#Number of Days of Feeling Sick: Conditional Growth Models

##Variable Distributions and Transformations

[1] 314 368
[1] 314 368
lambda
0.84

####Figure S1a. Comparison of Best Fitting Theoretical Distribution (Normal, Neg. Binomial, or Poisson) Through QQ Plots on Sick Days (Raw).

[1] 55 189
[1] 55 189
lambda
0.8

####Figure S1b. Comparison of Best Fitting Theoretical Distributions (Normal, Neg. Binomial, or Poisson) Through QQ Plots on Sick Days (Transformed).

##Growth Model Specifications

###Table S1a. Growth Models for Number of Sick Days (Transformed): Diagonal Covariance Matrix

  IO-GM U-GM: Unconditional U-GM: Additive C-GM: Conditional
Predictors Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p
Intercept 0.42
(-0.86)		 -0.86
(-6.10)		 0.00 0.36
(-1.01)		 -1.01
(-6.72)		 0.00 0.43
(-0.99)		 -0.99
(-6.71)		 0.00 0.47
(-1.00)		 -1.00
(-6.80)		 0.00
Time 0.98
(-0.22)		 -0.22
(-2.74)		 0.01 0.97
(-0.22)		 -0.22
(-2.82)		 0.00 1.00
(-0.24)		 -0.24
(-3.07)		 0.00
Treatment 0.52
(-0.25)		 -0.25
(-1.81)		 0.07 0.36
(-0.28)		 -0.28
(-2.04)		 0.04
Time X Treatment 0.91
(-0.14)		 -0.14
(-1.77)		 0.08
Random Effects
σ2 1.21 1.34 1.34 1.34
τ00 1.40 ID 1.43 ID 1.33 ID 1.28 ID
  0.02 ID.1 0.02 ID.1 0.01 ID.1
ICC 0.53 0.51 0.50 0.49
N 98 ID 98 ID 98 ID 98 ID
Observations 767 767 767 767
Marginal R2 / Conditional R2 0.000 / 0.535 0.001 / 0.516 0.041 / 0.517 0.040 / 0.509
Deviance 1719.144 1680.174 1674.789 1670.453
AIC 1723.144 1688.174 1684.789 1682.453

Note. IRR=Incidence Rate Ratio=Exp(B), representing the rate of incidence of sick days. Treatment IRR represents the instantaneous effect of Treatment during the last week of the intervention (0 = Control; 1 = Treatment. Time: 0 = Week 10 (last week); -9 = Week 1). PA = Positive Affect; NA = Negative Affect; SwL= Satisfaction with Life.

`

###Table S1b. Growth Models for Number of Sick Days (Transformed): Unconstrained Covariance Matrix

  IO-GM U-GM: Unconditional U-GM: Additive C-GM: Conditional
Predictors Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p
Intercept 0.42
(-0.86)		 -0.86
(-6.10)		 0.00 0.21
(-1.04)		 -1.04
(-6.66)		 0.00 0.27
(-1.01)		 -1.01
(-6.65)		 0.00 0.34
(-1.02)		 -1.02
(-6.73)		 0.00
Time 0.90
(-0.31)		 -0.31
(-2.98)		 0.00 0.90
(-0.29)		 -0.29
(-2.85)		 0.00 0.94
(-0.31)		 -0.31
(-3.08)		 0.00
Treatment 0.65
(-0.21)		 -0.21
(-1.51)		 0.13 0.35
(-0.28)		 -0.28
(-2.01)		 0.04
Time X Treatment 0.90
(-0.15)		 -0.15
(-1.81)		 0.07
Random Effects
σ2 1.21 1.32 1.32 1.32
τ00 1.40 ID 2.81 ID 2.58 ID 2.47 ID
τ11   0.03 ID.time 0.03 ID.time 0.03 ID.time
ρ01   0.73 ID 0.70 ID 0.69 ID
ICC 0.53 0.57 0.56 0.55
N 98 ID 98 ID 98 ID 98 ID
Observations 767 767 767 767
Marginal R2 / Conditional R2 0.000 / 0.535 0.030 / 0.581 0.040 / 0.574 0.060 / 0.576
Deviance 1719.144 1662.342 1660.064 1656.889
AIC 1723.144 1672.342 1672.064 1670.889

Note. IRR=Incidence Rate Ratio=Exp(B), representing the rate of incidence of sick days. Treatment IRR represents the instantaneous effect of Treatment during the last week of the intervention (0 = Control; 1 = Treatment. Time: 0 = Week 10 (last week); -9 = Week 1). PA = Positive Affect; NA = Negative Affect; SwL= Satisfaction with Life.

`

###Table S1c. Growth Models for Number of Sick Days (Raw Data)

  IO-GM U-GM: Unconditional U-GM: Additive C-GM: Conditional
Predictors Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p Incidence Rate Ratios std. Beta p
Intercept 0.43 (-0.85) -0.85 (-5.91) 0.00 0.31 (-1.02) -1.02 (-6.61) 0.00 0.43 (-0.99) -0.99 (-6.71) 0.00 0.47 (-1.01) -1.01 (-6.67) 0.00
Time 0.97 (-0.23) -0.23 (-2.77) 0.01 0.97 (-0.22) -0.22 (-2.82) 0.00 1.00 (-0.25) -0.25 (-3.09) 0.00
Treatment 0.52 (-0.25) -0.25 (-1.81) 0.07 0.35 (-0.28) -0.28 (-2.02) 0.04
Time X Treatment 0.91 (-0.14) -0.14 (-1.72) 0.09
Random Effects
σ2 1.21 1.36 1.34 1.36
τ00 1.49 ID 1.56 ID 1.33 ID 1.40 ID
  0.02 ID.1 0.02 ID.1 0.02 ID.1
ICC 0.55 0.54 0.50 0.51
N 98 ID 98 ID 98 ID 98 ID
Observations 767 767 767 767
Marginal R2 / Conditional R2 0.000 / 0.552 0.003 / 0.537 0.041 / 0.517 0.039 / 0.527
Deviance 1774.708 1718.309 1674.789 1709.528
AIC 1778.708 1726.309 1684.789 1721.528

Note. IRR=Incidence Rate Ratio=Exp(B), representing the rate of incidence of sick days. Treatment IRR represents the instantaneous effect of Treatment during the last week of the intervention (0 = Control; 1 = Treatment. Time: 0 = Week 10 (last week); -9 = Week 1). PA = Positive Affect; NA = Negative Affect; SwL= Satisfaction with Life.

##Growth Model Diagnostics

###Sick Days (Raw)

plotSimulatedResiduals is deprecated, switch your code to using the plot function

####Figure S2a. Similation-based (10000 samples) Scaled Residuals: Sick Days (Raw)

Note. The QQ-plot on the left shows the residuals against the predicted value as an indicator of deviations from the expected distribution. The Kolmogorov-Smirnov (KS) test indicates the overall uniformity of residuals. The quantile regression plot on the right provides 0.25, 0.5 and 0.75 quantile lines across the plots. Red lines that are straighter, more horizontal, and close to y-values of 0.25, 0.5, and 0.75 indicate better satisfaction of the assumption of residual distribution.

###Sick Days (Transformed)

plotSimulatedResiduals is deprecated, switch your code to using the plot function

####Figure S2b. Similation-based (10000 samples) Scaled Residuals: Sick Days (Transformed).

Note. The QQ-plot on the left shows the residuals against the predicted value as an indicator of deviations from the expected distribution. The Kolmogorov-Smirnov (KS) test indicates the overall uniformity of residuals (p < .05 indicates rejection of the assumption of uniformity). The quantile regression plot on the right provides 0.25, 0.5 and 0.75 quantile lines across the plots. Red lines that are straighter, more horizontal, and close to y-values of 0.25, 0.5, and 0.75 indicate better satisfaction of the assumption of residual distribution.