Analysis
Ashish
2024-11-15
1 Initialization
Need to install the relativeVariability package from source
install.packages("relativeVariability_1.0.tar.gz", repos=NULL, type="source")
rm(list=ls())
library(relativeVariability)
library(rsurveyutils)
library(here)
library(tidyverse)
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/proc/wave2/ESM_W2_WITH_MISSING_V1_no exclusions.csv")
df_raw <- read_csv(filepath) %>%
janitor::clean_names() ## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)names(df_raw)## [1] "x1"
## [2] "connection_id"
## [3] "invitation_code"
## [4] "alias"
## [5] "scheduled_beep_id"
## [6] "sent_beep_id"
## [7] "reminder_for_original_sent_beep_id"
## [8] "question_list_name"
## [9] "time_stamp_scheduled"
## [10] "time_stamp_sent"
## [11] "time_stamp_start"
## [12] "time_stamp_stop"
## [13] "original_time_stamp_sent"
## [14] "check_right_2"
## [15] "random_number_computation"
## [16] "n_computation"
## [17] "panas_happy"
## [18] "randomizercode_1_computation"
## [19] "panas_relaxed"
## [20] "randomizercode_2_computation"
## [21] "panas_energetic"
## [22] "randomizercode_3_computation"
## [23] "panas_sad"
## [24] "randomizercode_4_computation"
## [25] "panas_angry"
## [26] "randomizercode_5_computation"
## [27] "panas_anxious"
## [28] "randomizercode_6_computation"
## [29] "panas_uncertain"
## [30] "randomizercode_7_computation"
## [31] "panas_annoyed"
## [32] "randomizercode_8_computation"
## [33] "panas_stressed"
## [34] "randomizercode_9_computation"
## [35] "intense_emotion_multiple_choice_index"
## [36] "intense_emotion_multiple_choice_string"
## [37] "intense_emotion_multiple_choice_likert"
## [38] "intense_emotion_intensity_slider_neutral_pos"
## [39] "randomizercode_10_computation"
## [40] "emotionregulation_reappraisal"
## [41] "randomizercode_11_computation"
## [42] "emotionregulation_distraction"
## [43] "randomizercode_12_computation"
## [44] "emotionregulation_socialsupport"
## [45] "randomizercode_13_computation"
## [46] "emotionregulation_suppresion"
## [47] "randomizercode_14_computation"
## [48] "emotionregulation_rumination"
## [49] "randomizercode_15_computation"
## [50] "emotionregulation_selfcompassion1"
## [51] "randomizercode_16_computation"
## [52] "emotionregulation_selfcompassion2"
## [53] "randomizercode_17_computation"
## [54] "emotionregulation_engagement"
## [55] "randomizercode_18_computation"
## [56] "event_stressevaluation_choice"
## [57] "event_stressevaluation"
## [58] "stressevaluation_event_multiple_choice_likert"
## [59] "randomizercode_19_computation"
## [60] "event_stressevaluation_stress"
## [61] "randomizercode_20_computation"
## [62] "check_right_1"
## [63] "event_stressevaluation_control"
## [64] "randomizercode_21_computation"
## [65] "check_left_1"
## [66] "friend_interaction_yesno"
## [67] "friend_positive_listened"
## [68] "friend_positive_attention"
## [69] "friend_negative_irritated"
## [70] "friend_negative_argued"
## [71] "peer_exclusion_slider_neg_pos"
## [72] "peer_acceptance_slider_neg_pos"
## [73] "randomizercode_22_computation"
## [74] "sleep_time_time"
## [75] "sleep_quality_slider_neg_pos"
## [76] "check_right_4"
## [77] "sleep_awakening_time"
## [78] "intense_emotion_rating"
## [79] "randomizercode_23_computation"
## [80] "panas_happy_1_slider_neg_pos"
## [81] "friend_seen_last"
## [82] "friend_seen_last_time_multiple_choice_string"
## [83] "friend_seen_last_time_multiple_choice_likert"
## [84] "time_seen_friend_time"
## [85] "check_right_3"
## [86] "random_number_computation_1"
## [87] "n_computation_1"
## [88] "check_left_3"
## [89] "label_r_oe_z_basic"
## [90] "check_right_5"
## [91] "check_left_5"
## [92] "emotionregulation_socialsupport_slider_neutral_pos_1"
## [93] "panas_sad_slider_neg_pos_1"
## [94] "check_left_4"
## [95] "friend_interaction_yesno_1"
## [96] "panas_sad_slider_neg_pos_2"
## [97] "check_left_2"
## [98] "panas_happy_slider_neg_pos_1"
## [99] "panas_happy_slider_neg_pos_2"
## [100] "panas_anxious_slider_neg_pos_1"
## [101] "panas_angry_slider_neg_pos_1"
## [102] "time_seen_friend_time_1"
## [103] "emotionregulation_selfcompassion_1_slider_neutral_pos_1"
## [104] "peer_acceptance_slider_neg_pos_1"
## [105] "emotionregulation_reappraisal_slider_neutral_pos_1"
## [106] "panas_annoyed_slider_neg_pos_1"
## [107] "emotionregulation_distraction_slider_neutral_pos_1"
## [108] "friend_interaction_yesno_2"
## [109] "panas_relaxed_slider_neg_pos_1"
## [110] "panas_relaxed_slider_neg_pos_2"
## [111] "panas_uncertain_slider_neg_pos_1"
## [112] "panas_uncertain_slider_neg_pos_2"
## [113] "emotionregulation_selfcompassion_2_slider_neutral_pos_1"
## [114] "friend_negative2_slider_neg_pos_1"
## [115] "panas_energetic_slider_neg_pos_1"
## [116] "emotionregulation_rumination_slider_neutral_pos_1"
## [117] "friend_negative2_slider_neg_pos_2"
## [118] "intensity_neg_emotions_slider_neg_pos_1"
## [119] "intensity_neg_emotions_slider_neg_pos_2"
## [120] "intensity_neg_emotions_slider_neg_pos_3"
## [121] "friend_seen_last_time_multiple_choice_index_1"
## [122] "friend_seen_last_time_multiple_choice_string_1"
## [123] "friend_seen_last_time_multiple_choice_likert_1"
## [124] "emotionregulation_rumination_slider_neutral_pos_2"
## [125] "emotionregulation_suppresion_slider_neutral_pos_1"
## [126] "emotionregulation_distraction_slider_neutral_pos_2"
## [127] "emotionregulation_distraction_slider_neutral_pos_3"
## [128] "emotionregulation_distraction_slider_neutral_pos_4"
## [129] "emotionregulation_distraction_slider_neutral_pos_5"
## [130] "panas_stressed_slider_neg_pos_1"
## [131] "wave_text"
## [132] "wave"
## [133] "temporal"
## [134] "time_stamp_scheduled_transformed"
## [135] "time_stamp_sent_transformed"
## [136] "time_stamp_start_transformed"
## [137] "time_stamp_stop_transformed"
## [138] "temp"
## [139] "received_beeps"
## [140] "completed_beeps"
## [141] "check_right"
## [142] "check_left"
## [143] "check_correct"
## [144] "n_correct"
## [145] "n_incorrect"
## [146] "start_stop_difference_seconds"
## [147] "start_stop_difference_seconds_average"
## [148] "completed_enough"
## [149] "day"
## [150] "weekend"
## [151] "day_sequence"
## [152] "day_sequence_completed"
## [153] "time_stamp_diff_sent_start"
## [154] "time_stamp_survey_duration"
## [155] "marker"
## [156] "relative_compliance"
## [157] "beeps"
## [158] "beeps_sequence_completed"
## [159] "beeps_sequence"
## [160] "panas_positivescale_beep"
## [161] "panas_negativescale_beep"
## [162] "myna_pa"
## [163] "myna_na"
## [164] "panas_positivescale_day"
## [165] "panas_negativescale_day"
## [166] "emotionregulation_selfcompassion"
## [167] "was_er_used"
## [168] "betw_strat_mean"
## [169] "betw_strat_sd"
## [170] "betw_strat_sd_index"
## [171] "betw_strat_rel_sd"
## [172] "friend_positive_interaction"
## [173] "friend_negative_interaction"
## [174] "id"
## [175] "sex"
## [176] "birth_year"
## [177] "birth_month"
## [178] "ethnicity"
## [179] "education_mother"
## [180] "education_father"
## [181] "birth"
## [182] "wave_2"
## [183] "olw_v_gl_w2"
## [184] "olw_v_form_1_w2"
## [185] "olw_v_form_2_w2"
## [186] "olw_v_form_3_w2"
## [187] "olw_v_form_4_w2"
## [188] "olw_v_form_5_w2"
## [189] "olw_v_form_6_w2"
## [190] "olw_v_form_7_w2"
## [191] "olw_v_form_8_w2"
## [192] "olw_v_form_9_w2"
## [193] "olw_v_form_10_w2"
## [194] "olw_v_form_10_text_w2"
## [195] "olw_b_gl_w2"
## [196] "nri_supp_1_w2"
## [197] "nri_supp_2_w2"
## [198] "nri_supp_3_w2"
## [199] "nri_crit_1_w2"
## [200] "nri_crit_2_w2"
## [201] "nri_crit_3_w2"
## [202] "nri_conf_1_w2"
## [203] "nri_inti_1_w2"
## [204] "nri_conf_2_w2"
## [205] "nri_inti_2_w2"
## [206] "nri_conf_3_w2"
## [207] "nri_inti_3_w2"
## [208] "ccsq_peer_1_w2"
## [209] "ccsq_peer_2_w2"
## [210] "ccsq_peer_3_w2"
## [211] "ccsq_peer_4_w2"
## [212] "ccsq_peer_5_w2"
## [213] "ccsq_peer_6_w2"
## [214] "ccsq_peer_7_w2"
## [215] "ccsq_peer_8_w2"
## [216] "scared_1_w2"
## [217] "scared_2_w2"
## [218] "scared_3_w2"
## [219] "scared_4_w2"
## [220] "scared_5_w2"
## [221] "scared_6_w2"
## [222] "scared_7_w2"
## [223] "scared_8_w2"
## [224] "scared_9_w2"
## [225] "scared_10_w2"
## [226] "scared_11_w2"
## [227] "scared_12_w2"
## [228] "scared_13_w2"
## [229] "smfq_1_w2"
## [230] "smfq_2_w2"
## [231] "smfq_3_w2"
## [232] "smfq_4_w2"
## [233] "smfq_5_w2"
## [234] "smfq_6_w2"
## [235] "smfq_7_w2"
## [236] "smfq_8_w2"
## [237] "smfq_9_w2"
## [238] "smfq_10_w2"
## [239] "smfq_11_w2"
## [240] "smfq_12_w2"
## [241] "smfq_13_w2"
## [242] "rsq_12_w2"
## [243] "rsq_13_w2"
## [244] "rsq_16_w2"
## [245] "scared_scale_w2"
## [246] "smfq_scale_w2"
## [247] "smfq_scale_omit_w2"
## [248] "ccsq_scale_w2"
## [249] "nri_conf_scale_w2"
## [250] "nri_supp_scale_w2"
## [251] "nri_crit_scale_w2"
## [252] "nri_inti_scale_w2"
## [253] "rsq_scale_w2"
## [254] "experiment_start_date"
## [255] "week"
## [256] "day_sequence_study"
## [257] "beep_sequence_study"
## [258] "myrow"
## [259] "panas_negativescale_beep_new"
## [260] "myna_na_new"
## [261] "panas_negativescale_day_new"
## [262] "panas_negativescale_week_new"
## [263] "panas_negativescale_general_new"2.2 Process data
df_emos <- df_raw %>%
select(id, time_stamp_sent, 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, time_stamp_sent, matches("emotionregulation_[a-z]+$")) %>%
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 rows2.3 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## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## drop_na: removed 40,753 rows (32%), 88,110 rows remaining
## group_by: 2 grouping variables (id, strat)
## summarize: now 1,855 rows and 3 columns, one group variable remaining (id)
## ungroup: no grouping variables remaindf_mean_er <- df_mean_er_strat %>%
group_by(id) %>%
summarize(
mean_er = mean(mean_er)
) %>%
ungroup## group_by: one grouping variable (id)
## summarize: now 265 rows and 2 columns, ungrouped
## ungroup: no grouping variables remain2.3.2 Relative within SD
d <- df_er %>%
pivot_longer(cols=starts_with("er_"),
names_to="strat",
values_to="rating") %>%
drop_na(rating)## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## drop_na: removed 40,753 rows (32%), 88,110 rows remainingmin(d$rating)## [1] 1max(d$rating)## [1] 7d %>%
group_by(id, strat) %>%
summarize(
n = n()
) %>%
# arrange(desc(n))
arrange(n)## group_by: 2 grouping variables (id, strat)
## summarize: now 1,855 rows and 3 columns, one group variable remaining (id)## # A tibble: 1,855 × 3
## # Groups: id [265]
## id strat n
## <chr> <chr> <int>
## 1 1029 er_distraction 1
## 2 1029 er_engagement 1
## 3 1029 er_reappraisal 1
## 4 1029 er_rumination 1
## 5 1029 er_selfcompassion 1
## 6 1029 er_socialsupport 1
## 7 1029 er_suppresion 1
## 8 8048 er_distraction 1
## 9 8048 er_engagement 1
## 10 8048 er_reappraisal 1
## # ℹ 1,845 more rowsdf_rwsd <- d %>%
group_by(id, strat) %>%
mutate(n = n()) %>%
filter(n > 1) %>%
filter(mean(rating) < 7) %>%
filter(mean(rating) > 1) %>%
summarize(
rwsd = relativeSD(rating, 1, 7),
n = n()
) %>%
summarize(
rwsd = mean(rwsd),
n = mean(n)
) %>%
ungroup## group_by: 2 grouping variables (id, strat)
## mutate (grouped): new variable 'n' (integer) with 64 unique values and 0% NA
## filter (grouped): removed 14 rows (<1%), 88,096 rows remaining (removed 14 groups, 1,841 groups remaining)
## filter (grouped): no rows removed
## filter (grouped): removed 9,004 rows (10%), 79,092 rows remaining (removed 188 groups, 1,653 groups remaining)
## summarize: now 1,653 rows and 4 columns, one group variable remaining (id)
## summarize: now 254 rows and 3 columns, ungrouped
## ungroup: no grouping variables remainhist(df_rwsd$rwsd)
Technically, the arithmetic mean is not the correct way to aggregate standard deviations (i.e. SDs of each strategy per person). One should do a pooled standard deviation, but it’s complicated because we’re using the relative SD measure…
2.3.3 Relative between SD
d <- df_er %>%
pivot_longer(cols=starts_with("er_"),
names_to="strat",
values_to="rating") %>%
drop_na(rating)## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## drop_na: removed 40,753 rows (32%), 88,110 rows remainingmin(d$rating)## [1] 1max(d$rating)## [1] 7d %>%
group_by(id, time_stamp_sent) %>%
summarize(
n = n()
) %>%
# arrange(desc(n))
arrange(n)## group_by: 2 grouping variables (id, time_stamp_sent)
## summarize: now 12,590 rows and 3 columns, one group variable remaining (id)## # A tibble: 12,590 × 3
## # Groups: id [265]
## id time_stamp_sent n
## <chr> <dbl> <int>
## 1 7034 1652955304 4
## 2 11187 1653992106 5
## 3 11143 1653569405 6
## 4 11143 1653768014 6
## 5 11143 1653854414 6
## 6 11143 1653940813 6
## 7 11143 1654027212 6
## 8 11143 1654157103 6
## 9 11143 1654449309 6
## 10 11170 1653768014 6
## # ℹ 12,580 more rowsdf_rbsd <- d %>%
group_by(id, time_stamp_sent) %>%
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)
)## group_by: 2 grouping variables (id, time_stamp_sent)
## mutate (grouped): new variable 'n' (integer) with 4 unique values and 0% NA
## filter (grouped): no rows removed
## filter (grouped): removed 38,736 rows (44%), 49,374 rows remaining (removed 5,536 groups, 7,054 groups remaining)
## filter (grouped): removed 637 rows (1%), 48,737 rows remaining (removed 91 groups, 6,963 groups remaining)
## summarize: now 6,963 rows and 4 columns, one group variable remaining (id)
## summarize: now 256 rows and 3 columns, ungroupedhist(df_rbsd$rbsd)
2.3.4 Relative within RMSSD
d <- df_er %>%
pivot_longer(cols=starts_with("er_"),
names_to="strat",
values_to="rating") %>%
drop_na(rating)## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## drop_na: removed 40,753 rows (32%), 88,110 rows remainingmin(d$rating)## [1] 1max(d$rating)## [1] 7d %>%
group_by(id, strat) %>%
summarize(
n = n()
) %>%
# arrange(desc(n))
arrange(n)## group_by: 2 grouping variables (id, strat)
## summarize: now 1,855 rows and 3 columns, one group variable remaining (id)## # A tibble: 1,855 × 3
## # Groups: id [265]
## id strat n
## <chr> <chr> <int>
## 1 1029 er_distraction 1
## 2 1029 er_engagement 1
## 3 1029 er_reappraisal 1
## 4 1029 er_rumination 1
## 5 1029 er_selfcompassion 1
## 6 1029 er_socialsupport 1
## 7 1029 er_suppresion 1
## 8 8048 er_distraction 1
## 9 8048 er_engagement 1
## 10 8048 er_reappraisal 1
## # ℹ 1,845 more rowsdf_rwrmssd <- d %>%
group_by(id, strat) %>%
mutate(n = n()) %>%
filter(n > 1) %>%
filter(mean(rating) < 7) %>%
filter(mean(rating) > 1) %>%
summarize(
rwrmssd = relativeRMSSD(rating, 1, 7),
n = n()
) %>%
summarize(
rwrmssd = mean(rwrmssd),
n = mean(n)
) %>%
ungroup## group_by: 2 grouping variables (id, strat)
## mutate (grouped): new variable 'n' (integer) with 64 unique values and 0% NA
## filter (grouped): removed 14 rows (<1%), 88,096 rows remaining (removed 14 groups, 1,841 groups remaining)
## filter (grouped): no rows removed
## filter (grouped): removed 9,004 rows (10%), 79,092 rows remaining (removed 188 groups, 1,653 groups remaining)
## summarize: now 1,653 rows and 4 columns, one group variable remaining (id)
## summarize: now 254 rows and 3 columns, ungrouped
## ungroup: no grouping variables remainhist(df_rwrmssd$rwrmssd)
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(time_stamp_sent) %>%
mutate(
rating_abs_diff = abs(rating - lag(rating)),
rating_sum = rating + lag(rating),
) %>%
ungroup %>%
drop_na(rating_abs_diff)## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## group_by: 2 grouping variables (id, strat)
## mutate (grouped): new variable 'rating_abs_diff' (double) with 14 unique values and 46% NA
## new variable 'rating_sum' (double) with 26 unique values and 46% NA
## ungroup: no grouping variables remain
## drop_na: removed 58,907 rows (46%), 69,956 rows remaining# momentary level (state)
df_bc_st <- d %>%
group_by(id, time_stamp_sent) %>%
summarize(
bc = sum(rating_abs_diff, na.rm=T)/sum(rating_sum, na.rm=T)
)## group_by: 2 grouping variables (id, time_stamp_sent)
## summarize: now 9,999 rows and 3 columns, one group variable remaining (id)hist(df_bc_st$bc)
# individual level (trait)
# df_bc_tr <- df_bc_st %>%
# group_by(id) %>%
# summarize(
# bc = mean(bc, na.rm=T),
# n = n()
# )
#
# hist(df_bc_tr$bc)
df_bc_tr <- d %>%
group_by(id) %>%
summarize(
bc = sum(rating_abs_diff, na.rm=T)/sum(rating_sum, na.rm=T),
n = n()
) ## group_by: one grouping variable (id)
## summarize: now 257 rows and 3 columns, ungroupedhist(df_bc_tr$bc)
# checking for a relationship between number of observations and bc
df_bc_tr %>%
ggplot(aes(x = n, y = bc)) +
geom_smooth(se=F, size=.4, color="red") +
geom_smooth(method="lm") +
geom_point()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
2.3.6 Wentropy
# momentary (state)
df_wentropy_st <- df_er %>%
pivot_longer(cols=starts_with("er_"),
names_to="strat",
values_to="rating") %>%
drop_na(rating) %>%
group_by(id, time_stamp_sent) %>%
mutate(
pr = rating/7,
ln_pr = log(pr),
pr_ln_pr = pr*ln_pr
) %>%
summarize(
wentropy = -sum(pr_ln_pr)
) %>%
ungroup## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## drop_na: removed 40,753 rows (32%), 88,110 rows remaining
## group_by: 2 grouping variables (id, time_stamp_sent)
## mutate (grouped): new variable 'pr' (double) with 13 unique values and 0% NA
## new variable 'ln_pr' (double) with 13 unique values and 0% NA
## new variable 'pr_ln_pr' (double) with 13 unique values and 0% NA
## summarize: now 12,590 rows and 3 columns, one group variable remaining (id)
## ungroup: no grouping variables remainhist(df_wentropy_st$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## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## drop_na: removed 40,753 rows (32%), 88,110 rows remaining
## group_by: 2 grouping variables (id, strat)
## summarize: now 1,855 rows and 5 columns, one group variable remaining (id)
## summarize: now 265 rows and 2 columns, ungrouped
## ungroup: no grouping variables remainhist(df_wentropy_tr$wentropy)
2.3.7 Entropy
# momentary (state)
df_entropy_st <- df_er %>%
pivot_longer(cols=starts_with("er_"),
names_to="strat",
values_to="rating") %>%
drop_na(rating) %>%
group_by(id, time_stamp_sent) %>%
mutate(
pr = rating/sum(rating),
ln_pr = log(pr),
pr_ln_pr = pr*ln_pr
) %>%
summarize(
entropy = -sum(pr_ln_pr)
) %>%
ungroup## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## drop_na: removed 40,753 rows (32%), 88,110 rows remaining
## group_by: 2 grouping variables (id, time_stamp_sent)
## mutate (grouped): new variable 'pr' (double) with 452 unique values and 0% NA
## new variable 'ln_pr' (double) with 452 unique values and 0% NA
## new variable 'pr_ln_pr' (double) with 451 unique values and 0% NA
## summarize: now 12,590 rows and 3 columns, one group variable remaining (id)
## ungroup: no grouping variables remainhist(df_entropy_st$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## pivot_longer: reorganized (er_reappraisal, er_distraction, er_socialsupport, er_suppresion, er_rumination, …) into (strat, rating) [was 18409x9, now 128863x4]
## drop_na: removed 40,753 rows (32%), 88,110 rows remaining
## group_by: 2 grouping variables (id, strat)
## summarize: now 1,855 rows and 3 columns, one group variable remaining (id)
## mutate (grouped): new variable 'pr' (double) with 1,415 unique values and 0% NA
## new variable 'ln_pr' (double) with 1,415 unique values and 0% NA
## new variable 'pr_ln_pr' (double) with 1,415 unique values and 0% NA
## summarize: now 265 rows and 2 columns, ungrouped
## ungroup: no grouping variables remainhist(df_entropy_tr$entropy, breaks=30)
3 Trait
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, by="id") %>%
select(-id)## 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) 2
## > matched rows 254
## > =====
## > rows total 256
## select: dropped one variable (n)
## full_join: added one column (rwrmssd)
## > rows only in x 2
## > rows only in df_rwrmssd %>% select(-n) 0
## > matched rows 254
## > =====
## > rows total 256
## select: dropped one variable (n)
## full_join: added one column (bc)
## > rows only in x 7
## > rows only in df_bc_tr %>% select(-n) 8
## > matched rows 249
## > =====
## > rows total 264
## full_join: added one column (wentropy)
## > rows only in x 0
## > rows only in df_wentropy_tr 1
## > matched rows 264
## > =====
## > 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 one column (mean_er)
## > rows only in x 0
## > rows only in df_mean_er 0
## > matched rows 265
## > =====
## > rows total 265
## select: dropped one variable (id)corrplot::corrplot(cor(d, use="pairwise.complete.obs"))
cor(d, use="pairwise.complete.obs")## rwsd rbsd rwrmssd bc wentropy
## rwsd 1.00000000 0.70223069 0.911538061 -0.028013737 -0.09405299
## rbsd 0.70223069 1.00000000 0.647137143 -0.031475373 -0.23977250
## rwrmssd 0.91153806 0.64713714 1.000000000 -0.138209853 -0.14656079
## bc -0.02801374 -0.03147537 -0.138209853 1.000000000 0.57881635
## wentropy -0.09405299 -0.23977250 -0.146560790 0.578816354 1.00000000
## entropy -0.07516132 -0.32195712 -0.009524656 -0.007960902 0.17287186
## mean_er -0.53758175 -0.26931454 -0.646611160 0.212521644 -0.30937610
## entropy mean_er
## rwsd -0.075161321 -0.5375818
## rbsd -0.321957120 -0.2693145
## rwrmssd -0.009524656 -0.6466112
## bc -0.007960902 0.2125216
## wentropy 0.172871862 -0.3093761
## entropy 1.000000000 -0.1292289
## mean_er -0.129228899 1.00000003.2 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 ( 11)
## > matched rows 254
## > =====
## > rows total 2543.2.1 Depression
fit <- lm(smfq ~ rwsd, data=df_rwsd_outcomes)
tab_model(fit)| smfq | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.65 | 1.45 – 1.86 | <0.001 |
| rwsd | -0.29 | -0.56 – -0.02 | 0.036 |
| Observations | 246 | ||
| R2 / R2 adjusted | 0.018 / 0.014 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.2.2 Anxiety
fit <- lm(scared ~ rwsd, data=df_rwsd_outcomes)
tab_model(fit)| scared | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.19 | 1.99 – 2.40 | <0.001 |
| rwsd | -0.36 | -0.63 – -0.08 | 0.012 |
| Observations | 245 | ||
| R2 / R2 adjusted | 0.026 / 0.022 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.2.3 Neg
fit <- lm(panas_neg ~ rwsd, data=df_rwsd_outcomes)
tab_model(fit)| panas neg | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.79 | 2.43 – 3.16 | <0.001 |
| rwsd | -1.35 | -1.84 – -0.86 | <0.001 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.105 / 0.101 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.2.4 Pos
fit <- lm(panas_pos ~ rwsd, data=df_rwsd_outcomes)
tab_model(fit)| panas pos | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.02 | 3.47 – 4.57 | <0.001 |
| rwsd | 1.54 | 0.81 – 2.27 | <0.001 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.064 / 0.060 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.3 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 ( 9)
## > matched rows 256
## > =====
## > rows total 2563.3.1 Depression
fit <- lm(smfq ~ rbsd, data=df_rbsd_outcomes)
tab_model(fit)| smfq | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.55 | 1.39 – 1.70 | <0.001 |
| rbsd | -0.16 | -0.38 – 0.06 | 0.159 |
| Observations | 248 | ||
| R2 / R2 adjusted | 0.008 / 0.004 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
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 | -0.37 – 0.08 | 0.212 |
| Observations | 247 | ||
| 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
fit <- lm(panas_neg ~ rbsd, data=df_rbsd_outcomes)
tab_model(fit)| panas neg | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.22 | 1.93 – 2.51 | <0.001 |
| rbsd | -0.61 | -1.02 – -0.19 | 0.004 |
| Observations | 256 | ||
| R2 / R2 adjusted | 0.032 / 0.028 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.3.4 Pos
fit <- lm(panas_pos ~ rbsd, data=df_rbsd_outcomes)
tab_model(fit)| panas pos | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.61 | 4.18 – 5.03 | <0.001 |
| rbsd | 0.80 | 0.20 – 1.41 | 0.009 |
| Observations | 256 | ||
| R2 / R2 adjusted | 0.026 / 0.022 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.4 Relative within RMSSD
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 ( 11)
## > matched rows 254
## > =====
## > rows total 2543.4.1 Depression
fit <- lm(smfq ~ rwrmssd, data=df_rwrmssd_outcomes)
tab_model(fit)| smfq | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.65 | 1.49 – 1.81 | <0.001 |
| rwrmssd | -0.34 | -0.60 – -0.08 | 0.009 |
| Observations | 246 | ||
| R2 / R2 adjusted | 0.027 / 0.023 | ||
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) | 2.21 | 2.04 – 2.37 | <0.001 |
| rwrmssd | -0.45 | -0.71 – -0.19 | 0.001 |
| Observations | 245 | ||
| R2 / R2 adjusted | 0.047 / 0.043 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.4.3 Neg
fit <- lm(panas_neg ~ rwrmssd, data=df_rwrmssd_outcomes)
tab_model(fit)| panas neg | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.73 | 2.44 – 3.01 | <0.001 |
| rwrmssd | -1.52 | -1.97 – -1.07 | <0.001 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.150 / 0.147 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.4.4 Pos
fit <- lm(panas_pos ~ rwrmssd, data=df_rwrmssd_outcomes)
tab_model(fit)| panas pos | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.29 | 3.86 – 4.73 | <0.001 |
| rwrmssd | 1.40 | 0.71 – 2.10 | <0.001 |
| Observations | 254 | ||
| R2 / R2 adjusted | 0.060 / 0.056 | ||
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 2573.5.1 Depression
fit <- lm(smfq ~ bc, data=df_bc_tr_outcomes)
tab_model(fit)| smfq | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.35 | 1.25 – 1.45 | <0.001 |
| bc | 0.57 | 0.04 – 1.09 | 0.033 |
| Observations | 249 | ||
| R2 / R2 adjusted | 0.018 / 0.014 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.5.2 Anxiety
fit <- lm(scared ~ bc, data=df_bc_tr_outcomes)
tab_model(fit)| scared | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.76 | 1.66 – 1.87 | <0.001 |
| bc | 1.00 | 0.47 – 1.53 | <0.001 |
| Observations | 249 | ||
| R2 / R2 adjusted | 0.053 / 0.050 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.5.3 Neg
fit <- lm(panas_neg ~ bc, data=df_bc_tr_outcomes)
tab_model(fit)| panas neg | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.36 | 1.17 – 1.54 | <0.001 |
| bc | 2.76 | 1.82 – 3.69 | <0.001 |
| Observations | 257 | ||
| R2 / R2 adjusted | 0.117 / 0.113 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.5.4 Pos
fit <- lm(panas_pos ~ bc, data=df_bc_tr_outcomes)
tab_model(fit)| panas pos | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 5.69 | 5.42 – 5.97 | <0.001 |
| bc | -3.28 | -4.69 – -1.87 | <0.001 |
| Observations | 257 | ||
| R2 / R2 adjusted | 0.076 / 0.073 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.6 Wentropy
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 2653.6.1 Depression
fit <- lm(smfq ~ wentropy, data=df_wentropy_tr_outcomes)
tab_model(fit)| smfq | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.22 | 0.85 – 1.59 | <0.001 |
| wentropy | 0.10 | -0.07 – 0.27 | 0.248 |
| Observations | 257 | ||
| R2 / R2 adjusted | 0.005 / 0.001 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.6.2 Anxiety
fit <- lm(scared ~ wentropy, data=df_wentropy_tr_outcomes)
tab_model(fit)| scared | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.63 | 1.25 – 2.01 | <0.001 |
| wentropy | 0.14 | -0.04 – 0.31 | 0.134 |
| Observations | 256 | ||
| R2 / R2 adjusted | 0.009 / 0.005 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.6.3 Neg
fit <- lm(panas_neg ~ wentropy, data=df_wentropy_tr_outcomes)
tab_model(fit)| panas neg | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.75 | 0.06 – 1.43 | 0.033 |
| wentropy | 0.49 | 0.18 – 0.81 | 0.002 |
| Observations | 265 | ||
| R2 / R2 adjusted | 0.035 / 0.031 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.6.4 Pos
fit <- lm(panas_pos ~ wentropy, data=df_wentropy_tr_outcomes)
tab_model(fit)| panas pos | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 7.28 | 6.29 – 8.27 | <0.001 |
| wentropy | -0.99 | -1.45 – -0.53 | <0.001 |
| Observations | 265 | ||
| R2 / R2 adjusted | 0.064 / 0.060 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.7 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 2653.7.1 Depression
fit <- lm(smfq ~ entropy, data=df_entropy_tr_outcomes)
tab_model(fit)| smfq | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.21 | 2.32 – 6.11 | <0.001 |
| entropy | -1.45 | -2.43 – -0.46 | 0.004 |
| Observations | 257 | ||
| R2 / R2 adjusted | 0.032 / 0.028 | ||
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) | 3.91 | 1.92 – 5.91 | <0.001 |
| entropy | -1.04 | -2.08 – -0.00 | 0.050 |
| Observations | 256 | ||
| R2 / R2 adjusted | 0.015 / 0.011 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
3.7.3 Neg
fit <- lm(panas_neg ~ entropy, data=df_entropy_tr_outcomes)
tab_model(fit)| panas neg | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.95 | 0.32 – 7.58 | 0.033 |
| entropy | -1.12 | -3.01 – 0.77 | 0.244 |
| 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'
3.7.4 Pos
fit <- lm(panas_pos ~ entropy, data=df_entropy_tr_outcomes)
tab_model(fit)| panas pos | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.44 | -1.92 – 8.80 | 0.208 |
| entropy | 0.90 | -1.89 – 3.69 | 0.524 |
| Observations | 265 | ||
| R2 / R2 adjusted | 0.002 / -0.002 | ||
plot_model(fit, type="slope")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
Some issues I’m noticing: - not capturing uncertainty around SD estimates in models - not aggregating SDs correctly
4 Session Info
sessionInfo()## R version 4.4.0 (2024-04-24)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidylog_1.1.0 aTSA_3.1.2.1 sjPlot_2.8.16
## [4] lmerTest_3.1-3 lme4_1.1-35.3 Matrix_1.7-0
## [7] ggthemr_1.1.0 lubridate_1.9.3 forcats_1.0.0
## [10] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
## [13] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
## [16] ggplot2_3.5.1 tidyverse_2.0.0 here_1.0.1
## [19] rsurveyutils_0.1.0 relativeVariability_1.0
##
## loaded via a namespace (and not attached):
## [1] sjlabelled_1.2.0 tidyselect_1.2.1 farver_2.1.2
## [4] fastmap_1.2.0 janitor_2.2.0 bayestestR_0.13.2
## [7] sjstats_0.19.0 digest_0.6.35 timechange_0.3.0
## [10] estimability_1.5.1 lifecycle_1.0.4 magrittr_2.0.3
## [13] compiler_4.4.0 rlang_1.1.4 sass_0.4.9
## [16] tools_4.4.0 utf8_1.2.4 yaml_2.3.9
## [19] corrplot_0.95 knitr_1.47 labeling_0.4.3
## [22] bit_4.0.5 RColorBrewer_1.1-3 withr_3.0.0
## [25] numDeriv_2016.8-1.1 grid_4.4.0 datawizard_0.10.0
## [28] fansi_1.0.6 xtable_1.8-4 colorspace_2.1-0
## [31] emmeans_1.10.3 scales_1.3.0 MASS_7.3-60.2
## [34] insight_0.20.0 cli_3.6.3 mvtnorm_1.2-5
## [37] rmarkdown_2.27 crayon_1.5.3 generics_0.1.3
## [40] rstudioapi_0.16.0 performance_0.11.0 tzdb_0.4.0
## [43] parameters_0.21.7 minqa_1.2.7 cachem_1.1.0
## [46] splines_4.4.0 parallel_4.4.0 effectsize_0.8.8
## [49] vctrs_0.6.5 boot_1.3-30 jsonlite_1.8.8
## [52] hms_1.1.3 bit64_4.0.5 jquerylib_0.1.4
## [55] glue_1.7.0 nloptr_2.0.3 stringi_1.8.4
## [58] gtable_0.3.5 ggeffects_1.6.0 munsell_0.5.1
## [61] pillar_1.9.0 clisymbols_1.2.0 htmltools_0.5.8.1
## [64] R6_2.5.1 rprojroot_2.0.4 vroom_1.6.5
## [67] evaluate_0.23 lattice_0.22-6 highr_0.11
## [70] snakecase_0.11.1 bslib_0.7.0 Rcpp_1.0.12
## [73] coda_0.19-4.1 nlme_3.1-164 mgcv_1.9-1
## [76] xfun_0.44 sjmisc_2.8.10 pkgconfig_2.0.3