1 Initialization

rm(list=ls())
library(relativeVariability)
library(rsurveyutils)
library(here)
library(tidyverse)
library(corrplot)
# library(ggthemr)
# theme_set(theme_bw())
# ggthemr("fresh")

library(lmerTest)
library(sjPlot)

# library(aTSA)
library(tidylog)

2 Preprocessing

2.1 Read data

filepath <- here("data/ESM_W2_WITH_MISSING_V1_no exclusions.csv")
df_raw <- read_csv(filepath)

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

2.2 Process data

df_emos <- df_raw %>% 
  select(ID, timeStampSent, matches("panas.*scale.*beep"), -matches("new")) %>% 
  # TODO: what is panas scale new
  rename(
    panas_neg = PANAS_negativescale_beep,
    panas_pos = PANAS_positivescale_beep
  )

df_er <- df_raw %>% 
  select(ID, timeStampSent, matches("Emotionregulation_[a-z]+$"),
         -Emotionregulation_selfcompassion1,
         -Emotionregulation_selfcompassion2,
         -Emotionregulation_selfcompassion,
         -Emotionregulation_engagement) %>% 
  rename_all(~str_replace(., "Emotionregulation_", "er_"))

df_indiff <- df_raw %>% 
  select(ID, Sex, Birth_year, Birth_month, Ethnicity, 
         matches("SCARED.*scale"), matches("SMFQ.*scale"),
         # matches("panas"),
         -matches("omit")) %>% 
  rename_all(~str_remove(., "_scale_w2")) %>% 
  distinct() %>%
  left_join(
    df_emos %>% 
      filter(!is.na(panas_neg) & !is.na(panas_pos)) %>% 
      group_by(ID) %>% 
      summarize(
        panas_neg = mean(panas_neg, na.rm=T),
        panas_pos = mean(panas_pos, na.rm=T),
        n_beeps = n()
      )
  ) %>% 
  mutate_at(vars(SCARED, SMFQ, panas_neg, panas_pos), list(z=scale)) 
# confirm 1 row per person
df_indiff %>% 
  count(ID) %>% 
  arrange(desc(n))

## # A tibble: 265 × 2
##    ID        n
##    <chr> <int>
##  1 10015     1
##  2 10016     1
##  3 10017     1
##  4 10018     1
##  5 10020     1
##  6 10021     1
##  7 10022     1
##  8 10026     1
##  9 10027     1
## 10 10028     1
## # ℹ 255 more rows

df_indiff_2 <- df_raw %>% 
  select(ID, Sex, Birth_year, Birth_month, Ethnicity, 
         matches("SCARED.*scale"), matches("SMFQ.*scale"),
         # matches("panas"),
         -matches("omit")) %>% 
  rename_all(~str_remove(., "_scale_w2")) %>% 
  distinct() %>%
  left_join(
    df_emos # %>% 
      # filter(!is.na(panas_neg) & !is.na(panas_pos)) %>% 
  ) %>% 
  mutate_at(vars(SCARED, SMFQ, panas_neg, panas_pos), list(z=scale))

2.3 Calculate Metrics

2.3.1 Mean ER

df_mean_er_strat <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  drop_na(rating) %>% 
  group_by(ID, strat) %>%
  summarize(
    mean_er = mean(rating)
  ) %>%
  ungroup

2.3.2 Relative Within SD

d <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  drop_na(rating)

d %>% 
  group_by(ID, strat) %>% 
  summarize(
    n = n()
  ) %>%
  # arrange(desc(n))
  arrange(n)

## # A tibble: 1,325 × 3
## # Groups:   ID [265]
##    ID    strat                n
##    <chr> <chr>            <int>
##  1 1029  er_distraction       1
##  2 1029  er_reappraisal       1
##  3 1029  er_rumination        1
##  4 1029  er_socialsupport     1
##  5 1029  er_suppresion        1
##  6 8048  er_distraction       1
##  7 8048  er_reappraisal       1
##  8 8048  er_rumination        1
##  9 8048  er_socialsupport     1
## 10 8048  er_suppresion        1
## # ℹ 1,315 more rows

df_rwsd <- d %>% 
  group_by(ID, strat) %>% 
  mutate(n = n()) %>% 
  filter(n > 1) %>% 
  filter(mean(rating) < 7) %>%
  filter(mean(rating) > 1) %>%
  summarize(
    rwsd = relativeSD(rating, 1, 7),
    n = n()
  ) %>% 
  summarize(
    rwsd = mean(rwsd),
    n = mean(n)
  ) %>% 
  ungroup

hist(df_rwsd$rwsd)

# rwsd for each ER strat
df_rwsd_st <- d %>% 
  group_by(ID, strat) %>% 
  mutate(n = n()) %>% 
  filter(n > 1) %>% 
  filter(mean(rating) < 7) %>%
  filter(mean(rating) > 1) %>%
  summarize(
    rwsd = relativeSD(rating, 1, 7),
    n = n()
  )

2.3.3 Relative Between SD

d <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  drop_na(rating)

d %>% 
  group_by(ID, timeStampSent) %>% 
  summarize(
    n = n()
  ) %>%
  # arrange(desc(n))
  arrange(n)

## # A tibble: 12,590 × 3
## # Groups:   ID [265]
##    ID    timeStampSent     n
##    <chr>         <dbl> <int>
##  1 7034     1652955304     2
##  2 11187    1653992106     3
##  3 11143    1653768014     4
##  4 11143    1653940813     4
##  5 11143    1654027212     4
##  6 11143    1654157103     4
##  7 11143    1654449309     4
##  8 11170    1653768014     4
##  9 2026     1650967206     4
## 10 2026     1651782618     4
## # ℹ 12,580 more rows

df_rbsd <- d %>% 
  group_by(ID, timeStampSent) %>% 
  mutate(n = n()) %>% 
  filter(n > 1) %>% 
  filter(mean(rating) > 1) %>%
  filter(mean(rating) < 7) %>%
  summarize(
    rbsd = relativeSD(rating, 1, 7),
    n = n()
  ) %>% 
  summarize(
    rbsd = mean(rbsd),
    n = mean(n)
  )

hist(df_rbsd$rbsd)

# rbsd across moments
df_rbsd_st <- d %>% 
  group_by(ID, timeStampSent) %>% 
  mutate(n = n()) %>% 
  filter(n > 1) %>% 
  filter(mean(rating) > 1) %>%
  filter(mean(rating) < 7) %>%
  summarize(
    rbsd = relativeSD(rating, 1, 7),
    n = n()
  )

2.3.4 Successive Difference

d <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  drop_na(rating)

d %>% 
  group_by(ID, strat) %>% 
  summarize(
    n = n()
  ) %>%
  # arrange(desc(n))
  arrange(n)

## # A tibble: 1,325 × 3
## # Groups:   ID [265]
##    ID    strat                n
##    <chr> <chr>            <int>
##  1 1029  er_distraction       1
##  2 1029  er_reappraisal       1
##  3 1029  er_rumination        1
##  4 1029  er_socialsupport     1
##  5 1029  er_suppresion        1
##  6 8048  er_distraction       1
##  7 8048  er_reappraisal       1
##  8 8048  er_rumination        1
##  9 8048  er_socialsupport     1
## 10 8048  er_suppresion        1
## # ℹ 1,315 more rows

df_rwrmssd <- d %>% 
  group_by(ID, strat) %>% 
  mutate(n = n()) %>% 
  filter(n > 1) %>% 
  filter(mean(rating) < 7) %>%
  filter(mean(rating) > 1) %>%
  summarize(
    rwrmssd = relativeRMSSD(rating, 1, 7),
    n = n()
  ) %>% 
  summarize(
    rwrmssd = mean(rwrmssd),
    n = mean(n)
  ) %>% 
  ungroup

hist(df_rwrmssd$rwrmssd)

# rwrmssd for each ER strat
df_rwrmssd_st <- d %>% 
  group_by(ID, strat) %>% 
  mutate(n = n()) %>% 
  filter(n > 1) %>% 
  filter(mean(rating) < 7) %>%
  filter(mean(rating) > 1) %>%
  summarize(
    rwrmssd = relativeRMSSD(rating, 1, 7),
    n = n()
  ) %>%
  ungroup

2.3.5 Bray-Curtis

d <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  group_by(ID, strat) %>% 
  arrange(timeStampSent) %>% 
  mutate(
    rating_abs_diff = abs(rating - lag(rating)),
    rating_sum = rating + lag(rating),
  ) %>%
  ungroup %>% 
  drop_na(rating_abs_diff)

# momentary level (state)
df_bc_st <- d %>% 
  group_by(ID, timeStampSent) %>%
  summarize(
    bc = sum(rating_abs_diff, na.rm=T)/sum(rating_sum, na.rm=T)
  )

hist(df_bc_st$bc)

# individual level (trait)
df_bc_tr <- d %>%
  group_by(ID) %>%
  summarize(
    bc = sum(rating_abs_diff, na.rm=T)/sum(rating_sum, na.rm=T),
    n = n()
  ) 

hist(df_bc_tr$bc)

# checking for a relationship between number of observations and bc
df_bc_tr %>% 
  ggplot(aes(x = n, y = bc)) +
  geom_smooth(se=F, size=.4, color="red") + 
  geom_smooth(method="lm") + 
  geom_point()

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

2.3.6 Wentropy (Emotion Regulation Diversity)

# momentary (state)
df_wentropy_st <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  drop_na(rating) %>% 
  group_by(ID, timeStampSent) %>% 
  mutate(
    pr = rating/7,
    ln_pr = log(pr),
    pr_ln_pr = pr*ln_pr
    ) %>%
  summarize(
    wentropy = -sum(pr_ln_pr)
  ) %>% 
  ungroup

hist(df_wentropy_st$wentropy)

# individual (trait)
df_wentropy_tr <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  drop_na(rating) %>% 
  group_by(ID, strat) %>% 
  summarize(
    pr = sum(rating)/(7*n()),
    ln_pr = log(pr),
    pr_ln_pr = pr*ln_pr
    ) %>%
  summarize(
    wentropy = -sum(pr_ln_pr)
  ) %>% 
  ungroup

hist(df_wentropy_tr$wentropy)

2.3.7 Entropy

# momentary (state)
df_entropy_st <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  drop_na(rating) %>% 
  group_by(ID, timeStampSent) %>% 
  mutate(
    pr = rating/sum(rating),
    ln_pr = log(pr),
    pr_ln_pr = pr*ln_pr
    ) %>%
  summarize(
    entropy = -sum(pr_ln_pr)
  ) %>% 
  ungroup

hist(df_entropy_st$entropy, 50)

# individual (trait)
df_entropy_tr <- df_er %>% 
  pivot_longer(cols=starts_with("er_"), 
               names_to="strat", 
               values_to="rating") %>% 
  drop_na(rating) %>% 
  group_by(ID, strat) %>% 
  summarize(
    sum_strategy_rating = sum(rating),
    ) %>%
  mutate(
    pr = sum_strategy_rating/sum(sum_strategy_rating),
    ln_pr = log(pr),
    pr_ln_pr = pr*ln_pr
    ) %>%
  summarize(
    entropy = -sum(pr_ln_pr)
  ) %>% 
  ungroup

hist(df_entropy_tr$entropy, breaks=30)

3 Trait (between-person analysis)

3.1 Cor Between Metrics

d <- df_rwsd %>% 
  select(-n) %>% 
  full_join(df_rbsd %>% select(-n), by="ID") %>%
  full_join(df_rwrmssd %>% select(-n), by="ID") %>%
  full_join(df_bc_tr %>% select(-n), by="ID") %>%
  full_join(df_wentropy_tr, by="ID") %>%
  full_join(df_entropy_tr, by="ID") %>% 
  full_join(df_mean_er_strat, by="ID") %>%
  select(-ID) %>% 
  select(-strat)

## select: dropped one variable (n)
## select: dropped one variable (n)
## full_join: added one column (rbsd)
##            > rows only in x                         0
##            > rows only in df_rbsd %>% select(-n)    1
##            > matched rows                         253
##            >                                     =====
##            > rows total                           254
## select: dropped one variable (n)
## full_join: added one column (rwrmssd)
##            > rows only in x                            1
##            > rows only in df_rwrmssd %>% select(-n)    0
##            > matched rows                            253
##            >                                        =====
##            > rows total                              254
## select: dropped one variable (n)
## full_join: added one column (bc)
##            > rows only in x                          6
##            > rows only in df_bc_tr %>% select(-n)    9
##            > matched rows                          248
##            >                                      =====
##            > rows total                            263
## full_join: added one column (wentropy)
##            > rows only in x                 0
##            > rows only in df_wentropy_tr    2
##            > matched rows                 263
##            >                             =====
##            > rows total                   265
## full_join: added one column (entropy)
##            > rows only in x                0
##            > rows only in df_entropy_tr    0
##            > matched rows                265
##            >                            =====
##            > rows total                  265
## full_join: added 2 columns (strat, mean_er)
##            > rows only in x                     0
##            > rows only in df_mean_er_strat      0
##            > matched rows                   1,325    (includes duplicates)
##            >                               =======
##            > rows total                     1,325
## select: dropped one variable (ID)
## select: dropped one variable (strat)

corrplot::corrplot(cor(d, use="pairwise.complete.obs"))

cor(d, use="pairwise.complete.obs")

##                 rwsd        rbsd     rwrmssd          bc   wentropy     entropy
## rwsd      1.00000000  0.69307528  0.90168342 -0.01388751 -0.1229160 -0.05263748
## rbsd      0.69307528  1.00000000  0.64130309 -0.03940082 -0.2611491 -0.29040009
## rwrmssd   0.90168342  0.64130309  1.00000000 -0.11901781 -0.1643954  0.02033524
## bc       -0.01388751 -0.03940082 -0.11901781  1.00000000  0.5667578  0.01220801
## wentropy -0.12291598 -0.26114908 -0.16439543  0.56675784  1.0000000  0.21150563
## entropy  -0.05263748 -0.29040009  0.02033524  0.01220801  0.2115056  1.00000000
## mean_er  -0.44514455 -0.20574042 -0.55858523  0.21007821 -0.2679604 -0.16430195
##             mean_er
## rwsd     -0.4451445
## rbsd     -0.2057404
## rwrmssd  -0.5585852
## bc        0.2100782
## wentropy -0.2679604
## entropy  -0.1643019
## mean_er   1.0000000

pairwise_n <- matrix(NA, ncol = ncol(d), nrow = ncol(d))
colnames(pairwise_n) <- colnames(d)
rownames(pairwise_n) <- colnames(d)

for (i in 1:ncol(d)) {
  for (j in 1:ncol(d)) {
    pairwise_n[i, j] <- sum(!is.na(d[, i]) & !is.na(d[, j]))
  }
}

# Degrees of freedom = number of pairs - 2
df_matrix <- pairwise_n - 2

3.2 Relative Within SD

df_rwsd_outcomes <- df_rwsd %>% 
  left_join(df_indiff)

## Joining with `by = join_by(ID)`
## left_join: added 13 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff ( 12)
## > matched rows 253
## > =====
## > rows total 253

3.2.1 Depression

fit <- lm(SMFQ ~ rwsd, data=df_rwsd_outcomes)
tab_model(fit)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.66	1.45 – 1.88	<0.001
rwsd	-0.29	-0.57 – -0.01	0.039
Observations	245
R² / R² adjusted	0.017 / 0.013

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(SMFQ ~ poly(rwsd, 3), data=df_rwsd_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: SMFQ ~ rwsd
## Model 2: SMFQ ~ poly(rwsd, 3)
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    243 47.872                           
## 2    241 47.290  2   0.58157 1.4819 0.2293

tab_model(fit_2)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.45	1.39 – 1.50	<0.001
rwsd [1st degree]	-0.93	-1.80 – -0.05	0.039
rwsd [2nd degree]	-0.58	-1.46 – 0.30	0.194
rwsd [3rd degree]	-0.51	-1.38 – 0.37	0.259
Observations	245
R² / R² adjusted	0.029 / 0.017

3.2.2 Anxiety

fit <- lm(SCARED ~ rwsd, data=df_rwsd_outcomes)
tab_model(fit)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	2.19	1.98 – 2.41	<0.001
rwsd	-0.35	-0.63 – -0.06	0.017
Observations	244
R² / R² adjusted	0.023 / 0.019

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(SCARED ~ poly(rwsd, 2), data=df_rwsd_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: SCARED ~ rwsd
## Model 2: SCARED ~ poly(rwsd, 2)
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    242 49.273                                
## 2    241 47.769  1    1.5037 7.5866 0.006329 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	1.94	1.88 – 1.99	<0.001
rwsd [1st degree]	-1.11	-1.99 – -0.22	0.014
rwsd [2nd degree]	-1.24	-2.12 – -0.35	0.006
Observations	244
R² / R² adjusted	0.053 / 0.045

3.2.3 Neg Affect

fit <- lm(panas_neg ~ rwsd, data=df_rwsd_outcomes)
tab_model(fit)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	2.84	2.45 – 3.22	<0.001
rwsd	-1.37	-1.88 – -0.87	<0.001
Observations	253
R² / R² adjusted	0.103 / 0.100

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(panas_neg ~ poly(rwsd, 2), data=df_rwsd_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: panas_neg ~ rwsd
## Model 2: panas_neg ~ poly(rwsd, 2)
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    251 162.85                                  
## 2    250 154.21  1    8.6441 14.014 0.0002252 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	1.83	1.73 – 1.92	<0.001
rwsd [1st degree]	-4.33	-5.88 – -2.79	<0.001
rwsd [2nd degree]	-2.94	-4.49 – -1.39	<0.001
Observations	253
R² / R² adjusted	0.151 / 0.144

3.2.4 Pos Affect

fit <- lm(panas_pos ~ rwsd, data=df_rwsd_outcomes)
tab_model(fit)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	3.97	3.40 – 4.55	<0.001
rwsd	1.56	0.81 – 2.31	<0.001
Observations	253
R² / R² adjusted	0.062 / 0.059

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(panas_pos ~ poly(rwsd, 2), data=df_rwsd_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: panas_pos ~ rwsd
## Model 2: panas_pos ~ poly(rwsd, 2)
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    251 363.14                                
## 2    250 349.03  1    14.112 10.108 0.001662 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	5.12	4.97 – 5.27	<0.001
rwsd [1st degree]	4.92	2.59 – 7.24	<0.001
rwsd [2nd degree]	3.76	1.43 – 6.08	0.002
Observations	253
R² / R² adjusted	0.099 / 0.092

3.3 Relative Between SD

df_rbsd_outcomes <- df_rbsd %>% 
  left_join(df_indiff)

## Joining with `by = join_by(ID)`
## left_join: added 13 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff ( 11)
## > matched rows 254
## > =====
## > rows total 254

3.3.1 Depression

fit <- lm(SMFQ ~ rbsd, data=df_rbsd_outcomes)
tab_model(fit)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.55	1.40 – 1.70	<0.001
rbsd	-0.16	-0.38 – 0.05	0.141
Observations	246
R² / R² adjusted	0.009 / 0.005

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(SMFQ ~ poly(rbsd, 4), data=df_rbsd_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: SMFQ ~ rbsd
## Model 2: SMFQ ~ poly(rbsd, 4)
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    244 48.424                           
## 2    241 47.303  3    1.1203 1.9025 0.1298

tab_model(fit_2)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.44	1.39 – 1.50	<0.001
rbsd [1st degree]	-0.65	-1.52 – 0.23	0.149
rbsd [2nd degree]	0.24	-0.63 – 1.12	0.585
rbsd [3rd degree]	-0.31	-1.19 – 0.58	0.493
rbsd [4th degree]	-1.00	-1.88 – -0.12	0.027
Observations	246
R² / R² adjusted	0.032 / 0.016

3.3.2 Anxiety

fit <- lm(SCARED ~ rbsd, data=df_rbsd_outcomes)
tab_model(fit)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	2.03	1.87 – 2.19	<0.001
rbsd	-0.14	-0.36 – 0.08	0.212
Observations	245
R² / R² adjusted	0.006 / 0.002

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

3.3.3 Neg Affect

fit <- lm(panas_neg ~ rbsd, data=df_rbsd_outcomes)
tab_model(fit)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	2.23	1.94 – 2.51	<0.001
rbsd	-0.61	-1.01 – -0.21	0.003
Observations	254
R² / R² adjusted	0.034 / 0.030

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(panas_neg ~ poly(rbsd, 2), data=df_rbsd_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: panas_neg ~ rbsd
## Model 2: panas_neg ~ poly(rbsd, 2)
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    252 175.43                                  
## 2    251 167.97  1    7.4575 11.144 0.0009712 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	1.83	1.72 – 1.93	<0.001
rbsd [1st degree]	-2.49	-4.10 – -0.88	0.003
rbsd [2nd degree]	-2.73	-4.34 – -1.12	0.001
Observations	254
R² / R² adjusted	0.075 / 0.068

3.3.4 Pos Affect

fit <- lm(panas_pos ~ rbsd, data=df_rbsd_outcomes)
tab_model(fit)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	4.69	4.27 – 5.11	<0.001
rbsd	0.66	0.07 – 1.26	0.028
Observations	254
R² / R² adjusted	0.019 / 0.015

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

3.4 Successive Difference

df_rwrmssd_outcomes <- df_rwrmssd %>% 
  left_join(df_indiff)

## Joining with `by = join_by(ID)`
## left_join: added 13 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff ( 12)
## > matched rows 253
## > =====
## > rows total 253

3.4.1 Depression

fit <- lm(SMFQ ~ rwrmssd, data=df_rwrmssd_outcomes)
tab_model(fit)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.68	1.51 – 1.85	<0.001
rwrmssd	-0.39	-0.65 – -0.13	0.004
Observations	245
R² / R² adjusted	0.034 / 0.030

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

3.4.2 Anxiety

fit <- lm(SCARED ~ rwrmssd, data=df_rwrmssd_outcomes)
tab_model(fit)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	2.24	2.07 – 2.40	<0.001
rwrmssd	-0.49	-0.76 – -0.23	<0.001
Observations	244
R² / R² adjusted	0.053 / 0.049

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(SCARED ~ poly(rwrmssd, 2), data=df_rwrmssd_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: SCARED ~ rwrmssd
## Model 2: SCARED ~ poly(rwrmssd, 2)
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    242 47.770                                  
## 2    241 45.115  1    2.6555 14.185 0.0002082 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	1.94	1.88 – 1.99	<0.001
rwrmssd [1st degree]	-1.67	-2.54 – -0.80	<0.001
rwrmssd [2nd degree]	-1.64	-2.50 – -0.78	<0.001
Observations	244
R² / R² adjusted	0.106 / 0.098

3.4.3 Neg Affect

fit <- lm(panas_neg ~ rwrmssd, data=df_rwrmssd_outcomes)
tab_model(fit)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	2.78	2.49 – 3.08	<0.001
rwrmssd	-1.58	-2.03 – -1.12	<0.001
Observations	253
R² / R² adjusted	0.156 / 0.153

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

3.4.4 Pos Affect

fit <- lm(panas_pos ~ rwrmssd, data=df_rwrmssd_outcomes)
tab_model(fit)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	4.23	3.78 – 4.68	<0.001
rwrmssd	1.47	0.77 – 2.18	<0.001
Observations	253
R² / R² adjusted	0.064 / 0.060

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

3.5 Bray-Curtis

df_bc_tr_outcomes <- df_bc_tr %>% 
  left_join(df_indiff)

## Joining with `by = join_by(ID)`
## left_join: added 13 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff ( 8)
## > matched rows 257
## > =====
## > rows total 257

3.5.1 Depression

fit <- lm(SMFQ ~ bc, data=df_bc_tr_outcomes)
tab_model(fit)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.37	1.27 – 1.47	<0.001
bc	0.45	-0.04 – 0.95	0.074
Observations	249
R² / R² adjusted	0.013 / 0.009

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(SMFQ ~ poly(bc, 2), data=df_bc_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: SMFQ ~ bc
## Model 2: SMFQ ~ poly(bc, 2)
##   Res.Df    RSS Df Sum of Sq     F  Pr(>F)  
## 1    247 48.279                             
## 2    246 47.509  1   0.77018 3.988 0.04693 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.44	1.39 – 1.50	<0.001
bc [1st degree]	0.81	-0.06 – 1.69	0.069
bc [2nd degree]	-0.88	-1.75 – -0.01	0.047
Observations	249
R² / R² adjusted	0.029 / 0.021

3.5.2 Anxiety

fit <- lm(SCARED ~ bc, data=df_bc_tr_outcomes)
tab_model(fit)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	1.79	1.68 – 1.89	<0.001
bc	0.84	0.33 – 1.34	0.001
Observations	249
R² / R² adjusted	0.041 / 0.038

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(SCARED ~ poly(bc, 2), data=df_bc_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: SCARED ~ bc
## Model 2: SCARED ~ poly(bc, 2)
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    247 50.105                                  
## 2    246 47.480  1    2.6258 13.605 0.0002777 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	1.93	1.87 – 1.98	<0.001
bc [1st degree]	1.50	0.63 – 2.38	0.001
bc [2nd degree]	-1.63	-2.50 – -0.76	<0.001
Observations	249
R² / R² adjusted	0.092 / 0.084

3.5.3 Neg Affect

fit <- lm(panas_neg ~ bc, data=df_bc_tr_outcomes)
tab_model(fit)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	1.38	1.20 – 1.56	<0.001
bc	2.51	1.62 – 3.40	<0.001
Observations	257
R² / R² adjusted	0.108 / 0.105

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(panas_neg ~ poly(bc, 2), data=df_bc_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: panas_neg ~ bc
## Model 2: panas_neg ~ poly(bc, 2)
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    255 164.24                                  
## 2    254 153.35  1    10.882 18.023 3.062e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	1.80	1.71 – 1.90	<0.001
bc [1st degree]	4.46	2.93 – 5.99	<0.001
bc [2nd degree]	-3.30	-4.83 – -1.77	<0.001
Observations	257
R² / R² adjusted	0.167 / 0.161

3.5.4 Pos Affect

fit <- lm(panas_pos ~ bc, data=df_bc_tr_outcomes)
tab_model(fit)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	5.66	5.39 – 5.93	<0.001
bc	-2.97	-4.31 – -1.64	<0.001
Observations	257
R² / R² adjusted	0.070 / 0.066

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(panas_pos ~ poly(bc, 2), data=df_bc_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: panas_pos ~ bc
## Model 2: panas_pos ~ poly(bc, 2)
##   Res.Df    RSS Df Sum of Sq     F   Pr(>F)   
## 1    255 370.31                               
## 2    254 357.02  1    13.295 9.459 0.002331 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	5.16	5.02 – 5.31	<0.001
bc [1st degree]	-5.28	-7.62 – -2.95	<0.001
bc [2nd degree]	3.65	1.31 – 5.98	0.002
Observations	257
R² / R² adjusted	0.104 / 0.096

3.6 Wentropy (Emotion Regulation Diversity)

df_wentropy_tr_outcomes <- df_wentropy_tr %>% 
  left_join(df_indiff)

## Joining with `by = join_by(ID)`
## left_join: added 13 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff ( 0)
## > matched rows 265
## > =====
## > rows total 265

3.6.1 Depression

fit <- lm(SMFQ ~ wentropy, data=df_wentropy_tr_outcomes)
tab_model(fit)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.31	0.95 – 1.67	<0.001
wentropy	0.08	-0.15 – 0.32	0.497
Observations	257
R² / R² adjusted	0.002 / -0.002

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(SMFQ ~ poly(wentropy, 4), data=df_wentropy_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: SMFQ ~ wentropy
## Model 2: SMFQ ~ poly(wentropy, 4)
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    255 49.912                           
## 2    252 48.909  3    1.0033 1.7231 0.1627

tab_model(fit_2)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	1.43	1.38 – 1.49	<0.001
wentropy [1st degree]	0.30	-0.57 – 1.18	0.493
wentropy [2nd degree]	0.32	-0.55 – 1.20	0.470
wentropy [3rd degree]	-0.14	-1.02 – 0.73	0.748
wentropy [4th degree]	-0.94	-1.82 – -0.07	0.034
Observations	257
R² / R² adjusted	0.022 / 0.006

3.6.2 Anxiety

fit <- lm(SCARED ~ wentropy, data=df_wentropy_tr_outcomes)
tab_model(fit)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	1.71	1.33 – 2.09	<0.001
wentropy	0.13	-0.11 – 0.38	0.284
Observations	256
R² / R² adjusted	0.005 / 0.001

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(SCARED ~ poly(wentropy, 4), data=df_wentropy_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: SCARED ~ wentropy
## Model 2: SCARED ~ poly(wentropy, 4)
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)  
## 1    254 53.950                             
## 2    251 52.519  3    1.4311 2.2799 0.0799 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	1.92	1.86 – 1.97	<0.001
wentropy [1st degree]	0.50	-0.40 – 1.41	0.275
wentropy [2nd degree]	0.56	-0.35 – 1.47	0.225
wentropy [3rd degree]	-0.05	-0.96 – 0.86	0.910
wentropy [4th degree]	-1.06	-1.97 – -0.16	0.022
Observations	256
R² / R² adjusted	0.031 / 0.015

3.6.3 Neg Affect

fit <- lm(panas_neg ~ wentropy, data=df_wentropy_tr_outcomes)
tab_model(fit)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	0.89	0.21 – 1.57	0.010
wentropy	0.59	0.15 – 1.03	0.008
Observations	265
R² / R² adjusted	0.026 / 0.022

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(panas_neg ~ poly(wentropy, 3), data=df_wentropy_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: panas_neg ~ wentropy
## Model 2: panas_neg ~ poly(wentropy, 3)
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    263 182.27                              
## 2    261 176.09  2    6.1727 4.5744 0.01115 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	1.80	1.70 – 1.90	<0.001
wentropy [1st degree]	2.21	0.59 – 3.83	0.008
wentropy [2nd degree]	1.81	0.19 – 3.43	0.028
wentropy [3rd degree]	1.70	0.08 – 3.32	0.040
Observations	265
R² / R² adjusted	0.059 / 0.048

3.6.4 Pos Affect

fit <- lm(panas_pos ~ wentropy, data=df_wentropy_tr_outcomes)
tab_model(fit)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	7.08	6.09 – 8.06	<0.001
wentropy	-1.25	-1.88 – -0.61	<0.001
Observations	265
R² / R² adjusted	0.053 / 0.049

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

3.7 Entropy

df_entropy_tr_outcomes <- df_entropy_tr %>% 
  left_join(df_indiff)

## Joining with `by = join_by(ID)`
## left_join: added 13 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff ( 0)
## > matched rows 265
## > =====
## > rows total 265

3.7.1 Depression

fit <- lm(SMFQ ~ entropy, data=df_entropy_tr_outcomes)
tab_model(fit)

	SMFQ
Predictors	Estimates	CI	p
(Intercept)	3.78	2.13 – 5.42	<0.001
entropy	-1.47	-2.51 – -0.44	0.005
Observations	257
R² / R² adjusted	0.030 / 0.026

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

3.7.2 Anxiety

fit <- lm(SCARED ~ entropy, data=df_entropy_tr_outcomes)
tab_model(fit)

	SCARED
Predictors	Estimates	CI	p
(Intercept)	4.25	2.54 – 5.97	<0.001
entropy	-1.47	-2.55 – -0.39	0.008
Observations	256
R² / R² adjusted	0.028 / 0.024

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

3.7.3 Neg Affect

fit <- lm(panas_neg ~ entropy, data=df_entropy_tr_outcomes)
tab_model(fit)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	3.82	0.69 – 6.96	0.017
entropy	-1.28	-3.25 – 0.70	0.204
Observations	265
R² / R² adjusted	0.006 / 0.002

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(panas_neg ~ poly(entropy, 2), data=df_entropy_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: panas_neg ~ entropy
## Model 2: panas_neg ~ poly(entropy, 2)
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    263 186.00                              
## 2    262 182.69  1    3.3111 4.7485 0.03021 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	panas_neg
Predictors	Estimates	CI	p
(Intercept)	1.80	1.70 – 1.90	<0.001
entropy [1st degree]	-1.07	-2.71 – 0.57	0.201
entropy [2nd degree]	-1.82	-3.46 – -0.18	0.030
Observations	265
R² / R² adjusted	0.024 / 0.016

3.7.4 Pos Affect

fit <- lm(panas_pos ~ entropy, data=df_entropy_tr_outcomes)
tab_model(fit)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	2.60	-2.02 – 7.23	0.268
entropy	1.62	-1.29 – 4.53	0.274
Observations	265
R² / R² adjusted	0.005 / 0.001

plot_model(fit, type="slope")

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

fit_2 <- lm(panas_pos ~ poly(entropy, 2), data=df_entropy_tr_outcomes)
anova(fit, fit_2)

## Analysis of Variance Table
## 
## Model 1: panas_pos ~ entropy
## Model 2: panas_pos ~ poly(entropy, 2)
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    263 404.50                              
## 2    262 399.99  1    4.5077 2.9526 0.08692 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

tab_model(fit_2)

	panas_pos
Predictors	Estimates	CI	p
(Intercept)	5.18	5.03 – 5.33	<0.001
entropy [1st degree]	1.36	-1.07 – 3.79	0.272
entropy [2nd degree]	2.12	-0.31 – 4.56	0.087
Observations	265
R² / R² adjusted	0.016 / 0.008

4 Momentary (within-person analysis)

4.1 Cor Between Metrics

df_bc_st_mean <- df_bc_st %>% 
  group_by(ID) %>% 
  summarize(mean_bc = mean(bc, na.rm = TRUE))

## group_by: one grouping variable (ID)
## summarize: now 257 rows and 2 columns, ungrouped

df_wentropy_st_mean <- df_wentropy_st %>% 
  group_by(ID) %>% 
  summarize(mean_wentropy = mean(wentropy, na.rm = TRUE))

## group_by: one grouping variable (ID)
## summarize: now 265 rows and 2 columns, ungrouped

df_entropy_st_mean <- df_entropy_st %>% 
  group_by(ID) %>% 
  summarize(mean_entropy = mean(entropy, na.rm = TRUE))

## group_by: one grouping variable (ID)
## summarize: now 265 rows and 2 columns, ungrouped

d <- df_rwsd %>% 
  select(-n) %>% 
  full_join(df_rbsd %>% select(-n), by="ID") %>%
  full_join(df_rwrmssd %>% select(-n), by="ID") %>%
  full_join(df_bc_st_mean, by="ID") %>%
  full_join(df_wentropy_st_mean, by="ID") %>%
  full_join(df_entropy_st_mean, by="ID") %>% 
  full_join(df_mean_er_strat, by="ID") %>%
  select(-ID) %>% 
  select(-strat)

## select: dropped one variable (n)
## select: dropped one variable (n)
## full_join: added one column (rbsd)
##            > rows only in x                         0
##            > rows only in df_rbsd %>% select(-n)    1
##            > matched rows                         253
##            >                                     =====
##            > rows total                           254
## select: dropped one variable (n)
## full_join: added one column (rwrmssd)
##            > rows only in x                            1
##            > rows only in df_rwrmssd %>% select(-n)    0
##            > matched rows                            253
##            >                                        =====
##            > rows total                              254
## full_join: added one column (mean_bc)
##            > rows only in x                6
##            > rows only in df_bc_st_mean    9
##            > matched rows                248
##            >                            =====
##            > rows total                  263
## full_join: added one column (mean_wentropy)
##            > rows only in x                      0
##            > rows only in df_wentropy_st_mean    2
##            > matched rows                      263
##            >                                  =====
##            > rows total                        265
## full_join: added one column (mean_entropy)
##            > rows only in x                     0
##            > rows only in df_entropy_st_mean    0
##            > matched rows                     265
##            >                                 =====
##            > rows total                       265
## full_join: added 2 columns (strat, mean_er)
##            > rows only in x                     0
##            > rows only in df_mean_er_strat      0
##            > matched rows                   1,325    (includes duplicates)
##            >                               =======
##            > rows total                     1,325
## select: dropped one variable (ID)
## select: dropped one variable (strat)

corrplot::corrplot(cor(d, use="pairwise.complete.obs"))

cor(d, use="pairwise.complete.obs")

##                     rwsd        rbsd     rwrmssd     mean_bc mean_wentropy
## rwsd           1.0000000  0.69307528  0.90168342 -0.14082009   -0.23403019
## rbsd           0.6930753  1.00000000  0.64130309 -0.06915301   -0.36418921
## rwrmssd        0.9016834  0.64130309  1.00000000 -0.27621472   -0.05835954
## mean_bc       -0.1408201 -0.06915301 -0.27621472  1.00000000   -0.16626100
## mean_wentropy -0.2340302 -0.36418921 -0.05835954 -0.16626100    1.00000000
## mean_entropy  -0.0435414 -0.33794978  0.12736986 -0.48980349    0.43857187
## mean_er       -0.4451445 -0.20574042 -0.55858523  0.38774922   -0.57420085
##               mean_entropy    mean_er
## rwsd            -0.0435414 -0.4451445
## rbsd            -0.3379498 -0.2057404
## rwrmssd          0.1273699 -0.5585852
## mean_bc         -0.4898035  0.3877492
## mean_wentropy    0.4385719 -0.5742008
## mean_entropy     1.0000000 -0.3772359
## mean_er         -0.3772359  1.0000000

pairwise_n <- matrix(NA, ncol = ncol(d), nrow = ncol(d))
colnames(pairwise_n) <- colnames(d)
rownames(pairwise_n) <- colnames(d)

for (i in 1:ncol(d)) {
  for (j in 1:ncol(d)) {
    pairwise_n[i, j] <- sum(!is.na(d[, i]) & !is.na(d[, j]))
  }
}

# Degrees of freedom = number of pairs - 2
df_matrix <- pairwise_n - 2

4.2 Relative Within SD

# all ER strategies
df_rwsd_outcomes <- df_rwsd %>% 
  left_join(df_indiff_2) %>% 
  left_join(
    df_mean_er_strat %>%
      group_by(ID) %>%
      summarize(mean_ID_er = mean(mean_er, na.rm = TRUE)) %>%
      select(ID, mean_ID_er) %>% 
      ungroup() 
    )

## Joining with `by = join_by(ID)`
## left_join: added 13 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff_2 ( 836)
## > matched rows 17,573 (includes duplicates)
## > ========
## > rows total 17,573
## group_by: one grouping variable (ID)
## summarize: now 265 rows and 2 columns, ungrouped
## select: no changes
## ungroup: no grouping variables remain
## Joining with `by = join_by(ID)`
## left_join: added one column (mean_ID_er)
## > rows only in x 0
## > rows only in df_mean_er_strat %>% gr.. ( 12)
## > matched rows 17,573
## > ========
## > rows total 17,573

4.2.1 Neg Affect

fit <- df_rwsd_outcomes %>% 
  lmer(panas_neg_z ~ rwsd + (1|ID), data = .)
tab_model(fit)

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.95	0.61 – 1.30	<0.001
rwsd	-1.24	-1.69 – -0.79	<0.001
Random Effects
σ²	0.45
τ₀₀ _ID	0.51
ICC	0.53
N _ID	253
Observations	12099
Marginal R² / Conditional R²	0.060 / 0.561

# controlling for mean ER
df_rwsd_outcomes %>% 
  lmer(panas_neg_z ~ rwsd + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.05	-0.44 – 0.55	0.828
rwsd	-0.63	-1.13 – -0.13	0.013
mean ID er	0.18	0.11 – 0.25	<0.001
Random Effects
σ²	0.45
τ₀₀ _ID	0.47
ICC	0.51
N _ID	253
Observations	12099
Marginal R² / Conditional R²	0.106 / 0.563

relative within SD predicts less neg affect

4.2.2 Pos Affect

fit <- df_rwsd_outcomes %>% 
  lmer(panas_pos_z ~ rwsd + (1|ID), data = .)
tab_model(fit)

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.73	-1.08 – -0.39	<0.001
rwsd	0.95	0.49 – 1.40	<0.001
Random Effects
σ²	0.47
τ₀₀ _ID	0.51
ICC	0.52
N _ID	253
Observations	12096
Marginal R² / Conditional R²	0.035 / 0.538

# controlling for mean ER
df_rwsd_outcomes %>% 
  lmer(panas_pos_z ~ rwsd + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.80	-1.32 – -0.29	0.002
rwsd	0.99	0.47 – 1.51	<0.001
mean ID er	0.01	-0.06 – 0.09	0.717
Random Effects
σ²	0.47
τ₀₀ _ID	0.51
ICC	0.52
N _ID	253
Observations	12096
Marginal R² / Conditional R²	0.035 / 0.538

relative within SD predicts more pos affect

4.3 Relative Between SD

df_rbsd_outcomes <- df_rbsd_st %>% 
  left_join(df_indiff_2) %>% 
  left_join(
    df_mean_er_strat %>%
      group_by(ID) %>%
      summarize(mean_ID_er = mean(mean_er, na.rm = TRUE)) %>%
      select(ID, mean_ID_er) %>% 
      ungroup() 
    ) %>% 
  mutate(across(rbsd, ~scale(., scale = F), .names = "{.col}_pc")) %>% 
  mutate(across(rbsd_pc, ~scale(., scale = T), .names = "{.col}_z"))

## Joining with `by = join_by(ID, timeStampSent)`
## left_join: added 12 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff_2 (11,761)
## > matched rows 6,648
## > ========
## > rows total 6,648
## group_by: one grouping variable (ID)
## summarize: now 265 rows and 2 columns, ungrouped
## select: no changes
## ungroup: no grouping variables remain
## Joining with `by = join_by(ID)`
## left_join: added one column (mean_ID_er)
## > rows only in x 0
## > rows only in df_mean_er_strat %>% gr.. ( 11)
## > matched rows 6,648
## > =======
## > rows total 6,648
## mutate (grouped): new variable 'rbsd_pc' (double) with 3,128 unique values and
## 0% NA
## mutate (grouped): new variable 'rbsd_pc_z' (double) with 3,107 unique values
## and 1% NA

4.3.1 Neg Affect

fit <- df_rbsd_outcomes %>% 
  lmer(panas_neg_z ~ rbsd_pc_z + (1|ID), data = .)
tab_model(fit)

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.33	0.23 – 0.44	<0.001
rbsd pc z	-0.04	-0.06 – -0.03	<0.001
Random Effects
σ²	0.54
τ₀₀ _ID	0.60
ICC	0.53
N _ID	231
Observations	6612
Marginal R² / Conditional R²	0.002 / 0.528

# controlling for mean ER
df_rbsd_outcomes %>% 
  lmer(panas_neg_z ~ rbsd_pc_z + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.18	-0.05 – 0.42	0.130
rbsd pc z	-0.04	-0.06 – -0.03	<0.001
mean ID er	0.05	-0.02 – 0.13	0.167
Random Effects
σ²	0.54
τ₀₀ _ID	0.60
ICC	0.53
N _ID	231
Observations	6612
Marginal R² / Conditional R²	0.005 / 0.529

relative between SD predicts less neg affect (but effect size is really small)

4.3.2 Pos Affect

fit <- df_rbsd_outcomes %>% 
  lmer(panas_pos_z ~ rbsd_pc_z + (1|ID), data = .)
tab_model(fit)

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.18	-0.27 – -0.09	<0.001
rbsd pc z	0.04	0.02 – 0.05	<0.001
Random Effects
σ²	0.49
τ₀₀ _ID	0.48
ICC	0.49
N _ID	231
Observations	6610
Marginal R² / Conditional R²	0.001 / 0.495

# controlling for mean ER
df_rbsd_outcomes %>% 
  lmer(panas_pos_z ~ rbsd_pc_z + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.28	-0.49 – -0.06	0.011
rbsd pc z	0.04	0.02 – 0.05	<0.001
mean ID er	0.04	-0.03 – 0.10	0.322
Random Effects
σ²	0.49
τ₀₀ _ID	0.48
ICC	0.49
N _ID	231
Observations	6610
Marginal R² / Conditional R²	0.003 / 0.495

relative between SD predicts more pos affect (but effect size is really small)

4.4 Successive Difference

df_rwrmssd_outcomes <- df_rwrmssd %>% 
  left_join(df_indiff_2) %>% 
  left_join(
    df_mean_er_strat %>%
      group_by(ID) %>%
      summarize(mean_ID_er = mean(mean_er, na.rm = TRUE)) %>%
      select(ID, mean_ID_er) %>% 
      ungroup() 
    )

## Joining with `by = join_by(ID)`
## left_join: added 13 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff_2 ( 836)
## > matched rows 17,573 (includes duplicates)
## > ========
## > rows total 17,573
## group_by: one grouping variable (ID)
## summarize: now 265 rows and 2 columns, ungrouped
## select: no changes
## ungroup: no grouping variables remain
## Joining with `by = join_by(ID)`
## left_join: added one column (mean_ID_er)
## > rows only in x 0
## > rows only in df_mean_er_strat %>% gr.. ( 12)
## > matched rows 17,573
## > ========
## > rows total 17,573

4.4.1 Neg Affect

fit <- df_rwrmssd_outcomes %>% 
  lmer(panas_neg_z ~ rwrmssd + (1|ID), data = .)
tab_model(fit)

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.90	0.64 – 1.17	<0.001
rwrmssd	-1.42	-1.83 – -1.02	<0.001
Random Effects
σ²	0.45
τ₀₀ _ID	0.48
ICC	0.52
N _ID	253
Observations	12099
Marginal R² / Conditional R²	0.092 / 0.562

# controlling for mean ER
df_rwrmssd_outcomes %>% 
  lmer(panas_neg_z ~ rwrmssd + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.21	-0.26 – 0.68	0.378
rwrmssd	-0.86	-1.37 – -0.35	0.001
mean ID er	0.14	0.06 – 0.22	0.001
Random Effects
σ²	0.45
τ₀₀ _ID	0.46
ICC	0.51
N _ID	253
Observations	12099
Marginal R² / Conditional R²	0.116 / 0.564

successive differences predicts less neg affect

4.4.2 Pos Affect

fit <- df_rwrmssd_outcomes %>% 
  lmer(panas_pos_z ~ rwrmssd + (1|ID), data = .)
tab_model(fit)

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.58	-0.85 – -0.31	<0.001
rwrmssd	0.89	0.47 – 1.31	<0.001
Random Effects
σ²	0.47
τ₀₀ _ID	0.51
ICC	0.52
N _ID	253
Observations	12096
Marginal R² / Conditional R²	0.036 / 0.538

# controlling for mean ER
df_rwrmssd_outcomes %>% 
  lmer(panas_pos_z ~ rwrmssd + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.81	-1.31 – -0.32	0.001
rwrmssd	1.08	0.54 – 1.62	<0.001
mean ID er	0.05	-0.04 – 0.13	0.268
Random Effects
σ²	0.47
τ₀₀ _ID	0.51
ICC	0.52
N _ID	253
Observations	12096
Marginal R² / Conditional R²	0.038 / 0.539

successive differences predicts more pos affect (large effect size)

4.5 Bray-Curtis

df_bc_outcomes <- df_bc_st %>% 
  left_join(df_indiff_2) %>% 
  left_join(
    df_mean_er_strat %>%
      group_by(ID) %>%
      summarize(mean_ID_er = mean(mean_er, na.rm = TRUE)) %>%
      select(ID, mean_ID_er) %>% 
      ungroup() 
    ) %>% 
  mutate(across(bc, ~scale(., scale = F), .names = "{.col}_pc")) %>% 
  mutate(across(bc_pc, ~scale(., scale = T), .names = "{.col}_z"))

## Joining with `by = join_by(ID, timeStampSent)`
## left_join: added 12 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff_2 (8,410)
## > matched rows 9,999
## > =======
## > rows total 9,999
## group_by: one grouping variable (ID)
## summarize: now 265 rows and 2 columns, ungrouped
## select: no changes
## ungroup: no grouping variables remain
## Joining with `by = join_by(ID)`
## left_join: added one column (mean_ID_er)
## > rows only in x 0
## > rows only in df_mean_er_strat %>% gr.. ( 8)
## > matched rows 9,999
## > =======
## > rows total 9,999
## mutate (grouped): new variable 'bc_pc' (double) with 3,901 unique values and 0%
## NA
## mutate (grouped): new variable 'bc_pc_z' (double) with 3,901 unique values and
## 4% NA

4.5.1 Neg Affect

fit <- df_bc_outcomes %>% 
  lmer(panas_neg_z ~ bc_pc_z + (1|ID), data = .)
tab_model(fit)

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.04	-0.06 – 0.14	0.421
bc pc z	0.10	0.09 – 0.11	<0.001
Random Effects
σ²	0.44
τ₀₀ _ID	0.59
ICC	0.58
N _ID	244
Observations	9578
Marginal R² / Conditional R²	0.009 / 0.580

# controlling for mean ER
df_bc_outcomes %>% 
  lmer(panas_neg_z ~ bc_pc_z + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	-0.51	-0.70 – -0.32	<0.001
bc pc z	0.10	0.09 – 0.11	<0.001
mean ID er	0.22	0.15 – 0.28	<0.001
Random Effects
σ²	0.44
τ₀₀ _ID	0.50
ICC	0.54
N _ID	244
Observations	9578
Marginal R² / Conditional R²	0.101 / 0.583

BC predicts MORE neg affect (effect size does not change when controlling for mean ER; small effect size)

4.5.2 Pos Affect

fit <- df_bc_outcomes %>% 
  lmer(panas_pos_z ~ bc_pc_z + (1|ID), data = .)
tab_model(fit)

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.04	-0.14 – 0.05	0.381
bc pc z	-0.05	-0.06 – -0.04	<0.001
Random Effects
σ²	0.46
τ₀₀ _ID	0.56
ICC	0.55
N _ID	244
Observations	9575
Marginal R² / Conditional R²	0.002 / 0.547

# controlling for mean ER
df_bc_outcomes %>% 
  lmer(panas_pos_z ~ bc_pc_z + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	0.09	-0.11 – 0.29	0.382
bc pc z	-0.05	-0.06 – -0.04	<0.001
mean ID er	-0.05	-0.12 – 0.02	0.141
Random Effects
σ²	0.46
τ₀₀ _ID	0.55
ICC	0.54
N _ID	244
Observations	9575
Marginal R² / Conditional R²	0.008 / 0.548

BC predicts LESS pos affect (effect size does not change when controlling for mean ER; small effect size)

4.6 Wentropy (Emotion Regulation Diversity)

df_wentropy_outcomes <- df_wentropy_st %>% 
  left_join(df_indiff_2) %>% 
  left_join(
    df_mean_er_strat %>%
      group_by(ID) %>%
      summarize(mean_ID_er = mean(mean_er, na.rm = TRUE)) %>%
      select(ID, mean_ID_er) %>% 
      ungroup() 
    ) %>% 
  mutate(across(wentropy, ~scale(., scale = F), .names = "{.col}_pc")) %>% 
  mutate(across(wentropy_pc, ~scale(., scale = T), .names = "{.col}_z"))

## Joining with `by = join_by(ID, timeStampSent)`
## left_join: added 12 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff_2 ( 5,819)
## > matched rows 12,590
## > ========
## > rows total 12,590
## group_by: one grouping variable (ID)
## summarize: now 265 rows and 2 columns, ungrouped
## select: no changes
## ungroup: no grouping variables remain
## Joining with `by = join_by(ID)`
## left_join: added one column (mean_ID_er)
## > rows only in x 0
## > rows only in df_mean_er_strat %>% gr.. ( 0)
## > matched rows 12,590
## > ========
## > rows total 12,590
## mutate: new variable 'wentropy_pc' (double) with 436 unique values and 0% NA
## mutate: new variable 'wentropy_pc_z' (double) with 436 unique values and 0% NA

4.6.1 Neg Affect

fit <- df_wentropy_outcomes %>% 
  lmer(panas_neg_z ~ wentropy_pc_z + (1|ID), data = .)
tab_model(fit)

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.02	-0.07 – 0.11	0.724
wentropy pc z	-0.05	-0.07 – -0.03	<0.001
Random Effects
σ²	0.43
τ₀₀ _ID	0.56
ICC	0.56
N _ID	265
Observations	12590
Marginal R² / Conditional R²	0.003 / 0.566

# controlling for mean ER
df_wentropy_outcomes %>% 
  lmer(panas_neg_z ~ wentropy_pc_z + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	-0.52	-0.69 – -0.35	<0.001
wentropy pc z	-0.05	-0.06 – -0.03	<0.001
mean ID er	0.22	0.16 – 0.28	<0.001
Random Effects
σ²	0.43
τ₀₀ _ID	0.47
ICC	0.52
N _ID	265
Observations	12590
Marginal R² / Conditional R²	0.107 / 0.574

Wentropy predicts less neg affect (but small effect size)

4.6.2 Pos Affect

fit <- df_wentropy_outcomes %>% 
  lmer(panas_pos_z ~ wentropy_pc_z + (1|ID), data = .)
tab_model(fit)

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.00	-0.09 – 0.09	0.929
wentropy pc z	-0.04	-0.06 – -0.02	<0.001
Random Effects
σ²	0.46
τ₀₀ _ID	0.54
ICC	0.54
N _ID	265
Observations	12587
Marginal R² / Conditional R²	0.002 / 0.542

# controlling for mean ER
df_wentropy_outcomes %>% 
  lmer(panas_pos_z ~ wentropy_pc_z + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	0.23	0.04 – 0.41	0.016
wentropy pc z	-0.04	-0.06 – -0.02	<0.001
mean ID er	-0.09	-0.16 – -0.03	0.005
Random Effects
σ²	0.46
τ₀₀ _ID	0.53
ICC	0.53
N _ID	265
Observations	12587
Marginal R² / Conditional R²	0.013 / 0.541

Wentropy predicts LESS pos affecr (but small effect size)

4.7 Entropy

df_entropy_outcomes <- df_entropy_st %>% 
  left_join(df_indiff_2) %>% 
  left_join(
    df_mean_er_strat %>%
      group_by(ID) %>%
      summarize(mean_ID_er = mean(mean_er, na.rm = TRUE)) %>%
      select(ID, mean_ID_er) %>% 
      ungroup() 
    ) %>% 
  mutate(across(entropy, ~scale(., scale = F), .names = "{.col}_pc")) %>% 
  mutate(across(entropy_pc, ~scale(., scale = T), .names = "{.col}_z"))

## Joining with `by = join_by(ID, timeStampSent)`
## left_join: added 12 columns (Sex, Birth_year, Birth_month, Ethnicity, SCARED,
## …)
## > rows only in x 0
## > rows only in df_indiff_2 ( 5,819)
## > matched rows 12,590
## > ========
## > rows total 12,590
## group_by: one grouping variable (ID)
## summarize: now 265 rows and 2 columns, ungrouped
## select: no changes
## ungroup: no grouping variables remain
## Joining with `by = join_by(ID)`
## left_join: added one column (mean_ID_er)
## > rows only in x 0
## > rows only in df_mean_er_strat %>% gr.. ( 0)
## > matched rows 12,590
## > ========
## > rows total 12,590
## mutate: new variable 'entropy_pc' (double) with 416 unique values and 0% NA
## mutate: new variable 'entropy_pc_z' (double) with 416 unique values and 0% NA

4.7.1 Neg Affect

fit <- df_entropy_outcomes %>% 
  lmer(panas_neg_z ~ entropy_pc_z + (1|ID), data = .)
tab_model(fit)

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	0.01	-0.08 – 0.10	0.748
entropy pc z	-0.09	-0.11 – -0.07	<0.001
Random Effects
σ²	0.43
τ₀₀ _ID	0.54
ICC	0.56
N _ID	265
Observations	12590
Marginal R² / Conditional R²	0.008 / 0.562

# controlling for mean ER
df_entropy_outcomes %>% 
  lmer(panas_neg_z ~ entropy_pc_z + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_neg_z
Predictors	Estimates	CI	p
(Intercept)	-0.51	-0.68 – -0.34	<0.001
entropy pc z	-0.09	-0.10 – -0.07	<0.001
mean ID er	0.21	0.15 – 0.27	<0.001
Random Effects
σ²	0.43
τ₀₀ _ID	0.46
ICC	0.52
N _ID	265
Observations	12590
Marginal R² / Conditional R²	0.114 / 0.571

Entropy predicts less neg affect (but small effect size)

4.7.2 Pos Affect

fit <- df_entropy_outcomes %>% 
  lmer(panas_pos_z ~ entropy_pc_z + (1|ID), data = .)
tab_model(fit)

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	-0.00	-0.09 – 0.08	0.918
entropy pc z	0.05	0.03 – 0.06	<0.001
Random Effects
σ²	0.46
τ₀₀ _ID	0.54
ICC	0.54
N _ID	265
Observations	12587
Marginal R² / Conditional R²	0.002 / 0.541

# controlling for mean ER
df_entropy_outcomes %>% 
  lmer(panas_pos_z ~ entropy_pc_z + mean_ID_er + (1|ID), data = .) %>% 
  tab_model()

	panas_pos_z
Predictors	Estimates	CI	p
(Intercept)	0.17	-0.02 – 0.35	0.077
entropy pc z	0.05	0.03 – 0.06	<0.001
mean ID er	-0.07	-0.13 – -0.00	0.037
Random Effects
σ²	0.46
τ₀₀ _ID	0.53
ICC	0.54
N _ID	265
Observations	12587
Marginal R² / Conditional R²	0.014 / 0.543

Entropy predicts more pos affect (but small effect size)

wave2_analysis

Angela

2025-11-05

1 Initialization

2 Preprocessing

2.1 Read data

2.2 Process data

2.3 Calculate Metrics

2.3.1 Mean ER

2.3.2 Relative Within SD

2.3.3 Relative Between SD

2.3.4 Successive Difference

2.3.5 Bray-Curtis

2.3.6 Wentropy (Emotion Regulation Diversity)

2.3.7 Entropy

3 Trait (between-person analysis)

3.1 Cor Between Metrics

3.2 Relative Within SD

3.2.1 Depression

3.2.2 Anxiety

3.2.3 Neg Affect

3.2.4 Pos Affect

3.3 Relative Between SD

3.3.1 Depression

3.3.2 Anxiety

3.3.3 Neg Affect

3.3.4 Pos Affect

3.4 Successive Difference

3.4.1 Depression

3.4.2 Anxiety

3.4.3 Neg Affect

3.4.4 Pos Affect

3.5 Bray-Curtis

3.5.1 Depression

3.5.2 Anxiety

3.5.3 Neg Affect

3.5.4 Pos Affect

3.6 Wentropy (Emotion Regulation Diversity)

3.6.1 Depression

3.6.2 Anxiety

3.6.3 Neg Affect

3.6.4 Pos Affect

3.7 Entropy

3.7.1 Depression

3.7.2 Anxiety

3.7.3 Neg Affect

3.7.4 Pos Affect

4 Momentary (within-person analysis)

4.1 Cor Between Metrics

4.2 Relative Within SD

4.2.1 Neg Affect

4.2.2 Pos Affect

4.3 Relative Between SD

4.3.1 Neg Affect

4.3.2 Pos Affect

4.4 Successive Difference

4.4.1 Neg Affect

4.4.2 Pos Affect

4.5 Bray-Curtis

4.5.1 Neg Affect

4.5.2 Pos Affect

4.6 Wentropy (Emotion Regulation Diversity)

4.6.1 Neg Affect

4.6.2 Pos Affect

4.7 Entropy

4.7.1 Neg Affect

4.7.2 Pos Affect