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 35 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 35 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()`).
## 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
~ Group+age+Sex+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 ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: -907
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9665 -0.3449 -0.0514 0.2412 7.5620
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0008135 0.02852
## Residual 0.0022595 0.04753
## Number of obs: 316, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.1681510 0.1106580 30.1045116 1.520 0.139057
## Group -0.0493628 0.0119549 34.0119545 -4.129 0.000223 ***
## age -0.0003212 0.0011196 30.0054534 -0.287 0.776185
## Sex -0.0068679 0.0126947 31.2235143 -0.541 0.592346
## leg_l -0.0025199 0.0012226 34.6351666 -2.061 0.046865 *
## factor(Race)1 0.0044978 0.0177343 30.7382226 0.254 0.801478
## factor(Race)2 -0.0027812 0.0190236 31.6727677 -0.146 0.884694
## factor(Race)3 0.0620342 0.0220575 30.0662388 2.812 0.008580 **
## factor(Race)4 -0.0207480 0.0234337 32.1396041 -0.885 0.382521
## Speed 0.1199637 0.0374844 96.3207775 3.200 0.001859 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.154
## age -0.333 -0.159
## Sex -0.265 -0.124 0.155
## leg_l -0.850 0.007 0.044 0.250
## factor(Rc)1 -0.312 -0.025 0.210 0.078 0.168
## factor(Rc)2 -0.158 -0.140 0.042 -0.054 0.107 0.697
## factor(Rc)3 -0.172 -0.018 0.147 -0.158 0.085 0.623 0.581
## factor(Rc)4 -0.158 -0.141 0.286 0.011 0.134 0.630 0.572 0.530
## Speed -0.048 0.325 -0.007 -0.206 -0.386 -0.081 -0.129 -0.084 -0.324r2 <- r.squaredGLMM(m1)
print(r2)## R2m R2c
## [1,] 0.3499206 0.5220073# 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) -0.048734724 0.3850367507
## Group -0.072793897 -0.0259316706
## age -0.002515443 0.0018731165
## Sex -0.031749097 0.0180133730
## leg_l -0.004916195 -0.0001236388
## factor(Race)1 -0.030260757 0.0392562820
## factor(Race)2 -0.040066691 0.0345043824
## factor(Race)3 0.018802251 0.1052660789
## factor(Race)4 -0.066677205 0.0251811302
## Speed 0.046495685 0.1934316826Model 2: Peak Knee Angle
m2 <- lmer(Peak ~ Group+age+Sex+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 ~ Group + age + Sex + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ocha
##
## REML criterion at convergence: 1728.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2386 -0.5189 -0.0003 0.5459 3.2864
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 47.571 6.897
## Residual 9.011 3.002
## Number of obs: 320, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 31.31210 23.04943 32.82898 1.358 0.18357
## Group -7.89471 2.36898 33.99827 -3.333 0.00209 **
## age 0.09579 0.23355 32.79857 0.410 0.68437
## Sex 0.28180 2.60212 33.19242 0.108 0.91441
## leg_l -0.37482 0.23820 34.75462 -1.574 0.12466
## factor(Race)1 4.47890 3.67233 32.93183 1.220 0.23127
## factor(Race)2 3.28497 3.90084 33.12185 0.842 0.40576
## factor(Race)3 5.81778 4.59115 32.87893 1.267 0.21400
## factor(Race)4 2.93366 4.69906 33.96851 0.624 0.53660
## Speed 10.74321 3.26295 309.73554 3.292 0.00111 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.139
## age -0.346 -0.152
## Sex -0.276 -0.067 0.158
## leg_l -0.925 0.111 0.058 0.197
## factor(Rc)1 -0.311 -0.010 0.209 0.067 0.149
## factor(Rc)2 -0.153 -0.113 0.039 -0.097 0.061 0.697
## factor(Rc)3 -0.164 -0.008 0.151 -0.179 0.051 0.616 0.582
## factor(Rc)4 -0.175 -0.058 0.292 -0.047 0.034 0.630 0.565 0.526
## Speed -0.016 0.136 -0.004 -0.083 -0.176 -0.036 -0.057 -0.038 -0.143r2 <- r.squaredGLMM(m2)
print(r2)## R2m R2c
## [1,] 0.2939976 0.8875669# 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) -13.8639569 76.48814913
## Group -12.5378238 -3.25158676
## age -0.3619623 0.55354298
## Sex -4.8182547 5.38185077
## leg_l -0.8416808 0.09204852
## factor(Race)1 -2.7187324 11.67653253
## factor(Race)2 -4.3605326 10.93047347
## factor(Race)3 -3.1807110 14.81626585
## factor(Race)4 -6.2763286 12.14364946
## Speed 4.3479526 17.13846986Model 3: Initial_Peak Knee Angle Difference
m3 <- lmer(InitialPeak ~ Group+age+Sex+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 ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: 1641.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5822 -0.5404 -0.0084 0.5320 3.3135
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 28.267 5.317
## Residual 6.993 2.644
## Number of obs: 320, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.90428 17.83756 32.90707 1.845 0.0741 .
## Group -4.23194 1.83790 34.35008 -2.303 0.0275 *
## age -0.09573 0.18073 32.86892 -0.530 0.5999
## Sex -1.22712 2.01532 33.35248 -0.609 0.5467
## leg_l -0.32931 0.18508 35.26961 -1.779 0.0838 .
## factor(Race)1 -0.88518 2.84260 33.03525 -0.311 0.7575
## factor(Race)2 1.76001 3.02073 33.27321 0.583 0.5641
## factor(Race)3 0.93362 3.55340 32.96651 0.263 0.7944
## factor(Race)4 3.54444 3.64533 34.29586 0.972 0.3377
## Speed 3.61349 2.83404 309.13468 1.275 0.2033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.140
## age -0.346 -0.152
## Sex -0.276 -0.070 0.158
## leg_l -0.921 0.105 0.058 0.200
## factor(Rc)1 -0.311 -0.011 0.209 0.067 0.150
## factor(Rc)2 -0.153 -0.115 0.039 -0.095 0.064 0.697
## factor(Rc)3 -0.165 -0.009 0.151 -0.178 0.053 0.616 0.582
## factor(Rc)4 -0.174 -0.063 0.292 -0.044 0.040 0.630 0.565 0.526
## Speed -0.018 0.153 -0.004 -0.093 -0.196 -0.041 -0.064 -0.042 -0.160r2 <- r.squaredGLMM(m3)
print(r2)## R2m R2c
## [1,] 0.1962431 0.840597# 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) -2.0567035 67.86525986
## Group -7.8341465 -0.62972424
## age -0.4499564 0.25848990
## Sex -5.1770816 2.72283163
## leg_l -0.6920718 0.03344188
## factor(Race)1 -6.4565701 4.68620291
## factor(Race)2 -4.1605190 7.68053716
## factor(Race)3 -6.0309024 7.89815144
## factor(Race)4 -3.6002804 10.68916366
## Speed -1.9411228 9.16810667# Calculate marginal and conditional R²
r2 <- r.squaredGLMM(m3)
print(r2)## R2m R2c
## [1,] 0.1962431 0.840597Since 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 ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: -221.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0062 -0.5365 0.0146 0.4610 3.7335
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.07176 0.2679
## Residual 0.01691 0.1300
## Number of obs: 317, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.370046 0.898091 32.839880 1.526 0.13671
## Group -0.280158 0.092589 34.357529 -3.026 0.00467 **
## age -0.003064 0.009099 32.801396 -0.337 0.73846
## Sex 0.041257 0.101502 33.326176 0.406 0.68700
## leg_l -0.015660 0.009316 35.172220 -1.681 0.10164
## factor(Race)1 0.119234 0.143128 32.974367 0.833 0.41081
## factor(Race)2 0.129976 0.152110 33.222597 0.854 0.39895
## factor(Race)3 0.177360 0.178896 32.891192 0.991 0.32872
## factor(Race)4 0.156998 0.183530 34.225968 0.855 0.39826
## Speed 0.434376 0.142493 306.452066 3.048 0.00250 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.140
## age -0.346 -0.152
## Sex -0.276 -0.071 0.158
## leg_l -0.921 0.105 0.058 0.200
## factor(Rc)1 -0.311 -0.011 0.209 0.068 0.151
## factor(Rc)2 -0.153 -0.115 0.039 -0.095 0.064 0.697
## factor(Rc)3 -0.164 -0.009 0.151 -0.178 0.053 0.616 0.582
## factor(Rc)4 -0.174 -0.063 0.292 -0.044 0.040 0.630 0.565 0.526
## Speed -0.019 0.155 -0.005 -0.096 -0.195 -0.042 -0.064 -0.042 -0.160## R2m R2c
## [1,] 0.2873167 0.8641043## GVIF Df GVIF^(1/(2*Df))
## Group 1.115697 1 1.056265
## age 1.217995 1 1.103628
## Sex 1.191604 1 1.091606
## leg_l 1.133858 1 1.064828
## factor(Race) 1.406133 4 1.043526
## Speed 1.125107 1 1.060711## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.39017942 3.130271104
## Group -0.46162806 -0.098687795
## age -0.02089863 0.014770392
## Sex -0.15768359 0.240197104
## leg_l -0.03391946 0.002599896
## factor(Race)1 -0.16129203 0.399759957
## factor(Race)2 -0.16815358 0.428105646
## factor(Race)3 -0.17326881 0.527989259
## factor(Race)4 -0.20271403 0.516709075
## Speed 0.15509352 0.713657706Model 5: Moment Range
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: -183.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6766 -0.5267 -0.0202 0.5343 3.6786
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.05062 0.2250
## Residual 0.02020 0.1421
## Number of obs: 317, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.816e-01 7.626e-01 3.270e+01 1.156 0.256022
## Group -2.721e-01 7.916e-02 3.490e+01 -3.437 0.001537 **
## age 1.721e-04 7.725e-03 3.265e+01 0.022 0.982364
## Sex -2.121e-02 8.638e-02 3.339e+01 -0.245 0.807576
## leg_l -9.231e-03 7.991e-03 3.600e+01 -1.155 0.255652
## factor(Race)1 8.854e-02 1.216e-01 3.290e+01 0.728 0.471738
## factor(Race)2 6.678e-02 1.294e-01 3.328e+01 0.516 0.609177
## factor(Race)3 7.214e-02 1.519e-01 3.277e+01 0.475 0.638074
## factor(Race)4 1.909e-01 1.568e-01 3.463e+01 1.218 0.231539
## Speed 5.080e-01 1.500e-01 2.887e+02 3.388 0.000803 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.141
## age -0.345 -0.152
## Sex -0.274 -0.079 0.158
## leg_l -0.910 0.088 0.057 0.208
## factor(Rc)1 -0.311 -0.014 0.209 0.070 0.154
## factor(Rc)2 -0.153 -0.119 0.040 -0.090 0.071 0.697
## factor(Rc)3 -0.165 -0.012 0.151 -0.175 0.058 0.617 0.582
## factor(Rc)4 -0.171 -0.076 0.291 -0.035 0.056 0.630 0.566 0.526
## Speed -0.024 0.192 -0.006 -0.118 -0.239 -0.052 -0.079 -0.051 -0.197## R2m R2c
## [1,] 0.3258733 0.8076806## GVIF Df GVIF^(1/(2*Df))
## Group 1.131453 1 1.063698
## age 1.218573 1 1.103890
## Sex 1.197164 1 1.094150
## leg_l 1.157478 1 1.075861
## factor(Race) 1.430137 4 1.045736
## Speed 1.192566 1 1.092046## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.61301350 2.376204202
## Group -0.42721251 -0.116928723
## age -0.01496843 0.015312575
## Sex -0.19050227 0.148091881
## leg_l -0.02489388 0.006431865
## factor(Race)1 -0.14981080 0.326881082
## factor(Race)2 -0.18681729 0.320380617
## factor(Race)3 -0.22564647 0.369925647
## factor(Race)4 -0.11637019 0.498255682
## Speed 0.21408693 0.801934117## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
## 116 165 173 216 217 218 220 221 222 223 224 272 273 275 276 279
## 14 17 25 28 29 30 32 33 34 35 36 38 39 41 42 45
## 365 367 368 369 372 373 641 827 1007 1054 1381 1487 1490 1492 1638 1696
## 54 56 57 58 61 62 101 130 154 168 211 224 227 229 249 261
## 1938 1977
## 302 303##
## ====================
## Model: m2
## ====================
## 최대 Cook's distance:
## Index: 200 Value: 0.2036968
##
## 상위 5개 Cook's distance (Index / Value):
## Index CookD
## 1 200 0.2036968
## 2 205 0.1696459
## 3 261 0.1692407
## 4 300 0.1166685
## 5 201 0.1152959
##
## ====================
## Model: m3
## ====================
## 최대 Cook's distance:
## Index: 201 Value: 0.2417491
##
## 상위 5개 Cook's distance (Index / Value):
## Index CookD
## 1 201 0.2417491
## 2 205 0.2128716
## 3 200 0.2050525
## 4 12 0.1306174
## 5 283 0.1218922
##
## ====================
## Model: m4
## ====================
## 최대 Cook's distance:
## Index: 261 Value: 0.262505
##
## 상위 5개 Cook's distance (Index / Value):
## Index CookD
## 1 261 0.2625050
## 2 93 0.1746152
## 3 203 0.1696880
## 4 211 0.1436317
## 5 195 0.1344602
##
## ====================
## Model: m5
## ====================
## 최대 Cook's distance:
## Index: 261 Value: 0.2483553
##
## 상위 5개 Cook's distance (Index / Value):
## Index CookD
## 1 261 0.2483553
## 2 7 0.1509047
## 3 195 0.1378127
## 4 211 0.1323426
## 5 203 0.1311426## 영향치 인덱스:## 116 165 173 216 217 218 220 221 222 223 224 272 273 275 276 279
## 14 17 25 28 29 30 32 33 34 35 36 38 39 41 42 45
## 365 367 368 369 372 373 641 827 1007 1054 1381 1487 1490 1492 1638 1696
## 54 56 57 58 61 62 101 130 154 168 211 224 227 229 249 261
## 1938 1977
## 302 303##
## 영향치 관측치 데이터:## Stiffness Group age Sex leg_l factor(Race) Speed Subject
## 116 0.136036991 0 28 1 87.75 2 1.21 3
## 165 0.182624955 0 21 1 92.50 1 1.41 4
## 173 0.204310777 0 21 1 92.50 1 1.50 4
## 216 0.064440277 0 26 1 90.00 3 1.40 5
## 217 0.034949795 0 26 1 90.00 3 1.36 5
## 218 0.518037248 0 26 1 90.00 3 1.46 5
## 220 0.073798491 0 26 1 90.00 3 1.45 5
## 221 0.066178078 0 26 1 90.00 3 1.41 5
## 222 0.351057793 0 26 1 90.00 3 1.44 5
## 223 0.077144127 0 26 1 90.00 3 1.63 5
## 224 0.079147965 0 26 1 90.00 3 1.47 5
## 272 0.206098619 0 26 0 82.50 1 1.28 6
## 273 0.085550706 0 26 0 82.50 1 1.23 6
## 275 0.090767956 0 26 0 82.50 1 1.25 6
## 276 0.274885982 0 26 0 82.50 1 1.27 6
## 279 0.322678873 0 26 0 82.50 1 1.30 6
## 365 0.087542304 0 24 1 87.00 3 1.35 8
## 367 0.097144372 0 24 1 87.00 3 1.36 8
## 368 0.475717040 0 24 1 87.00 3 1.34 8
## 369 0.452519157 0 24 1 87.00 3 1.42 8
## 372 0.050722268 0 24 1 87.00 3 1.33 8
## 373 0.118854902 0 24 1 87.00 3 1.32 8
## 641 0.079923268 0 22 1 86.75 2 1.78 13
## 827 -0.042630135 1 19 1 95.00 3 1.28 17
## 1007 0.254859448 1 25 1 77.75 3 1.14 21
## 1054 -0.053429730 1 35 1 84.50 2 1.06 22
## 1381 0.111084669 1 37 1 90.00 0 1.32 29
## 1487 0.139220896 1 23 1 100.00 1 1.47 31
## 1490 0.166469109 1 23 1 100.00 1 1.39 31
## 1492 -0.000601481 1 23 1 100.00 1 1.40 31
## 1638 -0.082538884 1 38 0 86.25 2 1.11 34
## 1696 0.096320039 1 27 1 87.75 2 1.36 35
## 1938 0.134492460 1 29 0 90.00 1 1.15 40
## 1977 0.116302334 1 32 1 87.50 3 1.17 41## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: -907
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9665 -0.3449 -0.0514 0.2412 7.5620
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0008135 0.02852
## Residual 0.0022595 0.04753
## Number of obs: 316, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.1681510 0.1106580 30.1045116 1.520 0.139057
## Group -0.0493628 0.0119549 34.0119545 -4.129 0.000223 ***
## age -0.0003212 0.0011196 30.0054534 -0.287 0.776185
## Sex -0.0068679 0.0126947 31.2235143 -0.541 0.592346
## leg_l -0.0025199 0.0012226 34.6351666 -2.061 0.046865 *
## factor(Race)1 0.0044978 0.0177343 30.7382226 0.254 0.801478
## factor(Race)2 -0.0027812 0.0190236 31.6727677 -0.146 0.884694
## factor(Race)3 0.0620342 0.0220575 30.0662388 2.812 0.008580 **
## factor(Race)4 -0.0207480 0.0234337 32.1396041 -0.885 0.382521
## Speed 0.1199637 0.0374844 96.3207775 3.200 0.001859 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.154
## age -0.333 -0.159
## Sex -0.265 -0.124 0.155
## leg_l -0.850 0.007 0.044 0.250
## factor(Rc)1 -0.312 -0.025 0.210 0.078 0.168
## factor(Rc)2 -0.158 -0.140 0.042 -0.054 0.107 0.697
## factor(Rc)3 -0.172 -0.018 0.147 -0.158 0.085 0.623 0.581
## factor(Rc)4 -0.158 -0.141 0.286 0.011 0.134 0.630 0.572 0.530
## Speed -0.048 0.325 -0.007 -0.206 -0.386 -0.081 -0.129 -0.084 -0.324## 영향치 인덱스:## 116 165 173 216 217 218 220 221 222 223 224 272 273 275 276 279
## 14 17 25 28 29 30 32 33 34 35 36 38 39 41 42 45
## 365 367 368 369 372 373 641 827 1007 1054 1381 1487 1490 1492 1638 1696
## 54 56 57 58 61 62 101 130 154 168 211 224 227 229 249 261
## 1938 1977
## 302 303##
## 영향치 관측치 내용(df_ocha 기준):
## Subject studyid age Group Class BS Sex BMI Race height_m weight_kg
## 115 3 3 28 0 0 0 1 20.57653 2 1.650 56.0196
## 164 4 4 21 0 0 0 1 21.23567 1 1.720 62.8236
## 172 4 4 21 0 0 0 1 21.23567 1 1.720 62.8236
## 215 5 5 26 0 0 0 1 22.03837 3 1.670 61.4628
## 216 5 5 26 0 0 0 1 22.03837 3 1.670 61.4628
## 217 5 5 26 0 0 0 1 22.03837 3 1.670 61.4628
## 219 5 5 26 0 0 0 1 22.03837 3 1.670 61.4628
## 220 5 5 26 0 0 0 1 22.03837 3 1.670 61.4628
## 221 5 5 26 0 0 0 1 22.03837 3 1.670 61.4628
## 222 5 5 26 0 0 0 1 22.03837 3 1.670 61.4628
## 223 5 5 26 0 0 0 1 22.03837 3 1.670 61.4628
## 271 6 6 26 0 0 0 0 21.36102 1 1.690 61.0092
## 272 6 6 26 0 0 0 0 21.36102 1 1.690 61.0092
## 274 6 6 26 0 0 0 0 21.36102 1 1.690 61.0092
## 275 6 6 26 0 0 0 0 21.36102 1 1.690 61.0092
## 278 6 6 26 0 0 0 0 21.36102 1 1.690 61.0092
## 326 7 7 26 0 0 0 0 24.11522 0 1.775 75.9780
## 366 8 8 24 0 0 0 1 24.75517 3 1.630 65.7720
## 367 8 8 24 0 0 0 1 24.75517 3 1.630 65.7720
## 368 8 8 24 0 0 0 1 24.75517 3 1.630 65.7720
## 371 8 8 24 0 0 0 1 24.75517 3 1.630 65.7720
## 372 8 8 24 0 0 0 1 24.75517 3 1.630 65.7720
## 640 13 13 22 0 0 0 1 21.63454 2 1.635 57.8340
## 826 17 2 19 1 1 2 1 30.11415 3 1.740 91.1736
## 965 20 5 25 1 1 1 0 32.36631 1 1.670 90.2664
## 1053 22 7 35 1 1 1 1 31.51368 2 1.645 85.2768
## 1379 29 14 37 1 2 1 1 35.24519 0 1.675 98.8848
## 1440 30 15 31 1 1 2 1 33.50482 1 1.670 93.4416
## 1488 31 16 23 1 1 2 1 34.80018 1 1.765 108.4104
## 1490 31 16 23 1 1 2 1 34.80018 1 1.765 108.4104
## 1591 33 18 21 1 2 1 0 37.35529 1 1.700 107.9568
## 1694 35 20 27 1 1 2 1 33.44444 2 1.620 87.7716
## 1934 40 25 29 1 1 1 0 34.98061 1 1.710 102.2868
## 1935 40 25 29 1 1 1 0 34.98061 1 1.710 102.2868
## leg_l_l leg_l_r leg_l DST Stroop Stroop_Effect PA Task Trial Speed
## 115 87.5 88.0 87.75 23 120 126.615 9.750 OCHA 2 1.24
## 164 93.0 92.0 92.50 24 117 74.185 9.250 OCHA 1 1.47
## 172 93.0 92.0 92.50 24 117 74.185 9.250 OCHA 9 1.53
## 215 89.0 91.0 90.00 21 120 47.935 8.000 OCHA 1 1.50
## 216 89.0 91.0 90.00 21 120 47.935 8.000 OCHA 2 1.40
## 217 89.0 91.0 90.00 21 120 47.935 8.000 OCHA 3 1.36
## 219 89.0 91.0 90.00 21 120 47.935 8.000 OCHA 5 1.46
## 220 89.0 91.0 90.00 21 120 47.935 8.000 OCHA 6 1.45
## 221 89.0 91.0 90.00 21 120 47.935 8.000 OCHA 7 1.41
## 222 89.0 91.0 90.00 21 120 47.935 8.000 OCHA 8 1.44
## 223 89.0 91.0 90.00 21 120 47.935 8.000 OCHA 9 1.63
## 271 83.0 82.0 82.50 16 120 237.135 7.625 OCHA 1 1.28
## 272 83.0 82.0 82.50 16 120 237.135 7.625 OCHA 2 1.28
## 274 83.0 82.0 82.50 16 120 237.135 7.625 OCHA 4 1.30
## 275 83.0 82.0 82.50 16 120 237.135 7.625 OCHA 5 1.25
## 278 83.0 82.0 82.50 16 120 237.135 7.625 OCHA 8 1.30
## 326 93.5 94.0 93.75 18 120 110.335 8.000 OCHA 8 1.60
## 366 88.0 86.0 87.00 26 118 50.565 9.125 OCHA 2 1.33
## 367 88.0 86.0 87.00 26 118 50.565 9.125 OCHA 3 1.36
## 368 88.0 86.0 87.00 26 118 50.565 9.125 OCHA 4 1.34
## 371 88.0 86.0 87.00 26 118 50.565 9.125 OCHA 7 1.27
## 372 88.0 86.0 87.00 26 118 50.565 9.125 OCHA 8 1.33
## 640 86.5 87.0 86.75 25 119 235.100 9.125 OCHA 5 1.60
## 826 94.0 96.0 95.00 14 119 452.035 10.625 OCHA 2 1.32
## 965 88.0 86.0 87.00 28 119 352.965 7.375 OCHA 5 1.08
## 1053 83.5 85.5 84.50 17 118 296.135 6.750 OCHA 5 1.02
## 1379 90.5 89.5 90.00 19 118 314.315 7.750 OCHA 5 1.41
## 1440 90.5 89.5 90.00 20 119 129.315 7.625 OCHA 9 1.26
## 1488 101.0 99.0 100.00 22 117 163.865 5.500 OCHA 2 1.41
## 1490 101.0 99.0 100.00 22 117 163.865 5.500 OCHA 4 1.39
## 1591 80.5 81.0 80.75 25 118 212.950 6.875 OCHA 8 1.26
## 1694 87.5 88.0 87.75 18 120 76.465 6.375 OCHA 2 1.36
## 1934 90.5 89.5 90.00 27 120 406.700 6.500 OCHA 5 1.25
## 1935 90.5 89.5 90.00 27 120 406.700 6.500 OCHA 6 1.15
## Initial Peak InitialPeak Min MinPeak Stiffness MomentPeak
## 115 9.175464 16.009724 6.834260 0.455854 15.553870 0.073637589 0.723195
## 164 17.715038 22.466850 4.751812 1.843585 20.623265 0.110255958 0.853889
## 172 17.685530 22.821281 5.135751 2.472993 20.348288 0.153571540 0.918187
## 215 16.197014 21.529213 5.332199 -1.968023 23.497236 0.132135747 0.446350
## 216 11.556826 11.692035 0.135209 -1.657075 13.349110 0.064440277 0.349575
## 217 10.690295 5.755368 -4.934927 -0.982431 6.737799 0.034949795 0.143231
## 219 15.316833 20.155354 4.838521 0.878796 19.276558 0.173324264 0.526523
## 220 12.940715 16.350256 3.409541 -1.357867 17.708123 0.073798491 0.537022
## 221 9.427518 9.009793 -0.417725 -1.454494 10.464287 0.066178078 0.293274
## 222 14.909432 15.732693 0.823261 0.554453 15.178240 0.351057793 0.420379
## 223 12.603979 13.343647 0.739668 0.119079 13.224568 0.077144127 0.445633
## 271 14.231660 19.688225 5.456565 9.109503 10.578722 0.137211933 0.566892
## 272 12.403412 14.654442 2.251030 5.912299 8.742143 0.206098619 0.279914
## 274 13.796951 18.553205 4.756254 8.626437 9.926768 0.102409452 0.356648
## 275 14.069253 20.631458 6.562205 10.552197 10.079261 0.090767956 0.414597
## 278 15.724159 22.201521 6.477362 12.926221 9.275300 0.104453979 0.487971
## 326 11.528237 18.710653 7.182416 3.653500 15.057153 0.126703033 0.768804
## 366 21.074718 28.220461 7.145743 -0.346061 28.566522 0.162517221 0.908558
## 367 13.441177 20.861546 7.420369 0.208837 20.652709 0.097144372 0.849207
## 368 16.833082 17.830923 0.997841 0.588718 17.242205 0.475717040 0.625646
## 371 19.056789 23.221504 4.164715 0.421220 22.800284 0.202264223 0.890341
## 372 14.226321 23.948938 9.722617 4.015524 19.933414 0.050722268 0.645357
## 640 19.336845 22.175987 2.839142 9.271103 12.904884 0.162558859 1.009598
## 826 10.077677 13.184644 3.106967 5.673627 7.511017 0.002558751 0.469243
## 965 7.311799 13.158663 5.846864 3.824940 9.333723 0.051680825 0.442332
## 1053 7.634773 9.146136 1.511363 7.949317 1.196819 0.000337270 0.082703
## 1379 12.764028 17.671484 4.907456 16.663019 1.008465 0.059496081 0.360399
## 1440 4.549724 0.559313 -3.990411 0.178167 0.381146 0.043617116 0.044075
## 1488 7.219712 -2.257428 -9.477140 -2.561201 0.303773 0.065780432 -0.023526
## 1490 11.412565 -2.973567 -14.386132 -3.245015 0.271448 0.166469109 -0.003345
## 1591 8.340804 13.712532 5.371728 -2.980036 16.692568 0.033602686 0.461303
## 1694 1.376832 2.133925 0.757093 0.596095 1.537830 0.017721956 0.073103
## 1934 17.105021 20.363249 3.258228 4.498713 15.864536 0.059837698 0.525876
## 1935 15.250969 7.421558 -7.829411 3.285512 4.136046 NA 0.099427
## kneeMrange head_cir neck_cir SH_B SH_D CH_B CH_D WA_B WA_D HIP_B HIP_D
## 115 0.782259 NA 33.2 23.0 17.0 28.3 22.2 24.2 18.0 31.0 22.5
## 164 1.029662 54.8 33.0 24.6 16.1 28.0 18.0 27.2 18.4 34.5 20.3
## 172 1.060826 54.8 33.0 24.6 16.1 28.0 18.0 27.2 18.4 34.5 20.3
## 215 0.669863 55.5 32.0 32.0 14.0 27.5 21.0 26.0 17.0 35.0 22.2
## 216 0.619831 55.5 32.0 32.0 14.0 27.5 21.0 26.0 17.0 35.0 22.2
## 217 0.350666 55.5 32.0 32.0 14.0 27.5 21.0 26.0 17.0 35.0 22.2
## 219 0.968210 55.5 32.0 32.0 14.0 27.5 21.0 26.0 17.0 35.0 22.2
## 220 0.723871 55.5 32.0 32.0 14.0 27.5 21.0 26.0 17.0 35.0 22.2
## 221 0.485294 55.5 32.0 32.0 14.0 27.5 21.0 26.0 17.0 35.0 22.2
## 222 0.689116 55.5 32.0 32.0 14.0 27.5 21.0 26.0 17.0 35.0 22.2
## 223 0.655012 55.5 32.0 32.0 14.0 27.5 21.0 26.0 17.0 35.0 22.2
## 271 0.637930 58.0 38.0 35.5 15.8 30.2 18.3 26.8 17.2 32.4 21.0
## 272 0.444167 58.0 38.0 35.5 15.8 30.2 18.3 26.8 17.2 32.4 21.0
## 274 0.491933 58.0 38.0 35.5 15.8 30.2 18.3 26.8 17.2 32.4 21.0
## 275 0.610645 58.0 38.0 35.5 15.8 30.2 18.3 26.8 17.2 32.4 21.0
## 278 0.620854 58.0 38.0 35.5 15.8 30.2 18.3 26.8 17.2 32.4 21.0
## 326 0.993161 58.0 38.0 38.6 20.1 34.0 22.1 29.0 22.0 34.0 24.1
## 366 1.047587 56.0 32.0 32.6 13.0 29.5 23.0 34.0 22.0 39.0 23.0
## 367 0.833722 56.0 32.0 32.6 13.0 29.5 23.0 34.0 22.0 39.0 23.0
## 368 0.704887 56.0 32.0 32.6 13.0 29.5 23.0 34.0 22.0 39.0 23.0
## 371 0.937494 56.0 32.0 32.6 13.0 29.5 23.0 34.0 22.0 39.0 23.0
## 372 0.650540 56.0 32.0 32.6 13.0 29.5 23.0 34.0 22.0 39.0 23.0
## 640 0.942383 56.0 33.5 32.6 14.0 27.4 19.7 27.4 20.2 37.4 22.2
## 826 0.349515 56.0 39.0 35.0 19.5 33.0 25.1 34.5 25.6 41.4 25.1
## 965 0.542559 61.0 38.0 37.5 23.7 34.3 27.2 34.4 29.5 35.5 28.4
## 1053 0.073638 58.0 35.8 36.7 20.0 34.0 28.2 36.0 30.0 41.0 26.1
## 1379 0.468078 60.5 38.5 36.9 21.5 26.9 32.3 37.6 31.5 40.6 30.5
## 1440 0.214325 59.0 39.0 35.2 19.5 34.5 28.7 32.5 26.5 42.3 30.5
## 1488 0.304822 53.5 38.0 39.5 20.4 33.5 30.5 39.0 30.1 48.0 29.0
## 1490 0.351063 53.5 38.0 39.5 20.4 33.5 30.5 39.0 30.1 48.0 29.0
## 1591 0.525356 57.0 39.6 39.9 22.0 35.6 28.9 39.9 31.7 41.9 29.6
## 1694 0.305646 56.0 36.0 35.0 18.6 34.2 27.9 33.1 25.9 42.0 28.5
## 1934 0.637468 54.2 39.1 36.9 21.9 36.0 26.4 39.1 33.1 39.1 26.3
## 1935 0.311668 54.2 39.1 36.9 21.9 36.0 26.4 39.1 33.1 39.1 26.3
## ASIS waist_cir hip_cir thigh_cir L_thigh_cir shank_cir ankle_cir W.H.ratio
## 115 23.8 73.0 92.8 54.5 33.5 35.0 19.2 0.7866379
## 164 26.8 77.5 97.0 56.0 41.0 33.5 21.0 0.7989691
## 172 26.8 77.5 97.0 56.0 41.0 33.5 21.0 0.7989691
## 215 23.5 73.5 102.0 59.0 38.5 37.5 21.0 0.7205882
## 216 23.5 73.5 102.0 59.0 38.5 37.5 21.0 0.7205882
## 217 23.5 73.5 102.0 59.0 38.5 37.5 21.0 0.7205882
## 219 23.5 73.5 102.0 59.0 38.5 37.5 21.0 0.7205882
## 220 23.5 73.5 102.0 59.0 38.5 37.5 21.0 0.7205882
## 221 23.5 73.5 102.0 59.0 38.5 37.5 21.0 0.7205882
## 222 23.5 73.5 102.0 59.0 38.5 37.5 21.0 0.7205882
## 223 23.5 73.5 102.0 59.0 38.5 37.5 21.0 0.7205882
## 271 24.4 76.0 91.6 53.5 37.0 34.0 21.0 0.8296943
## 272 24.4 76.0 91.6 53.5 37.0 34.0 21.0 0.8296943
## 274 24.4 76.0 91.6 53.5 37.0 34.0 21.0 0.8296943
## 275 24.4 76.0 91.6 53.5 37.0 34.0 21.0 0.8296943
## 278 24.4 76.0 91.6 53.5 37.0 34.0 21.0 0.8296943
## 326 25.0 87.0 99.0 58.5 41.5 37.5 24.0 0.8787879
## 366 25.5 80.0 105.0 64.5 43.0 37.0 22.0 0.7619048
## 367 25.5 80.0 105.0 64.5 43.0 37.0 22.0 0.7619048
## 368 25.5 80.0 105.0 64.5 43.0 37.0 22.0 0.7619048
## 371 25.5 80.0 105.0 64.5 43.0 37.0 22.0 0.7619048
## 372 25.5 80.0 105.0 64.5 43.0 37.0 22.0 0.7619048
## 640 22.5 73.5 97.5 57.0 39.0 33.5 22.0 0.7538462
## 826 26.0 95.0 115.0 71.0 58.0 41.0 23.0 0.8260870
## 965 27.0 108.0 111.5 64.0 41.0 38.5 22.5 0.9686099
## 1053 28.9 101.0 112.5 69.4 45.2 43.0 23.0 0.8977778
## 1379 31.1 104.5 117.5 71.5 42.5 39.0 23.0 0.8893617
## 1440 28.2 92.6 116.0 75.0 51.0 45.0 24.0 0.7982759
## 1488 28.2 112.0 132.0 74.5 50.5 42.2 23.4 0.8484848
## 1490 28.2 112.0 132.0 74.5 50.5 42.2 23.4 0.8484848
## 1591 30.9 115.0 119.0 74.5 50.0 43.0 22.5 0.9663866
## 1694 25.6 91.5 118.5 73.6 44.0 42.0 22.9 0.7721519
## 1934 32.4 118.8 111.5 66.5 44.5 43.5 24.0 1.0654709
## 1935 32.4 118.8 111.5 66.5 44.5 43.5 24.0 1.0654709
## ABSI Hip.Index biceps_cir forearm_cir wrist_cir
## 115 75.68320 64.90350 NA NA NA
## 164 77.05962 65.02650 25.0 24.0 15.5
## 172 77.05962 65.02650 25.0 24.0 15.5
## 215 72.35627 68.47486 NA NA NA
## 216 72.35627 68.47486 NA NA NA
## 217 72.35627 68.47486 NA NA NA
## 219 72.35627 68.47486 NA NA NA
## 220 72.35627 68.47486 NA NA NA
## 221 72.35627 68.47486 NA NA NA
## 222 72.35627 68.47486 NA NA NA
## 223 72.35627 68.47486 NA NA NA
## 271 75.93738 49.35313 NA NA NA
## 272 75.93738 49.35313 NA NA NA
## 274 75.93738 49.35313 NA NA NA
## 275 75.93738 49.35313 NA NA NA
## 278 75.93738 49.35313 NA NA NA
## 326 78.23348 49.34007 NA NA NA
## 366 73.77099 67.71294 NA NA NA
## 367 73.77099 67.71294 NA NA NA
## 368 73.77099 67.71294 NA NA NA
## 371 73.77099 67.71294 NA NA NA
## 372 73.77099 67.71294 NA NA NA
## 640 74.03379 66.96113 26.7 19.0 15.0
## 826 74.40496 64.65635 NA NA NA
## 965 82.28804 51.23983 35.0 27.8 16.0
## 1053 78.92944 64.19533 37.5 29.0 17.0
## 1379 75.11193 62.78126 33.0 26.2 16.0
## 1440 68.94687 63.63553 37.2 26.5 16.0
## 1488 79.09056 68.57418 38.2 26.5 16.5
## 1490 79.09056 68.57418 38.2 26.5 16.5
## 1591 78.92936 61.22935 36.2 26.8 16.1
## 1694 69.25444 66.36993 31.0 28.7 16.2
## 1934 84.93763 58.98894 33.0 28.0 17.0
## 1935 84.93763 58.98894 33.0 28.0 17.0
##
## ========== 기존 모형 m1 ==========
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha
##
## REML criterion at convergence: -907
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9665 -0.3449 -0.0514 0.2412 7.5620
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0008135 0.02852
## Residual 0.0022595 0.04753
## Number of obs: 316, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.1681510 0.1106580 30.1045116 1.520 0.139057
## Group -0.0493628 0.0119549 34.0119545 -4.129 0.000223 ***
## age -0.0003212 0.0011196 30.0054534 -0.287 0.776185
## Sex -0.0068679 0.0126947 31.2235143 -0.541 0.592346
## leg_l -0.0025199 0.0012226 34.6351666 -2.061 0.046865 *
## factor(Race)1 0.0044978 0.0177343 30.7382226 0.254 0.801478
## factor(Race)2 -0.0027812 0.0190236 31.6727677 -0.146 0.884694
## factor(Race)3 0.0620342 0.0220575 30.0662388 2.812 0.008580 **
## factor(Race)4 -0.0207480 0.0234337 32.1396041 -0.885 0.382521
## Speed 0.1199637 0.0374844 96.3207775 3.200 0.001859 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.154
## age -0.333 -0.159
## Sex -0.265 -0.124 0.155
## leg_l -0.850 0.007 0.044 0.250
## factor(Rc)1 -0.312 -0.025 0.210 0.078 0.168
## factor(Rc)2 -0.158 -0.140 0.042 -0.054 0.107 0.697
## factor(Rc)3 -0.172 -0.018 0.147 -0.158 0.085 0.623 0.581
## factor(Rc)4 -0.158 -0.141 0.286 0.011 0.134 0.630 0.572 0.530
## Speed -0.048 0.325 -0.007 -0.206 -0.386 -0.081 -0.129 -0.084 -0.324
##
## ========== 영향치 제거 후 모형 m1_new ==========
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocha_clean
##
## REML criterion at convergence: -899
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7381 -0.3631 -0.0396 0.2722 6.7506
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.001314 0.03625
## Residual 0.001466 0.03829
## Number of obs: 283, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.985e-01 1.303e-01 2.706e+01 1.523 0.13930
## Group -6.223e-02 1.388e-02 3.039e+01 -4.482 9.77e-05 ***
## age -3.062e-05 1.307e-03 2.599e+01 -0.023 0.98148
## Sex -8.468e-03 1.482e-02 2.767e+01 -0.571 0.57238
## leg_l -2.619e-03 1.410e-03 3.143e+01 -1.857 0.07264 .
## factor(Race)1 6.833e-03 2.064e-02 2.647e+01 0.331 0.74319
## factor(Race)2 2.163e-03 2.207e-02 2.714e+01 0.098 0.92264
## factor(Race)3 8.747e-02 2.627e-02 2.816e+01 3.329 0.00244 **
## factor(Race)4 -1.422e-02 2.704e-02 2.831e+01 -0.526 0.60302
## Speed 1.024e-01 3.736e-02 1.447e+02 2.742 0.00688 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.153
## age -0.334 -0.158
## Sex -0.279 -0.101 0.158
## leg_l -0.877 0.045 0.051 0.245
## factor(Rc)1 -0.308 -0.023 0.207 0.076 0.166
## factor(Rc)2 -0.149 -0.139 0.042 -0.066 0.092 0.696
## factor(Rc)3 -0.165 -0.042 0.148 -0.157 0.077 0.607 0.572
## factor(Rc)4 -0.159 -0.117 0.288 -0.004 0.103 0.629 0.572 0.517
## Speed -0.034 0.275 -0.012 -0.181 -0.336 -0.080 -0.125 -0.077 -0.289
##
## 고정효과 계수 비교 (fixef):
## original cleaned
## (Intercept) 0.1681510133 1.984584e-01
## Group -0.0493627836 -6.223169e-02
## age -0.0003211635 -3.062416e-05
## Sex -0.0068678621 -8.468317e-03
## leg_l -0.0025199170 -2.619107e-03
## factor(Race)1 0.0044977623 6.832909e-03
## factor(Race)2 -0.0027811545 2.163071e-03
## factor(Race)3 0.0620341648 8.746717e-02
## factor(Race)4 -0.0207480374 -1.422256e-02
## Speed 0.1199636837 1.024405e-01
##
## 랜덤효과 분산/표준편차 비교 (VarCorr):
## Groups Name Std.Dev.
## Subject (Intercept) 0.028522
## Residual 0.047535
## Groups Name Std.Dev.
## Subject (Intercept) 0.036245
## Residual 0.038291
## $m1
## 116 165 173 216 217 218 220 221 222 223 224 272 273 275 276 279
## 14 17 25 28 29 30 32 33 34 35 36 38 39 41 42 45
## 365 367 368 369 372 373 641 827 1007 1054 1381 1487 1490 1492 1638 1696
## 54 56 57 58 61 62 101 130 154 168 211 224 227 229 249 261
## 1938 1977
## 302 303
##
## $m2
## 37 38 40 41 42 70 71 72 73 74 75 116 168 170 215 217
## 1 2 4 5 6 7 8 9 10 11 12 15 21 23 28 30
## 219 221 223 272 273 276 278 279 366 368 373 426 484 487 532 534
## 32 34 36 39 40 43 45 46 56 58 63 66 76 79 81 83
## 537 539 584 589 590 591 642 643 698 828 829 963 1008 1049 1050 1052
## 86 88 89 94 95 96 103 104 112 132 133 152 156 164 165 167
## 1054 1179 1180 1231 1276 1278 1279 1280 1330 1331 1333 1335 1379 1381 1487 1536
## 169 182 183 186 193 195 196 197 200 201 203 205 211 213 226 233
## 1537 1585 1586 1587 1589 1590 1694 1695 1696 1697 1748 1793 1800 1831 1833 1834
## 234 243 244 245 247 248 261 262 263 264 272 275 282 283 285 286
## 1836 1885 1887 1888 1890 1932 1934 1935 1936 1979 1983 2023
## 288 292 294 295 297 300 302 303 304 309 313 318
##
## $m3
## 40 42 70 72 74 75 114 215 217 219 272 273 276 279 320 368
## 4 6 7 9 11 12 13 28 30 32 39 40 43 46 48 58
## 369 372 426 429 531 534 585 588 589 591 637 693 784 785 828 829
## 59 62 66 69 80 83 90 93 94 96 98 107 126 127 132 133
## 871 872 920 923 924 926 962 1007 1050 1051 1052 1053 1054 1144 1179 1180
## 135 136 142 145 146 148 151 155 165 166 167 168 169 179 182 183
## 1181 1276 1278 1280 1330 1331 1332 1333 1335 1376 1438 1487 1489 1490 1584 1585
## 184 193 195 197 200 201 202 203 205 208 222 226 228 229 242 243
## 1586 1589 1590 1591 1638 1694 1695 1696 1697 1744 1745 1747 1748 1793 1794 1796
## 244 247 248 249 251 261 262 263 264 268 269 271 272 275 276 278
## 1800 1831 1834 1884 1885 1888 1890 1932 1934 1935 1936 1977 1980 1983 2019 2023
## 282 283 286 291 292 295 297 300 302 303 304 307 310 313 314 318
##
## $m4
## 42 71 72 75 166 167 168 170 173 217 218 219 220 271 272 273
## 6 7 8 11 18 19 20 22 25 29 30 31 32 37 38 39
## 276 279 322 324 326 366 369 371 425 426 429 480 487 532 534 537
## 42 45 49 51 53 55 58 60 64 65 68 71 78 80 82 85
## 539 584 587 588 589 591 642 643 691 698 755 876 962 1008 1049 1050
## 87 88 91 92 93 95 102 103 104 111 121 139 150 155 163 164
## 1052 1054 1093 1095 1141 1142 1144 1177 1178 1179 1231 1234 1276 1278 1279 1280
## 166 168 170 172 175 176 178 179 180 181 184 187 191 193 194 195
## 1282 1330 1331 1333 1335 1376 1378 1379 1381 1492 1539 1585 1587 1693 1694 1695
## 197 198 199 201 203 206 208 209 211 229 234 241 243 258 259 260
## 1696 1697 1698 1747 1793 1795 1800 1835 1885 1888 1890 1932 1934 1935 1936 1937
## 261 262 263 268 272 274 279 284 289 292 294 297 299 300 301 302
## 1979 1983 2022 2023
## 306 310 314 315
##
## $m5
## 39 71 72 73 75 116 117 166 167 168 170 173 217 219 273 276
## 3 7 8 9 11 14 15 18 19 20 22 25 29 31 39 42
## 321 322 324 326 366 371 372 424 425 426 478 480 481 483 486 487
## 48 49 51 53 55 60 61 63 64 65 69 71 72 74 77 78
## 532 534 537 539 584 586 587 588 589 591 638 640 642 698 756 829
## 80 82 85 87 88 90 91 92 93 95 98 100 102 111 122 132
## 871 876 961 964 1007 1053 1093 1094 1141 1142 1144 1177 1178 1181 1231 1234
## 134 139 149 152 154 167 170 171 175 176 178 179 180 182 184 187
## 1276 1278 1279 1280 1282 1330 1331 1334 1335 1376 1378 1379 1381 1492 1538 1585
## 191 193 194 195 197 198 199 202 203 206 208 209 211 229 233 241
## 1587 1645 1693 1694 1695 1696 1697 1698 1793 1795 1800 1888 1890 1930 1932 1935
## 243 256 258 259 260 261 262 263 272 274 279 292 294 295 297 300
## 1936 1938 1979 1982 1983 2023
## 301 303 306 309 310 315## [1] 0.04495882## [1] 0.03566365## [1] 0.009295174## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Stiffness ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocma
##
## REML criterion at convergence: -675.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7770 -0.1668 -0.0287 0.1198 15.5932
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.001679 0.04097
## Residual 0.005538 0.07442
## Number of obs: 333, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.243104 0.159478 28.680794 1.524 0.1384
## Group -0.021142 0.018216 33.921789 -1.161 0.2539
## age -0.001657 0.001636 29.940795 -1.013 0.3193
## Sex 0.002347 0.018805 31.803643 0.125 0.9015
## leg_l -0.003164 0.001829 32.918215 -1.729 0.0931 .
## factor(Race)1 -0.004752 0.026022 30.766230 -0.183 0.8563
## factor(Race)2 -0.009588 0.028184 32.275682 -0.340 0.7359
## factor(Race)3 0.046923 0.033066 32.260805 1.419 0.1655
## factor(Race)4 -0.032420 0.034208 31.405019 -0.948 0.3505
## Speed 0.122298 0.061005 58.129489 2.005 0.0497 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.136
## age -0.343 -0.115
## Sex -0.256 -0.173 0.148
## leg_l -0.819 -0.084 0.012 0.272
## factor(Rc)1 -0.300 -0.071 0.202 0.077 0.185
## factor(Rc)2 -0.150 -0.194 0.031 -0.040 0.148 0.707
## factor(Rc)3 -0.170 -0.078 0.130 -0.137 0.125 0.623 0.589
## factor(Rc)4 -0.163 -0.178 0.258 0.016 0.156 0.648 0.597 0.542
## Speed -0.037 0.424 0.068 -0.257 -0.460 -0.139 -0.213 -0.152 -0.317## R2m R2c
## [1,] 0.1305934 0.3328304## GVIF Df GVIF^(1/(2*Df))
## Group 1.334711 1 1.155297
## age 1.226769 1 1.107596
## Sex 1.260472 1 1.122708
## leg_l 1.377301 1 1.173585
## factor(Race) 1.520138 4 1.053745
## Speed 1.822107 1 1.349854## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.069467707 0.555676267
## Group -0.056844574 0.014560295
## age -0.004862571 0.001549222
## Sex -0.034510032 0.039203635
## leg_l -0.006749356 0.000421868
## factor(Race)1 -0.055754943 0.046250917
## factor(Race)2 -0.064827886 0.045651499
## factor(Race)3 -0.017885866 0.111731638
## factor(Race)4 -0.099465490 0.034626235
## Speed 0.002731428 0.241864756
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ Group + age + Sex + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ocma
##
## REML criterion at convergence: 1754
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8025 -0.4568 -0.0136 0.4597 4.1276
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 43.167 6.570
## Residual 7.636 2.763
## Number of obs: 335, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.04400 21.90239 32.71203 1.463 0.153000
## Group -6.12311 2.28556 35.56299 -2.679 0.011108 *
## age 0.09897 0.22223 32.85080 0.445 0.658975
## Sex 0.55191 2.48442 33.60643 0.222 0.825543
## leg_l -0.43230 0.22924 35.98358 -1.886 0.067423 .
## factor(Race)1 3.89158 3.49656 33.04263 1.113 0.273751
## factor(Race)2 1.72786 3.72157 33.45423 0.464 0.645452
## factor(Race)3 3.92657 4.38213 33.29505 0.896 0.376663
## factor(Race)4 1.75308 4.47827 34.11665 0.391 0.697888
## Speed 13.60971 3.90845 312.40208 3.482 0.000568 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.138
## age -0.348 -0.142
## Sex -0.274 -0.081 0.155
## leg_l -0.912 0.080 0.050 0.206
## factor(Rc)1 -0.309 -0.020 0.206 0.070 0.156
## factor(Rc)2 -0.151 -0.125 0.036 -0.090 0.074 0.698
## factor(Rc)3 -0.163 -0.022 0.148 -0.172 0.062 0.616 0.584
## factor(Rc)4 -0.174 -0.072 0.286 -0.041 0.046 0.633 0.570 0.529
## Speed -0.017 0.215 0.031 -0.118 -0.235 -0.066 -0.101 -0.076 -0.156## R2m R2c
## [1,] 0.2797752 0.8917411## GVIF Df GVIF^(1/(2*Df))
## Group 1.140346 1 1.067870
## age 1.218417 1 1.103819
## Sex 1.197424 1 1.094269
## leg_l 1.153339 1 1.073936
## factor(Race) 1.398714 4 1.042836
## Speed 1.182570 1 1.087460## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -10.8838933 74.97190157
## Group -10.6027207 -1.64350836
## age -0.3365890 0.53453757
## Sex -4.3174678 5.42128804
## leg_l -0.8816069 0.01701569
## factor(Race)1 -2.9615427 10.74470207
## factor(Race)2 -5.5662720 9.02199837
## factor(Race)3 -4.6622457 12.51539204
## factor(Race)4 -7.0241660 10.53033549
## Speed 5.9492791 21.27013929## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocma
##
## REML criterion at convergence: 1654.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5028 -0.6029 0.0246 0.5634 3.9744
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 20.925 4.574
## Residual 5.888 2.426
## Number of obs: 335, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 36.29142 15.34295 32.13016 2.365 0.0242 *
## Group -3.67121 1.61824 35.86173 -2.269 0.0294 *
## age -0.03308 0.15577 32.33571 -0.212 0.8331
## Sex -1.57323 1.74657 33.33052 -0.901 0.3742
## leg_l -0.40762 0.16254 36.36619 -2.508 0.0167 *
## factor(Race)1 -0.47560 2.45270 32.58570 -0.194 0.8475
## factor(Race)2 1.03015 2.61481 33.14662 0.394 0.6961
## factor(Race)3 0.54649 3.07709 32.95053 0.178 0.8601
## factor(Race)4 4.10047 3.15419 33.96234 1.300 0.2024
## Speed 5.88568 3.28415 272.36615 1.792 0.0742 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.138
## age -0.347 -0.137
## Sex -0.272 -0.092 0.153
## leg_l -0.900 0.057 0.046 0.215
## factor(Rc)1 -0.308 -0.026 0.205 0.072 0.160
## factor(Rc)2 -0.150 -0.134 0.034 -0.084 0.083 0.699
## factor(Rc)3 -0.163 -0.029 0.146 -0.167 0.070 0.617 0.584
## factor(Rc)4 -0.172 -0.086 0.282 -0.033 0.061 0.634 0.572 0.531
## Speed -0.020 0.255 0.037 -0.142 -0.279 -0.079 -0.121 -0.091 -0.186## R2m R2c
## [1,] 0.2514878 0.8356408## GVIF Df GVIF^(1/(2*Df))
## Group 1.163710 1 1.078754
## age 1.219002 1 1.104084
## Sex 1.204506 1 1.097500
## leg_l 1.181201 1 1.086831
## factor(Race) 1.414827 4 1.044330
## Speed 1.262440 1 1.123583## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) 6.2197925 66.36305079
## Group -6.8429083 -0.49951485
## age -0.3383809 0.27221647
## Sex -4.9964437 1.84997522
## leg_l -0.7261874 -0.08904589
## factor(Race)1 -5.2827950 4.33159581
## factor(Race)2 -4.0947916 6.15508822
## factor(Race)3 -5.4844897 6.57747155
## factor(Race)4 -2.0816331 10.28257178
## Speed -0.5511378 12.32250606## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocma
##
## REML criterion at convergence: -304.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1521 -0.5369 0.0324 0.4651 3.3323
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.06877 0.2622
## Residual 0.01365 0.1168
## Number of obs: 334, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.660299 0.875348 32.440663 1.897 0.066798 .
## Group -0.234420 0.091591 35.535959 -2.559 0.014891 *
## age -0.004765 0.008883 32.593975 -0.536 0.595298
## Sex -0.022416 0.099382 33.419479 -0.226 0.822921
## leg_l -0.019295 0.009187 35.944338 -2.100 0.042797 *
## factor(Race)1 0.074859 0.139792 32.805712 0.536 0.595913
## factor(Race)2 0.055816 0.148835 33.244940 0.375 0.710026
## factor(Race)3 0.142513 0.175227 33.074881 0.813 0.421860
## factor(Race)4 0.057045 0.179203 33.949476 0.318 0.752188
## Speed 0.583468 0.163914 304.532912 3.560 0.000431 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.138
## age -0.347 -0.141
## Sex -0.274 -0.084 0.155
## leg_l -0.909 0.074 0.049 0.209
## factor(Rc)1 -0.309 -0.021 0.206 0.070 0.157
## factor(Rc)2 -0.151 -0.127 0.035 -0.089 0.076 0.698
## factor(Rc)3 -0.163 -0.023 0.148 -0.171 0.064 0.616 0.584
## factor(Rc)4 -0.174 -0.075 0.285 -0.039 0.049 0.633 0.570 0.529
## Speed -0.018 0.226 0.032 -0.124 -0.246 -0.070 -0.106 -0.080 -0.164## R2m R2c
## [1,] 0.2864611 0.8818439## GVIF Df GVIF^(1/(2*Df))
## Group 1.146100 1 1.070560
## age 1.218561 1 1.103885
## Sex 1.199272 1 1.095113
## leg_l 1.159648 1 1.076870
## factor(Race) 1.402389 4 1.043178
## Speed 1.201180 1 1.095983## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.05535237 3.375950169
## Group -0.41393456 -0.054904735
## age -0.02217499 0.012644854
## Sex -0.21720229 0.172369709
## leg_l -0.03730156 -0.001287955
## factor(Race)1 -0.19912800 0.348846673
## factor(Race)2 -0.23589519 0.347527967
## factor(Race)3 -0.20092521 0.485950330
## factor(Race)4 -0.29418675 0.408276552
## Speed 0.26220318 0.904733006## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocma
##
## REML criterion at convergence: -255.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6421 -0.5404 0.0224 0.4714 3.7929
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.04485 0.2118
## Residual 0.01689 0.1300
## Number of obs: 334, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.298079 0.714223 32.310765 1.817 0.0784 .
## Group -0.198259 0.075978 36.702658 -2.609 0.0130 *
## age -0.005585 0.007255 32.577214 -0.770 0.4469
## Sex -0.087035 0.081547 33.764069 -1.067 0.2934
## leg_l -0.015517 0.007635 37.175027 -2.032 0.0493 *
## factor(Race)1 0.024027 0.114310 32.877396 0.210 0.8348
## factor(Race)2 -0.021167 0.122014 33.542989 -0.173 0.8633
## factor(Race)3 0.009514 0.143520 33.325882 0.066 0.9475
## factor(Race)4 0.038418 0.147446 34.437870 0.261 0.7960
## Speed 0.826062 0.169928 235.271217 4.861 2.13e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.138
## age -0.347 -0.135
## Sex -0.271 -0.100 0.152
## leg_l -0.890 0.039 0.043 0.222
## factor(Rc)1 -0.307 -0.031 0.205 0.074 0.164
## factor(Rc)2 -0.150 -0.140 0.034 -0.080 0.090 0.700
## factor(Rc)3 -0.163 -0.034 0.145 -0.164 0.076 0.617 0.585
## factor(Rc)4 -0.171 -0.096 0.279 -0.027 0.072 0.635 0.575 0.532
## Speed -0.023 0.282 0.041 -0.158 -0.307 -0.089 -0.134 -0.101 -0.206## R2m R2c
## [1,] 0.370058 0.827641## GVIF Df GVIF^(1/(2*Df))
## Group 1.182394 1 1.087380
## age 1.219496 1 1.104308
## Sex 1.210365 1 1.100166
## leg_l 1.202476 1 1.096575
## factor(Race) 1.427064 4 1.045455
## Speed 1.324292 1 1.150779## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.10177210 2.6979305101
## Group -0.34717405 -0.0493444656
## age -0.01980416 0.0086341483
## Sex -0.24686472 0.0727948764
## leg_l -0.03048199 -0.0005526187
## factor(Race)1 -0.20001626 0.2480696622
## factor(Race)2 -0.26030918 0.2179760620
## factor(Race)3 -0.27178019 0.2908076441
## factor(Race)4 -0.25056969 0.3274061039
## Speed 0.49300873 1.1591155176## 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 ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ochb
##
## REML criterion at convergence: -1515.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3484 -0.4441 -0.0515 0.2714 4.5773
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0009452 0.03074
## Residual 0.0004024 0.02006
## Number of obs: 343, groups: Subject, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.474e-02 1.040e-01 3.198e+01 0.430 0.670
## Group 5.732e-03 1.121e-02 3.785e+01 0.511 0.612
## age -1.226e-03 1.106e-03 3.215e+01 -1.109 0.276
## Sex 8.894e-03 1.228e-02 3.249e+01 0.724 0.474
## leg_l -3.185e-04 1.100e-03 3.567e+01 -0.289 0.774
## factor(Race)1 -1.140e-02 1.661e-02 3.224e+01 -0.686 0.497
## factor(Race)2 -8.539e-03 1.817e-02 3.243e+01 -0.470 0.642
## factor(Race)3 -2.379e-04 2.092e-02 3.241e+01 -0.011 0.991
## factor(Race)4 -1.666e-02 2.132e-02 3.266e+01 -0.781 0.440
## Speed 8.412e-02 2.062e-02 2.626e+02 4.080 5.98e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.135
## age -0.330 -0.121
## Sex -0.263 -0.092 0.058
## leg_l -0.903 0.038 0.024 0.216
## factor(Rc)1 -0.309 -0.008 0.214 0.047 0.137
## factor(Rc)2 -0.148 -0.110 0.108 -0.157 0.047 0.688
## factor(Rc)3 -0.165 -0.004 0.175 -0.204 0.041 0.616 0.588
## factor(Rc)4 -0.173 -0.068 0.313 -0.082 0.025 0.631 0.577 0.531
## Speed -0.013 0.327 0.020 -0.096 -0.262 -0.017 -0.068 -0.021 -0.108## R2m R2c
## [1,] 0.1616718 0.7496711## GVIF Df GVIF^(1/(2*Df))
## Group 1.203071 1 1.096846
## age 1.162061 1 1.077989
## Sex 1.217654 1 1.103474
## leg_l 1.174553 1 1.083768
## factor(Race) 1.398979 4 1.042861
## Speed 1.262160 1 1.123459## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.159071463 0.2485466697
## Group -0.016243967 0.0277082713
## age -0.003393680 0.0009416305
## Sex -0.015169084 0.0329571873
## leg_l -0.002475167 0.0018380788
## factor(Race)1 -0.043949446 0.0211557378
## factor(Race)2 -0.044148543 0.0270709818
## factor(Race)3 -0.041242557 0.0407666653
## factor(Race)4 -0.058433878 0.0251193163
## Speed 0.043710247 0.1245373844## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ Group + age + Sex + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ochb
##
## REML criterion at convergence: 1453.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2142 -0.5813 -0.0369 0.5578 3.3741
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 25.896 5.089
## Residual 2.285 1.512
## Number of obs: 353, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.04481 16.87490 32.84036 1.069 0.29272
## Group -5.47604 1.74508 34.82879 -3.138 0.00345 **
## age 0.02771 0.17108 32.87864 0.162 0.87230
## Sex 2.83352 1.90237 33.04494 1.489 0.14585
## leg_l -0.06865 0.17341 34.11545 -0.396 0.69465
## factor(Race)1 4.67265 2.68739 32.89355 1.739 0.09144 .
## factor(Race)2 -0.08575 2.85136 32.94273 -0.030 0.97619
## factor(Race)3 2.57563 3.36230 32.93891 0.766 0.44911
## factor(Race)4 2.78608 3.41289 33.03793 0.816 0.42015
## Speed 1.91633 1.72514 339.51562 1.111 0.26743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.137
## age -0.347 -0.146
## Sex -0.277 -0.065 0.158
## leg_l -0.933 0.110 0.056 0.191
## factor(Rc)1 -0.311 -0.006 0.209 0.064 0.144
## factor(Rc)2 -0.153 -0.110 0.038 -0.102 0.055 0.697
## factor(Rc)3 -0.164 -0.005 0.151 -0.182 0.045 0.615 0.581
## factor(Rc)4 -0.178 -0.048 0.293 -0.057 0.016 0.631 0.564 0.525
## Speed -0.007 0.176 0.017 -0.056 -0.141 -0.007 -0.031 -0.008 -0.054## R2m R2c
## [1,] 0.3399021 0.9464801## GVIF Df GVIF^(1/(2*Df))
## Group 1.122108 1 1.059296
## age 1.217433 1 1.103374
## Sex 1.184696 1 1.088437
## leg_l 1.111651 1 1.054349
## factor(Race) 1.367254 4 1.039875
## Speed 1.072782 1 1.035752## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -15.0293931 51.1190041
## Group -8.8963299 -2.0557594
## age -0.3075971 0.3630263
## Sex -0.8950659 6.5620980
## leg_l -0.4085240 0.2712236
## factor(Race)1 -0.5945385 9.9398465
## factor(Race)2 -5.6743151 5.5028245
## factor(Race)3 -4.0143504 9.1656126
## factor(Race)4 -3.9030513 9.4752122
## Speed -1.4648780 5.2975374## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ochb
##
## REML criterion at convergence: 1549.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.62718 -0.56966 0.00019 0.55281 2.43051
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 16.636 4.079
## Residual 3.259 1.805
## Number of obs: 353, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 21.02706 13.61222 32.99692 1.545 0.1320
## Group -3.45232 1.43006 36.77491 -2.414 0.0209 *
## age -0.06675 0.13805 33.07626 -0.484 0.6319
## Sex -0.42255 1.53721 33.39264 -0.275 0.7851
## leg_l -0.05329 0.14133 35.42006 -0.377 0.7084
## factor(Race)1 2.07260 2.16884 33.11445 0.956 0.3462
## factor(Race)2 0.72104 2.30213 33.20824 0.313 0.7561
## factor(Race)3 1.96431 2.71462 33.21287 0.724 0.4744
## factor(Race)4 4.05534 2.75763 33.38153 1.471 0.1508
## Speed -2.65732 1.98358 337.17042 -1.340 0.1813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.136
## age -0.347 -0.141
## Sex -0.277 -0.075 0.157
## leg_l -0.922 0.082 0.053 0.197
## factor(Rc)1 -0.311 -0.007 0.209 0.064 0.143
## factor(Rc)2 -0.153 -0.113 0.038 -0.099 0.059 0.697
## factor(Rc)3 -0.165 -0.006 0.150 -0.181 0.047 0.615 0.581
## factor(Rc)4 -0.177 -0.057 0.291 -0.053 0.023 0.631 0.565 0.524
## Speed -0.010 0.247 0.024 -0.081 -0.198 -0.011 -0.045 -0.012 -0.077## R2m R2c
## [1,] 0.1785766 0.865438## GVIF Df GVIF^(1/(2*Df))
## Group 1.158236 1 1.076214
## age 1.217722 1 1.103504
## Sex 1.188193 1 1.090043
## leg_l 1.134214 1 1.064995
## factor(Race) 1.373101 4 1.040430
## Speed 1.147855 1 1.071380## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -5.6523892 47.7065143
## Group -6.2551722 -0.6494593
## age -0.3373261 0.2038173
## Sex -3.4354256 2.5903331
## leg_l -0.3302968 0.2237200
## factor(Race)1 -2.1782541 6.3234578
## factor(Race)2 -3.7910434 5.2331264
## factor(Race)3 -3.3562572 7.2848766
## factor(Race)4 -1.3495192 9.4602015
## Speed -6.5450595 1.2304240## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ochb
##
## REML criterion at convergence: -515.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3002 -0.5323 0.0167 0.4702 4.0482
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.050236 0.22413
## Residual 0.007703 0.08777
## Number of obs: 352, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.968016 0.746133 32.609639 1.297 0.203605
## Group -0.167303 0.077946 35.753951 -2.146 0.038702 *
## age -0.006452 0.007566 32.675000 -0.853 0.400034
## Sex 0.071429 0.084212 32.943824 0.848 0.402445
## leg_l -0.006635 0.007717 34.606007 -0.860 0.395814
## factor(Race)1 0.129588 0.118864 32.705761 1.090 0.283582
## factor(Race)2 0.015855 0.126144 32.777917 0.126 0.900744
## factor(Race)3 0.217581 0.148746 32.777860 1.463 0.153054
## factor(Race)4 0.176011 0.151060 32.923465 1.165 0.252322
## Speed 0.363172 0.097882 341.347840 3.710 0.000242 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.136
## age -0.347 -0.143
## Sex -0.277 -0.071 0.157
## leg_l -0.926 0.092 0.054 0.195
## factor(Rc)1 -0.311 -0.007 0.209 0.064 0.143
## factor(Rc)2 -0.153 -0.112 0.038 -0.100 0.057 0.697
## factor(Rc)3 -0.165 -0.005 0.150 -0.181 0.046 0.615 0.581
## factor(Rc)4 -0.177 -0.054 0.292 -0.054 0.020 0.631 0.565 0.525
## Speed -0.009 0.224 0.021 -0.073 -0.179 -0.010 -0.040 -0.011 -0.069## R2m R2c
## [1,] 0.3394928 0.9121833## GVIF Df GVIF^(1/(2*Df))
## Group 1.144972 1 1.070034
## age 1.217658 1 1.103475
## Sex 1.187031 1 1.089510
## leg_l 1.125741 1 1.061009
## factor(Race) 1.370930 4 1.040224
## Speed 1.119952 1 1.058278## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.49437736 2.430409832
## Group -0.32007490 -0.014531179
## age -0.02128092 0.008377687
## Sex -0.09362350 0.236481723
## leg_l -0.02176079 0.008490175
## factor(Race)1 -0.10338053 0.362556815
## factor(Race)2 -0.23138223 0.263092793
## factor(Race)3 -0.07395561 0.509118058
## factor(Race)4 -0.12006122 0.472083509
## Speed 0.17132685 0.555016831## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ochb
##
## REML criterion at convergence: -459.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7281 -0.4985 -0.0183 0.4745 5.0177
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.051366 0.22664
## Residual 0.009188 0.09585
## Number of obs: 352, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.743518 0.755617 32.569878 0.984 0.3324
## Group -0.172256 0.079221 36.099631 -2.174 0.0363 *
## age -0.003815 0.007663 32.644813 -0.498 0.6219
## Sex 0.036306 0.085317 32.946944 0.426 0.6732
## leg_l -0.005324 0.007834 34.810836 -0.680 0.5013
## factor(Race)1 0.150660 0.120389 32.681598 1.251 0.2197
## factor(Race)2 0.025493 0.127774 32.762541 0.200 0.8431
## factor(Race)3 0.224784 0.150668 32.765093 1.492 0.1453
## factor(Race)4 0.175454 0.153039 32.924274 1.146 0.2599
## Speed 0.570809 0.105962 338.814207 5.387 1.34e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.136
## age -0.347 -0.142
## Sex -0.277 -0.074 0.157
## leg_l -0.924 0.086 0.053 0.196
## factor(Rc)1 -0.311 -0.007 0.209 0.064 0.143
## factor(Rc)2 -0.153 -0.113 0.038 -0.100 0.058 0.697
## factor(Rc)3 -0.165 -0.006 0.150 -0.181 0.047 0.615 0.581
## factor(Rc)4 -0.177 -0.056 0.292 -0.054 0.022 0.631 0.565 0.524
## Speed -0.009 0.239 0.023 -0.078 -0.191 -0.010 -0.043 -0.012 -0.074## R2m R2c
## [1,] 0.3781692 0.9056504## GVIF Df GVIF^(1/(2*Df))
## Group 1.153233 1 1.073887
## age 1.217730 1 1.103508
## Sex 1.187849 1 1.089885
## leg_l 1.130865 1 1.063421
## factor(Race) 1.372256 4 1.040350
## Speed 1.137061 1 1.066331## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.73746455 2.22450149
## Group -0.32752735 -0.01698536
## age -0.01883440 0.01120364
## Sex -0.13091338 0.20352441
## leg_l -0.02067733 0.01002996
## factor(Race)1 -0.08529821 0.38661845
## factor(Race)2 -0.22493973 0.27592489
## factor(Race)3 -0.07052028 0.52008864
## factor(Race)4 -0.12449680 0.47540500
## Speed 0.36312804 0.77848978## 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 ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocmb
##
## REML criterion at convergence: -1522.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8005 -0.4442 -0.0666 0.3243 6.3261
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0008799 0.02966
## Residual 0.0004034 0.02008
## Number of obs: 344, groups: Subject, 41
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.033869 0.100315 30.863975 -0.338 0.73793
## Group 0.001176 0.010776 35.769330 0.109 0.91369
## age -0.001000 0.001069 31.225702 -0.936 0.35638
## Sex 0.015846 0.011967 32.327388 1.324 0.19475
## leg_l 0.000638 0.001073 35.073015 0.595 0.55595
## factor(Race)1 -0.008085 0.016052 31.339346 -0.504 0.61804
## factor(Race)2 0.005702 0.017610 31.752364 0.324 0.74823
## factor(Race)3 0.010443 0.020209 31.390882 0.517 0.60894
## factor(Race)4 -0.012777 0.020592 31.646873 -0.621 0.53937
## Speed 0.064880 0.022733 214.111979 2.854 0.00474 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.134
## age -0.334 -0.118
## Sex -0.260 -0.110 0.056
## leg_l -0.892 0.027 0.026 0.233
## factor(Rc)1 -0.309 -0.012 0.214 0.047 0.139
## factor(Rc)2 -0.147 -0.121 0.106 -0.148 0.059 0.687
## factor(Rc)3 -0.163 -0.019 0.178 -0.198 0.050 0.617 0.591
## factor(Rc)4 -0.176 -0.067 0.312 -0.077 0.032 0.632 0.580 0.536
## Speed -0.012 0.311 0.024 -0.153 -0.301 -0.024 -0.101 -0.056 -0.110## R2m R2c
## [1,] 0.2081002 0.7510754## GVIF Df GVIF^(1/(2*Df))
## Group 1.187630 1 1.089784
## age 1.161420 1 1.077692
## Sex 1.237179 1 1.112285
## leg_l 1.202187 1 1.096443
## factor(Race) 1.405826 4 1.043498
## Speed 1.295350 1 1.138134## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.230482382 0.162743865
## Group -0.019944013 0.022296543
## age -0.003094927 0.001094101
## Sex -0.007609334 0.039302161
## leg_l -0.001465089 0.002741039
## factor(Race)1 -0.039546398 0.023377282
## factor(Race)2 -0.028813573 0.040216845
## factor(Race)3 -0.029164815 0.050050976
## factor(Race)4 -0.053136519 0.027581768
## Speed 0.020324294 0.109435220## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ Group + age + Sex + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ocmb
##
## REML criterion at convergence: 1408
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6659 -0.5932 0.0450 0.6071 3.3506
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 24.853 4.985
## Residual 1.961 1.400
## Number of obs: 354, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 12.15616 16.51616 32.72368 0.736 0.46696
## Group -4.73029 1.70327 34.33910 -2.777 0.00882 **
## age 0.05714 0.16749 32.79624 0.341 0.73518
## Sex 2.05898 1.86655 33.23281 1.103 0.27791
## leg_l -0.05473 0.17003 34.21025 -0.322 0.74951
## factor(Race)1 4.19425 2.63097 32.81135 1.594 0.12048
## factor(Race)2 -1.37514 2.79320 32.93441 -0.492 0.62576
## factor(Race)3 2.59251 3.29117 32.83353 0.788 0.43651
## factor(Race)4 2.01055 3.34063 32.93118 0.602 0.55140
## Speed 5.35608 1.82743 342.31597 2.931 0.00361 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.137
## age -0.348 -0.146
## Sex -0.277 -0.069 0.157
## leg_l -0.931 0.110 0.057 0.196
## factor(Rc)1 -0.311 -0.007 0.209 0.064 0.144
## factor(Rc)2 -0.152 -0.112 0.038 -0.100 0.058 0.697
## factor(Rc)3 -0.164 -0.009 0.151 -0.180 0.047 0.615 0.582
## factor(Rc)4 -0.178 -0.047 0.293 -0.056 0.017 0.631 0.565 0.526
## Speed -0.006 0.158 0.019 -0.084 -0.155 -0.010 -0.046 -0.025 -0.052## R2m R2c
## [1,] 0.3424011 0.9519077## GVIF Df GVIF^(1/(2*Df))
## Group 1.114855 1 1.055867
## age 1.217414 1 1.103365
## Sex 1.189644 1 1.090708
## leg_l 1.116179 1 1.056494
## factor(Race) 1.368289 4 1.039973
## Speed 1.074116 1 1.036396## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -20.2149179 44.5272393
## Group -8.0686298 -1.3919495
## age -0.2711356 0.3854069
## Sex -1.5993903 5.7173601
## leg_l -0.3879866 0.2785300
## factor(Race)1 -0.9623455 9.3508538
## factor(Race)2 -6.8497191 4.0994421
## factor(Race)3 -3.8580693 9.0430992
## factor(Race)4 -4.5369614 8.5580588
## Speed 1.7743797 8.9377873## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocmb
##
## REML criterion at convergence: 1504.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.66789 -0.58248 0.01772 0.55906 2.54914
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 16.652 4.081
## Residual 2.783 1.668
## Number of obs: 354, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 21.90183 13.58493 32.84697 1.612 0.116
## Group -2.25276 1.41785 35.77553 -1.589 0.121
## age -0.06823 0.13784 32.99227 -0.495 0.624
## Sex -0.98435 1.54133 33.78562 -0.639 0.527
## leg_l -0.11584 0.14141 35.52005 -0.819 0.418
## factor(Race)1 1.71004 2.16561 33.02938 0.790 0.435
## factor(Race)2 -0.45087 2.30132 33.25109 -0.196 0.846
## factor(Race)3 1.45817 2.70949 33.06711 0.538 0.594
## factor(Race)4 3.78782 2.75222 33.23548 1.376 0.178
## Speed 1.09662 2.09257 334.37547 0.524 0.601
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.136
## age -0.348 -0.141
## Sex -0.275 -0.081 0.155
## leg_l -0.920 0.084 0.053 0.205
## factor(Rc)1 -0.311 -0.009 0.208 0.064 0.144
## factor(Rc)2 -0.152 -0.118 0.037 -0.096 0.063 0.696
## factor(Rc)3 -0.164 -0.013 0.151 -0.178 0.051 0.615 0.582
## factor(Rc)4 -0.178 -0.054 0.291 -0.052 0.024 0.631 0.566 0.527
## Speed -0.008 0.218 0.026 -0.117 -0.213 -0.014 -0.065 -0.035 -0.072## R2m R2c
## [1,] 0.1809507 0.8827179## GVIF Df GVIF^(1/(2*Df))
## Group 1.140825 1 1.068094
## age 1.217630 1 1.103463
## Sex 1.197447 1 1.094279
## leg_l 1.140936 1 1.068146
## factor(Race) 1.374572 4 1.040569
## Speed 1.143460 1 1.069327## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -4.7241426 48.5278081
## Group -5.0316916 0.5261703
## age -0.3383969 0.2019442
## Sex -4.0052937 2.0366032
## leg_l -0.3929843 0.1613141
## factor(Race)1 -2.5344713 5.9545580
## factor(Race)2 -4.9613785 4.0596476
## factor(Race)3 -3.8523345 6.7686770
## factor(Race)4 -1.6064420 9.1820770
## Speed -3.0047385 5.1979701## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocmb
##
## REML criterion at convergence: -548.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3566 -0.4847 -0.0178 0.5441 3.7215
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.051339 0.22658
## Residual 0.006959 0.08342
## Number of obs: 353, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.771833 0.753010 31.947308 1.025 0.3131
## Group -0.172292 0.078295 34.429236 -2.201 0.0346 *
## age -0.006087 0.007639 32.067489 -0.797 0.4314
## Sex 0.099941 0.085331 32.741702 1.171 0.2500
## leg_l -0.004437 0.007809 34.189762 -0.568 0.5736
## factor(Race)1 0.135395 0.120014 32.098877 1.128 0.2676
## factor(Race)2 -0.014486 0.127488 32.278254 -0.114 0.9102
## factor(Race)3 0.242966 0.150139 32.124685 1.618 0.1154
## factor(Race)4 0.215710 0.152470 32.268344 1.415 0.1667
## Speed 0.321930 0.106114 340.301598 3.034 0.0026 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.136
## age -0.348 -0.143
## Sex -0.276 -0.077 0.156
## leg_l -0.923 0.093 0.054 0.202
## factor(Rc)1 -0.311 -0.008 0.208 0.064 0.144
## factor(Rc)2 -0.152 -0.116 0.037 -0.097 0.061 0.696
## factor(Rc)3 -0.164 -0.011 0.151 -0.179 0.050 0.615 0.582
## factor(Rc)4 -0.178 -0.052 0.292 -0.053 0.022 0.631 0.566 0.527
## Speed -0.008 0.200 0.023 -0.107 -0.195 -0.013 -0.059 -0.032 -0.066## R2m R2c
## [1,] 0.3593029 0.9235158## GVIF Df GVIF^(1/(2*Df))
## Group 1.132292 1 1.064092
## age 1.217619 1 1.103458
## Sex 1.195016 1 1.093168
## leg_l 1.132535 1 1.064206
## factor(Race) 1.372459 4 1.040369
## Speed 1.120216 1 1.058403## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.70404000 2.247706251
## Group -0.32574662 -0.018837722
## age -0.02105963 0.008885867
## Sex -0.06730510 0.267186980
## leg_l -0.01974304 0.010868336
## factor(Race)1 -0.09982842 0.370618789
## factor(Race)2 -0.26435794 0.235386800
## factor(Race)3 -0.05130130 0.537233879
## factor(Race)4 -0.08312653 0.514546117
## Speed 0.11394992 0.529910938## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ocmb
##
## REML criterion at convergence: -500.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8632 -0.5181 0.0011 0.5552 3.2753
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.049593 0.22270
## Residual 0.008146 0.09026
## Number of obs: 353, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.399945 0.741265 31.934742 0.540 0.5933
## Group -0.168561 0.077360 34.794212 -2.179 0.0362 *
## age -0.003954 0.007522 32.077704 -0.526 0.6027
## Sex 0.079693 0.084104 32.855432 0.948 0.3503
## leg_l -0.001654 0.007713 34.510105 -0.214 0.8314
## factor(Race)1 0.146500 0.118171 32.117077 1.240 0.2241
## factor(Race)2 0.004002 0.125566 32.323017 0.032 0.9748
## factor(Race)3 0.246068 0.147839 32.145751 1.664 0.1057
## factor(Race)4 0.203925 0.150168 32.308524 1.358 0.1839
## Speed 0.527126 0.113383 334.039915 4.649 4.81e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.136
## age -0.348 -0.142
## Sex -0.275 -0.081 0.155
## leg_l -0.920 0.085 0.053 0.205
## factor(Rc)1 -0.311 -0.009 0.208 0.064 0.144
## factor(Rc)2 -0.152 -0.118 0.037 -0.096 0.063 0.696
## factor(Rc)3 -0.164 -0.012 0.151 -0.178 0.050 0.615 0.582
## factor(Rc)4 -0.178 -0.054 0.291 -0.052 0.024 0.631 0.566 0.527
## Speed -0.008 0.216 0.025 -0.116 -0.211 -0.014 -0.064 -0.035 -0.072## R2m R2c
## [1,] 0.3883109 0.9137001## GVIF Df GVIF^(1/(2*Df))
## Group 1.140327 1 1.067861
## age 1.217694 1 1.103492
## Sex 1.197456 1 1.094283
## leg_l 1.140140 1 1.067773
## factor(Race) 1.374384 4 1.040551
## Speed 1.141576 1 1.068446## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -1.05290716 1.85279669
## Group -0.32018343 -0.01693867
## age -0.01869636 0.01078772
## Sex -0.08514833 0.24453434
## leg_l -0.01677208 0.01346340
## factor(Race)1 -0.08511083 0.37811143
## factor(Race)2 -0.24210148 0.25010639
## factor(Race)3 -0.04369200 0.53582778
## factor(Race)4 -0.09039968 0.49824889
## Speed 0.30489930 0.74935330## 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 ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ff
##
## REML criterion at convergence: -1591.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9637 -0.4114 -0.0401 0.3557 5.8619
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0007291 0.02700
## Residual 0.0002305 0.01518
## Number of obs: 325, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.056e-01 9.168e-02 3.379e+01 -1.152 0.2574
## Group -2.394e-03 9.370e-03 3.429e+01 -0.256 0.7999
## age 1.834e-04 9.224e-04 3.296e+01 0.199 0.8436
## Sex 1.927e-02 1.028e-02 3.333e+01 1.875 0.0695 .
## leg_l 9.526e-04 9.283e-04 3.326e+01 1.026 0.3122
## factor(Race)1 1.477e-02 1.462e-02 3.404e+01 1.011 0.3194
## factor(Race)2 -9.566e-04 1.551e-02 3.405e+01 -0.062 0.9512
## factor(Race)3 1.552e-03 1.822e-02 3.363e+01 0.085 0.9326
## factor(Race)4 -1.325e-02 1.889e-02 3.627e+01 -0.701 0.4877
## Speed 5.570e-02 1.289e-02 3.133e+02 4.320 2.09e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.155
## age -0.345 -0.153
## Sex -0.262 -0.069 0.158
## leg_l -0.916 0.121 0.061 0.191
## factor(Rc)1 -0.291 -0.021 0.205 0.073 0.153
## factor(Rc)2 -0.134 -0.121 0.037 -0.090 0.063 0.703
## factor(Rc)3 -0.149 -0.018 0.150 -0.172 0.054 0.621 0.588
## factor(Rc)4 -0.142 -0.071 0.285 -0.036 0.031 0.638 0.575 0.533
## Speed -0.134 0.151 -0.001 -0.088 -0.099 -0.111 -0.124 -0.098 -0.229## R2m R2c
## [1,] 0.1969276 0.807094## GVIF Df GVIF^(1/(2*Df))
## Group 1.114002 1 1.055463
## age 1.218146 1 1.103696
## Sex 1.188590 1 1.090225
## leg_l 1.099966 1 1.048793
## factor(Race) 1.439094 4 1.046553
## Speed 1.115464 1 1.056155## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.2852984789 0.074083246
## Group -0.0207590925 0.015970954
## age -0.0016244671 0.001991248
## Sex -0.0008685809 0.039413658
## leg_l -0.0008668995 0.002772067
## factor(Race)1 -0.0138780154 0.043421589
## factor(Race)2 -0.0313506339 0.029437466
## factor(Race)3 -0.0341636059 0.037268352
## factor(Race)4 -0.0502708217 0.023780344
## Speed 0.0304343815 0.080974724## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ Group + age + Sex + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_ff
##
## REML criterion at convergence: 1410.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8373 -0.5858 -0.0268 0.5197 3.2446
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 31.882 5.646
## Residual 2.786 1.669
## Number of obs: 325, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.5325 18.7825 33.0608 0.987 0.3310
## Group -4.8716 1.9143 33.2193 -2.545 0.0158 *
## age 0.2234 0.1899 32.7644 1.176 0.2479
## Sex -0.1427 2.1111 32.8946 -0.068 0.9465
## leg_l -0.1118 0.1908 32.8787 -0.586 0.5619
## factor(Race)1 5.9898 2.9911 33.1161 2.003 0.0535 .
## factor(Race)2 1.4083 3.1728 33.1293 0.444 0.6600
## factor(Race)3 4.4646 3.7373 32.9877 1.195 0.2408
## factor(Race)4 7.4451 3.8172 33.8916 1.950 0.0594 .
## Speed 3.6348 1.4866 301.3293 2.445 0.0151 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.143
## age -0.347 -0.152
## Sex -0.273 -0.060 0.158
## leg_l -0.935 0.132 0.060 0.187
## factor(Rc)1 -0.305 -0.010 0.208 0.067 0.148
## factor(Rc)2 -0.147 -0.111 0.038 -0.100 0.055 0.699
## factor(Rc)3 -0.159 -0.009 0.151 -0.180 0.047 0.617 0.583
## factor(Rc)4 -0.167 -0.049 0.291 -0.052 0.016 0.633 0.567 0.528
## Speed -0.076 0.085 0.000 -0.049 -0.055 -0.063 -0.070 -0.055 -0.131## R2m R2c
## [1,] 0.285975 0.942612## GVIF Df GVIF^(1/(2*Df))
## Group 1.095473 1 1.046649
## age 1.217424 1 1.103369
## Sex 1.183618 1 1.087942
## leg_l 1.092919 1 1.045428
## factor(Race) 1.385910 4 1.041638
## Speed 1.036117 1 1.017898## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -18.28050003 55.3455837
## Group -8.62352367 -1.1196389
## age -0.14882338 0.5956250
## Sex -4.28050827 3.9950113
## leg_l -0.48569683 0.2621349
## factor(Race)1 0.12723416 11.8523026
## factor(Race)2 -4.81021944 7.6267370
## factor(Race)3 -2.86043745 11.7895436
## factor(Race)4 -0.03650778 14.9266156
## Speed 0.72112637 6.5484650## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ff
##
## REML criterion at convergence: 1427.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2786 -0.5724 0.0150 0.5966 2.5470
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 16.103 4.013
## Residual 3.195 1.788
## Number of obs: 325, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 25.2253 13.4856 33.4311 1.871 0.0702 .
## Group -2.6824 1.3764 33.7688 -1.949 0.0597 .
## age 0.1731 0.1360 32.8401 1.273 0.2121
## Sex -2.3141 1.5136 33.1001 -1.529 0.1358
## leg_l -0.1757 0.1368 33.0607 -1.285 0.2079
## factor(Race)1 2.7740 2.1488 33.5736 1.291 0.2055
## factor(Race)2 1.7303 2.2795 33.5925 0.759 0.4531
## factor(Race)3 3.6153 2.6817 33.3021 1.348 0.1867
## factor(Race)4 6.3917 2.7604 35.1466 2.315 0.0265 *
## Speed 0.4207 1.5542 313.6908 0.271 0.7868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.149
## age -0.346 -0.152
## Sex -0.267 -0.065 0.158
## leg_l -0.925 0.126 0.060 0.189
## factor(Rc)1 -0.298 -0.016 0.207 0.070 0.151
## factor(Rc)2 -0.140 -0.116 0.037 -0.095 0.059 0.701
## factor(Rc)3 -0.154 -0.014 0.150 -0.176 0.051 0.619 0.586
## factor(Rc)4 -0.154 -0.061 0.288 -0.044 0.024 0.636 0.571 0.531
## Speed -0.110 0.124 0.000 -0.072 -0.081 -0.091 -0.102 -0.080 -0.189## R2m R2c
## [1,] 0.224873 0.87166## GVIF Df GVIF^(1/(2*Df))
## Group 1.105003 1 1.051191
## age 1.217775 1 1.103528
## Sex 1.186208 1 1.089132
## leg_l 1.096553 1 1.047164
## factor(Race) 1.413448 4 1.044203
## Speed 1.077079 1 1.037824## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -1.20600291 51.65666735
## Group -5.38008321 0.01528099
## age -0.09347241 0.43963933
## Sex -5.28064998 0.65242594
## leg_l -0.44371356 0.09235520
## factor(Race)1 -1.43758860 6.98549743
## factor(Race)2 -2.73735437 6.19802417
## factor(Race)3 -1.64080630 8.87136045
## factor(Race)4 0.98138413 11.80210595
## Speed -2.62552877 3.46689420## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ff
##
## REML criterion at convergence: -386.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7313 -0.6128 -0.0095 0.5761 3.4220
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.04889 0.2211
## Residual 0.01014 0.1007
## Number of obs: 325, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.831695 0.743647 33.113354 1.118 0.2714
## Group -0.182499 0.075908 33.461461 -2.404 0.0219 *
## age 0.005657 0.007498 32.507265 0.755 0.4560
## Sex -0.006518 0.083455 32.773914 -0.078 0.9382
## leg_l -0.009313 0.007540 32.732799 -1.235 0.2256
## factor(Race)1 0.264841 0.118498 33.262271 2.235 0.0322 *
## factor(Race)2 0.099450 0.125705 33.280972 0.791 0.4345
## factor(Race)3 0.338292 0.147874 32.982492 2.288 0.0287 *
## factor(Race)4 0.386316 0.152304 34.877719 2.536 0.0158 *
## Speed 0.434100 0.087410 314.099307 4.966 1.12e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.149
## age -0.346 -0.152
## Sex -0.267 -0.065 0.158
## leg_l -0.924 0.126 0.060 0.190
## factor(Rc)1 -0.297 -0.016 0.206 0.070 0.151
## factor(Rc)2 -0.140 -0.116 0.037 -0.095 0.059 0.701
## factor(Rc)3 -0.154 -0.014 0.150 -0.175 0.051 0.619 0.586
## factor(Rc)4 -0.153 -0.061 0.288 -0.044 0.025 0.636 0.571 0.531
## Speed -0.112 0.126 -0.001 -0.074 -0.082 -0.093 -0.104 -0.082 -0.193## R2m R2c
## [1,] 0.3928577 0.8956609## GVIF Df GVIF^(1/(2*Df))
## Group 1.105736 1 1.051540
## age 1.217804 1 1.103541
## Sex 1.186404 1 1.089222
## leg_l 1.096831 1 1.047297
## factor(Race) 1.415549 4 1.044397
## Speed 1.080215 1 1.039334## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.625826008 2.289216140
## Group -0.331275820 -0.033722746
## age -0.009038543 0.020353419
## Sex -0.170085929 0.157049881
## leg_l -0.024091863 0.005465312
## factor(Race)1 0.032590051 0.497091882
## factor(Race)2 -0.146928104 0.345827733
## factor(Race)3 0.048464145 0.628119200
## factor(Race)4 0.087806258 0.684825288
## Speed 0.262780292 0.605420549## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_ff
##
## REML criterion at convergence: -268.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3525 -0.5829 -0.0157 0.5663 3.6142
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.03845 0.1961
## Residual 0.01577 0.1256
## Number of obs: 325, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.487220 0.671037 32.364269 0.726 0.47301
## Group -0.215694 0.068651 32.960234 -3.142 0.00354 **
## age 0.007747 0.006740 31.435773 1.149 0.25908
## Sex -0.021221 0.075146 31.844884 -0.282 0.77947
## leg_l -0.008315 0.006787 31.759314 -1.225 0.22957
## factor(Race)1 0.281578 0.107046 32.692380 2.630 0.01290 *
## factor(Race)2 0.065957 0.113565 32.697643 0.581 0.56537
## factor(Race)3 0.338628 0.133340 32.216651 2.540 0.01612 *
## factor(Race)4 0.333648 0.138916 35.228866 2.402 0.02172 *
## Speed 0.740052 0.104788 305.230602 7.062 1.11e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.158
## age -0.345 -0.153
## Sex -0.258 -0.073 0.157
## leg_l -0.910 0.117 0.061 0.193
## factor(Rc)1 -0.287 -0.024 0.205 0.075 0.155
## factor(Rc)2 -0.130 -0.124 0.036 -0.087 0.066 0.705
## factor(Rc)3 -0.146 -0.021 0.149 -0.169 0.056 0.622 0.590
## factor(Rc)4 -0.134 -0.078 0.283 -0.031 0.037 0.640 0.577 0.535
## Speed -0.149 0.168 -0.001 -0.098 -0.110 -0.123 -0.138 -0.108 -0.253## R2m R2c
## [1,] 0.5185026 0.8599389## GVIF Df GVIF^(1/(2*Df))
## Group 1.120498 1 1.058536
## age 1.218440 1 1.103830
## Sex 1.190269 1 1.090994
## leg_l 1.102419 1 1.049962
## factor(Race) 1.457378 4 1.048206
## Speed 1.142984 1 1.069104## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.827987376 1.802428246
## Group -0.350248042 -0.081140550
## age -0.005463553 0.020957108
## Sex -0.168504227 0.126062210
## leg_l -0.021618068 0.004988287
## factor(Race)1 0.071771630 0.491384852
## factor(Race)2 -0.156627620 0.288540809
## factor(Race)3 0.077286519 0.599969765
## factor(Race)4 0.061378621 0.605917434
## Speed 0.534671603 0.945432466## 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 ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_prf
##
## REML criterion at convergence: -1531.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6999 -0.4451 0.0045 0.3256 7.0011
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0009437 0.03072
## Residual 0.0002996 0.01731
## Number of obs: 329, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.365e-01 1.035e-01 3.142e+01 1.319 0.197
## Group -6.217e-03 1.090e-02 3.465e+01 -0.570 0.572
## age -5.641e-04 1.053e-03 3.182e+01 -0.536 0.596
## Sex -2.372e-03 1.186e-02 3.307e+01 -0.200 0.843
## leg_l -9.927e-04 1.088e-03 3.449e+01 -0.912 0.368
## factor(Race)1 -1.469e-02 1.680e-02 3.332e+01 -0.874 0.388
## factor(Race)2 1.323e-02 1.789e-02 3.370e+01 0.739 0.465
## factor(Race)3 1.393e-03 2.115e-02 3.388e+01 0.066 0.948
## factor(Race)4 -4.304e-04 2.178e-02 3.534e+01 -0.020 0.984
## Speed 3.662e-02 2.380e-02 2.314e+02 1.539 0.125
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.137
## age -0.347 -0.166
## Sex -0.269 -0.101 0.165
## leg_l -0.906 0.062 0.079 0.224
## factor(Rc)1 -0.302 -0.051 0.216 0.097 0.186
## factor(Rc)2 -0.146 -0.152 0.054 -0.061 0.101 0.709
## factor(Rc)3 -0.153 -0.062 0.163 -0.135 0.096 0.630 0.601
## factor(Rc)4 -0.166 -0.108 0.301 -0.004 0.082 0.649 0.589 0.555
## Speed -0.020 0.255 -0.072 -0.185 -0.257 -0.188 -0.207 -0.221 -0.285## R2m R2c
## [1,] 0.1151962 0.7867893## GVIF Df GVIF^(1/(2*Df))
## Group 1.163481 1 1.078648
## age 1.220483 1 1.104755
## Sex 1.221804 1 1.105352
## leg_l 1.166847 1 1.080207
## factor(Race) 1.492882 4 1.051364
## Speed 1.296115 1 1.138470## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.066332321 0.339300288
## Group -0.027588689 0.015154035
## age -0.002627680 0.001499445
## Sex -0.025612828 0.020869525
## leg_l -0.003125651 0.001140250
## factor(Race)1 -0.047621608 0.018249258
## factor(Race)2 -0.021835900 0.048288390
## factor(Race)3 -0.040060654 0.042847128
## factor(Race)4 -0.043127352 0.042266569
## Speed -0.010020863 0.083269258## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Peak ~ Group + age + Sex + leg_l + factor(Race) + Speed + (1 |
## Subject)
## Data: df_prf
##
## REML criterion at convergence: 1376
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2823 -0.5943 0.0334 0.5790 3.1047
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 29.621 5.443
## Residual 2.107 1.452
## Number of obs: 338, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.35761 18.03645 32.91548 0.519 0.607
## Group -2.71090 1.85548 34.21063 -1.461 0.153
## age -0.01502 0.18294 33.00380 -0.082 0.935
## Sex 1.07584 2.04051 33.54270 0.527 0.601
## leg_l -0.13452 0.18535 34.16929 -0.726 0.473
## factor(Race)1 3.19310 2.88822 33.63854 1.106 0.277
## factor(Race)2 -2.77669 3.06598 33.74901 -0.906 0.372
## factor(Race)3 1.95010 3.61951 33.88199 0.539 0.594
## factor(Race)4 0.73896 3.68871 34.48199 0.200 0.842
## Speed 14.43258 2.25614 327.10258 6.397 5.48e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.137
## age -0.347 -0.156
## Sex -0.275 -0.070 0.161
## leg_l -0.932 0.114 0.066 0.197
## factor(Rc)1 -0.308 -0.020 0.211 0.074 0.157
## factor(Rc)2 -0.151 -0.120 0.042 -0.091 0.067 0.701
## factor(Rc)3 -0.161 -0.022 0.155 -0.168 0.060 0.620 0.587
## factor(Rc)4 -0.175 -0.061 0.296 -0.042 0.032 0.637 0.571 0.535
## Speed -0.010 0.142 -0.039 -0.103 -0.144 -0.104 -0.114 -0.122 -0.159## R2m R2c
## [1,] 0.3302585 0.9555201## GVIF Df GVIF^(1/(2*Df))
## Group 1.109923 1 1.053529
## age 1.218848 1 1.104014
## Sex 1.193410 1 1.092433
## leg_l 1.112535 1 1.054768
## factor(Race) 1.400100 4 1.042965
## Speed 1.087993 1 1.043069## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -25.9931770 44.7083886
## Group -6.3475609 0.9257701
## age -0.3735683 0.3435301
## Sex -2.9234777 5.0751559
## leg_l -0.4978065 0.2287672
## factor(Race)1 -2.4676972 8.8539051
## factor(Race)2 -8.7858952 3.2325093
## factor(Race)3 -5.1440078 9.0442116
## factor(Race)4 -6.4907839 7.9686976
## Speed 10.0106221 18.8545279## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: InitialPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_prf
##
## REML criterion at convergence: 1449.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.88078 -0.51175 -0.03963 0.57084 2.76940
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 19.386 4.403
## Residual 2.833 1.683
## Number of obs: 338, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.5140 14.6625 32.9756 1.126 0.268
## Group -0.5186 1.5221 35.2156 -0.341 0.735
## age -0.1490 0.1488 33.1249 -1.001 0.324
## Sex -1.2124 1.6662 34.0496 -0.728 0.472
## leg_l -0.1531 0.1520 35.1269 -1.007 0.321
## factor(Race)1 0.1651 2.3601 34.2389 0.070 0.945
## factor(Race)2 -2.6554 2.5072 34.4288 -1.059 0.297
## factor(Race)3 0.7986 2.9625 34.6507 0.270 0.789
## factor(Race)4 0.3659 3.0313 35.6741 0.121 0.905
## Speed 10.3940 2.5087 316.2120 4.143 4.4e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.137
## age -0.347 -0.160
## Sex -0.273 -0.082 0.164
## leg_l -0.922 0.094 0.070 0.207
## factor(Rc)1 -0.306 -0.032 0.213 0.083 0.168
## factor(Rc)2 -0.149 -0.132 0.046 -0.079 0.080 0.704
## factor(Rc)3 -0.158 -0.037 0.158 -0.155 0.074 0.624 0.592
## factor(Rc)4 -0.172 -0.079 0.298 -0.027 0.051 0.641 0.578 0.542
## Speed -0.014 0.193 -0.053 -0.140 -0.195 -0.141 -0.154 -0.166 -0.215## R2m R2c
## [1,] 0.2073673 0.8989244## GVIF Df GVIF^(1/(2*Df))
## Group 1.129754 1 1.062899
## age 1.220287 1 1.104666
## Sex 1.203858 1 1.097204
## leg_l 1.132345 1 1.064117
## factor(Race) 1.433570 4 1.046050
## Speed 1.164490 1 1.079115## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -12.2239114 45.2518902
## Group -3.5017417 2.4646353
## age -0.4406440 0.1426784
## Sex -4.4780567 2.0532276
## leg_l -0.4510285 0.1448017
## factor(Race)1 -4.4605360 4.7907035
## factor(Race)2 -7.5694571 2.2586441
## factor(Race)3 -5.0078511 6.6050484
## factor(Race)4 -5.5753700 6.3071033
## Speed 5.4770484 15.3108807## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MomentPeak ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_prf
##
## REML criterion at convergence: -539.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5742 -0.5498 0.0065 0.6312 3.5405
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.043875 0.20946
## Residual 0.006597 0.08122
## Number of obs: 338, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.954621 0.697741 32.839996 1.368 0.1806
## Group -0.014631 0.072463 35.114704 -0.202 0.8412
## age -0.005836 0.007082 32.991385 -0.824 0.4158
## Sex 0.003806 0.079306 33.930000 0.048 0.9620
## leg_l -0.013367 0.007236 35.023656 -1.847 0.0732 .
## factor(Race)1 0.027565 0.112337 34.123638 0.245 0.8076
## factor(Race)2 -0.151961 0.119346 34.316398 -1.273 0.2115
## factor(Race)3 0.151880 0.141026 34.541295 1.077 0.2890
## factor(Race)4 0.115746 0.144328 35.579614 0.802 0.4279
## Speed 0.748409 0.120783 314.647138 6.196 1.81e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.137
## age -0.347 -0.160
## Sex -0.273 -0.082 0.164
## leg_l -0.922 0.093 0.071 0.208
## factor(Rc)1 -0.306 -0.032 0.213 0.083 0.168
## factor(Rc)2 -0.149 -0.133 0.046 -0.078 0.080 0.704
## factor(Rc)3 -0.158 -0.038 0.158 -0.154 0.075 0.624 0.593
## factor(Rc)4 -0.171 -0.079 0.298 -0.027 0.052 0.642 0.578 0.543
## Speed -0.014 0.195 -0.054 -0.142 -0.197 -0.143 -0.156 -0.168 -0.218## R2m R2c
## [1,] 0.3819236 0.919212## GVIF Df GVIF^(1/(2*Df))
## Group 1.130760 1 1.063372
## age 1.220359 1 1.104699
## Sex 1.204385 1 1.097445
## leg_l 1.133350 1 1.064589
## factor(Race) 1.435266 4 1.046204
## Speed 1.168372 1 1.080912## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.4129254 2.3221673879
## Group -0.1566563 0.1273940362
## age -0.0197153 0.0080440602
## Sex -0.1516307 0.1592420977
## leg_l -0.0275505 0.0008155994
## factor(Race)1 -0.1926113 0.2477414763
## factor(Race)2 -0.3858754 0.0819534573
## factor(Race)3 -0.1245261 0.4282856044
## factor(Race)4 -0.1671322 0.3986243973
## Speed 0.5116776 0.9851395230## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: kneeMrange ~ Group + age + Sex + leg_l + factor(Race) + Speed +
## (1 | Subject)
## Data: df_prf
##
## REML criterion at convergence: -508.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6557 -0.6146 0.0070 0.5877 2.7132
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.035094 0.18733
## Residual 0.007501 0.08661
## Number of obs: 338, groups: Subject, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.697799 0.626578 33.056856 1.114 0.2735
## Group -0.004357 0.065485 35.894927 -0.067 0.9473
## age -0.005508 0.006362 33.242195 -0.866 0.3928
## Sex -0.027973 0.071438 34.406545 -0.392 0.6978
## leg_l -0.013415 0.006538 35.762233 -2.052 0.0476 *
## factor(Race)1 -0.005882 0.101250 34.674827 -0.058 0.9540
## factor(Race)2 -0.164425 0.107624 34.913888 -1.528 0.1356
## factor(Race)3 0.107354 0.127251 35.185004 0.844 0.4046
## factor(Race)4 0.029072 0.130585 36.460936 0.223 0.8251
## Speed 1.113116 0.124720 286.880401 8.925 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group age Sex leg_l fc(R)1 fc(R)2 fc(R)3 fc(R)4
## Group -0.137
## age -0.347 -0.163
## Sex -0.271 -0.091 0.165
## leg_l -0.915 0.080 0.074 0.214
## factor(Rc)1 -0.304 -0.040 0.214 0.089 0.176
## factor(Rc)2 -0.148 -0.141 0.048 -0.070 0.089 0.706
## factor(Rc)3 -0.156 -0.048 0.159 -0.146 0.084 0.627 0.596
## factor(Rc)4 -0.169 -0.092 0.299 -0.016 0.065 0.645 0.583 0.548
## Speed -0.017 0.223 -0.062 -0.162 -0.225 -0.164 -0.179 -0.192 -0.249## R2m R2c
## [1,] 0.479623 0.9083614## GVIF Df GVIF^(1/(2*Df))
## Group 1.144796 1 1.069951
## age 1.221348 1 1.105146
## Sex 1.211713 1 1.100778
## leg_l 1.147344 1 1.071142
## factor(Race) 1.458872 4 1.048340
## Speed 1.222472 1 1.105655## 2.5 % 97.5 %
## .sig01 NA NA
## .sigma NA NA
## (Intercept) -0.53027194 1.9258704500
## Group -0.13270381 0.1239907239
## age -0.01797764 0.0069614670
## Sex -0.16798958 0.1120433548
## leg_l -0.02622881 -0.0006011951
## factor(Race)1 -0.20432756 0.1925641667
## factor(Race)2 -0.37536422 0.0465138197
## factor(Race)3 -0.14205219 0.3567608038
## factor(Race)4 -0.22686958 0.2850126354
## Speed 0.86866845 1.3575631828## Residual analysis for Stiffness
##
##
## Residual analysis for Peak
##
##
## Residual analysis for InitialPeak
##
##
## Residual analysis for PeakMoment
##
##
## Residual analysis for kneeRange
