# Analysis script
### Data curation ----
dt_isaac1 = read.csv("dt/isaac-ai_Report.xlsx - Variables.csv")
dt_isaac2 = read.csv("dt/isaac-ai_Report.xlsx - Answers.csv")
dt_isaac3 = subset(read.csv("dt/isaac-ai_Report.xlsx - Variables2.csv"), select = c(EMail,`X_bloodpressure`))
dt_isaac = merge(dt_isaac1, dt_isaac2, by = "Order.id")
dt_shc1 = read.csv("dt/shc-b2c_Report.xlsx - Variables.csv")
dt_shc2 = read.csv("dt/shc-b2c_Report.xlsx - Answers.csv")
dt_shc = merge(dt_shc1, dt_shc2, by = "Order.id")
dt_shc = subset(dt_shc, I.agree.that.data.collected.about.me.during.this.research.can.be.used.to.improve.the.services.offered.by.SleepHubs.Ltd.and.understand.that.said.data.will.be.anonymised.before.being.submitted.for.publication. == "I agree")
### Remove testing samples ----
dt_shc_removed_test = subset(dt_shc,
Your.first.name != "Chaz")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "chaz")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "TEST")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "TEST KATHRYN")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "Kathryn test")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "Kathryn TEST")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "Kathryn NEW TEST")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "test")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "Chaz test")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "Filip")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "ddddd")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "chaz+1")
dt_shc_removed_test = subset(dt_shc_removed_test,
Your.first.name != "Charlie")
### Select columns ----
dt_shc_sel_col = subset(dt_shc_removed_test, select =
c(EMail,
How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep.,
Are.you.happy.with.your.sleep.,
What.are.your.problems..You.can.select.any.combination.of.these.from.the.most.to.least.frequent...1...Biggest.problem.,
Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason.,
Has.anyone.ever.complained.about.your.sleep.,
What.are.the.complaints.about..You.can.select.any.combination.of.these.from.the.most.to.least.frequent...1...Biggest.complaint.,
What.other.sleep.problem.,
What.other.complaints.))
dt_shc_sel_col=dt_shc_sel_col[!duplicated(dt_shc_sel_col), ]
dt_shc_sel_col = dt_shc_sel_col[!duplicated(dt_shc_sel_col$EMail),]
dt_isaac_solvedSHC = subset(dt_isaac, SolvedSHC == "1")
# remove columns with all NAs
dt_isaac_solvedSHC_noNA = dt_isaac_solvedSHC[colSums(!is.na(dt_isaac_solvedSHC)) > 0]
dt_isaac_solvedSHC_noNA_unique = dt_isaac_solvedSHC_noNA[!duplicated(dt_isaac_solvedSHC_noNA$EMail),]
dt_shc_sel_col_addIsaac = merge(dt_shc_sel_col,
dt_isaac_solvedSHC_noNA_unique, by = "EMail")
write.csv(dt_isaac_solvedSHC_noNA_unique,"dt_out/dt_isaac_solvedSHC_noNA_unique_merged_dt.csv", row.names = F)
dt_isaac_solvedSHC_noNA_unique_addBP = merge(dt_shc_sel_col_addIsaac,
dt_isaac3[!duplicated(dt_isaac3), ], by = "EMail")
dt_isaac_solvedSHC_noNA_unique_addBP = dt_isaac_solvedSHC_noNA_unique_addBP[!duplicated(dt_isaac_solvedSHC_noNA_unique_addBP$EMail),]
write.csv(dt_isaac_solvedSHC_noNA_unique_addBP,"dt_out/dt_isaac_solvedSHC_noNA_unique_addBP_deduplicated.csv", row.names = F)
isi_vars = c(
"In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep",
"In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep",
"In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early",
"How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern.",
"To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc.",
"How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life.",
"How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem.")
SHC_vars = c(
"How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep.",
"Are.you.happy.with.your.sleep.",
"Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason.",
"Has.anyone.ever.complained.about.your.sleep.",
"What.are.your.problems..You.can.select.any.combination.of.these.from.the.most.to.least.frequent...1...Biggest.problem.",
"What.other.sleep.problem.",
"What.are.the.complaints.about..You.can.select.any.combination.of.these.from.the.most.to.least.frequent...1...Biggest.complaint.")
SB_vars = c(
"Do.you.snore.",
"How.often.do.you.feel.tired.or.fatigued.after.your.sleep.",
"Has.anyone.ever.noticed.that.you.stop.breathing.during.your.sleep.",
"Age",
"Gender",
"BMI",
"X_bloodpressure",
"Is.your.neck.size.shirt.collar.16.inches.41cm.or.larger")
MAPI_vars = c("Loud.snoring",
"Snorting.or.gasping.for.breath",
"Your.breathing.stops.or.you.choke.or.struggle",
"Do.you.snore.",
"BMI",'Age',"Gender")
# Get dataset for all combined variables
all_vars = c("EMail",isi_vars,SB_vars,SHC_vars,MAPI_vars)
dt_isaac_solvedSHC_noNA_unique_addBP$X_bloodpressure[is.na(dt_isaac_solvedSHC_noNA_unique_addBP$X_bloodpressure)] = 0
all_dt_target_variables = subset(dt_isaac_solvedSHC_noNA_unique_addBP, select = all_vars)
# Create a function: rotate through all mapping variables, find the correct one, then replace
ISI_pattern = c("","Never","Rarely (less than once a week)",
"Sometimes (1-2 times a week)",
"Frequently (3-4 times a week)",
"Always (5-7 times a week)",
"Very Satisfied","Satisfied",
"Moderately Satisfied",
"Dissatisfied","Very Dissatisfied",
"Not at all Worried","A Little","Somewhat","Very","Extremely",
"Not at all Noticeable",
"Not at all Interfering")
MAPI_pattern = c("No","Never", "Don't know",
"Rarely (less than once a week)","Yes",
"Sometimes (1-2 times a week)",
"Frequently (3-4 times a week)",
"Always (5-7 times a week)")
SB_pattern = c("Don't know","Never or nearly never",
"1-2 times per month",
"1-2 times per week",
"3-4 times per week",
"Nearly every day",
"Nearly every night")
SHC_pattern = c("Always","Regularly","Sometimes","Never")
ISI_pattern_replace = c(NA,0,1,2,3,4,
0,1,2,3,4,
0,1,2,3,4,
0,
0)
MAPI_pattern_replace = c(0,0,0,
1,1,
2,
3,
4)
SB_pattern_replace = c(0,0,1,2,3,4,4)
SHC_pattern_replace = c(3,2,1,0)
all_test_pattern = c(ISI_pattern,SB_pattern,MAPI_pattern,SHC_pattern)
all_test_replace = c(ISI_pattern_replace,SB_pattern_replace,MAPI_pattern_replace,SHC_pattern_replace)
curate_all_test_vars = Vectorize(function(df_element){
for (pattern_i in 1:length(all_test_pattern)){
if (df_element == all_test_pattern[pattern_i]){
replacement = all_test_replace[pattern_i]
return(replacement)
}
else {replacement = NULL}
}
ifelse(is.null(replacement), yes = return(df_element), no = NA)
})
all_dt_target_variables_curated = all_dt_target_variables
all_dt_target_variables_curated[] = sapply(all_dt_target_variables_curated,curate_all_test_vars)
write.csv(all_dt_target_variables_curated,"dt_out/all_dt_target_variables_curated.csv",row.names = F)
#Generate MAPI score
all_dt_target_variables_curated$index1 = (as.numeric(all_dt_target_variables_curated$Loud.snoring) +
as.numeric(all_dt_target_variables_curated$Snorting.or.gasping.for.breath) +
as.numeric(all_dt_target_variables_curated$Your.breathing.stops.or.you.choke.or.struggle))/3
all_dt_target_variables_curated$index1[all_dt_target_variables_curated$Do.you.snore.==0] <- 0
all_dt_target_variables_curated$MAPI = exp(
-8.16+(1.299 * all_dt_target_variables_curated$index1)+
(0.163 * as.numeric(all_dt_target_variables_curated$BMI)) - 0.028 *all_dt_target_variables_curated$index1 * as.numeric(all_dt_target_variables_curated$BMI) +
0.032 * as.numeric(all_dt_target_variables_curated$Age) +
1.278 * as.numeric(all_dt_target_variables_curated$Gender))/(1+exp(-8.16+(1.299 * all_dt_target_variables_curated$index1)+
(0.163 * as.numeric(all_dt_target_variables_curated$BMI)) -
0.028 *all_dt_target_variables_curated$index1 *
as.numeric(all_dt_target_variables_curated$BMI) +
0.032 * as.numeric(all_dt_target_variables_curated$Age) +
1.278 * as.numeric(all_dt_target_variables_curated$Gender)))*100
# Generate STOPBANG score
all_dt_target_variables_curated$Has.anyone.ever.noticed.that.you.stop.breathing.during.your.sleep.[is.na(all_dt_target_variables_curated$Has.anyone.ever.noticed.that.you.stop.breathing.during.your.sleep.)] <- 0
# no one replied yes to Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason.
all_dt_target_variables_curated$STOPBANG =
as.numeric(all_dt_target_variables_curated$Do.you.snore.) +
#as.numeric(all_dt_target_variables_curated$Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason.) +
as.numeric(all_dt_target_variables_curated$Has.anyone.ever.noticed.that.you.stop.breathing.during.your.sleep.) +
as.numeric(all_dt_target_variables_curated$X_bloodpressure) +
ifelse(as.numeric(all_dt_target_variables_curated$BMI) > 35,1,0) +
as.numeric(all_dt_target_variables_curated$Is.your.neck.size.shirt.collar.16.inches.41cm.or.larger) +
as.numeric(all_dt_target_variables_curated$Gender)
# Generate ISI score
all_dt_target_variables_curated$ISI =
as.numeric(all_dt_target_variables_curated$How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern.) +
as.numeric(all_dt_target_variables_curated$To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc.) +
as.numeric(all_dt_target_variables_curated$How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life.) +
as.numeric(all_dt_target_variables_curated$How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem.) +
as.numeric(all_dt_target_variables_curated$In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep) +
as.numeric(all_dt_target_variables_curated$In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early) +
as.numeric(all_dt_target_variables_curated$In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep)
write.csv(all_dt_target_variables_curated,"dt_out/all_dt_target_variables_curated_added_tests.csv",row.names = F)
### Make plotting fuction ----
yy_plt_forest_lm = function(lm_model, savePDF = TRUE,saveCSV = TRUE){
coef_mat = as.data.frame(summary(lm_model)$coefficients)
coef_mat = na.omit(coef_mat)
conf = na.omit(confint.default(lm_model, level = 0.95))
coef_mat$lci = conf[,1]
coef_mat$uci = conf[,2]
coef_mat$label = row.names(coef_mat)
coef_mat = subset(coef_mat, label != "(Intercept)")
library(ggplot2)
out_plt = ggplot(data=coef_mat, aes(x=label,y=Estimate, fill = `Pr(>|t|)` < 0.05,
color = `Pr(>|t|)` < 0.05)) +
geom_errorbar(aes(ymin=lci, ymax=uci),
width=0, # Width of the error bars
position=position_dodge(.9), color = "grey", size = 1.5) +
geom_point(shape=21, size = 2.5)+
#geom_pointrange(aes(fill = `Pr(>|t|)` < 0.05)) +
geom_hline(yintercept=0, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("") + ylab("Effect (95% CI)") +
scale_fill_manual(values = hcl.colors(18, "ag_Sunset"))+
scale_color_manual(values = hcl.colors(18, "ag_Sunset"))+
theme_classic()+
theme(axis.text.x=element_text(colour="black"),
axis.text.y=element_text(colour="black")) +
theme(text=element_text(family="Arial", size=14))
out_plt
if(isTRUE(savePDF)) {
save_name_pdf = paste0("fig/",deparse(substitute(lm_model)),"_figure.pdf")
ggsave(save_name_pdf,plot = out_plt,device = cairo_pdf, width = 6, height = 4)
}
if(isTRUE(saveCSV)) {
save_name_csv = paste0("dt_out/",deparse(substitute(lm_model)),"_summary.csv")
write.csv(coef_mat,save_name_csv)
}
}
### Analysis - General scores ----
curated_SB_MAPI_ISI_scaled = subset(all_dt_target_variables_curated,
select = c(MAPI, STOPBANG, ISI, Age, Gender))
curated_SB_MAPI_ISI_scaled = as.data.frame(scale(dplyr::mutate_all(curated_SB_MAPI_ISI_scaled, function(x) as.numeric(as.character(x)))))
MAPI_ISI_lm = lm(data = curated_SB_MAPI_ISI_scaled,
MAPI ~ ISI+Age+Gender)
summary(MAPI_ISI_lm)
##
## Call:
## lm(formula = MAPI ~ ISI + Age + Gender, data = curated_SB_MAPI_ISI_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6684 -0.4918 -0.2221 0.2599 3.6742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.002066 0.022371 0.092 0.926
## ISI 0.119056 0.022740 5.235 1.92e-07 ***
## Age 0.280332 0.023808 11.775 < 2e-16 ***
## Gender 0.467090 0.024008 19.456 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7978 on 1268 degrees of freedom
## (84 observations deleted due to missingness)
## Multiple R-squared: 0.3713, Adjusted R-squared: 0.3698
## F-statistic: 249.6 on 3 and 1268 DF, p-value: < 2.2e-16
yy_plt_forest_lm(MAPI_ISI_lm)
ISI_MAPI_lm = lm(data = curated_SB_MAPI_ISI_scaled,
ISI ~ MAPI+as.numeric(Age)+Gender)
summary(ISI_MAPI_lm)
##
## Call:
## lm(formula = ISI ~ MAPI + as.numeric(Age) + Gender, data = curated_SB_MAPI_ISI_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.70267 -0.69807 0.01231 0.71538 2.48375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0006377 0.0273326 0.023 0.981
## MAPI 0.1777283 0.0339469 5.235 1.92e-07 ***
## as.numeric(Age) -0.0498340 0.0306054 -1.628 0.104
## Gender -0.2564307 0.0326405 -7.856 8.39e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9748 on 1268 degrees of freedom
## (84 observations deleted due to missingness)
## Multiple R-squared: 0.05205, Adjusted R-squared: 0.04981
## F-statistic: 23.21 on 3 and 1268 DF, p-value: 1.26e-14
yy_plt_forest_lm(ISI_MAPI_lm)
STOPBANG_ISI_lm = lm(data = curated_SB_MAPI_ISI_scaled,
STOPBANG ~ ISI+as.numeric(Age)+Gender)
summary(STOPBANG_ISI_lm)
##
## Call:
## lm(formula = STOPBANG ~ ISI + as.numeric(Age) + Gender, data = curated_SB_MAPI_ISI_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4076 -0.6650 -0.2727 0.2361 4.8802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.30540 0.08484 3.600 0.000336 ***
## ISI 0.12092 0.03312 3.651 0.000277 ***
## as.numeric(Age) 0.16538 0.03648 4.533 6.59e-06 ***
## Gender 0.47875 0.13045 3.670 0.000257 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9728 on 908 degrees of freedom
## (444 observations deleted due to missingness)
## Multiple R-squared: 0.05426, Adjusted R-squared: 0.05114
## F-statistic: 17.37 on 3 and 908 DF, p-value: 5.71e-11
yy_plt_forest_lm(STOPBANG_ISI_lm)
ISI_STOPBANG_lm = lm(data = curated_SB_MAPI_ISI_scaled,
ISI ~ STOPBANG+as.numeric(Age)+Gender)
summary(ISI_STOPBANG_lm)
##
## Call:
## lm(formula = ISI ~ STOPBANG + as.numeric(Age) + Gender, data = curated_SB_MAPI_ISI_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.45252 -0.67110 -0.01415 0.70499 2.55655
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.17761 0.08479 -2.095 0.036469 *
## STOPBANG 0.11963 0.03277 3.651 0.000277 ***
## as.numeric(Age) 0.03974 0.03667 1.084 0.278750
## Gender -0.47348 0.12977 -3.649 0.000279 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9676 on 908 degrees of freedom
## (444 observations deleted due to missingness)
## Multiple R-squared: 0.0273, Adjusted R-squared: 0.02408
## F-statistic: 8.494 on 3 and 908 DF, p-value: 1.44e-05
yy_plt_forest_lm(ISI_STOPBANG_lm)
### Analysis - Sub-levels ----
sublevels_vars = c(MAPI_vars,isi_vars,SB_vars, "Age", "Gender", "ISI","STOPBANG","MAPI")
curated_SB_MAPI_ISI_sublevels_scaled = subset(all_dt_target_variables_curated,
select = sublevels_vars)
curated_SB_MAPI_ISI_sublevels_scaled = as.data.frame(scale(dplyr::mutate_all(curated_SB_MAPI_ISI_sublevels_scaled, function(x) as.numeric(as.character(x)))))
# apnoea variables on insomnia
ISI_MAPISB_all_vars_lm_formula = paste("ISI ~", paste(MAPI_vars,collapse=" + "), " + ", paste(SB_vars,collapse=" + "))
ISI_MAPISB_all_vars_lm = lm(data = curated_SB_MAPI_ISI_sublevels_scaled,
formula = ISI_MAPISB_all_vars_lm_formula)
summary(ISI_MAPISB_all_vars_lm)
##
## Call:
## lm(formula = ISI_MAPISB_all_vars_lm_formula, data = curated_SB_MAPI_ISI_sublevels_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.69352 -0.56156 0.03546 0.51656 2.22449
##
## Coefficients: (1 not defined because of singularities)
## Estimate
## (Intercept) 0.028980
## Loud.snoring 0.010658
## Snorting.or.gasping.for.breath 0.050293
## Your.breathing.stops.or.you.choke.or.struggle 0.061559
## Do.you.snore. NA
## BMI 0.141539
## Age 0.062302
## Gender -0.044614
## How.often.do.you.feel.tired.or.fatigued.after.your.sleep. 0.540074
## Has.anyone.ever.noticed.that.you.stop.breathing.during.your.sleep. -0.035540
## X_bloodpressure -0.005045
## Is.your.neck.size.shirt.collar.16.inches.41cm.or.larger -0.122339
## Std. Error
## (Intercept) 0.098211
## Loud.snoring 0.042365
## Snorting.or.gasping.for.breath 0.056377
## Your.breathing.stops.or.you.choke.or.struggle 0.061368
## Do.you.snore. NA
## BMI 0.039627
## Age 0.049314
## Gender 0.148437
## How.often.do.you.feel.tired.or.fatigued.after.your.sleep. 0.044749
## Has.anyone.ever.noticed.that.you.stop.breathing.during.your.sleep. 0.053823
## X_bloodpressure 0.043167
## Is.your.neck.size.shirt.collar.16.inches.41cm.or.larger 0.039132
## t value
## (Intercept) 0.295
## Loud.snoring 0.252
## Snorting.or.gasping.for.breath 0.892
## Your.breathing.stops.or.you.choke.or.struggle 1.003
## Do.you.snore. NA
## BMI 3.572
## Age 1.263
## Gender -0.301
## How.often.do.you.feel.tired.or.fatigued.after.your.sleep. 12.069
## Has.anyone.ever.noticed.that.you.stop.breathing.during.your.sleep. -0.660
## X_bloodpressure -0.117
## Is.your.neck.size.shirt.collar.16.inches.41cm.or.larger -3.126
## Pr(>|t|)
## (Intercept) 0.768085
## Loud.snoring 0.801502
## Snorting.or.gasping.for.breath 0.372891
## Your.breathing.stops.or.you.choke.or.struggle 0.316413
## Do.you.snore. NA
## BMI 0.000398 ***
## Age 0.207195
## Gender 0.763910
## How.often.do.you.feel.tired.or.fatigued.after.your.sleep. < 2e-16 ***
## Has.anyone.ever.noticed.that.you.stop.breathing.during.your.sleep. 0.509439
## X_bloodpressure 0.907026
## Is.your.neck.size.shirt.collar.16.inches.41cm.or.larger 0.001900 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8177 on 397 degrees of freedom
## (948 observations deleted due to missingness)
## Multiple R-squared: 0.3263, Adjusted R-squared: 0.3093
## F-statistic: 19.23 on 10 and 397 DF, p-value: < 2.2e-16
yy_plt_forest_lm(ISI_MAPISB_all_vars_lm)
#insomnia variables on stopbang and mapi
MAPI_ISIall_vars_lm_formula = paste("MAPI ~", paste(isi_vars,collapse=" + "), " + Age + Gender")
MAPI_ISIall_vars_lm = lm(data = curated_SB_MAPI_ISI_sublevels_scaled,
formula = MAPI_ISIall_vars_lm_formula)
summary(MAPI_ISIall_vars_lm)
##
## Call:
## lm(formula = MAPI_ISIall_vars_lm_formula, data = curated_SB_MAPI_ISI_sublevels_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6298 -0.4770 -0.2195 0.2550 3.6577
##
## Coefficients:
## Estimate
## (Intercept) 0.002071
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep -0.046853
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep -0.012850
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early 0.009296
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern. 0.025225
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc. 0.027702
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. 0.134136
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem. 0.005690
## Age 0.292904
## Gender 0.448448
## Std. Error
## (Intercept) 0.022236
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep 0.027496
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep 0.031521
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early 0.028032
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern. 0.036229
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc. 0.037208
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. 0.035042
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem. 0.040167
## Age 0.024691
## Gender 0.024452
## t value
## (Intercept) 0.093
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep -1.704
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep -0.408
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early 0.332
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern. 0.696
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc. 0.745
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. 3.828
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem. 0.142
## Age 11.863
## Gender 18.340
## Pr(>|t|)
## (Intercept) 0.925805
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep 0.088635
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep 0.683599
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early 0.740216
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern. 0.486386
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc. 0.456695
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. 0.000136
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem. 0.887373
## Age < 2e-16
## Gender < 2e-16
##
## (Intercept)
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep .
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern.
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc.
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. ***
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem.
## Age ***
## Gender ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.793 on 1262 degrees of freedom
## (84 observations deleted due to missingness)
## Multiple R-squared: 0.3817, Adjusted R-squared: 0.3773
## F-statistic: 86.58 on 9 and 1262 DF, p-value: < 2.2e-16
yy_plt_forest_lm(MAPI_ISIall_vars_lm)
ISI_SBall_vars_lm_formula = paste("STOPBANG ~", paste(isi_vars,collapse=" + "), " + Age + Gender")
ISI_SBall_vars_lm = lm(data = curated_SB_MAPI_ISI_sublevels_scaled,
formula = ISI_SBall_vars_lm_formula)
summary(ISI_SBall_vars_lm)
##
## Call:
## lm(formula = ISI_SBall_vars_lm_formula, data = curated_SB_MAPI_ISI_sublevels_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4180 -0.6253 -0.2837 0.3039 4.8514
##
## Coefficients:
## Estimate
## (Intercept) 0.319269
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep 0.005442
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep -0.059532
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early -0.007877
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern. 0.043155
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc. 0.041509
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. 0.195312
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem. -0.067450
## Age 0.202841
## Gender 0.478452
## Std. Error
## (Intercept) 0.084517
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep 0.039581
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep 0.044682
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early 0.039331
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern. 0.053735
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc. 0.052138
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. 0.049872
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem. 0.055871
## Age 0.037516
## Gender 0.130204
## t value
## (Intercept) 3.778
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep 0.137
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep -1.332
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early -0.200
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern. 0.803
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc. 0.796
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. 3.916
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem. -1.207
## Age 5.407
## Gender 3.675
## Pr(>|t|)
## (Intercept) 0.000169
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep 0.890683
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep 0.183083
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early 0.841321
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern. 0.422131
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc. 0.426167
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. 9.67e-05
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem. 0.227660
## Age 8.22e-08
## Gender 0.000252
##
## (Intercept) ***
## In.the.last.2.weeks..please.select.how.many.times.it.has.been.difficult.for.you.to.fall.asleep
## In.the.last.2.weeks.please.select.how.difficult.it.has.been.for.you.to.stay.asleep
## In.the.last.2.weeks.please.select.how.many.times.you.have.woken.too.early
## How.SATISFIED.DISSATISFIED.are.you.with.your.CURRENT.sleep.pattern.
## To.what.extent.do.your.sleep.problems.INTERFERE.with.your.daily.functioning.e.g..daytime.fatigue..mood..ability.to.function.at.work.daily.chores..concentration..memory..mood..etc.
## How.NOTICEABLE.to.others.do.you.think.your.sleep.problem.is.in.terms.of.impairing.the.quality.of.your.life. ***
## How.WORRIED.DISTRESSED.are.you.about.your.current.sleep.problem.
## Age ***
## Gender ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9641 on 902 degrees of freedom
## (444 observations deleted due to missingness)
## Multiple R-squared: 0.07716, Adjusted R-squared: 0.06795
## F-statistic: 8.379 on 9 and 902 DF, p-value: 4.476e-12
yy_plt_forest_lm(ISI_SBall_vars_lm)
stats::cor.test(curated_SB_MAPI_ISI_sublevels_scaled$STOPBANG,curated_SB_MAPI_ISI_sublevels_scaled$ISI, method = "spearman")
## Warning in cor.test.default(curated_SB_MAPI_ISI_sublevels_scaled$STOPBANG, :
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: curated_SB_MAPI_ISI_sublevels_scaled$STOPBANG and curated_SB_MAPI_ISI_sublevels_scaled$ISI
## S = 112607793, p-value = 0.0009467
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1092913
ggplot(curated_SB_MAPI_ISI_sublevels_scaled,aes(x=ISI,y=STOPBANG)) +
#geom_point()+
geom_smooth(method = "lm")+
theme_classic()
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 444 rows containing non-finite values (stat_smooth).
# network plot
network1_vars = unique(c(MAPI_vars,isi_vars,SB_vars))
network1_vars = network1_vars[network1_vars != "Do.you.snore."]
curated_SB_MAPI_ISI_network_scaled = subset(all_dt_target_variables_curated,
select = network1_vars)
curated_SB_MAPI_ISI_network_scaled = as.data.frame(scale(dplyr::mutate_all(curated_SB_MAPI_ISI_network_scaled, function(x) as.numeric(as.character(x)))))
cor_matrix_network1 = psych::corr.test(na.omit(curated_SB_MAPI_ISI_network_scaled), adjust = "none", method = "spearman")
cor_matrix_network1_diag = cor_matrix_network1$p
cor_matrix_network1_diag[cor_matrix_network1_diag >= 0.05/length(cor_matrix_network1$p)] <- NA
cor_matrix_network1_diag[cor_matrix_network1_diag == 0] <- min(cor_matrix_network1_diag)
cor_matrix_network1_diag = -log2(as.matrix(cor_matrix_network1_diag))
circlize::chordDiagram(cor_matrix_network1_diag, annotationTrack = c("name","grid"), grid.col =hcl.colors(ncol(cor_matrix_network1_diag), "ag_Sunset"),annotationTrackHeight = c(0.04, 0.02))
library(corrr)
network_plot(correlate(curated_SB_MAPI_ISI_network_scaled, method = "spearman"),min_cor = .5)
## Correlation computed with
## • Method: 'spearman'
## • Missing treated using: 'pairwise.complete.obs'
### Analysis of SHC variables to additionally predict ISI and MAPI/STOPBANG ----
SHC_vars_sim = c(
"How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep.",
"Are.you.happy.with.your.sleep.",
"Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason.",
"Has.anyone.ever.complained.about.your.sleep.")
sublevels_SHC_vars = unique(c(MAPI_vars,isi_vars,SB_vars, SHC_vars_sim, "ISI","STOPBANG","MAPI"))
curated_SB_MAPI_ISI_SHC_scaled = subset(all_dt_target_variables_curated,
select = sublevels_SHC_vars)
curated_SB_MAPI_ISI_SHC_scaled = as.data.frame(scale(dplyr::mutate_all(curated_SB_MAPI_ISI_SHC_scaled, function(x) as.numeric(as.character(x)))))
ISI_SHCvars_lm_formula = paste("ISI ~", paste(SHC_vars_sim,collapse=" + "), " + Age + Gender")
SB_SHCvars_lm_formula = paste("STOPBANG ~", paste(SHC_vars_sim,collapse=" + "), " + Age + Gender")
MAPI_SHCvars_lm_formula = paste("MAPI ~", paste(SHC_vars_sim,collapse=" + "), " + Age + Gender")
ISI_SHCvars_lm = lm(data = curated_SB_MAPI_ISI_SHC_scaled,
formula = ISI_SHCvars_lm_formula)
summary(ISI_SHCvars_lm)
##
## Call:
## lm(formula = ISI_SHCvars_lm_formula, data = curated_SB_MAPI_ISI_SHC_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.98418 -0.44861 -0.00163 0.44331 3.01731
##
## Coefficients:
## Estimate
## (Intercept) 0.003239
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 0.186845
## Are.you.happy.with.your.sleep. -0.573854
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 0.021476
## Has.anyone.ever.complained.about.your.sleep. 0.126026
## Age -0.007167
## Gender -0.056722
## Std. Error
## (Intercept) 0.018972
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 0.022162
## Are.you.happy.with.your.sleep. 0.021910
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 0.020547
## Has.anyone.ever.complained.about.your.sleep. 0.020270
## Age 0.020423
## Gender 0.020720
## t value
## (Intercept) 0.171
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 8.431
## Are.you.happy.with.your.sleep. -26.191
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 1.045
## Has.anyone.ever.complained.about.your.sleep. 6.217
## Age -0.351
## Gender -2.737
## Pr(>|t|)
## (Intercept) 0.86447
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. < 2e-16
## Are.you.happy.with.your.sleep. < 2e-16
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 0.29613
## Has.anyone.ever.complained.about.your.sleep. 6.85e-10
## Age 0.72568
## Gender 0.00628
##
## (Intercept)
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. ***
## Are.you.happy.with.your.sleep. ***
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason.
## Has.anyone.ever.complained.about.your.sleep. ***
## Age
## Gender **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6765 on 1265 degrees of freedom
## (84 observations deleted due to missingness)
## Multiple R-squared: 0.5445, Adjusted R-squared: 0.5423
## F-statistic: 252 on 6 and 1265 DF, p-value: < 2.2e-16
yy_plt_forest_lm(ISI_SHCvars_lm)
SB_SHCvars_lm = lm(data = curated_SB_MAPI_ISI_SHC_scaled,
formula = SB_SHCvars_lm_formula)
summary(SB_SHCvars_lm)
##
## Call:
## lm(formula = SB_SHCvars_lm_formula, data = curated_SB_MAPI_ISI_SHC_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3222 -0.5195 -0.2315 0.3677 4.7871
##
## Coefficients:
## Estimate
## (Intercept) 0.38238
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 0.09372
## Are.you.happy.with.your.sleep. 0.05029
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 0.13162
## Has.anyone.ever.complained.about.your.sleep. 0.33303
## Age 0.20027
## Gender 0.53497
## Std. Error
## (Intercept) 0.07836
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 0.03415
## Are.you.happy.with.your.sleep. 0.03447
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 0.03041
## Has.anyone.ever.complained.about.your.sleep. 0.03095
## Age 0.03287
## Gender 0.12071
## t value
## (Intercept) 4.880
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 2.744
## Are.you.happy.with.your.sleep. 1.459
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 4.328
## Has.anyone.ever.complained.about.your.sleep. 10.760
## Age 6.092
## Gender 4.432
## Pr(>|t|)
## (Intercept) 1.25e-06
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 0.00618
## Are.you.happy.with.your.sleep. 0.14491
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 1.67e-05
## Has.anyone.ever.complained.about.your.sleep. < 2e-16
## Age 1.62e-09
## Gender 1.04e-05
##
## (Intercept) ***
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. **
## Are.you.happy.with.your.sleep.
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. ***
## Has.anyone.ever.complained.about.your.sleep. ***
## Age ***
## Gender ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.897 on 953 degrees of freedom
## (396 observations deleted due to missingness)
## Multiple R-squared: 0.2005, Adjusted R-squared: 0.1954
## F-statistic: 39.82 on 6 and 953 DF, p-value: < 2.2e-16
yy_plt_forest_lm(SB_SHCvars_lm)
MAPI_SHCvars_lm = lm(data = curated_SB_MAPI_ISI_SHC_scaled,
formula = MAPI_SHCvars_lm_formula)
summary(MAPI_SHCvars_lm)
##
## Call:
## lm(formula = MAPI_SHCvars_lm_formula, data = curated_SB_MAPI_ISI_SHC_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0381 -0.4630 -0.1714 0.2457 3.4830
##
## Coefficients:
## Estimate
## (Intercept) 0.0007624
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 0.0851261
## Are.you.happy.with.your.sleep. 0.0103611
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 0.1034736
## Has.anyone.ever.complained.about.your.sleep. 0.2019033
## Age 0.2950866
## Gender 0.4322187
## Std. Error
## (Intercept) 0.0206630
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 0.0242117
## Are.you.happy.with.your.sleep. 0.0237721
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 0.0223140
## Has.anyone.ever.complained.about.your.sleep. 0.0220572
## Age 0.0220955
## Gender 0.0226409
## t value
## (Intercept) 0.037
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 3.516
## Are.you.happy.with.your.sleep. 0.436
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 4.637
## Has.anyone.ever.complained.about.your.sleep. 9.154
## Age 13.355
## Gender 19.090
## Pr(>|t|)
## (Intercept) 0.970571
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. 0.000453
## Are.you.happy.with.your.sleep. 0.663015
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. 3.88e-06
## Has.anyone.ever.complained.about.your.sleep. < 2e-16
## Age < 2e-16
## Gender < 2e-16
##
## (Intercept)
## How.do.you.feel.during.the.day..Thinking.over.the.past.week..how.tired.have.you.felt.during.the.day..1...extremely.alert..full.of.energy..9...very.sleepy..great.effort.to.stay.awake..fighting.sleep. ***
## Are.you.happy.with.your.sleep.
## Do.you..nod.off...doze.or.fall.asleep.during.the.day.without.obvious.reason. ***
## Has.anyone.ever.complained.about.your.sleep. ***
## Age ***
## Gender ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7544 on 1326 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.4335, Adjusted R-squared: 0.4309
## F-statistic: 169.1 on 6 and 1326 DF, p-value: < 2.2e-16
yy_plt_forest_lm(MAPI_SHCvars_lm)