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data-analysis-multiple-imputation.R
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data-analysis-multiple-imputation.R
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# This file contains code to perform analysis with mulyiple imputed data for
# a given aim. Estimation of model parameters is done by
# primary_and_secondary_analysis.R
# -----------------------------------------------------------------------------
# Preliminary steps: Read in file paths and data for analysis
# -----------------------------------------------------------------------------
path.code <- Sys.getenv("path.code") # Location of all code
path.dataforanalysis <- Sys.getenv("path.dataforanalysis") # Location of files to be used for data analysis
path.analysis <- Sys.getenv("path.analysis")
this.participant.withdrew <- Sys.getenv("this.participant.withdrew")
source(file.path(path.code, "main-utils.R"))
source(file.path(path.analysis, "primary_and_secondary_analysis.R"))
# Aim 1: Read in data for analysis
#dataforanalysis.aim1 <- read.csv(file.path(path.dataforanalysis,"dataforanalysis.aim1.csv"))
#dataforanalysis.aim1$username <- as.character(dataforanalysis.aim1$username)
# Aim 2: Read in data for analysis
#dataforanalysis.aim2 <- read.csv(file.path(path.dataforanalysis,"dataforanalysis.aim2.csv"))
#dataforanalysis.aim2$username <- as.character(dataforanalysis.aim2$username)
# Aim 4: Read in data for analysis
#dataforanalysis.aim4 <- read.csv(file.path(path.dataforanalysis,"dataforanalysis.aim4.csv"))
#dataforanalysis.aim4$username <- as.character(dataforanalysis.aim4$username)
# -----------------------------------------------------------------------------
# Portion of code to change: uncomment section corresponding to desired aim
# -----------------------------------------------------------------------------
# Aim 1 -------------------------------
#dataforanalysis.aimX <- dataforanalysis.aim1
# In data analyses, study_day is zero-indexed
#dataforanalysis.aimX$study_day <- dataforanalysis.aimX$study_day - 1
#dataforanalysis.aimX$study_day_squared <- (dataforanalysis.aimX$study_day)^2
#drop.criteria.aimX <- NULL
# my_outcome_variable <- "isCompleted"
# Aim 2 -------------------------------
#dataforanalysis.aimX <- dataforanalysis.aim2
# In data analyses, study_day is zero-indexed
#dataforanalysis.aimX$study_day <- dataforanalysis.aimX$study_day - 1
#dataforanalysis.aimX$study_day_squared <- (dataforanalysis.aimX$study_day)^2
#drop.criteria.aimX <- (dataforanalysis.aimX[,"username"] != this.participant.withdrew & dataforanalysis.aimX[,"study_day"] == 28) |
# (dataforanalysis.aimX[,"username"] == this.participant.withdrew & dataforanalysis.aimX[,"study_day"] == 10) |
# (dataforanalysis.aimX[,"memegifbug"] == 1)
# my_outcome_variable <- "isCompleted_tomorrow"
# Aim 4 -------------------------------
# Drop study_day 1 and 2: mathematically equivalent to setting availability=0 for these study days
#dataforanalysis.aim4$availability <- replace(dataforanalysis.aim4$availability,
# (dataforanalysis.aim4$study_day==1) | (dataforanalysis.aim4$study_day==2) ,
# 0)
#dataforanalysis.aimX <- dataforanalysis.aim4
# In data analyses, study_day is zero-indexed
#dataforanalysis.aimX$study_day <- dataforanalysis.aimX$study_day - 1
#dataforanalysis.aimX$study_day_squared <- (dataforanalysis.aimX$study_day)^2
#drop.criteria.aimX <- (dataforanalysis.aimX[,"username"] != this.participant.withdrew & dataforanalysis.aimX[,"study_day"] == 28) |
# (dataforanalysis.aimX[,"username"] == this.participant.withdrew & dataforanalysis.aimX[,"study_day"] == 10) |
# (dataforanalysis.aimX[,"memegifbug"] == 1)
# my_outcome_variable <- "isCompleted_tomorrow"
# -----------------------------------------------------------------------------
# Create new time variables, create data for complete case analysis and for
# analysis with multiply imputed data
# -----------------------------------------------------------------------------
dataforanalysis.aimX$day.of.week <- strptime(dataforanalysis.aimX$calendar_date, format = "%m/%d/%Y", tz = "EST5EDT")
dataforanalysis.aimX$day.of.week <- strftime(dataforanalysis.aimX$day.of.week, format = "%u") # Monday = 1
dataforanalysis.aimX$weekend <- ifelse(dataforanalysis.aimX$day.of.week >=6, 1, 0)
dataforanalysis.aimX$sunday <- ifelse(dataforanalysis.aimX$day.of.week ==7, 1, 0)
# Multiply imputed data -------------------------------------------------------
# Generate 10 imputed datasets
all.seeds <- c(398783,737213,980124,201881,471247,809199,342341,895465,786324,213627)
m <- length(all.seeds)
mi.data.aimX <- mapply(ImputeInterventionAssignment,
use.this.seed = all.seeds,
MoreArgs = list(dataforanalysis.aimX = dataforanalysis.aimX),
SIMPLIFY = FALSE)
mi.data.aimX <- lapply(mi.data.aimX, FUN = function(dat){
return(dat[order(dat$username, dat$study_day),])
})
# Other parameters ------------------------------------------------------------
n <- length(unique(dataforanalysis.aimX$username))
# -----------------------------------------------------------------------------
# Data analysis: main analyses
# -----------------------------------------------------------------------------
if(my_outcome_variable == "isCompleted"){
# Aim 1 or Aim 3
mi.main.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
main.analysis.aimX <- SARA_primary_hypothesis_1(dta = this.imputed.data,
control_var = c("appusage_yes","isCompleted_yesterday_yes","contact_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = "isCompleted",
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.025)
return(main.analysis.aimX)
})
mi.main.aimX <- do.call(cbind, mi.main.aimX)
mi.main.aimX <- apply(mi.main.aimX, 2, unlist)
est.mi.main.aimX <- PoolEstimatesMI(inputs.beta = mi.main.aimX[c(1,6:9),],
inputs.stderr = mi.main.aimX[c(2, 10:13),],
n = n,
p = 1,
q = 4,
m = m,
p.val.one.sided = TRUE)
est.mi.main.aimX <- do.call(cbind, est.mi.main.aimX)
row.names(est.mi.main.aimX) <- c("beta","Intercept","appusage_yes","isCompleted_yesterday_yes","contact_yes")
}else if(my_outcome_variable == "isCompleted_tomorrow"){
# !is.null(drop.criteria.aimX)
# Aim 2 or Aim 4
mi.main.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
main.analysis.aimX <- SARA_primary_hypothesis_2(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
survey_completion_var = "isCompleted",
outcome_var = "isCompleted",
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.025)
return(main.analysis.aimX)
})
mi.main.aimX <- do.call(cbind, mi.main.aimX)
mi.main.aimX <- apply(mi.main.aimX, 2, unlist)
est.mi.main.aimX <- PoolEstimatesMI(inputs.beta = mi.main.aimX[c(1,6:9),],
inputs.stderr = mi.main.aimX[c(2, 10:13),],
n = n,
p = 1,
q = 4,
m = m,
p.val.one.sided = TRUE)
est.mi.main.aimX <- do.call(cbind, est.mi.main.aimX)
row.names(est.mi.main.aimX) <- c("beta","Intercept","appusage_yes", "isCompleted_yesterday_yes", "contact_yes")
}else{
print("Please enter a valid outcome variable.")
}
# -----------------------------------------------------------------------------
# Data analysis: moderators analyses
# -----------------------------------------------------------------------------
# Obtain subset of data if drop.criteria.aimX is provided
if(is.null(drop.criteria.aimX)){
# Main analysis Aim 1 or Aim 3
mi.data.aimX <- mi.data.aimX
}else{
# !is.null(drop.criteria.aimX)
# Main analysis Aim 2 or Aim 4
mi.data.aimX <- lapply(mi.data.aimX, FUN = function(dat){
dat <- dat[!drop.criteria.aimX,]
return(dat[order(dat$username, dat$study_day),])
})
}
# -----------------------------------------------------------------------------
# Data analysis: more moderators analyses
# -----------------------------------------------------------------------------
# female vs. male
mi.exploratory.female.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.female <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("female"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,0,
1,1), nrow=2, byrow=TRUE),
F_test_c = NULL)
return(aimX.exploratory.female)
})
mi.exploratory.female.aimX <- do.call(cbind, mi.exploratory.female.aimX)
mi.exploratory.female.aimX <- apply(mi.exploratory.female.aimX, 2, unlist)
est.mi.exploratory.female.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.female.aimX[c(1:2, 21:22, 13:16),],
inputs.stderr = mi.exploratory.female.aimX[c(3:4, 23:24, 17:20),],
n = n,
p = 2,
q = 4,
m = m)
est.mi.exploratory.female.aimX <- do.call(cbind, est.mi.exploratory.female.aimX)
row.names(est.mi.exploratory.female.aimX) <- c("beta1","beta2",
"contrast: male","contrast: female",
"Intercept","appusage_yes", "isCompleted_yesterday_yes", "contact_yes")
# weekend = 1 vs. weekend = 0
mi.exploratory.weekend.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.weekend <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("weekend"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,0,
1,1), byrow=TRUE, nrow=2),
F_test_c = NULL)
return(aimX.exploratory.weekend)
})
mi.exploratory.weekend.aimX <- do.call(cbind, mi.exploratory.weekend.aimX)
mi.exploratory.weekend.aimX <- apply(mi.exploratory.weekend.aimX, 2, unlist)
est.mi.exploratory.weekend.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.weekend.aimX[c(1:2, 21:22, 13:16),],
inputs.stderr = mi.exploratory.weekend.aimX[c(3:4, 23:24, 17:20),],
n = n,
p = 2,
q = 4,
m = m)
est.mi.exploratory.weekend.aimX <- do.call(cbind, est.mi.exploratory.weekend.aimX)
row.names(est.mi.exploratory.weekend.aimX) <- c("beta1","beta2",
"contrast: weekday","contrast: weekend",
"Intercept","appusage_yes", "isCompleted_yesterday_yes", "contact_yes")
# appusage_yes=1 vs. appusage_yes=0
mi.exploratory.appusage_yes.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.appusage_yes <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("appusage_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,0,
1,1), byrow=TRUE, nrow=2),
F_test_c = NULL)
return(aimX.exploratory.appusage_yes)
})
mi.exploratory.appusage_yes.aimX <- do.call(cbind, mi.exploratory.appusage_yes.aimX)
mi.exploratory.appusage_yes.aimX <- apply(mi.exploratory.appusage_yes.aimX, 2, unlist)
est.mi.exploratory.appusage_yes.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.appusage_yes.aimX[c(1:2,21:22,13:16),],
inputs.stderr = mi.exploratory.appusage_yes.aimX[c(3:4,23:24,17:20),],
n = n,
p = 2,
q = 4,
m = m)
est.mi.exploratory.appusage_yes.aimX <- do.call(cbind, est.mi.exploratory.appusage_yes.aimX)
row.names(est.mi.exploratory.appusage_yes.aimX) <- c("beta1","beta2",
"contrast: appusage_yes=0","contrast: appusage_yes=1",
"Intercept","appusage_yes", "isCompleted_yesterday_yes", "contact_yes")
# isCompleted_yesterday_yes=1 vs. isCompleted_yesterday_yes=0
mi.exploratory.isCompleted_yesterday_yes.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.isCompleted_yesterday_yes <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("isCompleted_yesterday_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,0,
1,1), byrow=TRUE, nrow=2),
F_test_c = NULL)
return(aimX.exploratory.isCompleted_yesterday_yes)
})
mi.exploratory.isCompleted_yesterday_yes.aimX <- do.call(cbind, mi.exploratory.isCompleted_yesterday_yes.aimX)
mi.exploratory.isCompleted_yesterday_yes.aimX <- apply(mi.exploratory.isCompleted_yesterday_yes.aimX, 2, unlist)
est.mi.exploratory.isCompleted_yesterday_yes.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.isCompleted_yesterday_yes.aimX[c(1:2,21:22,13:16),],
inputs.stderr = mi.exploratory.isCompleted_yesterday_yes.aimX[c(3:4, 23:24, 17:20),],
n = n,
p = 2,
q = 4,
m = m)
est.mi.exploratory.isCompleted_yesterday_yes.aimX <- do.call(cbind, est.mi.exploratory.isCompleted_yesterday_yes.aimX)
row.names(est.mi.exploratory.isCompleted_yesterday_yes.aimX) <- c("beta1","beta2",
"contrast: isCompleted_yesterday_yes=0","contrast: isCompleted_yesterday_yes=1",
"Intercept","appusage_yes", "isCompleted_yesterday_yes", "contact_yes")
# contact_yes=1 vs. contact_yes=0
mi.exploratory.contact_yes.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.contact_yes <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("contact_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,0,
1,1), byrow=TRUE, nrow=2),
F_test_c = NULL)
return(aimX.exploratory.contact_yes)
})
mi.exploratory.contact_yes.aimX <- do.call(cbind, mi.exploratory.contact_yes.aimX)
mi.exploratory.contact_yes.aimX <- apply(mi.exploratory.contact_yes.aimX, 2, unlist)
est.mi.exploratory.contact_yes.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.contact_yes.aimX[c(1:2,21:22,13:16),],
inputs.stderr = mi.exploratory.contact_yes.aimX[c(3:4,23:24,17:20),],
n = n,
p = 2,
q = 4,
m = m)
est.mi.exploratory.contact_yes.aimX <- do.call(cbind, est.mi.exploratory.contact_yes.aimX)
row.names(est.mi.exploratory.contact_yes.aimX) <- c("beta1","beta2",
"contrast: contact_yes=0","contrast: contact_yes=1",
"Intercept","appusage_yes", "isCompleted_yesterday_yes", "contact_yes")
# Time trend: study_day
mi.exploratory.study_day.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.study_day <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("study_day"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = c(1,8),
F_test_c = NULL)
return(aimX.exploratory.study_day)
})
mi.exploratory.study_day.aimX <- do.call(cbind, mi.exploratory.study_day.aimX)
mi.exploratory.study_day.aimX <- apply(mi.exploratory.study_day.aimX, 2, unlist)
est.mi.exploratory.study_day.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.study_day.aimX[c(1:2,13:16),],
inputs.stderr = mi.exploratory.study_day.aimX[c(3:4,17:20),],
n = n,
p = 2,
q = 4,
m = m)
est.mi.exploratory.study_day.aimX <- do.call(cbind, est.mi.exploratory.study_day.aimX)
row.names(est.mi.exploratory.study_day.aimX) <- c("beta1","beta2",
"Intercept","appusage_yes", "isCompleted_yesterday_yes", "contact_yes")
# Time trend: study_day and study_day_squared
mi.exploratory.study_day_squared.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.study_day <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("study_day","study_day_squared"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = c(1,8,64),
F_test_c = NULL)
return(aimX.exploratory.study_day)
})
mi.exploratory.study_day_squared.aimX <- do.call(cbind, mi.exploratory.study_day_squared.aimX)
mi.exploratory.study_day_squared.aimX <- apply(mi.exploratory.study_day_squared.aimX, 2, unlist)
est.mi.exploratory.study_day_squared.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.study_day_squared.aimX[c(1:3,17:20),],
inputs.stderr = mi.exploratory.study_day_squared.aimX[c(4:6,21:24),],
n = n,
p = 3,
q = 4,
m = m)
est.mi.exploratory.study_day_squared.aimX <- do.call(cbind, est.mi.exploratory.study_day_squared.aimX)
row.names(est.mi.exploratory.study_day_squared.aimX) <- c("beta1","beta2","beta3",
"Intercept","appusage_yes", "isCompleted_yesterday_yes", "contact_yes")
# all four moderators in one model --------------------------------------------
mi.exploratory.all.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.all <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("weekend",
"contact_yes",
"appusage_yes",
"isCompleted_yesterday_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,0,0,0,0), byrow=TRUE, nrow=1),
F_test_c = NULL)
return(aimX.exploratory.all)
})
mi.exploratory.all.aimX <- do.call(cbind, mi.exploratory.all.aimX)
mi.exploratory.all.aimX <- apply(mi.exploratory.all.aimX, 2, unlist)
est.mi.exploratory.all.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.all.aimX[c(1:5),],
inputs.stderr = mi.exploratory.all.aimX[c(6:10),],
n = n,
p = 5,
q = 4,
m = m)
est.mi.exploratory.all.aimX <- do.call(cbind, est.mi.exploratory.all.aimX)
# R & V: all four moderators in one model -------------------------------------
mi.exploratory.all.R_V.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.all <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("weekend",
"contact_yes",
"appusage_yes",
"isCompleted_yesterday_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,1,1,0,0,
1,1,0,0,0,
1,0,1,0,0), byrow=TRUE, nrow=3),
F_test_c = NULL)
return(aimX.exploratory.all)
})
mi.exploratory.all.R_V.aimX <- do.call(cbind, mi.exploratory.all.R_V.aimX)
mi.exploratory.all.R_V.aimX <- apply(mi.exploratory.all.R_V.aimX, 2, unlist)
est.mi.exploratory.all.R_V.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.all.R_V.aimX[c("beta_contrast1","beta_contrast2","beta_contrast3"),],
inputs.stderr = mi.exploratory.all.R_V.aimX[c("se_beta_contrast1","se_beta_contrast2","se_beta_contrast3"),],
n = n,
p = 5,
q = 4,
m = m)
est.mi.exploratory.all.R_V.aimX <- do.call(cbind, est.mi.exploratory.all.R_V.aimX)
row.names(est.mi.exploratory.all.R_V.aimX) <- c("R&V: (1,1,1,0,0)", "R&V: (1,1,0,0,0)", "R&V: (1,0,1,0,0)")
est.mi.exploratory.all.R_V.aimX_01 <- est.mi.exploratory.all.R_V.aimX
mi.exploratory.all.R_V.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.all <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("weekend",
"contact_yes",
"appusage_yes",
"isCompleted_yesterday_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,1,1,0,1,
1,1,0,0,1,
1,0,1,0,1), byrow=TRUE, nrow=3),
F_test_c = NULL)
return(aimX.exploratory.all)
})
mi.exploratory.all.R_V.aimX <- do.call(cbind, mi.exploratory.all.R_V.aimX)
mi.exploratory.all.R_V.aimX <- apply(mi.exploratory.all.R_V.aimX, 2, unlist)
est.mi.exploratory.all.R_V.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.all.R_V.aimX[c("beta_contrast1","beta_contrast2","beta_contrast3"),],
inputs.stderr = mi.exploratory.all.R_V.aimX[c("se_beta_contrast1","se_beta_contrast2","se_beta_contrast3"),],
n = n,
p = 5,
q = 4,
m = m)
est.mi.exploratory.all.R_V.aimX <- do.call(cbind, est.mi.exploratory.all.R_V.aimX)
row.names(est.mi.exploratory.all.R_V.aimX) <- c("R&V: (1,1,1,0,1)", "R&V: (1,1,0,0,1)", "R&V: (1,0,1,0,1)")
est.mi.exploratory.all.R_V.aimX_02 <- est.mi.exploratory.all.R_V.aimX
mi.exploratory.all.R_V.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.all <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("weekend",
"contact_yes",
"appusage_yes",
"isCompleted_yesterday_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,1,1,1,0,
1,1,0,1,0,
1,0,1,1,0), byrow=TRUE, nrow=3),
F_test_c = NULL)
return(aimX.exploratory.all)
})
mi.exploratory.all.R_V.aimX <- do.call(cbind, mi.exploratory.all.R_V.aimX)
mi.exploratory.all.R_V.aimX <- apply(mi.exploratory.all.R_V.aimX, 2, unlist)
est.mi.exploratory.all.R_V.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.all.R_V.aimX[c("beta_contrast1","beta_contrast2","beta_contrast3"),],
inputs.stderr = mi.exploratory.all.R_V.aimX[c("se_beta_contrast1","se_beta_contrast2","se_beta_contrast3"),],
n = n,
p = 5,
q = 4,
m = m)
est.mi.exploratory.all.R_V.aimX <- do.call(cbind, est.mi.exploratory.all.R_V.aimX)
row.names(est.mi.exploratory.all.R_V.aimX) <- c("R&V: (1,1,1,1,0)", "R&V: (1,1,0,1,0)", "R&V: (1,0,1,1,0)")
est.mi.exploratory.all.R_V.aimX_03 <- est.mi.exploratory.all.R_V.aimX
# R & NV
mi.exploratory.all.R_NV.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.all <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("weekend",
"contact_yes",
"appusage_yes",
"isCompleted_yesterday_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,1,1,1,1,
1,1,0,1,1,
1,0,1,1,1), byrow=TRUE, nrow=3),
F_test_c = NULL)
return(aimX.exploratory.all)
})
mi.exploratory.all.R_NV.aimX <- do.call(cbind, mi.exploratory.all.R_NV.aimX)
mi.exploratory.all.R_NV.aimX <- apply(mi.exploratory.all.R_NV.aimX, 2, unlist)
est.mi.exploratory.all.R_NV.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.all.R_NV.aimX[c("beta_contrast1","beta_contrast2","beta_contrast3"),],
inputs.stderr = mi.exploratory.all.R_NV.aimX[c("se_beta_contrast1","se_beta_contrast2","se_beta_contrast3"),],
n = n,
p = 5,
q = 4,
m = m)
est.mi.exploratory.all.R_NV.aimX <- do.call(cbind, est.mi.exploratory.all.R_NV.aimX)
row.names(est.mi.exploratory.all.R_NV.aimX) <- c("R&NV: (1,1,1,1,1)", "R&NV: (1,1,0,1,1)", "R&NV: (1,0,1,1,1)")
# NR_V
mi.exploratory.all.NR_V.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.all <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("weekend",
"contact_yes",
"appusage_yes",
"isCompleted_yesterday_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,0,0,0,0,
1,0,0,0,1,
1,0,0,1,0), byrow=TRUE, nrow=3),
F_test_c = NULL)
return(aimX.exploratory.all)
})
mi.exploratory.all.NR_V.aimX <- do.call(cbind, mi.exploratory.all.NR_V.aimX)
mi.exploratory.all.NR_V.aimX <- apply(mi.exploratory.all.NR_V.aimX, 2, unlist)
est.mi.exploratory.all.NR_V.aimX <- PoolEstimatesMI(inputs.beta = mi.exploratory.all.NR_V.aimX[c("beta_contrast1","beta_contrast2","beta_contrast3"),],
inputs.stderr = mi.exploratory.all.NR_V.aimX[c("se_beta_contrast1","se_beta_contrast2","se_beta_contrast3"),],
n = n,
p = 5,
q = 4,
m = m)
est.mi.exploratory.all.NR_V.aimX <- do.call(cbind, est.mi.exploratory.all.NR_V.aimX)
row.names(est.mi.exploratory.all.NR_V.aimX) <- c("NR&V: (1,0,0,0,0)", "NR&V: (1,0,0,0,1)", "NR&V: (1,0,0,1,0)")
# NR & NV
mi.exploratory.all.NR_NV.aimX <- lapply(mi.data.aimX, FUN = function(this.imputed.data){
aimX.exploratory.all <- SARA_exploratory_analysis_general_F_test(dta = this.imputed.data,
control_var = c("appusage_yes", "isCompleted_yesterday_yes", "contact_yes"),
moderator = c("weekend",
"contact_yes",
"appusage_yes",
"isCompleted_yesterday_yes"),
id_var = "username",
day_var = "study_day",
trt_var = "isRandomized",
outcome_var = my_outcome_variable,
avail_var = "availability",
prob_treatment = 1/2,
significance_level = 0.05,
F_test_L = matrix(c(1,0,0,1,1), byrow=TRUE, nrow=1),
F_test_c = NULL)
return(aimX.exploratory.all)
})
mi.exploratory.all.NR_NV.aimX <- do.call(cbind, mi.exploratory.all.NR_NV.aimX)
mi.exploratory.all.NR_NV.aimX <- apply(mi.exploratory.all.NR_NV.aimX, 2, unlist)
est.mi.exploratory.all.NR_NV.aimX <- PoolEstimatesMI(inputs.beta = matrix(mi.exploratory.all.NR_NV.aimX[c("beta_contrast"),], nrow=1),
inputs.stderr = matrix(mi.exploratory.all.NR_NV.aimX[c("se_beta_contrast"),], nrow=1),
n = n,
p = 5,
q = 4,
m = m)
est.mi.exploratory.all.NR_NV.aimX <- do.call(cbind, est.mi.exploratory.all.NR_NV.aimX)
row.names(est.mi.exploratory.all.NR_NV.aimX) <- c("NR&NV: (1,0,0,1,1)")
row.names(est.mi.exploratory.all.R_V.aimX_01) <- substring(row.names(est.mi.exploratory.all.R_V.aimX_01), 6)
row.names(est.mi.exploratory.all.R_V.aimX_02) <- substring(row.names(est.mi.exploratory.all.R_V.aimX_02), 6)
row.names(est.mi.exploratory.all.R_V.aimX_03) <- substring(row.names(est.mi.exploratory.all.R_V.aimX_03), 6)
row.names(est.mi.exploratory.all.R_NV.aimX) <- substring(row.names(est.mi.exploratory.all.R_NV.aimX), 7)
row.names(est.mi.exploratory.all.NR_V.aimX) <- substring(row.names(est.mi.exploratory.all.NR_V.aimX), 7)
row.names(est.mi.exploratory.all.NR_NV.aimX) <- substring(row.names(est.mi.exploratory.all.NR_NV.aimX), 8)
est.mi.exploratory.all.aimX <- rbind(est.mi.exploratory.all.aimX,
est.mi.exploratory.all.R_V.aimX_01,
est.mi.exploratory.all.R_V.aimX_02,
est.mi.exploratory.all.R_V.aimX_03,
est.mi.exploratory.all.R_NV.aimX,
est.mi.exploratory.all.NR_V.aimX,
est.mi.exploratory.all.NR_NV.aimX)
# -----------------------------------------------------------------------------
# Save all objects into a list
# -----------------------------------------------------------------------------
results.names <- c("est.mi.main.aimX",
"est.mi.exploratory.appusage_yes.aimX",
"est.mi.exploratory.contact_yes.aimX",
"est.mi.exploratory.isCompleted_yesterday_yes.aimX",
"est.mi.exploratory.female.aimX",
"est.mi.exploratory.study_day.aimX",
#"est.mi.exploratory.study_day_squared.aimX",
"est.mi.exploratory.weekend.aimX",
"est.mi.exploratory.all.aimX")
objects.aimX <- lapply(results.names, function(this.string){
return(get(this.string))
})
names(objects.aimX) <- results.names
table.labels <- c("Analysis with Multiple Imputed Data: Main Analysis",
"Analysis with Multiple Imputed Data: appusage_yes=1 vs. appusage_yes=0",
"Analysis with Multiple Imputed Data: contact_yes=1 vs. contact_yes=0",
"Analysis with Multiple Imputed Data: isCompleted_yesterday_yes=1 vs. isCompleted_yesterday_yes=0",
"Analysis with Multiple Imputed Data: female=1 vs. female=0",
"Analysis with Multiple Imputed Data: study_day",
#"Analysis with Multiple Imputed Data: study_day_squared",
"Analysis with Multiple Imputed Data: weekend=1 vs. weekend=0",
"Analysis with Multiple Imputed Data: Four Moderators in One Model")