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MIssMechanismsCombinations.v2.R
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####################################################################################
# M C A R
####################################################################################
#MCAR BIAS ***ALL DATA*** AT IMPLEMENTATION + all validation methods + mechanisms
# Sort the validation dataset by the "Iteration" column in ascending order
df_val <- df_val[order(df_val$Iteration), ]
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MCAR_alldata <- subset(df_imp, DAG_type == "MCAR" & dataset == "all_data_imp") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MCAR_alldata <- left_join(df_val, imp_MCAR_alldata,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MCAR_alldata$DAG_combined = paste(joined_imp_MCAR_alldata$DAG_type.x, joined_imp_MCAR_alldata$DAG_type.y, sep = "+")
#calculate bias mean and CI
BIAS_MCAR_ALLDATA <- joined_imp_MCAR_alldata %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
### SANITY CHECKS
which(BIAS_MCAR_ALLDATA$scenario_number.x == 1472 &
BIAS_MCAR_ALLDATA$scenario_number.y == 1472 &
BIAS_MCAR_ALLDATA$R_prev == 0.5 &
BIAS_MCAR_ALLDATA$Y_prev == 0.1)
which(bias_imp_all_data$scenario_number == 1472 &
bias_imp_all_data$R_prev.x == 0.5 &
bias_imp_all_data$Y_prev.x == 0.1)
####################################################################################
#MCAR BIAS ***MEAN*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MCAR_mean <- subset(df_imp, DAG_type == "MCAR" & dataset == "mean_imp") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MCAR_mean <- left_join(df_val, imp_MCAR_mean,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MCAR_mean$DAG_combined = paste(joined_imp_MCAR_mean$DAG_type.x, joined_imp_MCAR_mean$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MCAR_mean$dataset_combined = paste(joined_imp_MCAR_mean$dataset.x, joined_imp_MCAR_mean$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MCAR_mean$scenario_combined = paste(joined_imp_MCAR_mean$scenario_number.x, joined_imp_MCAR_mean$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MCAR_MEAN <- joined_imp_MCAR_mean %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MCAR_MEAN$plot_number <- 1:nrow(BIAS_MCAR_MEAN)
which(bias_imp_mean$scenario_number == 1472 &
bias_imp_mean$R_prev.x == 0.5 &
bias_imp_mean$Y_prev.x == 0.1)
which(BIAS_MCAR_MEAN$scenario_number.x == 1472 &
BIAS_MCAR_MEAN$scenario_number.y == 1472 &
BIAS_MCAR_MEAN$R_prev == 0.5 &
BIAS_MCAR_MEAN$Y_prev == 0.1)
######################################################################################
#MCAR BIAS ***MI without Y*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MCAR_MInoY <- subset(df_imp, DAG_type == "MCAR" & dataset == "MI_imp_data_noY") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MCAR_MInoY <- left_join(df_val, imp_MCAR_MInoY,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MCAR_MInoY$DAG_combined = paste(joined_imp_MCAR_MInoY$DAG_type.x, joined_imp_MCAR_MInoY$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MCAR_MInoY$dataset_combined = paste(joined_imp_MCAR_MInoY$dataset.x, joined_imp_MCAR_MInoY$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MCAR_MInoY$scenario_combined = paste(joined_imp_MCAR_MInoY$scenario_number.x, joined_imp_MCAR_MInoY$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MCAR_MInoY <- joined_imp_MCAR_MInoY %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MCAR_MInoY$plot_number <- 1:nrow(BIAS_MCAR_MInoY)
which(bias_imp_MI_noY$scenario_number == 1472 &
bias_imp_MI_noY$R_prev.x == 0.5 &
bias_imp_MI_noY$Y_prev.x == 0.1)
which(BIAS_MCAR_MInoY$scenario_number.x == 1472 &
BIAS_MCAR_MInoY$scenario_number.y == 1472 &
BIAS_MCAR_MInoY$R_prev == 0.5 &
BIAS_MCAR_MInoY$Y_prev == 0.1)
######################################################################################
#MCAR BIAS ***MI withY*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MCAR_MIwithY <- subset(df_imp, DAG_type == "MCAR" & dataset == "MI_imp_data_withY") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MCAR_MIwithY <- left_join(df_val, imp_MCAR_MIwithY,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MCAR_MIwithY$DAG_combined = paste(joined_imp_MCAR_MIwithY$DAG_type.x, joined_imp_MCAR_MIwithY$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MCAR_MIwithY$dataset_combined = paste(joined_imp_MCAR_MIwithY$dataset.x, joined_imp_MCAR_MIwithY$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MCAR_MIwithY$scenario_combined = paste(joined_imp_MCAR_MIwithY$scenario_number.x, joined_imp_MCAR_MIwithY$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MCAR_MIwithY <- joined_imp_MCAR_MIwithY %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MCAR_MIwithY$plot_number <- 1:nrow(BIAS_MCAR_MIwithY)
which(bias_imp_MI_withY$scenario_number == 1472 &
bias_imp_MI_withY$R_prev.x == 0.5 &
bias_imp_MI_withY$Y_prev.x == 0.1)
which(BIAS_MCAR_MIwithY$scenario_number.x == 1472 &
BIAS_MCAR_MIwithY$scenario_number.y == 1472 &
BIAS_MCAR_MIwithY$R_prev == 0.5 &
BIAS_MCAR_MIwithY$Y_prev == 0.1)
#combine all BIAS dataframes into one BIAS_MCAR
####################################################################################
# M A R
####################################################################################
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MAR_alldata <- subset(df_imp, DAG_type == "MAR" & dataset == "all_data_imp") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MAR_alldata <- left_join(df_val, imp_MAR_alldata,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MAR_alldata$DAG_combined = paste(joined_imp_MAR_alldata$DAG_type.x, joined_imp_MAR_alldata$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MAR_alldata$dataset_combined = paste(joined_imp_MAR_alldata$dataset.x, joined_imp_MAR_alldata$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MAR_alldata$scenario_combined = paste(joined_imp_MAR_alldata$scenario_number.x, joined_imp_MAR_alldata$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MAR_ALLDATA <- joined_imp_MAR_alldata %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MAR_ALLDATA$plot_number <- 1:nrow(BIAS_MAR_ALLDATA)
### SANITY CHECKS
which(BIAS_MAR_ALLDATA$scenario_number.x == 1553 &
BIAS_MAR_ALLDATA$scenario_number.y == 1553 &
BIAS_MAR_ALLDATA$R_prev == 0.5 &
BIAS_MAR_ALLDATA$Y_prev == 0.1)
which(bias_imp_all_data$scenario_number == 1553 &
bias_imp_all_data$R_prev.x == 0.5 &
bias_imp_all_data$Y_prev.x == 0.1)
row_new_MAR <- BIAS_MAR_ALLDATA[134325, ]
row_old_MAR <- bias_imp_all_data[58700, ]
####################################################################################
#MAR BIAS ***MEAN*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MAR_mean <- subset(df_imp, DAG_type == "MAR" & dataset == "mean_imp") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MAR_mean <- left_join(df_val, imp_MAR_mean,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MAR_mean$DAG_combined = paste(joined_imp_MAR_mean$DAG_type.x, joined_imp_MAR_mean$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MAR_mean$dataset_combined = paste(joined_imp_MAR_mean$dataset.x, joined_imp_MAR_mean$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MAR_mean$scenario_combined = paste(joined_imp_MAR_mean$scenario_number.x, joined_imp_MAR_mean$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MAR_MEAN <- joined_imp_MAR_mean %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MAR_MEAN$plot_number <- 1:nrow(BIAS_MAR_MEAN)
which(bias_imp_mean$scenario_number == 1553 &
bias_imp_mean$R_prev.x == 0.5 &
bias_imp_mean$Y_prev.x == 0.1)
which(BIAS_MAR_MEAN$scenario_number.x == 1553 &
BIAS_MAR_MEAN$scenario_number.y == 1553 &
BIAS_MAR_MEAN$R_prev == 0.5 &
BIAS_MAR_MEAN$Y_prev == 0.1)
row_old <- bias_imp_mean[58700, ]
row_new <- BIAS_MAR_MEAN[134325, ]
######################################################################################
#MAR BIAS ***MI without Y*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MAR_MInoY <- subset(df_imp, DAG_type == "MAR" & dataset == "MI_imp_data_noY") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MAR_MInoY <- left_join(df_val, imp_MAR_MInoY,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MAR_MInoY$DAG_combined = paste(joined_imp_MAR_MInoY$DAG_type.x, joined_imp_MAR_MInoY$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MAR_MInoY$dataset_combined = paste(joined_imp_MAR_MInoY$dataset.x, joined_imp_MAR_MInoY$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MAR_MInoY$scenario_combined = paste(joined_imp_MAR_MInoY$scenario_number.x, joined_imp_MAR_MInoY$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MAR_MInoY <- joined_imp_MAR_MInoY %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MAR_MInoY$plot_number <- 1:nrow(BIAS_MAR_MInoY)
which(bias_imp_MI_noY$scenario_number == 1553 &
bias_imp_MI_noY$R_prev.x == 0.5 &
bias_imp_MI_noY$Y_prev.x == 0.1)
which(BIAS_MAR_MInoY$scenario_number.x == 1553 &
BIAS_MAR_MInoY$scenario_number.y == 1553 &
BIAS_MAR_MInoY$R_prev == 0.5 &
BIAS_MAR_MInoY$Y_prev == 0.1)
row_old <- bias_imp_MI_noY[58700, ]
row_new <- BIAS_MAR_MInoY[134325, ]
######################################################################################
#MAR BIAS ***MI withY*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MAR_MIwithY <- subset(df_imp, DAG_type == "MAR" & dataset == "MI_imp_data_withY") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MAR_MIwithY <- left_join(df_val, imp_MAR_MIwithY,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MAR_MIwithY$DAG_combined = paste(joined_imp_MAR_MIwithY$DAG_type.x, joined_imp_MAR_MIwithY$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MAR_MIwithY$dataset_combined = paste(joined_imp_MAR_MIwithY$dataset.x, joined_imp_MAR_MIwithY$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MAR_MIwithY$scenario_combined = paste(joined_imp_MAR_MIwithY$scenario_number.x, joined_imp_MAR_MIwithY$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MAR_MIwithY <- joined_imp_MAR_MIwithY %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MAR_MIwithY$plot_number <- 1:nrow(BIAS_MAR_MIwithY)
which(bias_imp_MI_withY$scenario_number == 1553 &
bias_imp_MI_withY$R_prev.x == 0.5 &
bias_imp_MI_withY$Y_prev.x == 0.1)
which(BIAS_MAR_MIwithY$scenario_number.x == 1553 &
BIAS_MAR_MIwithY$scenario_number.y == 1553 &
BIAS_MAR_MIwithY$R_prev == 0.5 &
BIAS_MAR_MIwithY$Y_prev == 0.1)
row_old2 <- bias_imp_MI_withY[58700, ]
row_new_2 <- BIAS_MAR_MIwithY[134325, ]
#combine all BIAS dataframes into one BIAS_MCAR
####################################################################################
# M N A R - X
####################################################################################
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MNAR1_alldata <- subset(df_imp, DAG_type == "MNAR1" & dataset == "all_data_imp") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MNAR1_alldata <- left_join(df_val, imp_MNAR1_alldata,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MNAR1_alldata$DAG_combined = paste(joined_imp_MNAR1_alldata$DAG_type.x, joined_imp_MNAR1_alldata$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MNAR1_alldata$dataset_combined = paste(joined_imp_MNAR1_alldata$dataset.x, joined_imp_MNAR1_alldata$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MNAR1_alldata$scenario_combined = paste(joined_imp_MNAR1_alldata$scenario_number.x, joined_imp_MNAR1_alldata$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MNAR1_ALLDATA <- joined_imp_MNAR1_alldata %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MNAR1_ALLDATA$plot_number <- 1:nrow(BIAS_MNAR1_ALLDATA)
### SANITY CHECKS
which(BIAS_MNAR1_ALLDATA$scenario_number.x == 1796 &
BIAS_MNAR1_ALLDATA$scenario_number.y == 1796 &
BIAS_MNAR1_ALLDATA$R_prev == 0.5 &
BIAS_MNAR1_ALLDATA$Y_prev == 0.1)
which(bias_imp_all_data$scenario_number == 1796 &
bias_imp_all_data$R_prev.x == 0.5 &
bias_imp_all_data$Y_prev.x == 0.1)
row_new_MNAR1 <- BIAS_MNAR1_ALLDATA[269621, ]
row_old_MNAR1 <- bias_imp_all_data[59672, ]
####################################################################################
#MAR BIAS ***MEAN*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MNAR1_mean <- subset(df_imp, DAG_type == "MNAR1" & dataset == "mean_imp") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MNAR1_mean <- left_join(df_val, imp_MNAR1_mean,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MNAR1_mean$DAG_combined = paste(joined_imp_MNAR1_mean$DAG_type.x, joined_imp_MNAR1_mean$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MNAR1_mean$dataset_combined = paste(joined_imp_MNAR1_mean$dataset.x, joined_imp_MNAR1_mean$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MNAR1_mean$scenario_combined = paste(joined_imp_MNAR1_mean$scenario_number.x, joined_imp_MNAR1_mean$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MNAR1_MEAN <- joined_imp_MNAR1_mean %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MNAR1_MEAN$plot_number <- 1:nrow(BIAS_MNAR1_MEAN)
which(bias_imp_mean$scenario_number == 1796 &
bias_imp_mean$R_prev.x == 0.5 &
bias_imp_mean$Y_prev.x == 0.1)
which(BIAS_MNAR1_MEAN$scenario_number.x == 1796 &
BIAS_MNAR1_MEAN$scenario_number.y == 1796 &
BIAS_MNAR1_MEAN$R_prev == 0.5 &
BIAS_MNAR1_MEAN$Y_prev == 0.1)
row_old <- bias_imp_mean[59672, ]
row_new <- BIAS_MNAR1_MEAN[269621, ]
######################################################################################
#MAR BIAS ***MI without Y*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MNAR1_MInoY <- subset(df_imp, DAG_type == "MNAR1" & dataset == "MI_imp_data_noY") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MNAR1_MInoY <- left_join(df_val, imp_MNAR1_MInoY,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MNAR1_MInoY$DAG_combined = paste(joined_imp_MNAR1_MInoY$DAG_type.x, joined_imp_MNAR1_MInoY$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MNAR1_MInoY$dataset_combined = paste(joined_imp_MNAR1_MInoY$dataset.x, joined_imp_MNAR1_MInoY$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MNAR1_MInoY$scenario_combined = paste(joined_imp_MNAR1_MInoY$scenario_number.x, joined_imp_MNAR1_MInoY$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MNAR1_MInoY <- joined_imp_MNAR1_MInoY %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MNAR1_MInoY$plot_number <- 1:nrow(BIAS_MNAR1_MInoY)
which(bias_imp_MI_noY$scenario_number == 1796 &
bias_imp_MI_noY$R_prev.x == 0.5 &
bias_imp_MI_noY$Y_prev.x == 0.1)
which(BIAS_MNAR1_MInoY$scenario_number.x == 1796 &
BIAS_MNAR1_MInoY$scenario_number.y == 1796 &
BIAS_MNAR1_MInoY$R_prev == 0.5 &
BIAS_MNAR1_MInoY$Y_prev == 0.1)
row_old <- bias_imp_MI_noY[59672, ]
row_new <- BIAS_MNAR1_MInoY[269621, ]
######################################################################################
#MAR BIAS ***MI withY*** AT IMPLEMENTATION + all validation methods + mechanisms
#subset only rows with DAG == MCAR and dataset == all data required (this will be the true estimates)
imp_MNAR1_MIwithY <- subset(df_imp, DAG_type == "MNAR1" & dataset == "MI_imp_data_withY") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MNAR1_MIwithY <- left_join(df_val, imp_MNAR1_MIwithY,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MNAR1_MIwithY$DAG_combined = paste(joined_imp_MNAR1_MIwithY$DAG_type.x, joined_imp_MNAR1_MIwithY$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MNAR1_MIwithY$dataset_combined = paste(joined_imp_MNAR1_MIwithY$dataset.x, joined_imp_MNAR1_MIwithY$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MNAR1_MIwithY$scenario_combined = paste(joined_imp_MNAR1_MIwithY$scenario_number.x, joined_imp_MNAR1_MIwithY$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MNAR1_MIwithY <- joined_imp_MNAR1_MIwithY %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,
beta_x1.x,
beta_x2.x,
beta_U.x,
beta_x1.y,
beta_x2.y,
beta_U.y,
gamma_x1,
gamma_x2,
gamma_U,
DAG_combined) %>%
summarise(bias_mean = mean(bias), LCI = quantile(bias, 0.025), UCI = quantile(bias, 0.975))
BIAS_MNAR1_MIwithY$plot_number <- 1:nrow(BIAS_MNAR1_MIwithY)
which(bias_imp_MI_withY$scenario_number == 1796 &
bias_imp_MI_withY$R_prev.x == 0.5 &
bias_imp_MI_withY$Y_prev.x == 0.1)
which(BIAS_MNAR1_MIwithY$scenario_number.x == 1796 &
BIAS_MNAR1_MIwithY$scenario_number.y == 1796 &
BIAS_MNAR1_MIwithY$R_prev == 0.5 &
BIAS_MNAR1_MIwithY$Y_prev == 0.1)
#combine all BIAS dataframes into one BIAS_MCAR
####################################################################################
# M N A R - Y
####################################################################################
#subset only rows with DAG == MNARY and dataset == all data required (this will be the true estimates)
imp_MNAR2_alldata <- subset(df_imp, DAG_type == "MNAR2" & dataset == "all_data_imp") %>%
rename_at(vars("estimates"), function(x) paste0("true_", x))
# join the subset with df_val and calculate bias
joined_imp_MNAR2_alldata <- left_join(df_val, imp_MNAR2_alldata,
multiple = "all",
by = c("Iteration",
"mod",
"target_measures",
"n_iter",
"N",
"N_dev",
"N_imp",
"N_val",
"Y_prev",
"X_categorical",
"R_prev",
"gamma_x1",
"gamma_x2",
"gamma_U")) %>%
mutate(bias = estimates - true_estimates)
#create a new column DAG_combined NB: scenario_number.x = VAL and scenario_number.y = IMP
joined_imp_MNAR2_alldata$DAG_combined = paste(joined_imp_MNAR2_alldata$DAG_type.x, joined_imp_MNAR2_alldata$DAG_type.y, sep = "+")
#create a new column dataset_combined
joined_imp_MNAR2_alldata$dataset_combined = paste(joined_imp_MNAR2_alldata$dataset.x, joined_imp_MNAR2_alldata$dataset.y, sep = "+")
#create a new column scenario_combined
joined_imp_MNAR2_alldata$scenario_combined = paste(joined_imp_MNAR2_alldata$scenario_number.x, joined_imp_MNAR2_alldata$scenario_number.y, sep = "+")
#calculate bias mean and CI
BIAS_MNAR2_ALLDATA <- joined_imp_MNAR2_alldata %>%
group_by(dataset.x,
target_measures,
scenario_number.x,
scenario_number.y,
N,
N_dev,
N_val,
N_imp,
Y_prev,
R_prev,
X_categorical,