##import dataset
tournament_data <- read.csv(here("raw data","tournamentData.csv"))
tournament_data$unspokenUnderstanding_MidTournament <- (tournament_data$unspokenUnderstanding_day1 + tournament_data$unspokenUnderstanding_day2) / 2
tournament_data$teamAura_MidTournament <- (tournament_data$teamAura_day1 + tournament_data$teamAura_day2) / 2
tournament_data$clickPictorial_MidTournament <- (tournament_data$clickPictorial_day1 + tournament_data$clickPictorial_day2) / 2
###view(tournament_data)Since we know the 3 factors we want and know explicitly which indicators we want to link them with (e.g., visceral_interdependence_day1 with unspokenUnderstanding_day1, teamAura_day1, clickPictorial_day1), Confirmed Factors Analysis might be more appropriate in this case.
#CFA
cfa_model <- 'visceral_interdependence_day1 =~ unspokenUnderstanding_day1 + teamAura_day1 + clickPictorial_day1
visceral_interdependence_day2 =~ unspokenUnderstanding_day2 + teamAura_day2 + clickPictorial_day2
social_bonding_day1 =~ emotionalSupport_day1 + sharedGoal_day1 + fusionPictorialTeam_day1
social_bonding_day2 =~ emotionalSupport_day2 + sharedGoal_day2 + fusionPictorialTeam_day2'
tournament_data_complete <- tournament_data[complete.cases(tournament_data[, c("unspokenUnderstanding_day1","teamAura_day1","clickPictorial_day1", "unspokenUnderstanding_day2", "teamAura_day2","clickPictorial_day2","emotionalSupport_day1","sharedGoal_day1","fusionPictorialTeam_day1", "emotionalSupport_day2","sharedGoal_day2","fusionPictorialTeam_day2")]), ]
fit_tournament <- cfa(cfa_model, data = tournament_data_complete)## Warning in lav_data_full(data = data, group = group, cluster = cluster, :
## lavaan WARNING: some observed variances are (at least) a factor 1000 times
## larger than others; use varTable(fit) to investigate## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negativeThis will produce output that includes the standardized factor loadings (labeled as “Std.lv”), along with other information about the model. Set rsq to TRUE, we will also get the variance explained by each latent variable.
#report factor loadings (>.3 considered adequate)
summary(fit_tournament, standardized = TRUE, rsq = TRUE)## lavaan 0.6.15 ended normally after 382 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Number of observations 125
##
## Model Test User Model:
##
## Test statistic 199.587
## Degrees of freedom 48
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv
## visceral_interdependence_day1 =~
## unspknUndrst_1 1.000 1.724
## teamAura_day1 -1.783 0.916 -1.947 0.052 -3.075
## clickPctrl_dy1 2.993 52.734 0.057 0.955 5.160
## visceral_interdependence_day2 =~
## unspknUndrst_2 1.000 18.575
## teamAura_day2 1.213 0.114 10.606 0.000 22.540
## clickPctrl_dy2 0.039 0.005 8.079 0.000 0.716
## social_bonding_day1 =~
## emtnlSpprt_dy1 1.000 19.209
## sharedGoal_dy1 0.875 0.122 7.145 0.000 16.811
## fsnPctrlTm_dy1 0.015 0.004 4.345 0.000 0.298
## social_bonding_day2 =~
## emtnlSpprt_dy2 1.000 17.389
## sharedGoal_dy2 1.217 0.123 9.896 0.000 21.164
## fsnPctrlTm_dy2 0.024 0.004 6.205 0.000 0.424
## Std.all
##
## 0.080
## -0.033
## 5.315
##
## 0.841
## 0.893
## 0.679
##
## 0.844
## 0.864
## 0.413
##
## 0.826
## 0.887
## 0.554
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## visceral_interdependence_day1 ~~
## vscrl_ntrdpn_2 1.505 26.711 0.056 0.955 0.047
## socl_bndng_dy1 0.745 13.236 0.056 0.955 0.022
## socl_bndng_dy2 1.304 23.149 0.056 0.955 0.043
## visceral_interdependence_day2 ~~
## socl_bndng_dy1 17.519 37.036 0.473 0.636 0.049
## socl_bndng_dy2 202.921 41.772 4.858 0.000 0.628
## social_bonding_day1 ~~
## socl_bndng_dy2 136.644 39.412 3.467 0.001 0.409
## Std.all
##
## 0.047
## 0.022
## 0.043
##
## 0.049
## 0.628
##
## 0.409
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .unspknUndrst_1 459.291 78.074 5.883 0.000 459.291 0.994
## .teamAura_day1 8775.694 1121.670 7.824 0.000 8775.694 0.999
## .clickPctrl_dy1 -25.683 467.526 -0.055 0.956 -25.683 -27.254
## .unspknUndrst_2 143.150 29.221 4.899 0.000 143.150 0.293
## .teamAura_day2 129.282 36.993 3.495 0.000 129.282 0.203
## .clickPctrl_dy2 0.600 0.086 6.992 0.000 0.600 0.540
## .emtnlSpprt_dy1 149.162 48.613 3.068 0.002 149.162 0.288
## .sharedGoal_dy1 95.998 36.368 2.640 0.008 95.998 0.254
## .fsnPctrlTm_dy1 0.431 0.057 7.623 0.000 0.431 0.830
## .emtnlSpprt_dy2 141.211 28.474 4.959 0.000 141.211 0.318
## .sharedGoal_dy2 120.817 35.722 3.382 0.001 120.817 0.212
## .fsnPctrlTm_dy2 0.405 0.055 7.401 0.000 0.405 0.693
## vscrl_ntrdpn_1 2.973 52.575 0.057 0.955 1.000 1.000
## vscrl_ntrdpn_2 345.032 63.334 5.448 0.000 1.000 1.000
## socl_bndng_dy1 368.977 77.115 4.785 0.000 1.000 1.000
## socl_bndng_dy2 302.383 57.629 5.247 0.000 1.000 1.000
##
## R-Square:
## Estimate
## unspknUndrst_1 0.006
## teamAura_day1 0.001
## clickPctrl_dy1 NA
## unspknUndrst_2 0.707
## teamAura_day2 0.797
## clickPctrl_dy2 0.460
## emtnlSpprt_dy1 0.712
## sharedGoal_dy1 0.746
## fsnPctrlTm_dy1 0.170
## emtnlSpprt_dy2 0.682
## sharedGoal_dy2 0.788
## fsnPctrlTm_dy2 0.307#Sum of Squares Loadings (SS Loadings) for each factor
#std_lv <- lavInspect(fit_tournament, "std.lv")
#print(std_lv)
ss_loadings <- (unlist(lavInspect(fit_tournament, "std.lv")))^2 * nrow(tournament_data_complete)
print(ss_loadings)## lambda1 lambda2 lambda3 lambda4 lambda5 lambda6
## 3.715941e+02 1.181581e+03 3.328152e+03 0.000000e+00 0.000000e+00 0.000000e+00
## lambda7 lambda8 lambda9 lambda10 lambda11 lambda12
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## lambda13 lambda14 lambda15 lambda16 lambda17 lambda18
## 0.000000e+00 0.000000e+00 0.000000e+00 4.312901e+04 6.350380e+04 6.405727e+01
## lambda19 lambda20 lambda21 lambda22 lambda23 lambda24
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## lambda25 lambda26 lambda27 lambda28 lambda29 lambda30
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## lambda31 lambda32 lambda33 lambda34 lambda35 lambda36
## 4.612214e+04 3.532511e+04 1.107174e+01 0.000000e+00 0.000000e+00 0.000000e+00
## lambda37 lambda38 lambda39 lambda40 lambda41 lambda42
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## lambda43 lambda44 lambda45 lambda46 lambda47 lambda48
## 0.000000e+00 0.000000e+00 0.000000e+00 3.779783e+04 5.599181e+04 2.242539e+01
## theta1 theta2 theta3 theta4 theta5 theta6
## 2.636857e+07 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta7 theta8 theta9 theta10 theta11 theta12
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta13 theta14 theta15 theta16 theta17 theta18
## 0.000000e+00 9.626602e+09 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta19 theta20 theta21 theta22 theta23 theta24
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta25 theta26 theta27 theta28 theta29 theta30
## 0.000000e+00 0.000000e+00 8.245116e+04 0.000000e+00 0.000000e+00 0.000000e+00
## theta31 theta32 theta33 theta34 theta35 theta36
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta37 theta38 theta39 theta40 theta41 theta42
## 0.000000e+00 0.000000e+00 0.000000e+00 2.561489e+06 0.000000e+00 0.000000e+00
## theta43 theta44 theta45 theta46 theta47 theta48
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta49 theta50 theta51 theta52 theta53 theta54
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.089238e+06 0.000000e+00
## theta55 theta56 theta57 theta58 theta59 theta60
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta61 theta62 theta63 theta64 theta65 theta66
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.506919e+01
## theta67 theta68 theta69 theta70 theta71 theta72
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta73 theta74 theta75 theta76 theta77 theta78
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta79 theta80 theta81 theta82 theta83 theta84
## 2.781151e+06 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta85 theta86 theta87 theta88 theta89 theta90
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta91 theta92 theta93 theta94 theta95 theta96
## 0.000000e+00 1.151942e+06 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta97 theta98 theta99 theta100 theta101 theta102
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta103 theta104 theta105 theta106 theta107 theta108
## 0.000000e+00 0.000000e+00 2.323096e+01 0.000000e+00 0.000000e+00 0.000000e+00
## theta109 theta110 theta111 theta112 theta113 theta114
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta115 theta116 theta117 theta118 theta119 theta120
## 0.000000e+00 0.000000e+00 0.000000e+00 2.492570e+06 0.000000e+00 0.000000e+00
## theta121 theta122 theta123 theta124 theta125 theta126
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta127 theta128 theta129 theta130 theta131 theta132
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.824590e+06 0.000000e+00
## theta133 theta134 theta135 theta136 theta137 theta138
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## theta139 theta140 theta141 theta142 theta143 theta144
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.052630e+01
## psi1 psi2 psi3 psi4 psi5 psi6
## 1.250000e+02 2.758574e-01 6.325913e-02 2.363937e-01 2.758574e-01 1.250000e+02
## psi7 psi8 psi9 psi10 psi11 psi12
## 3.013475e-01 4.933398e+01 6.325913e-02 3.013475e-01 1.250000e+02 2.091878e+01
## psi13 psi14 psi15 psi16
## 2.363937e-01 4.933398e+01 2.091878e+01 1.250000e+02library(psych)
# Select the variables for each group
group1_vars <- c("unspokenUnderstanding_day1", "teamAura_day1", "clickPictorial_day1")
group2_vars <- c("unspokenUnderstanding_day2", "teamAura_day2", "clickPictorial_day2")
group3_vars <- c("emotionalSupport_day1", "sharedGoal_day1", "fusionPictorialTeam_day1")
group4_vars <- c("emotionalSupport_day2", "sharedGoal_day2", "fusionPictorialTeam_day2")
cor_matrix1 <- cor(tournament_data_complete[, group1_vars])
cor_matrix2 <- cor(tournament_data_complete[, group2_vars])
cor_matrix3 <- cor(tournament_data_complete[, group3_vars])
cor_matrix4 <- cor(tournament_data_complete[, group4_vars])
# Calculate the correlation matrix and perform KMO test
kmo1 <- KMO(cor_matrix1)
kmo2 <- KMO(cor_matrix2)
kmo3 <- KMO(cor_matrix3)
kmo4 <- KMO(cor_matrix4)
# Print the results
print(kmo1)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor_matrix1)
## Overall MSA = 0.43
## MSA for each item =
## unspokenUnderstanding_day1 teamAura_day1
## 0.46 0.36
## clickPictorial_day1
## 0.44print(kmo2)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor_matrix2)
## Overall MSA = 0.68
## MSA for each item =
## unspokenUnderstanding_day2 teamAura_day2
## 0.66 0.63
## clickPictorial_day2
## 0.81print(kmo3)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor_matrix3)
## Overall MSA = 0.59
## MSA for each item =
## emotionalSupport_day1 sharedGoal_day1 fusionPictorialTeam_day1
## 0.56 0.56 0.82print(kmo4)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor_matrix4)
## Overall MSA = 0.61
## MSA for each item =
## emotionalSupport_day2 sharedGoal_day2 fusionPictorialTeam_day2
## 0.59 0.57 0.78#Bartlett's test for each group
#bartlett1 <- bartlett.test(cor_matrix1, ncol(cor_matrix1))
#bartlett2 <- bartlett.test(cor_matrix2)
#bartlett3 <- bartlett.test(cor_matrix3)
#bartlett4 <- bartlett.test(cor_matrix4)
# Print the results
#print(bartlett1)
#print(bartlett2)
#print(bartlett3)
#print(bartlett4)
###Error in bartlett.test.default(cor_matrix1, ncol(cor_matrix1)) :
###'x' and 'g' must have the same length# Extract the standardized factor loadings from the CFA model
#factor_loadings <- fa(fit_tournament, standardized = TRUE)$loadings
#std_lv <- lavInspect(fit_tournament, "std.lv")
# Calculate Cronbach's alpha
#alpha(factor_loadings)
# Calculate McDonald's omega
#omega(factor_loadings)
### Error in UseMethod("isSymmetric") :
### no applicable method for 'isSymmetric' applied to an object of class "S4"#predicted scores
predicted_scores <- predict(fit_tournament, tournament_data_complete)## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negativetournament_data_complete$predicted_visceral_interdependence_day1 <- predicted_scores[,1]
tournament_data_complete$predicted_visceral_interdependence_day2 <- predicted_scores[,2]
tournament_data_complete$predicted_social_bonding_day1 <- predicted_scores[,3]
tournament_data_complete$predicted_social_bonding_day2 <- predicted_scores[,4]
head(tournament_data_complete)## athleteID team sex age yearsTeam position trainingAge startingReserve X
## 1 1 AHM male 21 2 Back 5 Starting Team NA
## 2 2 AHM male 19 3 Forward 4 Reserve NA
## 3 3 AHM male 18 0 Forward 3 Reserve NA
## 4 4 AHM male 20 1 Back 3 Starting Team NA
## 5 5 AHM male 18 2 Back 2 Reserve NA
## 6 6 AHM male 19 2 Back 5 Starting Team NA
## tipiExtraverted tipiAgreeableness tipiConscientiousness
## 1 5.0 5.5 5.5
## 2 2.5 5.5 6.5
## 3 6.5 4.0 7.0
## 4 5.5 6.0 5.5
## 5 4.0 4.0 3.5
## 6 5.0 4.5 6.5
## tipiEmotionalStability tipiOpenness injury_pre indPerformanceExpectations_pre
## 1 4.0 5.5 100 81
## 2 5.0 6.0 85 90
## 3 7.0 3.5 92 72
## 4 5.5 5.5 41 80
## 5 4.0 4.5 84 71
## 6 2.5 5.0 80 90
## passingTech_pre supportAttack_pre indDefense_pre effectContact_pre
## 1 100 100 86 78
## 2 85 85 86 90
## 3 51 79 59 79
## 4 80 82 89 79
## 5 67 69 64 65
## 6 90 80 45 50
## decisionAttack_pre abilityTeammatesBaseline abilityChineseProsBaseline
## 1 80 29 0
## 2 80 1 -24
## 3 71 45 37
## 4 81 30 23
## 5 71 -15 -16
## 6 60 25 50
## abilityInternationalProsBaseline teamPerformanceExpectations_pre
## 1 0 0
## 2 -40 85
## 3 35 93
## 4 19 80
## 5 -12 68
## 6 50 95
## teamAbilityChineseProvinces_pre teamDefence_pre teamAttack_pre
## 1 -20 90 71
## 2 20 85 91
## 3 17 83 84
## 4 21 41 44
## 5 8 65 37
## 6 25 70 90
## teamSupportPlay_pre teamCommunication_pre reliabilityForOthers_pre
## 1 82 100 100
## 2 90 85 75
## 3 82 86 85
## 4 42 50 60
## 5 38 36 62
## 6 85 75 80
## reliabilityOfOthers_pre unspokenUnderstanding_pre generalAtmosphere_pre
## 1 100 100 100
## 2 69 88 90
## 3 95 90 93
## 4 64 34 34
## 5 82 68 68
## 6 91 90 85
## abilityExtended_pre clickPictorial_pre emotionalSupport_pre sharedGoal_pre
## 1 32 3 78 81
## 2 70 5 70 80
## 3 93 3 92 92
## 4 44 3 40 41
## 5 74 4 49 60
## 6 100 4 10 80
## fusionOne_pre fusionTwo_pre fusionThree_pre fusionFour_pre fusionFive_pre
## 1 3 4 5 1 5
## 2 5 4 4 5 5
## 3 5 5 5 5 3
## 4 2 3 3 3 3
## 5 3 4 4 1 4
## 6 3 3 5 1 5
## fusionSix_pre fusionSeven_pre fusionVerbal_pre groupIDOne_pre groupIDTwo_pre
## 1 5 3 3.714286 5 3
## 2 5 5 4.714286 5 2
## 3 3 3 4.142857 1 2
## 4 3 3 2.857143 5 5
## 5 4 2 3.142857 4 4
## 6 5 5 3.857143 5 5
## groupIDThree_pre groupIDFour_pre groupIDFive_pre groupIDSix_pre groupId_pre
## 1 5 5 5 5 4.666667
## 2 5 5 5 5 4.500000
## 3 5 5 1 3 2.833333
## 4 3 5 5 5 4.666667
## 5 3 3 4 4 3.666667
## 6 3 3 3 3 3.666667
## fusionPictorialTeam_pre fusionPictorialFamily_pre fusionPictorialCountry_pre
## 1 5 3 4
## 2 4 5 5
## 3 5 5 5
## 4 4 5 5
## 5 5 5 5
## 6 5 4 4
## rankCountry_pre rankFamily_pre rankTeam_pre aroused_day1 relaxed_day1
## 1 2 3 3 8 7
## 2 3 1 2 8 7
## 3 3 2 1 8 7
## 4 1 1 2 10
## 5 3 2 1 5 5
## 6 1 1 1 NA 10
## excited_day1 prpe_day1 mental_day1 fatigue_day1
## 1 10 12.847826 7.336957 70.00000
## 2 9 13.000000 6.000000 45.00000
## 3 5 17.000000 6.000000 80.00000
## 4 NA 12.847826 1.554348 70.00000
## 5 5 8.434783 5.739130 65.21739
## 6 NA 14.369565 5.739130 56.52174
## indPerformanceExpectations_day1 teamPerformanceExpectations_day1
## 1 80.00000 40.00000
## 2 70.00000 60.00000
## 3 15.00000 70.00000
## 4 70.00000 55.00000
## 5 48.36957 51.08696
## 6 49.45652 60.32609
## unspokenUnderstanding_day1 teamAura_day1 clickPictorial_day1
## 1 60.00000 50.00000 3
## 2 60.00000 70.00000 4
## 3 80.00000 50.00000 3
## 4 60.00000 75.00000 3
## 5 46.19565 44.56522 3
## 6 40.21739 50.54348 3
## emotionalSupport_day1 sharedGoal_day1 fusionPictorialTeam_day1 injury_day1
## 1 75.00000 100.00000 4 85.00000
## 2 75.00000 85.00000 4 75.00000
## 3 90.00000 90.00000 4 100.00000
## 4 70.00000 80.00000 4 NA
## 5 48.36957 61.95652 5 69.56522
## 6 74.45652 55.43478 4 53.80435
## aroused_day2 relaxed_day2 excited_day2 prpe_day2 mental_day2 fatigue_day2
## 1 8 2 7 13.15217 5.891304 68.47826
## 2 8 8 9 14.00000 6.000000 70.00000
## 3 8 6 6 17.41304 4.445652 83.15217
## 4 1 1 NA 16.50000 5.130435 80.00000
## 5 4 3 3 16.80435 7.336957 75.00000
## 6 NA 1 NA 16.04348 6.043478 84.78261
## indPerformance_day2 teamPerformance_day2 unspokenUnderstanding_day2
## 1 64.13043 23.91304 60.00000
## 2 25.00000 45.00000 60.00000
## 3 38.58696 66.30435 64.67391
## 4 20.00000 10.00000 70.00000
## 5 34.23913 41.30435 41.84783
## 6 78.80435 42.93478 40.76087
## teamAura_day2 clickPictorial_day2 emotionalSupport_day2 sharedGoal_day2
## 1 0.00000 1 100.00000 50.00000
## 2 70.00000 3 60.00000 75.00000
## 3 65.76087 4 77.71739 79.89130
## 4 75.00000 3 50.00000 60.00000
## 5 37.50000 3 46.73913 42.93478
## 6 52.17391 3 75.54348 82.06522
## fusionPictorialTeam_day2 injury_day2 injury_post aroused_post relaxed_post
## 1 3 50.00000 60 6 5
## 2 4 70.00000 69 8 8
## 3 4 75.00000 85 9 10
## 4 4 NA 22 10 10
## 5 5 38.58696 68 6 6
## 6 4 34.78261 80 10 10
## excited_post prpe_post mental_post fatigue_post indPerformance_post
## 1 5 16 8 84 66
## 2 9 15 6 43 70
## 3 7 10 7 85 74
## 4 10 16 6 80 50
## 5 5 16 7 45 36
## 6 10 16 8 70 80
## passingTech_post supportAttack_post indDefense_post effectContact_post
## 1 86 93 98 77
## 2 70 75 80 80
## 3 69 80 52 83
## 4 35 74 75 36
## 5 37 63 40 37
## 6 95 94 80 85
## decisionAttack_post challengesTournamentPost teamPerformanceExpectations_post
## 1 100 84 87
## 2 70 68 68
## 3 71 80 80
## 4 57 77 24
## 5 37 83 66
## 6 90 100 100
## teamDefence_post teamAttack_post teamSupportPlay_post teamCommunication_post
## 1 78 88 62 65
## 2 75 70 70 74
## 3 81 74 79 80
## 4 62 59 56 60
## 5 38 70 67 36
## 6 88 95 95 95
## unspokenUnderstanding_post teamAura_post reliabilityForOthers_post
## 1 70 81 100
## 2 75 87 70
## 3 95 84 87
## 4 37 77 74
## 5 66 65 64
## 6 90 100 95
## abilityExtended_post reliabilityOfOthers_post clickPictorial_post
## 1 100 93 3
## 2 45 60 4
## 3 88 96 4
## 4 70 81 3
## 5 68 65 4
## 6 95 50 5
## emotionalSupport_post sharedGoal_post fusionPictorialTeam_post fusionOne_post
## 1 95 87 5 5
## 2 60 68 4 3
## 3 92 93 4 5
## 4 63 74 4 3
## 5 67 69 5 3
## 6 100 100 5 5
## fusionTwo_post fusionThree_post fusionFour_post fusionFive_post
## 1 4 5 3 5
## 2 3 4 4 4
## 3 5 5 2 5
## 4 3 3 3 3
## 5 3 4 2 4
## 6 5 5 1 4
## fusionSix_post fusionSeven_post fusionVerbal_post groupIDOne_post
## 1 5 4 4.428571 5
## 2 4 4 3.714286 5
## 3 5 2 4.142857 4
## 4 3 3 3.000000 3
## 5 4 3 3.285714 4
## 6 5 4 4.142857 5
## groupIDTwo_post groupIDThree_post groupIDFour_post groupIDFive_post
## 1 3 5 5 5
## 2 2 5 5 5
## 3 4 5 5 5
## 4 3 3 3 3
## 5 4 4 4 4
## 6 5 5 3 5
## groupIDSix_post groupId_post fusionPictorialFamily_post
## 1 5 4.666667 5
## 2 3 4.166667 5
## 3 4 4.500000 4
## 4 3 3.000000 5
## 5 4 4.000000 5
## 6 3 4.333333 5
## fusionPictorialCountry_post rankCountry_post rankFamily_post rankTeam_post
## 1 4 2 3 3
## 2 3 3 1 2
## 3 5 3 2 1
## 4 5 1 1 2
## 5 5 3 2 1
## 6 3 3 1 1
## gameNumber_game1 opponent_game1 result_game1 starting_game1 subInjury_game1
## 1 7 JT 0 1 0
## 2 7 JT 0 0 0
## 3 7 JT 0 0 0
## 4 7 JT 0 1 0
## 5 7 JT 0 0 0
## 6 7 JT 0 0 0
## pointsFor_game1 pointsAgainst_game1 minutes_game1 indPoints_game1
## 1 7 10 14 0
## 2 7 10 0 0
## 3 7 10 0 0
## 4 7 10 8 0
## 5 7 10 6 0
## 6 7 10 0 0
## cumulativeWL_game1 gameNumber_game2 opponent_game2 result_game2
## 1 -1 13 TJM 0
## 2 -1 13 TJM 0
## 3 -1 13 TJM 0
## 4 -1 13 TJM 0
## 5 -1 13 TJM 0
## 6 -1 13 TJM 0
## starting_game2 subYellow_game2 subInjury_game2 pointsFor_game2
## 1 1 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 1 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## pointsAgainst_game2 minutes_game2 indPoints_game2 cumulativeMinutes_game2
## 1 40 14 0 28
## 2 40 6 0 6
## 3 40 0 0 0
## 4 40 8 0 16
## 5 40 0 0 6
## 6 40 6 0 6
## cumulativePoints_game2 cumulativeWL_game2 gameNumber_game3 opponent_game3
## 1 0 -2 21 FJM
## 2 0 -2 21 FJM
## 3 0 -2 21 FJM
## 4 0 -2 21 FJM
## 5 0 -2 21 FJM
## 6 0 -2 21 FJM
## result_game3 starting_game3 subYellow_game3 subInjury_game3 pointsFor_game3
## 1 1 1 0 0 27
## 2 1 0 0 0 27
## 3 1 0 0 0 27
## 4 1 0 0 0 27
## 5 1 0 0 0 27
## 6 1 1 0 0 27
## pointsAgainst_game3 minutes_game3 indPoints_game3 cumulativeMinutes_game3
## 1 0 14 0 42
## 2 0 0 0 6
## 3 0 0 0 0
## 4 0 6 0 22
## 5 0 1 0 7
## 6 0 8 0 14
## cumulativePoints_game3 cumulativeWL_game3 gameNumber_game4 opponent_game4
## 1 0 -1 24 BJM
## 2 0 -1 24 BJM
## 3 0 -1 24 BJM
## 4 0 -1 24 BJM
## 5 0 -1 24 BJM
## 6 0 -1 24 BJM
## result_game4 starting_game4 subYellow_game4 subInjury_game4 pointsFor_game4
## 1 0 0 0 0 0
## 2 0 1 0 0 0
## 3 0 1 0 0 0
## 4 0 0 0 0 0
## 5 0 1 0 0 0
## 6 0 1 0 0 0
## pointsAgainst_game4 minutes_game4 indPoints_game4 cumulativeMinutes_game4
## 1 35 0 0 42
## 2 35 8 0 14
## 3 35 6 0 6
## 4 35 6 0 28
## 5 35 14 0 21
## 6 35 8 0 22
## cumulativePoints_game4 cumulativeWL_game4 gameNumber_game5 opponent_game5
## 1 0 -2 30 FJM
## 2 0 -2 30 FJM
## 3 0 -2 30 FJM
## 4 0 -2 30 FJM
## 5 0 -2 30 FJM
## 6 0 -2 30 FJM
## result_game5 starting_game5 subYellow_game5 subInjury_game5 pointsFor_game5
## 1 1 1 0 0 22
## 2 1 1 0 0 22
## 3 1 1 0 0 22
## 4 1 0 0 0 22
## 5 1 1 0 1 22
## 6 1 1 0 0 22
## pointsAgainst_game5 minutes_game5 indPoints_game5 cumulativeMinutes_game5
## 1 0 14 5 56
## 2 0 8 0 22
## 3 0 6 0 12
## 4 0 6 0 34
## 5 0 6 0 27
## 6 0 8 0 30
## cumulativePoints_game5 cumulativeWL_game5 gameNumber_game6 opponent_game6
## 1 5 -1 36 HBM
## 2 0 -1 36 HBM
## 3 0 -1 36 HBM
## 4 0 -1 36 HBM
## 5 0 -1 36 HBM
## 6 0 -1 36 HBM
## result_game6 starting_game6 subYellow_game6 subInjury_game6 pointsFor_game6
## 1 1 1 0 0 19
## 2 1 0 0 0 19
## 3 1 0 0 0 19
## 4 1 0 0 0 19
## 5 1 0 0 0 19
## 6 1 1 0 0 19
## pointsAgainst_game6 minutes_game6 indPoints_game6 cumulativeMinutes_game6
## 1 12 14 0 70
## 2 12 0 0 22
## 3 12 0 0 12
## 4 12 0 0 34
## 5 12 0 0 27
## 6 12 14 0 44
## cumulativePoints_game6 cumulativeWL_game6 gamesTotal minutesTotal pointsTotal
## 1 5 0 6 70 5
## 2 0 0 6 22 0
## 3 0 0 6 12 0
## 4 0 0 6 34 0
## 5 0 0 6 27 0
## 6 0 0 6 44 0
## WL_total startingAvg cumulativeMinutes_Day1 cumulativeMinutes_day2
## 1 0 0.2500000 28 56
## 2 0 0.5000000 6 22
## 3 0 1.0000000 0 12
## 4 0 0.6000000 16 34
## 5 0 0.8333333 6 27
## 6 0 1.0000000 6 30
## cumulativePoints_day1 cumulativePoints_day2 WL_day1 WL_day2 finalRank
## 1 0 5 0 0 4
## 2 0 0 0 0 4
## 3 0 0 0 0 4
## 4 0 0 0 0 4
## 5 0 0 0 0 4
## 6 0 0 0 0 4
## teamPerformanceComponents_post teamPerformanceComponents_pre
## 1 0.6864194 0.7809592
## 2 0.3647715 1.0501978
## 3 0.6458610 0.8318607
## 4 -0.2637021 -0.8880669
## 5 -0.4150587 -1.0531050
## 6 1.4497511 0.7992097
## indPerformanceComponents_post teamClick_post socialBonding_post
## 1 1.5064683 0.22675461 0.5315840
## 2 0.6493488 -0.01358004 -1.0364085
## 3 0.5956892 0.90349839 0.4702404
## 4 -0.1070546 -0.92017449 -0.8973874
## 5 -0.7292898 -0.36857145 -0.7848899
## 6 1.4020583 1.00405539 0.9632656
## socialBonding_pre fatigueFactor_post indPerformanceExpectations_preNorm
## 1 0.1816229 0.6045565 0.56669437
## 2 0.1318736 -0.5009079 0.98878396
## 3 0.6810516 -0.4855037 0.14460477
## 4 -1.4207263 0.3768963 0.51979552
## 5 -0.7680741 -0.2148382 0.09770592
## 6 -0.7397663 0.3375862 0.98878396
## indPerformanceExpectations_day1Norm indPerformanceExpectations_day2Norm
## 1 1.3827708 0.7058322
## 2 0.9853669 -0.8692098
## 3 -1.2003549 -0.3223202
## 4 0.9853669 -1.0704652
## 5 0.1257648 -0.4973249
## 6 0.1689609 1.2964729
## indPerformanceExpectations_postNorm teamPerformanceExpectations_preNorm
## 1 0.4108446 -3.2520881
## 2 0.5812476 0.6353253
## 3 0.7516506 1.0011995
## 4 -0.2707675 0.4066539
## 5 -0.8671781 -0.1421574
## 6 1.0072551 1.0926681
## teamPerformanceExpectations_day1Norm teamPerformanceExpectations_day2Norm
## 1 -0.80824428 -1.2670837
## 2 -0.03758367 -0.5270855
## 3 0.34774664 0.2205416
## 4 -0.23024882 -1.7553300
## 5 -0.38103025 -0.6567759
## 6 -0.02501855 -0.5995595
## teamPerformanceExpectations_postNorm objectiveCompetenceFactor
## 1 0.9587160 0.01490811
## 2 0.1539832 -0.17421958
## 3 0.6622355 -1.32101507
## 4 -1.7096085 -0.71270435
## 5 0.0692745 -0.78637241
## 6 1.5093227 -0.85023179
## subjectiveCompetenceFactor fusionPictorialFamilyChange
## 1 -0.6909715 2
## 2 -2.0061019 0
## 3 1.2749742 -1
## 4 0.4328477 0
## 5 -1.4072495 0
## 6 1.7249003 1
## teamPerformanceComponentsPrePost_pre teamPerformanceComponentsPrePost_post
## 1 0.8507844 0.4972990
## 2 1.1194659 0.3234245
## 3 0.8957069 0.6039347
## 4 -0.9696120 -0.2751380
## 5 -1.0944980 -0.3745075
## 6 0.8291875 1.3712542
## indPerformanceComponentsPrePost_pre indPerformanceComponents_post.1
## 1 1.2778512 1.3158036
## 2 0.9983334 0.5124933
## 3 0.3284804 0.4659734
## 4 0.8580448 -0.2337555
## 5 0.2001746 -0.7960595
## 6 0.2314547 1.2554505
## teamClickPrePost_pre teamClickPrePost_post socialBondingPrePost_pre
## 1 0.9172223 0.33426870 0.05188940
## 2 0.6657200 0.04032163 -0.06575643
## 3 0.8898100 0.93907889 0.61717935
## 4 -1.7399060 -0.82518066 -1.75035467
## 5 -0.1426176 -0.32711326 -1.03868256
## 6 0.8637609 0.99041284 -1.20466647
## socialBondingPrePost_post fatiguePrePost_pre fatiguePrePost_post
## 1 0.5917504 -0.08051824 0.83755038
## 2 -0.6510972 -0.44429115 -0.02606701
## 3 0.5697324 0.89868447 -0.53656863
## 4 -0.5449662 -0.39571188 0.67658452
## 5 -0.4813802 -1.20986392 0.27651588
## 6 0.9497229 -0.00468708 0.65572005
## teamPerformanceComponentsChange indPerformanceComponentsChange
## 1 -0.3534853 0.03795234
## 2 -0.7960414 -0.48584006
## 3 -0.2917722 0.13749296
## 4 0.6944741 -1.09180025
## 5 0.7199905 -0.99623406
## 6 0.5420668 1.02399575
## teamClickChange socialBondingChange fatigueChange
## 1 -0.58295361 0.53986102 0.9180686
## 2 -0.62539835 -0.58534073 0.4182241
## 3 0.04926885 -0.04744695 -1.4352531
## 4 0.91472536 1.20538850 1.0722964
## 5 -0.18449569 0.55730239 1.4863798
## 6 0.12665193 2.15438942 0.6604071
## unspokenUnderstanding_MidTournament teamAura_MidTournament
## 1 60.00000 25.00000
## 2 60.00000 70.00000
## 3 72.33696 57.88043
## 4 65.00000 75.00000
## 5 44.02174 41.03261
## 6 40.48913 51.35870
## clickPictorial_MidTournament predicted_visceral_interdependence_day1
## 1 2.0 -6.695276
## 2 3.5 2.835342
## 3 3.5 -16.659172
## 4 3.0 -9.819551
## 5 3.0 -3.098275
## 6 3.0 -3.953598
## predicted_visceral_interdependence_day2 predicted_social_bonding_day1
## 1 -36.154383 7.7480265
## 2 -4.413756 -0.7697906
## 3 -2.414695 7.3022878
## 4 -1.516309 -6.5543438
## 5 -22.978742 -20.6851345
## 6 -15.061615 -14.2735321
## predicted_social_bonding_day2
## 1 -13.774182
## 2 -8.705139
## 3 -1.774874
## 4 -16.932507
## 5 -26.021705
## 6 -5.076659lme() is in the nlme package in R. It is used to fit linear mixed-effects models with both fixed and random effects. The syntax of lme() is similar to lmer() from the lme4 package, but lme() uses maximum likelihood estimation by default instead of restricted maximum likelihood estimation (REML) used by lmer().
Outcome (Y1) - Visceral Interdependence: Visceral Interdependence Factor - Day 1
Predictor (X) - Team performance ← Fixed effect (x) - Perceptions of team performance relative to prior expectations teamPerformanceExpectations_day1Norm
library(nlme)##
## Attaching package: 'nlme'## The following object is masked from 'package:lme4':
##
## lmList## The following object is masked from 'package:dplyr':
##
## collapsey1d1 <- lme(predicted_visceral_interdependence_day1 ~
teamPerformanceExpectations_day1Norm +
indPerformanceExpectations_day1Norm +
#indPerformanceExpectations_day2Norm +
#indPerformance_average +
objectiveCompetenceFactor +
subjectiveCompetenceFactor +
finalRank +
minutesTotal +
pointsTotal +
fatigue_day1 +
#fatigue_day2 +
#fatigue_average +
tipiExtraverted,
data = tournament_data_complete,
random = ~ 1|team,
na.action = na.exclude)
summary(y1d1)## Linear mixed-effects model fit by REML
## Data: tournament_data_complete
## AIC BIC logLik
## 698.2709 726.7042 -337.1354
##
## Random effects:
## Formula: ~1 | team
## (Intercept) Residual
## StdDev: 1.931353 11.48562
##
## Fixed effects: predicted_visceral_interdependence_day1 ~ teamPerformanceExpectations_day1Norm + indPerformanceExpectations_day1Norm + objectiveCompetenceFactor + subjectiveCompetenceFactor + finalRank + minutesTotal + pointsTotal + fatigue_day1 + tipiExtraverted
## Value Std.Error DF t-value p-value
## (Intercept) -2.9528938 7.826925 68 -0.3772738 0.7071
## teamPerformanceExpectations_day1Norm 1.9860845 1.500475 68 1.3236372 0.1901
## indPerformanceExpectations_day1Norm 0.4941000 1.440712 68 0.3429554 0.7327
## objectiveCompetenceFactor 1.0132782 1.709670 68 0.5926748 0.5554
## subjectiveCompetenceFactor 1.8252545 1.252243 68 1.4575877 0.1496
## finalRank 0.5660130 0.755520 11 0.7491701 0.4695
## minutesTotal -0.0141503 0.082815 68 -0.1708666 0.8648
## pointsTotal 0.0135198 0.108499 68 0.1246069 0.9012
## fatigue_day1 -0.0487659 0.058502 68 -0.8335738 0.4074
## tipiExtraverted 0.4549187 0.925675 68 0.4914455 0.6247
## Correlation:
## (Intr) tPE_1N iPE_1N objcCF sbjcCF fnlRnk
## teamPerformanceExpectations_day1Norm -0.217
## indPerformanceExpectations_day1Norm 0.051 -0.366
## objectiveCompetenceFactor 0.217 -0.236 -0.083
## subjectiveCompetenceFactor 0.117 0.053 -0.057 0.193
## finalRank -0.613 0.125 -0.009 -0.330 -0.155
## minutesTotal -0.326 0.358 -0.122 -0.239 -0.002 0.106
## pointsTotal 0.161 -0.144 0.025 -0.062 -0.041 -0.298
## fatigue_day1 -0.414 0.048 -0.169 0.077 -0.033 0.181
## tipiExtraverted -0.622 -0.020 0.100 0.062 -0.033 0.093
## mntsTt pntsTt ftg_d1
## teamPerformanceExpectations_day1Norm
## indPerformanceExpectations_day1Norm
## objectiveCompetenceFactor
## subjectiveCompetenceFactor
## finalRank
## minutesTotal
## pointsTotal -0.382
## fatigue_day1 -0.278 0.114
## tipiExtraverted -0.091 -0.031 0.073
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.84151566 -0.58923213 -0.03316593 0.53098778 3.29822039
##
## Number of Observations: 89
## Number of Groups: 13library(r2glmm)
# Calculate R-squared
r2beta(y1d1)## Effect Rsq upper.CL lower.CL
## 1 Model 0.121 0.354 0.083
## 5 subjectiveCompetenceFactor 0.028 0.144 0.000
## 2 teamPerformanceExpectations_day1Norm 0.025 0.137 0.000
## 9 fatigue_day1 0.009 0.101 0.000
## 6 finalRank 0.009 0.099 0.000
## 4 objectiveCompetenceFactor 0.005 0.087 0.000
## 10 tipiExtraverted 0.003 0.081 0.000
## 3 indPerformanceExpectations_day1Norm 0.002 0.074 0.000
## 7 minutesTotal 0.000 0.069 0.000
## 8 pointsTotal 0.000 0.068 0.000