BMI Walking Stance Project Final Report
Chiwhan Choi
2025-11-01
Abstract
Obesity significantly impacts musculoskeletal health and alters walking biomechanics. This study aimed to quantify the influence of obesity on knee angle changes during various walking tasks and identify associated anthropometric predictors. We analyzed motion sensor data from 35 participants (12 normal-weight, 23 obese) performing six distinct walking tasks (e.g., preferred speed, obstacle negotiation). Knee angle difference (InitialPeak) was the primary outcome. Exploratory data analysis (EDA), including correlation analysis and Variance Inflation Factor (VIF) assessment, guided variable selection. Linear Mixed-Effects Models (LMM), Generalized Additive Mixed Models (GAMM), and Bayesian Mixed Models were developed, incorporating fixed effects for group, demographics, and selected body measurements, with random intercepts for participant and task. Model performance was compared using ANOVA and 5-fold cross-validation (RMSE, R²). Two-sample t-tests compared knee angle differences between groups for each task. EDA and t-tests revealed significantly reduced knee angle differences in the obese group across most tasks (p < 0.05), suggesting less knee flexion. The final LMM demonstrated the best fit and predictive performance (Avg CV RMSE \(\approx\) 2.59, Avg CV R² \(\approx\) 0.68), identifying significant associations between knee angle difference and Shoulder Breadth (positive), Chest Breadth (negative), Lower Thigh Circumference (negative), Shank Circumference (negative), and A Body Shape Index (ABSI, negative). Significant variability was attributed to both participant and task random effects. In conclusion, obesity is associated with reduced knee flexion during walking, and specific body dimensions beyond BMI contribute significantly to these biomechanical alterations.
Introduction
Obesity is a growing public health concern worldwide, with well-documented impacts on musculoskeletal health and mobility. Excess body weight alters biomechanical loading patterns during everyday activities, increasing the risk of joint degeneration and chronic musculoskeletal conditions. In particular, deviations in walking mechanics—such as reduced knee flexion and altered foot–ground contact—have been observed in individuals with obesity, suggesting a potential pathway by which obesity contributes to long-term joint dysfunction and osteoarthritis.
Knee angle during gait is a critical marker of walking stance and limb mechanics. This project aims to quantify how obesity influences knee-angle trajectories across a series of controlled walking conditions. Thirty-five participants (12 normal-weight, 23 obese) underwent six distinct walking tasks—ranging from preferred and fast speeds to obstacle approaches and crossings at two heights—while equipped with motion sensors on the upper leg, lower leg, and shoes. By comparing knee-angle profiles between obese and non-obese groups, we seek to identify the association between knee angles and people’s body measurements.
In this report, we will mainly talk about the EDA of the dataset, the models used to assess the relationship and interpret our models’ results to reach a final conclusion.
Data Description
## 'data.frame': 2025 obs. of 52 variables:
## $ Subject : int 1 1 1 1 1 1 1 1 1 1 ...
## $ studyid : int 1 1 1 1 1 1 1 1 1 1 ...
## $ age : int 36 36 36 36 36 36 36 36 36 36 ...
## $ Group : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Class : int 0 0 0 0 0 0 0 0 0 0 ...
## $ BS : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Sex : int 0 0 0 0 0 0 0 0 0 0 ...
## $ BMI : num 20.1 20.1 20.1 20.1 20.1 ...
## $ Race : int 0 0 0 0 0 0 0 0 0 0 ...
## $ height_m : num 1.66 1.66 1.66 1.66 1.66 1.66 1.66 1.66 1.66 1.66 ...
## $ weight_kg : num 55.3 55.3 55.3 55.3 55.3 ...
## $ leg_l_l : num 92 92 92 92 92 92 92 92 92 92 ...
## $ leg_l_r : num 93 93 93 93 93 93 93 93 93 93 ...
## $ leg_l : num 92.5 92.5 92.5 92.5 92.5 92.5 92.5 92.5 92.5 92.5 ...
## $ DST : int 22 22 22 22 22 22 22 22 22 22 ...
## $ Stroop : int 119 119 119 119 119 119 119 119 119 119 ...
## $ Stroop_Effect: num 483 483 483 483 483 ...
## $ PA : num 7.12 7.12 7.12 7.12 7.12 ...
## $ Task : chr "PRF" "PRF" "PRF" "PRF" ...
## $ Trial : int 1 2 3 4 1 2 3 4 1 2 ...
## $ Speed : num 1.29 1.32 1.32 1.32 1.55 1.51 1.57 1.53 1.4 1.35 ...
## $ Initial : num 4.5 3.63 3.22 3.95 6.88 ...
## $ Peak : num 10.4 11.8 10.1 12.8 16.3 ...
## $ InitialPeak : num 5.9 8.21 6.93 8.83 9.47 ...
## $ Min : num 1.4 1.51 1.59 2.34 1.5 ...
## $ MinPeak : num 9 10.33 8.56 10.44 14.84 ...
## $ Stiffness : num 0.1153 0.1114 0.0969 0.0902 0.1088 ...
## $ MomentPeak : num 0.518 0.738 0.553 0.705 0.748 ...
## $ kneeMrange : num 0.75 0.925 0.647 0.771 0.992 ...
## $ head_cir : num 56.5 56.5 56.5 56.5 56.5 56.5 56.5 56.5 56.5 56.5 ...
## $ neck_cir : num 36 36 36 36 36 36 36 36 36 36 ...
## $ SH_B : num 34.6 34.6 34.6 34.6 34.6 34.6 34.6 34.6 34.6 34.6 ...
## $ SH_D : num 19 19 19 19 19 19 19 19 19 19 ...
## $ CH_B : num 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 ...
## $ CH_D : num 18 18 18 18 18 18 18 18 18 18 ...
## $ WA_B : num 23 23 23 23 23 23 23 23 23 23 ...
## $ WA_D : num 19 19 19 19 19 19 19 19 19 19 ...
## $ HIP_B : num 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.4 ...
## $ HIP_D : num 20.5 20.5 20.5 20.5 20.5 20.5 20.5 20.5 20.5 20.5 ...
## $ ASIS : num 21.3 21.3 21.3 21.3 21.3 21.3 21.3 21.3 21.3 21.3 ...
## $ waist_cir : num 76 76 76 76 76 76 76 76 76 76 ...
## $ hip_cir : num 90 90 90 90 90 90 90 90 90 90 ...
## $ thigh_cir : num 49 49 49 49 49 49 49 49 49 49 ...
## $ L_thigh_cir : num 35 35 35 35 35 35 35 35 35 35 ...
## $ shank_cir : num 29 29 29 29 29 29 29 29 29 29 ...
## $ ankle_cir : num 21 21 21 21 21 21 21 21 21 21 ...
## $ W.H.ratio : num 0.844 0.844 0.844 0.844 0.844 ...
## $ ABSI : num 79.8 79.8 79.8 79.8 79.8 ...
## $ Hip.Index : num 50.2 50.2 50.2 50.2 50.2 ...
## $ biceps_cir : num 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 ...
## $ forearm_cir : num 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5 ...
## $ wrist_cir : num 15 15 15 15 15 15 15 15 15 15 ...The main dataset used is “BodyShape.csv”. It contains the body shape measures, such as BMI, hip circumference, waist hip ratio, cognitive test scores, physical activity scores and the knee angle before and after conducting the task and the difference between those two measures for all 38 participants. However, there are many repeated measures for each participants, which means that linear mixed effect model would be a good baseline model to start with. Next, we will perform EDA to select the important variables related to knee angles.
EDA
Based on the histogram, for normal people, the knee angle difference
is mostly centered around 10 degrees, for obese people, the knee angle
difference is more right tailed, there are more observations distributed
around 0 to 5 degrees; for boxplot, the median of obese people is
slightly lower than normal people. Both plots have showed that the knee
angle difference is smaller for obese group, meaning they tend to bend
less their knees during the six walking stances.
From the correlation plot above, InitialPeak does not have high
correlation with any of the body measurements, also, many of the body
measurements are highly correlated with each other, and they are also
tend to correlate with Group, Body Shape, BMI and weight. We could
further use VIF value to test for the multicollinearity.
## Warning in summary.lm(object, ...): essentially perfect fit: summary may be
## unreliable## age Group Class BS Sex
## 6.847906 71.981158 99.320093 39.180965 41.588461
## BMI Race height_m weight_kg leg_l
## 1722.631669 8.040939 146.804583 60.103351 30.476475
## DST Stroop Stroop_Effect PA Speed
## 6.176927 6.628922 8.074536 6.408408 3.649911
## Initial Peak Min MinPeak Stiffness
## 2.962855 397.294243 191.851953 309.612294 1.528218
## MomentPeak kneeMrange neck_cir SH_B SH_D
## 31.754977 24.552982 19.213967 9.284384 34.179679
## CH_B CH_D WA_B WA_D HIP_B
## 16.135450 36.112680 36.035933 46.457726 27.415390
## HIP_D ASIS waist_cir hip_cir thigh_cir
## 44.179809 10.930804 8221.128851 4914.450223 37.235729
## L_thigh_cir shank_cir ankle_cir W.H.ratio ABSI
## 16.264640 36.981547 24.051762 2683.961251 47.511878
## Hip.Index
## 17.390758The values above are the values of VIF, and it is clear that most of the body measurement are high correlated with each other since many of the VIF values are far exceeding 10. We will try to remove some of the variables to see if VIF values change.
## Warning in summary.lm(object, ...): essentially perfect fit: summary may be
## unreliable## age Group Sex Race leg_l
## 1.778794 9.995250 4.502893 2.044137 1.990723
## DST Stroop Stroop_Effect PA Speed
## 1.840140 2.299483 1.970921 2.141428 2.775249
## Initial Peak Min MinPeak Stiffness
## 2.066236 376.571767 183.235431 293.522531 1.382954
## MomentPeak kneeMrange neck_cir SH_B CH_B
## 15.834178 13.863525 6.013996 3.475902 4.883854
## WA_B HIP_D ASIS shank_cir ankle_cir
## 6.438265 8.361000 4.826496 6.084880 5.056890
## ABSI
## 2.166755As the results shown above, after removing leg_l_l, leg_l_r, BMI,
waist_cir, hip_cir, thigh_cir, height_m, weight_kg, W.H.ratio,
Hip.Index, WA_B, WA_D, BS, SH_D and CH_D, most of the VIF become lower
than 10, thus, we will try include those variables in the later modeling
part.
## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
Based on the boxplot shown above, both groups bent their knees the most when they walk fast, and for all six tasks, people in normal group tend to have greater knee angle difference than the obese group.
Modeling
Model 1: Stiffness
library(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## stepm1 <- lmer(Stiffness
~ factor(BS)+age+leg_l+factor(Race)+Speed+(1|Subject), data=df_ocha)
summary(m1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: -593.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6900 -0.3168 -0.0515 0.2125 6.8678
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0008254 0.02873
## Residual 0.0027145 0.05210
## Number of obs: 225, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.075306 0.126874 18.308566 0.594 0.56008
## factor(BS)1 -0.043521 0.021287 19.691852 -2.045 0.05450 .
## factor(BS)2 -0.060763 0.016466 19.445403 -3.690 0.00151 **
## age 0.000289 0.001689 18.426326 0.171 0.86599
## leg_l -0.001685 0.001563 23.019037 -1.078 0.29210
## factor(Race)1 0.002302 0.025337 18.649465 0.091 0.92859
## factor(Race)2 -0.001789 0.025430 20.572824 -0.070 0.94461
## factor(Race)3 0.064453 0.027126 18.681403 2.376 0.02836 *
## factor(Race)4 -0.012498 0.029953 20.846463 -0.417 0.68077
## Speed 0.118924 0.046888 60.491145 2.536 0.01380 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.141
## factor(BS)2 0.000 0.472
## age -0.182 -0.546 -0.119
## leg_l -0.847 0.153 -0.161 -0.113
## factor(Rc)1 -0.166 0.319 0.089 -0.073 0.114
## factor(Rc)2 -0.128 0.140 0.024 -0.103 0.144 0.749
## factor(Rc)3 -0.151 0.234 0.060 -0.048 0.111 0.742 0.696
## factor(Rc)4 -0.177 -0.048 -0.057 0.188 0.180 0.653 0.658 0.617
## Speed 0.017 0.111 0.282 0.003 -0.437 -0.218 -0.306 -0.226 -0.430r2 <- r.squaredGLMM(m1)
print(r2)## R2m R2c
## [1,] 0.3543964 0.5049386# 95% Confidence Intervals for fixed effects (beta coefficients)
ci <- confint(m1, level = 0.95, method = "Wald") # Or method = "profile"
print(ci)## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.173362427 0.323973593
## factor(BS)1 -0.085242627 -0.001800058
## factor(BS)2 -0.093035933 -0.028489441
## age -0.003020904 0.003598884
## leg_l -0.004748523 0.001378079
## factor(Race)1 -0.047357474 0.051960808
## factor(Race)2 -0.051629758 0.048052539
## factor(Race)3 0.011287693 0.117617831
## factor(Race)4 -0.071204718 0.046209445
## Speed 0.027025529 0.210822001Model 2: Peak Knee Angle
m2 <- lmer(Peak ~ factor(BS)+age+leg_l+factor(Race)+Speed+(1|Subject), data=df_ocha)
summary(m2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ factor(BS) + age + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ocha
##
## REML criterion at convergence: 1209.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2792 -0.5176 -0.0294 0.5801 3.3780
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 44.520 6.672
## Residual 8.727 2.954
## Number of obs: 228, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 21.6647 24.6589 20.9291 0.879 0.389607
## factor(BS)1 -9.6998 4.0482 21.1049 -2.396 0.025915 *
## factor(BS)2 -13.3625 3.1062 21.3460 -4.302 0.000306 ***
## age 0.3798 0.3276 20.9418 1.159 0.259420
## leg_l -0.3173 0.2757 22.5107 -1.151 0.261797
## factor(Race)1 2.0431 4.8469 21.2178 0.422 0.677614
## factor(Race)2 1.7259 4.7125 21.6080 0.366 0.717748
## factor(Race)3 4.7502 5.1871 21.2374 0.916 0.370076
## factor(Race)4 3.6106 5.4094 22.2288 0.667 0.511340
## Speed 11.7757 3.6313 217.7806 3.243 0.001370 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.098
## factor(BS)2 0.011 0.462
## age -0.228 -0.534 -0.104
## leg_l -0.917 0.163 -0.087 -0.076
## factor(Rc)1 -0.140 0.352 0.153 -0.084 0.014
## factor(Rc)2 -0.097 0.176 0.112 -0.116 0.006 0.742
## factor(Rc)3 -0.124 0.258 0.109 -0.050 0.006 0.727 0.682
## factor(Rc)4 -0.179 -0.002 0.057 0.192 0.025 0.629 0.620 0.587
## Speed 0.008 0.036 0.109 0.007 -0.193 -0.092 -0.128 -0.095 -0.187r2 <- r.squaredGLMM(m2)
print(r2)## R2m R2c
## [1,] 0.4482139 0.9095632# 95% Confidence Intervals for fixed effects (beta coefficients)
ci <- confint(m2, level = 0.95, method = "Wald") # Or method = "profile"
print(ci)## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -26.6658830 69.9953302
## factor(BS)1 -17.6340629 -1.7654624
## factor(BS)2 -19.4504242 -7.2745101
## age -0.2623566 1.0219505
## leg_l -0.8576983 0.2230198
## factor(Race)1 -7.4567002 11.5428567
## factor(Race)2 -7.5104088 10.9622114
## factor(Race)3 -5.4164534 14.9167714
## factor(Race)4 -6.9915969 14.2128672
## Speed 4.6584134 18.8929001Model 3: Initial_Peak Knee Angle Difference
m3 <- lmer(InitialPeak ~ factor(BS)+age+leg_l+factor(Race)+Speed+(1|Subject), data=df_ocha)
summary(m3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: 1154.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5716 -0.5119 0.0265 0.5108 3.2914
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 23.996 4.899
## Residual 7.055 2.656
## Number of obs: 228, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 28.6316 18.2266 20.8257 1.571 0.1313
## factor(BS)1 -5.9192 2.9952 21.0774 -1.976 0.0614 .
## factor(BS)2 -6.3489 2.3011 21.3908 -2.759 0.0116 *
## age 0.1713 0.2422 20.8440 0.707 0.4871
## leg_l -0.3647 0.2055 22.9906 -1.775 0.0891 .
## factor(Race)1 -1.0532 3.5882 21.2099 -0.294 0.7720
## factor(Race)2 1.1469 3.4960 21.7536 0.328 0.7460
## factor(Race)3 1.1039 3.8404 21.2379 0.287 0.7766
## factor(Race)4 5.7272 4.0255 22.5793 1.423 0.1685
## Speed 3.9267 3.1991 216.1422 1.227 0.2210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.099
## factor(BS)2 0.011 0.462
## age -0.226 -0.535 -0.104
## leg_l -0.911 0.161 -0.094 -0.078
## factor(Rc)1 -0.141 0.349 0.148 -0.084 0.022
## factor(Rc)2 -0.099 0.173 0.105 -0.116 0.018 0.743
## factor(Rc)3 -0.125 0.256 0.105 -0.051 0.015 0.728 0.684
## factor(Rc)4 -0.179 -0.005 0.048 0.190 0.040 0.631 0.624 0.589
## Speed 0.010 0.043 0.130 0.008 -0.228 -0.109 -0.152 -0.113 -0.221r2 <- r.squaredGLMM(m3)
print(r2)## R2m R2c
## [1,] 0.3177622 0.8449954# 95% Confidence Intervals for fixed effects (beta coefficients)
ci <- confint(m3, level = 0.95, method = "Wald") # Or method = "profile"
print(ci)## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -7.0918682 64.35510008
## factor(BS)1 -11.7896486 -0.04883345
## factor(BS)2 -10.8589798 -1.83887138
## age -0.3033504 0.64601132
## leg_l -0.7674139 0.03798025
## factor(Race)1 -8.0859176 5.97942185
## factor(Race)2 -5.7050410 7.99883291
## factor(Race)3 -6.4231926 8.63108304
## factor(Race)4 -2.1626765 13.61706818
## Speed -2.3434518 10.19690687# Calculate marginal and conditional R²
r2 <- r.squaredGLMM(m3)
print(r2)## R2m R2c
## [1,] 0.3177622 0.8449954Since SH_B(Shoulder Breadth), CH_B(Chest Breadth), L_thigh_cir(lower thigh circumference), shank_cir(shank circumference) and ABSI(a body shape index) are the only statistically significant variables based on the model output above. We can try only including those variables as fixed effects and check the performance using ANOVA later.
Model 4: Moment Peak
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: -162.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0445 -0.5343 0.0231 0.4568 3.7881
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.05631 0.2373
## Residual 0.01648 0.1284
## Number of obs: 225, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.05077 0.88296 20.86307 1.190 0.247391
## factor(BS)1 -0.43776 0.14521 21.17799 -3.015 0.006555 **
## factor(BS)2 -0.46792 0.11158 21.50135 -4.194 0.000392 ***
## age 0.01443 0.01173 20.88662 1.230 0.232324
## leg_l -0.01657 0.00996 23.07367 -1.664 0.109606
## factor(Race)1 0.06631 0.17389 21.27186 0.381 0.706711
## factor(Race)2 0.10023 0.16949 21.84945 0.591 0.560345
## factor(Race)3 0.16783 0.18606 21.27891 0.902 0.377152
## factor(Race)4 0.28019 0.19522 22.69371 1.435 0.164846
## Speed 0.52236 0.15849 212.90505 3.296 0.001150 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.100
## factor(BS)2 0.010 0.463
## age -0.226 -0.535 -0.105
## leg_l -0.910 0.160 -0.095 -0.078
## factor(Rc)1 -0.141 0.348 0.147 -0.083 0.023
## factor(Rc)2 -0.099 0.172 0.104 -0.115 0.019 0.743
## factor(Rc)3 -0.125 0.256 0.104 -0.050 0.015 0.728 0.684
## factor(Rc)4 -0.178 -0.007 0.046 0.191 0.042 0.631 0.624 0.589
## Speed 0.008 0.050 0.136 0.004 -0.231 -0.113 -0.156 -0.115 -0.226## R2m R2c
## [1,] 0.4748231 0.8811105## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 1.995822 2 1.188586
## age 1.648239 1 1.283838
## leg_l 1.138110 1 1.066822
## factor(Race) 1.571311 4 1.058115
## Speed 1.140020 1 1.067717## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.679796138 2.781333479
## factor(BS)1 -0.722367270 -0.153159230
## factor(BS)2 -0.686606058 -0.249226485
## age -0.008563237 0.037429848
## leg_l -0.036095713 0.002946269
## factor(Race)1 -0.274494396 0.407123758
## factor(Race)2 -0.231962747 0.432422607
## factor(Race)3 -0.196844285 0.532498556
## factor(Race)4 -0.102426962 0.662814218
## Speed 0.211713955 0.833004753Model 5: Moment Range
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: -133.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6250 -0.4440 -0.0591 0.5449 3.6850
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.02721 0.1649
## Residual 0.02060 0.1435
## Number of obs: 225, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.644426 0.633055 20.494570 1.018 0.320563
## factor(BS)1 -0.413719 0.104634 21.167222 -3.954 0.000716 ***
## factor(BS)2 -0.457539 0.080722 21.588858 -5.668 1.14e-05 ***
## age 0.011283 0.008416 20.546939 1.341 0.194629
## leg_l -0.009949 0.007353 24.356264 -1.353 0.188508
## factor(Race)1 -0.017367 0.125365 21.123383 -0.139 0.891134
## factor(Race)2 0.005427 0.123214 22.230830 0.044 0.965258
## factor(Race)3 0.014412 0.134160 21.142097 0.107 0.915466
## factor(Race)4 0.227688 0.143365 23.482062 1.588 0.125625
## Speed 0.628287 0.163080 163.804191 3.853 0.000167 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.109
## factor(BS)2 0.008 0.466
## age -0.217 -0.537 -0.107
## leg_l -0.887 0.153 -0.120 -0.085
## factor(Rc)1 -0.146 0.339 0.128 -0.082 0.053
## factor(Rc)2 -0.105 0.162 0.078 -0.113 0.061 0.746
## factor(Rc)3 -0.130 0.248 0.088 -0.050 0.047 0.733 0.688
## factor(Rc)4 -0.177 -0.020 0.012 0.188 0.092 0.638 0.636 0.598
## Speed 0.012 0.073 0.194 0.005 -0.322 -0.159 -0.220 -0.163 -0.316## R2m R2c
## [1,] 0.5761764 0.8173572## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.046464 2 1.196055
## age 1.653760 1 1.285986
## leg_l 1.206084 1 1.098219
## factor(Race) 1.664657 4 1.065775
## Speed 1.289762 1 1.135677## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.596339452 1.885191838
## factor(BS)1 -0.618797726 -0.208639357
## factor(BS)2 -0.615751951 -0.299325803
## age -0.005210833 0.027777736
## leg_l -0.024361171 0.004463902
## factor(Race)1 -0.263078553 0.228344841
## factor(Race)2 -0.236067208 0.246922146
## factor(Race)3 -0.248536582 0.277360331
## factor(Race)4 -0.053303536 0.508678771
## Speed 0.308655233 0.947918753## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocma
##
## REML criterion at convergence: -402.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5780 -0.1687 -0.0396 0.0881 13.3630
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.001999 0.04471
## Residual 0.007487 0.08653
## Number of obs: 238, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.259214 0.196080 17.952438 1.322 0.203
## factor(BS)1 0.009513 0.033957 20.753437 0.280 0.782
## factor(BS)2 -0.027236 0.027144 20.865841 -1.003 0.327
## age -0.002472 0.002666 19.613698 -0.927 0.365
## leg_l -0.003475 0.002521 21.976076 -1.378 0.182
## factor(Race)1 0.022579 0.040634 20.463141 0.556 0.584
## factor(Race)2 0.016806 0.041476 22.950912 0.405 0.689
## factor(Race)3 0.073138 0.044218 21.157487 1.654 0.113
## factor(Race)4 -0.016071 0.047503 21.317920 -0.338 0.738
## Speed 0.125155 0.081937 37.905511 1.527 0.135
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.101
## factor(BS)2 0.024 0.509
## age -0.206 -0.527 -0.107
## leg_l -0.817 0.056 -0.253 -0.110
## factor(Rc)1 -0.142 0.267 0.046 -0.091 0.124
## factor(Rc)2 -0.110 0.081 -0.035 -0.124 0.178 0.766
## factor(Rc)3 -0.142 0.168 -0.007 -0.068 0.150 0.745 0.710
## factor(Rc)4 -0.172 -0.094 -0.105 0.154 0.202 0.675 0.685 0.636
## Speed 0.035 0.208 0.401 0.046 -0.512 -0.264 -0.372 -0.290 -0.431## R2m R2c
## [1,] 0.1316237 0.3146344## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.345072 2 1.237482
## age 1.662664 1 1.289443
## leg_l 1.474573 1 1.214320
## factor(Race) 1.863030 4 1.080880
## Speed 1.910343 1 1.382152## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.125094906 0.643523360
## factor(BS)1 -0.057041295 0.076067256
## factor(BS)2 -0.080436451 0.025964990
## age -0.007697637 0.002753231
## leg_l -0.008415494 0.001465964
## factor(Race)1 -0.057063479 0.102220811
## factor(Race)2 -0.064484601 0.098096461
## factor(Race)3 -0.013528316 0.159804990
## factor(Race)4 -0.109175585 0.077033126
## Speed -0.035438836 0.285748285## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ factor(BS) + age + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ocma
##
## REML criterion at convergence: 1278.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2526 -0.4295 -0.0235 0.4249 3.7690
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 46.711 6.835
## Residual 9.039 3.006
## Number of obs: 240, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 30.8178 25.2027 20.7003 1.223 0.235146
## factor(BS)1 -7.3740 4.1611 21.2843 -1.772 0.090691 .
## factor(BS)2 -9.9505 3.2246 22.2193 -3.086 0.005360 **
## age 0.2467 0.3354 20.8470 0.736 0.470183
## leg_l -0.4680 0.2868 23.4682 -1.632 0.116079
## factor(Race)1 1.4443 4.9790 21.3461 0.290 0.774560
## factor(Race)2 0.1026 4.8649 22.0820 0.021 0.983368
## factor(Race)3 2.5832 5.3448 21.5903 0.483 0.633736
## factor(Race)4 1.7485 5.5742 22.5009 0.314 0.756661
## Speed 16.8159 4.8864 216.6442 3.441 0.000694 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.093
## factor(BS)2 0.014 0.471
## age -0.230 -0.529 -0.099
## leg_l -0.903 0.136 -0.118 -0.078
## factor(Rc)1 -0.138 0.339 0.136 -0.087 0.027
## factor(Rc)2 -0.096 0.159 0.088 -0.119 0.028 0.745
## factor(Rc)3 -0.123 0.243 0.087 -0.054 0.025 0.729 0.687
## factor(Rc)4 -0.179 -0.018 0.033 0.185 0.047 0.633 0.628 0.593
## Speed 0.015 0.103 0.199 0.022 -0.267 -0.125 -0.181 -0.143 -0.219## R2m R2c
## [1,] 0.378309 0.899203## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.033846 2 1.194207
## age 1.645912 1 1.282931
## leg_l 1.158645 1 1.076404
## factor(Race) 1.569484 4 1.057961
## Speed 1.197344 1 1.094232## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -18.5786417 80.21425361
## factor(BS)1 -15.5295564 0.78164662
## factor(BS)2 -16.2706019 -3.63036277
## age -0.4106695 0.90410847
## leg_l -1.0301388 0.09413347
## factor(Race)1 -8.3144032 11.20294969
## factor(Race)2 -9.4325245 9.63767306
## factor(Race)3 -7.8924019 13.05888456
## factor(Race)4 -9.1768429 12.67379817
## Speed 7.2386942 26.39313968## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocma
##
## REML criterion at convergence: 1205.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1712 -0.5701 0.0326 0.5264 3.6932
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 20.488 4.526
## Residual 6.894 2.626
## Number of obs: 240, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.63678 16.83172 20.18115 2.058 0.0528 .
## factor(BS)1 -4.29280 2.79167 21.03059 -1.538 0.1390
## factor(BS)2 -5.07396 2.17749 22.24728 -2.330 0.0293 *
## age 0.09442 0.22428 20.42259 0.421 0.6781
## leg_l -0.42532 0.19525 23.90934 -2.178 0.0395 *
## factor(Race)1 0.65003 3.34170 21.08859 0.195 0.8476
## factor(Race)2 1.29077 3.28259 22.12878 0.393 0.6979
## factor(Race)3 1.62470 3.59368 21.43276 0.452 0.6557
## factor(Race)4 6.63053 3.77119 22.61646 1.758 0.0922 .
## Speed 4.41487 4.04269 175.81807 1.092 0.2763
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.093
## factor(BS)2 0.016 0.476
## age -0.228 -0.528 -0.098
## leg_l -0.888 0.120 -0.142 -0.081
## factor(Rc)1 -0.138 0.329 0.121 -0.088 0.044
## factor(Rc)2 -0.097 0.148 0.068 -0.121 0.053 0.748
## factor(Rc)3 -0.124 0.232 0.071 -0.056 0.046 0.731 0.690
## factor(Rc)4 -0.178 -0.030 0.010 0.180 0.075 0.638 0.636 0.599
## Speed 0.019 0.127 0.243 0.027 -0.325 -0.155 -0.223 -0.177 -0.268## R2m R2c
## [1,] 0.3428894 0.8345491## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.077662 2 1.200587
## age 1.646845 1 1.283295
## leg_l 1.203498 1 1.097040
## factor(Race) 1.615001 4 1.061748
## Speed 1.302817 1 1.141410## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 1.6472172 67.62633799
## factor(BS)1 -9.7643795 1.17878107
## factor(BS)2 -9.3417553 -0.80616007
## age -0.3451593 0.53400789
## leg_l -0.8079922 -0.04264462
## factor(Race)1 -5.8995790 7.19964318
## factor(Race)2 -5.1429898 7.72452028
## factor(Race)3 -5.4187842 8.66818640
## factor(Race)4 -0.7608625 14.02193152
## Speed -3.5086526 12.33838710## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocma
##
## REML criterion at convergence: -182.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8305 -0.5319 0.0567 0.4540 3.1528
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.06926 0.2632
## Residual 0.01586 0.1260
## Number of obs: 239, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.344732 0.972519 20.487492 1.383 0.181635
## factor(BS)1 -0.349821 0.160794 21.160429 -2.176 0.041061 *
## factor(BS)2 -0.370858 0.124850 22.196168 -2.970 0.007018 **
## age 0.006448 0.012947 20.660576 0.498 0.623748
## leg_l -0.019946 0.011127 23.547824 -1.793 0.085889 .
## factor(Race)1 0.050559 0.192401 21.213844 0.263 0.795257
## factor(Race)2 0.066557 0.188256 22.032288 0.354 0.727040
## factor(Race)3 0.158225 0.206623 21.481237 0.766 0.452148
## factor(Race)4 0.202765 0.215884 22.484973 0.939 0.357594
## Speed 0.680171 0.202215 206.545771 3.364 0.000917 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.093
## factor(BS)2 0.014 0.473
## age -0.230 -0.529 -0.099
## leg_l -0.899 0.131 -0.125 -0.078
## factor(Rc)1 -0.138 0.336 0.132 -0.087 0.032
## factor(Rc)2 -0.096 0.156 0.082 -0.120 0.035 0.746
## factor(Rc)3 -0.123 0.240 0.083 -0.054 0.031 0.729 0.688
## factor(Rc)4 -0.178 -0.022 0.026 0.184 0.055 0.634 0.630 0.594
## Speed 0.016 0.111 0.213 0.023 -0.285 -0.134 -0.193 -0.153 -0.234## R2m R2c
## [1,] 0.4090695 0.8898725## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.046819 2 1.196107
## age 1.646257 1 1.283066
## leg_l 1.170973 1 1.082115
## factor(Race) 1.581932 4 1.059006
## Speed 1.227068 1 1.107731## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.56137045 3.250834321
## factor(BS)1 -0.66497180 -0.034670511
## factor(BS)2 -0.61555901 -0.126156270
## age -0.01892833 0.031823503
## leg_l -0.04175337 0.001861782
## factor(Race)1 -0.32653994 0.427656993
## factor(Race)2 -0.30241705 0.435531038
## factor(Race)3 -0.24674938 0.563199790
## factor(Race)4 -0.22036086 0.625890183
## Speed 0.28383688 1.076504433## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocma
##
## REML criterion at convergence: -143.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2938 -0.5617 0.0128 0.5080 3.5606
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.04213 0.2053
## Residual 0.02009 0.1417
## Number of obs: 239, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.096e+00 7.695e-01 2.010e+01 1.424 0.16986
## factor(BS)1 -3.004e-01 1.282e-01 2.118e+01 -2.344 0.02891 *
## factor(BS)2 -3.261e-01 1.005e-01 2.255e+01 -3.246 0.00363 **
## age -7.318e-04 1.027e-02 2.043e+01 -0.071 0.94387
## leg_l -1.609e-02 9.057e-03 2.439e+01 -1.777 0.08808 .
## factor(Race)1 -7.779e-02 1.534e-01 2.120e+01 -0.507 0.61733
## factor(Race)2 -6.729e-02 1.513e-01 2.245e+01 -0.445 0.66082
## factor(Race)3 -4.557e-02 1.652e-01 2.161e+01 -0.276 0.78526
## factor(Race)4 6.643e-02 1.742e-01 2.293e+01 0.381 0.70639
## Speed 9.537e-01 2.088e-01 1.415e+02 4.568 1.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.093
## factor(BS)2 0.017 0.481
## age -0.227 -0.526 -0.098
## leg_l -0.877 0.108 -0.160 -0.083
## factor(Rc)1 -0.139 0.321 0.110 -0.089 0.057
## factor(Rc)2 -0.098 0.138 0.052 -0.121 0.072 0.750
## factor(Rc)3 -0.126 0.224 0.059 -0.057 0.061 0.733 0.693
## factor(Rc)4 -0.177 -0.039 -0.009 0.176 0.096 0.642 0.642 0.603
## Speed 0.021 0.144 0.274 0.030 -0.361 -0.175 -0.250 -0.198 -0.300## R2m R2c
## [1,] 0.4938239 0.8365632## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.113744 2 1.205766
## age 1.647880 1 1.283698
## leg_l 1.238722 1 1.112979
## factor(Race) 1.650431 4 1.064632
## Speed 1.386599 1 1.177539## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.41262052 2.603836255
## factor(BS)1 -0.55159333 -0.049203897
## factor(BS)2 -0.52304063 -0.129216342
## age -0.02085329 0.019389781
## leg_l -0.03384468 0.001658975
## factor(Race)1 -0.37846485 0.222883334
## factor(Race)2 -0.36386957 0.229295382
## factor(Race)3 -0.36934447 0.278198213
## factor(Race)4 -0.27491489 0.407776099
## Speed 0.54444966 1.362876635## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ochb
##
## REML criterion at convergence: -1057.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0449 -0.4633 -0.0451 0.2852 4.1364
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0011660 0.03415
## Residual 0.0004871 0.02207
## Number of obs: 253, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.126e-03 1.277e-01 2.093e+01 0.056 0.95602
## factor(BS)1 2.135e-02 2.132e-02 2.227e+01 1.001 0.32743
## factor(BS)2 5.850e-03 1.667e-02 2.373e+01 0.351 0.72873
## age -2.743e-03 1.700e-03 2.112e+01 -1.614 0.12140
## leg_l 7.079e-04 1.470e-03 2.432e+01 0.482 0.63432
## factor(Race)1 -4.025e-03 2.516e-02 2.137e+01 -0.160 0.87442
## factor(Race)2 6.203e-03 2.458e-02 2.205e+01 0.252 0.80306
## factor(Race)3 9.719e-03 2.697e-02 2.155e+01 0.360 0.72209
## factor(Race)4 -8.979e-03 2.823e-02 2.261e+01 -0.318 0.75338
## Speed 6.949e-02 2.652e-02 1.932e+02 2.621 0.00947 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.086
## factor(BS)2 0.021 0.488
## age -0.226 -0.523 -0.097
## leg_l -0.902 0.105 -0.147 -0.081
## factor(Rc)1 -0.140 0.325 0.124 -0.086 0.028
## factor(Rc)2 -0.101 0.145 0.074 -0.120 0.032 0.745
## factor(Rc)3 -0.126 0.236 0.086 -0.055 0.022 0.727 0.684
## factor(Rc)4 -0.183 -0.038 0.011 0.185 0.054 0.632 0.627 0.587
## Speed 0.045 0.186 0.286 0.020 -0.303 -0.116 -0.171 -0.113 -0.221## R2m R2c
## [1,] 0.153847 0.7506696## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.131945 2 1.208353
## age 1.647853 1 1.283687
## leg_l 1.183640 1 1.087952
## factor(Race) 1.567707 4 1.057811
## Speed 1.295325 1 1.138123## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.243085187 0.2573364060
## factor(BS)1 -0.020439885 0.0631384874
## factor(BS)2 -0.026821888 0.0385217482
## age -0.006073736 0.0005882379
## leg_l -0.002172267 0.0035879839
## factor(Race)1 -0.053336642 0.0452875951
## factor(Race)2 -0.041964878 0.0543713137
## factor(Race)3 -0.043143524 0.0625817463
## factor(Race)4 -0.064316896 0.0463580309
## Speed 0.017521683 0.1214595858## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ factor(BS) + age + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ochb
##
## REML criterion at convergence: 1034.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1431 -0.6256 -0.0559 0.5943 3.3581
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 22.581 4.752
## Residual 2.337 1.529
## Number of obs: 254, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 26.16203 17.43275 20.74503 1.501 0.14849
## factor(BS)1 -3.35476 2.87234 21.20203 -1.168 0.25579
## factor(BS)2 -8.08628 2.21279 21.77568 -3.654 0.00141 **
## age -0.02952 0.23170 20.78580 -0.127 0.89985
## leg_l -0.12353 0.19408 21.98725 -0.636 0.53103
## factor(Race)1 4.25999 3.42010 20.89736 1.246 0.22671
## factor(Race)2 0.34625 3.31868 21.12727 0.104 0.91789
## factor(Race)3 2.90366 3.66112 20.93997 0.793 0.43661
## factor(Race)4 1.70953 3.79020 21.35302 0.451 0.65651
## Speed 2.92508 2.01366 243.23897 1.453 0.14762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.091
## factor(BS)2 0.015 0.469
## age -0.231 -0.530 -0.101
## leg_l -0.923 0.148 -0.095 -0.075
## factor(Rc)1 -0.138 0.347 0.152 -0.085 0.005
## factor(Rc)2 -0.096 0.170 0.111 -0.118 -0.005 0.742
## factor(Rc)3 -0.122 0.254 0.108 -0.052 -0.003 0.725 0.681
## factor(Rc)4 -0.181 -0.009 0.058 0.192 0.010 0.627 0.617 0.583
## Speed 0.025 0.104 0.164 0.011 -0.175 -0.065 -0.096 -0.063 -0.125## R2m R2c
## [1,] 0.4186347 0.9454745## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.007523 2 1.190324
## age 1.645217 1 1.282660
## leg_l 1.108842 1 1.053016
## factor(Race) 1.510351 4 1.052894
## Speed 1.090995 1 1.044507## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -8.0055445 60.3295993
## factor(BS)1 -8.9844470 2.2749278
## factor(BS)2 -12.4232715 -3.7492857
## age -0.4836367 0.4245991
## leg_l -0.5039144 0.2568569
## factor(Race)1 -2.4432800 10.9632516
## factor(Race)2 -6.1582415 6.8507506
## factor(Race)3 -4.2719937 10.0793148
## factor(Race)4 -5.7191231 9.1381857
## Speed -1.0216141 6.8717815## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ochb
##
## REML criterion at convergence: 1104.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.65482 -0.57197 0.00578 0.53647 2.34678
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 16.479 4.059
## Residual 3.295 1.815
## Number of obs: 254, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 23.4752 14.9805 20.9875 1.567 0.1321
## factor(BS)1 -4.1866 2.4798 21.7809 -1.688 0.1056
## factor(BS)2 -4.5258 1.9209 22.7443 -2.356 0.0275 *
## age 0.1318 0.1992 21.0704 0.662 0.5152
## leg_l -0.1568 0.1688 23.1187 -0.929 0.3626
## factor(Race)1 1.2701 2.9436 21.2526 0.431 0.6705
## factor(Race)2 0.3990 2.8630 21.6568 0.139 0.8905
## factor(Race)3 1.6541 3.1525 21.3373 0.525 0.6052
## factor(Race)4 3.8983 3.2770 22.0327 1.190 0.2469
## Speed -0.9929 2.3182 238.9786 -0.428 0.6688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.089
## factor(BS)2 0.017 0.476
## age -0.229 -0.527 -0.099
## leg_l -0.915 0.132 -0.114 -0.077
## factor(Rc)1 -0.139 0.339 0.142 -0.085 0.013
## factor(Rc)2 -0.098 0.161 0.097 -0.119 0.008 0.743
## factor(Rc)3 -0.124 0.248 0.100 -0.053 0.006 0.726 0.682
## factor(Rc)4 -0.182 -0.019 0.041 0.190 0.026 0.629 0.620 0.585
## Speed 0.034 0.139 0.217 0.015 -0.231 -0.086 -0.128 -0.084 -0.166## R2m R2c
## [1,] 0.2088503 0.8681542## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.051554 2 1.196798
## age 1.645926 1 1.282937
## leg_l 1.135587 1 1.065639
## factor(Race) 1.530993 4 1.054682
## Speed 1.163478 1 1.078646## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -5.8860385 52.8363479
## factor(BS)1 -9.0469034 0.6736646
## factor(BS)2 -8.2905788 -0.7609267
## age -0.2585804 0.5222506
## leg_l -0.4876683 0.1740594
## factor(Race)1 -4.4993229 7.0394328
## factor(Race)2 -5.2124525 6.0104122
## factor(Race)3 -4.5247618 7.8329045
## factor(Race)4 -2.5244654 10.3211009
## Speed -5.5365510 3.5507181## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ochb
##
## REML criterion at convergence: -354.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4930 -0.5532 0.0234 0.4766 3.9640
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.056079 0.23681
## Residual 0.008049 0.08972
## Number of obs: 253, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.111843 0.870902 20.719788 1.277 0.215847
## factor(BS)1 -0.142997 0.143806 21.334661 -0.994 0.331185
## factor(BS)2 -0.229233 0.111056 22.084269 -2.064 0.050949 .
## age -0.004521 0.011578 20.780864 -0.390 0.700160
## leg_l -0.008993 0.009747 22.348778 -0.923 0.366048
## factor(Race)1 0.154275 0.170980 20.922331 0.902 0.377168
## factor(Race)2 0.089568 0.166072 21.224497 0.539 0.595273
## factor(Race)3 0.271577 0.183059 20.979464 1.484 0.152797
## factor(Race)4 0.236509 0.189853 21.517890 1.246 0.226251
## Speed 0.404309 0.116680 242.780073 3.465 0.000627 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.091
## factor(BS)2 0.016 0.472
## age -0.230 -0.529 -0.100
## leg_l -0.919 0.141 -0.104 -0.075
## factor(Rc)1 -0.139 0.343 0.147 -0.085 0.008
## factor(Rc)2 -0.097 0.167 0.105 -0.118 0.001 0.742
## factor(Rc)3 -0.123 0.251 0.105 -0.052 0.001 0.726 0.681
## factor(Rc)4 -0.182 -0.013 0.050 0.191 0.017 0.628 0.618 0.584
## Speed 0.029 0.121 0.189 0.013 -0.201 -0.075 -0.111 -0.073 -0.144## R2m R2c
## [1,] 0.3535708 0.9188654## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.026960 2 1.193195
## age 1.645670 1 1.282837
## leg_l 1.120396 1 1.058488
## factor(Race) 1.519102 4 1.053655
## Speed 1.122572 1 1.059515## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.59509252 2.81877949
## factor(BS)1 -0.42485224 0.13885745
## factor(BS)2 -0.44689823 -0.01156680
## age -0.02721278 0.01817116
## leg_l -0.02809663 0.01011074
## factor(Race)1 -0.18083917 0.48938939
## factor(Race)2 -0.23592831 0.41506339
## factor(Race)3 -0.08721198 0.63036601
## factor(Race)4 -0.13559561 0.60861316
## Speed 0.17562073 0.63299704## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ochb
##
## REML criterion at convergence: -313.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6606 -0.4766 -0.0319 0.4868 4.9085
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.055950 0.23654
## Residual 0.009654 0.09826
## Number of obs: 253, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.776894 0.871444 20.617410 0.892 0.3829
## factor(BS)1 -0.149490 0.144099 21.328240 -1.037 0.3112
## factor(BS)2 -0.241206 0.111464 22.185824 -2.164 0.0415 *
## age -0.003854 0.011587 20.691317 -0.333 0.7427
## leg_l -0.005607 0.009788 22.492884 -0.573 0.5724
## factor(Race)1 0.116251 0.171167 20.851652 0.679 0.5045
## factor(Race)2 0.075698 0.166369 21.202099 0.455 0.6537
## factor(Race)3 0.237526 0.183284 20.920454 1.296 0.2091
## factor(Race)4 0.185624 0.190316 21.536860 0.975 0.3402
## Speed 0.602181 0.126609 241.032361 4.756 3.4e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.090
## factor(BS)2 0.016 0.474
## age -0.229 -0.528 -0.100
## leg_l -0.917 0.136 -0.109 -0.076
## factor(Rc)1 -0.139 0.341 0.144 -0.085 0.011
## factor(Rc)2 -0.098 0.164 0.101 -0.118 0.005 0.743
## factor(Rc)3 -0.124 0.250 0.102 -0.053 0.003 0.726 0.682
## factor(Rc)4 -0.182 -0.016 0.045 0.190 0.022 0.628 0.619 0.584
## Speed 0.032 0.131 0.205 0.014 -0.217 -0.081 -0.120 -0.079 -0.156## R2m R2c
## [1,] 0.4020639 0.9120083## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.040128 2 1.195128
## age 1.645913 1 1.282931
## leg_l 1.128348 1 1.062237
## factor(Race) 1.525207 4 1.054183
## Speed 1.144184 1 1.069665## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.93110389 2.48489193
## factor(BS)1 -0.43191830 0.13293910
## factor(BS)2 -0.45967081 -0.02274171
## age -0.02656364 0.01885519
## leg_l -0.02479160 0.01357741
## factor(Race)1 -0.21922985 0.45173255
## factor(Race)2 -0.25037984 0.40177612
## factor(Race)3 -0.12170371 0.59675670
## factor(Race)4 -0.18738877 0.55863762
## Speed 0.35403203 0.85032981## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocmb
##
## REML criterion at convergence: -1063.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5518 -0.4703 -0.0899 0.3095 5.7021
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0010909 0.03303
## Residual 0.0004975 0.02230
## Number of obs: 255, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.076914 0.123366 20.177355 -0.623 0.540
## factor(BS)1 0.019653 0.020414 20.832557 0.963 0.347
## factor(BS)2 0.002102 0.016242 23.133262 0.129 0.898
## age -0.002499 0.001648 20.556769 -1.517 0.145
## leg_l 0.001852 0.001445 24.098449 1.281 0.212
## factor(Race)1 0.003720 0.024608 21.357058 0.151 0.881
## factor(Race)2 0.024487 0.024197 22.356228 1.012 0.322
## factor(Race)3 0.024488 0.026391 21.481218 0.928 0.364
## factor(Race)4 -0.002521 0.027474 22.113905 -0.092 0.928
## Speed 0.043781 0.030796 144.732930 1.422 0.157
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.090
## factor(BS)2 0.020 0.473
## age -0.229 -0.520 -0.079
## leg_l -0.884 0.115 -0.169 -0.092
## factor(Rc)1 -0.139 0.325 0.103 -0.093 0.052
## factor(Rc)2 -0.098 0.146 0.048 -0.130 0.063 0.750
## factor(Rc)3 -0.124 0.230 0.056 -0.060 0.050 0.734 0.694
## factor(Rc)4 -0.182 -0.025 0.004 0.173 0.072 0.641 0.636 0.601
## Speed 0.032 0.121 0.303 0.066 -0.353 -0.171 -0.239 -0.186 -0.237## R2m R2c
## [1,] 0.1989545 0.7490954## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.143717 2 1.210018
## age 1.649766 1 1.284432
## leg_l 1.229303 1 1.108739
## factor(Race) 1.594393 4 1.060045
## Speed 1.360464 1 1.166389## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.3187078530 0.164879301
## factor(BS)1 -0.0203584733 0.059664791
## factor(BS)2 -0.0297317365 0.033935705
## age -0.0057280385 0.000730059
## leg_l -0.0009808461 0.004684377
## factor(Race)1 -0.0445116282 0.051951168
## factor(Race)2 -0.0229376117 0.071912268
## factor(Race)3 -0.0272380644 0.076214084
## factor(Race)4 -0.0563690250 0.051327923
## Speed -0.0165780589 0.104140697## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ factor(BS) + age + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ocmb
##
## REML criterion at convergence: 989.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7277 -0.6065 0.0954 0.6488 2.5681
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 18.18 4.264
## Residual 1.89 1.375
## Number of obs: 256, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.06212 15.63131 20.59674 1.028 0.31607
## factor(BS)1 -2.94769 2.56776 20.81084 -1.148 0.26401
## factor(BS)2 -8.32595 1.98923 21.79323 -4.186 0.00039 ***
## age -0.01286 0.20794 20.70372 -0.062 0.95126
## leg_l -0.04811 0.17510 22.27333 -0.275 0.78605
## factor(Race)1 2.50345 3.07748 21.01623 0.813 0.42507
## factor(Race)2 -2.25097 2.99286 21.41059 -0.752 0.46017
## factor(Race)3 1.79382 3.29542 21.07785 0.544 0.59192
## factor(Race)4 -0.11367 3.40553 21.34578 -0.033 0.97368
## Speed 6.28376 2.17643 245.41041 2.887 0.00423 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.092
## factor(BS)2 0.014 0.464
## age -0.231 -0.529 -0.095
## leg_l -0.917 0.151 -0.103 -0.079
## factor(Rc)1 -0.138 0.347 0.145 -0.088 0.013
## factor(Rc)2 -0.096 0.171 0.102 -0.121 0.006 0.743
## factor(Rc)3 -0.122 0.253 0.099 -0.053 0.007 0.727 0.684
## factor(Rc)4 -0.181 -0.005 0.055 0.188 0.016 0.630 0.620 0.587
## Speed 0.019 0.067 0.174 0.038 -0.207 -0.096 -0.136 -0.105 -0.135## R2m R2c
## [1,] 0.496924 0.9526259## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.012205 2 1.191017
## age 1.646150 1 1.283024
## leg_l 1.123332 1 1.059874
## factor(Race) 1.519289 4 1.053671
## Speed 1.112332 1 1.054672## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -14.5746827 46.6989155
## factor(BS)1 -7.9804021 2.0850201
## factor(BS)2 -12.2247631 -4.4271340
## age -0.4204126 0.3946839
## leg_l -0.3912876 0.2950753
## factor(Race)1 -3.5283082 8.5352032
## factor(Race)2 -8.1168569 3.6149245
## factor(Race)3 -4.6650923 8.2527285
## factor(Race)4 -6.7883930 6.5610472
## Speed 2.0180414 10.5494859## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocmb
##
## REML criterion at convergence: 1084.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5972 -0.5670 0.0362 0.5327 2.5230
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 14.947 3.866
## Residual 2.934 1.713
## Number of obs: 256, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 22.2678 14.2436 20.8356 1.563 0.1330
## factor(BS)1 -3.0655 2.3447 21.1962 -1.307 0.2051
## factor(BS)2 -3.8673 1.8328 22.7666 -2.110 0.0461 *
## age 0.1466 0.1897 21.0254 0.773 0.4483
## leg_l -0.2042 0.1620 23.5217 -1.261 0.2198
## factor(Race)1 0.1840 2.8157 21.5365 0.065 0.9485
## factor(Race)2 -1.6826 2.7483 22.1815 -0.612 0.5466
## factor(Race)3 0.4781 3.0168 21.6333 0.158 0.8755
## factor(Race)4 3.0568 3.1253 22.0625 0.978 0.3386
## Speed 3.1960 2.6028 225.2509 1.228 0.2208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.091
## factor(BS)2 0.016 0.467
## age -0.231 -0.526 -0.089
## leg_l -0.905 0.138 -0.126 -0.083
## factor(Rc)1 -0.138 0.340 0.131 -0.090 0.026
## factor(Rc)2 -0.097 0.162 0.083 -0.124 0.026 0.746
## factor(Rc)3 -0.123 0.245 0.084 -0.056 0.021 0.729 0.687
## factor(Rc)4 -0.181 -0.011 0.038 0.183 0.035 0.633 0.625 0.592
## Speed 0.024 0.088 0.226 0.049 -0.267 -0.126 -0.178 -0.138 -0.176## R2m R2c
## [1,] 0.2577701 0.8781971## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.055292 2 1.197342
## age 1.647285 1 1.283466
## leg_l 1.158086 1 1.076144
## factor(Race) 1.544345 4 1.055828
## Speed 1.193374 1 1.092416## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -5.6490401 50.1847077
## factor(BS)1 -7.6610421 1.5301358
## factor(BS)2 -7.4594955 -0.2751288
## age -0.2251896 0.5183505
## leg_l -0.5216693 0.1132972
## factor(Race)1 -5.3347031 5.7027227
## factor(Race)2 -7.0692333 3.7040013
## factor(Race)3 -5.4347929 6.3910031
## factor(Race)4 -3.0687366 9.1823002
## Speed -1.9053838 8.2974431## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocmb
##
## REML criterion at convergence: -402.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3575 -0.4845 -0.0198 0.5388 3.0980
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.063481 0.25195
## Residual 0.006541 0.08088
## Number of obs: 255, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.802525 0.923578 20.125927 0.869 0.3951
## factor(BS)1 -0.162758 0.151732 20.344325 -1.073 0.2960
## factor(BS)2 -0.291152 0.117540 21.304140 -2.477 0.0217 *
## age -0.002277 0.012286 20.232908 -0.185 0.8548
## leg_l -0.003386 0.010344 21.755496 -0.327 0.7465
## factor(Race)1 0.163252 0.181830 20.535480 0.898 0.3797
## factor(Race)2 0.035068 0.176817 20.916149 0.198 0.8447
## factor(Race)3 0.295881 0.194699 20.592677 1.520 0.1438
## factor(Race)4 0.283771 0.201200 20.853826 1.410 0.1732
## Speed 0.225882 0.128133 244.496234 1.763 0.0792 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.092
## factor(BS)2 0.014 0.465
## age -0.231 -0.529 -0.095
## leg_l -0.917 0.151 -0.103 -0.079
## factor(Rc)1 -0.138 0.347 0.145 -0.087 0.013
## factor(Rc)2 -0.096 0.171 0.102 -0.121 0.006 0.743
## factor(Rc)3 -0.122 0.253 0.099 -0.053 0.006 0.727 0.684
## factor(Rc)4 -0.181 -0.004 0.055 0.188 0.016 0.629 0.620 0.587
## Speed 0.018 0.068 0.174 0.037 -0.206 -0.096 -0.136 -0.105 -0.134## R2m R2c
## [1,] 0.3445915 0.938773## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.011984 2 1.190985
## age 1.646282 1 1.283075
## leg_l 1.122923 1 1.059681
## factor(Race) 1.518792 4 1.053628
## Speed 1.111583 1 1.054316## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -1.00765349 2.61270393
## factor(BS)1 -0.46014845 0.13463173
## factor(BS)2 -0.52152743 -0.06077726
## age -0.02635810 0.02180326
## leg_l -0.02365921 0.01688744
## factor(Race)1 -0.19312798 0.51963111
## factor(Race)2 -0.31148552 0.38162246
## factor(Race)3 -0.08572143 0.67748267
## factor(Race)4 -0.11057377 0.67811584
## Speed -0.02525464 0.47701894## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocmb
##
## REML criterion at convergence: -371.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7381 -0.5278 -0.0364 0.5639 2.8682
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.059798 0.24454
## Residual 0.007549 0.08689
## Number of obs: 255, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.341305 0.897523 19.986719 0.380 0.70775
## factor(BS)1 -0.161016 0.147538 20.244209 -1.091 0.28794
## factor(BS)2 -0.298530 0.114572 21.360933 -2.606 0.01637 *
## age -0.000621 0.011943 20.114474 -0.052 0.95905
## leg_l 0.000766 0.010094 21.883651 0.076 0.94019
## factor(Race)1 0.118978 0.176893 20.468200 0.673 0.50872
## factor(Race)2 0.023727 0.172185 20.913391 0.138 0.89172
## factor(Race)3 0.257380 0.189441 20.534320 1.359 0.18900
## factor(Race)4 0.213126 0.195898 20.838261 1.088 0.28904
## Speed 0.437096 0.136185 241.755775 3.210 0.00151 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.092
## factor(BS)2 0.015 0.465
## age -0.231 -0.529 -0.094
## leg_l -0.914 0.148 -0.109 -0.080
## factor(Rc)1 -0.138 0.345 0.141 -0.088 0.016
## factor(Rc)2 -0.096 0.168 0.097 -0.122 0.011 0.744
## factor(Rc)3 -0.122 0.251 0.095 -0.054 0.010 0.728 0.685
## factor(Rc)4 -0.181 -0.006 0.050 0.187 0.021 0.630 0.621 0.588
## Speed 0.020 0.074 0.190 0.041 -0.224 -0.105 -0.148 -0.114 -0.147## R2m R2c
## [1,] 0.3814397 0.9306615## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.023672 2 1.192710
## age 1.646630 1 1.283211
## leg_l 1.132286 1 1.064089
## factor(Race) 1.525510 4 1.054209
## Speed 1.133453 1 1.064638## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -1.41780792 2.10041847
## factor(BS)1 -0.45018600 0.12815449
## factor(BS)2 -0.52308671 -0.07397324
## age -0.02402913 0.02278719
## leg_l -0.01901683 0.02054889
## factor(Race)1 -0.22772569 0.46568201
## factor(Race)2 -0.31374915 0.36120245
## factor(Race)3 -0.11391631 0.62867694
## factor(Race)4 -0.17082658 0.59707918
## Speed 0.17017730 0.70401424## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ff
##
## REML criterion at convergence: -1077
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3581 -0.3891 -0.0303 0.3658 5.2558
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0008831 0.02972
## Residual 0.0002859 0.01691
## Number of obs: 233, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.022e-01 1.120e-01 2.193e+01 -0.913 0.371254
## factor(BS)1 -9.017e-03 1.814e-02 2.106e+01 -0.497 0.624211
## factor(BS)2 6.537e-03 1.409e-02 2.227e+01 0.464 0.647284
## age 4.599e-04 1.470e-03 2.102e+01 0.313 0.757482
## leg_l 8.704e-04 1.219e-03 2.138e+01 0.714 0.482792
## factor(Race)1 2.208e-02 2.179e-02 2.147e+01 1.013 0.322234
## factor(Race)2 1.270e-02 2.112e-02 2.161e+01 0.601 0.554070
## factor(Race)3 9.654e-03 2.332e-02 2.148e+01 0.414 0.682993
## factor(Race)4 1.218e-03 2.434e-02 2.252e+01 0.050 0.960522
## Speed 5.906e-02 1.659e-02 2.221e+02 3.559 0.000455 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.100
## factor(BS)2 -0.018 0.460
## age -0.232 -0.533 -0.099
## leg_l -0.901 0.165 -0.086 -0.075
## factor(Rc)1 -0.122 0.348 0.143 -0.083 0.003
## factor(Rc)2 -0.078 0.176 0.106 -0.117 -0.010 0.744
## factor(Rc)3 -0.106 0.255 0.099 -0.050 -0.005 0.728 0.684
## factor(Rc)4 -0.148 -0.003 0.044 0.189 0.008 0.630 0.620 0.588
## Speed -0.155 0.041 0.185 0.021 -0.102 -0.092 -0.108 -0.095 -0.184## R2m R2c
## [1,] 0.1642074 0.7955816## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.024033 2 1.192764
## age 1.647461 1 1.283534
## leg_l 1.087239 1 1.042708
## factor(Race) 1.534826 4 1.055012
## Speed 1.091415 1 1.044708## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.321709695 0.117262030
## factor(BS)1 -0.044564965 0.026530264
## factor(BS)2 -0.021087149 0.034161339
## age -0.002421200 0.003340937
## leg_l -0.001518024 0.003258844
## factor(Race)1 -0.020630205 0.064789049
## factor(Race)2 -0.028704116 0.054094989
## factor(Race)3 -0.036050949 0.055358272
## factor(Race)4 -0.046484019 0.048920388
## Speed 0.026534330 0.091585077## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ factor(BS) + age + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ff
##
## REML criterion at convergence: 1003.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6455 -0.5885 -0.0515 0.5158 3.2445
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 29.206 5.404
## Residual 2.896 1.702
## Number of obs: 233, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.609077 19.910746 20.971078 0.834 0.41358
## factor(BS)1 -2.440743 3.248935 20.657541 -0.751 0.46098
## factor(BS)2 -8.870354 2.498627 21.102437 -3.550 0.00188 **
## age -0.002559 0.263421 20.641086 -0.010 0.99234
## leg_l 0.029956 0.217690 20.772969 0.138 0.89187
## factor(Race)1 3.191576 3.890137 20.796418 0.820 0.42128
## factor(Race)2 -1.869161 3.766243 20.847201 -0.496 0.62488
## factor(Race)3 2.320141 4.162467 20.801381 0.557 0.58321
## factor(Race)4 4.588738 4.306483 21.178056 1.066 0.29863
## Speed 2.667695 1.737896 214.948721 1.535 0.12625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.096
## factor(BS)2 0.001 0.460
## age -0.232 -0.533 -0.102
## leg_l -0.922 0.167 -0.075 -0.073
## factor(Rc)1 -0.132 0.354 0.157 -0.084 -0.003
## factor(Rc)2 -0.088 0.180 0.121 -0.117 -0.018 0.742
## factor(Rc)3 -0.116 0.260 0.112 -0.050 -0.012 0.725 0.681
## factor(Rc)4 -0.169 0.002 0.067 0.193 -0.004 0.627 0.615 0.583
## Speed -0.091 0.024 0.110 0.012 -0.060 -0.054 -0.063 -0.056 -0.109## R2m R2c
## [1,] 0.405887 0.9463985## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 1.976834 2 1.185748
## age 1.645400 1 1.282732
## leg_l 1.079248 1 1.038869
## factor(Race) 1.501778 4 1.052145
## Speed 1.031311 1 1.015535## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -22.4152686 55.6334223
## factor(BS)1 -8.8085380 3.9270524
## factor(BS)2 -13.7675732 -3.9731353
## age -0.5188539 0.5137368
## leg_l -0.3967094 0.4566216
## factor(Race)1 -4.4329528 10.8161058
## factor(Race)2 -9.2508630 5.5125402
## factor(Race)3 -5.8381445 10.4784262
## factor(Race)4 -3.8518134 13.0292897
## Speed -0.7385181 6.0739091## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ff
##
## REML criterion at convergence: 997.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.34604 -0.58005 -0.00842 0.60925 2.60915
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 13.450 3.667
## Residual 3.032 1.741
## Number of obs: 233, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 24.20954 13.68787 21.41645 1.769 0.0912 .
## factor(BS)1 -2.45341 2.22368 20.76922 -1.103 0.2825
## factor(BS)2 -4.00556 1.72074 21.67485 -2.328 0.0297 *
## age 0.23296 0.18025 20.73714 1.292 0.2104
## leg_l -0.16688 0.14924 21.00575 -1.118 0.2761
## factor(Race)1 0.45634 2.66788 21.06415 0.171 0.8658
## factor(Race)2 -1.86519 2.58476 21.16921 -0.722 0.4784
## factor(Race)3 1.34890 2.85483 21.07367 0.472 0.6414
## factor(Race)4 4.11304 2.96900 21.84926 1.385 0.1799
## Speed -0.06028 1.73782 222.59319 -0.035 0.9724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.098
## factor(BS)2 -0.011 0.460
## age -0.232 -0.533 -0.100
## leg_l -0.910 0.166 -0.082 -0.074
## factor(Rc)1 -0.126 0.350 0.149 -0.083 0.000
## factor(Rc)2 -0.082 0.177 0.112 -0.117 -0.013 0.743
## factor(Rc)3 -0.110 0.257 0.104 -0.050 -0.008 0.727 0.683
## factor(Rc)4 -0.157 -0.001 0.053 0.191 0.003 0.629 0.618 0.586
## Speed -0.133 0.035 0.159 0.017 -0.087 -0.079 -0.092 -0.081 -0.158## R2m R2c
## [1,] 0.2844185 0.8683504## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.004708 2 1.189906
## age 1.646585 1 1.283193
## leg_l 1.083947 1 1.041128
## factor(Race) 1.521333 4 1.053848
## Speed 1.066753 1 1.032837## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -2.6181938 51.0372782
## factor(BS)1 -6.8117495 1.9049353
## factor(BS)2 -7.3781598 -0.6329663
## age -0.1203318 0.5862474
## leg_l -0.4593814 0.1256243
## factor(Race)1 -4.7726004 5.6852827
## factor(Race)2 -6.9312152 3.2008402
## factor(Race)3 -4.2464667 6.9442767
## factor(Race)4 -1.7060832 9.9321715
## Speed -3.4663409 3.3457743## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ff
##
## REML criterion at convergence: -283.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.96545 -0.63923 -0.02816 0.61231 3.03251
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.048368 0.21993
## Residual 0.009588 0.09792
## Number of obs: 233, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.530190 0.818606 21.110129 0.648 0.524178
## factor(BS)1 -0.103009 0.133108 20.534638 -0.774 0.447824
## factor(BS)2 -0.285120 0.102873 21.342422 -2.772 0.011327 *
## age 0.004643 0.010790 20.505744 0.430 0.671500
## leg_l -0.004340 0.008930 20.745255 -0.486 0.632111
## factor(Race)1 0.245047 0.159631 20.795269 1.535 0.139838
## factor(Race)2 0.028482 0.154635 20.888639 0.184 0.855640
## factor(Race)3 0.327794 0.170815 20.803869 1.919 0.068808 .
## factor(Race)4 0.403101 0.177458 21.493998 2.272 0.033496 *
## Speed 0.388262 0.098193 221.782014 3.954 0.000103 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.098
## factor(BS)2 -0.008 0.460
## age -0.232 -0.533 -0.100
## leg_l -0.912 0.166 -0.080 -0.074
## factor(Rc)1 -0.127 0.351 0.151 -0.083 -0.001
## factor(Rc)2 -0.083 0.178 0.114 -0.117 -0.014 0.743
## factor(Rc)3 -0.111 0.258 0.106 -0.050 -0.008 0.727 0.682
## factor(Rc)4 -0.159 0.000 0.056 0.191 0.002 0.628 0.617 0.585
## Speed -0.126 0.033 0.150 0.017 -0.083 -0.074 -0.087 -0.077 -0.149## R2m R2c
## [1,] 0.4604656 0.9107384## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 1.999008 2 1.189060
## age 1.646336 1 1.283096
## leg_l 1.082982 1 1.040664
## factor(Race) 1.517343 4 1.053502
## Speed 1.059495 1 1.029318## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -1.074248158 2.13462760
## factor(BS)1 -0.363895938 0.15787822
## factor(BS)2 -0.486747402 -0.08349186
## age -0.016506063 0.02579119
## leg_l -0.021842637 0.01316364
## factor(Race)1 -0.067823660 0.55791749
## factor(Race)2 -0.274595992 0.33156073
## factor(Race)3 -0.006997252 0.66258518
## factor(Race)4 0.055289599 0.75091219
## Speed 0.195806455 0.58071709## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ff
##
## REML criterion at convergence: -191.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3346 -0.6056 -0.0013 0.6360 3.0438
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.02825 0.1681
## Residual 0.01590 0.1261
## Number of obs: 233, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.053594 0.647326 20.498944 0.083 0.934820
## factor(BS)1 -0.167998 0.104171 19.311654 -1.613 0.123027
## factor(BS)2 -0.350719 0.081652 20.903296 -4.295 0.000324 ***
## age 0.007012 0.008440 19.262651 0.831 0.416301
## leg_l -0.001753 0.007016 19.738941 -0.250 0.805327
## factor(Race)1 0.157210 0.125550 19.894966 1.252 0.225027
## factor(Race)2 -0.088044 0.121825 20.088523 -0.723 0.478189
## factor(Race)3 0.241394 0.134365 19.909024 1.797 0.087596 .
## factor(Race)4 0.252941 0.141265 21.312744 1.791 0.087584 .
## Speed 0.739337 0.119245 204.934048 6.200 3.06e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.104
## factor(BS)2 -0.035 0.459
## age -0.232 -0.532 -0.097
## leg_l -0.883 0.164 -0.094 -0.076
## factor(Rc)1 -0.113 0.343 0.131 -0.081 0.008
## factor(Rc)2 -0.069 0.172 0.093 -0.117 -0.003 0.747
## factor(Rc)3 -0.098 0.251 0.088 -0.050 0.001 0.730 0.687
## factor(Rc)4 -0.131 -0.008 0.024 0.186 0.019 0.634 0.624 0.592
## Speed -0.193 0.051 0.229 0.026 -0.127 -0.115 -0.134 -0.119 -0.227## R2m R2c
## [1,] 0.6188809 0.8627328## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.064034 2 1.198614
## age 1.649438 1 1.284305
## leg_l 1.094161 1 1.046022
## factor(Race) 1.562565 4 1.057377
## Speed 1.142736 1 1.068988## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -1.215141588 1.32232869
## factor(BS)1 -0.372168738 0.03617239
## factor(BS)2 -0.510754138 -0.19068368
## age -0.009531064 0.02355446
## leg_l -0.015503238 0.01199822
## factor(Race)1 -0.088862687 0.40328309
## factor(Race)2 -0.326816543 0.15072852
## factor(Race)3 -0.021956008 0.50474461
## factor(Race)4 -0.023934235 0.52981586
## Speed 0.505622085 0.97305208## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_prf
##
## REML criterion at convergence: -1049.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9823 -0.4093 -0.0156 0.3092 6.2498
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0010319 0.03212
## Residual 0.0003758 0.01938
## Number of obs: 239, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.676e-02 1.200e-01 2.025e+01 0.640 0.5295
## factor(BS)1 1.096e-02 1.972e-02 2.046e+01 0.556 0.5843
## factor(BS)2 -9.234e-03 1.569e-02 2.289e+01 -0.588 0.5621
## age -2.711e-03 1.592e-03 2.016e+01 -1.703 0.1040
## leg_l -4.888e-05 1.380e-03 2.319e+01 -0.035 0.9720
## factor(Race)1 -1.212e-02 2.396e-02 2.145e+01 -0.506 0.6181
## factor(Race)2 2.036e-02 2.339e-02 2.200e+01 0.870 0.3934
## factor(Race)3 5.015e-03 2.600e-02 2.233e+01 0.193 0.8488
## factor(Race)4 1.430e-03 2.668e-02 2.208e+01 0.054 0.9577
## Speed 5.294e-02 3.009e-02 1.639e+02 1.759 0.0804 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.091
## factor(BS)2 0.011 0.461
## age -0.235 -0.534 -0.107
## leg_l -0.888 0.138 -0.152 -0.060
## factor(Rc)1 -0.134 0.337 0.100 -0.078 0.053
## factor(Rc)2 -0.092 0.160 0.050 -0.108 0.053 0.752
## factor(Rc)3 -0.114 0.236 0.035 -0.044 0.062 0.736 0.699
## factor(Rc)4 -0.175 -0.011 0.003 0.194 0.066 0.643 0.636 0.608
## Speed -0.003 0.064 0.287 -0.017 -0.306 -0.195 -0.239 -0.255 -0.248## R2m R2c
## [1,] 0.2162569 0.7907845## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.144012 2 1.210060
## age 1.646752 1 1.283258
## leg_l 1.185985 1 1.089029
## factor(Race) 1.621027 4 1.062243
## Speed 1.296800 1 1.138771## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.158391608 0.3119164240
## factor(BS)1 -0.027684985 0.0496049250
## factor(BS)2 -0.039994984 0.0215269118
## age -0.005831471 0.0004095251
## leg_l -0.002752861 0.0026551101
## factor(Race)1 -0.059081881 0.0348385941
## factor(Race)2 -0.025480997 0.0661976409
## factor(Race)3 -0.045944448 0.0559751594
## factor(Race)4 -0.050863676 0.0537235861
## Speed -0.006038418 0.1119262171## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ factor(BS) + age + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_prf
##
## REML criterion at convergence: 975.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1723 -0.5589 -0.0071 0.5172 3.0697
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 21.852 4.675
## Residual 2.167 1.472
## Number of obs: 243, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.2941 17.1531 20.9467 0.833 0.41407
## factor(BS)1 -2.4423 2.8131 21.0352 -0.868 0.39510
## factor(BS)2 -6.8299 2.1827 22.1515 -3.129 0.00485 **
## age -0.0463 0.2278 20.9055 -0.203 0.84088
## leg_l -0.1614 0.1913 22.3100 -0.844 0.40776
## factor(Race)1 0.6942 3.3822 21.4832 0.205 0.83931
## factor(Race)2 -4.3834 3.2831 21.7360 -1.335 0.19564
## factor(Race)3 0.4416 3.6376 21.8886 0.121 0.90449
## factor(Race)4 -0.8768 3.7414 21.7901 -0.234 0.81690
## Speed 16.1276 2.5425 232.9483 6.343 1.16e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.093
## factor(BS)2 0.011 0.461
## age -0.233 -0.534 -0.105
## leg_l -0.917 0.158 -0.100 -0.068
## factor(Rc)1 -0.136 0.350 0.141 -0.083 0.015
## factor(Rc)2 -0.093 0.174 0.100 -0.114 0.005 0.745
## factor(Rc)3 -0.119 0.253 0.088 -0.048 0.013 0.729 0.686
## factor(Rc)4 -0.178 -0.001 0.051 0.194 0.017 0.631 0.621 0.591
## Speed -0.001 0.039 0.175 -0.011 -0.187 -0.117 -0.143 -0.154 -0.149## R2m R2c
## [1,] 0.4756612 0.9526852## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.019036 2 1.192027
## age 1.644668 1 1.282446
## leg_l 1.114104 1 1.055511
## factor(Race) 1.531999 4 1.054769
## Speed 1.104165 1 1.050792## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -19.3254846 47.9135904
## factor(BS)1 -7.9558321 3.0713237
## factor(BS)2 -11.1078715 -2.5518674
## age -0.4927071 0.4001043
## leg_l -0.5364305 0.2135587
## factor(Race)1 -5.9348834 7.3232935
## factor(Race)2 -10.8181117 2.0512493
## factor(Race)3 -6.6879739 7.5711271
## factor(Race)4 -8.2098079 6.4562110
## Speed 11.1444206 21.1107864## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_prf
##
## REML criterion at convergence: 1031
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.84777 -0.50469 -0.03149 0.57533 2.72548
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 15.084 3.884
## Residual 2.922 1.709
## Number of obs: 243, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.12458 14.34150 21.08176 1.334 0.196594
## factor(BS)1 -2.11913 2.35415 21.23335 -0.900 0.378128
## factor(BS)2 -2.66646 1.84623 23.04294 -1.444 0.162121
## age 0.09444 0.19034 21.00016 0.496 0.624928
## leg_l -0.23964 0.16207 23.30206 -1.479 0.152629
## factor(Race)1 -1.55310 2.84300 21.96298 -0.546 0.590375
## factor(Race)2 -4.07044 2.76635 22.37960 -1.471 0.155102
## factor(Race)3 -0.25031 3.06948 22.62806 -0.082 0.935724
## factor(Race)4 0.04007 3.15407 22.45740 0.013 0.989977
## Speed 10.28814 2.83099 217.31943 3.634 0.000348 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.093
## factor(BS)2 0.012 0.461
## age -0.234 -0.534 -0.106
## leg_l -0.904 0.149 -0.123 -0.065
## factor(Rc)1 -0.135 0.344 0.123 -0.081 0.031
## factor(Rc)2 -0.093 0.168 0.079 -0.112 0.026 0.748
## factor(Rc)3 -0.117 0.246 0.066 -0.046 0.034 0.732 0.691
## factor(Rc)4 -0.177 -0.005 0.030 0.194 0.038 0.636 0.627 0.598
## Speed -0.001 0.051 0.230 -0.014 -0.245 -0.155 -0.189 -0.203 -0.197## R2m R2c
## [1,] 0.2644345 0.880645## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.070649 2 1.199573
## age 1.644863 1 1.282522
## leg_l 1.144150 1 1.069650
## factor(Race) 1.568934 4 1.057915
## Speed 1.184352 1 1.088279## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -8.9842473 47.2333975
## factor(BS)1 -6.7331865 2.4949253
## factor(BS)2 -6.2849994 0.9520696
## age -0.2786194 0.4675022
## leg_l -0.5572849 0.0780073
## factor(Race)1 -7.1252687 4.0190778
## factor(Race)2 -9.4923843 1.3515006
## factor(Race)3 -6.2663861 5.7657718
## factor(Race)4 -6.1418049 6.2219374
## Speed 4.7395046 15.8367717## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_prf
##
## REML criterion at convergence: -396.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6371 -0.5458 0.0000 0.6401 3.5590
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.033274 0.18241
## Residual 0.006367 0.07979
## Number of obs: 243, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.455e-01 6.735e-01 2.047e+01 1.255 0.2235
## factor(BS)1 -7.234e-02 1.105e-01 2.062e+01 -0.654 0.5201
## factor(BS)2 -1.104e-01 8.668e-02 2.237e+01 -1.274 0.2157
## age -4.877e-04 8.938e-03 2.039e+01 -0.055 0.9570
## leg_l -1.572e-02 7.608e-03 2.262e+01 -2.065 0.0505 .
## factor(Race)1 -6.390e-03 1.335e-01 2.132e+01 -0.048 0.9623
## factor(Race)2 -1.592e-01 1.299e-01 2.173e+01 -1.226 0.2335
## factor(Race)3 1.430e-01 1.441e-01 2.197e+01 0.992 0.3318
## factor(Race)4 2.065e-01 1.481e-01 2.180e+01 1.395 0.1772
## Speed 9.202e-01 1.323e-01 2.175e+02 6.956 4.05e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.093
## factor(BS)2 0.012 0.461
## age -0.234 -0.534 -0.106
## leg_l -0.905 0.150 -0.122 -0.065
## factor(Rc)1 -0.135 0.344 0.124 -0.081 0.031
## factor(Rc)2 -0.093 0.168 0.079 -0.112 0.025 0.748
## factor(Rc)3 -0.117 0.246 0.066 -0.046 0.034 0.732 0.691
## factor(Rc)4 -0.177 -0.005 0.031 0.194 0.038 0.636 0.627 0.598
## Speed -0.001 0.051 0.229 -0.014 -0.244 -0.154 -0.189 -0.202 -0.196## R2m R2c
## [1,] 0.5129273 0.9217663## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.069490 2 1.199405
## age 1.644859 1 1.282520
## leg_l 1.143476 1 1.069334
## factor(Race) 1.568102 4 1.057844
## Speed 1.182552 1 1.087452## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.47447497 2.1654775784
## factor(BS)1 -0.28900772 0.1443306782
## factor(BS)2 -0.28031237 0.0594494043
## age -0.01800669 0.0170312100
## leg_l -0.03062725 -0.0008028644
## factor(Race)1 -0.26802522 0.2552451390
## factor(Race)2 -0.41374769 0.0953872570
## factor(Race)3 -0.13944571 0.4254623806
## factor(Race)4 -0.08371166 0.4967768306
## Speed 0.66089518 1.1794535641## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ factor(BS) + age + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_prf
##
## REML criterion at convergence: -375.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.60807 -0.61281 -0.00528 0.60741 2.25768
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.025351 0.1592
## Residual 0.007208 0.0849
## Number of obs: 243, groups: Subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.997e-01 5.915e-01 2.092e+01 0.845 0.4077
## factor(BS)1 -8.523e-02 9.716e-02 2.112e+01 -0.877 0.3902
## factor(BS)2 -1.213e-01 7.683e-02 2.334e+01 -1.579 0.1279
## age 1.065e-04 7.846e-03 2.080e+01 0.014 0.9893
## leg_l -1.404e-02 6.752e-03 2.366e+01 -2.079 0.0486 *
## factor(Race)1 -1.042e-01 1.177e-01 2.202e+01 -0.885 0.3859
## factor(Race)2 -2.138e-01 1.148e-01 2.254e+01 -1.862 0.0757 .
## factor(Race)3 5.007e-02 1.275e-01 2.285e+01 0.393 0.6982
## factor(Race)4 6.781e-02 1.309e-01 2.262e+01 0.518 0.6095
## Speed 1.257e+00 1.356e-01 1.904e+02 9.271 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) f(BS)1 f(BS)2 age leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## factor(BS)1 -0.092
## factor(BS)2 0.012 0.462
## age -0.234 -0.534 -0.107
## leg_l -0.895 0.143 -0.140 -0.062
## factor(Rc)1 -0.134 0.340 0.110 -0.080 0.043
## factor(Rc)2 -0.092 0.164 0.062 -0.110 0.041 0.750
## factor(Rc)3 -0.115 0.240 0.048 -0.045 0.050 0.735 0.695
## factor(Rc)4 -0.176 -0.008 0.014 0.194 0.054 0.640 0.633 0.604
## Speed -0.002 0.059 0.264 -0.016 -0.282 -0.179 -0.219 -0.234 -0.228## R2m R2c
## [1,] 0.6022749 0.9119508## GVIF Df GVIF^(1/(2*Df))
## factor(BS) 2.111867 2 1.205498
## age 1.645019 1 1.282583
## leg_l 1.168124 1 1.080798
## factor(Race) 1.598579 4 1.060393
## Speed 1.248252 1 1.117252## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.65952266 1.6589290757
## factor(BS)1 -0.27566316 0.1052011176
## factor(BS)2 -0.27186703 0.0293005936
## age -0.01527200 0.0154849460
## leg_l -0.02727009 -0.0008035685
## factor(Race)1 -0.33493075 0.1266217883
## factor(Race)2 -0.43874392 0.0112234364
## factor(Race)3 -0.19984354 0.2999831636
## factor(Race)4 -0.18879207 0.3244087740
## Speed 0.99155029 1.5231605147## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
