sleep psych within sibship
2023-03-04
Colorado twin sample
all data has been read in, cleaned and scored else where
read in cleaned data
HF_sleep<- fread("/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_sleep_psych/HF_sleep_dat.csv", header=T, data.table = F)
cadd<- fread("/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_sleep_psych/LTS_psych_dat.csv", header=T, data.table=F)
HF_sleep$project<- NULL
HF_sleep$family<- NULL
HF_sleep$bestbet<- NULL
HF_sleep$Trand<- NULL
HF_sleep$racecat<- NULL
HF_sleep$hispanic<- NULL
dat<- merge(HF_sleep, cadd, by='twinid')z transform
dat$sleep_satisfaction<- scale(dat$sleep_satisfaction)
dat$weekday_dur<- scale(dat$weekday_dur)
dat$weekend_dur<- scale(dat$weekend_dur)
dat$total_alertness<- scale(dat$total_alertness)
dat$insom<- scale(dat$insom)create between and within variables
dat<- dat %>% group_by(family) %>%
mutate(
avg_satisf = mean(sleep_satisfaction,na.rm=T),
satisf_diff = sleep_satisfaction-avg_satisf,
avg_weekday_dur= mean(weekday_dur,na.rm=T),
weekday_dur_diff = weekday_dur-avg_weekday_dur,
avg_weekend_dur = mean(weekend_dur,na.rm=T),
weekend_dur_diff = weekend_dur-avg_weekend_dur,
avg_meq = mean(total_meq,na.rm=T),
meq_diff = total_meq-avg_meq,
avg_alertness = mean(total_alertness,na.rm=T),
alertness_diff = total_alertness-avg_alertness,
avg_insom= mean(insom, na.rm=T),
insom_diff=insom-avg_insom
)regress out covariates
# look at stucture
str(dat$hispanic); table(dat$hispanic)
str(dat$racecat); table(dat$racecat)
str(dat$work_schedule); table(dat$work_schedule)
# turn race and work sched into factors
dat$race_fact<- as.factor(dat$racecat)
dat$work_schedule<- as.factor(dat$work_schedule)
str(dat$race_fact)
# non normal
hist(dat$INT)
hist(dat$EXT)
# sqrt root transform
dat$INT_SQRT<- sqrt(dat$INT) ## Warning in sqrt(dat$INT): NaNs produceddat$EXT_SQRT<- sqrt(dat$EXT) ## Warning in sqrt(dat$EXT): NaNs produced# more normal!
hist(dat$INT_SQRT)
hist(dat$EXT_SQRT)
summary(lm(INT_SQRT~sex+Age+hispanic+race_fact+work_schedule, data=dat, na.action = "na.exclude"))
summary(lm(EXT_SQRT~sex+Age+hispanic+race_fact+work_schedule, data=dat, na.action = "na.exclude"))
# regress out and save residuals
dat$INT_resid<- resid(lm(INT_SQRT~sex+Age+hispanic+race_fact+work_schedule, data=dat, na.action = "na.exclude"))
dat$EXT_resid<- resid(lm(EXT_SQRT~sex+Age+hispanic+race_fact+work_schedule, data=dat, na.action = "na.exclude"))
# looks good
hist(dat$INT_resid)
hist(dat$EXT_resid)
# finally, standardize
dat$INT_resid<- scale(dat$INT_resid)
dat$EXT_resid<- scale(dat$EXT_resid)
# mean around 0 and SD=1!
mean(dat$INT_resid, na.rm=T); sd(dat$INT_resid, na.rm=T)
mean(dat$EXT_resid, na.rm=T); sd(dat$EXT_resid, na.rm=T)code zygosity
dat$family<- as.numeric(dat$family)
dat$zyg<- ifelse(dat$bestbet=="MZ", 1/2, ifelse(dat$bestbet=="DZ" | dat$bestbet=="OS", -1/2, NA))
dat$zygMZ<- ifelse(dat$bestbet=="MZ", 0, ifelse(dat$bestbet=="DZ" | dat$bestbet=="OS", 1, NA))
dat$zygDZ<- ifelse(dat$bestbet=="MZ", 1, ifelse(dat$bestbet=="DZ" | dat$bestbet=="OS", 0, NA))
table(dat$zyg)##
## -0.5 0.5
## 1074 1037table(dat$sex)##
## 0 1
## 1181 932create data frame to save models
col_out<- data.frame()internalizing
satisfaction
# satisfaction
satisf_int_pheno<- lmer(INT_resid~sleep_satisfaction+(1|family), data=dat)
summary(satisf_int_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ sleep_satisfaction + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2319.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22787 -0.64216 0.01849 0.67537 2.61698
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1518 0.3897
## Residual 0.8163 0.9035
## Number of obs: 826, groups: family, 606
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01531 0.03683 604.61341 -0.416 0.678
## sleep_satisfaction -0.13687 0.03424 823.94777 -3.997 6.99e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## slp_stsfctn 0.254out<- satisf_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Satisfaction",
sleep="Satisfaction",
Psychiatric="Internalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
satisf_int<- lmer(INT_resid~avg_satisf+satisf_diff+(1|family), data=dat)
summary(satisf_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_satisf + satisf_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2319.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.28104 -0.64978 0.01876 0.66461 2.65307
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1472 0.3837
## Residual 0.8178 0.9043
## Number of obs: 826, groups: family, 606
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01749 0.03676 601.45769 -0.476 0.634
## avg_satisf -0.18793 0.04414 633.87961 -4.258 2.37e-05 ***
## satisf_diff -0.06273 0.05310 511.85440 -1.181 0.238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_st
## avg_satisf 0.219
## satisf_diff 0.138 0.015anova(satisf_int_pheno,satisf_int)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## satisf_int_pheno: INT_resid ~ sleep_satisfaction + (1 | family)
## satisf_int: INT_resid ~ avg_satisf + satisf_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## satisf_int_pheno 4 2317.5 2336.4 -1154.8 2309.5
## satisf_int 5 2316.2 2339.8 -1153.1 2306.2 3.3404 1 0.0676 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1satisf_int_zyg<- lmer(INT_resid~avg_satisf+satisf_diff+zyg+zyg*satisf_diff+(1|family), data=dat)
summary(satisf_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_satisf + satisf_diff + zyg + zyg * satisf_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2320.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25803 -0.65569 0.02182 0.65536 2.65216
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1467 0.3831
## Residual 0.8212 0.9062
## Number of obs: 824, groups: family, 605
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.018033 0.036865 596.586339 -0.489 0.625
## avg_satisf -0.185897 0.044290 630.507469 -4.197 3.09e-05 ***
## satisf_diff -0.057312 0.053508 514.501087 -1.071 0.285
## zyg 0.008143 0.071651 627.015829 0.114 0.910
## satisf_diff:zyg 0.072475 0.107354 535.755867 0.675 0.500
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_st stsf_d zyg
## avg_satisf 0.219
## satisf_diff 0.138 0.019
## zyg 0.038 0.058 0.020
## stsf_dff:zy 0.028 0.021 0.093 0.138out<- satisf_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Satisfaction",
sleep="Satisfaction",
Psychiatric="Internalizing",
model="Between Within")
col_out<- rbind(col_out, out)
satisf_int_MZ<- lmer(INT_resid~avg_satisf+satisf_diff+zygMZ+zygMZ*satisf_diff+(1|family), data=dat)
summary(satisf_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_satisf + satisf_diff + zygMZ + zygMZ * satisf_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2320.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25803 -0.65569 0.02182 0.65536 2.65216
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1467 0.3831
## Residual 0.8212 0.9062
## Number of obs: 824, groups: family, 605
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.013962 0.052379 593.898162 -0.267 0.79
## avg_satisf -0.185897 0.044290 630.507469 -4.197 3.09e-05 ***
## satisf_diff -0.021074 0.079222 559.518103 -0.266 0.79
## zygMZ -0.008143 0.071651 627.015829 -0.114 0.91
## satisf_diff:zygMZ -0.072475 0.107354 535.755867 -0.675 0.50
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_st stsf_d zygMZ
## avg_satisf 0.194
## satisf_diff 0.152 0.027
## zygMZ -0.711 -0.058 -0.107
## stsf_dff:MZ -0.114 -0.021 -0.740 0.138out<- satisf_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Satisfaction",
sleep="Satisfaction",
Psychiatric="Internalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
satisf_int_DZ<- lmer(INT_resid~avg_satisf+satisf_diff+zygDZ+zygDZ*satisf_diff+(1|family), data=dat)
summary(satisf_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_satisf + satisf_diff + zygDZ + zygDZ * satisf_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2320.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25803 -0.65569 0.02182 0.65536 2.65216
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1467 0.3831
## Residual 0.8212 0.9062
## Number of obs: 824, groups: family, 605
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.022104 0.050413 630.225809 -0.438 0.661
## avg_satisf -0.185897 0.044290 630.507469 -4.197 3.09e-05 ***
## satisf_diff -0.093549 0.072197 485.417033 -1.296 0.196
## zygDZ 0.008143 0.071651 627.015829 0.114 0.910
## satisf_diff:zygDZ 0.072475 0.107354 535.755867 0.675 0.500
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_st stsf_d zygDZ
## avg_satisf 0.120
## satisf_diff 0.122 -0.002
## zygDZ -0.683 0.058 -0.088
## stsf_dff:DZ -0.078 0.021 -0.675 0.138out<- satisf_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Satisfaction",
sleep="Satisfaction",
Psychiatric="Internalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)weekend duration
# weekend duration
weekend_dur_int_pheno<- lmer(INT_resid~weekend_dur+(1|family), data=dat)
summary(weekend_dur_int_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ weekend_dur + (1 | family)
## Data: dat
##
## REML criterion at convergence: 1818.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0360 -0.6272 0.0215 0.6060 2.1402
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2573 0.5072
## Residual 0.7235 0.8506
## Number of obs: 646, groups: family, 514
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01176 0.04107 504.54740 -0.286 0.775
## weekend_dur -0.02017 0.03475 642.86233 -0.580 0.562
##
## Correlation of Fixed Effects:
## (Intr)
## weekend_dur 0.111out<- weekend_dur_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
weekend_dur_int<- lmer(INT_resid~avg_weekend_dur+weekend_dur_diff+(1|family), data=dat)
summary(weekend_dur_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekend_dur + weekend_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 1821.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.03432 -0.62735 0.02152 0.60601 2.14049
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2566 0.5065
## Residual 0.7256 0.8518
## Number of obs: 646, groups: family, 514
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01173 0.04116 502.47708 -0.285 0.776
## avg_weekend_dur -0.02008 0.04274 562.83184 -0.470 0.639
## weekend_dur_diff -0.02031 0.06237 337.69125 -0.326 0.745
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_dr 0.124
## wknd_dr_dff 0.014 -0.029anova(weekend_dur_int_pheno,weekend_dur_int)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## weekend_dur_int_pheno: INT_resid ~ weekend_dur + (1 | family)
## weekend_dur_int: INT_resid ~ avg_weekend_dur + weekend_dur_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekend_dur_int_pheno 4 1816.7 1834.6 -904.37 1808.7
## weekend_dur_int 5 1818.7 1841.1 -904.37 1808.7 0 1 0.9977weekend_dur_int_zyg<- lmer(INT_resid~avg_weekend_dur+weekend_dur_diff+zyg+zyg*weekend_dur_diff+(1|family), data=dat)
summary(weekend_dur_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekend_dur + weekend_dur_diff + zyg + zyg *
## weekend_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 1823.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.03034 -0.63891 0.02638 0.61539 2.15762
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2582 0.5081
## Residual 0.7266 0.8524
## Number of obs: 645, groups: family, 513
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.013003 0.041260 499.847134 -0.315 0.753
## avg_weekend_dur -0.019064 0.042907 561.613689 -0.444 0.657
## weekend_dur_diff -0.002501 0.064909 327.768914 -0.039 0.969
## zyg -0.008321 0.081372 538.900478 -0.102 0.919
## weekend_dur_diff:zyg 0.106223 0.129932 331.589538 0.818 0.414
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wknd__ zyg
## avg_wknd_dr 0.121
## wknd_dr_dff 0.011 -0.026
## zyg 0.018 -0.066 0.008
## wknd_dr_df: 0.003 0.002 0.264 0.016out<- weekend_dur_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Between Within")
col_out<- rbind(col_out, out)
weekend_dur_int_MZ<- lmer(INT_resid~avg_weekend_dur+weekend_dur_diff+zygMZ+zygMZ*weekend_dur_diff+(1|family), data=dat)
summary(weekend_dur_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekend_dur + weekend_dur_diff + zygMZ + zygMZ *
## weekend_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 1823.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.03034 -0.63891 0.02638 0.61539 2.15762
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2582 0.5081
## Residual 0.7266 0.8524
## Number of obs: 645, groups: family, 513
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.017164 0.058456 525.795560 -0.294 0.769
## avg_weekend_dur -0.019064 0.042907 561.613689 -0.444 0.657
## weekend_dur_diff 0.050611 0.103241 322.824071 0.490 0.624
## zygMZ 0.008321 0.081372 538.900478 0.102 0.919
## weekend_dur_diff:zygMZ -0.106223 0.129932 331.589538 -0.818 0.414
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wknd__ zygMZ
## avg_wknd_dr 0.040
## wknd_dr_dff 0.017 -0.016
## zygMZ -0.708 0.066 -0.015
## wknd_dr_:MZ -0.013 -0.002 -0.795 0.016out<- weekend_dur_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
weekend_dur_int_DZ<- lmer(INT_resid~avg_weekend_dur+weekend_dur_diff+zygDZ+zygDZ*weekend_dur_diff+(1|family), data=dat)
summary(weekend_dur_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekend_dur + weekend_dur_diff + zygDZ + zygDZ *
## weekend_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 1823.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.03034 -0.63891 0.02638 0.61539 2.15762
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2582 0.5081
## Residual 0.7266 0.8524
## Number of obs: 645, groups: family, 513
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.008843 0.057432 512.603238 -0.154 0.878
## avg_weekend_dur -0.019064 0.042907 561.613689 -0.444 0.657
## weekend_dur_diff -0.055612 0.078796 341.765256 -0.706 0.481
## zygDZ -0.008321 0.081372 538.900478 -0.102 0.919
## weekend_dur_diff:zygDZ 0.106223 0.129932 331.589538 0.818 0.414
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wknd__ zygDZ
## avg_wknd_dr 0.134
## wknd_dr_dff 0.010 -0.023
## zygDZ -0.696 -0.066 -0.007
## wknd_dr_:DZ -0.009 0.002 -0.607 0.016out<- weekend_dur_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)weekday duration
# weekday duration
weekday_dur_int_pheno<- lmer(INT_resid~weekday_dur+(1|family), data=dat)
summary(weekday_dur_int_pheno) ## NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ weekday_dur + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2466.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.20270 -0.63883 0.00684 0.66145 2.58429
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1697 0.4120
## Residual 0.8333 0.9128
## Number of obs: 868, groups: family, 630
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01360 0.03582 608.79003 -0.380 0.704
## weekday_dur -0.02894 0.03061 864.79284 -0.946 0.345
##
## Correlation of Fixed Effects:
## (Intr)
## weekday_dur 0.133out<- weekday_dur_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
weekday_dur_int<- lmer(INT_resid~avg_weekday_dur+weekday_dur_diff+(1|family), data=dat)
summary(weekday_dur_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekday_dur + weekday_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2470.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21078 -0.63574 0.00454 0.66712 2.58366
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1699 0.4122
## Residual 0.8342 0.9133
## Number of obs: 868, groups: family, 630
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01403 0.03590 604.61803 -0.391 0.696
## avg_weekday_dur -0.03500 0.04210 617.43553 -0.831 0.406
## weekday_dur_diff -0.02150 0.04692 475.58949 -0.458 0.647
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_dr 0.136
## wkdy_dr_dff 0.044 -0.045anova(weekday_dur_int_pheno,weekday_dur_int)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## weekday_dur_int_pheno: INT_resid ~ weekday_dur + (1 | family)
## weekday_dur_int: INT_resid ~ avg_weekday_dur + weekday_dur_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekday_dur_int_pheno 4 2464.6 2483.6 -1228.3 2456.6
## weekday_dur_int 5 2466.5 2490.3 -1228.3 2456.5 0.0439 1 0.8341weekday_dur_int_zyg<- lmer(INT_resid~avg_weekday_dur+weekday_dur_diff+zyg+weekday_dur_diff*zyg+(1|family), data=dat)
summary(weekday_dur_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekday_dur + weekday_dur_diff + zyg + weekday_dur_diff *
## zyg + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2468.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13701 -0.63600 0.02837 0.65850 2.69363
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1693 0.4115
## Residual 0.8342 0.9133
## Number of obs: 866, groups: family, 629
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01343 0.03595 602.26963 -0.374 0.7087
## avg_weekday_dur -0.03141 0.04215 616.67975 -0.745 0.4565
## weekday_dur_diff -0.01412 0.04709 474.68160 -0.300 0.7644
## zyg 0.03507 0.07094 637.78355 0.494 0.6212
## weekday_dur_diff:zyg 0.16235 0.09471 505.53236 1.714 0.0871 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wkdy__ zyg
## avg_wkdy_dr 0.135
## wkdy_dr_dff 0.046 -0.043
## zyg 0.029 -0.025 0.033
## wkdy_dr_df: 0.030 0.031 0.074 0.052out<- weekday_dur_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Between Within")
col_out<- rbind(col_out, out)
weekday_dur_int_MZ<- lmer(INT_resid~avg_weekday_dur+weekday_dur_diff+zygMZ+weekday_dur_diff*zygMZ+(1|family), data=dat)
summary(weekday_dur_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekday_dur + weekday_dur_diff + zygMZ + weekday_dur_diff *
## zygMZ + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2468.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13701 -0.63600 0.02837 0.65850 2.69363
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1693 0.4115
## Residual 0.8342 0.9133
## Number of obs: 866, groups: family, 629
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.004103 0.051229 605.918025 0.080 0.9362
## avg_weekday_dur -0.031408 0.042150 616.679752 -0.745 0.4565
## weekday_dur_diff 0.067055 0.069214 500.267571 0.969 0.3331
## zygMZ -0.035074 0.070941 637.783549 -0.494 0.6212
## weekday_dur_diff:zygMZ -0.162349 0.094711 505.532364 -1.714 0.0871 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wkdy__ zygMZ
## avg_wkdy_dr 0.077
## wkdy_dr_dff 0.076 -0.008
## zygMZ -0.713 0.025 -0.058
## wkdy_dr_:MZ -0.057 -0.031 -0.735 0.052out<- weekday_dur_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
weekday_dur_int_DZ<- lmer(INT_resid~avg_weekday_dur+weekday_dur_diff+zygDZ+weekday_dur_diff*zygDZ+(1|family), data=dat)
summary(weekday_dur_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekday_dur + weekday_dur_diff + zygDZ + weekday_dur_diff *
## zygDZ + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2468.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13701 -0.63600 0.02837 0.65850 2.69363
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1693 0.4115
## Residual 0.8342 0.9133
## Number of obs: 866, groups: family, 629
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03097 0.04976 634.45294 -0.622 0.5339
## avg_weekday_dur -0.03141 0.04215 616.67975 -0.745 0.4565
## weekday_dur_diff -0.09529 0.06426 478.29103 -1.483 0.1387
## zygDZ 0.03507 0.07094 637.78355 0.494 0.6212
## weekday_dur_diff:zygDZ 0.16235 0.09471 505.53236 1.714 0.0871 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wkdy__ zygDZ
## avg_wkdy_dr 0.115
## wkdy_dr_dff 0.018 -0.054
## zygDZ -0.692 -0.025 -0.014
## wkdy_dr_:DZ -0.015 0.031 -0.683 0.052out<- weekday_dur_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)alertness
# alertness
alertness_int_pheno<- lmer(INT_resid~total_alertness+(1|family), data=dat)
summary(alertness_int_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ total_alertness + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2497.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16967 -0.66525 0.01919 0.67470 2.48863
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1542 0.3926
## Residual 0.8408 0.9169
## Number of obs: 881, groups: family, 635
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.009938 0.035106 606.540008 -0.283 0.7772
## total_alertness -0.068799 0.032675 878.955927 -2.106 0.0355 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## totl_lrtnss 0.073out<- alertness_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Alertness",
sleep="Alertness",
Psychiatric="Internalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
alertness_int<- lmer(INT_resid~avg_alertness+alertness_diff+(1|family), data=dat)
summary(alertness_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_alertness + alertness_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2498.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17598 -0.66299 0.02227 0.65645 2.54371
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1518 0.3897
## Residual 0.8415 0.9173
## Number of obs: 881, groups: family, 635
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01244 0.03510 604.71402 -0.354 0.7233
## avg_alertness -0.11206 0.04355 621.56914 -2.573 0.0103 *
## alertness_diff -0.01208 0.04998 501.94387 -0.242 0.8091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_lr
## avg_alrtnss 0.087
## alrtnss_dff 0.011 -0.011anova(alertness_int_pheno,alertness_int)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## alertness_int_pheno: INT_resid ~ total_alertness + (1 | family)
## alertness_int: INT_resid ~ avg_alertness + alertness_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## alertness_int_pheno 4 2495.3 2514.5 -1243.7 2487.3
## alertness_int 5 2495.1 2519.0 -1242.5 2485.1 2.254 1 0.1333alertness_int_zyg<- lmer(INT_resid~avg_alertness+alertness_diff+zyg+zyg*alertness_diff+(1|family), data=dat)
summary(alertness_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_alertness + alertness_diff + zyg + zyg * alertness_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2498.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16794 -0.66094 0.02024 0.65348 2.56869
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1477 0.3843
## Residual 0.8472 0.9204
## Number of obs: 879, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01442 0.03515 602.55798 -0.410 0.682
## avg_alertness -0.11254 0.04357 619.05485 -2.583 0.010 *
## alertness_diff -0.02390 0.05163 500.76318 -0.463 0.644
## zyg 0.01831 0.06972 647.54417 0.263 0.793
## alertness_diff:zyg -0.07978 0.10356 520.56731 -0.770 0.441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_lr alrtn_ zyg
## avg_alrtnss 0.088
## alrtnss_dff 0.015 -0.009
## zyg 0.033 0.030 0.015
## alrtnss_df: 0.012 0.005 0.237 0.017out<- alertness_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Alertness",
sleep="Alertness",
Psychiatric="Internalizing",
model="Between Within")
col_out<- rbind(col_out, out)
alertness_int_MZ<- lmer(INT_resid~avg_alertness+alertness_diff+zygMZ+zygMZ*alertness_diff+(1|family), data=dat)
summary(alertness_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_alertness + alertness_diff + zygMZ + zygMZ *
## alertness_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2498.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16794 -0.66094 0.02024 0.65348 2.56869
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1477 0.3843
## Residual 0.8472 0.9204
## Number of obs: 879, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.005267 0.050312 611.280376 -0.105 0.917
## avg_alertness -0.112545 0.043568 619.054854 -2.583 0.010 *
## alertness_diff -0.063788 0.081311 510.662126 -0.784 0.433
## zygMZ -0.018311 0.069716 647.544173 -0.263 0.793
## alertness_diff:zygMZ 0.079775 0.103561 520.567309 0.770 0.441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_lr alrtn_ zygMZ
## avg_alrtnss 0.083
## alrtnss_dff 0.026 -0.002
## zygMZ -0.716 -0.030 -0.020
## alrtnss_:MZ -0.020 -0.005 -0.787 0.017out<- alertness_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Alertness",
sleep="Alertness",
Psychiatric="Internalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
alertness_int_DZ<- lmer(INT_resid~avg_alertness+alertness_diff+zygDZ+zygDZ*alertness_diff+(1|family), data=dat)
summary(alertness_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_alertness + alertness_diff + zygDZ + zygDZ *
## alertness_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2498.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16794 -0.66094 0.02024 0.65348 2.56869
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1477 0.3843
## Residual 0.8472 0.9204
## Number of obs: 879, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02358 0.04869 639.23603 -0.484 0.628
## avg_alertness -0.11254 0.04357 619.05485 -2.583 0.010 *
## alertness_diff 0.01599 0.06389 510.54559 0.250 0.803
## zygDZ 0.01831 0.06972 647.54417 0.263 0.793
## alertness_diff:zygDZ -0.07978 0.10356 520.56731 -0.770 0.441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_lr alrtn_ zygDZ
## avg_alrtnss 0.042
## alrtnss_dff 0.003 -0.011
## zygDZ -0.692 0.030 -0.001
## alrtnss_:DZ -0.003 0.005 -0.619 0.017out<- alertness_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Alertness",
sleep="Alertness",
Psychiatric="Internalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)chronotype
# chrono
chrono_int_pheno<- lmer(INT_resid~total_meq+(1|family), data=dat)
summary(chrono_int_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ total_meq + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2500.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17198 -0.64944 0.02028 0.66973 2.55691
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1463 0.3825
## Residual 0.8471 0.9204
## Number of obs: 881, groups: family, 635
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.38603 0.17422 841.63027 2.216 0.0270 *
## total_meq -0.00805 0.00352 850.30815 -2.287 0.0224 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## total_meq -0.980out<- chrono_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
chrono_int<- lmer(INT_resid~avg_meq+meq_diff+(1|family), data=dat)
summary(chrono_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_meq + meq_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2508.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15030 -0.65079 0.01626 0.65438 2.54576
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1468 0.3832
## Residual 0.8475 0.9206
## Number of obs: 881, groups: family, 635
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.434848 0.213421 628.149493 2.038 0.0420 *
## avg_meq -0.009035 0.004309 634.297962 -2.096 0.0364 *
## meq_diff -0.006088 0.006062 585.133566 -1.004 0.3156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_mq
## avg_meq -0.986
## meq_diff 0.005 0.006anova(chrono_int_pheno,chrono_int)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## chrono_int_pheno: INT_resid ~ total_meq + (1 | family)
## chrono_int: INT_resid ~ avg_meq + meq_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## chrono_int_pheno 4 2494.6 2513.7 -1243.3 2486.6
## chrono_int 5 2496.4 2520.3 -1243.2 2486.4 0.1578 1 0.6912chrono_int_zyg<- lmer(INT_resid~avg_meq+meq_diff+zyg+zyg*meq_diff+(1|family), data=dat)
summary(chrono_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_meq + meq_diff + zyg + zyg * meq_diff + (1 |
## family)
## Data: dat
##
## REML criterion at convergence: 2511.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17950 -0.63906 0.02351 0.66436 2.59925
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1440 0.3795
## Residual 0.8503 0.9221
## Number of obs: 879, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.444335 0.213469 624.383272 2.081 0.0378 *
## avg_meq -0.009239 0.004312 630.509540 -2.142 0.0325 *
## meq_diff -0.004848 0.006115 558.597124 -0.793 0.4282
## zyg 0.030000 0.069751 646.676633 0.430 0.6673
## meq_diff:zyg 0.017464 0.012270 583.215183 1.423 0.1552
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_mq mq_dff zyg
## avg_meq -0.986
## meq_diff 0.005 0.006
## zyg 0.001 0.004 0.023
## meq_dff:zyg -0.008 0.012 0.122 0.064out<- chrono_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Between Within")
col_out<- rbind(col_out, out)
chrono_int_MZ<- lmer(INT_resid~avg_meq+meq_diff+zygMZ+zygMZ*meq_diff+(1|family), data=dat)
summary(chrono_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_meq + meq_diff + zygMZ + zygMZ * meq_diff + (1 |
## family)
## Data: dat
##
## REML criterion at convergence: 2511.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17950 -0.63906 0.02351 0.66436 2.59925
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1440 0.3795
## Residual 0.8503 0.9221
## Number of obs: 879, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.459335 0.216349 631.798042 2.123 0.0341 *
## avg_meq -0.009239 0.004312 630.509539 -2.142 0.0325 *
## meq_diff 0.003884 0.009174 493.456243 0.423 0.6722
## zygMZ -0.030000 0.069751 646.676633 -0.430 0.6673
## meq_diff:zygMZ -0.017464 0.012270 583.215182 -1.423 0.1552
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_mq mq_dff zygMZ
## avg_meq -0.973
## meq_diff 0.007 0.012
## zygMZ -0.163 -0.004 -0.058
## mq_dff:zyMZ -0.002 -0.012 -0.750 0.064out<- chrono_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
chrono_int_DZ<- lmer(INT_resid~avg_meq+meq_diff+zygDZ+zygDZ*meq_diff+(1|family), data=dat)
summary(chrono_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_meq + meq_diff + zygDZ + zygDZ * meq_diff + (1 |
## family)
## Data: dat
##
## REML criterion at convergence: 2511.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17950 -0.63906 0.02351 0.66436 2.59925
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1440 0.3795
## Residual 0.8503 0.9221
## Number of obs: 879, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.429335 0.216250 618.135845 1.985 0.0475 *
## avg_meq -0.009239 0.004312 630.509531 -2.142 0.0325 *
## meq_diff -0.013580 0.008118 677.942308 -1.673 0.0948 .
## zygDZ 0.030000 0.069751 646.676634 0.430 0.6673
## meq_diff:zygDZ 0.017464 0.012270 583.215189 1.423 0.1552
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_mq mq_dff zygDZ
## avg_meq -0.974
## meq_diff 0.015 -0.005
## zygDZ -0.160 0.004 -0.031
## mq_dff:zyDZ -0.018 0.012 -0.664 0.064out<- chrono_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)insomnia
# insom
insom_int_pheno<- lmer(INT_resid~insom + (1|family), data=dat)
summary(insom_int_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ insom + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2464.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.20505 -0.64305 -0.01324 0.67939 2.63702
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1377 0.3710
## Residual 0.8290 0.9105
## Number of obs: 878, groups: family, 635
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04649 0.03542 618.90946 -1.313 0.19
## insom 0.15418 0.02888 875.63839 5.338 1.2e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## insom -0.231out<- insom_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Insomnia",
sleep="Insomnia",
Psychiatric="Internalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
insom_int<- lmer(INT_resid~avg_insom+insom_diff+(1|family), data=dat)
summary(insom_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_insom + insom_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2467.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25923 -0.64374 -0.00273 0.69035 2.65341
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1363 0.3691
## Residual 0.8299 0.9110
## Number of obs: 878, groups: family, 635
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04949 0.03549 611.52481 -1.394 0.1637
## avg_insom 0.18400 0.03855 642.01783 4.772 2.25e-06 ***
## insom_diff 0.11191 0.04635 513.43267 2.414 0.0161 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ns
## avg_insom -0.221
## insom_diff -0.086 -0.052anova(insom_int_pheno,insom_int)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## insom_int_pheno: INT_resid ~ insom + (1 | family)
## insom_int: INT_resid ~ avg_insom + insom_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## insom_int_pheno 4 2462.6 2481.7 -1227.3 2454.6
## insom_int 5 2463.2 2487.1 -1226.6 2453.2 1.3644 1 0.2428insom_int_zyg<- lmer(INT_resid~avg_insom+insom_diff+zyg+zyg*insom_diff+(1|family), data=dat)
summary(insom_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_insom + insom_diff + zyg + zyg * insom_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2466.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25166 -0.64421 -0.00997 0.68601 2.65174
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1371 0.3702
## Residual 0.8301 0.9111
## Number of obs: 876, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05138 0.03558 607.38894 -1.444 0.1492
## avg_insom 0.18254 0.03866 639.64040 4.722 2.87e-06 ***
## insom_diff 0.11486 0.04666 507.75560 2.461 0.0142 *
## zyg -0.00206 0.06917 645.27142 -0.030 0.9762
## insom_diff:zyg 0.13745 0.09329 513.23038 1.473 0.1413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ns insm_d zyg
## avg_insom -0.222
## insom_diff -0.085 -0.050
## zyg 0.044 -0.057 -0.015
## insm_dff:zy -0.018 -0.007 0.088 -0.098out<- insom_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Insomnia",
sleep="Insomnia",
Psychiatric="Internalizing",
model="Between Within")
col_out<- rbind(col_out, out)
insom_int_MZ<- lmer(INT_resid~avg_insom+insom_diff+zygMZ+zygMZ*insom_diff+(1|family), data=dat)
summary(insom_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_insom + insom_diff + zygMZ + zygMZ * insom_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2466.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25166 -0.64421 -0.00997 0.68601 2.65174
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1371 0.3702
## Residual 0.8301 0.9111
## Number of obs: 876, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05241 0.05069 612.29172 -1.034 0.30153
## avg_insom 0.18254 0.03866 639.64040 4.722 2.87e-06 ***
## insom_diff 0.18359 0.06883 524.87593 2.667 0.00788 **
## zygMZ 0.00206 0.06917 645.27142 0.030 0.97625
## insom_diff:zygMZ -0.13745 0.09329 513.23038 -1.473 0.14125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ns insm_d zygMZ
## avg_insom -0.195
## insom_diff -0.101 -0.038
## zygMZ -0.713 0.057 0.076
## insm_dff:MZ 0.079 0.007 -0.737 -0.098out<- insom_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Insomnia",
sleep="Insomnia",
Psychiatric="Internalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
insom_int_DZ<- lmer(INT_resid~avg_insom+insom_diff+zygDZ+zygDZ*insom_diff+(1|family), data=dat)
summary(insom_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_insom + insom_diff + zygDZ + zygDZ * insom_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2466.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.25166 -0.64421 -0.00997 0.68601 2.65174
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.1371 0.3702
## Residual 0.8301 0.9111
## Number of obs: 876, groups: family, 634
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05035 0.04852 640.47145 -1.038 0.300
## avg_insom 0.18254 0.03866 639.64040 4.722 2.87e-06 ***
## insom_diff 0.04614 0.06300 493.72454 0.732 0.464
## zygDZ -0.00206 0.06917 645.27142 -0.030 0.976
## insom_diff:zygDZ 0.13745 0.09329 513.23038 1.473 0.141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ns insm_d zygDZ
## avg_insom -0.122
## insom_diff -0.080 -0.032
## zygDZ -0.681 -0.057 0.061
## insm_dff:DZ 0.056 -0.007 -0.675 -0.098out<- insom_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Insomnia",
sleep="Insomnia",
Psychiatric="Internalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)externalizing
satisfaction
# satisfaction
satisf_ext_pheno<- lmer(EXT_resid~sleep_satisfaction+(1|family), data=dat)
summary(satisf_ext_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ sleep_satisfaction + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2526.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.14117 -0.66122 -0.02402 0.62103 2.31853
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2627 0.5125
## Residual 0.7380 0.8591
## Number of obs: 894, groups: family, 642
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01823 0.03604 613.12580 -0.506 0.6131
## sleep_satisfaction -0.08509 0.03317 891.76428 -2.565 0.0105 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## slp_stsfctn 0.139out<- satisf_ext_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Satisfaction",
sleep="Satisfaction",
Psychiatric="Externalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
satisf_ext<- lmer(EXT_resid~avg_satisf+satisf_diff+(1|family), data=dat)
summary(satisf_ext)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_satisf + satisf_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2528.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13693 -0.65055 -0.01632 0.62088 2.35209
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2632 0.5130
## Residual 0.7371 0.8586
## Number of obs: 894, groups: family, 642
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01974 0.03605 611.55095 -0.548 0.58414
## avg_satisf -0.11722 0.04275 648.24792 -2.742 0.00627 **
## satisf_diff -0.03843 0.05130 489.21508 -0.749 0.45408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_st
## avg_satisf 0.129
## satisf_diff 0.064 0.020anova(satisf_ext_pheno,satisf_ext)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## satisf_ext_pheno: EXT_resid ~ sleep_satisfaction + (1 | family)
## satisf_ext: EXT_resid ~ avg_satisf + satisf_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## satisf_ext_pheno 4 2524.9 2544.1 -1258.5 2516.9
## satisf_ext 5 2525.5 2549.5 -1257.7 2515.5 1.4235 1 0.2328satisf_ext_zyg<- lmer(EXT_resid~avg_satisf+satisf_diff+zyg+zyg*satisf_diff+(1|family), data=dat)
summary(satisf_ext_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_satisf + satisf_diff + zyg + zyg * satisf_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2532.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15370 -0.65043 -0.01981 0.61586 2.32689
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2636 0.5134
## Residual 0.7396 0.8600
## Number of obs: 893, groups: family, 641
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.018447 0.036188 605.822140 -0.510 0.61041
## avg_satisf -0.116067 0.042844 647.023114 -2.709 0.00693 **
## satisf_diff -0.037909 0.051506 485.889111 -0.736 0.46209
## zyg 0.046478 0.071256 644.451614 0.652 0.51446
## satisf_diff:zyg 0.006891 0.103071 486.863686 0.067 0.94672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_st stsf_d zyg
## avg_satisf 0.130
## satisf_diff 0.064 0.021
## zyg 0.061 0.035 0.010
## stsf_dff:zy 0.009 0.003 0.069 0.064out<- satisf_ext_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Satisfaction",
sleep="Satisfaction",
Psychiatric="Externalizing",
model="Between Within")
col_out<- rbind(col_out, out)
satisf_ext_MZ<- lmer(EXT_resid~avg_satisf+satisf_diff+zygMZ+zygMZ*satisf_diff+(1|family), data=dat)
summary(satisf_ext_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_satisf + satisf_diff + zygMZ + zygMZ * satisf_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2532.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15370 -0.65043 -0.01981 0.61586 2.32689
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2636 0.5134
## Residual 0.7396 0.8600
## Number of obs: 893, groups: family, 641
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.004792 0.052314 595.508635 0.092 0.92705
## avg_satisf -0.116067 0.042844 647.023114 -2.709 0.00693 **
## satisf_diff -0.034463 0.075330 472.359974 -0.457 0.64752
## zygMZ -0.046478 0.071256 644.451614 -0.652 0.51446
## satisf_diff:zygMZ -0.006891 0.103071 486.863686 -0.067 0.94672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_st stsf_d zygMZ
## avg_satisf 0.114
## satisf_diff 0.069 0.016
## zygMZ -0.723 -0.035 -0.050
## stsf_dff:MZ -0.050 -0.003 -0.731 0.064out<- satisf_ext_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Satisfaction",
sleep="Satisfaction",
Psychiatric="Externalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
satisf_ext_DZ<- lmer(EXT_resid~avg_satisf+satisf_diff+zygDZ+zygDZ*satisf_diff+(1|family), data=dat)
summary(satisf_ext_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_satisf + satisf_diff + zygDZ + zygDZ * satisf_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2532.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15370 -0.65043 -0.01981 0.61586 2.32689
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2636 0.5134
## Residual 0.7396 0.8600
## Number of obs: 893, groups: family, 641
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.041686 0.049205 658.291607 -0.847 0.39719
## avg_satisf -0.116067 0.042844 647.023114 -2.709 0.00693 **
## satisf_diff -0.041354 0.070307 502.923823 -0.588 0.55666
## zygDZ 0.046478 0.071256 644.451614 0.652 0.51446
## satisf_diff:zygDZ 0.006891 0.103071 486.863686 0.067 0.94672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_st stsf_d zygDZ
## avg_satisf 0.070
## satisf_diff 0.058 0.013
## zygDZ -0.679 0.035 -0.039
## stsf_dff:DZ -0.040 0.003 -0.683 0.064out<- satisf_ext_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Satisfaction",
sleep="Satisfaction",
Psychiatric="Externalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)weekend duration
# weekend duration
weekend_dur_ext_pheno<- lmer(EXT_resid~weekend_dur+(1|family), data=dat)
summary(weekend_dur_ext_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ weekend_dur + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2040.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.97233 -0.65387 -0.05305 0.66820 2.28146
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2588 0.5087
## Residual 0.7614 0.8726
## Number of obs: 715, groups: family, 562
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.048074 0.039721 528.768985 -1.210 0.227
## weekend_dur -0.006897 0.034280 712.999739 -0.201 0.841
##
## Correlation of Fixed Effects:
## (Intr)
## weekend_dur 0.089out<- weekend_dur_ext_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
weekend_dur_ext<- lmer(EXT_resid~avg_weekend_dur+weekend_dur_diff+(1|family), data=dat)
summary(weekend_dur_ext)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekend_dur + weekend_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2042
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95017 -0.65068 -0.04906 0.64403 2.30019
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2571 0.5070
## Residual 0.7619 0.8728
## Number of obs: 715, groups: family, 562
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04950 0.03971 527.24819 -1.247 0.213
## avg_weekend_dur -0.03851 0.04180 584.48321 -0.921 0.357
## weekend_dur_diff 0.06177 0.06236 331.80180 0.991 0.323
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_dr 0.089
## wknd_dr_dff 0.025 -0.028anova(weekend_dur_ext_pheno,weekend_dur_ext)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## weekend_dur_ext_pheno: EXT_resid ~ weekend_dur + (1 | family)
## weekend_dur_ext: EXT_resid ~ avg_weekend_dur + weekend_dur_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekend_dur_ext_pheno 4 2038.9 2057.2 -1015.4 2030.9
## weekend_dur_ext 5 2039.1 2062.0 -1014.6 2029.1 1.7435 1 0.1867weekend_dur_ext_zyg<- lmer(EXT_resid~avg_weekend_dur+weekend_dur_diff+zyg+zyg*weekend_dur_diff+(1|family), data=dat)
summary(weekend_dur_ext_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekend_dur + weekend_dur_diff + zyg + zyg *
## weekend_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2046.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95679 -0.64754 -0.03161 0.64949 2.30929
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2542 0.5042
## Residual 0.7659 0.8752
## Number of obs: 715, groups: family, 562
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04697 0.03979 524.31584 -1.180 0.238
## avg_weekend_dur -0.03960 0.04184 584.54456 -0.946 0.344
## weekend_dur_diff 0.07091 0.06450 323.62470 1.099 0.272
## zyg 0.06534 0.07892 556.68962 0.828 0.408
## weekend_dur_diff:zyg 0.06573 0.12946 337.15939 0.508 0.612
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wknd__ zyg
## avg_wknd_dr 0.087
## wknd_dr_dff 0.031 -0.026
## zyg 0.059 -0.035 0.027
## wknd_dr_df: 0.025 0.007 0.247 0.032out<- weekend_dur_ext_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Between Within")
col_out<- rbind(col_out, out)
weekend_dur_ext_MZ<- lmer(EXT_resid~avg_weekend_dur+weekend_dur_diff+zygMZ+zygMZ*weekend_dur_diff+(1|family), data=dat)
summary(weekend_dur_ext_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekend_dur + weekend_dur_diff + zygMZ + zygMZ *
## weekend_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2046.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95679 -0.64754 -0.03161 0.64949 2.30929
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2542 0.5042
## Residual 0.7659 0.8752
## Number of obs: 715, groups: family, 562
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01430 0.05766 526.93607 -0.248 0.804
## avg_weekend_dur -0.03960 0.04184 584.54456 -0.946 0.344
## weekend_dur_diff 0.10377 0.10204 318.15006 1.017 0.310
## zygMZ -0.06534 0.07892 556.68962 -0.828 0.408
## weekend_dur_diff:zygMZ -0.06573 0.12946 337.15939 -0.508 0.612
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wknd__ zygMZ
## avg_wknd_dr 0.036
## wknd_dr_dff 0.050 -0.012
## zygMZ -0.725 0.035 -0.038
## wknd_dr_:MZ -0.039 -0.007 -0.790 0.032out<- weekend_dur_ext_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
weekend_dur_ext_DZ<- lmer(EXT_resid~avg_weekend_dur+weekend_dur_diff+zygDZ+zygDZ*weekend_dur_diff+(1|family), data=dat)
summary(weekend_dur_ext_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekend_dur + weekend_dur_diff + zygDZ + zygDZ *
## weekend_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2046.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95679 -0.64754 -0.03161 0.64949 2.30929
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2542 0.5042
## Residual 0.7659 0.8752
## Number of obs: 715, groups: family, 562
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07963 0.05437 555.48495 -1.465 0.144
## avg_weekend_dur -0.03960 0.04184 584.54456 -0.946 0.344
## weekend_dur_diff 0.03804 0.07929 351.63546 0.480 0.632
## zygDZ 0.06534 0.07892 556.68962 0.828 0.408
## weekend_dur_diff:zygDZ 0.06573 0.12946 337.15939 0.508 0.612
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wknd__ zygDZ
## avg_wknd_dr 0.089
## wknd_dr_dff 0.007 -0.027
## zygDZ -0.683 -0.035 -0.004
## wknd_dr_:DZ -0.005 0.007 -0.615 0.032out<- weekend_dur_ext_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)weekday duration
# weekday duration
weekday_dur_ext_pheno<- lmer(EXT_resid~weekday_dur+(1|family), data=dat)
summary(weekday_dur_ext_pheno) ## NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ weekday_dur + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2689.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.09660 -0.65357 -0.03686 0.62579 2.40788
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2484 0.4984
## Residual 0.7559 0.8694
## Number of obs: 950, groups: family, 666
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.021266 0.034815 622.387290 -0.611 0.542
## weekday_dur 0.007801 0.029849 938.637919 0.261 0.794
##
## Correlation of Fixed Effects:
## (Intr)
## weekday_dur 0.079out<- weekday_dur_ext_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
weekday_dur_ext<- lmer(EXT_resid~avg_weekday_dur+weekday_dur_diff+(1|family), data=dat)
summary(weekday_dur_ext)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekday_dur + weekday_dur_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2692.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13539 -0.65035 -0.03949 0.62831 2.38410
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2502 0.5002
## Residual 0.7544 0.8686
## Number of obs: 950, groups: family, 666
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02220 0.03484 620.79885 -0.637 0.524
## avg_weekday_dur -0.02074 0.04131 647.12839 -0.502 0.616
## weekday_dur_diff 0.03973 0.04369 470.62440 0.909 0.364
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_dr 0.073
## wkdy_dr_dff 0.037 -0.011anova(weekday_dur_ext_pheno,weekday_dur_ext)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## weekday_dur_ext_pheno: EXT_resid ~ weekday_dur + (1 | family)
## weekday_dur_ext: EXT_resid ~ avg_weekday_dur + weekday_dur_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekday_dur_ext_pheno 4 2687.4 2706.8 -1339.7 2679.4
## weekday_dur_ext 5 2688.4 2712.7 -1339.2 2678.4 1.001 1 0.3171weekday_dur_ext_zyg<- lmer(EXT_resid~avg_weekday_dur+weekday_dur_diff+zyg+weekday_dur_diff*zyg+(1|family), data=dat)
summary(weekday_dur_ext_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekday_dur + weekday_dur_diff + zyg + weekday_dur_diff *
## zyg + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2696.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15677 -0.64608 -0.03938 0.61902 2.36283
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2504 0.5004
## Residual 0.7567 0.8699
## Number of obs: 949, groups: family, 665
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02078 0.03499 614.38630 -0.594 0.553
## avg_weekday_dur -0.01970 0.04139 645.79910 -0.476 0.634
## weekday_dur_diff 0.04063 0.04400 464.69887 0.924 0.356
## zyg 0.04528 0.06928 666.43236 0.654 0.514
## weekday_dur_diff:zyg 0.01635 0.08861 488.69493 0.185 0.854
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wkdy__ zyg
## avg_wkdy_dr 0.074
## wkdy_dr_dff 0.038 -0.010
## zyg 0.071 0.024 0.006
## wkdy_dr_df: 0.005 0.012 0.105 0.035out<- weekday_dur_ext_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Between Within")
col_out<- rbind(col_out, out)
weekday_dur_ext_MZ<- lmer(EXT_resid~avg_weekday_dur+weekday_dur_diff+zygMZ+weekday_dur_diff*zygMZ+(1|family), data=dat)
summary(weekday_dur_ext_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekday_dur + weekday_dur_diff + zygMZ + weekday_dur_diff *
## zygMZ + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2696.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15677 -0.64608 -0.03938 0.61902 2.36283
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2504 0.5004
## Residual 0.7567 0.8699
## Number of obs: 949, groups: family, 665
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.001857 0.050967 608.497632 0.036 0.971
## avg_weekday_dur -0.019701 0.041390 645.799100 -0.476 0.634
## weekday_dur_diff 0.048810 0.065634 454.633873 0.744 0.457
## zygMZ -0.045279 0.069281 666.432356 -0.654 0.514
## weekday_dur_diff:zygMZ -0.016353 0.088609 488.694926 -0.185 0.854
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wkdy__ zygMZ
## avg_wkdy_dr 0.067
## wkdy_dr_dff 0.039 0.002
## zygMZ -0.729 -0.024 -0.028
## wkdy_dr_:MZ -0.027 -0.012 -0.745 0.035out<- weekday_dur_ext_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
weekday_dur_ext_DZ<- lmer(EXT_resid~avg_weekday_dur+weekday_dur_diff+zygDZ+weekday_dur_diff*zygDZ+(1|family), data=dat)
summary(weekday_dur_ext_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekday_dur + weekday_dur_diff + zygDZ + weekday_dur_diff *
## zygDZ + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2696.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15677 -0.64608 -0.03938 0.61902 2.36283
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2504 0.5004
## Residual 0.7567 0.8699
## Number of obs: 949, groups: family, 665
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04342 0.04745 677.01409 -0.915 0.360
## avg_weekday_dur -0.01970 0.04139 645.79910 -0.476 0.634
## weekday_dur_diff 0.03246 0.05907 505.23826 0.549 0.583
## zygDZ 0.04528 0.06928 666.43236 0.654 0.514
## weekday_dur_diff:zygDZ 0.01635 0.08861 488.69493 0.185 0.854
##
## Correlation of Fixed Effects:
## (Intr) avg_w_ wkdy__ zygDZ
## avg_wkdy_dr 0.037
## wkdy_dr_dff 0.034 -0.017
## zygDZ -0.677 0.024 -0.022
## wkdy_dr_:DZ -0.021 0.012 -0.672 0.035out<- weekday_dur_ext_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)alertness
# alertness
alertness_ext_pheno<- lmer(EXT_resid~total_alertness+(1|family), data=dat)
summary(alertness_ext_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ total_alertness + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2728.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.08492 -0.64389 -0.03954 0.63341 2.39921
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2514 0.5014
## Residual 0.7504 0.8662
## Number of obs: 965, groups: family, 673
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02418 0.03455 626.29314 -0.700 0.484
## total_alertness -0.02535 0.03138 961.54010 -0.808 0.419
##
## Correlation of Fixed Effects:
## (Intr)
## totl_lrtnss 0.074out<- alertness_ext_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Alertness",
sleep="Alertness",
Psychiatric="Externalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
alertness_ext<- lmer(EXT_resid~avg_alertness+alertness_diff+(1|family), data=dat)
summary(alertness_ext)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_alertness + alertness_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2732
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.08112 -0.64492 -0.03831 0.63625 2.39848
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2509 0.5009
## Residual 0.7518 0.8670
## Number of obs: 965, groups: family, 673
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02411 0.03456 625.37802 -0.698 0.486
## avg_alertness -0.03207 0.04271 659.31457 -0.751 0.453
## alertness_diff -0.01710 0.04724 518.27262 -0.362 0.717
##
## Correlation of Fixed Effects:
## (Intr) avg_lr
## avg_alrtnss 0.054
## alrtnss_dff 0.050 -0.018anova(alertness_ext_pheno,alertness_ext)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## alertness_ext_pheno: EXT_resid ~ total_alertness + (1 | family)
## alertness_ext: EXT_resid ~ avg_alertness + alertness_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## alertness_ext_pheno 4 2726.4 2745.9 -1359.2 2718.4
## alertness_ext 5 2728.4 2752.8 -1359.2 2718.4 0.0546 1 0.8153alertness_ext_zyg<- lmer(EXT_resid~avg_alertness+alertness_diff+zyg+zyg*alertness_diff+(1|family), data=dat)
summary(alertness_ext_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_alertness + alertness_diff + zyg + zyg * alertness_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2735.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.09545 -0.64554 -0.04097 0.62474 2.37343
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2510 0.5010
## Residual 0.7541 0.8684
## Number of obs: 964, groups: family, 672
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02267 0.03471 618.50108 -0.653 0.514
## avg_alertness -0.03200 0.04279 657.95931 -0.748 0.455
## alertness_diff -0.02154 0.04864 510.16461 -0.443 0.658
## zyg 0.04337 0.06878 671.50914 0.631 0.529
## alertness_diff:zyg -0.02769 0.09775 527.30111 -0.283 0.777
##
## Correlation of Fixed Effects:
## (Intr) avg_lr alrtn_ zyg
## avg_alrtnss 0.055
## alrtnss_dff 0.045 -0.010
## zyg 0.071 0.022 -0.013
## alrtnss_df: -0.014 0.033 0.227 0.051out<- alertness_ext_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Alertness",
sleep="Alertness",
Psychiatric="Externalizing",
model="Between Within")
col_out<- rbind(col_out, out)
alertness_ext_MZ<- lmer(EXT_resid~avg_alertness+alertness_diff+zygMZ+zygMZ*alertness_diff+(1|family), data=dat)
summary(alertness_ext_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_alertness + alertness_diff + zygMZ + zygMZ *
## alertness_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2735.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.09545 -0.64554 -0.04097 0.62474 2.37343
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2510 0.5010
## Residual 0.7541 0.8684
## Number of obs: 964, groups: family, 672
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -9.843e-04 5.057e-02 6.113e+02 -0.019 0.984
## avg_alertness -3.200e-02 4.279e-02 6.580e+02 -0.748 0.455
## alertness_diff -3.538e-02 7.639e-02 5.088e+02 -0.463 0.643
## zygMZ -4.337e-02 6.878e-02 6.715e+02 -0.631 0.529
## alertness_diff:zygMZ 2.769e-02 9.775e-02 5.273e+02 0.283 0.777
##
## Correlation of Fixed Effects:
## (Intr) avg_lr alrtn_ zygMZ
## avg_alrtnss 0.052
## alrtnss_dff 0.030 0.015
## zygMZ -0.729 -0.022 -0.024
## alrtnss_:MZ -0.025 -0.033 -0.785 0.051out<- alertness_ext_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Alertness",
sleep="Alertness",
Psychiatric="Externalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
alertness_ext_DZ<- lmer(EXT_resid~avg_alertness+alertness_diff+zygDZ+zygDZ*alertness_diff+(1|family), data=dat)
summary(alertness_ext_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_alertness + alertness_diff + zygDZ + zygDZ *
## alertness_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2735.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.09545 -0.64554 -0.04097 0.62474 2.37343
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2510 0.5010
## Residual 0.7541 0.8684
## Number of obs: 964, groups: family, 672
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.044352 0.047098 683.761702 -0.942 0.347
## avg_alertness -0.032003 0.042792 657.959314 -0.748 0.455
## alertness_diff -0.007696 0.060618 534.733731 -0.127 0.899
## zygDZ 0.043368 0.068782 671.509138 0.631 0.529
## alertness_diff:zygDZ -0.027685 0.097754 527.301108 -0.283 0.777
##
## Correlation of Fixed Effects:
## (Intr) avg_lr alrtn_ zygDZ
## avg_alrtnss 0.024
## alrtnss_dff 0.073 -0.035
## zygDZ -0.678 0.022 -0.052
## alrtnss_:DZ -0.048 0.033 -0.624 0.051out<- alertness_ext_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Alertness",
sleep="Alertness",
Psychiatric="Externalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)chronotype
# chrono
chrono_ext_pheno<- lmer(EXT_resid~total_meq+(1|family), data=dat)
summary(chrono_ext_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ total_meq + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2724.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02217 -0.64259 -0.01496 0.64325 2.40555
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2466 0.4965
## Residual 0.7488 0.8653
## Number of obs: 964, groups: family, 672
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.396378 0.165260 938.793002 2.399 0.0167 *
## total_meq -0.008551 0.003313 948.988695 -2.581 0.0100 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## total_meq -0.978out<- chrono_ext_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
chrono_ext<- lmer(EXT_resid~avg_meq+meq_diff+(1|family), data=dat)
summary(chrono_ext)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_meq + meq_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2732.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.01561 -0.63805 -0.02112 0.62834 2.41097
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2449 0.4948
## Residual 0.7509 0.8665
## Number of obs: 964, groups: family, 672
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.469829 0.210939 631.886171 2.227 0.0263 *
## avg_meq -0.010030 0.004239 642.414371 -2.366 0.0183 *
## meq_diff -0.006129 0.005452 549.029772 -1.124 0.2614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_mq
## avg_meq -0.987
## meq_diff 0.027 -0.020anova(chrono_ext_pheno,chrono_ext)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## chrono_ext_pheno: EXT_resid ~ total_meq + (1 | family)
## chrono_ext: EXT_resid ~ avg_meq + meq_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## chrono_ext_pheno 4 2717.9 2737.4 -1355.0 2709.9
## chrono_ext 5 2719.6 2744.0 -1354.8 2709.6 0.3139 1 0.5753chrono_ext_zyg<- lmer(EXT_resid~avg_meq+meq_diff+zyg+zyg*meq_diff+(1|family), data=dat)
summary(chrono_ext_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_meq + meq_diff + zyg + zyg * meq_diff + (1 |
## family)
## Data: dat
##
## REML criterion at convergence: 2740.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0272 -0.6360 -0.0163 0.6352 2.3938
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2455 0.4955
## Residual 0.7529 0.8677
## Number of obs: 963, groups: family, 671
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.471792 0.211364 629.953889 2.232 0.0260 *
## avg_meq -0.010047 0.004248 640.057318 -2.365 0.0183 *
## meq_diff -0.006063 0.005523 532.569255 -1.098 0.2729
## zyg 0.043012 0.068488 668.508569 0.628 0.5302
## meq_diff:zyg 0.001209 0.011112 559.528573 0.109 0.9134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_mq mq_dff zyg
## avg_meq -0.987
## meq_diff 0.024 -0.017
## zyg 0.002 0.009 -0.006
## meq_dff:zyg -0.023 0.023 0.152 0.045out<- chrono_ext_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Between Within")
col_out<- rbind(col_out, out)
chrono_ext_MZ<- lmer(EXT_resid~avg_meq+meq_diff+zygMZ+zygMZ*meq_diff+(1|family), data=dat)
summary(chrono_ext_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_meq + meq_diff + zygMZ + zygMZ * meq_diff + (1 |
## family)
## Data: dat
##
## REML criterion at convergence: 2740.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0272 -0.6360 -0.0163 0.6352 2.3938
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2455 0.4955
## Residual 0.7529 0.8677
## Number of obs: 963, groups: family, 671
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.493298 0.214203 633.917063 2.303 0.0216 *
## avg_meq -0.010047 0.004248 640.057331 -2.365 0.0183 *
## meq_diff -0.005458 0.008407 504.075703 -0.649 0.5165
## zygMZ -0.043012 0.068488 668.508570 -0.628 0.5302
## meq_diff:zygMZ -0.001209 0.011112 559.528573 -0.109 0.9134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_mq mq_dff zygMZ
## avg_meq -0.972
## meq_diff 0.004 0.004
## zygMZ -0.162 -0.009 -0.026
## mq_dff:zyMZ 0.016 -0.023 -0.760 0.045out<- chrono_ext_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
chrono_ext_DZ<- lmer(EXT_resid~avg_meq+meq_diff+zygDZ+zygDZ*meq_diff+(1|family), data=dat)
summary(chrono_ext_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_meq + meq_diff + zygDZ + zygDZ * meq_diff + (1 |
## family)
## Data: dat
##
## REML criterion at convergence: 2740.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0272 -0.6360 -0.0163 0.6352 2.3938
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2455 0.4955
## Residual 0.7529 0.8677
## Number of obs: 963, groups: family, 671
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.450286 0.214037 627.938669 2.104 0.0358 *
## avg_meq -0.010047 0.004248 640.057336 -2.365 0.0183 *
## meq_diff -0.006667 0.007216 607.396683 -0.924 0.3559
## zygDZ 0.043012 0.068488 668.508570 0.628 0.5302
## meq_diff:zygDZ 0.001209 0.011112 559.528573 0.109 0.9134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_mq mq_dff zygDZ
## avg_meq -0.976
## meq_diff 0.042 -0.030
## zygDZ -0.158 0.009 -0.039
## mq_dff:zyDZ -0.030 0.023 -0.654 0.045out<- chrono_ext_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)insomnia
# insom
insom_ext_pheno<- lmer(EXT_resid~insom + (1|family), data=dat)
summary(insom_ext_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ insom + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2713
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13746 -0.63937 -0.03125 0.61784 2.32122
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2455 0.4955
## Residual 0.7480 0.8648
## Number of obs: 962, groups: family, 673
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03713 0.03464 637.45030 -1.072 0.28426
## insom 0.07884 0.02916 959.59169 2.703 0.00698 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## insom -0.138out<- insom_ext_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Insomnia",
sleep="Insomnia",
Psychiatric="Externalizing",
model="Phenotypic")
col_out<- rbind(col_out, out)
insom_ext<- lmer(EXT_resid~avg_insom+insom_diff+(1|family), data=dat)
summary(insom_ext)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_insom + insom_diff + (1 | family)
## Data: dat
##
## REML criterion at convergence: 2716.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1378 -0.6386 -0.0312 0.6169 2.3212
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2454 0.4954
## Residual 0.7490 0.8655
## Number of obs: 962, groups: family, 673
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03698 0.03475 632.44634 -1.064 0.2877
## avg_insom 0.07768 0.03920 650.07462 1.982 0.0479 *
## insom_diff 0.08033 0.04482 500.14811 1.792 0.0737 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ns
## avg_insom -0.152
## insom_diff -0.033 -0.022anova(insom_ext_pheno,insom_ext)## refitting model(s) with ML (instead of REML)## Data: dat
## Models:
## insom_ext_pheno: EXT_resid ~ insom + (1 | family)
## insom_ext: EXT_resid ~ avg_insom + insom_diff + (1 | family)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## insom_ext_pheno 4 2710.8 2730.3 -1351.4 2702.8
## insom_ext 5 2712.8 2737.2 -1351.4 2702.8 0.002 1 0.9648insom_ext_zyg<- lmer(EXT_resid~avg_insom+insom_diff+zyg+zyg*insom_diff+(1|family), data=dat)
summary(insom_ext_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_insom + insom_diff + zyg + zyg * insom_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2718
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02727 -0.65081 -0.03621 0.62551 2.31694
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2518 0.5018
## Residual 0.7433 0.8621
## Number of obs: 961, groups: family, 672
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03615 0.03494 625.84415 -1.035 0.3012
## avg_insom 0.07661 0.03939 649.50440 1.945 0.0522 .
## insom_diff 0.08763 0.04495 492.84141 1.949 0.0518 .
## zyg 0.03247 0.06872 670.58745 0.473 0.6367
## insom_diff:zyg 0.16038 0.09044 516.27024 1.773 0.0768 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ns insm_d zyg
## avg_insom -0.157
## insom_diff -0.031 -0.021
## zyg 0.083 -0.078 0.000
## insm_dff:zy -0.002 0.006 0.092 -0.036out<- insom_ext_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Insomnia",
sleep="Insomnia",
Psychiatric="Externalizing",
model="Between Within")
col_out<- rbind(col_out, out)
insom_ext_MZ<- lmer(EXT_resid~avg_insom+insom_diff+zygMZ+zygMZ*insom_diff+(1|family), data=dat)
summary(insom_ext_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_insom + insom_diff + zygMZ + zygMZ * insom_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2718
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02727 -0.65081 -0.03621 0.62551 2.31694
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2518 0.5018
## Residual 0.7433 0.8621
## Number of obs: 961, groups: family, 672
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01992 0.05100 616.89262 -0.391 0.6962
## avg_insom 0.07661 0.03939 649.50440 1.945 0.0522 .
## insom_diff 0.16782 0.06664 491.73593 2.518 0.0121 *
## zygMZ -0.03247 0.06872 670.58745 -0.473 0.6367
## insom_diff:zygMZ -0.16038 0.09044 516.27024 -1.773 0.0768 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ns insm_d zygMZ
## avg_insom -0.160
## insom_diff -0.031 -0.010
## zygMZ -0.731 0.078 0.024
## insm_dff:MZ 0.025 -0.006 -0.741 -0.036out<- insom_ext_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Insomnia",
sleep="Insomnia",
Psychiatric="Externalizing",
model="Dummy MZ")
col_out<- rbind(col_out, out)
insom_ext_DZ<- lmer(EXT_resid~avg_insom+insom_diff+zygDZ+zygDZ*insom_diff+(1|family), data=dat)
summary(insom_ext_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_insom + insom_diff + zygDZ + zygDZ * insom_diff +
## (1 | family)
## Data: dat
##
## REML criterion at convergence: 2718
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02727 -0.65081 -0.03621 0.62551 2.31694
##
## Random effects:
## Groups Name Variance Std.Dev.
## family (Intercept) 0.2518 0.5018
## Residual 0.7433 0.8621
## Number of obs: 961, groups: family, 672
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.052392 0.046932 684.836538 -1.116 0.2647
## avg_insom 0.076614 0.039390 649.504402 1.945 0.0522 .
## insom_diff 0.007434 0.060749 520.266732 0.122 0.9027
## zygDZ 0.032474 0.068716 670.587453 0.473 0.6367
## insom_diff:zygDZ 0.160384 0.090443 516.270235 1.773 0.0768 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ns insm_d zygDZ
## avg_insom -0.060
## insom_diff -0.036 -0.020
## zygDZ -0.670 -0.078 0.027
## insm_dff:DZ 0.025 0.006 -0.676 -0.036out<- insom_ext_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Insomnia",
sleep="Insomnia",
Psychiatric="Externalizing",
model="Dummy DZ")
col_out<- rbind(col_out, out)write file
fwrite(col_out, "/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_results_colorado.csv", sep="\t")ABCD sample
get data
# get covariates
acs<- fread("/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_sleep_psych//acspsw03.txt", header = T, data.table = F)
acsvars<- c('subjectkey', 'eventname', 'sex', 'race_ethnicity')
acs<- acs %>% dplyr::select(all_of(acsvars))
subs<- fread("/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_sleep_psych/abcd_suss01.txt", header=T, data.table = F)
pube<- fread("/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_sleep_psych/abcd_ssphp01.txt", header=T, data.table = F)
subsvars<- c('subjectkey', 'eventname','su_caff_ss_sum_calc', 'su_caff_ss_sum_l')
pubevars<- c('subjectkey', 'eventname','pds_p_ss_female_category_2', 'pds_p_ss_male_category_2')
subs<- subs %>% dplyr::select(all_of(subsvars))
pube<- pube %>% dplyr::select(all_of(pubevars)) %>%
mutate(pubertal_score= ifelse(!is.na(pube$pds_p_ss_female_category_2), pube$pds_p_ss_female_category_2, ifelse(!is.na(pube$pds_p_ss_male_category_2), pube$pds_p_ss_male_category_2, NA))) %>%
select(subjectkey, eventname, pubertal_score)
acs<- subset(acs, acs$eventname=="1_year_follow_up_y_arm_1")
#abcd<- fread("/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_sleep_psych/ABCD_sleep_psych.csv", header=T, data.table=F)
### new file with all sibs
abcd<- fread("/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_sleep_psych/ABCD_sleep_psych_ALL_sibs.csv", header=T, data.table=F)
colnames(abcd)## [1] "subjectkey" "eventname_wave_2.x"
## [3] "weekend_dur_mcq_wave_2" "weekday_dur_mcq_wave_2"
## [5] "chronotype_wave_2" "social_jet_lag_wave_2"
## [7] "eventname" "avg_weekend_dur"
## [9] "avg_weekday_dur" "avg_weekend_effic"
## [11] "avg_weekday_effic" "variability"
## [13] "eventname_wave_2.y" "interview_age_wave_2"
## [15] "sex_wave_2" "cbcl_scr_syn_attention_r_wave_2"
## [17] "cbcl_scr_syn_internal_r_wave_2" "cbcl_scr_syn_external_r_wave_2"
## [19] "visit_wave_2" "total_score_wave_2"
## [21] "distress_score_wave_2" "rel_family_id"
## [23] "zyg" "matched_subject"
## [25] "rel_relationship" "twin"abcd_all<- merge(abcd, acs, by="subjectkey", all.x=T)z transform sleep before creating between/within scores
abcd_all$avg_weekend_dur<- scale(abcd_all$avg_weekend_dur)
abcd_all$avg_weekday_dur<- scale(abcd_all$avg_weekday_dur)
abcd_all$weekend_dur_mcq_wave_2<- scale(abcd_all$weekend_dur_mcq_wave_2)
abcd_all$weekday_dur_mcq_wave_2<- scale(abcd_all$weekday_dur_mcq_wave_2)
abcd_all$avg_weekend_effic<- scale(abcd_all$avg_weekend_effic)
abcd_all$avg_weekday_effic<- scale(abcd_all$avg_weekday_effic)
abcd_all$variability<- scale(abcd_all$variability)
abcd_all$chronotype_wave_2<- scale(abcd_all$chronotype_wave_2)
abcd_all$social_jet_lag_wave_2<- scale(abcd_all$social_jet_lag_wave_2)set up between and within family variables
abcd_all <- abcd_all%>% group_by(rel_family_id) %>%
mutate(
fam_avg_weekend_dur = mean(avg_weekend_dur,na.rm=T),
weekend_dur_diff = avg_weekend_dur-fam_avg_weekend_dur,
fam_avg_weekday_dur = mean(avg_weekday_dur,na.rm=T),
weekday_dur_diff = avg_weekday_dur-fam_avg_weekday_dur,
avg_weekend_dur_mcq = mean(weekend_dur_mcq_wave_2,na.rm=T),
weekend_dur_mcq_diff = weekend_dur_mcq_wave_2-avg_weekend_dur_mcq,
avg_weekday_dur_mcq = mean(weekday_dur_mcq_wave_2,na.rm=T),
weekday_dur_mcq_diff = weekday_dur_mcq_wave_2-avg_weekday_dur_mcq,
fam_avg_weekend_effic = mean(avg_weekend_effic,na.rm=T),
weekend_effic_diff = avg_weekend_effic-fam_avg_weekend_effic,
fam_avg_weekday_effic = mean(avg_weekday_effic,na.rm=T),
weekday_effic_diff = avg_weekday_effic-fam_avg_weekday_effic,
avg_variabilitiy = mean(variability,na.rm=T),
variabiltiy_diff = variability-avg_variabilitiy,
avg_chrono = mean(chronotype_wave_2,na.rm=T),
chrono_diff = chronotype_wave_2-avg_chrono,
avg_jetlag = mean(social_jet_lag_wave_2,na.rm=T),
jetlag_diff = social_jet_lag_wave_2-avg_jetlag)
head(abcd_all)
hist(abcd_all$weekend_dur_diff)
hist(abcd_all$weekday_dur_diff)
hist(abcd_all$weekend_effic_diff)
hist(abcd_all$weekday_effic_diff)
hist(abcd_all$variabiltiy_diff)
hist(abcd_all$chrono_diff) ### should we do something about this distribution?
hist(abcd_all$jetlag_diff)
code zygosity
# create contrast codes for zygosity
table(abcd_all$zyg)##
## 1 2 3
## 602 794 1567# MZ DZ SIB
# sibDZ_MZ 2/3 -1/3 -1/3
# sib_DZ 0 1/2 -1/2
abcd_all$sibDZ_MZ<- ifelse(abcd_all$zyg==1, 2/3, ifelse(abcd_all$zyg==2, -1/3, ifelse(abcd_all$zyg==3, -1/3, NA)))
abcd_all$sib_DZ<- ifelse(abcd_all$zyg==1, 0, ifelse(abcd_all$zyg==2, 1/2, ifelse(abcd_all$zyg==3, -1/2, NA)))
table(abcd_all$sibDZ_MZ)##
## -0.333333333333333 0.666666666666667
## 2361 602table(abcd_all$sib_DZ)##
## -0.5 0 0.5
## 1567 602 794# if sib_DZ is not significant--> dummy code so MZ is 0 on all, then DZ 0 on all
abcd_all$MZ_dummy_DZ<- ifelse(abcd_all$zyg==1, 0, ifelse(abcd_all$zyg==2, 1, ifelse(abcd_all$zyg==3, 0, NA)))
abcd_all$MZ_dummy_sib<- ifelse(abcd_all$zyg==1, 0, ifelse(abcd_all$zyg==2, 0, ifelse(abcd_all$zyg==3, 1, NA)))
abcd_all$DZ_dummy_MZ<- ifelse(abcd_all$zyg==1, 1, ifelse(abcd_all$zyg==2, 0, ifelse(abcd_all$zyg==3, 0, NA)))
abcd_all$DZ_dummy_sib<- ifelse(abcd_all$zyg==1, 0, ifelse(abcd_all$zyg==2, 0, ifelse(abcd_all$zyg==3, 1, NA)))
abcd_all$sib_dummy_MZ<- ifelse(abcd_all$zyg==1, 1, ifelse(abcd_all$zyg==2, 0, ifelse(abcd_all$zyg==3, 0, NA)))
abcd_all$sib_dummy_DZ<- ifelse(abcd_all$zyg==1, 0, ifelse(abcd_all$zyg==2, 1, ifelse(abcd_all$zyg==3, 0, NA)))regress out covariates
table(abcd_all$sex)
# get caffeine intake variable
subs<- subs %>% filter(eventname=="2_year_follow_up_y_arm_1") %>%
select(subjectkey, su_caff_ss_sum_l) %>%
rename(caff_intake=su_caff_ss_sum_l)
hist(subs$caff_intake)
# get puberty rating
pube<- pube %>% filter(eventname=="2_year_follow_up_y_arm_1") %>%
select(subjectkey, pubertal_score)
hist(pube$pubertal_score)
pube$pubertal_score<- as.numeric(pube$pubertal_score)
# merge in
cov<- merge(subs, pube, by="subjectkey", all=T)
abcd_all<- merge(abcd_all, cov, by="subjectkey", all=T)
# check structure
str(abcd_all$race_ethnicity); table(abcd_all$sex)
# convert to factor
abcd_all$race_ethnicity_fact<- as.factor(abcd_all$race_ethnicity)
str(abcd_all$race_ethnicity_fact)
# look at psych variables
hist(abcd_all$cbcl_scr_syn_internal_r_wave_2)
hist(abcd_all$cbcl_scr_syn_external_r_wave_2)
hist(abcd_all$cbcl_scr_syn_attention_r_wave_2) 
hist(abcd_all$total_score_wave_2)
# not normal so transform
abcd_all$int_SQRT<- sqrt(abcd_all$cbcl_scr_syn_internal_r_wave_2)
abcd_all$ext_SQRT<- sqrt(abcd_all$cbcl_scr_syn_external_r_wave_2)
abcd_all$att_SQRT<- sqrt(abcd_all$cbcl_scr_syn_attention_r_wave_2)
abcd_all$psychosis_SQRT<- sqrt(abcd_all$total_score_wave_2)
# somewhat better
hist(abcd_all$int_SQRT)
hist(abcd_all$ext_SQRT)
hist(abcd_all$att_SQRT) 
hist(abcd_all$psychosis_SQRT)
# regress out covarars
abcd_all$INT_resid<- resid(lm(int_SQRT~sex+interview_age_wave_2+race_ethnicity_fact + caff_intake + pubertal_score, data=abcd_all, na.action = "na.exclude"))
abcd_all$EXT_resid<- resid(lm(ext_SQRT~sex+interview_age_wave_2+race_ethnicity_fact + caff_intake + pubertal_score, data=abcd_all, na.action = "na.exclude"))
abcd_all$ATT_resid<- resid(lm(att_SQRT~sex+interview_age_wave_2+race_ethnicity_fact + caff_intake + pubertal_score, data=abcd_all, na.action = "na.exclude"))
abcd_all$PSYCH_resid<- resid(lm(psychosis_SQRT~sex+interview_age_wave_2+race_ethnicity_fact + caff_intake + pubertal_score, data=abcd_all, na.action = "na.exclude"))
# scale dependent vars
abcd_all$INT_resid<- scale(abcd_all$INT_resid)
abcd_all$EXT_resid<- scale(abcd_all$EXT_resid)
abcd_all$ATT_resid<- scale(abcd_all$ATT_resid)
abcd_all$PSYCH_resid<- scale(abcd_all$PSYCH_resid)
# mean around 0 and SD at 1
mean(abcd_all$INT_resid, na.rm=T); sd(abcd_all$INT_resid, na.rm=T)
mean(abcd_all$EXT_resid, na.rm=T); sd(abcd_all$EXT_resid, na.rm=T)
mean(abcd_all$ATT_resid, na.rm=T); sd(abcd_all$ATT_resid, na.rm=T)
mean(abcd_all$PSYCH_resid, na.rm=T); sd(abcd_all$PSYCH_resid, na.rm=T)
# add whether or not someone was in a covid arm for the fitbit analyses.
abcd_all$covid<- ifelse(abcd_all$eventname.x=="covid19_cv1_arm_2", 1, ifelse( abcd_all$eventname.x=="baseline_year_1_arm_1" | abcd_all$eventname.x=="2_year_follow_up_y_arm_1", 0, NA))create data frame to save models
abcd_out<- data.frame()internalizing
variability
# variability (fitbit)
variability_int_pheno<- lmer(INT_resid~variability+covid+(1|rel_family_id), data=abcd_all)
summary(variability_int_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ variability + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3414.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22635 -0.54369 -0.01005 0.47599 2.86361
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4938 0.7027
## Residual 0.4432 0.6657
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.10092 0.03134 827.55703 -3.220 0.00133 **
## variability 0.11794 0.02767 1271.00050 4.262 2.17e-05 ***
## covid -0.04309 0.19246 1058.66787 -0.224 0.82289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vrblty
## variability 0.037
## covid -0.147 -0.065out<- variability_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Internalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
variability_int<- lmer(INT_resid~avg_variabilitiy+variabiltiy_diff+covid+(1|rel_family_id), data=abcd_all)
summary(variability_int) ### don't need to save this one, just for model comparison## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3417.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1917 -0.5485 0.0015 0.4807 2.8753
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4944 0.7031
## Residual 0.4428 0.6654
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.10022 0.03135 827.59257 -3.197 0.00144 **
## avg_variabilitiy 0.14082 0.03487 878.93476 4.038 5.86e-05 ***
## variabiltiy_diff 0.07928 0.04526 465.73794 1.752 0.08049 .
## covid -0.04859 0.19254 1058.78023 -0.252 0.80082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_
## avg_varblty 0.041
## varblty_dff 0.006 0.003
## covid -0.147 -0.067 -0.019variability_int_zyg<- lmer(INT_resid~avg_variabilitiy+variabiltiy_diff+covid+sibDZ_MZ+sib_DZ+variabiltiy_diff*sibDZ_MZ+variabiltiy_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(variability_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + sibDZ_MZ +
## sib_DZ + variabiltiy_diff * sibDZ_MZ + variabiltiy_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3425.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15060 -0.55376 -0.00432 0.47879 2.89755
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4919 0.7014
## Residual 0.4443 0.6665
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.124e-01 3.257e-02 7.940e+02 -3.452 0.000586 ***
## avg_variabilitiy 1.421e-01 3.489e-02 8.782e+02 4.073 5.07e-05 ***
## variabiltiy_diff 7.950e-02 4.741e-02 4.612e+02 1.677 0.094205 .
## covid -6.502e-02 1.927e-01 1.056e+03 -0.337 0.735836
## sibDZ_MZ -3.054e-02 7.432e-02 7.660e+02 -0.411 0.681180
## sib_DZ -1.348e-01 7.169e-02 8.133e+02 -1.881 0.060345 .
## variabiltiy_diff:sibDZ_MZ -1.369e-03 1.115e-01 4.567e+02 -0.012 0.990209
## variabiltiy_diff:sib_DZ -1.166e-02 1.020e-01 4.681e+02 -0.114 0.908977
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid sDZ_MZ sib_DZ v_:DZ_
## avg_varblty 0.045
## varblty_dff 0.004 0.002
## covid -0.136 -0.069 -0.015
## sibDZ_MZ 0.232 0.041 -0.002 -0.007
## sib_DZ 0.124 -0.031 -0.002 0.047 -0.088
## vrbl_:DZ_MZ -0.004 -0.004 0.292 0.010 0.000 0.001
## vrblty_:_DZ -0.003 -0.005 -0.013 0.011 0.001 0.005 0.008out<- variability_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Internalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
variability_int_MZ<- lmer(INT_resid~avg_variabilitiy+variabiltiy_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+variabiltiy_diff*MZ_dummy_DZ+variabiltiy_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(variability_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + MZ_dummy_DZ +
## MZ_dummy_sib + variabiltiy_diff * MZ_dummy_DZ + variabiltiy_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3425.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15060 -0.55376 -0.00432 0.47879 2.89755
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4919 0.7014
## Residual 0.4443 0.6665
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.328e-01 6.531e-02 7.559e+02 -2.033 0.0424
## avg_variabilitiy 1.421e-01 3.489e-02 8.782e+02 4.073 5.07e-05
## variabiltiy_diff 7.859e-02 9.912e-02 4.537e+02 0.793 0.4283
## covid -6.502e-02 1.927e-01 1.056e+03 -0.337 0.7358
## MZ_dummy_DZ -3.688e-02 8.529e-02 7.553e+02 -0.432 0.6656
## MZ_dummy_sib 9.797e-02 7.963e-02 7.976e+02 1.230 0.2189
## variabiltiy_diff:MZ_dummy_DZ -4.463e-03 1.222e-01 4.530e+02 -0.037 0.9709
## variabiltiy_diff:MZ_dummy_sib 7.201e-03 1.230e-01 4.644e+02 0.059 0.9533
##
## (Intercept) *
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib
## variabiltiy_diff:MZ_dummy_DZ
## variabiltiy_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid MZ__DZ MZ_dm_ v_:MZ__D
## avg_varblty 0.054
## varblty_dff -0.001 -0.002
## covid -0.073 -0.069 0.000
## MZ_dummy_DZ -0.764 -0.049 0.001 0.026
## MZ_dummy_sb -0.814 -0.024 0.001 -0.014 0.624
## vrb_:MZ__DZ 0.001 0.001 -0.811 -0.005 0.000 -0.001
## vrblt_:MZ__ 0.002 0.005 -0.806 -0.014 -0.001 0.002 0.654out<- variability_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Internalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
variability_int_DZ<- lmer(INT_resid~avg_variabilitiy+variabiltiy_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+variabiltiy_diff*DZ_dummy_MZ+variabiltiy_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(variability_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + DZ_dummy_MZ +
## DZ_dummy_sib + variabiltiy_diff * DZ_dummy_MZ + variabiltiy_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3425.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15060 -0.55376 -0.00432 0.47879 2.89755
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4919 0.7014
## Residual 0.4443 0.6665
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.697e-01 5.504e-02 7.585e+02 -3.083 0.00212
## avg_variabilitiy 1.421e-01 3.489e-02 8.782e+02 4.073 5.07e-05
## variabiltiy_diff 7.413e-02 7.144e-02 4.515e+02 1.038 0.30000
## covid -6.502e-02 1.927e-01 1.056e+03 -0.337 0.73584
## DZ_dummy_MZ 3.688e-02 8.529e-02 7.553e+02 0.432 0.66559
## DZ_dummy_sib 1.348e-01 7.169e-02 8.133e+02 1.881 0.06034
## variabiltiy_diff:DZ_dummy_MZ 4.463e-03 1.222e-01 4.530e+02 0.037 0.97087
## variabiltiy_diff:DZ_dummy_sib 1.166e-02 1.020e-01 4.681e+02 0.114 0.90898
##
## (Intercept) **
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib .
## variabiltiy_diff:DZ_dummy_MZ
## variabiltiy_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid DZ__MZ DZ_dm_ v_:DZ__M
## avg_varblty -0.012
## varblty_dff 0.003 0.000
## covid -0.047 -0.069 -0.008
## DZ_dummy_MZ -0.643 0.049 -0.001 -0.026
## DZ_dummy_sb -0.764 0.031 -0.001 -0.047 0.497
## vrb_:DZ__MZ -0.002 -0.001 -0.585 0.005 0.000 0.001
## vrblt_:DZ__ -0.001 0.005 -0.700 -0.011 0.002 0.005 0.410out<- variability_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Internalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
variability_int_sib<- lmer(INT_resid~avg_variabilitiy+variabiltiy_diff+covid+sib_dummy_MZ+sib_dummy_DZ+variabiltiy_diff*sib_dummy_MZ+variabiltiy_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(variability_int_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + sib_dummy_MZ +
## sib_dummy_DZ + variabiltiy_diff * sib_dummy_MZ + variabiltiy_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3425.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15060 -0.55376 -0.00432 0.47879 2.89755
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4919 0.7014
## Residual 0.4443 0.6665
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.483e-02 4.626e-02 9.005e+02 -0.753 0.4517
## avg_variabilitiy 1.421e-01 3.489e-02 8.782e+02 4.073 5.07e-05
## variabiltiy_diff 8.579e-02 7.277e-02 4.851e+02 1.179 0.2390
## covid -6.502e-02 1.927e-01 1.056e+03 -0.337 0.7358
## sib_dummy_MZ -9.797e-02 7.963e-02 7.976e+02 -1.230 0.2189
## sib_dummy_DZ -1.348e-01 7.169e-02 8.133e+02 -1.881 0.0603
## variabiltiy_diff:sib_dummy_MZ -7.201e-03 1.230e-01 4.644e+02 -0.059 0.9533
## variabiltiy_diff:sib_dummy_DZ -1.166e-02 1.020e-01 4.681e+02 -0.114 0.9090
##
## (Intercept)
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## sib_dummy_MZ
## sib_dummy_DZ .
## variabiltiy_diff:sib_dummy_MZ
## variabiltiy_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid sb__MZ sb__DZ v_:__M
## avg_varblty 0.034
## varblty_dff 0.010 0.007
## covid -0.128 -0.069 -0.023
## sib_dmmy_MZ -0.572 0.024 -0.004 0.014
## sib_dmmy_DZ -0.641 -0.031 -0.006 0.047 0.368
## vrblt_:__MZ -0.006 -0.005 -0.592 0.014 0.002 0.003
## vrblt_:__DZ -0.006 -0.005 -0.714 0.011 0.003 0.005 0.422out<- variability_int_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Internalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend duration (fitbit)
# weekend duration (fitbit)
weekend_dur_int_pheno<- lmer(INT_resid~avg_weekend_dur+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_int_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekend_dur + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3396.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.97119 -0.56201 -0.00897 0.47773 2.95205
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5015 0.7082
## Residual 0.4491 0.6702
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.10083 0.03172 818.29393 -3.178 0.00154 **
## avg_weekend_dur -0.03143 0.02838 1261.89216 -1.108 0.26828
## covid 0.00203 0.19352 1045.17647 0.010 0.99163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_dr -0.045
## covid -0.146 0.023out<- weekend_dur_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_dur_int<- lmer(INT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3397.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95922 -0.54649 0.00496 0.48377 2.97222
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5030 0.7092
## Residual 0.4472 0.6687
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.026e-01 3.175e-02 8.189e+02 -3.232 0.00128 **
## fam_avg_weekend_dur 3.121e-03 3.531e-02 8.591e+02 0.088 0.92960
## weekend_dur_diff -9.432e-02 4.752e-02 4.574e+02 -1.985 0.04776 *
## covid 1.455e-02 1.936e-01 1.046e+03 0.075 0.94014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__
## fm_vg_wknd_ -0.057
## wknd_dr_dff 0.002 0.001
## covid -0.147 0.042 -0.019weekend_dur_int_zyg<- lmer(INT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekend_dur_diff+sib_DZ*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekend_dur_diff + sib_DZ *
## weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3404.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90792 -0.53575 -0.00982 0.48149 3.00247
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5018 0.7084
## Residual 0.4474 0.6689
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.164e-01 3.301e-02 7.863e+02 -3.528 0.000444 ***
## fam_avg_weekend_dur 7.068e-03 3.542e-02 8.592e+02 0.200 0.841866
## weekend_dur_diff -7.316e-02 5.300e-02 4.493e+02 -1.381 0.168103
## covid 3.079e-03 1.937e-01 1.044e+03 0.016 0.987323
## sibDZ_MZ -4.740e-02 7.523e-02 7.588e+02 -0.630 0.528855
## sib_DZ -1.260e-01 7.264e-02 8.032e+02 -1.735 0.083201 .
## weekend_dur_diff:sibDZ_MZ 1.013e-01 1.311e-01 4.466e+02 0.772 0.440286
## weekend_dur_diff:sib_DZ 8.339e-02 1.038e-01 4.547e+02 0.803 0.422252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wknd_ -0.077
## wknd_dr_dff 0.000 0.002
## covid -0.137 0.041 -0.013
## sibDZ_MZ 0.232 -0.078 -0.001 -0.007
## sib_DZ 0.130 -0.027 -0.002 0.043 -0.086
## wkn__:DZ_MZ -0.002 0.001 0.437 0.008 -0.002 0.001
## wknd_d_:_DZ -0.002 0.002 0.055 0.007 0.001 -0.001 -0.033out<- weekend_dur_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_dur_int_MZ<- lmer(INT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekend_dur_diff+MZ_dummy_sib*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekend_dur_diff +
## MZ_dummy_sib * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3404.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90792 -0.53575 -0.00982 0.48149 3.00247
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5018 0.7084
## Residual 0.4474 0.6689
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.480e-01 6.613e-02 7.501e+02 -2.239 0.0255
## fam_avg_weekend_dur 7.068e-03 3.542e-02 8.592e+02 0.200 0.8419
## weekend_dur_diff -5.649e-03 1.204e-01 4.450e+02 -0.047 0.9626
## covid 3.079e-03 1.937e-01 1.044e+03 0.016 0.9873
## MZ_dummy_DZ -1.560e-02 8.630e-02 7.472e+02 -0.181 0.8566
## MZ_dummy_sib 1.104e-01 8.068e-02 7.903e+02 1.368 0.1716
## weekend_dur_diff:MZ_dummy_DZ -5.958e-02 1.426e-01 4.437e+02 -0.418 0.6763
## weekend_dur_diff:MZ_dummy_sib -1.430e-01 1.394e-01 4.519e+02 -1.026 0.3056
##
## (Intercept) *
## fam_avg_weekend_dur
## weekend_dur_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib
## weekend_dur_diff:MZ_dummy_DZ
## weekend_dur_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wknd_ -0.097
## wknd_dr_dff -0.002 0.002
## covid -0.074 0.041 0.000
## MZ_dummy_DZ -0.762 0.056 0.002 0.024
## MZ_dummy_sb -0.815 0.084 0.002 -0.013 0.623
## wk__:MZ__DZ 0.002 0.000 -0.844 -0.005 -0.002 -0.001
## wknd__:MZ__ 0.003 -0.002 -0.864 -0.010 -0.002 -0.001 0.729out<- weekend_dur_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_int_DZ<- lmer(INT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekend_dur_diff+DZ_dummy_sib*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekend_dur_diff +
## DZ_dummy_sib * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3404.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90792 -0.53575 -0.00982 0.48149 3.00247
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5018 0.7084
## Residual 0.4474 0.6689
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.636e-01 5.586e-02 7.506e+02 -2.929 0.0035
## fam_avg_weekend_dur 7.068e-03 3.542e-02 8.592e+02 0.200 0.8419
## weekend_dur_diff -6.523e-02 7.645e-02 4.403e+02 -0.853 0.3940
## covid 3.079e-03 1.937e-01 1.044e+03 0.016 0.9873
## DZ_dummy_MZ 1.560e-02 8.630e-02 7.472e+02 0.181 0.8566
## DZ_dummy_sib 1.260e-01 7.264e-02 8.032e+02 1.735 0.0832
## weekend_dur_diff:DZ_dummy_MZ 5.958e-02 1.426e-01 4.437e+02 0.418 0.6763
## weekend_dur_diff:DZ_dummy_sib -8.339e-02 1.038e-01 4.547e+02 -0.803 0.4223
##
## (Intercept) **
## fam_avg_weekend_dur
## weekend_dur_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib .
## weekend_dur_diff:DZ_dummy_MZ
## weekend_dur_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wknd_ -0.028
## wknd_dr_dff -0.002 0.002
## covid -0.050 0.041 -0.009
## DZ_dummy_MZ -0.643 -0.056 0.002 -0.024
## DZ_dummy_sb -0.765 0.027 0.002 -0.043 0.496
## wk__:DZ__MZ 0.001 0.000 -0.536 0.005 -0.002 -0.001
## wknd__:DZ__ 0.002 -0.002 -0.736 -0.007 -0.001 -0.001 0.395out<- weekend_dur_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_int_sib<- lmer(INT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekend_dur_diff+sib_dummy_DZ*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_int_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekend_dur_diff +
## sib_dummy_DZ * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3404.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.90792 -0.53575 -0.00982 0.48149 3.00247
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5018 0.7084
## Residual 0.4474 0.6689
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.764e-02 4.676e-02 8.874e+02 -0.805 0.4210
## fam_avg_weekend_dur 7.068e-03 3.542e-02 8.592e+02 0.200 0.8419
## weekend_dur_diff -1.486e-01 7.025e-02 4.729e+02 -2.116 0.0349
## covid 3.079e-03 1.937e-01 1.044e+03 0.016 0.9873
## sib_dummy_MZ -1.104e-01 8.068e-02 7.903e+02 -1.368 0.1716
## sib_dummy_DZ -1.260e-01 7.264e-02 8.032e+02 -1.735 0.0832
## weekend_dur_diff:sib_dummy_MZ 1.430e-01 1.394e-01 4.519e+02 1.026 0.3056
## weekend_dur_diff:sib_dummy_DZ 8.339e-02 1.038e-01 4.547e+02 0.803 0.4223
##
## (Intercept)
## fam_avg_weekend_dur
## weekend_dur_diff *
## covid
## sib_dummy_MZ
## sib_dummy_DZ .
## weekend_dur_diff:sib_dummy_MZ
## weekend_dur_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wknd_ 0.008
## wknd_dr_dff 0.003 -0.001
## covid -0.126 0.041 -0.020
## sib_dmmy_MZ -0.573 -0.084 0.000 0.013
## sib_dmmy_DZ -0.639 -0.027 -0.001 0.043 0.370
## wknd__:__MZ -0.001 0.002 -0.504 0.010 -0.001 0.000
## wknd__:__DZ -0.001 0.002 -0.677 0.007 0.000 -0.001 0.341out<- weekend_dur_int_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday duration (fitbit)
# weekday duration (fitbit)
weekday_dur_int_pheno<- lmer(INT_resid~avg_weekday_dur+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_int_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekday_dur + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3432.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.00240 -0.55350 -0.00242 0.47872 2.97936
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5072 0.7122
## Residual 0.4454 0.6674
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.048e-01 3.164e-02 8.257e+02 -3.314 0.000961 ***
## avg_weekday_dur -1.957e-02 2.867e-02 1.275e+03 -0.683 0.494974
## covid 7.273e-03 1.937e-01 1.058e+03 0.038 0.970053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_dr -0.038
## covid -0.145 0.013out<- weekday_dur_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_dur_int<- lmer(INT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3433.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.96811 -0.54608 -0.00177 0.47021 3.03987
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5079 0.7127
## Residual 0.4444 0.6666
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.056e-01 3.164e-02 8.262e+02 -3.338 0.000882 ***
## fam_avg_weekday_dur 8.430e-03 3.479e-02 8.604e+02 0.242 0.808608
## weekday_dur_diff -7.809e-02 5.013e-02 4.672e+02 -1.558 0.119983
## covid 7.009e-03 1.937e-01 1.059e+03 0.036 0.971137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__
## fm_vg_wkdy_ -0.042
## wkdy_dr_dff -0.007 0.006
## covid -0.145 0.011 0.008weekday_dur_int_zyg<- lmer(INT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekday_dur_diff+sib_DZ*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekday_dur_diff + sib_DZ *
## weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3441.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9449 -0.5393 -0.0008 0.4715 3.0293
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5058 0.7112
## Residual 0.4455 0.6675
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11897 0.03290 793.59009 -3.616 0.000318 ***
## fam_avg_weekday_dur 0.01125 0.03485 860.33500 0.323 0.746973
## weekday_dur_diff -0.07228 0.05229 462.60405 -1.382 0.167540
## covid -0.00651 0.19375 1056.42943 -0.034 0.973201
## sibDZ_MZ -0.04410 0.07511 764.76888 -0.587 0.557252
## sib_DZ -0.12601 0.07234 810.77014 -1.742 0.081914 .
## weekday_dur_diff:sibDZ_MZ 0.05579 0.12236 457.16343 0.456 0.648628
## weekday_dur_diff:sib_DZ 0.07126 0.11334 471.26503 0.629 0.529836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wkdy_ -0.059
## wkdy_dr_dff -0.008 0.005
## covid -0.134 0.010 0.006
## sibDZ_MZ 0.234 -0.062 -0.004 -0.005
## sib_DZ 0.127 -0.022 -0.001 0.044 -0.085
## wkd__:DZ_MZ -0.003 -0.003 0.278 -0.003 -0.008 0.001
## wkdy_d_:_DZ -0.002 -0.006 -0.028 0.009 0.001 -0.005 0.018out<- weekday_dur_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_dur_int_MZ<- lmer(INT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekday_dur_diff+MZ_dummy_sib*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekday_dur_diff +
## MZ_dummy_sib * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3441.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9449 -0.5393 -0.0008 0.4715 3.0293
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5058 0.7112
## Residual 0.4455 0.6675
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14837 0.06604 756.04166 -2.247 0.0249
## fam_avg_weekday_dur 0.01125 0.03485 860.33500 0.323 0.7470
## weekday_dur_diff -0.03508 0.10845 453.41122 -0.324 0.7465
## covid -0.00651 0.19375 1056.42943 -0.034 0.9732
## MZ_dummy_DZ -0.01890 0.08610 753.54939 -0.220 0.8263
## MZ_dummy_sib 0.10711 0.08054 796.29471 1.330 0.1839
## weekday_dur_diff:MZ_dummy_DZ -0.02016 0.13391 452.97103 -0.151 0.8804
## weekday_dur_diff:MZ_dummy_sib -0.09143 0.13578 466.19344 -0.673 0.5011
##
## (Intercept) *
## fam_avg_weekday_dur
## weekday_dur_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib
## weekday_dur_diff:MZ_dummy_DZ
## weekday_dur_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wkdy_ -0.076
## wkdy_dr_dff -0.010 0.000
## covid -0.070 0.010 0.001
## MZ_dummy_DZ -0.764 0.045 0.007 0.023
## MZ_dummy_sb -0.815 0.068 0.008 -0.015 0.625
## wk__:MZ__DZ 0.007 0.000 -0.810 0.007 -0.008 -0.006
## wkdy__:MZ__ 0.007 0.005 -0.799 -0.001 -0.006 -0.007 0.647out<- weekday_dur_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_int_DZ<- lmer(INT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekday_dur_diff+DZ_dummy_sib*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekday_dur_diff +
## DZ_dummy_sib * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3441.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9449 -0.5393 -0.0008 0.4715 3.0293
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5058 0.7112
## Residual 0.4455 0.6675
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.16727 0.05557 757.51907 -3.010 0.0027
## fam_avg_weekday_dur 0.01125 0.03485 860.33500 0.323 0.7470
## weekday_dur_diff -0.05525 0.07855 452.13150 -0.703 0.4822
## covid -0.00651 0.19375 1056.42943 -0.034 0.9732
## DZ_dummy_MZ 0.01890 0.08610 753.54939 0.220 0.8263
## DZ_dummy_sib 0.12601 0.07234 810.77014 1.742 0.0819
## weekday_dur_diff:DZ_dummy_MZ 0.02016 0.13391 452.97103 0.151 0.8804
## weekday_dur_diff:DZ_dummy_sib -0.07126 0.11334 471.26503 -0.629 0.5298
##
## (Intercept) **
## fam_avg_weekday_dur
## weekday_dur_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib .
## weekday_dur_diff:DZ_dummy_MZ
## weekday_dur_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wkdy_ -0.021
## wkdy_dr_dff -0.008 0.001
## covid -0.048 0.010 0.013
## DZ_dummy_MZ -0.642 -0.045 0.004 -0.023
## DZ_dummy_sb -0.764 0.022 0.005 -0.044 0.494
## wk__:DZ__MZ 0.004 0.000 -0.587 -0.007 -0.008 -0.003
## wkdy__:DZ__ 0.005 0.006 -0.693 -0.009 -0.003 -0.005 0.407out<- weekday_dur_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_int_sib<- lmer(INT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekday_dur_diff+sib_dummy_DZ*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_int_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekday_dur_diff +
## sib_dummy_DZ * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3441.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9449 -0.5393 -0.0008 0.4715 3.0293
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5058 0.7112
## Residual 0.4455 0.6675
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04126 0.04664 895.74828 -0.885 0.3766
## fam_avg_weekday_dur 0.01125 0.03485 860.33500 0.323 0.7470
## weekday_dur_diff -0.12651 0.08171 489.78106 -1.548 0.1222
## covid -0.00651 0.19375 1056.42943 -0.034 0.9732
## sib_dummy_MZ -0.10711 0.08054 796.29471 -1.330 0.1839
## sib_dummy_DZ -0.12601 0.07234 810.77014 -1.742 0.0819
## weekday_dur_diff:sib_dummy_MZ 0.09143 0.13578 466.19344 0.673 0.5011
## weekday_dur_diff:sib_dummy_DZ 0.07126 0.11334 471.26503 0.629 0.5298
##
## (Intercept)
## fam_avg_weekday_dur
## weekday_dur_diff
## covid
## sib_dummy_MZ
## sib_dummy_DZ .
## weekday_dur_diff:sib_dummy_MZ
## weekday_dur_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wkdy_ 0.009
## wkdy_dr_dff -0.003 0.009
## covid -0.126 0.010 -0.001
## sib_dmmy_MZ -0.573 -0.068 0.001 0.015
## sib_dmmy_DZ -0.640 -0.022 0.002 0.044 0.370
## wkdy__:__MZ 0.002 -0.005 -0.602 0.001 -0.007 -0.001
## wkdy__:__DZ 0.001 -0.006 -0.721 0.009 -0.001 -0.005 0.434out<- weekday_dur_int_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend duration (MCQ)
# weekend duration (MCQ)
weekend_dur_MCQ_int_pheno<- lmer(INT_resid~weekend_dur_mcq_wave_2+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_int_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ weekend_dur_mcq_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5839.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5110 -0.5843 0.0013 0.4943 3.4126
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4941 0.7029
## Residual 0.4798 0.6927
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.258e-01 2.506e-02 1.218e+03 -5.020 5.93e-07 ***
## weekend_dur_mcq_wave_2 6.732e-03 2.100e-02 2.009e+03 0.321 0.749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wknd_dr___2 -0.041out<- weekend_dur_MCQ_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_int<- lmer(INT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5839.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4394 -0.5883 -0.0071 0.4935 3.4323
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4934 0.7024
## Residual 0.4790 0.6921
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.12836 0.02507 1218.01847 -5.120 3.56e-07 ***
## avg_weekend_dur_mcq 0.05984 0.03286 1257.14824 1.821 0.0688 .
## weekend_dur_mcq_diff -0.02940 0.02713 1035.20656 -1.083 0.2788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___
## avg_wknd_d_ -0.064
## wknd_dr_mc_ -0.001 0.006weekend_dur_MCQ_int_zyg<- lmer(INT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+sibDZ_MZ+sib_DZ+weekend_dur_mcq_diff*sibDZ_MZ+weekend_dur_mcq_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + sibDZ_MZ +
## sib_DZ + weekend_dur_mcq_diff * sibDZ_MZ + weekend_dur_mcq_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4090 -0.5874 -0.0042 0.5003 3.4321
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4914 0.7010
## Residual 0.4781 0.6915
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.15081 0.02668 1165.61397 -5.652 1.99e-08
## avg_weekend_dur_mcq 0.05540 0.03284 1256.84267 1.687 0.0919
## weekend_dur_mcq_diff -0.02460 0.02911 961.70775 -0.845 0.3984
## sibDZ_MZ -0.06299 0.06139 1148.43577 -1.026 0.3050
## sib_DZ -0.12943 0.05923 1188.76340 -2.185 0.0291
## weekend_dur_mcq_diff:sibDZ_MZ -0.06587 0.06717 937.48670 -0.981 0.3270
## weekend_dur_mcq_diff:sib_DZ 0.10260 0.06447 997.85323 1.591 0.1118
##
## (Intercept) ***
## avg_weekend_dur_mcq .
## weekend_dur_mcq_diff
## sibDZ_MZ
## sib_DZ *
## weekend_dur_mcq_diff:sibDZ_MZ
## weekend_dur_mcq_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ sDZ_MZ sib_DZ w___:D
## avg_wknd_d_ -0.044
## wknd_dr_mc_ 0.000 0.002
## sibDZ_MZ 0.231 0.019 0.000
## sib_DZ 0.221 0.039 0.002 -0.145
## wk___:DZ_MZ 0.000 -0.001 0.238 0.000 -0.001
## wknd___:_DZ 0.002 -0.012 0.237 -0.001 0.000 -0.154out<- weekend_dur_MCQ_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_int_MZ<- lmer(INT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+MZ_dummy_DZ+MZ_dummy_sib+weekend_dur_mcq_diff*MZ_dummy_DZ+weekend_dur_mcq_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + MZ_dummy_DZ +
## MZ_dummy_sib + weekend_dur_mcq_diff * MZ_dummy_DZ + weekend_dur_mcq_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4090 -0.5874 -0.0042 0.5003 3.4321
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4914 0.7010
## Residual 0.4781 0.6915
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.928e-01 5.378e-02 1.137e+03 -3.585
## avg_weekend_dur_mcq 5.539e-02 3.284e-02 1.257e+03 1.687
## weekend_dur_mcq_diff -6.851e-02 5.893e-02 9.202e+02 -1.163
## MZ_dummy_DZ -1.718e-03 7.191e-02 1.135e+03 -0.024
## MZ_dummy_sib 1.277e-01 6.419e-02 1.183e+03 1.990
## weekend_dur_mcq_diff:MZ_dummy_DZ 1.172e-01 7.885e-02 9.244e+02 1.486
## weekend_dur_mcq_diff:MZ_dummy_sib 1.457e-02 6.989e-02 9.801e+02 0.208
## Pr(>|t|)
## (Intercept) 0.000351 ***
## avg_weekend_dur_mcq 0.091933 .
## weekend_dur_mcq_diff 0.245323
## MZ_dummy_DZ 0.980940
## MZ_dummy_sib 0.046856 *
## weekend_dur_mcq_diff:MZ_dummy_DZ 0.137658
## weekend_dur_mcq_diff:MZ_dummy_sib 0.834895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ MZ__DZ MZ_dm_ w___:MZ__D
## avg_wknd_d_ -0.007
## wknd_dr_mc_ 0.000 0.000
## MZ_dummy_DZ -0.748 -0.001 0.000
## MZ_dummy_sb -0.838 -0.036 0.000 0.626
## w___:MZ__DZ 0.000 -0.004 -0.747 0.001 0.000
## wkn___:MZ__ 0.000 0.006 -0.843 0.000 -0.001 0.630out<- weekend_dur_MCQ_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_int_DZ<- lmer(INT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+DZ_dummy_MZ+DZ_dummy_sib+weekend_dur_mcq_diff*DZ_dummy_MZ+weekend_dur_mcq_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + DZ_dummy_MZ +
## DZ_dummy_sib + weekend_dur_mcq_diff * DZ_dummy_MZ + weekend_dur_mcq_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4090 -0.5874 -0.0042 0.5003 3.4321
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4914 0.7010
## Residual 0.4781 0.6915
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.945e-01 4.775e-02 1.132e+03 -4.074
## avg_weekend_dur_mcq 5.539e-02 3.284e-02 1.257e+03 1.687
## weekend_dur_mcq_diff 4.866e-02 5.239e-02 9.299e+02 0.929
## DZ_dummy_MZ 1.718e-03 7.191e-02 1.135e+03 0.024
## DZ_dummy_sib 1.294e-01 5.923e-02 1.189e+03 2.185
## weekend_dur_mcq_diff:DZ_dummy_MZ -1.172e-01 7.885e-02 9.244e+02 -1.486
## weekend_dur_mcq_diff:DZ_dummy_sib -1.026e-01 6.447e-02 9.979e+02 -1.591
## Pr(>|t|)
## (Intercept) 4.95e-05 ***
## avg_weekend_dur_mcq 0.0919 .
## weekend_dur_mcq_diff 0.3533
## DZ_dummy_MZ 0.9809
## DZ_dummy_sib 0.0291 *
## weekend_dur_mcq_diff:DZ_dummy_MZ 0.1377
## weekend_dur_mcq_diff:DZ_dummy_sib 0.1118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ DZ__MZ DZ_dm_ w___:DZ__M
## avg_wknd_d_ -0.009
## wknd_dr_mc_ 0.002 -0.006
## DZ_dummy_MZ -0.664 0.001 -0.001
## DZ_dummy_sb -0.806 -0.039 -0.001 0.535
## w___:DZ__MZ -0.001 0.004 -0.664 0.001 0.001
## wkn___:DZ__ -0.002 0.012 -0.813 0.001 0.000 0.540out<- weekend_dur_MCQ_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_int_sib<- lmer(INT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+sib_dummy_MZ+sib_dummy_DZ+weekend_dur_mcq_diff*sib_dummy_MZ+weekend_dur_mcq_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_int_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + sib_dummy_MZ +
## sib_dummy_DZ + weekend_dur_mcq_diff * sib_dummy_MZ + weekend_dur_mcq_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4090 -0.5874 -0.0042 0.5003 3.4321
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4914 0.7010
## Residual 0.4781 0.6915
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.06510 0.03507 1300.22386 -1.856
## avg_weekend_dur_mcq 0.05540 0.03284 1256.84267 1.687
## weekend_dur_mcq_diff -0.05394 0.03756 1145.79701 -1.436
## sib_dummy_MZ -0.12771 0.06418 1183.02718 -1.990
## sib_dummy_DZ -0.12943 0.05923 1188.76340 -2.185
## weekend_dur_mcq_diff:sib_dummy_MZ -0.01457 0.06988 980.07860 -0.208
## weekend_dur_mcq_diff:sib_dummy_DZ 0.10260 0.06447 997.85323 1.591
## Pr(>|t|)
## (Intercept) 0.0637 .
## avg_weekend_dur_mcq 0.0919 .
## weekend_dur_mcq_diff 0.1513
## sib_dummy_MZ 0.0469 *
## sib_dummy_DZ 0.0291 *
## weekend_dur_mcq_diff:sib_dummy_MZ 0.8349
## weekend_dur_mcq_diff:sib_dummy_DZ 0.1118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ sb__MZ sb__DZ w___:__M
## avg_wknd_d_ -0.078
## wknd_dr_mc_ -0.004 0.012
## sib_dmmy_MZ -0.546 0.036 0.002
## sib_dmmy_DZ -0.592 0.039 0.002 0.323
## wkn___:__MZ 0.002 -0.006 -0.537 -0.001 -0.001
## wkn___:__DZ 0.003 -0.012 -0.583 -0.001 0.000 0.313out<- weekend_dur_MCQ_int_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday duration (MCQ)
# weekday duration (MCQ)
weekday_dur_MCQ_int_pheno<- lmer(INT_resid~weekday_dur_mcq_wave_2+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_int_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ weekday_dur_mcq_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5838.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5723 -0.5835 -0.0019 0.4968 3.4284
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4959 0.7042
## Residual 0.4783 0.6916
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.12609 0.02506 1217.11301 -5.031 5.61e-07 ***
## weekday_dur_mcq_wave_2 -0.02492 0.02069 2025.29896 -1.204 0.229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wkdy_dr___2 0.021out<- weekday_dur_MCQ_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_int<- lmer(INT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5840.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5939 -0.5830 -0.0101 0.4965 3.5026
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4953 0.7037
## Residual 0.4780 0.6914
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.12572 0.02505 1217.36119 -5.019 5.97e-07 ***
## avg_weekday_dur_mcq 0.01593 0.03157 1239.50011 0.505 0.6139
## weekday_dur_mcq_diff -0.05472 0.02703 1003.60970 -2.024 0.0432 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___
## avg_wkdy_d_ 0.020
## wkdy_dr_mc_ 0.010 0.015weekday_dur_MCQ_int_zyg<- lmer(INT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+sibDZ_MZ+sib_DZ+weekday_dur_mcq_diff*sibDZ_MZ+weekday_dur_mcq_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + sibDZ_MZ +
## sib_DZ + weekday_dur_mcq_diff * sibDZ_MZ + weekday_dur_mcq_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5847.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5228 -0.5761 -0.0155 0.5004 3.4843
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4925 0.7018
## Residual 0.4784 0.6917
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.490e-01 2.673e-02 1.165e+03 -5.576 3.06e-08
## avg_weekday_dur_mcq 5.929e-03 3.176e-02 1.238e+03 0.187 0.8519
## weekday_dur_mcq_diff -4.560e-02 2.940e-02 9.627e+02 -1.551 0.1212
## sibDZ_MZ -6.352e-02 6.162e-02 1.148e+03 -1.031 0.3029
## sib_DZ -1.310e-01 5.942e-02 1.188e+03 -2.204 0.0277
## weekday_dur_mcq_diff:sibDZ_MZ 2.770e-02 6.896e-02 9.561e+02 0.402 0.6881
## weekday_dur_mcq_diff:sib_DZ 3.797e-02 6.350e-02 9.726e+02 0.598 0.5501
##
## (Intercept) ***
## avg_weekday_dur_mcq
## weekday_dur_mcq_diff
## sibDZ_MZ
## sib_DZ *
## weekday_dur_mcq_diff:sibDZ_MZ
## weekday_dur_mcq_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ sDZ_MZ sib_DZ w___:D
## avg_wkdy_d_ 0.061
## wkdy_dr_mc_ 0.005 0.004
## sibDZ_MZ 0.236 0.079 0.003
## sib_DZ 0.227 0.078 -0.006 -0.138
## wk___:DZ_MZ 0.002 -0.013 0.285 0.005 0.003
## wkdy___:_DZ -0.007 -0.018 0.226 0.003 0.003 -0.144out<- weekday_dur_MCQ_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_int_MZ<- lmer(INT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+MZ_dummy_DZ+MZ_dummy_sib+weekday_dur_mcq_diff*MZ_dummy_DZ+weekday_dur_mcq_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + MZ_dummy_DZ +
## MZ_dummy_sib + weekday_dur_mcq_diff * MZ_dummy_DZ + weekday_dur_mcq_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5847.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5228 -0.5761 -0.0155 0.5004 3.4843
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4925 0.7018
## Residual 0.4784 0.6917
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.914e-01 5.404e-02 1.137e+03 -3.541
## avg_weekday_dur_mcq 5.929e-03 3.176e-02 1.238e+03 0.187
## weekday_dur_mcq_diff -2.714e-02 6.122e-02 9.517e+02 -0.443
## MZ_dummy_DZ -1.958e-03 7.202e-02 1.135e+03 -0.027
## MZ_dummy_sib 1.290e-01 6.460e-02 1.182e+03 1.997
## weekday_dur_mcq_diff:MZ_dummy_DZ -8.712e-03 7.998e-02 9.421e+02 -0.109
## weekday_dur_mcq_diff:MZ_dummy_sib -4.668e-02 7.164e-02 9.805e+02 -0.652
## Pr(>|t|)
## (Intercept) 0.000414 ***
## avg_weekday_dur_mcq 0.851915
## weekday_dur_mcq_diff 0.657649
## MZ_dummy_DZ 0.978313
## MZ_dummy_sib 0.046071 *
## weekday_dur_mcq_diff:MZ_dummy_DZ 0.913281
## weekday_dur_mcq_diff:MZ_dummy_sib 0.514820
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ MZ__DZ MZ_dm_ w___:MZ__D
## avg_wkdy_d_ 0.090
## wkdy_dr_mc_ 0.006 -0.008
## MZ_dummy_DZ -0.747 -0.035 -0.004
## MZ_dummy_sb -0.840 -0.111 -0.004 0.626
## w___:MZ__DZ -0.004 0.004 -0.765 0.002 0.004
## wkd___:MZ__ -0.004 0.020 -0.855 0.003 0.007 0.654out<- weekday_dur_MCQ_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_int_DZ<- lmer(INT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+DZ_dummy_MZ+DZ_dummy_sib+weekday_dur_mcq_diff*DZ_dummy_MZ+weekday_dur_mcq_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + DZ_dummy_MZ +
## DZ_dummy_sib + weekday_dur_mcq_diff * DZ_dummy_MZ + weekday_dur_mcq_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5847.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5228 -0.5761 -0.0155 0.5004 3.4843
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4925 0.7018
## Residual 0.4784 0.6917
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.933e-01 4.785e-02 1.132e+03 -4.041
## avg_weekday_dur_mcq 5.929e-03 3.176e-02 1.238e+03 0.187
## weekday_dur_mcq_diff -3.585e-02 5.147e-02 9.285e+02 -0.697
## DZ_dummy_MZ 1.958e-03 7.202e-02 1.135e+03 0.027
## DZ_dummy_sib 1.310e-01 5.942e-02 1.188e+03 2.204
## weekday_dur_mcq_diff:DZ_dummy_MZ 8.712e-03 7.998e-02 9.421e+02 0.109
## weekday_dur_mcq_diff:DZ_dummy_sib -3.797e-02 6.350e-02 9.726e+02 -0.598
## Pr(>|t|)
## (Intercept) 5.69e-05 ***
## avg_weekday_dur_mcq 0.8519
## weekday_dur_mcq_diff 0.4863
## DZ_dummy_MZ 0.9783
## DZ_dummy_sib 0.0277 *
## weekday_dur_mcq_diff:DZ_dummy_MZ 0.9133
## weekday_dur_mcq_diff:DZ_dummy_sib 0.5501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ DZ__MZ DZ_dm_ w___:DZ__M
## avg_wkdy_d_ 0.049
## wkdy_dr_mc_ -0.003 -0.003
## DZ_dummy_MZ -0.661 0.035 0.002
## DZ_dummy_sb -0.807 -0.078 0.003 0.531
## w___:DZ__MZ 0.002 -0.004 -0.644 0.002 -0.001
## wkd___:DZ__ 0.004 0.018 -0.811 -0.001 0.003 0.521out<- weekday_dur_MCQ_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_int_sib<- lmer(INT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+sib_dummy_MZ+sib_dummy_DZ+weekday_dur_mcq_diff*sib_dummy_MZ+weekday_dur_mcq_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_int_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## INT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + sib_dummy_MZ +
## sib_dummy_DZ + weekday_dur_mcq_diff * sib_dummy_MZ + weekday_dur_mcq_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5847.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5228 -0.5761 -0.0155 0.5004 3.4843
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4925 0.7018
## Residual 0.4784 0.6917
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -6.238e-02 3.508e-02 1.298e+03 -1.778
## avg_weekday_dur_mcq 5.929e-03 3.176e-02 1.238e+03 0.187
## weekday_dur_mcq_diff -7.382e-02 3.720e-02 1.064e+03 -1.985
## sib_dummy_MZ -1.290e-01 6.460e-02 1.182e+03 -1.997
## sib_dummy_DZ -1.310e-01 5.942e-02 1.188e+03 -2.204
## weekday_dur_mcq_diff:sib_dummy_MZ 4.668e-02 7.164e-02 9.805e+02 0.652
## weekday_dur_mcq_diff:sib_dummy_DZ 3.797e-02 6.350e-02 9.726e+02 0.598
## Pr(>|t|)
## (Intercept) 0.0756 .
## avg_weekday_dur_mcq 0.8519
## weekday_dur_mcq_diff 0.0475 *
## sib_dummy_MZ 0.0461 *
## sib_dummy_DZ 0.0277 *
## weekday_dur_mcq_diff:sib_dummy_MZ 0.5148
## weekday_dur_mcq_diff:sib_dummy_DZ 0.5501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ sb__MZ sb__DZ w___:__M
## avg_wkdy_d_ -0.066
## wkdy_dr_mc_ 0.017 0.027
## sib_dmmy_MZ -0.548 0.111 -0.007
## sib_dmmy_DZ -0.593 0.078 -0.009 0.328
## wkd___:__MZ -0.008 -0.020 -0.519 0.007 0.004
## wkd___:__DZ -0.010 -0.018 -0.586 0.004 0.003 0.304out<- weekday_dur_MCQ_int_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Internalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)chronotype
# chrono
chrono_int_pheno<- lmer(INT_resid~chronotype_wave_2+(1|rel_family_id), data=abcd_all)
summary(chrono_int_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ chronotype_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5157.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4804 -0.5776 0.0011 0.4891 3.3738
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4813 0.6938
## Residual 0.4897 0.6998
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11834 0.02610 1161.25992 -4.534 6.39e-06 ***
## chronotype_wave_2 -0.01239 0.02120 1762.33662 -0.584 0.559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## chrntyp_w_2 -0.084out<- chrono_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
chrono_int<- lmer(INT_resid~avg_chrono+chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_chrono + chrono_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5161.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4752 -0.5767 -0.0029 0.4907 3.3786
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4814 0.6938
## Residual 0.4899 0.6999
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.193e-01 2.616e-02 1.155e+03 -4.561 5.63e-06 ***
## avg_chrono 4.867e-04 3.115e-02 1.200e+03 0.016 0.988
## chrono_diff -2.350e-02 2.894e-02 7.919e+02 -0.812 0.417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch
## avg_chrono -0.106
## chrono_diff -0.016 0.000chrono_int_zyg<- lmer(INT_resid~avg_chrono+chrono_diff+sibDZ_MZ+sib_DZ+sibDZ_MZ*chrono_diff+sib_DZ*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_chrono + chrono_diff + sibDZ_MZ + sib_DZ + sibDZ_MZ *
## chrono_diff + sib_DZ * chrono_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5168.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4360 -0.5729 -0.0049 0.4958 3.4120
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4780 0.6914
## Residual 0.4905 0.7004
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.437e-01 2.785e-02 1.115e+03 -5.162 2.89e-07 ***
## avg_chrono 2.303e-03 3.110e-02 1.199e+03 0.074 0.9410
## chrono_diff -1.864e-02 3.033e-02 7.482e+02 -0.615 0.5389
## sibDZ_MZ -6.731e-02 6.363e-02 1.099e+03 -1.058 0.2904
## sib_DZ -1.391e-01 6.168e-02 1.129e+03 -2.255 0.0243 *
## chrono_diff:sibDZ_MZ 2.673e-02 6.820e-02 7.301e+02 0.392 0.6952
## chrono_diff:sib_DZ 3.012e-02 6.953e-02 7.722e+02 0.433 0.6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d sDZ_MZ sib_DZ c_:DZ_
## avg_chrono -0.106
## chrono_diff -0.009 0.000
## sibDZ_MZ 0.225 -0.003 0.000
## sib_DZ 0.228 -0.022 0.006 -0.148
## chrn_:DZ_MZ 0.000 0.001 0.165 -0.008 -0.004
## chrn_df:_DZ 0.006 0.000 0.220 -0.004 -0.011 -0.147out<- chrono_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
chrono_int_MZ<- lmer(INT_resid~avg_chrono+chrono_diff+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*chrono_diff+MZ_dummy_sib*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_chrono + chrono_diff + MZ_dummy_DZ + MZ_dummy_sib +
## MZ_dummy_DZ * chrono_diff + MZ_dummy_sib * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5168.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4360 -0.5729 -0.0049 0.4958 3.4120
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4780 0.6914
## Residual 0.4905 0.7004
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.886e-01 5.574e-02 1.092e+03 -3.384 0.00074 ***
## avg_chrono 2.303e-03 3.110e-02 1.199e+03 0.074 0.94098
## chrono_diff -8.230e-04 5.867e-02 7.160e+02 -0.014 0.98881
## MZ_dummy_DZ -2.230e-03 7.470e-02 1.087e+03 -0.030 0.97619
## MZ_dummy_sib 1.368e-01 6.648e-02 1.126e+03 2.058 0.03977 *
## chrono_diff:MZ_dummy_DZ -1.167e-02 8.097e-02 7.177e+02 -0.144 0.88546
## chrono_diff:MZ_dummy_sib -4.179e-02 7.185e-02 7.661e+02 -0.582 0.56099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d MZ__DZ MZ_dm_ c_:MZ__D
## avg_chrono -0.055
## chrono_diff -0.006 0.001
## MZ_dummy_DZ -0.744 -0.007 0.005
## MZ_dummy_sb -0.837 0.013 0.005 0.624
## chr_:MZ__DZ 0.005 -0.001 -0.725 -0.005 -0.004
## chrn_d:MZ__ 0.005 0.000 -0.817 -0.004 -0.013 0.592out<- chrono_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
chrono_int_DZ<- lmer(INT_resid~avg_chrono+chrono_diff+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*chrono_diff+DZ_dummy_sib*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_chrono + chrono_diff + DZ_dummy_MZ + DZ_dummy_sib +
## DZ_dummy_MZ * chrono_diff + DZ_dummy_sib * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5168.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4360 -0.5729 -0.0049 0.4958 3.4120
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4780 0.6914
## Residual 0.4905 0.7004
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.908e-01 4.995e-02 1.086e+03 -3.821 0.00014 ***
## avg_chrono 2.303e-03 3.110e-02 1.199e+03 0.074 0.94098
## chrono_diff -1.249e-02 5.580e-02 7.196e+02 -0.224 0.82295
## DZ_dummy_MZ 2.230e-03 7.470e-02 1.087e+03 0.030 0.97619
## DZ_dummy_sib 1.391e-01 6.168e-02 1.129e+03 2.255 0.02434 *
## chrono_diff:DZ_dummy_MZ 1.167e-02 8.097e-02 7.177e+02 0.144 0.88546
## chrono_diff:DZ_dummy_sib -3.012e-02 6.953e-02 7.722e+02 -0.433 0.66495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d DZ__MZ DZ_dm_ c_:DZ__M
## avg_chrono -0.072
## chrono_diff -0.002 0.000
## DZ_dummy_MZ -0.666 0.007 0.002
## DZ_dummy_sb -0.807 0.022 0.002 0.539
## chr_:DZ__MZ 0.002 0.001 -0.689 -0.005 -0.001
## chrn_d:DZ__ 0.002 0.000 -0.803 -0.001 -0.011 0.553out<- chrono_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
chrono_int_sib<- lmer(INT_resid~avg_chrono+chrono_diff+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*chrono_diff+sib_dummy_DZ*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_int_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_chrono + chrono_diff + sib_dummy_MZ + sib_dummy_DZ +
## sib_dummy_MZ * chrono_diff + sib_dummy_DZ * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5168.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4360 -0.5729 -0.0049 0.4958 3.4120
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4780 0.6914
## Residual 0.4905 0.7004
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.177e-02 3.641e-02 1.215e+03 -1.422 0.1554
## avg_chrono 2.303e-03 3.110e-02 1.199e+03 0.074 0.9410
## chrono_diff -4.261e-02 4.148e-02 8.790e+02 -1.027 0.3045
## sib_dummy_MZ -1.368e-01 6.648e-02 1.126e+03 -2.058 0.0398 *
## sib_dummy_DZ -1.391e-01 6.168e-02 1.129e+03 -2.255 0.0243 *
## chrono_diff:sib_dummy_MZ 4.179e-02 7.185e-02 7.661e+02 0.582 0.5610
## chrono_diff:sib_dummy_DZ 3.012e-02 6.953e-02 7.722e+02 0.433 0.6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d sb__MZ sb__DZ c_:__M
## avg_chrono -0.061
## chrono_diff -0.027 0.000
## sib_dmmy_MZ -0.545 -0.013 0.015
## sib_dmmy_DZ -0.587 -0.022 0.016 0.322
## chrn_d:__MZ 0.015 0.000 -0.577 -0.013 -0.009
## chrn_d:__DZ 0.016 0.000 -0.597 -0.009 -0.011 0.344out<- chrono_int_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Internalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend efficiency
weekend_effic_int_pheno<- lmer(INT_resid~avg_weekend_effic+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_int_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekend_effic + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3397
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9844 -0.5517 -0.0011 0.4782 2.9682
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4996 0.7068
## Residual 0.4508 0.6714
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.010e-01 3.176e-02 8.202e+02 -3.179 0.00153 **
## avg_weekend_effic -1.859e-02 3.008e-02 1.131e+03 -0.618 0.53682
## covid 2.954e-03 1.936e-01 1.043e+03 0.015 0.98783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_ff -0.074
## covid -0.148 0.036out<- weekend_effic_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_effic_int<- lmer(INT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3400
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95509 -0.54438 -0.00036 0.47555 2.96943
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4999 0.7070
## Residual 0.4507 0.6714
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.029e-01 3.184e-02 8.154e+02 -3.233 0.00128 **
## fam_avg_weekend_effic 8.691e-03 4.287e-02 8.666e+02 0.203 0.83941
## weekend_effic_diff -4.503e-02 4.221e-02 4.555e+02 -1.067 0.28656
## covid 5.444e-03 1.936e-01 1.042e+03 0.028 0.97758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__
## fm_vg_wknd_ -0.101
## wknd_ffc_df -0.004 0.000
## covid -0.148 0.036 0.015weekend_effic_int_zyg<- lmer(INT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekend_effic_diff+sib_DZ*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekend_effic_diff + sib_DZ *
## weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3408.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.93639 -0.54578 0.00584 0.47325 2.98502
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4975 0.7053
## Residual 0.4524 0.6726
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.162e-01 3.306e-02 7.829e+02 -3.516 0.000463
## fam_avg_weekend_effic 6.838e-03 4.293e-02 8.651e+02 0.159 0.873474
## weekend_effic_diff -3.841e-02 4.619e-02 4.457e+02 -0.832 0.406096
## covid -9.813e-03 1.937e-01 1.039e+03 -0.051 0.959614
## sibDZ_MZ -4.673e-02 7.503e-02 7.578e+02 -0.623 0.533657
## sib_DZ -1.253e-01 7.264e-02 8.026e+02 -1.725 0.084950
## weekend_effic_diff:sibDZ_MZ 2.039e-02 1.105e-01 4.427e+02 0.185 0.853651
## weekend_effic_diff:sib_DZ 3.250e-02 9.659e-02 4.508e+02 0.336 0.736678
##
## (Intercept) ***
## fam_avg_weekend_effic
## weekend_effic_diff
## covid
## sibDZ_MZ
## sib_DZ .
## weekend_effic_diff:sibDZ_MZ
## weekend_effic_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wknd_ -0.102
## wknd_ffc_df -0.005 0.000
## covid -0.137 0.038 0.011
## sibDZ_MZ 0.230 -0.048 -0.002 -0.006
## sib_DZ 0.122 0.045 -0.002 0.046 -0.090
## wkn__:DZ_MZ -0.001 -0.001 0.340 -0.006 -0.004 0.001
## wknd_f_:_DZ -0.002 -0.001 0.177 -0.004 0.002 -0.003 -0.111out<- weekend_effic_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_effic_int_MZ<- lmer(INT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekend_effic_diff+MZ_dummy_sib*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekend_effic_diff +
## MZ_dummy_sib * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3408.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.93639 -0.54578 0.00584 0.47325 2.98502
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4975 0.7053
## Residual 0.4524 0.6726
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.474e-01 6.601e-02 7.472e+02 -2.233
## fam_avg_weekend_effic 6.838e-03 4.293e-02 8.651e+02 0.159
## weekend_effic_diff -2.482e-02 9.934e-02 4.407e+02 -0.250
## covid -9.813e-03 1.937e-01 1.039e+03 -0.051
## MZ_dummy_DZ -1.592e-02 8.625e-02 7.467e+02 -0.185
## MZ_dummy_sib 1.094e-01 8.037e-02 7.891e+02 1.361
## weekend_effic_diff:MZ_dummy_DZ -4.138e-03 1.254e-01 4.412e+02 -0.033
## weekend_effic_diff:MZ_dummy_sib -3.664e-02 1.156e-01 4.472e+02 -0.317
## Pr(>|t|)
## (Intercept) 0.0259 *
## fam_avg_weekend_effic 0.8735
## weekend_effic_diff 0.8028
## covid 0.9596
## MZ_dummy_DZ 0.8536
## MZ_dummy_sib 0.1739
## weekend_effic_diff:MZ_dummy_DZ 0.9737
## weekend_effic_diff:MZ_dummy_sib 0.7513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wknd_ -0.088
## wknd_ffc_df -0.004 -0.001
## covid -0.073 0.038 0.000
## MZ_dummy_DZ -0.763 0.061 0.003 0.024
## MZ_dummy_sb -0.812 0.025 0.004 -0.016 0.622
## wk__:MZ__DZ 0.003 0.001 -0.792 0.004 -0.004 -0.003
## wknd__:MZ__ 0.003 0.001 -0.860 0.008 -0.003 -0.003 0.681out<- weekend_effic_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_effic_int_DZ<- lmer(INT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekend_effic_diff+DZ_dummy_sib*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekend_effic_diff +
## DZ_dummy_sib * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3408.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.93639 -0.54578 0.00584 0.47325 2.98502
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4975 0.7053
## Residual 0.4524 0.6726
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.633e-01 5.580e-02 7.496e+02 -2.927
## fam_avg_weekend_effic 6.838e-03 4.293e-02 8.651e+02 0.159
## weekend_effic_diff -2.896e-02 7.647e-02 4.420e+02 -0.379
## covid -9.813e-03 1.937e-01 1.039e+03 -0.051
## DZ_dummy_MZ 1.592e-02 8.625e-02 7.467e+02 0.185
## DZ_dummy_sib 1.253e-01 7.264e-02 8.026e+02 1.725
## weekend_effic_diff:DZ_dummy_MZ 4.138e-03 1.254e-01 4.412e+02 0.033
## weekend_effic_diff:DZ_dummy_sib -3.250e-02 9.659e-02 4.508e+02 -0.336
## Pr(>|t|)
## (Intercept) 0.00353 **
## fam_avg_weekend_effic 0.87347
## weekend_effic_diff 0.70512
## covid 0.95961
## DZ_dummy_MZ 0.85360
## DZ_dummy_sib 0.08495 .
## weekend_effic_diff:DZ_dummy_MZ 0.97368
## weekend_effic_diff:DZ_dummy_sib 0.73668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wknd_ -0.010
## wknd_ffc_df -0.005 0.000
## covid -0.049 0.038 0.007
## DZ_dummy_MZ -0.644 -0.061 0.003 -0.024
## DZ_dummy_sb -0.764 -0.045 0.003 -0.046 0.499
## wk__:DZ__MZ 0.003 -0.001 -0.610 -0.004 -0.004 -0.002
## wknd__:DZ__ 0.004 0.001 -0.792 0.004 -0.003 -0.003 0.483out<- weekend_effic_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_effic_int_sib<- lmer(INT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekend_effic_diff+sib_dummy_DZ*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_int_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekend_effic_diff +
## sib_dummy_DZ * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3408.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.93639 -0.54578 0.00584 0.47325 2.98502
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4975 0.7053
## Residual 0.4524 0.6726
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -3.803e-02 4.690e-02 8.865e+02 -0.811
## fam_avg_weekend_effic 6.838e-03 4.293e-02 8.651e+02 0.159
## weekend_effic_diff -6.145e-02 5.902e-02 4.663e+02 -1.041
## covid -9.813e-03 1.937e-01 1.039e+03 -0.051
## sib_dummy_MZ -1.094e-01 8.037e-02 7.891e+02 -1.361
## sib_dummy_DZ -1.253e-01 7.264e-02 8.026e+02 -1.725
## weekend_effic_diff:sib_dummy_MZ 3.664e-02 1.156e-01 4.472e+02 0.317
## weekend_effic_diff:sib_dummy_DZ 3.250e-02 9.659e-02 4.508e+02 0.336
## Pr(>|t|)
## (Intercept) 0.4177
## fam_avg_weekend_effic 0.8735
## weekend_effic_diff 0.2983
## covid 0.9596
## sib_dummy_MZ 0.1739
## sib_dummy_DZ 0.0849 .
## weekend_effic_diff:sib_dummy_MZ 0.7513
## weekend_effic_diff:sib_dummy_DZ 0.7367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wknd_ -0.081
## wknd_ffc_df -0.002 0.001
## covid -0.129 0.038 0.016
## sib_dmmy_MZ -0.571 -0.025 0.000 0.016
## sib_dmmy_DZ -0.640 0.045 0.001 0.046 0.368
## wknd__:__MZ 0.001 -0.001 -0.511 -0.008 -0.003 0.000
## wknd__:__DZ 0.000 -0.001 -0.611 -0.004 0.000 -0.003 0.312out<- weekend_effic_int_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday efficiency
weekday_effic_int_pheno<- lmer(INT_resid~avg_weekday_effic+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_int_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ avg_weekday_effic + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3426.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9911 -0.5401 0.0119 0.4754 3.0210
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5077 0.7125
## Residual 0.4419 0.6648
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.025e-01 3.160e-02 8.255e+02 -3.243 0.00123 **
## avg_weekday_effic -6.770e-02 2.791e-02 1.191e+03 -2.426 0.01542 *
## covid -8.587e-03 1.935e-01 1.060e+03 -0.044 0.96461
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_ff -0.040
## covid -0.146 0.037out<- weekday_effic_int_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_effic_int<- lmer(INT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_int)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3429.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.96670 -0.53274 0.00719 0.47414 3.04649
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5074 0.7123
## Residual 0.4421 0.6649
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.037e-01 3.162e-02 8.248e+02 -3.279 0.00108 **
## fam_avg_weekday_effic -3.981e-02 3.878e-02 8.889e+02 -1.027 0.30492
## weekday_effic_diff -9.830e-02 4.065e-02 4.705e+02 -2.419 0.01596 *
## covid -6.565e-03 1.935e-01 1.060e+03 -0.034 0.97294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__
## fm_vg_wkdy_ -0.054
## wkdy_ffc_df -0.002 -0.011
## covid -0.146 0.033 0.018weekday_effic_int_zyg<- lmer(INT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekday_effic_diff+sib_DZ*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_int_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekday_effic_diff + sib_DZ *
## weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3436.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.97509 -0.53583 0.00413 0.47269 2.99064
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5070 0.7120
## Residual 0.4414 0.6643
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11605 0.03291 791.78451 -3.526 0.000446
## fam_avg_weekday_effic -0.03593 0.03894 887.44225 -0.923 0.356452
## weekday_effic_diff -0.08092 0.04215 460.70910 -1.920 0.055501
## covid -0.02144 0.19359 1058.37767 -0.111 0.911849
## sibDZ_MZ -0.03590 0.07523 763.32579 -0.477 0.633359
## sib_DZ -0.12286 0.07226 811.40558 -1.700 0.089468
## weekday_effic_diff:sibDZ_MZ 0.13199 0.09789 452.62518 1.348 0.178212
## weekday_effic_diff:sib_DZ 0.06142 0.09242 472.79572 0.665 0.506610
##
## (Intercept) ***
## fam_avg_weekday_effic
## weekday_effic_diff .
## covid
## sibDZ_MZ
## sib_DZ .
## weekday_effic_diff:sibDZ_MZ
## weekday_effic_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wkdy_ -0.077
## wkdy_ffc_df -0.002 -0.008
## covid -0.135 0.033 0.015
## sibDZ_MZ 0.237 -0.094 0.002 -0.007
## sib_DZ 0.127 -0.014 -0.007 0.044 -0.085
## wkd__:DZ_MZ 0.003 0.004 0.256 -0.010 0.000 0.004
## wkdy_f_:_DZ -0.010 0.015 0.065 0.013 0.003 -0.002 -0.042out<- weekday_effic_int_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_effic_int_MZ<- lmer(INT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekday_effic_diff+MZ_dummy_sib*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_int_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekday_effic_diff +
## MZ_dummy_sib * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3436.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.97509 -0.53583 0.00413 0.47269 2.99064
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5070 0.7120
## Residual 0.4414 0.6643
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.400e-01 6.618e-02 7.537e+02 -2.115
## fam_avg_weekday_effic -3.593e-02 3.894e-02 8.874e+02 -0.923
## weekday_effic_diff 7.074e-03 8.629e-02 4.469e+02 0.082
## covid -2.144e-02 1.936e-01 1.058e+03 -0.111
## MZ_dummy_DZ -2.553e-02 8.619e-02 7.519e+02 -0.296
## MZ_dummy_sib 9.733e-02 8.063e-02 7.958e+02 1.207
## weekday_effic_diff:MZ_dummy_DZ -1.013e-01 1.100e-01 4.519e+02 -0.921
## weekday_effic_diff:MZ_dummy_sib -1.627e-01 1.065e-01 4.609e+02 -1.528
## Pr(>|t|)
## (Intercept) 0.0347 *
## fam_avg_weekday_effic 0.3565
## weekday_effic_diff 0.9347
## covid 0.9118
## MZ_dummy_DZ 0.7671
## MZ_dummy_sib 0.2278
## weekday_effic_diff:MZ_dummy_DZ 0.3576
## weekday_effic_diff:MZ_dummy_sib 0.1272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wkdy_ -0.110
## wkdy_ffc_df 0.001 -0.001
## covid -0.073 0.033 0.000
## MZ_dummy_DZ -0.765 0.076 -0.001 0.024
## MZ_dummy_sb -0.816 0.094 -0.001 -0.013 0.627
## wk__:MZ__DZ -0.002 0.003 -0.785 0.014 -0.003 0.001
## wkdy__:MZ__ 0.000 -0.010 -0.810 0.004 0.000 0.003 0.636out<- weekday_effic_int_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_effic_int_DZ<- lmer(INT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekday_effic_diff+DZ_dummy_sib*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_int_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekday_effic_diff +
## DZ_dummy_sib * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3436.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.97509 -0.53583 0.00413 0.47269 2.99064
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5070 0.7120
## Residual 0.4414 0.6643
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.16552 0.05552 757.87805 -2.981
## fam_avg_weekday_effic -0.03593 0.03894 887.44225 -0.923
## weekday_effic_diff -0.09420 0.06818 459.95513 -1.382
## covid -0.02144 0.19359 1058.37767 -0.111
## DZ_dummy_MZ 0.02553 0.08619 751.90174 0.296
## DZ_dummy_sib 0.12286 0.07226 811.40558 1.700
## weekday_effic_diff:DZ_dummy_MZ 0.10127 0.10997 451.87838 0.921
## weekday_effic_diff:DZ_dummy_sib -0.06142 0.09242 472.79572 -0.665
## Pr(>|t|)
## (Intercept) 0.00296 **
## fam_avg_weekday_effic 0.35645
## weekday_effic_diff 0.16774
## covid 0.91185
## DZ_dummy_MZ 0.76712
## DZ_dummy_sib 0.08947 .
## weekday_effic_diff:DZ_dummy_MZ 0.35760
## weekday_effic_diff:DZ_dummy_sib 0.50661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wkdy_ -0.013
## wkdy_ffc_df -0.012 0.003
## covid -0.049 0.033 0.023
## DZ_dummy_MZ -0.641 -0.076 0.006 -0.024
## DZ_dummy_sb -0.764 0.014 0.008 -0.044 0.494
## wk__:DZ__MZ 0.008 -0.003 -0.620 -0.014 -0.003 -0.005
## wkdy__:DZ__ 0.009 -0.015 -0.738 -0.013 -0.004 -0.002 0.457out<- weekday_effic_int_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_effic_int_sib<- lmer(INT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekday_effic_diff+sib_dummy_DZ*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_int_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: INT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekday_effic_diff +
## sib_dummy_DZ * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3436.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.97509 -0.53583 0.00413 0.47269 2.99064
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5070 0.7120
## Residual 0.4414 0.6643
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.04266 0.04659 895.89008 -0.916
## fam_avg_weekday_effic -0.03593 0.03894 887.44225 -0.923
## weekday_effic_diff -0.15563 0.06240 489.11194 -2.494
## covid -0.02144 0.19359 1058.37767 -0.111
## sib_dummy_MZ -0.09733 0.08063 795.83298 -1.207
## sib_dummy_DZ -0.12286 0.07226 811.40558 -1.700
## weekday_effic_diff:sib_dummy_MZ 0.16270 0.10649 460.91086 1.528
## weekday_effic_diff:sib_dummy_DZ 0.06142 0.09242 472.79572 0.665
## Pr(>|t|)
## (Intercept) 0.3601
## fam_avg_weekday_effic 0.3565
## weekday_effic_diff 0.0130 *
## covid 0.9118
## sib_dummy_MZ 0.2278
## sib_dummy_DZ 0.0895 .
## weekday_effic_diff:sib_dummy_MZ 0.1272
## weekday_effic_diff:sib_dummy_DZ 0.5066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wkdy_ 0.007
## wkdy_ffc_df 0.008 -0.018
## covid -0.126 0.033 0.006
## sib_dmmy_MZ -0.571 -0.094 -0.003 0.013
## sib_dmmy_DZ -0.640 -0.014 -0.005 0.044 0.369
## wkdy__:__MZ -0.005 0.010 -0.586 -0.004 0.003 0.003
## wkdy__:__DZ -0.008 0.015 -0.675 0.013 0.002 -0.002 0.396out<- weekday_effic_int_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Internalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)Externalizing
variability
# variability (fitbit)
variability_EXT_pheno<- lmer(EXT_resid~variability+covid+(1|rel_family_id), data=abcd_all)
summary(variability_EXT_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ variability + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3445.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1982 -0.5364 -0.1090 0.4728 3.2038
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5304 0.7283
## Residual 0.4398 0.6631
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07411 0.03201 806.63674 -2.315 0.0208 *
## variability 0.11449 0.02798 1265.54373 4.091 4.56e-05 ***
## covid -0.02343 0.19587 1061.25015 -0.120 0.9048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vrblty
## variability 0.036
## covid -0.146 -0.064out<- variability_EXT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Externalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
variability_EXT<- lmer(EXT_resid~avg_variabilitiy+variabiltiy_diff+covid+(1|rel_family_id), data=abcd_all)
summary(variability_EXT) ### don't need to save this one, just for model comparison## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3445.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2147 -0.5282 -0.1145 0.4777 3.1458
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5306 0.7285
## Residual 0.4379 0.6618
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07281 0.03200 807.99106 -2.276 0.0231 *
## avg_variabilitiy 0.15743 0.03556 857.73363 4.427 1.08e-05 ***
## variabiltiy_diff 0.04531 0.04503 442.96213 1.006 0.3149
## covid -0.03299 0.19578 1062.70893 -0.169 0.8662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_
## avg_varblty 0.041
## varblty_dff 0.006 0.003
## covid -0.146 -0.067 -0.019variability_EXT_zyg<- lmer(EXT_resid~avg_variabilitiy+variabiltiy_diff+covid+sibDZ_MZ+sib_DZ+variabiltiy_diff*sibDZ_MZ+variabiltiy_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(variability_EXT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + sibDZ_MZ +
## sib_DZ + variabiltiy_diff * sibDZ_MZ + variabiltiy_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3446.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1847 -0.5357 -0.1104 0.4630 3.2031
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5227 0.7230
## Residual 0.4380 0.6618
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.09556 0.03312 772.63029 -2.885 0.00402 **
## avg_variabilitiy 0.15866 0.03544 855.19929 4.477 8.6e-06 ***
## variabiltiy_diff 0.03821 0.04708 436.76289 0.812 0.41745
## covid -0.06171 0.19521 1056.98885 -0.316 0.75199
## sibDZ_MZ -0.09467 0.07561 744.97566 -1.252 0.21091
## sib_DZ -0.19057 0.07289 791.23501 -2.614 0.00911 **
## variabiltiy_diff:sibDZ_MZ -0.07103 0.11069 432.54500 -0.642 0.52144
## variabiltiy_diff:sib_DZ -0.16019 0.10129 443.28957 -1.582 0.11447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid sDZ_MZ sib_DZ v_:DZ_
## avg_varblty 0.045
## varblty_dff 0.004 0.002
## covid -0.136 -0.068 -0.016
## sibDZ_MZ 0.233 0.041 -0.002 -0.007
## sib_DZ 0.125 -0.031 -0.002 0.046 -0.088
## vrbl_:DZ_MZ -0.004 -0.004 0.291 0.010 0.000 0.001
## vrblty_:_DZ -0.003 -0.005 -0.014 0.011 0.001 0.005 0.009out<- variability_EXT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Externalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
variability_EXT_MZ<- lmer(EXT_resid~avg_variabilitiy+variabiltiy_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+variabiltiy_diff*MZ_dummy_DZ+variabiltiy_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(variability_EXT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + MZ_dummy_DZ +
## MZ_dummy_sib + variabiltiy_diff * MZ_dummy_DZ + variabiltiy_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3446.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1847 -0.5357 -0.1104 0.4630 3.2031
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5227 0.7230
## Residual 0.4380 0.6618
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.587e-01 6.646e-02 7.352e+02 -2.388 0.0172
## avg_variabilitiy 1.587e-01 3.544e-02 8.552e+02 4.477 8.6e-06
## variabiltiy_diff -9.139e-03 9.842e-02 4.297e+02 -0.093 0.9261
## covid -6.171e-02 1.952e-01 1.057e+03 -0.316 0.7520
## MZ_dummy_DZ -6.124e-04 8.679e-02 7.346e+02 -0.007 0.9944
## MZ_dummy_sib 1.900e-01 8.098e-02 7.758e+02 2.346 0.0192
## variabiltiy_diff:MZ_dummy_DZ -9.069e-03 1.213e-01 4.290e+02 -0.075 0.9404
## variabiltiy_diff:MZ_dummy_sib 1.511e-01 1.221e-01 4.398e+02 1.237 0.2166
##
## (Intercept) *
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib *
## variabiltiy_diff:MZ_dummy_DZ
## variabiltiy_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid MZ__DZ MZ_dm_ v_:MZ__D
## avg_varblty 0.053
## varblty_dff -0.001 -0.002
## covid -0.073 -0.068 0.000
## MZ_dummy_DZ -0.764 -0.049 0.001 0.025
## MZ_dummy_sb -0.815 -0.024 0.001 -0.014 0.624
## vrb_:MZ__DZ 0.001 0.001 -0.811 -0.005 0.000 -0.001
## vrblt_:MZ__ 0.002 0.005 -0.806 -0.014 -0.001 0.002 0.654out<- variability_EXT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Externalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
variability_EXT_DZ<- lmer(EXT_resid~avg_variabilitiy+variabiltiy_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+variabiltiy_diff*DZ_dummy_MZ+variabiltiy_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(variability_EXT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + DZ_dummy_MZ +
## DZ_dummy_sib + variabiltiy_diff * DZ_dummy_MZ + variabiltiy_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3446.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1847 -0.5357 -0.1104 0.4630 3.2031
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5227 0.7230
## Residual 0.4380 0.6618
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.593e-01 5.600e-02 7.377e+02 -2.844 0.00457
## avg_variabilitiy 1.587e-01 3.544e-02 8.552e+02 4.477 8.6e-06
## variabiltiy_diff -1.821e-02 7.093e-02 4.276e+02 -0.257 0.79753
## covid -6.171e-02 1.952e-01 1.057e+03 -0.316 0.75199
## DZ_dummy_MZ 6.124e-04 8.679e-02 7.346e+02 0.007 0.99437
## DZ_dummy_sib 1.906e-01 7.289e-02 7.912e+02 2.614 0.00911
## variabiltiy_diff:DZ_dummy_MZ 9.069e-03 1.213e-01 4.290e+02 0.075 0.94044
## variabiltiy_diff:DZ_dummy_sib 1.602e-01 1.013e-01 4.433e+02 1.582 0.11447
##
## (Intercept) **
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib **
## variabiltiy_diff:DZ_dummy_MZ
## variabiltiy_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid DZ__MZ DZ_dm_ v_:DZ__M
## avg_varblty -0.012
## varblty_dff 0.003 0.000
## covid -0.047 -0.068 -0.008
## DZ_dummy_MZ -0.643 0.049 -0.001 -0.025
## DZ_dummy_sb -0.765 0.031 -0.001 -0.046 0.497
## vrb_:DZ__MZ -0.002 -0.001 -0.585 0.005 0.000 0.001
## vrblt_:DZ__ -0.001 0.005 -0.700 -0.011 0.002 0.005 0.409out<- variability_EXT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Externalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
variability_EXT_sib<- lmer(EXT_resid~avg_variabilitiy+variabiltiy_diff+covid+sib_dummy_MZ+sib_dummy_DZ+variabiltiy_diff*sib_dummy_MZ+variabiltiy_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(variability_EXT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + sib_dummy_MZ +
## sib_dummy_DZ + variabiltiy_diff * sib_dummy_MZ + variabiltiy_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3446.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1847 -0.5357 -0.1104 0.4630 3.2031
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5227 0.7230
## Residual 0.4380 0.6618
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.03128 0.04697 877.44676 0.666 0.50566
## avg_variabilitiy 0.15866 0.03544 855.19929 4.477 8.6e-06
## variabiltiy_diff 0.14198 0.07232 459.37681 1.963 0.05021
## covid -0.06171 0.19521 1056.98885 -0.316 0.75199
## sib_dummy_MZ -0.18996 0.08098 775.80899 -2.346 0.01924
## sib_dummy_DZ -0.19057 0.07289 791.23501 -2.614 0.00911
## variabiltiy_diff:sib_dummy_MZ -0.15112 0.12214 439.81301 -1.237 0.21662
## variabiltiy_diff:sib_dummy_DZ -0.16019 0.10129 443.28957 -1.582 0.11447
##
## (Intercept)
## avg_variabilitiy ***
## variabiltiy_diff .
## covid
## sib_dummy_MZ *
## sib_dummy_DZ **
## variabiltiy_diff:sib_dummy_MZ
## variabiltiy_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid sb__MZ sb__DZ v_:__M
## avg_varblty 0.034
## varblty_dff 0.010 0.006
## covid -0.128 -0.068 -0.023
## sib_dmmy_MZ -0.571 0.024 -0.004 0.014
## sib_dmmy_DZ -0.640 -0.031 -0.006 0.046 0.367
## vrblt_:__MZ -0.006 -0.005 -0.592 0.014 0.002 0.003
## vrblt_:__DZ -0.006 -0.005 -0.714 0.011 0.003 0.005 0.423out<- variability_EXT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Externalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend duration (fitbit)
# weekend duration (fitbit)
weekend_dur_EXT_pheno<- lmer(EXT_resid~avg_weekend_dur+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_EXT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekend_dur + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3425.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21789 -0.53619 -0.09613 0.46480 3.05538
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5469 0.7395
## Residual 0.4398 0.6632
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07884 0.03248 803.49681 -2.428 0.0154 *
## avg_weekend_dur -0.04270 0.02869 1258.01454 -1.488 0.1370
## covid 0.02633 0.19719 1057.33661 0.134 0.8938
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_dr -0.043
## covid -0.145 0.021out<- weekend_dur_EXT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_dur_EXT<- lmer(EXT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_EXT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3427.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.24835 -0.55695 -0.09273 0.46221 3.02440
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5481 0.7403
## Residual 0.4388 0.6624
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08024 0.03251 803.41618 -2.469 0.0138 *
## fam_avg_weekend_dur -0.01534 0.03613 841.46377 -0.425 0.6712
## weekend_dur_diff -0.08937 0.04710 437.73416 -1.897 0.0584 .
## covid 0.03674 0.19737 1057.34383 0.186 0.8524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__
## fm_vg_wknd_ -0.056
## wknd_dr_dff 0.002 0.001
## covid -0.146 0.042 -0.019weekend_dur_EXT_zyg<- lmer(EXT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekend_dur_diff+sib_DZ*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_EXT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekend_dur_diff + sib_DZ *
## weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3431.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21876 -0.54914 -0.08233 0.46307 2.99550
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5388 0.7341
## Residual 0.4417 0.6646
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.037e-01 3.369e-02 7.691e+02 -3.079 0.00215 **
## fam_avg_weekend_dur -8.534e-03 3.611e-02 8.399e+02 -0.236 0.81324
## weekend_dur_diff -8.613e-02 5.268e-02 4.286e+02 -1.635 0.10278
## covid 1.596e-02 1.969e-01 1.049e+03 0.081 0.93543
## sibDZ_MZ -1.087e-01 7.683e-02 7.421e+02 -1.415 0.15743
## sib_DZ -1.681e-01 7.413e-02 7.852e+02 -2.267 0.02365 *
## weekend_dur_diff:sibDZ_MZ 2.784e-02 1.303e-01 4.260e+02 0.214 0.83093
## weekend_dur_diff:sib_DZ -6.120e-02 1.032e-01 4.338e+02 -0.593 0.55352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wknd_ -0.077
## wknd_dr_dff 0.000 0.002
## covid -0.136 0.041 -0.013
## sibDZ_MZ 0.233 -0.077 -0.001 -0.007
## sib_DZ 0.131 -0.027 -0.002 0.043 -0.087
## wkn__:DZ_MZ -0.002 0.001 0.437 0.008 -0.002 0.001
## wknd_d_:_DZ -0.002 0.002 0.055 0.007 0.001 -0.001 -0.033out<- weekend_dur_EXT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_dur_EXT_MZ<- lmer(EXT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekend_dur_diff+MZ_dummy_sib*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_EXT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekend_dur_diff +
## MZ_dummy_sib * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3431.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21876 -0.54914 -0.08233 0.46307 2.99550
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5388 0.7341
## Residual 0.4417 0.6646
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.762e-01 6.754e-02 7.338e+02 -2.609 0.00926
## fam_avg_weekend_dur -8.534e-03 3.611e-02 8.399e+02 -0.236 0.81324
## weekend_dur_diff -6.757e-02 1.197e-01 4.246e+02 -0.565 0.57260
## covid 1.596e-02 1.969e-01 1.049e+03 0.081 0.93543
## MZ_dummy_DZ 2.469e-02 8.815e-02 7.309e+02 0.280 0.77945
## MZ_dummy_sib 1.928e-01 8.235e-02 7.726e+02 2.341 0.01950
## weekend_dur_diff:MZ_dummy_DZ -5.844e-02 1.417e-01 4.233e+02 -0.412 0.68032
## weekend_dur_diff:MZ_dummy_sib 2.759e-03 1.386e-01 4.311e+02 0.020 0.98413
##
## (Intercept) **
## fam_avg_weekend_dur
## weekend_dur_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib *
## weekend_dur_diff:MZ_dummy_DZ
## weekend_dur_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wknd_ -0.097
## wknd_dr_dff -0.002 0.002
## covid -0.073 0.041 0.000
## MZ_dummy_DZ -0.762 0.056 0.002 0.024
## MZ_dummy_sb -0.816 0.084 0.002 -0.013 0.624
## wk__:MZ__DZ 0.002 0.000 -0.844 -0.005 -0.002 -0.001
## wknd__:MZ__ 0.003 -0.002 -0.864 -0.011 -0.002 -0.001 0.729out<- weekend_dur_EXT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_EXT_DZ<- lmer(EXT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekend_dur_diff+DZ_dummy_sib*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_EXT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekend_dur_diff +
## DZ_dummy_sib * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3431.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21876 -0.54914 -0.08233 0.46307 2.99550
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5388 0.7341
## Residual 0.4417 0.6646
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.515e-01 5.706e-02 7.342e+02 -2.656 0.00809
## fam_avg_weekend_dur -8.534e-03 3.611e-02 8.399e+02 -0.236 0.81324
## weekend_dur_diff -1.260e-01 7.597e-02 4.201e+02 -1.659 0.09795
## covid 1.596e-02 1.969e-01 1.049e+03 0.081 0.93543
## DZ_dummy_MZ -2.469e-02 8.815e-02 7.309e+02 -0.280 0.77945
## DZ_dummy_sib 1.681e-01 7.413e-02 7.852e+02 2.267 0.02365
## weekend_dur_diff:DZ_dummy_MZ 5.844e-02 1.417e-01 4.233e+02 0.412 0.68032
## weekend_dur_diff:DZ_dummy_sib 6.120e-02 1.032e-01 4.338e+02 0.593 0.55352
##
## (Intercept) **
## fam_avg_weekend_dur
## weekend_dur_diff .
## covid
## DZ_dummy_MZ
## DZ_dummy_sib *
## weekend_dur_diff:DZ_dummy_MZ
## weekend_dur_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wknd_ -0.028
## wknd_dr_dff -0.002 0.002
## covid -0.049 0.041 -0.009
## DZ_dummy_MZ -0.643 -0.056 0.002 -0.024
## DZ_dummy_sb -0.766 0.027 0.002 -0.043 0.496
## wk__:DZ__MZ 0.001 0.000 -0.536 0.005 -0.002 -0.001
## wknd__:DZ__ 0.002 -0.002 -0.736 -0.007 -0.001 -0.001 0.394out<- weekend_dur_EXT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_EXT_sib<- lmer(EXT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekend_dur_diff+sib_dummy_DZ*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_EXT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekend_dur_diff +
## sib_dummy_DZ * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3431.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21876 -0.54914 -0.08233 0.46307 2.99550
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5388 0.7341
## Residual 0.4417 0.6646
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.654e-02 4.766e-02 8.677e+02 0.347 0.7286
## fam_avg_weekend_dur -8.534e-03 3.611e-02 8.399e+02 -0.236 0.8132
## weekend_dur_diff -6.481e-02 6.987e-02 4.509e+02 -0.928 0.3541
## covid 1.596e-02 1.969e-01 1.049e+03 0.081 0.9354
## sib_dummy_MZ -1.928e-01 8.235e-02 7.726e+02 -2.341 0.0195
## sib_dummy_DZ -1.681e-01 7.413e-02 7.852e+02 -2.267 0.0236
## weekend_dur_diff:sib_dummy_MZ -2.759e-03 1.386e-01 4.311e+02 -0.020 0.9841
## weekend_dur_diff:sib_dummy_DZ -6.120e-02 1.032e-01 4.338e+02 -0.593 0.5535
##
## (Intercept)
## fam_avg_weekend_dur
## weekend_dur_diff
## covid
## sib_dummy_MZ *
## sib_dummy_DZ *
## weekend_dur_diff:sib_dummy_MZ
## weekend_dur_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wknd_ 0.008
## wknd_dr_dff 0.003 -0.001
## covid -0.126 0.041 -0.021
## sib_dmmy_MZ -0.572 -0.084 0.000 0.013
## sib_dmmy_DZ -0.638 -0.027 -0.001 0.043 0.369
## wknd__:__MZ -0.001 0.002 -0.504 0.011 -0.001 0.001
## wknd__:__DZ -0.001 0.002 -0.677 0.007 0.000 -0.001 0.341out<- weekend_dur_EXT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday duration (fitbit)
# weekday duration (fitbit)
weekday_dur_EXT_pheno<- lmer(EXT_resid~avg_weekday_dur+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_EXT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekday_dur + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3462.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2276 -0.5242 -0.1026 0.4658 3.0624
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5509 0.7422
## Residual 0.4376 0.6615
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.821e-02 3.237e-02 8.088e+02 -2.416 0.0159 *
## avg_weekday_dur -6.335e-03 2.903e-02 1.277e+03 -0.218 0.8273
## covid 3.100e-02 1.973e-01 1.068e+03 0.157 0.8752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_dr -0.037
## covid -0.144 0.013out<- weekday_dur_EXT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_dur_EXT<- lmer(EXT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_EXT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3463.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1248 -0.5237 -0.1056 0.4680 3.0665
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5516 0.7427
## Residual 0.4362 0.6605
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07911 0.03237 809.72111 -2.444 0.0147 *
## fam_avg_weekday_dur 0.02718 0.03558 841.99327 0.764 0.4450
## weekday_dur_diff -0.07218 0.04970 446.67600 -1.452 0.1471
## covid 0.03119 0.19724 1069.39917 0.158 0.8744
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__
## fm_vg_wkdy_ -0.040
## wkdy_dr_dff -0.007 0.006
## covid -0.144 0.010 0.008weekday_dur_EXT_zyg<- lmer(EXT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekday_dur_diff+sib_DZ*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_EXT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekday_dur_diff + sib_DZ *
## weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3464.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9466 -0.5221 -0.1053 0.4619 3.0166
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5437 0.7374
## Residual 0.4366 0.6608
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.037e-01 3.356e-02 7.756e+02 -3.091 0.00207 **
## fam_avg_weekday_dur 3.310e-02 3.550e-02 8.401e+02 0.932 0.35141
## weekday_dur_diff -6.005e-02 5.179e-02 4.405e+02 -1.159 0.24692
## covid 6.259e-03 1.967e-01 1.063e+03 0.032 0.97462
## sibDZ_MZ -1.129e-01 7.665e-02 7.475e+02 -1.474 0.14104
## sib_DZ -1.802e-01 7.376e-02 7.919e+02 -2.443 0.01479 *
## weekday_dur_diff:sibDZ_MZ 7.881e-02 1.212e-01 4.354e+02 0.650 0.51580
## weekday_dur_diff:sib_DZ -1.468e-01 1.123e-01 4.487e+02 -1.307 0.19172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wkdy_ -0.058
## wkdy_dr_dff -0.008 0.005
## covid -0.133 0.009 0.006
## sibDZ_MZ 0.235 -0.062 -0.004 -0.005
## sib_DZ 0.129 -0.022 -0.001 0.044 -0.086
## wkd__:DZ_MZ -0.003 -0.003 0.278 -0.003 -0.008 0.001
## wkdy_d_:_DZ -0.003 -0.006 -0.029 0.010 0.001 -0.005 0.019out<- weekday_dur_EXT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_dur_EXT_MZ<- lmer(EXT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekday_dur_diff+MZ_dummy_sib*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_EXT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekday_dur_diff +
## MZ_dummy_sib * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3464.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9466 -0.5221 -0.1053 0.4619 3.0166
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5437 0.7374
## Residual 0.4366 0.6608
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.790e-01 6.740e-02 7.391e+02 -2.656 0.00808
## fam_avg_weekday_dur 3.310e-02 3.550e-02 8.401e+02 0.932 0.35141
## weekday_dur_diff -7.507e-03 1.074e-01 4.319e+02 -0.070 0.94430
## covid 6.259e-03 1.967e-01 1.063e+03 0.032 0.97462
## MZ_dummy_DZ 2.285e-02 8.788e-02 7.367e+02 0.260 0.79497
## MZ_dummy_sib 2.030e-01 8.214e-02 7.778e+02 2.472 0.01366
## weekday_dur_diff:MZ_dummy_DZ -1.522e-01 1.326e-01 4.314e+02 -1.148 0.25159
## weekday_dur_diff:MZ_dummy_sib -5.400e-03 1.345e-01 4.439e+02 -0.040 0.96799
##
## (Intercept) **
## fam_avg_weekday_dur
## weekday_dur_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib *
## weekday_dur_diff:MZ_dummy_DZ
## weekday_dur_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wkdy_ -0.075
## wkdy_dr_dff -0.009 0.000
## covid -0.070 0.009 0.001
## MZ_dummy_DZ -0.764 0.044 0.007 0.022
## MZ_dummy_sb -0.816 0.068 0.008 -0.015 0.625
## wk__:MZ__DZ 0.007 0.000 -0.810 0.007 -0.008 -0.006
## wkdy__:MZ__ 0.007 0.005 -0.798 -0.001 -0.006 -0.007 0.647out<- weekday_dur_EXT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_EXT_DZ<- lmer(EXT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekday_dur_diff+DZ_dummy_sib*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_EXT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekday_dur_diff +
## DZ_dummy_sib * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3464.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9466 -0.5221 -0.1053 0.4619 3.0166
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5437 0.7374
## Residual 0.4366 0.6608
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.562e-01 5.672e-02 7.406e+02 -2.753 0.00604
## fam_avg_weekday_dur 3.310e-02 3.550e-02 8.401e+02 0.932 0.35141
## weekday_dur_diff -1.597e-01 7.777e-02 4.307e+02 -2.054 0.04061
## covid 6.259e-03 1.967e-01 1.063e+03 0.032 0.97462
## DZ_dummy_MZ -2.285e-02 8.788e-02 7.367e+02 -0.260 0.79497
## DZ_dummy_sib 1.802e-01 7.376e-02 7.919e+02 2.443 0.01479
## weekday_dur_diff:DZ_dummy_MZ 1.522e-01 1.326e-01 4.314e+02 1.148 0.25159
## weekday_dur_diff:DZ_dummy_sib 1.468e-01 1.123e-01 4.487e+02 1.307 0.19172
##
## (Intercept) **
## fam_avg_weekday_dur
## weekday_dur_diff *
## covid
## DZ_dummy_MZ
## DZ_dummy_sib *
## weekday_dur_diff:DZ_dummy_MZ
## weekday_dur_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wkdy_ -0.021
## wkdy_dr_dff -0.007 0.001
## covid -0.048 0.009 0.013
## DZ_dummy_MZ -0.642 -0.044 0.004 -0.022
## DZ_dummy_sb -0.765 0.022 0.005 -0.044 0.495
## wk__:DZ__MZ 0.004 0.000 -0.587 -0.007 -0.008 -0.003
## wkdy__:DZ__ 0.005 0.006 -0.693 -0.010 -0.003 -0.005 0.406out<- weekday_dur_EXT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_EXT_sib<- lmer(EXT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekday_dur_diff+sib_dummy_DZ*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_EXT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekday_dur_diff +
## sib_dummy_DZ * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3464.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9466 -0.5221 -0.1053 0.4619 3.0166
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5437 0.7374
## Residual 0.4366 0.6608
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.402e-02 4.749e-02 8.749e+02 0.506 0.6132
## fam_avg_weekday_dur 3.310e-02 3.550e-02 8.401e+02 0.932 0.3514
## weekday_dur_diff -1.291e-02 8.099e-02 4.661e+02 -0.159 0.8735
## covid 6.259e-03 1.967e-01 1.063e+03 0.032 0.9746
## sib_dummy_MZ -2.030e-01 8.214e-02 7.778e+02 -2.472 0.0137
## sib_dummy_DZ -1.802e-01 7.376e-02 7.919e+02 -2.443 0.0148
## weekday_dur_diff:sib_dummy_MZ 5.400e-03 1.345e-01 4.439e+02 0.040 0.9680
## weekday_dur_diff:sib_dummy_DZ -1.468e-01 1.123e-01 4.487e+02 -1.307 0.1917
##
## (Intercept)
## fam_avg_weekday_dur
## weekday_dur_diff
## covid
## sib_dummy_MZ *
## sib_dummy_DZ *
## weekday_dur_diff:sib_dummy_MZ
## weekday_dur_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wkdy_ 0.010
## wkdy_dr_dff -0.003 0.009
## covid -0.125 0.009 -0.001
## sib_dmmy_MZ -0.572 -0.068 0.001 0.015
## sib_dmmy_DZ -0.639 -0.022 0.002 0.044 0.369
## wkdy__:__MZ 0.002 -0.005 -0.602 0.001 -0.007 -0.001
## wkdy__:__DZ 0.001 -0.006 -0.721 0.010 -0.001 -0.005 0.434out<- weekday_dur_EXT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend duration (MCQ)
# weekend duration (MCQ)
weekend_dur_MCQ_EXT_pheno<- lmer(EXT_resid~weekend_dur_mcq_wave_2+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_EXT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ weekend_dur_mcq_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5914
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7374 -0.5367 -0.1149 0.4756 3.6673
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5208 0.7217
## Residual 0.4913 0.7009
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.604e-02 2.560e-02 1.226e+03 -2.58 0.010 **
## weekend_dur_mcq_wave_2 -8.748e-03 2.133e-02 2.002e+03 -0.41 0.682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wknd_dr___2 -0.041out<- weekend_dur_MCQ_EXT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_EXT<- lmer(EXT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_EXT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5913.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7841 -0.5397 -0.1139 0.4747 3.6565
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5189 0.7203
## Residual 0.4909 0.7006
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06880 0.02559 1225.26055 -2.688 0.00728 **
## avg_weekend_dur_mcq 0.04876 0.03354 1263.87665 1.454 0.14624
## weekend_dur_mcq_diff -0.04722 0.02748 1040.50524 -1.719 0.08600 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___
## avg_wknd_d_ -0.063
## wknd_dr_mc_ -0.001 0.006weekend_dur_MCQ_EXT_zyg<- lmer(EXT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+sibDZ_MZ+sib_DZ+weekend_dur_mcq_diff*sibDZ_MZ+weekend_dur_mcq_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_EXT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + sibDZ_MZ +
## sib_DZ + weekend_dur_mcq_diff * sibDZ_MZ + weekend_dur_mcq_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8140 -0.5279 -0.1150 0.4907 3.6278
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5126 0.7160
## Residual 0.4921 0.7015
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.09829 0.02719 1171.33550 -3.615 0.000313
## avg_weekend_dur_mcq 0.04371 0.03346 1261.97315 1.306 0.191719
## weekend_dur_mcq_diff -0.04456 0.02954 966.49315 -1.509 0.131753
## sibDZ_MZ -0.11198 0.06256 1154.27126 -1.790 0.073721
## sib_DZ -0.13972 0.06035 1194.32898 -2.315 0.020771
## weekend_dur_mcq_diff:sibDZ_MZ -0.01073 0.06815 942.33716 -0.157 0.874907
## weekend_dur_mcq_diff:sib_DZ 0.03384 0.06542 1002.51739 0.517 0.605023
##
## (Intercept) ***
## avg_weekend_dur_mcq
## weekend_dur_mcq_diff
## sibDZ_MZ .
## sib_DZ *
## weekend_dur_mcq_diff:sibDZ_MZ
## weekend_dur_mcq_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ sDZ_MZ sib_DZ w___:D
## avg_wknd_d_ -0.044
## wknd_dr_mc_ 0.000 0.002
## sibDZ_MZ 0.231 0.019 0.000
## sib_DZ 0.222 0.039 0.002 -0.145
## wk___:DZ_MZ 0.000 -0.001 0.238 0.000 -0.001
## wknd___:_DZ 0.002 -0.012 0.237 -0.001 0.000 -0.154out<- weekend_dur_MCQ_EXT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_EXT_MZ<- lmer(EXT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+MZ_dummy_DZ+MZ_dummy_sib+weekend_dur_mcq_diff*MZ_dummy_DZ+weekend_dur_mcq_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_EXT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + MZ_dummy_DZ +
## MZ_dummy_sib + weekend_dur_mcq_diff * MZ_dummy_DZ + weekend_dur_mcq_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8140 -0.5279 -0.1150 0.4907 3.6278
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5126 0.7160
## Residual 0.4921 0.7015
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.729e-01 5.480e-02 1.143e+03 -3.156
## avg_weekend_dur_mcq 4.371e-02 3.346e-02 1.262e+03 1.306
## weekend_dur_mcq_diff -5.171e-02 5.979e-02 9.251e+02 -0.865
## MZ_dummy_DZ 4.211e-02 7.328e-02 1.141e+03 0.575
## MZ_dummy_sib 1.818e-01 6.540e-02 1.189e+03 2.780
## weekend_dur_mcq_diff:MZ_dummy_DZ 2.765e-02 8.000e-02 9.293e+02 0.346
## weekend_dur_mcq_diff:MZ_dummy_sib -6.191e-03 7.091e-02 9.848e+02 -0.087
## Pr(>|t|)
## (Intercept) 0.00164 **
## avg_weekend_dur_mcq 0.19172
## weekend_dur_mcq_diff 0.38727
## MZ_dummy_DZ 0.56562
## MZ_dummy_sib 0.00552 **
## weekend_dur_mcq_diff:MZ_dummy_DZ 0.72967
## weekend_dur_mcq_diff:MZ_dummy_sib 0.93045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ MZ__DZ MZ_dm_ w___:MZ__D
## avg_wknd_d_ -0.007
## wknd_dr_mc_ 0.000 0.000
## MZ_dummy_DZ -0.748 -0.001 0.000
## MZ_dummy_sb -0.838 -0.036 0.000 0.627
## w___:MZ__DZ 0.000 -0.004 -0.747 0.001 0.000
## wkn___:MZ__ 0.000 0.006 -0.843 0.000 -0.001 0.630out<- weekend_dur_MCQ_EXT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_EXT_DZ<- lmer(EXT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+DZ_dummy_MZ+DZ_dummy_sib+weekend_dur_mcq_diff*DZ_dummy_MZ+weekend_dur_mcq_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_EXT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + DZ_dummy_MZ +
## DZ_dummy_sib + weekend_dur_mcq_diff * DZ_dummy_MZ + weekend_dur_mcq_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8140 -0.5279 -0.1150 0.4907 3.6278
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5126 0.7160
## Residual 0.4921 0.7015
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.13082 0.04866 1138.32031 -2.688
## avg_weekend_dur_mcq 0.04371 0.03346 1261.97315 1.306
## weekend_dur_mcq_diff -0.02406 0.05316 934.71710 -0.453
## DZ_dummy_MZ -0.04212 0.07328 1140.71479 -0.575
## DZ_dummy_sib 0.13972 0.06035 1194.32898 2.315
## weekend_dur_mcq_diff:DZ_dummy_MZ -0.02765 0.08000 929.31539 -0.346
## weekend_dur_mcq_diff:DZ_dummy_sib -0.03384 0.06542 1002.51739 -0.517
## Pr(>|t|)
## (Intercept) 0.00728 **
## avg_weekend_dur_mcq 0.19172
## weekend_dur_mcq_diff 0.65090
## DZ_dummy_MZ 0.56562
## DZ_dummy_sib 0.02077 *
## weekend_dur_mcq_diff:DZ_dummy_MZ 0.72967
## weekend_dur_mcq_diff:DZ_dummy_sib 0.60502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ DZ__MZ DZ_dm_ w___:DZ__M
## avg_wknd_d_ -0.009
## wknd_dr_mc_ 0.002 -0.006
## DZ_dummy_MZ -0.664 0.001 -0.001
## DZ_dummy_sb -0.806 -0.039 -0.001 0.535
## w___:DZ__MZ -0.001 0.004 -0.664 0.001 0.001
## wkn___:DZ__ -0.002 0.012 -0.813 0.001 0.000 0.540out<- weekend_dur_MCQ_EXT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_EXT_sib<- lmer(EXT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+sib_dummy_MZ+sib_dummy_DZ+weekend_dur_mcq_diff*sib_dummy_MZ+weekend_dur_mcq_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_EXT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + sib_dummy_MZ +
## sib_dummy_DZ + weekend_dur_mcq_diff * sib_dummy_MZ + weekend_dur_mcq_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8140 -0.5279 -0.1150 0.4907 3.6278
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5126 0.7160
## Residual 0.4921 0.7015
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.898e-03 3.573e-02 1.305e+03 0.249
## avg_weekend_dur_mcq 4.371e-02 3.346e-02 1.262e+03 1.306
## weekend_dur_mcq_diff -5.791e-02 3.813e-02 1.150e+03 -1.519
## sib_dummy_MZ -1.818e-01 6.540e-02 1.189e+03 -2.780
## sib_dummy_DZ -1.397e-01 6.035e-02 1.194e+03 -2.315
## weekend_dur_mcq_diff:sib_dummy_MZ 6.191e-03 7.091e-02 9.848e+02 0.087
## weekend_dur_mcq_diff:sib_dummy_DZ 3.384e-02 6.542e-02 1.003e+03 0.517
## Pr(>|t|)
## (Intercept) 0.80337
## avg_weekend_dur_mcq 0.19172
## weekend_dur_mcq_diff 0.12908
## sib_dummy_MZ 0.00552 **
## sib_dummy_DZ 0.02077 *
## weekend_dur_mcq_diff:sib_dummy_MZ 0.93045
## weekend_dur_mcq_diff:sib_dummy_DZ 0.60502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ sb__MZ sb__DZ w___:__M
## avg_wknd_d_ -0.078
## wknd_dr_mc_ -0.004 0.012
## sib_dmmy_MZ -0.546 0.036 0.002
## sib_dmmy_DZ -0.591 0.039 0.002 0.323
## wkn___:__MZ 0.002 -0.006 -0.538 -0.001 -0.001
## wkn___:__DZ 0.003 -0.012 -0.583 -0.001 0.000 0.313out<- weekend_dur_MCQ_EXT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday duration (MCQ)
# weekday duration (MCQ)
weekday_dur_MCQ_EXT_pheno<- lmer(EXT_resid~weekday_dur_mcq_wave_2+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_EXT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ weekday_dur_mcq_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5906.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7224 -0.5408 -0.1176 0.4849 3.6537
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5224 0.7228
## Residual 0.4878 0.6984
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06790 0.02557 1222.83217 -2.655 0.00804 **
## weekday_dur_mcq_wave_2 -0.05746 0.02099 2015.88052 -2.737 0.00625 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wkdy_dr___2 0.021out<- weekday_dur_MCQ_EXT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_EXT<- lmer(EXT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_EXT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5908.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7176 -0.5425 -0.1200 0.4917 3.6395
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5222 0.7226
## Residual 0.4872 0.6980
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06750 0.02557 1223.60066 -2.640 0.00839 **
## avg_weekday_dur_mcq -0.01483 0.03222 1245.38593 -0.460 0.64539
## weekday_dur_mcq_diff -0.08794 0.02730 1007.59183 -3.221 0.00132 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___
## avg_wkdy_d_ 0.020
## wkdy_dr_mc_ 0.010 0.015weekday_dur_MCQ_EXT_zyg<- lmer(EXT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+sibDZ_MZ+sib_DZ+weekday_dur_mcq_diff*sibDZ_MZ+weekday_dur_mcq_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_EXT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + sibDZ_MZ +
## sib_DZ + weekday_dur_mcq_diff * sibDZ_MZ + weekday_dur_mcq_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5910.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7344 -0.5284 -0.1191 0.4915 3.5936
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5160 0.7183
## Residual 0.4875 0.6982
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.09898 0.02723 1169.51415 -3.635 0.00029
## avg_weekday_dur_mcq -0.02855 0.03234 1242.04966 -0.883 0.37746
## weekday_dur_mcq_diff -0.07306 0.02968 965.62714 -2.461 0.01401
## sibDZ_MZ -0.11715 0.06278 1153.55577 -1.866 0.06227
## sib_DZ -0.14443 0.06052 1192.43900 -2.386 0.01717
## weekday_dur_mcq_diff:sibDZ_MZ 0.02964 0.06963 959.06458 0.426 0.67042
## weekday_dur_mcq_diff:sib_DZ 0.08119 0.06412 975.45979 1.266 0.20574
##
## (Intercept) ***
## avg_weekday_dur_mcq
## weekday_dur_mcq_diff *
## sibDZ_MZ .
## sib_DZ *
## weekday_dur_mcq_diff:sibDZ_MZ
## weekday_dur_mcq_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ sDZ_MZ sib_DZ w___:D
## avg_wkdy_d_ 0.061
## wkdy_dr_mc_ 0.005 0.004
## sibDZ_MZ 0.236 0.079 0.003
## sib_DZ 0.227 0.078 -0.006 -0.139
## wk___:DZ_MZ 0.002 -0.013 0.285 0.005 0.003
## wkdy___:_DZ -0.007 -0.018 0.226 0.003 0.003 -0.144out<- weekday_dur_MCQ_EXT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_EXT_MZ<- lmer(EXT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+MZ_dummy_DZ+MZ_dummy_sib+weekday_dur_mcq_diff*MZ_dummy_DZ+weekday_dur_mcq_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_EXT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + MZ_dummy_DZ +
## MZ_dummy_sib + weekday_dur_mcq_diff * MZ_dummy_DZ + weekday_dur_mcq_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5910.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7344 -0.5284 -0.1191 0.4915 3.5936
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5160 0.7183
## Residual 0.4875 0.6982
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.17709 0.05506 1142.04313 -3.217
## avg_weekday_dur_mcq -0.02855 0.03234 1242.04966 -0.883
## weekday_dur_mcq_diff -0.05330 0.06181 954.76025 -0.862
## MZ_dummy_DZ 0.04494 0.07337 1140.09551 0.612
## MZ_dummy_sib 0.18937 0.06580 1187.01913 2.878
## weekday_dur_mcq_diff:MZ_dummy_DZ 0.01095 0.08074 945.16462 0.136
## weekday_dur_mcq_diff:MZ_dummy_sib -0.07024 0.07234 983.23254 -0.971
## Pr(>|t|)
## (Intercept) 0.00133 **
## avg_weekday_dur_mcq 0.37746
## weekday_dur_mcq_diff 0.38867
## MZ_dummy_DZ 0.54035
## MZ_dummy_sib 0.00408 **
## weekday_dur_mcq_diff:MZ_dummy_DZ 0.89211
## weekday_dur_mcq_diff:MZ_dummy_sib 0.33180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ MZ__DZ MZ_dm_ w___:MZ__D
## avg_wkdy_d_ 0.090
## wkdy_dr_mc_ 0.006 -0.008
## MZ_dummy_DZ -0.747 -0.035 -0.004
## MZ_dummy_sb -0.840 -0.111 -0.004 0.627
## w___:MZ__DZ -0.004 0.004 -0.765 0.002 0.004
## wkd___:MZ__ -0.004 0.020 -0.855 0.003 0.007 0.654out<- weekday_dur_MCQ_EXT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_EXT_DZ<- lmer(EXT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+DZ_dummy_MZ+DZ_dummy_sib+weekday_dur_mcq_diff*DZ_dummy_MZ+weekday_dur_mcq_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_EXT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + DZ_dummy_MZ +
## DZ_dummy_sib + weekday_dur_mcq_diff * DZ_dummy_MZ + weekday_dur_mcq_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5910.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7344 -0.5284 -0.1191 0.4915 3.5936
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5160 0.7183
## Residual 0.4875 0.6982
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.13215 0.04875 1137.68563 -2.711
## avg_weekday_dur_mcq -0.02855 0.03234 1242.04966 -0.883
## weekday_dur_mcq_diff -0.04235 0.05196 931.69938 -0.815
## DZ_dummy_MZ -0.04494 0.07337 1140.09551 -0.612
## DZ_dummy_sib 0.14443 0.06052 1192.43900 2.386
## weekday_dur_mcq_diff:DZ_dummy_MZ -0.01095 0.08074 945.16462 -0.136
## weekday_dur_mcq_diff:DZ_dummy_sib -0.08119 0.06412 975.45979 -1.266
## Pr(>|t|)
## (Intercept) 0.00681 **
## avg_weekday_dur_mcq 0.37746
## weekday_dur_mcq_diff 0.41525
## DZ_dummy_MZ 0.54035
## DZ_dummy_sib 0.01717 *
## weekday_dur_mcq_diff:DZ_dummy_MZ 0.89211
## weekday_dur_mcq_diff:DZ_dummy_sib 0.20574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ DZ__MZ DZ_dm_ w___:DZ__M
## avg_wkdy_d_ 0.049
## wkdy_dr_mc_ -0.003 -0.003
## DZ_dummy_MZ -0.661 0.035 0.002
## DZ_dummy_sb -0.807 -0.078 0.003 0.531
## w___:DZ__MZ 0.002 -0.004 -0.643 0.002 -0.001
## wkd___:DZ__ 0.004 0.018 -0.810 -0.001 0.003 0.521out<- weekday_dur_MCQ_EXT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_EXT_sib<- lmer(EXT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+sib_dummy_MZ+sib_dummy_DZ+weekday_dur_mcq_diff*sib_dummy_MZ+weekday_dur_mcq_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_EXT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## EXT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + sib_dummy_MZ +
## sib_dummy_DZ + weekday_dur_mcq_diff * sib_dummy_MZ + weekday_dur_mcq_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5910.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7344 -0.5284 -0.1191 0.4915 3.5936
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5160 0.7183
## Residual 0.4875 0.6982
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.01228 0.03572 1301.07874 0.344
## avg_weekday_dur_mcq -0.02855 0.03234 1242.04966 -0.883
## weekday_dur_mcq_diff -0.12354 0.03757 1065.73924 -3.288
## sib_dummy_MZ -0.18937 0.06580 1187.01914 -2.878
## sib_dummy_DZ -0.14443 0.06052 1192.43900 -2.386
## weekday_dur_mcq_diff:sib_dummy_MZ 0.07024 0.07234 983.23255 0.971
## weekday_dur_mcq_diff:sib_dummy_DZ 0.08119 0.06412 975.45979 1.266
## Pr(>|t|)
## (Intercept) 0.73095
## avg_weekday_dur_mcq 0.37746
## weekday_dur_mcq_diff 0.00104 **
## sib_dummy_MZ 0.00408 **
## sib_dummy_DZ 0.01717 *
## weekday_dur_mcq_diff:sib_dummy_MZ 0.33180
## weekday_dur_mcq_diff:sib_dummy_DZ 0.20574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ sb__MZ sb__DZ w___:__M
## avg_wkdy_d_ -0.066
## wkdy_dr_mc_ 0.017 0.026
## sib_dmmy_MZ -0.548 0.111 -0.007
## sib_dmmy_DZ -0.593 0.078 -0.009 0.328
## wkd___:__MZ -0.008 -0.020 -0.520 0.007 0.004
## wkd___:__DZ -0.010 -0.018 -0.586 0.004 0.003 0.305out<- weekday_dur_MCQ_EXT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Externalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)chronotype
# chrono
chrono_EXT_pheno<- lmer(EXT_resid~chronotype_wave_2+(1|rel_family_id), data=abcd_all)
summary(chrono_EXT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ chronotype_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5239.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7087 -0.5695 -0.0924 0.4609 3.6615
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4996 0.7068
## Residual 0.5131 0.7163
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05863 0.02664 1176.86306 -2.201 0.0280 *
## chronotype_wave_2 -0.03935 0.02167 1769.38706 -1.815 0.0696 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## chrntyp_w_2 -0.084out<- chrono_EXT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
chrono_EXT<- lmer(EXT_resid~avg_chrono+chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_EXT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_chrono + chrono_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5243.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6845 -0.5621 -0.0940 0.4683 3.6385
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5001 0.7072
## Residual 0.5129 0.7162
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05711 0.02671 1171.13608 -2.138 0.0327 *
## avg_chrono -0.05912 0.03180 1215.37191 -1.859 0.0632 .
## chrono_diff -0.02218 0.02960 808.97615 -0.749 0.4539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch
## avg_chrono -0.106
## chrono_diff -0.016 0.000chrono_EXT_zyg<- lmer(EXT_resid~avg_chrono+chrono_diff+sibDZ_MZ+sib_DZ+sibDZ_MZ*chrono_diff+sib_DZ*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_EXT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_chrono + chrono_diff + sibDZ_MZ + sib_DZ + sibDZ_MZ *
## chrono_diff + sib_DZ * chrono_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5243.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7064 -0.5581 -0.1027 0.4714 3.5839
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4930 0.7022
## Residual 0.5129 0.7162
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.09162 0.02835 1128.89255 -3.231 0.00127 **
## avg_chrono -0.05687 0.03167 1212.75654 -1.795 0.07284 .
## chrono_diff -0.02550 0.03100 762.62333 -0.822 0.41114
## sibDZ_MZ -0.12820 0.06479 1112.22011 -1.979 0.04808 *
## sib_DZ -0.16166 0.06281 1142.34998 -2.574 0.01018 *
## chrono_diff:sibDZ_MZ 0.06749 0.06973 744.33758 0.968 0.33342
## chrono_diff:sib_DZ -0.07500 0.07108 786.85407 -1.055 0.29167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d sDZ_MZ sib_DZ c_:DZ_
## avg_chrono -0.106
## chrono_diff -0.009 0.000
## sibDZ_MZ 0.225 -0.003 0.000
## sib_DZ 0.227 -0.022 0.006 -0.148
## chrn_:DZ_MZ 0.000 0.001 0.165 -0.008 -0.004
## chrn_df:_DZ 0.006 0.000 0.221 -0.004 -0.011 -0.147out<- chrono_EXT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
chrono_EXT_MZ<- lmer(EXT_resid~avg_chrono+chrono_diff+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*chrono_diff+MZ_dummy_sib*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_EXT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_chrono + chrono_diff + MZ_dummy_DZ + MZ_dummy_sib +
## MZ_dummy_DZ * chrono_diff + MZ_dummy_sib * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5243.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7064 -0.5581 -0.1027 0.4714 3.5839
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4930 0.7022
## Residual 0.5129 0.7162
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.17709 0.05676 1105.17724 -3.120 0.00185 **
## avg_chrono -0.05687 0.03167 1212.75654 -1.795 0.07284 .
## chrono_diff 0.01950 0.05999 730.05654 0.325 0.74528
## MZ_dummy_DZ 0.04738 0.07606 1100.76548 0.623 0.53349
## MZ_dummy_sib 0.20903 0.06769 1139.83750 3.088 0.00206 **
## chrono_diff:MZ_dummy_DZ -0.10499 0.08279 731.78600 -1.268 0.20515
## chrono_diff:MZ_dummy_sib -0.02999 0.07346 780.71302 -0.408 0.68322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d MZ__DZ MZ_dm_ c_:MZ__D
## avg_chrono -0.055
## chrono_diff -0.006 0.001
## MZ_dummy_DZ -0.744 -0.007 0.005
## MZ_dummy_sb -0.837 0.013 0.005 0.624
## chr_:MZ__DZ 0.005 -0.001 -0.725 -0.005 -0.004
## chrn_d:MZ__ 0.005 0.000 -0.817 -0.004 -0.013 0.592out<- chrono_EXT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
chrono_EXT_DZ<- lmer(EXT_resid~avg_chrono+chrono_diff+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*chrono_diff+DZ_dummy_sib*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_EXT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_chrono + chrono_diff + DZ_dummy_MZ + DZ_dummy_sib +
## DZ_dummy_MZ * chrono_diff + DZ_dummy_sib * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5243.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7064 -0.5581 -0.1027 0.4714 3.5839
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4930 0.7022
## Residual 0.5129 0.7162
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.12972 0.05085 1099.69486 -2.551 0.0109 *
## avg_chrono -0.05687 0.03167 1212.75654 -1.795 0.0728 .
## chrono_diff -0.08549 0.05706 733.70402 -1.498 0.1345
## DZ_dummy_MZ -0.04738 0.07606 1100.76548 -0.623 0.5335
## DZ_dummy_sib 0.16166 0.06281 1142.34998 2.574 0.0102 *
## chrono_diff:DZ_dummy_MZ 0.10499 0.08279 731.78600 1.268 0.2052
## chrono_diff:DZ_dummy_sib 0.07500 0.07108 786.85407 1.055 0.2917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d DZ__MZ DZ_dm_ c_:DZ__M
## avg_chrono -0.072
## chrono_diff -0.002 0.000
## DZ_dummy_MZ -0.666 0.007 0.002
## DZ_dummy_sb -0.807 0.022 0.002 0.539
## chr_:DZ__MZ 0.002 0.001 -0.689 -0.005 -0.001
## chrn_d:DZ__ 0.002 0.000 -0.803 -0.001 -0.011 0.553out<- chrono_EXT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
chrono_EXT_sib<- lmer(EXT_resid~avg_chrono+chrono_diff+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*chrono_diff+sib_dummy_DZ*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_EXT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_chrono + chrono_diff + sib_dummy_MZ + sib_dummy_DZ +
## sib_dummy_MZ * chrono_diff + sib_dummy_DZ * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5243.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7064 -0.5581 -0.1027 0.4714 3.5839
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.4930 0.7022
## Residual 0.5129 0.7162
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.03194 0.03708 1228.43664 0.861 0.38923
## avg_chrono -0.05687 0.03167 1212.75654 -1.795 0.07284 .
## chrono_diff -0.01049 0.04239 894.61755 -0.247 0.80461
## sib_dummy_MZ -0.20903 0.06769 1139.83750 -3.088 0.00206 **
## sib_dummy_DZ -0.16166 0.06281 1142.34998 -2.574 0.01018 *
## chrono_diff:sib_dummy_MZ 0.02999 0.07346 780.71302 0.408 0.68322
## chrono_diff:sib_dummy_DZ -0.07500 0.07108 786.85407 -1.055 0.29167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d sb__MZ sb__DZ c_:__M
## avg_chrono -0.061
## chrono_diff -0.027 0.000
## sib_dmmy_MZ -0.545 -0.013 0.015
## sib_dmmy_DZ -0.587 -0.022 0.016 0.323
## chrn_d:__MZ 0.016 0.000 -0.577 -0.013 -0.009
## chrn_d:__DZ 0.016 0.000 -0.596 -0.009 -0.011 0.344out<- chrono_EXT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Externalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend efficiency
weekend_effic_EXT_pheno<- lmer(EXT_resid~avg_weekend_effic+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_EXT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekend_effic + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3427.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2205 -0.5317 -0.1016 0.4667 3.0539
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5430 0.7369
## Residual 0.4433 0.6658
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08010 0.03251 805.20198 -2.464 0.0139 *
## avg_weekend_effic -0.01188 0.03029 1099.56709 -0.392 0.6950
## covid 0.02852 0.19724 1053.03304 0.145 0.8850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_ff -0.073
## covid -0.147 0.036out<- weekend_effic_EXT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_effic_EXT<- lmer(EXT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_EXT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3431.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21374 -0.53017 -0.09913 0.46631 3.05581
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5432 0.7370
## Residual 0.4438 0.6662
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.963e-02 3.260e-02 7.998e+02 -2.443 0.0148 *
## fam_avg_weekend_effic -1.857e-02 4.384e-02 8.486e+02 -0.423 0.6721
## weekend_effic_diff -5.779e-03 4.190e-02 4.358e+02 -0.138 0.8904
## covid 2.784e-02 1.973e-01 1.052e+03 0.141 0.8878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__
## fm_vg_wknd_ -0.101
## wknd_ffc_df -0.004 0.000
## covid -0.147 0.036 0.016weekend_effic_EXT_zyg<- lmer(EXT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekend_effic_diff+sib_DZ*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_EXT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekend_effic_diff + sib_DZ *
## weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3435.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.18530 -0.51629 -0.08785 0.46137 3.02123
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5343 0.7309
## Residual 0.4466 0.6682
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.028e-01 3.374e-02 7.654e+02 -3.046 0.0024
## fam_avg_weekend_effic -2.002e-02 4.376e-02 8.452e+02 -0.457 0.6475
## weekend_effic_diff -6.174e-03 4.590e-02 4.247e+02 -0.135 0.8931
## covid 5.796e-03 1.969e-01 1.044e+03 0.029 0.9765
## sibDZ_MZ -1.085e-01 7.661e-02 7.407e+02 -1.416 0.1572
## sib_DZ -1.704e-01 7.412e-02 7.842e+02 -2.299 0.0217
## weekend_effic_diff:sibDZ_MZ 2.148e-02 1.098e-01 4.219e+02 0.196 0.8450
## weekend_effic_diff:sib_DZ -3.623e-02 9.600e-02 4.295e+02 -0.377 0.7061
##
## (Intercept) **
## fam_avg_weekend_effic
## weekend_effic_diff
## covid
## sibDZ_MZ
## sib_DZ *
## weekend_effic_diff:sibDZ_MZ
## weekend_effic_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wknd_ -0.102
## wknd_ffc_df -0.005 0.000
## covid -0.136 0.038 0.011
## sibDZ_MZ 0.231 -0.048 -0.002 -0.005
## sib_DZ 0.124 0.045 -0.002 0.046 -0.091
## wkn__:DZ_MZ -0.001 -0.001 0.340 -0.007 -0.004 0.001
## wknd_f_:_DZ -0.002 -0.001 0.176 -0.004 0.002 -0.003 -0.111out<- weekend_effic_EXT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_effic_EXT_MZ<- lmer(EXT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekend_effic_diff+MZ_dummy_sib*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_EXT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekend_effic_diff +
## MZ_dummy_sib * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3435.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.18530 -0.51629 -0.08785 0.46137 3.02123
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5343 0.7309
## Residual 0.4466 0.6682
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.751e-01 6.742e-02 7.305e+02 -2.597
## fam_avg_weekend_effic -2.002e-02 4.376e-02 8.452e+02 -0.457
## weekend_effic_diff 8.144e-03 9.871e-02 4.201e+02 0.083
## covid 5.796e-03 1.969e-01 1.044e+03 0.029
## MZ_dummy_DZ 2.328e-02 8.809e-02 7.300e+02 0.264
## MZ_dummy_sib 1.937e-01 8.202e-02 7.710e+02 2.362
## weekend_effic_diff:MZ_dummy_DZ -3.959e-02 1.246e-01 4.205e+02 -0.318
## weekend_effic_diff:MZ_dummy_sib -3.362e-03 1.148e-01 4.262e+02 -0.029
## Pr(>|t|)
## (Intercept) 0.00959 **
## fam_avg_weekend_effic 0.64749
## weekend_effic_diff 0.93428
## covid 0.97652
## MZ_dummy_DZ 0.79167
## MZ_dummy_sib 0.01843 *
## weekend_effic_diff:MZ_dummy_DZ 0.75076
## weekend_effic_diff:MZ_dummy_sib 0.97666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wknd_ -0.088
## wknd_ffc_df -0.004 -0.001
## covid -0.072 0.038 0.000
## MZ_dummy_DZ -0.763 0.061 0.003 0.024
## MZ_dummy_sb -0.813 0.025 0.003 -0.015 0.622
## wk__:MZ__DZ 0.003 0.001 -0.792 0.004 -0.004 -0.003
## wknd__:MZ__ 0.003 0.001 -0.860 0.008 -0.003 -0.003 0.681out<- weekend_effic_EXT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_effic_EXT_DZ<- lmer(EXT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekend_effic_diff+DZ_dummy_sib*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_EXT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekend_effic_diff +
## DZ_dummy_sib * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3435.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.18530 -0.51629 -0.08785 0.46137 3.02123
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5343 0.7309
## Residual 0.4466 0.6682
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.518e-01 5.698e-02 7.328e+02 -2.664
## fam_avg_weekend_effic -2.002e-02 4.376e-02 8.452e+02 -0.457
## weekend_effic_diff -3.145e-02 7.598e-02 4.212e+02 -0.414
## covid 5.796e-03 1.969e-01 1.044e+03 0.029
## DZ_dummy_MZ -2.328e-02 8.809e-02 7.300e+02 -0.264
## DZ_dummy_sib 1.704e-01 7.412e-02 7.842e+02 2.299
## weekend_effic_diff:DZ_dummy_MZ 3.959e-02 1.246e-01 4.205e+02 0.318
## weekend_effic_diff:DZ_dummy_sib 3.623e-02 9.600e-02 4.295e+02 0.377
## Pr(>|t|)
## (Intercept) 0.00788 **
## fam_avg_weekend_effic 0.64749
## weekend_effic_diff 0.67916
## covid 0.97652
## DZ_dummy_MZ 0.79167
## DZ_dummy_sib 0.02174 *
## weekend_effic_diff:DZ_dummy_MZ 0.75076
## weekend_effic_diff:DZ_dummy_sib 0.70607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wknd_ -0.010
## wknd_ffc_df -0.005 0.000
## covid -0.049 0.038 0.008
## DZ_dummy_MZ -0.644 -0.061 0.003 -0.024
## DZ_dummy_sb -0.764 -0.045 0.003 -0.046 0.500
## wk__:DZ__MZ 0.003 -0.001 -0.610 -0.004 -0.004 -0.002
## wknd__:DZ__ 0.004 0.001 -0.791 0.004 -0.003 -0.003 0.483out<- weekend_effic_EXT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_effic_EXT_sib<- lmer(EXT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekend_effic_diff+sib_dummy_DZ*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_EXT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekend_effic_diff +
## sib_dummy_DZ * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3435.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.18530 -0.51629 -0.08785 0.46137 3.02123
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5343 0.7309
## Residual 0.4466 0.6682
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.861e-02 4.779e-02 8.666e+02 0.389
## fam_avg_weekend_effic -2.002e-02 4.376e-02 8.452e+02 -0.457
## weekend_effic_diff 4.782e-03 5.869e-02 4.442e+02 0.081
## covid 5.796e-03 1.969e-01 1.044e+03 0.029
## sib_dummy_MZ -1.937e-01 8.202e-02 7.710e+02 -2.362
## sib_dummy_DZ -1.704e-01 7.412e-02 7.842e+02 -2.299
## weekend_effic_diff:sib_dummy_MZ 3.362e-03 1.148e-01 4.262e+02 0.029
## weekend_effic_diff:sib_dummy_DZ -3.623e-02 9.600e-02 4.295e+02 -0.377
## Pr(>|t|)
## (Intercept) 0.6970
## fam_avg_weekend_effic 0.6475
## weekend_effic_diff 0.9351
## covid 0.9765
## sib_dummy_MZ 0.0184 *
## sib_dummy_DZ 0.0217 *
## weekend_effic_diff:sib_dummy_MZ 0.9767
## weekend_effic_diff:sib_dummy_DZ 0.7061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wknd_ -0.081
## wknd_ffc_df -0.002 0.001
## covid -0.129 0.038 0.016
## sib_dmmy_MZ -0.570 -0.025 0.000 0.015
## sib_dmmy_DZ -0.640 0.045 0.001 0.046 0.367
## wknd__:__MZ 0.001 -0.001 -0.511 -0.008 -0.003 0.000
## wknd__:__DZ 0.000 -0.001 -0.611 -0.004 0.000 -0.003 0.312out<- weekend_effic_EXT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday efficiency
weekday_effic_EXT_pheno<- lmer(EXT_resid~avg_weekday_effic+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_EXT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ avg_weekday_effic + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3459.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1974 -0.5280 -0.0958 0.4579 3.1272
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5488 0.7408
## Residual 0.4370 0.6611
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07625 0.03233 808.77436 -2.359 0.0186 *
## avg_weekday_effic -0.04906 0.02816 1167.78575 -1.742 0.0817 .
## covid 0.01874 0.19716 1068.15366 0.095 0.9243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_ff -0.040
## covid -0.145 0.037out<- weekday_effic_EXT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_effic_EXT<- lmer(EXT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_EXT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3463.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.18931 -0.52826 -0.09887 0.46029 3.14016
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5488 0.7408
## Residual 0.4375 0.6615
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07659 0.03236 807.68824 -2.367 0.0182 *
## fam_avg_weekday_effic -0.04130 0.03964 869.32239 -1.042 0.2978
## weekday_effic_diff -0.05715 0.04047 449.54685 -1.412 0.1586
## covid 0.01918 0.19723 1067.11883 0.097 0.9225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__
## fm_vg_wkdy_ -0.054
## wkdy_ffc_df -0.002 -0.010
## covid -0.145 0.033 0.018weekday_effic_EXT_zyg<- lmer(EXT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekday_effic_diff+sib_DZ*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_EXT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekday_effic_diff + sib_DZ *
## weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3456
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0366 -0.5365 -0.1110 0.4828 2.9436
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5505 0.7419
## Residual 0.4274 0.6538
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -9.859e-02 3.362e-02 7.738e+02 -2.933 0.00346
## fam_avg_weekday_effic -3.578e-02 3.970e-02 8.651e+02 -0.901 0.36767
## weekday_effic_diff -5.348e-02 4.150e-02 4.375e+02 -1.289 0.19824
## covid -8.568e-03 1.966e-01 1.070e+03 -0.044 0.96524
## sibDZ_MZ -1.029e-01 7.689e-02 7.461e+02 -1.338 0.18118
## sib_DZ -1.767e-01 7.378e-02 7.920e+02 -2.394 0.01688
## weekday_effic_diff:sibDZ_MZ 1.155e-01 9.635e-02 4.300e+02 1.199 0.23130
## weekday_effic_diff:sib_DZ -2.953e-01 9.104e-02 4.487e+02 -3.244 0.00127
##
## (Intercept) **
## fam_avg_weekday_effic
## weekday_effic_diff
## covid
## sibDZ_MZ
## sib_DZ *
## weekday_effic_diff:sibDZ_MZ
## weekday_effic_diff:sib_DZ **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wkdy_ -0.077
## wkdy_ffc_df -0.002 -0.008
## covid -0.134 0.033 0.016
## sibDZ_MZ 0.238 -0.094 0.002 -0.007
## sib_DZ 0.129 -0.013 -0.007 0.043 -0.087
## wkd__:DZ_MZ 0.003 0.004 0.256 -0.010 0.000 0.004
## wkdy_f_:_DZ -0.010 0.014 0.064 0.013 0.003 -0.002 -0.041out<- weekday_effic_EXT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_effic_EXT_MZ<- lmer(EXT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekday_effic_diff+MZ_dummy_sib*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_EXT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekday_effic_diff +
## MZ_dummy_sib * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3456
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0366 -0.5365 -0.1110 0.4828 2.9436
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5505 0.7419
## Residual 0.4274 0.6538
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.672e-01 6.766e-02 7.371e+02 -2.471
## fam_avg_weekday_effic -3.578e-02 3.970e-02 8.651e+02 -0.901
## weekday_effic_diff 2.352e-02 8.492e-02 4.247e+02 0.277
## covid -8.568e-03 1.966e-01 1.070e+03 -0.044
## MZ_dummy_DZ 1.458e-02 8.811e-02 7.353e+02 0.165
## MZ_dummy_sib 1.912e-01 8.235e-02 7.770e+02 2.322
## weekday_effic_diff:MZ_dummy_DZ -2.632e-01 1.082e-01 4.293e+02 -2.431
## weekday_effic_diff:MZ_dummy_sib 3.216e-02 1.049e-01 4.377e+02 0.307
## Pr(>|t|)
## (Intercept) 0.0137 *
## fam_avg_weekday_effic 0.3677
## weekday_effic_diff 0.7819
## covid 0.9652
## MZ_dummy_DZ 0.8686
## MZ_dummy_sib 0.0205 *
## weekday_effic_diff:MZ_dummy_DZ 0.0155 *
## weekday_effic_diff:MZ_dummy_sib 0.7592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wkdy_ -0.110
## wkdy_ffc_df 0.001 -0.001
## covid -0.072 0.033 0.000
## MZ_dummy_DZ -0.765 0.077 -0.001 0.024
## MZ_dummy_sb -0.817 0.094 -0.001 -0.013 0.627
## wk__:MZ__DZ -0.002 0.003 -0.784 0.015 -0.003 0.001
## wkdy__:MZ__ 0.000 -0.010 -0.810 0.004 0.000 0.003 0.635out<- weekday_effic_EXT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_effic_EXT_DZ<- lmer(EXT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekday_effic_diff+DZ_dummy_sib*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_EXT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekday_effic_diff +
## DZ_dummy_sib * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3456
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0366 -0.5365 -0.1110 0.4828 2.9436
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5505 0.7419
## Residual 0.4274 0.6538
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -1.526e-01 5.675e-02 7.410e+02 -2.689
## fam_avg_weekday_effic -3.578e-02 3.970e-02 8.651e+02 -0.901
## weekday_effic_diff -2.396e-01 6.713e-02 4.368e+02 -3.570
## covid -8.568e-03 1.966e-01 1.070e+03 -0.044
## DZ_dummy_MZ -1.458e-02 8.811e-02 7.353e+02 -0.165
## DZ_dummy_sib 1.767e-01 7.378e-02 7.920e+02 2.394
## weekday_effic_diff:DZ_dummy_MZ 2.632e-01 1.082e-01 4.293e+02 2.431
## weekday_effic_diff:DZ_dummy_sib 2.953e-01 9.104e-02 4.487e+02 3.244
## Pr(>|t|)
## (Intercept) 0.007324 **
## fam_avg_weekday_effic 0.367669
## weekday_effic_diff 0.000397 ***
## covid 0.965241
## DZ_dummy_MZ 0.868646
## DZ_dummy_sib 0.016877 *
## weekday_effic_diff:DZ_dummy_MZ 0.015465 *
## weekday_effic_diff:DZ_dummy_sib 0.001268 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wkdy_ -0.012
## wkdy_ffc_df -0.012 0.003
## covid -0.048 0.033 0.024
## DZ_dummy_MZ -0.641 -0.077 0.006 -0.024
## DZ_dummy_sb -0.765 0.013 0.007 -0.043 0.494
## wk__:DZ__MZ 0.008 -0.003 -0.620 -0.015 -0.003 -0.005
## wkdy__:DZ__ 0.009 -0.014 -0.737 -0.013 -0.004 -0.002 0.457out<- weekday_effic_EXT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_effic_EXT_sib<- lmer(EXT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekday_effic_diff+sib_dummy_DZ*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_EXT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EXT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekday_effic_diff +
## sib_dummy_DZ * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3456
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0366 -0.5365 -0.1110 0.4828 2.9436
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.5505 0.7419
## Residual 0.4274 0.6538
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.404e-02 4.748e-02 8.737e+02 0.506
## fam_avg_weekday_effic -3.578e-02 3.970e-02 8.651e+02 -0.901
## weekday_effic_diff 5.568e-02 6.151e-02 4.639e+02 0.905
## covid -8.568e-03 1.966e-01 1.070e+03 -0.044
## sib_dummy_MZ -1.912e-01 8.235e-02 7.770e+02 -2.322
## sib_dummy_DZ -1.767e-01 7.378e-02 7.920e+02 -2.394
## weekday_effic_diff:sib_dummy_MZ -3.216e-02 1.049e-01 4.377e+02 -0.307
## weekday_effic_diff:sib_dummy_DZ -2.953e-01 9.104e-02 4.487e+02 -3.244
## Pr(>|t|)
## (Intercept) 0.61275
## fam_avg_weekday_effic 0.36767
## weekday_effic_diff 0.36578
## covid 0.96524
## sib_dummy_MZ 0.02048 *
## sib_dummy_DZ 0.01688 *
## weekday_effic_diff:sib_dummy_MZ 0.75922
## weekday_effic_diff:sib_dummy_DZ 0.00127 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wkdy_ 0.007
## wkdy_ffc_df 0.008 -0.018
## covid -0.125 0.033 0.006
## sib_dmmy_MZ -0.570 -0.094 -0.003 0.013
## sib_dmmy_DZ -0.639 -0.013 -0.005 0.043 0.367
## wkdy__:__MZ -0.005 0.010 -0.587 -0.004 0.003 0.003
## wkdy__:__DZ -0.007 0.014 -0.676 0.013 0.002 -0.002 0.396out<- weekday_effic_EXT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Externalizing",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)Attention Problems
variability
# variability (fitbit)
variability_ATT_pheno<- lmer(ATT_resid~variability+covid+(1|rel_family_id), data=abcd_all)
summary(variability_ATT_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ variability + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3484.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.86194 -0.69699 -0.09916 0.61917 2.72535
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2906 0.5391
## Residual 0.6267 0.7916
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13139 0.02968 803.09488 -4.427 1.09e-05 ***
## variability 0.12399 0.02834 1256.85608 4.375 1.31e-05 ***
## covid -0.12294 0.18641 912.21602 -0.660 0.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vrblty
## variability 0.045
## covid -0.154 -0.071out<- variability_ATT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Attention Problems",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
variability_ATT<- lmer(ATT_resid~avg_variabilitiy+variabiltiy_diff+covid+(1|rel_family_id), data=abcd_all)
summary(variability_ATT) ### don't need to save this one, just for model comparison## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3487.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83343 -0.69661 -0.09523 0.61766 2.69296
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2912 0.5396
## Residual 0.6264 0.7915
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13083 0.02969 803.06106 -4.406 1.20e-05 ***
## avg_variabilitiy 0.13982 0.03332 882.25916 4.196 2.99e-05 ***
## variabiltiy_diff 0.08285 0.05361 483.39448 1.545 0.123
## covid -0.12733 0.18652 912.28708 -0.683 0.495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_
## avg_varblty 0.049
## varblty_dff 0.006 0.003
## covid -0.154 -0.074 -0.016variability_ATT_zyg<- lmer(ATT_resid~avg_variabilitiy+variabiltiy_diff+covid+sibDZ_MZ+sib_DZ+variabiltiy_diff*sibDZ_MZ+variabiltiy_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(variability_ATT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + sibDZ_MZ +
## sib_DZ + variabiltiy_diff * sibDZ_MZ + variabiltiy_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3488.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7785 -0.6902 -0.1245 0.6340 2.6535
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2817 0.5308
## Residual 0.6293 0.7933
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.153884 0.030543 759.082862 -5.038 5.87e-07 ***
## avg_variabilitiy 0.139571 0.033196 880.895671 4.204 2.89e-05 ***
## variabiltiy_diff 0.084789 0.056239 477.480348 1.508 0.1323
## covid -0.148820 0.185844 906.683991 -0.801 0.4235
## sibDZ_MZ -0.132094 0.069351 721.585626 -1.905 0.0572 .
## sib_DZ -0.165768 0.067475 789.661688 -2.457 0.0142 *
## variabiltiy_diff:sibDZ_MZ 0.007741 0.132407 471.743848 0.058 0.9534
## variabiltiy_diff:sib_DZ -0.059206 0.120733 486.628361 -0.490 0.6241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid sDZ_MZ sib_DZ v_:DZ_
## avg_varblty 0.053
## varblty_dff 0.004 0.002
## covid -0.145 -0.076 -0.013
## sibDZ_MZ 0.223 0.042 -0.003 -0.008
## sib_DZ 0.106 -0.032 -0.002 0.050 -0.077
## vrbl_:DZ_MZ -0.004 -0.004 0.295 0.008 0.000 0.002
## vrblty_:_DZ -0.003 -0.005 -0.006 0.009 0.001 0.006 0.004out<- variability_ATT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Attention Problems",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
variability_ATT_MZ<- lmer(ATT_resid~avg_variabilitiy+variabiltiy_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+variabiltiy_diff*MZ_dummy_DZ+variabiltiy_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(variability_ATT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + MZ_dummy_DZ +
## MZ_dummy_sib + variabiltiy_diff * MZ_dummy_DZ + variabiltiy_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3488.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7785 -0.6902 -0.1245 0.6340 2.6535
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2817 0.5308
## Residual 0.6293 0.7933
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.24195 0.06083 706.15666 -3.978 7.68e-05
## avg_variabilitiy 0.13957 0.03320 880.89567 4.204 2.89e-05
## variabiltiy_diff 0.08995 0.11784 467.87930 0.763 0.44566
## covid -0.14882 0.18584 906.68399 -0.801 0.42347
## MZ_dummy_DZ 0.04921 0.07943 705.72476 0.620 0.53578
## MZ_dummy_sib 0.21498 0.07474 767.57692 2.876 0.00413
## variabiltiy_diff:MZ_dummy_DZ -0.03734 0.14529 466.83603 -0.257 0.79727
## variabiltiy_diff:MZ_dummy_sib 0.02186 0.14575 481.79107 0.150 0.88083
##
## (Intercept) ***
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib **
## variabiltiy_diff:MZ_dummy_DZ
## variabiltiy_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid MZ__DZ MZ_dm_ v_:MZ__D
## avg_varblty 0.059
## varblty_dff -0.002 -0.002
## covid -0.079 -0.076 0.000
## MZ_dummy_DZ -0.764 -0.050 0.001 0.029
## MZ_dummy_sb -0.807 -0.025 0.001 -0.015 0.618
## vrb_:MZ__DZ 0.002 0.001 -0.811 -0.004 0.000 -0.001
## vrblt_:MZ__ 0.002 0.006 -0.809 -0.011 -0.001 0.002 0.656out<- variability_ATT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Attention Problems",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
variability_ATT_DZ<- lmer(ATT_resid~avg_variabilitiy+variabiltiy_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+variabiltiy_diff*DZ_dummy_MZ+variabiltiy_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(variability_ATT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + DZ_dummy_MZ +
## DZ_dummy_sib + variabiltiy_diff * DZ_dummy_MZ + variabiltiy_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3488.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7785 -0.6902 -0.1245 0.6340 2.6535
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2817 0.5308
## Residual 0.6293 0.7933
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.19274 0.05129 709.57278 -3.758 0.000185
## avg_variabilitiy 0.13957 0.03320 880.89567 4.204 2.89e-05
## variabiltiy_diff 0.05261 0.08499 464.83582 0.619 0.536240
## covid -0.14882 0.18584 906.68399 -0.801 0.423470
## DZ_dummy_MZ -0.04921 0.07943 705.72476 -0.620 0.535777
## DZ_dummy_sib 0.16577 0.06748 789.66169 2.457 0.014235
## variabiltiy_diff:DZ_dummy_MZ 0.03734 0.14529 466.83603 0.257 0.797269
## variabiltiy_diff:DZ_dummy_sib 0.05921 0.12073 486.62836 0.490 0.624079
##
## (Intercept) ***
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib *
## variabiltiy_diff:DZ_dummy_MZ
## variabiltiy_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid DZ__MZ DZ_dm_ v_:DZ__M
## avg_varblty -0.008
## varblty_dff 0.003 0.000
## covid -0.049 -0.076 -0.006
## DZ_dummy_MZ -0.643 0.050 -0.002 -0.029
## DZ_dummy_sb -0.756 0.032 -0.002 -0.050 0.492
## vrb_:DZ__MZ -0.002 -0.001 -0.585 0.004 0.000 0.001
## vrblt_:DZ__ -0.002 0.005 -0.704 -0.009 0.002 0.006 0.412out<- variability_ATT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Attention Problems",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
variability_ATT_sib<- lmer(ATT_resid~avg_variabilitiy+variabiltiy_diff+covid+sib_dummy_MZ+sib_dummy_DZ+variabiltiy_diff*sib_dummy_MZ+variabiltiy_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(variability_ATT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + sib_dummy_MZ +
## sib_dummy_DZ + variabiltiy_diff * sib_dummy_MZ + variabiltiy_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3488.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7785 -0.6902 -0.1245 0.6340 2.6535
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2817 0.5308
## Residual 0.6293 0.7933
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02697 0.04417 911.84545 -0.611 0.54166
## avg_variabilitiy 0.13957 0.03320 880.89567 4.204 2.89e-05
## variabiltiy_diff 0.11181 0.08576 509.44231 1.304 0.19291
## covid -0.14882 0.18584 906.68399 -0.801 0.42347
## sib_dummy_MZ -0.21498 0.07474 767.57691 -2.876 0.00413
## sib_dummy_DZ -0.16577 0.06748 789.66169 -2.457 0.01424
## variabiltiy_diff:sib_dummy_MZ -0.02186 0.14575 481.79107 -0.150 0.88083
## variabiltiy_diff:sib_dummy_DZ -0.05921 0.12073 486.62836 -0.490 0.62408
##
## (Intercept)
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## sib_dummy_MZ **
## sib_dummy_DZ *
## variabiltiy_diff:sib_dummy_MZ
## variabiltiy_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid sb__MZ sb__DZ v_:__M
## avg_varblty 0.039
## varblty_dff 0.011 0.007
## covid -0.134 -0.076 -0.019
## sib_dmmy_MZ -0.581 0.025 -0.005 0.015
## sib_dmmy_DZ -0.650 -0.032 -0.006 0.050 0.380
## vrblt_:__MZ -0.006 -0.006 -0.588 0.011 0.002 0.004
## vrblt_:__DZ -0.007 -0.005 -0.710 0.009 0.003 0.006 0.418out<- variability_ATT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Attention Problems",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend duration (fitbit)
# weekend duration (fitbit)
weekend_dur_ATT_pheno<- lmer(ATT_resid~avg_weekend_dur+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_ATT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_weekend_dur + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3457.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7712 -0.6903 -0.1134 0.6232 2.6863
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.3052 0.5524
## Residual 0.6231 0.7893
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14069 0.03010 793.97071 -4.674 3.47e-06 ***
## avg_weekend_dur -0.03083 0.02889 1232.05911 -1.067 0.286
## covid -0.06682 0.18754 902.50138 -0.356 0.722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_dr -0.054
## covid -0.153 0.033out<- weekend_dur_ATT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_dur_ATT<- lmer(ATT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_ATT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3461.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7780 -0.6836 -0.1178 0.6263 2.6961
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.3053 0.5526
## Residual 0.6234 0.7896
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14125 0.03013 793.37305 -4.689 3.24e-06 ***
## fam_avg_weekend_dur -0.02139 0.03374 855.82724 -0.634 0.526
## weekend_dur_diff -0.05675 0.05583 472.34716 -1.017 0.310
## covid -0.06299 0.18772 901.23312 -0.336 0.737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__
## fm_vg_wknd_ -0.064
## wknd_dr_dff 0.001 0.001
## covid -0.154 0.047 -0.015weekend_dur_ATT_zyg<- lmer(ATT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekend_dur_diff+sib_DZ*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_ATT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekend_dur_diff + sib_DZ *
## weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3460.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7469 -0.6974 -0.1282 0.6146 2.6502
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2979 0.5458
## Residual 0.6240 0.7899
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.648e-01 3.103e-02 7.510e+02 -5.311 1.44e-07 ***
## fam_avg_weekend_dur -1.337e-02 3.371e-02 8.559e+02 -0.397 0.6918
## weekend_dur_diff -6.299e-02 6.238e-02 4.620e+02 -1.010 0.3132
## covid -8.256e-02 1.871e-01 8.964e+02 -0.441 0.6591
## sibDZ_MZ -1.355e-01 7.041e-02 7.146e+02 -1.925 0.0547 .
## sib_DZ -1.603e-01 6.851e-02 7.780e+02 -2.340 0.0196 *
## weekend_dur_diff:sibDZ_MZ -8.802e-04 1.544e-01 4.586e+02 -0.006 0.9955
## weekend_dur_diff:sib_DZ -1.641e-01 1.220e-01 4.692e+02 -1.345 0.1794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wknd_ -0.084
## wknd_dr_dff -0.001 0.002
## covid -0.145 0.046 -0.010
## sibDZ_MZ 0.224 -0.081 -0.001 -0.009
## sib_DZ 0.114 -0.029 -0.002 0.046 -0.076
## wkn__:DZ_MZ -0.002 0.001 0.439 0.007 -0.002 0.001
## wknd_d_:_DZ -0.002 0.002 0.061 0.006 0.001 -0.001 -0.037out<- weekend_dur_ATT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_dur_ATT_MZ<- lmer(ATT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekend_dur_diff+MZ_dummy_sib*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_ATT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekend_dur_diff +
## MZ_dummy_sib * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3460.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7469 -0.6974 -0.1282 0.6146 2.6502
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2979 0.5458
## Residual 0.6240 0.7899
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.25517 0.06179 701.33387 -4.130 4.07e-05
## fam_avg_weekend_dur -0.01337 0.03371 855.94749 -0.397 0.69175
## weekend_dur_diff -0.06357 0.14188 456.68609 -0.448 0.65432
## covid -0.08256 0.18706 896.36700 -0.441 0.65905
## MZ_dummy_DZ 0.05539 0.08060 697.63272 0.687 0.49221
## MZ_dummy_sib 0.21566 0.07593 760.03238 2.840 0.00463
## weekend_dur_diff:MZ_dummy_DZ -0.08116 0.16814 454.87260 -0.483 0.62954
## weekend_dur_diff:MZ_dummy_sib 0.08292 0.16395 465.55828 0.506 0.61327
##
## (Intercept) ***
## fam_avg_weekend_dur
## weekend_dur_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib **
## weekend_dur_diff:MZ_dummy_DZ
## weekend_dur_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wknd_ -0.104
## wknd_dr_dff -0.003 0.002
## covid -0.079 0.046 0.000
## MZ_dummy_DZ -0.762 0.058 0.002 0.027
## MZ_dummy_sb -0.808 0.088 0.002 -0.013 0.618
## wk__:MZ__DZ 0.002 0.000 -0.844 -0.004 -0.003 -0.002
## wknd__:MZ__ 0.003 -0.002 -0.865 -0.008 -0.002 -0.002 0.730out<- weekend_dur_ATT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_ATT_DZ<- lmer(ATT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekend_dur_diff+DZ_dummy_sib*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_ATT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekend_dur_diff +
## DZ_dummy_sib * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3460.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7469 -0.6974 -0.1282 0.6146 2.6502
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2979 0.5458
## Residual 0.6240 0.7899
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.19978 0.05221 701.92460 -3.827 0.000141
## fam_avg_weekend_dur -0.01337 0.03371 855.94749 -0.397 0.691752
## weekend_dur_diff -0.14473 0.09022 450.41745 -1.604 0.109362
## covid -0.08256 0.18706 896.36700 -0.441 0.659052
## DZ_dummy_MZ -0.05539 0.08060 697.63272 -0.687 0.492208
## DZ_dummy_sib 0.16028 0.06851 778.03377 2.340 0.019559
## weekend_dur_diff:DZ_dummy_MZ 0.08116 0.16814 454.87260 0.483 0.629542
## weekend_dur_diff:DZ_dummy_sib 0.16408 0.12202 469.15987 1.345 0.179363
##
## (Intercept) ***
## fam_avg_weekend_dur
## weekend_dur_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib *
## weekend_dur_diff:DZ_dummy_MZ
## weekend_dur_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wknd_ -0.033
## wknd_dr_dff -0.002 0.002
## covid -0.052 0.046 -0.007
## DZ_dummy_MZ -0.642 -0.058 0.002 -0.027
## DZ_dummy_sb -0.758 0.029 0.002 -0.046 0.491
## wk__:DZ__MZ 0.001 0.000 -0.537 0.004 -0.003 -0.001
## wknd__:DZ__ 0.002 -0.002 -0.739 -0.006 -0.001 -0.001 0.397out<- weekend_dur_ATT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_ATT_sib<- lmer(ATT_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekend_dur_diff+sib_dummy_DZ*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_ATT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekend_dur_diff +
## sib_dummy_DZ * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3460.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7469 -0.6974 -0.1282 0.6146 2.6502
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2979 0.5458
## Residual 0.6240 0.7899
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03951 0.04470 895.06511 -0.884 0.37699
## fam_avg_weekend_dur -0.01337 0.03371 855.94749 -0.397 0.69175
## weekend_dur_diff 0.01935 0.08216 493.27035 0.235 0.81394
## covid -0.08256 0.18706 896.36700 -0.441 0.65905
## sib_dummy_MZ -0.21566 0.07593 760.03238 -2.840 0.00463
## sib_dummy_DZ -0.16028 0.06851 778.03376 -2.340 0.01956
## weekend_dur_diff:sib_dummy_MZ -0.08292 0.16395 465.55828 -0.506 0.61327
## weekend_dur_diff:sib_dummy_DZ -0.16408 0.12202 469.15987 -1.345 0.17936
##
## (Intercept)
## fam_avg_weekend_dur
## weekend_dur_diff
## covid
## sib_dummy_MZ **
## sib_dummy_DZ *
## weekend_dur_diff:sib_dummy_MZ
## weekend_dur_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wknd_ 0.006
## wknd_dr_dff 0.002 -0.001
## covid -0.132 0.046 -0.016
## sib_dmmy_MZ -0.581 -0.088 0.000 0.013
## sib_dmmy_DZ -0.648 -0.029 -0.001 0.046 0.381
## wknd__:__MZ -0.001 0.002 -0.501 0.008 -0.002 0.000
## wknd__:__DZ -0.001 0.002 -0.673 0.006 0.000 -0.001 0.337out<- weekend_dur_ATT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday duration (fitbit)
# weekday duration (fitbit)
weekday_dur_ATT_pheno<- lmer(ATT_resid~avg_weekday_dur+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_ATT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_weekday_dur + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3501.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7688 -0.6932 -0.1168 0.6227 2.6742
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.3039 0.5513
## Residual 0.6280 0.7925
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13486 0.02999 799.85520 -4.497 7.91e-06 ***
## avg_weekday_dur -0.03848 0.02904 1213.38899 -1.325 0.185
## covid -0.06932 0.18773 908.31453 -0.369 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_dr -0.051
## covid -0.152 0.015out<- weekday_dur_ATT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_dur_ATT<- lmer(ATT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_ATT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3499.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8578 -0.6816 -0.1216 0.6156 2.6026
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.3057 0.5529
## Residual 0.6237 0.7897
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.136093 0.029973 801.486761 -4.541 6.47e-06 ***
## fam_avg_weekday_dur -0.001226 0.033154 855.537311 -0.037 0.97051
## weekday_dur_diff -0.158382 0.059073 483.277774 -2.681 0.00759 **
## covid -0.069076 0.187562 910.916388 -0.368 0.71275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__
## fm_vg_wkdy_ -0.053
## wkdy_dr_dff -0.008 0.007
## covid -0.151 0.014 0.007weekday_dur_ATT_zyg<- lmer(ATT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekday_dur_diff+sib_DZ*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_ATT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekday_dur_diff + sib_DZ *
## weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3498.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9674 -0.6809 -0.1327 0.6216 2.6451
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2976 0.5455
## Residual 0.6245 0.7903
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.160366 0.030868 758.108736 -5.195 2.63e-07 ***
## fam_avg_weekday_dur 0.005573 0.033067 854.420099 0.169 0.8662
## weekday_dur_diff -0.139538 0.061630 476.423423 -2.264 0.0240 *
## covid -0.089796 0.186848 905.586338 -0.481 0.6309
## sibDZ_MZ -0.144912 0.070124 718.894460 -2.067 0.0391 *
## sib_DZ -0.155900 0.068086 784.851800 -2.290 0.0223 *
## weekday_dur_diff:sibDZ_MZ 0.139130 0.144437 469.399255 0.963 0.3359
## weekday_dur_diff:sib_DZ -0.127263 0.133274 487.723411 -0.955 0.3401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wkdy_ -0.070
## wkdy_dr_dff -0.009 0.006
## covid -0.142 0.013 0.005
## sibDZ_MZ 0.225 -0.065 -0.005 -0.006
## sib_DZ 0.111 -0.023 -0.002 0.047 -0.075
## wkd__:DZ_MZ -0.004 -0.004 0.282 -0.002 -0.010 0.001
## wkdy_d_:_DZ -0.002 -0.007 -0.021 0.008 0.002 -0.005 0.013out<- weekday_dur_ATT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_dur_ATT_MZ<- lmer(ATT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekday_dur_diff+MZ_dummy_sib*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_ATT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekday_dur_diff +
## MZ_dummy_sib * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3498.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9674 -0.6809 -0.1327 0.6216 2.6451
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2976 0.5455
## Residual 0.6245 0.7903
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.256974 0.061554 705.793883 -4.175 3.36e-05
## fam_avg_weekday_dur 0.005573 0.033067 854.420099 0.169 0.8662
## weekday_dur_diff -0.046785 0.128147 464.593958 -0.365 0.7152
## covid -0.089796 0.186848 905.586338 -0.481 0.6309
## MZ_dummy_DZ 0.066962 0.080219 702.460414 0.835 0.4041
## MZ_dummy_sib 0.222862 0.075614 764.521353 2.947 0.0033
## weekday_dur_diff:MZ_dummy_DZ -0.202761 0.158251 463.979057 -1.281 0.2007
## weekday_dur_diff:MZ_dummy_sib -0.075498 0.159880 481.141762 -0.472 0.6370
##
## (Intercept) ***
## fam_avg_weekday_dur
## weekday_dur_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib **
## weekday_dur_diff:MZ_dummy_DZ
## weekday_dur_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wkdy_ -0.084
## wkdy_dr_dff -0.011 0.000
## covid -0.076 0.013 0.001
## MZ_dummy_DZ -0.763 0.047 0.008 0.025
## MZ_dummy_sb -0.808 0.071 0.009 -0.016 0.620
## wk__:MZ__DZ 0.008 0.000 -0.810 0.005 -0.010 -0.007
## wkdy__:MZ__ 0.008 0.006 -0.802 -0.001 -0.006 -0.008 0.649out<- weekday_dur_ATT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_ATT_DZ<- lmer(ATT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekday_dur_diff+DZ_dummy_sib*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_ATT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekday_dur_diff +
## DZ_dummy_sib * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3498.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9674 -0.6809 -0.1327 0.6216 2.6451
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2976 0.5455
## Residual 0.6245 0.7903
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.190012 0.051816 707.611295 -3.667 0.000264
## fam_avg_weekday_dur 0.005573 0.033067 854.420099 0.169 0.866189
## weekday_dur_diff -0.249547 0.092854 462.811099 -2.688 0.007458
## covid -0.089796 0.186848 905.586338 -0.481 0.630928
## DZ_dummy_MZ -0.066962 0.080219 702.460414 -0.835 0.404149
## DZ_dummy_sib 0.155900 0.068086 784.851799 2.290 0.022299
## weekday_dur_diff:DZ_dummy_MZ 0.202761 0.158251 463.979057 1.281 0.200739
## weekday_dur_diff:DZ_dummy_sib 0.127263 0.133274 487.723411 0.955 0.340101
##
## (Intercept) ***
## fam_avg_weekday_dur
## weekday_dur_diff **
## covid
## DZ_dummy_MZ
## DZ_dummy_sib *
## weekday_dur_diff:DZ_dummy_MZ
## weekday_dur_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wkdy_ -0.028
## wkdy_dr_dff -0.008 0.001
## covid -0.050 0.013 0.010
## DZ_dummy_MZ -0.641 -0.047 0.005 -0.025
## DZ_dummy_sb -0.757 0.023 0.006 -0.047 0.490
## wk__:DZ__MZ 0.005 0.000 -0.587 -0.005 -0.010 -0.003
## wkdy__:DZ__ 0.006 0.007 -0.697 -0.008 -0.004 -0.005 0.409out<- weekday_dur_ATT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_ATT_sib<- lmer(ATT_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekday_dur_diff+sib_dummy_DZ*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_ATT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekday_dur_diff +
## sib_dummy_DZ * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3498.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9674 -0.6809 -0.1327 0.6216 2.6451
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2976 0.5455
## Residual 0.6245 0.7903
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.034112 0.044506 904.082492 -0.766 0.4436
## fam_avg_weekday_dur 0.005573 0.033067 854.420099 0.169 0.8662
## weekday_dur_diff -0.122283 0.095604 512.661498 -1.279 0.2015
## covid -0.089796 0.186848 905.586338 -0.481 0.6309
## sib_dummy_MZ -0.222862 0.075614 764.521354 -2.947 0.0033
## sib_dummy_DZ -0.155900 0.068086 784.851800 -2.290 0.0223
## weekday_dur_diff:sib_dummy_MZ 0.075498 0.159880 481.141762 0.472 0.6370
## weekday_dur_diff:sib_dummy_DZ -0.127263 0.133274 487.723411 -0.955 0.3401
##
## (Intercept)
## fam_avg_weekday_dur
## weekday_dur_diff
## covid
## sib_dummy_MZ **
## sib_dummy_DZ *
## weekday_dur_diff:sib_dummy_MZ
## weekday_dur_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wkdy_ 0.003
## wkdy_dr_dff -0.004 0.010
## covid -0.131 0.013 -0.001
## sib_dmmy_MZ -0.581 -0.071 0.001 0.016
## sib_dmmy_DZ -0.649 -0.023 0.002 0.047 0.381
## wkdy__:__MZ 0.002 -0.006 -0.598 0.001 -0.008 -0.001
## wkdy__:__DZ 0.002 -0.007 -0.717 0.008 -0.001 -0.005 0.429out<- weekday_dur_ATT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend duration (MCQ)
# weekend duration (MCQ)
weekend_dur_MCQ_ATT_pheno<- lmer(ATT_resid~weekend_dur_mcq_wave_2+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_ATT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ weekend_dur_mcq_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5922.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9849 -0.7315 -0.0818 0.6567 3.2851
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2820 0.5311
## Residual 0.6473 0.8045
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14901 0.02311 1202.40667 -6.447 1.65e-10 ***
## weekend_dur_mcq_wave_2 0.03349 0.02182 2162.08863 1.535 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wknd_dr___2 -0.048out<- weekend_dur_MCQ_ATT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_ATT<- lmer(ATT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_ATT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5926.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9957 -0.7305 -0.0887 0.6588 3.2840
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2818 0.5309
## Residual 0.6476 0.8047
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14981 0.02314 1200.26957 -6.475 1.38e-10 ***
## avg_weekend_dur_mcq 0.04981 0.03045 1251.65851 1.636 0.102
## weekend_dur_mcq_diff 0.01650 0.03107 1082.64408 0.531 0.595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___
## avg_wknd_d_ -0.066
## wknd_dr_mc_ -0.001 0.007weekend_dur_MCQ_ATT_zyg<- lmer(ATT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+sibDZ_MZ+sib_DZ+weekend_dur_mcq_diff*sibDZ_MZ+weekend_dur_mcq_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_ATT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + sibDZ_MZ +
## sib_DZ + weekend_dur_mcq_diff * sibDZ_MZ + weekend_dur_mcq_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5934.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0263 -0.7172 -0.0837 0.6398 3.2483
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2789 0.5281
## Residual 0.6488 0.8055
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.17025 0.02448 1134.73721 -6.955 5.95e-12
## avg_weekend_dur_mcq 0.04619 0.03044 1250.43710 1.517 0.1294
## weekend_dur_mcq_diff 0.01487 0.03371 987.41309 0.441 0.6592
## sibDZ_MZ -0.08772 0.05621 1112.01508 -1.560 0.1189
## sib_DZ -0.09544 0.05449 1165.19103 -1.752 0.0801
## weekend_dur_mcq_diff:sibDZ_MZ -0.02153 0.07802 959.12389 -0.276 0.7826
## weekend_dur_mcq_diff:sib_DZ 0.01007 0.07430 1030.53647 0.136 0.8922
##
## (Intercept) ***
## avg_weekend_dur_mcq
## weekend_dur_mcq_diff
## sibDZ_MZ
## sib_DZ .
## weekend_dur_mcq_diff:sibDZ_MZ
## weekend_dur_mcq_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ sDZ_MZ sib_DZ w___:D
## avg_wknd_d_ -0.047
## wknd_dr_mc_ 0.000 0.002
## sibDZ_MZ 0.226 0.019 0.000
## sib_DZ 0.209 0.041 0.002 -0.137
## wk___:DZ_MZ 0.000 -0.001 0.247 0.000 -0.001
## wknd___:_DZ 0.002 -0.013 0.253 -0.001 0.000 -0.164out<- weekend_dur_MCQ_ATT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_ATT_MZ<- lmer(ATT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+MZ_dummy_DZ+MZ_dummy_sib+weekend_dur_mcq_diff*MZ_dummy_DZ+weekend_dur_mcq_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_ATT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + MZ_dummy_DZ +
## MZ_dummy_sib + weekend_dur_mcq_diff * MZ_dummy_DZ + weekend_dur_mcq_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5934.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0263 -0.7172 -0.0837 0.6398 3.2483
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2789 0.5281
## Residual 0.6488 0.8055
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -2.287e-01 4.917e-02 1.097e+03 -4.651
## avg_weekend_dur_mcq 4.619e-02 3.044e-02 1.250e+03 1.517
## weekend_dur_mcq_diff 5.175e-04 6.860e-02 9.392e+02 0.008
## MZ_dummy_DZ 4.000e-02 6.575e-02 1.094e+03 0.608
## MZ_dummy_sib 1.354e-01 5.901e-02 1.157e+03 2.295
## weekend_dur_mcq_diff:MZ_dummy_DZ 2.657e-02 9.174e-02 9.446e+02 0.290
## weekend_dur_mcq_diff:MZ_dummy_sib 1.649e-02 8.073e-02 1.009e+03 0.204
## Pr(>|t|)
## (Intercept) 3.7e-06 ***
## avg_weekend_dur_mcq 0.1294
## weekend_dur_mcq_diff 0.9940
## MZ_dummy_DZ 0.5431
## MZ_dummy_sib 0.0219 *
## weekend_dur_mcq_diff:MZ_dummy_DZ 0.7722
## weekend_dur_mcq_diff:MZ_dummy_sib 0.8382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ MZ__DZ MZ_dm_ w___:MZ__D
## avg_wknd_d_ -0.009
## wknd_dr_mc_ 0.000 0.000
## MZ_dummy_DZ -0.748 0.000 0.000
## MZ_dummy_sb -0.833 -0.037 0.000 0.623
## w___:MZ__DZ 0.000 -0.004 -0.748 0.001 0.000
## wkn___:MZ__ 0.000 0.007 -0.850 0.000 -0.001 0.635out<- weekend_dur_MCQ_ATT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_ATT_DZ<- lmer(ATT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+DZ_dummy_MZ+DZ_dummy_sib+weekend_dur_mcq_diff*DZ_dummy_MZ+weekend_dur_mcq_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_ATT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + DZ_dummy_MZ +
## DZ_dummy_sib + weekend_dur_mcq_diff * DZ_dummy_MZ + weekend_dur_mcq_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5934.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0263 -0.7172 -0.0837 0.6398 3.2483
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2789 0.5281
## Residual 0.6488 0.8055
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.18873 0.04364 1091.19825 -4.324
## avg_weekend_dur_mcq 0.04619 0.03044 1250.43710 1.517
## weekend_dur_mcq_diff 0.02708 0.06091 951.44568 0.445
## DZ_dummy_MZ -0.04000 0.06575 1094.16121 -0.608
## DZ_dummy_sib 0.09544 0.05449 1165.19103 1.752
## weekend_dur_mcq_diff:DZ_dummy_MZ -0.02657 0.09174 944.57057 -0.290
## weekend_dur_mcq_diff:DZ_dummy_sib -0.01007 0.07430 1030.53647 -0.136
## Pr(>|t|)
## (Intercept) 1.67e-05 ***
## avg_weekend_dur_mcq 0.1294
## weekend_dur_mcq_diff 0.6567
## DZ_dummy_MZ 0.5431
## DZ_dummy_sib 0.0801 .
## weekend_dur_mcq_diff:DZ_dummy_MZ 0.7722
## weekend_dur_mcq_diff:DZ_dummy_sib 0.8922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ DZ__MZ DZ_dm_ w___:DZ__M
## avg_wknd_d_ -0.009
## wknd_dr_mc_ 0.002 -0.006
## DZ_dummy_MZ -0.664 0.000 -0.001
## DZ_dummy_sb -0.801 -0.041 -0.001 0.532
## w___:DZ__MZ -0.001 0.004 -0.664 0.001 0.001
## wkn___:DZ__ -0.002 0.013 -0.820 0.001 0.000 0.544out<- weekend_dur_MCQ_ATT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_ATT_sib<- lmer(ATT_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+sib_dummy_MZ+sib_dummy_DZ+weekend_dur_mcq_diff*sib_dummy_MZ+weekend_dur_mcq_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_ATT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + sib_dummy_MZ +
## sib_dummy_DZ + weekend_dur_mcq_diff * sib_dummy_MZ + weekend_dur_mcq_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5934.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0263 -0.7172 -0.0837 0.6398 3.2483
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2789 0.5281
## Residual 0.6488 0.8055
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.09329 0.03265 1309.23554 -2.857
## avg_weekend_dur_mcq 0.04619 0.03044 1250.43710 1.517
## weekend_dur_mcq_diff 0.01701 0.04255 1214.38265 0.400
## sib_dummy_MZ -0.13544 0.05901 1157.38481 -2.295
## sib_dummy_DZ -0.09544 0.05449 1165.19103 -1.752
## weekend_dur_mcq_diff:sib_dummy_MZ -0.01649 0.08073 1008.50607 -0.204
## weekend_dur_mcq_diff:sib_dummy_DZ 0.01007 0.07430 1030.53647 0.136
## Pr(>|t|)
## (Intercept) 0.00434 **
## avg_weekend_dur_mcq 0.12940
## weekend_dur_mcq_diff 0.68938
## sib_dummy_MZ 0.02190 *
## sib_dummy_DZ 0.08010 .
## weekend_dur_mcq_diff:sib_dummy_MZ 0.83816
## weekend_dur_mcq_diff:sib_dummy_DZ 0.89217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ sb__MZ sb__DZ w___:__M
## avg_wknd_d_ -0.080
## wknd_dr_mc_ -0.004 0.013
## sib_dmmy_MZ -0.553 0.037 0.002
## sib_dmmy_DZ -0.599 0.041 0.002 0.331
## wkn___:__MZ 0.002 -0.007 -0.527 -0.001 -0.001
## wkn___:__DZ 0.003 -0.013 -0.573 -0.002 0.000 0.302out<- weekend_dur_MCQ_ATT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday duration (MCQ)
# weekday duration (MCQ)
weekday_dur_MCQ_ATT_pheno<- lmer(ATT_resid~weekday_dur_mcq_wave_2+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_ATT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ weekday_dur_mcq_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5922.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0315 -0.7240 -0.0866 0.6624 3.3523
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2826 0.5316
## Residual 0.6467 0.8042
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14822 0.02310 1199.23795 -6.417 1.99e-10 ***
## weekday_dur_mcq_wave_2 -0.03595 0.02145 2167.54607 -1.676 0.0938 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wkdy_dr___2 0.024out<- weekday_dur_MCQ_ATT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_ATT<- lmer(ATT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_ATT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5926.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0527 -0.7214 -0.0898 0.6572 3.3750
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2827 0.5317
## Residual 0.6467 0.8042
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14810 0.02310 1198.75685 -6.411 2.07e-10 ***
## avg_weekday_dur_mcq -0.02035 0.02918 1227.64043 -0.697 0.4857
## weekday_dur_mcq_diff -0.05370 0.03108 1043.70592 -1.728 0.0844 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___
## avg_wkdy_d_ 0.022
## wkdy_dr_mc_ 0.012 0.017weekday_dur_MCQ_ATT_zyg<- lmer(ATT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+sibDZ_MZ+sib_DZ+weekday_dur_mcq_diff*sibDZ_MZ+weekday_dur_mcq_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_ATT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + sibDZ_MZ +
## sib_DZ + weekday_dur_mcq_diff * sibDZ_MZ + weekday_dur_mcq_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5933
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0631 -0.7057 -0.0777 0.6378 3.3106
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2796 0.5288
## Residual 0.6476 0.8047
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.17025 0.02450 1133.13477 -6.949 6.21e-12
## avg_weekday_dur_mcq -0.02918 0.02934 1224.68467 -0.995 0.3202
## weekday_dur_mcq_diff -0.06287 0.03399 991.11500 -1.850 0.0646
## sibDZ_MZ -0.09404 0.05638 1111.83669 -1.668 0.0956
## sib_DZ -0.10244 0.05461 1163.66827 -1.876 0.0609
## weekday_dur_mcq_diff:sibDZ_MZ -0.03739 0.07979 983.71441 -0.469 0.6394
## weekday_dur_mcq_diff:sib_DZ -0.03395 0.07333 1002.47784 -0.463 0.6435
##
## (Intercept) ***
## avg_weekday_dur_mcq
## weekday_dur_mcq_diff .
## sibDZ_MZ .
## sib_DZ .
## weekday_dur_mcq_diff:sibDZ_MZ
## weekday_dur_mcq_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ sDZ_MZ sib_DZ w___:D
## avg_wkdy_d_ 0.061
## wkdy_dr_mc_ 0.006 0.004
## sibDZ_MZ 0.231 0.079 0.003
## sib_DZ 0.215 0.078 -0.007 -0.131
## wk___:DZ_MZ 0.002 -0.014 0.287 0.005 0.003
## wkdy___:_DZ -0.008 -0.020 0.237 0.003 0.003 -0.151out<- weekday_dur_MCQ_ATT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_ATT_MZ<- lmer(ATT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+MZ_dummy_DZ+MZ_dummy_sib+weekday_dur_mcq_diff*MZ_dummy_DZ+weekday_dur_mcq_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_ATT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + MZ_dummy_DZ +
## MZ_dummy_sib + weekday_dur_mcq_diff * MZ_dummy_DZ + weekday_dur_mcq_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5933
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0631 -0.7057 -0.0777 0.6378 3.3106
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2796 0.5288
## Residual 0.6476 0.8047
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.23294 0.04938 1096.43698 -4.718
## avg_weekday_dur_mcq -0.02918 0.02934 1224.68467 -0.995
## weekday_dur_mcq_diff -0.08780 0.07087 978.79606 -1.239
## MZ_dummy_DZ 0.04282 0.06579 1094.02260 0.651
## MZ_dummy_sib 0.14526 0.05934 1156.21452 2.448
## weekday_dur_mcq_diff:MZ_dummy_DZ 0.02042 0.09271 966.50778 0.220
## weekday_dur_mcq_diff:MZ_dummy_sib 0.05437 0.08263 1013.36896 0.658
## Pr(>|t|)
## (Intercept) 2.69e-06 ***
## avg_weekday_dur_mcq 0.3202
## weekday_dur_mcq_diff 0.2157
## MZ_dummy_DZ 0.5153
## MZ_dummy_sib 0.0145 *
## weekday_dur_mcq_diff:MZ_dummy_DZ 0.8257
## weekday_dur_mcq_diff:MZ_dummy_sib 0.5107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ MZ__DZ MZ_dm_ w___:MZ__D
## avg_wkdy_d_ 0.090
## wkdy_dr_mc_ 0.006 -0.009
## MZ_dummy_DZ -0.748 -0.035 -0.005
## MZ_dummy_sb -0.835 -0.111 -0.005 0.623
## w___:MZ__DZ -0.005 0.004 -0.764 0.002 0.004
## wkd___:MZ__ -0.004 0.023 -0.858 0.004 0.008 0.656out<- weekday_dur_MCQ_ATT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_ATT_DZ<- lmer(ATT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+DZ_dummy_MZ+DZ_dummy_sib+weekday_dur_mcq_diff*DZ_dummy_MZ+weekday_dur_mcq_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_ATT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + DZ_dummy_MZ +
## DZ_dummy_sib + weekday_dur_mcq_diff * DZ_dummy_MZ + weekday_dur_mcq_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5933
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0631 -0.7057 -0.0777 0.6378 3.3106
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2796 0.5288
## Residual 0.6476 0.8047
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.19012 0.04370 1091.04774 -4.351
## avg_weekday_dur_mcq -0.02918 0.02934 1224.68467 -0.995
## weekday_dur_mcq_diff -0.06738 0.05977 949.45881 -1.127
## DZ_dummy_MZ -0.04282 0.06579 1094.02260 -0.651
## DZ_dummy_sib 0.10244 0.05461 1163.66827 1.876
## weekday_dur_mcq_diff:DZ_dummy_MZ -0.02042 0.09271 966.50778 -0.220
## weekday_dur_mcq_diff:DZ_dummy_sib 0.03395 0.07333 1002.47784 0.463
## Pr(>|t|)
## (Intercept) 1.48e-05 ***
## avg_weekday_dur_mcq 0.3202
## weekday_dur_mcq_diff 0.2599
## DZ_dummy_MZ 0.5153
## DZ_dummy_sib 0.0609 .
## weekday_dur_mcq_diff:DZ_dummy_MZ 0.8257
## weekday_dur_mcq_diff:DZ_dummy_sib 0.6435
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ DZ__MZ DZ_dm_ w___:DZ__M
## avg_wkdy_d_ 0.049
## wkdy_dr_mc_ -0.004 -0.004
## DZ_dummy_MZ -0.661 0.035 0.002
## DZ_dummy_sb -0.802 -0.078 0.003 0.527
## w___:DZ__MZ 0.002 -0.004 -0.645 0.002 -0.002
## wkd___:DZ__ 0.004 0.020 -0.815 -0.001 0.003 0.525out<- weekday_dur_MCQ_ATT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_ATT_sib<- lmer(ATT_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+sib_dummy_MZ+sib_dummy_DZ+weekday_dur_mcq_diff*sib_dummy_MZ+weekday_dur_mcq_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_ATT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ATT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + sib_dummy_MZ +
## sib_dummy_DZ + weekday_dur_mcq_diff * sib_dummy_MZ + weekday_dur_mcq_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5933
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0631 -0.7057 -0.0777 0.6378 3.3106
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2796 0.5288
## Residual 0.6476 0.8047
## Number of obs: 2171, groups: rel_family_id, 1253
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.08768 0.03262 1305.58243 -2.688
## avg_weekday_dur_mcq -0.02918 0.02934 1224.68467 -0.995
## weekday_dur_mcq_diff -0.03343 0.04246 1117.18999 -0.787
## sib_dummy_MZ -0.14526 0.05934 1156.21452 -2.448
## sib_dummy_DZ -0.10244 0.05461 1163.66827 -1.876
## weekday_dur_mcq_diff:sib_dummy_MZ -0.05437 0.08263 1013.36896 -0.658
## weekday_dur_mcq_diff:sib_dummy_DZ -0.03395 0.07333 1002.47784 -0.463
## Pr(>|t|)
## (Intercept) 0.00728 **
## avg_weekday_dur_mcq 0.32015
## weekday_dur_mcq_diff 0.43125
## sib_dummy_MZ 0.01451 *
## sib_dummy_DZ 0.06095 .
## weekday_dur_mcq_diff:sib_dummy_MZ 0.51071
## weekday_dur_mcq_diff:sib_dummy_DZ 0.64348
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ sb__MZ sb__DZ w___:__M
## avg_wkdy_d_ -0.065
## wkdy_dr_mc_ 0.019 0.029
## sib_dmmy_MZ -0.555 0.111 -0.008
## sib_dmmy_DZ -0.600 0.078 -0.010 0.336
## wkd___:__MZ -0.009 -0.023 -0.514 0.008 0.005
## wkd___:__DZ -0.011 -0.020 -0.579 0.004 0.003 0.298out<- weekday_dur_MCQ_ATT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Attention Problems",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)chronotype
# chrono
chrono_ATT_pheno<- lmer(ATT_resid~chronotype_wave_2+(1|rel_family_id), data=abcd_all)
summary(chrono_ATT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ chronotype_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5202.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.78274 -0.73984 -0.07492 0.67237 2.76426
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2644 0.5142
## Residual 0.6599 0.8124
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.15338 0.02422 1142.09351 -6.333 3.46e-10 ***
## chronotype_wave_2 -0.01342 0.02184 1897.07203 -0.615 0.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## chrntyp_w_2 -0.094out<- chrono_ATT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Attention Problems",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
chrono_ATT<- lmer(ATT_resid~avg_chrono+chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_ATT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_chrono + chrono_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5204.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.80098 -0.73192 -0.07079 0.66460 2.78559
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2647 0.5145
## Residual 0.6591 0.8118
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.15125 0.02426 1137.65907 -6.234 6.39e-10 ***
## avg_chrono -0.04167 0.02903 1198.50051 -1.435 0.151
## chrono_diff 0.02337 0.03311 848.73200 0.706 0.480
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch
## avg_chrono -0.110
## chrono_diff -0.017 0.001chrono_ATT_zyg<- lmer(ATT_resid~avg_chrono+chrono_diff+sibDZ_MZ+sib_DZ+sibDZ_MZ*chrono_diff+sib_DZ*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_ATT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_chrono + chrono_diff + sibDZ_MZ + sib_DZ + sibDZ_MZ *
## chrono_diff + sib_DZ * chrono_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5210.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.84048 -0.72781 -0.06079 0.64698 2.73666
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2616 0.5115
## Residual 0.6596 0.8121
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.749e-01 2.570e-02 1.084e+03 -6.805 1.67e-11 ***
## avg_chrono -3.984e-02 2.898e-02 1.196e+03 -1.375 0.1694
## chrono_diff 1.469e-02 3.493e-02 7.876e+02 0.421 0.6742
## sibDZ_MZ -8.860e-02 5.862e-02 1.061e+03 -1.511 0.1310
## sib_DZ -1.163e-01 5.702e-02 1.102e+03 -2.040 0.0416 *
## chrono_diff:sibDZ_MZ 9.752e-03 7.878e-02 7.658e+02 0.124 0.9015
## chrono_diff:sib_DZ -7.185e-02 7.978e-02 8.169e+02 -0.901 0.3681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d sDZ_MZ sib_DZ c_:DZ_
## avg_chrono -0.111
## chrono_diff -0.010 0.000
## sibDZ_MZ 0.221 -0.005 0.000
## sib_DZ 0.218 -0.023 0.007 -0.142
## chrn_:DZ_MZ 0.000 0.001 0.173 -0.008 -0.004
## chrn_df:_DZ 0.006 0.000 0.236 -0.004 -0.012 -0.157out<- chrono_ATT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Attention Problems",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
chrono_ATT_MZ<- lmer(ATT_resid~avg_chrono+chrono_diff+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*chrono_diff+MZ_dummy_sib*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_ATT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_chrono + chrono_diff + MZ_dummy_DZ + MZ_dummy_sib +
## MZ_dummy_DZ * chrono_diff + MZ_dummy_sib * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5210.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.84048 -0.72781 -0.06079 0.64698 2.73666
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2616 0.5115
## Residual 0.6596 0.8121
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.23397 0.05131 1051.72616 -4.560 5.72e-06 ***
## avg_chrono -0.03984 0.02898 1195.52617 -1.375 0.1694
## chrono_diff 0.02119 0.06793 749.04297 0.312 0.7551
## MZ_dummy_DZ 0.03045 0.06872 1045.82832 0.443 0.6578
## MZ_dummy_sib 0.14675 0.06144 1098.35624 2.389 0.0171 *
## chrono_diff:MZ_dummy_DZ -0.04568 0.09372 751.24295 -0.487 0.6261
## chrono_diff:MZ_dummy_sib 0.02617 0.08253 809.15879 0.317 0.7512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d MZ__DZ MZ_dm_ c_:MZ__D
## avg_chrono -0.059
## chrono_diff -0.007 0.001
## MZ_dummy_DZ -0.744 -0.005 0.005
## MZ_dummy_sb -0.833 0.016 0.006 0.621
## chr_:MZ__DZ 0.005 -0.001 -0.725 -0.005 -0.004
## chrn_d:MZ__ 0.006 0.000 -0.823 -0.004 -0.014 0.597out<- chrono_ATT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Attention Problems",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
chrono_ATT_DZ<- lmer(ATT_resid~avg_chrono+chrono_diff+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*chrono_diff+DZ_dummy_sib*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_ATT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_chrono + chrono_diff + DZ_dummy_MZ + DZ_dummy_sib +
## DZ_dummy_MZ * chrono_diff + DZ_dummy_sib * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5210.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.84048 -0.72781 -0.06079 0.64698 2.73666
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2616 0.5115
## Residual 0.6596 0.8121
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.20352 0.04594 1044.59000 -4.430 1.04e-05 ***
## avg_chrono -0.03984 0.02898 1195.52617 -1.375 0.1694
## chrono_diff -0.02448 0.06457 753.68730 -0.379 0.7047
## DZ_dummy_MZ -0.03045 0.06872 1045.82832 -0.443 0.6578
## DZ_dummy_sib 0.11630 0.05702 1101.91162 2.040 0.0416 *
## chrono_diff:DZ_dummy_MZ 0.04568 0.09372 751.24295 0.487 0.6261
## chrono_diff:DZ_dummy_sib 0.07185 0.07978 816.90793 0.901 0.3681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d DZ__MZ DZ_dm_ c_:DZ__M
## avg_chrono -0.074
## chrono_diff -0.003 0.000
## DZ_dummy_MZ -0.665 0.005 0.002
## DZ_dummy_sb -0.803 0.023 0.002 0.536
## chr_:DZ__MZ 0.002 0.001 -0.689 -0.005 -0.001
## chrn_d:DZ__ 0.002 0.000 -0.809 -0.001 -0.012 0.558out<- chrono_ATT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Attention Problems",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
chrono_ATT_sib<- lmer(ATT_resid~avg_chrono+chrono_diff+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*chrono_diff+sib_dummy_DZ*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_ATT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_chrono + chrono_diff + sib_dummy_MZ + sib_dummy_DZ +
## sib_dummy_MZ * chrono_diff + sib_dummy_DZ * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 5210.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.84048 -0.72781 -0.06079 0.64698 2.73666
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2616 0.5115
## Residual 0.6596 0.8121
## Number of obs: 1904, groups: rel_family_id, 1197
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08722 0.03398 1216.62792 -2.567 0.0104 *
## avg_chrono -0.03984 0.02898 1195.52617 -1.375 0.1694
## chrono_diff 0.04736 0.04687 953.62436 1.011 0.3125
## sib_dummy_MZ -0.14675 0.06144 1098.35624 -2.389 0.0171 *
## sib_dummy_DZ -0.11630 0.05702 1101.91162 -2.040 0.0416 *
## chrono_diff:sib_dummy_MZ -0.02617 0.08253 809.15879 -0.317 0.7512
## chrono_diff:sib_dummy_DZ -0.07185 0.07978 816.90793 -0.901 0.3681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d sb__MZ sb__DZ c_:__M
## avg_chrono -0.061
## chrono_diff -0.030 0.000
## sib_dmmy_MZ -0.550 -0.016 0.016
## sib_dmmy_DZ -0.592 -0.023 0.018 0.329
## chrn_d:__MZ 0.017 0.000 -0.568 -0.014 -0.010
## chrn_d:__DZ 0.018 0.000 -0.587 -0.010 -0.012 0.334out<- chrono_ATT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Attention Problems",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend efficiency
weekend_effic_ATT_pheno<- lmer(ATT_resid~avg_weekend_effic+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_ATT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_weekend_effic + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3458.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7899 -0.6949 -0.1194 0.6203 2.6652
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.3053 0.5526
## Residual 0.6237 0.7898
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.425e-01 3.016e-02 7.926e+02 -4.723 2.74e-06 ***
## avg_weekend_effic 4.459e-04 3.157e-02 1.243e+03 0.014 0.989
## covid -6.019e-02 1.876e-01 8.998e+02 -0.321 0.748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_ff -0.079
## covid -0.154 0.035out<- weekend_effic_ATT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_effic_ATT<- lmer(ATT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_ATT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3462
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7774 -0.6945 -0.1180 0.6238 2.6028
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.3051 0.5524
## Residual 0.6242 0.7901
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14115 0.03022 787.92162 -4.671 3.53e-06 ***
## fam_avg_weekend_effic -0.01887 0.04102 865.97110 -0.460 0.646
## weekend_effic_diff 0.02850 0.04946 469.22647 0.576 0.565
## covid -0.06186 0.18766 898.88688 -0.330 0.742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__
## fm_vg_wknd_ -0.099
## wknd_ffc_df -0.004 0.000
## covid -0.154 0.035 0.013weekend_effic_ATT_zyg<- lmer(ATT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekend_effic_diff+sib_DZ*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_ATT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekend_effic_diff + sib_DZ *
## weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3461.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7514 -0.6929 -0.1286 0.6295 2.6575
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2973 0.5453
## Residual 0.6243 0.7901
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.16450 0.03108 745.77810 -5.293 1.58e-07 ***
## fam_avg_weekend_effic -0.01898 0.04089 863.44631 -0.464 0.6427
## weekend_effic_diff 0.03360 0.05416 456.33869 0.620 0.5353
## covid -0.08252 0.18694 893.96524 -0.441 0.6590
## sibDZ_MZ -0.13657 0.07026 713.65337 -1.944 0.0523 .
## sib_DZ -0.16239 0.06852 776.87933 -2.370 0.0180 *
## weekend_effic_diff:sibDZ_MZ 0.13383 0.12962 452.49055 1.032 0.3024
## weekend_effic_diff:sib_DZ -0.14768 0.11309 463.03941 -1.306 0.1923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wknd_ -0.102
## wknd_ffc_df -0.005 0.000
## covid -0.144 0.037 0.009
## sibDZ_MZ 0.222 -0.050 -0.002 -0.007
## sib_DZ 0.107 0.042 -0.002 0.049 -0.080
## wkn__:DZ_MZ -0.001 -0.001 0.342 -0.005 -0.004 0.001
## wknd_f_:_DZ -0.002 -0.001 0.181 -0.003 0.002 -0.003 -0.113out<- weekend_effic_ATT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_effic_ATT_MZ<- lmer(ATT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekend_effic_diff+MZ_dummy_sib*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_ATT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekend_effic_diff +
## MZ_dummy_sib * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3461.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7514 -0.6929 -0.1286 0.6295 2.6575
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2973 0.5453
## Residual 0.6243 0.7901
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.25555 0.06169 697.56618 -4.142 3.86e-05
## fam_avg_weekend_effic -0.01898 0.04089 863.44631 -0.464 0.64268
## weekend_effic_diff 0.12281 0.11664 450.02322 1.053 0.29292
## covid -0.08252 0.18694 893.96524 -0.441 0.65902
## MZ_dummy_DZ 0.05537 0.08060 697.51646 0.687 0.49232
## MZ_dummy_sib 0.21776 0.07565 758.31951 2.879 0.00411
## weekend_effic_diff:MZ_dummy_DZ -0.20766 0.14717 450.62826 -1.411 0.15891
## weekend_effic_diff:MZ_dummy_sib -0.05999 0.13543 458.36836 -0.443 0.65802
##
## (Intercept) ***
## fam_avg_weekend_effic
## weekend_effic_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib **
## weekend_effic_diff:MZ_dummy_DZ
## weekend_effic_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wknd_ -0.090
## wknd_ffc_df -0.005 -0.001
## covid -0.078 0.037 0.000
## MZ_dummy_DZ -0.762 0.062 0.004 0.027
## MZ_dummy_sb -0.806 0.027 0.004 -0.016 0.617
## wk__:MZ__DZ 0.004 0.001 -0.793 0.004 -0.005 -0.003
## wknd__:MZ__ 0.004 0.001 -0.861 0.006 -0.003 -0.003 0.683out<- weekend_effic_ATT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_effic_ATT_DZ<- lmer(ATT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekend_effic_diff+DZ_dummy_sib*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_ATT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekend_effic_diff +
## DZ_dummy_sib * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3461.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7514 -0.6929 -0.1286 0.6295 2.6575
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2973 0.5453
## Residual 0.6243 0.7901
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.20017 0.05217 700.94229 -3.837 0.000136
## fam_avg_weekend_effic -0.01898 0.04089 863.44631 -0.464 0.642678
## weekend_effic_diff -0.08485 0.08975 451.65189 -0.945 0.344941
## covid -0.08252 0.18694 893.96524 -0.441 0.659018
## DZ_dummy_MZ -0.05537 0.08060 697.51646 -0.687 0.492324
## DZ_dummy_sib 0.16239 0.06852 776.87932 2.370 0.018043
## weekend_effic_diff:DZ_dummy_MZ 0.20766 0.14717 450.62826 1.411 0.158914
## weekend_effic_diff:DZ_dummy_sib 0.14768 0.11309 463.03941 1.306 0.192265
##
## (Intercept) ***
## fam_avg_weekend_effic
## weekend_effic_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib *
## weekend_effic_diff:DZ_dummy_MZ
## weekend_effic_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wknd_ -0.010
## wknd_ffc_df -0.006 0.000
## covid -0.051 0.037 0.006
## DZ_dummy_MZ -0.644 -0.062 0.003 -0.027
## DZ_dummy_sb -0.756 -0.042 0.004 -0.049 0.495
## wk__:DZ__MZ 0.004 -0.001 -0.610 -0.004 -0.005 -0.002
## wknd__:DZ__ 0.004 0.001 -0.794 0.003 -0.003 -0.003 0.484out<- weekend_effic_ATT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_effic_ATT_sib<- lmer(ATT_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekend_effic_diff+sib_dummy_DZ*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_ATT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekend_effic_diff +
## sib_dummy_DZ * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3461.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7514 -0.6929 -0.1286 0.6295 2.6575
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2973 0.5453
## Residual 0.6243 0.7901
## Number of obs: 1266, groups: rel_family_id, 833
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03779 0.04482 892.48001 -0.843 0.39943
## fam_avg_weekend_effic -0.01898 0.04089 863.44631 -0.464 0.64268
## weekend_effic_diff 0.06283 0.06882 483.40012 0.913 0.36175
## covid -0.08252 0.18694 893.96524 -0.441 0.65902
## sib_dummy_MZ -0.21776 0.07565 758.31951 -2.879 0.00411
## sib_dummy_DZ -0.16239 0.06852 776.87932 -2.370 0.01804
## weekend_effic_diff:sib_dummy_MZ 0.05999 0.13543 458.36836 0.443 0.65802
## weekend_effic_diff:sib_dummy_DZ -0.14768 0.11309 463.03941 -1.306 0.19226
##
## (Intercept)
## fam_avg_weekend_effic
## weekend_effic_diff
## covid
## sib_dummy_MZ **
## sib_dummy_DZ *
## weekend_effic_diff:sib_dummy_MZ
## weekend_effic_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wknd_ -0.077
## wknd_ffc_df -0.001 0.001
## covid -0.134 0.037 0.013
## sib_dmmy_MZ -0.579 -0.027 0.000 0.016
## sib_dmmy_DZ -0.648 0.042 0.000 0.049 0.378
## wknd__:__MZ 0.001 -0.001 -0.508 -0.006 -0.003 0.000
## wknd__:__DZ 0.000 -0.001 -0.608 -0.003 0.000 -0.003 0.309out<- weekend_effic_ATT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday efficiency
weekday_effic_ATT_pheno<- lmer(ATT_resid~avg_weekday_effic+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_ATT_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ avg_weekday_effic + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3501.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7553 -0.6964 -0.1164 0.6269 2.6917
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.3007 0.5483
## Residual 0.6304 0.7940
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13500 0.02995 798.66768 -4.508 7.52e-06 ***
## avg_weekday_effic -0.04101 0.02926 1272.36481 -1.402 0.161
## covid -0.07489 0.18764 907.91452 -0.399 0.690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_ff -0.047
## covid -0.152 0.037out<- weekday_effic_ATT_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_effic_ATT<- lmer(ATT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_ATT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3504.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7434 -0.6965 -0.1240 0.6214 2.7066
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.3004 0.5481
## Residual 0.6310 0.7943
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13572 0.02997 798.03516 -4.529 6.84e-06 ***
## fam_avg_weekday_effic -0.02563 0.03717 898.07277 -0.690 0.491
## weekday_effic_diff -0.06673 0.04824 488.99617 -1.383 0.167
## covid -0.07361 0.18767 907.26968 -0.392 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__
## fm_vg_wkdy_ -0.059
## wkdy_ffc_df -0.001 -0.013
## covid -0.153 0.035 0.014weekday_effic_ATT_zyg<- lmer(ATT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekday_effic_diff+sib_DZ*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_ATT_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekday_effic_diff + sib_DZ *
## weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3499.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7385 -0.6923 -0.1229 0.6291 2.6351
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2972 0.5452
## Residual 0.6244 0.7902
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.15851 0.03089 754.19101 -5.132 3.64e-07 ***
## fam_avg_weekday_effic -0.01752 0.03715 895.37492 -0.471 0.6374
## weekday_effic_diff -0.05649 0.04993 474.45442 -1.131 0.2585
## covid -0.09851 0.18692 905.20733 -0.527 0.5983
## sibDZ_MZ -0.14166 0.07025 716.61259 -2.016 0.0441 *
## sib_DZ -0.15409 0.06806 786.14029 -2.264 0.0239 *
## weekday_effic_diff:sibDZ_MZ 0.16724 0.11622 463.87486 1.439 0.1508
## weekday_effic_diff:sib_DZ -0.25980 0.10911 490.76152 -2.381 0.0176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wkdy_ -0.081
## wkdy_ffc_df -0.002 -0.009
## covid -0.143 0.034 0.012
## sibDZ_MZ 0.228 -0.093 0.003 -0.008
## sib_DZ 0.111 -0.020 -0.008 0.047 -0.075
## wkd__:DZ_MZ 0.003 0.004 0.263 -0.008 0.000 0.005
## wkdy_f_:_DZ -0.011 0.017 0.070 0.011 0.004 -0.002 -0.045out<- weekday_effic_ATT_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_effic_ATT_MZ<- lmer(ATT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekday_effic_diff+MZ_dummy_sib*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_ATT_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekday_effic_diff +
## MZ_dummy_sib * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3499.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7385 -0.6923 -0.1229 0.6291 2.6351
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2972 0.5452
## Residual 0.6244 0.7902
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.25295 0.06169 701.55752 -4.100 4.61e-05
## fam_avg_weekday_effic -0.01752 0.03715 895.37492 -0.471 0.63742
## weekday_effic_diff 0.05500 0.10263 456.51711 0.536 0.59224
## covid -0.09851 0.18692 905.20733 -0.527 0.59831
## MZ_dummy_DZ 0.06462 0.08032 699.76054 0.805 0.42137
## MZ_dummy_sib 0.21870 0.07574 764.03143 2.887 0.00399
## weekday_effic_diff:MZ_dummy_DZ -0.29714 0.13060 463.01743 -2.275 0.02335
## weekday_effic_diff:MZ_dummy_sib -0.03734 0.12614 474.66579 -0.296 0.76733
##
## (Intercept) ***
## fam_avg_weekday_effic
## weekday_effic_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib **
## weekday_effic_diff:MZ_dummy_DZ *
## weekday_effic_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wkdy_ -0.111
## wkdy_ffc_df 0.002 -0.001
## covid -0.078 0.034 0.000
## MZ_dummy_DZ -0.764 0.072 -0.001 0.027
## MZ_dummy_sb -0.809 0.095 -0.001 -0.013 0.621
## wk__:MZ__DZ -0.002 0.003 -0.786 0.012 -0.004 0.001
## wkdy__:MZ__ 0.000 -0.011 -0.814 0.002 0.000 0.003 0.639out<- weekday_effic_ATT_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_effic_ATT_DZ<- lmer(ATT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekday_effic_diff+DZ_dummy_sib*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_ATT_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekday_effic_diff +
## DZ_dummy_sib * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3499.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7385 -0.6923 -0.1229 0.6291 2.6351
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2972 0.5452
## Residual 0.6244 0.7902
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.18834 0.05179 707.69881 -3.636 0.000297
## fam_avg_weekday_effic -0.01752 0.03715 895.37492 -0.471 0.637416
## weekday_effic_diff -0.24214 0.08078 473.75376 -2.998 0.002865
## covid -0.09851 0.18692 905.20733 -0.527 0.598310
## DZ_dummy_MZ -0.06462 0.08032 699.76054 -0.805 0.421371
## DZ_dummy_sib 0.15409 0.06806 786.14029 2.264 0.023857
## weekday_effic_diff:DZ_dummy_MZ 0.29714 0.13060 463.01743 2.275 0.023355
## weekday_effic_diff:DZ_dummy_sib 0.25980 0.10911 490.76152 2.381 0.017639
##
## (Intercept) ***
## fam_avg_weekday_effic
## weekday_effic_diff **
## covid
## DZ_dummy_MZ
## DZ_dummy_sib *
## weekday_effic_diff:DZ_dummy_MZ *
## weekday_effic_diff:DZ_dummy_sib *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wkdy_ -0.020
## wkdy_ffc_df -0.014 0.004
## covid -0.051 0.034 0.019
## DZ_dummy_MZ -0.640 -0.072 0.007 -0.027
## DZ_dummy_sb -0.757 0.020 0.009 -0.047 0.489
## wk__:DZ__MZ 0.009 -0.003 -0.619 -0.012 -0.004 -0.005
## wkdy__:DZ__ 0.010 -0.017 -0.740 -0.011 -0.005 -0.002 0.458out<- weekday_effic_ATT_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_effic_ATT_sib<- lmer(ATT_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekday_effic_diff+sib_dummy_DZ*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_ATT_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ATT_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekday_effic_diff +
## sib_dummy_DZ * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3499.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7385 -0.6923 -0.1229 0.6291 2.6351
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2972 0.5452
## Residual 0.6244 0.7902
## Number of obs: 1280, groups: rel_family_id, 839
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03425 0.04450 904.69956 -0.770 0.44168
## fam_avg_weekday_effic -0.01752 0.03715 895.37492 -0.471 0.63742
## weekday_effic_diff 0.01766 0.07334 512.71295 0.241 0.80980
## covid -0.09851 0.18692 905.20733 -0.527 0.59831
## sib_dummy_MZ -0.21870 0.07574 764.03143 -2.887 0.00399
## sib_dummy_DZ -0.15409 0.06806 786.14029 -2.264 0.02386
## weekday_effic_diff:sib_dummy_MZ 0.03734 0.12614 474.66579 0.296 0.76733
## weekday_effic_diff:sib_dummy_DZ -0.25980 0.10911 490.76152 -2.381 0.01764
##
## (Intercept)
## fam_avg_weekday_effic
## weekday_effic_diff
## covid
## sib_dummy_MZ **
## sib_dummy_DZ *
## weekday_effic_diff:sib_dummy_MZ
## weekday_effic_diff:sib_dummy_DZ *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wkdy_ 0.008
## wkdy_ffc_df 0.010 -0.021
## covid -0.131 0.034 0.004
## sib_dmmy_MZ -0.580 -0.095 -0.004 0.013
## sib_dmmy_DZ -0.649 -0.020 -0.006 0.047 0.380
## wkdy__:__MZ -0.006 0.011 -0.581 -0.002 0.003 0.004
## wkdy__:__DZ -0.008 0.017 -0.672 0.011 0.003 -0.002 0.391out<- weekday_effic_ATT_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Attention Problems",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)Psychosis
variability
# variability (fitbit)
variability_PSYCH_pheno<- lmer(PSYCH_resid~variability+covid+(1|rel_family_id), data=abcd_all)
summary(variability_PSYCH_pheno)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ variability + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3654.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8645 -0.5515 -0.3336 0.5467 3.7364
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2352 0.4850
## Residual 0.6039 0.7771
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05297 0.02707 868.00919 -1.956 0.050733 .
## variability 0.08588 0.02578 1359.24430 3.331 0.000889 ***
## covid -0.07608 0.17691 975.63392 -0.430 0.667267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vrblty
## variability 0.038
## covid -0.149 -0.068out<- variability_PSYCH_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Psychosis",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
variability_PSYCH<- lmer(PSYCH_resid~avg_variabilitiy+variabiltiy_diff+covid+(1|rel_family_id), data=abcd_all)
summary(variability_PSYCH) ### don't need to save this one, just for model comparison## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_variabilitiy + variabiltiy_diff + covid + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3657.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8555 -0.5504 -0.3340 0.5508 3.6912
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2349 0.4847
## Residual 0.6040 0.7772
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05242 0.02707 868.10866 -1.936 0.053157 .
## avg_variabilitiy 0.10328 0.03030 967.71255 3.409 0.000679 ***
## variabiltiy_diff 0.04024 0.04909 533.81072 0.820 0.412660
## covid -0.08106 0.17694 975.35507 -0.458 0.646972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_
## avg_varblty 0.042
## varblty_dff 0.004 0.000
## covid -0.149 -0.071 -0.014variability_PSYCH_zyg<- lmer(PSYCH_resid~avg_variabilitiy+variabiltiy_diff+covid+sibDZ_MZ+sib_DZ+variabiltiy_diff*sibDZ_MZ+variabiltiy_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(variability_PSYCH_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_variabilitiy + variabiltiy_diff + covid + sibDZ_MZ +
## sib_DZ + variabiltiy_diff * sibDZ_MZ + variabiltiy_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3664.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8564 -0.5520 -0.3208 0.5430 3.7334
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2309 0.4805
## Residual 0.6067 0.7789
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06746 0.02805 823.62056 -2.405 0.016375 *
## avg_variabilitiy 0.10157 0.03030 964.84587 3.352 0.000833 ***
## variabiltiy_diff 0.04368 0.05258 529.78934 0.831 0.406496
## covid -0.08737 0.17687 970.30342 -0.494 0.621460
## sibDZ_MZ -0.10156 0.06412 787.86285 -1.584 0.113631
## sib_DZ -0.07085 0.06151 853.92144 -1.152 0.249740
## variabiltiy_diff:sibDZ_MZ 0.01990 0.12633 529.13059 0.158 0.874887
## variabiltiy_diff:sib_DZ 0.10061 0.10907 530.79735 0.922 0.356722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid sDZ_MZ sib_DZ v_:DZ_
## avg_varblty 0.051
## varblty_dff 0.002 -0.001
## covid -0.141 -0.073 -0.010
## sibDZ_MZ 0.239 0.055 -0.002 -0.012
## sib_DZ 0.102 -0.030 -0.001 0.049 -0.074
## vrbl_:DZ_MZ -0.003 -0.002 0.353 0.007 -0.001 0.001
## vrblty_:_DZ -0.002 -0.003 0.005 0.007 0.000 0.003 -0.003out<- variability_PSYCH_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Psychosis",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
variability_PSYCH_MZ<- lmer(PSYCH_resid~avg_variabilitiy+variabiltiy_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+variabiltiy_diff*MZ_dummy_DZ+variabiltiy_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(variability_PSYCH_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_variabilitiy + variabiltiy_diff + covid + MZ_dummy_DZ +
## MZ_dummy_sib + variabiltiy_diff * MZ_dummy_DZ + variabiltiy_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3664.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8564 -0.5520 -0.3208 0.5430 3.7334
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2309 0.4805
## Residual 0.6067 0.7789
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13517 0.05646 773.83349 -2.394 0.016899
## avg_variabilitiy 0.10157 0.03030 964.84587 3.352 0.000833
## variabiltiy_diff 0.05695 0.11395 528.70994 0.500 0.617454
## covid -0.08737 0.17687 970.30342 -0.494 0.621460
## MZ_dummy_DZ 0.06614 0.07315 772.69406 0.904 0.366201
## MZ_dummy_sib 0.13698 0.06903 831.37703 1.985 0.047522
## variabiltiy_diff:MZ_dummy_DZ 0.03040 0.13775 527.75052 0.221 0.825399
## variabiltiy_diff:MZ_dummy_sib -0.07021 0.13745 531.04662 -0.511 0.609729
##
## (Intercept) *
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib *
## variabiltiy_diff:MZ_dummy_DZ
## variabiltiy_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid MZ__DZ MZ_dm_ v_:MZ__D
## avg_varblty 0.067
## varblty_dff -0.002 -0.002
## covid -0.079 -0.073 0.000
## MZ_dummy_DZ -0.770 -0.061 0.001 0.031
## MZ_dummy_sb -0.811 -0.038 0.001 -0.011 0.627
## vrb_:MZ__DZ 0.002 0.001 -0.827 -0.003 -0.001 -0.001
## vrblt_:MZ__ 0.002 0.003 -0.829 -0.009 -0.001 0.001 0.686out<- variability_PSYCH_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Psychosis",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
variability_PSYCH_DZ<- lmer(PSYCH_resid~avg_variabilitiy+variabiltiy_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+variabiltiy_diff*DZ_dummy_MZ+variabiltiy_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(variability_PSYCH_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_variabilitiy + variabiltiy_diff + covid + DZ_dummy_MZ +
## DZ_dummy_sib + variabiltiy_diff * DZ_dummy_MZ + variabiltiy_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3664.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8564 -0.5520 -0.3208 0.5430 3.7334
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2309 0.4805
## Residual 0.6067 0.7789
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06903 0.04666 775.28319 -1.480 0.139393
## avg_variabilitiy 0.10157 0.03030 964.84587 3.352 0.000833
## variabiltiy_diff 0.08735 0.07739 525.67238 1.129 0.259533
## covid -0.08737 0.17687 970.30342 -0.494 0.621460
## DZ_dummy_MZ -0.06614 0.07315 772.69406 -0.904 0.366201
## DZ_dummy_sib 0.07085 0.06151 853.92145 1.152 0.249740
## variabiltiy_diff:DZ_dummy_MZ -0.03040 0.13775 527.75052 -0.221 0.825399
## variabiltiy_diff:DZ_dummy_sib -0.10061 0.10907 530.79735 -0.922 0.356722
##
## (Intercept)
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib
## variabiltiy_diff:DZ_dummy_MZ
## variabiltiy_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid DZ__MZ DZ_dm_ v_:DZ__M
## avg_varblty -0.015
## varblty_dff 0.002 -0.001
## covid -0.047 -0.073 -0.005
## DZ_dummy_MZ -0.636 0.061 -0.001 -0.031
## DZ_dummy_sb -0.755 0.030 -0.001 -0.049 0.486
## vrb_:DZ__MZ -0.001 -0.001 -0.562 0.003 -0.001 0.000
## vrblt_:DZ__ -0.001 0.003 -0.709 -0.007 0.001 0.003 0.399out<- variability_PSYCH_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Psychosis",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
variability_PSYCH_sib<- lmer(PSYCH_resid~avg_variabilitiy+variabiltiy_diff+covid+sib_dummy_MZ+sib_dummy_DZ+variabiltiy_diff*sib_dummy_MZ+variabiltiy_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(variability_PSYCH_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_variabilitiy + variabiltiy_diff + covid + sib_dummy_MZ +
## sib_dummy_DZ + variabiltiy_diff * sib_dummy_MZ + variabiltiy_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3664.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8564 -0.5520 -0.3208 0.5430 3.7334
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2309 0.4805
## Residual 0.6067 0.7789
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.001815 0.040352 970.746108 0.045 0.964125
## avg_variabilitiy 0.101574 0.030302 964.845869 3.352 0.000833
## variabiltiy_diff -0.013257 0.076861 536.230394 -0.172 0.863129
## covid -0.087365 0.176873 970.303423 -0.494 0.621460
## sib_dummy_MZ -0.136984 0.069025 831.377030 -1.985 0.047522
## sib_dummy_DZ -0.070846 0.061511 853.921445 -1.152 0.249740
## variabiltiy_diff:sib_dummy_MZ 0.070205 0.137451 531.046621 0.511 0.609729
## variabiltiy_diff:sib_dummy_DZ 0.100608 0.109068 530.797350 0.922 0.356722
##
## (Intercept)
## avg_variabilitiy ***
## variabiltiy_diff
## covid
## sib_dummy_MZ *
## sib_dummy_DZ
## variabiltiy_diff:sib_dummy_MZ
## variabiltiy_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_vr vrblt_ covid sb__MZ sb__DZ v_:__M
## avg_varblty 0.029
## varblty_dff 0.007 0.002
## covid -0.129 -0.073 -0.016
## sib_dmmy_MZ -0.575 0.038 -0.003 0.011
## sib_dmmy_DZ -0.652 -0.030 -0.004 0.049 0.377
## vrblt_:__MZ -0.004 -0.003 -0.559 0.009 0.001 0.002
## vrblt_:__DZ -0.004 -0.003 -0.705 0.007 0.002 0.003 0.394out<- variability_PSYCH_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Variability",
sleep="Variability",
Psychiatric="Psychosis",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend duration (fitbit)
# weekend duration (fitbit)
weekend_dur_PSYCH_pheno<- lmer(PSYCH_resid~avg_weekend_dur+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_PSYCH_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_weekend_dur + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8263 -0.5511 -0.3489 0.5582 3.6442
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2255 0.4748
## Residual 0.6071 0.7792
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05522 0.02705 864.65361 -2.041 0.04151 *
## avg_weekend_dur -0.08058 0.02604 1333.29328 -3.094 0.00201 **
## covid -0.05109 0.17561 959.27130 -0.291 0.77115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_dr -0.055
## covid -0.149 0.033out<- weekend_dur_PSYCH_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_dur_PSYCH<- lmer(PSYCH_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_PSYCH)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3613.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8655 -0.5514 -0.3507 0.5638 3.6514
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2251 0.4744
## Residual 0.6077 0.7796
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05455 0.02707 864.61388 -2.015 0.04416 *
## fam_avg_weekend_dur -0.09123 0.03034 946.88733 -3.007 0.00271 **
## weekend_dur_diff -0.05097 0.05056 535.80246 -1.008 0.31382
## covid -0.05539 0.17572 958.02749 -0.315 0.75266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__
## fm_vg_wknd_ -0.066
## wknd_dr_dff 0.002 0.003
## covid -0.150 0.047 -0.013weekend_dur_PSYCH_zyg<- lmer(PSYCH_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekend_dur_diff+sib_DZ*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_PSYCH_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekend_dur_diff + sib_DZ *
## weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3622.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8255 -0.5570 -0.3370 0.5579 3.7486
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2228 0.4721
## Residual 0.6100 0.7810
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06718 0.02810 822.43353 -2.391 0.01704 *
## fam_avg_weekend_dur -0.08623 0.03048 945.80357 -2.829 0.00476 **
## weekend_dur_diff -0.03383 0.05896 528.96403 -0.574 0.56638
## covid -0.05926 0.17582 954.03821 -0.337 0.73616
## sibDZ_MZ -0.08954 0.06411 784.37848 -1.397 0.16293
## sib_DZ -0.04938 0.06160 847.53332 -0.802 0.42300
## weekend_dur_diff:sibDZ_MZ 0.09341 0.14873 528.50470 0.628 0.53023
## weekend_dur_diff:sib_DZ -0.02366 0.11053 529.92008 -0.214 0.83055
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wknd_ -0.090
## wknd_dr_dff 0.000 0.003
## covid -0.141 0.045 -0.008
## sibDZ_MZ 0.239 -0.084 -0.002 -0.011
## sib_DZ 0.109 -0.046 -0.003 0.045 -0.070
## wkn__:DZ_MZ -0.002 0.001 0.492 0.005 -0.002 0.002
## wknd_d_:_DZ -0.004 0.002 0.108 0.004 0.002 0.000 -0.064out<- weekend_dur_PSYCH_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_dur_PSYCH_MZ<- lmer(PSYCH_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekend_dur_diff+MZ_dummy_sib*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_PSYCH_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekend_dur_diff +
## MZ_dummy_sib * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3622.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8255 -0.5570 -0.3370 0.5579 3.7486
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2228 0.4721
## Residual 0.6100 0.7810
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.12687 0.05649 773.23349 -2.246 0.02500
## fam_avg_weekend_dur -0.08623 0.03048 945.80357 -2.829 0.00476
## weekend_dur_diff 0.02845 0.13808 528.24800 0.206 0.83685
## covid -0.05926 0.17582 954.03821 -0.337 0.73616
## MZ_dummy_DZ 0.06485 0.07304 767.50075 0.888 0.37487
## MZ_dummy_sib 0.11423 0.06916 828.62587 1.652 0.09899
## weekend_dur_diff:MZ_dummy_DZ -0.10524 0.16194 526.49274 -0.650 0.51606
## weekend_dur_diff:MZ_dummy_sib -0.08158 0.15531 531.06228 -0.525 0.59962
##
## (Intercept) *
## fam_avg_weekend_dur **
## weekend_dur_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib .
## weekend_dur_diff:MZ_dummy_DZ
## weekend_dur_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wknd_ -0.108
## wknd_dr_dff -0.003 0.002
## covid -0.079 0.045 0.000
## MZ_dummy_DZ -0.768 0.054 0.002 0.029
## MZ_dummy_sb -0.812 0.099 0.002 -0.010 0.626
## wk__:MZ__DZ 0.002 0.000 -0.853 -0.003 -0.003 -0.002
## wknd__:MZ__ 0.003 -0.001 -0.889 -0.007 -0.002 0.000 0.758out<- weekend_dur_PSYCH_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_PSYCH_DZ<- lmer(PSYCH_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekend_dur_diff+DZ_dummy_sib*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_PSYCH_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekend_dur_diff +
## DZ_dummy_sib * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3622.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8255 -0.5570 -0.3370 0.5579 3.7486
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2228 0.4721
## Residual 0.6100 0.7810
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06202 0.04678 772.32552 -1.326 0.18531
## fam_avg_weekend_dur -0.08623 0.03048 945.80357 -2.829 0.00476
## weekend_dur_diff -0.07680 0.08462 521.84120 -0.908 0.36456
## covid -0.05926 0.17582 954.03821 -0.337 0.73616
## DZ_dummy_MZ -0.06485 0.07304 767.50075 -0.888 0.37487
## DZ_dummy_sib 0.04938 0.06160 847.53332 0.802 0.42300
## weekend_dur_diff:DZ_dummy_MZ 0.10524 0.16194 526.49274 0.650 0.51606
## weekend_dur_diff:DZ_dummy_sib 0.02366 0.11053 529.92008 0.214 0.83055
##
## (Intercept)
## fam_avg_weekend_dur **
## weekend_dur_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib
## weekend_dur_diff:DZ_dummy_MZ
## weekend_dur_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wknd_ -0.046
## wknd_dr_dff -0.004 0.003
## covid -0.051 0.045 -0.006
## DZ_dummy_MZ -0.634 -0.054 0.003 -0.029
## DZ_dummy_sb -0.756 0.046 0.004 -0.045 0.483
## wk__:DZ__MZ 0.002 0.000 -0.523 0.003 -0.003 -0.002
## wknd__:DZ__ 0.004 -0.002 -0.766 -0.004 -0.002 0.000 0.400out<- weekend_dur_PSYCH_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_PSYCH_sib<- lmer(PSYCH_resid~fam_avg_weekend_dur+weekend_dur_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekend_dur_diff+sib_dummy_DZ*weekend_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_PSYCH_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekend_dur_diff +
## sib_dummy_DZ * weekend_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3622.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8255 -0.5570 -0.3370 0.5579 3.7486
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2228 0.4721
## Residual 0.6100 0.7810
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01264 0.04033 960.64817 -0.313 0.75400
## fam_avg_weekend_dur -0.08623 0.03048 945.80357 -2.829 0.00476
## weekend_dur_diff -0.05313 0.07111 541.82915 -0.747 0.45528
## covid -0.05926 0.17582 954.03821 -0.337 0.73616
## sib_dummy_MZ -0.11423 0.06916 828.62587 -1.652 0.09899
## sib_dummy_DZ -0.04938 0.06160 847.53332 -0.802 0.42300
## weekend_dur_diff:sib_dummy_MZ 0.08158 0.15531 531.06228 0.525 0.59962
## weekend_dur_diff:sib_dummy_DZ -0.02366 0.11053 529.92008 -0.214 0.83055
##
## (Intercept)
## fam_avg_weekend_dur **
## weekend_dur_diff
## covid
## sib_dummy_MZ .
## sib_dummy_DZ
## weekend_dur_diff:sib_dummy_MZ
## weekend_dur_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wknd_ 0.017
## wknd_dr_dff 0.007 0.000
## covid -0.127 0.045 -0.014
## sib_dmmy_MZ -0.577 -0.099 -0.003 0.010
## sib_dmmy_DZ -0.651 -0.046 -0.004 0.045 0.381
## wknd__:__MZ -0.003 0.001 -0.458 0.007 0.000 0.002
## wknd__:__DZ -0.004 0.002 -0.643 0.004 0.002 0.000 0.295out<- weekend_dur_PSYCH_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday duration (fitbit)
# weekday duration (fitbit)
weekday_dur_PSYCH_pheno<- lmer(PSYCH_resid~avg_weekday_dur+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_PSYCH_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_weekday_dur + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3658
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7866 -0.5460 -0.3418 0.5483 3.6078
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2406 0.4905
## Residual 0.6015 0.7756
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05395 0.02715 868.25263 -1.987 0.04722 *
## avg_weekday_dur -0.07276 0.02615 1306.69212 -2.783 0.00547 **
## covid -0.04217 0.17702 973.85159 -0.238 0.81175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_dr -0.031
## covid -0.147 0.012out<- weekday_dur_PSYCH_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_dur_PSYCH<- lmer(PSYCH_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_PSYCH)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3661.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7964 -0.5482 -0.3419 0.5455 3.6078
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2404 0.4903
## Residual 0.6021 0.7760
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05408 0.02716 868.27113 -1.991 0.0468 *
## fam_avg_weekday_dur -0.06741 0.02979 937.40975 -2.263 0.0239 *
## weekday_dur_diff -0.09060 0.05422 539.20267 -1.671 0.0953 .
## covid -0.04215 0.17705 973.29856 -0.238 0.8119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__
## fm_vg_wkdy_ -0.033
## wkdy_dr_dff -0.004 0.005
## covid -0.147 0.011 0.006weekday_dur_PSYCH_zyg<- lmer(PSYCH_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekday_dur_diff+sib_DZ*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_PSYCH_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekday_dur_diff + sib_DZ *
## weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3668.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8451 -0.5577 -0.3300 0.5414 3.6637
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2369 0.4867
## Residual 0.6045 0.7775
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.068828 0.028162 825.824802 -2.444 0.0147 *
## fam_avg_weekday_dur -0.062703 0.029849 935.688365 -2.101 0.0359 *
## weekday_dur_diff -0.071145 0.057189 534.797832 -1.244 0.2140
## covid -0.049101 0.176992 968.572268 -0.277 0.7815
## sibDZ_MZ -0.102815 0.064482 788.854960 -1.594 0.1112
## sib_DZ -0.060816 0.061737 852.884884 -0.985 0.3249
## weekday_dur_diff:sibDZ_MZ 0.145182 0.135358 532.337682 1.073 0.2839
## weekday_dur_diff:sib_DZ 0.005553 0.121730 538.919287 0.046 0.9636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wkdy_ -0.055
## wkdy_dr_dff -0.007 0.004
## covid -0.137 0.010 0.004
## sibDZ_MZ 0.241 -0.079 -0.006 -0.008
## sib_DZ 0.106 -0.028 -0.001 0.046 -0.071
## wkd__:DZ_MZ -0.005 -0.002 0.310 -0.002 -0.008 0.001
## wkdy_d_:_DZ -0.002 -0.003 -0.031 0.007 0.001 -0.001 0.019out<- weekday_dur_PSYCH_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_dur_PSYCH_MZ<- lmer(PSYCH_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekday_dur_diff+MZ_dummy_sib*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_PSYCH_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekday_dur_diff +
## MZ_dummy_sib * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3668.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8451 -0.5577 -0.3300 0.5414 3.6637
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2369 0.4867
## Residual 0.6045 0.7775
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.13737 0.05678 777.14067 -2.419 0.0158 *
## fam_avg_weekday_dur -0.06270 0.02985 935.68837 -2.101 0.0359 *
## weekday_dur_diff 0.02564 0.12090 530.69103 0.212 0.8321
## covid -0.04910 0.17699 968.57227 -0.277 0.7815
## MZ_dummy_DZ 0.07241 0.07343 772.63231 0.986 0.3244
## MZ_dummy_sib 0.13322 0.06949 832.56037 1.917 0.0556 .
## weekday_dur_diff:MZ_dummy_DZ -0.14241 0.14733 528.74272 -0.967 0.3342
## weekday_dur_diff:MZ_dummy_sib -0.14796 0.14949 538.04551 -0.990 0.3227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wkdy_ -0.087
## wkdy_dr_dff -0.010 0.000
## covid -0.074 0.010 0.001
## MZ_dummy_DZ -0.770 0.057 0.008 0.027
## MZ_dummy_sb -0.813 0.085 0.008 -0.013 0.628
## wk__:MZ__DZ 0.008 0.001 -0.821 0.004 -0.008 -0.007
## wkdy__:MZ__ 0.008 0.003 -0.809 -0.001 -0.006 -0.007 0.664out<- weekday_dur_PSYCH_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_PSYCH_DZ<- lmer(PSYCH_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekday_dur_diff+DZ_dummy_sib*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_PSYCH_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekday_dur_diff +
## DZ_dummy_sib * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3668.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8451 -0.5577 -0.3300 0.5414 3.6637
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2369 0.4867
## Residual 0.6045 0.7775
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.064964 0.046844 776.267955 -1.387 0.1659
## fam_avg_weekday_dur -0.062703 0.029849 935.688365 -2.101 0.0359
## weekday_dur_diff -0.116763 0.084190 524.752108 -1.387 0.1661
## covid -0.049101 0.176992 968.572268 -0.277 0.7815
## DZ_dummy_MZ -0.072407 0.073431 772.632315 -0.986 0.3244
## DZ_dummy_sib 0.060816 0.061737 852.884884 0.985 0.3249
## weekday_dur_diff:DZ_dummy_MZ 0.142406 0.147326 528.742719 0.967 0.3342
## weekday_dur_diff:DZ_dummy_sib -0.005553 0.121730 538.919287 -0.046 0.9636
##
## (Intercept)
## fam_avg_weekday_dur *
## weekday_dur_diff
## covid
## DZ_dummy_MZ
## DZ_dummy_sib
## weekday_dur_diff:DZ_dummy_MZ
## weekday_dur_diff:DZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wkdy_ -0.016
## wkdy_dr_dff -0.004 0.002
## covid -0.048 0.010 0.009
## DZ_dummy_MZ -0.634 -0.057 0.002 -0.027
## DZ_dummy_sb -0.755 0.028 0.002 -0.046 0.482
## wk__:DZ__MZ 0.002 -0.001 -0.571 -0.004 -0.008 -0.001
## wkdy__:DZ__ 0.003 0.003 -0.692 -0.007 -0.001 -0.001 0.395out<- weekday_dur_PSYCH_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_PSYCH_sib<- lmer(PSYCH_resid~fam_avg_weekday_dur+weekday_dur_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekday_dur_diff+sib_dummy_DZ*weekday_dur_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_PSYCH_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekday_dur_diff +
## sib_dummy_DZ * weekday_dur_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3668.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8451 -0.5577 -0.3300 0.5414 3.6637
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2369 0.4867
## Residual 0.6045 0.7775
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004149 0.040483 966.222147 -0.102 0.9184
## fam_avg_weekday_dur -0.062703 0.029849 935.688365 -2.101 0.0359
## weekday_dur_diff -0.122316 0.087922 552.276620 -1.391 0.1647
## covid -0.049101 0.176992 968.572269 -0.277 0.7815
## sib_dummy_MZ -0.133222 0.069495 832.560374 -1.917 0.0556
## sib_dummy_DZ -0.060816 0.061737 852.884884 -0.985 0.3249
## weekday_dur_diff:sib_dummy_MZ 0.147959 0.149491 538.045514 0.990 0.3227
## weekday_dur_diff:sib_dummy_DZ 0.005553 0.121730 538.919287 0.046 0.9636
##
## (Intercept)
## fam_avg_weekday_dur *
## weekday_dur_diff
## covid
## sib_dummy_MZ .
## sib_dummy_DZ
## weekday_dur_diff:sib_dummy_MZ
## weekday_dur_diff:sib_dummy_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wkdy_ 0.024
## wkdy_dr_dff 0.000 0.006
## covid -0.127 0.010 -0.001
## sib_dmmy_MZ -0.577 -0.085 -0.001 0.013
## sib_dmmy_DZ -0.651 -0.028 0.000 0.046 0.379
## wkdy__:__MZ 0.000 -0.003 -0.588 0.001 -0.007 0.000
## wkdy__:__DZ -0.001 -0.003 -0.722 0.007 0.000 -0.001 0.425out<- weekday_dur_PSYCH_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend duration (MCQ)
# weekend duration (MCQ)
weekend_dur_MCQ_PSYCH_pheno<- lmer(PSYCH_resid~weekend_dur_mcq_wave_2+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_PSYCH_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ weekend_dur_mcq_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7272.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8331 -0.5792 -0.3522 0.6042 3.5570
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2446 0.4945
## Residual 0.6746 0.8214
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02199 0.02066 1418.61738 -1.064 0.287452
## weekend_dur_mcq_wave_2 -0.07422 0.01941 2652.27074 -3.824 0.000134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wknd_dr___2 -0.041out<- weekend_dur_MCQ_PSYCH_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_PSYCH<- lmer(PSYCH_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_PSYCH)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7275.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8352 -0.5803 -0.3482 0.6059 3.5415
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2442 0.4942
## Residual 0.6748 0.8215
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02100 0.02068 1415.86750 -1.016 0.309964
## avg_weekend_dur_mcq -0.09608 0.02773 1483.53364 -3.465 0.000546 ***
## weekend_dur_mcq_diff -0.05314 0.02724 1314.74320 -1.951 0.051268 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___
## avg_wknd_d_ -0.059
## wknd_dr_mc_ 0.001 -0.003weekend_dur_MCQ_PSYCH_zyg<- lmer(PSYCH_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+sibDZ_MZ+sib_DZ+weekend_dur_mcq_diff*sibDZ_MZ+weekend_dur_mcq_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_PSYCH_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + sibDZ_MZ +
## sib_DZ + weekend_dur_mcq_diff * sibDZ_MZ + weekend_dur_mcq_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7289.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8499 -0.5814 -0.3450 0.6071 3.5649
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2449 0.4949
## Residual 0.6751 0.8216
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.508e-02 2.232e-02 1.394e+03 -1.124 0.261250
## avg_weekend_dur_mcq -9.662e-02 2.778e-02 1.481e+03 -3.478 0.000519
## weekend_dur_mcq_diff -5.987e-02 3.105e-02 1.283e+03 -1.928 0.054055
## sibDZ_MZ 2.045e-03 5.170e-02 1.389e+03 0.040 0.968450
## sib_DZ -3.306e-02 4.907e-02 1.400e+03 -0.674 0.500550
## weekend_dur_mcq_diff:sibDZ_MZ 4.213e-02 7.214e-02 1.276e+03 0.584 0.559267
## weekend_dur_mcq_diff:sib_DZ -6.870e-02 6.805e-02 1.294e+03 -1.010 0.312877
##
## (Intercept)
## avg_weekend_dur_mcq ***
## weekend_dur_mcq_diff .
## sibDZ_MZ
## sib_DZ
## weekend_dur_mcq_diff:sibDZ_MZ
## weekend_dur_mcq_diff:sib_DZ
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ sDZ_MZ sib_DZ w___:D
## avg_wknd_d_ -0.040
## wknd_dr_mc_ 0.001 -0.004
## sibDZ_MZ 0.251 0.011 -0.005
## sib_DZ 0.237 0.040 0.004 -0.154
## wk___:DZ_MZ -0.005 0.003 0.258 -0.003 -0.002
## wknd___:_DZ 0.004 -0.008 0.339 -0.003 0.005 -0.219out<- weekend_dur_MCQ_PSYCH_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_PSYCH_MZ<- lmer(PSYCH_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+MZ_dummy_DZ+MZ_dummy_sib+weekend_dur_mcq_diff*MZ_dummy_DZ+weekend_dur_mcq_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_PSYCH_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + MZ_dummy_DZ +
## MZ_dummy_sib + weekend_dur_mcq_diff * MZ_dummy_DZ + weekend_dur_mcq_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7289.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8499 -0.5814 -0.3450 0.6071 3.5649
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2449 0.4949
## Residual 0.6751 0.8216
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -2.372e-02 4.552e-02 1.386e+03 -0.521
## avg_weekend_dur_mcq -9.662e-02 2.778e-02 1.481e+03 -3.478
## weekend_dur_mcq_diff -3.178e-02 6.361e-02 1.271e+03 -0.500
## MZ_dummy_DZ -1.858e-02 6.054e-02 1.383e+03 -0.307
## MZ_dummy_sib 1.449e-02 5.371e-02 1.401e+03 0.270
## weekend_dur_mcq_diff:MZ_dummy_DZ -7.648e-02 8.622e-02 1.274e+03 -0.887
## weekend_dur_mcq_diff:MZ_dummy_sib -7.785e-03 7.272e-02 1.286e+03 -0.107
## Pr(>|t|)
## (Intercept) 0.602370
## avg_weekend_dur_mcq 0.000519 ***
## weekend_dur_mcq_diff 0.617460
## MZ_dummy_DZ 0.758995
## MZ_dummy_sib 0.787420
## weekend_dur_mcq_diff:MZ_dummy_DZ 0.375218
## weekend_dur_mcq_diff:MZ_dummy_sib 0.914765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ MZ__DZ MZ_dm_ w___:MZ__D
## avg_wknd_d_ -0.011
## wknd_dr_mc_ -0.005 0.001
## MZ_dummy_DZ -0.752 0.007 0.004
## MZ_dummy_sb -0.847 -0.029 0.004 0.637
## w___:MZ__DZ 0.004 -0.006 -0.738 0.001 -0.003
## wkn___:MZ__ 0.004 0.000 -0.875 -0.003 -0.004 0.645out<- weekend_dur_MCQ_PSYCH_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_PSYCH_DZ<- lmer(PSYCH_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+DZ_dummy_MZ+DZ_dummy_sib+weekend_dur_mcq_diff*DZ_dummy_MZ+weekend_dur_mcq_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_PSYCH_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + DZ_dummy_MZ +
## DZ_dummy_sib + weekend_dur_mcq_diff * DZ_dummy_MZ + weekend_dur_mcq_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7289.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8499 -0.5814 -0.3450 0.6071 3.5649
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2449 0.4949
## Residual 0.6751 0.8216
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.04230 0.03992 1379.49375 -1.060
## avg_weekend_dur_mcq -0.09662 0.02778 1480.56877 -3.478
## weekend_dur_mcq_diff -0.10826 0.05821 1277.88539 -1.860
## DZ_dummy_MZ 0.01858 0.06054 1383.16505 0.307
## DZ_dummy_sib 0.03306 0.04907 1400.05248 0.674
## weekend_dur_mcq_diff:DZ_dummy_MZ 0.07648 0.08622 1273.96884 0.887
## weekend_dur_mcq_diff:DZ_dummy_sib 0.06870 0.06805 1293.91772 1.010
## Pr(>|t|)
## (Intercept) 0.289481
## avg_weekend_dur_mcq 0.000519 ***
## weekend_dur_mcq_diff 0.063121 .
## DZ_dummy_MZ 0.758995
## DZ_dummy_sib 0.500550
## weekend_dur_mcq_diff:DZ_dummy_MZ 0.375218
## weekend_dur_mcq_diff:DZ_dummy_sib 0.312877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ DZ__MZ DZ_dm_ w___:DZ__M
## avg_wknd_d_ -0.003
## wknd_dr_mc_ 0.008 -0.008
## DZ_dummy_MZ -0.659 -0.007 -0.005
## DZ_dummy_sb -0.813 -0.040 -0.006 0.537
## w___:DZ__MZ -0.005 0.006 -0.675 0.001 0.004
## wkn___:DZ__ -0.007 0.008 -0.855 0.004 0.005 0.577out<- weekend_dur_MCQ_PSYCH_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_dur_MCQ_PSYCH_sib<- lmer(PSYCH_resid~avg_weekend_dur_mcq+weekend_dur_mcq_diff+sib_dummy_MZ+sib_dummy_DZ+weekend_dur_mcq_diff*sib_dummy_MZ+weekend_dur_mcq_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(weekend_dur_MCQ_PSYCH_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + sib_dummy_MZ +
## sib_dummy_DZ + weekend_dur_mcq_diff * sib_dummy_MZ + weekend_dur_mcq_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7289.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8499 -0.5814 -0.3450 0.6071 3.5649
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2449 0.4949
## Residual 0.6751 0.8216
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -9.233e-03 2.855e-02 1.442e+03 -0.323
## avg_weekend_dur_mcq -9.662e-02 2.778e-02 1.481e+03 -3.478
## weekend_dur_mcq_diff -3.956e-02 3.524e-02 1.339e+03 -1.123
## sib_dummy_MZ -1.449e-02 5.371e-02 1.401e+03 -0.270
## sib_dummy_DZ -3.306e-02 4.907e-02 1.400e+03 -0.674
## weekend_dur_mcq_diff:sib_dummy_MZ 7.785e-03 7.272e-02 1.286e+03 0.107
## weekend_dur_mcq_diff:sib_dummy_DZ -6.870e-02 6.805e-02 1.294e+03 -1.010
## Pr(>|t|)
## (Intercept) 0.746440
## avg_weekend_dur_mcq 0.000519 ***
## weekend_dur_mcq_diff 0.261840
## sib_dummy_MZ 0.787420
## sib_dummy_DZ 0.500550
## weekend_dur_mcq_diff:sib_dummy_MZ 0.914765
## weekend_dur_mcq_diff:sib_dummy_DZ 0.312877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkn___ sb__MZ sb__DZ w___:__M
## avg_wknd_d_ -0.073
## wknd_dr_mc_ 0.000 0.001
## sib_dmmy_MZ -0.531 0.029 0.000
## sib_dmmy_DZ -0.582 0.040 0.000 0.309
## wkn___:__MZ 0.000 0.000 -0.485 -0.004 0.000
## wkn___:__DZ 0.000 -0.008 -0.518 0.000 0.005 0.251out<- weekend_dur_MCQ_PSYCH_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekend Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday duration (MCQ)
# weekday duration (MCQ)
weekday_dur_MCQ_PSYCH_pheno<- lmer(PSYCH_resid~weekday_dur_mcq_wave_2+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_PSYCH_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ weekday_dur_mcq_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7215.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0684 -0.5755 -0.3216 0.6079 3.5277
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2368 0.4866
## Residual 0.6622 0.8138
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02512 0.02040 1419.68293 -1.232 0.218
## weekday_dur_mcq_wave_2 -0.15977 0.01871 2664.17148 -8.538 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wkdy_dr___2 -0.001out<- weekday_dur_MCQ_PSYCH_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_PSYCH<- lmer(PSYCH_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_PSYCH)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + (1 |
## rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7219.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0544 -0.5713 -0.3175 0.6118 3.5041
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2369 0.4867
## Residual 0.6623 0.8138
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02514 0.02040 1419.27256 -1.232 0.218
## avg_weekday_dur_mcq -0.17307 0.02541 1453.80062 -6.810 1.42e-11 ***
## weekday_dur_mcq_diff -0.14387 0.02779 1318.07504 -5.177 2.61e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___
## avg_wkdy_d_ 0.001
## wkdy_dr_mc_ -0.001 -0.004weekday_dur_MCQ_PSYCH_zyg<- lmer(PSYCH_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+sibDZ_MZ+sib_DZ+weekday_dur_mcq_diff*sibDZ_MZ+weekday_dur_mcq_diff*sib_DZ+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_PSYCH_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + sibDZ_MZ +
## sib_DZ + weekday_dur_mcq_diff * sibDZ_MZ + weekday_dur_mcq_diff *
## sib_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7224.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0234 -0.5730 -0.3102 0.6099 3.4138
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2389 0.4888
## Residual 0.6588 0.8117
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03434 0.02205 1396.53366 -1.558 0.11957
## avg_weekday_dur_mcq -0.17602 0.02557 1453.53745 -6.883 8.71e-12
## weekday_dur_mcq_diff -0.17394 0.03156 1300.09132 -5.512 4.27e-08
## sibDZ_MZ -0.02429 0.05121 1393.37500 -0.474 0.63541
## sib_DZ -0.04787 0.04855 1405.17812 -0.986 0.32435
## weekday_dur_mcq_diff:sibDZ_MZ 0.01689 0.07513 1302.17075 0.225 0.82219
## weekday_dur_mcq_diff:sib_DZ -0.19561 0.06650 1296.61127 -2.941 0.00332
##
## (Intercept)
## avg_weekday_dur_mcq ***
## weekday_dur_mcq_diff ***
## sibDZ_MZ
## sib_DZ
## weekday_dur_mcq_diff:sibDZ_MZ
## weekday_dur_mcq_diff:sib_DZ **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ sDZ_MZ sib_DZ w___:D
## avg_wkdy_d_ 0.043
## wkdy_dr_mc_ 0.001 -0.006
## sibDZ_MZ 0.254 0.075 0.007
## sib_DZ 0.241 0.070 -0.003 -0.148
## wk___:DZ_MZ 0.007 -0.005 0.327 0.005 0.001
## wkdy___:_DZ -0.003 -0.002 0.284 0.002 -0.005 -0.179out<- weekday_dur_MCQ_PSYCH_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_PSYCH_MZ<- lmer(PSYCH_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+MZ_dummy_DZ+MZ_dummy_sib+weekday_dur_mcq_diff*MZ_dummy_DZ+weekday_dur_mcq_diff*MZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_PSYCH_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + MZ_dummy_DZ +
## MZ_dummy_sib + weekday_dur_mcq_diff * MZ_dummy_DZ + weekday_dur_mcq_diff *
## MZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7224.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0234 -0.5730 -0.3102 0.6099 3.4138
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2389 0.4888
## Residual 0.6588 0.8117
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -5.053e-02 4.510e-02 1.389e+03 -1.121
## avg_weekday_dur_mcq -1.760e-01 2.557e-02 1.454e+03 -6.883
## weekday_dur_mcq_diff -1.627e-01 6.738e-02 1.304e+03 -2.415
## MZ_dummy_DZ 3.515e-04 5.984e-02 1.387e+03 0.006
## MZ_dummy_sib 4.822e-02 5.332e-02 1.407e+03 0.904
## weekday_dur_mcq_diff:MZ_dummy_DZ -1.147e-01 8.743e-02 1.294e+03 -1.312
## weekday_dur_mcq_diff:MZ_dummy_sib 8.092e-02 7.653e-02 1.310e+03 1.057
## Pr(>|t|)
## (Intercept) 0.2627
## avg_weekday_dur_mcq 8.71e-12 ***
## weekday_dur_mcq_diff 0.0159 *
## MZ_dummy_DZ 0.9953
## MZ_dummy_sib 0.3660
## weekday_dur_mcq_diff:MZ_dummy_DZ 0.1898
## weekday_dur_mcq_diff:MZ_dummy_sib 0.2906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ MZ__DZ MZ_dm_ w___:MZ__D
## avg_wkdy_d_ 0.077
## wkdy_dr_mc_ 0.008 -0.007
## MZ_dummy_DZ -0.752 -0.035 -0.006
## MZ_dummy_sb -0.849 -0.103 -0.007 0.637
## w___:MZ__DZ -0.006 0.003 -0.771 0.002 0.005
## wkd___:MZ__ -0.007 0.006 -0.880 0.005 0.005 0.678out<- weekday_dur_MCQ_PSYCH_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_PSYCH_DZ<- lmer(PSYCH_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+DZ_dummy_MZ+DZ_dummy_sib+weekday_dur_mcq_diff*DZ_dummy_MZ+weekday_dur_mcq_diff*DZ_dummy_sib+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_PSYCH_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + DZ_dummy_MZ +
## DZ_dummy_sib + weekday_dur_mcq_diff * DZ_dummy_MZ + weekday_dur_mcq_diff *
## DZ_dummy_sib + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7224.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0234 -0.5730 -0.3102 0.6099 3.4138
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2389 0.4888
## Residual 0.6588 0.8117
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -5.018e-02 3.945e-02 1.383e+03 -1.272
## avg_weekday_dur_mcq -1.760e-01 2.557e-02 1.454e+03 -6.883
## weekday_dur_mcq_diff -2.774e-01 5.572e-02 1.281e+03 -4.978
## DZ_dummy_MZ -3.515e-04 5.984e-02 1.387e+03 -0.006
## DZ_dummy_sib 4.787e-02 4.855e-02 1.405e+03 0.986
## weekday_dur_mcq_diff:DZ_dummy_MZ 1.147e-01 8.743e-02 1.294e+03 1.312
## weekday_dur_mcq_diff:DZ_dummy_sib 1.956e-01 6.650e-02 1.297e+03 2.941
## Pr(>|t|)
## (Intercept) 0.20361
## avg_weekday_dur_mcq 8.71e-12 ***
## weekday_dur_mcq_diff 7.31e-07 ***
## DZ_dummy_MZ 0.99531
## DZ_dummy_sib 0.32435
## weekday_dur_mcq_diff:DZ_dummy_MZ 0.18981
## weekday_dur_mcq_diff:DZ_dummy_sib 0.00332 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ DZ__MZ DZ_dm_ w___:DZ__M
## avg_wkdy_d_ 0.035
## wkdy_dr_mc_ -0.007 -0.003
## DZ_dummy_MZ -0.657 0.035 0.004
## DZ_dummy_sb -0.814 -0.070 0.006 0.533
## w___:DZ__MZ 0.004 -0.003 -0.637 0.002 -0.003
## wkd___:DZ__ 0.006 0.002 -0.838 -0.004 -0.005 0.534out<- weekday_dur_MCQ_PSYCH_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_dur_MCQ_PSYCH_sib<- lmer(PSYCH_resid~avg_weekday_dur_mcq+weekday_dur_mcq_diff+sib_dummy_MZ+sib_dummy_DZ+weekday_dur_mcq_diff*sib_dummy_MZ+weekday_dur_mcq_diff*sib_dummy_DZ+(1|rel_family_id), data=abcd_all)
summary(weekday_dur_MCQ_PSYCH_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + sib_dummy_MZ +
## sib_dummy_DZ + weekday_dur_mcq_diff * sib_dummy_MZ + weekday_dur_mcq_diff *
## sib_dummy_DZ + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 7224.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0234 -0.5730 -0.3102 0.6099 3.4138
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2389 0.4888
## Residual 0.6588 0.8117
## Number of obs: 2670, groups: rel_family_id, 1448
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -2.312e-03 2.820e-02 1.448e+03 -0.082
## avg_weekday_dur_mcq -1.760e-01 2.557e-02 1.454e+03 -6.883
## weekday_dur_mcq_diff -8.176e-02 3.630e-02 1.334e+03 -2.252
## sib_dummy_MZ -4.822e-02 5.332e-02 1.407e+03 -0.904
## sib_dummy_DZ -4.787e-02 4.855e-02 1.405e+03 -0.986
## weekday_dur_mcq_diff:sib_dummy_MZ -8.092e-02 7.653e-02 1.310e+03 -1.057
## weekday_dur_mcq_diff:sib_dummy_DZ -1.956e-01 6.650e-02 1.297e+03 -2.941
## Pr(>|t|)
## (Intercept) 0.93467
## avg_weekday_dur_mcq 8.71e-12 ***
## weekday_dur_mcq_diff 0.02446 *
## sib_dummy_MZ 0.36598
## sib_dummy_DZ 0.32435
## weekday_dur_mcq_diff:sib_dummy_MZ 0.29056
## weekday_dur_mcq_diff:sib_dummy_DZ 0.00332 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg___ wkd___ sb__MZ sb__DZ w___:__M
## avg_wkdy_d_ -0.072
## wkdy_dr_mc_ -0.002 0.000
## sib_dmmy_MZ -0.534 0.103 0.001
## sib_dmmy_DZ -0.583 0.070 0.001 0.313
## wkd___:__MZ 0.002 -0.006 -0.474 0.005 -0.001
## wkd___:__DZ 0.001 -0.002 -0.546 -0.001 -0.005 0.259out<- weekday_dur_MCQ_PSYCH_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Weekday Duration",
sleep="Duration",
Psychiatric="Psychosis",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)chronotype
# chrono
chrono_PSYCH_pheno<- lmer(PSYCH_resid~chronotype_wave_2+(1|rel_family_id), data=abcd_all)
summary(chrono_PSYCH_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ chronotype_wave_2 + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 6310.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8703 -0.5830 -0.3122 0.5856 3.5383
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2715 0.5211
## Residual 0.6496 0.8060
## Number of obs: 2317, groups: rel_family_id, 1388
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01605 0.02209 1341.80242 -0.727 0.468
## chronotype_wave_2 -0.10600 0.01971 2306.13491 -5.377 8.32e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## chrntyp_w_2 -0.054out<- chrono_PSYCH_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Psychosis",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
chrono_PSYCH<- lmer(PSYCH_resid~avg_chrono+chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_PSYCH)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_chrono + chrono_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 6310.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8951 -0.5840 -0.3060 0.5913 3.5241
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2702 0.5198
## Residual 0.6495 0.8059
## Number of obs: 2317, groups: rel_family_id, 1388
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01424 0.02208 1339.87628 -0.645 0.5190
## avg_chrono -0.14162 0.02640 1423.23800 -5.365 9.43e-08 ***
## chrono_diff -0.06131 0.02960 1013.62574 -2.071 0.0386 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch
## avg_chrono -0.067
## chrono_diff -0.006 0.000chrono_PSYCH_zyg<- lmer(PSYCH_resid~avg_chrono+chrono_diff+sibDZ_MZ+sib_DZ+sibDZ_MZ*chrono_diff+sib_DZ*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_PSYCH_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_chrono + chrono_diff + sibDZ_MZ + sib_DZ +
## sibDZ_MZ * chrono_diff + sib_DZ * chrono_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 6324.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8944 -0.5852 -0.3021 0.5879 3.5212
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2706 0.5202
## Residual 0.6501 0.8063
## Number of obs: 2317, groups: rel_family_id, 1388
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.409e-02 2.385e-02 1.321e+03 -0.591 0.5547
## avg_chrono -1.416e-01 2.644e-02 1.424e+03 -5.356 9.89e-08 ***
## chrono_diff -5.920e-02 3.212e-02 9.946e+02 -1.843 0.0656 .
## sibDZ_MZ -6.465e-03 5.504e-02 1.315e+03 -0.117 0.9065
## sib_DZ 7.616e-03 5.245e-02 1.324e+03 0.145 0.8846
## chrono_diff:sibDZ_MZ 7.900e-02 7.358e-02 9.895e+02 1.074 0.2832
## chrono_diff:sib_DZ -5.106e-02 7.184e-02 1.002e+03 -0.711 0.4774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d sDZ_MZ sib_DZ c_:DZ_
## avg_chrono -0.081
## chrono_diff -0.005 0.000
## sibDZ_MZ 0.248 -0.031 -0.002
## sib_DZ 0.242 -0.032 0.000 -0.155
## chrn_:DZ_MZ -0.002 0.001 0.218 -0.006 0.000
## chrn_df:_DZ 0.000 0.000 0.276 0.000 -0.004 -0.181out<- chrono_PSYCH_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Psychosis",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
chrono_PSYCH_MZ<- lmer(PSYCH_resid~avg_chrono+chrono_diff+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*chrono_diff+MZ_dummy_sib*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_PSYCH_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_chrono + chrono_diff + MZ_dummy_DZ + MZ_dummy_sib +
## MZ_dummy_DZ * chrono_diff + MZ_dummy_sib * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 6324.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8944 -0.5852 -0.3021 0.5879 3.5212
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2706 0.5202
## Residual 0.6501 0.8063
## Number of obs: 2317, groups: rel_family_id, 1388
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.840e-02 4.846e-02 1.315e+03 -0.380 0.704
## avg_chrono -1.416e-01 2.644e-02 1.424e+03 -5.356 9.89e-08 ***
## chrono_diff -6.537e-03 6.421e-02 9.857e+02 -0.102 0.919
## MZ_dummy_DZ 1.027e-02 6.453e-02 1.308e+03 0.159 0.874
## MZ_dummy_sib 2.657e-03 5.718e-02 1.328e+03 0.046 0.963
## chrono_diff:MZ_dummy_DZ -1.045e-01 8.751e-02 9.866e+02 -1.194 0.233
## chrono_diff:MZ_dummy_sib -5.347e-02 7.583e-02 9.990e+02 -0.705 0.481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d MZ__DZ MZ_dm_ c_:MZ__D
## avg_chrono -0.063
## chrono_diff -0.007 0.001
## MZ_dummy_DZ -0.749 0.013 0.005
## MZ_dummy_sb -0.847 0.045 0.006 0.635
## chr_:MZ__DZ 0.005 0.000 -0.734 -0.005 -0.004
## chrn_d:MZ__ 0.006 -0.001 -0.847 -0.004 -0.007 0.621out<- chrono_PSYCH_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Psychosis",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
chrono_PSYCH_DZ<- lmer(PSYCH_resid~avg_chrono+chrono_diff+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*chrono_diff+DZ_dummy_sib*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_PSYCH_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_chrono + chrono_diff + DZ_dummy_MZ + DZ_dummy_sib +
## DZ_dummy_MZ * chrono_diff + DZ_dummy_sib * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 6324.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8944 -0.5852 -0.3021 0.5879 3.5212
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2706 0.5202
## Residual 0.6501 0.8063
## Number of obs: 2317, groups: rel_family_id, 1388
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.130e-03 4.276e-02 1.305e+03 -0.190 0.8492
## avg_chrono -1.416e-01 2.644e-02 1.424e+03 -5.356 9.89e-08 ***
## chrono_diff -1.111e-01 5.946e-02 9.876e+02 -1.868 0.0621 .
## DZ_dummy_MZ -1.027e-02 6.453e-02 1.308e+03 -0.159 0.8735
## DZ_dummy_sib -7.616e-03 5.245e-02 1.324e+03 -0.145 0.8846
## chrono_diff:DZ_dummy_MZ 1.045e-01 8.751e-02 9.866e+02 1.194 0.2326
## chrono_diff:DZ_dummy_sib 5.106e-02 7.184e-02 1.002e+03 0.711 0.4774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d DZ__MZ DZ_dm_ c_:DZ__M
## avg_chrono -0.051
## chrono_diff -0.003 0.000
## DZ_dummy_MZ -0.660 -0.013 0.002
## DZ_dummy_sb -0.815 0.032 0.003 0.538
## chr_:DZ__MZ 0.002 0.000 -0.679 -0.005 -0.002
## chrn_d:DZ__ 0.003 0.000 -0.828 -0.002 -0.004 0.562out<- chrono_PSYCH_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Psychosis",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
chrono_PSYCH_sib<- lmer(PSYCH_resid~avg_chrono+chrono_diff+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*chrono_diff+sib_dummy_DZ*chrono_diff+(1|rel_family_id), data=abcd_all)
summary(chrono_PSYCH_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PSYCH_resid ~ avg_chrono + chrono_diff + sib_dummy_MZ + sib_dummy_DZ +
## sib_dummy_MZ * chrono_diff + sib_dummy_DZ * chrono_diff +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 6324.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8944 -0.5852 -0.3021 0.5879 3.5212
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2706 0.5202
## Residual 0.6501 0.8063
## Number of obs: 2317, groups: rel_family_id, 1388
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.575e-02 3.040e-02 1.362e+03 -0.518 0.605
## avg_chrono -1.416e-01 2.644e-02 1.424e+03 -5.356 9.89e-08 ***
## chrono_diff -6.001e-02 4.033e-02 1.034e+03 -1.488 0.137
## sib_dummy_MZ -2.657e-03 5.718e-02 1.328e+03 -0.046 0.963
## sib_dummy_DZ 7.616e-03 5.245e-02 1.324e+03 0.145 0.885
## chrono_diff:sib_dummy_MZ 5.347e-02 7.583e-02 9.990e+02 0.705 0.481
## chrono_diff:sib_dummy_DZ -5.106e-02 7.184e-02 1.002e+03 -0.711 0.477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_ch chrn_d sb__MZ sb__DZ c_:__M
## avg_chrono -0.017
## chrono_diff -0.007 -0.001
## sib_dmmy_MZ -0.531 -0.045 0.004
## sib_dmmy_DZ -0.579 -0.032 0.004 0.310
## chrn_d:__MZ 0.004 0.001 -0.532 -0.007 -0.002
## chrn_d:__DZ 0.004 0.000 -0.561 -0.002 -0.004 0.299out<- chrono_PSYCH_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Chronotype",
sleep="Chronotype",
Psychiatric="Psychosis",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekend efficiency
weekend_effic_PSYCH_pheno<- lmer(PSYCH_resid~avg_weekend_effic+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_PSYCH_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_weekend_effic + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3619.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6960 -0.5436 -0.3701 0.5566 3.7775
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2306 0.4802
## Residual 0.6084 0.7800
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05900 0.02723 863.43630 -2.167 0.0305 *
## avg_weekend_effic -0.01033 0.02910 1355.74735 -0.355 0.7226
## covid -0.03511 0.17643 958.65639 -0.199 0.8423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wknd_ff -0.083
## covid -0.149 0.033out<- weekend_effic_PSYCH_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekend_effic_PSYCH<- lmer(PSYCH_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_PSYCH)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3622.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6959 -0.5444 -0.3687 0.5539 3.7809
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2304 0.4800
## Residual 0.6091 0.7805
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.849e-02 2.729e-02 8.589e+02 -2.143 0.0324 *
## fam_avg_weekend_effic -1.752e-02 3.728e-02 9.538e+02 -0.470 0.6385
## weekend_effic_diff 8.054e-04 4.642e-02 5.325e+02 0.017 0.9862
## covid -3.577e-02 1.765e-01 9.575e+02 -0.203 0.8394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__
## fm_vg_wknd_ -0.103
## wknd_ffc_df -0.003 0.003
## covid -0.150 0.034 0.012weekend_effic_PSYCH_zyg<- lmer(PSYCH_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekend_effic_diff+sib_DZ*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_PSYCH_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekend_effic_diff + sib_DZ *
## weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3628.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6797 -0.5572 -0.3564 0.5599 3.7730
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2272 0.4767
## Residual 0.6101 0.7811
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.07303 0.02825 816.98209 -2.586 0.0099 **
## fam_avg_weekend_effic -0.01662 0.03726 951.16126 -0.446 0.6556
## weekend_effic_diff 0.03050 0.05140 523.05927 0.593 0.5531
## covid -0.04349 0.17635 953.80184 -0.247 0.8052
## sibDZ_MZ -0.10396 0.06418 784.40693 -1.620 0.1057
## sib_DZ -0.05879 0.06180 847.06679 -0.951 0.3417
## weekend_effic_diff:sibDZ_MZ 0.20871 0.12431 521.14237 1.679 0.0938 .
## weekend_effic_diff:sib_DZ -0.04265 0.10532 526.51630 -0.405 0.6857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wknd_ -0.105
## wknd_ffc_df -0.004 0.002
## covid -0.141 0.035 0.008
## sibDZ_MZ 0.236 -0.040 -0.003 -0.009
## sib_DZ 0.102 0.036 0.001 0.048 -0.075
## wkn__:DZ_MZ -0.002 -0.002 0.372 -0.005 -0.004 -0.001
## wknd_f_:_DZ 0.001 -0.002 0.170 -0.003 0.000 0.000 -0.105out<- weekend_effic_PSYCH_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekend_effic_PSYCH_MZ<- lmer(PSYCH_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekend_effic_diff+MZ_dummy_sib*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_PSYCH_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekend_effic_diff +
## MZ_dummy_sib * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3628.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6797 -0.5572 -0.3564 0.5599 3.7730
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2272 0.4767
## Residual 0.6101 0.7811
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14234 0.05657 769.86305 -2.516 0.0121
## fam_avg_weekend_effic -0.01662 0.03726 951.16126 -0.446 0.6556
## weekend_effic_diff 0.16964 0.11261 519.95223 1.507 0.1325
## covid -0.04349 0.17635 953.80184 -0.247 0.8052
## MZ_dummy_DZ 0.07456 0.07330 768.70112 1.017 0.3094
## MZ_dummy_sib 0.13335 0.06910 827.45360 1.930 0.0540
## weekend_effic_diff:MZ_dummy_DZ -0.23004 0.14002 519.50125 -1.643 0.1010
## weekend_effic_diff:MZ_dummy_sib -0.18739 0.12979 524.82996 -1.444 0.1494
##
## (Intercept) *
## fam_avg_weekend_effic
## weekend_effic_diff
## covid
## MZ_dummy_DZ
## MZ_dummy_sib .
## weekend_effic_diff:MZ_dummy_DZ
## weekend_effic_diff:MZ_dummy_sib
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wknd_ -0.082
## wknd_ffc_df -0.005 -0.001
## covid -0.077 0.035 0.000
## MZ_dummy_DZ -0.768 0.050 0.004 0.028
## MZ_dummy_sb -0.809 0.021 0.004 -0.013 0.625
## wk__:MZ__DZ 0.004 0.001 -0.804 0.003 -0.003 -0.003
## wknd__:MZ__ 0.004 0.003 -0.868 0.006 -0.003 -0.004 0.698out<- weekend_effic_PSYCH_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekend_effic_PSYCH_DZ<- lmer(PSYCH_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekend_effic_diff+DZ_dummy_sib*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_PSYCH_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekend_effic_diff +
## DZ_dummy_sib * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3628.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6797 -0.5572 -0.3564 0.5599 3.7730
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2272 0.4767
## Residual 0.6101 0.7811
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06777 0.04692 770.92173 -1.444 0.149
## fam_avg_weekend_effic -0.01662 0.03726 951.16126 -0.446 0.656
## weekend_effic_diff -0.06040 0.08323 518.67807 -0.726 0.468
## covid -0.04349 0.17635 953.80184 -0.247 0.805
## DZ_dummy_MZ -0.07456 0.07330 768.70112 -1.017 0.309
## DZ_dummy_sib 0.05879 0.06180 847.06679 0.951 0.342
## weekend_effic_diff:DZ_dummy_MZ 0.23004 0.14002 519.50125 1.643 0.101
## weekend_effic_diff:DZ_dummy_sib 0.04265 0.10532 526.51630 0.405 0.686
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wknd_ -0.022
## wknd_ffc_df 0.000 0.001
## covid -0.049 0.035 0.005
## DZ_dummy_MZ -0.636 -0.050 0.000 -0.028
## DZ_dummy_sb -0.754 -0.036 -0.001 -0.048 0.488
## wk__:DZ__MZ 0.000 -0.001 -0.594 -0.003 -0.003 0.000
## wknd__:DZ__ -0.001 0.002 -0.790 0.003 0.000 0.000 0.470out<- weekend_effic_PSYCH_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekend_effic_PSYCH_sib<- lmer(PSYCH_resid~fam_avg_weekend_effic+weekend_effic_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekend_effic_diff+sib_dummy_DZ*weekend_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekend_effic_PSYCH_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekend_effic + weekend_effic_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekend_effic_diff +
## sib_dummy_DZ * weekend_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3628.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6797 -0.5572 -0.3564 0.5599 3.7730
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2272 0.4767
## Residual 0.6101 0.7811
## Number of obs: 1370, groups: rel_family_id, 893
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.008983 0.040588 960.084284 -0.221
## fam_avg_weekend_effic -0.016620 0.037255 951.161257 -0.446
## weekend_effic_diff -0.017747 0.064540 540.024408 -0.275
## covid -0.043494 0.176352 953.801842 -0.247
## sib_dummy_MZ -0.133352 0.069095 827.453598 -1.930
## sib_dummy_DZ -0.058791 0.061796 847.066792 -0.951
## weekend_effic_diff:sib_dummy_MZ 0.187390 0.129790 524.829956 1.444
## weekend_effic_diff:sib_dummy_DZ -0.042649 0.105317 526.516296 -0.405
## Pr(>|t|)
## (Intercept) 0.825
## fam_avg_weekend_effic 0.656
## weekend_effic_diff 0.783
## covid 0.805
## sib_dummy_MZ 0.054 .
## sib_dummy_DZ 0.342
## weekend_effic_diff:sib_dummy_MZ 0.149
## weekend_effic_diff:sib_dummy_DZ 0.686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wknd__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wknd_ -0.079
## wknd_ffc_df -0.003 0.005
## covid -0.130 0.035 0.012
## sib_dmmy_MZ -0.574 -0.021 0.001 0.013
## sib_dmmy_DZ -0.651 0.036 0.002 0.048 0.377
## wknd__:__MZ 0.002 -0.003 -0.497 -0.006 -0.004 -0.001
## wknd__:__DZ 0.001 -0.002 -0.613 -0.003 0.000 0.000 0.305out<- weekend_effic_PSYCH_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekend Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)weekday efficiency
weekday_effic_PSYCH_pheno<- lmer(PSYCH_resid~avg_weekday_effic+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_PSYCH_pheno) ### NOT sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ avg_weekday_effic + covid + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3665.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7295 -0.5395 -0.3648 0.5327 3.7550
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2427 0.4926
## Residual 0.6042 0.7773
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.587e-02 2.725e-02 8.693e+02 -2.051 0.0406 *
## avg_weekday_effic -9.868e-03 2.637e-02 1.377e+03 -0.374 0.7083
## covid -3.845e-02 1.776e-01 9.757e+02 -0.216 0.8287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) avg_w_
## avg_wkdy_ff -0.043
## covid -0.148 0.034out<- weekday_effic_PSYCH_pheno %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Phenotypic")
abcd_out<- rbind(abcd_out, out)
weekday_effic_PSYCH<- lmer(PSYCH_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_PSYCH)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3669.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7251 -0.5394 -0.3651 0.5328 3.7517
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2425 0.4924
## Residual 0.6050 0.7778
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.055967 0.027272 868.910242 -2.052 0.0405 *
## fam_avg_weekday_effic -0.008098 0.033435 984.874807 -0.242 0.8087
## weekday_effic_diff -0.012855 0.043484 551.725843 -0.296 0.7676
## covid -0.038288 0.177689 974.988958 -0.215 0.8294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__
## fm_vg_wkdy_ -0.058
## wkdy_ffc_df 0.004 -0.010
## covid -0.148 0.033 0.012weekday_effic_PSYCH_zyg<- lmer(PSYCH_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+sibDZ_MZ+sib_DZ+sibDZ_MZ*weekday_effic_diff+sib_DZ*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_PSYCH_zyg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## sibDZ_MZ + sib_DZ + sibDZ_MZ * weekday_effic_diff + sib_DZ *
## weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3675.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7198 -0.5539 -0.3474 0.5428 3.7236
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2388 0.4887
## Residual 0.6064 0.7787
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.071842 0.028285 824.718923 -2.540 0.0113 *
## fam_avg_weekday_effic -0.002053 0.033525 980.604391 -0.061 0.9512
## weekday_effic_diff 0.010354 0.046532 538.995715 0.223 0.8240
## covid -0.046692 0.177538 970.178271 -0.263 0.7926
## sibDZ_MZ -0.112898 0.064729 787.559270 -1.744 0.0815 .
## sib_DZ -0.064131 0.061883 854.849129 -1.036 0.3003
## weekday_effic_diff:sibDZ_MZ 0.144836 0.111655 529.266270 1.297 0.1951
## weekday_effic_diff:sib_DZ -0.060756 0.096744 556.622847 -0.628 0.5303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sDZ_MZ sib_DZ w__:DZ
## fm_vg_wkdy_ -0.081
## wkdy_ffc_df 0.003 -0.008
## covid -0.139 0.033 0.008
## sibDZ_MZ 0.243 -0.093 0.000 -0.011
## sib_DZ 0.105 -0.013 -0.009 0.046 -0.072
## wkd__:DZ_MZ 0.000 0.003 0.349 -0.005 0.002 0.005
## wkdy_f_:_DZ -0.012 0.014 -0.041 0.008 0.005 0.006 0.025out<- weekday_effic_PSYCH_zyg %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Between Within")
abcd_out<- rbind(abcd_out, out)
weekday_effic_PSYCH_MZ<- lmer(PSYCH_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+MZ_dummy_DZ+MZ_dummy_sib+MZ_dummy_DZ*weekday_effic_diff+MZ_dummy_sib*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_PSYCH_MZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## MZ_dummy_DZ + MZ_dummy_sib + MZ_dummy_DZ * weekday_effic_diff +
## MZ_dummy_sib * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3675.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7198 -0.5539 -0.3474 0.5428 3.7236
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2388 0.4887
## Residual 0.6064 0.7787
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.147107 0.057056 774.065911 -2.578
## fam_avg_weekday_effic -0.002053 0.033525 980.604391 -0.061
## weekday_effic_diff 0.106911 0.100634 523.119633 1.062
## covid -0.046692 0.177538 970.178271 -0.263
## MZ_dummy_DZ 0.080833 0.073713 771.496513 1.097
## MZ_dummy_sib 0.144964 0.069719 832.316240 2.079
## weekday_effic_diff:MZ_dummy_DZ -0.175214 0.120547 527.059613 -1.453
## weekday_effic_diff:MZ_dummy_sib -0.114458 0.122808 539.758227 -0.932
## Pr(>|t|)
## (Intercept) 0.0101 *
## fam_avg_weekday_effic 0.9512
## weekday_effic_diff 0.2886
## covid 0.7926
## MZ_dummy_DZ 0.2732
## MZ_dummy_sib 0.0379 *
## weekday_effic_diff:MZ_dummy_DZ 0.1467
## weekday_effic_diff:MZ_dummy_sib 0.3517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid MZ__DZ MZ_dm_ w__:MZ__D
## fm_vg_wkdy_ -0.110
## wkdy_ffc_df 0.002 -0.001
## covid -0.077 0.033 0.000
## MZ_dummy_DZ -0.771 0.076 -0.001 0.029
## MZ_dummy_sb -0.813 0.092 -0.001 -0.011 0.629
## wk__:MZ__DZ -0.002 0.002 -0.835 0.008 -0.001 0.001
## wkdy__:MZ__ 0.000 -0.008 -0.819 0.002 0.000 0.007 0.684out<- weekday_effic_PSYCH_MZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Dummy MZ")
abcd_out<- rbind(abcd_out, out)
weekday_effic_PSYCH_DZ<- lmer(PSYCH_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+DZ_dummy_MZ+DZ_dummy_sib+DZ_dummy_MZ*weekday_effic_diff+DZ_dummy_sib*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_PSYCH_DZ)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## DZ_dummy_MZ + DZ_dummy_sib + DZ_dummy_MZ * weekday_effic_diff +
## DZ_dummy_sib * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3675.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7198 -0.5539 -0.3474 0.5428 3.7236
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2388 0.4887
## Residual 0.6064 0.7787
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.066274 0.046967 777.802825 -1.411
## fam_avg_weekday_effic -0.002053 0.033525 980.604391 -0.061
## weekday_effic_diff -0.068303 0.066366 536.257700 -1.029
## covid -0.046692 0.177538 970.178271 -0.263
## DZ_dummy_MZ -0.080833 0.073713 771.496513 -1.097
## DZ_dummy_sib 0.064131 0.061883 854.849129 1.036
## weekday_effic_diff:DZ_dummy_MZ 0.175214 0.120547 527.059613 1.453
## weekday_effic_diff:DZ_dummy_sib 0.060756 0.096744 556.622847 0.628
## Pr(>|t|)
## (Intercept) 0.159
## fam_avg_weekday_effic 0.951
## weekday_effic_diff 0.304
## covid 0.793
## DZ_dummy_MZ 0.273
## DZ_dummy_sib 0.300
## weekday_effic_diff:DZ_dummy_MZ 0.147
## weekday_effic_diff:DZ_dummy_sib 0.530
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid DZ__MZ DZ_dm_ w__:DZ__M
## fm_vg_wkdy_ -0.015
## wkdy_ffc_df -0.008 0.002
## covid -0.049 0.033 0.015
## DZ_dummy_MZ -0.633 -0.076 0.004 -0.029
## DZ_dummy_sb -0.755 0.013 0.005 -0.046 0.483
## wk__:DZ__MZ 0.005 -0.002 -0.551 -0.008 -0.001 -0.003
## wkdy__:DZ__ 0.006 -0.014 -0.686 -0.008 -0.002 0.006 0.378out<- weekday_effic_PSYCH_DZ %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Dummy DZ")
abcd_out<- rbind(abcd_out, out)
weekday_effic_PSYCH_sib<- lmer(PSYCH_resid~fam_avg_weekday_effic+weekday_effic_diff+covid+sib_dummy_MZ+sib_dummy_DZ+sib_dummy_MZ*weekday_effic_diff+sib_dummy_DZ*weekday_effic_diff+(1|rel_family_id), data=abcd_all)
summary(weekday_effic_PSYCH_sib)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PSYCH_resid ~ fam_avg_weekday_effic + weekday_effic_diff + covid +
## sib_dummy_MZ + sib_dummy_DZ + sib_dummy_MZ * weekday_effic_diff +
## sib_dummy_DZ * weekday_effic_diff + (1 | rel_family_id)
## Data: abcd_all
##
## REML criterion at convergence: 3675.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7198 -0.5539 -0.3474 0.5428 3.7236
##
## Random effects:
## Groups Name Variance Std.Dev.
## rel_family_id (Intercept) 0.2388 0.4887
## Residual 0.6064 0.7787
## Number of obs: 1384, groups: rel_family_id, 899
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -0.002143 0.040580 969.399986 -0.053
## fam_avg_weekday_effic -0.002053 0.033525 980.604391 -0.061
## weekday_effic_diff -0.007546 0.070392 575.691279 -0.107
## covid -0.046692 0.177538 970.178271 -0.263
## sib_dummy_MZ -0.144964 0.069719 832.316239 -2.079
## sib_dummy_DZ -0.064131 0.061883 854.849129 -1.036
## weekday_effic_diff:sib_dummy_MZ 0.114458 0.122808 539.758227 0.932
## weekday_effic_diff:sib_dummy_DZ -0.060756 0.096744 556.622847 -0.628
## Pr(>|t|)
## (Intercept) 0.9579
## fam_avg_weekday_effic 0.9512
## weekday_effic_diff 0.9147
## covid 0.7926
## sib_dummy_MZ 0.0379 *
## sib_dummy_DZ 0.3003
## weekday_effic_diff:sib_dummy_MZ 0.3517
## weekday_effic_diff:sib_dummy_DZ 0.5303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fm_v__ wkdy__ covid sb__MZ sb__DZ w__:__M
## fm_vg_wkdy_ 0.003
## wkdy_ffc_df 0.019 -0.017
## covid -0.127 0.033 0.003
## sib_dmmy_MZ -0.575 -0.092 -0.010 0.011
## sib_dmmy_DZ -0.651 -0.013 -0.012 0.046 0.377
## wkdy__:__MZ -0.011 0.008 -0.573 -0.002 0.007 0.007
## wkdy__:__DZ -0.015 0.014 -0.728 0.008 0.007 0.006 0.417out<- weekday_effic_PSYCH_sib %>%
tidy() %>%
filter(effect=="fixed") %>%
select(term, estimate, std.error, p.value) %>%
mutate(sleep_trait_specific="Accelerometer Weekday Efficiency",
sleep="Efficiency",
Psychiatric="Psychosis",
model="Dummy Sib")
abcd_out<- rbind(abcd_out, out)write file
fwrite(abcd_out, "/Users/claire/Desktop/dissertation/cotwin_mendelian/ctc_results_abcd.csv", sep="\t")model comparisons for ABCD
did not embed these in the model code chunks as I did for Colorado (though this is prob easier so here are all model comparisons for ABCD)
# int
anova(variability_int_pheno,variability_int)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## variability_int_pheno: INT_resid ~ variability + covid + (1 | rel_family_id)
## variability_int: INT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## variability_int_pheno 5 3412.7 3438.5 -1701.3 3402.7
## variability_int 6 3413.5 3444.4 -1700.8 3401.5 1.1652 1 0.2804anova(weekend_dur_int_pheno,weekend_dur_int)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekend_dur_int_pheno: INT_resid ~ avg_weekend_dur + covid + (1 | rel_family_id)
## weekend_dur_int: INT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekend_dur_int_pheno 5 3394.4 3420.2 -1692.2 3384.4
## weekend_dur_int 6 3393.7 3424.6 -1690.9 3381.7 2.7113 1 0.09964
##
## weekend_dur_int_pheno
## weekend_dur_int .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(weekday_dur_int_pheno,weekday_dur_int)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_dur_int_pheno: INT_resid ~ avg_weekday_dur + covid + (1 | rel_family_id)
## weekday_dur_int: INT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekday_dur_int_pheno 5 3430.3 3456.1 -1710.2 3420.3
## weekday_dur_int 6 3430.3 3461.2 -1709.1 3418.3 2.0236 1 0.1549anova(weekend_dur_MCQ_int_pheno,weekend_dur_MCQ_int)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekend_dur_MCQ_int_pheno: INT_resid ~ weekend_dur_mcq_wave_2 + (1 | rel_family_id)
## weekend_dur_MCQ_int: INT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekend_dur_MCQ_int_pheno 4 5836.4 5859.1 -2914.2 5828.4
## weekend_dur_MCQ_int 5 5833.9 5862.4 -2912.0 5823.9 4.4127 1
## Pr(>Chisq)
## weekend_dur_MCQ_int_pheno
## weekend_dur_MCQ_int 0.03567 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(weekday_dur_MCQ_int_pheno,weekday_dur_MCQ_int)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_dur_MCQ_int_pheno: INT_resid ~ weekday_dur_mcq_wave_2 + (1 | rel_family_id)
## weekday_dur_MCQ_int: INT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekday_dur_MCQ_int_pheno 4 5835.0 5857.7 -2913.5 5827.0
## weekday_dur_MCQ_int 5 5834.1 5862.5 -2912.0 5824.1 2.9354 1
## Pr(>Chisq)
## weekday_dur_MCQ_int_pheno
## weekday_dur_MCQ_int 0.08666 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(chrono_int_pheno,chrono_int)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## chrono_int_pheno: INT_resid ~ chronotype_wave_2 + (1 | rel_family_id)
## chrono_int: INT_resid ~ avg_chrono + chrono_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## chrono_int_pheno 4 5153.8 5176.1 -2572.9 5145.8
## chrono_int 5 5155.5 5183.3 -2572.8 5145.5 0.3189 1 0.5723anova(weekday_effic_int_pheno,weekday_dur_int)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_effic_int_pheno: INT_resid ~ avg_weekday_effic + covid + (1 | rel_family_id)
## weekday_dur_int: INT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekday_effic_int_pheno 5 3424.9 3450.7 -1707.4 3414.9
## weekday_dur_int 6 3430.3 3461.2 -1709.1 3418.3 0 1 1anova(jetlag_int_pheno,jetlag_int)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## jetlag_int_pheno: INT_resid ~ social_jet_lag_wave_2 + (1 | rel_family_id)
## jetlag_int: INT_resid ~ avg_jetlag + jetlag_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## jetlag_int_pheno 4 5835.0 5857.8 -2913.5 5827.0
## jetlag_int 5 5833.9 5862.3 -2911.9 5823.9 3.1409 1 0.07635 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# ext
anova(variability_EXT_pheno,variability_EXT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## variability_EXT_pheno: EXT_resid ~ variability + covid + (1 | rel_family_id)
## variability_EXT: EXT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## variability_EXT_pheno 5 3444.1 3469.8 -1717.0 3434.1
## variability_EXT 6 3442.2 3473.2 -1715.1 3430.2 3.8326 1 0.05026
##
## variability_EXT_pheno
## variability_EXT .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(weekend_dur_EXT_pheno,weekend_dur_EXT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekend_dur_EXT_pheno: EXT_resid ~ avg_weekend_dur + covid + (1 | rel_family_id)
## weekend_dur_EXT: EXT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekend_dur_EXT_pheno 5 3423.9 3449.6 -1707.0 3413.9
## weekend_dur_EXT 6 3424.4 3455.2 -1706.2 3412.4 1.5575 1 0.212anova(weekday_dur_EXT_pheno,weekday_dur_EXT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_dur_EXT_pheno: EXT_resid ~ avg_weekday_dur + covid + (1 | rel_family_id)
## weekday_dur_EXT: EXT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekday_dur_EXT_pheno 5 3460.6 3486.4 -1725.3 3450.6
## weekday_dur_EXT 6 3460.0 3490.9 -1724.0 3448.0 2.6598 1 0.1029anova(weekend_dur_MCQ_EXT_pheno,weekend_dur_MCQ_EXT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekend_dur_MCQ_EXT_pheno: EXT_resid ~ weekend_dur_mcq_wave_2 + (1 | rel_family_id)
## weekend_dur_MCQ_EXT: EXT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekend_dur_MCQ_EXT_pheno 4 5910.7 5933.4 -2951.3 5902.7
## weekend_dur_MCQ_EXT 5 5907.7 5936.1 -2948.9 5897.7 4.9298 1
## Pr(>Chisq)
## weekend_dur_MCQ_EXT_pheno
## weekend_dur_MCQ_EXT 0.0264 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(weekday_dur_MCQ_EXT_pheno,weekday_dur_MCQ_EXT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_dur_MCQ_EXT_pheno: EXT_resid ~ weekday_dur_mcq_wave_2 + (1 | rel_family_id)
## weekday_dur_MCQ_EXT: EXT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekday_dur_MCQ_EXT_pheno 4 5903.4 5926.1 -2947.7 5895.4
## weekday_dur_MCQ_EXT 5 5902.3 5930.7 -2946.2 5892.3 3.0435 1
## Pr(>Chisq)
## weekday_dur_MCQ_EXT_pheno
## weekday_dur_MCQ_EXT 0.08106 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(chrono_EXT_pheno,chrono_EXT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## chrono_EXT_pheno: EXT_resid ~ chronotype_wave_2 + (1 | rel_family_id)
## chrono_EXT: EXT_resid ~ avg_chrono + chrono_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## chrono_EXT_pheno 4 5236.1 5258.3 -2614.1 5228.1
## chrono_EXT 5 5237.4 5265.2 -2613.7 5227.4 0.724 1 0.3948anova(weekday_effic_EXT_pheno,weekday_dur_EXT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_effic_EXT_pheno: EXT_resid ~ avg_weekday_effic + covid + (1 | rel_family_id)
## weekday_dur_EXT: EXT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekday_effic_EXT_pheno 5 3457.6 3483.4 -1723.8 3447.6
## weekday_dur_EXT 6 3460.0 3490.9 -1724.0 3448.0 0 1 1anova(jetlag_EXT_pheno,jetlag_EXT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## jetlag_EXT_pheno: EXT_resid ~ social_jet_lag_wave_2 + (1 | rel_family_id)
## jetlag_EXT: EXT_resid ~ avg_jetlag + jetlag_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## jetlag_EXT_pheno 4 5910.3 5933.1 -2951.2 5902.3
## jetlag_EXT 5 5912.3 5940.7 -2951.1 5902.3 0.0574 1 0.8107# att
anova(variability_ATT_pheno,variability_ATT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## variability_ATT_pheno: ATT_resid ~ variability + covid + (1 | rel_family_id)
## variability_ATT: ATT_resid ~ avg_variabilitiy + variabiltiy_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## variability_ATT_pheno 5 3482.4 3508.1 -1736.2 3472.4
## variability_ATT 6 3483.5 3514.5 -1735.8 3471.5 0.8176 1 0.3659anova(weekend_dur_ATT_pheno,weekend_dur_ATT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekend_dur_ATT_pheno: ATT_resid ~ avg_weekend_dur + covid + (1 | rel_family_id)
## weekend_dur_ATT: ATT_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekend_dur_ATT_pheno 5 3455.9 3481.7 -1723.0 3445.9
## weekend_dur_ATT 6 3457.7 3488.5 -1722.8 3445.7 0.2946 1 0.5873anova(weekday_dur_ATT_pheno,weekday_dur_ATT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_dur_ATT_pheno: ATT_resid ~ avg_weekday_dur + covid + (1 | rel_family_id)
## weekday_dur_ATT: ATT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## weekday_dur_ATT_pheno 5 3499.7 3525.4 -1744.8 3489.7
## weekday_dur_ATT 6 3496.2 3527.2 -1742.1 3484.2 5.41 1 0.02002 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(weekend_dur_MCQ_ATT_pheno,weekend_dur_MCQ_ATT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekend_dur_MCQ_ATT_pheno: ATT_resid ~ weekend_dur_mcq_wave_2 + (1 | rel_family_id)
## weekend_dur_MCQ_ATT: ATT_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekend_dur_MCQ_ATT_pheno 4 5919.2 5941.9 -2955.6 5911.2
## weekend_dur_MCQ_ATT 5 5920.6 5949.0 -2955.3 5910.6 0.5909 1
## Pr(>Chisq)
## weekend_dur_MCQ_ATT_pheno
## weekend_dur_MCQ_ATT 0.4421anova(weekday_dur_MCQ_ATT_pheno,weekday_dur_MCQ_ATT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_dur_MCQ_ATT_pheno: ATT_resid ~ weekday_dur_mcq_wave_2 + (1 | rel_family_id)
## weekday_dur_MCQ_ATT: ATT_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekday_dur_MCQ_ATT_pheno 4 5918.7 5941.4 -2955.4 5910.7
## weekday_dur_MCQ_ATT 5 5920.1 5948.5 -2955.1 5910.1 0.6227 1
## Pr(>Chisq)
## weekday_dur_MCQ_ATT_pheno
## weekday_dur_MCQ_ATT 0.43anova(chrono_ATT_pheno,chrono_ATT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## chrono_ATT_pheno: ATT_resid ~ chronotype_wave_2 + (1 | rel_family_id)
## chrono_ATT: ATT_resid ~ avg_chrono + chrono_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## chrono_ATT_pheno 4 5199.0 5221.3 -2595.5 5191.0
## chrono_ATT 5 5198.9 5226.6 -2594.4 5188.9 2.185 1 0.1394anova(weekday_effic_ATT_pheno,weekday_dur_ATT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_effic_ATT_pheno: ATT_resid ~ avg_weekday_effic + covid + (1 | rel_family_id)
## weekday_dur_ATT: ATT_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekday_effic_ATT_pheno 5 3499.4 3525.2 -1744.7 3489.4
## weekday_dur_ATT 6 3496.2 3527.2 -1742.1 3484.2 5.1888 1
## Pr(>Chisq)
## weekday_effic_ATT_pheno
## weekday_dur_ATT 0.02273 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(jetlag_ATT_pheno,jetlag_ATT)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## jetlag_ATT_pheno: ATT_resid ~ social_jet_lag_wave_2 + (1 | rel_family_id)
## jetlag_ATT: ATT_resid ~ avg_jetlag + jetlag_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## jetlag_ATT_pheno 4 5921.5 5944.2 -2956.8 5913.5
## jetlag_ATT 5 5923.5 5951.9 -2956.7 5913.5 0.0176 1 0.8943# psych
anova(variability_PSYCH_pheno,variability_PSYCH)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## variability_PSYCH_pheno: PSYCH_resid ~ variability + covid + (1 | rel_family_id)
## variability_PSYCH: PSYCH_resid ~ avg_variabilitiy + variabiltiy_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## variability_PSYCH_pheno 5 3652.2 3678.3 -1821.1 3642.2
## variability_PSYCH 6 3653.0 3684.4 -1820.5 3641.0 1.1968 1
## Pr(>Chisq)
## variability_PSYCH_pheno
## variability_PSYCH 0.274anova(weekend_dur_PSYCH_pheno,weekend_dur_PSYCH)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekend_dur_PSYCH_pheno: PSYCH_resid ~ avg_weekend_dur + covid + (1 | rel_family_id)
## weekend_dur_PSYCH: PSYCH_resid ~ fam_avg_weekend_dur + weekend_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekend_dur_PSYCH_pheno 5 3607.5 3633.6 -1798.8 3597.5
## weekend_dur_PSYCH 6 3609.1 3640.4 -1798.5 3597.1 0.4686 1
## Pr(>Chisq)
## weekend_dur_PSYCH_pheno
## weekend_dur_PSYCH 0.4936anova(weekday_dur_PSYCH_pheno,weekday_dur_PSYCH)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_dur_PSYCH_pheno: PSYCH_resid ~ avg_weekday_dur + covid + (1 | rel_family_id)
## weekday_dur_PSYCH: PSYCH_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekday_dur_PSYCH_pheno 5 3655.5 3681.7 -1822.7 3645.5
## weekday_dur_PSYCH 6 3657.3 3688.7 -1822.7 3645.3 0.1412 1
## Pr(>Chisq)
## weekday_dur_PSYCH_pheno
## weekday_dur_PSYCH 0.7071anova(weekend_dur_MCQ_PSYCH_pheno,weekend_dur_MCQ_PSYCH)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekend_dur_MCQ_PSYCH_pheno: PSYCH_resid ~ weekend_dur_mcq_wave_2 + (1 | rel_family_id)
## weekend_dur_MCQ_PSYCH: PSYCH_resid ~ avg_weekend_dur_mcq + weekend_dur_mcq_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekend_dur_MCQ_PSYCH_pheno 4 7268.4 7292.0 -3630.2 7260.4
## weekend_dur_MCQ_PSYCH 5 7269.2 7298.7 -3629.6 7259.2 1.2182 1
## Pr(>Chisq)
## weekend_dur_MCQ_PSYCH_pheno
## weekend_dur_MCQ_PSYCH 0.2697anova(weekday_dur_MCQ_PSYCH_pheno,weekday_dur_MCQ_PSYCH)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_dur_MCQ_PSYCH_pheno: PSYCH_resid ~ weekday_dur_mcq_wave_2 + (1 | rel_family_id)
## weekday_dur_MCQ_PSYCH: PSYCH_resid ~ avg_weekday_dur_mcq + weekday_dur_mcq_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekday_dur_MCQ_PSYCH_pheno 4 7211.1 7234.6 -3601.5 7203.1
## weekday_dur_MCQ_PSYCH 5 7212.5 7241.9 -3601.2 7202.5 0.5992 1
## Pr(>Chisq)
## weekday_dur_MCQ_PSYCH_pheno
## weekday_dur_MCQ_PSYCH 0.4389anova(chrono_PSYCH_pheno,chrono_PSYCH)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## chrono_PSYCH_pheno: PSYCH_resid ~ chronotype_wave_2 + (1 | rel_family_id)
## chrono_PSYCH: PSYCH_resid ~ avg_chrono + chrono_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## chrono_PSYCH_pheno 4 6306.5 6329.5 -3149.2 6298.5
## chrono_PSYCH 5 6304.4 6333.1 -3147.2 6294.4 4.1011 1 0.04286 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(weekday_effic_PSYCH_pheno,weekday_dur_PSYCH)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## weekday_effic_PSYCH_pheno: PSYCH_resid ~ avg_weekday_effic + covid + (1 | rel_family_id)
## weekday_dur_PSYCH: PSYCH_resid ~ fam_avg_weekday_dur + weekday_dur_diff + covid + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df
## weekday_effic_PSYCH_pheno 5 3663.1 3689.3 -1826.5 3653.1
## weekday_dur_PSYCH 6 3657.3 3688.7 -1822.7 3645.3 7.7402 1
## Pr(>Chisq)
## weekday_effic_PSYCH_pheno
## weekday_dur_PSYCH 0.005401 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(jetlag_PSYCH_pheno,jetlag_PSYCH)## refitting model(s) with ML (instead of REML)## Data: abcd_all
## Models:
## jetlag_PSYCH_pheno: PSYCH_resid ~ social_jet_lag_wave_2 + (1 | rel_family_id)
## jetlag_PSYCH: PSYCH_resid ~ avg_jetlag + jetlag_diff + (1 | rel_family_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## jetlag_PSYCH_pheno 4 7267.3 7290.9 -3629.7 7259.3
## jetlag_PSYCH 5 7268.5 7297.9 -3629.2 7258.5 0.8469 1 0.3574getting simple effects for two model comparisons:
## weekday effic on att
# want avg of DZ and sib
summary(weekday_effic_ATT_zyg)
summary(weekday_effic_ATT_DZ)
summary(weekday_effic_ATT_sib)
# y = -.24-.003between-.183within-.478covid-.011sibDZ_vs_MZ+.104siv_vs_DZ+.357win_X_sibDZ_MZ+.114win_X_sib_DZ
### rearrange
# simple slope = -.183within+.357win_X_sibDZ_MZ+.114win_X_sib_DZ
# = (-.183+.357sibDZ_MZ+.114sib_DZ)within
# sib:
-.183 +.357*(-1/3)+.114*(-1/2)
# = -.359
# DZ
-.183 +.357*(-1/3)+.114*(1/2)
# = -.245
(-.245+-.359)/2
# -.302
summary(chrono_EXT_DZ)
summary(chrono_EXT_sib)
(-0.08546-0.42456)/2zygosity interaction plots
| variable | MZ | DZ | sib |
|---|---|---|---|
| sibDZ_MZ | 2/3 | -1/3 | -1/3 |
| sib_DZ | 0 | 1/2 | -1/2 |
p<- plot_model(chrono_EXT_zyg, type = "pred", terms = c("chrono_diff","sibDZ_MZ[-0.333333333333333, 0.666666666666667]"),
title="Effect of the interaction between within-family \nchronotype differences and MZs vs DZs & siblings on externalizing",
legend.title = "DZ & sibs (-0.33) vs MZ (0.67)")
(p<- p + xlab("Within-family chronotype differences") +
ylab("Externalizing"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_CHRONO_EXT_INTERACT.png", width = 7, height = 5, units = "in")p<- plot_model(jetlag_int_zyg, type = "pred", terms = c("jetlag_diff","sibDZ_MZ[-0.333333333333333, 0.666666666666667]"),
title="Effect of the interaction between within-family \nsocial jet lag differences and MZs vs DZs & siblings on internalizing",
legend.title = "DZ & sibs (-0.33) vs MZ (0.67)")
(p<- p + xlab("Within-family social jet lag differences") +
ylab("Internalizing"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_JETLAG_INT_INTERACT.png", width = 7, height = 5, units = "in")p<- plot_model(jetlag_EXT_zyg, type = "pred", terms = c("jetlag_diff","sibDZ_MZ[-0.333333333333333, 0.666666666666667]"),
title="Effect of the interaction between within-family \nsocial jet lag differences and MZs vs DZs & siblings on externalizing",
legend.title = "DZ & sibs(-0.33) vs MZ (0.67)")
(p<- p + xlab("Within-family social jet lag differences") +
ylab("Externalizing"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_JETLAG_EXT_INTERACT.png", width = 7, height = 5, units = "in")p<- plot_model(jetlag_int_zyg, type = "pred", terms = c("jetlag_diff","sib_DZ[-0.5,0, 0.5]"),
title="Effect of the interaction between within-family \nsocial jet lag differences and MZs vs DZs & siblings on internalizing",
legend.title = "DZ (0.5) vs sib(-0.5) \nMZ (0)")
(p<- p + xlab("Within-family social jet lag differences") +
ylab("Internalizing"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_JETLAG_INT_INTERACT2.png", width = 7, height = 5, units = "in")p<- plot_model(jetlag_EXT_zyg, type = "pred", terms = c("jetlag_diff","sib_DZ[-0.5,0, 0.5]"),
title="Effect of the interaction between within-family \nsocial jet lag differences and siblings vs DZs on externalizing",
legend.title = "DZ (0.5) vs sib(-0.5) \nMZ (0)")
(p<- p + xlab("Within-family social jet lag differences") +
ylab("Externalizing"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_JETLAG_EXT_INTERACT2.png", width = 7, height = 5, units = "in")p<- plot_model(jetlag_ATT_zyg, type = "pred", terms = c("jetlag_diff","sib_DZ[-0.5,0, 0.5]"),
title="Effect of the interaction between within-family \nsocial jet lag differences and siblings vs DZs on attention problems",
legend.title = "DZ (0.5) vs sib(-0.5) \nMZ (0)")
(p<- p + xlab("Within-family social jet lag differences") +
ylab("Attention problems"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_JETLAG_ATT_INTERACT2.png", width = 7, height = 5, units = "in")p<- plot_model(weekend_effic_PSYCH_zyg, type = "pred", terms = c("weekend_effic_diff","sibDZ_MZ[-0.333333333333333, 0.666666666666667]"),
title="Effect of the interaction between within-family \nweekend efficiency differences and MZs vs DZs & \nsiblings on psychosis",
legend.title = "DZ & sibs(-1/3) vs MZ(2/3)")
(p<- p + xlab("Within-family weekend efficiency differences") +
ylab("Psychosis"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_WEEKEND_EFFIC_PSYCH_INTERACT.png", width = 7, height = 5, units = "in")p<- plot_model(weekend_effic_PSYCH_zyg, type = "pred", terms = c("weekend_effic_diff","sib_DZ[-0.5,0, 0.5]"),
title="Effect of the interaction between within-family \nweekend efficiency differences and siblings vs DZs on psychosis",
legend.title = "DZ (0.5) vs sib(-0.5) \nMZ (0)")
(p<- p + xlab("Within-family weekend efficiency differences") +
ylab("Psychosis"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_WEEKEND_EFFIC_PSYCH_INTERACT2.png", width = 7, height = 5, units = "in")p<- plot_model(weekday_effic_ATT_zyg, type = "pred", terms = c("weekday_effic_diff","sibDZ_MZ[-0.333333333333333, 0.666666666666667]"),
title="Effect of the interaction between within-family \nweekday efficiency differences and MZs vs DZs & \nsiblings on attention problems",
legend.title = "DZ & sibs(-1/3) vs MZ(2/3)")
(p<- p + xlab("Within-family weekend efficiency differences") +
ylab("Attention problems"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_WEEKEND_EFFIC_ATT_INTERACT.png", width = 7, height = 5, units = "in")p<- plot_model(weekday_dur_PSYCH_zyg, type = "pred", terms = c("weekday_dur_diff","sib_DZ[-0.5,0, 0.5]"),
title="Effect of the interaction between within-family \nweekday duration differences and siblings vs DZs on psychosis",
legend.title = "DZ (0.5) vs sib(-0.5) \nMZ (0)")
(p<- p + xlab("Within-family weekday duration differences") +
ylab("Psychosis"))
ggsave(plot = p,filename = "/Users/claire/Desktop/dissertation/figs/ABCD_WEEKDAY_DUR_PSYCH_INTERACT.png", width = 7, height = 5, units = "in")psych::describe(abcd_all)## vars n mean sd median trimmed
## subjectkey* 1 10418 5209.50 3007.56 5209.50 5209.50
## eventname_wave_2.x* 2 10418 1.97 0.18 2.00 2.00
## weekend_dur_mcq_wave_2 3 10054 0.00 1.00 0.09 0.05
## weekday_dur_mcq_wave_2 4 10054 0.00 1.00 0.15 0.07
## chronotype_wave_2 5 8771 0.00 1.00 -0.63 0.00
## social_jet_lag_wave_2 6 10054 0.00 1.00 -0.20 -0.10
## eventname.x* 7 10418 1.49 0.58 1.00 1.45
## avg_weekend_dur 8 4755 0.00 1.00 0.10 0.06
## avg_weekday_dur 9 4793 0.00 1.00 0.07 0.05
## avg_weekend_effic 10 4755 0.00 1.00 0.25 0.15
## avg_weekday_effic 11 4793 0.00 1.00 0.22 0.13
## variability 12 4788 0.00 1.00 -0.21 -0.11
## eventname_wave_2.y* 13 10418 2.00 0.02 2.00 2.00
## interview_age_wave_2 14 10414 144.04 7.95 144.00 143.94
## sex_wave_2* 15 10418 2.52 0.50 3.00 2.53
## cbcl_scr_syn_attention_r_wave_2 16 8085 2.70 3.31 1.00 2.09
## cbcl_scr_syn_internal_r_wave_2 17 8085 4.94 5.62 3.00 3.91
## cbcl_scr_syn_external_r_wave_2 18 8085 3.93 5.52 2.00 2.77
## visit_wave_2* 19 10418 2.00 0.02 2.00 2.00
## total_score_wave_2 20 10414 1.56 2.79 0.00 0.88
## distress_score_wave_2 21 10414 1.99 5.30 0.00 0.66
## rel_family_id 22 2963 6073.81 3469.91 5897.00 6023.14
## zyg 23 2963 2.33 0.79 3.00 2.41
## matched_subject* 24 10418 105.77 303.70 1.00 12.43
## rel_relationship 25 2963 1.47 0.50 1.00 1.46
## twin 26 1396 1.50 0.50 1.50 1.50
## eventname.y* 27 10190 1.00 0.00 1.00 1.00
## sex* 28 10190 1.52 0.50 2.00 1.53
## race_ethnicity 29 10187 2.01 1.33 1.00 1.76
## fam_avg_weekend_dur 30 9270 -0.01 0.40 -0.02 -0.02
## weekend_dur_diff 31 4755 0.00 0.88 0.00 0.04
## fam_avg_weekday_dur 32 9282 -0.01 0.41 -0.02 -0.02
## weekday_dur_diff 33 4793 0.00 0.87 0.00 0.03
## avg_weekend_dur_mcq 34 10408 0.00 0.40 -0.01 -0.01
## weekend_dur_mcq_diff 35 10054 0.00 0.92 0.04 0.04
## avg_weekday_dur_mcq 36 10408 0.00 0.43 0.00 0.01
## weekday_dur_mcq_diff 37 10054 0.00 0.90 0.07 0.06
## fam_avg_weekend_effic 38 9270 -0.01 0.33 -0.03 -0.03
## weekend_effic_diff 39 4755 0.00 0.92 0.08 0.12
## fam_avg_weekday_effic 40 9282 -0.01 0.37 -0.02 -0.02
## weekday_effic_diff 41 4793 0.00 0.90 0.07 0.10
## avg_variabilitiy 42 9282 0.01 0.41 0.01 0.01
## variabiltiy_diff 43 4788 0.00 0.88 -0.02 -0.08
## avg_chrono 44 10285 0.00 0.45 -0.02 -0.02
## chrono_diff 45 8771 0.00 0.90 0.00 0.00
## avg_jetlag 46 10408 0.00 0.45 0.01 -0.01
## jetlag_diff 47 10054 0.00 0.90 -0.08 -0.08
## sibDZ_MZ 48 2963 -0.13 0.40 -0.33 -0.20
## sib_DZ 49 2963 -0.13 0.43 -0.50 -0.16
## MZ_dummy_DZ 50 2963 0.27 0.44 0.00 0.21
## MZ_dummy_sib 51 2963 0.53 0.50 1.00 0.54
## DZ_dummy_MZ 52 2963 0.20 0.40 0.00 0.13
## DZ_dummy_sib 53 2963 0.53 0.50 1.00 0.54
## sib_dummy_MZ 54 2963 0.20 0.40 0.00 0.13
## sib_dummy_DZ 55 2963 0.27 0.44 0.00 0.21
## caff_intake 56 10317 1.95 5.12 0.60 0.99
## pubertal_score 57 9797 2.60 1.07 3.00 2.61
## race_ethnicity_fact* 58 10187 2.01 1.33 1.00 1.76
## int_SQRT 59 8085 1.83 1.27 1.73 1.76
## ext_SQRT 60 8085 1.48 1.32 1.41 1.34
## att_SQRT 61 8085 1.22 1.10 1.00 1.13
## psychosis_SQRT 62 10414 0.75 1.00 0.00 0.58
## INT_resid 63 7468 0.00 1.00 -0.03 -0.04
## EXT_resid 64 7468 0.00 1.00 -0.10 -0.09
## ATT_resid 65 7468 0.00 1.00 0.01 -0.06
## PSYCH_resid 66 9513 0.00 1.00 -0.52 -0.13
## covid 67 4793 0.02 0.15 0.00 0.00
## mad min max range skew
## subjectkey* 3861.43 1.00 10418.00 10417.00 0.00
## eventname_wave_2.x* 0.00 1.00 2.00 1.00 -5.28
## weekend_dur_mcq_wave_2 0.77 -4.86 2.96 7.82 -0.65
## weekday_dur_mcq_wave_2 0.79 -5.69 4.56 10.25 -1.03
## chronotype_wave_2 0.81 -1.35 1.30 2.65 0.06
## social_jet_lag_wave_2 0.77 -8.60 6.27 14.87 0.69
## eventname.x* 0.00 1.00 4.00 3.00 1.04
## avg_weekend_dur 0.86 -6.42 5.25 11.66 -0.77
## avg_weekday_dur 0.90 -5.90 3.40 9.30 -0.59
## avg_weekend_effic 0.64 -14.66 1.16 15.82 -3.65
## avg_weekday_effic 0.72 -13.15 1.54 14.69 -2.99
## variability 0.90 -1.92 5.62 7.54 1.10
## eventname_wave_2.y* 0.00 1.00 2.00 1.00 -51.00
## interview_age_wave_2 10.38 127.00 168.00 41.00 0.11
## sex_wave_2* 0.00 1.00 3.00 2.00 -0.10
## cbcl_scr_syn_attention_r_wave_2 1.48 0.00 19.00 19.00 1.51
## cbcl_scr_syn_internal_r_wave_2 4.45 0.00 50.00 50.00 2.04
## cbcl_scr_syn_external_r_wave_2 2.97 0.00 50.00 50.00 2.51
## visit_wave_2* 0.00 1.00 2.00 1.00 -51.00
## total_score_wave_2 0.00 0.00 21.00 21.00 2.56
## distress_score_wave_2 0.00 0.00 69.00 69.00 4.59
## rel_family_id 3891.82 1.00 11880.00 11879.00 0.16
## zyg 0.00 1.00 3.00 2.00 -0.65
## matched_subject* 0.00 1.00 1478.00 1477.00 3.01
## rel_relationship 0.00 1.00 3.00 2.00 0.14
## twin 0.74 1.00 2.00 1.00 0.00
## eventname.y* 0.00 1.00 1.00 0.00 NaN
## sex* 0.00 1.00 2.00 1.00 -0.09
## race_ethnicity 0.00 1.00 5.00 4.00 1.11
## fam_avg_weekend_dur 0.00 -3.93 2.53 6.46 -1.38
## weekend_dur_diff 0.65 -6.39 5.27 11.66 -0.82
## fam_avg_weekday_dur 0.00 -3.94 2.76 6.70 -1.26
## weekday_dur_diff 0.66 -5.88 3.42 9.30 -0.67
## avg_weekend_dur_mcq 0.00 -4.53 2.63 7.16 -0.46
## weekend_dur_mcq_diff 0.70 -4.84 2.98 7.82 -0.66
## avg_weekday_dur_mcq 0.00 -4.28 2.44 6.72 -1.83
## weekday_dur_mcq_diff 0.69 -5.69 4.21 9.90 -1.02
## fam_avg_weekend_effic 0.00 -6.25 1.16 7.41 -3.48
## weekend_effic_diff 0.61 -14.63 3.13 17.76 -4.19
## fam_avg_weekday_effic 0.00 -6.18 1.54 7.73 -4.19
## weekday_effic_diff 0.64 -13.13 4.33 17.46 -3.39
## avg_variabilitiy 0.00 -1.71 4.15 5.87 2.51
## variabiltiy_diff 0.72 -2.41 5.60 8.01 1.15
## avg_chrono 0.00 -1.29 1.21 2.50 0.29
## chrono_diff 1.46 -1.33 1.32 2.65 0.10
## avg_jetlag 0.00 -4.71 4.61 9.32 1.43
## jetlag_diff 0.69 -8.42 6.26 14.68 0.80
## sibDZ_MZ 0.00 -0.33 0.67 1.00 1.47
## sib_DZ 0.00 -0.50 0.50 1.00 0.53
## MZ_dummy_DZ 0.00 0.00 1.00 1.00 1.05
## MZ_dummy_sib 0.00 0.00 1.00 1.00 -0.12
## DZ_dummy_MZ 0.00 0.00 1.00 1.00 1.47
## DZ_dummy_sib 0.00 0.00 1.00 1.00 -0.12
## sib_dummy_MZ 0.00 0.00 1.00 1.00 1.47
## sib_dummy_DZ 0.00 0.00 1.00 1.00 1.05
## caff_intake 0.89 0.00 116.00 116.00 9.48
## pubertal_score 1.48 1.00 5.00 4.00 -0.09
## race_ethnicity_fact* 0.00 1.00 5.00 4.00 1.11
## int_SQRT 1.09 0.00 7.07 7.07 0.36
## ext_SQRT 1.83 0.00 7.07 7.07 0.67
## att_SQRT 1.48 0.00 4.36 4.36 0.35
## psychosis_SQRT 0.00 0.00 4.58 4.58 1.10
## INT_resid 0.96 -2.02 4.04 6.05 0.36
## EXT_resid 1.26 -2.47 4.09 6.56 0.66
## ATT_resid 1.36 -2.16 3.17 5.33 0.33
## PSYCH_resid 0.66 -2.23 3.93 6.15 1.05
## covid 0.00 0.00 1.00 1.00 6.13
## kurtosis se
## subjectkey* -1.20 29.47
## eventname_wave_2.x* 25.85 0.00
## weekend_dur_mcq_wave_2 1.82 0.01
## weekday_dur_mcq_wave_2 3.40 0.01
## chronotype_wave_2 -1.94 0.01
## social_jet_lag_wave_2 7.04 0.01
## eventname.x* 1.96 0.01
## avg_weekend_dur 2.31 0.01
## avg_weekday_dur 1.22 0.01
## avg_weekend_effic 28.57 0.01
## avg_weekday_effic 21.24 0.01
## variability 1.51 0.01
## eventname_wave_2.y* 2599.00 0.00
## interview_age_wave_2 -0.98 0.08
## sex_wave_2* -1.97 0.00
## cbcl_scr_syn_attention_r_wave_2 2.08 0.04
## cbcl_scr_syn_internal_r_wave_2 5.60 0.06
## cbcl_scr_syn_external_r_wave_2 8.50 0.06
## visit_wave_2* 2599.00 0.00
## total_score_wave_2 7.56 0.03
## distress_score_wave_2 28.10 0.05
## rel_family_id -1.07 63.75
## zyg -1.11 0.01
## matched_subject* 8.01 2.98
## rel_relationship -1.90 0.01
## twin -2.00 0.01
## eventname.y* NaN 0.00
## sex* -1.99 0.00
## race_ethnicity 0.03 0.01
## fam_avg_weekend_dur 19.71 0.00
## weekend_dur_diff 3.86 0.01
## fam_avg_weekday_dur 17.46 0.00
## weekday_dur_diff 2.58 0.01
## avg_weekend_dur_mcq 12.93 0.00
## weekend_dur_mcq_diff 2.47 0.01
## avg_weekday_dur_mcq 16.58 0.00
## weekday_dur_mcq_diff 4.18 0.01
## fam_avg_weekend_effic 46.32 0.00
## weekend_effic_diff 38.65 0.01
## fam_avg_weekday_effic 50.89 0.00
## weekday_effic_diff 29.97 0.01
## avg_variabilitiy 20.70 0.00
## variabiltiy_diff 2.58 0.01
## avg_chrono 2.18 0.00
## chrono_diff -1.68 0.01
## avg_jetlag 20.19 0.00
## jetlag_diff 7.83 0.01
## sibDZ_MZ 0.17 0.01
## sib_DZ -1.43 0.01
## MZ_dummy_DZ -0.90 0.01
## MZ_dummy_sib -1.99 0.01
## DZ_dummy_MZ 0.17 0.01
## DZ_dummy_sib -1.99 0.01
## sib_dummy_MZ 0.17 0.01
## sib_dummy_DZ -0.90 0.01
## caff_intake 131.87 0.05
## pubertal_score -1.04 0.01
## race_ethnicity_fact* 0.03 0.01
## int_SQRT -0.13 0.01
## ext_SQRT 0.01 0.01
## att_SQRT -0.97 0.01
## psychosis_SQRT 0.26 0.01
## INT_resid -0.10 0.01
## EXT_resid 0.08 0.01
## ATT_resid -0.89 0.01
## PSYCH_resid 0.36 0.01
## covid 35.63 0.00
social jet lag