 ***
## dowTuesday 0.000012011526524385 ***
## dowWednesday 0.042611 *
## novel_locations:pre_covid_re 0.164451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling