R/data.R
rads2.RdResponses of 917 adolescents from Lima (Peru) to the 25 items retained in the four-factor version of the Reynolds Adolescent Depression Scale, second edition (RADS-2), each answered on a four-point Likert scale. The data come from Ramos-Vera et al. (2023), who also grouped the items into four clusters (stored with the data).
rads2A data frame with 917 rows (adolescents) and 25 integer columns (items), with values 1 to 4. Higher values
indicate more frequent depressive symptoms for every item. The columns keep the item numbering of the RADS-2
(D1, D3, ..., D30). The attribute "clusters" is a named character vector giving the
cluster of each item (see Details).
Ramos-Vera, C., Quispe Callo, G., Basauri Delgado, M., Vallejos Saldarriaga, J., and Saintila, J. (2023). "S1 Data. Reynolds Adolescent Depression Scale", supporting information of the article below, doi:10.1371/journal.pone.0286081.s001 . Licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/); the changes are listed under Details. The rest of the package is licensed under the MIT license.
The items are grouped into four clusters (Ramos-Vera et al., 2023), stored in attr(rads2, "clusters"):
Dysphoria: D3, D6, D7, D8, D16, D21, D26
Anhedonia/Negative Affect: D1, D5, D12
Negative Self-Assessment: D4, D9, D13, D14, D15, D19, D20, D30
Somatic Complaint: D11, D17, D18, D22, D24, D27, D28
Subsetting the data frame (e.g. rads2[, 1:10]) drops the attribute; retrieve it first with
attr(rads2, "clusters").
The data are derived from "S1 Data" of Ramos-Vera et al. (2023), with the following changes: the 25 items of
the four-factor version were kept (items 2, 10, 23, 25 and 29, and the age and sex of the respondents, were removed),
the items were renamed from A<k> to D<k>, and the positively worded items D1, D5 and
D12 were reverse-coded (5 - x), so that higher values indicate more symptoms for every item. The rows
are in the original order. The item wording of the RADS-2 is not included.
Ramos-Vera, C., Quispe Callo, G., Basauri Delgado, M., Vallejos Saldarriaga, J., and Saintila, J. (2023). Factorial and network structure of the Reynolds Adolescent Depression Scale (RADS-2) in Peruvian adolescents. PLOS ONE, 18(5), e0286081. doi:10.1371/journal.pone.0286081
Reynolds, W. M. (2002). Reynolds Adolescent Depression Scale, 2nd Edition: Professional Manual. Lutz, FL: Psychological Assessment Resources.
data(rads2)
dim(rads2)
#> [1] 917 25
table(attr(rads2, "clusters"))
#>
#> Anhedonia/Negative Affect Dysphoria Negative Self-Assessment
#> 3 7 8
#> Somatic Complaint
#> 7
# point estimates and Godambe-Huber-White standard errors for the seven dysphoria items
dysphoria <- names(which(attr(rads2, "clusters") == "Dysphoria"))
fit <- dmrfit(rads2[, dysphoria], with_prior = TRUE)
summary(fit)
#>
#> Call:
#> dmrfit(data = rads2[, dysphoria], with_prior = TRUE)
#>
#> Discrete Markov Random Field
#> Estimation method: maximum pseudo-likelihood (with prior)
#> Nodes: 7 Observations: 917 Free parameters: 42
#> Standard errors: Godambe-Huber-White (GHW) estimator
#> ------------------------------------------------------------
#>
#> Thresholds:
#> Post.Mode Post.SD z value Pr(>|z|)
#> mu[1,1] -0.993877 0.108647 -9.1477 < 2.2e-16
#> mu[1,2] -2.033318 0.184850 -10.9998 < 2.2e-16
#> mu[1,3] -4.856100 0.352168 -13.7891 < 2.2e-16
#> mu[2,1] -1.393145 0.109043 -12.7761 < 2.2e-16
#> mu[2,2] -2.967213 0.219885 -13.4944 < 2.2e-16
#> mu[2,3] -5.408165 0.388149 -13.9332 < 2.2e-16
#> mu[3,1] -1.347157 0.123918 -10.8714 < 2.2e-16
#> mu[3,2] -3.556990 0.286449 -12.4175 < 2.2e-16
#> mu[3,3] -8.179832 0.578213 -14.1467 < 2.2e-16
#> mu[4,1] -1.495516 0.120498 -12.4111 < 2.2e-16
#> mu[4,2] -3.937368 0.269144 -14.6292 < 2.2e-16
#> mu[4,3] -7.836540 0.492864 -15.9000 < 2.2e-16
#> mu[5,1] -0.844315 0.102145 -8.2659 < 2.2e-16
#> mu[5,2] -2.498274 0.200847 -12.4387 < 2.2e-16
#> mu[5,3] -5.420323 0.377907 -14.3430 < 2.2e-16
#> mu[6,1] -2.092922 0.121261 -17.2596 < 2.2e-16
#> mu[6,2] -4.192847 0.257757 -16.2666 < 2.2e-16
#> mu[6,3] -7.419170 0.469632 -15.7978 < 2.2e-16
#> mu[7,1] 0.031543 0.102258 0.3085 0.7577
#> mu[7,2] -1.082361 0.158731 -6.8188 9.178e-12
#> mu[7,3] -3.121824 0.290390 -10.7505 < 2.2e-16
#>
#> Pairwise interactions:
#> Post.Mode Post.SD z value Pr(>|z|)
#> theta[2,1] 0.262511 0.049439 5.3098 1.097e-07 ***
#> theta[3,1] 0.315228 0.060200 5.2364 1.638e-07 ***
#> theta[4,1] 0.215751 0.055752 3.8699 0.0001089 ***
#> theta[5,1] 0.102765 0.051743 1.9860 0.0470278 *
#> theta[6,1] 0.215105 0.055583 3.8700 0.0001089 ***
#> theta[7,1] 0.086309 0.049577 1.7409 0.0817005 .
#> theta[3,2] 0.207482 0.057861 3.5859 0.0003360 ***
#> theta[4,2] 0.078529 0.054518 1.4404 0.1497441
#> theta[5,2] 0.094153 0.049816 1.8900 0.0587562 .
#> theta[6,2] 0.273918 0.050015 5.4768 4.332e-08 ***
#> theta[7,2] 0.050617 0.048043 1.0536 0.2920747
#> theta[4,3] 0.799282 0.084451 9.4645 < 2.2e-16 ***
#> theta[5,3] 0.181065 0.066751 2.7125 0.0066771 **
#> theta[6,3] 0.207936 0.075347 2.7597 0.0057852 **
#> theta[7,3] 0.176420 0.060693 2.9068 0.0036520 **
#> theta[5,4] 0.173476 0.061509 2.8203 0.0047978 **
#> theta[6,4] 0.173787 0.068501 2.5370 0.0111810 *
#> theta[7,4] 0.210921 0.059018 3.5738 0.0003518 ***
#> theta[6,5] 0.227905 0.058071 3.9246 8.688e-05 ***
#> theta[7,5] 0.185144 0.052731 3.5111 0.0004462 ***
#> theta[7,6] 0.147535 0.057995 2.5439 0.0109609 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Intervals (95%): Wald (GHW standard errors)
#> Wald lower Wald upper
#> mu[1,1] -1.2068 -0.7809
#> mu[1,2] -2.3956 -1.6710
#> mu[1,3] -5.5463 -4.1659
#> mu[2,1] -1.6069 -1.1794
#> mu[2,2] -3.3982 -2.5362
#> mu[2,3] -6.1689 -4.6474
#> mu[3,1] -1.5900 -1.1043
#> mu[3,2] -4.1184 -2.9956
#> mu[3,3] -9.3131 -7.0466
#> mu[4,1] -1.7317 -1.2593
#> mu[4,2] -4.4649 -3.4099
#> mu[4,3] -8.8025 -6.8705
#> mu[5,1] -1.0445 -0.6441
#> mu[5,2] -2.8919 -2.1046
#> mu[5,3] -6.1610 -4.6796
#> mu[6,1] -2.3306 -1.8553
#> mu[6,2] -4.6980 -3.6877
#> mu[6,3] -8.3396 -6.4987
#> mu[7,1] -0.1689 0.2320
#> mu[7,2] -1.3935 -0.7713
#> mu[7,3] -3.6910 -2.5527
#> theta[2,1] 0.1656 0.3594
#> theta[3,1] 0.1972 0.4332
#> theta[4,1] 0.1065 0.3250
#> theta[5,1] 0.0013 0.2042
#> theta[6,1] 0.1062 0.3240
#> theta[7,1] -0.0109 0.1835
#> theta[3,2] 0.0941 0.3209
#> theta[4,2] -0.0283 0.1854
#> theta[5,2] -0.0035 0.1918
#> theta[6,2] 0.1759 0.3719
#> theta[7,2] -0.0435 0.1448
#> theta[4,3] 0.6338 0.9648
#> theta[5,3] 0.0502 0.3119
#> theta[6,3] 0.0603 0.3556
#> theta[7,3] 0.0575 0.2954
#> theta[5,4] 0.0529 0.2940
#> theta[6,4] 0.0395 0.3080
#> theta[7,4] 0.0952 0.3266
#> theta[6,5] 0.1141 0.3417
#> theta[7,5] 0.0818 0.2885
#> theta[7,6] 0.0339 0.2612
#>
#> Negative log pseudo-likelihood: 6876.938