Model 1 - Picewise Effect of testosterone across time
P.Model1 <- lme(fixed=Test.l~Sex_1+P.time1*InterventionC+P.time2*InterventionC+InterventionC*RinseC,
random=list(ID=pdDiag(~P.time1+P.time2),Session=pdDiag(~P.time1+P.time2)),
correlation = corAR1(),
data=T_DF.NA,
method="ML")
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA
AIC BIC logLik
111.998 170.3713 -38.999
Random effects:
Formula: ~P.time1 + P.time2 | ID
Structure: Diagonal
(Intercept) P.time1 P.time2
StdDev: 0.2140401 0.07067603 0.08691495
Formula: ~P.time1 + P.time2 | Session %in% ID
Structure: Diagonal
(Intercept) P.time1 P.time2 Residual
StdDev: 1.357778e-05 0.08649717 0.09082493 0.2355644
Correlation Structure: AR(1)
Formula: ~1 | ID/Session
Parameter estimate(s):
Phi
0.1817981
Fixed effects: Test.l ~ Sex_1 + P.time1 * InterventionC + P.time2 * InterventionC + InterventionC * RinseC
Value Std.Error DF t-value p-value
(Intercept) 4.601876 0.11252488 179 40.89652 0.0000
Sex_1Male 0.348336 0.13800896 10 2.52401 0.0302
P.time1 0.082122 0.06990500 179 1.17477 0.2416
InterventionCTestosterone -0.133148 0.08676743 31 -1.53454 0.1350
P.time2 -0.022732 0.04017463 179 -0.56582 0.5722
RinseCRinse -0.136550 0.06958717 31 -1.96228 0.0588
P.time1:InterventionCTestosterone 1.996552 0.09171052 179 21.77015 0.0000
InterventionCTestosterone:P.time2 -0.361670 0.04283781 179 -8.44277 0.0000
InterventionCTestosterone:RinseCRinse 0.060602 0.09725234 31 0.62314 0.5377
Correlation:
(Intr) Sx_1Ml P.tim1 IntrCT P.tim2 RnsCRn P.1:IC ICT:P.
Sex_1Male -0.617
P.time1 -0.289 -0.008
InterventionCTestosterone -0.419 0.031 0.381
P.time2 -0.034 -0.001 -0.244 0.045
RinseCRinse -0.316 0.036 -0.022 0.402 0.000
P.time1:InterventionCTestosterone 0.222 0.003 -0.695 -0.547 0.186 0.017
InterventionCTestosterone:P.time2 0.035 -0.004 0.229 -0.080 -0.556 0.000 -0.334
InterventionCTestosterone:RinseCRinse 0.242 -0.046 0.016 -0.558 -0.001 -0.735 -0.013 0.000
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.32992834 -0.65035630 -0.06395292 0.53513715 2.67551469
Number of Observations: 229
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 179 5528.158 <.0001
Sex_1 1 10 6.017 0.0341
P.time1 1 179 466.654 <.0001
InterventionC 1 31 368.228 <.0001
P.time2 1 179 40.071 <.0001
RinseC 1 31 6.087 0.0193
P.time1:InterventionC 1 179 404.625 <.0001
InterventionC:P.time2 1 179 71.275 <.0001
InterventionC:RinseC 1 31 0.388 0.5377
Model 2 - Picewise Simple Slopes only looking at testosterone and sex
P.Model2 <- lme(fixed=Test.l~Sex_1+P.time1*Sex_1+P.time2*Sex_1,
random=list(ID=pdDiag(~P.time1+P.time2),Session=pdDiag(~P.time1+P.time2)),
correlation = corAR1(),
data=subset(T_DF.NA,InterventionC=="Testosterone"),
method="ML")
Linear mixed-effects model fit by maximum likelihood
Data: subset(T_DF.NA, InterventionC == "Testosterone")
AIC BIC logLik
57.31685 96.34174 -14.65843
Random effects:
Formula: ~P.time1 + P.time2 | ID
Structure: Diagonal
(Intercept) P.time1 P.time2
StdDev: 0.1231745 1.427718e-06 0.1656147
Formula: ~P.time1 + P.time2 | Session %in% ID
Structure: Diagonal
(Intercept) P.time1 P.time2 Residual
StdDev: 5.323992e-06 6.745817e-06 0.09616876 0.2071279
Correlation Structure: AR(1)
Formula: ~1 | ID/Session
Parameter estimate(s):
Phi
-0.02620728
Fixed effects: Test.l ~ Sex_1 + P.time1 * Sex_1 + P.time2 * Sex_1
Value Std.Error DF t-value p-value
(Intercept) 4.415080 0.07846658 92 56.26702 0.0000
Sex_1Male 0.376273 0.11391719 10 3.30304 0.0080
P.time1 2.283573 0.07729339 92 29.54422 0.0000
P.time2 -0.513190 0.07917641 92 -6.48160 0.0000
Sex_1Male:P.time1 -0.406867 0.11416991 92 -3.56370 0.0006
Sex_1Male:P.time2 0.259593 0.11330705 92 2.29106 0.0242
Correlation:
(Intr) Sx_1Ml P.tim1 P.tim2 S_1M:P.1
Sex_1Male -0.689
P.time1 -0.583 0.402
P.time2 0.003 -0.002 -0.172
Sex_1Male:P.time1 0.395 -0.593 -0.677 0.117
Sex_1Male:P.time2 -0.002 0.003 0.120 -0.699 -0.178
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.53349101 -0.50623943 0.06975269 0.42895700 2.06232323
Number of Observations: 120
Number of Groups:
ID Session %in% ID
12 24
numDF denDF F-value p-value
(Intercept) 1 92 16459.724 <.0001
Sex_1 1 10 2.916 0.1185
P.time1 1 92 1312.070 <.0001
P.time2 1 92 47.032 <.0001
Sex_1:P.time1 1 92 10.287 0.0018
Sex_1:P.time2 1 92 5.249 0.0242
Total Aggression
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
605.5606 633.18 -291.7803
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 7.621099
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 1.338002 4.768877
Fixed effects: Total.Aggression ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 23.541610 3.617005 41 6.508593 0.0000
Pre.PostPost 1.818182 2.129200 41 0.853927 0.3981
SexMale 7.810447 5.142191 10 1.518895 0.1598
InterventionTestosterone 1.461643 2.144077 32 0.681712 0.5003
Pre.PostPost:SexMale -2.579198 3.055619 41 -0.844084 0.4035
Pre.PostPost:InterventionTestosterone 3.874126 2.893013 41 1.339132 0.1879
SexMale:InterventionTestosterone 0.140978 3.123660 32 0.045132 0.9643
Pre.PostPost:SexMale:InterventionTestosterone -5.385837 4.207889 41 -1.279938 0.2078
Correlation:
(Intr) Pr.PsP SexMal IntrvT Pr.PP:SM P.PP:I SxM:IT
Pre.PostPost -0.294
SexMale -0.703 0.207
InterventionTestosterone -0.321 0.497 0.226
Pre.PostPost:SexMale 0.205 -0.697 -0.305 -0.346
Pre.PostPost:InterventionTestosterone 0.217 -0.736 -0.152 -0.675 0.513
SexMale:InterventionTestosterone 0.220 -0.341 -0.324 -0.686 0.503 0.463
Pre.PostPost:SexMale:InterventionTestosterone -0.149 0.506 0.222 0.464 -0.726 -0.688 -0.684
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.4851087 -0.5981793 0.0109560 0.5627690 2.1926572
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 147.52444 <.0001
Pre.Post 1 41 1.63232 0.2086
Sex 1 10 1.14769 0.3092
Intervention 1 32 3.72055 0.0627
Pre.Post:Sex 1 41 6.66959 0.0135
Pre.Post:Intervention 1 41 0.38093 0.5405
Sex:Intervention 1 32 1.29430 0.2637
Pre.Post:Sex:Intervention 1 41 1.63824 0.2078
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 23.54161 3.617005 11 15.58064 31.50258
Post 25.35979 3.617005 11 17.39882 33.32077
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 31.35206 3.655052 10 23.20810 39.49602
Post 30.59104 3.619360 10 22.52661 38.65548
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 25.00325 3.563655 11 17.15970 32.84680
Post 30.69556 3.563655 11 22.85201 38.53911
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 32.95468 3.619360 10 24.89024 41.01911
Post 30.68195 3.619360 10 22.61751 38.74639
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -1.818182 2.129200 41 -0.854 0.3981
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 0.761016 2.191647 41 0.347 0.7302
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -5.692308 1.958579 41 -2.906 0.0059
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 2.272727 2.129200 41 1.067 0.2920
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Physical Aggression
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
397.0568 424.6762 -187.5284
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 2.490678
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 0.56272 1.475714
Fixed effects: Physical.Aggression ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 4.251836 1.1767679 41 3.613147 0.0008
Pre.PostPost 0.272727 0.6588743 41 0.413929 0.6811
SexMale 4.760393 1.6725227 10 2.846235 0.0174
InterventionTestosterone 0.150457 0.6839883 32 0.219970 0.8273
Pre.PostPost:SexMale -0.545731 0.9461480 41 -0.576793 0.5672
Pre.PostPost:InterventionTestosterone 1.342657 0.8952339 41 1.499784 0.1413
SexMale:InterventionTestosterone -1.241643 0.9960137 32 -1.246612 0.2216
Pre.PostPost:SexMale:InterventionTestosterone -1.342381 1.3025512 41 -1.030578 0.3088
Correlation:
(Intr) Pr.PsP SexMal IntrvT Pr.PP:SM P.PP:I SxM:IT
Pre.PostPost -0.280
SexMale -0.704 0.197
InterventionTestosterone -0.315 0.482 0.222
Pre.PostPost:SexMale 0.195 -0.696 -0.291 -0.335
Pre.PostPost:InterventionTestosterone 0.206 -0.736 -0.145 -0.654 0.513
SexMale:InterventionTestosterone 0.216 -0.331 -0.317 -0.687 0.489 0.449
Pre.PostPost:SexMale:InterventionTestosterone -0.142 0.506 0.211 0.450 -0.726 -0.687 -0.664
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.31287462 -0.62168809 0.04883832 0.60603591 2.23693095
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 71.64327 <.0001
Pre.Post 1 41 1.62987 0.2089
Sex 1 10 5.02133 0.0489
Intervention 1 32 0.06129 0.8060
Pre.Post:Sex 1 41 3.70007 0.0614
Pre.Post:Intervention 1 41 1.11454 0.2973
Sex:Intervention 1 32 6.67490 0.0146
Pre.Post:Sex:Intervention 1 41 1.06209 0.3088
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 4.251836 1.176768 11 1.661787 6.841884
Post 4.524563 1.176768 11 1.934514 7.114612
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 9.012229 1.188507 10 6.364070 11.660388
Post 8.739225 1.177541 10 6.115501 11.362949
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 4.402293 1.160085 11 1.848964 6.955622
Post 6.017677 1.160085 11 3.464349 8.571006
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 7.921043 1.177541 10 5.297319 10.544767
Post 7.648316 1.177541 10 5.024592 10.272040
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -0.2727273 0.6588743 41 -0.414 0.6811
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 0.2730041 0.6790292 41 0.402 0.6897
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -1.6153846 0.6060762 41 -2.665 0.0110
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 0.2727273 0.6588743 41 0.414 0.6811
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Verbal Aggression
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
401.3351 428.9545 -189.6675
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 2.181625
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 0.7481456 1.494487
Fixed effects: Verbal.Aggression ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 7.346714 1.0729000 41 6.847529 0.0000
Pre.PostPost 0.545455 0.6672558 41 0.817459 0.4184
SexMale 1.624848 1.5271905 10 1.063946 0.3124
InterventionTestosterone -0.486241 0.7240290 32 -0.671577 0.5067
Pre.PostPost:SexMale -1.230903 0.9589514 41 -1.283592 0.2065
Pre.PostPost:InterventionTestosterone 1.762238 0.9066222 41 1.943740 0.0588
SexMale:InterventionTestosterone 1.346247 1.0536375 32 1.277714 0.2105
Pre.PostPost:SexMale:InterventionTestosterone -1.985881 1.3196786 41 -1.504821 0.1400
Correlation:
(Intr) Pr.PsP SexMal IntrvT Pr.PP:SM P.PP:I SxM:IT
Pre.PostPost -0.311
SexMale -0.703 0.218
InterventionTestosterone -0.366 0.461 0.257
Pre.PostPost:SexMale 0.216 -0.696 -0.324 -0.321
Pre.PostPost:InterventionTestosterone 0.229 -0.736 -0.161 -0.626 0.512
SexMale:InterventionTestosterone 0.251 -0.317 -0.367 -0.687 0.469 0.430
Pre.PostPost:SexMale:InterventionTestosterone -0.157 0.506 0.235 0.430 -0.727 -0.687 -0.637
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.43919401 -0.38927298 0.09737046 0.58049346 2.07676605
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 148.53731 <.0001
Pre.Post 1 41 1.56236 0.2184
Sex 1 10 0.70752 0.4199
Intervention 1 32 2.05492 0.1614
Pre.Post:Sex 1 41 12.24693 0.0011
Pre.Post:Intervention 1 41 1.58165 0.2156
Sex:Intervention 1 32 0.17175 0.6813
Pre.Post:Sex:Intervention 1 41 2.26449 0.1400
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 7.346714 1.072900 11 4.985277 9.708151
Post 7.892168 1.072900 11 5.530731 10.253605
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 8.971562 1.086829 10 6.549957 11.393167
Post 8.286114 1.073832 10 5.893468 10.678760
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 6.860473 1.052345 11 4.544278 9.176668
Post 9.168165 1.052345 11 6.851970 11.484361
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 9.831569 1.073832 10 7.438923 12.224214
Post 8.922478 1.073832 10 6.529832 11.315123
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -0.5454545 0.6672558 41 -0.817 0.4184
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 0.6854481 0.6887362 41 0.995 0.3255
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -2.3076923 0.6137861 41 -3.760 0.0005
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 0.9090909 0.6672558 41 1.362 0.1805
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Anger
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
418.0813 445.7008 -198.0407
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 2.319354
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 0.6210482 1.707768
Fixed effects: Anger ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 6.701245 0.7331684 41 9.140117 0.0000
Pre.Post1 -0.060202 0.1884060 41 -0.319535 0.7509
Sex1 -0.255319 0.7331684 10 -0.348241 0.7349
Intervention1 -0.414366 0.2131753 32 -1.943779 0.0608
Pre.Post1:Sex1 -0.175812 0.1884060 41 -0.933153 0.3562
Pre.Post1:Intervention1 -0.210552 0.1884060 41 -1.117544 0.2703
Sex1:Intervention1 0.015480 0.2131753 32 0.072615 0.9426
Pre.Post1:Sex1:Intervention1 0.128384 0.1884060 41 0.681423 0.4994
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.004
Sex1 -0.007 -0.004
Intervention1 0.015 0.014 0.008
Pre.Post1:Sex1 -0.004 -0.055 0.004 -0.014
Pre.Post1:Intervention1 0.004 0.055 -0.004 0.014 0.024
Sex1:Intervention1 0.008 -0.014 0.015 -0.036 0.014 -0.014
Pre.Post1:Sex1:Intervention1 -0.004 0.024 0.004 -0.014 0.055 -0.055 0.014
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.28620697 -0.45611486 -0.09021087 0.54395482 2.06876022
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 84.13264 <.0001
Pre.Post 1 41 0.09429 0.7603
Sex 1 10 0.11202 0.7448
Intervention 1 32 3.73411 0.0622
Pre.Post:Sex 1 41 0.89173 0.3505
Pre.Post:Intervention 1 41 1.16835 0.2861
Sex:Intervention 1 32 0.00399 0.9500
Pre.Post:Sex:Intervention 1 41 0.46434 0.4994
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 5.728858 1.146921 10 3.173359 8.284358
Post 6.365222 1.146921 10 3.809722 8.920722
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 6.303392 1.162792 10 3.712530 8.894254
Post 6.750046 1.147886 10 4.192396 9.307695
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 6.690966 1.124212 10 4.186066 9.195866
Post 6.998658 1.124212 10 4.493759 9.503558
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 7.840955 1.147886 10 5.283305 10.398604
Post 6.931864 1.147886 10 4.374215 9.489513
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -0.6363636 0.7624811 41 -0.835 0.4088
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -0.4466539 0.7855616 41 -0.569 0.5727
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -0.3076923 0.7013806 41 -0.439 0.6632
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 0.9090909 0.7624811 41 1.192 0.2400
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Hostility
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
383.4122 411.0317 -180.7061
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 2.149448
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 1.046028 1.195227
Fixed effects: Hostility ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 7.100458 0.6834277 41 10.389479 0.0000
Pre.Post1 -0.174749 0.1321078 41 -1.322779 0.1932
Sex1 -0.012620 0.6834277 10 -0.018465 0.9856
Intervention1 -0.430935 0.2106004 32 -2.046222 0.0490
Pre.Post1:Sex1 -0.281544 0.1321078 41 -2.131172 0.0391
Pre.Post1:Intervention1 0.145181 0.1321078 41 1.098956 0.2782
Sex1:Intervention1 -0.265003 0.2106004 32 -1.258320 0.2174
Pre.Post1:Sex1:Intervention1 0.129295 0.1321078 41 0.978707 0.3335
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.003
Sex1 -0.007 -0.003
Intervention1 0.015 0.012 0.010
Pre.Post1:Sex1 -0.003 -0.059 0.003 -0.012
Pre.Post1:Intervention1 0.003 0.059 -0.003 0.012 0.020
Sex1:Intervention1 0.010 -0.012 0.015 -0.031 0.012 -0.012
Pre.Post1:Sex1:Intervention1 -0.003 0.020 0.003 -0.012 0.059 -0.059 0.012
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.51342419 -0.43631181 0.01467211 0.43841887 2.12039068
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 108.84685 <.0001
Pre.Post 1 41 2.38619 0.1301
Sex 1 10 0.00091 0.9765
Intervention 1 32 4.46537 0.0425
Pre.Post:Sex 1 41 4.86353 0.0331
Pre.Post:Intervention 1 41 1.30833 0.2593
Sex:Intervention 1 32 1.61373 0.2131
Pre.Post:Sex:Intervention 1 41 0.95787 0.3335
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 6.210083 1.048283 10 3.874363 8.545803
Post 6.573719 1.048283 10 4.237999 8.909439
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 7.069826 1.059199 10 4.709783 9.429869
Post 6.824465 1.049285 10 4.486512 9.162417
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 7.053007 1.029223 10 4.759754 9.346260
Post 8.514545 1.029223 10 6.221293 10.807798
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 7.369919 1.049285 10 5.031967 9.707871
Post 7.188101 1.049285 10 4.850149 9.526053
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -0.3636364 0.5336429 41 -0.681 0.4994
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 0.2453615 0.5535745 41 0.443 0.6599
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -1.4615385 0.4908801 41 -2.977 0.0049
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 0.1818182 0.5336429 41 0.341 0.7351
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Total Risk
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
735.8626 763.4821 -356.9313
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 16.18329
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 0.8562673 9.984673
Fixed effects: Total.Risk ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 122.88101 5.019526 41 24.480602 0.0000
Pre.Post1 -1.19823 1.100821 41 -1.088486 0.2827
Sex1 -8.37550 5.019526 10 -1.668585 0.1262
Intervention1 -0.40468 1.117786 32 -0.362041 0.7197
Pre.Post1:Sex1 -3.32275 1.100821 41 -3.018430 0.0044
Pre.Post1:Intervention1 1.50457 1.100821 41 1.366770 0.1791
Sex1:Intervention1 -1.02921 1.117786 32 -0.920755 0.3641
Pre.Post1:Sex1:Intervention1 0.74368 1.100821 41 0.675571 0.5031
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.003
Sex1 -0.004 -0.003
Intervention1 0.012 0.014 0.006
Pre.Post1:Sex1 -0.003 -0.054 0.003 -0.014
Pre.Post1:Intervention1 0.003 0.054 -0.003 0.014 0.025
Sex1:Intervention1 0.006 -0.014 0.012 -0.037 0.014 -0.014
Pre.Post1:Sex1:Intervention1 -0.003 0.025 0.003 -0.014 0.054 -0.054 0.014
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.58275535 -0.54790763 0.06764513 0.42338187 3.94295963
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 599.2693 <.0001
Pre.Post 1 41 1.8888 0.1768
Sex 1 10 2.7057 0.1310
Intervention 1 32 0.1991 0.6585
Pre.Post:Sex 1 41 9.5322 0.0036
Pre.Post:Intervention 1 41 1.9399 0.1712
Sex:Intervention 1 32 0.8658 0.3591
Pre.Post:Sex:Intervention 1 41 0.4564 0.5031
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 110.7989 7.613207 10 93.83561 127.7622
Post 115.3443 7.613207 10 98.38106 132.3076
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 134.7664 7.687990 10 117.63654 151.8964
Post 128.9956 7.617606 10 112.02255 145.9687
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 109.1702 7.509607 10 92.43772 125.9026
Post 122.7086 7.509607 10 105.97618 139.4411
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 131.9956 7.617606 10 115.02255 148.9687
Post 129.2684 7.617606 10 112.29527 146.2414
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -4.545455 4.457939 41 -1.020 0.3139
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 5.770820 4.581853 41 1.259 0.2150
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -13.538462 4.100708 41 -3.301 0.0020
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 2.727273 4.457939 41 0.612 0.5441
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Financial Risk
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
503.375 530.9945 -240.6875
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 3.597261
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 1.220844 2.672917
Fixed effects: Finacial.Risk ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 17.847788 1.1439204 41 15.602299 0.0000
Pre.Post1 0.117701 0.2949821 41 0.399009 0.6920
Sex1 -2.883152 1.1439204 10 -2.520413 0.0304
Intervention1 -0.466859 0.3530446 32 -1.322380 0.1954
Pre.Post1:Sex1 -0.463854 0.2949821 41 -1.572483 0.1235
Pre.Post1:Intervention1 0.259309 0.2949821 41 0.879067 0.3845
Sex1:Intervention1 -0.457505 0.3530446 32 -1.295884 0.2043
Pre.Post1:Sex1:Intervention1 0.086845 0.2949821 41 0.294408 0.7699
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.004
Sex1 -0.007 -0.004
Intervention1 0.016 0.014 0.009
Pre.Post1:Sex1 -0.004 -0.056 0.004 -0.014
Pre.Post1:Intervention1 0.004 0.056 -0.004 0.014 0.023
Sex1:Intervention1 0.009 -0.014 0.016 -0.035 0.014 -0.014
Pre.Post1:Sex1:Intervention1 -0.004 0.023 0.004 -0.014 0.056 -0.056 0.014
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.7551738 -0.3660375 -0.1168382 0.3643547 3.9662413
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 243.71657 <.0001
Pre.Post 1 41 0.05741 0.8118
Sex 1 10 6.14615 0.0326
Intervention 1 32 1.94968 0.1722
Pre.Post:Sex 1 41 2.55483 0.1176
Pre.Post:Intervention 1 41 0.77459 0.3839
Sex:Intervention 1 32 1.69009 0.2029
Pre.Post:Sex:Intervention 1 41 0.08668 0.7699
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 14.04027 1.798399 10 10.03319 18.04736
Post 14.04027 1.798399 10 10.03319 18.04736
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 21.47560 1.824318 10 17.41077 25.54044
Post 19.96757 1.800037 10 15.95683 23.97830
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 15.19669 1.760476 10 11.27411 19.11928
Post 16.58131 1.760476 10 12.65872 20.50389
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 21.14938 1.800037 10 17.13865 25.16012
Post 20.33120 1.800037 10 16.32047 24.34194
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 2.109424e-15 1.193399 41 0.000 1.0000
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 1.508038e+00 1.231027 41 1.225 0.2276
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -1.384615e+00 1.097768 41 -1.261 0.2143
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 8.181818e-01 1.193399 41 0.686 0.4968
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Ethical Risk
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
517.5502 545.1696 -247.7751
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 3.955385
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 0.0003743873 3.108673
Fixed effects: Ethical.Risk ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 19.427060 1.2451138 41 15.602637 0.0000
Pre.Post1 -0.479160 0.3427107 41 -1.398149 0.1696
Sex1 -1.460429 1.2451138 10 -1.172928 0.2680
Intervention1 -0.263292 0.3454198 32 -0.762237 0.4515
Pre.Post1:Sex1 -0.648462 0.3427107 41 -1.892156 0.0655
Pre.Post1:Intervention1 0.603007 0.3427107 41 1.759523 0.0859
Sex1:Intervention1 -0.387139 0.3454198 32 -1.120778 0.2707
Pre.Post1:Sex1:Intervention1 0.115524 0.3427107 41 0.337089 0.7378
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.004
Sex1 -0.006 -0.004
Intervention1 0.015 0.014 0.008
Pre.Post1:Sex1 -0.004 -0.054 0.004 -0.014
Pre.Post1:Intervention1 0.004 0.054 -0.004 0.014 0.025
Sex1:Intervention1 0.008 -0.014 0.015 -0.037 0.014 -0.014
Pre.Post1:Sex1:Intervention1 -0.004 0.025 0.004 -0.014 0.054 -0.054 0.014
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.5973768 -0.4497526 -0.1082360 0.4969251 3.0938972
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 243.74789 <.0001
Pre.Post 1 41 2.65428 0.1109
Sex 1 10 1.29627 0.2814
Intervention 1 32 0.72052 0.4023
Pre.Post:Sex 1 41 3.79764 0.0582
Pre.Post:Intervention 1 41 3.11602 0.0850
Sex:Intervention 1 32 1.26715 0.2687
Pre.Post:Sex:Intervention 1 41 0.11363 0.7378
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 16.90711 1.957243 10 12.54610 21.26812
Post 17.72529 1.957243 10 13.36428 22.08630
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 21.66812 1.985030 10 17.24520 26.09104
Post 20.35455 1.958758 10 15.99017 24.71894
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 16.77091 1.918366 10 12.49652 21.04529
Post 20.46322 1.918366 10 16.18883 24.73760
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 20.44546 1.958758 10 16.08108 24.80984
Post 21.08182 1.958758 10 16.71744 25.44621
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -0.8181818 1.387955 41 -0.589 0.5588
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 1.3135695 1.426168 41 0.921 0.3624
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -3.6923077 1.276733 41 -2.892 0.0061
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -0.6363636 1.387955 41 -0.458 0.6490
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Health Safety risk
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
486.8408 514.4603 -232.4204
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 4.324106
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 0.8125637 2.42408
Fixed effects: Health_Safety.Risk ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 22.366692 1.3410361 41 16.678665 0.0000
Pre.Post1 -0.337761 0.2674136 41 -1.263066 0.2137
Sex1 -0.392460 1.3410361 10 -0.292654 0.7758
Intervention1 0.046093 0.2979223 32 0.154716 0.8780
Pre.Post1:Sex1 -0.489162 0.2674136 41 -1.829235 0.0746
Pre.Post1:Intervention1 0.125526 0.2674136 41 0.469407 0.6413
Sex1:Intervention1 -0.105308 0.2979223 32 -0.353475 0.7261
Pre.Post1:Sex1:Intervention1 0.201397 0.2674136 41 0.753130 0.4557
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.003
Sex1 -0.004 -0.003
Intervention1 0.012 0.014 0.006
Pre.Post1:Sex1 -0.003 -0.055 0.003 -0.014
Pre.Post1:Intervention1 0.003 0.055 -0.003 0.014 0.024
Sex1:Intervention1 0.006 -0.014 0.012 -0.035 0.014 -0.014
Pre.Post1:Sex1:Intervention1 -0.003 0.024 0.003 -0.014 0.055 -0.055 0.014
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.69178992 -0.42644215 -0.01037085 0.48637162 3.67093205
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 278.19743 <.0001
Pre.Post 1 41 2.04751 0.1600
Sex 1 10 0.08136 0.7813
Intervention 1 32 0.01473 0.9042
Pre.Post:Sex 1 41 3.54650 0.0668
Pre.Post:Intervention 1 41 0.25685 0.6150
Sex:Intervention 1 32 0.13253 0.7182
Pre.Post:Sex:Intervention 1 41 0.56721 0.4557
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 21.41502 2.018634 10 16.91722 25.91281
Post 22.41502 2.018634 10 17.91722 26.91281
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 22.98608 2.036733 10 18.44796 27.52421
Post 22.83502 2.019814 10 18.33460 27.33545
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 20.87960 1.993176 10 16.43853 25.32067
Post 23.18729 1.993176 10 18.74622 27.62836
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 22.83502 2.019814 10 18.33460 27.33545
Post 22.38048 2.019814 10 17.88005 26.88090
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -1.0000000 1.0822992 41 -0.924 0.3609
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 0.1510600 1.1147811 41 0.136 0.8929
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -2.3076923 0.9955705 41 -2.318 0.0255
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 0.4545455 1.0822992 41 0.420 0.6767
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Recreational Risk
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
513.192 540.8115 -245.596
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 6.473518
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 1.682729 2.442329
Fixed effects: Recreational.Risk ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 32.88364 1.9934830 41 16.495572 0.0000
Pre.Post1 -0.16713 0.2697859 41 -0.619484 0.5390
Sex1 -1.75055 1.9934830 10 -0.878137 0.4005
Intervention1 0.19961 0.3781087 32 0.527904 0.6012
Pre.Post1:Sex1 -0.87833 0.2697859 41 -3.255643 0.0023
Pre.Post1:Intervention1 0.21924 0.2697859 41 0.812628 0.4211
Sex1:Intervention1 -0.10250 0.3781087 32 -0.271093 0.7881
Pre.Post1:Sex1:Intervention1 0.23531 0.2697859 41 0.872209 0.3882
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.002
Sex1 -0.003 -0.002
Intervention1 0.009 0.013 0.006
Pre.Post1:Sex1 -0.002 -0.057 0.002 -0.013
Pre.Post1:Intervention1 0.002 0.057 -0.002 0.013 0.021
Sex1:Intervention1 0.006 -0.013 0.009 -0.032 0.013 -0.013
Pre.Post1:Sex1:Intervention1 -0.002 0.021 0.002 -0.013 0.057 -0.057 0.013
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.365962669 -0.552012559 0.006438086 0.498206336 2.643651873
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 271.78081 <.0001
Pre.Post 1 41 0.81537 0.3718
Sex 1 10 0.76032 0.4037
Intervention 1 32 0.23026 0.6346
Pre.Post:Sex 1 41 11.09037 0.0018
Pre.Post:Intervention 1 41 0.74097 0.3944
Sex:Intervention 1 32 0.07978 0.7794
Pre.Post:Sex:Intervention 1 41 0.76075 0.3882
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 30.63929 2.923242 10 24.12590 37.15267
Post 31.82110 2.923242 10 25.30771 38.33449
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 35.63143 2.938509 10 29.08402 42.17883
Post 34.24118 2.924605 10 27.72475 40.75760
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 29.53599 2.899541 10 23.07541 35.99657
Post 32.53599 2.899541 10 26.07541 38.99657
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 35.05936 2.924605 10 28.54293 41.57579
Post 33.60481 2.924605 10 27.08839 40.12124
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -1.181818 1.090447 41 -1.084 0.2848
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 1.390249 1.128679 41 1.232 0.2251
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -3.000000 1.003065 41 -2.991 0.0047
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 1.454545 1.090447 41 1.334 0.1896
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Social Risk
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
468.0657 495.6851 -223.0328
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 3.935943
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 1.574873 1.890705
Fixed effects: Social ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 30.365879 1.2335552 41 24.616555 0.0000
Pre.Post1 -0.320071 0.2089532 41 -1.531782 0.1333
Sex1 -1.902173 1.2335552 10 -1.542025 0.1541
Intervention1 0.072304 0.3236451 32 0.223404 0.8246
Pre.Post1:Sex1 -0.854754 0.2089532 41 -4.090649 0.0002
Pre.Post1:Intervention1 0.309300 0.2089532 41 1.480235 0.1465
Sex1:Intervention1 -0.007834 0.3236451 32 -0.024207 0.9808
Pre.Post1:Sex1:Intervention1 0.092798 0.2089532 41 0.444110 0.6593
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.003
Sex1 -0.005 -0.003
Intervention1 0.013 0.012 0.009
Pre.Post1:Sex1 -0.003 -0.058 0.003 -0.012
Pre.Post1:Intervention1 0.003 0.058 -0.003 0.012 0.020
Sex1:Intervention1 0.009 -0.012 0.013 -0.031 0.012 -0.012
Pre.Post1:Sex1:Intervention1 -0.003 0.020 0.003 -0.012 0.058 -0.058 0.012
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-1.43182407 -0.51978560 -0.02934255 0.57896508 1.94272833
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 605.1353 <.0001
Pre.Post 1 41 3.6020 0.0648
Sex 1 10 2.3383 0.1572
Intervention 1 32 0.0265 0.8717
Pre.Post:Sex 1 41 17.3096 0.0002
Pre.Post:Intervention 1 41 2.2756 0.1391
Sex:Intervention 1 32 0.0009 0.9765
Pre.Post:Sex:Intervention 1 41 0.1972 0.6593
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 27.75545 1.855291 10 23.62160 31.88929
Post 29.30090 1.855291 10 25.16706 33.43475
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 33.09938 1.870565 10 28.93150 37.26725
Post 31.59701 1.856729 10 27.45995 35.73406
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 26.82231 1.829493 10 22.74595 30.89868
Post 29.97616 1.829493 10 25.89980 34.05252
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 32.50610 1.856729 10 28.36905 36.64315
Post 31.86973 1.856729 10 27.73268 36.00678
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post -1.5454545 0.8441587 41 -1.831 0.0744
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 1.5023703 0.8753013 41 1.716 0.0936
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post -3.1538462 0.7765130 41 -4.062 0.0002
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 0.6363636 0.8441587 41 0.754 0.4553
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Empathy
Linear mixed-effects model fit by maximum likelihood
Data: T_DF.NA2
AIC BIC logLik
569.3296 596.9491 -273.6648
Random effects:
Formula: ~1 | ID
(Intercept)
StdDev: 12.22933
Formula: ~1 | Session %in% ID
(Intercept) Residual
StdDev: 2.996947 2.844551
Fixed effects: Empathy ~ Pre.Post * Sex * Intervention
Value Std.Error DF t-value p-value
(Intercept) 37.22673 3.740190 41 9.953166 0.0000
Pre.Post1 0.92165 0.314583 41 2.929748 0.0055
Sex1 4.21454 3.740190 10 1.126826 0.2861
Intervention1 -0.14401 0.565215 32 -0.254789 0.8005
Pre.Post1:Sex1 0.14129 0.314583 41 0.449127 0.6557
Pre.Post1:Intervention1 0.42689 0.314583 41 1.357015 0.1822
Sex1:Intervention1 -0.56074 0.565215 32 -0.992075 0.3286
Pre.Post1:Sex1:Intervention1 0.32835 0.314583 41 1.043765 0.3027
Correlation:
(Intr) Pr.Ps1 Sex1 Intrv1 Pr.P1:S1 P.P1:I Sx1:I1
Pre.Post1 0.002
Sex1 -0.002 -0.002
Intervention1 0.007 0.011 0.005
Pre.Post1:Sex1 -0.002 -0.060 0.002 -0.011
Pre.Post1:Intervention1 0.002 0.060 -0.002 0.011 0.019
Sex1:Intervention1 0.005 -0.011 0.007 -0.029 0.011 -0.011
Pre.Post1:Sex1:Intervention1 -0.002 0.019 0.002 -0.011 0.060 -0.060 0.011
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.0199431135 -0.5239276876 0.0003454661 0.4477106080 2.3362976142
Number of Observations: 91
Number of Groups:
ID Session %in% ID
12 46
numDF denDF F-value p-value
(Intercept) 1 41 99.18199 <.0001
Pre.Post 1 41 8.13204 0.0068
Sex 1 10 1.28879 0.2827
Intervention 1 32 0.07990 0.7793
Pre.Post:Sex 1 41 0.13452 0.7157
Pre.Post:Intervention 1 41 1.99129 0.1657
Sex:Intervention 1 32 1.00812 0.3229
Pre.Post:Sex:Intervention 1 41 1.08944 0.3027
$lsmeans
Sex = Female, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 42.55471 5.390290 10 30.54439 54.56502
Post 38.91834 5.390290 10 26.90803 50.92866
Sex = Male, Intervention = Placebo:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 34.30782 5.403550 10 22.26796 46.34768
Post 32.55001 5.392004 10 20.53587 44.56414
Sex = Female, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 42.45371 5.365291 10 30.49910 54.40832
Post 41.83832 5.365291 10 29.88371 53.79294
Sex = Male, Intervention = Testosterone:
Pre.Post lsmean SE df lower.CL upper.CL
Pre 33.27728 5.392004 10 21.26315 45.29142
Post 31.91364 5.392004 10 19.89951 43.92778
Confidence level used: 0.95
$contrasts
Sex = Female, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 3.6363636 1.270030 41 2.863 0.0066
Sex = Male, Intervention = Placebo:
contrast estimate SE df t.ratio p.value
Pre - Post 1.7578093 1.320161 41 1.332 0.1904
Sex = Female, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 0.6153846 1.168258 41 0.527 0.6012
Sex = Male, Intervention = Testosterone:
contrast estimate SE df t.ratio p.value
Pre - Post 1.3636364 1.270030 41 1.074 0.2892
TableGrob (1 x 2) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
Plots Across Time (log)
Only Looking in the Testosterone Condition (log)
#Total Aggression
#All data
p3 <- ggplot(subset(T_DF.NA2,InterventionC=="Testosterone"),aes(y=Test.l,x=Total.Aggression)) + geom_point(size=2) + geom_smooth(method="lm",se=TRUE)
p3
#Seperated by ID
p3 <- ggplot(subset(T_DF.NA2,InterventionC=="Testosterone"),aes(y=Test.l,x=Total.Aggression,color=ID)) + geom_point(size=2) + geom_smooth(method="lm",se=FALSE)
p3
p3 <- ggplot(subset(T_DF.NA2,Sex_1=="Male" & InterventionC=="Testosterone"),aes(y=Test.l,x=Total.Aggression,color=ID)) + geom_point(size=2) + geom_smooth(method="lm",se=FALSE)
p3
#Seperate Plots for each individual
uni <- unique(T_DF.NA2$ID)
for (i in 1:length(uni)) {
p3 <- ggplot(subset(T_DF.NA2,InterventionC=="Testosterone" & ID==uni[i]),aes(y=Test.l,x=Total.Aggression,color=ID,linetype=Session)) + geom_point(size=2) + geom_smooth(method="lm",se=FALSE); print(p3)
}
Social Risk