Statistics within and between groups
Call: statsBy(data = subset(df, select = c(opplev0, wbgt, tdist, tpace,
hit_distrun, veryhigh_distrun)), group = df$idg, cors = FALSE,
method = "pearson", use = "pairwise", alpha = 0.05)
Intraclass Correlation 1 (Percentage of variance due to groups)
opplev0 wbgt tdist tpace
0.66 0.96 0.07 0.12
hit_distrun veryhigh_distrun group
0.13 0.40 1.00
Intraclass Correlation 2 (Reliability of group differences)
opplev0 wbgt tdist tpace
0.95 0.99 0.39 0.55
hit_distrun veryhigh_distrun group
0.56 0.86 1.00
eta^2 between groups
opplev0.bg wbgt.bg tdist.bg tpace.bg
0.69 0.96 0.17 0.22
hit_distrun.bg veryhigh_distrun.bg
0.22 0.46
Correlation between groups
opp0. wbgt. tdst. tpc.b ht_d. vry_.
opplev0.bg 1.00
wbgt.bg 0.18 1.00
tdist.bg 0.10 -0.37 1.00
tpace.bg 0.10 -0.48 0.86 1.00
hit_distrun.bg 0.15 -0.14 0.58 0.51 1.00
veryhigh_distrun.bg 0.11 0.03 0.11 0.11 0.82 1.00
Correlation within groups
opp0. wbgt. tdst. tpc.w ht_d. vry_.
opplev0.wg 1.00
wbgt.wg 0.16 1.00
tdist.wg 0.07 -0.03 1.00
tpace.wg 0.02 0.04 0.64 1.00
hit_distrun.wg 0.07 -0.02 0.74 0.63 1.00
veryhigh_distrun.wg 0.09 -0.02 0.40 0.37 0.78 1.00
Many results are not shown directly. To see specific objects select from the following list:
mean sd n F ICC1 ICC2 ci1 ci2 raw rbg ci.bg pbg rwg nw ci.wg pwg etabg etawg nwg nG Call opp0. wbgt. tdst. tpc.b ht_d. vry_.
opplev0.bg 0.00
wbgt.bg 0.19 0.00
tdist.bg 0.48 0.01 0.00
tpace.bg 0.50 0.00 0.00 0.00
hit_distrun.bg 0.28 0.31 0.00 0.00 0.00
veryhigh_distrun.bg 0.42 0.84 0.42 0.45 0.00 0.00 opp0. wbgt. tdst. tpc.w ht_d. vry_.
opplev0.wg 0.00
wbgt.wg 0.00 0.00
tdist.wg 0.11 0.55 0.00
tpace.wg 0.63 0.41 0.00 0.00
hit_distrun.wg 0.15 0.64 0.00 0.00 0.00
veryhigh_distrun.wg 0.06 0.69 0.00 0.00 0.00 0.00
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: tdist ~ grand.cent.wbgt + opplev0 + pos + grand.cent.wbgt * pos +
(1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
8384.9 8438.9 -4179.4 8358.9 457
Scaled residuals:
Min 1Q Median 3Q Max
-3.7246 -0.4061 0.1499 0.5796 2.5631
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 207037 455
Residual 2936741 1714
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8470.795 226.399 278.806 37.415 < 2e-16
grand.cent.wbgt 3.473 23.853 356.163 0.146 0.884318
opplev0 209.267 113.244 151.276 1.848 0.066564
posforward -556.564 297.122 427.163 -1.873 0.061726
posmidfielder 808.526 234.123 434.600 3.453 0.000608
poswide_midf -1306.937 267.808 432.678 -4.880 1.49e-06
poswing_back 350.678 237.553 434.135 1.476 0.140612
grand.cent.wbgt:posforward -32.851 37.882 427.969 -0.867 0.386327
grand.cent.wbgt:posmidfielder -51.658 30.130 433.589 -1.714 0.087154
grand.cent.wbgt:poswide_midf -99.392 34.503 436.308 -2.881 0.004164
grand.cent.wbgt:poswing_back -53.281 29.842 433.263 -1.785 0.074894
(Intercept) ***
grand.cent.wbgt
opplev0 .
posforward .
posmidfielder ***
poswide_midf ***
poswing_back
grand.cent.wbgt:posforward
grand.cent.wbgt:posmidfielder .
grand.cent.wbgt:poswide_midf **
grand.cent.wbgt:poswing_back .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0 psfrwr psmdfl pswd_m pswng_
grnd.cnt.wb 0.105
opplev0 -0.552 -0.110
posforward -0.480 -0.033 0.019
posmidfildr -0.611 -0.043 0.026 0.454
poswide_mdf -0.543 -0.038 0.042 0.397 0.505
poswing_bck -0.596 -0.041 0.014 0.447 0.570 0.497
grnd.cnt.wbgt:psf -0.033 -0.557 0.014 0.017 0.025 0.022 0.024
grnd.cnt.wbgt:psm -0.053 -0.698 0.039 0.025 0.009 0.028 0.031
grnd.cnt.wbgt:pswd_ -0.037 -0.608 0.017 0.022 0.027 0.081 0.027
grnd.cnt.wbgt:pswn_ -0.035 -0.701 0.006 0.024 0.031 0.027 0.014
grnd.cnt.wbgt:psf grnd.cnt.wbgt:psm grnd.cnt.wbgt:pswd_
grnd.cnt.wb
opplev0
posforward
posmidfildr
poswide_mdf
poswing_bck
grnd.cnt.wbgt:psf
grnd.cnt.wbgt:psm 0.435
grnd.cnt.wbgt:pswd_ 0.378 0.483
grnd.cnt.wbgt:pswn_ 0.440 0.557 0.487
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: tpace ~ grand.cent.wbgt + opplev0 + (1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
4072.8 4093.5 -2031.4 4062.8 465
Scaled residuals:
Min 1Q Median 3Q Max
-4.2311 -0.4074 0.0955 0.6545 2.7495
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 23.77 4.875
Residual 313.89 17.717
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 99.8685 1.6424 100.2824 60.807 < 2e-16 ***
grand.cent.wbgt -0.4803 0.1375 66.8954 -3.492 0.000855 ***
opplev0 1.5534 1.1781 164.6799 1.319 0.189148
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd..
grnd.cnt.wb 0.134
opplev0 -0.760 -0.161
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: stand_walk ~ grand.cent.wbgt + opplev0 + (1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
7119.3 7140.1 -3554.7 7109.3 465
Scaled residuals:
Min 1Q Median 3Q Max
-3.2159 -0.6878 0.1522 0.7271 2.6447
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 8053 89.74
Residual 210149 458.42
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1677.938 38.943 93.928 43.087 <2e-16 ***
grand.cent.wbgt -2.851 3.198 64.894 -0.892 0.3758
opplev0 68.413 28.489 140.436 2.401 0.0176 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd..
grnd.cnt.wb 0.135
opplev0 -0.774 -0.164
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: hit_distrun ~ grand.cent.wbgt + opplev0 + (1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
7354.6 7375.4 -3672.3 7344.6 465
Scaled residuals:
Min 1Q Median 3Q Max
-2.68454 -0.74244 0.03125 0.65197 3.15622
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 41519 203.8
Residual 329578 574.1
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1677.538 58.177 95.847 28.835 <2e-16 ***
grand.cent.wbgt -5.626 4.981 61.909 -1.130 0.2630
opplev0 75.098 40.712 181.788 1.845 0.0667 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd..
grnd.cnt.wb 0.131
opplev0 -0.743 -0.158
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: veryhigh_distrun ~ grand.cent.wbgt + opplev0 + grand.cent.wbgt *
opplev0 + (1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
6859.1 6884.0 -3423.5 6847.1 464
Scaled residuals:
Min 1Q Median 3Q Max
-4.2793 -0.6508 -0.0322 0.5832 4.3169
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 63093 251.2
Residual 100974 317.8
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 602.995 48.265 100.832 12.494 <2e-16 ***
grand.cent.wbgt -4.199 6.067 192.332 -0.692 0.4897
opplev0 54.378 27.756 396.694 1.959 0.0508 .
grand.cent.wbgt:opplev0 3.581 3.868 332.510 0.926 0.3553
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0
grnd.cnt.wb 0.141
opplev0 -0.613 -0.111
grnd.cnt.:0 -0.089 -0.675 0.003
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: thr_less_80 ~ grand.cent.wbgt + opplev0 + (1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
3841.6 3862.4 -1915.8 3831.6 465
Scaled residuals:
Min 1Q Median 3Q Max
-1.9493 -0.7845 -0.1305 0.6456 3.5969
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 10.47 3.235
Residual 194.61 13.950
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 24.0940 1.2323 78.9504 19.553 < 2e-16 ***
grand.cent.wbgt -0.4056 0.1020 52.8270 -3.974 0.000215 ***
opplev0 0.7571 0.8940 126.1001 0.847 0.398712
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd..
grnd.cnt.wb 0.134
opplev0 -0.769 -0.163
boundary (singular) fit: see help('isSingular')Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: thr_over_80 ~ grand.cent.wbgt + opplev0 + (1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
4058.8 4079.5 -2024.4 4048.8 465
Scaled residuals:
Min 1Q Median 3Q Max
-3.02990 -0.65020 0.08811 0.71160 2.27362
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 5.920e-16 2.433e-08
Residual 3.226e+02 1.796e+01
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 54.67647 1.34854 470.00000 40.55 < 2e-16 ***
grand.cent.wbgt 0.08539 0.10808 470.00000 0.79 0.429917
opplev0 3.38762 1.00823 470.00000 3.36 0.000843 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd..
grnd.cnt.wb 0.132
opplev0 -0.789 -0.168
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: sprints ~ grand.cent.wbgt + opplev0 + grand.cent.wbgt * opplev0 +
(1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
3066.5 3091.4 -1527.2 3054.5 464
Scaled residuals:
Min 1Q Median 3Q Max
-1.8759 -0.8397 -0.2059 0.6376 2.8907
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 0.0 0.000
Residual 38.9 6.237
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 10.93843 0.46918 470.00000 23.314 <2e-16 ***
grand.cent.wbgt 0.04235 0.06457 470.00000 0.656 0.512
opplev0 0.38179 0.35062 470.00000 1.089 0.277
grand.cent.wbgt:opplev0 -0.04002 0.04555 470.00000 -0.878 0.380
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0
grnd.cnt.wb 0.128
opplev0 -0.783 -0.053
grnd.cnt.:0 -0.063 -0.814 -0.054
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: decel_hit ~ grand.cent.wbgt + opplev0 + grand.cent.wbgt * opplev0 +
(1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
3963.7 3988.6 -1975.8 3951.7 464
Scaled residuals:
Min 1Q Median 3Q Max
-2.9368 -0.6036 -0.1132 0.5090 3.6527
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 160.4 12.67
Residual 209.1 14.46
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 34.1631 2.3401 92.4406 14.599 < 2e-16 ***
grand.cent.wbgt -0.8971 0.2879 198.7958 -3.116 0.00211 **
opplev0 3.7520 1.2889 420.4462 2.911 0.00379 **
grand.cent.wbgt:opplev0 0.1636 0.1807 359.9103 0.905 0.36590
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0
grnd.cnt.wb 0.137
opplev0 -0.587 -0.115
grnd.cnt.:0 -0.087 -0.654 0.006
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: accel_hit ~ grand.cent.wbgt + opplev0 + (1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
3941.0 3961.8 -1965.5 3931.0 465
Scaled residuals:
Min 1Q Median 3Q Max
-3.8540 -0.4743 -0.1056 0.3634 3.5265
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 280.3 16.74
Residual 187.2 13.68
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 26.9460 2.7718 80.7073 9.722 3.1e-15 ***
grand.cent.wbgt -0.8276 0.2538 125.3967 -3.261 0.00143 **
opplev0 4.2819 1.2838 467.8990 3.335 0.00092 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd..
grnd.cnt.wb 0.092
opplev0 -0.494 -0.146
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: distzone3 ~ grand.cent.wbgt + opplev0 + grand.cent.wbgt * opplev0 +
(1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
6955.1 6980.0 -3471.5 6943.1 464
Scaled residuals:
Min 1Q Median 3Q Max
-2.3150 -0.7051 -0.1342 0.6042 3.9554
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 9464 97.28
Residual 144873 380.62
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1063.9974 34.6287 80.6146 30.726 <2e-16 ***
grand.cent.wbgt -5.3762 4.7309 89.8009 -1.136 0.259
opplev0 25.8979 24.9043 135.0412 1.040 0.300
grand.cent.wbgt:opplev0 -0.7761 3.2926 108.9282 -0.236 0.814
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0
grnd.cnt.wb 0.145
opplev0 -0.759 -0.074
grnd.cnt.:0 -0.080 -0.794 -0.031
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: distzone4 ~ grand.cent.wbgt + opplev0 + grand.cent.wbgt * opplev0 +
(1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
6445.6 6470.5 -3216.8 6433.6 463
Scaled residuals:
Min 1Q Median 3Q Max
-4.1183 -0.6423 -0.0195 0.5144 4.7247
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 25290 159.0
Residual 43402 208.3
Number of obs: 469, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 442.637 30.988 101.785 14.284 <2e-16 ***
grand.cent.wbgt -2.693 3.920 187.691 -0.687 0.4930
opplev0 31.531 18.065 386.452 1.745 0.0817 .
grand.cent.wbgt:opplev0 2.096 2.513 321.213 0.834 0.4049
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0
grnd.cnt.wb 0.142
opplev0 -0.621 -0.110
grnd.cnt.:0 -0.090 -0.682 0.002
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: distzone5 ~ grand.cent.wbgt + opplev0 + grand.cent.wbgt * opplev0 +
(1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
6051.6 6076.5 -3019.8 6039.6 464
Scaled residuals:
Min 1Q Median 3Q Max
-3.4987 -0.6107 -0.2089 0.4658 5.8319
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 8311 91.16
Residual 18685 136.69
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 161.6388 18.8064 104.2133 8.595 8.95e-14 ***
grand.cent.wbgt -0.9496 2.4304 170.6504 -0.391 0.6965
opplev0 21.8546 11.4924 344.3103 1.902 0.0581 .
grand.cent.wbgt:opplev0 1.1670 1.5862 277.9183 0.736 0.4625
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0
grnd.cnt.wb 0.146
opplev0 -0.650 -0.105
grnd.cnt.:0 -0.091 -0.706 -0.001
boundary (singular) fit: see help('isSingular')Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: tload ~ grand.cent.wbgt + opplev0 + grand.cent.wbgt * opplev0 +
(1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
5276.2 5301.1 -2632.1 5264.2 464
Scaled residuals:
Min 1Q Median 3Q Max
-3.2915 -0.6351 0.0484 0.6534 2.3178
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 6.082e-14 2.466e-07
Residual 4.283e+03 6.545e+01
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 228.8387 4.9231 470.0000 46.482 < 2e-16 ***
grand.cent.wbgt 0.7990 0.6776 470.0000 1.179 0.238910
opplev0 13.1613 3.6790 470.0000 3.577 0.000383 ***
grand.cent.wbgt:opplev0 -0.5488 0.4780 470.0000 -1.148 0.251490
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0
grnd.cnt.wb 0.128
opplev0 -0.783 -0.053
grnd.cnt.:0 -0.063 -0.814 -0.054
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: cload ~ grand.cent.wbgt + opplev0 + grand.cent.wbgt * opplev0 +
(1 | idg)
Data: df
Control: lmerControl(optimizer = "bobyqa")
AIC BIC logLik -2*log(L) df.resid
5072.4 5097.3 -2530.2 5060.4 464
Scaled residuals:
Min 1Q Median 3Q Max
-3.2284 -0.5074 0.1248 0.7031 2.5012
Random effects:
Groups Name Variance Std.Dev.
idg (Intercept) 1.305 1.143
Residual 2775.191 52.680
Number of obs: 470, groups: idg, 52
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 175.4663 3.9702 74.5172 44.196 < 2e-16 ***
grand.cent.wbgt 0.8382 0.5464 78.5689 1.534 0.12904
opplev0 8.6924 2.9660 105.6633 2.931 0.00415 **
grand.cent.wbgt:opplev0 -0.4064 0.3854 87.2580 -1.054 0.29458
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) grnd.. opplv0
grnd.cnt.wb 0.128
opplev0 -0.783 -0.053
grnd.cnt.:0 -0.063 -0.814 -0.054