Cognition in Mothers - Perinatal Intrusions
Sarah Marzari
2024-08-26
Set-up
# load in packages
library(tidyverse)
library(janitor)
library(psych)
library(haven)
library(naniar)
library(ggpubr)
library(report)
library(ggplot2)
library(reshape2)
library(lme4)
library(sjPlot)
library(parameters)
library(mediation)
library(lavaan)
library(lmerTest)
library(modEvA)## Warning: package 'modEvA' was built under R version 4.4.1library(rsconnect)
library(effectsize)
library(emmeans)## Warning: package 'emmeans' was built under R version 4.4.1library(performance)
library(tinytex)
library(latexpdf)
library(interactions)
library(lm.beta)
library(car)
library(apaTables)
library(sjPlot)
library(papaja)
library(broom.mixed)# load in data
full_data <- read.csv("full_data_b_wide.csv")Cleaning
# check exclusion criteria
# check minimum age
min(full_data$age_years)## [1] 19# check if all participants were either currently or previously pregnant
full_data %>%
filter(t1_pregnant == "No" & t1_number_of_children == "None")## X Prolific_ID ID.x hitsNeg_1 hitsNeut_1 hitsPNeg_1
## 1 31 5d01006f8a840b00195c2159 11284603 3 2 4
## hitsNeg_2 hitsNeut_2 hitsPNeg_2 hitsNeg_3 hitsNeut_3 hitsPNeg_3 missesNeg_1
## 1 1 3 3 1 2 0 2
## missesNeut_1 missesPNeg_1 missesNeg_2 missesNeut_2 missesPNeg_2 missesNeg_3
## 1 3 1 4 2 2 4
## missesNeut_3 missesPNeg_3 false_alarmsNeg_1 false_alarmsNeut_1
## 1 3 5 4 5
## false_alarmsPNeg_1 false_alarmsNeg_2 false_alarmsNeut_2 false_alarmsPNeg_2
## 1 4 4 2 4
## false_alarmsNeg_3 false_alarmsNeut_3 false_alarmsPNeg_3 correct_rejectNeg_1
## 1 4 5 3 7
## correct_rejectNeut_1 correct_rejectPNeg_1 correct_rejectNeg_2
## 1 6 7 8
## correct_rejectNeut_2 correct_rejectPNeg_2 correct_rejectNeg_3
## 1 10 8 9
## correct_rejectNeut_3 correct_rejectPNeg_3 hit_rateNeg_1 hit_rateNeut_1
## 1 8 10 0.6 0.4
## hit_ratePNeg_1 hit_rateNeg_2 hit_rateNeut_2 hit_ratePNeg_2 hit_rateNeg_3
## 1 0.8 0.2 0.6 0.6 0.2
## hit_rateNeut_3 hit_ratePNeg_3 false_alarm_rateNeg_1 false_alarm_rateNeut_1
## 1 0.4 1e-05 0.3636364 0.4545455
## false_alarm_ratePNeg_1 false_alarm_rateNeg_2 false_alarm_rateNeut_2
## 1 0.3636364 0.3333333 0.1666667
## false_alarm_ratePNeg_2 false_alarm_rateNeg_3 false_alarm_rateNeut_3
## 1 0.3333333 0.3076923 0.3846154
## false_alarm_ratePNeg_3 DprimeNeg_1 DprimeNeut_1 DprimePNeg_1 DprimeNeg_2
## 1 0.2307692 0.6021028 -0.1391618 1.190377 -0.4108939
## DprimeNeut_2 DprimePNeg_2 DprimeNeg_3 DprimeNeut_3 DprimePNeg_3 RTNeg_1
## 1 1.220769 0.6840744 -0.339219 0.04003413 -3.528575 666.944
## RTNeut_1 RTPNeg_1 RTNeg_2 RTNeut_2 RTPNeg_2 RTNeg_3 RTNeut_3 RTPNeg_3
## 1 666.5333 797.637 826.0923 795.8833 698.8444 619.4464 692.5571 608.4571
## totaltrialsNeg_1 totaltrialsNeut_1 totaltrialsPNeg_1 totaltrialsNeg_2
## 1 16 16 16 17
## totaltrialsNeut_2 totaltrialsPNeg_2 totaltrialsNeg_3 totaltrialsNeut_3
## 1 17 17 18 18
## totaltrialsPNeg_3 totalcorrectNeg_1 totalcorrectNeut_1 totalcorrectPNeg_1
## 1 18 10 8 11
## totalcorrectNeg_2 totalcorrectNeut_2 totalcorrectPNeg_2 totalcorrectNeg_3
## 1 9 13 11 10
## totalcorrectNeut_3 totalcorrectPNeg_3 percentcorrectNeg_1
## 1 10 10 62.5
## percentcorrectNeut_1 percentcorrectPNeg_1 percentcorrectNeg_2
## 1 50 68.75 52.94118
## percentcorrectNeut_2 percentcorrectPNeg_2 percentcorrectNeg_3
## 1 76.47059 64.70588 55.55556
## percentcorrectNeut_3 percentcorrectPNeg_3 study.x Task_randomiser t1_date
## 1 55.55556 55.55556 A PERI-NEG-NEU 2024-06-20
## t1_phq_1 t1_phq_2 t1_phq_3 t1_phq_4 t1_phq_5 t1_phq_6 t1_phq_7 t1_phq_8
## 1 2 2 3 1 2 3 2 2
## t1_gad_1 t1_gad_2 t1_gad_3 t1_gad_4 t1_gad_5 t1_gad_6 t1_gad_7 t1_cfq_1
## 1 1 1 1 1 1 1 1 7
## t1_cfq_2 t1_cfq_3 t1_cfq_4 t1_cfq_5 t1_cfq_6 t1_cfq_7 t1_rtq_1 t1_rtq_2
## 1 7 7 7 7 7 7 5 5
## t1_rtq_3 t1_rtq_4 t1_rtq_5 t1_rtq_6 t1_rtq_7 t1_rtq_8 t1_rtq_9 t1_rtq_10
## 1 5 5 5 5 5 5 5 5
## t1_ocd_1 t1_ocd_2 t1_ocd_3 t1_ocd_4 t1_ocd_5 t1_ocd_6 t1_ocd_7 t1_ocd_8
## 1 2 3 2 4 4 4 4 4
## t1_ocd_9 t1_ocd_10 t1_ocd_11 t1_ocd_12 t1_ocd_13 t1_ocd_14 t1_ocd_15
## 1 4 4 4 4 4 4 4
## t1_ocd_16 t1_ocd_17 t1_ocd_18 t1_pnd_ocd1 t1_pnd_ocd2 t1_pnd_ocd3 t1_pnd_ocd4
## 1 4 4 4 1 1 NA 2
## t1_thoughts_frequency t1_thoughts_interfere_functioning
## 1 2 3
## t1_thoughts_distress_level t1_mental_health_diagnosis t1_anxiety_diagnosis
## 1 4 1 1
## t1_bipolar_diagnosis t1_depression_diagnosis t1_eating_disorder_diagnosis
## 1 0 0 0
## t1_ocd_diagnosis t1_ptsd_diagnosis t1_schizophrenia_diagnosis
## 1 1 0 0
## t1_prefer_not_to_say_diagnosis t1_other_diagnosis t1_other_type_diagnosis
## 1 0 0 <NA>
## t1_current_anxiety_disorder t1_current_bipolar_disorder
## 1 1 0
## t1_current_depression_disorder t1_current_eating_disorder
## 1 0 0
## t1_current_ocd_disorder t1_current_ptsd_disorder
## 1 1 0
## t1_current_schizophrenia_disorder t1_no_current_disorder
## 1 0 0
## t1_current_disorder_prefer_not_to_say t1_other_current_disorder
## 1 0 0
## t1_other_current_type_disorder t1_neurodevelopmental_neurological_disorder
## 1 <NA> 0
## t1_adhd_disorder t1_autism_spectrum_disorder t1_asperger_syndrome_disorder
## 1 NA NA NA
## t1_epilepsy t1_seizures t1_dyslexia_reading_disorder_
## 1 NA NA NA
## t1_dysgraphia_writing_disorder t1_dyscalculia_calculation_disorder
## 1 NA NA
## t1_prefer_not_to_say t1_other_neuro_disorder t1_other_type_neuro_disorder
## 1 NA NA <NA>
## age_years gender_identity gender_identity_quant gender_identity_other
## 1 24 Female 1 NA
## resident_country resident_country_quant ethnicity ethnicity_quant
## 1 United Kingdom 11 White 6
## ethnicity_other level_of_education level_of_education_quant socioeconomics
## 1 <NA> University/college 4 Fairly well off
## socioeconomics_quant t1_pregnant t1_pregnant_quant t1_due_day t1_due_month
## 1 3 No 0 NA <NA>
## t1_due_month_quant t1_due_year t1_number_of_children t1_child_1_age
## 1 NA NA None NA
## t1_child_1_age_units t1_child_1_week_born t1_child_2_age t1_child_2_age_units
## 1 <NA> NA NA <NA>
## t1_child_2_week_born t1_child_3_age t1_child_3_age_units t1_child_3_week_born
## 1 NA NA <NA> NA
## t1_child_4_age t1_child_4_age_units t1_child_4_week_born t1_child_5_age
## 1 NA <NA> NA NA
## t1_child_5_age_units t1_child_5_week_born t1_child_6_age t1_child_6_age_units
## 1 <NA> NA NA <NA>
## t1_child_6_week_born t1_birth_complications t1_birth_complications_quant
## 1 NA <NA> NA
## t1_birth_complications_type t1phqTotal t1gadTotal t1cfqTotal t1rtqTotal
## 1 <NA> 17 7 49 50
## t1ocdTotal t1intrusionsTotal t1_due_date t1_days_until_birth
## 1 67 4 <NA> NA
## t1_weeks_until_birth t1_gest_age_weeks t1_child_1_age_weeks
## 1 NA NA NA
## t1_child_2_age_weeks t1_child_3_age_weeks t1_child_4_age_weeks
## 1 NA NA NA
## t1_child_5_age_weeks t1_child_6_age_weeks ID_t2 t2_date completed_t2
## 1 NA NA NA <NA> <NA>
## t2_pregnancy_child_loss t2_pregnancy_child_loss_quant t2_phq_1 t2_phq_2
## 1 <NA> NA NA NA
## t2_phq_3 t2_phq_4 t2_phq_5 t2_phq_6 t2_phq_7 t2_phq_8 t2_gad_1 t2_gad_2
## 1 NA NA NA NA NA NA NA NA
## t2_gad_3 t2_gad_4 t2_gad_5 t2_gad_6 t2_gad_7 t2_cfq_1 t2_cfq_2 t2_cfq_3
## 1 NA NA NA NA NA NA NA NA
## t2_cfq_4 t2_cfq_5 t2_cfq_6 t2_cfq_7 t2_rtq_1 t2_rtq_2 t2_rtq_3 t2_rtq_4
## 1 NA NA NA NA NA NA NA NA
## t2_rtq_5 t2_rtq_6 t2_rtq_7 t2_rtq_8 t2_rtq_9 t2_rtq_10 t2_ocd_1 t2_ocd_2
## 1 NA NA NA NA NA NA NA NA
## t2_ocd_3 t2_ocd_4 t2_ocd_5 t2_ocd_6 t2_ocd_7 t2_ocd_8 t2_ocd_9 t2_ocd_10
## 1 NA NA NA NA NA NA NA NA
## t2_ocd_11 t2_ocd_12 t2_ocd_13 t2_ocd_14 t2_ocd_15 t2_ocd_16 t2_ocd_17
## 1 NA NA NA NA NA NA NA
## t2_ocd_18 t2_pnd_ocd1 t2_pnd_ocd2 t2_pnd_ocd3 t2_pnd_ocd4
## 1 NA NA NA NA NA
## t2_thoughts_frequency t2_thoughts_interfere_functioning
## 1 NA NA
## t2_thoughts_distress_level t2_new_mental_health_diagnosis_last_month
## 1 NA NA
## t2_new_anxiety_diagnosis t2_new_bipolar_diagnosis t2_new_depression_diagnosis
## 1 NA NA NA
## t2_new_eating_disorder_diagnosis t2_new_ocd_diagnosis t2_new_ptsd_diagnosis
## 1 NA NA NA
## t2_new_schizophrenia_diagnosis t2_prefer_not_to_say_diagnosis
## 1 NA NA
## t2_new_other_diagnosis t2_new_other_type_diagnosis
## 1 NA <NA>
## t2_current_anxiety_disorder t2_current_bipolar_disorder
## 1 NA NA
## t2_current_depression_disorder t2_current_eating_disorder
## 1 NA NA
## t2_current_ocd_disorder t2_current_ptsd_disorder
## 1 NA NA
## t2_current_schizophrenia_disorder t2_current_disorder_prefer_not_to_say
## 1 NA NA
## t2_other_current_disorder t2_other_current_type_disorder t2_pregnant
## 1 NA <NA> <NA>
## t2_pregnant_quant t2_due_day t2_due_month t2_due_month_quant t2_due_year
## 1 NA NA <NA> NA NA
## t2_number_of_pregnancies birth_since_t1 birth_since_t1_quant
## 1 NA <NA> NA
## birth_since_t1_child_1_age birth_since_t1_child_1_age_units
## 1 NA <NA>
## birth_since_t1_child_1_week_born birth_since_t1_child_2_age
## 1 NA NA
## birth_since_t1_child_2_age_units birth_since_t1_child_2_week_born
## 1 NA NA
## birth_since_t1_child_3_age birth_since_t1_child_3_age_units
## 1 NA NA
## birth_since_t1_child_3_week_born birth_since_t1_child_4_age
## 1 NA NA
## birth_since_t1_child_4_age_units birth_since_t1_child_4_week_born
## 1 NA NA
## t2_birth_complications t2_birth_complications_quant
## 1 <NA> NA
## t2_birth_complications_type t2_phq_total t2_gad_total t2_cfq_total
## 1 <NA> NA NA NA
## t2_rtq_total t2_ocd_total t2_intrusions_total t2_due_date t2_days_until_birth
## 1 NA NA NA <NA> NA
## t2_weeks_until_birth t2_gest_age_weeks
## 1 NA NA # exclude participant Prolific ID = 5d01006f8a840b00195c2159
# check if all pregnant participants had a gestational age > than 13 weeks
full_data %>%
filter(t1_pregnant == "Yes" & t1_gest_age_weeks < 13)## X Prolific_ID ID.x hitsNeg_1 hitsNeut_1 hitsPNeg_1
## 1 57 5f262b3018c68e27aebdd170 11331455 4 1 1
## 2 76 606be651a89a7baf6141e8b9 11308857 1 1 3
## 3 101 631910aab5323b5658ca4a39 11308763 3 0 4
## hitsNeg_2 hitsNeut_2 hitsPNeg_2 hitsNeg_3 hitsNeut_3 hitsPNeg_3 missesNeg_1
## 1 4 4 5 4 4 5 1
## 2 1 1 2 0 1 1 4
## 3 0 1 0 0 2 0 2
## missesNeut_1 missesPNeg_1 missesNeg_2 missesNeut_2 missesPNeg_2 missesNeg_3
## 1 4 4 1 1 0 1
## 2 4 2 4 4 3 5
## 3 5 1 5 4 5 5
## missesNeut_3 missesPNeg_3 false_alarmsNeg_1 false_alarmsNeut_1
## 1 1 0 0 6
## 2 4 4 4 4
## 3 3 5 6 9
## false_alarmsPNeg_1 false_alarmsNeg_2 false_alarmsNeut_2 false_alarmsPNeg_2
## 1 3 2 0 0
## 2 5 6 7 10
## 3 7 0 5 2
## false_alarmsNeg_3 false_alarmsNeut_3 false_alarmsPNeg_3 correct_rejectNeg_1
## 1 3 4 1 11
## 2 6 5 7 7
## 3 0 4 0 5
## correct_rejectNeut_1 correct_rejectPNeg_1 correct_rejectNeg_2
## 1 5 8 10
## 2 7 6 6
## 3 2 4 12
## correct_rejectNeut_2 correct_rejectPNeg_2 correct_rejectNeg_3
## 1 12 12 10
## 2 5 2 7
## 3 7 10 13
## correct_rejectNeut_3 correct_rejectPNeg_3 hit_rateNeg_1 hit_rateNeut_1
## 1 9 12 0.8 2e-01
## 2 8 6 0.2 2e-01
## 3 9 13 0.6 1e-05
## hit_ratePNeg_1 hit_rateNeg_2 hit_rateNeut_2 hit_ratePNeg_2 hit_rateNeg_3
## 1 0.2 8e-01 0.8 0.99999 8e-01
## 2 0.6 2e-01 0.2 0.40000 1e-05
## 3 0.8 1e-05 0.2 0.00001 1e-05
## hit_rateNeut_3 hit_ratePNeg_3 false_alarm_rateNeg_1 false_alarm_rateNeut_1
## 1 0.8 0.99999 0.0000100 0.5454545
## 2 0.2 0.20000 0.3636364 0.3636364
## 3 0.4 0.00001 0.5454545 0.8181818
## false_alarm_ratePNeg_1 false_alarm_rateNeg_2 false_alarm_rateNeut_2
## 1 0.2727273 0.1666667 0.0000100
## 2 0.4545455 0.5000000 0.5833333
## 3 0.6363636 0.0000100 0.4166667
## false_alarm_ratePNeg_2 false_alarm_rateNeg_3 false_alarm_rateNeut_3
## 1 0.0000100 0.2307692 0.3076923
## 2 0.8333333 0.4615385 0.3846154
## 3 0.1666667 0.0000100 0.3076923
## false_alarm_ratePNeg_3 DprimeNeg_1 DprimeNeut_1 DprimePNeg_1 DprimeNeg_2
## 1 0.07692308 5.1065120 -0.9558065 -0.2370359 1.8090428
## 2 0.53846154 -0.4928655 -0.4928655 0.3675324 -0.8416212
## 3 0.00001000 0.1391618 -5.1733487 0.4928655 0.0000000
## DprimeNeut_2 DprimePNeg_2 DprimeNeg_3 DprimeNeut_3 DprimePNeg_3 RTNeg_1
## 1 5.1065120 8.529782 1.577937 1.3440235 5.6909677 728.7097
## 2 -1.0520496 -1.220769 -4.168332 -0.5482400 -0.9381798 700.6217
## 3 -0.6311928 -3.297469 0.000000 0.2490551 0.0000000 784.8667
## RTNeut_1 RTPNeg_1 RTNeg_2 RTNeut_2 RTPNeg_2 RTNeg_3 RTNeut_3 RTPNeg_3
## 1 709.3636 731.3750 611.4839 623.4545 687.1176 564.6250 581.6129 674.8571
## 2 650.7917 820.3160 583.5042 610.5739 586.9286 501.8360 568.3630 502.3000
## 3 546.1765 858.4348 443.3034 616.0080 658.3963 469.7968 694.8724 389.4323
## totaltrialsNeg_1 totaltrialsNeut_1 totaltrialsPNeg_1 totaltrialsNeg_2
## 1 16 16 16 17
## 2 16 16 16 17
## 3 16 16 16 17
## totaltrialsNeut_2 totaltrialsPNeg_2 totaltrialsNeg_3 totaltrialsNeut_3
## 1 17 17 18 18
## 2 17 17 18 18
## 3 17 17 18 18
## totaltrialsPNeg_3 totalcorrectNeg_1 totalcorrectNeut_1 totalcorrectPNeg_1
## 1 18 15 6 9
## 2 18 8 8 9
## 3 18 8 2 8
## totalcorrectNeg_2 totalcorrectNeut_2 totalcorrectPNeg_2 totalcorrectNeg_3
## 1 14 16 17 14
## 2 7 6 4 7
## 3 12 8 10 13
## totalcorrectNeut_3 totalcorrectPNeg_3 percentcorrectNeg_1
## 1 13 17 93.75
## 2 9 7 50.00
## 3 11 13 50.00
## percentcorrectNeut_1 percentcorrectPNeg_1 percentcorrectNeg_2
## 1 37.5 56.25 82.35294
## 2 50.0 56.25 41.17647
## 3 12.5 50.00 70.58824
## percentcorrectNeut_2 percentcorrectPNeg_2 percentcorrectNeg_3
## 1 94.11765 100.00000 77.77778
## 2 35.29412 23.52941 38.88889
## 3 47.05882 58.82353 72.22222
## percentcorrectNeut_3 percentcorrectPNeg_3 study.x Task_randomiser t1_date
## 1 72.22222 94.44444 C PERI-NEU-NEG 2024-06-28
## 2 50.00000 38.88889 A NEG-PERI-NEU 2024-06-24
## 3 61.11111 72.22222 A NEU-PERI-NEG 2024-06-24
## t1_phq_1 t1_phq_2 t1_phq_3 t1_phq_4 t1_phq_5 t1_phq_6 t1_phq_7 t1_phq_8
## 1 1 1 0 2 0 0 0 0
## 2 2 2 1 0 0 0 0 0
## 3 1 1 1 1 0 1 1 0
## t1_gad_1 t1_gad_2 t1_gad_3 t1_gad_4 t1_gad_5 t1_gad_6 t1_gad_7 t1_cfq_1
## 1 1 1 1 0 0 0 0 4
## 2 1 1 2 2 0 0 0 6
## 3 1 1 1 1 1 0 0 1
## t1_cfq_2 t1_cfq_3 t1_cfq_4 t1_cfq_5 t1_cfq_6 t1_cfq_7 t1_rtq_1 t1_rtq_2
## 1 4 3 4 3 3 3 2 2
## 2 5 1 1 1 1 1 1 1
## 3 1 3 2 2 2 1 1 1
## t1_rtq_3 t1_rtq_4 t1_rtq_5 t1_rtq_6 t1_rtq_7 t1_rtq_8 t1_rtq_9 t1_rtq_10
## 1 3 2 3 2 3 3 3 2
## 2 1 1 1 1 1 3 3 3
## 3 1 1 1 2 2 1 1 1
## t1_ocd_1 t1_ocd_2 t1_ocd_3 t1_ocd_4 t1_ocd_5 t1_ocd_6 t1_ocd_7 t1_ocd_8
## 1 2 2 2 1 1 3 1 1
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## t1_ocd_9 t1_ocd_10 t1_ocd_11 t1_ocd_12 t1_ocd_13 t1_ocd_14 t1_ocd_15
## 1 1 0 1 1 2 0 2
## 2 0 0 0 0 0 0 4
## 3 0 0 0 0 1 1 0
## t1_ocd_16 t1_ocd_17 t1_ocd_18 t1_pnd_ocd1 t1_pnd_ocd2 t1_pnd_ocd3 t1_pnd_ocd4
## 1 0 0 0 1 1 1 1
## 2 0 0 4 1 1 1 1
## 3 0 0 0 0 0 1 0
## t1_thoughts_frequency t1_thoughts_interfere_functioning
## 1 3 3
## 2 1 1
## 3 1 1
## t1_thoughts_distress_level t1_mental_health_diagnosis t1_anxiety_diagnosis
## 1 2 0 NA
## 2 1 0 NA
## 3 1 0 NA
## t1_bipolar_diagnosis t1_depression_diagnosis t1_eating_disorder_diagnosis
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## t1_ocd_diagnosis t1_ptsd_diagnosis t1_schizophrenia_diagnosis
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## t1_prefer_not_to_say_diagnosis t1_other_diagnosis t1_other_type_diagnosis
## 1 NA NA <NA>
## 2 NA NA <NA>
## 3 NA NA <NA>
## t1_current_anxiety_disorder t1_current_bipolar_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t1_current_depression_disorder t1_current_eating_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t1_current_ocd_disorder t1_current_ptsd_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t1_current_schizophrenia_disorder t1_no_current_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t1_current_disorder_prefer_not_to_say t1_other_current_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t1_other_current_type_disorder t1_neurodevelopmental_neurological_disorder
## 1 <NA> 0
## 2 <NA> 0
## 3 <NA> 0
## t1_adhd_disorder t1_autism_spectrum_disorder t1_asperger_syndrome_disorder
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## t1_epilepsy t1_seizures t1_dyslexia_reading_disorder_
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## t1_dysgraphia_writing_disorder t1_dyscalculia_calculation_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t1_prefer_not_to_say t1_other_neuro_disorder t1_other_type_neuro_disorder
## 1 NA NA <NA>
## 2 NA NA <NA>
## 3 NA NA <NA>
## age_years gender_identity gender_identity_quant gender_identity_other
## 1 28 Female 1 NA
## 2 28 Female 1 NA
## 3 35 Female 1 NA
## resident_country resident_country_quant ethnicity ethnicity_quant
## 1 United Kingdom 11 Mixed 5
## 2 United States of America 12 Black 3
## 3 United States of America 12 White 6
## ethnicity_other level_of_education level_of_education_quant socioeconomics
## 1 <NA> University/college 4 Not very well off
## 2 <NA> University/college 4 Not very well off
## 3 <NA> University/college 4 Rather well off
## socioeconomics_quant t1_pregnant t1_pregnant_quant t1_due_day t1_due_month
## 1 2 Yes 1 5 January
## 2 2 Yes 1 16 December
## 3 4 Yes 1 3 February
## t1_due_month_quant t1_due_year t1_number_of_children t1_child_1_age
## 1 1 2025 None NA
## 2 12 2025 None NA
## 3 2 2025 None NA
## t1_child_1_age_units t1_child_1_week_born t1_child_2_age t1_child_2_age_units
## 1 <NA> NA NA <NA>
## 2 <NA> NA NA <NA>
## 3 <NA> NA NA <NA>
## t1_child_2_week_born t1_child_3_age t1_child_3_age_units t1_child_3_week_born
## 1 NA NA <NA> NA
## 2 NA NA <NA> NA
## 3 NA NA <NA> NA
## t1_child_4_age t1_child_4_age_units t1_child_4_week_born t1_child_5_age
## 1 NA <NA> NA NA
## 2 NA <NA> NA NA
## 3 NA <NA> NA NA
## t1_child_5_age_units t1_child_5_week_born t1_child_6_age t1_child_6_age_units
## 1 <NA> NA NA <NA>
## 2 <NA> NA NA <NA>
## 3 <NA> NA NA <NA>
## t1_child_6_week_born t1_birth_complications t1_birth_complications_quant
## 1 NA <NA> NA
## 2 NA <NA> NA
## 3 NA <NA> NA
## t1_birth_complications_type t1phqTotal t1gadTotal t1cfqTotal t1rtqTotal
## 1 <NA> 4 3 24 25
## 2 <NA> 5 6 16 16
## 3 <NA> 6 5 12 12
## t1ocdTotal t1intrusionsTotal t1_due_date t1_days_until_birth
## 1 20 4 2025-01-05 191
## 2 8 4 2025-12-16 540
## 3 2 1 2025-02-03 224
## t1_weeks_until_birth t1_gest_age_weeks t1_child_1_age_weeks
## 1 27.28571 12.71429 NA
## 2 77.14286 -37.14286 NA
## 3 32.00000 8.00000 NA
## t1_child_2_age_weeks t1_child_3_age_weeks t1_child_4_age_weeks
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## t1_child_5_age_weeks t1_child_6_age_weeks ID_t2 t2_date completed_t2
## 1 NA NA 11516554 2024-07-30 complete
## 2 NA NA 11472949 2024-07-21 complete
## 3 NA NA 11473575 2024-07-21 complete
## t2_pregnancy_child_loss t2_pregnancy_child_loss_quant t2_phq_1 t2_phq_2
## 1 No 0 1 1
## 2 No 0 0 0
## 3 No 0 1 1
## t2_phq_3 t2_phq_4 t2_phq_5 t2_phq_6 t2_phq_7 t2_phq_8 t2_gad_1 t2_gad_2
## 1 0 2 1 0 0 0 1 1
## 2 0 0 0 0 0 0 1 1
## 3 3 1 1 1 1 0 1 1
## t2_gad_3 t2_gad_4 t2_gad_5 t2_gad_6 t2_gad_7 t2_cfq_1 t2_cfq_2 t2_cfq_3
## 1 2 1 1 1 1 4 4 5
## 2 1 1 1 3 2 3 3 3
## 3 1 2 2 1 0 4 4 5
## t2_cfq_4 t2_cfq_5 t2_cfq_6 t2_cfq_7 t2_rtq_1 t2_rtq_2 t2_rtq_3 t2_rtq_4
## 1 4 4 4 4 3 3 2 3
## 2 3 3 2 2 1 1 1 1
## 3 4 3 5 5 2 3 2 3
## t2_rtq_5 t2_rtq_6 t2_rtq_7 t2_rtq_8 t2_rtq_9 t2_rtq_10 t2_ocd_1 t2_ocd_2
## 1 4 3 3 3 2 2 2 1
## 2 2 2 1 1 1 1 0 2
## 3 2 3 2 2 2 2 1 1
## t2_ocd_3 t2_ocd_4 t2_ocd_5 t2_ocd_6 t2_ocd_7 t2_ocd_8 t2_ocd_9 t2_ocd_10
## 1 1 1 0 2 1 1 1 1
## 2 0 0 0 3 0 0 0 0
## 3 2 1 0 2 2 1 2 0
## t2_ocd_11 t2_ocd_12 t2_ocd_13 t2_ocd_14 t2_ocd_15 t2_ocd_16 t2_ocd_17
## 1 2 0 4 1 3 0 0
## 2 0 0 0 0 0 0 0
## 3 2 1 1 1 2 0 2
## t2_ocd_18 t2_pnd_ocd1 t2_pnd_ocd2 t2_pnd_ocd3 t2_pnd_ocd4
## 1 1 0 1 0 1
## 2 0 2 1 1 2
## 3 2 0 0 2 0
## t2_thoughts_frequency t2_thoughts_interfere_functioning
## 1 3 2
## 2 3 3
## 3 NA 2
## t2_thoughts_distress_level t2_new_mental_health_diagnosis_last_month
## 1 2 0
## 2 1 0
## 3 3 0
## t2_new_anxiety_diagnosis t2_new_bipolar_diagnosis t2_new_depression_diagnosis
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## t2_new_eating_disorder_diagnosis t2_new_ocd_diagnosis t2_new_ptsd_diagnosis
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## t2_new_schizophrenia_diagnosis t2_prefer_not_to_say_diagnosis
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t2_new_other_diagnosis t2_new_other_type_diagnosis
## 1 NA
## 2 NA
## 3 NA
## t2_current_anxiety_disorder t2_current_bipolar_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t2_current_depression_disorder t2_current_eating_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t2_current_ocd_disorder t2_current_ptsd_disorder
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t2_current_schizophrenia_disorder t2_current_disorder_prefer_not_to_say
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t2_other_current_disorder t2_other_current_type_disorder t2_pregnant
## 1 NA Yes
## 2 NA Yes
## 3 NA Yes
## t2_pregnant_quant t2_due_day t2_due_month t2_due_month_quant t2_due_year
## 1 1 20 January 1 2025
## 2 1 16 December 12 2025
## 3 1 15 February 2 2025
## t2_number_of_pregnancies birth_since_t1 birth_since_t1_quant
## 1 1 NA
## 2 1 NA
## 3 1 NA
## birth_since_t1_child_1_age birth_since_t1_child_1_age_units
## 1 NA
## 2 NA
## 3 NA
## birth_since_t1_child_1_week_born birth_since_t1_child_2_age
## 1 NA NA
## 2 NA NA
## 3 NA NA
## birth_since_t1_child_2_age_units birth_since_t1_child_2_week_born
## 1 NA NA
## 2 NA NA
## 3 NA NA
## birth_since_t1_child_3_age birth_since_t1_child_3_age_units
## 1 NA NA
## 2 NA NA
## 3 NA NA
## birth_since_t1_child_3_week_born birth_since_t1_child_4_age
## 1 NA NA
## 2 NA NA
## 3 NA NA
## birth_since_t1_child_4_age_units birth_since_t1_child_4_week_born
## 1 NA NA
## 2 NA NA
## 3 NA NA
## t2_birth_complications t2_birth_complications_quant
## 1 NA
## 2 NA
## 3 NA
## t2_birth_complications_type t2_phq_total t2_gad_total t2_cfq_total
## 1 5 8 29
## 2 0 10 19
## 3 9 8 30
## t2_rtq_total t2_ocd_total t2_intrusions_total t2_due_date t2_days_until_birth
## 1 28 22 2 2025-01-20 174
## 2 12 5 6 2025-12-16 513
## 3 23 23 2 2025-02-15 209
## t2_weeks_until_birth t2_gest_age_weeks
## 1 24.85714 15.14286
## 2 73.28571 -33.28571
## 3 29.85714 10.14286 # exclude participant Prolific ID = 5f262b3018c68e27aebdd170,
# 606be651a89a7baf6141e8b9, 631910aab5323b5658ca4a39# check if non-pregnant participants' youngest child < 104.4 weeks (24 months)
child_weeks <- c("t1_child_1_age_weeks", "t1_child_2_age_weeks",
"t1_child_3_age_weeks",
"t1_child_4_age_weeks", "t1_child_5_age_weeks",
"t1_child_6_age_weeks")
full_data %>%
filter(t1_pregnant == "No") %>%
dplyr::select(c(Prolific_ID, child_weeks)) %>%
pivot_longer(cols = child_weeks,
names_to = "child", values_to = "weeks") %>%
na.omit() %>%
group_by(Prolific_ID) %>%
summarise(youngest_child_age = min(weeks),
exclude = youngest_child_age > 104.4 | youngest_child_age < 0) %>%
filter(exclude == TRUE & youngest_child_age != Inf) ## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(child_weeks)
##
## # Now:
## data %>% select(all_of(child_weeks))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## # A tibble: 3 × 3
## Prolific_ID youngest_child_age exclude
## <chr> <dbl> <lgl>
## 1 5f565a2b33c152071057bb04 -117. TRUE
## 2 650062cd6cef58594c7a5c78 126. TRUE
## 3 663259adf676257069e1a6e1 -52.1 TRUE # exclude participant Prolific ID == 650062cd6cef58594c7a5c78
# assuming that age input by participant Prolific ID == 5f565a2b33c152071057bb04
# and 663259adf676257069e1a6e1 was a typo, exclude
# Prolific ID == 5f565a2b33c152071057bb04 and include
# Prolific ID == 663259adf676257069e1a6e1 final_data <- full_data
# find row and column index of negative value of participant ID == 11308955
which(final_data == -52.14, arr.ind = TRUE)## row col
## [1,] 156 249# change participant ID == 11308955 input value to positive,
# assuming negative sign is a typo
final_data[156,249] = -1 * final_data[156,249]
# check value change
final_data[156,249]## [1] 52.14# remove excluded participants and create final data frame
final_data <- final_data %>%
filter(Prolific_ID != "5d01006f8a840b00195c2159" & # not currently or
#previously pregnant
Prolific_ID != "5f262b3018c68e27aebdd170" & # gest age < 13 wks
Prolific_ID != "606be651a89a7baf6141e8b9" &
Prolific_ID != "631910aab5323b5658ca4a39" &
Prolific_ID != "650062cd6cef58594c7a5c78" & # not preg.
#& youngest child > 104.4 wks
Prolific_ID != "5f565a2b33c152071057bb04") # calculate weeks since conception
final_data <- final_data %>%
rowwise() %>%
mutate(t1_post_child1 = sum(t1_child_1_age_weeks, t1_child_1_week_born),
t1_post_child2 = sum(t1_child_2_age_weeks, t1_child_2_week_born),
t1_post_child3 = sum(t1_child_3_age_weeks, t1_child_3_week_born),
t1_post_child4 = sum(t1_child_4_age_weeks, t1_child_4_week_born),
t1_post_child5 = sum(t1_child_5_age_weeks, t1_child_5_week_born),
t1_post_child6 = sum(t1_child_6_age_weeks, t1_child_6_week_born)) %>%
mutate(min_postpartum = min(c(t1_post_child1, t1_post_child2, t1_post_child3,
t1_post_child4, t1_post_child5, t1_post_child6),
na.rm = TRUE),
t1_gest_age_weeks_dup = t1_gest_age_weeks,
t1_wks_since_conception = case_when(is.na(t1_gest_age_weeks) == TRUE ~
min_postpartum,
.default = t1_gest_age_weeks)) %>%
ungroup()## Warning: There were 26 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `min_postpartum = min(...)`.
## ℹ In row 12.
## Caused by warning in `min()`:
## ! no non-missing arguments to min; returning Inf
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 25 remaining warnings.Check distribution and outliers
Intrusions, distress and mental health
# check distributions of T1 Intrusions
ggplot(final_data, aes(t1intrusionsTotal)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "T1 Intrusions Total Distribution", x= "T1 Intrusions Total Score (0-12: No this is Not True - this is True Most of the Time)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
theme(plot.title = element_text(size = 18)) ## List of 136
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## $ legend.box : NULL
## $ legend.box.just : NULL
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## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$t1intrusionsTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 4.87 3.31 5 4.74 4.45 0 12 12 0.26 -0.98 0.21# First, calculate the mean and standard deviation
descriptives <- describe(final_data$t1intrusionsTotal)
mean_intrusions <- descriptives$mean # Extract mean from describe output
sd_intrusions <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in intrusions total
z_scores <- (final_data$t1intrusionsTotal - mean_intrusions) / sd_intrusions
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$t1intrusionsTotal[abs(z_scores) > 3]
# Print outliers
outliers## integer(0)# check distributions of T1 Intrusions Distress
ggplot(final_data, aes(t1_thoughts_distress_level)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "T1 Intrusions Distress Total Distribution", x= "T1 Intrusions Distress Total Score (1-5: None to Near Constant Disabling Distress)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_bin()`).
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## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
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## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$t1_thoughts_distress_level)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 246 2.04 0.89 2 2 1.48 1 4 3 0.3 -0.94 0.06# First, calculate the mean and standard deviation
descriptives <- describe(final_data$t1_thoughts_distress_level)
mean_intrusionsdistress <- descriptives$mean # Extract mean from describe output
sd_intrusionsdistress <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in intrustions distress total
z_scores <- (final_data$t1_thoughts_distress_level - mean_intrusionsdistress) / sd_intrusionsdistress
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$t1_thoughts_distress_level[abs(z_scores) > 3]
# Print outliers
outliers## [1] NA# check distributions of T1 PHQ Data
ggplot(final_data, aes(t1phqTotal)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "T1 PHQ Total Distribution", x= "T1 PHQ Total Score, (0-24: Not at All to Every Day)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
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## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$t1phqTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 7.77 5.3 7 7.34 5.93 0 24 24 0.69 -0.15 0.34# First, calculate the mean and standard deviation
descriptives <- describe(final_data$t1phqTotal)
mean_phq <- descriptives$mean # Extract mean from describe output
sd_phq <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in t1phqTotal
z_scores <- (final_data$t1phqTotal - mean_phq) / sd_phq
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$t1phqTotal[abs(z_scores) > 3]
# Print outliers
outliers## [1] 24# check distributions of T1 GAD Data
ggplot(final_data, aes(t1gadTotal)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "T1 GAD Total Distribution", x= "T1 GAD Total Score (0-21: Not at All to Nearly Every Day)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
theme(plot.title = element_text(size = 18)) ## List of 136
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## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
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## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$t1gadTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 7.28 5.02 6 6.95 4.45 0 21 21 0.53 -0.55 0.32# First, calculate the mean and standard deviation
descriptives <- describe(final_data$t1gadTotal)
mean_gad <- descriptives$mean # Extract mean from describe output
sd_gad <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in t1gadTotal
z_scores <- (final_data$t1gadTotal - mean_gad) / sd_gad
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$t1gadTotal[abs(z_scores) > 3]
# Print outliers
outliers## integer(0)# check distributions of T1 OCD Data
ggplot(final_data, aes(t1ocdTotal)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "T1 OCD Total Distribution", x= "T1 OCD Total Score (0-72: Not At All to Extermely", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
theme(plot.title = element_text(size = 18)) ## List of 136
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## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$t1ocdTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 16.31 13.57 12 14.77 13.34 0 54 54 0.82 -0.31 0.86# First, calculate the mean and standard deviation
descriptives <- describe(final_data$t1ocdTotal)
mean_ocd <- descriptives$mean # Extract mean from describe output
sd_ocd <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in t1ocdTotal
z_scores <- (final_data$t1ocdTotal - mean_ocd) / sd_ocd
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$t1ocdTotal[abs(z_scores) > 3]
# Print outliers
outliers## integer(0)# check distributions of T1 RNT Data
ggplot(final_data, aes(t1rtqTotal)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "T1 RTQ Total Distribution", x= "T1 RTQ Total Score (0-50: Not True at All to Very True)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
theme(plot.title = element_text(size = 18)) ## List of 136
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## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$t1rtqTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 26.33 10.56 27 25.99 13.34 10 50 40 0.19 -0.94 0.67# First, calculate the mean and standard deviation
descriptives <- describe(final_data$t1rtqTotal)
mean_rtq <- descriptives$mean # Extract mean from describe output
sd_rtq <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in t1rtqTotal
z_scores <- (final_data$t1rtqTotal - mean_rtq) / sd_rtq
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$t1rtqTotal[abs(z_scores) > 3]
# Print outliers
outliers## integer(0)#Descriptives ### T1 Descriptives
### T1
describe(final_data$age_years)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 32.13 5.05 32 32.09 5.93 19 45 26 0.05 -0.28 0.32describe(final_data$t1_wks_since_conception)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 247 63.16 31.22 65.1 62.42 39.97 13.57 145.28 131.71 0.11 -1.12
## se
## X1 1.99describe(final_data$t1phqTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 7.77 5.3 7 7.34 5.93 0 24 24 0.69 -0.15 0.34describe(final_data$t1gadTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 7.28 5.02 6 6.95 4.45 0 21 21 0.53 -0.55 0.32describe(final_data$t1cfqTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 24.02 10.04 25 24.01 11.86 7 47 40 -0.02 -1 0.64describe(final_data$t1rtqTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 26.33 10.56 27 25.99 13.34 10 50 40 0.19 -0.94 0.67describe(final_data$t1ocdTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 16.31 13.57 12 14.77 13.34 0 54 54 0.82 -0.31 0.86describe(final_data$t1intrusionsTotal)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 4.87 3.31 5 4.74 4.45 0 12 12 0.26 -0.98 0.21describe(final_data$t1_thoughts_distress_level)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 246 2.04 0.89 2 2 1.48 1 4 3 0.3 -0.94 0.06#Calculate levels of mental health
# gad
final_data %>%
dplyr::select(t1gadTotal) %>%
mutate(scoring = case_when(t1gadTotal <= 4 ~ "minimal anxiety",
t1gadTotal <= 9 ~ "mild anxiety",
t1gadTotal <= 14 ~ "moderate anxiety",
t1gadTotal <= 21 ~ "severe anxiety")) %>%
tabyl(scoring)## scoring n percent
## mild anxiety 77 0.3117409
## minimal anxiety 90 0.3643725
## moderate anxiety 55 0.2226721
## severe anxiety 25 0.1012146# phq
final_data %>%
dplyr::select(t1phqTotal) %>%
mutate(scoring = case_when(t1phqTotal <= 4 ~ "none/minimal depression",
t1phqTotal <= 9 ~ "mild depression",
t1phqTotal <= 14 ~ "moderate depression",
t1phqTotal <= 19 ~ "moderately severe depression",
t1phqTotal <= 27 ~ "severe depression")) %>%
tabyl(scoring)## scoring n percent
## mild depression 73 0.29554656
## moderate depression 57 0.23076923
## moderately severe depression 24 0.09716599
## none/minimal depression 86 0.34817814
## severe depression 7 0.02834008# rtq
final_data %>%
dplyr::select(t1rtqTotal) %>%
mutate(scoring = ifelse(t1rtqTotal < 32, "non-clinical", "clinical")) %>%
tabyl(scoring)## scoring n percent
## clinical 77 0.3117409
## non-clinical 170 0.6882591# ocd
final_data %>%
dplyr::select(t1ocdTotal) %>%
mutate(scoring = case_when(t1ocdTotal <= 15 ~ "mild",
t1ocdTotal <= 27 ~ "moderate",
t1ocdTotal <= 72 ~ "severe")) %>%
tabyl(scoring)## scoring n percent
## mild 143 0.5789474
## moderate 51 0.2064777
## severe 53 0.2145749tabyl(final_data$gender_identity)## final_data$gender_identity n percent
## Female 246 0.995951417
## Male 1 0.004048583tabyl(final_data$ethnicity)## final_data$ethnicity n percent
## Asian 22 0.089068826
## Black 35 0.141700405
## Hispanic 8 0.032388664
## Mixed 10 0.040485830
## Prefer not to say 1 0.004048583
## White 167 0.676113360
## __other 4 0.016194332tabyl(final_data$resident_country)## final_data$resident_country n percent
## Australia 14 0.056680162
## Canada 20 0.080971660
## Germany 4 0.016194332
## Netherlands 8 0.032388664
## New Zealand 2 0.008097166
## Sweden 1 0.004048583
## United Kingdom 125 0.506072874
## United States of America 73 0.295546559tabyl(final_data$level_of_education)## final_data$level_of_education n percent
## High School 36 0.1457490
## Professional/vocational training 25 0.1012146
## University/college 186 0.7530364tabyl(final_data$socioeconomics)## final_data$socioeconomics n percent
## Fairly well off 123 0.497975709
## Not at all well off 9 0.036437247
## Not very well off 64 0.259109312
## Prefer not to say 2 0.008097166
## Rather well off 45 0.182186235
## Very well off 4 0.016194332tabyl(final_data$t1_pregnant)## final_data$t1_pregnant n percent
## No 172 0.6963563
## Yes 75 0.3036437tabyl(final_data$t1_number_of_children)## final_data$t1_number_of_children n percent
## 1 107 0.43319838
## 2 69 0.27935223
## 3 32 0.12955466
## 4 5 0.02024291
## 5 4 0.01619433
## 6 4 0.01619433
## None 26 0.10526316tabyl(final_data$t1_birth_complications)## final_data$t1_birth_complications n percent valid_percent
## No 118 0.477732794 0.533936652
## Prefer not to say 2 0.008097166 0.009049774
## Yes 101 0.408906883 0.457013575
## <NA> 26 0.105263158 NAtabyl(final_data$t1_mental_health_diagnosis)## final_data$t1_mental_health_diagnosis n percent
## 0 165 0.6680162
## 1 82 0.3319838tabyl(final_data$t1_anxiety_diagnosis)## final_data$t1_anxiety_diagnosis n percent valid_percent
## 0 19 0.07692308 0.2317073
## 1 63 0.25506073 0.7682927
## NA 165 0.66801619 NAtabyl(final_data$t1_bipolar_diagnosis)## final_data$t1_bipolar_diagnosis n percent valid_percent
## 0 77 0.31174089 0.93902439
## 1 5 0.02024291 0.06097561
## NA 165 0.66801619 NAtabyl(final_data$t1_depression_diagnosis)## final_data$t1_depression_diagnosis n percent valid_percent
## 0 25 0.1012146 0.304878
## 1 57 0.2307692 0.695122
## NA 165 0.6680162 NAtabyl(final_data$t1_eating_disorder_diagnosis)## final_data$t1_eating_disorder_diagnosis n percent valid_percent
## 0 70 0.2834008 0.8536585
## 1 12 0.0485830 0.1463415
## NA 165 0.6680162 NAtabyl(final_data$t1_ocd_diagnosis)## final_data$t1_ocd_diagnosis n percent valid_percent
## 0 77 0.31174089 0.93902439
## 1 5 0.02024291 0.06097561
## NA 165 0.66801619 NAtabyl(final_data$t1_ptsd_diagnosis)## final_data$t1_ptsd_diagnosis n percent valid_percent
## 0 68 0.27530364 0.8292683
## 1 14 0.05668016 0.1707317
## NA 165 0.66801619 NAtabyl(final_data$t1_schizophrenia_diagnosis)## final_data$t1_schizophrenia_diagnosis n percent valid_percent
## 0 82 0.3319838 1
## NA 165 0.6680162 NAtabyl(final_data$t1_prefer_not_to_say_diagnosis)## final_data$t1_prefer_not_to_say_diagnosis n percent valid_percent
## 0 81 0.327935223 0.98780488
## 1 1 0.004048583 0.01219512
## NA 165 0.668016194 NAtabyl(final_data$t1_other_diagnosis)## final_data$t1_other_diagnosis n percent valid_percent
## 0 78 0.31578947 0.95121951
## 1 4 0.01619433 0.04878049
## NA 165 0.66801619 NAtabyl(final_data$t1_other_type_diagnosis)## final_data$t1_other_type_diagnosis n percent valid_percent
## BPD 1 0.004048583 0.25
## Borderline Personality Disorder 1 0.004048583 0.25
## Borderline Personality Disorder, ADHD 1 0.004048583 0.25
## grief 1 0.004048583 0.25
## <NA> 243 0.983805668 NAtabyl(final_data$t1_current_anxiety_disorder)## final_data$t1_current_anxiety_disorder n percent valid_percent
## 0 31 0.1255061 0.3780488
## 1 51 0.2064777 0.6219512
## NA 165 0.6680162 NAtabyl(final_data$t1_current_bipolar_disorder)## final_data$t1_current_bipolar_disorder n percent valid_percent
## 0 79 0.31983806 0.96341463
## 1 3 0.01214575 0.03658537
## NA 165 0.66801619 NAtabyl(final_data$t1_current_depression_disorder)## final_data$t1_current_depression_disorder n percent valid_percent
## 0 49 0.1983806 0.597561
## 1 33 0.1336032 0.402439
## NA 165 0.6680162 NAtabyl(final_data$t1_current_eating_disorder)## final_data$t1_current_eating_disorder n percent valid_percent
## 0 77 0.31174089 0.93902439
## 1 5 0.02024291 0.06097561
## NA 165 0.66801619 NAtabyl(final_data$t1_current_ocd_disorder)## final_data$t1_current_ocd_disorder n percent valid_percent
## 0 76 0.3076923 0.92682927
## 1 6 0.0242915 0.07317073
## NA 165 0.6680162 NAtabyl(final_data$t1_current_ptsd_disorder)## final_data$t1_current_ptsd_disorder n percent valid_percent
## 0 73 0.29554656 0.8902439
## 1 9 0.03643725 0.1097561
## NA 165 0.66801619 NAtabyl(final_data$t1_current_schizophrenia_disorder)## final_data$t1_current_schizophrenia_disorder n percent valid_percent
## 0 82 0.3319838 1
## NA 165 0.6680162 NAtabyl(final_data$t1_no_current_disorder)## final_data$t1_no_current_disorder n percent valid_percent
## 0 61 0.24696356 0.7439024
## 1 21 0.08502024 0.2560976
## NA 165 0.66801619 NAtabyl(final_data$t1_current_disorder_prefer_not_to_say)## final_data$t1_current_disorder_prefer_not_to_say n percent valid_percent
## 0 82 0.3319838 1
## NA 165 0.6680162 NAtabyl(final_data$t1_other_current_disorder)## final_data$t1_other_current_disorder n percent valid_percent
## 0 78 0.31578947 0.95121951
## 1 4 0.01619433 0.04878049
## NA 165 0.66801619 NAtabyl(final_data$t1_other_current_type_disorder)## final_data$t1_other_current_type_disorder n percent valid_percent
## BPD 1 0.004048583 0.25
## Borderline Personality Disorder 1 0.004048583 0.25
## Borderline Personality Disorder, ADHD 1 0.004048583 0.25
## grief 1 0.004048583 0.25
## <NA> 243 0.983805668 NAtabyl(final_data$t1_neurodevelopmental_neurological_disorder)## final_data$t1_neurodevelopmental_neurological_disorder n percent
## 0 228 0.923076923
## 1 17 0.068825911
## NA 2 0.008097166
## valid_percent
## 0.93061224
## 0.06938776
## NAtabyl(final_data$t1_adhd_disorder)## final_data$t1_adhd_disorder n percent valid_percent
## 0 4 0.01619433 0.2352941
## 1 13 0.05263158 0.7647059
## NA 230 0.93117409 NAtabyl(final_data$t1_autism_spectrum_disorder)## final_data$t1_autism_spectrum_disorder n percent valid_percent
## 0 12 0.04858300 0.7058824
## 1 5 0.02024291 0.2941176
## NA 230 0.93117409 NAtabyl(final_data$t1_asperger_syndrome_disorder)## final_data$t1_asperger_syndrome_disorder n percent valid_percent
## 0 17 0.06882591 1
## NA 230 0.93117409 NAtabyl(final_data$t1_epilepsy)## final_data$t1_epilepsy n percent valid_percent
## 0 17 0.06882591 1
## NA 230 0.93117409 NAtabyl(final_data$t1_seizures)## final_data$t1_seizures n percent valid_percent
## 0 17 0.06882591 1
## NA 230 0.93117409 NAtabyl(final_data$t1_dyslexia_reading_disorder_)## final_data$t1_dyslexia_reading_disorder_ n percent valid_percent
## 0 16 0.064777328 0.94117647
## 1 1 0.004048583 0.05882353
## NA 230 0.931174089 NAtabyl(final_data$t1_dysgraphia_writing_disorder)## final_data$t1_dysgraphia_writing_disorder n percent valid_percent
## 0 17 0.06882591 1
## NA 230 0.93117409 NAtabyl(final_data$t1_dyscalculia_calculation_disorder)## final_data$t1_dyscalculia_calculation_disorder n percent valid_percent
## 0 16 0.064777328 0.94117647
## 1 1 0.004048583 0.05882353
## NA 230 0.931174089 NAtabyl(final_data$t1_prefer_not_to_say)## final_data$t1_prefer_not_to_say n percent valid_percent
## 0 17 0.06882591 1
## NA 230 0.93117409 NAtabyl(final_data$t1_other_neuro_disorder)## final_data$t1_other_neuro_disorder n percent valid_percent
## 0 16 0.064777328 0.94117647
## 1 1 0.004048583 0.05882353
## NA 230 0.931174089 NAtabyl(final_data$t1_other_type_neuro_disorder)## final_data$t1_other_type_neuro_disorder n percent valid_percent
## Dispraxia 1 0.004048583 1
## <NA> 246 0.995951417 NAT2 Descriptives
t2_data <- final_data %>%
dplyr::select(c(Prolific_ID, completed_t2, starts_with(c("t2_", "birth_since")))) %>%
filter(completed_t2 != is.na(TRUE),
t2_pregnancy_child_loss != "Yes")describe(t2_data$t2_phq_total)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 206 7.07 4.84 6 6.65 4.45 0 24 24 0.85 0.42 0.34describe(t2_data$t2_gad_total)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 206 6.83 4.75 6 6.43 4.45 0 20 20 0.64 -0.23 0.33describe(t2_data$t2_cfq_total)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 206 23.27 10.05 24 23.08 10.38 7 49 42 0.08 -0.68 0.7describe(t2_data$t2_rtq_total)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 206 25.13 9.75 24 24.57 8.9 10 50 40 0.48 -0.42 0.68describe(t2_data$t2_ocd_total)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 206 14.22 11.84 12 12.83 11.86 0 52 52 0.92 0.18 0.83describe(t2_data$t2_intrusions_total)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 206 4.36 3.26 4 4.12 2.97 0 12 12 0.55 -0.47 0.23describe(t2_data$t2_thoughts_distress_level)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 206 2 0.97 2 1.92 1.48 1 5 4 0.57 -0.47 0.07#Calculate levels of mental health
# gad
t2_data %>%
dplyr::select(t2_gad_total) %>%
mutate(scoring = case_when(t2_gad_total <= 4 ~ "minimal anxiety",
t2_gad_total <= 9 ~ "mild anxiety",
t2_gad_total <= 14 ~ "moderate anxiety",
t2_gad_total <= 21 ~ "severe anxiety")) %>%
tabyl(scoring)## scoring n percent
## mild anxiety 67 0.32524272
## minimal anxiety 80 0.38834951
## moderate anxiety 42 0.20388350
## severe anxiety 17 0.08252427# phq
t2_data %>%
dplyr::select(t2_phq_total) %>%
mutate(scoring = case_when(t2_phq_total <= 4 ~ "none/minimal depression",
t2_phq_total <= 9 ~ "mild depression",
t2_phq_total <= 14 ~ "moderate depression",
t2_phq_total <= 19 ~ "moderately severe depression",
t2_phq_total <= 27 ~ "severe depression")) %>%
tabyl(scoring)## scoring n percent
## mild depression 72 0.34951456
## moderate depression 40 0.19417476
## moderately severe depression 15 0.07281553
## none/minimal depression 76 0.36893204
## severe depression 3 0.01456311# rtq
t2_data %>%
dplyr::select(t2_rtq_total) %>%
mutate(scoring = ifelse(t2_rtq_total < 32, "non-clinical", "clinical")) %>%
tabyl(scoring)## scoring n percent
## clinical 47 0.2281553
## non-clinical 159 0.7718447# ocd
t2_data %>%
dplyr::select(t2_ocd_total) %>%
mutate(scoring = case_when(t2_ocd_total <= 15 ~ "mild",
t2_ocd_total <= 27 ~ "moderate",
t2_ocd_total <= 72 ~ "severe")) %>%
tabyl(scoring)## scoring n percent
## mild 127 0.6165049
## moderate 48 0.2330097
## severe 31 0.1504854tabyl(final_data$t2_pregnancy_child_loss)## final_data$t2_pregnancy_child_loss n percent valid_percent
## No 206 0.83400810 0.94930876
## Yes 11 0.04453441 0.05069124
## <NA> 30 0.12145749 NAtabyl(final_data$completed_t2)## final_data$completed_t2 n percent valid_percent
## complete 206 0.83400810 0.94930876
## rejected 11 0.04453441 0.05069124
## <NA> 30 0.12145749 NAtabyl(t2_data$t2_pregnant)## t2_data$t2_pregnant n percent
## No 154 0.7475728
## Yes 52 0.2524272tabyl(t2_data$t2_number_of_pregnancies)## t2_data$t2_number_of_pregnancies n percent valid_percent
## 1 19 0.092233010 0.36538462
## 2 15 0.072815534 0.28846154
## 3 13 0.063106796 0.25000000
## 4 1 0.004854369 0.01923077
## 5 2 0.009708738 0.03846154
## 7 1 0.004854369 0.01923077
## 10 1 0.004854369 0.01923077
## NA 154 0.747572816 NAtabyl(t2_data$birth_since_t1)## t2_data$birth_since_t1 n percent
## 52 0.25242718
## No 146 0.70873786
## Yes 8 0.03883495tabyl(t2_data$t2_new_mental_health_diagnosis_last_month)## t2_data$t2_new_mental_health_diagnosis_last_month n percent
## 0 202 0.980582524
## 1 3 0.014563107
## NA 1 0.004854369
## valid_percent
## 0.98536585
## 0.01463415
## NAtabyl(t2_data$t2_new_anxiety_diagnosis)## t2_data$t2_new_anxiety_diagnosis n percent valid_percent
## 0 2 0.009708738 0.6666667
## 1 1 0.004854369 0.3333333
## NA 203 0.985436893 NAtabyl(t2_data$t2_new_bipolar_diagnosis)## t2_data$t2_new_bipolar_diagnosis n percent valid_percent
## 0 2 0.009708738 0.6666667
## 1 1 0.004854369 0.3333333
## NA 203 0.985436893 NAtabyl(t2_data$t2_new_depression_diagnosis)## t2_data$t2_new_depression_diagnosis n percent valid_percent
## 0 2 0.009708738 0.6666667
## 1 1 0.004854369 0.3333333
## NA 203 0.985436893 NAtabyl(t2_data$t2_new_eating_disorder_diagnosis)## t2_data$t2_new_eating_disorder_diagnosis n percent valid_percent
## 0 3 0.01456311 1
## NA 203 0.98543689 NAtabyl(t2_data$t2_new_ocd_diagnosis)## t2_data$t2_new_ocd_diagnosis n percent valid_percent
## 0 2 0.009708738 0.6666667
## 1 1 0.004854369 0.3333333
## NA 203 0.985436893 NAtabyl(t2_data$t2_new_other_diagnosis)## t2_data$t2_new_other_diagnosis n percent valid_percent
## 0 2 0.009708738 0.6666667
## 1 1 0.004854369 0.3333333
## NA 203 0.985436893 NAtabyl(t2_data$t2_new_other_type_diagnosis)## t2_data$t2_new_other_type_diagnosis n percent
## 205 0.995145631
## Postpartum depression 1 0.004854369tabyl(t2_data$t2_current_anxiety_disorder)## t2_data$t2_current_anxiety_disorder n percent valid_percent
## 1 3 0.01456311 1
## NA 203 0.98543689 NAtabyl(t2_data$t2_current_bipolar_disorder)## t2_data$t2_current_bipolar_disorder n percent valid_percent
## 0 2 0.009708738 0.6666667
## 1 1 0.004854369 0.3333333
## NA 203 0.985436893 NAtabyl(t2_data$t2_current_depression_disorder)## t2_data$t2_current_depression_disorder n percent valid_percent
## 1 3 0.01456311 1
## NA 203 0.98543689 NAtabyl(t2_data$t2_current_eating_disorder)## t2_data$t2_current_eating_disorder n percent valid_percent
## 0 2 0.009708738 0.6666667
## 1 1 0.004854369 0.3333333
## NA 203 0.985436893 NAtabyl(t2_data$t2_current_ocd_disorder)## t2_data$t2_current_ocd_disorder n percent valid_percent
## 0 1 0.004854369 0.3333333
## 1 2 0.009708738 0.6666667
## NA 203 0.985436893 NAtabyl(t2_data$t2_current_ptsd_disorder)## t2_data$t2_current_ptsd_disorder n percent valid_percent
## 1 3 0.01456311 1
## NA 203 0.98543689 NAtabyl(t2_data$t2_current_schizophrenia_disorder)## t2_data$t2_current_schizophrenia_disorder n percent valid_percent
## 0 3 0.01456311 1
## NA 203 0.98543689 NAtabyl(t2_data$t2_current_disorder_prefer_not_to_say)## t2_data$t2_current_disorder_prefer_not_to_say n percent valid_percent
## 0 3 0.01456311 1
## NA 203 0.98543689 NAtabyl(t2_data$t2_other_current_disorder)## t2_data$t2_other_current_disorder n percent valid_percent
## 0 2 0.009708738 0.6666667
## 1 1 0.004854369 0.3333333
## NA 203 0.985436893 NAtabyl(t2_data$t2_other_current_type_disorder)## t2_data$t2_other_current_type_disorder n percent
## 205 0.995145631
## BPD, PPD 1 0.004854369#Internal Consistency Checks for Measures
#T1
phq_items <- dplyr::select(final_data, starts_with("t1_phq_"))
gad_items <- dplyr::select(final_data, starts_with("t1_gad_"))
cfq_items <- dplyr::select(final_data, starts_with("t1_cfq_"))
rtq_items <- dplyr::select(final_data, starts_with("t1_rtq_"))
ocd_items <- dplyr::select(final_data, c(starts_with("t1_ocd_"), -t1_ocd_diagnosis))
pnd_items <- dplyr::select(final_data, starts_with("t1_pnd_ocd"))
omega(phq_items)## Loading required namespace: GPArotation
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.84
## G.6: 0.84
## Omega Hierarchical: 0.7
## Omega H asymptotic: 0.8
## Omega Total 0.88
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t1_phq_1 0.67 0.34 0.58 0.58 0.42 0.77 1.56
## t1_phq_2 0.73 0.50 0.79 0.79 0.21 0.68 1.77
## t1_phq_3 0.50 0.26 0.35 0.35 0.65 0.71 1.81
## t1_phq_4 0.46 0.65 0.63 0.63 0.37 0.34 1.82
## t1_phq_5 0.57 0.28 0.28 0.48 0.48 0.52 0.67 1.97
## t1_phq_6 0.67 0.32 0.57 0.57 0.43 0.78 1.55
## t1_phq_7 0.50 0.29 0.36 0.36 0.64 0.69 1.86
## t1_phq_8 0.54 0.43 0.48 0.48 0.52 0.60 1.95
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 2.75 0.50 0.37 0.60 2.39
##
## general/max 1.15 max/min = 6.39
## mean percent general = 0.66 with sd = 0.14 and cv of 0.21
## Explained Common Variance of the general factor = 0.65
##
## The degrees of freedom are 7 and the fit is 0.03
## The number of observations was 247 with Chi Square = 7.04 with prob < 0.42
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.002 and the 10 % confidence intervals are 0 0.079
## BIC = -31.53
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 20 and the fit is 0.35
## The number of observations was 247 with Chi Square = 83.98 with prob < 8.2e-10
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.114 and the 10 % confidence intervals are 0.089 0.14
## BIC = -26.21
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.85 0.58 0.56 0.72
## Multiple R square of scores with factors 0.73 0.34 0.32 0.53
## Minimum correlation of factor score estimates 0.46 -0.32 -0.36 0.05
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.88 0.84 0.57 0.68
## Omega general for total scores and subscales 0.70 0.64 0.39 0.43
## Omega group for total scores and subscales 0.11 0.20 0.19 0.26omega(gad_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.88
## G.6: 0.87
## Omega Hierarchical: 0.73
## Omega H asymptotic: 0.8
## Omega Total 0.92
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t1_gad_1 0.68 0.43 0.66 0.66 0.34 0.71 1.72
## t1_gad_2 0.72 0.51 0.78 0.78 0.22 0.67 1.82
## t1_gad_3 0.70 0.51 0.75 0.75 0.25 0.66 1.82
## t1_gad_4 0.61 0.35 0.53 0.53 0.47 0.70 1.82
## t1_gad_5 0.58 0.59 0.68 0.68 0.32 0.49 2.00
## t1_gad_6 0.54 0.37 0.37 0.63 0.79 1.57
## t1_gad_7 0.70 0.71 1.00 1.00 0.00 0.49 2.00
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 2.96 0.76 0.51 0.53 3.48
##
## general/max 0.85 max/min = 6.77
## mean percent general = 0.64 with sd = 0.11 and cv of 0.18
## Explained Common Variance of the general factor = 0.62
##
## The degrees of freedom are 3 and the fit is 0.02
## The number of observations was 247 with Chi Square = 4.37 with prob < 0.22
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0.043 and the 10 % confidence intervals are 0 0.123
## BIC = -12.16
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.57
## The number of observations was 247 with Chi Square = 139.14 with prob < 1.1e-22
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.14
##
## RMSEA index = 0.19 and the 10 % confidence intervals are 0.163 0.22
## BIC = 62.01
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.87 0.68 0.71 0.87
## Multiple R square of scores with factors 0.75 0.46 0.50 0.76
## Minimum correlation of factor score estimates 0.51 -0.08 0.00 0.52
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.92 0.89 0.73 1.00
## Omega general for total scores and subscales 0.73 0.61 0.51 0.49
## Omega group for total scores and subscales 0.14 0.29 0.22 0.51omega(cfq_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.95
## G.6: 0.94
## Omega Hierarchical: 0.9
## Omega H asymptotic: 0.93
## Omega Total 0.96
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t1_cfq_1 0.81 0.30 0.75 0.75 0.25 0.88 1.27
## t1_cfq_2 0.77 0.34 0.72 0.72 0.28 0.83 1.38
## t1_cfq_3 0.78 0.62 1.00 1.00 0.00 0.62 1.89
## t1_cfq_4 0.90 0.86 0.86 0.14 0.94 1.14
## t1_cfq_5 0.82 0.32 0.77 0.77 0.23 0.86 1.31
## t1_cfq_6 0.88 0.25 0.83 0.83 0.17 0.92 1.17
## t1_cfq_7 0.83 0.21 0.74 0.74 0.26 0.93 1.16
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 4.81 0.25 0.23 0.40 4.66
##
## general/max 1.03 max/min = 20.57
## mean percent general = 0.85 with sd = 0.11 and cv of 0.13
## Explained Common Variance of the general factor = 0.85
##
## The degrees of freedom are 3 and the fit is 0.01
## The number of observations was 247 with Chi Square = 3.11 with prob < 0.38
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.01
## RMSEA index = 0.011 and the 10 % confidence intervals are 0 0.109
## BIC = -13.42
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.25
## The number of observations was 247 with Chi Square = 60.87 with prob < 8.3e-08
## The root mean square of the residuals is 0.05
## The df corrected root mean square of the residuals is 0.06
##
## RMSEA index = 0.116 and the 10 % confidence intervals are 0.088 0.147
## BIC = -16.26
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.95 0.51 0.56 0.91
## Multiple R square of scores with factors 0.90 0.26 0.32 0.83
## Minimum correlation of factor score estimates 0.81 -0.48 -0.36 0.67
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.96 0.94 0.85 1.00
## Omega general for total scores and subscales 0.90 0.87 0.73 0.62
## Omega group for total scores and subscales 0.05 0.07 0.12 0.38omega(rtq_items)## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in cov2cor(t(w) %*% r %*% w): diag(V) had non-positive or NA entries;
## the non-finite result may be dubious
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.95
## G.6: 0.96
## Omega Hierarchical: 0.85
## Omega H asymptotic: 0.88
## Omega Total 0.96
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t1_rtq_1 0.61 0.49 0.60 0.60 0.40 0.61 1.91
## t1_rtq_2 0.69 0.55 0.79 0.79 0.21 0.60 1.93
## t1_rtq_3 0.58 0.39 0.51 0.51 0.49 0.67 1.96
## t1_rtq_4 0.72 0.45 0.73 0.73 0.27 0.72 1.72
## t1_rtq_5 0.79 0.38 0.81 0.81 0.19 0.77 1.58
## t1_rtq_6 0.78 0.26 0.21 0.72 0.72 0.28 0.85 1.36
## t1_rtq_7 0.81 0.27 0.75 0.75 0.25 0.88 1.27
## t1_rtq_8 0.93 0.85 0.85 0.15 1.02 1.02
## t1_rtq_9 0.93 0.85 0.85 0.15 1.02 1.00
## t1_rtq_10 0.86 0.75 0.75 0.25 0.97 1.09
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 6.07 1.09 0.00 0.27 5.53
##
## general/max 1.1 max/min = Inf
## mean percent general = 0.81 with sd = 0.16 and cv of 0.2
## Explained Common Variance of the general factor = 0.82
##
## The degrees of freedom are 18 and the fit is 0.2
## The number of observations was 247 with Chi Square = 46.92 with prob < 0.00022
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0.081 and the 10 % confidence intervals are 0.053 0.11
## BIC = -52.24
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 35 and the fit is 1.17
## The number of observations was 247 with Chi Square = 281.36 with prob < 3e-40
## The root mean square of the residuals is 0.11
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.169 and the 10 % confidence intervals are 0.151 0.188
## BIC = 88.53
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.98 0.88 0 0.72
## Multiple R square of scores with factors 0.96 0.77 0 0.52
## Minimum correlation of factor score estimates 0.92 0.53 -1 0.04
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.96 0.93 NA 0.93
## Omega general for total scores and subscales 0.85 0.74 NA 0.91
## Omega group for total scores and subscales 0.10 0.19 NA 0.03omega(ocd_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.93
## G.6: 0.95
## Omega Hierarchical: 0.72
## Omega H asymptotic: 0.76
## Omega Total 0.94
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t1_ocd_1 0.54 0.39 0.47 0.47 0.53 0.62 1.99
## t1_ocd_2 0.62 0.27 0.22 0.52 0.52 0.48 0.74 1.73
## t1_ocd_3 0.70 0.55 0.79 0.79 0.21 0.62 1.89
## t1_ocd_4 0.56 0.24 0.41 0.41 0.59 0.75 1.70
## t1_ocd_5 0.60 0.25 0.47 0.47 0.53 0.77 1.64
## t1_ocd_6 0.58 0.59 0.68 0.68 0.32 0.49 2.01
## t1_ocd_7 0.46 0.34 0.34 0.34 0.66 0.63 1.95
## t1_ocd_8 0.56 0.62 0.70 0.70 0.30 0.45 1.99
## t1_ocd_9 0.67 0.48 0.68 0.68 0.32 0.66 1.84
## t1_ocd_10 0.47 0.28 0.32 0.32 0.68 0.68 1.90
## t1_ocd_11 0.66 0.33 0.58 0.58 0.42 0.74 1.70
## t1_ocd_12 0.55 0.65 0.73 0.73 0.27 0.42 1.96
## t1_ocd_13 0.44 0.28 0.28 0.28 0.72 0.68 1.85
## t1_ocd_14 0.50 0.59 0.60 0.60 0.40 0.42 1.97
## t1_ocd_15 0.67 0.51 0.72 0.72 0.28 0.62 1.91
## t1_ocd_16 0.48 0.30 0.33 0.33 0.67 0.69 1.84
## t1_ocd_17 0.59 0.47 0.58 0.58 0.42 0.60 1.96
## t1_ocd_18 0.53 0.57 0.61 0.61 0.39 0.46 1.99
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 5.9 1.7 1.4 0.9 5.8
##
## general/max 1.01 max/min = 6.45
## mean percent general = 0.61 with sd = 0.12 and cv of 0.19
## Explained Common Variance of the general factor = 0.6
##
## The degrees of freedom are 102 and the fit is 2.1
## The number of observations was 247 with Chi Square = 497.34 with prob < 2.5e-53
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.07
## RMSEA index = 0.125 and the 10 % confidence intervals are 0.115 0.137
## BIC = -64.62
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 135 and the fit is 4.48
## The number of observations was 247 with Chi Square = 1068.65 with prob < 5.7e-145
## The root mean square of the residuals is 0.14
## The df corrected root mean square of the residuals is 0.15
##
## RMSEA index = 0.167 and the 10 % confidence intervals are 0.158 0.177
## BIC = 324.88
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.86 0.80 0.78 0.70
## Multiple R square of scores with factors 0.75 0.64 0.61 0.49
## Minimum correlation of factor score estimates 0.49 0.29 0.23 -0.01
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.94 0.86 0.85 0.88
## Omega general for total scores and subscales 0.72 0.52 0.57 0.57
## Omega group for total scores and subscales 0.15 0.34 0.28 0.32omega(pnd_items)## Warning in GPFoblq(A, Tmat = Tmat, normalize = normalize, eps = eps, maxit =
## maxit, : convergence not obtained in GPFoblq. 1000 iterations used.
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.82
## G.6: 0.8
## Omega Hierarchical: 0.5
## Omega H asymptotic: 0.59
## Omega Total 0.85
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t1_pnd_ocd1 0.63 0.59 0.76 0.76 0.24 0.53 2.05
## t1_pnd_ocd2 0.65 0.57 0.76 0.76 0.24 0.55 2.05
## t1_pnd_ocd3 0.40 0.27 0.21 0.28 0.28 0.72 0.58 2.35
## t1_pnd_ocd4 0.59 0.45 0.60 0.60 0.40 0.59 2.12
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 1.34 0.95 0.07 0.04 1.59
##
## general/max 0.84 max/min = 37.01
## mean percent general = 0.56 with sd = 0.03 and cv of 0.05
## Explained Common Variance of the general factor = 0.56
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 247 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.4
## The number of observations was 247 with Chi Square = 98.1 with prob < 5e-22
## The root mean square of the residuals is 0.23
## The df corrected root mean square of the residuals is 0.39
##
## RMSEA index = 0.441 and the 10 % confidence intervals are 0.37 0.519
## BIC = 87.08
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.71 0.62 0.35 0.33
## Multiple R square of scores with factors 0.51 0.39 0.12 0.11
## Minimum correlation of factor score estimates 0.02 -0.22 -0.76 -0.78
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.85 0.84 NA NA
## Omega general for total scores and subscales 0.50 0.50 NA NA
## Omega group for total scores and subscales 0.34 0.34 NA NA# T2
t2_phq_items <- dplyr::select(final_data, c(starts_with("t2_phq_")), -t2_phq_total)
t2_gad_items <- dplyr::select(final_data, c(starts_with("t2_gad_")), -t2_gad_total)
t2_cfq_items <- dplyr::select(final_data, c(starts_with("t2_cfq_"), -t2_cfq_total))
t2_rtq_items <- dplyr::select(final_data, c(starts_with("t2_rtq_"), -t2_rtq_total))
t2_ocd_items <- dplyr::select(final_data, c(starts_with("t2_ocd_"), -t2_ocd_total))
t2_pnd_items <- dplyr::select(final_data, starts_with("t2_pnd_ocd"))
omega(t2_phq_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.84
## G.6: 0.84
## Omega Hierarchical: 0.7
## Omega H asymptotic: 0.79
## Omega Total 0.88
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t2_phq_1 0.70 0.24 0.57 0.57 0.43 0.86 1.34
## t2_phq_2 0.79 0.36 0.76 0.76 0.24 0.83 1.40
## t2_phq_3 0.42 0.36 0.23 0.36 0.36 0.64 0.49 2.54
## t2_phq_4 0.47 0.49 0.47 0.47 0.53 0.46 2.09
## t2_phq_5 0.50 0.38 0.39 0.39 0.61 0.63 1.90
## t2_phq_6 0.63 0.20 0.46 0.46 0.54 0.86 1.33
## t2_phq_7 0.55 0.35 0.45 0.45 0.55 0.67 1.89
## t2_phq_8 0.54 0.84 1.00 1.00 0.00 0.29 1.70
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 2.75 0.23 0.69 0.80 2.82
##
## general/max 0.98 max/min = 12.33
## mean percent general = 0.64 with sd = 0.21 and cv of 0.33
## Explained Common Variance of the general factor = 0.62
##
## The degrees of freedom are 7 and the fit is 0.05
## The number of observations was 247 with Chi Square = 11.55 with prob < 0.12
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.051 and the 10 % confidence intervals are 0 0.102
## BIC = -27.01
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 20 and the fit is 0.36
## The number of observations was 247 with Chi Square = 87.03 with prob < 2.4e-10
## The root mean square of the residuals is 0.1
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.116 and the 10 % confidence intervals are 0.092 0.142
## BIC = -23.15
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.88 0.43 0.68 0.95
## Multiple R square of scores with factors 0.77 0.19 0.46 0.90
## Minimum correlation of factor score estimates 0.54 -0.63 -0.08 0.81
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.88 0.8 0.72 1.00
## Omega general for total scores and subscales 0.70 0.7 0.43 0.29
## Omega group for total scores and subscales 0.13 0.1 0.29 0.71omega(t2_gad_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.88
## G.6: 0.88
## Omega Hierarchical: 0.76
## Omega H asymptotic: 0.82
## Omega Total 0.92
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t2_gad_1 0.75 0.22 0.23 0.66 0.66 0.34 0.85 1.37
## t2_gad_2 0.84 0.28 0.20 0.83 0.83 0.17 0.86 1.34
## t2_gad_3 0.84 0.40 0.87 0.87 0.13 0.81 1.45
## t2_gad_4 0.63 0.30 0.51 0.51 0.49 0.77 1.60
## t2_gad_5 0.49 0.73 0.77 0.77 0.23 0.32 1.76
## t2_gad_6 0.47 0.36 0.37 0.37 0.63 0.61 1.98
## t2_gad_7 0.63 0.39 0.58 0.58 0.42 0.69 1.82
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 3.23 0.31 0.77 0.26 3.20
##
## general/max 1.01 max/min = 12.1
## mean percent general = 0.7 with sd = 0.19 and cv of 0.27
## Explained Common Variance of the general factor = 0.7
##
## The degrees of freedom are 3 and the fit is 0.01
## The number of observations was 247 with Chi Square = 1.73 with prob < 0.63
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.02
## RMSEA index = 0 and the 10 % confidence intervals are 0 0.087
## BIC = -14.8
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.4
## The number of observations was 247 with Chi Square = 97.39 with prob < 1.5e-14
## The root mean square of the residuals is 0.1
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.155 and the 10 % confidence intervals are 0.127 0.185
## BIC = 20.26
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.90 0.48 0.82 0.60
## Multiple R square of scores with factors 0.82 0.23 0.67 0.36
## Minimum correlation of factor score estimates 0.64 -0.54 0.34 -0.28
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.92 0.91 0.76 0.73
## Omega general for total scores and subscales 0.76 0.78 0.43 0.60
## Omega group for total scores and subscales 0.10 0.13 0.33 0.13omega(t2_cfq_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.95
## G.6: 0.95
## Omega Hierarchical: 0.86
## Omega H asymptotic: 0.89
## Omega Total 0.96
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t2_cfq_1 0.93 0.87 0.87 0.13 0.99 1.02
## t2_cfq_2 0.81 0.23 0.72 0.72 0.28 0.91 1.19
## t2_cfq_3 0.73 0.44 0.74 0.74 0.26 0.72 1.72
## t2_cfq_4 0.84 0.32 0.81 0.81 0.19 0.87 1.28
## t2_cfq_5 0.80 0.34 0.79 0.79 0.21 0.80 1.50
## t2_cfq_6 0.83 0.42 0.88 0.88 0.12 0.79 1.50
## t2_cfq_7 0.80 0.39 0.81 0.81 0.19 0.79 1.52
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 4.73 0.72 0.17 0.01 4.54
##
## general/max 1.04 max/min = 337.11
## mean percent general = 0.84 with sd = 0.09 and cv of 0.11
## Explained Common Variance of the general factor = 0.84
##
## The degrees of freedom are 3 and the fit is 0
## The number of observations was 247 with Chi Square = 0.22 with prob < 0.97
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is 0
## RMSEA index = 0 and the 10 % confidence intervals are 0 0
## BIC = -16.31
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.68
## The number of observations was 247 with Chi Square = 165.45 with prob < 5.7e-28
## The root mean square of the residuals is 0.1
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.209 and the 10 % confidence intervals are 0.182 0.239
## BIC = 88.32
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.96 0.74 0.63 0.17
## Multiple R square of scores with factors 0.91 0.54 0.40 0.03
## Minimum correlation of factor score estimates 0.82 0.09 -0.20 -0.95
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.96 0.95 0.75 0.87
## Omega general for total scores and subscales 0.86 0.79 0.64 0.86
## Omega group for total scores and subscales 0.09 0.16 0.12 0.01omega(t2_rtq_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.95
## G.6: 0.95
## Omega Hierarchical: 0.89
## Omega H asymptotic: 0.92
## Omega Total 0.96
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t2_rtq_1 0.67 0.50 0.50 0.50 0.88 1.28
## t2_rtq_2 0.73 0.49 0.78 0.78 0.22 0.69 1.77
## t2_rtq_3 0.66 0.46 0.46 0.54 0.94 1.13
## t2_rtq_4 0.77 0.33 0.72 0.72 0.28 0.83 1.39
## t2_rtq_5 0.82 0.47 0.89 0.89 0.11 0.75 1.60
## t2_rtq_6 0.77 0.26 0.67 0.67 0.33 0.88 1.27
## t2_rtq_7 0.82 0.24 0.75 0.75 0.25 0.89 1.26
## t2_rtq_8 0.85 0.26 0.80 0.80 0.20 0.90 1.22
## t2_rtq_9 0.88 0.23 0.84 0.84 0.16 0.93 1.15
## t2_rtq_10 0.81 0.71 0.71 0.29 0.93 1.15
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 6.10 0.21 0.48 0.34 5.25
##
## general/max 1.16 max/min = 24.74
## mean percent general = 0.86 with sd = 0.08 and cv of 0.1
## Explained Common Variance of the general factor = 0.86
##
## The degrees of freedom are 18 and the fit is 0.15
## The number of observations was 247 with Chi Square = 35.12 with prob < 0.0091
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0.062 and the 10 % confidence intervals are 0.03 0.093
## BIC = -64.05
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 35 and the fit is 0.6
## The number of observations was 247 with Chi Square = 144.02 with prob < 3.6e-15
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.07
##
## RMSEA index = 0.112 and the 10 % confidence intervals are 0.094 0.132
## BIC = -48.8
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.95 0.43 0.74 0.73
## Multiple R square of scores with factors 0.90 0.19 0.55 0.53
## Minimum correlation of factor score estimates 0.81 -0.62 0.10 0.06
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.96 0.90 0.88 0.87
## Omega general for total scores and subscales 0.89 0.85 0.75 0.72
## Omega group for total scores and subscales 0.04 0.05 0.13 0.15omega(t2_ocd_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.92
## G.6: 0.94
## Omega Hierarchical: 0.7
## Omega H asymptotic: 0.75
## Omega Total 0.94
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t2_ocd_1 0.47 0.36 0.37 0.37 0.63 0.61 2.03
## t2_ocd_2 0.63 0.21 0.26 0.54 0.54 0.46 0.75 1.71
## t2_ocd_3 0.61 0.53 0.66 0.66 0.34 0.57 1.96
## t2_ocd_4 0.57 0.30 0.44 0.44 0.56 0.73 1.74
## t2_ocd_5 0.54 0.40 0.46 0.46 0.54 0.63 1.93
## t2_ocd_6 0.51 0.65 0.68 0.68 0.32 0.38 1.90
## t2_ocd_7 0.41 0.24 0.25 0.25 0.75 0.67 1.97
## t2_ocd_8 0.51 0.46 0.50 0.50 0.50 0.52 2.24
## t2_ocd_9 0.65 0.59 0.76 0.76 0.24 0.55 1.99
## t2_ocd_10 0.45 0.21 0.28 0.28 0.72 0.73 1.77
## t2_ocd_11 0.65 0.45 0.65 0.65 0.35 0.66 1.86
## t2_ocd_12 0.57 0.64 0.74 0.74 0.26 0.43 2.02
## t2_ocd_13 0.45 0.21 0.28 0.28 0.72 0.73 1.78
## t2_ocd_14 0.50 0.40 0.42 0.42 0.58 0.60 1.94
## t2_ocd_15 0.62 0.51 0.65 0.65 0.35 0.59 1.95
## t2_ocd_16 0.48 0.31 0.31 0.69 0.75 1.70
## t2_ocd_17 0.58 0.61 0.71 0.71 0.29 0.48 2.02
## t2_ocd_18 0.55 0.57 0.64 0.64 0.36 0.47 2.08
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 5.4 1.6 1.2 1.1 5.4
##
## general/max 1 max/min = 4.74
## mean percent general = 0.6 with sd = 0.11 and cv of 0.19
## Explained Common Variance of the general factor = 0.58
##
## The degrees of freedom are 102 and the fit is 1.66
## The number of observations was 247 with Chi Square = 394.34 with prob < 5.7e-36
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.07
## RMSEA index = 0.108 and the 10 % confidence intervals are 0.097 0.119
## BIC = -167.62
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 135 and the fit is 3.96
## The number of observations was 247 with Chi Square = 944.02 with prob < 1.8e-121
## The root mean square of the residuals is 0.13
## The df corrected root mean square of the residuals is 0.14
##
## RMSEA index = 0.156 and the 10 % confidence intervals are 0.147 0.166
## BIC = 200.25
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.85 0.82 0.75 0.74
## Multiple R square of scores with factors 0.72 0.67 0.56 0.54
## Minimum correlation of factor score estimates 0.44 0.33 0.11 0.09
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.94 0.81 0.86 0.87
## Omega general for total scores and subscales 0.70 0.50 0.55 0.53
## Omega group for total scores and subscales 0.15 0.31 0.31 0.33omega(t2_pnd_items)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.83
## G.6: 0.81
## Omega Hierarchical: 0.81
## Omega H asymptotic: 0.94
## Omega Total 0.87
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 h2 u2 p2 com
## t2_pnd_ocd1 0.90 0.24 0.89 0.89 0.11 0.91 1.20
## t2_pnd_ocd2 0.77 0.23 0.68 0.68 0.32 0.88 1.27
## t2_pnd_ocd3 0.52 0.26 -0.20 0.37 0.37 0.63 0.72 1.79
## t2_pnd_ocd4 0.76 0.31 0.68 0.68 0.32 0.84 1.37
##
## With Sums of squares of:
## g F1* F2* F3* h2
## 2.25 0.11 0.16 0.10 1.85
##
## general/max 1.21 max/min = 19.34
## mean percent general = 0.84 with sd = 0.08 and cv of 0.1
## Explained Common Variance of the general factor = 0.86
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 247 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.04
## The number of observations was 247 with Chi Square = 8.75 with prob < 0.013
## The root mean square of the residuals is 0.03
## The df corrected root mean square of the residuals is 0.06
##
## RMSEA index = 0.117 and the 10 % confidence intervals are 0.046 0.201
## BIC = -2.26
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.93 0.35 0.47 0.48
## Multiple R square of scores with factors 0.87 0.13 0.22 0.23
## Minimum correlation of factor score estimates 0.73 -0.75 -0.56 -0.53
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.87 0.87 0.67 NA
## Omega general for total scores and subscales 0.81 0.81 0.56 NA
## Omega group for total scores and subscales 0.05 0.06 0.11 NABehavioural task
###Create cognitive and affective control variables
#Calculate and create average DPrime (accuracy) scores for each condition
#across n-back loads
final_data$average_DPrime_negative_score <- rowMeans(final_data[,
c("DprimeNeg_1", "DprimeNeg_2", "DprimeNeg_3")], na.rm = TRUE) #Dprime negative
final_data$average_DPrime_neutral_score <- rowMeans(final_data[,
c("DprimeNeut_1", "DprimeNeut_2", "DprimeNeut_3")], na.rm = TRUE) #Dprime neutral
final_data$average_DPrime_perineg_score <- rowMeans(final_data[,
c("DprimePNeg_1", "DprimePNeg_2", "DprimePNeg_3")], na.rm = TRUE) #Dprime peri-negative#Calculate and create average Reaction Time (RT) correct trials scores for each
#condition across n-back loads
final_data$average_RT_negative_score <- rowMeans(final_data[, c("RTNeg_1",
"RTNeg_2", "RTNeg_3")], na.rm = TRUE) #RT negative
final_data$average_RT_neutral_score <- rowMeans(final_data[, c("RTNeut_1",
"RTNeut_2", "RTNeut_3")], na.rm = TRUE) #RT neutral
final_data$average_RT_perineg_score <- rowMeans(final_data[, c("RTPNeg_1",
"RTPNeg_2", "RTPNeg_3")], na.rm = TRUE) #RT peri-negativefinal_data_test <- final_dataCheck distribution and outliers of Dprime and Reaction Time
# check distributions of Neutral Dprime condition (average of Dprime Neutral tasks)
ggplot(final_data, aes(average_DPrime_neutral_score)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "Neutral Dprime Distribution", x= "(Dprime - Accuracy)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
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## [list output truncated]
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## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$average_DPrime_neutral_score)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 2.76 2.94 3.33 2.78 4.13 -3.48 8.53 12.01 -0.07 -1.2 0.19# First, calculate the mean and standard deviation
descriptives <- describe(final_data$average_DPrime_neutral_score)
mean_dp_neut <- descriptives$mean # Extract mean from describe output
sd_dp_neut <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in Dprime neutral
z_scores <- (final_data$average_DPrime_neutral_score - mean_dp_neut) / sd_dp_neut
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$average_DPrime_neutral_score[abs(z_scores) > 3]
# Print outliers
outliers## numeric(0)# check distributions of Negative Dprime condition (average of Dprime Negative tasks)
ggplot(final_data, aes(average_DPrime_negative_score)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "Negative Dprime Distribution", x= "(Dprime - Accuracy)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
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## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$average_DPrime_negative_score)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 3.33 3.08 3.67 3.31 4.1 -2.79 8.53 11.32 0.02 -1.33 0.2# First, calculate the mean and standard deviation
descriptives <- describe(final_data$average_DPrime_negative_score)
mean_dp_neg <- descriptives$mean # Extract mean from describe output
sd_dp_neg <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in Dprime neutral
z_scores <- (final_data$average_DPrime_negative_score - mean_dp_neg) / sd_dp_neg
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$average_DPrime_negative_score[abs(z_scores) > 3]
# Print outliers
outliers## numeric(0)# check distributions of Peri-Negative Dprime condition (average of Dprime Peri-Negative tasks )
ggplot(final_data, aes(average_DPrime_perineg_score)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "Peri-Negative Dprime Distribution", x= "(Dprime - Accuracy)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
theme(plot.title = element_text(size = 18)) ## List of 136
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## - attr(*, "class")= chr [1:2] "theme" "gg"
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## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$average_DPrime_perineg_score)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 3.24 3.16 3.87 3.25 4.45 -3.56 8.53 12.09 -0.08 -1.36 0.2# First, calculate the mean and standard deviation
descriptives <- describe(final_data$average_DPrime_perineg_score)
mean_dp_pneg <- descriptives$mean # Extract mean from describe output
sd_dp_pneg <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in Dprime neutral
z_scores <- (final_data$average_DPrime_perineg_score - mean_dp_pneg) / sd_dp_pneg
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$average_DPrime_perineg_score[abs(z_scores) > 3]
# Print outliers
outliers## numeric(0)# check distributions of Neutral Reaction Time condition (average of Reaction Time for Neutral tasks)
ggplot(final_data, aes(average_RT_neutral_score)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "Neutral Reaction Time Distribution", x= "(Reaction Time Correct Trials ms)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
theme(plot.title = element_text(size = 18)) ## List of 136
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## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$average_RT_neutral_score)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 247 728.64 85.66 731.01 729.79 76.3 476.37 939.67 463.3 -0.12 0.14
## se
## X1 5.45# First, calculate the mean and standard deviation
descriptives <- describe(final_data$average_RT_neutral_score)
mean_rt_neut <- descriptives$mean # Extract mean from describe output
sd_rt_neut <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in Dprime neutral
z_scores <- (final_data$average_RT_neutral_score - mean_rt_neut) / sd_rt_neut
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$average_RT_neutral_score[abs(z_scores) > 3]
# Print outliers
outliers## numeric(0)# check distributions of Negative Reaction Time condition (average of Reaction Time for Negative tasks across loads)
ggplot(final_data, aes(average_RT_negative_score)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "Negative Reaction Time Distribution", x= "(Reaction Time Correct Trials ms)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
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## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$average_RT_negative_score)## vars n mean sd median trimmed mad min max range skew
## X1 1 247 731.18 89.67 733.42 732.54 76.02 442.64 999.06 556.42 -0.14
## kurtosis se
## X1 0.48 5.71# First, calculate the mean and standard deviation
descriptives <- describe(final_data$average_RT_negative_score)
mean_rt_neg <- descriptives$mean # Extract mean from describe output
sd_rt_neg <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in Dprime neutral
z_scores <- (final_data$average_RT_negative_score - mean_rt_neg) / sd_rt_neg
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$average_RT_negative_score[abs(z_scores) > 3]
# Print outliers
outliers## [1] 442.6396# check distributions of Peri-Negative Reaction Time condition (average of Reaction Time for Peri-Negative tasks across loads)
ggplot(final_data, aes(average_RT_perineg_score)) +
geom_histogram(color = "#000000", fill = "#0099F8") +
labs (title = "Peri-Negative Reaction Time Distribution", x= "(Reaction Time Correct Trials ms)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
theme_classic() +
theme(plot.title = element_text(size = 18)) ## List of 136
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## [list output truncated]
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUE# check descriptives
describe(final_data$average_RT_perineg_score)## vars n mean sd median trimmed mad min max range skew
## X1 1 247 738.22 93.11 730.84 738.01 93.55 470.06 997.06 527.01 0
## kurtosis se
## X1 0.04 5.92# First, calculate the mean and standard deviation
descriptives <- describe(final_data$average_RT_perineg_score)
mean_rt_pneg <- descriptives$mean # Extract mean from describe output
sd_rt_pneg <- descriptives$sd # Extract standard deviation from describe output
# Calculate Z-scores for each data point in Dprime neutral
z_scores <- (final_data$average_RT_perineg_score - mean_rt_pneg) / sd_rt_pneg
# Identify outliers based on a threshold, e.g., Z-scores greater than 3 or less than -3
outliers <- final_data$average_RT_perineg_score[abs(z_scores) > 3]
# Print outliers
outliers## numeric(0)#Create affective control indices
#Calculate and create affective difference scores
final_data$ACdP_neg<-((final_data$average_DPrime_negative_score-final_data$average_DPrime_neutral_score)) #negative-neutral Dprime
final_data$ACdP_peri<-((final_data$average_DPrime_perineg_score-final_data$average_DPrime_neutral_score)) #perineg-neutral Dprime
final_data$ACdP_Nperi<-((final_data$average_DPrime_perineg_score-final_data$average_DPrime_negative_score)) #perineg-negative Dprime
final_data$ACRT_neg<-((final_data$average_RT_negative_score-final_data$average_RT_neutral_score)) #negative-neutral Reaction Time
final_data$ACRT_peri<-((final_data$average_RT_perineg_score-final_data$average_RT_neutral_score)) #perineg-negative Reaction Time
final_data$ACRT_Nperi<-((final_data$average_RT_perineg_score-final_data$average_RT_negative_score)) #perineg-negative Reaction Timefinal_data_test2 <- final_dataAnalyses
Associations between perinatal stage (wks since conception) and mental health
Y ~ X
# phq
ggplot(final_data, aes(t1_wks_since_conception, t1phqTotal)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "PHQ Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
t1_peri_phq <- lm(t1phqTotal ~ t1_wks_since_conception, data = final_data)
summary(t1_peri_phq)##
## Call:
## lm(formula = t1phqTotal ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.4894 -4.2857 -0.8804 3.4306 15.3656
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.87438 0.75998 11.677 <2e-16 ***
## t1_wks_since_conception -0.01750 0.01079 -1.622 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.285 on 245 degrees of freedom
## Multiple R-squared: 0.01062, Adjusted R-squared: 0.00658
## F-statistic: 2.629 on 1 and 245 DF, p-value: 0.1062# gad
ggplot(final_data, aes(t1_wks_since_conception, t1gadTotal)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "GAD Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
t1_peri_gad <- lm(t1gadTotal ~ t1_wks_since_conception, data = final_data)
summary(t1_peri_gad)##
## Call:
## lm(formula = t1gadTotal ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.886 -3.781 -1.018 3.341 13.266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.05181 0.72061 11.174 <2e-16 ***
## t1_wks_since_conception -0.01223 0.01023 -1.195 0.233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.011 on 245 degrees of freedom
## Multiple R-squared: 0.005798, Adjusted R-squared: 0.00174
## F-statistic: 1.429 on 1 and 245 DF, p-value: 0.2331# RNT
ggplot(final_data, aes(t1_wks_since_conception, t1rtqTotal)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "RNT Total Score") +
scale_x_continuous(n.breaks = 15) +
scale_y_continuous(n.breaks = 10, limits = c(0,55), expand = c(0,0)) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
t1_peri_rtq <- lm(t1rtqTotal ~ t1_wks_since_conception, data = final_data)
summary(t1_peri_rtq)##
## Call:
## lm(formula = t1rtqTotal ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.4354 -8.4729 0.2875 7.6391 26.8086
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.17633 1.50811 19.346 <2e-16 ***
## t1_wks_since_conception -0.04510 0.02141 -2.106 0.0362 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.49 on 245 degrees of freedom
## Multiple R-squared: 0.01778, Adjusted R-squared: 0.01377
## F-statistic: 4.436 on 1 and 245 DF, p-value: 0.03621standardized_model_t1_peri_rtq <- standardize_parameters(t1_peri_rtq)
print(standardized_model_t1_peri_rtq)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------
## (Intercept) | 2.77e-17 | [-0.12, 0.12]
## t1 wks since conception | -0.13 | [-0.26, -0.01]# ocd
ggplot(final_data, aes(t1_wks_since_conception, t1ocdTotal)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "OCD Total Score") +
scale_x_continuous(n.breaks = 10) +
scale_y_continuous(n.breaks = 10) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
t1_peri_ocd <- lm(t1ocdTotal ~ t1_wks_since_conception, data = final_data)
summary(t1_peri_ocd)##
## Call:
## lm(formula = t1ocdTotal ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.581 -11.130 -3.373 8.453 36.977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.70139 1.94093 10.15 <2e-16 ***
## t1_wks_since_conception -0.05374 0.02756 -1.95 0.0524 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.5 on 245 degrees of freedom
## Multiple R-squared: 0.01528, Adjusted R-squared: 0.01126
## F-statistic: 3.801 on 1 and 245 DF, p-value: 0.05235# intrusions
ggplot(final_data, aes(t1_wks_since_conception, t1intrusionsTotal)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "Intrusions Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
t1_peri_intrusions <-
lm(t1intrusionsTotal ~ t1_wks_since_conception, data = final_data)
summary(t1_peri_intrusions)##
## Call:
## lm(formula = t1intrusionsTotal ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3370 -2.9007 -0.2406 2.6933 7.5374
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.502444 0.474759 11.590 <2e-16 ***
## t1_wks_since_conception -0.010071 0.006741 -1.494 0.136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.301 on 245 degrees of freedom
## Multiple R-squared: 0.009027, Adjusted R-squared: 0.004982
## F-statistic: 2.232 on 1 and 245 DF, p-value: 0.1365standardized_model_t1_peri_intrusions <- standardize_parameters(t1_peri_intrusions)
print(standardized_model_t1_peri_intrusions)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------
## (Intercept) | 1.21e-16 | [-0.13, 0.13]
## t1 wks since conception | -0.10 | [-0.22, 0.03]# intrusion-related distress
ggplot(final_data, aes(t1_wks_since_conception, t1_thoughts_distress_level)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "Intrusion Related Distress") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
t1_peri_thought_distress <-
lm(t1_thoughts_distress_level ~ t1_wks_since_conception, data = final_data)
summary(t1_peri_thought_distress)##
## Call:
## lm(formula = t1_thoughts_distress_level ~ t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.08841 -1.01892 -0.04904 0.92900 2.00368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1019419 0.1286523 16.338 <2e-16 ***
## t1_wks_since_conception -0.0009109 0.0018390 -0.495 0.621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8878 on 244 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.001005, Adjusted R-squared: -0.00309
## F-statistic: 0.2454 on 1 and 244 DF, p-value: 0.6208standardized_model_t1_peri_thought_distress <- standardize_parameters(t1_peri_thought_distress)
print(standardized_model_t1_peri_thought_distress)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------
## (Intercept) | -3.61e-16 | [-0.13, 0.13]
## t1 wks since conception | -0.03 | [-0.16, 0.09]# relevant graphs
# phq
phq_t1 <- ggplot(final_data, aes(t1_wks_since_conception, t1phqTotal)) +
geom_point() +
geom_smooth(method = "lm") +
labs(x = "Weeks Since Conception",
y = "Depression Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()
# gad
gad_t1 <-ggplot(final_data, aes(t1_wks_since_conception, t1gadTotal)) +
geom_point() +
geom_smooth(method = "lm") +
labs(x = "Weeks Since Conception",
y = "Anxiety Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()
# rtq
rtq_t1 <-ggplot(final_data, aes(t1_wks_since_conception, t1rtqTotal)) +
geom_point() +
geom_smooth(method = "lm") +
labs(x = "Weeks Since Conception",
y = "Repetitive Negative Thinking Total Score") +
scale_x_continuous(n.breaks = 15) +
scale_y_continuous(n.breaks = 10, limits = c(0,55), expand = c(0,0)) +
theme_apa()
# ocd
ocd_t1 <-ggplot(final_data, aes(t1_wks_since_conception, t1ocdTotal)) +
geom_point() +
geom_smooth(method = "lm") +
labs(x = "Weeks Since Conception",
y = "Obsessive-Compulsive Total Score") +
scale_x_continuous(n.breaks = 15) +
scale_y_continuous(n.breaks = 15) +
theme_apa()
# intrusions
intr_t1 <- ggplot(final_data, aes(t1_wks_since_conception, t1intrusionsTotal)) +
geom_point() +
geom_smooth(method = "lm") +
labs(x = "Weeks Since Conception",
y = "Intrusions Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()
# intrusion distress
thought_dist_t1 <- ggplot(final_data, aes(t1_wks_since_conception, t1_thoughts_distress_level)) +
geom_point() +
geom_smooth(method = "lm") +
labs(x = "Weeks Since Conception",
y = "Intrusion Distress Level") +
scale_x_continuous(n.breaks = 15) +
theme_apa()### T2
# phq
ggplot(final_data, aes(t1_wks_since_conception, t2_phq_total)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "Depression Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).
t2_peri_phq <- lm(t2_phq_total ~ t1_wks_since_conception, data = final_data)
summary(t2_peri_phq)##
## Call:
## lm(formula = t2_phq_total ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7316 -3.7162 -0.7277 3.2814 17.2783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.7367893 0.7813065 8.622 1.44e-15 ***
## t1_wks_since_conception -0.0003502 0.0109666 -0.032 0.975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.973 on 215 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 4.744e-06, Adjusted R-squared: -0.004646
## F-statistic: 0.00102 on 1 and 215 DF, p-value: 0.9746# gad
ggplot(final_data, aes(t1_wks_since_conception, t2_gad_total)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "Anxiety Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).
t2_peri_gad <- lm(t2_gad_total ~ t1_wks_since_conception, data = final_data)
summary(t2_peri_gad)##
## Call:
## lm(formula = t2_gad_total ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5722 -3.5495 -0.4904 3.4763 13.4772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.597427 0.766871 8.603 1.64e-15 ***
## t1_wks_since_conception -0.001696 0.010764 -0.158 0.875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.881 on 215 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.0001154, Adjusted R-squared: -0.004535
## F-statistic: 0.02482 on 1 and 215 DF, p-value: 0.875# RNT
ggplot(final_data, aes(t1_wks_since_conception, t2_rtq_total)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "RNT Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).
t2_peri_rtq <- lm(t2_rtq_total ~ t1_wks_since_conception, data = final_data)
summary(t2_peri_rtq)##
## Call:
## lm(formula = t2_rtq_total ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.0781 -6.7998 -0.6409 6.2566 26.4601
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.145975 1.730004 13.957 <2e-16 ***
## t1_wks_since_conception -0.004567 0.024283 -0.188 0.851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.01 on 215 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.0001645, Adjusted R-squared: -0.004486
## F-statistic: 0.03537 on 1 and 215 DF, p-value: 0.851# ocd
ggplot(final_data, aes(t1_wks_since_conception, t2_ocd_total)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "OCD Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).
t2_peri_ocd <- lm(t2_ocd_total ~ t1_wks_since_conception, data = final_data)
summary(t2_peri_ocd)##
## Call:
## lm(formula = t2_ocd_total ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.220 -9.721 -2.942 7.387 38.564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.43732 1.88102 7.675 5.71e-13 ***
## t1_wks_since_conception -0.01462 0.02640 -0.554 0.58
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.97 on 215 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.001425, Adjusted R-squared: -0.00322
## F-statistic: 0.3068 on 1 and 215 DF, p-value: 0.5802# intrusions
ggplot(final_data, aes(t1_wks_since_conception, t2_intrusions_total)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "Intrusions Total Score") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).
t2_peri_intrusions <-
lm(t2_intrusions_total ~ t1_wks_since_conception, data = final_data)
summary(t2_peri_intrusions)##
## Call:
## lm(formula = t2_intrusions_total ~ t1_wks_since_conception, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2206 -3.0636 -0.1969 2.8147 7.9255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.021560 0.522455 7.697 4.98e-13 ***
## t1_wks_since_conception 0.001816 0.007333 0.248 0.805
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.325 on 215 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.0002852, Adjusted R-squared: -0.004365
## F-statistic: 0.06133 on 1 and 215 DF, p-value: 0.8046# intrusion distress
ggplot(final_data, aes(t1_wks_since_conception, t2_thoughts_distress_level)) +
geom_point() +
geom_smooth() +
labs(x = "Weeks Since Conception",
y = "Intrusion Distress Level") +
scale_x_continuous(n.breaks = 15) +
theme_apa()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 41 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 41 rows containing missing values or values outside the scale range
## (`geom_point()`).
t2_peri_thought_distress <-
lm(t2_thoughts_distress_level ~ t1_wks_since_conception, data = final_data)
summary(t2_peri_thought_distress)##
## Call:
## lm(formula = t2_thoughts_distress_level ~ t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.16523 -0.94925 -0.01165 0.88349 2.92398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.213720 0.158142 13.998 <2e-16 ***
## t1_wks_since_conception -0.003202 0.002196 -1.458 0.146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9626 on 204 degrees of freedom
## (41 observations deleted due to missingness)
## Multiple R-squared: 0.01032, Adjusted R-squared: 0.005467
## F-statistic: 2.127 on 1 and 204 DF, p-value: 0.1463Performance of task
#Rename variables for separation
final_data_rename <- final_data %>%
rename(intrusions_T1 = t1intrusionsTotal,
intrusionsdistress_T1 = t1_thoughts_distress_level,
rtq_T1 = t1rtqTotal,
intrusions_T2 = t2_intrusions_total,
intrusionsdistress_T2 = t2_thoughts_distress_level,
rtq_T2 = t2_rtq_total
)#Select variables for pivot
final_data_select <- final_data_rename %>%
dplyr::select (Prolific_ID, intrusions_T1, intrusionsdistress_T1, rtq_T1, intrusions_T2, intrusionsdistress_T2,rtq_T2, t1_wks_since_conception, average_DPrime_neutral_score, average_DPrime_negative_score, average_DPrime_perineg_score, average_RT_neutral_score, average_RT_negative_score, average_RT_perineg_score,DprimeNeg_1,DprimeNeut_1,DprimePNeg_1,DprimeNeg_2,DprimeNeut_2,DprimePNeg_2,DprimeNeg_3, DprimeNeut_3, DprimePNeg_3,RTNeg_1,RTNeut_1,RTPNeg_1,RTNeg_2,RTNeut_2, RTPNeg_2,RTNeg_3,RTNeut_3,RTPNeg_3,percentcorrectNeg_1,percentcorrectNeut_1,percentcorrectPNeg_1,percentcorrectNeg_2,percentcorrectNeut_2,percentcorrectPNeg_2,percentcorrectNeg_3,percentcorrectNeut_3,percentcorrectPNeg_3,ACdP_neg,ACdP_peri, ACdP_Nperi,ACRT_neg,ACRT_peri,ACRT_Nperi)Valence on each load - Dprime
#Load 1 Dprime
task_data_measures_dpL1 <- final_data_select %>%
dplyr::select(Prolific_ID,DprimeNeg_1,DprimeNeut_1,DprimePNeg_1)#Pivot to long
dprimeL1 <- task_data_measures_dpL1 %>%
pivot_longer(
cols = -Prolific_ID,
names_to = "Valence",
values_to = "Value"
)#Valence as factor and reference group to neutral
dprimeL1$Valence <- as.factor(dprimeL1$Valence)
dprimeL1$Valence <- relevel(dprimeL1$Valence, ref = "DprimeNeut_1")model_dprimeL1 <- lmer(Value ~ Valence + (1 | Prolific_ID),
data = dprimeL1)
summary(model_dprimeL1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Value ~ Valence + (1 | Prolific_ID)
## Data: dprimeL1
##
## REML criterion at convergence: 3927.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0651 -0.4856 0.1461 0.4631 2.6043
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 11.987 3.462
## Residual 6.184 2.487
## Number of obs: 741, groups: Prolific_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1094 0.2712 394.5891 11.464 <2e-16 ***
## ValenceDprimeNeg_1 0.6545 0.2238 492.0000 2.925 0.0036 **
## ValenceDprimePNeg_1 0.4187 0.2238 492.0000 1.871 0.0619 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) VlDN_1
## VlncDprmN_1 -0.413
## VlncDprPN_1 -0.413 0.500standardized_model_dprimeL1 <- standardize_parameters(model_dprimeL1)
print(standardized_model_dprimeL1)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------
## (Intercept) | -0.08 | [-0.21, 0.04]
## Valence [DprimeNeg_1] | 0.15 | [ 0.05, 0.26]
## Valence [DprimePNeg_1] | 0.10 | [ 0.00, 0.20]anova(model_dprimeL1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Valence 54.287 27.144 2 492 4.389 0.0129 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Load 2 Dprime
task_data_measures_dpL2 <- final_data_select %>%
dplyr::select(Prolific_ID,DprimeNeg_2,DprimeNeut_2,DprimePNeg_2)#Pivot to long
dprimeL2 <- task_data_measures_dpL2 %>%
pivot_longer(
cols = -Prolific_ID,
names_to = "Valence",
values_to = "Value"
)#Valence as factor and reference group to neutral
dprimeL2$Valence <- as.factor(dprimeL2$Valence)
dprimeL2$Valence <- relevel(dprimeL2$Valence, ref = "DprimeNeut_2")model_dprimeL2 <- lmer(Value ~ Valence + (1 | Prolific_ID),
data = dprimeL2)
summary(model_dprimeL2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Value ~ Valence + (1 | Prolific_ID)
## Data: dprimeL2
##
## REML criterion at convergence: 3675.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.66603 -0.44300 0.06092 0.50285 2.56681
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 10.989 3.315
## Residual 3.955 1.989
## Number of obs: 741, groups: Prolific_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.4357 0.2460 354.5517 13.968 <2e-16 ***
## ValenceDprimeNeg_2 0.2977 0.1789 492.0000 1.664 0.0968 .
## ValenceDprimePNeg_2 0.1847 0.1789 492.0000 1.032 0.3026
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) VlDN_2
## VlncDprmN_2 -0.364
## VlncDprPN_2 -0.364 0.500standardized_model_dprimeL2 <- standardize_parameters(model_dprimeL2)
print(standardized_model_dprimeL2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------
## (Intercept) | -0.04 | [-0.17, 0.08]
## Valence [DprimeNeg_2] | 0.08 | [-0.01, 0.17]
## Valence [DprimePNeg_2] | 0.05 | [-0.04, 0.14]anova(model_dprimeL2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Valence 11.16 5.5798 2 492 1.4109 0.2449#Load 3 Dprime
task_data_measures_dpL3 <- final_data_select %>%
dplyr::select(Prolific_ID,DprimeNeg_3,DprimeNeut_3,DprimePNeg_3)#Pivot to long
dprimeL3 <- task_data_measures_dpL3 %>%
pivot_longer(
cols = -Prolific_ID,
names_to = "Valence",
values_to = "Value"
)#Valence as factor and reference group to neutral
dprimeL3$Valence <- as.factor(dprimeL3$Valence)
dprimeL3$Valence <- relevel(dprimeL3$Valence, ref = "DprimeNeut_3")model_dprimeL3 <- lmer(Value ~ Valence + (1 | Prolific_ID),
data = dprimeL3)
summary(model_dprimeL3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Value ~ Valence + (1 | Prolific_ID)
## Data: dprimeL3
##
## REML criterion at convergence: 3505.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4713 -0.5624 -0.1237 0.4987 2.6024
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 4.092 2.023
## Residual 4.194 2.048
## Number of obs: 741, groups: Prolific_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7482 0.1832 496.0778 9.545 < 2e-16 ***
## ValenceDprimeNeg_3 0.7473 0.1843 492.0000 4.055 5.83e-05 ***
## ValenceDprimePNeg_3 0.8240 0.1843 492.0000 4.471 9.67e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) VlDN_3
## VlncDprmN_3 -0.503
## VlncDprPN_3 -0.503 0.500standardized_model_dprimeL3 <- standardize_parameters(model_dprimeL3)
print(standardized_model_dprimeL3)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------
## (Intercept) | -0.18 | [-0.30, -0.06]
## Valence [DprimeNeg_3] | 0.26 | [ 0.13, 0.38]
## Valence [DprimePNeg_3] | 0.28 | [ 0.16, 0.41]anova(model_dprimeL3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Valence 102.36 51.18 2 492 12.202 6.731e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Valence on each load - Reaction Time
#Load 1 Reaction Time
task_data_measures_rtL1 <- final_data_select %>%
dplyr::select(Prolific_ID,RTNeg_1,RTNeut_1,RTPNeg_1)#Pivot to long
rtL1 <- task_data_measures_rtL1 %>%
pivot_longer(
cols = -Prolific_ID,
names_to = "Valence",
values_to = "Value"
)#Valence as factor and reference group to neutral
rtL1$Valence <- as.factor(rtL1$Valence)
rtL1$Valence <- relevel(rtL1$Valence, ref = "RTNeut_1")model_rtL1 <- lmer(Value ~ Valence + (1 | Prolific_ID),
data = rtL1)
summary(model_rtL1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Value ~ Valence + (1 | Prolific_ID)
## Data: rtL1
##
## REML criterion at convergence: 8677.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2362 -0.5479 -0.0195 0.5344 3.4341
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 4709 68.63
## Residual 4573 67.62
## Number of obs: 741, groups: Prolific_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 740.131 6.130 487.182 120.734 <2e-16 ***
## ValenceRTNeg_1 13.725 6.085 492.000 2.256 0.0245 *
## ValenceRTPNeg_1 9.253 6.085 492.000 1.521 0.1290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) VRTN_1
## ValncRTNg_1 -0.496
## VlncRTPNg_1 -0.496 0.500standardized_model_rtL1 <- standardize_parameters(model_rtL1)
print(standardized_model_rtL1)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | -0.08 | [-0.20, 0.05]
## Valence [RTNeg_1] | 0.14 | [ 0.02, 0.27]
## Valence [RTPNeg_1] | 0.10 | [-0.03, 0.22]anova(model_rtL1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Valence 24206 12103 2 492 2.6467 0.07189 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Load 2 Reaction Time
task_data_measures_rtL2 <- final_data_select %>%
dplyr::select(Prolific_ID,RTNeg_2,RTNeut_2,RTPNeg_2)#Pivot to long
rtL2 <- task_data_measures_rtL2 %>%
pivot_longer(
cols = -Prolific_ID,
names_to = "Valence",
values_to = "Value"
)#Valence as factor and reference group to neutral
rtL2$Valence <- as.factor(rtL2$Valence)
rtL2$Valence <- relevel(rtL2$Valence, ref = "RTNeut_2")model_rtL2 <- lmer(Value ~ Valence + (1 | Prolific_ID),
data = rtL2)
summary(model_rtL2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Value ~ Valence + (1 | Prolific_ID)
## Data: rtL2
##
## REML criterion at convergence: 8694.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.32648 -0.56870 -0.06428 0.46270 3.04830
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 8505 92.22
## Residual 3784 61.52
## Number of obs: 741, groups: Prolific_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 741.481 7.054 376.932 105.118 < 2e-16 ***
## ValenceRTNeg_2 -1.554 5.536 492.000 -0.281 0.778996
## ValenceRTPNeg_2 19.504 5.536 492.000 3.523 0.000466 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) VRTN_2
## ValncRTNg_2 -0.392
## VlncRTPNg_2 -0.392 0.500standardized_model_rtL2 <- standardize_parameters(model_rtL2)
print(standardized_model_rtL2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | -0.05 | [-0.18, 0.07]
## Valence [RTNeg_2] | -0.01 | [-0.11, 0.08]
## Valence [RTPNeg_2] | 0.18 | [ 0.08, 0.27]anova(model_rtL2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Valence 68032 34016 2 492 8.9885 0.0001466 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_rtL2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------
## Valence | 0.04 | [0.01, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].#Run pairwise valence on Load 2
model_rtL2pair <- lsmeans(model_rtL2,
pairwise~Valence,
adjust = "tukey")
summary(model_rtL2pair)## $lsmeans
## Valence lsmean SE df lower.CL upper.CL
## RTNeut_2 741 7.05 377 728 755
## RTNeg_2 740 7.05 377 726 754
## RTPNeg_2 761 7.05 377 747 775
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## RTNeut_2 - RTNeg_2 1.55 5.54 492 0.281 0.9575
## RTNeut_2 - RTPNeg_2 -19.50 5.54 492 -3.523 0.0014
## RTNeg_2 - RTPNeg_2 -21.06 5.54 492 -3.804 0.0005
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates# Convert contrasts to a data frame to extract estimates
contrast_results <- as.data.frame(model_rtL2pair$contrasts)
# Extract the estimates for standardization
estimates <- contrast_results$estimate
# Standard deviation of the dependent variable (replace with the actual dependent variable)
# In your example, `dprime$Value` refers to your dependent variable (replace if needed)
sd_y <- sd(rtL2$Value)
# Standardize the estimates
standardized_estimates <- estimates / sd_y
# Create a summary table
summary_table <- data.frame(
Contrast = contrast_results$contrast,
Estimate = estimates,
t_value = contrast_results$t.ratio, # t-value
p_value = contrast_results$p.value, # p-value
Standardized_Estimate = standardized_estimates
)
# Print the summary table
print(summary_table)## Contrast Estimate t_value p_value Standardized_Estimate
## 1 RTNeut_2 - RTNeg_2 1.55430 0.2807812 0.9574760037 0.01398712
## 2 RTNeut_2 - RTPNeg_2 -19.50437 -3.5234259 0.0013509311 -0.17551955
## 3 RTNeg_2 - RTPNeg_2 -21.05867 -3.8042072 0.0004695539 -0.18950668#Load 3 Reaction Time
task_data_measures_rtL3 <- final_data_select %>%
dplyr::select(Prolific_ID,RTNeg_3,RTNeut_3,RTPNeg_3)#Pivot to long
rtL3 <- task_data_measures_rtL3 %>%
pivot_longer(
cols = -Prolific_ID,
names_to = "Valence",
values_to = "Value"
)#Valence as factor and reference group to neutral
rtL3$Valence <- as.factor(rtL3$Valence)
rtL3$Valence <- relevel(rtL3$Valence, ref = "RTNeut_3")model_rtL3 <- lmer(Value ~ Valence + (1 | Prolific_ID),
data = rtL3)
summary(model_rtL3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Value ~ Valence + (1 | Prolific_ID)
## Data: rtL3
##
## REML criterion at convergence: 8602.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90567 -0.52090 -0.00871 0.44566 3.02579
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 8277 90.98
## Residual 3210 56.66
## Number of obs: 741, groups: Prolific_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 704.301301 6.819568 362.043233 103.277 <2e-16 ***
## ValenceRTNeg_3 -4.545418 5.098140 492.000054 -0.892 0.373
## ValenceRTPNeg_3 0.002429 5.098140 492.000054 0.000 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) VRTN_3
## ValncRTNg_3 -0.374
## VlncRTPNg_3 -0.374 0.500standardized_model_rtL3 <- standardize_parameters(model_rtL3)
print(standardized_model_rtL3)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | 0.01 | [-0.11, 0.14]
## Valence [RTNeg_3] | -0.04 | [-0.14, 0.05]
## Valence [RTPNeg_3] | 2.27e-05 | [-0.09, 0.09]anova(model_rtL3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Valence 3404 1702 2 492 0.5302 0.5888#Valence * Load Interactions
###Dprime
#Select task variables
task_data_measures <- final_data %>%
dplyr::select(Prolific_ID,DprimeNeg_1,DprimeNeut_1,DprimePNeg_1,DprimeNeg_2,
DprimeNeut_2,DprimePNeg_2,DprimeNeg_3, DprimeNeut_3,
DprimePNeg_3,RTNeg_1,RTNeut_1,RTPNeg_1,RTNeg_2,RTNeut_2,RTPNeg_2,
RTNeg_3,RTNeut_3,RTPNeg_3,percentcorrectNeg_1,
percentcorrectNeut_1,percentcorrectPNeg_1,percentcorrectNeg_2,
percentcorrectNeut_2,percentcorrectPNeg_2,percentcorrectNeg_3,
percentcorrectNeut_3,percentcorrectPNeg_3,t1rtqTotal,t1intrusionsTotal)#Select dprime measures
dprime_select <- task_data_measures %>%
dplyr::select("Prolific_ID", "DprimeNeg_1","DprimeNeut_1","DprimePNeg_1",
"DprimeNeg_2", "DprimeNeut_2", "DprimePNeg_2", "DprimeNeg_3",
"DprimeNeut_3", "DprimePNeg_3","t1rtqTotal","t1intrusionsTotal")#Pivot to long
dprime <- dprime_select %>% pivot_longer(
cols = -c (Prolific_ID, t1rtqTotal,t1intrusionsTotal),
names_to = c("Valence", "Load"),
names_sep = "_", values_to = "Value"
)#Change to factor variable
dprime$Valence <- factor(dprime$Valence)#Change reference group to "Neutral"
dprime$Valence <- relevel(dprime$Valence, ref = "DprimeNeut")#Run main effects and interactions of valence and load on Dprime
model_dprime_interactions <- lmer(Value ~ Valence * Load + (1 | Prolific_ID),
data = dprime)
summary(model_dprime_interactions)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Value ~ Valence * Load + (1 | Prolific_ID)
## Data: dprime
##
## REML criterion at convergence: 10985.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6826 -0.6057 0.0004 0.6792 3.0701
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 7.595 2.756
## Residual 6.205 2.491
## Number of obs: 2223, groups: Prolific_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.10942 0.23637 646.77995 13.155 < 2e-16 ***
## ValenceDprimeNeg 0.65455 0.22416 1968.00000 2.920 0.00354 **
## ValenceDprimePNeg 0.41868 0.22416 1968.00000 1.868 0.06194 .
## Load2 0.32625 0.22416 1968.00000 1.455 0.14570
## Load3 -1.36118 0.22416 1968.00000 -6.072 1.51e-09 ***
## ValenceDprimeNeg:Load2 -0.35680 0.31700 1968.00000 -1.126 0.26050
## ValenceDprimePNeg:Load2 -0.23403 0.31700 1968.00000 -0.738 0.46044
## ValenceDprimeNeg:Load3 0.09273 0.31700 1968.00000 0.293 0.76992
## ValenceDprimePNeg:Load3 0.40529 0.31700 1968.00000 1.279 0.20122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) VlncDN VlnDPN Load2 Load3 VDN:L2 VDPN:L2 VDN:L3
## ValncDprmNg -0.474
## VlncDprmPNg -0.474 0.500
## Load2 -0.474 0.500 0.500
## Load3 -0.474 0.500 0.500 0.500
## VlncDprN:L2 0.335 -0.707 -0.354 -0.707 -0.354
## VlncDpPN:L2 0.335 -0.354 -0.707 -0.707 -0.354 0.500
## VlncDprN:L3 0.335 -0.707 -0.354 -0.354 -0.707 0.500 0.250
## VlncDpPN:L3 0.335 -0.354 -0.707 -0.354 -0.707 0.250 0.500 0.500standardized_model_dprime_interactions <- standardize_parameters(model_dprime_interactions)
print(standardized_model_dprime_interactions)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | -6.52e-04 | [-0.12, 0.12]
## Valence [DprimeNeg] | 0.17 | [ 0.06, 0.29]
## Valence [DprimePNeg] | 0.11 | [-0.01, 0.23]
## Load [2] | 0.09 | [-0.03, 0.20]
## Load [3] | -0.36 | [-0.48, -0.24]
## Valence [DprimeNeg] × Load [2] | -0.09 | [-0.26, 0.07]
## Valence [DprimePNeg] × Load [2] | -0.06 | [-0.23, 0.10]
## Valence [DprimeNeg] × Load [3] | 0.02 | [-0.14, 0.19]
## Valence [DprimePNeg] × Load [3] | 0.11 | [-0.06, 0.27]anova(model_dprime_interactions)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Valence 137.22 68.61 2 1968 11.0566 1.679e-05 ***
## Load 790.25 395.12 2 1968 63.6748 < 2.2e-16 ***
## Valence:Load 30.59 7.65 4 1968 1.2323 0.295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_dprime_interactions)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------
## Valence | 0.01 | [0.00, 1.00]
## Load | 0.06 | [0.04, 1.00]
## Valence:Load | 2.50e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].#Run posthoc pairwise valence and load on Dprime
posthoc_model_dp_interact <- lsmeans(model_dprime_interactions,
pairwise~Valence*Load, adjust = "tukey")
summary(posthoc_model_dp_interact)## $lsmeans
## Valence Load lsmean SE df lower.CL upper.CL
## DprimeNeut 1 3.11 0.236 647 2.65 3.57
## DprimeNeg 1 3.76 0.236 647 3.30 4.23
## DprimePNeg 1 3.53 0.236 647 3.06 3.99
## DprimeNeut 2 3.44 0.236 647 2.97 3.90
## DprimeNeg 2 3.73 0.236 647 3.27 4.20
## DprimePNeg 2 3.62 0.236 647 3.16 4.08
## DprimeNeut 3 1.75 0.236 647 1.28 2.21
## DprimeNeg 3 2.50 0.236 647 2.03 2.96
## DprimePNeg 3 2.57 0.236 647 2.11 3.04
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## DprimeNeut Load1 - DprimeNeg Load1 -0.6545 0.224 1968 -2.920 0.0846
## DprimeNeut Load1 - DprimePNeg Load1 -0.4187 0.224 1968 -1.868 0.6362
## DprimeNeut Load1 - DprimeNeut Load2 -0.3262 0.224 1968 -1.455 0.8758
## DprimeNeut Load1 - DprimeNeg Load2 -0.6240 0.224 1968 -2.784 0.1207
## DprimeNeut Load1 - DprimePNeg Load2 -0.5109 0.224 1968 -2.279 0.3552
## DprimeNeut Load1 - DprimeNeut Load3 1.3612 0.224 1968 6.072 <.0001
## DprimeNeut Load1 - DprimeNeg Load3 0.6139 0.224 1968 2.739 0.1350
## DprimeNeut Load1 - DprimePNeg Load3 0.5372 0.224 1968 2.397 0.2862
## DprimeNeg Load1 - DprimePNeg Load1 0.2359 0.224 1968 1.052 0.9805
## DprimeNeg Load1 - DprimeNeut Load2 0.3283 0.224 1968 1.465 0.8718
## DprimeNeg Load1 - DprimeNeg Load2 0.0306 0.224 1968 0.136 1.0000
## DprimeNeg Load1 - DprimePNeg Load2 0.1437 0.224 1968 0.641 0.9994
## DprimeNeg Load1 - DprimeNeut Load3 2.0157 0.224 1968 8.993 <.0001
## DprimeNeg Load1 - DprimeNeg Load3 1.2684 0.224 1968 5.659 <.0001
## DprimeNeg Load1 - DprimePNeg Load3 1.1917 0.224 1968 5.317 <.0001
## DprimePNeg Load1 - DprimeNeut Load2 0.0924 0.224 1968 0.412 1.0000
## DprimePNeg Load1 - DprimeNeg Load2 -0.2053 0.224 1968 -0.916 0.9921
## DprimePNeg Load1 - DprimePNeg Load2 -0.0922 0.224 1968 -0.411 1.0000
## DprimePNeg Load1 - DprimeNeut Load3 1.7799 0.224 1968 7.940 <.0001
## DprimePNeg Load1 - DprimeNeg Load3 1.0326 0.224 1968 4.607 0.0002
## DprimePNeg Load1 - DprimePNeg Load3 0.9559 0.224 1968 4.264 0.0007
## DprimeNeut Load2 - DprimeNeg Load2 -0.2977 0.224 1968 -1.328 0.9230
## DprimeNeut Load2 - DprimePNeg Load2 -0.1847 0.224 1968 -0.824 0.9962
## DprimeNeut Load2 - DprimeNeut Load3 1.6874 0.224 1968 7.528 <.0001
## DprimeNeut Load2 - DprimeNeg Load3 0.9401 0.224 1968 4.194 0.0010
## DprimeNeut Load2 - DprimePNeg Load3 0.8634 0.224 1968 3.852 0.0038
## DprimeNeg Load2 - DprimePNeg Load2 0.1131 0.224 1968 0.505 0.9999
## DprimeNeg Load2 - DprimeNeut Load3 1.9852 0.224 1968 8.856 <.0001
## DprimeNeg Load2 - DprimeNeg Load3 1.2379 0.224 1968 5.522 <.0001
## DprimeNeg Load2 - DprimePNeg Load3 1.1612 0.224 1968 5.180 <.0001
## DprimePNeg Load2 - DprimeNeut Load3 1.8721 0.224 1968 8.352 <.0001
## DprimePNeg Load2 - DprimeNeg Load3 1.1248 0.224 1968 5.018 <.0001
## DprimePNeg Load2 - DprimePNeg Load3 1.0481 0.224 1968 4.676 0.0001
## DprimeNeut Load3 - DprimeNeg Load3 -0.7473 0.224 1968 -3.334 0.0245
## DprimeNeut Load3 - DprimePNeg Load3 -0.8240 0.224 1968 -3.676 0.0075
## DprimeNeg Load3 - DprimePNeg Load3 -0.0767 0.224 1968 -0.342 1.0000
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 9 estimates#Run posthoc pairwise valence on Dprime
posthoc_model_dp_interact_valence <- lsmeans(posthoc_model_dp_interact,
pairwise~Valence, adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionssummary(posthoc_model_dp_interact_valence)## $lsmeans
## Valence lsmean SE df lower.CL upper.CL
## DprimeNeut 2.76 0.198 334 2.38 3.15
## DprimeNeg 3.33 0.198 334 2.94 3.72
## DprimePNeg 3.24 0.198 334 2.85 3.63
##
## Results are averaged over the levels of: Load
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## DprimeNeut - DprimeNeg -0.5665 0.129 1968 -4.378 <.0001
## DprimeNeut - DprimePNeg -0.4758 0.129 1968 -3.676 0.0007
## DprimeNeg - DprimePNeg 0.0908 0.129 1968 0.701 0.7628
##
## Results are averaged over the levels of: Load
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates# Post-hoc pairwise comparisons for Valence & standardise coefficents
posthoc_model_dp_interact_valence <- lsmeans(posthoc_model_dp_interact,
pairwise ~ Valence, adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactions# Convert contrasts to a data frame to extract estimates
contrast_results <- as.data.frame(posthoc_model_dp_interact_valence$contrasts)
# Extract the estimates for standardization
estimates <- contrast_results$estimate
# Standard deviation of the dependent variable (replace with the actual dependent variable)
# In your example, `dprime$Value` refers to your dependent variable (replace if needed)
sd_y <- sd(dprime$Value)
# Standardize the estimates
standardized_estimates <- estimates / sd_y
# Create a summary table
summary_table <- data.frame(
Contrast = contrast_results$contrast,
Estimate = estimates,
t_value = contrast_results$t.ratio, # t-value
p_value = contrast_results$p.value, # p-value
Standardized_Estimate = standardized_estimates
)
# Print the summary table
print(summary_table)## Contrast Estimate t_value p_value
## 1 DprimeNeut - DprimeNeg -0.56652467 -4.3775459 3.755999e-05
## 2 DprimeNeut - DprimePNeg -0.47577010 -3.6762838 7.105122e-04
## 3 DprimeNeg - DprimePNeg 0.09075456 0.7012621 7.627624e-01
## Standardized_Estimate
## 1 -0.1504358
## 2 -0.1263367
## 3 0.0240991#Run posthoc pairwise load on Dprime
posthoc_model_dp_interact_load <- lsmeans(posthoc_model_dp_interact,
pairwise~Load, adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionssummary(posthoc_model_dp_interact_load)## $lsmeans
## Load lsmean SE df lower.CL upper.CL
## 1 3.47 0.198 334 3.08 3.86
## 2 3.60 0.198 334 3.21 3.99
## 3 2.27 0.198 334 1.88 2.66
##
## Results are averaged over the levels of: Valence
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Load1 - Load2 -0.129 0.129 1968 -0.999 0.5775
## Load1 - Load3 1.195 0.129 1968 9.235 <.0001
## Load2 - Load3 1.324 0.129 1968 10.234 <.0001
##
## Results are averaged over the levels of: Valence
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates# Convert contrasts to a data frame to extract estimates
contrast_results <- as.data.frame(posthoc_model_dp_interact_load$contrasts)
# Extract the estimates for standardization
estimates <- contrast_results$estimate
# Standard deviation of the dependent variable (replace with the actual dependent variable)
# In your example, `dprime$Value` refers to your dependent variable (replace if needed)
sd_y <- sd(dprime$Value)
# Standardize the estimates
standardized_estimates <- estimates / sd_y
# Create a summary table
summary_table <- data.frame(
Contrast = contrast_results$contrast,
Estimate = estimates,
t_value = contrast_results$t.ratio, # t-value
p_value = contrast_results$p.value, # p-value
Standardized_Estimate = standardized_estimates
)
# Print the summary table
print(summary_table)## Contrast Estimate t_value p_value Standardized_Estimate
## 1 Load1 - Load2 -0.1293033 -0.999129 5.774875e-01 -0.03433539
## 2 Load1 - Load3 1.1951689 9.235091 3.961564e-11 0.31736691
## 3 Load2 - Load3 1.3244723 10.234220 3.961309e-11 0.35170230# Descriptive statistics for Dprime Value, grouped by Valence
describe.by(dprime$Value, group = dprime$Valence)## Warning in describe.by(dprime$Value, group = dprime$Valence): describe.by is
## deprecated. Please use the describeBy function##
## Descriptive statistics by group
## group: DprimeNeut
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 741 2.76 3.76 1.64 2.79 3.52 -4.94 8.53 13.47 0.23 -1.01 0.14
## ------------------------------------------------------------
## group: DprimeNeg
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 741 3.33 3.78 2.18 3.38 4.12 -5.65 8.53 14.18 0.13 -1.2 0.14
## ------------------------------------------------------------
## group: DprimePNeg
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 741 3.24 3.74 2.27 3.29 4.21 -4.94 8.53 13.47 0.08 -1.17 0.14# Descriptive statistics for Dprime Value, grouped by Load
describeBy(dprime$Value, group = dprime$Load)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 741 3.47 4.27 3.92 3.69 6.54 -4.87 8.53 13.4 -0.08 -1.43 0.16
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 741 3.6 3.86 4.01 3.71 6.01 -5.65 8.53 14.18 -0.03 -1.28 0.14
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 741 2.27 2.9 1.68 2.11 2.43 -4.56 8.53 13.09 0.48 -0.08 0.11# Descriptive statistics for Dprime Value, grouped by the interaction of Valence and Load
describeBy(dprime$Value, group = list(dprime$Valence, dprime$Load))##
## Descriptive statistics by group
## : DprimeNeut
## : 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 3.11 4.54 4.01 3.31 6.7 -4.38 8.53 12.91 -0.09 -1.48 0.29
## ------------------------------------------------------------
## : DprimeNeg
## : 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 3.76 4.08 3.92 3.92 6.33 -4.87 8.53 13.4 -0.05 -1.46 0.26
## ------------------------------------------------------------
## : DprimePNeg
## : 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 3.53 4.15 4.15 3.67 6.49 -4.87 8.53 13.4 -0.04 -1.48 0.26
## ------------------------------------------------------------
## : DprimeNeut
## : 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 3.44 3.86 2.22 3.55 4.27 -4.94 8.53 13.47 -0.01 -1.22 0.25
## ------------------------------------------------------------
## : DprimeNeg
## : 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 3.73 4.04 3.83 3.9 6.29 -5.65 8.53 14.18 -0.08 -1.37 0.26
## ------------------------------------------------------------
## : DprimePNeg
## : 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 3.62 3.69 4.52 3.68 5.95 -4.94 8.53 13.47 0.01 -1.27 0.23
## ------------------------------------------------------------
## : DprimeNeut
## : 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 1.75 2.32 1.27 1.57 1.47 -3.53 8.53 12.06 0.85 1.05 0.15
## ------------------------------------------------------------
## : DprimeNeg
## : 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 2.5 2.97 1.68 2.31 2.43 -4.36 8.53 12.89 0.44 -0.26 0.19
## ------------------------------------------------------------
## : DprimePNeg
## : 3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 247 2.57 3.26 1.86 2.5 3.57 -4.56 8.53 13.09 0.17 -0.57 0.21Reaction Time - Valence * Load
#Select reaction time measures
rt_select <- task_data_measures %>%
dplyr::select(Prolific_ID, RTNeg_1,RTNeut_1,RTPNeg_1, RTNeg_2, RTNeut_2,
RTPNeg_2, RTNeg_3,RTNeut_3, RTPNeg_3)#Pivot to long
rt <- rt_select %>% pivot_longer( cols = -Prolific_ID,
names_to = c("Valence", "Load"),
names_sep = "_", values_to = "Value" ) #Change to factor variable
rt$Valence <- factor(rt$Valence)#Change reference group to "Neutral"
rt$Valence <- relevel(rt$Valence, ref = "RTNeut")#Run main effects and interactions of valence and load on Reaction Time
model_rt_interactions <- lmer(Value ~ Valence * Load + (1 | Prolific_ID),
data = rt)
summary(model_rt_interactions)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Value ~ Valence * Load + (1 | Prolific_ID)
## Data: rt
##
## REML criterion at convergence: 25676.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5359 -0.5937 -0.0307 0.5470 5.2987
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6343 79.64
## Residual 4677 68.39
## Number of obs: 2223, groups: Prolific_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 740.131 6.679 606.524 110.808 < 2e-16 ***
## ValenceRTNeg 13.725 6.154 1968.000 2.230 0.0258 *
## ValenceRTPNeg 9.253 6.154 1968.000 1.504 0.1329
## Load2 1.351 6.154 1968.000 0.219 0.8263
## Load3 -35.829 6.154 1968.000 -5.822 6.77e-09 ***
## ValenceRTNeg:Load2 -15.280 8.703 1968.000 -1.756 0.0793 .
## ValenceRTPNeg:Load2 10.252 8.703 1968.000 1.178 0.2390
## ValenceRTNeg:Load3 -18.271 8.703 1968.000 -2.099 0.0359 *
## ValenceRTPNeg:Load3 -9.250 8.703 1968.000 -1.063 0.2880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) VlnRTN VlRTPN Load2 Load3 VRTN:L2 VRTPN:L2 VRTN:L3
## ValenceRTNg -0.461
## ValencRTPNg -0.461 0.500
## Load2 -0.461 0.500 0.500
## Load3 -0.461 0.500 0.500 0.500
## VlncRTNg:L2 0.326 -0.707 -0.354 -0.707 -0.354
## VlncRTPN:L2 0.326 -0.354 -0.707 -0.707 -0.354 0.500
## VlncRTNg:L3 0.326 -0.707 -0.354 -0.354 -0.707 0.500 0.250
## VlncRTPN:L3 0.326 -0.354 -0.707 -0.354 -0.707 0.250 0.500 0.500standardized_model_rt_interactions <- standardize_parameters(model_rt_interactions)
print(standardized_model_rt_interactions)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 0.07 | [-0.05, 0.19]
## Valence [RTNeg] | 0.13 | [ 0.02, 0.24]
## Valence [RTPNeg] | 0.09 | [-0.03, 0.20]
## Load [2] | 0.01 | [-0.10, 0.13]
## Load [3] | -0.33 | [-0.45, -0.22]
## Valence [RTNeg] × Load [2] | -0.14 | [-0.30, 0.02]
## Valence [RTPNeg] × Load [2] | 0.10 | [-0.06, 0.26]
## Valence [RTNeg] × Load [3] | -0.17 | [-0.33, -0.01]
## Valence [RTPNeg] × Load [3] | -0.09 | [-0.25, 0.07]anova(model_rt_interactions)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Valence 36553 18277 2 1968 3.9078 0.02024 *
## Load 993309 496654 2 1968 106.1904 < 2e-16 ***
## Valence:Load 59089 14772 4 1968 3.1585 0.01341 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_rt_interactions)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------
## Valence | 3.96e-03 | [0.00, 1.00]
## Load | 0.10 | [0.08, 1.00]
## Valence:Load | 6.38e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].#Run posthoc pairwise valence and load on Reaction Time
posthoc_model_rt_interact <- lsmeans(model_rt_interactions,
pairwise~Valence*Load, adjust = "tukey")
summary(posthoc_model_rt_interact)## $lsmeans
## Valence Load lsmean SE df lower.CL upper.CL
## RTNeut 1 740 6.68 607 727 753
## RTNeg 1 754 6.68 607 741 767
## RTPNeg 1 749 6.68 607 736 763
## RTNeut 2 741 6.68 607 728 755
## RTNeg 2 740 6.68 607 727 753
## RTPNeg 2 761 6.68 607 748 774
## RTNeut 3 704 6.68 607 691 717
## RTNeg 3 700 6.68 607 687 713
## RTPNeg 3 704 6.68 607 691 717
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## RTNeut Load1 - RTNeg Load1 -13.72522 6.15 1968 -2.230 0.3862
## RTNeut Load1 - RTPNeg Load1 -9.25267 6.15 1968 -1.504 0.8542
## RTNeut Load1 - RTNeut Load2 -1.35066 6.15 1968 -0.219 1.0000
## RTNeut Load1 - RTNeg Load2 0.20364 6.15 1968 0.033 1.0000
## RTNeut Load1 - RTPNeg Load2 -20.85503 6.15 1968 -3.389 0.0205
## RTNeut Load1 - RTNeut Load3 35.82948 6.15 1968 5.822 <.0001
## RTNeut Load1 - RTNeg Load3 40.37489 6.15 1968 6.561 <.0001
## RTNeut Load1 - RTPNeg Load3 35.82705 6.15 1968 5.822 <.0001
## RTNeg Load1 - RTPNeg Load1 4.47256 6.15 1968 0.727 0.9984
## RTNeg Load1 - RTNeut Load2 12.37456 6.15 1968 2.011 0.5359
## RTNeg Load1 - RTNeg Load2 13.92886 6.15 1968 2.263 0.3651
## RTNeg Load1 - RTPNeg Load2 -7.12981 6.15 1968 -1.159 0.9648
## RTNeg Load1 - RTNeut Load3 49.55470 6.15 1968 8.053 <.0001
## RTNeg Load1 - RTNeg Load3 54.10012 6.15 1968 8.791 <.0001
## RTNeg Load1 - RTPNeg Load3 49.55227 6.15 1968 8.052 <.0001
## RTPNeg Load1 - RTNeut Load2 7.90201 6.15 1968 1.284 0.9361
## RTPNeg Load1 - RTNeg Load2 9.45631 6.15 1968 1.537 0.8382
## RTPNeg Load1 - RTPNeg Load2 -11.60236 6.15 1968 -1.885 0.6240
## RTPNeg Load1 - RTNeut Load3 45.08214 6.15 1968 7.326 <.0001
## RTPNeg Load1 - RTNeg Load3 49.62756 6.15 1968 8.064 <.0001
## RTPNeg Load1 - RTPNeg Load3 45.07971 6.15 1968 7.325 <.0001
## RTNeut Load2 - RTNeg Load2 1.55430 6.15 1968 0.253 1.0000
## RTNeut Load2 - RTPNeg Load2 -19.50437 6.15 1968 -3.169 0.0412
## RTNeut Load2 - RTNeut Load3 37.18013 6.15 1968 6.042 <.0001
## RTNeut Load2 - RTNeg Load3 41.72555 6.15 1968 6.780 <.0001
## RTNeut Load2 - RTPNeg Load3 37.17771 6.15 1968 6.041 <.0001
## RTNeg Load2 - RTPNeg Load2 -21.05867 6.15 1968 -3.422 0.0183
## RTNeg Load2 - RTNeut Load3 35.62583 6.15 1968 5.789 <.0001
## RTNeg Load2 - RTNeg Load3 40.17125 6.15 1968 6.528 <.0001
## RTNeg Load2 - RTPNeg Load3 35.62341 6.15 1968 5.789 <.0001
## RTPNeg Load2 - RTNeut Load3 56.68451 6.15 1968 9.211 <.0001
## RTPNeg Load2 - RTNeg Load3 61.22992 6.15 1968 9.950 <.0001
## RTPNeg Load2 - RTPNeg Load3 56.68208 6.15 1968 9.211 <.0001
## RTNeut Load3 - RTNeg Load3 4.54542 6.15 1968 0.739 0.9982
## RTNeut Load3 - RTPNeg Load3 -0.00243 6.15 1968 0.000 1.0000
## RTNeg Load3 - RTPNeg Load3 -4.54785 6.15 1968 -0.739 0.9982
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 9 estimates#Run posthoc pairwise valence on Reaction Time
posthoc_model_rt_interact_valence <- lsmeans(posthoc_model_rt_interact,
pairwise~Valence, adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionssummary(posthoc_model_rt_interact_valence )## $lsmeans
## Valence lsmean SE df lower.CL upper.CL
## RTNeut 729 5.66 325 718 740
## RTNeg 731 5.66 325 720 742
## RTPNeg 738 5.66 325 727 749
##
## Results are averaged over the levels of: Load
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## RTNeut - RTNeg -2.54 3.55 1968 -0.715 0.7544
## RTNeut - RTPNeg -9.59 3.55 1968 -2.698 0.0193
## RTNeg - RTPNeg -7.04 3.55 1968 -1.983 0.1167
##
## Results are averaged over the levels of: Load
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates#Run posthoc pairwise load on Reaction Time
posthoc_model_rt_interact_load <- lsmeans(posthoc_model_rt_interact,
pairwise~Load, adjust = "tukey")## NOTE: Results may be misleading due to involvement in interactionssummary(posthoc_model_rt_interact_load)## $lsmeans
## Load lsmean SE df lower.CL upper.CL
## 1 748 5.66 325 737 759
## 2 747 5.66 325 736 759
## 3 703 5.66 325 692 714
##
## Results are averaged over the levels of: Valence
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Load1 - Load2 0.325 3.55 1968 0.092 0.9954
## Load1 - Load3 45.003 3.55 1968 12.666 <.0001
## Load2 - Load3 44.678 3.55 1968 12.575 <.0001
##
## Results are averaged over the levels of: Valence
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates# Descriptive statistics for Reaction Time Value, grouped by Valence
describeBy(rt$Value, group = rt$Valence)##
## Descriptive statistics by group
## group: RTNeut
## vars n mean sd median trimmed mad min max range skew
## X1 1 741 728.64 101.81 728.5 728.29 93.48 372.12 1027.51 655.39 0
## kurtosis se
## X1 0.45 3.74
## ------------------------------------------------------------
## group: RTNeg
## vars n mean sd median trimmed mad min max range skew
## X1 1 741 731.18 108.17 729.97 731.89 99.22 378.76 1092.24 713.48 -0.1
## kurtosis se
## X1 0.56 3.97
## ------------------------------------------------------------
## group: RTPNeg
## vars n mean sd median trimmed mad min max range skew
## X1 1 741 738.22 111.02 734.89 739.07 103.02 377.58 1110.53 732.95 -0.08
## kurtosis se
## X1 0.5 4.08# Descriptive statistics for Reaction Time Value, grouped by Load
describeBy(rt$Value, group = rt$Load)##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 741 747.79 96.38 742.27 745.19 92.67 386.85 1062.33 675.47 0.2
## kurtosis se
## X1 0.36 3.54
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew
## X1 1 741 747.46 111.12 744.01 746.9 101.26 382.68 1110.53 727.85 0.05
## kurtosis se
## X1 0.34 4.08
## ------------------------------------------------------------
## group: 3
## vars n mean sd median trimmed mad min max range skew
## X1 1 741 702.79 107.05 705.97 706.39 95.21 372.12 999.91 627.78 -0.3
## kurtosis se
## X1 0.36 3.93# Descriptive statistics for Reaction Time Value, grouped by the interaction of Valence and Load
describeBy(rt$Value, group = list(rt$Valence, rt$Load))##
## Descriptive statistics by group
## : RTNeut
## : 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 740.13 93.63 732.83 737.56 90.27 459.53 1009.98 550.45 0.28
## kurtosis se
## X1 0.26 5.96
## ------------------------------------------------------------
## : RTNeg
## : 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 753.86 98.01 752.54 751.83 89.51 386.85 1048.25 661.4 0.02
## kurtosis se
## X1 0.7 6.24
## ------------------------------------------------------------
## : RTPNeg
## : 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 749.38 97.33 743.74 746.59 96.34 500.55 1062.33 561.78 0.28
## kurtosis se
## X1 0.1 6.19
## ------------------------------------------------------------
## : RTNeut
## : 2
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 741.48 106.43 738.4 740.11 96.31 447.58 1027.51 579.94 0.11
## kurtosis se
## X1 0.03 6.77
## ------------------------------------------------------------
## : RTNeg
## : 2
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 739.93 110.56 739.66 739.86 101.96 382.68 1092.24 709.56 -0.03
## kurtosis se
## X1 0.49 7.03
## ------------------------------------------------------------
## : RTPNeg
## : 2
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 760.99 115.4 761.48 761.2 102.95 420.37 1110.53 690.16 0.02
## kurtosis se
## X1 0.36 7.34
## ------------------------------------------------------------
## : RTNeut
## : 3
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 704.3 100.96 709.58 707.55 91.24 372.12 979.88 607.76 -0.33
## kurtosis se
## X1 0.52 6.42
## ------------------------------------------------------------
## : RTNeg
## : 3
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 699.76 108.54 697.02 702.21 90.42 378.76 999.91 621.15 -0.16
## kurtosis se
## X1 0.35 6.91
## ------------------------------------------------------------
## : RTPNeg
## : 3
## vars n mean sd median trimmed mad min max range skew
## X1 1 247 704.3 111.75 710.41 709.23 100.36 377.58 977.39 599.81 -0.39
## kurtosis se
## X1 0.19 7.11##H1T1 Cross-sectional Associations between affective control & intrusions/distress ###H1-T1 Intrusions
#Intrusions
model_intrusions_dp_neg <- lm(t1intrusionsTotal ~
ACdP_neg, data = final_data)
summary(model_intrusions_dp_neg) #Intrusions symptoms Dprime ACdP_neg##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_neg, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0311 -2.8465 0.0681 2.5275 7.5453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.8162 0.2212 21.770 <2e-16 ***
## ACdP_neg 0.0886 0.1186 0.747 0.456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.313 on 245 degrees of freedom
## Multiple R-squared: 0.002272, Adjusted R-squared: -0.0018
## F-statistic: 0.5579 on 1 and 245 DF, p-value: 0.4558standardized_model_intrusions_dp_neg <- standardize_parameters(model_intrusions_dp_neg)
print(standardized_model_intrusions_dp_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 1.33e-16 | [-0.13, 0.13]
## ACdP neg | 0.05 | [-0.08, 0.17]anova(model_intrusions_dp_neg)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_neg 1 6.12 6.1222 0.5579 0.4558
## Residuals 245 2688.47 10.9733model_intrusions_dp_peri <- lm(t1intrusionsTotal ~
ACdP_peri, data = final_data)
summary(model_intrusions_dp_peri) #Intrusions symptoms Dprime ACdP_peri##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_peri, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.349 -2.900 0.091 2.732 7.761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.8037 0.2170 22.132 <2e-16 ***
## ACdP_peri 0.1317 0.1118 1.178 0.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.307 on 245 degrees of freedom
## Multiple R-squared: 0.005628, Adjusted R-squared: 0.00157
## F-statistic: 1.387 on 1 and 245 DF, p-value: 0.2401standardized_model_intrusions_dp_peri <- standardize_parameters(model_intrusions_dp_peri)
print(standardized_model_intrusions_dp_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 1.25e-16 | [-0.13, 0.13]
## ACdP peri | 0.08 | [-0.05, 0.20]anova(model_intrusions_dp_peri)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_peri 1 15.17 15.166 1.3867 0.2401
## Residuals 245 2679.43 10.936model_intrusions_dp_Nperi <- lm(t1intrusionsTotal ~
ACdP_Nperi, data = final_data)
summary(model_intrusions_dp_Nperi) #Intrusions symptoms Dprime ACdP_Nperi##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_Nperi, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0212 -2.8838 0.0773 2.9190 7.2805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.87198 0.21119 23.069 <2e-16 ***
## ACdP_Nperi 0.06152 0.12116 0.508 0.612
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.315 on 245 degrees of freedom
## Multiple R-squared: 0.001051, Adjusted R-squared: -0.003026
## F-statistic: 0.2578 on 1 and 245 DF, p-value: 0.6121standardized_model_intrusions_dp_Nperi <- standardize_parameters(model_intrusions_dp_Nperi)
print(standardized_model_intrusions_dp_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 1.34e-16 | [-0.13, 0.13]
## ACdP Nperi | 0.03 | [-0.09, 0.16]anova(model_intrusions_dp_Nperi)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_Nperi 1 2.83 2.8328 0.2578 0.6121
## Residuals 245 2691.76 10.9868model_intrusions_rt_neg <- lm(t1intrusionsTotal ~ ACRT_neg, data = final_data)
summary(model_intrusions_rt_neg) #Intrusions symptoms Reaction Time ACRT_neg##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_neg, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5527 -2.7975 0.0154 2.6147 7.5540
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.882069 0.209943 23.254 <2e-16 ***
## ACRT_neg -0.006166 0.003575 -1.725 0.0858 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.296 on 245 degrees of freedom
## Multiple R-squared: 0.012, Adjusted R-squared: 0.007964
## F-statistic: 2.975 on 1 and 245 DF, p-value: 0.08583standardized_model_intrusions_rt_neg <- standardize_parameters(model_intrusions_rt_neg)
print(standardized_model_intrusions_rt_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 1.28e-16 | [-0.12, 0.12]
## ACRT neg | -0.11 | [-0.23, 0.02]anova(model_intrusions_rt_neg)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_neg 1 32.33 32.325 2.9748 0.08583 .
## Residuals 245 2662.27 10.866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_intrusions_rt_peri<- lm(t1intrusionsTotal ~ ACRT_peri, data = final_data)
summary(model_intrusions_rt_peri) #Intrusions symptoms Reaction Time ACRT_peri##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_peri, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4898 -2.8295 0.1208 2.6272 7.4364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.916046 0.212962 23.084 <2e-16 ***
## ACRT_peri -0.005179 0.003608 -1.435 0.152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.303 on 245 degrees of freedom
## Multiple R-squared: 0.00834, Adjusted R-squared: 0.004292
## F-statistic: 2.06 on 1 and 245 DF, p-value: 0.1524standardized_model_intrusions_rt_peri <- standardize_parameters(model_intrusions_rt_peri)
print(standardized_model_intrusions_rt_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 1.25e-16 | [-0.13, 0.13]
## ACRT peri | -0.09 | [-0.22, 0.03]anova(model_intrusions_rt_peri)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_peri 1 22.47 22.472 2.0604 0.1524
## Residuals 245 2672.12 10.907model_intrusions_rt_Nperi<- lm(t1intrusionsTotal ~ ACRT_Nperi, data = final_data)
summary(model_intrusions_rt_Nperi) #Intrusions symptoms Reaction Time ACRT_Nperi##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_Nperi, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9858 -2.8770 0.0741 3.0372 7.2552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.859043 0.212467 22.870 <2e-16 ***
## ACRT_Nperi 0.001044 0.003564 0.293 0.77
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.316 on 245 degrees of freedom
## Multiple R-squared: 0.0003501, Adjusted R-squared: -0.00373
## F-statistic: 0.08581 on 1 and 245 DF, p-value: 0.7698standardized_model_intrusions_rt_Nperi <- standardize_parameters(model_intrusions_rt_Nperi)
print(standardized_model_intrusions_rt_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 1.33e-16 | [-0.13, 0.13]
## ACRT Nperi | 0.02 | [-0.11, 0.14]anova(model_intrusions_rt_Nperi)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_Nperi 1 0.94 0.9435 0.0858 0.7698
## Residuals 245 2693.65 10.9945# final data T2
final_dataT2<-final_data
final_dataT2<-final_dataT2[which(final_dataT2$completed_t2=="complete"),]#check mental health differences between T1 and T2
#gad
t_test_result_gad <- t.test(final_dataT2$t1gadTotal,final_dataT2$t2_gad_total, paired = TRUE)
print(t_test_result_gad)##
## Paired t-test
##
## data: final_dataT2$t1gadTotal and final_dataT2$t2_gad_total
## t = 1.0678, df = 205, p-value = 0.2869
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.2424151 0.8152306
## sample estimates:
## mean difference
## 0.2864078#phq
t_test_result_phq<- t.test(final_dataT2$t1phqTotal, final_dataT2$t2_phq_total, paired = TRUE)
print(t_test_result_phq)##
## Paired t-test
##
## data: final_dataT2$t1phqTotal and final_dataT2$t2_phq_total
## t = 1.6844, df = 205, p-value = 0.09364
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.07616456 0.96936844
## sample estimates:
## mean difference
## 0.4466019#ocd
t_test_result_ocd<- t.test(final_dataT2$t1ocdTotal, final_dataT2$t2_ocd_total, paired = TRUE)
print(t_test_result_ocd)##
## Paired t-test
##
## data: final_dataT2$t1ocdTotal and final_dataT2$t2_ocd_total
## t = 1.8653, df = 205, p-value = 0.06356
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.0594611 2.1468397
## sample estimates:
## mean difference
## 1.043689#intrusions
t_test_result_intrusions<- t.test(final_dataT2$t1intrusionsTotal,final_dataT2$t2_intrusions_total, paired = TRUE)
print(t_test_result_intrusions)##
## Paired t-test
##
## data: final_dataT2$t1intrusionsTotal and final_dataT2$t2_intrusions_total
## t = 1.9464, df = 205, p-value = 0.05298
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.005352203 0.830594922
## sample estimates:
## mean difference
## 0.4126214#intrusion distress
t_test_result_intrusions_distress<- t.test(final_dataT2$t1_thoughts_distress_level,final_dataT2$t2_thoughts_distress_level, paired = TRUE)
print(t_test_result_intrusions_distress)##
## Paired t-test
##
## data: final_dataT2$t1_thoughts_distress_level and final_dataT2$t2_thoughts_distress_level
## t = 1.0447, df = 205, p-value = 0.2974
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.05168435 0.16818921
## sample estimates:
## mean difference
## 0.05825243###H1-T1 Distress
#Intrusion distress
model_intrusions_distress_dp_neg <- lm(t1_thoughts_distress_level ~
ACdP_neg, data = final_data)
summary(model_intrusions_distress_dp_neg) #Intrusions symptoms Dprime ACdP_neg##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_neg, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.16092 -1.01210 -0.04284 0.92698 2.01306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.02651 0.05931 34.165 <2e-16 ***
## ACdP_neg 0.03209 0.03174 1.011 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8864 on 244 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.004171, Adjusted R-squared: 8.971e-05
## F-statistic: 1.022 on 1 and 244 DF, p-value: 0.3131standardized_model_intrusions_distress_dp_neg<- standardize_parameters(model_intrusions_distress_dp_neg)
print(standardized_model_intrusions_distress_dp_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | -3.61e-16 | [-0.13, 0.13]
## ACdP neg | 0.06 | [-0.06, 0.19]anova(model_intrusions_distress_dp_neg)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_neg 1 0.803 0.80295 1.022 0.3131
## Residuals 244 191.705 0.78568model_intrusions_distress_dp_peri <- lm(t1_thoughts_distress_level ~
ACdP_peri, data = final_data)
summary(model_intrusions_distress_dp_peri) #Intrusions distress Dprime ACdP_peri##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_peri, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.22239 -0.98439 -0.03503 0.90357 2.04287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.02550 0.05818 34.812 <2e-16 ***
## ACdP_peri 0.04046 0.02993 1.352 0.178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8849 on 244 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.007435, Adjusted R-squared: 0.003368
## F-statistic: 1.828 on 1 and 244 DF, p-value: 0.1776standardized_model_intrusions_distress_dp_peri <- standardize_parameters(model_intrusions_distress_dp_peri)
print(standardized_model_intrusions_distress_dp_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | -3.65e-16 | [-0.13, 0.13]
## ACdP peri | 0.09 | [-0.04, 0.21]anova(model_intrusions_distress_dp_peri)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_peri 1 1.431 1.4314 1.8278 0.1776
## Residuals 244 191.077 0.7831model_intrusions_distress_dp_Nperi <- lm(t1_thoughts_distress_level ~
ACdP_Nperi, data = final_data)
summary(model_intrusions_distress_dp_Nperi) #Intrusions distress Dprime ACdP_Nperi##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_Nperi, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.11772 -1.02925 -0.04351 0.93877 1.98288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.04600 0.05669 36.091 <2e-16 ***
## ACdP_Nperi 0.01383 0.03246 0.426 0.67
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8879 on 244 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0007437, Adjusted R-squared: -0.003352
## F-statistic: 0.1816 on 1 and 244 DF, p-value: 0.6704standardized_model_intrusions_distress_dp_Nperi <- standardize_parameters(model_intrusions_distress_dp_Nperi)
print(standardized_model_intrusions_distress_dp_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | -3.68e-16 | [-0.13, 0.13]
## ACdP Nperi | 0.03 | [-0.10, 0.15]anova(model_intrusions_distress_dp_Nperi)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_Nperi 1 0.143 0.14318 0.1816 0.6704
## Residuals 244 192.365 0.78838model_intrusions_distress_rt_neg<- lm(t1_thoughts_distress_level ~ ACRT_neg, data = final_data)
summary(model_intrusions_distress_rt_neg) #Intrusions distress Reaction Time ACRT_neg##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_neg, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.08141 -1.02832 -0.04458 0.94472 2.00274
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0455465 0.0566796 36.09 <2e-16 ***
## ACRT_neg -0.0003085 0.0009638 -0.32 0.749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8881 on 244 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0004196, Adjusted R-squared: -0.003677
## F-statistic: 0.1024 on 1 and 244 DF, p-value: 0.7492standardized_model_intrusions_distress_rt_neg <- standardize_parameters(model_intrusions_distress_rt_neg)
print(standardized_model_intrusions_distress_rt_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | -3.65e-16 | [-0.13, 0.13]
## ACRT neg | -0.02 | [-0.15, 0.11]anova(model_intrusions_distress_rt_neg)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_neg 1 0.081 0.08078 0.1024 0.7492
## Residuals 244 192.427 0.78864model_intrusions_distress_rt_peri<- lm(t1_thoughts_distress_level ~ ACRT_peri, data = final_data)
summary(model_intrusions_distress_rt_peri) #Intrusions distress Reaction Time ACRT_peri##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_peri, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.08482 -1.03117 -0.04535 0.94549 1.98423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0477059 0.0574071 35.670 <2e-16 ***
## ACRT_peri -0.0003067 0.0009712 -0.316 0.752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8881 on 244 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0004084, Adjusted R-squared: -0.003688
## F-statistic: 0.0997 on 1 and 244 DF, p-value: 0.7525standardized_model_intrusions_distress_rt_peri <- standardize_parameters(model_intrusions_distress_rt_peri)
print(standardized_model_intrusions_distress_rt_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | -3.67e-16 | [-0.13, 0.13]
## ACRT peri | -0.02 | [-0.15, 0.11]anova(model_intrusions_distress_rt_peri)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_peri 1 0.079 0.07863 0.0997 0.7525
## Residuals 244 192.430 0.78865model_intrusions_distress_rt_Nperi<- lm(t1_thoughts_distress_level ~ ACRT_Nperi, data = final_data)
summary(model_intrusions_distress_rt_Nperi) #Intrusions distress Reaction Time##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_Nperi, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.04569 -1.04445 -0.04475 0.95493 1.95643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.045e+00 5.703e-02 35.852 <2e-16 ***
## ACRT_Nperi 6.315e-06 9.547e-04 0.007 0.995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8882 on 244 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 1.794e-07, Adjusted R-squared: -0.004098
## F-statistic: 4.376e-05 on 1 and 244 DF, p-value: 0.9947standardized_model_intrusions_distress_rt_Nperi <- standardize_parameters(model_intrusions_distress_rt_Nperi)
print(standardized_model_intrusions_distress_rt_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | -3.65e-16 | [-0.13, 0.13]
## ACRT Nperi | 4.24e-04 | [-0.13, 0.13]anova(model_intrusions_distress_rt_Nperi)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_Nperi 1 0.00 0.00003 0 0.9947
## Residuals 244 192.51 0.78897###trialling
#Rename variables for separation
final_data_rename <- final_data %>%
rename(intrusions_T1 = t1intrusionsTotal,
intrusionsdistress_T1 = t1_thoughts_distress_level,
rtq_T1 = t1rtqTotal,
intrusions_T2 = t2_intrusions_total,
intrusionsdistress_T2 = t2_thoughts_distress_level,
rtq_T2 = t2_rtq_total
)#Select variables for pivot
final_data_select <- final_data_rename %>%
dplyr::select (Prolific_ID, intrusions_T1, intrusionsdistress_T1, rtq_T1, intrusions_T2, intrusionsdistress_T2,rtq_T2, t1_wks_since_conception, average_DPrime_neutral_score, average_DPrime_negative_score, average_DPrime_perineg_score, average_RT_neutral_score, average_RT_negative_score, average_RT_perineg_score,DprimeNeg_1,DprimeNeut_1,DprimePNeg_1,DprimeNeg_2,DprimeNeut_2,DprimePNeg_2,DprimeNeg_3, DprimeNeut_3, DprimePNeg_3,RTNeg_1,RTNeut_1,RTPNeg_1,RTNeg_2,RTNeut_2, RTPNeg_2,RTNeg_3,RTNeut_3,RTPNeg_3,ACdP_neg,ACdP_peri,ACdP_Nperi,ACRT_neg,ACRT_peri,ACRT_Nperi)final_data_select <- final_data_rename %>%
dplyr::select (Prolific_ID, intrusions_T1, intrusionsdistress_T1, rtq_T1, intrusions_T2, intrusionsdistress_T2,rtq_T2, t1_wks_since_conception, average_DPrime_neutral_score, average_DPrime_negative_score, average_DPrime_perineg_score, average_RT_neutral_score, average_RT_negative_score, average_RT_perineg_score,DprimeNeg_1,DprimeNeut_1,DprimePNeg_1,DprimeNeg_2,DprimeNeut_2,DprimePNeg_2,DprimeNeg_3, DprimeNeut_3, DprimePNeg_3,RTNeg_1,RTNeut_1,RTPNeg_1,RTNeg_2,RTNeut_2, RTPNeg_2,RTNeg_3,RTNeut_3,RTPNeg_3,ACdP_neg,ACdP_peri,ACdP_Nperi,ACRT_neg,ACRT_peri,ACRT_Nperi)#Pivot to long
final_data_long <- final_data_select %>%
pivot_longer(
cols = c(intrusions_T1, intrusions_T2, intrusionsdistress_T1, intrusionsdistress_T2, rtq_T1, rtq_T2),
names_to = c(".value", "time"),
names_sep = "_T"
)#Pivot to long
final_data_long <- final_data_select %>%
pivot_longer(
cols = c(intrusions_T1, intrusions_T2, intrusionsdistress_T1, intrusionsdistress_T2, rtq_T1, rtq_T2),
names_to = c(".value", "time"),
names_sep = "_T"
)# Converting time to a factor
final_data_long$time <- as.factor(final_data_long$time)H1 - T2 1-Month follow-up
#Rename variables for separation
final_dataT2_rename <- final_dataT2 %>%
rename(intrusions_T1 = t1intrusionsTotal,
intrusionsdistress_T1 = t1_thoughts_distress_level,
rtq_T1 = t1rtqTotal,
intrusions_T2 = t2_intrusions_total,
intrusionsdistress_T2 = t2_thoughts_distress_level,
rtq_T2 = t2_rtq_total
)final_dataT2_select <- final_dataT2_rename %>%
dplyr::select (Prolific_ID, intrusions_T1, intrusionsdistress_T1, rtq_T1, intrusions_T2, intrusionsdistress_T2,rtq_T2, t1_wks_since_conception, average_DPrime_neutral_score, average_DPrime_negative_score, average_DPrime_perineg_score, average_RT_neutral_score, average_RT_negative_score, average_RT_perineg_score,DprimeNeg_1,DprimeNeut_1,DprimePNeg_1,DprimeNeg_2,DprimeNeut_2,DprimePNeg_2,DprimeNeg_3, DprimeNeut_3, DprimePNeg_3,RTNeg_1,RTNeut_1,RTPNeg_1,RTNeg_2,RTNeut_2, RTPNeg_2,RTNeg_3,RTNeut_3,RTPNeg_3,ACdP_neg,ACdP_peri,ACdP_Nperi,ACRT_neg,ACRT_peri,ACRT_Nperi)#Pivot to long
final_dataT2_long <- final_dataT2_select %>%
pivot_longer(
cols = c(intrusions_T1, intrusions_T2, intrusionsdistress_T1, intrusionsdistress_T2, rtq_T1, rtq_T2),
names_to = c(".value", "time"),
names_sep = "_T"
)# Converting time to a factor
final_dataT2_long$time <- as.factor(final_dataT2_long$time)###H1-T2 Intrusions
#Intrusions - Dprime
model_intrusions_dp_neg_t2 <- lmer(intrusions ~ time * ACdP_neg + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_neg_t2) #Intrusions symptoms Dprime ACdP_neg T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ time * ACdP_neg + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2072
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7271 -0.4885 -0.1003 0.4775 3.0625
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.133 2.477
## Residual 4.650 2.156
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.71039 0.24183 308.27461 19.478 <2e-16 ***
## time2 -0.39429 0.22459 204.00000 -1.756 0.0807 .
## ACdP_neg 0.09956 0.12690 308.27461 0.785 0.4333
## time2:ACdP_neg -0.02970 0.11785 204.00000 -0.252 0.8013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACdP_n
## time2 -0.464
## ACdP_neg -0.324 0.150
## tm2:ACdP_ng 0.150 -0.324 -0.464standardized_model_intrusions_dp_neg_t2 <- standardize_parameters(model_intrusions_dp_neg_t2)
print(standardized_model_intrusions_dp_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.06 | [-0.07, 0.20]
## time [2] | -0.13 | [-0.25, 0.00]
## ACdP neg | 0.05 | [-0.08, 0.19]
## time [2] × ACdP neg | -0.02 | [-0.14, 0.11]anova(model_intrusions_dp_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 14.3327 14.3327 1 204 3.0821 0.08066 .
## ACdP_neg 2.6419 2.6419 1 204 0.5681 0.45188
## time:ACdP_neg 0.2954 0.2954 1 204 0.0635 0.80125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_dp_neg_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------
## time | 0.01 | [0.00, 1.00]
## ACdP_neg | 2.78e-03 | [0.00, 1.00]
## time:ACdP_neg | 3.11e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.571
## Marginal R2: 0.006model_intrusions_dp_peri_t2 <- lmer(intrusions ~ time * ACdP_peri + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_peri_t2) #Intrusions symptoms Dprime ACdP_peri T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ time * ACdP_peri + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2070.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.78454 -0.48903 -0.09454 0.47979 3.09188
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.121 2.474
## Residual 4.628 2.151
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.6749 0.2384 308.0975 19.607 <2e-16 ***
## time2 -0.3480 0.2213 204.0000 -1.573 0.117
## ACdP_peri 0.1715 0.1208 308.0975 1.419 0.157
## time2:ACdP_peri -0.1143 0.1121 204.0000 -1.020 0.309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACdP_p
## time2 -0.464
## ACdP_peri -0.287 0.133
## tm2:ACdP_pr 0.133 -0.287 -0.464standardized_model_intrusions_dp_peri_t2 <- standardize_parameters(model_intrusions_dp_peri_t2)
print(standardized_model_intrusions_dp_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------
## (Intercept) | 0.06 | [-0.07, 0.20]
## time [2] | -0.13 | [-0.25, 0.00]
## ACdP peri | 0.10 | [-0.04, 0.24]
## time [2] × ACdP peri | -0.07 | [-0.19, 0.06]anova(model_intrusions_dp_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 11.4467 11.4467 1 204 2.4733 0.1173
## ACdP_peri 5.2794 5.2794 1 204 1.1407 0.2868
## time:ACdP_peri 4.8120 4.8120 1 204 1.0397 0.3091eta_squared(model_intrusions_dp_peri_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## time | 0.01 | [0.00, 1.00]
## ACdP_peri | 5.56e-03 | [0.00, 1.00]
## time:ACdP_peri | 5.07e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.573
## Marginal R2: 0.009model_intrusions_dp_Nperi_t2 <- lmer(intrusions ~ time * ACdP_Nperi + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_Nperi_t2) #Intrusions symptoms Dprime ACdP_Nperi T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ time * ACdP_Nperi + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2071.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.72882 -0.48046 -0.09531 0.46963 3.08238
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.158 2.482
## Residual 4.636 2.153
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.77663 0.22901 307.81073 20.858 <2e-16 ***
## time2 -0.41778 0.21225 204.00000 -1.968 0.0504 .
## ACdP_Nperi 0.09263 0.12953 307.81073 0.715 0.4751
## time2:ACdP_Nperi -0.09993 0.12005 204.00000 -0.832 0.4061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACdP_N
## time2 -0.463
## ACdP_Nperi 0.029 -0.014
## tm2:ACdP_Np -0.014 0.029 -0.463standardized_model_intrusions_dp_Nperi_t2 <- standardize_parameters(model_intrusions_dp_Nperi_t2)
print(standardized_model_intrusions_dp_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.06 | [-0.07, 0.20]
## time [2] | -0.13 | [-0.25, 0.00]
## ACdP Nperi | 0.05 | [-0.09, 0.19]
## time [2] × ACdP Nperi | -0.05 | [-0.18, 0.07]anova(model_intrusions_dp_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 17.9626 17.9626 1 204 3.8746 0.05038 .
## ACdP_Nperi 0.6405 0.6405 1 204 0.1382 0.71051
## time:ACdP_Nperi 3.2125 3.2125 1 204 0.6929 0.40614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_dp_Nperi_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------
## time | 0.02 | [0.00, 1.00]
## ACdP_Nperi | 6.77e-04 | [0.00, 1.00]
## time:ACdP_Nperi | 3.39e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.573
## Marginal R2: 0.005#Intrusions - Reaction Time
model_intrusions_rt_neg_t2 <- lmer(intrusions ~ time * ACRT_neg + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_rt_neg_t2) #Intrusions symptoms Reaction Time ACRT_neg T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ time * ACRT_neg + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2080.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.80627 -0.50119 -0.09415 0.49336 3.09832
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 5.914 2.432
## Residual 4.652 2.157
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.801e+00 2.269e-01 3.107e+02 21.161 <2e-16 ***
## time2 -4.131e-01 2.129e-01 2.040e+02 -1.940 0.0537 .
## ACRT_neg -8.156e-03 3.746e-03 3.107e+02 -2.177 0.0302 *
## time2:ACRT_neg 1.239e-04 3.515e-03 2.040e+02 0.035 0.9719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACRT_n
## time2 -0.469
## ACRT_neg -0.059 0.028
## tm2:ACRT_ng 0.028 -0.059 -0.469standardized_model_intrusions_rt_neg_t2 <- standardize_parameters(model_intrusions_rt_neg_t2)
print(standardized_model_intrusions_rt_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------
## (Intercept) | 0.06 | [-0.07, 0.20]
## time [2] | -0.13 | [-0.25, 0.00]
## ACRT neg | -0.15 | [-0.29, -0.01]
## time [2] × ACRT neg | 2.29e-03 | [-0.13, 0.13]anova(model_intrusions_rt_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 17.5130 17.5130 1 204 3.7648 0.05372 .
## ACRT_neg 27.8524 27.8524 1 204 5.9875 0.01525 *
## time:ACRT_neg 0.0058 0.0058 1 204 0.0012 0.97190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_rt_neg_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------
## time | 0.02 | [0.00, 1.00]
## ACRT_neg | 0.03 | [0.00, 1.00]
## time:ACRT_neg | 6.10e-06 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.571
## Marginal R2: 0.026model_intrusions_rt_peri_t2<- lmer(intrusions ~ time * ACRT_peri + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_rt_peri_t2) #Intrusions symptoms Reaction Time ACRT_peri T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ time * ACRT_peri + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2084.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8443 -0.4863 -0.1262 0.4601 3.1526
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.158 2.482
## Residual 4.607 2.146
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.818740 0.231550 307.404637 20.811 <2e-16 ***
## time2 -0.460604 0.214218 204.000002 -2.150 0.0327 *
## ACRT_peri -0.004924 0.003867 307.404637 -1.273 0.2039
## time2:ACRT_peri 0.005038 0.003578 204.000002 1.408 0.1606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACRT_p
## time2 -0.463
## ACRT_peri -0.159 0.074
## tm2:ACRT_pr 0.074 -0.159 -0.463standardized_model_intrusions_rt_peri_t2 <- standardize_parameters(model_intrusions_rt_peri_t2)
print(standardized_model_intrusions_rt_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------
## (Intercept) | 0.06 | [-0.07, 0.20]
## time [2] | -0.13 | [-0.25, 0.00]
## ACRT peri | -0.09 | [-0.23, 0.05]
## time [2] × ACRT peri | 0.09 | [-0.04, 0.22]anova(model_intrusions_rt_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 21.2992 21.2992 1 204 4.6232 0.03272 *
## ACRT_peri 2.2664 2.2664 1 204 0.4919 0.48386
## time:ACRT_peri 9.1346 9.1346 1 204 1.9828 0.16062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_rt_peri_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## time | 0.02 | [0.00, 1.00]
## ACRT_peri | 2.41e-03 | [0.00, 1.00]
## time:ACRT_peri | 9.63e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.575
## Marginal R2: 0.008model_intrusions_rt_Nperi_t2 <- lmer(intrusions ~ time * ACRT_Nperi + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_rt_Nperi_t2) #Intrusions symptoms Reaction Time ACRT_Nperi T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ time * ACRT_Nperi + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2081.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7037 -0.4987 -0.1001 0.4295 3.0613
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.049 2.459
## Residual 4.610 2.147
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.750506 0.228605 308.608289 20.780 <2e-16 ***
## time2 -0.441636 0.212613 204.000000 -2.077 0.039 *
## ACRT_Nperi 0.003583 0.003833 308.608289 0.935 0.351
## time2:ACRT_Nperi 0.004871 0.003565 204.000000 1.366 0.173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACRT_N
## time2 -0.465
## ACRT_Nperi -0.100 0.046
## tm2:ACRT_Np 0.046 -0.100 -0.465standardized_model_intrusions_rt_Nperi_t2 <- standardize_parameters(model_intrusions_rt_Nperi_t2)
print(standardized_model_intrusions_rt_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.06 | [-0.07, 0.20]
## time [2] | -0.13 | [-0.25, 0.00]
## ACRT Nperi | 0.06 | [-0.07, 0.20]
## time [2] × ACRT Nperi | 0.09 | [-0.04, 0.22]anova(model_intrusions_rt_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 19.8890 19.8890 1 204 4.3147 0.03904 *
## ACRT_Nperi 14.4967 14.4967 1 204 3.1449 0.07766 .
## time:ACRT_Nperi 8.6057 8.6057 1 204 1.8669 0.17333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_rt_Nperi_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------
## time | 0.02 | [0.00, 1.00]
## ACRT_Nperi | 0.02 | [0.00, 1.00]
## time:ACRT_Nperi | 9.07e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.575
## Marginal R2: 0.018H1-T2 Distress
#Intrusion distress -Dprime
model_intrusions_distress_dp_neg_t2 <- lmer(intrusionsdistress ~ time * ACdP_neg + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_neg_t2) #Intrusions symptoms Dprime ACdP_neg T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ time * ACdP_neg + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1016.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.50800 -0.47251 -0.07393 0.49178 2.86521
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5397 0.7346
## Residual 0.3187 0.5646
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.05087 0.06823 292.41513 30.058 <2e-16 ***
## time2 -0.08507 0.05880 204.00001 -1.447 0.149
## ACdP_neg 0.01982 0.03580 292.41513 0.554 0.580
## time2:ACdP_neg 0.04344 0.03085 204.00001 1.408 0.161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACdP_n
## time2 -0.431
## ACdP_neg -0.324 0.140
## tm2:ACdP_ng 0.140 -0.324 -0.431standardized_model_intrusions_distress_dp_neg_t2<- standardize_parameters(model_intrusions_distress_dp_neg_t2)
print(standardized_model_intrusions_distress_dp_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.17]
## time [2] | -0.06 | [-0.18, 0.06]
## ACdP neg | 0.04 | [-0.10, 0.18]
## time [2] × ACdP neg | 0.08 | [-0.03, 0.20]anova(model_intrusions_distress_dp_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 0.66713 0.66713 1 204 2.0931 0.1495
## ACdP_neg 0.52694 0.52694 1 204 1.6533 0.2000
## time:ACdP_neg 0.63181 0.63181 1 204 1.9823 0.1607eta_squared(model_intrusions_distress_dp_neg_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------
## time | 0.01 | [0.00, 1.00]
## ACdP_neg | 8.04e-03 | [0.00, 1.00]
## time:ACdP_neg | 9.62e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.632
## Marginal R2: 0.009model_intrusions_distress_dp_peri_t2 <- lmer(intrusionsdistress ~ time * ACdP_peri + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_peri_t2) #Intrusions symptoms Dprime ACdP_peri T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ time * ACdP_peri + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1018.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.52712 -0.46811 -0.05872 0.46403 2.82564
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5364 0.7324
## Residual 0.3216 0.5671
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.04031 0.06737 293.34724 30.287 <2e-16 ***
## time2 -0.06413 0.05833 204.00000 -1.099 0.273
## ACdP_peri 0.04030 0.03413 293.34724 1.181 0.239
## time2:ACdP_peri 0.01039 0.02955 204.00000 0.352 0.726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACdP_p
## time2 -0.433
## ACdP_peri -0.287 0.124
## tm2:ACdP_pr 0.124 -0.287 -0.433standardized_model_intrusions_distress_dp_peri_t2 <- standardize_parameters(model_intrusions_distress_dp_peri_t2)
print(standardized_model_intrusions_distress_dp_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.17]
## time [2] | -0.06 | [-0.18, 0.06]
## ACdP peri | 0.08 | [-0.05, 0.22]
## time [2] × ACdP peri | 0.02 | [-0.10, 0.14]anova(model_intrusions_distress_dp_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 0.38880 0.38880 1 204 1.2089 0.2729
## ACdP_peri 0.70302 0.70302 1 204 2.1859 0.1408
## time:ACdP_peri 0.03975 0.03975 1 204 0.1236 0.7255eta_squared(model_intrusions_distress_dp_peri_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## time | 5.89e-03 | [0.00, 1.00]
## ACdP_peri | 0.01 | [0.00, 1.00]
## time:ACdP_peri | 6.05e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.629
## Marginal R2: 0.010model_intrusions_distress_dp_Nperi_t2 <- lmer(intrusionsdistress ~ time * ACdP_Nperi + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_Nperi_t2) #Intrusions symptoms Dprime ACdP_Nperi T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ time * ACdP_Nperi + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1019
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.56871 -0.46445 -0.04134 0.48083 2.93157
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5444 0.7379
## Residual 0.3201 0.5657
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.06442 0.06481 292.13594 31.854 <2e-16 ***
## time2 -0.05997 0.05577 204.00001 -1.075 0.283
## ACdP_Nperi 0.02549 0.03666 292.13594 0.695 0.487
## time2:ACdP_Nperi -0.03332 0.03154 204.00001 -1.056 0.292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACdP_N
## time2 -0.430
## ACdP_Nperi 0.029 -0.013
## tm2:ACdP_Np -0.013 0.029 -0.430standardized_model_intrusions_distress_dp_Nperi_t2 <- standardize_parameters(model_intrusions_distress_dp_Nperi_t2)
print(standardized_model_intrusions_distress_dp_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.17]
## time [2] | -0.06 | [-0.18, 0.06]
## ACdP Nperi | 0.05 | [-0.09, 0.19]
## time [2] × ACdP Nperi | -0.06 | [-0.18, 0.05]anova(model_intrusions_distress_dp_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 0.37016 0.37016 1 204 1.1565 0.2835
## ACdP_Nperi 0.02279 0.02279 1 204 0.0712 0.7899
## time:ACdP_Nperi 0.35720 0.35720 1 204 1.1160 0.2920eta_squared(model_intrusions_distress_dp_Nperi_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------
## time | 5.64e-03 | [0.00, 1.00]
## ACdP_Nperi | 3.49e-04 | [0.00, 1.00]
## time:ACdP_Nperi | 5.44e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_Nperi_t2 )## # R2 for Mixed Models
##
## Conditional R2: 0.631
## Marginal R2: 0.002#Intrusion distress - Reaction Time
model_intrusions_distress_rt_neg_t2<- lmer(intrusionsdistress ~ time * ACRT_neg + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_rt_neg_t2) #Intrusions symptoms Reaction Time ACRT_neg T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ time * ACRT_neg + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1032.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54088 -0.47904 -0.06403 0.45818 2.95496
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5423 0.7364
## Residual 0.3205 0.5661
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.064e+00 6.483e-02 2.925e+02 31.844 <2e-16 ***
## time2 -5.518e-02 5.588e-02 2.040e+02 -0.988 0.324
## ACRT_neg -3.480e-04 1.070e-03 2.925e+02 -0.325 0.745
## time2:ACRT_neg -8.597e-04 9.225e-04 2.040e+02 -0.932 0.352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACRT_n
## time2 -0.431
## ACRT_neg -0.059 0.025
## tm2:ACRT_ng 0.025 -0.059 -0.431standardized_model_intrusions_distress_rt_neg_t2 <- standardize_parameters(model_intrusions_distress_rt_neg_t2)
print(standardized_model_intrusions_distress_rt_neg_t2 )## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.17]
## time [2] | -0.06 | [-0.18, 0.06]
## ACRT neg | -0.02 | [-0.16, 0.11]
## time [2] × ACRT neg | -0.06 | [-0.17, 0.06]anova(model_intrusions_distress_rt_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 0.31258 0.31258 1 204 0.9754 0.3245
## ACRT_neg 0.20790 0.20790 1 204 0.6488 0.4215
## time:ACRT_neg 0.27833 0.27833 1 204 0.8686 0.3525eta_squared(model_intrusions_distress_rt_neg_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------
## time | 4.76e-03 | [0.00, 1.00]
## ACRT_neg | 3.17e-03 | [0.00, 1.00]
## time:ACRT_neg | 4.24e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.630
## Marginal R2: 0.004model_intrusions_distress_rt_peri_t2<- lmer(intrusionsdistress ~ time * ACRT_peri + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_rt_peri_t2) #Intrusions symptoms Reaction Time ACRT_peri T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ time * ACRT_peri + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1034
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.50197 -0.46473 -0.06301 0.44375 2.88532
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5432 0.7370
## Residual 0.3218 0.5673
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.068e+00 6.564e-02 2.926e+02 31.501 <2e-16 ***
## time2 -5.915e-02 5.662e-02 2.040e+02 -1.045 0.297
## ACRT_peri -4.678e-04 1.096e-03 2.926e+02 -0.427 0.670
## time2:ACRT_peri 9.383e-05 9.455e-04 2.040e+02 0.099 0.921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACRT_p
## time2 -0.431
## ACRT_peri -0.159 0.069
## tm2:ACRT_pr 0.069 -0.159 -0.431standardized_model_intrusions_distress_rt_peri_t2 <- standardize_parameters(model_intrusions_distress_rt_peri_t2)
print(standardized_model_intrusions_distress_rt_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.17]
## time [2] | -0.06 | [-0.18, 0.06]
## ACRT peri | -0.03 | [-0.17, 0.11]
## time [2] × ACRT peri | 5.99e-03 | [-0.11, 0.12]anova(model_intrusions_distress_rt_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 0.35120 0.35120 1 204 1.0914 0.2974
## ACRT_peri 0.05828 0.05828 1 204 0.1811 0.6709
## time:ACRT_peri 0.00317 0.00317 1 204 0.0098 0.9211eta_squared(model_intrusions_distress_rt_peri_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## time | 5.32e-03 | [0.00, 1.00]
## ACRT_peri | 8.87e-04 | [0.00, 1.00]
## time:ACRT_peri | 4.83e-05 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.629
## Marginal R2: 0.002model_intrusions_distress_rt_Nperi_t2<- lmer(intrusionsdistress ~ time * ACRT_Nperi + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_rt_Nperi_t2) #Intrusions symptoms Reaction Time ACRT_Nperi T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ time * ACRT_Nperi + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1033
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54289 -0.47303 -0.05587 0.45914 2.92929
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5441 0.7377
## Residual 0.3201 0.5658
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.064e+00 6.510e-02 2.922e+02 31.703 <2e-16 ***
## time2 -6.412e-02 5.603e-02 2.040e+02 -1.145 0.254
## ACRT_Nperi -1.029e-04 1.092e-03 2.922e+02 -0.094 0.925
## time2:ACRT_Nperi 9.859e-04 9.395e-04 2.040e+02 1.049 0.295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) time2 ACRT_N
## time2 -0.430
## ACRT_Nperi -0.100 0.043
## tm2:ACRT_Np 0.043 -0.100 -0.430standardized_model_intrusions_distress_rt_Nperi_t2 <- standardize_parameters(model_intrusions_distress_rt_Nperi_t2)
print(standardized_model_intrusions_distress_rt_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.17]
## time [2] | -0.06 | [-0.18, 0.06]
## ACRT Nperi | -6.59e-03 | [-0.14, 0.13]
## time [2] × ACRT Nperi | 0.06 | [-0.06, 0.18]anova(model_intrusions_distress_rt_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## time 0.41931 0.41931 1 204 1.3100 0.2537
## ACRT_Nperi 0.05016 0.05016 1 204 0.1567 0.6926
## time:ACRT_Nperi 0.35250 0.35250 1 204 1.1013 0.2952eta_squared(model_intrusions_distress_rt_Nperi_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------
## time | 6.38e-03 | [0.00, 1.00]
## ACRT_Nperi | 7.68e-04 | [0.00, 1.00]
## time:ACRT_Nperi | 5.37e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.631
## Marginal R2: 0.003##H2 - RNT X Affective Control ##Cross Sectional ### H2-T1 Intrusions
#Intrusions
model_intrusions_dp_mod_neg <- lm(t1intrusionsTotal ~ ACdP_neg*t1rtqTotal, data = final_data)
summary(model_intrusions_dp_mod_neg) #Intrusions mod Dprime ACdP_neg##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_neg * t1rtqTotal, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7608 -2.3543 -0.4392 1.9350 8.9451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.366796 0.545824 2.504 0.0129 *
## ACdP_neg -0.220755 0.273523 -0.807 0.4204
## t1rtqTotal 0.131848 0.019528 6.752 1.07e-10 ***
## ACdP_neg:t1rtqTotal 0.009566 0.009275 1.031 0.3034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.971 on 243 degrees of freedom
## Multiple R-squared: 0.2038, Adjusted R-squared: 0.194
## F-statistic: 20.73 on 3 and 243 DF, p-value: 5.387e-12standardized_model_intrusions_dp_mod_neg <- standardize_parameters(model_intrusions_dp_mod_neg)
print(standardized_model_intrusions_dp_mod_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | -3.24e-03 | [-0.12, 0.11]
## ACdP neg | 0.02 | [-0.10, 0.13]
## t1rtqTotal | 0.44 | [ 0.32, 0.55]
## ACdP neg × t1rtqTotal | 0.05 | [-0.05, 0.16]anova(model_intrusions_dp_mod_neg)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_neg 1 6.12 6.12 0.6934 0.4058
## t1rtqTotal 1 533.66 533.66 60.4445 2.142e-13 ***
## ACdP_neg:t1rtqTotal 1 9.39 9.39 1.0637 0.3034
## Residuals 243 2145.42 8.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_dp_mod_neg)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------
## ACdP_neg | 2.85e-03 | [0.00, 1.00]
## t1rtqTotal | 0.20 | [0.13, 1.00]
## ACdP_neg:t1rtqTotal | 4.36e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_mod_neg)## # R2 for Linear Regression
## R2: 0.204
## adj. R2: 0.194model_intrusions_dp_mod_peri <- lm(t1intrusionsTotal ~ ACdP_peri*t1rtqTotal, data = final_data)
summary(model_intrusions_dp_mod_peri) #Intrusions mod Dprime ACdP_peri##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_peri * t1rtqTotal, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7045 -2.1760 -0.4892 2.0958 8.9942
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.003280 0.525183 1.910 0.0573 .
## ACdP_peri 0.337907 0.240393 1.406 0.1611
## t1rtqTotal 0.145117 0.018678 7.769 2.22e-13 ***
## ACdP_peri:t1rtqTotal -0.009063 0.008726 -1.039 0.3000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.965 on 243 degrees of freedom
## Multiple R-squared: 0.2074, Adjusted R-squared: 0.1976
## F-statistic: 21.19 on 3 and 243 DF, p-value: 3.138e-12standardized_model_intrusions_dp_mod_peri <- standardize_parameters(model_intrusions_dp_mod_peri)
print(standardized_model_intrusions_dp_mod_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------
## (Intercept) | 1.44e-03 | [-0.11, 0.11]
## ACdP peri | 0.06 | [-0.06, 0.17]
## t1rtqTotal | 0.45 | [ 0.34, 0.56]
## ACdP peri × t1rtqTotal | -0.05 | [-0.16, 0.05]anova(model_intrusions_dp_mod_peri)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_peri 1 15.17 15.17 1.7255 0.1902
## t1rtqTotal 1 534.19 534.19 60.7792 1.869e-13 ***
## ACdP_peri:t1rtqTotal 1 9.48 9.48 1.0787 0.3000
## Residuals 243 2135.75 8.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_dp_mod_peri)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------
## ACdP_peri | 7.05e-03 | [0.00, 1.00]
## t1rtqTotal | 0.20 | [0.13, 1.00]
## ACdP_peri:t1rtqTotal | 4.42e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_mod_peri)## # R2 for Linear Regression
## R2: 0.207
## adj. R2: 0.198model_intrusions_dp_mod_Nperi <- lm(t1intrusionsTotal ~ ACdP_Nperi*t1rtqTotal, data = final_data)
summary(model_intrusions_dp_mod_Nperi) #Intrusions mod Dprime ACdP_Nperi##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_Nperi * t1rtqTotal, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9575 -2.2282 -0.3998 2.1549 8.9579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.297807 0.506455 2.563 0.0110 *
## ACdP_Nperi 0.660199 0.265930 2.483 0.0137 *
## t1rtqTotal 0.135240 0.017914 7.549 8.8e-13 ***
## ACdP_Nperi:t1rtqTotal -0.022756 0.009691 -2.348 0.0197 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.941 on 243 degrees of freedom
## Multiple R-squared: 0.2198, Adjusted R-squared: 0.2102
## F-statistic: 22.82 on 3 and 243 DF, p-value: 4.755e-13standardized_model_intrusions_dp_mod_Nperi <- standardize_parameters(model_intrusions_dp_mod_Nperi )
print(standardized_model_intrusions_dp_mod_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------
## (Intercept) | -4.09e-03 | [-0.12, 0.11]
## ACdP Nperi | 0.03 | [-0.08, 0.14]
## t1rtqTotal | 0.44 | [ 0.33, 0.55]
## ACdP Nperi × t1rtqTotal | -0.13 | [-0.23, -0.02]anova(model_intrusions_dp_mod_Nperi)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_Nperi 1 2.83 2.83 0.3274 0.56771
## t1rtqTotal 1 541.70 541.70 62.6123 8.897e-14 ***
## ACdP_Nperi:t1rtqTotal 1 47.70 47.70 5.5138 0.01967 *
## Residuals 243 2102.35 8.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_dp_mod_Nperi)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------
## ACdP_Nperi | 1.35e-03 | [0.00, 1.00]
## t1rtqTotal | 0.20 | [0.13, 1.00]
## ACdP_Nperi:t1rtqTotal | 0.02 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_mod_Nperi)## # R2 for Linear Regression
## R2: 0.220
## adj. R2: 0.210model_intrusions_RT_mod_neg <- lm(t1intrusionsTotal ~ ACRT_neg*t1rtqTotal, data = final_data)
summary(model_intrusions_RT_mod_neg) #Intrusions mod Reaction time ACRT_neg##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_neg * t1rtqTotal, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.362 -2.330 -0.433 2.118 9.410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.202e+00 5.071e-01 2.370 0.0185 *
## ACRT_neg -3.273e-03 8.987e-03 -0.364 0.7160
## t1rtqTotal 1.397e-01 1.787e-02 7.817 1.64e-13 ***
## ACRT_neg:t1rtqTotal -9.515e-05 3.238e-04 -0.294 0.7691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.959 on 243 degrees of freedom
## Multiple R-squared: 0.2106, Adjusted R-squared: 0.2008
## F-statistic: 21.6 on 3 and 243 DF, p-value: 1.945e-12standardized_model_intrusions_RT_mod_neg <- standardize_parameters(model_intrusions_RT_mod_neg)
print(standardized_model_intrusions_RT_mod_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | -3.02e-04 | [-0.11, 0.11]
## ACRT neg | -0.10 | [-0.22, 0.01]
## t1rtqTotal | 0.44 | [ 0.33, 0.56]
## ACRT neg × t1rtqTotal | -0.02 | [-0.14, 0.10]anova(model_intrusions_RT_mod_neg)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_neg 1 32.33 32.33 3.6926 0.05582 .
## t1rtqTotal 1 534.27 534.27 61.0316 1.687e-13 ***
## ACRT_neg:t1rtqTotal 1 0.76 0.76 0.0863 0.76915
## Residuals 243 2127.24 8.75
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_RT_mod_neg)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------
## ACRT_neg | 0.01 | [0.00, 1.00]
## t1rtqTotal | 0.20 | [0.13, 1.00]
## ACRT_neg:t1rtqTotal | 3.55e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_RT_mod_neg)## # R2 for Linear Regression
## R2: 0.211
## adj. R2: 0.201model_intrusions_RT_mod_peri <- lm(t1intrusionsTotal ~ ACRT_peri*t1rtqTotal, data = final_data)
summary(model_intrusions_RT_mod_peri) #Intrusions mod Reaction time ACRT_peri##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_peri * t1rtqTotal, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4163 -2.3333 -0.3523 1.9921 9.7996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.211e+00 5.119e-01 2.365 0.0188 *
## ACRT_peri -8.150e-03 8.558e-03 -0.952 0.3419
## t1rtqTotal 1.411e-01 1.820e-02 7.752 2.47e-13 ***
## ACRT_peri:t1rtqTotal 6.942e-05 3.184e-04 0.218 0.8276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.954 on 243 degrees of freedom
## Multiple R-squared: 0.2128, Adjusted R-squared: 0.2031
## F-statistic: 21.9 on 3 and 243 DF, p-value: 1.377e-12standardized_model_intrusions_RT_mod_peri <- standardize_parameters(model_intrusions_RT_mod_peri)
print(standardized_model_intrusions_RT_mod_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------
## (Intercept) | -6.24e-04 | [-0.11, 0.11]
## ACRT peri | -0.11 | [-0.22, 0.00]
## t1rtqTotal | 0.45 | [ 0.34, 0.56]
## ACRT peri × t1rtqTotal | 0.01 | [-0.10, 0.13]anova(model_intrusions_RT_mod_peri )## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_peri 1 22.47 22.47 2.5745 0.1099
## t1rtqTotal 1 550.60 550.60 63.0777 7.374e-14 ***
## ACRT_peri:t1rtqTotal 1 0.41 0.41 0.0475 0.8276
## Residuals 243 2121.11 8.73
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_RT_mod_peri)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------
## ACRT_peri | 0.01 | [0.00, 1.00]
## t1rtqTotal | 0.21 | [0.14, 1.00]
## ACRT_peri:t1rtqTotal | 1.96e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_RT_mod_peri)## # R2 for Linear Regression
## R2: 0.213
## adj. R2: 0.203model_intrusions_RT_mod_Nperi <- lm(t1intrusionsTotal ~ ACRT_Nperi*t1rtqTotal, data = final_data)
summary(model_intrusions_RT_mod_Nperi) #Intrusions mod reaction time ACRT_Nperi ##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_Nperi * t1rtqTotal, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6656 -2.3396 -0.4466 2.0969 9.6081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2180036 0.5133632 2.373 0.0184 *
## ACRT_Nperi -0.0059341 0.0085764 -0.692 0.4897
## t1rtqTotal 0.1383890 0.0182308 7.591 6.79e-13 ***
## ACRT_Nperi:t1rtqTotal 0.0002069 0.0003067 0.675 0.5005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.976 on 243 degrees of freedom
## Multiple R-squared: 0.2015, Adjusted R-squared: 0.1916
## F-statistic: 20.44 on 3 and 243 DF, p-value: 7.636e-12standardized_model_intrusions_RT_mod_Nperi <- standardize_parameters(model_intrusions_RT_mod_Nperi)
print(standardized_model_intrusions_RT_mod_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------
## (Intercept) | -2.52e-03 | [-0.12, 0.11]
## ACRT Nperi | -8.72e-03 | [-0.12, 0.10]
## t1rtqTotal | 0.45 | [ 0.33, 0.56]
## ACRT Nperi × t1rtqTotal | 0.04 | [-0.08, 0.15]anova(model_intrusions_RT_mod_Nperi)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_Nperi 1 0.94 0.94 0.1065 0.7444
## t1rtqTotal 1 537.93 537.93 60.7507 1.891e-13 ***
## ACRT_Nperi:t1rtqTotal 1 4.03 4.03 0.4552 0.5005
## Residuals 243 2151.69 8.85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_RT_mod_Nperi)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------
## ACRT_Nperi | 4.38e-04 | [0.00, 1.00]
## t1rtqTotal | 0.20 | [0.13, 1.00]
## ACRT_Nperi:t1rtqTotal | 1.87e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_RT_mod_Nperi)## # R2 for Linear Regression
## R2: 0.201
## adj. R2: 0.192H2-T1 Distress
#Intrusions distress
model_intrusions_distress_dp_mod_neg <- lm(t1_thoughts_distress_level ~ ACdP_neg*t1rtqTotal, data = final_data)
summary(model_intrusions_distress_dp_mod_neg) #Intrusions distress mod Dprime ACdP_neg##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_neg * t1rtqTotal,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.79530 -0.48686 -0.03673 0.48087 1.88114
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.861673 0.137153 6.283 1.54e-09 ***
## ACdP_neg -0.025280 0.068594 -0.369 0.713
## t1rtqTotal 0.044502 0.004902 9.079 < 2e-16 ***
## ACdP_neg:t1rtqTotal 0.001519 0.002326 0.653 0.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7451 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3021, Adjusted R-squared: 0.2934
## F-statistic: 34.91 on 3 and 242 DF, p-value: < 2.2e-16standardized_model_intrusions_distress_dp_mod_neg <- standardize_parameters(model_intrusions_distress_dp_mod_neg)
print(standardized_model_intrusions_distress_dp_mod_neg )## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | -1.91e-03 | [-0.11, 0.10]
## ACdP neg | 0.03 | [-0.08, 0.14]
## t1rtqTotal | 0.54 | [ 0.43, 0.65]
## ACdP neg × t1rtqTotal | 0.03 | [-0.07, 0.13]anova(model_intrusions_distress_dp_mod_neg)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_neg 1 0.803 0.803 1.4463 0.2303
## t1rtqTotal 1 57.112 57.112 102.8698 <2e-16 ***
## ACdP_neg:t1rtqTotal 1 0.237 0.237 0.4263 0.5144
## Residuals 242 134.356 0.555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_mod_neg)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------
## ACdP_neg | 5.94e-03 | [0.00, 1.00]
## t1rtqTotal | 0.30 | [0.22, 1.00]
## ACdP_neg:t1rtqTotal | 1.76e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_mod_neg)## # R2 for Linear Regression
## R2: 0.302
## adj. R2: 0.293model_intrusions_distress_dp_mod_peri <- lm(t1_thoughts_distress_level ~ ACdP_peri*t1rtqTotal, data = final_data)
summary(model_intrusions_distress_dp_mod_peri) #Intrusions distress mod Dprime ACdP_peri##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_peri * t1rtqTotal,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7793 -0.4852 -0.0435 0.4813 1.8592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.857424 0.131666 6.512 4.25e-10 ***
## ACdP_peri -0.024595 0.060155 -0.409 0.683
## t1rtqTotal 0.044338 0.004678 9.478 < 2e-16 ***
## ACdP_peri:t1rtqTotal 0.002324 0.002184 1.064 0.288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7418 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3082, Adjusted R-squared: 0.2996
## F-statistic: 35.93 on 3 and 242 DF, p-value: < 2.2e-16standardized_model_intrusions_distress_dp_mod_peri <- standardize_parameters(model_intrusions_distress_dp_mod_peri)
print(standardized_model_intrusions_distress_dp_mod_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------
## (Intercept) | -1.40e-03 | [-0.11, 0.10]
## ACdP peri | 0.08 | [-0.03, 0.18]
## t1rtqTotal | 0.54 | [ 0.44, 0.65]
## ACdP peri × t1rtqTotal | 0.05 | [-0.04, 0.15]anova(model_intrusions_distress_dp_mod_peri)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_peri 1 1.431 1.431 2.6009 0.1081
## t1rtqTotal 1 57.272 57.272 104.0673 <2e-16 ***
## ACdP_peri:t1rtqTotal 1 0.624 0.624 1.1331 0.2882
## Residuals 242 133.181 0.550
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_mod_peri)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------
## ACdP_peri | 0.01 | [0.00, 1.00]
## t1rtqTotal | 0.30 | [0.22, 1.00]
## ACdP_peri:t1rtqTotal | 4.66e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_mod_peri)## # R2 for Linear Regression
## R2: 0.308
## adj. R2: 0.300model_intrusions_distress_dp_mod_Nperi <- lm(t1_thoughts_distress_level ~ ACdP_Nperi*t1rtqTotal, data = final_data)
summary(model_intrusions_distress_dp_mod_Nperi) #Intrusions distress mod Dprime ACdP_Nperi##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_Nperi * t1rtqTotal,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80558 -0.47995 -0.03517 0.48786 1.86668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8283827 0.1285576 6.444 6.25e-10 ***
## ACdP_Nperi -0.0005007 0.0673672 -0.007 0.994
## t1rtqTotal 0.0462513 0.0045421 10.183 < 2e-16 ***
## ACdP_Nperi:t1rtqTotal 0.0009231 0.0024549 0.376 0.707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.745 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3022, Adjusted R-squared: 0.2936
## F-statistic: 34.94 on 3 and 242 DF, p-value: < 2.2e-16standardized_model_intrusions_distress_dp_mod_Nperi <- standardize_parameters(model_intrusions_distress_dp_mod_Nperi)
print(standardized_model_intrusions_distress_dp_mod_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------
## (Intercept) | 6.08e-04 | [-0.10, 0.11]
## ACdP Nperi | 0.05 | [-0.06, 0.15]
## t1rtqTotal | 0.55 | [ 0.44, 0.66]
## ACdP Nperi × t1rtqTotal | 0.02 | [-0.08, 0.12]anova(model_intrusions_distress_dp_mod_Nperi)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_Nperi 1 0.143 0.143 0.2579 0.6120
## t1rtqTotal 1 57.958 57.958 104.4148 <2e-16 ***
## ACdP_Nperi:t1rtqTotal 1 0.078 0.078 0.1414 0.7072
## Residuals 242 134.328 0.555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_mod_Nperi)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------
## ACdP_Nperi | 1.06e-03 | [0.00, 1.00]
## t1rtqTotal | 0.30 | [0.23, 1.00]
## ACdP_Nperi:t1rtqTotal | 5.84e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_mod_Nperi)## # R2 for Linear Regression
## R2: 0.302
## adj. R2: 0.294model_intrusions_distress_RT_mod_neg <- lm(t1_thoughts_distress_level ~ ACRT_neg*t1rtqTotal, data = final_data)
summary(model_intrusions_distress_RT_mod_neg) #Intrusions distress mod Reaction time ACRT_neg##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_neg * t1rtqTotal,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8068 -0.4710 -0.0375 0.4705 1.8811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.355e-01 1.282e-01 6.518 4.1e-10 ***
## ACRT_neg -2.303e-04 2.269e-03 -0.102 0.919
## t1rtqTotal 4.589e-02 4.512e-03 10.171 < 2e-16 ***
## ACRT_neg:t1rtqTotal 2.970e-06 8.172e-05 0.036 0.971
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7463 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2999, Adjusted R-squared: 0.2912
## F-statistic: 34.56 on 3 and 242 DF, p-value: < 2.2e-16standardized_model_intrusions_distress_RT_mod_neg <- standardize_parameters(model_intrusions_distress_RT_mod_neg)
print(standardized_model_intrusions_distress_RT_mod_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 3.91e-05 | [-0.11, 0.11]
## ACRT neg | -0.01 | [-0.12, 0.10]
## t1rtqTotal | 0.55 | [ 0.44, 0.65]
## ACRT neg × t1rtqTotal | 2.08e-03 | [-0.11, 0.12]anova(model_intrusions_distress_RT_mod_neg)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_neg 1 0.081 0.081 0.1451 0.7036
## t1rtqTotal 1 57.656 57.656 103.5306 <2e-16 ***
## ACRT_neg:t1rtqTotal 1 0.001 0.001 0.0013 0.9710
## Residuals 242 134.770 0.557
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_RT_mod_neg)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------
## ACRT_neg | 5.99e-04 | [0.00, 1.00]
## t1rtqTotal | 0.30 | [0.22, 1.00]
## ACRT_neg:t1rtqTotal | 5.46e-06 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_RT_mod_neg)## # R2 for Linear Regression
## R2: 0.300
## adj. R2: 0.291model_intrusions_distress_RT_mod_peri <- lm(t1_thoughts_distress_level ~ ACRT_peri*t1rtqTotal, data = final_data)
summary(model_intrusions_distress_RT_mod_peri) #Intrusions distress mod Reaction time ACRT_peri##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_peri * t1rtqTotal,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.77449 -0.49776 -0.00132 0.51894 1.94514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.173e-01 1.292e-01 6.327 1.2e-09 ***
## ACRT_peri 1.244e-03 2.156e-03 0.577 0.564
## t1rtqTotal 4.695e-02 4.586e-03 10.238 < 2e-16 ***
## ACRT_peri:t1rtqTotal -7.788e-05 8.018e-05 -0.971 0.332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7438 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3046, Adjusted R-squared: 0.296
## F-statistic: 35.34 on 3 and 242 DF, p-value: < 2.2e-16standardized_model_intrusions_distress_RT_mod_peri <- standardize_parameters(model_intrusions_distress_RT_mod_peri)
print(standardized_model_intrusions_distress_RT_mod_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------
## (Intercept) | 2.52e-03 | [-0.10, 0.11]
## ACRT peri | -0.05 | [-0.16, 0.05]
## t1rtqTotal | 0.55 | [ 0.45, 0.66]
## ACRT peri × t1rtqTotal | -0.05 | [-0.16, 0.06]anova(model_intrusions_distress_RT_mod_peri)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_peri 1 0.079 0.079 0.1421 0.7065
## t1rtqTotal 1 58.041 58.041 104.9260 <2e-16 ***
## ACRT_peri:t1rtqTotal 1 0.522 0.522 0.9434 0.3324
## Residuals 242 133.866 0.553
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_RT_mod_peri)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------
## ACRT_peri | 5.87e-04 | [0.00, 1.00]
## t1rtqTotal | 0.30 | [0.23, 1.00]
## ACRT_peri:t1rtqTotal | 3.88e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_RT_mod_peri)## # R2 for Linear Regression
## R2: 0.305
## adj. R2: 0.296model_intrusions_distress_RT_mod_Nperi <- lm(t1_thoughts_distress_level ~ ACRT_Nperi*t1rtqTotal, data = final_data)
summary(model_intrusions_distress_RT_mod_Nperi) #Intrusions distress mod Reaction time ACRT_Nperi##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_Nperi * t1rtqTotal,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.79694 -0.47655 -0.02866 0.50165 1.91914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.195e-01 1.287e-01 6.367 9.58e-10 ***
## ACRT_Nperi 1.227e-03 2.146e-03 0.572 0.568
## t1rtqTotal 4.673e-02 4.565e-03 10.236 < 2e-16 ***
## ACRT_Nperi:t1rtqTotal -6.740e-05 7.673e-05 -0.878 0.381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7445 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3033, Adjusted R-squared: 0.2946
## F-statistic: 35.11 on 3 and 242 DF, p-value: < 2.2e-16standardized_model_intrusions_distress_RT_mod_Nperi <- standardize_parameters(model_intrusions_distress_RT_mod_Nperi)
print(standardized_model_intrusions_distress_RT_mod_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------
## (Intercept) | 3.06e-03 | [-0.10, 0.11]
## ACRT Nperi | -0.04 | [-0.14, 0.07]
## t1rtqTotal | 0.55 | [ 0.45, 0.66]
## ACRT Nperi × t1rtqTotal | -0.05 | [-0.15, 0.06]anova(model_intrusions_distress_RT_mod_Nperi)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_Nperi 1 0.000 0.000 0.0001 0.9937
## t1rtqTotal 1 57.952 57.952 104.5587 <2e-16 ***
## ACRT_Nperi:t1rtqTotal 1 0.428 0.428 0.7716 0.3806
## Residuals 242 134.129 0.554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_RT_mod_Nperi)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------
## ACRT_Nperi | 2.57e-07 | [0.00, 1.00]
## t1rtqTotal | 0.30 | [0.23, 1.00]
## ACRT_Nperi:t1rtqTotal | 3.18e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_RT_mod_Nperi)## # R2 for Linear Regression
## R2: 0.303
## adj. R2: 0.295#run simple slopes on intrusions ACdpNperi (Perinatal Negative over Negative Generic)
probe_interaction(model_intrusions_dp_mod_Nperi, pred = ACdP_Nperi, modx = t1rtqTotal, johnson_neyman = TRUE, jnplot = TRUE, interval = TRUE, x.label = "Affective control Nperi",
y.label = "Intrusions", main.title = "Moderation of Intrusions",
color.class = "Paired")## The color.class argument is deprecated. Please use 'colors' instead.## JOHNSON-NEYMAN INTERVAL
##
## When t1rtqTotal is OUTSIDE the interval [17.93, 58.70], the slope of
## ACdP_Nperi is p < .05.
##
## Note: The range of observed values of t1rtqTotal is [10.00, 50.00]
## SIMPLE SLOPES ANALYSIS
##
## Slope of ACdP_Nperi when t1rtqTotal = 15.76792 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.30 0.14 2.14 0.03
##
## Slope of ACdP_Nperi when t1rtqTotal = 26.32794 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.06 0.11 0.56 0.57
##
## Slope of ACdP_Nperi when t1rtqTotal = 36.88795 (+ 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.18 0.16 -1.14 0.25
#Calculate standardized betas for simple slopes
# 1 below SD
# Step 1: Calculate SD
N <- 247
SE <- 0.14
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- 0.30
standardised_below <- SD * beta
# Print the result
print(standardised_below)## [1] 0.6600818# Average
# Step 1: Calculate SD
N <- 247
SE <- 0.11
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- 0.06
standardised_av <- SD * beta
# Print the result
print(standardised_av)## [1] 0.1037271# Above
# Step 1: Calculate SD
N <- 247
SE <- 0.16
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- -0.18
standardised_above <- SD * beta
# Print the result
print(standardised_above)## [1] -0.4526275#relevant graph
# Create interaction plot with simple slopes and data points
interaction_plot_intrusions_ACdP_Nperi <- interact_plot(model_intrusions_dp_mod_Nperi,
pred = ACdP_Nperi,
modx = t1rtqTotal,
plot.points = TRUE,
point.alpha = 0.5,
legend.main = "Levels of Repetitive Negative Thinking") +
theme_minimal() +
labs(x = "Affective Control: Negative Perinatal over Generic Negative (D-Prime)",
y = "Intrusions") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line()) # Keep axis lines
# Show the plot
print(interaction_plot_intrusions_ACdP_Nperi)
#1-Month follow up
# Converting time to a factor
final_dataT2_long$time <- as.factor(final_dataT2_long$time)###H2 -T2 Intrusions
#T2 moderation RNT (1 Month - follow up)
model_intrusions_dp_mod_neg_t2 <- lmer(intrusions ~ (ACdP_neg*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_mod_neg_t2) #Intrusions mod Dprime ACdP_neg T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACdP_neg * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2048.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3075 -0.5531 -0.0785 0.4520 3.4241
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 4.243 2.060
## Residual 4.731 2.175
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.703961 0.562993 403.993294 3.027 0.00263 **
## ACdP_neg -0.147361 0.283769 396.240785 -0.519 0.60384
## rtq 0.116919 0.020146 400.636342 5.804 1.32e-08 ***
## time2 0.457262 0.671287 222.804360 0.681 0.49647
## ACdP_neg:rtq 0.007485 0.009453 402.034164 0.792 0.42893
## ACdP_neg:time2 -0.324222 0.322443 212.857097 -1.006 0.31579
## rtq:time2 -0.030218 0.025283 227.813575 -1.195 0.23325
## ACdP_neg:rtq:time2 0.009915 0.011093 216.410493 0.894 0.37242
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_n rtq time2 ACdP_: ACP_:2 rtq:t2
## ACdP_neg -0.369
## rtq -0.920 0.345
## time2 -0.526 0.181 0.498
## ACdP_ng:rtq 0.368 -0.913 -0.400 -0.185
## ACdP_ng:tm2 0.197 -0.515 -0.188 -0.400 0.480
## rtq:time2 0.461 -0.154 -0.501 -0.941 0.180 0.389
## ACdP_ng:r:2 -0.185 0.458 0.201 0.423 -0.502 -0.928 -0.457standardized_model_intrusions_dp_mod_neg_t2 <- standardize_parameters(model_intrusions_dp_mod_neg_t2)
print(standardized_model_intrusions_dp_mod_neg_t2 )## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------
## (Intercept) | 0.05 | [-0.08, 0.17]
## ACdP neg | 0.02 | [-0.10, 0.15]
## rtq | 0.38 | [ 0.26, 0.49]
## time [2] | -0.11 | [-0.24, 0.02]
## ACdP neg × rtq | 0.04 | [-0.06, 0.15]
## ACdP neg × time [2] | -0.04 | [-0.17, 0.09]
## rtq × time [2] | -0.07 | [-0.21, 0.07]
## (ACdP neg × rtq) × time [2] | 0.06 | [-0.07, 0.18]anova(model_intrusions_dp_mod_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACdP_neg 7.630 7.630 1 294.67 1.6129 0.2051
## rtq 157.818 157.818 1 367.45 33.3601 1.633e-08 ***
## time 2.195 2.195 1 222.80 0.4640 0.4965
## ACdP_neg:rtq 10.858 10.858 1 317.45 2.2952 0.1308
## ACdP_neg:time 4.783 4.783 1 212.86 1.0111 0.3158
## rtq:time 6.758 6.758 1 227.81 1.4285 0.2333
## ACdP_neg:rtq:time 3.779 3.779 1 216.41 0.7989 0.3724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_dp_mod_neg_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -------------------------------------------------
## ACdP_neg | 5.44e-03 | [0.00, 1.00]
## rtq | 0.08 | [0.04, 1.00]
## time | 2.08e-03 | [0.00, 1.00]
## ACdP_neg:rtq | 7.18e-03 | [0.00, 1.00]
## ACdP_neg:time | 4.73e-03 | [0.00, 1.00]
## rtq:time | 6.23e-03 | [0.00, 1.00]
## ACdP_neg:rtq:time | 3.68e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_mod_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.549
## Marginal R2: 0.144model_intrusions_dp_mod_peri_t2 <- lmer(intrusions ~ (ACdP_peri*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_mod_peri_t2 ) #Intrusions mod Dprime ACdP_peri T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACdP_peri * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2048.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4499 -0.5426 -0.0906 0.4542 3.3835
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 4.276 2.068
## Residual 4.712 2.171
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.366229 0.541422 403.998927 2.523 0.012 *
## ACdP_peri 0.376039 0.242870 403.797955 1.548 0.122
## rtq 0.128909 0.019391 400.550240 6.648 9.75e-11 ***
## time2 0.384747 0.643059 221.743343 0.598 0.550
## ACdP_peri:rtq -0.009627 0.008994 400.120916 -1.070 0.285
## ACdP_peri:time2 -0.373284 0.289436 221.668651 -1.290 0.198
## rtq:time2 -0.025800 0.024034 226.122802 -1.073 0.284
## ACdP_peri:rtq:time2 0.010675 0.010958 225.979309 0.974 0.331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_p rtq time2 ACdP_: ACP_:2 rtq:t2
## ACdP_peri -0.265
## rtq -0.915 0.259
## time2 -0.526 0.133 0.496
## ACdP_pr:rtq 0.255 -0.890 -0.308 -0.133
## ACdP_pr:tm2 0.130 -0.517 -0.130 -0.321 0.478
## rtq:time2 0.467 -0.129 -0.511 -0.937 0.154 0.322
## ACdP_pr:r:2 -0.126 0.474 0.153 0.324 -0.532 -0.920 -0.369standardized_model_intrusions_dp_mod_peri_t2 <- standardize_parameters(model_intrusions_dp_mod_peri_t2)
print(standardized_model_intrusions_dp_mod_peri_t2 )## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 0.05 | [-0.08, 0.18]
## ACdP peri | 0.08 | [-0.05, 0.20]
## rtq | 0.38 | [ 0.27, 0.50]
## time [2] | -0.10 | [-0.23, 0.03]
## ACdP peri × rtq | -0.06 | [-0.16, 0.05]
## ACdP peri × time [2] | -0.06 | [-0.19, 0.07]
## rtq × time [2] | -0.06 | [-0.20, 0.08]
## (ACdP peri × rtq) × time [2] | 0.06 | [-0.06, 0.19]anova(model_intrusions_dp_mod_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACdP_peri 3.879 3.879 1 335.38 0.8232 0.3649
## rtq 224.552 224.552 1 368.17 47.6524 2.237e-11 ***
## time 1.687 1.687 1 221.74 0.3580 0.5502
## ACdP_peri:rtq 1.482 1.482 1 369.73 0.3144 0.5753
## ACdP_peri:time 7.838 7.838 1 221.67 1.6633 0.1985
## rtq:time 5.430 5.430 1 226.12 1.1523 0.2842
## ACdP_peri:rtq:time 4.472 4.472 1 225.98 0.9491 0.3310
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_dp_mod_peri_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------
## ACdP_peri | 2.45e-03 | [0.00, 1.00]
## rtq | 0.11 | [0.07, 1.00]
## time | 1.61e-03 | [0.00, 1.00]
## ACdP_peri:rtq | 8.50e-04 | [0.00, 1.00]
## ACdP_peri:time | 7.45e-03 | [0.00, 1.00]
## rtq:time | 5.07e-03 | [0.00, 1.00]
## ACdP_peri:rtq:time | 4.18e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_mod_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.549
## Marginal R2: 0.141model_intrusions_dp_mod_Nperi_t2 <- lmer(intrusions ~ (ACdP_Nperi*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_mod_Nperi_t2) #Intrusions mod Dprime ACdP_Nperi T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACdP_Nperi * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2044.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3032 -0.5523 -0.0756 0.4481 3.3431
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 4.096 2.024
## Residual 4.752 2.180
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.656282 0.521923 403.987487 3.173 0.00162 **
## ACdP_Nperi 0.625119 0.267982 403.975743 2.333 0.02015 *
## rtq 0.120069 0.018498 401.295944 6.491 2.52e-10 ***
## time2 0.141196 0.614157 221.644946 0.230 0.81838
## ACdP_Nperi:rtq -0.021358 0.009901 399.819294 -2.157 0.03158 *
## ACdP_Nperi:time2 -0.111216 0.329743 224.629266 -0.337 0.73622
## rtq:time2 -0.019128 0.022668 226.044578 -0.844 0.39964
## ACdP_Nperi:rtq:time2 0.001745 0.012632 229.467040 0.138 0.89022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_N rtq time2 ACdP_N: ACP_N:2 rtq:t2
## ACdP_Nperi 0.087
## rtq -0.918 -0.096
## time2 -0.546 -0.063 0.517
## ACdP_Npr:rt -0.098 -0.899 0.115 0.071
## ACdP_Npr:t2 -0.058 -0.504 0.065 0.104 0.467
## rtq:time2 0.488 0.061 -0.532 -0.936 -0.072 -0.127
## ACdP_Npr::2 0.057 0.450 -0.067 -0.127 -0.501 -0.929 0.158standardized_model_intrusions_dp_mod_Nperi_t2 <- standardize_parameters(model_intrusions_dp_mod_Nperi_t2)
print(standardized_model_intrusions_dp_mod_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------
## (Intercept) | 0.05 | [-0.08, 0.17]
## ACdP Nperi | 0.04 | [-0.08, 0.17]
## rtq | 0.38 | [ 0.26, 0.49]
## time [2] | -0.10 | [-0.23, 0.02]
## ACdP Nperi × rtq | -0.12 | [-0.22, -0.01]
## ACdP Nperi × time [2] | -0.04 | [-0.17, 0.09]
## rtq × time [2] | -0.06 | [-0.20, 0.08]
## (ACdP Nperi × rtq) × time [2] | 9.57e-03 | [-0.13, 0.15]anova(model_intrusions_dp_mod_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACdP_Nperi 28.291 28.291 1 338.96 5.9539 0.01520 *
## rtq 234.258 234.258 1 363.38 49.3005 1.085e-11 ***
## time 0.251 0.251 1 221.64 0.0529 0.81838
## ACdP_Nperi:rtq 26.486 26.486 1 369.77 5.5741 0.01875 *
## ACdP_Nperi:time 0.541 0.541 1 224.63 0.1138 0.73622
## rtq:time 3.384 3.384 1 226.04 0.7121 0.39964
## ACdP_Nperi:rtq:time 0.091 0.091 1 229.47 0.0191 0.89022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_dp_mod_Nperi_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------
## ACdP_Nperi | 0.02 | [0.00, 1.00]
## rtq | 0.12 | [0.07, 1.00]
## time | 2.38e-04 | [0.00, 1.00]
## ACdP_Nperi:rtq | 0.01 | [0.00, 1.00]
## ACdP_Nperi:time | 5.06e-04 | [0.00, 1.00]
## rtq:time | 3.14e-03 | [0.00, 1.00]
## ACdP_Nperi:rtq:time | 8.32e-05 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_mod_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.545
## Marginal R2: 0.154model_intrusions_RT_mod_neg_t2 <- lmer(intrusions ~ (ACRT_neg*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_RT_mod_neg_t2) #Intrusions mod Reaction time ACRT_neg T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACRT_neg * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2070
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2994 -0.5637 -0.0545 0.4318 3.3117
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 4.085 2.021
## Residual 4.715 2.171
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.624e+00 5.185e-01 4.039e+02 3.133 0.00186 **
## ACRT_neg -7.894e-03 8.994e-03 4.036e+02 -0.878 0.38065
## rtq 1.225e-01 1.832e-02 4.019e+02 6.684 7.78e-11 ***
## time2 1.405e-01 6.084e-01 2.211e+02 0.231 0.81762
## ACRT_neg:rtq 1.420e-05 3.249e-04 4.032e+02 0.044 0.96515
## ACRT_neg:time2 -1.315e-02 1.030e-02 2.191e+02 -1.277 0.20294
## rtq:time2 -1.726e-02 2.234e-02 2.255e+02 -0.773 0.44060
## ACRT_neg:rtq:time2 5.961e-04 3.958e-04 2.237e+02 1.506 0.13344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_n rtq time2 ACRT_n: ACRT_:2 rtq:t2
## ACRT_neg -0.038
## rtq -0.917 0.020
## time2 -0.544 0.010 0.514
## ACRT_ng:rtq 0.021 -0.925 -0.011 -0.001
## ACRT_ng:tm2 0.010 -0.563 0.000 -0.002 0.536
## rtq:time2 0.487 -0.005 -0.532 -0.935 0.000 -0.025
## ACRT_ng:r:2 -0.003 0.481 -0.002 -0.026 -0.520 -0.937 0.050
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_RT_mod_neg_t2 <- standardize_parameters(model_intrusions_RT_mod_neg_t2)
print(standardized_model_intrusions_RT_mod_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 0.05 | [-0.08, 0.17]
## ACRT neg | -0.14 | [-0.26, -0.01]
## rtq | 0.38 | [ 0.27, 0.49]
## time [2] | -0.09 | [-0.22, 0.04]
## ACRT neg × rtq | 2.67e-03 | [-0.12, 0.12]
## ACRT neg × time [2] | 0.04 | [-0.09, 0.17]
## rtq × time [2] | -0.05 | [-0.18, 0.09]
## (ACRT neg × rtq) × time [2] | 0.11 | [-0.03, 0.26]anova(model_intrusions_RT_mod_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACRT_neg 17.861 17.861 1 327.41 3.7884 0.05246 .
## rtq 251.535 251.535 1 360.96 53.3517 1.8e-12 ***
## time 0.251 0.251 1 221.14 0.0533 0.81762
## ACRT_neg:rtq 5.907 5.907 1 352.65 1.2530 0.26374
## ACRT_neg:time 7.689 7.689 1 219.14 1.6308 0.20294
## rtq:time 2.814 2.814 1 225.51 0.5968 0.44060
## ACRT_neg:rtq:time 10.695 10.695 1 223.71 2.2685 0.13344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_RT_mod_neg_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -------------------------------------------------
## ACRT_neg | 0.01 | [0.00, 1.00]
## rtq | 0.13 | [0.08, 1.00]
## time | 2.41e-04 | [0.00, 1.00]
## ACRT_neg:rtq | 3.54e-03 | [0.00, 1.00]
## ACRT_neg:time | 7.39e-03 | [0.00, 1.00]
## rtq:time | 2.64e-03 | [0.00, 1.00]
## ACRT_neg:rtq:time | 0.01 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_RT_mod_neg_t2 )## # R2 for Mixed Models
##
## Conditional R2: 0.550
## Marginal R2: 0.160model_intrusions_RT_mod_peri_t2 <- lmer(intrusions ~ (ACRT_peri*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_RT_mod_peri_t2) #Intrusions mod Reaction time ACRT_peri T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACRT_peri * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2073.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4352 -0.5421 -0.0843 0.4504 3.2727
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 4.318 2.078
## Residual 4.659 2.158
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.628e+00 5.269e-01 4.038e+02 3.089 0.00215 **
## ACRT_peri -8.703e-03 8.712e-03 4.026e+02 -0.999 0.31843
## rtq 1.233e-01 1.875e-02 4.024e+02 6.572 1.54e-10 ***
## time2 1.920e-01 6.089e-01 2.193e+02 0.315 0.75279
## ACRT_peri:rtq 1.175e-04 3.260e-04 4.037e+02 0.360 0.71869
## ACRT_peri:time2 -1.075e-03 9.809e-03 2.165e+02 -0.110 0.91282
## rtq:time2 -2.255e-02 2.244e-02 2.229e+02 -1.005 0.31603
## ACRT_peri:rtq:time2 3.101e-04 3.845e-04 2.202e+02 0.807 0.42075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_p rtq time2 ACRT_p: ACRT_:2 rtq:t2
## ACRT_peri -0.152
## rtq -0.916 0.161
## time2 -0.538 0.068 0.508
## ACRT_pr:rtq 0.153 -0.914 -0.192 -0.070
## ACRT_pr:tm2 0.073 -0.552 -0.080 -0.133 0.520
## rtq:time2 0.484 -0.073 -0.528 -0.935 0.089 0.138
## ACRT_pr:r:2 -0.074 0.481 0.094 0.129 -0.526 -0.929 -0.156
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_RT_mod_peri_t2 <- standardize_parameters(model_intrusions_RT_mod_peri_t2)
print(standardized_model_intrusions_RT_mod_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 0.05 | [-0.08, 0.17]
## ACRT peri | -0.10 | [-0.23, 0.02]
## rtq | 0.39 | [ 0.27, 0.50]
## time [2] | -0.10 | [-0.22, 0.03]
## ACRT peri × rtq | 0.02 | [-0.10, 0.14]
## ACRT peri × time [2] | 0.12 | [-0.01, 0.25]
## rtq × time [2] | -0.06 | [-0.20, 0.07]
## (ACRT peri × rtq) × time [2] | 0.06 | [-0.08, 0.20]anova(model_intrusions_RT_mod_peri_t2 )## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACRT_peri 7.531 7.531 1 321.47 1.6164 0.2045
## rtq 228.747 228.747 1 359.96 49.0997 1.204e-11 ***
## time 0.463 0.463 1 219.33 0.0994 0.7528
## ACRT_peri:rtq 4.480 4.480 1 348.51 0.9616 0.3275
## ACRT_peri:time 0.056 0.056 1 216.51 0.0120 0.9128
## rtq:time 4.705 4.705 1 222.91 1.0099 0.3160
## ACRT_peri:rtq:time 3.031 3.031 1 220.22 0.6507 0.4207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_RT_mod_peri_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------
## ACRT_peri | 5.00e-03 | [0.00, 1.00]
## rtq | 0.12 | [0.07, 1.00]
## time | 4.53e-04 | [0.00, 1.00]
## ACRT_peri:rtq | 2.75e-03 | [0.00, 1.00]
## ACRT_peri:time | 5.55e-05 | [0.00, 1.00]
## rtq:time | 4.51e-03 | [0.00, 1.00]
## ACRT_peri:rtq:time | 2.95e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_RT_mod_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.555
## Marginal R2: 0.143model_intrusions_RT_mod_Nperi_t2 <- lmer(intrusions ~ (ACRT_Nperi*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_RT_mod_Nperi_t2) #Intrusions mod reaction time ACRT_Nperi T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACRT_Nperi * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2074.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2774 -0.5503 -0.0540 0.4440 3.2891
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 4.267 2.066
## Residual 4.688 2.165
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.603e+00 5.247e-01 4.040e+02 3.054 0.0024 **
## ACRT_Nperi -1.838e-03 8.698e-03 4.040e+02 -0.211 0.8327
## rtq 1.215e-01 1.869e-02 4.011e+02 6.501 2.37e-10 ***
## time2 1.237e-01 6.125e-01 2.206e+02 0.202 0.8401
## ACRT_Nperi:rtq 1.502e-04 3.081e-04 4.013e+02 0.488 0.6261
## ACRT_Nperi:time2 1.285e-02 9.958e-03 2.198e+02 1.291 0.1981
## rtq:time2 -1.809e-02 2.271e-02 2.247e+02 -0.796 0.4267
## ACRT_Nperi:rtq:time2 -3.125e-04 3.732e-04 2.243e+02 -0.837 0.4032
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_Np rtq time2 ACRT_Np: ACRT_N:2 rtq:t2
## ACRT_Nperi -0.108
## rtq -0.916 0.125
## time2 -0.540 0.057 0.511
## ACRT_Npr:rt 0.127 -0.914 -0.166 -0.070
## ACRT_Npr:t2 0.061 -0.559 -0.073 -0.124 0.530
## rtq:time2 0.482 -0.063 -0.526 -0.936 0.084 0.148
## ACRT_Npr::2 -0.067 0.483 0.087 0.149 -0.528 -0.931 -0.190
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_RT_mod_Nperi_t2<- standardize_parameters(model_intrusions_RT_mod_Nperi_t2)
print(standardized_model_intrusions_RT_mod_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------
## (Intercept) | 0.05 | [-0.08, 0.17]
## ACRT Nperi | 0.04 | [-0.09, 0.16]
## rtq | 0.38 | [ 0.27, 0.49]
## time [2] | -0.09 | [-0.22, 0.03]
## ACRT Nperi × rtq | 0.03 | [-0.08, 0.14]
## ACRT Nperi × time [2] | 0.09 | [-0.04, 0.22]
## rtq × time [2] | -0.06 | [-0.20, 0.07]
## (ACRT Nperi × rtq) × time [2] | -0.06 | [-0.19, 0.08]anova(model_intrusions_RT_mod_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACRT_Nperi 1.898 1.898 1 335.98 0.4049 0.5250
## rtq 232.645 232.645 1 365.79 49.6252 9.291e-12 ***
## time 0.191 0.191 1 220.55 0.0408 0.8401
## ACRT_Nperi:rtq 0.002 0.002 1 364.88 0.0005 0.9817
## ACRT_Nperi:time 7.811 7.811 1 219.79 1.6661 0.1981
## rtq:time 2.973 2.973 1 224.69 0.6341 0.4267
## ACRT_Nperi:rtq:time 3.288 3.288 1 224.34 0.7013 0.4032
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_RT_mod_Nperi_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------
## ACRT_Nperi | 1.20e-03 | [0.00, 1.00]
## rtq | 0.12 | [0.07, 1.00]
## time | 1.85e-04 | [0.00, 1.00]
## ACRT_Nperi:rtq | 1.45e-06 | [0.00, 1.00]
## ACRT_Nperi:time | 7.52e-03 | [0.00, 1.00]
## rtq:time | 2.81e-03 | [0.00, 1.00]
## ACRT_Nperi:rtq:time | 3.12e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_RT_mod_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.553
## Marginal R2: 0.146#Run simple slopes for Nperi x RNT interaction at 1-Month
sim_slopes(model_intrusions_dp_mod_Nperi_t2, pred = ACdP_Nperi, modx = rtq)## JOHNSON-NEYMAN INTERVAL
##
## When rtq is OUTSIDE the interval [16.14, 90.70], the slope of ACdP_Nperi is
## p < .05.
##
## Note: The range of observed values of rtq is [10.00, 50.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of ACdP_Nperi when rtq = 15.35146 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.30 0.15 2.02 0.04
##
## Slope of ACdP_Nperi when rtq = 25.52427 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.08 0.12 0.68 0.50
##
## Slope of ACdP_Nperi when rtq = 35.69708 (+ 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.14 0.16 -0.84 0.40# Create interaction plot with simple slopes and data points
interaction_plot_intrusions_ACdP_Nperi_1M <- interact_plot(model_intrusions_dp_mod_Nperi_t2,
pred = ACdP_Nperi,
modx = rtq,
plot.points = TRUE,
point.alpha = 0.5,
legend.main = "Levels of Repetitive Negative Thinking") +
theme_minimal() +
labs(x = "Affective Control: Perinatal over Generic Negative D-Prime",
y = "Intrusions 1-Month") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line()) # Keep axis lines
# Show the plot
print(interaction_plot_intrusions_ACdP_Nperi_1M)
###H2-T2 Distress
#Intrusion Distress - Dprime
model_intrusions_distress_dp_mod_neg_t2 <- lmer(intrusionsdistress ~ (ACdP_neg*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_mod_neg_t2) #Intrusions distress mod Dprime ACdP_neg T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACdP_neg * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 937.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25876 -0.55186 -0.05809 0.56694 2.45811
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.2208 0.4699
## Residual 0.3309 0.5753
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.277e-01 1.423e-01 4.040e+02 6.517 2.13e-10 ***
## ACdP_neg -5.217e-02 7.132e-02 3.968e+02 -0.731 0.465
## rtq 4.368e-02 5.111e-03 4.023e+02 8.546 2.68e-16 ***
## time2 -2.326e-01 1.765e-01 2.201e+02 -1.318 0.189
## ACdP_neg:rtq 2.065e-03 2.382e-03 4.016e+02 0.867 0.387
## ACdP_neg:time2 1.302e-02 8.499e-02 2.083e+02 0.153 0.878
## rtq:time2 7.722e-03 6.642e-03 2.258e+02 1.163 0.246
## ACdP_neg:rtq:time2 3.105e-04 2.922e-03 2.124e+02 0.106 0.915
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_n rtq time2 ACdP_: ACP_:2 rtq:t2
## ACdP_neg -0.370
## rtq -0.923 0.347
## time2 -0.552 0.194 0.521
## ACdP_ng:rtq 0.370 -0.915 -0.401 -0.197
## ACdP_ng:tm2 0.207 -0.545 -0.197 -0.400 0.506
## rtq:time2 0.487 -0.168 -0.527 -0.940 0.195 0.388
## ACdP_ng:r:2 -0.197 0.487 0.214 0.422 -0.532 -0.928 -0.456standardized_model_intrusions_distress_dp_mod_neg_t2 <- standardize_parameters(model_intrusions_distress_dp_mod_neg_t2)
print(standardized_model_intrusions_distress_dp_mod_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------
## (Intercept) | 9.65e-03 | [-0.10, 0.12]
## ACdP neg | 1.04e-03 | [-0.11, 0.11]
## rtq | 0.49 | [ 0.39, 0.59]
## time [2] | -0.02 | [-0.15, 0.10]
## ACdP neg × rtq | 0.04 | [-0.05, 0.13]
## ACdP neg × time [2] | 0.04 | [-0.08, 0.16]
## rtq × time [2] | 0.09 | [-0.04, 0.22]
## (ACdP neg × rtq) × time [2] | 6.15e-03 | [-0.11, 0.12]anova(model_intrusions_distress_dp_mod_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACdP_neg 0.192 0.192 1 275.31 0.5808 0.4466
## rtq 38.848 38.848 1 346.00 117.3958 <2e-16 ***
## time 0.575 0.575 1 220.14 1.7362 0.1890
## ACdP_neg:rtq 0.397 0.397 1 295.27 1.2002 0.2742
## ACdP_neg:time 0.008 0.008 1 208.32 0.0235 0.8784
## rtq:time 0.447 0.447 1 225.81 1.3514 0.2463
## ACdP_neg:rtq:time 0.004 0.004 1 212.37 0.0113 0.9155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_mod_neg_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -------------------------------------------------
## ACdP_neg | 2.11e-03 | [0.00, 1.00]
## rtq | 0.25 | [0.19, 1.00]
## time | 7.82e-03 | [0.00, 1.00]
## ACdP_neg:rtq | 4.05e-03 | [0.00, 1.00]
## ACdP_neg:time | 1.13e-04 | [0.00, 1.00]
## rtq:time | 5.95e-03 | [0.00, 1.00]
## ACdP_neg:rtq:time | 5.32e-05 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_mod_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.593
## Marginal R2: 0.321model_intrusions_distress_dp_mod_peri_t2 <- lmer(intrusionsdistress ~ (ACdP_peri*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_mod_peri_t2) #Intrusions distress mod Dprime ACdP_peri T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACdP_peri * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 933.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21069 -0.55292 -0.05819 0.57813 2.42472
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.2100 0.4582
## Residual 0.3323 0.5765
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.912248 0.136084 403.925383 6.704 6.86e-11 ***
## ACdP_peri -0.041525 0.060986 403.655630 -0.681 0.496
## rtq 0.043522 0.004895 402.606226 8.891 < 2e-16 ***
## time2 -0.199272 0.169607 222.461720 -1.175 0.241
## ACdP_peri:rtq 0.002987 0.002271 402.371409 1.315 0.189
## ACdP_peri:time2 -0.038722 0.076342 222.215764 -0.507 0.613
## rtq:time2 0.006841 0.006332 227.770624 1.080 0.281
## ACdP_peri:rtq:time2 0.001740 0.002887 227.769315 0.603 0.547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_p rtq time2 ACdP_: ACP_:2 rtq:t2
## ACdP_peri -0.267
## rtq -0.919 0.262
## time2 -0.558 0.144 0.524
## ACdP_pr:rtq 0.258 -0.895 -0.310 -0.143
## ACdP_pr:tm2 0.141 -0.550 -0.141 -0.319 0.506
## rtq:time2 0.498 -0.140 -0.542 -0.936 0.166 0.321
## ACdP_pr:r:2 -0.137 0.503 0.166 0.322 -0.561 -0.919 -0.368standardized_model_intrusions_distress_dp_mod_peri_t2 <- standardize_parameters(model_intrusions_distress_dp_mod_peri_t2)
print(standardized_model_intrusions_distress_dp_mod_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 9.47e-03 | [-0.10, 0.12]
## ACdP peri | 0.07 | [-0.04, 0.18]
## rtq | 0.50 | [ 0.40, 0.60]
## time [2] | -0.02 | [-0.14, 0.10]
## ACdP peri × rtq | 0.06 | [-0.03, 0.15]
## ACdP peri × time [2] | 0.01 | [-0.11, 0.13]
## rtq × time [2] | 0.09 | [-0.04, 0.21]
## (ACdP peri × rtq) × time [2] | 0.04 | [-0.08, 0.15]anova(model_intrusions_distress_dp_mod_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACdP_peri 0.471 0.471 1 313.94 1.4174 0.23473
## rtq 42.621 42.621 1 343.72 128.2487 < 2e-16 ***
## time 0.459 0.459 1 222.46 1.3804 0.24129
## ACdP_peri:rtq 1.388 1.388 1 345.39 4.1761 0.04176 *
## ACdP_peri:time 0.086 0.086 1 222.22 0.2573 0.61250
## rtq:time 0.388 0.388 1 227.77 1.1671 0.28113
## ACdP_peri:rtq:time 0.121 0.121 1 227.77 0.3631 0.54737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_mod_peri_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------
## ACdP_peri | 4.49e-03 | [0.00, 1.00]
## rtq | 0.27 | [0.21, 1.00]
## time | 6.17e-03 | [0.00, 1.00]
## ACdP_peri:rtq | 0.01 | [0.00, 1.00]
## ACdP_peri:time | 1.16e-03 | [0.00, 1.00]
## rtq:time | 5.10e-03 | [0.00, 1.00]
## ACdP_peri:rtq:time | 1.59e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_mod_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.592
## Marginal R2: 0.334model_intrusions_distress_dp_mod_Nperi_t2 <- lmer(intrusionsdistress ~ (ACdP_Nperi*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_mod_Nperi_t2) #Intrusions distress mod Dprime ACdP_Nperi T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACdP_Nperi * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 936.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2230 -0.5475 -0.0336 0.5592 2.4666
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.2098 0.4580
## Residual 0.3372 0.5807
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.866684 0.132384 403.904262 6.547 1.79e-10 ***
## ACdP_Nperi -0.005965 0.067969 403.898723 -0.088 0.930
## rtq 0.046237 0.004709 402.820965 9.818 < 2e-16 ***
## time2 -0.257147 0.162637 220.781698 -1.581 0.115
## ACdP_Nperi:rtq 0.001485 0.002523 401.943986 0.589 0.557
## ACdP_Nperi:time2 -0.053350 0.087264 223.887410 -0.611 0.542
## rtq:time2 0.009502 0.005997 226.057397 1.584 0.114
## ACdP_Nperi:rtq:time2 0.001624 0.003339 229.792758 0.486 0.627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_N rtq time2 ACdP_N: ACP_N:2 rtq:t2
## ACdP_Nperi 0.090
## rtq -0.921 -0.100
## time2 -0.574 -0.064 0.540
## ACdP_Npr:rt -0.102 -0.903 0.119 0.072
## ACdP_Npr:t2 -0.060 -0.534 0.067 0.103 0.494
## rtq:time2 0.514 0.064 -0.559 -0.935 -0.075 -0.125
## ACdP_Npr::2 0.061 0.478 -0.070 -0.125 -0.530 -0.928 0.156standardized_model_intrusions_distress_dp_mod_Nperi_t2 <- standardize_parameters(model_intrusions_distress_dp_mod_Nperi_t2)
print(standardized_model_intrusions_distress_dp_mod_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------
## (Intercept) | 0.01 | [-0.10, 0.12]
## ACdP Nperi | 0.06 | [-0.05, 0.17]
## rtq | 0.51 | [ 0.41, 0.61]
## time [2] | -0.02 | [-0.14, 0.11]
## ACdP Nperi × rtq | 0.03 | [-0.07, 0.13]
## ACdP Nperi × time [2] | -0.02 | [-0.15, 0.10]
## rtq × time [2] | 0.10 | [-0.03, 0.23]
## (ACdP Nperi × rtq) × time [2] | 0.03 | [-0.10, 0.16]anova(model_intrusions_distress_dp_mod_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACdP_Nperi 0.107 0.107 1 318.66 0.3176 0.5735
## rtq 56.967 56.967 1 340.58 168.9228 <2e-16 ***
## time 0.843 0.843 1 220.78 2.4999 0.1153
## ACdP_Nperi:rtq 0.379 0.379 1 347.77 1.1246 0.2897
## ACdP_Nperi:time 0.126 0.126 1 223.89 0.3738 0.5416
## rtq:time 0.847 0.847 1 226.06 2.5106 0.1145
## ACdP_Nperi:rtq:time 0.080 0.080 1 229.79 0.2367 0.6271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_mod_Nperi_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------
## ACdP_Nperi | 9.96e-04 | [0.00, 1.00]
## rtq | 0.33 | [0.27, 1.00]
## time | 0.01 | [0.00, 1.00]
## ACdP_Nperi:rtq | 3.22e-03 | [0.00, 1.00]
## ACdP_Nperi:time | 1.67e-03 | [0.00, 1.00]
## rtq:time | 0.01 | [0.00, 1.00]
## ACdP_Nperi:rtq:time | 1.03e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_mod_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.584
## Marginal R2: 0.325#Intrusion Distress - Reaction Time
model_intrusions_distress_RT_mod_neg_t2 <- lmer(intrusionsdistress ~ (ACRT_neg*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_distress_RT_mod_neg_t2) #Intrusions distress mod Reaction time ACRT_neg T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACRT_neg * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 963.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.27002 -0.55003 -0.04139 0.56740 2.40566
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.2122 0.4607
## Residual 0.3344 0.5783
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.812e-01 1.317e-01 4.038e+02 6.691 7.4e-11 ***
## ACRT_neg -1.431e-03 2.283e-03 4.034e+02 -0.627 0.531
## rtq 4.565e-02 4.670e-03 4.031e+02 9.775 < 2e-16 ***
## time2 -2.492e-01 1.611e-01 2.200e+02 -1.547 0.123
## ACRT_neg:rtq 5.141e-05 8.272e-05 4.038e+02 0.622 0.535
## ACRT_neg:time2 -2.837e-03 2.728e-03 2.177e+02 -1.040 0.300
## rtq:time2 9.328e-03 5.912e-03 2.252e+02 1.578 0.116
## ACRT_neg:rtq:time2 1.086e-04 1.048e-04 2.229e+02 1.037 0.301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_n rtq time2 ACRT_n: ACRT_:2 rtq:t2
## ACRT_neg -0.034
## rtq -0.920 0.016
## time2 -0.571 0.011 0.537
## ACRT_ng:rtq 0.017 -0.928 -0.006 -0.001
## ACRT_ng:tm2 0.011 -0.589 0.000 -0.004 0.558
## rtq:time2 0.513 -0.005 -0.558 -0.935 -0.001 -0.023
## ACRT_ng:r:2 -0.003 0.508 -0.003 -0.024 -0.548 -0.937 0.048
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_distress_RT_mod_neg_t2<- standardize_parameters(model_intrusions_distress_RT_mod_neg_t2)
print(standardized_model_intrusions_distress_RT_mod_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------
## (Intercept) | 0.01 | [-0.10, 0.12]
## ACRT neg | -7.77e-03 | [-0.12, 0.10]
## rtq | 0.50 | [ 0.40, 0.60]
## time [2] | -0.01 | [-0.13, 0.11]
## ACRT neg × rtq | 0.03 | [-0.07, 0.14]
## ACRT neg × time [2] | -4.20e-03 | [-0.13, 0.12]
## rtq × time [2] | 0.11 | [-0.02, 0.23]
## (ACRT neg × rtq) × time [2] | 0.07 | [-0.06, 0.21]anova(model_intrusions_distress_RT_mod_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACRT_neg 0.797 0.797 1 307.11 2.3839 0.1236
## rtq 55.861 55.861 1 338.93 167.0450 <2e-16 ***
## time 0.800 0.800 1 219.99 2.3923 0.1234
## ACRT_neg:rtq 0.772 0.772 1 330.20 2.3100 0.1295
## ACRT_neg:time 0.362 0.362 1 217.68 1.0814 0.2995
## rtq:time 0.833 0.833 1 225.18 2.4899 0.1160
## ACRT_neg:rtq:time 0.360 0.360 1 222.93 1.0751 0.3009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_RT_mod_neg_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -------------------------------------------------
## ACRT_neg | 7.70e-03 | [0.00, 1.00]
## rtq | 0.33 | [0.27, 1.00]
## time | 0.01 | [0.00, 1.00]
## ACRT_neg:rtq | 6.95e-03 | [0.00, 1.00]
## ACRT_neg:time | 4.94e-03 | [0.00, 1.00]
## rtq:time | 0.01 | [0.00, 1.00]
## ACRT_neg:rtq:time | 4.80e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_RT_mod_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.587
## Marginal R2: 0.325model_intrusions_distress_RT_mod_peri_t2 <- lmer(intrusionsdistress ~ (ACRT_peri*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_distress_RT_mod_peri_t2) #Intrusions distress mod Reaction time ACRT_peri T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACRT_peri * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 964.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22845 -0.54103 -0.03809 0.57382 2.41056
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.2139 0.4625
## Residual 0.3343 0.5782
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.721e-01 1.332e-01 4.036e+02 6.548 1.78e-10 ***
## ACRT_peri 1.991e-04 2.199e-03 4.024e+02 0.091 0.928
## rtq 4.630e-02 4.761e-03 4.036e+02 9.726 < 2e-16 ***
## time2 -2.206e-01 1.621e-01 2.192e+02 -1.361 0.175
## ACRT_peri:rtq -4.099e-05 8.259e-05 4.040e+02 -0.496 0.620
## ACRT_peri:time2 -2.760e-03 2.613e-03 2.158e+02 -1.056 0.292
## rtq:time2 7.630e-03 5.967e-03 2.237e+02 1.279 0.202
## ACRT_peri:rtq:time2 1.355e-04 1.023e-04 2.203e+02 1.325 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_p rtq time2 ACRT_p: ACRT_:2 rtq:t2
## ACRT_peri -0.148
## rtq -0.920 0.157
## time2 -0.570 0.074 0.536
## ACRT_pr:rtq 0.150 -0.918 -0.188 -0.076
## ACRT_pr:tm2 0.078 -0.583 -0.085 -0.135 0.547
## rtq:time2 0.515 -0.079 -0.560 -0.934 0.096 0.139
## ACRT_pr:r:2 -0.079 0.513 0.100 0.130 -0.559 -0.929 -0.157
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_distress_RT_mod_peri_t2 <- standardize_parameters(model_intrusions_distress_RT_mod_peri_t2)
print(standardized_model_intrusions_RT_mod_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 0.05 | [-0.08, 0.17]
## ACRT peri | -0.10 | [-0.23, 0.02]
## rtq | 0.39 | [ 0.27, 0.50]
## time [2] | -0.10 | [-0.22, 0.03]
## ACRT peri × rtq | 0.02 | [-0.10, 0.14]
## ACRT peri × time [2] | 0.12 | [-0.01, 0.25]
## rtq × time [2] | -0.06 | [-0.20, 0.07]
## (ACRT peri × rtq) × time [2] | 0.06 | [-0.08, 0.20]anova(model_intrusions_distress_RT_mod_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACRT_peri 0.146 0.146 1 298.67 0.4371 0.5091
## rtq 53.576 53.576 1 333.97 160.2778 <2e-16 ***
## time 0.619 0.619 1 219.24 1.8525 0.1749
## ACRT_peri:rtq 0.051 0.051 1 322.11 0.1521 0.6968
## ACRT_peri:time 0.373 0.373 1 215.81 1.1154 0.2921
## rtq:time 0.546 0.546 1 223.67 1.6349 0.2024
## ACRT_peri:rtq:time 0.587 0.587 1 220.33 1.7548 0.1866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_RT_mod_peri_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## --------------------------------------------------
## ACRT_peri | 1.46e-03 | [0.00, 1.00]
## rtq | 0.32 | [0.26, 1.00]
## time | 8.38e-03 | [0.00, 1.00]
## ACRT_peri:rtq | 4.72e-04 | [0.00, 1.00]
## ACRT_peri:time | 5.14e-03 | [0.00, 1.00]
## rtq:time | 7.26e-03 | [0.00, 1.00]
## ACRT_peri:rtq:time | 7.90e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_RT_mod_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.588
## Marginal R2: 0.324model_intrusions_distress_RT_mod_Nperi_t2 <- lmer(intrusionsdistress ~ (ACRT_Nperi*rtq)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_distress_RT_mod_Nperi_t2) #Intrusions distress mod Reaction time ACRT_Nperi T2## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACRT_Nperi * rtq) * time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 965
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.23753 -0.54946 -0.05375 0.56995 2.32058
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.2161 0.4649
## Residual 0.3338 0.5778
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.600e-01 1.329e-01 4.039e+02 6.471 2.82e-10 ***
## ACRT_Nperi 1.241e-03 2.202e-03 4.038e+02 0.564 0.573
## rtq 4.670e-02 4.753e-03 4.028e+02 9.825 < 2e-16 ***
## time2 -2.378e-01 1.624e-01 2.204e+02 -1.464 0.145
## ACRT_Nperi:rtq -7.438e-05 7.832e-05 4.029e+02 -0.950 0.343
## ACRT_Nperi:time2 1.298e-04 2.641e-03 2.196e+02 0.049 0.961
## rtq:time2 8.403e-03 6.017e-03 2.254e+02 1.396 0.164
## ACRT_Nperi:rtq:time2 2.405e-05 9.887e-05 2.250e+02 0.243 0.808
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_Np rtq time2 ACRT_Np: ACRT_N:2 rtq:t2
## ACRT_Nperi -0.109
## rtq -0.920 0.127
## time2 -0.570 0.061 0.536
## ACRT_Npr:rt 0.128 -0.918 -0.167 -0.074
## ACRT_Npr:t2 0.065 -0.587 -0.077 -0.124 0.553
## rtq:time2 0.511 -0.068 -0.555 -0.935 0.090 0.147
## ACRT_Npr::2 -0.071 0.511 0.093 0.149 -0.557 -0.930 -0.190
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_distress_RT_mod_Nperi_t2 <- standardize_parameters(model_intrusions_distress_RT_mod_Nperi_t2)
print(standardized_model_intrusions_RT_mod_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------
## (Intercept) | 0.05 | [-0.08, 0.17]
## ACRT Nperi | 0.04 | [-0.09, 0.16]
## rtq | 0.38 | [ 0.27, 0.49]
## time [2] | -0.09 | [-0.22, 0.03]
## ACRT Nperi × rtq | 0.03 | [-0.08, 0.14]
## ACRT Nperi × time [2] | 0.09 | [-0.04, 0.22]
## rtq × time [2] | -0.06 | [-0.20, 0.07]
## (ACRT Nperi × rtq) × time [2] | -0.06 | [-0.19, 0.08]anova(model_intrusions_distress_RT_mod_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACRT_Nperi 0.179 0.179 1 314.47 0.5366 0.4644
## rtq 54.839 54.839 1 342.07 164.2792 <2e-16 ***
## time 0.716 0.716 1 220.44 2.1442 0.1445
## ACRT_Nperi:rtq 0.304 0.304 1 341.07 0.9112 0.3405
## ACRT_Nperi:time 0.001 0.001 1 219.59 0.0024 0.9608
## rtq:time 0.651 0.651 1 225.43 1.9499 0.1640
## ACRT_Nperi:rtq:time 0.020 0.020 1 225.03 0.0591 0.8081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_RT_mod_Nperi_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------
## ACRT_Nperi | 1.70e-03 | [0.00, 1.00]
## rtq | 0.32 | [0.26, 1.00]
## time | 9.63e-03 | [0.00, 1.00]
## ACRT_Nperi:rtq | 2.66e-03 | [0.00, 1.00]
## ACRT_Nperi:time | 1.10e-05 | [0.00, 1.00]
## rtq:time | 8.58e-03 | [0.00, 1.00]
## ACRT_Nperi:rtq:time | 2.63e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_RT_mod_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.588
## Marginal R2: 0.322#check simple slopes of ACdp_peri (Perinegative - Neutral)
probe_interaction(model_intrusions_distress_dp_mod_peri_t2 , pred = ACdP_peri, modx = rtq, johnson_neyman = TRUE, jnplot = TRUE, interval = TRUE, x.label = "Affective control index - Perinegative Dprime (Peri -Neutral)",
y.label = "Intrusions Distress", main.title = "Moderation of Intrusion Distress",
color.class = "Paired")## The color.class argument is deprecated. Please use 'colors' instead.## JOHNSON-NEYMAN INTERVAL
##
## The Johnson-Neyman interval could not be found. Is the p value for your
## interaction term below the specified alpha?
## SIMPLE SLOPES ANALYSIS
##
## Slope of ACdP_peri when rtq = 15.35146 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.00 0.03 0.13 0.90
##
## Slope of ACdP_peri when rtq = 25.52427 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.03 0.03 1.27 0.21
##
## Slope of ACdP_peri when rtq = 35.69708 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.07 0.04 1.72 0.09
#standardise coeffcients
# 1 below SD
# Step 1: Calculate SD
N <- 206
SE <- 0.13
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- 0.00
standardised_below_distress <- SD * beta
# Print the result
print(standardised_below_distress)## [1] 0# Average
# Step 1: Calculate SD
N <- 206
SE <- 0.03
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- 0.03
standardised_av_distress <- SD * beta
# Print the result
print(standardised_av_distress)## [1] 0.01291743# 1 above SD
# Step 1: Calculate SD
N <- 206
SE <- 0.04
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- 0.07
standardised_above_distress <- SD * beta
# Print the result
print(standardised_above_distress)## [1] 0.04018756#relevent graph
interaction_plot_model_intrusions_distress_dp_mod_peri_t2 <- interact_plot(model_intrusions_distress_dp_mod_peri_t2,
pred = ACdP_peri,
modx = rtq,
plot.points = TRUE,
point.alpha = 0.5, # Keep points semi-transparent
point.color = "grey", # Set points to black
legend.main = "Levels of Repetitive Negative Thinking") +
theme_minimal() +
labs(x = "Affective Control: Negative Perinatal (D-Prime)",
y = "Intrusion-Related Distress") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line())
# Show the plot
print(interaction_plot_model_intrusions_distress_dp_mod_peri_t2)
##H3 - Weeks since conception ##Cross sectional ### H3 -T1 Intrusions
#Intrusions
model_intrusions_dp_c_neg <- lm(t1intrusionsTotal ~ ACdP_neg*t1_wks_since_conception, data = final_data)
summary(model_intrusions_dp_c_neg) #Intrusions conception Dprime##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_neg * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3264 -2.9297 -0.1921 2.6528 7.6386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.451e+00 5.023e-01 10.852 <2e-16 ***
## ACdP_neg 8.430e-02 2.511e-01 0.336 0.737
## t1_wks_since_conception -1.004e-02 7.154e-03 -1.403 0.162
## ACdP_neg:t1_wks_since_conception 3.889e-05 3.702e-03 0.011 0.992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.311 on 243 degrees of freedom
## Multiple R-squared: 0.0112, Adjusted R-squared: -0.001008
## F-statistic: 0.9174 on 3 and 243 DF, p-value: 0.4331standardized_model_intrusions_dp_c_neg <- standardize_parameters(model_intrusions_dp_c_neg)
print(standardized_model_intrusions_dp_c_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------
## (Intercept) | 7.31e-06 | [-0.13, 0.13]
## ACdP neg | 0.05 | [-0.08, 0.17]
## t1 wks since conception | -0.09 | [-0.22, 0.03]
## ACdP neg × t1 wks since conception | 6.53e-04 | [-0.12, 0.12]anova(model_intrusions_dp_c_neg)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_neg 1 6.12 6.1222 0.5584 0.4556
## t1_wks_since_conception 1 24.05 24.0539 2.1938 0.1399
## ACdP_neg:t1_wks_since_conception 1 0.00 0.0012 0.0001 0.9916
## Residuals 243 2664.41 10.9647eta_squared(model_intrusions_dp_c_neg)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------------------
## ACdP_neg | 2.29e-03 | [0.00, 1.00]
## t1_wks_since_conception | 8.95e-03 | [0.00, 1.00]
## ACdP_neg:t1_wks_since_conception | 4.54e-07 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_c_neg)## # R2 for Linear Regression
## R2: 0.011
## adj. R2: -0.001model_intrusions_dp_c_peri <- lm(t1intrusionsTotal ~ ACdP_peri*t1_wks_since_conception, data = final_data)
summary(model_intrusions_dp_c_peri) #Intrusions conception Dprime##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_peri * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4928 -2.8809 -0.2598 2.4779 7.6809
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.341005 0.492424 10.846 <2e-16 ***
## ACdP_peri 0.309801 0.248383 1.247 0.213
## t1_wks_since_conception -0.008467 0.007005 -1.209 0.228
## ACdP_peri:t1_wks_since_conception -0.002952 0.003649 -0.809 0.419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.301 on 243 degrees of freedom
## Multiple R-squared: 0.01718, Adjusted R-squared: 0.005051
## F-statistic: 1.416 on 3 and 243 DF, p-value: 0.2386standardized_model_intrusions_dp_c_peri <- standardize_parameters(model_intrusions_dp_c_peri)
print(standardized_model_intrusions_dp_c_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------
## (Intercept) | -4.30e-04 | [-0.13, 0.12]
## ACdP peri | 0.07 | [-0.06, 0.20]
## t1 wks since conception | -0.09 | [-0.22, 0.03]
## ACdP peri × t1 wks since conception | -0.05 | [-0.18, 0.08]anova(model_intrusions_dp_c_peri)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_peri 1 15.17 15.1656 1.3916 0.2393
## t1_wks_since_conception 1 24.01 24.0111 2.2032 0.1390
## ACdP_peri:t1_wks_since_conception 1 7.13 7.1298 0.6542 0.4194
## Residuals 243 2648.28 10.8983eta_squared(model_intrusions_dp_c_peri)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------------------
## ACdP_peri | 5.69e-03 | [0.00, 1.00]
## t1_wks_since_conception | 8.99e-03 | [0.00, 1.00]
## ACdP_peri:t1_wks_since_conception | 2.68e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_c_peri)## # R2 for Linear Regression
## R2: 0.017
## adj. R2: 0.005model_intrusions_dp_c_Nperi <- lm(t1intrusionsTotal ~ ACdP_Nperi*t1_wks_since_conception, data = final_data)
summary(model_intrusions_dp_c_Nperi) #Intrusions conception Dprime##
## Call:
## lm(formula = t1intrusionsTotal ~ ACdP_Nperi * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.509 -2.918 -0.223 2.629 7.648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.534633 0.476669 11.611 <2e-16 ***
## ACdP_Nperi 0.291695 0.280991 1.038 0.300
## t1_wks_since_conception -0.010499 0.006770 -1.551 0.122
## ACdP_Nperi:t1_wks_since_conception -0.003814 0.004212 -0.906 0.366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.308 on 243 degrees of freedom
## Multiple R-squared: 0.01342, Adjusted R-squared: 0.001244
## F-statistic: 1.102 on 3 and 243 DF, p-value: 0.3489standardized_model_intrusions_dp_c_Nperi <- standardize_parameters(model_intrusions_dp_c_Nperi)
print(standardized_model_intrusions_dp_c_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------
## (Intercept) | 1.61e-04 | [-0.13, 0.13]
## ACdP Nperi | 0.03 | [-0.10, 0.15]
## t1 wks since conception | -0.10 | [-0.22, 0.03]
## ACdP Nperi × t1 wks since conception | -0.06 | [-0.20, 0.07]anova(model_intrusions_dp_c_Nperi)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_Nperi 1 2.83 2.8328 0.2589 0.6113
## t1_wks_since_conception 1 24.37 24.3673 2.2274 0.1369
## ACdP_Nperi:t1_wks_since_conception 1 8.97 8.9721 0.8201 0.3660
## Residuals 243 2658.42 10.9400eta_squared(model_intrusions_dp_c_Nperi)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ------------------------------------------------------------------
## ACdP_Nperi | 1.06e-03 | [0.00, 1.00]
## t1_wks_since_conception | 9.08e-03 | [0.00, 1.00]
## ACdP_Nperi:t1_wks_since_conception | 3.36e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_c_Nperi)## # R2 for Linear Regression
## R2: 0.013
## adj. R2: 0.001model_intrusions_rt_c_neg <- lm(t1intrusionsTotal ~ ACRT_neg*t1_wks_since_conception, data = final_data)
summary(model_intrusions_rt_c_neg) #Intrusions conception Reaction time##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_neg * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5321 -2.8350 -0.2801 2.5303 7.9839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.587e+00 4.757e-01 11.746 <2e-16 ***
## ACRT_neg -7.671e-03 7.937e-03 -0.967 0.335
## t1_wks_since_conception -1.110e-02 6.744e-03 -1.646 0.101
## ACRT_neg:t1_wks_since_conception 1.617e-05 1.125e-04 0.144 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.292 on 243 degrees of freedom
## Multiple R-squared: 0.02298, Adjusted R-squared: 0.01092
## F-statistic: 1.906 on 3 and 243 DF, p-value: 0.1293standardized_model_intrusions_rt_c_neg <- standardize_parameters(model_intrusions_rt_c_neg)
print(standardized_model_intrusions_rt_c_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------
## (Intercept) | 7.39e-04 | [-0.12, 0.13]
## ACRT neg | -0.12 | [-0.24, 0.01]
## t1 wks since conception | -0.10 | [-0.23, 0.02]
## ACRT neg × t1 wks since conception | 8.97e-03 | [-0.11, 0.13]anova(model_intrusions_rt_c_neg)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_neg 1 32.33 32.325 2.9837 0.08538 .
## t1_wks_since_conception 1 29.38 29.385 2.7123 0.10087
## ACRT_neg:t1_wks_since_conception 1 0.22 0.224 0.0207 0.88584
## Residuals 243 2632.66 10.834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_rt_c_neg)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------------------
## ACRT_neg | 0.01 | [0.00, 1.00]
## t1_wks_since_conception | 0.01 | [0.00, 1.00]
## ACRT_neg:t1_wks_since_conception | 8.50e-05 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_c_neg)## # R2 for Linear Regression
## R2: 0.023
## adj. R2: 0.011model_intrusions_rt_c_peri <- lm(t1intrusionsTotal ~ ACRT_peri*t1_wks_since_conception, data = final_data)
summary(model_intrusions_rt_c_peri) #Intrusions conception Reaction time##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_peri * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3161 -2.7817 -0.1098 2.5979 7.5235
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.571e+00 4.822e-01 11.553 <2e-16 ***
## ACRT_peri -5.526e-03 7.963e-03 -0.694 0.488
## t1_wks_since_conception -1.034e-02 6.827e-03 -1.515 0.131
## ACRT_peri:t1_wks_since_conception 3.421e-06 1.131e-04 0.030 0.976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.3 on 243 degrees of freedom
## Multiple R-squared: 0.0178, Adjusted R-squared: 0.005672
## F-statistic: 1.468 on 3 and 243 DF, p-value: 0.2239standardized_model_intrusions_rt_c_peri <- standardize_parameters(model_intrusions_rt_c_peri)
print(standardized_model_intrusions_rt_c_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------
## (Intercept) | 4.51e-05 | [-0.12, 0.13]
## ACRT peri | -0.09 | [-0.22, 0.03]
## t1 wks since conception | -0.10 | [-0.22, 0.03]
## ACRT peri × t1 wks since conception | 1.88e-03 | [-0.12, 0.12]anova(model_intrusions_rt_c_peri)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_peri 1 22.47 22.472 2.0633 0.1522
## t1_wks_since_conception 1 25.48 25.476 2.3391 0.1275
## ACRT_peri:t1_wks_since_conception 1 0.01 0.010 0.0009 0.9759
## Residuals 243 2646.63 10.892eta_squared(model_intrusions_rt_c_peri)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------------------
## ACRT_peri | 8.42e-03 | [0.00, 1.00]
## t1_wks_since_conception | 9.53e-03 | [0.00, 1.00]
## ACRT_peri:t1_wks_since_conception | 3.77e-06 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_c_peri)## # R2 for Linear Regression
## R2: 0.018
## adj. R2: 0.006model_intrusions_rt_c_Nperi <- lm(t1intrusionsTotal ~ ACRT_Nperi*t1_wks_since_conception, data = final_data)
summary(model_intrusions_rt_c_Nperi) #Intrusions conception Reaction time##
## Call:
## lm(formula = t1intrusionsTotal ~ ACRT_Nperi * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3875 -2.9112 -0.2692 2.6478 7.6146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.498e+00 4.803e-01 11.448 <2e-16 ***
## ACRT_Nperi 1.842e-03 7.861e-03 0.234 0.815
## t1_wks_since_conception -1.014e-02 6.882e-03 -1.473 0.142
## ACRT_Nperi:t1_wks_since_conception -8.103e-06 1.172e-04 -0.069 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.314 on 243 degrees of freedom
## Multiple R-squared: 0.009637, Adjusted R-squared: -0.00259
## F-statistic: 0.7882 on 3 and 243 DF, p-value: 0.5015standardized_model_intrusions_rt_c_Nperi <- standardize_parameters(model_intrusions_rt_c_Nperi)
print(standardized_model_intrusions_rt_c_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------
## (Intercept) | 2.63e-04 | [-0.13, 0.13]
## ACRT Nperi | 0.02 | [-0.10, 0.15]
## t1 wks since conception | -0.10 | [-0.22, 0.03]
## ACRT Nperi × t1 wks since conception | -4.54e-03 | [-0.13, 0.12]anova(model_intrusions_rt_c_Nperi)## Analysis of Variance Table
##
## Response: t1intrusionsTotal
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_Nperi 1 0.94 0.9435 0.0859 0.7697
## t1_wks_since_conception 1 24.97 24.9713 2.2738 0.1329
## ACRT_Nperi:t1_wks_since_conception 1 0.05 0.0525 0.0048 0.9450
## Residuals 243 2668.62 10.9820eta_squared(model_intrusions_rt_c_Nperi)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ------------------------------------------------------------------
## ACRT_Nperi | 3.53e-04 | [0.00, 1.00]
## t1_wks_since_conception | 9.27e-03 | [0.00, 1.00]
## ACRT_Nperi:t1_wks_since_conception | 1.97e-05 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_c_Nperi)## # R2 for Linear Regression
## R2: 0.010
## adj. R2: -0.003###H3 -T1 Distress
#Intrusion Distress Dprime
model_intrusions_distress_dp_c_neg <- lm(t1_thoughts_distress_level ~ ACdP_neg*t1_wks_since_conception, data = final_data)
summary(model_intrusions_distress_dp_c_neg) #Intrusions distress conception Dprime##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_neg * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.22128 -0.98320 -0.03716 0.89697 2.14243
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9963915 0.1346084 14.831 <2e-16 ***
## ACdP_neg 0.1641265 0.0667967 2.457 0.0147 *
## t1_wks_since_conception 0.0005222 0.0019298 0.271 0.7869
## ACdP_neg:t1_wks_since_conception -0.0022109 0.0009849 -2.245 0.0257 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8805 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02543, Adjusted R-squared: 0.01335
## F-statistic: 2.105 on 3 and 242 DF, p-value: 0.1002standardized_model_intrusions_distress_dp_c_neg <- standardize_parameters(model_intrusions_distress_dp_c_neg)
print(standardized_model_intrusions_distress_dp_c_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------
## (Intercept) | -1.36e-03 | [-0.13, 0.12]
## ACdP neg | 0.05 | [-0.07, 0.18]
## t1 wks since conception | -0.03 | [-0.15, 0.10]
## ACdP neg × t1 wks since conception | -0.14 | [-0.26, -0.02]anova(model_intrusions_distress_dp_c_neg)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_neg 1 0.803 0.8029 1.0357 0.30984
## t1_wks_since_conception 1 0.186 0.1857 0.2395 0.62502
## ACdP_neg:t1_wks_since_conception 1 3.907 3.9069 5.0394 0.02568 *
## Residuals 242 187.613 0.7753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_c_neg)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------------------
## ACdP_neg | 4.26e-03 | [0.00, 1.00]
## t1_wks_since_conception | 9.89e-04 | [0.00, 1.00]
## ACdP_neg:t1_wks_since_conception | 0.02 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_c_neg)## # R2 for Linear Regression
## R2: 0.025
## adj. R2: 0.013model_intrusions_distress_dp_c_peri <- lm(t1_thoughts_distress_level ~ ACdP_peri*t1_wks_since_conception, data = final_data)
summary(model_intrusions_distress_dp_c_peri) #Intrusions distress conception Dprime##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_peri * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.42413 -0.99111 -0.03394 0.88702 2.03343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.023e+00 1.324e-01 15.275 <2e-16 ***
## ACdP_peri 1.516e-01 6.635e-02 2.285 0.0232 *
## t1_wks_since_conception 5.801e-05 1.895e-03 0.031 0.9756
## ACdP_peri:t1_wks_since_conception -1.830e-03 9.748e-04 -1.878 0.0616 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8818 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02263, Adjusted R-squared: 0.01051
## F-statistic: 1.868 on 3 and 242 DF, p-value: 0.1357standardized_model_intrusions_distress_dp_c_peri <- standardize_parameters(model_intrusions_distress_dp_c_peri)
print(standardized_model_intrusions_distress_dp_c_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------
## (Intercept) | -1.14e-03 | [-0.13, 0.12]
## ACdP peri | 0.08 | [-0.05, 0.20]
## t1 wks since conception | -0.03 | [-0.15, 0.10]
## ACdP peri × t1 wks since conception | -0.12 | [-0.25, 0.01]anova(model_intrusions_distress_dp_c_peri)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_peri 1 1.431 1.43138 1.8410 0.17609
## t1_wks_since_conception 1 0.183 0.18348 0.2360 0.62756
## ACdP_peri:t1_wks_since_conception 1 2.741 2.74118 3.5257 0.06163 .
## Residuals 242 188.152 0.77749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_c_peri)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------------------
## ACdP_peri | 7.55e-03 | [0.00, 1.00]
## t1_wks_since_conception | 9.74e-04 | [0.00, 1.00]
## ACdP_peri:t1_wks_since_conception | 0.01 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_c_peri)## # R2 for Linear Regression
## R2: 0.023
## adj. R2: 0.011model_intrusions_distress_dp_c_Nperi <- lm(t1_thoughts_distress_level ~ ACdP_Nperi*t1_wks_since_conception, data = final_data)
summary(model_intrusions_distress_dp_c_Nperi) #Intrusions distress conception Dprime##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACdP_Nperi * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.11297 -1.01737 -0.05792 0.92648 2.02222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0995282 0.1294036 16.225 <2e-16 ***
## ACdP_Nperi -0.0155945 0.0757348 -0.206 0.837
## t1_wks_since_conception -0.0008502 0.0018506 -0.459 0.646
## ACdP_Nperi:t1_wks_since_conception 0.0004887 0.0011358 0.430 0.667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8908 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.002511, Adjusted R-squared: -0.009854
## F-statistic: 0.2031 on 3 and 242 DF, p-value: 0.8942standardized_model_intrusions_distress_dp_c_Nperi <- standardize_parameters(model_intrusions_distress_dp_c_Nperi)
print(standardized_model_intrusions_distress_dp_c_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------
## (Intercept) | 5.96e-06 | [-0.13, 0.13]
## ACdP Nperi | 0.03 | [-0.10, 0.16]
## t1 wks since conception | -0.03 | [-0.16, 0.10]
## ACdP Nperi × t1 wks since conception | 0.03 | [-0.11, 0.17]anova(model_intrusions_distress_dp_c_Nperi)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACdP_Nperi 1 0.143 0.14318 0.1804 0.6714
## t1_wks_since_conception 1 0.193 0.19332 0.2436 0.6220
## ACdP_Nperi:t1_wks_since_conception 1 0.147 0.14694 0.1852 0.6673
## Residuals 242 192.025 0.79349eta_squared(model_intrusions_distress_dp_c_Nperi)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ------------------------------------------------------------------
## ACdP_Nperi | 7.45e-04 | [0.00, 1.00]
## t1_wks_since_conception | 1.01e-03 | [0.00, 1.00]
## ACdP_Nperi:t1_wks_since_conception | 7.65e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_c_Nperi)## # R2 for Linear Regression
## R2: 0.003
## adj. R2: -0.010#Intrusion Distress Reaction Time
model_intrusions_distress_rt_c_neg <- lm(t1_thoughts_distress_level ~ ACRT_neg*t1_wks_since_conception, data = final_data)
summary(model_intrusions_distress_rt_c_neg) #Intrusions distress conception Reaction time##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_neg * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.19132 -1.01057 -0.05488 0.92835 2.02421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.102e+00 1.296e-01 16.213 <2e-16 ***
## ACRT_neg 1.044e-03 2.152e-03 0.485 0.628
## t1_wks_since_conception -9.465e-04 1.850e-03 -0.512 0.609
## ACRT_neg:t1_wks_since_conception -2.213e-05 3.057e-05 -0.724 0.470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8903 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.003691, Adjusted R-squared: -0.00866
## F-statistic: 0.2988 on 3 and 242 DF, p-value: 0.8262standardized_model_intrusions_distress_rt_c_neg <- standardize_parameters(model_intrusions_distress_rt_c_neg)
print(standardized_model_intrusions_distress_rt_c_neg)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------
## (Intercept) | -3.48e-03 | [-0.13, 0.12]
## ACRT neg | -0.02 | [-0.15, 0.10]
## t1 wks since conception | -0.04 | [-0.16, 0.09]
## ACRT neg × t1 wks since conception | -0.05 | [-0.17, 0.08]anova(model_intrusions_distress_rt_c_neg)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_neg 1 0.081 0.08078 0.1019 0.7498
## t1_wks_since_conception 1 0.214 0.21439 0.2705 0.6035
## ACRT_neg:t1_wks_since_conception 1 0.415 0.41531 0.5240 0.4698
## Residuals 242 191.798 0.79255eta_squared(model_intrusions_distress_rt_c_neg)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------------------
## ACRT_neg | 4.21e-04 | [0.00, 1.00]
## t1_wks_since_conception | 1.12e-03 | [0.00, 1.00]
## ACRT_neg:t1_wks_since_conception | 2.16e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_c_neg)## # R2 for Linear Regression
## R2: 0.004
## adj. R2: -0.009model_intrusions_distress_rt_c_peri <- lm(t1_thoughts_distress_level ~ ACRT_peri*t1_wks_since_conception, data = final_data)
summary(model_intrusions_distress_rt_c_peri) #Intrusions distress mod Reaction time##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_peri * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.15977 -1.02346 -0.03469 0.93798 2.01506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.122e+00 1.313e-01 16.160 <2e-16 ***
## ACRT_peri -1.740e-03 2.156e-03 -0.807 0.420
## t1_wks_since_conception -1.164e-03 1.873e-03 -0.621 0.535
## ACRT_peri:t1_wks_since_conception 2.275e-05 3.071e-05 0.741 0.459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8903 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.003695, Adjusted R-squared: -0.008656
## F-statistic: 0.2992 on 3 and 242 DF, p-value: 0.826standardized_model_intrusions_distress_rt_c_peri <- standardize_parameters(model_intrusions_distress_rt_c_peri)
print(standardized_model_intrusions_distress_rt_c_peri)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------
## (Intercept) | 7.74e-04 | [-0.13, 0.13]
## ACRT peri | -0.02 | [-0.15, 0.11]
## t1 wks since conception | -0.03 | [-0.16, 0.09]
## ACRT peri × t1 wks since conception | 0.05 | [-0.08, 0.17]anova(model_intrusions_distress_rt_c_peri)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_peri 1 0.079 0.07863 0.0992 0.7531
## t1_wks_since_conception 1 0.198 0.19760 0.2493 0.6180
## ACRT_peri:t1_wks_since_conception 1 0.435 0.43510 0.5490 0.4594
## Residuals 242 191.797 0.79255eta_squared(model_intrusions_distress_rt_c_peri)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------------------
## ACRT_peri | 4.10e-04 | [0.00, 1.00]
## t1_wks_since_conception | 1.03e-03 | [0.00, 1.00]
## ACRT_peri:t1_wks_since_conception | 2.26e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_c_peri)## # R2 for Linear Regression
## R2: 0.004
## adj. R2: -0.009model_intrusions_distress_rt_c_Nperi <- lm(t1_thoughts_distress_level ~ ACRT_Nperi*t1_wks_since_conception, data = final_data)
summary(model_intrusions_distress_rt_c_Nperi) #Intrusions distress mod reaction time##
## Call:
## lm(formula = t1_thoughts_distress_level ~ ACRT_Nperi * t1_wks_since_conception,
## data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.36692 -0.99847 -0.04071 0.90899 1.96996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.127e+00 1.296e-01 16.409 <2e-16 ***
## ACRT_Nperi -2.811e-03 2.105e-03 -1.336 0.183
## t1_wks_since_conception -1.408e-03 1.870e-03 -0.753 0.452
## ACRT_Nperi:t1_wks_since_conception 4.765e-05 3.140e-05 1.518 0.130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8872 on 242 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01043, Adjusted R-squared: -0.001841
## F-statistic: 0.8499 on 3 and 242 DF, p-value: 0.4678standardized_model_intrusions_distress_rt_c_Nperi <- standardize_parameters(model_intrusions_distress_rt_c_Nperi)
print(standardized_model_intrusions_distress_rt_c_Nperi)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------
## (Intercept) | -5.87e-03 | [-0.13, 0.12]
## ACRT Nperi | 0.01 | [-0.11, 0.14]
## t1 wks since conception | -0.04 | [-0.16, 0.09]
## ACRT Nperi × t1 wks since conception | 0.10 | [-0.03, 0.23]anova(model_intrusions_distress_rt_c_Nperi)## Analysis of Variance Table
##
## Response: t1_thoughts_distress_level
## Df Sum Sq Mean Sq F value Pr(>F)
## ACRT_Nperi 1 0.000 0.00003 0.0000 0.9947
## t1_wks_since_conception 1 0.194 0.19439 0.2469 0.6197
## ACRT_Nperi:t1_wks_since_conception 1 1.813 1.81277 2.3028 0.1304
## Residuals 242 190.501 0.78719eta_squared(model_intrusions_distress_rt_c_Nperi)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## ------------------------------------------------------------------
## ACRT_Nperi | 1.81e-07 | [0.00, 1.00]
## t1_wks_since_conception | 1.02e-03 | [0.00, 1.00]
## ACRT_Nperi:t1_wks_since_conception | 9.43e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_c_Nperi)## # R2 for Linear Regression
## R2: 0.010
## adj. R2: -0.002tab_model (model_intrusions_distress_rt_c_Nperi)|
t 1 thoughts distress level |
|||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.13 | 1.87 – 2.38 | <0.001 |
| ACRT Nperi | -0.00 | -0.01 – 0.00 | 0.183 |
| t1 wks since conception | -0.00 | -0.01 – 0.00 | 0.452 |
|
ACRT Nperi × t1 wks since conception |
0.00 | -0.00 – 0.00 | 0.130 |
| Observations | 246 | ||
| R2 / R2 adjusted | 0.010 / -0.002 | ||
#simple slopes for ACdp_neg and weeks since conception
probe_interaction(model_intrusions_distress_dp_c_neg , pred = ACdP_neg, modx = t1_wks_since_conception, johnson_neyman = TRUE, jnplot = TRUE, interval = TRUE, x.label = "Affective control index - Negative Dprime (Neg -Neutral)",
y.label = "Intrusions Distress", main.title = "Moderation of Intrusion Distress",
color.class = "Paired")## The color.class argument is deprecated. Please use 'colors' instead.## JOHNSON-NEYMAN INTERVAL
##
## When t1_wks_since_conception is OUTSIDE the interval [42.17, 202.99], the
## slope of ACdP_neg is p < .05.
##
## Note: The range of observed values of t1_wks_since_conception is [13.57,
## 132.70]
## SIMPLE SLOPES ANALYSIS
##
## Slope of ACdP_neg when t1_wks_since_conception = 31.97976 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.09 0.04 2.24 0.03
##
## Slope of ACdP_neg when t1_wks_since_conception = 62.82129 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.03 0.03 0.80 0.43
##
## Slope of ACdP_neg when t1_wks_since_conception = 93.66282 (+ 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.04 0.05 -0.94 0.35
#calculate coefficients
# 1 below SD
# Step 1: Calculate SD
N <- 247
SE <- 0.04
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- 0.09
standardised_below_distress_c <- SD * beta
# Print the result
print(standardised_below_distress_c)## [1] 0.05657844# average
# Step 1: Calculate SD
N <- 247
SE <- 0.03
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- 0.03
standardised_av_distress_c <- SD * beta
# Print the result
print(standardised_av_distress_c)## [1] 0.01414461# 1 above SD
# Step 1: Calculate SD
N <- 247
SE <- 0.05
SD <- SE * sqrt(N)
# Step 2: Calculate the standardized beta
beta <- -0.04
standardised_above_distress_c <- SD * beta
# Print the result
print(standardised_above_distress_c)## [1] -0.03143247# relevant graph
# create interaction plot with simple slopes and data points
interaction_plot_model_intrusions_distress_dp_c_neg <- interact_plot(model_intrusions_distress_dp_c_neg,
pred = ACdP_neg,
modx = t1_wks_since_conception,
plot.points = TRUE,
point.alpha = 0.5,
legend.main = "Peripartum Stage") +
theme_minimal() +
labs(x = "Affective Control: Negative Generic Content (D-Prime)",
y = "Intrusion-Related Distress") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line()) # Keep axis lines
# Show the plot
print(interaction_plot_model_intrusions_distress_dp_c_neg)
##1-Month follow up
# Converting time to a factor
final_dataT2_long$time <- as.factor(final_dataT2_long$time)###H3 -T2 Intrusions
#Intrusions Dprime
model_intrusions_dp_c_neg_t2 <- lmer(intrusions ~ (ACdP_neg*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_c_neg_t2) #Intrusions conception Dprime## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACdP_neg * t1_wks_since_conception) * time + (1 |
## Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2105.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6926 -0.5020 -0.1173 0.4466 3.0527
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.185 2.487
## Residual 4.678 2.163
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.087e+00 5.789e-01 3.051e+02 8.787
## ACdP_neg 1.477e-01 2.743e-01 3.051e+02 0.538
## t1_wks_since_conception -5.735e-03 8.018e-03 3.051e+02 -0.715
## time2 -5.308e-01 5.373e-01 2.020e+02 -0.988
## ACdP_neg:t1_wks_since_conception -8.475e-04 3.941e-03 3.051e+02 -0.215
## ACdP_neg:time2 -1.870e-01 2.546e-01 2.020e+02 -0.735
## t1_wks_since_conception:time2 2.065e-03 7.441e-03 2.020e+02 0.278
## ACdP_neg:t1_wks_since_conception:time2 2.592e-03 3.657e-03 2.020e+02 0.709
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ACdP_neg 0.591
## t1_wks_since_conception 0.475
## time2 0.324
## ACdP_neg:t1_wks_since_conception 0.830
## ACdP_neg:time2 0.463
## t1_wks_since_conception:time2 0.782
## ACdP_neg:t1_wks_since_conception:time2 0.479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_n t1_w__ time2 ACdP_:1___ ACP_:2 t1___:
## ACdP_neg -0.354
## t1_wks_snc_ -0.908 0.323
## time2 -0.464 0.164 0.421
## ACdP_n:1___ 0.311 -0.886 -0.346 -0.144
## ACdP_ng:tm2 0.164 -0.464 -0.150 -0.354 0.411
## t1_wks_s_:2 0.421 -0.150 -0.464 -0.908 0.160 0.323
## ACP_:1___:2 -0.144 0.411 0.160 0.311 -0.464 -0.886 -0.346standardized_model_intrusions_dp_c_neg_t2 <- standardize_parameters(model_intrusions_dp_c_neg_t2)
print(standardized_model_intrusions_dp_c_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------------
## (Intercept) | 0.06 | [-0.08, 0.20]
## ACdP neg | 0.05 | [-0.09, 0.19]
## t1 wks since conception | -0.06 | [-0.20, 0.08]
## time [2] | -0.12 | [-0.25, 0.00]
## ACdP neg × t1 wks since conception | -0.01 | [-0.14, 0.12]
## ACdP neg × time [2] | -9.86e-03 | [-0.14, 0.12]
## t1 wks since conception × time [2] | 0.03 | [-0.09, 0.16]
## (ACdP neg × t1 wks since conception) × time [2] | 0.04 | [-0.08, 0.16]anova(model_intrusions_dp_c_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACdP_neg 0.2329 0.2329 1 202 0.0498 0.8237
## t1_wks_since_conception 2.0508 2.0508 1 202 0.4384 0.5086
## time 4.5659 4.5659 1 202 0.9761 0.3243
## ACdP_neg:t1_wks_since_conception 0.0773 0.0773 1 202 0.0165 0.8979
## ACdP_neg:time 2.5238 2.5238 1 202 0.5396 0.4635
## t1_wks_since_conception:time 0.3603 0.3603 1 202 0.0770 0.7817
## ACdP_neg:t1_wks_since_conception:time 2.3503 2.3503 1 202 0.5025 0.4792eta_squared(model_intrusions_dp_c_neg_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------------------------
## ACdP_neg | 2.46e-04 | [0.00, 1.00]
## t1_wks_since_conception | 2.17e-03 | [0.00, 1.00]
## time | 4.81e-03 | [0.00, 1.00]
## ACdP_neg:t1_wks_since_conception | 8.18e-05 | [0.00, 1.00]
## ACdP_neg:time | 2.66e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 3.81e-04 | [0.00, 1.00]
## ACdP_neg:t1_wks_since_conception:time | 2.48e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_c_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.573
## Marginal R2: 0.009model_intrusions_dp_c_peri_t2 <- lmer(intrusions ~ (ACdP_peri*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_c_peri_t2) #Intrusions conception Dprime## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACdP_peri * t1_wks_since_conception) * time + (1 |
## Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2103.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6999 -0.5025 -0.1065 0.4752 3.0620
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.150 2.480
## Residual 4.649 2.156
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 4.898666 0.569088 305.056571
## ACdP_peri 0.439639 0.270252 305.056572
## t1_wks_since_conception -0.003402 0.007872 305.056571
## time2 -0.455953 0.528050 202.000000
## ACdP_peri:t1_wks_since_conception -0.004474 0.003965 305.056572
## ACdP_peri:time2 -0.315002 0.250763 202.000000
## t1_wks_since_conception:time2 0.001639 0.007305 202.000000
## ACdP_peri:t1_wks_since_conception:time2 0.003337 0.003679 202.000000
## t value Pr(>|t|)
## (Intercept) 8.608 4.02e-16 ***
## ACdP_peri 1.627 0.105
## t1_wks_since_conception -0.432 0.666
## time2 -0.863 0.389
## ACdP_peri:t1_wks_since_conception -1.128 0.260
## ACdP_peri:time2 -1.256 0.211
## t1_wks_since_conception:time2 0.224 0.823
## ACdP_peri:t1_wks_since_conception:time2 0.907 0.365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_p t1_w__ time2 ACdP_:1___ ACP_:2 t1___:
## ACdP_peri -0.316
## t1_wks_snc_ -0.908 0.289
## time2 -0.464 0.146 0.421
## ACdP_p:1___ 0.274 -0.894 -0.302 -0.127
## ACdP_pr:tm2 0.146 -0.464 -0.134 -0.316 0.415
## t1_wks_s_:2 0.421 -0.134 -0.464 -0.908 0.140 0.289
## ACP_:1___:2 -0.127 0.415 0.140 0.274 -0.464 -0.894 -0.302standardized_model_intrusions_dp_c_peri_t2 <- standardize_parameters(model_intrusions_dp_c_peri_t2)
print(standardized_model_intrusions_dp_c_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------------------
## (Intercept) | 0.06 | [-0.08, 0.20]
## ACdP peri | 0.09 | [-0.05, 0.22]
## t1 wks since conception | -0.06 | [-0.19, 0.08]
## time [2] | -0.12 | [-0.25, 0.00]
## ACdP peri × t1 wks since conception | -0.08 | [-0.22, 0.06]
## ACdP peri × time [2] | -0.06 | [-0.18, 0.07]
## t1 wks since conception × time [2] | 0.03 | [-0.09, 0.16]
## (ACdP peri × t1 wks since conception) × time [2] | 0.06 | [-0.07, 0.19]anova(model_intrusions_dp_c_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACdP_peri 6.4564 6.4564 1 202 1.3888
## t1_wks_since_conception 0.6373 0.6373 1 202 0.1371
## time 3.4660 3.4660 1 202 0.7456
## ACdP_peri:t1_wks_since_conception 2.9648 2.9648 1 202 0.6378
## ACdP_peri:time 7.3357 7.3357 1 202 1.5780
## t1_wks_since_conception:time 0.2341 0.2341 1 202 0.0504
## ACdP_peri:t1_wks_since_conception:time 3.8245 3.8245 1 202 0.8227
## Pr(>F)
## ACdP_peri 0.2400
## t1_wks_since_conception 0.7116
## time 0.3889
## ACdP_peri:t1_wks_since_conception 0.4255
## ACdP_peri:time 0.2105
## t1_wks_since_conception:time 0.8227
## ACdP_peri:t1_wks_since_conception:time 0.3655eta_squared(model_intrusions_dp_c_peri_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------------------------
## ACdP_peri | 6.83e-03 | [0.00, 1.00]
## t1_wks_since_conception | 6.78e-04 | [0.00, 1.00]
## time | 3.68e-03 | [0.00, 1.00]
## ACdP_peri:t1_wks_since_conception | 3.15e-03 | [0.00, 1.00]
## ACdP_peri:time | 7.75e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 2.49e-04 | [0.00, 1.00]
## ACdP_peri:t1_wks_since_conception:time | 4.06e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_c_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.576
## Marginal R2: 0.014model_intrusions_dp_c_Nperi_t2 <- lmer(intrusions ~ (ACdP_Nperi*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_dp_c_Nperi_t2 ) #Intrusions conception Dprime## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACdP_Nperi * t1_wks_since_conception) * time +
## (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2104.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6552 -0.4916 -0.1074 0.4530 3.0746
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.155 2.481
## Residual 4.674 2.162
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 5.229e+00 5.413e-01 3.054e+02
## ACdP_Nperi 3.785e-01 3.037e-01 3.054e+02
## t1_wks_since_conception -6.985e-03 7.521e-03 3.054e+02
## time2 -6.770e-01 5.029e-01 2.020e+02
## ACdP_Nperi:t1_wks_since_conception -4.784e-03 4.579e-03 3.054e+02
## ACdP_Nperi:time2 -1.495e-01 2.822e-01 2.020e+02
## t1_wks_since_conception:time2 3.985e-03 6.988e-03 2.020e+02
## ACdP_Nperi:t1_wks_since_conception:time2 8.354e-04 4.254e-03 2.020e+02
## t value Pr(>|t|)
## (Intercept) 9.660 <2e-16 ***
## ACdP_Nperi 1.246 0.214
## t1_wks_since_conception -0.929 0.354
## time2 -1.346 0.180
## ACdP_Nperi:t1_wks_since_conception -1.045 0.297
## ACdP_Nperi:time2 -0.530 0.597
## t1_wks_since_conception:time2 0.570 0.569
## ACdP_Nperi:t1_wks_since_conception:time2 0.196 0.845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_N t1_w__ time2 ACdP_N:1___ ACP_N:2 t1___:
## ACdP_Nperi 0.047
## t1_wks_snc_ -0.906 -0.052
## time2 -0.465 -0.022 0.421
## ACdP_N:1___ -0.050 -0.904 0.062 0.023
## ACdP_Npr:t2 -0.022 -0.465 0.024 0.047 0.420
## t1_wks_s_:2 0.421 0.024 -0.465 -0.906 -0.029 -0.052
## ACP_N:1___: 0.023 0.420 -0.029 -0.050 -0.465 -0.904 0.062standardized_model_intrusions_dp_c_Nperi_t2 <- standardize_parameters(model_intrusions_dp_c_Nperi_t2 )
print(standardized_model_intrusions_dp_c_Nperi_t2 )## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------------------
## (Intercept) | 0.06 | [-0.08, 0.20]
## ACdP Nperi | 0.04 | [-0.10, 0.18]
## t1 wks since conception | -0.06 | [-0.20, 0.07]
## time [2] | -0.13 | [-0.25, 0.00]
## ACdP Nperi × t1 wks since conception | -0.08 | [-0.23, 0.07]
## ACdP Nperi × time [2] | -0.05 | [-0.18, 0.08]
## t1 wks since conception × time [2] | 0.04 | [-0.09, 0.16]
## (ACdP Nperi × t1 wks since conception) × time [2] | 0.01 | [-0.12, 0.15]anova(model_intrusions_dp_c_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACdP_Nperi 5.9646 5.9646 1 202 1.2762
## t1_wks_since_conception 2.6267 2.6267 1 202 0.5620
## time 8.4705 8.4705 1 202 1.8123
## ACdP_Nperi:t1_wks_since_conception 5.4184 5.4184 1 202 1.1593
## ACdP_Nperi:time 1.3113 1.3113 1 202 0.2806
## t1_wks_since_conception:time 1.5198 1.5198 1 202 0.3252
## ACdP_Nperi:t1_wks_since_conception:time 0.1802 0.1802 1 202 0.0386
## Pr(>F)
## ACdP_Nperi 0.2600
## t1_wks_since_conception 0.4543
## time 0.1797
## ACdP_Nperi:t1_wks_since_conception 0.2829
## ACdP_Nperi:time 0.5969
## t1_wks_since_conception:time 0.5691
## ACdP_Nperi:t1_wks_since_conception:time 0.8445eta_squared(model_intrusions_dp_c_Nperi_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------------------------
## ACdP_Nperi | 6.28e-03 | [0.00, 1.00]
## t1_wks_since_conception | 2.77e-03 | [0.00, 1.00]
## time | 8.89e-03 | [0.00, 1.00]
## ACdP_Nperi:t1_wks_since_conception | 5.71e-03 | [0.00, 1.00]
## ACdP_Nperi:time | 1.39e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 1.61e-03 | [0.00, 1.00]
## ACdP_Nperi:t1_wks_since_conception:time | 1.91e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_dp_c_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.573
## Marginal R2: 0.012model_intrusions_rt_c_neg_t2 <- lmer(intrusions ~ (ACRT_neg*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_rt_c_neg_t2) #Intrusions conception Reaction time## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACRT_neg * t1_wks_since_conception) * time + (1 |
## Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2127.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82682 -0.50459 -0.09861 0.45897 3.06314
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 5.954 2.440
## Residual 4.663 2.159
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.313e+00 5.405e-01 3.073e+02 9.831
## ACRT_neg -6.097e-03 8.473e-03 3.073e+02 -0.720
## t1_wks_since_conception -7.931e-03 7.476e-03 3.073e+02 -1.061
## time2 -6.261e-01 5.065e-01 2.020e+02 -1.236
## ACRT_neg:t1_wks_since_conception -3.780e-05 1.182e-04 3.073e+02 -0.320
## ACRT_neg:time2 -7.487e-03 7.941e-03 2.020e+02 -0.943
## t1_wks_since_conception:time2 3.560e-03 7.006e-03 2.020e+02 0.508
## ACRT_neg:t1_wks_since_conception:time2 1.214e-04 1.108e-04 2.020e+02 1.096
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ACRT_neg 0.472
## t1_wks_since_conception 0.290
## time2 0.218
## ACRT_neg:t1_wks_since_conception 0.749
## ACRT_neg:time2 0.347
## t1_wks_since_conception:time2 0.612
## ACRT_neg:t1_wks_since_conception:time2 0.274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_n t1_w__ time2 ACRT_n:1___ ACRT_:2 t1___:
## ACRT_neg -0.126
## t1_wks_snc_ -0.906 0.092
## time2 -0.469 0.059 0.425
## ACRT_n:1___ 0.088 -0.896 -0.058 -0.041
## ACRT_ng:tm2 0.059 -0.469 -0.043 -0.126 0.420
## t1_wks_s_:2 0.425 -0.043 -0.469 -0.906 0.027 0.092
## ACRT_:1___: -0.041 0.420 0.027 0.088 -0.469 -0.896 -0.058
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_rt_c_neg_t2 <- standardize_parameters(model_intrusions_rt_c_neg_t2)
print(standardized_model_intrusions_rt_c_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------------------
## (Intercept) | 0.06 | [-0.08, 0.20]
## ACRT neg | -0.16 | [-0.29, -0.02]
## t1 wks since conception | -0.08 | [-0.21, 0.06]
## time [2] | -0.12 | [-0.25, 0.01]
## ACRT neg × t1 wks since conception | -0.02 | [-0.15, 0.11]
## ACRT neg × time [2] | 7.95e-03 | [-0.12, 0.14]
## t1 wks since conception × time [2] | 0.04 | [-0.09, 0.17]
## (ACRT neg × t1 wks since conception) × time [2] | 0.07 | [-0.05, 0.19]anova(model_intrusions_rt_c_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## ACRT_neg 8.0582 8.0582 1 202 1.7283 0.1901
## t1_wks_since_conception 4.0455 4.0455 1 202 0.8677 0.3527
## time 7.1234 7.1234 1 202 1.5278 0.2179
## ACRT_neg:t1_wks_since_conception 0.2242 0.2242 1 202 0.0481 0.8267
## ACRT_neg:time 4.1440 4.1440 1 202 0.8888 0.3469
## t1_wks_since_conception:time 1.2035 1.2035 1 202 0.2581 0.6120
## ACRT_neg:t1_wks_since_conception:time 5.5986 5.5986 1 202 1.2008 0.2745eta_squared(model_intrusions_rt_c_neg_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------------------------
## ACRT_neg | 8.48e-03 | [0.00, 1.00]
## t1_wks_since_conception | 4.28e-03 | [0.00, 1.00]
## time | 7.51e-03 | [0.00, 1.00]
## ACRT_neg:t1_wks_since_conception | 2.38e-04 | [0.00, 1.00]
## ACRT_neg:time | 4.38e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 1.28e-03 | [0.00, 1.00]
## ACRT_neg:t1_wks_since_conception:time | 5.91e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_c_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.574
## Marginal R2: 0.031model_intrusions_rt_c_peri_t2 <- lmer(intrusions ~ (ACRT_peri*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_rt_c_peri_t2) #Intrusions conception Reaction time## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACRT_peri * t1_wks_since_conception) * time + (1 |
## Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2131.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8409 -0.4807 -0.1258 0.4496 3.2305
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.203 2.491
## Residual 4.627 2.151
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 5.247e+00 5.485e-01 3.042e+02
## ACRT_peri -4.918e-03 8.766e-03 3.042e+02
## t1_wks_since_conception -6.566e-03 7.618e-03 3.042e+02
## time2 -6.507e-01 5.071e-01 2.020e+02
## ACRT_peri:t1_wks_since_conception -2.154e-07 1.214e-04 3.042e+02
## ACRT_peri:time2 -1.376e-03 8.104e-03 2.020e+02
## t1_wks_since_conception:time2 2.914e-03 7.042e-03 2.020e+02
## ACRT_peri:t1_wks_since_conception:time2 9.909e-05 1.122e-04 2.020e+02
## t value Pr(>|t|)
## (Intercept) 9.565 <2e-16 ***
## ACRT_peri -0.561 0.575
## t1_wks_since_conception -0.862 0.389
## time2 -1.283 0.201
## ACRT_peri:t1_wks_since_conception -0.002 0.999
## ACRT_peri:time2 -0.170 0.865
## t1_wks_since_conception:time2 0.414 0.679
## ACRT_peri:t1_wks_since_conception:time2 0.883 0.378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_p t1_w__ time2 ACRT_p:1___ ACRT_:2 t1___:
## ACRT_peri -0.169
## t1_wks_snc_ -0.906 0.154
## time2 -0.462 0.078 0.419
## ACRT_p:1___ 0.154 -0.897 -0.170 -0.071
## ACRT_pr:tm2 0.078 -0.462 -0.071 -0.169 0.414
## t1_wks_s_:2 0.419 -0.071 -0.462 -0.906 0.079 0.154
## ACRT_:1___: -0.071 0.414 0.079 0.154 -0.462 -0.897 -0.170
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_rt_c_peri_t2 <- standardize_parameters(model_intrusions_rt_c_peri_t2)
print(standardized_model_intrusions_rt_c_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------------------
## (Intercept) | 0.06 | [-0.07, 0.20]
## ACRT peri | -0.09 | [-0.23, 0.05]
## t1 wks since conception | -0.06 | [-0.20, 0.08]
## time [2] | -0.13 | [-0.25, 0.00]
## ACRT peri × t1 wks since conception | -1.19e-04 | [-0.13, 0.13]
## ACRT peri × time [2] | 0.09 | [-0.04, 0.22]
## t1 wks since conception × time [2] | 0.04 | [-0.09, 0.16]
## (ACRT peri × t1 wks since conception) × time [2] | 0.05 | [-0.07, 0.18]anova(model_intrusions_rt_c_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACRT_peri 2.4063 2.4063 1 202 0.5200
## t1_wks_since_conception 2.6462 2.6462 1 202 0.5719
## time 7.6204 7.6204 1 202 1.6469
## ACRT_peri:t1_wks_since_conception 0.9722 0.9722 1 202 0.2101
## ACRT_peri:time 0.1335 0.1335 1 202 0.0288
## t1_wks_since_conception:time 0.7926 0.7926 1 202 0.1713
## ACRT_peri:t1_wks_since_conception:time 3.6102 3.6102 1 202 0.7802
## Pr(>F)
## ACRT_peri 0.4717
## t1_wks_since_conception 0.4504
## time 0.2009
## ACRT_peri:t1_wks_since_conception 0.6472
## ACRT_peri:time 0.8653
## t1_wks_since_conception:time 0.6794
## ACRT_peri:t1_wks_since_conception:time 0.3781eta_squared(model_intrusions_rt_c_peri_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------------------------
## ACRT_peri | 2.57e-03 | [0.00, 1.00]
## t1_wks_since_conception | 2.82e-03 | [0.00, 1.00]
## time | 8.09e-03 | [0.00, 1.00]
## ACRT_peri:t1_wks_since_conception | 1.04e-03 | [0.00, 1.00]
## ACRT_peri:time | 1.43e-04 | [0.00, 1.00]
## t1_wks_since_conception:time | 8.47e-04 | [0.00, 1.00]
## ACRT_peri:t1_wks_since_conception:time | 3.85e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_c_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.578
## Marginal R2: 0.011model_intrusions_rt_c_Nperi_t2 <- lmer(intrusions ~ (ACRT_Nperi*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_rt_c_Nperi_t2) #Intrusions conception Reaction time## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusions ~ (ACRT_Nperi * t1_wks_since_conception) * time +
## (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 2129.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6705 -0.5010 -0.1144 0.4288 3.0482
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 6.072 2.464
## Residual 4.651 2.157
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 5.236e+00 5.397e-01 3.059e+02
## ACRT_Nperi 6.957e-04 8.509e-03 3.059e+02
## t1_wks_since_conception -7.632e-03 7.560e-03 3.059e+02
## time2 -6.470e-01 5.027e-01 2.020e+02
## ACRT_Nperi:t1_wks_since_conception 5.308e-05 1.250e-04 3.059e+02
## ACRT_Nperi:time2 5.192e-03 7.925e-03 2.020e+02
## t1_wks_since_conception:time2 3.184e-03 7.041e-03 2.020e+02
## ACRT_Nperi:t1_wks_since_conception:time2 -7.657e-06 1.164e-04 2.020e+02
## t value Pr(>|t|)
## (Intercept) 9.701 <2e-16 ***
## ACRT_Nperi 0.082 0.935
## t1_wks_since_conception -1.010 0.314
## time2 -1.287 0.200
## ACRT_Nperi:t1_wks_since_conception 0.425 0.671
## ACRT_Nperi:time2 0.655 0.513
## t1_wks_since_conception:time2 0.452 0.652
## ACRT_Nperi:t1_wks_since_conception:time2 -0.066 0.948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_Np t1_w__ time2 ACRT_Np:1___ ACRT_N:2 t1___:
## ACRT_Nperi -0.046
## t1_wks_snc_ -0.904 0.072
## time2 -0.466 0.021 0.421
## ACRT_Np:1___ 0.071 -0.891 -0.126 -0.033
## ACRT_Npr:t2 0.021 -0.466 -0.034 -0.046 0.415
## t1_wks_s_:2 0.421 -0.034 -0.466 -0.904 0.059 0.072
## ACRT_N:1___: -0.033 0.415 0.059 0.071 -0.466 -0.891 -0.126
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_rt_c_Nperi_t2 <- standardize_parameters(model_intrusions_rt_c_Nperi_t2)
print(standardized_model_intrusions_rt_c_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------------------
## (Intercept) | 0.06 | [-0.08, 0.20]
## ACRT Nperi | 0.08 | [-0.06, 0.21]
## t1 wks since conception | -0.07 | [-0.21, 0.07]
## time [2] | -0.13 | [-0.25, 0.00]
## ACRT Nperi × t1 wks since conception | 0.03 | [-0.11, 0.17]
## ACRT Nperi × time [2] | 0.08 | [-0.04, 0.21]
## t1 wks since conception × time [2] | 0.03 | [-0.10, 0.16]
## (ACRT Nperi × t1 wks since conception) × time [2] | -4.24e-03 | [-0.13, 0.12]anova(model_intrusions_rt_c_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACRT_Nperi 0.8886 0.8886 1 202 0.1911
## t1_wks_since_conception 3.7909 3.7909 1 202 0.8152
## time 7.7033 7.7033 1 202 1.6564
## ACRT_Nperi:t1_wks_since_conception 0.9222 0.9222 1 202 0.1983
## ACRT_Nperi:time 1.9959 1.9959 1 202 0.4292
## t1_wks_since_conception:time 0.9508 0.9508 1 202 0.2044
## ACRT_Nperi:t1_wks_since_conception:time 0.0201 0.0201 1 202 0.0043
## Pr(>F)
## ACRT_Nperi 0.6625
## t1_wks_since_conception 0.3677
## time 0.1996
## ACRT_Nperi:t1_wks_since_conception 0.6566
## ACRT_Nperi:time 0.5131
## t1_wks_since_conception:time 0.6516
## ACRT_Nperi:t1_wks_since_conception:time 0.9476eta_squared(model_intrusions_rt_c_Nperi_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------------------------
## ACRT_Nperi | 9.45e-04 | [0.00, 1.00]
## t1_wks_since_conception | 4.02e-03 | [0.00, 1.00]
## time | 8.13e-03 | [0.00, 1.00]
## ACRT_Nperi:t1_wks_since_conception | 9.81e-04 | [0.00, 1.00]
## ACRT_Nperi:time | 2.12e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 1.01e-03 | [0.00, 1.00]
## ACRT_Nperi:t1_wks_since_conception:time | 2.14e-05 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_rt_c_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.575
## Marginal R2: 0.021###H3 - T2 Distress
#Intrusion distress Dprime
model_intrusions_distress_dp_c_neg_t2 <- lmer(intrusionsdistress ~ (ACdP_neg*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_c_neg_t2) #Intrusions distress conception Dprime## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACdP_neg * t1_wks_since_conception) * time +
## (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1056.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.53968 -0.47978 -0.05965 0.50667 2.83190
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5316 0.7291
## Residual 0.3199 0.5656
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.068e+00 1.621e-01 2.907e+02 12.758
## ACdP_neg 1.423e-01 7.681e-02 2.907e+02 1.853
## t1_wks_since_conception -2.474e-04 2.245e-03 2.907e+02 -0.110
## time2 4.252e-02 1.405e-01 2.020e+02 0.303
## ACdP_neg:t1_wks_since_conception -2.005e-03 1.103e-03 2.907e+02 -1.817
## ACdP_neg:time2 -4.904e-03 6.658e-02 2.020e+02 -0.074
## t1_wks_since_conception:time2 -1.949e-03 1.946e-03 2.020e+02 -1.002
## ACdP_neg:t1_wks_since_conception:time2 7.694e-04 9.564e-04 2.020e+02 0.805
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ACdP_neg 0.0649 .
## t1_wks_since_conception 0.9123
## time2 0.7625
## ACdP_neg:t1_wks_since_conception 0.0703 .
## ACdP_neg:time2 0.9414
## t1_wks_since_conception:time2 0.3178
## ACdP_neg:t1_wks_since_conception:time2 0.4220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_n t1_w__ time2 ACdP_:1___ ACP_:2 t1___:
## ACdP_neg -0.354
## t1_wks_snc_ -0.908 0.323
## time2 -0.433 0.153 0.393
## ACdP_n:1___ 0.311 -0.886 -0.346 -0.135
## ACdP_ng:tm2 0.153 -0.433 -0.140 -0.354 0.384
## t1_wks_s_:2 0.393 -0.140 -0.433 -0.908 0.150 0.323
## ACP_:1___:2 -0.135 0.384 0.150 0.311 -0.433 -0.886 -0.346standardized_model_intrusions_distress_dp_c_neg_t2 <- standardize_parameters(model_intrusions_distress_dp_c_neg_t2)
print(standardized_model_intrusions_distress_dp_c_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.16]
## ACdP neg | 0.02 | [-0.11, 0.16]
## t1 wks since conception | -0.05 | [-0.19, 0.09]
## time [2] | -0.06 | [-0.18, 0.06]
## ACdP neg × t1 wks since conception | -0.12 | [-0.25, 0.01]
## ACdP neg × time [2] | 0.09 | [-0.03, 0.21]
## t1 wks since conception × time [2] | -0.05 | [-0.17, 0.07]
## (ACdP neg × t1 wks since conception) × time [2] | 0.05 | [-0.07, 0.16]anova(model_intrusions_distress_dp_c_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACdP_neg 1.30634 1.30634 1 202 4.0835
## t1_wks_since_conception 0.11670 0.11670 1 202 0.3648
## time 0.02930 0.02930 1 202 0.0916
## ACdP_neg:t1_wks_since_conception 0.84901 0.84901 1 202 2.6539
## ACdP_neg:time 0.00174 0.00174 1 202 0.0054
## t1_wks_since_conception:time 0.32091 0.32091 1 202 1.0031
## ACdP_neg:t1_wks_since_conception:time 0.20706 0.20706 1 202 0.6472
## Pr(>F)
## ACdP_neg 0.04462 *
## t1_wks_since_conception 0.54653
## time 0.76248
## ACdP_neg:t1_wks_since_conception 0.10485
## ACdP_neg:time 0.94136
## t1_wks_since_conception:time 0.31775
## ACdP_neg:t1_wks_since_conception:time 0.42205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_c_neg_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------------------------
## ACdP_neg | 0.02 | [0.00, 1.00]
## t1_wks_since_conception | 1.80e-03 | [0.00, 1.00]
## time | 4.53e-04 | [0.00, 1.00]
## ACdP_neg:t1_wks_since_conception | 0.01 | [0.00, 1.00]
## ACdP_neg:time | 2.69e-05 | [0.00, 1.00]
## t1_wks_since_conception:time | 4.94e-03 | [0.00, 1.00]
## ACdP_neg:t1_wks_since_conception:time | 3.19e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_c_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.634
## Marginal R2: 0.026model_intrusions_distress_dp_c_peri_t2 <- lmer(intrusionsdistress ~ (ACdP_peri*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_c_peri_t2) #Intrusions distress conception Dprime## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACdP_peri * t1_wks_since_conception) *
## time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1058.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.55031 -0.47174 -0.06301 0.50304 2.80773
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5333 0.7302
## Residual 0.3215 0.5670
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.075e+00 1.601e-01 2.908e+02
## ACdP_peri 1.478e-01 7.603e-02 2.908e+02
## t1_wks_since_conception -5.207e-04 2.215e-03 2.908e+02
## time2 7.795e-02 1.389e-01 2.020e+02
## ACdP_peri:t1_wks_since_conception -1.784e-03 1.115e-03 2.908e+02
## ACdP_peri:time2 -6.058e-02 6.595e-02 2.020e+02
## t1_wks_since_conception:time2 -2.167e-03 1.921e-03 2.020e+02
## ACdP_peri:t1_wks_since_conception:time2 1.148e-03 9.676e-04 2.020e+02
## t value Pr(>|t|)
## (Intercept) 12.958 <2e-16 ***
## ACdP_peri 1.944 0.0529 .
## t1_wks_since_conception -0.235 0.8143
## time2 0.561 0.5752
## ACdP_peri:t1_wks_since_conception -1.599 0.1109
## ACdP_peri:time2 -0.919 0.3594
## t1_wks_since_conception:time2 -1.128 0.2606
## ACdP_peri:t1_wks_since_conception:time2 1.186 0.2369
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_p t1_w__ time2 ACdP_:1___ ACP_:2 t1___:
## ACdP_peri -0.316
## t1_wks_snc_ -0.908 0.289
## time2 -0.434 0.137 0.394
## ACdP_p:1___ 0.274 -0.894 -0.302 -0.119
## ACdP_pr:tm2 0.137 -0.434 -0.125 -0.316 0.388
## t1_wks_s_:2 0.394 -0.125 -0.434 -0.908 0.131 0.289
## ACP_:1___:2 -0.119 0.388 0.131 0.274 -0.434 -0.894 -0.302standardized_model_intrusions_distress_dp_c_peri_t2 <- standardize_parameters(model_intrusions_distress_dp_c_peri_t2)
print(standardized_model_intrusions_distress_dp_c_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.16]
## ACdP peri | 0.06 | [-0.07, 0.20]
## t1 wks since conception | -0.05 | [-0.19, 0.09]
## time [2] | -0.06 | [-0.18, 0.06]
## ACdP peri × t1 wks since conception | -0.11 | [-0.25, 0.03]
## ACdP peri × time [2] | 0.03 | [-0.09, 0.15]
## t1 wks since conception × time [2] | -0.05 | [-0.17, 0.07]
## (ACdP peri × t1 wks since conception) × time [2] | 0.07 | [-0.05, 0.19]anova(model_intrusions_distress_dp_c_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACdP_peri 0.94608 0.94608 1 202 2.9424
## t1_wks_since_conception 0.20778 0.20778 1 202 0.6462
## time 0.10130 0.10130 1 202 0.3151
## ACdP_peri:t1_wks_since_conception 0.46568 0.46568 1 202 1.4483
## ACdP_peri:time 0.27136 0.27136 1 202 0.8440
## t1_wks_since_conception:time 0.40919 0.40919 1 202 1.2726
## ACdP_peri:t1_wks_since_conception:time 0.45243 0.45243 1 202 1.4071
## Pr(>F)
## ACdP_peri 0.08781 .
## t1_wks_since_conception 0.42241
## time 0.57522
## ACdP_peri:t1_wks_since_conception 0.23020
## ACdP_peri:time 0.35936
## t1_wks_since_conception:time 0.26061
## ACdP_peri:t1_wks_since_conception:time 0.23693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_dp_c_peri_t2 )## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------------------------
## ACdP_peri | 0.01 | [0.00, 1.00]
## t1_wks_since_conception | 3.19e-03 | [0.00, 1.00]
## time | 1.56e-03 | [0.00, 1.00]
## ACdP_peri:t1_wks_since_conception | 7.12e-03 | [0.00, 1.00]
## ACdP_peri:time | 4.16e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 6.26e-03 | [0.00, 1.00]
## ACdP_peri:t1_wks_since_conception:time | 6.92e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_c_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.632
## Marginal R2: 0.023model_intrusions_distress_dp_c_Nperi_t2 <- lmer(intrusionsdistress ~ (ACdP_Nperi*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)
summary(model_intrusions_distress_dp_c_Nperi_t2) #Intrusions distress conception Dprime## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACdP_Nperi * t1_wks_since_conception) *
## time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1061.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.55732 -0.47744 -0.05537 0.50896 2.90001
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5440 0.7376
## Residual 0.3219 0.5674
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.172e+00 1.531e-01 2.897e+02
## ACdP_Nperi 3.958e-03 8.588e-02 2.897e+02
## t1_wks_since_conception -1.651e-03 2.127e-03 2.897e+02
## time2 3.676e-02 1.320e-01 2.020e+02
## ACdP_Nperi:t1_wks_since_conception 3.550e-04 1.295e-03 2.897e+02
## ACdP_Nperi:time2 -5.780e-02 7.405e-02 2.020e+02
## t1_wks_since_conception:time2 -1.479e-03 1.834e-03 2.020e+02
## ACdP_Nperi:t1_wks_since_conception:time2 4.045e-04 1.117e-03 2.020e+02
## t value Pr(>|t|)
## (Intercept) 14.192 <2e-16 ***
## ACdP_Nperi 0.046 0.963
## t1_wks_since_conception -0.776 0.438
## time2 0.279 0.781
## ACdP_Nperi:t1_wks_since_conception 0.274 0.784
## ACdP_Nperi:time2 -0.781 0.436
## t1_wks_since_conception:time2 -0.806 0.421
## ACdP_Nperi:t1_wks_since_conception:time2 0.362 0.718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACdP_N t1_w__ time2 ACdP_N:1___ ACP_N:2 t1___:
## ACdP_Nperi 0.047
## t1_wks_snc_ -0.906 -0.052
## time2 -0.431 -0.020 0.391
## ACdP_N:1___ -0.050 -0.904 0.062 0.021
## ACdP_Npr:t2 -0.020 -0.431 0.023 0.047 0.390
## t1_wks_s_:2 0.391 0.023 -0.431 -0.906 -0.027 -0.052
## ACP_N:1___: 0.021 0.390 -0.027 -0.050 -0.431 -0.904 0.062standardized_model_intrusions_distress_dp_c_Nperi_t2 <- standardize_parameters(model_intrusions_distress_dp_c_Nperi_t2)
print(standardized_model_intrusions_distress_dp_c_Nperi_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.17]
## ACdP Nperi | 0.05 | [-0.09, 0.19]
## t1 wks since conception | -0.06 | [-0.19, 0.08]
## time [2] | -0.06 | [-0.18, 0.06]
## ACdP Nperi × t1 wks since conception | 0.02 | [-0.13, 0.17]
## ACdP Nperi × time [2] | -0.06 | [-0.18, 0.06]
## t1 wks since conception × time [2] | -0.05 | [-0.17, 0.07]
## (ACdP Nperi × t1 wks since conception) × time [2] | 0.02 | [-0.10, 0.15]anova(model_intrusions_distress_dp_c_Nperi_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACdP_Nperi 0.03335 0.03335 1 202 0.1036
## t1_wks_since_conception 0.49935 0.49935 1 202 1.5511
## time 0.02497 0.02497 1 202 0.0776
## ACdP_Nperi:t1_wks_since_conception 0.07323 0.07323 1 202 0.2275
## ACdP_Nperi:time 0.19612 0.19612 1 202 0.6092
## t1_wks_since_conception:time 0.20932 0.20932 1 202 0.6502
## ACdP_Nperi:t1_wks_since_conception:time 0.04225 0.04225 1 202 0.1312
## Pr(>F)
## ACdP_Nperi 0.7479
## t1_wks_since_conception 0.2144
## time 0.7809
## ACdP_Nperi:t1_wks_since_conception 0.6339
## ACdP_Nperi:time 0.4360
## t1_wks_since_conception:time 0.4210
## ACdP_Nperi:t1_wks_since_conception:time 0.7175eta_squared(model_intrusions_distress_dp_c_Nperi_t2)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------------------------
## ACdP_Nperi | 5.13e-04 | [0.00, 1.00]
## t1_wks_since_conception | 7.62e-03 | [0.00, 1.00]
## time | 3.84e-04 | [0.00, 1.00]
## ACdP_Nperi:t1_wks_since_conception | 1.12e-03 | [0.00, 1.00]
## ACdP_Nperi:time | 3.01e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 3.21e-03 | [0.00, 1.00]
## ACdP_Nperi:t1_wks_since_conception:time | 6.49e-04 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_dp_c_Nperi_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.632
## Marginal R2: 0.010#Intrusion Distress - Reaction Time
model_intrusions_distress_rt_c_neg_t2 <- lmer(intrusionsdistress ~ (ACRT_neg*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_distress_rt_c_neg_t2) #Intrusions distress conception Reaction time## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACRT_neg * t1_wks_since_conception) * time +
## (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1089
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54342 -0.47713 -0.06544 0.48929 2.92637
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5412 0.7357
## Residual 0.3218 0.5673
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.171e+00 1.541e-01 2.900e+02 14.088
## ACRT_neg 1.111e-03 2.416e-03 2.900e+02 0.460
## t1_wks_since_conception -1.688e-03 2.131e-03 2.900e+02 -0.792
## time2 6.077e-02 1.331e-01 2.020e+02 0.457
## ACRT_neg:t1_wks_since_conception -2.400e-05 3.370e-05 2.900e+02 -0.712
## ACRT_neg:time2 -2.008e-03 2.086e-03 2.020e+02 -0.963
## t1_wks_since_conception:time2 -1.732e-03 1.841e-03 2.020e+02 -0.941
## ACRT_neg:t1_wks_since_conception:time2 1.670e-05 2.910e-05 2.020e+02 0.574
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ACRT_neg 0.646
## t1_wks_since_conception 0.429
## time2 0.648
## ACRT_neg:t1_wks_since_conception 0.477
## ACRT_neg:time2 0.337
## t1_wks_since_conception:time2 0.348
## ACRT_neg:t1_wks_since_conception:time2 0.567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_n t1_w__ time2 ACRT_n:1___ ACRT_:2 t1___:
## ACRT_neg -0.126
## t1_wks_snc_ -0.906 0.092
## time2 -0.432 0.054 0.391
## ACRT_n:1___ 0.088 -0.896 -0.058 -0.038
## ACRT_ng:tm2 0.054 -0.432 -0.040 -0.126 0.387
## t1_wks_s_:2 0.391 -0.040 -0.432 -0.906 0.025 0.092
## ACRT_:1___: -0.038 0.387 0.025 0.088 -0.432 -0.896 -0.058
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_distress_rt_c_neg_t2 <- standardize_parameters(model_intrusions_distress_rt_c_neg_t2)
print(standardized_model_intrusions_distress_rt_c_neg_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.16]
## ACRT neg | -0.03 | [-0.17, 0.11]
## t1 wks since conception | -0.06 | [-0.20, 0.08]
## time [2] | -0.06 | [-0.18, 0.06]
## ACRT neg × t1 wks since conception | -0.05 | [-0.18, 0.08]
## ACRT neg × time [2] | -0.06 | [-0.18, 0.06]
## t1 wks since conception × time [2] | -0.06 | [-0.17, 0.06]
## (ACRT neg × t1 wks since conception) × time [2] | 0.03 | [-0.08, 0.15]anova(model_intrusions_distress_rt_c_neg_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACRT_neg 0.00078 0.00078 1 202 0.0024
## t1_wks_since_conception 0.56787 0.56787 1 202 1.7648
## time 0.06711 0.06711 1 202 0.2086
## ACRT_neg:t1_wks_since_conception 0.08524 0.08524 1 202 0.2649
## ACRT_neg:time 0.29820 0.29820 1 202 0.9267
## t1_wks_since_conception:time 0.28489 0.28489 1 202 0.8854
## ACRT_neg:t1_wks_since_conception:time 0.10601 0.10601 1 202 0.3294
## Pr(>F)
## ACRT_neg 0.9608
## t1_wks_since_conception 0.1855
## time 0.6484
## ACRT_neg:t1_wks_since_conception 0.6073
## ACRT_neg:time 0.3369
## t1_wks_since_conception:time 0.3479
## ACRT_neg:t1_wks_since_conception:time 0.5666eta_squared(model_intrusions_distress_rt_c_neg_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ---------------------------------------------------------------------
## ACRT_neg | 1.20e-05 | [0.00, 1.00]
## t1_wks_since_conception | 8.66e-03 | [0.00, 1.00]
## time | 1.03e-03 | [0.00, 1.00]
## ACRT_neg:t1_wks_since_conception | 1.31e-03 | [0.00, 1.00]
## ACRT_neg:time | 4.57e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 4.36e-03 | [0.00, 1.00]
## ACRT_neg:t1_wks_since_conception:time | 1.63e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_c_neg_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.632
## Marginal R2: 0.014model_intrusions_distress_rt_c_peri_t2 <- lmer(intrusionsdistress ~ (ACRT_peri*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_distress_rt_c_peri_t2) #Intrusions distress mod Reaction time## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACRT_peri * t1_wks_since_conception) *
## time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1090.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.48413 -0.47284 -0.05694 0.49905 2.84394
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5429 0.7368
## Residual 0.3229 0.5683
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.198e+00 1.551e-01 2.900e+02
## ACRT_peri -2.298e-03 2.479e-03 2.900e+02
## t1_wks_since_conception -2.002e-03 2.154e-03 2.900e+02
## time2 2.273e-02 1.340e-01 2.020e+02
## ACRT_peri:t1_wks_since_conception 2.822e-05 3.431e-05 2.900e+02
## ACRT_peri:time2 1.596e-03 2.141e-03 2.020e+02
## t1_wks_since_conception:time2 -1.255e-03 1.860e-03 2.020e+02
## ACRT_peri:t1_wks_since_conception:time2 -2.322e-05 2.963e-05 2.020e+02
## t value Pr(>|t|)
## (Intercept) 14.173 <2e-16 ***
## ACRT_peri -0.927 0.355
## t1_wks_since_conception -0.930 0.353
## time2 0.170 0.865
## ACRT_peri:t1_wks_since_conception 0.822 0.412
## ACRT_peri:time2 0.746 0.457
## t1_wks_since_conception:time2 -0.675 0.501
## ACRT_peri:t1_wks_since_conception:time2 -0.784 0.434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_p t1_w__ time2 ACRT_p:1___ ACRT_:2 t1___:
## ACRT_peri -0.169
## t1_wks_snc_ -0.906 0.154
## time2 -0.432 0.073 0.391
## ACRT_p:1___ 0.154 -0.897 -0.170 -0.067
## ACRT_pr:tm2 0.073 -0.432 -0.066 -0.169 0.387
## t1_wks_s_:2 0.391 -0.066 -0.432 -0.906 0.073 0.154
## ACRT_:1___: -0.067 0.387 0.073 0.154 -0.432 -0.897 -0.170
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_distress_rt_c_peri_t2 <- standardize_parameters(model_intrusions_distress_rt_c_peri_t2)
print(standardized_model_intrusions_distress_rt_c_peri_t2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------------------------
## (Intercept) | 0.03 | [-0.11, 0.17]
## ACRT peri | -0.03 | [-0.17, 0.11]
## t1 wks since conception | -0.06 | [-0.19, 0.08]
## time [2] | -0.06 | [-0.18, 0.06]
## ACRT peri × t1 wks since conception | 0.06 | [-0.08, 0.19]
## ACRT peri × time [2] | 5.22e-03 | [-0.11, 0.12]
## t1 wks since conception × time [2] | -0.05 | [-0.17, 0.07]
## (ACRT peri × t1 wks since conception) × time [2] | -0.05 | [-0.16, 0.07]anova(model_intrusions_distress_rt_c_peri_t2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACRT_peri 0.14532 0.14532 1 202 0.4500
## t1_wks_since_conception 0.59177 0.59177 1 202 1.8325
## time 0.00930 0.00930 1 202 0.0288
## ACRT_peri:t1_wks_since_conception 0.09298 0.09298 1 202 0.2879
## ACRT_peri:time 0.17956 0.17956 1 202 0.5560
## t1_wks_since_conception:time 0.14699 0.14699 1 202 0.4552
## ACRT_peri:t1_wks_since_conception:time 0.19829 0.19829 1 202 0.6140
## Pr(>F)
## ACRT_peri 0.5031
## t1_wks_since_conception 0.1773
## time 0.8654
## ACRT_peri:t1_wks_since_conception 0.5921
## ACRT_peri:time 0.4567
## t1_wks_since_conception:time 0.5007
## ACRT_peri:t1_wks_since_conception:time 0.4342eta_squared(model_intrusions_distress_rt_c_peri_t2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------------------------------
## ACRT_peri | 2.22e-03 | [0.00, 1.00]
## t1_wks_since_conception | 8.99e-03 | [0.00, 1.00]
## time | 1.43e-04 | [0.00, 1.00]
## ACRT_peri:t1_wks_since_conception | 1.42e-03 | [0.00, 1.00]
## ACRT_peri:time | 2.75e-03 | [0.00, 1.00]
## t1_wks_since_conception:time | 2.25e-03 | [0.00, 1.00]
## ACRT_peri:t1_wks_since_conception:time | 3.03e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_c_peri_t2)## # R2 for Mixed Models
##
## Conditional R2: 0.631
## Marginal R2: 0.010model_intrusions_distress_rt_c_NperiT2 <- lmer(intrusionsdistress ~ (ACRT_Nperi*t1_wks_since_conception)*time + (1 | Prolific_ID), data = final_dataT2_long)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(model_intrusions_distress_rt_c_NperiT2) #Intrusions distress mod reaction time## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: intrusionsdistress ~ (ACRT_Nperi * t1_wks_since_conception) *
## time + (1 | Prolific_ID)
## Data: final_dataT2_long
##
## REML criterion at convergence: 1086.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.55121 -0.46936 -0.05707 0.49928 2.90680
##
## Random effects:
## Groups Name Variance Std.Dev.
## Prolific_ID (Intercept) 0.5408 0.7354
## Residual 0.3193 0.5650
## Number of obs: 412, groups: Prolific_ID, 206
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.191e+00 1.529e-01 2.895e+02
## ACRT_Nperi -3.448e-03 2.410e-03 2.895e+02
## t1_wks_since_conception -2.125e-03 2.141e-03 2.895e+02
## time2 3.343e-02 1.317e-01 2.020e+02
## ACRT_Nperi:t1_wks_since_conception 5.642e-05 3.540e-05 2.895e+02
## ACRT_Nperi:time2 3.453e-03 2.077e-03 2.020e+02
## t1_wks_since_conception:time2 -1.388e-03 1.845e-03 2.020e+02
## ACRT_Nperi:t1_wks_since_conception:time2 -3.938e-05 3.050e-05 2.020e+02
## t value Pr(>|t|)
## (Intercept) 14.334 <2e-16 ***
## ACRT_Nperi -1.431 0.1536
## t1_wks_since_conception -0.992 0.3218
## time2 0.254 0.7999
## ACRT_Nperi:t1_wks_since_conception 1.594 0.1121
## ACRT_Nperi:time2 1.663 0.0979 .
## t1_wks_since_conception:time2 -0.752 0.4526
## ACRT_Nperi:t1_wks_since_conception:time2 -1.291 0.1981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ACRT_Np t1_w__ time2 ACRT_Np:1___ ACRT_N:2 t1___:
## ACRT_Nperi -0.046
## t1_wks_snc_ -0.904 0.072
## time2 -0.431 0.020 0.390
## ACRT_Np:1___ 0.071 -0.891 -0.126 -0.031
## ACRT_Npr:t2 0.020 -0.431 -0.031 -0.046 0.384
## t1_wks_s_:2 0.390 -0.031 -0.431 -0.904 0.054 0.072
## ACRT_N:1___: -0.031 0.384 0.054 0.071 -0.431 -0.891 -0.126
## fit warnings:
## Some predictor variables are on very different scales: consider rescalingstandardized_model_intrusions_distress_rt_c_NperiT2 <- standardize_parameters(model_intrusions_distress_rt_c_NperiT2)
print(standardized_model_intrusions_distress_rt_c_NperiT2)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------------------
## (Intercept) | 0.02 | [-0.12, 0.16]
## ACRT Nperi | 0.01 | [-0.12, 0.15]
## t1 wks since conception | -0.06 | [-0.20, 0.08]
## time [2] | -0.06 | [-0.17, 0.06]
## ACRT Nperi × t1 wks since conception | 0.11 | [-0.03, 0.25]
## ACRT Nperi × time [2] | 0.06 | [-0.06, 0.18]
## t1 wks since conception × time [2] | -0.05 | [-0.17, 0.07]
## (ACRT Nperi × t1 wks since conception) × time [2] | -0.08 | [-0.19, 0.04]anova(model_intrusions_distress_rt_c_NperiT2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value
## ACRT_Nperi 0.19993 0.19993 1 202 0.6262
## t1_wks_since_conception 0.67960 0.67960 1 202 2.1285
## time 0.02056 0.02056 1 202 0.0644
## ACRT_Nperi:t1_wks_since_conception 0.42203 0.42203 1 202 1.3218
## ACRT_Nperi:time 0.88294 0.88294 1 202 2.7654
## t1_wks_since_conception:time 0.18079 0.18079 1 202 0.5662
## ACRT_Nperi:t1_wks_since_conception:time 0.53226 0.53226 1 202 1.6671
## Pr(>F)
## ACRT_Nperi 0.42968
## t1_wks_since_conception 0.14613
## time 0.79992
## ACRT_Nperi:t1_wks_since_conception 0.25163
## ACRT_Nperi:time 0.09787 .
## t1_wks_since_conception:time 0.45263
## ACRT_Nperi:t1_wks_since_conception:time 0.19812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_squared(model_intrusions_distress_rt_c_NperiT2)## Warning: Some predictor variables are on very different scales: consider
## rescaling## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------------------------------
## ACRT_Nperi | 3.09e-03 | [0.00, 1.00]
## t1_wks_since_conception | 0.01 | [0.00, 1.00]
## time | 3.19e-04 | [0.00, 1.00]
## ACRT_Nperi:t1_wks_since_conception | 6.50e-03 | [0.00, 1.00]
## ACRT_Nperi:time | 0.01 | [0.00, 1.00]
## t1_wks_since_conception:time | 2.80e-03 | [0.00, 1.00]
## ACRT_Nperi:t1_wks_since_conception:time | 8.19e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].r2(model_intrusions_distress_rt_c_NperiT2)## # R2 for Mixed Models
##
## Conditional R2: 0.635
## Marginal R2: 0.017