## [1] "ID" "Condition"
## [3] "Site" "Coach"
## [5] "Session" "Morn_Aft"
## [7] "Day" "Age_month"
## [9] "Waist_Circumference_cm" "BMI"
## [11] "OrgSport" "MVPA_ATHLETE_EV"
## [13] "Min_MVPA_Ath_EV" "VPA_ATHLETE_EV"
## [15] "Min_VPA_Ath_EV" "MPA_ATHLETE_EV"
## [17] "Min_MPA_Ath_EV" "SED_ATHLETE_EV"
## [19] "Min_SED_Ath_EV" "MVPA_COACH_TRO"
## [21] "MIN_MVPA_COACH_TRO" "VPA_COACH_TRO"
## [23] "MIN_VPA_COACH_TRO" "MPA_COACH_TRO"
## [25] "MIN_MPA_COACH_TRO" "SED_COACH_TRO"
## [27] "MIN_SED_COACH_TRO" "Management"
## [29] "Knowledge" "Promo_PA"
## [31] "Demo_PA" "Dis"
## [33] "MVPA_EV_CHANGE" "VPA_EV_CHANGE"
## [35] "MPA_EV_CHANGE" "SED_EV_CHANGE"
## [37] "MVPA_COACH_TRO_CHANGE" "VPA_COACH_TRO_CHANGE"
## [39] "MPA_COACH_TRO_CHANGE" "SED_COACH_TRO_CHANGE"
## [41] "Management_change" "Knowledge_change"
## [43] "Promo_PA_change" "Demo_PA_change"
## [45] "Dis_change" "Man_Knowl_change"
## Compare linear to quadratic
## Data: dataExclude
## Models:
## TE_base: MVPA_ATHLETE_EV ~ Condition + baseline + Condition:baseline +
## TE_base: Session + (1 | ID) + (1 | Coach) + (1 | Site)
## TE: MVPA_ATHLETE_EV ~ Condition + baseline + Condition:baseline +
## TE: Session + I(Session^2) + (1 | ID) + (1 | Coach) + (1 | Site)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## TE_base 9 3825 3864 -1903 3807
## TE 10 3816 3860 -1898 3796 10.6 1 0.0011 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Compare baseline vs session as treatment variable of interest
## Data: dataExclude
## Models:
## TE: MVPA_ATHLETE_EV ~ Condition + baseline + Condition:baseline +
## TE: Session + I(Session^2) + (1 | ID) + (1 | Coach) + (1 | Site)
## TE_1: MVPA_ATHLETE_EV ~ Condition + baseline + Session + I(Session^2) +
## TE_1: (1 | ID) + (1 | Coach) + (1 | Site) + Condition:baseline +
## TE_1: Condition:Session
## TE_2: MVPA_ATHLETE_EV ~ Condition + baseline + Session + I(Session^2) +
## TE_2: (1 | ID) + (1 | Coach) + (1 | Site) + Condition:baseline +
## TE_2: Condition:Session + Condition:baseline:Session
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## TE 10 3816 3860 -1898 3796
## TE_1 11 3818 3866 -1898 3796 0.00 1 0.98
## TE_2 12 3818 3870 -1897 3794 2.69 1 0.10
## Data: dataExclude
## Models:
## TE: MVPA_ATHLETE_EV ~ Condition + baseline + Condition:baseline +
## TE: Session + I(Session^2) + (1 | ID) + (1 | Coach) + (1 | Site)
## TE_2: MVPA_ATHLETE_EV ~ Condition + baseline + Session + I(Session^2) +
## TE_2: (1 | ID) + (1 | Coach) + (1 | Site) + Condition:baseline +
## TE_2: Condition:Session + Condition:baseline:Session
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## TE 10 3816 3860 -1898 3796
## TE_2 12 3818 3870 -1897 3794 2.69 2 0.26
I will using quasi-bayes simulation for confidence intervals (see Galman and Hills, 2008)
## (Intercept) Condition baseline
## 26.8055 0.2352 25.5013
## Session I(Session^2) Condition:baseline
## -3.9013 0.2241 -13.7000
## Confidence Intervals
## -----------------------------------
## 0.5% 2.5% 97.5% 99.5%
## (Intercept) 12.98826 16.94369 37.2252 39.5108
## Condition -2.79589 -2.08619 2.4502 3.0846
## baseline 19.36811 20.38859 30.7433 32.2609
## Session -6.37138 -5.98148 -1.8280 -1.3443
## I(Session^2) 0.04782 0.09054 0.3614 0.3968
## Condition:baseline -17.75197 -16.84303 -10.5833 -9.5742
## (Intercept) Condition baseline
## 17.72171 0.26752 25.32159
## Session I(Session^2) Management
## -3.95978 0.22577 0.14600
## Knowledge Demo_PA Promo_PA
## 0.04263 -0.12606 0.24775
## MVPA_COACH_TRO Condition:baseline
## 0.25537 -12.25455
## Confidence Intervals
## -----------------------------------
## 0.5% 2.5% 97.5% 99.5%
## (Intercept) 1.93642 4.95321 30.25236 33.5966
## Condition -2.85467 -2.13581 2.61086 3.4642
## baseline 17.46253 19.07084 32.03565 34.2883
## Session -6.78857 -6.11862 -1.81952 -1.0805
## I(Session^2) 0.03845 0.08213 0.36949 0.4093
## Management -0.14457 -0.09221 0.38539 0.4640
## Knowledge -0.09152 -0.06014 0.13301 0.1659
## Demo_PA -0.38554 -0.33080 0.07364 0.1246
## Promo_PA -0.08879 -0.01713 0.48723 0.5635
## MVPA_COACH_TRO 0.10632 0.13988 0.37573 0.4027
## Condition:baseline -17.29238 -15.90451 -8.78486 -7.2383
## Total IE
## Point Estimate: -1.401
## CIs: -7.149 -6.204 3.378 4.952
Done equation by equation - equivilent to seemingly unrelated regression
## Results: Mangement
## -----------------------
## (Intercept) Condition baseline
## 11.4304 0.2534 -18.0873
## Session I(Session^2) Condition:baseline
## 4.3270 -0.2753 6.5948
## Confidence Intervals
## -----------------------------------
## 0.5% 2.5% 97.5% 99.5%
## (Intercept) 8.9065 9.6300 13.2936 13.7440
## Condition -0.6043 -0.4514 0.9730 1.2012
## baseline -20.5852 -19.9602 -16.2292 -15.6886
## Session 3.3972 3.6120 5.0840 5.3667
## I(Session^2) -0.3444 -0.3268 -0.2291 -0.2116
## Condition:baseline 4.9605 5.4302 7.7014 7.8918
## Results: Knowledge
## -----------------------
## (Intercept) Condition baseline
## 19.9848 1.2490 -11.3706
## Session I(Session^2) Condition:baseline
## 2.2159 -0.1481 5.5283
## Confidence Intervals
## -----------------------------------
## 0.5% 2.5% 97.5% 99.5%
## (Intercept) 11.997665 14.0145 25.41674 27.40383
## Condition -0.970703 -0.5217 2.93592 3.41690
## baseline -17.215003 -16.1875 -7.05115 -6.23353
## Session 0.006251 0.5401 3.98207 4.43052
## I(Session^2) -0.292297 -0.2651 -0.02876 0.01158
## Condition:baseline 2.096885 2.9629 8.37407 9.38584
## Results: Demo_PA
## -----------------------
## (Intercept) Condition baseline
## 8.79105 0.22304 4.99048
## Session I(Session^2) Condition:baseline
## 0.37032 -0.04814 -4.71777
## Confidence Intervals
## -----------------------------------
## 0.5% 2.5% 97.5% 99.5%
## (Intercept) 3.5103 4.8397 12.80427 14.09212
## Condition -0.9654 -0.6884 1.20835 1.56503
## baseline 1.7866 2.5104 7.56626 8.24396
## Session -0.8738 -0.6074 1.33970 1.65997
## I(Session^2) -0.1329 -0.1152 0.01902 0.03782
## Condition:baseline -6.7239 -6.1864 -3.28233 -2.68465
## Results: Promo_PA
## -----------------------
## (Intercept) Condition baseline
## 18.39170 -0.24385 9.67185
## Session I(Session^2) Condition:baseline
## -1.56333 0.08988 -5.98779
## Confidence Intervals
## -----------------------------------
## 0.5% 2.5% 97.5% 99.5%
## (Intercept) 14.67840 15.72859 21.1301 21.6091
## Condition -1.30971 -1.03478 0.6124 0.8486
## baseline 6.87957 7.53514 11.7304 12.3630
## Session -2.55235 -2.32427 -0.7921 -0.4773
## I(Session^2) 0.01779 0.03789 0.1423 0.1559
## Condition:baseline -7.81013 -7.34607 -4.8125 -4.4073
## Results: MVPA_COACH
## -----------------------
## (Intercept) Condition baseline
## 12.57561 -0.13269 6.62460
## Session I(Session^2) Condition:baseline
## -1.20288 0.08826 -7.04944
## Confidence Intervals
## -----------------------------------
## 0.5% 2.5% 97.5% 99.5%
## (Intercept) 6.07076 7.599435 17.8032 18.8340
## Condition -1.95380 -1.536699 1.3330 1.8088
## baseline 2.10269 3.098076 10.3780 10.8770
## Session -2.99353 -2.726896 0.2270 0.5074
## I(Session^2) -0.03248 -0.007469 0.1876 0.2149
## Condition:baseline -9.97075 -9.257899 -4.7835 -4.0742
## Results: MVPA_ATHLETE_EV
## -----------------------
## (Intercept) baseline Management
## 17.72171 25.32159 0.14600
## Knowledge Demo_PA Promo_PA
## 0.04263 -0.12606 0.24775
## MVPA_COACH_TRO Session I(Session^2)
## 0.25537 -3.95978 0.22577
## Condition baseline:Condition
## 0.26752 -12.25455
## Confidence Intervals
## -----------------------------------
## 0.5% 2.5% 97.5% 99.5%
## (Intercept) 1.83855 4.98057 30.09535 33.7229
## baseline 16.43514 18.61208 32.58542 34.4226
## Management -0.14566 -0.09020 0.39679 0.4881
## Knowledge -0.08835 -0.05316 0.14095 0.1680
## Demo_PA -0.38918 -0.33717 0.08948 0.1626
## Promo_PA -0.08911 -0.02022 0.51120 0.6047
## MVPA_COACH_TRO 0.09013 0.13466 0.37617 0.4111
## Session -6.93133 -6.22692 -1.64079 -0.8995
## I(Session^2) 0.02851 0.07092 0.37266 0.4137
## Condition -3.00229 -2.11716 2.66843 3.3266
## baseline:Condition -17.54876 -16.17026 -8.43904 -7.6280
Again all done via Quasi-bayes
## Management
## Point Estimate: 0.9741
## CIs: -0.9688 -0.5772 2.679 3.309
## Knowledge
## Point Estimate: 0.2468
## CIs: -0.4936 -0.2955 0.8752 1.101
## Demo_PA
## Point Estimate: 0.5909
## CIs: -0.7542 -0.4166 1.675 1.899
## Promo_PA
## Point Estimate: -1.49
## CIs: -3.63 -3.171 0.1266 0.5299
## MVPA_Coach
## Point Estimate: -1.797
## CIs: -3.221 -2.854 -0.8471 -0.5933
## Total Indirect Effect:
## Total IE
## Point Estimate: -1.475
## CIs: -4.25 -3.746 0.9647 1.574