This file is one of a series of supplemental explanatory documents for the study “Determining optimal parameters of the Self Referent Encoding Task: A large-scale examination of self-referent cognition and depression”. Data and code are located at doi: 10.18738/T8/XK5PXX, and websites with visual R Markdown explanations are located and navigable on the paper’s github pages website.
This file makes smaller models created in MASS
using MASS::glm.nb() to model the depressive symptom severity as a negative binomial distribution. It makes specific comparisons between variables using beset
function beset::r2d() to compare them by R2D.
If you are viewing this as an HTML file, and wish to see the code, please download the R Markdown file from the Texas Data Repository.
Based on trends highlighted in the plots of the models, we ran small, specific comparisons within each sample. For example, we hypothesized that endorsements of positive and negative word alone were substantially better at predicting depression symptoms than so-called negative/positive processing biases (e.g., the ratio of the number of negative words endorsed to the total number of words endorsed).
We tested this with R2D for each sample, and for each model.
For the model with variables num.neg.endorsed and num.pos.endorsed:
Model-fit R-squared = 0.42
Cross-validated R-squared = 0.42, 95% CI [0.4, 0.43]
For the model with only num.pos.endorsed:
Model-fit R-squared = 0.27
Cross-validated R-squared = 0.26, 95% CI [0.24, 0.28]
For the model with only num.neg.endorsed:
Model-fit R-squared = 0.36
Cross-validated R-squared = 0.36, 95% CI [0.34, 0.37]
For the model with variables num.neg.endorsed and num.pos.endorsed:
Model-fit R-squared = 0.36
Cross-validated R-squared = 0.35, 95% CI [0.33, 0.37]
For the model with only num.pos.endorsed:
Model-fit R-squared = 0.26
Cross-validated R-squared = 0.25, 95% CI [0.23, 0.27]
For the model with only num.neg.endorsed:
Model-fit R-squared = 0.33
Cross-validated R-squared = 0.32, 95% CI [0.31, 0.34]
For the model with variables num.neg.endorsed and num.pos.endorsed:
Model-fit R-squared = 0.33
Cross-validated R-squared = 0.31, 95% CI [0.29, 0.33]
For the model with only num.pos.endorsed:
Model-fit R-squared = 0.2
Cross-validated R-squared = 0.19, 95% CI [0.16, 0.21]
For the model with only num.neg.endorsed:
Model-fit R-squared = 0.27
Cross-validated R-squared = 0.26, 95% CI [0.23, 0.28]
For the model with variables v.negative and v.positive:
Model-fit R-squared = 0.41
Cross-validated R-squared = 0.4, 95% CI [0.38, 0.42]
For the model with only v.positive:
Model-fit R-squared = 0.33
Cross-validated R-squared = 0.33, 95% CI [0.31, 0.35]
For the model with only v.negative:
Model-fit R-squared = 0.38
Cross-validated R-squared = 0.37, 95% CI [0.35, 0.39]
For the model with variables v.negative and v.positive:
Model-fit R-squared = 0.4
Cross-validated R-squared = 0.39, 95% CI [0.37, 0.41]
For the model with only v.positive:
Model-fit R-squared = 0.33
Cross-validated R-squared = 0.32, 95% CI [0.3, 0.34]
For the model with only v.negative:
Model-fit R-squared = 0.38
Cross-validated R-squared = 0.38, 95% CI [0.35, 0.4]
For the model with variables v.negative and v.positive:
Model-fit R-squared = 0.36
Cross-validated R-squared = 0.34, 95% CI [0.29, 0.37]
For the model with only v.positive:
Model-fit R-squared = 0.27
Cross-validated R-squared = 0.26, 95% CI [0.21, 0.29]
For the model with only v.negative:
Model-fit R-squared = 0.29
Cross-validated R-squared = 0.28, 95% CI [0.24, 0.31]
For the model with variables numSRposrecalled and numSRnegrecalled:
Model-fit R-squared = 0.36
Cross-validated R-squared = 0.35, 95% CI [0.33, 0.36]
For the model with only numSRposrecalled:
Model-fit R-squared = 0.13
Cross-validated R-squared = 0.12, 95% CI [0.1, 0.13]
For the model with only numSRnegrecalled:
Model-fit R-squared = 0.31
Cross-validated R-squared = 0.3, 95% CI [0.28, 0.32]
For the model with variables numSRposrecalled and numSRnegrecalled:
Model-fit R-squared = 0.28
Cross-validated R-squared = 0.27, 95% CI [0.25, 0.29]
For the model with only numSRposrecalled:
Model-fit R-squared = 0.08
Cross-validated R-squared = 0.07, 95% CI [0.04, 0.09]
For the model with only numSRnegrecalled:
Model-fit R-squared = 0.23
Cross-validated R-squared = 0.22, 95% CI [0.2, 0.24]
For the model with variables numSRposrecalled and numSRnegrecalled:
Model-fit R-squared = 0.19
Cross-validated R-squared = 0.16, 95% CI [0.14, 0.18]
For the model with only numSRposrecalled:
Model-fit R-squared = 0.06
Cross-validated R-squared = 0.04, 95% CI [0.02, 0.06]
For the model with only numSRnegrecalled:
Model-fit R-squared = 0.16
Cross-validated R-squared = 0.14, 95% CI [0.12, 0.16]
For the model with variables numposrecalled and numnegrecalled:
Model-fit R-squared = 0.08
Cross-validated R-squared = 0.07, 95% CI [0.05, 0.08]
For the model with only numposrecalled:
Model-fit R-squared = 0
Cross-validated R-squared = -0.01, 95% CI [-0.01, -0.01]
For the model with only numnegrecalled:
Model-fit R-squared = 0.05
Cross-validated R-squared = 0.04, 95% CI [0.03, 0.05]
For the model with variables numposrecalled and numnegrecalled:
Model-fit R-squared = 0.07
Cross-validated R-squared = 0.05, 95% CI [0.03, 0.07]
For the model with only numposrecalled:
Model-fit R-squared = 0
Cross-validated R-squared = -0.01, 95% CI [-0.01, -0.01]
For the model with only numnegrecalled:
Model-fit R-squared = 0.04
Cross-validated R-squared = 0.03, 95% CI [0.01, 0.04]
For the model with variables numposrecalled and numnegrecalled:
Model-fit R-squared = 0.07
Cross-validated R-squared = 0.04, 95% CI [0.02, 0.07]
For the model with only numposrecalled:
Model-fit R-squared = 0.01
Cross-validated R-squared = -0.01, 95% CI [-0.02, 0]
For the model with only numnegrecalled:
Model-fit R-squared = 0.04
Cross-validated R-squared = 0.02, 95% CI [0.01, 0.03]
For the model with variables negRT and posRT:
Model-fit R-squared = 0.09
Cross-validated R-squared = 0.08, 95% CI [0.07, 0.09]
For the model with only posRT:
Model-fit R-squared = 0.09
Cross-validated R-squared = 0.09, 95% CI [0.07, 0.1]
For the model with only negRT:
Model-fit R-squared = 0.07
Cross-validated R-squared = 0.06, 95% CI [0.05, 0.07]
For the model with variables negRT and posRT:
Model-fit R-squared = 0.08
Cross-validated R-squared = 0.06, 95% CI [0.04, 0.08]
For the model with only posRT:
Model-fit R-squared = 0.08
Cross-validated R-squared = 0.07, 95% CI [0.04, 0.09]
For the model with only negRT:
Model-fit R-squared = 0.08
Cross-validated R-squared = 0.06, 95% CI [0.04, 0.08]
For the model with variables negRT and posRT:
Model-fit R-squared = 0.11
Cross-validated R-squared = 0.09, 95% CI [0.07, 0.1]
For the model with only posRT:
Model-fit R-squared = 0.09
Cross-validated R-squared = 0.07, 95% CI [0.05, 0.08]
For the model with only negRT:
Model-fit R-squared = 0.11
Cross-validated R-squared = 0.09, 95% CI [0.07, 0.11]
For the model with variables zr.negative and zr.positive:
Model-fit R-squared = 0.05
Cross-validated R-squared = 0.03, 95% CI [0.02, 0.04]
For the model with only zr.positive:
Model-fit R-squared = 0.01
Cross-validated R-squared = -0.01, 95% CI [-0.01, 0]
For the model with only zr.negative:
Model-fit R-squared = 0.04
Cross-validated R-squared = 0.03, 95% CI [0.02, 0.05]
For the model with variables zr.negative and zr.positive:
Model-fit R-squared = 0.09
Cross-validated R-squared = 0.08, 95% CI [0.06, 0.09]
For the model with only zr.positive:
Model-fit R-squared = 0.06
Cross-validated R-squared = 0.05, 95% CI [0.03, 0.06]
For the model with only zr.negative:
Model-fit R-squared = 0.07
Cross-validated R-squared = 0.06, 95% CI [0.05, 0.08]
For the model with variables zr.negative and zr.positive:
Model-fit R-squared = 0.06
Cross-validated R-squared = 0.04, 95% CI [0.02, 0.05]
For the model with only zr.positive:
Model-fit R-squared = 0.03
Cross-validated R-squared = 0.01, 95% CI [0, 0.02]
For the model with only zr.negative:
Model-fit R-squared = 0.03
Cross-validated R-squared = 0.02, 95% CI [0, 0.03]
=======================================================
Best Model:
dep ~ num.neg.endorsed + num.pos.endorsed
Deviance Residuals:
Min 1Q Median 3Q Max
-3.6444 -0.7255 -0.0523 0.4610 2.2455
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.793786 0.096221 29.035 < 2e-16 ***
num.neg.endorsed 0.054583 0.005071 10.764 < 2e-16 ***
num.pos.endorsed -0.024927 0.003752 -6.644 3.05e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Negative Binomial(5.7328) family taken to be 1)
Log-likelihood: -1447 on 4 Df
AIC: 2902.2
Number of Fisher Scoring iterations: 1
Train-sample R-squared = 0.42, Test-sample R-squared = 0.44
Cross-validated R-squared = 0.42, 95% CI [0.4, 0.43]
=======================================================
=======================================================
Best Model:
dep ~ num.neg.endorsed + num.pos.endorsed
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6264 -0.8479 -0.1471 0.3945 2.3788
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.590585 0.243528 10.638 < 2e-16 ***
num.neg.endorsed 0.066591 0.011303 5.891 3.83e-09 ***
num.pos.endorsed -0.028475 0.009467 -3.008 0.00263 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Negative Binomial(1.9496) family taken to be 1)
Log-likelihood: -760.4 on 4 Df
AIC: 1528.8
Number of Fisher Scoring iterations: 1
Train-sample R-squared = 0.36, Test-sample R-squared = 0.29
Cross-validated R-squared = 0.35, 95% CI [0.33, 0.37]
=======================================================
=======================================================
Best Model:
dep ~ num.neg.endorsed + num.pos.endorsed
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3123 -1.3478 -0.3062 0.4898 2.0378
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.43207 0.31201 4.59 4.44e-06 ***
num.neg.endorsed 0.11597 0.01787 6.49 8.57e-11 ***
num.pos.endorsed -0.05609 0.01420 -3.95 7.82e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Negative Binomial(1.9485) family taken to be 1)
Log-likelihood: -399.9 on 4 Df
AIC: 807.82
Number of Fisher Scoring iterations: 1
Train-sample R-squared = 0.33, Test-sample R-squared = 0.29
Cross-validated R-squared = 0.31, 95% CI [0.29, 0.33]
=======================================================