Go to the RMD, R, PDF, or HTML version of this file. Go back to fan’s REconTools Package, R4Econ Repository (bookdown site), or Intro Stats with R Repository.
There are multiple or a single continuous variables. Find which quantile each observation belongs to for each of the variables. Then also generate a joint/interaction variable of all combinations of quantiles from different variables.
The program has these features:
When joining multiple quantile variables together:
# Quantiles for any variable
gen_quantiles <- function(var, df, prob=c(0.25, 0.50, 0.75)) {
enframe(quantile(as.numeric(df[[var]]), prob, na.rm=TRUE), 'quant.perc', var)
}
# Support Functions for Variable Suffix
f_Q_suffix <- function(seq.quantiles) {
quantile.suffix <- paste0('Qs', min(seq.quantiles),
'e', max(seq.quantiles),
'n', (length(seq.quantiles)-1))
}
# Support Functions for Quantile Labeling
f_Q_label <- function(arr.quantiles,
arr.sort.unique.quantile,
seq.quantiles) {
paste0('(',
paste0(which(arr.quantiles %in% arr.sort.unique.quantile), collapse=','),
') of ', f_Q_suffix(seq.quantiles))
}
# Generate New Variable Names with Quantile Suffix
f_var_rename <- function(name, seq.quantiles) {
quantile.suffix <- paste0('_', f_Q_suffix(seq.quantiles))
return(sub('_q', quantile.suffix, name))
}
# Check Are Values within Group By Unique? If not, STOP
f_check_distinct_ingroup <- function(df, vars.group_by, vars.values_in_group) {
df.uniqus.in.group <- df %>% group_by(!!!syms(vars.group_by)) %>%
mutate(quant_vars_paste = paste(!!!(syms(vars.values_in_group)), sep='-')) %>%
mutate(unique_in_group = n_distinct(quant_vars_paste)) %>%
slice(1L) %>%
ungroup() %>%
group_by(unique_in_group) %>%
summarise(n=n())
if (sum(df.uniqus.in.group$unique_in_group) > 1) {
print(df.uniqus.in.group)
print(paste('vars.values_in_group', vars.values_in_group, sep=':'))
print(paste('vars.group_by', vars.group_by, sep=':'))
stop("The variables for which quantiles are to be taken are not identical within the group variables")
}
}# First Step, given groups, generate quantiles based on group characteristics
# vars.cts2quantile <- c('wealthIdx', 'hgt0', 'wgt0')
# seq.quantiles <- c(0, 0.3333, 0.6666, 1.0)
# vars.group_by <- c('indi.id')
# vars.arrange <- c('indi.id', 'svymthRound')
# vars.continuous <- c('wealthIdx', 'hgt0', 'wgt0')
df_sliced_quantiles <- function(df, vars.cts2quantile, seq.quantiles,
vars.group_by, vars.arrange) {
# Slicing data
df.grp.L1 <- df %>% group_by(!!!syms(vars.group_by)) %>% arrange(!!!syms(vars.arrange)) %>% slice(1L) %>% ungroup()
# Quantiles based on sliced data
df.sliced.quantiles <- lapply(vars.cts2quantile, gen_quantiles, df=df.grp.L1, prob=seq.quantiles) %>% reduce(full_join)
return(list(df.sliced.quantiles=df.sliced.quantiles,
df.grp.L1=df.grp.L1))
}# Cutting Function, Cut Continuous Variables into Quantiles with labeing
f_cut <- function(var, df.sliced.quantiles, seq.quantiles, include.lowest=TRUE, fan.labels=TRUE, print=FALSE) {
# unparsed string variable name
var.str <- substitute(var)
# Breaks
arr.quantiles <- df.sliced.quantiles[[var.str]]
arr.sort.unique.quantiles <- sort(unique(arr.quantiles))
if (print) {
print(arr.sort.unique.quantiles)
}
# Regular cutting With Standard Labels
# TRUE, means the lowest group has closed bracket left and right
var.quantile <- cut(var, breaks=arr.sort.unique.quantiles, include.lowest=include.lowest)
# Use my custom labels
if (fan.labels) {
levels.suffix <- lapply(arr.sort.unique.quantiles[1:(length(arr.sort.unique.quantiles)-1)],
f_Q_label,
arr.quantiles=arr.quantiles,
seq.quantiles=seq.quantiles)
if (print) {
print(levels.suffix)
}
levels(var.quantile) <- paste0(levels(var.quantile), '; ', levels.suffix)
}
# Return
return(var.quantile)
}# Combo Quantile Function
# vars.cts2quantile <- c('wealthIdx', 'hgt0', 'wgt0')
# seq.quantiles <- c(0, 0.3333, 0.6666, 1.0)
# vars.group_by <- c('indi.id')
# vars.arrange <- c('indi.id', 'svymthRound')
# vars.continuous <- c('wealthIdx', 'hgt0', 'wgt0')
df_cut_by_sliced_quantiles <- function(df, vars.cts2quantile, seq.quantiles,
vars.group_by, vars.arrange) {
# Check Are Values within Group By Unique? If not, STOP
f_check_distinct_ingroup(df, vars.group_by, vars.values_in_group=vars.cts2quantile)
# First Step Slicing
df.sliced <- df_sliced_quantiles(df, vars.cts2quantile, seq.quantiles, vars.group_by, vars.arrange)
# Second Step Generate Categorical Variables of Quantiles
df.with.cut.quant <- df %>% mutate_at(vars.cts2quantile,
funs(q=f_cut(., df.sliced$df.sliced.quantiles,
seq.quantiles=seq.quantiles,
include.lowest=TRUE, fan.labels=TRUE)))
if (length(vars.cts2quantile) > 1) {
df.with.cut.quant <- df.with.cut.quant %>%
rename_at(vars(contains('_q')),
funs(f_var_rename(., seq.quantiles=seq.quantiles)))
} else {
new.var.name <- paste0(vars.cts2quantile[1], '_', f_Q_suffix(seq.quantiles))
df.with.cut.quant <- df.with.cut.quant %>% rename(!!new.var.name := q)
}
# Newly Generated Quantile-Cut Variables
vars.quantile.cut <- df.with.cut.quant %>%
select(matches(paste0(vars.cts2quantile, collapse='|'))) %>%
select(matches(f_Q_suffix(seq.quantiles)))
# Return
return(list(df.with.cut.quant = df.with.cut.quant,
df.sliced.quantiles=df.sliced$df.sliced.quantiles,
df.grp.L1=df.sliced$df.grp.L1,
vars.quantile.cut=vars.quantile.cut))
}# Function to handle list inputs with different quantiles vars and probabilities
df_cut_by_sliced_quantiles_grps <- function(quantile.grp.list, df, vars.group_by, vars.arrange) {
vars.cts2quantile <- quantile.grp.list$vars
seq.quantiles <- quantile.grp.list$prob
return(df_cut_by_sliced_quantiles(df, vars.cts2quantile, seq.quantiles, vars.group_by, vars.arrange))
}
# Show Results
df_cut_by_sliced_quantiles_joint_results_grped <- function(df.with.cut.quant.all, vars.cts2quantile, vars.group_by, vars.arrange,
vars.quantile.cut.all, var.qjnt.grp.idx) {
# Show ALL
df.group.panel.cnt.mean <- df.with.cut.quant.all %>% group_by(!!!syms(vars.quantile.cut.all), !!sym(var.qjnt.grp.idx)) %>%
summarise_at(vars.cts2quantile, funs(mean, n()))
# Show Based on SLicing first
df.group.slice1.cnt.mean <- df.with.cut.quant.all %>% group_by(!!!syms(vars.group_by)) %>% arrange(!!!syms(vars.arrange)) %>% slice(1L) %>%
group_by(!!!syms(vars.quantile.cut.all), !!sym(var.qjnt.grp.idx)) %>%
summarise_at(vars.cts2quantile, funs(mean, n()))
return(list(df.group.panel.cnt.mean=df.group.panel.cnt.mean,
df.group.slice1.cnt.mean=df.group.slice1.cnt.mean))
}# # Joint Quantile Group Name
# var.qjnt.grp.idx <- 'group.index'
# # Generate Categorical Variables of Quantiles
# vars.group_by <- c('indi.id')
# vars.arrange <- c('indi.id', 'svymthRound')
# # Quantile Variables and Quantiles
# vars.cts2quantile.wealth <- c('wealthIdx')
# seq.quantiles.wealth <- c(0, .5, 1.0)
# vars.cts2quantile.wgthgt <- c('hgt0', 'wgt0')
# seq.quantiles.wgthgt <- c(0, .3333, 0.6666, 1.0)
# drop.any.quantile.na <- TRUE
# # collect to list
# list.cts2quantile <- list(list(vars=vars.cts2quantile.wealth,
# prob=seq.quantiles.wealth),
# list(vars=vars.cts2quantile.wgthgt,
# prob=seq.quantiles.wgthgt))
df_cut_by_sliced_quantiles_joint <- function(df, var.qjnt.grp.idx,
list.cts2quantile,
vars.group_by, vars.arrange,
drop.any.quantile.na = TRUE,
toprint = TRUE) {
# Original dimensions
if(toprint) {
print(dim(df))
}
# All Continuous Variables from lists
vars.cts2quantile <- unlist(lapply(list.cts2quantile, function(elist) elist$vars))
vars.cts2quantile
# Keep only if not NA for all Quantile variables
if (drop.any.quantile.na) {
df.select <- df %>% drop_na(c(vars.group_by, vars.arrange, vars.cts2quantile))
} else {
df.select <- df
}
if(toprint) {
print(dim(df.select))
}
# Apply qunatile function to all elements of list of list
df.cut.list <- lapply(list.cts2quantile, df_cut_by_sliced_quantiles_grps,
df=df.select, vars.group_by=vars.group_by, vars.arrange=vars.arrange)
# Reduce Resulting Core Panel Matrix Together
df.with.cut.quant.all <- lapply(df.cut.list, function(elist) elist$df.with.cut.quant) %>% reduce(left_join)
df.sliced.quantiles.all <- lapply(df.cut.list, function(elist) elist$df.sliced.quantiles)
if(toprint) {
print(dim(df.with.cut.quant.all))
}
# Obrain Newly Created Quantile Group Variables
vars.quantile.cut.all <- unlist(lapply(df.cut.list, function(elist) names(elist$vars.quantile.cut)))
if(toprint) {
print(vars.quantile.cut.all)
print(summary(df.with.cut.quant.all %>% select(one_of(vars.quantile.cut.all))))
}
# Generate Joint Quantile Index Variable
df.with.cut.quant.all <- df.with.cut.quant.all %>% mutate(!!var.qjnt.grp.idx := group_indices(., !!!syms(vars.quantile.cut.all)))
# Quantile Groups
arr.group.idx <- t(sort(unique(df.with.cut.quant.all[[var.qjnt.grp.idx]])))
# Results Display
df.group.print <- df_cut_by_sliced_quantiles_joint_results_grped(df.with.cut.quant.all, vars.cts2quantile,
vars.group_by, vars.arrange,
vars.quantile.cut.all, var.qjnt.grp.idx)
# list to Return
# These returns are the same as returns earlier: df_cut_by_sliced_quantiles
# Except that they are combined together
return(list(df.with.cut.quant = df.with.cut.quant.all,
df.sliced.quantiles = df.sliced.quantiles.all,
df.grp.L1 = (df.cut.list[[1]])$df.grp.L1,
vars.quantile.cut = vars.quantile.cut.all,
df.group.panel.cnt.mean = df.group.print$df.group.panel.cnt.mean,
df.group.slice1.cnt.mean = df.group.print$df.group.slice1.cnt.mean))
}Load Data
# Library
library(tidyverse)
# Load Sample Data
setwd('C:/Users/fan/R4Econ/_data/')
df <- read_csv('height_weight.csv')## Parsed with column specification:
## cols(
## S.country = col_character(),
## vil.id = col_double(),
## indi.id = col_double(),
## sex = col_character(),
## svymthRound = col_double(),
## momEdu = col_double(),
## wealthIdx = col_double(),
## hgt = col_double(),
## wgt = col_double(),
## hgt0 = col_double(),
## wgt0 = col_double(),
## prot = col_double(),
## cal = col_double(),
## p.A.prot = col_double(),
## p.A.nProt = col_double()
## )# Joint Quantile Group Name
var.qjnt.grp.idx <- 'group.index'
list.cts2quantile <- list(list(vars=c('hgt0'), prob=c(0, .3333, 0.6666, 1.0)))
results <- df_cut_by_sliced_quantiles_joint(df, var.qjnt.grp.idx, list.cts2quantile,
vars.group_by = c('indi.id'), vars.arrange = c('indi.id', 'svymthRound'),
drop.any.quantile.na = TRUE, toprint = FALSE)
# Show Results
results$df.group.slice1.cnt.mean## # A tibble: 3 x 4
## # Groups: hgt0_Qs0e1n3 [3]
## hgt0_Qs0e1n3 group.index mean n
## <fct> <int> <dbl> <int>
## 1 [40.6,48.5]; (1) of Qs0e1n3 1 47.0 580
## 2 (48.5,50.2]; (2) of Qs0e1n3 2 49.4 561
## 3 (50.2,58]; (3) of Qs0e1n3 3 51.7 568# Joint Quantile Group Name
var.qjnt.grp.idx <- 'wltQuintle.index'
list.cts2quantile <- list(list(vars=c('wealthIdx'), prob=seq(0, 1.0, 0.20)))
results <- df_cut_by_sliced_quantiles_joint((df %>% filter(S.country == 'Guatemala')),
var.qjnt.grp.idx, list.cts2quantile,
vars.group_by = c('indi.id'), vars.arrange = c('indi.id', 'svymthRound'),
drop.any.quantile.na = TRUE, toprint = FALSE)
# Show Results
results$df.group.slice1.cnt.mean## # A tibble: 5 x 4
## # Groups: wealthIdx_Qs0e1n5 [5]
## wealthIdx_Qs0e1n5 wltQuintle.index mean n
## <fct> <int> <dbl> <int>
## 1 [1,1.6]; (1) of Qs0e1n5 1 1.25 151
## 2 (1.6,2.1]; (2) of Qs0e1n5 2 1.82 139
## 3 (2.1,2.3]; (3) of Qs0e1n5 3 2.25 139
## 4 (2.3,2.9]; (4) of Qs0e1n5 4 2.70 134
## 5 (2.9,6.6]; (5) of Qs0e1n5 5 3.77 111# Joint Quantile Group Name
var.qjnt.grp.idx <- 'group.index'
list.cts2quantile <- list(list(vars=c('hgt0', 'wgt0'), prob=c(0, .5, 1.0)))
results <- df_cut_by_sliced_quantiles_joint(df, var.qjnt.grp.idx, list.cts2quantile,
vars.group_by = c('indi.id'), vars.arrange = c('indi.id', 'svymthRound'),
drop.any.quantile.na = TRUE, toprint = FALSE)## Joining, by = "quant.perc"# Show Results
results$df.group.slice1.cnt.mean## # A tibble: 4 x 7
## # Groups: hgt0_Qs0e1n2, wgt0_Qs0e1n2 [4]
## hgt0_Qs0e1n2 wgt0_Qs0e1n2 group.index hgt0_mean wgt0_mean hgt0_n wgt0_n
## <fct> <fct> <int> <dbl> <dbl> <int> <int>
## 1 [40.6,49.4]; (1) of Qs0e1n2 [1.4e+03,3.01e+03]; (1) of Qs0e1n2 1 47.4 2650. 652 652
## 2 [40.6,49.4]; (1) of Qs0e1n2 (3.01e+03,5.49e+03]; (2) of Qs0e1n2 2 48.5 3244. 228 228
## 3 (49.4,58]; (2) of Qs0e1n2 [1.4e+03,3.01e+03]; (1) of Qs0e1n2 3 50.4 2829. 202 202
## 4 (49.4,58]; (2) of Qs0e1n2 (3.01e+03,5.49e+03]; (2) of Qs0e1n2 4 51.3 3483. 626 626# Joint Quantile Group Name
var.qjnt.grp.idx <- 'group.index'
list.cts2quantile <- list(list(vars=c('wealthIdx'), prob=c(0, .5, 1.0)), list(vars=c('hgt0'), prob=c(0, .333, 0.666, 1.0)))
results <- df_cut_by_sliced_quantiles_joint((df %>% filter(S.country == 'Cebu')),
var.qjnt.grp.idx, list.cts2quantile,
vars.group_by = c('indi.id'), vars.arrange = c('indi.id', 'svymthRound'),
drop.any.quantile.na = TRUE, toprint = FALSE)## Joining, by = c("S.country", "vil.id", "indi.id", "sex", "svymthRound", "momEdu", "wealthIdx", "hgt", "wgt", "hgt0", "wgt0", "prot", "cal", "p.A.prot",
## "p.A.nProt")# Show Results
results$df.group.slice1.cnt.mean## # A tibble: 6 x 7
## # Groups: wealthIdx_Qs0e1n2, hgt0_Qs0e1n3 [6]
## wealthIdx_Qs0e1n2 hgt0_Qs0e1n3 group.index wealthIdx_mean hgt0_mean wealthIdx_n hgt0_n
## <fct> <fct> <int> <dbl> <dbl> <int> <int>
## 1 [5.2,8.3]; (1) of Qs0e1n2 [41.1,48.4]; (1) of Qs0e1n3 1 7.15 46.9 270 270
## 2 [5.2,8.3]; (1) of Qs0e1n2 (48.4,50.1]; (2) of Qs0e1n3 2 7.18 49.2 269 269
## 3 [5.2,8.3]; (1) of Qs0e1n2 (50.1,58]; (3) of Qs0e1n3 3 7.13 51.3 236 236
## 4 (8.3,19.3]; (2) of Qs0e1n2 [41.1,48.4]; (1) of Qs0e1n3 4 11.1 47.2 179 179
## 5 (8.3,19.3]; (2) of Qs0e1n2 (48.4,50.1]; (2) of Qs0e1n3 5 11.2 49.3 185 185
## 6 (8.3,19.3]; (2) of Qs0e1n2 (50.1,58]; (3) of Qs0e1n3 6 11.6 51.7 207 207Weight at month 0 below and above median, height at month zero into three terciles.
# Joint Quantile Group Name
var.qjnt.grp.idx <- 'wltHgt0Wgt0.index'
list.cts2quantile <- list(list(vars=c('wealthIdx'), prob=c(0, .5, 1.0)), list(vars=c('hgt0', 'wgt0'), prob=c(0, .5, 1.0)))
results <- df_cut_by_sliced_quantiles_joint((df %>% filter(S.country == 'Cebu')),
var.qjnt.grp.idx, list.cts2quantile,
vars.group_by = c('indi.id'), vars.arrange = c('indi.id', 'svymthRound'),
drop.any.quantile.na = TRUE, toprint = FALSE)## Joining, by = "quant.perc"Joining, by = c("S.country", "vil.id", "indi.id", "sex", "svymthRound", "momEdu", "wealthIdx", "hgt", "wgt", "hgt0", "wgt0",
## "prot", "cal", "p.A.prot", "p.A.nProt")# Show Results
results$df.group.slice1.cnt.mean## # A tibble: 8 x 10
## # Groups: wealthIdx_Qs0e1n2, hgt0_Qs0e1n2, wgt0_Qs0e1n2 [8]
## wealthIdx_Qs0e1n2 hgt0_Qs0e1n2 wgt0_Qs0e1n2 wltHgt0Wgt0.ind~ wealthIdx_mean hgt0_mean wgt0_mean wealthIdx_n hgt0_n wgt0_n
## <fct> <fct> <fct> <int> <dbl> <dbl> <dbl> <int> <int> <int>
## 1 [5.2,8.3]; (1) of Qs0e~ [41.1,49.2]; (1) of Qs~ [1.4e+03,2.98e+03]; (1) of ~ 1 7.16 47.3 2607. 308 308 308
## 2 [5.2,8.3]; (1) of Qs0e~ [41.1,49.2]; (1) of Qs~ (2.98e+03,5.49e+03]; (2) of~ 2 7.27 48.4 3156. 102 102 102
## 3 [5.2,8.3]; (1) of Qs0e~ (49.2,58]; (2) of Qs0e~ [1.4e+03,2.98e+03]; (1) of ~ 3 7.00 50.2 2781. 97 97 97
## 4 [5.2,8.3]; (1) of Qs0e~ (49.2,58]; (2) of Qs0e~ (2.98e+03,5.49e+03]; (2) of~ 4 7.16 51.0 3328. 268 268 268
## 5 (8.3,19.3]; (2) of Qs0~ [41.1,49.2]; (1) of Qs~ [1.4e+03,2.98e+03]; (1) of ~ 5 10.9 47.4 2632. 186 186 186
## 6 (8.3,19.3]; (2) of Qs0~ [41.1,49.2]; (1) of Qs~ (2.98e+03,5.49e+03]; (2) of~ 6 11.3 48.5 3196. 81 81 81
## 7 (8.3,19.3]; (2) of Qs0~ (49.2,58]; (2) of Qs0e~ [1.4e+03,2.98e+03]; (1) of ~ 7 11.3 50.2 2779. 82 82 82
## 8 (8.3,19.3]; (2) of Qs0~ (49.2,58]; (2) of Qs0e~ (2.98e+03,5.49e+03]; (2) of~ 8 11.7 51.4 3431. 222 222 222The idea of the function is to generate quantiles levels first, and then use those to generate the categories based on quantiles. Rather than doing this in one step. These are done in two steps, to increase clarity in the quantiles used for quantile category generation. And a dataframe with these quantiles are saved as a separate output of the function.
Quantiles from Different Variables. Note that these variables are specific to the individual, not individual/month. So we need to first slick the data, so that we only get the first rows.
Do this in several steps to clarify group_by level. No speed loss.
# Selected Variables, many Percentiles
vars.group_by <- c('indi.id')
vars.arrange <- c('indi.id', 'svymthRound')
vars.cts2quantile <- c('wealthIdx', 'hgt0', 'wgt0')
seq.quantiles <- c(0, 0.3333, 0.6666, 1.0)
df.sliced <- df_sliced_quantiles(df, vars.cts2quantile, seq.quantiles, vars.group_by, vars.arrange)## Joining, by = "quant.perc"Joining, by = "quant.perc"df.sliced.quantiles <- df.sliced$df.sliced.quantiles
df.grp.L1 <- df.sliced$df.grp.L1df.sliced.quantiles## # A tibble: 4 x 4
## quant.perc wealthIdx hgt0 wgt0
## <chr> <dbl> <dbl> <dbl>
## 1 0% 1 40.6 1402.
## 2 33.33% 5.2 48.5 2843.
## 3 66.66% 8.3 50.2 3209.
## 4 100% 19.3 58 5494.# Quantiles all Variables
suppressMessages(lapply(names(df), gen_quantiles, df=df.grp.L1, prob=seq(0.1,0.9,0.10)) %>% reduce(full_join))## Warning in quantile(as.numeric(df[[var]]), prob, na.rm = TRUE): NAs introduced by coercion
## Warning in quantile(as.numeric(df[[var]]), prob, na.rm = TRUE): NAs introduced by coercion## # A tibble: 9 x 16
## quant.perc S.country vil.id indi.id sex svymthRound momEdu wealthIdx hgt wgt hgt0 wgt0 prot cal p.A.prot p.A.nProt
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 10% NA 3 203. NA 0 5.7 1.7 46.3 1397. 46.6 2500. 0.5 0.5 24.3 0.5
## 2 20% NA 4 405. NA 0 6.9 2.3 47.3 1840. 47.7 2686. 0.5 0.5 172. 0.5
## 3 30% NA 6 608. NA 0 7.7 3.3 48 2272. 48.3 2804. 0.5 0.5 721. 1.06
## 4 40% NA 8 810. NA 0 8.6 6.3 48.7 2669. 48.8 2910. 0.5 0.5 1010. 19
## 5 50% NA 9 1012 NA 0 9.3 7.3 49.4 3050. 49.4 3013 0.5 0.5 1273. 111.
## 6 60% NA 13 1214. NA 0 10.4 8.3 49.9 3440. 49.9 3126. 0.5 3.88 1614. 222.
## 7 70% NA 14 1416. NA 0 11.4 8.3 50.5 3857. 50.4 3250. 0.7 8.26 2680. 257.
## 8 80% NA 17 1619. NA 0 12.7 9.3 51.2 4258. 51.0 3418. 1.2 11.5 4761. 298.
## 9 90% NA 26 1821. NA 0 14.6 11.3 52.3 4704. 52 3683. 1.6 15.6 10868. 365.Using the Quantiles we have generate, cut the continuous variables to generate categorical quantile variables in the full dataframe.
Note that we can only cut based on unique breaks, but sometimes quantile break-points are the same if some values are often observed, and also if there are too few observations with respect to quantile groups.
To resolve this issue, we only look at unique quantiles.
We need several support Functions: 1. support functions to generate suffix for quantile variables based on quantile cuts 2. support for labeling variables of resulting quantiles beyond bracketing
# Function Testing
arr.quantiles <- df.sliced.quantiles[[substitute('wealthIdx')]]
arr.quantiles## [1] 1.0 5.2 8.3 19.3arr.sort.unique.quantiles <- sort(unique(df.sliced.quantiles[[substitute('wealthIdx')]]))
arr.sort.unique.quantiles## [1] 1.0 5.2 8.3 19.3f_Q_label(arr.quantiles, arr.sort.unique.quantiles[1], seq.quantiles)## [1] "(1) of Qs0e1n3"f_Q_label(arr.quantiles, arr.sort.unique.quantiles[2], seq.quantiles)## [1] "(2) of Qs0e1n3"lapply(arr.sort.unique.quantiles[1:(length(arr.sort.unique.quantiles)-1)],
f_Q_label,
arr.quantiles=arr.quantiles,
seq.quantiles=seq.quantiles)## [[1]]
## [1] "(1) of Qs0e1n3"
##
## [[2]]
## [1] "(2) of Qs0e1n3"
##
## [[3]]
## [1] "(3) of Qs0e1n3"# Generate Categorical Variables of Quantiles
vars.group_by <- c('indi.id')
vars.arrange <- c('indi.id', 'svymthRound')
vars.cts2quantile <- c('wealthIdx', 'hgt0', 'wgt0')
seq.quantiles <- c(0, 0.3333, 0.6666, 1.0)
df.cut <- df_cut_by_sliced_quantiles(df, vars.cts2quantile, seq.quantiles, vars.group_by, vars.arrange)## Joining, by = "quant.perc"Joining, by = "quant.perc"vars.quantile.cut <- df.cut$vars.quantile.cut
df.with.cut.quant <- df.cut$df.with.cut.quant
df.grp.L1 <- df.cut$df.grp.L1# Cut Variables Generated
names(vars.quantile.cut)## [1] "wealthIdx_Qs0e1n3" "hgt0_Qs0e1n3" "wgt0_Qs0e1n3"summary(vars.quantile.cut)## wealthIdx_Qs0e1n3 hgt0_Qs0e1n3 wgt0_Qs0e1n3
## [1,5.2]; (1) of Qs0e1n3 :10958 [40.6,48.5]; (1) of Qs0e1n3:10232 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 :10105
## (5.2,8.3]; (2) of Qs0e1n3 :13812 (48.5,50.2]; (2) of Qs0e1n3: 9895 (2.84e+03,3.21e+03]; (2) of Qs0e1n3:10056
## (8.3,19.3]; (3) of Qs0e1n3:10295 (50.2,58]; (3) of Qs0e1n3 : 9908 (3.21e+03,5.49e+03]; (3) of Qs0e1n3: 9858
## NA's : 5030 NA's : 5046# options(repr.matrix.max.rows=50, repr.matrix.max.cols=20)
# df.with.cut.quant# Group By Results
f.count <- function(df, var.cts, seq.quantiles) {
df %>% select(S.country, indi.id, svymthRound, matches(paste0(var.cts, collapse='|'))) %>%
group_by(!!sym(f_var_rename(paste0(var.cts,'_q'), seq.quantiles))) %>%
summarise_all(funs(n=n()))
}# Full Panel Results
lapply(vars.cts2quantile, f.count, df=df.with.cut.quant, seq.quantiles=seq.quantiles)## Warning: Factor `hgt0_Qs0e1n3` contains implicit NA, consider using `forcats::fct_explicit_na`## Warning: Factor `wgt0_Qs0e1n3` contains implicit NA, consider using `forcats::fct_explicit_na`## [[1]]
## # A tibble: 3 x 5
## wealthIdx_Qs0e1n3 S.country_n indi.id_n svymthRound_n wealthIdx_n
## <fct> <int> <int> <int> <int>
## 1 [1,5.2]; (1) of Qs0e1n3 10958 10958 10958 10958
## 2 (5.2,8.3]; (2) of Qs0e1n3 13812 13812 13812 13812
## 3 (8.3,19.3]; (3) of Qs0e1n3 10295 10295 10295 10295
##
## [[2]]
## # A tibble: 4 x 5
## hgt0_Qs0e1n3 S.country_n indi.id_n svymthRound_n hgt0_n
## <fct> <int> <int> <int> <int>
## 1 [40.6,48.5]; (1) of Qs0e1n3 10232 10232 10232 10232
## 2 (48.5,50.2]; (2) of Qs0e1n3 9895 9895 9895 9895
## 3 (50.2,58]; (3) of Qs0e1n3 9908 9908 9908 9908
## 4 <NA> 5030 5030 5030 5030
##
## [[3]]
## # A tibble: 4 x 5
## wgt0_Qs0e1n3 S.country_n indi.id_n svymthRound_n wgt0_n
## <fct> <int> <int> <int> <int>
## 1 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 10105 10105 10105 10105
## 2 (2.84e+03,3.21e+03]; (2) of Qs0e1n3 10056 10056 10056 10056
## 3 (3.21e+03,5.49e+03]; (3) of Qs0e1n3 9858 9858 9858 9858
## 4 <NA> 5046 5046 5046 5046# Results Individual Slice
lapply(vars.cts2quantile, f.count,
df=(df.with.cut.quant %>% group_by(!!!syms(vars.group_by)) %>% arrange(!!!syms(vars.arrange)) %>% slice(1L)),
seq.quantiles = seq.quantiles)## Warning: Factor `hgt0_Qs0e1n3` contains implicit NA, consider using `forcats::fct_explicit_na`## Warning: Factor `wgt0_Qs0e1n3` contains implicit NA, consider using `forcats::fct_explicit_na`## [[1]]
## # A tibble: 3 x 5
## wealthIdx_Qs0e1n3 S.country_n indi.id_n svymthRound_n wealthIdx_n
## <fct> <int> <int> <int> <int>
## 1 [1,5.2]; (1) of Qs0e1n3 683 683 683 683
## 2 (5.2,8.3]; (2) of Qs0e1n3 768 768 768 768
## 3 (8.3,19.3]; (3) of Qs0e1n3 572 572 572 572
##
## [[2]]
## # A tibble: 4 x 5
## hgt0_Qs0e1n3 S.country_n indi.id_n svymthRound_n hgt0_n
## <fct> <int> <int> <int> <int>
## 1 [40.6,48.5]; (1) of Qs0e1n3 580 580 580 580
## 2 (48.5,50.2]; (2) of Qs0e1n3 561 561 561 561
## 3 (50.2,58]; (3) of Qs0e1n3 568 568 568 568
## 4 <NA> 314 314 314 314
##
## [[3]]
## # A tibble: 4 x 5
## wgt0_Qs0e1n3 S.country_n indi.id_n svymthRound_n wgt0_n
## <fct> <int> <int> <int> <int>
## 1 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 569 569 569 569
## 2 (2.84e+03,3.21e+03]; (2) of Qs0e1n3 569 569 569 569
## 3 (3.21e+03,5.49e+03]; (3) of Qs0e1n3 570 570 570 570
## 4 <NA> 315 315 315 315Collect together different quantile base variables and their percentile cuttings quantile rules. Input Parameters.
# Generate Categorical Variables of Quantiles
vars.group_by <- c('indi.id')
vars.arrange <- c('indi.id', 'svymthRound')# Quantile Variables and Quantiles
vars.cts2quantile.wealth <- c('wealthIdx')
seq.quantiles.wealth <- c(0, .5, 1.0)
vars.cts2quantile.wgthgt <- c('hgt0', 'wgt0')
seq.quantiles.wgthgt <- c(0, .3333, 0.6666, 1.0)
drop.any.quantile.na <- TRUE
# collect to list
list.cts2quantile <- list(list(vars=vars.cts2quantile.wealth,
prob=seq.quantiles.wealth),
list(vars=vars.cts2quantile.wgthgt,
prob=seq.quantiles.wgthgt))Need to make sure quantile variables are unique within groups
vars.cts2quantile <- unlist(lapply(list.cts2quantile, function(elist) elist$vars))
f_check_distinct_ingroup(df, vars.group_by, vars.values_in_group=vars.cts2quantile)# Original dimensions
dim(df)## [1] 35065 15# All Continuous Variables from lists
vars.cts2quantile <- unlist(lapply(list.cts2quantile, function(elist) elist$vars))
vars.cts2quantile## [1] "wealthIdx" "hgt0" "wgt0"# Keep only if not NA for all Quantile variables
if (drop.any.quantile.na) {
df.select <- df %>% drop_na(c(vars.group_by, vars.arrange, vars.cts2quantile))
}
dim(df.select)## [1] 30019 15# Dealing with a list of quantile variables
df.cut.wealth <- df_cut_by_sliced_quantiles(df.select, vars.cts2quantile.wealth, seq.quantiles.wealth, vars.group_by, vars.arrange)
summary(df.cut.wealth$vars.quantile.cut)## wealthIdx_Qs0e1n2
## [1,7.3]; (1) of Qs0e1n2 :14936
## (7.3,19.3]; (2) of Qs0e1n2:15083# summary((df.cut.wealth$df.with.cut.quant)[['wealthIdx_Qs0e1n2']])
# df.cut.wealth$df.with.cut.quant %>% filter(is.na(wealthIdx_Qs0e1n2))
# df.cut.wealth$df.with.cut.quant %>% filter(indi.id == 500)df.cut.wgthgt <- df_cut_by_sliced_quantiles(df.select, vars.cts2quantile.wgthgt, seq.quantiles.wgthgt, vars.group_by, vars.arrange)## Joining, by = "quant.perc"summary(df.cut.wgthgt$vars.quantile.cut)## hgt0_Qs0e1n3 wgt0_Qs0e1n3
## [40.6,48.5]; (1) of Qs0e1n3:10216 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 :10105
## (48.5,50.2]; (2) of Qs0e1n3: 9895 (2.84e+03,3.21e+03]; (2) of Qs0e1n3:10056
## (50.2,58]; (3) of Qs0e1n3 : 9908 (3.21e+03,5.49e+03]; (3) of Qs0e1n3: 9858# Function to handle list inputs with different quantiles vars and probabilities
df_cut_by_sliced_quantiles_grps <- function(quantile.grp.list, df, vars.group_by, vars.arrange) {
vars.cts2quantile <- quantile.grp.list$vars
seq.quantiles <- quantile.grp.list$prob
return(df_cut_by_sliced_quantiles(df, vars.cts2quantile, seq.quantiles, vars.group_by, vars.arrange))
}# Apply function
df.cut.list <- lapply(list.cts2quantile, df_cut_by_sliced_quantiles_grps,
df=df.select, vars.group_by=vars.group_by, vars.arrange=vars.arrange)## Joining, by = "quant.perc"# Reduce Resulting Matrixes Together
df.with.cut.quant.all <- lapply(df.cut.list, function(elist) elist$df.with.cut.quant) %>% reduce(left_join)## Joining, by = c("S.country", "vil.id", "indi.id", "sex", "svymthRound", "momEdu", "wealthIdx", "hgt", "wgt", "hgt0", "wgt0", "prot", "cal", "p.A.prot",
## "p.A.nProt")dim(df.with.cut.quant.all)## [1] 30019 18# Obrain Newly Created Quantile Group Variables
vars.quantile.cut.all <- unlist(lapply(df.cut.list, function(elist) names(elist$vars.quantile.cut)))
vars.quantile.cut.all## [1] "wealthIdx_Qs0e1n2" "hgt0_Qs0e1n3" "wgt0_Qs0e1n3"Summarize by all groups.
summary(df.with.cut.quant.all %>% select(one_of(vars.quantile.cut.all)))## wealthIdx_Qs0e1n2 hgt0_Qs0e1n3 wgt0_Qs0e1n3
## [1,7.3]; (1) of Qs0e1n2 :14936 [40.6,48.5]; (1) of Qs0e1n3:10216 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 :10105
## (7.3,19.3]; (2) of Qs0e1n2:15083 (48.5,50.2]; (2) of Qs0e1n3: 9895 (2.84e+03,3.21e+03]; (2) of Qs0e1n3:10056
## (50.2,58]; (3) of Qs0e1n3 : 9908 (3.21e+03,5.49e+03]; (3) of Qs0e1n3: 9858# df.with.cut.quant.all %>%
# group_by(!!!syms(vars.quantile.cut.all)) %>%
# summarise_at(vars.cts2quantile, funs(mean, n()))# Generate Joint Quantile Index Variable
var.qjnt.grp.idx <- 'group.index'
df.with.cut.quant.all <- df.with.cut.quant.all %>% mutate(!!var.qjnt.grp.idx := group_indices(., !!!syms(vars.quantile.cut.all)))arr.group.idx <- t(sort(unique(df.with.cut.quant.all[[var.qjnt.grp.idx]])))
arr.group.idx## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18df.with.cut.quant.all %>% group_by(!!!syms(vars.quantile.cut.all), !!sym(var.qjnt.grp.idx)) %>%
summarise_at(vars.cts2quantile, funs(mean, n()))## # A tibble: 18 x 10
## # Groups: wealthIdx_Qs0e1n2, hgt0_Qs0e1n3, wgt0_Qs0e1n3 [18]
## wealthIdx_Qs0e1n2 hgt0_Qs0e1n3 wgt0_Qs0e1n3 group.index wealthIdx_mean hgt0_mean wgt0_mean wealthIdx_n hgt0_n wgt0_n
## <fct> <fct> <fct> <int> <dbl> <dbl> <dbl> <int> <int> <int>
## 1 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0~ [1.4e+03,2.84e+03]; (1) of Qs~ 1 5.31 46.6 2498. 3304 3304 3304
## 2 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0~ (2.84e+03,3.21e+03]; (2) of Q~ 2 5.08 47.6 2993. 1348 1348 1348
## 3 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0~ (3.21e+03,5.49e+03]; (3) of Q~ 3 3.64 47.7 3429. 362 362 362
## 4 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0~ [1.4e+03,2.84e+03]; (1) of Qs~ 4 6.04 49.2 2671. 1134 1134 1134
## 5 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0~ (2.84e+03,3.21e+03]; (2) of Q~ 5 5.36 49.3 3030. 2184 2184 2184
## 6 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0~ (3.21e+03,5.49e+03]; (3) of Q~ 6 4.36 49.6 3481. 1484 1484 1484
## 7 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1~ [1.4e+03,2.84e+03]; (1) of Qs~ 7 6.25 51.2 2666. 196 196 196
## 8 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1~ (2.84e+03,3.21e+03]; (2) of Q~ 8 5.45 51.0 3048. 1466 1466 1466
## 9 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1~ (3.21e+03,5.49e+03]; (3) of Q~ 9 4.06 51.8 3660. 3458 3458 3458
## 10 (7.3,19.3]; (2) of Qs0e~ [40.6,48.5]; (1) of Qs0~ [1.4e+03,2.84e+03]; (1) of Qs~ 10 9.86 46.8 2540. 3438 3438 3438
## 11 (7.3,19.3]; (2) of Qs0e~ [40.6,48.5]; (1) of Qs0~ (2.84e+03,3.21e+03]; (2) of Q~ 11 10.5 47.8 2980. 1440 1440 1440
## 12 (7.3,19.3]; (2) of Qs0e~ [40.6,48.5]; (1) of Qs0~ (3.21e+03,5.49e+03]; (3) of Q~ 12 11.2 48.0 3403. 324 324 324
## 13 (7.3,19.3]; (2) of Qs0e~ (48.5,50.2]; (2) of Qs0~ [1.4e+03,2.84e+03]; (1) of Qs~ 13 10.2 49.4 2679. 1709 1709 1709
## 14 (7.3,19.3]; (2) of Qs0e~ (48.5,50.2]; (2) of Qs0~ (2.84e+03,3.21e+03]; (2) of Q~ 14 10.3 49.3 3024. 2034 2034 2034
## 15 (7.3,19.3]; (2) of Qs0e~ (48.5,50.2]; (2) of Qs0~ (3.21e+03,5.49e+03]; (3) of Q~ 15 10.3 49.4 3387. 1350 1350 1350
## 16 (7.3,19.3]; (2) of Qs0e~ (50.2,58]; (3) of Qs0e1~ [1.4e+03,2.84e+03]; (1) of Qs~ 16 10.5 50.9 2677. 324 324 324
## 17 (7.3,19.3]; (2) of Qs0e~ (50.2,58]; (3) of Qs0e1~ (2.84e+03,3.21e+03]; (2) of Q~ 17 10.3 51.3 3060. 1584 1584 1584
## 18 (7.3,19.3]; (2) of Qs0e~ (50.2,58]; (3) of Qs0e1~ (3.21e+03,5.49e+03]; (3) of Q~ 18 11.0 52.1 3623. 2880 2880 2880df.with.cut.quant.all %>% group_by(!!!syms(vars.group_by)) %>% arrange(!!!syms(vars.arrange)) %>% slice(1L) %>%
group_by(!!!syms(vars.quantile.cut.all), !!sym(var.qjnt.grp.idx)) %>%
summarise_at(vars.cts2quantile, funs(mean, n()))## # A tibble: 18 x 10
## # Groups: wealthIdx_Qs0e1n2, hgt0_Qs0e1n3, wgt0_Qs0e1n3 [18]
## wealthIdx_Qs0e1n2 hgt0_Qs0e1n3 wgt0_Qs0e1n3 group.index wealthIdx_mean hgt0_mean wgt0_mean wealthIdx_n hgt0_n wgt0_n
## <fct> <fct> <fct> <int> <dbl> <dbl> <dbl> <int> <int> <int>
## 1 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0~ [1.4e+03,2.84e+03]; (1) of Qs~ 1 5.20 46.6 2499. 190 190 190
## 2 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0~ (2.84e+03,3.21e+03]; (2) of Q~ 2 4.96 47.6 2993. 78 78 78
## 3 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0~ (3.21e+03,5.49e+03]; (3) of Q~ 3 3.56 47.7 3431. 22 22 22
## 4 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0~ [1.4e+03,2.84e+03]; (1) of Qs~ 4 5.99 49.2 2671. 64 64 64
## 5 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0~ (2.84e+03,3.21e+03]; (2) of Q~ 5 5.25 49.3 3031. 126 126 126
## 6 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0~ (3.21e+03,5.49e+03]; (3) of Q~ 6 4.24 49.6 3485. 88 88 88
## 7 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1~ [1.4e+03,2.84e+03]; (1) of Qs~ 7 6.22 51.2 2666. 11 11 11
## 8 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1~ (2.84e+03,3.21e+03]; (2) of Q~ 8 5.36 51.0 3048. 84 84 84
## 9 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1~ (3.21e+03,5.49e+03]; (3) of Q~ 9 3.94 51.8 3667. 207 207 207
## 10 (7.3,19.3]; (2) of Qs0e~ [40.6,48.5]; (1) of Qs0~ [1.4e+03,2.84e+03]; (1) of Qs~ 10 9.86 46.8 2540. 191 191 191
## 11 (7.3,19.3]; (2) of Qs0e~ [40.6,48.5]; (1) of Qs0~ (2.84e+03,3.21e+03]; (2) of Q~ 11 10.5 47.8 2980. 80 80 80
## 12 (7.3,19.3]; (2) of Qs0e~ [40.6,48.5]; (1) of Qs0~ (3.21e+03,5.49e+03]; (3) of Q~ 12 11.2 48.0 3403. 18 18 18
## 13 (7.3,19.3]; (2) of Qs0e~ (48.5,50.2]; (2) of Qs0~ [1.4e+03,2.84e+03]; (1) of Qs~ 13 10.2 49.4 2678. 95 95 95
## 14 (7.3,19.3]; (2) of Qs0e~ (48.5,50.2]; (2) of Qs0~ (2.84e+03,3.21e+03]; (2) of Q~ 14 10.3 49.3 3024. 113 113 113
## 15 (7.3,19.3]; (2) of Qs0e~ (48.5,50.2]; (2) of Qs0~ (3.21e+03,5.49e+03]; (3) of Q~ 15 10.3 49.4 3387. 75 75 75
## 16 (7.3,19.3]; (2) of Qs0e~ (50.2,58]; (3) of Qs0e1~ [1.4e+03,2.84e+03]; (1) of Qs~ 16 10.5 50.9 2677. 18 18 18
## 17 (7.3,19.3]; (2) of Qs0e~ (50.2,58]; (3) of Qs0e1~ (2.84e+03,3.21e+03]; (2) of Q~ 17 10.3 51.3 3060. 88 88 88
## 18 (7.3,19.3]; (2) of Qs0e~ (50.2,58]; (3) of Qs0e1~ (3.21e+03,5.49e+03]; (3) of Q~ 18 11.0 52.1 3623. 160 160 160Index from 1 to 18, change input values based on index
# arr.group.idx.subsidy <- arr.group.idx*2 - ((arr.group.idx)^2)*0.01
arr.group.idx.subsidy <- arr.group.idx*2
df.with.cut.quant.all %>%
mutate(more_prot = prot + arr.group.idx.subsidy[!!sym(var.qjnt.grp.idx)]) %>%
group_by(!!!syms(vars.quantile.cut.all), !!sym(var.qjnt.grp.idx)) %>%
summarise_at(c('more_prot', 'prot'), funs(mean(., na.rm=TRUE)))## # A tibble: 18 x 6
## # Groups: wealthIdx_Qs0e1n2, hgt0_Qs0e1n3, wgt0_Qs0e1n3 [18]
## wealthIdx_Qs0e1n2 hgt0_Qs0e1n3 wgt0_Qs0e1n3 group.index more_prot prot
## <fct> <fct> <fct> <int> <dbl> <dbl>
## 1 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0e1n3 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 1 14.1 12.1
## 2 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0e1n3 (2.84e+03,3.21e+03]; (2) of Qs0e1n3 2 15.9 11.9
## 3 [1,7.3]; (1) of Qs0e1n2 [40.6,48.5]; (1) of Qs0e1n3 (3.21e+03,5.49e+03]; (3) of Qs0e1n3 3 27.2 21.2
## 4 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0e1n3 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 4 18.9 10.9
## 5 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0e1n3 (2.84e+03,3.21e+03]; (2) of Qs0e1n3 5 22.3 12.3
## 6 [1,7.3]; (1) of Qs0e1n2 (48.5,50.2]; (2) of Qs0e1n3 (3.21e+03,5.49e+03]; (3) of Qs0e1n3 6 28.6 16.6
## 7 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1n3 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 7 25.5 11.5
## 8 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1n3 (2.84e+03,3.21e+03]; (2) of Qs0e1n3 8 28.0 12.0
## 9 [1,7.3]; (1) of Qs0e1n2 (50.2,58]; (3) of Qs0e1n3 (3.21e+03,5.49e+03]; (3) of Qs0e1n3 9 34.7 16.7
## 10 (7.3,19.3]; (2) of Qs0e1n2 [40.6,48.5]; (1) of Qs0e1n3 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 10 30.7 10.7
## 11 (7.3,19.3]; (2) of Qs0e1n2 [40.6,48.5]; (1) of Qs0e1n3 (2.84e+03,3.21e+03]; (2) of Qs0e1n3 11 34.8 12.8
## 12 (7.3,19.3]; (2) of Qs0e1n2 [40.6,48.5]; (1) of Qs0e1n3 (3.21e+03,5.49e+03]; (3) of Qs0e1n3 12 37.4 13.4
## 13 (7.3,19.3]; (2) of Qs0e1n2 (48.5,50.2]; (2) of Qs0e1n3 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 13 37.4 11.4
## 14 (7.3,19.3]; (2) of Qs0e1n2 (48.5,50.2]; (2) of Qs0e1n3 (2.84e+03,3.21e+03]; (2) of Qs0e1n3 14 41.5 13.5
## 15 (7.3,19.3]; (2) of Qs0e1n2 (48.5,50.2]; (2) of Qs0e1n3 (3.21e+03,5.49e+03]; (3) of Qs0e1n3 15 43.9 13.9
## 16 (7.3,19.3]; (2) of Qs0e1n2 (50.2,58]; (3) of Qs0e1n3 [1.4e+03,2.84e+03]; (1) of Qs0e1n3 16 43.8 11.8
## 17 (7.3,19.3]; (2) of Qs0e1n2 (50.2,58]; (3) of Qs0e1n3 (2.84e+03,3.21e+03]; (2) of Qs0e1n3 17 47.9 13.9
## 18 (7.3,19.3]; (2) of Qs0e1n2 (50.2,58]; (3) of Qs0e1n3 (3.21e+03,5.49e+03]; (3) of Qs0e1n3 18 50.3 14.3