| Frequencies of emot_probs_binomial | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Group | |||||||||
| emot_probs_binomial | Time | Gender | Control | Intervention | |||||
| low | T1 | F | 2 | 6 | |||||
| M | 5 | 6 | |||||||
| T2 | F | 4 | 8 | ||||||
| M | 3 | 7 | |||||||
| high | T1 | F | 8 | 5 | |||||
| M | 4 | 1 | |||||||
| T2 | F | 6 | 3 | ||||||
| M | 6 | 0 | |||||||
| Model Info | |||||
|---|---|---|---|---|---|
| Info | Value | Comment | |||
| Model Type | Logistic | Model for binary y | |||
| Call | glm | emot_probs_binomial ~ 1 + Group + Time + Time:Group + (1 | Participant) | |||
| Link function | Logit | Log of the odd of y=1 over y=0 | |||
| Direction | P(y=1)/P(y=0) | P( emot_probs_binomial = ) / P( emot_probs_binomial = ) | |||
| Distribution | Binomial | Dichotomous event distribution of y | |||
| LogLikel. | -36.206 | Less is better | |||
| R-squared | 0.256 | Marginal | |||
| R-squared | 0.993 | Conditional | |||
| AIC | 82.410 | Less is better | |||
| BIC | 93.932 | Less is better | |||
| Deviance | 12.023 | Conditional | |||
| Residual DF | 69.000 | ||||
| Converged | yes | ||||
| Optimizer | bobyqa | ||||
| [3] | |||||
| Fixed Effect Omnibus tests | |||||||
|---|---|---|---|---|---|---|---|
| X² | df | p | |||||
| Group | 18.65 | 1.00 | < .001 | ||||
| Time | 1.33e-15 | 1.00 | 1.000 | ||||
| Time ✻ Group | 6.04 | 1.00 | 0.014 | ||||
| Fixed Effects Parameter Estimates | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 95% Exp(B) Confidence Interval | |||||||||||||||||
| Names | Effect | Estimate | SE | exp(B) | Lower | Upper | z | p | |||||||||
| (Intercept) | (Intercept) | 8.77 | 2.46 | 6428.41 | 51.5143 | 802193.811 | 3.56 | < .001 | |||||||||
| Group1 | Intervention - Control | -17.20 | 3.98 | 3.39e-8 | 1.38e-11 | 8.33e-5 | -4.32 | < .001 | |||||||||
| Time1 | T2 - T1 | 5.61e-8 | 1.54 | 1.00 | 0.0488 | 20.493 | 3.64e-8 | 1.000 | |||||||||
| Group1 ✻ Time1 | Intervention - Control ✻ T2 - T1 | -9.00 | 3.66 | 1.23e-4 | 9.44e-8 | 0.161 | -2.46 | 0.014 | |||||||||
| Random Components | |||||||
|---|---|---|---|---|---|---|---|
| Groups | Name | SD | Variance | ||||
| Participant | (Intercept) | 19.29 | 372.08 | ||||
| Residuals | 1.00 | 1.00 | |||||
| Note. Number of Obs: 74 , groups: Participant 37 | |||||||
| Time | |||||||
|---|---|---|---|---|---|---|---|
| Name | Contrast | level=T1 | level=T2 | ||||
| Time1 | T2 - T1 | 0 | 1 | ||||
| Note. Intercept computed for Time=T1 | |||||||
| Group | |||||||
|---|---|---|---|---|---|---|---|
| Name | Contrast | level=Control | level=Intervention | ||||
| Group1 | Intervention - Control | 0 | 1 | ||||
| Note. Intercept computed for Group=Control | |||||||
| Gender | |||||||
|---|---|---|---|---|---|---|---|
| Name | Contrast | level=F | level=M | ||||
| Gender1 | M - F | 0 | 1 | ||||
| Note. Intercept computed for Gender=F | |||||||
| Group | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 95% Confidence Interval | |||||||||||
| Group | Prob. | SE | df | Lower | Upper | ||||||
| Control | 1.000 | 3.64e-4 | Inf | 0.985 | 1.00000 | ||||||
| Intervention | 2.42e-6 | 7.91e-6 | Inf | 4.00e-9 | 0.00146 | ||||||
| Time | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 95% Confidence Interval | |||||||||||
| Time | Prob. | SE | df | Lower | Upper | ||||||
| T1 | 0.5420 | 0.3229 | Inf | 0.0846 | 0.938 | ||||||
| T2 | 0.0130 | 0.0258 | Inf | 2.52e-4 | 0.407 | ||||||
| Group:Time | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 95% Confidence Interval | |||||||||||||
| Group | Time | Prob. | SE | df | Lower | Upper | |||||||
| Control | T1 | 1.000 | 3.83e-4 | Inf | 0.981 | 1.0000 | |||||||
| Intervention | T1 | 2.18e-4 | 4.99e-4 | Inf | 2.44e-6 | 0.0191 | |||||||
| Control | T2 | 1.000 | 3.83e-4 | Inf | 0.981 | 1.0000 | |||||||
| Intervention | T2 | 2.69e-8 | 1.25e-7 | Inf | 2.96e-12 | 2.44e-4 | |||||||
[1] The jamovi project (2020). jamovi. (Version 1.2) [Computer Software]. Retrieved from https://www.jamovi.org.
[2] R Core Team (2019). R: A Language and environment for statistical computing. (Version 3.6) [Computer software]. Retrieved from https://cran.r-project.org/.
[3] Gallucci, M. (2019). GAMLj: General analyses for linear models. [jamovi module]. Retrieved from https://gamlj.github.io/.