LDA2
2024-05-13
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# Practical 1 - Preparing Understanding Society for LDA
# # install packages
# install.packages("tidyverse")
# install.packages("haven")
# install.packages("dplyr")
# install.packages("stringr")
# install.packages("haven")
# load packages
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(haven)
library(dplyr)
library(stringr)
library(haven)
# wave 6 (12 variables)
# --------------------------------------------------------------------------------
wave6_ifsdata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_6_ifs_derived_variables.tab", header=TRUE, sep="\t")
wave6_ifsdata2 <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_6_elsa_data_v2.tab", header=TRUE, sep="\t")
wave6_finsdata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_6_financial_derived_variables.tab", header=TRUE, sep="\t")
# merge data
w6 <- full_join(wave6_ifsdata,wave6_ifsdata2,by = "idauniq")
w6 <- full_join(w6,wave6_finsdata,by = "idauniq")
vars1 <-
c(
"idauniq", # 个人唯一标识符
"age", #年龄
"sex", # 性别
"marstat", # 婚姻状况：marital status - couple1 combined with dimar
"edqual", # 教育水平：educational qualification - info merged from current and previous waves
"famtype", # 家庭类型：household type
"srh_hrs", # 自评健康状况：self-reported health: HRS version
"tenure", # 住房保有权：housing tenure - copy of hotenu
"spen_r_i", # 国家养老金:state pension income (iapam/iappam) - value (incl. imputed values)
"totinc_bu_s" # BU总收入: BU total income - summary var
)
us6 <- w6 %>% select(all_of(vars1))
head(us6) # see first few cases and variables## idauniq age sex marstat edqual famtype srh_hrs tenure spen_r_i totinc_bu_s
## 1 160322 61 2 5 7 1 1 1 129.75 287.2558
## 2 165974 56 2 1 4 6 4 1 0.00 NA
## 3 105333 71 1 1 2 6 2 1 112.00 917.8748
## 4 108170 68 2 1 2 6 1 1 92.00 917.8748
## 5 161180 66 1 3 5 1 3 1 132.25 296.7415
## 6 151048 58 1 1 7 6 2 2 0.00 904.2505glimpse(us6) # another description of the data## Rows: 10,601
## Columns: 10
## $ idauniq <int> 160322, 165974, 105333, 108170, 161180, 151048, 150615, 11…
## $ age <int> 61, 56, 71, 68, 66, 58, 57, 65, 55, 99, 73, 69, 73, 65, 73…
## $ sex <int> 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1…
## $ marstat <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 4, 1, 1, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 3, 7, 4, 7, 2, 2, 7, 6, 4, 7, 6, 1, 4, 7…
## $ famtype <int> 1, 6, 6, 6, 1, 6, 6, 6, 6, 1, 6, 6, 6, 6, 1, 1, 6, 6, 6, 6…
## $ srh_hrs <int> 1, 4, 2, 1, 3, 2, 3, 2, 2, -1, 1, 1, 5, 2, 2, 4, 2, 3, 3, …
## $ tenure <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 2, 2…
## $ spen_r_i <dbl> 129.75, 0.00, 112.00, 92.00, 132.25, 0.00, 0.00, 139.25, 0…
## $ totinc_bu_s <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 904.25055,…# get names of us8
names(us6)## [1] "idauniq" "age" "sex" "marstat" "edqual"
## [6] "famtype" "srh_hrs" "tenure" "spen_r_i" "totinc_bu_s"# rename just variable
us6 <- us6 %>%
rename(
pid = idauniq,
health = srh_hrs,
gndr = sex,
peninc = spen_r_i,
totincome = totinc_bu_s
)
names(us6)## [1] "pid" "age" "gndr" "marstat" "edqual" "famtype"
## [7] "health" "tenure" "peninc" "totincome"# add wave signal to variable
# 时间常量：pid,age,gndr,edqual
us6 <-
rename_at(us6,
vars(-pid, -age, -gndr, -edqual),
~ str_c(., "_1"))
# inspect the results
glimpse(us6)## Rows: 10,601
## Columns: 10
## $ pid <int> 160322, 165974, 105333, 108170, 161180, 151048, 150615, 11…
## $ age <int> 61, 56, 71, 68, 66, 58, 57, 65, 55, 99, 73, 69, 73, 65, 73…
## $ gndr <int> 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1…
## $ marstat_1 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 4, 1, 1, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 3, 7, 4, 7, 2, 2, 7, 6, 4, 7, 6, 1, 4, 7…
## $ famtype_1 <int> 1, 6, 6, 6, 1, 6, 6, 6, 6, 1, 6, 6, 6, 6, 1, 1, 6, 6, 6, 6…
## $ health_1 <int> 1, 4, 2, 1, 3, 2, 3, 2, 2, -1, 1, 1, 5, 2, 2, 4, 2, 3, 3, …
## $ tenure_1 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 2, 2…
## $ peninc_1 <dbl> 129.75, 0.00, 112.00, 92.00, 132.25, 0.00, 0.00, 139.25, 0…
## $ totincome_1 <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 904.25055,…# make a variable that gets value 1
us6 <- mutate(us6, present_1 = 1)
glimpse(us6) # investigate result## Rows: 10,601
## Columns: 11
## $ pid <int> 160322, 165974, 105333, 108170, 161180, 151048, 150615, 11…
## $ age <int> 61, 56, 71, 68, 66, 58, 57, 65, 55, 99, 73, 69, 73, 65, 73…
## $ gndr <int> 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1…
## $ marstat_1 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 4, 1, 1, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 3, 7, 4, 7, 2, 2, 7, 6, 4, 7, 6, 1, 4, 7…
## $ famtype_1 <int> 1, 6, 6, 6, 1, 6, 6, 6, 6, 1, 6, 6, 6, 6, 1, 1, 6, 6, 6, 6…
## $ health_1 <int> 1, 4, 2, 1, 3, 2, 3, 2, 2, -1, 1, 1, 5, 2, 2, 4, 2, 3, 3, …
## $ tenure_1 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 2, 2…
## $ peninc_1 <dbl> 129.75, 0.00, 112.00, 92.00, 132.25, 0.00, 0.00, 139.25, 0…
## $ totincome_1 <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 904.25055,…
## $ present_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…# wave 7 (12 variables)
# --------------------------------------------------------------------------------
wave7_elsadata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_7_elsa_data.tab", header=TRUE, sep="\t")
wave7_ifsdata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_7_ifs_derived_variables.tab", header=TRUE, sep="\t")
wave7_findata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_7_financial_derived_variables.tab", header=TRUE, sep="\t")
# merge data
w7 <- full_join(wave7_elsadata, wave7_ifsdata, by = "idauniq")
w7 <- full_join(w7,wave7_findata,by = "idauniq")
# select variables
vars2 <-
c(
"idauniq", # 个人唯一标识符
"marstat", # 婚姻状况：marital status - couple1 combined with dimar
"famtype", # 家庭类型：household type
"srh_hrs", # 自评健康状况：self-reported health: HRS version
"tenure", # 住房保有权：housing tenure - copy of hotenu
"spen_r_i", # 国家养老金:state pension income (iapam/iappam) - value (incl. imputed values)
"totinc_bu_s" # BU总收入: BU total income - summary var
)
us7 <- w7 %>% select(all_of(vars2))
head(us7) # see first few cases and variables## idauniq marstat famtype srh_hrs tenure spen_r_i totinc_bu_s
## 1 104178 1 6 3 1 132.50000 805.1105
## 2 106612 1 6 2 1 113.00000 805.1105
## 3 104826 1 8 4 2 161.25000 725.9358
## 4 105887 1 8 3 2 71.25000 725.9358
## 5 106269 1 6 3 1 153.84616 264.0000
## 6 103787 1 6 3 1 57.69231 264.0000glimpse(us7) # another description of the data## Rows: 9,666
## Columns: 7
## $ idauniq <int> 104178, 106612, 104826, 105887, 106269, 103787, 106891, 10…
## $ marstat <int> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4, 4, 4, 1, 1, 3, 1, 1, 5…
## $ famtype <int> 6, 6, 8, 8, 6, 6, 1, 8, 8, 6, 6, 1, 1, 1, 6, 6, 1, 6, 6, 1…
## $ srh_hrs <int> 3, 2, 4, 3, 3, 3, 3, 3, 3, 2, 3, -1, 2, 2, 2, 2, 5, 3, 5, …
## $ tenure <int> 1, 1, 2, 2, 1, 1, 4, 3, 3, 1, 1, 4, 1, 1, 1, 1, 4, 4, 4, 4…
## $ spen_r_i <dbl> 132.50000, 113.00000, 161.25000, 71.25000, 153.84616, 57.6…
## $ totinc_bu_s <dbl> 805.1105, 805.1105, 725.9358, 725.9358, 264.0000, 264.0000…# get names of us7
names(us7)## [1] "idauniq" "marstat" "famtype" "srh_hrs" "tenure"
## [6] "spen_r_i" "totinc_bu_s"# rename just variable
us7 <- us7 %>%
rename(
pid = idauniq,
health = srh_hrs,
peninc = spen_r_i,
totincome = totinc_bu_s
)
names(us7)## [1] "pid" "marstat" "famtype" "health" "tenure" "peninc"
## [7] "totincome"# add wave signal to variable
# 时间常量：pid,age,gndr,edqual
us7 <-
rename_at(us7,
vars(-pid),
~ str_c(., "_2"))
# inspect the results
glimpse(us7)## Rows: 9,666
## Columns: 7
## $ pid <int> 104178, 106612, 104826, 105887, 106269, 103787, 106891, 10…
## $ marstat_2 <int> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4, 4, 4, 1, 1, 3, 1, 1, 5…
## $ famtype_2 <int> 6, 6, 8, 8, 6, 6, 1, 8, 8, 6, 6, 1, 1, 1, 6, 6, 1, 6, 6, 1…
## $ health_2 <int> 3, 2, 4, 3, 3, 3, 3, 3, 3, 2, 3, -1, 2, 2, 2, 2, 5, 3, 5, …
## $ tenure_2 <int> 1, 1, 2, 2, 1, 1, 4, 3, 3, 1, 1, 4, 1, 1, 1, 1, 4, 4, 4, 4…
## $ peninc_2 <dbl> 132.50000, 113.00000, 161.25000, 71.25000, 153.84616, 57.6…
## $ totincome_2 <dbl> 805.1105, 805.1105, 725.9358, 725.9358, 264.0000, 264.0000…# make a variable that gets value 1
us7 <- mutate(us7, present_2 = 1)
glimpse(us7) # investigate result## Rows: 9,666
## Columns: 8
## $ pid <int> 104178, 106612, 104826, 105887, 106269, 103787, 106891, 10…
## $ marstat_2 <int> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4, 4, 4, 1, 1, 3, 1, 1, 5…
## $ famtype_2 <int> 6, 6, 8, 8, 6, 6, 1, 8, 8, 6, 6, 1, 1, 1, 6, 6, 1, 6, 6, 1…
## $ health_2 <int> 3, 2, 4, 3, 3, 3, 3, 3, 3, 2, 3, -1, 2, 2, 2, 2, 5, 3, 5, …
## $ tenure_2 <int> 1, 1, 2, 2, 1, 1, 4, 3, 3, 1, 1, 4, 1, 1, 1, 1, 4, 4, 4, 4…
## $ peninc_2 <dbl> 132.50000, 113.00000, 161.25000, 71.25000, 153.84616, 57.6…
## $ totincome_2 <dbl> 805.1105, 805.1105, 725.9358, 725.9358, 264.0000, 264.0000…
## $ present_2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…# wave 8 (12 variables)
# --------------------------------------------------------------------------------
wave8_ifsdata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_8_elsa_ifs_dvs_eul_v1.tab", header=TRUE, sep="\t")
wave8_ifsdata2 <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_8_elsa_data_eul_v2.tab", header=TRUE, sep="\t")
wave8_finsdata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_8_elsa_financial_dvs_eul_v1.tab", header=TRUE, sep="\t")
# merge data
w8 <- full_join(wave8_ifsdata,wave8_finsdata,by = "idauniq")
w8 <- full_join(w8,wave8_ifsdata2,by = "idauniq")
vars3 <-
c(
"idauniq", # 个人唯一标识符
"marstat", # 婚姻状况：marital status - couple1 combined with dimar
"famtype", # 家庭类型：household type
"srh_hrs", # 自评健康状况：self-reported health: HRS version
"tenure", # 住房保有权：housing tenure - copy of hotenu
"spen_r_i", # 国家养老金收入:state pension income (iapam/iappam) - value (incl. imputed values)
"totinc_bu_s" # BU总收入: BU total income - summary var
)
us8 <- w8 %>% select(all_of(vars3))
head(us8) # see first few cases and variables## idauniq marstat famtype srh_hrs tenure spen_r_i totinc_bu_s
## 1 105658 4 1 5 1 165.0 324.3533
## 2 108340 1 6 3 1 136.0 671.7735
## 3 103727 1 6 2 1 156.0 671.7735
## 4 104135 1 6 4 1 197.5 722.2353
## 5 151605 1 6 4 1 0.0 748.0327
## 6 151543 1 6 4 1 0.0 748.0327glimpse(us8) # another description of the data## Rows: 8,445
## Columns: 7
## $ idauniq <int> 105658, 108340, 103727, 104135, 151605, 151543, 117373, 10…
## $ marstat <int> 4, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1…
## $ famtype <int> 1, 6, 6, 6, 6, 6, 1, 6, 6, 6, 6, 12, 12, 6, 6, 11, 11, 1, …
## $ srh_hrs <int> 5, 3, 2, 4, 4, 4, 4, 3, 4, 2, 3, 1, 2, -1, 2, 3, 4, 1, 3, …
## $ tenure <int> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ spen_r_i <dbl> 165.0000, 136.0000, 156.0000, 197.5000, 0.0000, 0.0000, 20…
## $ totinc_bu_s <dbl> 324.3533, 671.7735, 671.7735, 722.2353, 748.0327, 748.0327…# get names of us8
names(us8)## [1] "idauniq" "marstat" "famtype" "srh_hrs" "tenure"
## [6] "spen_r_i" "totinc_bu_s"# rename just variable
us8 <- us8 %>%
rename(
pid = idauniq,
health = srh_hrs,
peninc = spen_r_i,
totincome = totinc_bu_s
)
names(us8)## [1] "pid" "marstat" "famtype" "health" "tenure" "peninc"
## [7] "totincome"# add wave signal to variable
# 时间常量：pid,age,gndr,edqual
us8 <-
rename_at(us8,
vars(-pid),
~ str_c(., "_3"))
# inspect the results
glimpse(us8)## Rows: 8,445
## Columns: 7
## $ pid <int> 105658, 108340, 103727, 104135, 151605, 151543, 117373, 10…
## $ marstat_3 <int> 4, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1…
## $ famtype_3 <int> 1, 6, 6, 6, 6, 6, 1, 6, 6, 6, 6, 12, 12, 6, 6, 11, 11, 1, …
## $ health_3 <int> 5, 3, 2, 4, 4, 4, 4, 3, 4, 2, 3, 1, 2, -1, 2, 3, 4, 1, 3, …
## $ tenure_3 <int> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ peninc_3 <dbl> 165.0000, 136.0000, 156.0000, 197.5000, 0.0000, 0.0000, 20…
## $ totincome_3 <dbl> 324.3533, 671.7735, 671.7735, 722.2353, 748.0327, 748.0327…# make a variable that gets value 1
us8 <- mutate(us8, present_3 = 1)
glimpse(us8) # investigate result## Rows: 8,445
## Columns: 8
## $ pid <int> 105658, 108340, 103727, 104135, 151605, 151543, 117373, 10…
## $ marstat_3 <int> 4, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1…
## $ famtype_3 <int> 1, 6, 6, 6, 6, 6, 1, 6, 6, 6, 6, 12, 12, 6, 6, 11, 11, 1, …
## $ health_3 <int> 5, 3, 2, 4, 4, 4, 4, 3, 4, 2, 3, 1, 2, -1, 2, 3, 4, 1, 3, …
## $ tenure_3 <int> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ peninc_3 <dbl> 165.0000, 136.0000, 156.0000, 197.5000, 0.0000, 0.0000, 20…
## $ totincome_3 <dbl> 324.3533, 671.7735, 671.7735, 722.2353, 748.0327, 748.0327…
## $ present_3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…# wave 9 (14 variables)
# --------------------------------------------------------------------------------
wave9_ifsdata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_9_ifs_derived_variables.tab", header=TRUE, sep="\t")
wave9_ifsdata2 <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_9_elsa_data_eul_v1.tab", header=TRUE, sep="\t")
wave9_finsdata <- read.table("~/Downloads/UKDA-5050-tab/tab/wave_9_financial_derived_variables.tab", header=TRUE, sep="\t")
# merge data
w9 <- full_join(wave9_ifsdata,wave9_ifsdata2,by = "idauniq")
w9 <- full_join(w9,wave9_finsdata,by = "idauniq")
vars4 <-
c(
"idauniq", # 个人唯一标识符
"marstat", # 婚姻状况：marital status - couple1 combined with dimar
"famtype", # 家庭类型：household type
"srh_hrs", # 自评健康状况：self-reported health: HRS version
"tenure", # 住房保有权：housing tenure - copy of hotenu
"spen_r_i", # 国家养老金收入:state pension income (iapam/iappam) - value (incl. imputed values)
"totinc_bu_s" # BU总收入: BU total income - summary var
)
us9 <- w9 %>% select(all_of(vars4))
head(us9) # see first few cases and variables## idauniq marstat famtype srh_hrs tenure spen_r_i totinc_bu_s
## 1 160346 1 6 4 4 129.3303 560.5591
## 2 161815 1 6 2 4 200.0000 560.5591
## 3 908556 3 1 3 4 0.0000 214.7806
## 4 111568 1 6 3 4 176.0000 361.2610
## 5 112208 1 6 3 4 146.0000 361.2610
## 6 107581 1 6 4 1 134.0000 451.2576glimpse(us9) # another description of the data## Rows: 8,736
## Columns: 7
## $ idauniq <int> 160346, 161815, 908556, 111568, 112208, 107581, 107626, 12…
## $ marstat <int> 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 4, 4…
## $ famtype <int> 6, 6, 1, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 1, 1…
## $ srh_hrs <int> 4, 2, 3, 3, 3, 4, 3, 4, 4, 1, 3, 3, 3, 3, -1, 3, 2, -1, 3,…
## $ tenure <int> 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2…
## $ spen_r_i <dbl> 129.3303, 200.0000, 0.0000, 176.0000, 146.0000, 134.0000, …
## $ totinc_bu_s <dbl> 560.5591, 560.5591, 214.7806, 361.2610, 361.2610, 451.2576…# get names of us8
names(us9)## [1] "idauniq" "marstat" "famtype" "srh_hrs" "tenure"
## [6] "spen_r_i" "totinc_bu_s"# rename just variable
us9 <- us9 %>%
rename(
pid = idauniq,
health = srh_hrs,
peninc = spen_r_i,
totincome = totinc_bu_s
)
names(us9)## [1] "pid" "marstat" "famtype" "health" "tenure" "peninc"
## [7] "totincome"# add wave signal to variable
# 时间常量：pid,age,gndr,edqual
us9 <-
rename_at(us9,
vars(-pid),
~ str_c(., "_4"))
# inspect the results
glimpse(us9)## Rows: 8,736
## Columns: 7
## $ pid <int> 160346, 161815, 908556, 111568, 112208, 107581, 107626, 12…
## $ marstat_4 <int> 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 4, 4…
## $ famtype_4 <int> 6, 6, 1, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 1, 1…
## $ health_4 <int> 4, 2, 3, 3, 3, 4, 3, 4, 4, 1, 3, 3, 3, 3, -1, 3, 2, -1, 3,…
## $ tenure_4 <int> 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2…
## $ peninc_4 <dbl> 129.3303, 200.0000, 0.0000, 176.0000, 146.0000, 134.0000, …
## $ totincome_4 <dbl> 560.5591, 560.5591, 214.7806, 361.2610, 361.2610, 451.2576…# make a variable that gets value 1
us9 <- mutate(us9, present_4 = 1)
glimpse(us9) # investigate result## Rows: 8,736
## Columns: 8
## $ pid <int> 160346, 161815, 908556, 111568, 112208, 107581, 107626, 12…
## $ marstat_4 <int> 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 4, 4…
## $ famtype_4 <int> 6, 6, 1, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 1, 1…
## $ health_4 <int> 4, 2, 3, 3, 3, 4, 3, 4, 4, 1, 3, 3, 3, 3, -1, 3, 2, -1, 3,…
## $ tenure_4 <int> 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2…
## $ peninc_4 <dbl> 129.3303, 200.0000, 0.0000, 176.0000, 146.0000, 134.0000, …
## $ totincome_4 <dbl> 560.5591, 560.5591, 214.7806, 361.2610, 361.2610, 451.2576…
## $ present_4 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…#------------------------------------------------------------------------------
# create new data frame to store data
us1 <- us6
us2 <- us7
us3 <- us8
us4 <- us9
glimpse(us1)## Rows: 10,601
## Columns: 11
## $ pid <int> 160322, 165974, 105333, 108170, 161180, 151048, 150615, 11…
## $ age <int> 61, 56, 71, 68, 66, 58, 57, 65, 55, 99, 73, 69, 73, 65, 73…
## $ gndr <int> 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1…
## $ marstat_1 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 4, 1, 1, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 3, 7, 4, 7, 2, 2, 7, 6, 4, 7, 6, 1, 4, 7…
## $ famtype_1 <int> 1, 6, 6, 6, 1, 6, 6, 6, 6, 1, 6, 6, 6, 6, 1, 1, 6, 6, 6, 6…
## $ health_1 <int> 1, 4, 2, 1, 3, 2, 3, 2, 2, -1, 1, 1, 5, 2, 2, 4, 2, 3, 3, …
## $ tenure_1 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 2, 2…
## $ peninc_1 <dbl> 129.75, 0.00, 112.00, 92.00, 132.25, 0.00, 0.00, 139.25, 0…
## $ totincome_1 <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 904.25055,…
## $ present_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…glimpse(us2)## Rows: 9,666
## Columns: 8
## $ pid <int> 104178, 106612, 104826, 105887, 106269, 103787, 106891, 10…
## $ marstat_2 <int> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4, 4, 4, 1, 1, 3, 1, 1, 5…
## $ famtype_2 <int> 6, 6, 8, 8, 6, 6, 1, 8, 8, 6, 6, 1, 1, 1, 6, 6, 1, 6, 6, 1…
## $ health_2 <int> 3, 2, 4, 3, 3, 3, 3, 3, 3, 2, 3, -1, 2, 2, 2, 2, 5, 3, 5, …
## $ tenure_2 <int> 1, 1, 2, 2, 1, 1, 4, 3, 3, 1, 1, 4, 1, 1, 1, 1, 4, 4, 4, 4…
## $ peninc_2 <dbl> 132.50000, 113.00000, 161.25000, 71.25000, 153.84616, 57.6…
## $ totincome_2 <dbl> 805.1105, 805.1105, 725.9358, 725.9358, 264.0000, 264.0000…
## $ present_2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…glimpse(us3)## Rows: 8,445
## Columns: 8
## $ pid <int> 105658, 108340, 103727, 104135, 151605, 151543, 117373, 10…
## $ marstat_3 <int> 4, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1…
## $ famtype_3 <int> 1, 6, 6, 6, 6, 6, 1, 6, 6, 6, 6, 12, 12, 6, 6, 11, 11, 1, …
## $ health_3 <int> 5, 3, 2, 4, 4, 4, 4, 3, 4, 2, 3, 1, 2, -1, 2, 3, 4, 1, 3, …
## $ tenure_3 <int> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ peninc_3 <dbl> 165.0000, 136.0000, 156.0000, 197.5000, 0.0000, 0.0000, 20…
## $ totincome_3 <dbl> 324.3533, 671.7735, 671.7735, 722.2353, 748.0327, 748.0327…
## $ present_3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…glimpse(us4)## Rows: 8,736
## Columns: 8
## $ pid <int> 160346, 161815, 908556, 111568, 112208, 107581, 107626, 12…
## $ marstat_4 <int> 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 4, 4…
## $ famtype_4 <int> 6, 6, 1, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 1, 1…
## $ health_4 <int> 4, 2, 3, 3, 3, 4, 3, 4, 4, 1, 3, 3, 3, 3, -1, 3, 2, -1, 3,…
## $ tenure_4 <int> 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2…
## $ peninc_4 <dbl> 129.3303, 200.0000, 0.0000, 176.0000, 146.0000, 134.0000, …
## $ totincome_4 <dbl> 560.5591, 560.5591, 214.7806, 361.2610, 361.2610, 451.2576…
## $ present_4 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…#------------------------------------------------------------------------------
# # merge data: wave1,2,3,4
# # merge waves 1 and 2 based on pidp
us12 <- full_join(us1, us2, by = "pid")
# look at the data
glimpse(us12)## Rows: 11,401
## Columns: 18
## $ pid <int> 160322, 165974, 105333, 108170, 161180, 151048, 150615, 11…
## $ age <int> 61, 56, 71, 68, 66, 58, 57, 65, 55, 99, 73, 69, 73, 65, 73…
## $ gndr <int> 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1…
## $ marstat_1 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 4, 1, 1, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 3, 7, 4, 7, 2, 2, 7, 6, 4, 7, 6, 1, 4, 7…
## $ famtype_1 <int> 1, 6, 6, 6, 1, 6, 6, 6, 6, 1, 6, 6, 6, 6, 1, 1, 6, 6, 6, 6…
## $ health_1 <int> 1, 4, 2, 1, 3, 2, 3, 2, 2, -1, 1, 1, 5, 2, 2, 4, 2, 3, 3, …
## $ tenure_1 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 2, 2…
## $ peninc_1 <dbl> 129.75, 0.00, 112.00, 92.00, 132.25, 0.00, 0.00, 139.25, 0…
## $ totincome_1 <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 904.25055,…
## $ present_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ marstat_2 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, NA, NA, 4, NA, 4, 6, 4, 1, 1, 1…
## $ famtype_2 <int> 1, 1, 6, 6, 1, 6, 6, 6, 6, NA, NA, 1, NA, 1, 1, 1, 6, 6, 6…
## $ health_2 <int> 2, 4, 2, 1, 2, 2, 2, 4, 3, NA, NA, 3, NA, 2, 1, 3, 2, 2, 3…
## $ tenure_2 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, NA, NA, 1, NA, 1, 5, 1, 1, 1, 2…
## $ peninc_2 <dbl> 127.50000, 0.00000, 135.00000, 100.00000, 143.25000, 0.000…
## $ totincome_2 <dbl> 276.99368, 0.00000, 637.97339, 637.97339, 319.36539, 900.6…
## $ present_2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, NA, 1, NA, 1, 1, 1, 1, 1, 1…us123 <- full_join(us12, us3, by = "pid")
glimpse(us123)## Rows: 11,468
## Columns: 25
## $ pid <int> 160322, 165974, 105333, 108170, 161180, 151048, 150615, 11…
## $ age <int> 61, 56, 71, 68, 66, 58, 57, 65, 55, 99, 73, 69, 73, 65, 73…
## $ gndr <int> 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1…
## $ marstat_1 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 4, 1, 1, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 3, 7, 4, 7, 2, 2, 7, 6, 4, 7, 6, 1, 4, 7…
## $ famtype_1 <int> 1, 6, 6, 6, 1, 6, 6, 6, 6, 1, 6, 6, 6, 6, 1, 1, 6, 6, 6, 6…
## $ health_1 <int> 1, 4, 2, 1, 3, 2, 3, 2, 2, -1, 1, 1, 5, 2, 2, 4, 2, 3, 3, …
## $ tenure_1 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 2, 2…
## $ peninc_1 <dbl> 129.75, 0.00, 112.00, 92.00, 132.25, 0.00, 0.00, 139.25, 0…
## $ totincome_1 <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 904.25055,…
## $ present_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ marstat_2 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, NA, NA, 4, NA, 4, 6, 4, 1, 1, 1…
## $ famtype_2 <int> 1, 1, 6, 6, 1, 6, 6, 6, 6, NA, NA, 1, NA, 1, 1, 1, 6, 6, 6…
## $ health_2 <int> 2, 4, 2, 1, 2, 2, 2, 4, 3, NA, NA, 3, NA, 2, 1, 3, 2, 2, 3…
## $ tenure_2 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, NA, NA, 1, NA, 1, 5, 1, 1, 1, 2…
## $ peninc_2 <dbl> 127.50000, 0.00000, 135.00000, 100.00000, 143.25000, 0.000…
## $ totincome_2 <dbl> 276.99368, 0.00000, 637.97339, 637.97339, 319.36539, 900.6…
## $ present_2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, NA, 1, NA, 1, 1, 1, 1, 1, 1…
## $ marstat_3 <int> 5, 6, 1, 1, 3, 1, 1, 1, 1, NA, NA, 4, NA, NA, 6, 4, 1, 1, …
## $ famtype_3 <int> 1, 1, 6, 6, 1, 6, 6, 6, 6, NA, NA, 1, NA, NA, 1, 1, 6, 6, …
## $ health_3 <int> 2, 4, 2, 1, 3, -1, 1, 4, 3, NA, NA, 1, NA, NA, 2, 3, 3, 3,…
## $ tenure_3 <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, NA, 1, NA, NA, 5, 1, 1, 1, …
## $ peninc_3 <dbl> 137.50, 0.00, 138.50, 102.75, 146.75, 0.00, 0.00, 39.25, 0…
## $ totincome_3 <dbl> 345.96155, 213.44342, 451.45447, 451.45447, 310.29816, 715…
## $ present_3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, NA, 1, NA, NA, 1, 1, 1, 1, …us1234 <- full_join(us123, us4, by = "pid")
glimpse(us1234)## Rows: 12,758
## Columns: 32
## $ pid <int> 160322, 165974, 105333, 108170, 161180, 151048, 150615, 11…
## $ age <int> 61, 56, 71, 68, 66, 58, 57, 65, 55, 99, 73, 69, 73, 65, 73…
## $ gndr <int> 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1…
## $ marstat_1 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 4, 1, 1, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 3, 7, 4, 7, 2, 2, 7, 6, 4, 7, 6, 1, 4, 7…
## $ famtype_1 <int> 1, 6, 6, 6, 1, 6, 6, 6, 6, 1, 6, 6, 6, 6, 1, 1, 6, 6, 6, 6…
## $ health_1 <int> 1, 4, 2, 1, 3, 2, 3, 2, 2, -1, 1, 1, 5, 2, 2, 4, 2, 3, 3, …
## $ tenure_1 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 2, 2…
## $ peninc_1 <dbl> 129.75, 0.00, 112.00, 92.00, 132.25, 0.00, 0.00, 139.25, 0…
## $ totincome_1 <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 904.25055,…
## $ present_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ marstat_2 <int> 5, 1, 1, 1, 3, 1, 1, 1, 1, NA, NA, 4, NA, 4, 6, 4, 1, 1, 1…
## $ famtype_2 <int> 1, 1, 6, 6, 1, 6, 6, 6, 6, NA, NA, 1, NA, 1, 1, 1, 6, 6, 6…
## $ health_2 <int> 2, 4, 2, 1, 2, 2, 2, 4, 3, NA, NA, 3, NA, 2, 1, 3, 2, 2, 3…
## $ tenure_2 <int> 1, 1, 1, 1, 1, 2, 2, 1, 1, NA, NA, 1, NA, 1, 5, 1, 1, 1, 2…
## $ peninc_2 <dbl> 127.50000, 0.00000, 135.00000, 100.00000, 143.25000, 0.000…
## $ totincome_2 <dbl> 276.99368, 0.00000, 637.97339, 637.97339, 319.36539, 900.6…
## $ present_2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, NA, 1, NA, 1, 1, 1, 1, 1, 1…
## $ marstat_3 <int> 5, 6, 1, 1, 3, 1, 1, 1, 1, NA, NA, 4, NA, NA, 6, 4, 1, 1, …
## $ famtype_3 <int> 1, 1, 6, 6, 1, 6, 6, 6, 6, NA, NA, 1, NA, NA, 1, 1, 6, 6, …
## $ health_3 <int> 2, 4, 2, 1, 3, -1, 1, 4, 3, NA, NA, 1, NA, NA, 2, 3, 3, 3,…
## $ tenure_3 <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, NA, 1, NA, NA, 5, 1, 1, 1, …
## $ peninc_3 <dbl> 137.50, 0.00, 138.50, 102.75, 146.75, 0.00, 0.00, 39.25, 0…
## $ totincome_3 <dbl> 345.96155, 213.44342, 451.45447, 451.45447, 310.29816, 715…
## $ present_3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, NA, 1, NA, NA, 1, 1, 1, 1, …
## $ marstat_4 <int> 5, 6, 1, 1, 3, NA, NA, 1, 1, NA, NA, NA, NA, 4, 6, 4, 1, 1…
## $ famtype_4 <int> 1, 4, 6, 6, 1, NA, NA, 6, 6, NA, NA, NA, NA, 1, 1, 1, 1, 1…
## $ health_4 <int> 2, 4, 3, 2, 2, NA, NA, 4, 3, NA, NA, NA, NA, 2, 1, 3, -1, …
## $ tenure_4 <int> 1, 1, 1, 1, 1, NA, NA, 1, 1, NA, NA, NA, NA, 1, 5, 1, -7, …
## $ peninc_4 <dbl> 140.0000, 0.0000, 146.0000, 108.0000, 152.5000, NA, NA, 16…
## $ totincome_4 <dbl> 267.02078, 195.61201, 650.78229, 650.78229, 296.93707, NA,…
## $ present_4 <dbl> 1, 1, 1, 1, 1, NA, NA, 1, 1, NA, NA, NA, NA, 1, 1, 1, 1, 1…# Rows: 12758 Columns: 32
#------------------------------------------------------------------------------
# missing patterns waves 1 and 2
count(us1234, present_1, present_2)## present_1 present_2 n
## 1 1 1 8866
## 2 1 NA 1735
## 3 NA 1 800
## 4 NA NA 1357count(us1234, present_1, present_3)## present_1 present_3 n
## 1 1 1 7825
## 2 1 NA 2776
## 3 NA 1 620
## 4 NA NA 1537count(us1234, present_1, present_4)## present_1 present_4 n
## 1 1 1 6920
## 2 1 NA 3681
## 3 NA 1 1816
## 4 NA NA 341# keep all people who appeared in all the waves
# keep balanced panel
us <-
filter(us1234, present_1 == 1, present_2 == 1, present_3 == 1,present_4 == 1)
# look at result
glimpse(us)## Rows: 6,459
## Columns: 32
## $ pid <int> 160322, 165974, 105333, 108170, 161180, 119052, 121367, 11…
## $ age <int> 61, 56, 71, 68, 66, 65, 55, 73, 73, 71, 73, 66, 67, 67, 64…
## $ gndr <int> 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1…
## $ marstat_1 <int> 5, 1, 1, 1, 3, 1, 1, 6, 4, 1, 1, 1, 1, 1, 1, 1, 2, 5, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 4, 4, 7, 6, 1, 1, 1, 2, 7, 6, 7, 5, 4, 5…
## $ famtype_1 <int> 1, 6, 6, 6, 1, 6, 6, 1, 1, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6…
## $ health_1 <int> 1, 4, 2, 1, 3, 2, 2, 2, 4, 2, 3, 2, 3, 2, 2, 3, 2, 4, 3, 3…
## $ tenure_1 <int> 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ peninc_1 <dbl> 129.7500, 0.0000, 112.0000, 92.0000, 132.2500, 139.2500, 0…
## $ totincome_1 <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 255.62947,…
## $ present_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ marstat_2 <int> 5, 1, 1, 1, 3, 1, 1, 6, 4, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1, 1…
## $ famtype_2 <int> 1, 1, 6, 6, 1, 6, 6, 1, 1, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6…
## $ health_2 <int> 2, 4, 2, 1, 2, 4, 3, 1, 3, 2, 2, 3, 4, 2, 1, 3, 2, 3, 3, 3…
## $ tenure_2 <int> 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ peninc_2 <dbl> 127.50000, 0.00000, 135.00000, 100.00000, 143.25000, 145.0…
## $ totincome_2 <dbl> 276.99368, 0.00000, 637.97339, 637.97339, 319.36539, 292.1…
## $ present_2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ marstat_3 <int> 5, 6, 1, 1, 3, 1, 1, 6, 4, 1, 1, 1, 1, 1, 1, 4, 2, 3, 4, 1…
## $ famtype_3 <int> 1, 1, 6, 6, 1, 6, 6, 1, 1, 6, 6, 6, 6, 6, 6, 1, 6, 1, 1, 6…
## $ health_3 <int> 2, 4, 2, 1, 3, 4, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 5, 3, 4…
## $ tenure_3 <int> 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ peninc_3 <dbl> 137.50, 0.00, 138.50, 102.75, 146.75, 39.25, 0.00, 72.75, …
## $ totincome_3 <dbl> 345.96155, 213.44342, 451.45447, 451.45447, 310.29816, 143…
## $ present_3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ marstat_4 <int> 5, 6, 1, 1, 3, 1, 1, 6, 4, 1, 1, 1, 1, 1, 1, 4, 2, 3, 4, 1…
## $ famtype_4 <int> 1, 4, 6, 6, 1, 6, 6, 1, 1, 1, 1, 6, 6, 6, 6, 1, 6, 1, 1, 6…
## $ health_4 <int> 2, 4, 3, 2, 2, 4, 3, 1, 3, -1, 3, 2, 3, 2, 3, 3, 3, 5, 4, …
## $ tenure_4 <int> 1, 1, 1, 1, 1, 1, 1, 5, 1, -7, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peninc_4 <dbl> 140.0000, 0.0000, 146.0000, 108.0000, 152.5000, 161.5000, …
## $ totincome_4 <dbl> 267.02078, 195.61201, 650.78229, 650.78229, 296.93707, 288…
## $ present_4 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…# Rows: 6,459
# Columns: 32
# remove present variables
# eliminate all variables starting with "present"
us <- select(us, -starts_with("present"))
# check the result
glimpse(us)## Rows: 6,459
## Columns: 28
## $ pid <int> 160322, 165974, 105333, 108170, 161180, 119052, 121367, 11…
## $ age <int> 61, 56, 71, 68, 66, 65, 55, 73, 73, 71, 73, 66, 67, 67, 64…
## $ gndr <int> 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1…
## $ marstat_1 <int> 5, 1, 1, 1, 3, 1, 1, 6, 4, 1, 1, 1, 1, 1, 1, 1, 2, 5, 1, 1…
## $ edqual <int> 7, 4, 2, 2, 5, 7, 4, 4, 7, 6, 1, 1, 1, 2, 7, 6, 7, 5, 4, 5…
## $ famtype_1 <int> 1, 6, 6, 6, 1, 6, 6, 1, 1, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6…
## $ health_1 <int> 1, 4, 2, 1, 3, 2, 2, 2, 4, 2, 3, 2, 3, 2, 2, 3, 2, 4, 3, 3…
## $ tenure_1 <int> 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ peninc_1 <dbl> 129.7500, 0.0000, 112.0000, 92.0000, 132.2500, 139.2500, 0…
## $ totincome_1 <dbl> 287.25577, NA, 917.87476, 917.87476, 296.74152, 255.62947,…
## $ marstat_2 <int> 5, 1, 1, 1, 3, 1, 1, 6, 4, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1, 1…
## $ famtype_2 <int> 1, 1, 6, 6, 1, 6, 6, 1, 1, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6…
## $ health_2 <int> 2, 4, 2, 1, 2, 4, 3, 1, 3, 2, 2, 3, 4, 2, 1, 3, 2, 3, 3, 3…
## $ tenure_2 <int> 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ peninc_2 <dbl> 127.50000, 0.00000, 135.00000, 100.00000, 143.25000, 145.0…
## $ totincome_2 <dbl> 276.99368, 0.00000, 637.97339, 637.97339, 319.36539, 292.1…
## $ marstat_3 <int> 5, 6, 1, 1, 3, 1, 1, 6, 4, 1, 1, 1, 1, 1, 1, 4, 2, 3, 4, 1…
## $ famtype_3 <int> 1, 1, 6, 6, 1, 6, 6, 1, 1, 6, 6, 6, 6, 6, 6, 1, 6, 1, 1, 6…
## $ health_3 <int> 2, 4, 2, 1, 3, 4, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 5, 3, 4…
## $ tenure_3 <int> 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ peninc_3 <dbl> 137.50, 0.00, 138.50, 102.75, 146.75, 39.25, 0.00, 72.75, …
## $ totincome_3 <dbl> 345.96155, 213.44342, 451.45447, 451.45447, 310.29816, 143…
## $ marstat_4 <int> 5, 6, 1, 1, 3, 1, 1, 6, 4, 1, 1, 1, 1, 1, 1, 4, 2, 3, 4, 1…
## $ famtype_4 <int> 1, 4, 6, 6, 1, 6, 6, 1, 1, 1, 1, 6, 6, 6, 6, 1, 6, 1, 1, 6…
## $ health_4 <int> 2, 4, 3, 2, 2, 4, 3, 1, 3, -1, 3, 2, 3, 2, 3, 3, 3, 5, 4, …
## $ tenure_4 <int> 1, 1, 1, 1, 1, 1, 1, 5, 1, -7, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peninc_4 <dbl> 140.0000, 0.0000, 146.0000, 108.0000, 152.5000, 161.5000, …
## $ totincome_4 <dbl> 267.02078, 195.61201, 650.78229, 650.78229, 296.93707, 288…#------------------------------------------------------------------------------
# transfer wide data to long data
usl <- pivot_longer(
us,
cols = !c(pid, gndr, age, edqual),
names_sep = "_",
names_to = c(".value", "wave")
)
glimpse(usl)## Rows: 25,836
## Columns: 11
## $ pid <int> 160322, 160322, 160322, 160322, 165974, 165974, 165974, 1659…
## $ age <int> 61, 61, 61, 61, 56, 56, 56, 56, 71, 71, 71, 71, 68, 68, 68, …
## $ gndr <int> 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, …
## $ edqual <int> 7, 7, 7, 7, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, …
## $ wave <chr> "1", "2", "3", "4", "1", "2", "3", "4", "1", "2", "3", "4", …
## $ marstat <int> 5, 5, 5, 5, 1, 1, 6, 6, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, …
## $ famtype <int> 1, 1, 1, 1, 6, 1, 1, 4, 6, 6, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, …
## $ health <int> 1, 2, 2, 2, 4, 4, 4, 4, 2, 2, 2, 3, 1, 1, 1, 2, 3, 2, 3, 2, …
## $ tenure <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peninc <dbl> 129.75, 127.50, 137.50, 140.00, 0.00, 0.00, 0.00, 0.00, 112.…
## $ totincome <dbl> 287.2558, 276.9937, 345.9615, 267.0208, NA, 0.0000, 213.4434…# change the order of variables
# put pidp and wave at start of the dataset
usl <- select(usl, pid, wave, gndr, everything())
# arrange rows by individual id and wave
usl <- arrange(usl, pid, wave)
# check result
glimpse(usl)## Rows: 25,836
## Columns: 11
## $ pid <int> 100007, 100007, 100007, 100007, 100023, 100023, 100023, 1000…
## $ wave <chr> "1", "2", "3", "4", "1", "2", "3", "4", "1", "2", "3", "4", …
## $ gndr <int> 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ age <int> 64, 64, 64, 64, 53, 53, 53, 53, 81, 81, 81, 81, 70, 70, 70, …
## $ edqual <int> 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 5, 5, 5, 5, 2, 2, 2, 2, …
## $ marstat <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, …
## $ famtype <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, …
## $ health <int> 3, 4, 3, 4, -1, -1, 3, 3, 1, 3, 2, 2, 3, 3, 3, 3, 5, 5, 4, 5…
## $ tenure <int> 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peninc <dbl> 0.00, 139.25, 125.00, 150.00, 0.00, 0.00, 0.00, 0.00, 126.50…
## $ totincome <dbl> 553.7556, 502.4079, 415.7344, 514.9683, 597.7595, 896.1353, …# check if it's balanced
count(usl, wave)## # A tibble: 4 × 2
## wave n
## <chr> <int>
## 1 1 6459
## 2 2 6459
## 3 3 6459
## 4 4 6459#------------------------------------------------------------------------------
# cleaning data
# 1-gndr-------------
# describe gndr：1-male 2-female
# no NA
count(usl, gndr)## # A tibble: 2 × 2
## gndr n
## <int> <int>
## 1 1 11336
## 2 2 14500attributes(usl$gndr)## NULL# make new variable as factor
usl <-
mutate(usl, gndr_f = factor(gndr, labels = c("Male", "Female")))
# check if the new variable is correct
count(usl, gndr, gndr_f)## # A tibble: 2 × 3
## gndr gndr_f n
## <int> <fct> <int>
## 1 1 Male 11336
## 2 2 Female 14500# 2-age-------------
# no NA
count(usl, age)## # A tibble: 57 × 2
## age n
## <int> <int>
## 1 31 4
## 2 36 12
## 3 37 8
## 4 38 12
## 5 39 4
## 6 40 12
## 7 41 20
## 8 42 12
## 9 43 20
## 10 44 32
## # ℹ 47 more rowsattributes(usl$age)## NULLsummary(usl$age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 31.00 59.00 64.00 65.14 71.00 99.00# 3-edqual-------------
# no NA
count(usl, edqual)## # A tibble: 10 × 2
## edqual n
## <int> <int>
## 1 -9 44
## 2 -8 76
## 3 -2 124
## 4 1 5032
## 5 2 3732
## 6 3 2404
## 7 4 5044
## 8 5 928
## 9 6 3224
## 10 7 5228attributes(usl$edqual)## NULLsummary(usl$edqual)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -9.00 2.00 4.00 3.84 6.00 7.00# make new variable as factor
usl <- mutate(
usl,
edqual = ifelse(edqual < 0, NA, edqual),
degree = case_when(edqual %in% 1:2 ~ "High Education",
edqual %in% 3:4 ~ "Secondary Education",
edqual %in% 5:7 ~ "Basic Education"),
degree = as.factor(degree)
)
# check result
count(usl, edqual, degree)## # A tibble: 8 × 3
## edqual degree n
## <int> <fct> <int>
## 1 1 High Education 5032
## 2 2 High Education 3732
## 3 3 Secondary Education 2404
## 4 4 Secondary Education 5044
## 5 5 Basic Education 928
## 6 6 Basic Education 3224
## 7 7 Basic Education 5228
## 8 NA <NA> 244# 4-marstat-------------
# no NA,1-yes 2-no
# Value = 1.0 Label = married (inc civ pship 06 onwards)
# Value = 2.0 Label = cohabiting
# Value = 3.0 Label = single, never married
# Value = 4.0 Label = widowed
# Value = 5.0 Label = divorced
# Value = 6.0 Label = separated
count(usl, marstat)## # A tibble: 6 × 2
## marstat n
## <int> <int>
## 1 1 17319
## 2 2 1487
## 3 3 1202
## 4 4 3309
## 5 5 2230
## 6 6 289attributes(usl$marstat)## NULLsummary(usl$marstat)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 1.936 3.000 6.000usl <- mutate(
usl,
marstat_f = case_when(marstat == 1 ~ "Married",
marstat == 2 ~ "Cohabiting",
marstat == 3 ~ "Single",
marstat == 4 ~ "Widowed",
marstat == 5 ~ "Divorced",
marstat == 6 ~ "Separated"),
marstat_f = as.factor(marstat_f)
)
# check result
count(usl, marstat, marstat_f)## # A tibble: 6 × 3
## marstat marstat_f n
## <int> <fct> <int>
## 1 1 Married 17319
## 2 2 Cohabiting 1487
## 3 3 Single 1202
## 4 4 Widowed 3309
## 5 5 Divorced 2230
## 6 6 Separated 289# 5-famtype-------------
# no NA,1-yes 2-no
count(usl, famtype)## # A tibble: 15 × 2
## famtype n
## <int> <int>
## 1 1 5887
## 2 2 53
## 3 3 237
## 4 4 362
## 5 5 27
## 6 6 14954
## 7 7 395
## 8 8 2492
## 9 9 200
## 10 10 4
## 11 11 672
## 12 12 255
## 13 13 271
## 14 14 26
## 15 NA 1attributes(usl$famtype)## NULLsummary(usl$famtype)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 6.000 5.299 6.000 14.000 1usl <- mutate(
usl,
famtype_f = case_when(famtype == 1 ~ "Single Households",
famtype %in% 2:5 ~ "Lone Households",
famtype %in% 6:10 ~ "Couple Households",
famtype %in% 11:14 ~ "Extended Family and Other"),
famtype_f = as.factor(famtype_f)
)
# check result
count(usl, famtype, famtype_f)## # A tibble: 15 × 3
## famtype famtype_f n
## <int> <fct> <int>
## 1 1 Single Households 5887
## 2 2 Lone Households 53
## 3 3 Lone Households 237
## 4 4 Lone Households 362
## 5 5 Lone Households 27
## 6 6 Couple Households 14954
## 7 7 Couple Households 395
## 8 8 Couple Households 2492
## 9 9 Couple Households 200
## 10 10 Couple Households 4
## 11 11 Extended Family and Other 672
## 12 12 Extended Family and Other 255
## 13 13 Extended Family and Other 271
## 14 14 Extended Family and Other 26
## 15 NA <NA> 1# 7-tenure-------------
# Value = 1.0 Label = own it outright
# Value = 2.0 Label = buying it with the help of a mortgage or loan
# Value = 3.0 Label = pay part rent and part mortgage (shared ownership)
# Value = 4.0 Label = rent it
# Value = 5.0 Label = live here rent free (including rent free in relative/friends)
# Value = 6.0 Label = squatting
count(usl, tenure)## # A tibble: 10 × 2
## tenure n
## <int> <int>
## 1 -9 2
## 2 -8 10
## 3 -7 64
## 4 -3 9
## 5 -1 29
## 6 1 18545
## 7 2 3628
## 8 3 24
## 9 4 3289
## 10 5 236attributes(usl$tenure)## NULLsummary(usl$tenure)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -9.000 1.000 1.000 1.533 2.000 5.000usl <- mutate(
usl,
tenure = ifelse(tenure < 0, NA, tenure),
tenure_f = case_when(tenure %in% 1:3 ~ "Own",
tenure %in% 4:6 ~ "Not Own"),
tenure_f = as.factor(tenure_f)
)
# check result
count(usl, tenure, tenure_f)## # A tibble: 6 × 3
## tenure tenure_f n
## <int> <fct> <int>
## 1 1 Own 18545
## 2 2 Own 3628
## 3 3 Own 24
## 4 4 Not Own 3289
## 5 5 Not Own 236
## 6 NA <NA> 114# 7-health-------------
# Value = 1.0 Label = excellent
# Value = 2.0 Label = very good
# Value = 3.0 Label = good
# Value = 4.0 Label = fair
# Value = 5.0 Label = poor
count(usl, health)## # A tibble: 8 × 2
## health n
## <int> <int>
## 1 -9 1
## 2 -8 3
## 3 -1 847
## 4 1 3091
## 5 2 7600
## 6 3 8368
## 7 4 4429
## 8 5 1497attributes(usl$health)## NULLsummary(usl$health)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -9.000 2.000 3.000 2.621 3.000 5.000usl <- mutate(
usl,
health = ifelse(health < 0, NA, health),
health_f = case_when(health == 1 ~ "excellent",
health == 2 ~ "very good",
health == 3 ~ "good",
health == 4 ~ "fair",
health == 5 ~ "poor"),
health_f = as.factor(health_f)
)
# check result
count(usl, health, health_f)## # A tibble: 6 × 3
## health health_f n
## <int> <fct> <int>
## 1 1 excellent 3091
## 2 2 very good 7600
## 3 3 good 8368
## 4 4 fair 4429
## 5 5 poor 1497
## 6 NA <NA> 851#------------------------------------------------------------------------------
# cleaning continuous variables
# 8-peninc-------------
hist(usl$peninc)
summary(usl$peninc)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.00 105.76 88.86 147.75 769.23 128# cap income to 400
usl <- mutate(usl,
peninc = ifelse(peninc > 400, 400, peninc))
# check the result
hist(usl$peninc)
# usl <- mutate(usl,
# penincnew = ifelse(peninc > 400, 400, peninc))
# hist(usl$penincnew)
# 9-totincome-------------
hist(usl$totincome)
summary(usl$totincome)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -322.5 310.6 482.8 597.5 738.5 16236.2 367# cap income to 0
usl <- mutate(usl,
totincome = ifelse(totincome < 0, 0, totincome))
# check the result
hist(usl$totincome)
# cap income to 5000
usl <- mutate(usl,
totincome = ifelse(totincome > 5000, 5000, totincome))
# check the result
hist(usl$totincome)
# cap income to 3000
usl <- mutate(usl,
totincome = ifelse(totincome > 3000, 3000, totincome))
# check the result
hist(usl$totincome)
summary(usl$totincome)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 310.6 482.8 582.4 738.6 3000.0 367hist(log(usl$totincome + 10)) # histogram of log income
# store log total income
usl <- mutate(usl, logincome = log(totincome + 10))
hist(usl$logincome)
# # delete totincomenew
# usl <- subset(usl, select = -totincomenew)
# # end of clean data
#------------------------------------------------------------------------------
summary(usl)## pid wave gndr age
## Min. :100007 Length:25836 Min. :1.000 Min. :31.00
## 1st Qu.:111035 Class :character 1st Qu.:1.000 1st Qu.:59.00
## Median :119888 Mode :character Median :2.000 Median :64.00
## Mean :133090 Mean :1.561 Mean :65.14
## 3rd Qu.:160763 3rd Qu.:2.000 3rd Qu.:71.00
## Max. :705927 Max. :2.000 Max. :99.00
##
## edqual marstat famtype health
## Min. :1.000 Min. :1.000 Min. : 1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 4.000 1st Qu.:2.000
## Median :4.000 Median :1.000 Median : 6.000 Median :3.000
## Mean :3.926 Mean :1.936 Mean : 5.299 Mean :2.745
## 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.: 6.000 3rd Qu.:3.000
## Max. :7.000 Max. :6.000 Max. :14.000 Max. :5.000
## NA's :244 NA's :1 NA's :851
## tenure peninc totincome gndr_f
## Min. :1.000 Min. : 0.00 Min. : 0.0 Male :11336
## 1st Qu.:1.000 1st Qu.: 0.00 1st Qu.: 310.6 Female:14500
## Median :1.000 Median :105.76 Median : 482.8
## Mean :1.563 Mean : 88.83 Mean : 582.4
## 3rd Qu.:2.000 3rd Qu.:147.75 3rd Qu.: 738.6
## Max. :5.000 Max. :400.00 Max. :3000.0
## NA's :114 NA's :128 NA's :367
## degree marstat_f
## Basic Education :9380 Cohabiting: 1487
## High Education :8764 Divorced : 2230
## Secondary Education:7448 Married :17319
## NA's : 244 Separated : 289
## Single : 1202
## Widowed : 3309
##
## famtype_f tenure_f health_f
## Couple Households :18045 Not Own: 3525 excellent:3091
## Extended Family and Other: 1224 Own :22197 fair :4429
## Lone Households : 679 NA's : 114 good :8368
## Single Households : 5887 poor :1497
## NA's : 1 very good:7600
## NA's : 851
##
## logincome
## Min. :2.303
## 1st Qu.:5.770
## Median :6.200
## Mean :6.169
## 3rd Qu.:6.618
## Max. :8.010
## NA's :367#------------------------------------------------------------------------------
usl %>%
group_by(wave) %>%
summarise(income = mean(totincome, na.rm = T))## # A tibble: 4 × 2
## wave income
## <chr> <dbl>
## 1 1 571.
## 2 2 577.
## 3 3 576.
## 4 4 605.usl %>%
group_by(wave) %>%
summarise(
mean_totincome = mean(totincome, na.rm = T),
var_totincome = var(totincome, na.rm = T),
mean_age = mean(age, na.rm = T),
prop_marstat = mean(marstat, na.rm = T)
)## # A tibble: 4 × 5
## wave mean_totincome var_totincome mean_age prop_marstat
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 571. 175343. 65.1 1.88
## 2 2 577. 169571. 65.1 1.91
## 3 3 576. 163724. 65.1 1.96
## 4 4 605. 194853. 65.1 2.00usl %>%
group_by(gndr_f,wave) %>%
summarise(
mean_totincome = mean(totincome, na.rm = T),
var_totincome = var(totincome, na.rm = T),
mean_age = mean(age, na.rm = T),
prop_marstat = mean(marstat, na.rm = T)
)## `summarise()` has grouped output by 'gndr_f'. You can override using the
## `.groups` argument.## # A tibble: 8 × 6
## # Groups: gndr_f [2]
## gndr_f wave mean_totincome var_totincome mean_age prop_marstat
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Male 1 613. 184130. 65.3 1.62
## 2 Male 2 618. 177698. 65.3 1.64
## 3 Male 3 617. 171000. 65.3 1.68
## 4 Male 4 650. 202991. 65.3 1.70
## 5 Female 1 538. 166041. 65.0 2.09
## 6 Female 2 545. 160868. 65.0 2.12
## 7 Female 3 543. 155637. 65.0 2.17
## 8 Female 4 570. 185725. 65.0 2.23# make wide data
usw <- pivot_wider(
usl,
values_from = !c(pid, gndr, age, edqual, degree, gndr_f), # time consistent variables
names_sep = "_",
names_from = wave
)
# check result
glimpse(usw)## Rows: 6,459
## Columns: 54
## $ pid <int> 100007, 100023, 100025, 100026, 100039, 100048, 100052, 10…
## $ gndr <int> 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1…
## $ age <int> 64, 53, 81, 70, 63, 44, 61, 64, 64, 59, 65, 72, 60, 74, 57…
## $ edqual <int> 6, 5, 4, 5, 2, 7, 1, 4, 4, 6, 2, 4, 1, 6, 5, 5, 4, 4, 4, N…
## $ gndr_f <fct> Male, Female, Male, Male, Male, Female, Female, Male, Fema…
## $ degree <fct> Basic Education, Basic Education, Secondary Education, Bas…
## $ wave_1 <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"…
## $ wave_2 <chr> "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2"…
## $ wave_3 <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3"…
## $ wave_4 <chr> "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4"…
## $ marstat_1 <int> 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1…
## $ marstat_2 <int> 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1…
## $ marstat_3 <int> 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1…
## $ marstat_4 <int> 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 4, 1, 1, 1, 1, 2, 1, 1, 1…
## $ famtype_1 <int> 6, 6, 6, 6, 6, 8, 6, 6, 8, 8, 6, 6, 6, 6, 6, 6, 7, 6, 7, 6…
## $ famtype_2 <int> 6, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 7, 6, 7, 6…
## $ famtype_3 <int> 6, 6, 6, 6, 6, 11, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 7, 6, 7, …
## $ famtype_4 <int> 6, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 1, 6, 6, 6, 6, 9, 6, 6, 6…
## $ health_1 <int> 3, NA, 1, 3, 5, 4, 3, 4, 2, 2, 3, 4, 1, 4, 2, 1, 3, 3, 4, …
## $ health_2 <int> 4, NA, 3, 3, 5, 4, 2, 4, 2, 3, 2, 4, NA, 4, 3, 3, 3, 4, 4,…
## $ health_3 <int> 3, 3, 2, 3, 4, 4, 2, 4, 2, 1, 2, 4, 1, 3, 2, 3, 2, 3, 4, 2…
## $ health_4 <int> 4, 3, 2, 3, 5, 3, 2, 4, 2, 1, 2, 4, 1, 4, 2, 2, 3, 3, 5, N…
## $ tenure_1 <int> 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 2, 4, 2, 1, 4, 1, 4, 1…
## $ tenure_2 <int> 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 4, 1, 1, 4, 1, 4, 1…
## $ tenure_3 <int> 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 4, 1, 1, 4, 1, 4, 1…
## $ tenure_4 <int> 1, 2, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 4, 1, 1, 4, 1, 4, 1…
## $ peninc_1 <dbl> 0.0000, 0.0000, 126.5000, 112.7500, 0.0000, 0.0000, 0.0000…
## $ peninc_2 <dbl> 139.2500, 0.0000, 125.0000, 118.7500, 104.7500, 0.0000, 0.…
## $ peninc_3 <dbl> 125.00000, 0.00000, 160.75000, 121.75000, 0.00000, 0.00000…
## $ peninc_4 <dbl> 150.0000, 0.0000, 150.0000, 131.7500, 124.0000, 0.0000, 0.…
## $ totincome_1 <dbl> 553.7556, 597.7595, 454.3551, 375.6178, 454.6623, 897.4019…
## $ totincome_2 <dbl> 502.4079, 896.1353, 433.1628, 394.1068, 325.9038, 691.7783…
## $ totincome_3 <dbl> 415.7344, 595.4955, 532.6436, 414.3903, 254.5891, 339.0300…
## $ totincome_4 <dbl> 514.9683, 648.6823, 508.6244, 437.1307, 452.5142, 521.8880…
## $ marstat_f_1 <fct> Cohabiting, Cohabiting, Cohabiting, Cohabiting, Married, C…
## $ marstat_f_2 <fct> Cohabiting, Cohabiting, Cohabiting, Cohabiting, Married, C…
## $ marstat_f_3 <fct> Cohabiting, Cohabiting, Cohabiting, Cohabiting, Married, C…
## $ marstat_f_4 <fct> Cohabiting, Cohabiting, Cohabiting, Cohabiting, Married, C…
## $ famtype_f_1 <fct> Couple Households, Couple Households, Couple Households, C…
## $ famtype_f_2 <fct> Couple Households, Couple Households, Couple Households, C…
## $ famtype_f_3 <fct> Couple Households, Couple Households, Couple Households, C…
## $ famtype_f_4 <fct> Couple Households, Couple Households, Couple Households, C…
## $ tenure_f_1 <fct> Own, Own, Own, Own, Own, Not Own, Not Own, Own, Own, Own, …
## $ tenure_f_2 <fct> Own, Own, Own, Own, Own, Not Own, Not Own, Own, Own, Own, …
## $ tenure_f_3 <fct> Own, Own, Own, Own, Own, Not Own, Not Own, Own, Own, Own, …
## $ tenure_f_4 <fct> Own, Own, Own, Own, Own, Not Own, Not Own, Own, Own, Own, …
## $ health_f_1 <fct> good, NA, excellent, good, poor, fair, good, fair, very go…
## $ health_f_2 <fct> fair, NA, good, good, poor, fair, very good, fair, very go…
## $ health_f_3 <fct> good, good, very good, good, fair, fair, very good, fair, …
## $ health_f_4 <fct> fair, good, very good, good, poor, good, very good, fair, …
## $ logincome_1 <dbl> 6.334621, 6.409779, 6.140650, 5.954847, 6.141311, 6.810585…
## $ logincome_2 <dbl> 6.239121, 6.809189, 6.093937, 6.001679, 5.816825, 6.553618…
## $ logincome_3 <dbl> 6.053816, 6.406047, 6.296453, 6.050654, 5.578178, 5.855158…
## $ logincome_4 <dbl> 6.263338, 6.490241, 6.251180, 6.102851, 6.136677, 6.276433…# save data, both wide data and long data
#------------------------------------------------------------------------------
# # save the balanced and clean data
# save(usl, usw, file = "~/Downloads/UKDA-5050-tab/data/us_clean.RData")
#
# # save the unbalanced data
# saveRDS(us1234, "~/Downloads/UKDA-5050-tab/data/us_unbalanced.rds")
#------------------------------------------------------------------------------
# EDA
#------------------------------------------------------------------------------
# 查看时间相关性-对于连续变量
# nice correlation matrix
usw %>%
select(matches("totincome")) %>% # select vars. that have this in name
na.omit() %>% # omit missing from data
cor() %>% # do correlations
round(2) # round to 2 decimal points## totincome_1 totincome_2 totincome_3 totincome_4
## totincome_1 1.00 0.66 0.62 0.58
## totincome_2 0.66 1.00 0.68 0.63
## totincome_3 0.62 0.68 1.00 0.70
## totincome_4 0.58 0.63 0.70 1.00usw %>%
select(matches("logincome")) %>% # select vars. that have this in name
na.omit() %>% # omit missing from data
cor() %>% # do correlations
round(2) # round to 2 decimal points## logincome_1 logincome_2 logincome_3 logincome_4
## logincome_1 1.00 0.65 0.64 0.58
## logincome_2 0.65 1.00 0.66 0.60
## logincome_3 0.64 0.66 1.00 0.69
## logincome_4 0.58 0.60 0.69 1.00usw %>%
select(matches("peninc")) %>% # select vars. that have this in name
na.omit() %>% # omit missing from data
cor() %>% # do correlations
round(2) # round to 2 decimal points## peninc_1 peninc_2 peninc_3 peninc_4
## peninc_1 1.00 0.78 0.69 0.60
## peninc_2 0.78 1.00 0.78 0.68
## peninc_3 0.69 0.78 1.00 0.77
## peninc_4 0.60 0.68 0.77 1.00# 分类变量
# 1-health-------------
# wave 1 and wave2----
# table in long format tidyverse way
count(usw, health_f_1, health_f_2) %>%
mutate(prop = n / sum(n)) %>%
print(n = 50) # make sure it prints all rows## # A tibble: 36 × 4
## health_f_1 health_f_2 n prop
## <fct> <fct> <int> <dbl>
## 1 excellent excellent 439 0.0680
## 2 excellent fair 14 0.00217
## 3 excellent good 93 0.0144
## 4 excellent poor 4 0.000619
## 5 excellent very good 306 0.0474
## 6 excellent <NA> 12 0.00186
## 7 fair excellent 9 0.00139
## 8 fair fair 525 0.0813
## 9 fair good 330 0.0511
## 10 fair poor 114 0.0176
## 11 fair very good 74 0.0115
## 12 fair <NA> 8 0.00124
## 13 good excellent 59 0.00913
## 14 good fair 327 0.0506
## 15 good good 1080 0.167
## 16 good poor 25 0.00387
## 17 good very good 536 0.0830
## 18 good <NA> 16 0.00248
## 19 poor excellent 1 0.000155
## 20 poor fair 110 0.0170
## 21 poor good 32 0.00495
## 22 poor poor 170 0.0263
## 23 poor very good 2 0.000310
## 24 poor <NA> 7 0.00108
## 25 very good excellent 275 0.0426
## 26 very good fair 95 0.0147
## 27 very good good 580 0.0898
## 28 very good poor 8 0.00124
## 29 very good very good 1025 0.159
## 30 very good <NA> 12 0.00186
## 31 <NA> excellent 5 0.000774
## 32 <NA> fair 9 0.00139
## 33 <NA> good 14 0.00217
## 34 <NA> poor 2 0.000310
## 35 <NA> very good 13 0.00201
## 36 <NA> <NA> 128 0.0198# transition matrix R base way
trans_freq12 <-
table(usw$health_f_1, usw$health_f_2, useNA = "always")
trans_freq12##
## excellent fair good poor very good <NA>
## excellent 439 14 93 4 306 12
## fair 9 525 330 114 74 8
## good 59 327 1080 25 536 16
## poor 1 110 32 170 2 7
## very good 275 95 580 8 1025 12
## <NA> 5 9 14 2 13 128# get row proportions
prop.table(trans_freq12, 1) %>%
round(2) # round to make easier to read##
## excellent fair good poor very good <NA>
## excellent 0.51 0.02 0.11 0.00 0.35 0.01
## fair 0.01 0.50 0.31 0.11 0.07 0.01
## good 0.03 0.16 0.53 0.01 0.26 0.01
## poor 0.00 0.34 0.10 0.53 0.01 0.02
## very good 0.14 0.05 0.29 0.00 0.51 0.01
## <NA> 0.03 0.05 0.08 0.01 0.08 0.75# wave 1 and wave 4----
# transition matrix R base way
trans_freq14 <-
table(usw$health_f_1, usw$health_f_4, useNA = "always")
trans_freq14##
## excellent fair good poor very good <NA>
## excellent 344 24 128 9 349 14
## fair 12 450 284 192 78 44
## good 76 433 988 78 408 60
## poor 0 101 34 157 9 21
## very good 241 140 648 20 904 42
## <NA> 7 10 17 6 19 112# get row proportions
prop.table(trans_freq14, 1) %>%
round(2) # round to make easier to read##
## excellent fair good poor very good <NA>
## excellent 0.40 0.03 0.15 0.01 0.40 0.02
## fair 0.01 0.42 0.27 0.18 0.07 0.04
## good 0.04 0.21 0.48 0.04 0.20 0.03
## poor 0.00 0.31 0.11 0.49 0.03 0.07
## very good 0.12 0.07 0.32 0.01 0.45 0.02
## <NA> 0.04 0.06 0.10 0.04 0.11 0.65# wave 1 and wave 4----
# transition matrix R base way
trans_freq23 <-
table(usw$health_f_2, usw$health_f_3, useNA = "always")
trans_freq23##
## excellent fair good poor very good <NA>
## excellent 419 10 85 1 268 5
## fair 14 537 302 144 70 13
## good 66 381 1116 44 501 21
## poor 0 104 21 183 6 9
## very good 246 90 554 15 1026 25
## <NA> 10 9 19 3 11 131# get row proportions
prop.table(trans_freq23, 1) %>%
round(2) # round to make easier to read##
## excellent fair good poor very good <NA>
## excellent 0.53 0.01 0.11 0.00 0.34 0.01
## fair 0.01 0.50 0.28 0.13 0.06 0.01
## good 0.03 0.18 0.52 0.02 0.24 0.01
## poor 0.00 0.32 0.07 0.57 0.02 0.03
## very good 0.13 0.05 0.28 0.01 0.52 0.01
## <NA> 0.05 0.05 0.10 0.02 0.06 0.72count(usw, famtype_f_1, famtype_f_2) %>%
mutate(prop = n / sum(n)) %>%
print(n = 50) # make sure it prints all rows## # A tibble: 16 × 4
## famtype_f_1 famtype_f_2 n prop
## <fct> <fct> <int> <dbl>
## 1 Couple Households Couple Households 4472 0.692
## 2 Couple Households Extended Family and Other 71 0.0110
## 3 Couple Households Lone Households 15 0.00232
## 4 Couple Households Single Households 81 0.0125
## 5 Extended Family and Other Couple Households 71 0.0110
## 6 Extended Family and Other Extended Family and Other 200 0.0310
## 7 Extended Family and Other Lone Households 7 0.00108
## 8 Extended Family and Other Single Households 22 0.00341
## 9 Lone Households Couple Households 3 0.000464
## 10 Lone Households Extended Family and Other 11 0.00170
## 11 Lone Households Lone Households 126 0.0195
## 12 Lone Households Single Households 36 0.00557
## 13 Single Households Couple Households 28 0.00434
## 14 Single Households Extended Family and Other 19 0.00294
## 15 Single Households Lone Households 32 0.00495
## 16 Single Households Single Households 1265 0.196# transition matrix R base way
trans_freq_famtype <-
table(usw$famtype_f_1, usw$famtype_f_2, useNA = "always")
trans_freq_famtype##
## Couple Households Extended Family and Other
## Couple Households 4472 71
## Extended Family and Other 71 200
## Lone Households 3 11
## Single Households 28 19
## <NA> 0 0
##
## Lone Households Single Households <NA>
## Couple Households 15 81 0
## Extended Family and Other 7 22 0
## Lone Households 126 36 0
## Single Households 32 1265 0
## <NA> 0 0 0## Graphs, graphs and more graphs (with long data)
#------------------------------------------------------------------------------
# ----------
# plot total income
set.seed(2222)
# randomly select 20 pidps
random_people <- unique(usl$pid) %>%
sample(20)
# filter just the 20 people
susl <- filter(usl, pid %in% random_people)
# plot total income of these 20 peaple among 4 waves
(g1 <- ggplot(data = susl,
aes(
x = wave, y = totincome, group = pid
)) +
geom_line())
# change theme
(g2 <- g1 + theme_bw())
# add label
(g3 <- g2 + labs(y = "Total income", x = "Wave"))
# -----
# plot for each people in sample which include 20 people
(
g4 <- g3 + facet_wrap( ~ pid, as.table = F) +
theme(strip.background = element_blank(),
strip.text.x = element_blank())
)
# add regression line
g4 + stat_smooth(method = "lm", se = F)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 2 rows containing non-finite values (`stat_smooth()`).
# -----
# plot point
g1 <- ggplot(susl, aes(wave, totincome, group = pid)) +
geom_point() +
stat_smooth(se = F) +
facet_wrap( ~ pid) +
labs(y = "Total income", x = "Wave") +
theme(strip.background = element_blank(),
strip.text.x = element_blank())
g1## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 2 rows containing non-finite values (`stat_smooth()`).## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : radius 0.000225## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 0.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : at 4.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : radius 0.000225## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 0.000225## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : zero-width neighborhood. make span bigger
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : zero-width neighborhood. make span bigger## Warning: Computation failed in `stat_smooth()`
## Caused by error in `predLoess()`:
## ! NA/NaN/Inf in foreign function call (arg 5)## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning: Removed 2 rows containing missing values (`geom_point()`).
# -----
# plot mean line
g1 <- ggplot(susl, aes(wave, totincome)) +
geom_point(alpha = .3) +
stat_summary(fun = mean,
geom = "line",
group = 1) +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave") +
theme_bw()
g1## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).## Warning: Removed 2 rows containing missing values (`geom_point()`).
# -----
# add catogory variables
# 1-gndr
ggplot(susl, aes(wave, totincome, color = gndr_f, group = gndr_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Gender") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
# 2-degree
ggplot(susl, aes(wave, totincome, color = degree, group = degree)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Education level") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
## Removed 2 rows containing non-finite values (`stat_summary()`).
# 3-marstat_f
ggplot(susl, aes(wave, totincome, color = marstat_f, group = marstat_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Marrital status") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
## Removed 2 rows containing non-finite values (`stat_summary()`).
# 4-health_f
ggplot(susl, aes(wave, totincome, color = health_f, group = health_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Health status") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
## Removed 2 rows containing non-finite values (`stat_summary()`).
# 5-tenure_f
ggplot(susl, aes(wave, totincome, color = tenure_f, group = tenure_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Tenure") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
## Removed 2 rows containing non-finite values (`stat_summary()`).
# 6-famtype_f
ggplot(susl, aes(wave, totincome, color = famtype_f, group = famtype_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Family type") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
## Removed 2 rows containing non-finite values (`stat_summary()`).
# -----
# gndr + health
ggplot(susl,
aes(
wave,
totincome,
color = gndr_f:health_f,
group = gndr_f:health_f
)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Gender x Health") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
## Removed 2 rows containing non-finite values (`stat_summary()`).
# gndr + tenure
ggplot(susl,
aes(
wave,
totincome,
color = gndr_f:tenure_f,
group = gndr_f:tenure_f
)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Gender x Tenure") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
## Removed 2 rows containing non-finite values (`stat_summary()`).
# gndr + degree
ggplot(susl,
aes(
wave,
totincome,
color = gndr_f:degree,
group = gndr_f:degree
)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Total income", x = "Wave", color = "Gender x Education level") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
## Removed 2 rows containing non-finite values (`stat_summary()`).
# 分面：gndr
ggplot(susl, aes(wave, totincome, group = pid)) +
geom_line() +
stat_summary(aes(group = gndr_f),
fun = mean,
geom = "line",
lwd = 2) +
facet_grid( ~ gndr_f) +
labs(y = "Total income", x = "Wave") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
# 分面组合：gndr+hleath
ggplot(susl, aes(wave, totincome, group = pid)) +
geom_line() +
stat_summary(
aes(group = gndr_f:health_f, color = health_f),
fun = mean,
geom = "line",
lwd = 2
) +
facet_grid( ~ gndr_f) +
labs(y = "Total income", x = "Wave", color = "Health") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
# 分面组合：gndr+marstat
ggplot(susl, aes(wave, totincome, group = pid)) +
geom_line() +
stat_summary(
aes(group = gndr_f:marstat_f, color = marstat_f),
fun = mean,
geom = "line",
lwd = 2
) +
facet_grid( ~ gndr_f) +
labs(y = "Total income", x = "Wave", color = "Marstat") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
# 分面组合：gndr+degree
ggplot(susl, aes(wave, totincome, group = pid)) +
geom_line() +
stat_summary(
aes(group = gndr_f:degree, color = degree),
fun = mean,
geom = "line",
lwd = 2
) +
facet_grid( ~ gndr_f) +
labs(y = "Total income", x = "Wave", color = "Education level") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
# 分面组合：gndr+tenure
ggplot(susl, aes(wave, totincome, group = pid)) +
geom_line() +
stat_summary(
aes(group = gndr_f:tenure_f, color = tenure_f),
fun = mean,
geom = "line",
lwd = 2
) +
facet_grid( ~ gndr_f) +
labs(y = "Total income", x = "Wave", color = "Tenure") +
theme_bw()## Warning: Removed 2 rows containing non-finite values (`stat_summary()`).
# ----------
## Graphs, graphs and more graphs (with long data)
#------------------------------------------------------------------------------
# ----------
# plot state pension income
set.seed(1234)
# randomly select 20 pidps
random_people2 <- unique(usl$pid) %>%
sample(20)
# filter just the 20 people
susl2 <- filter(usl, pid %in% random_people2)
# plot total income of these 20 peaple among 4 waves
(p1 <- ggplot(data = susl2,
aes(
x = wave, y = peninc, group = pid
)) +
geom_line())
# change theme
(p2 <- p1 + theme_bw())
# add label
(p3 <- p2 + labs(y = "Pension income", x = "Wave"))
# -----
# plot for each people in sample which include 20 people
(
p4 <- p3 + facet_wrap( ~ pid, as.table = F) +
theme(strip.background = element_blank(),
strip.text.x = element_blank())
)
# add regression line
p4 + stat_smooth(method = "lm", se = F)## `geom_smooth()` using formula = 'y ~ x'
# -----
# plot point
p1 <- ggplot(susl2, aes(wave, peninc, group = pid)) +
geom_point() +
stat_smooth(se = F) +
facet_wrap( ~ pid) +
labs(y = "Pension income", x = "Wave") +
theme(strip.background = element_blank(),
strip.text.x = element_blank())
p1## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.985## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.015## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.0602
# -----
# plot mean line
p1 <- ggplot(susl2, aes(wave, peninc)) +
geom_point(alpha = .3) +
stat_summary(fun = mean,
geom = "line",
group = 1) +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave") +
theme_bw()
p1
# -----
# add catogory variables
# 1-gndr
ggplot(susl2, aes(wave, peninc, color = gndr_f, group = gndr_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Gender") +
theme_bw()
# 2-degree
ggplot(susl2, aes(wave, peninc, color = degree, group = degree)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Education level") +
theme_bw()
# 3-marstat_f
ggplot(susl2, aes(wave, peninc, color = marstat_f, group = marstat_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Marrital status") +
theme_bw()
# 4-health_f
ggplot(susl2, aes(wave, peninc, color = health_f, group = health_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Health status") +
theme_bw()
# 5-tenure_f
ggplot(susl2, aes(wave, peninc, color = tenure_f, group = tenure_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Tenure") +
theme_bw()
# 6-famtype_f
ggplot(susl2, aes(wave, peninc, color = famtype_f, group = famtype_f)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Family type") +
theme_bw()
# -----
# gndr + health
ggplot(susl2,
aes(
wave,
peninc,
color = gndr_f:health_f,
group = gndr_f:health_f
)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Gender x Health") +
theme_bw()
# gndr + tenure
ggplot(susl2,
aes(
wave,
peninc,
color = gndr_f:tenure_f,
group = gndr_f:tenure_f
)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Gender x Tenure") +
theme_bw()
# gndr + degree
ggplot(susl2,
aes(
wave,
peninc,
color = gndr_f:degree,
group = gndr_f:degree
)) +
# geom_point(alpha = .3) +
stat_summary(fun = mean, geom = "line") +
stat_summary(fun = mean, geom = "point", size = 3) +
labs(y = "Pension income", x = "Wave", color = "Gender x Education level") +
theme_bw()
# 分面：gndr
ggplot(susl2, aes(wave, peninc, group = pid)) +
geom_line() +
stat_summary(aes(group = gndr_f),
fun = mean,
geom = "line",
lwd = 2) +
facet_grid( ~ gndr_f) +
labs(y = "Pension income", x = "Wave") +
theme_bw()
# 分面组合：gndr+hleath
ggplot(susl2, aes(wave, peninc, group = pid)) +
geom_line() +
stat_summary(
aes(group = gndr_f:health_f, color = gndr_f),
fun = mean,
geom = "line",
lwd = 2
) +
facet_grid( ~ health_f) +
labs(y = "Pension income", x = "Wave", color = "Gender") +
theme_bw()
# 分面组合：gndr+marstat
ggplot(susl2, aes(wave, peninc, group = pid)) +
geom_line() +
stat_summary(
aes(group = gndr_f:marstat_f, color = gndr_f),
fun = mean,
geom = "line",
lwd = 2
) +
facet_grid( ~ marstat_f) +
labs(y = "Pension income", x = "Wave", color = "Gender") +
theme_bw()
# 分面组合：gndr+degree
ggplot(susl2, aes(wave, peninc, group = pid)) +
geom_line() +
stat_summary(
aes(group = gndr_f:degree, color = gndr_f),
fun = mean,
geom = "line",
lwd = 2
) +
facet_grid( ~ degree) +
labs(y = "Pension income", x = "Wave", color = "Gender") +
theme_bw()
# 分面组合：gndr+tenure
ggplot(susl2, aes(wave, peninc, group = pid)) +
geom_line() +
stat_summary(
aes(group = gndr_f:tenure_f, color = gndr_f),
fun = mean,
geom = "line",
lwd = 2
) +
facet_grid( ~ tenure_f) +
labs(y = "Pension income", x = "Wave", color = "Gender") +
theme_bw()
# ----------
# Cross lagged
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
# load packages
library("lavaan")## This is lavaan 0.6-17
## lavaan is FREE software! Please report any bugs.usw # A tibble: 6,459 × 54## # A tibble: 6,459 × 54
## pid gndr age edqual gndr_f degree wave_1 wave_2 wave_3 wave_4 marstat_1
## <int> <int> <int> <int> <fct> <fct> <chr> <chr> <chr> <chr> <int>
## 1 100007 1 64 6 Male Basic… 1 2 3 4 2
## 2 100023 2 53 5 Female Basic… 1 2 3 4 2
## 3 100025 1 81 4 Male Secon… 1 2 3 4 2
## 4 100026 1 70 5 Male Basic… 1 2 3 4 2
## 5 100039 1 63 2 Male High … 1 2 3 4 1
## 6 100048 2 44 7 Female Basic… 1 2 3 4 2
## 7 100052 2 61 1 Female High … 1 2 3 4 2
## 8 100054 1 64 4 Male Secon… 1 2 3 4 1
## 9 100057 2 64 4 Female Secon… 1 2 3 4 1
## 10 100063 1 59 6 Male Basic… 1 2 3 4 2
## # ℹ 6,449 more rows
## # ℹ 43 more variables: marstat_2 <int>, marstat_3 <int>, marstat_4 <int>,
## # famtype_1 <int>, famtype_2 <int>, famtype_3 <int>, famtype_4 <int>,
## # health_1 <int>, health_2 <int>, health_3 <int>, health_4 <int>,
## # tenure_1 <int>, tenure_2 <int>, tenure_3 <int>, tenure_4 <int>,
## # peninc_1 <dbl>, peninc_2 <dbl>, peninc_3 <dbl>, peninc_4 <dbl>,
## # totincome_1 <dbl>, totincome_2 <dbl>, totincome_3 <dbl>, …usl # A tibble: 25,836 × 18## # A tibble: 25,836 × 18
## pid wave gndr age edqual marstat famtype health tenure peninc
## <int> <chr> <int> <int> <int> <int> <int> <int> <int> <dbl>
## 1 100007 1 1 64 6 2 6 3 1 0
## 2 100007 2 1 64 6 2 6 4 1 139.
## 3 100007 3 1 64 6 2 6 3 1 125
## 4 100007 4 1 64 6 2 6 4 1 150
## 5 100023 1 2 53 5 2 6 NA 1 0
## 6 100023 2 2 53 5 2 6 NA 1 0
## 7 100023 3 2 53 5 2 6 3 1 0
## 8 100023 4 2 53 5 2 6 3 2 0
## 9 100025 1 1 81 4 2 6 1 1 126.
## 10 100025 2 1 81 4 2 6 3 1 125
## # ℹ 25,826 more rows
## # ℹ 8 more variables: totincome <dbl>, gndr_f <fct>, degree <fct>,
## # marstat_f <fct>, famtype_f <fct>, tenure_f <fct>, health_f <fct>,
## # logincome <dbl># look at variables
hist(usw$peninc_1)
hist(usw$peninc_2)
hist(usw$peninc_3)
hist(usw$peninc_4)
# hist(usw$logincome_1)
# hist(usw$logincome_2)
# hist(usw$logincome_3)
# hist(usw$logincome_4)
# nice correlation matrix
usw %>%
select(matches("peninc")) %>% # select vars. that have this in name
na.omit() %>% # omit missing from data
cor() %>% # do correlations
round(2) # round to 2 decimal points## peninc_1 peninc_2 peninc_3 peninc_4
## peninc_1 1.00 0.78 0.69 0.60
## peninc_2 0.78 1.00 0.78 0.68
## peninc_3 0.69 0.78 1.00 0.77
## peninc_4 0.60 0.68 0.77 1.00# Create modle
#------------------------------------------------------------------------------
# model1
# save model
model <- 'peninc_2 ~ 1 + peninc_1'
# estimate and save results
fit <- sem(model, data = usw)
# print summary with standardized results
summary(fit, standardized = TRUE) ## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 3
##
## Used Total
## Number of observations 6425 6459
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 0.826 0.008 100.748 0.000 0.826 0.783
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 25.029 0.809 30.938 0.000 25.029 0.337
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2138.731 37.734 56.679 0.000 2138.731 0.388# # #-----数据居中处理
# check results
usw %>%
select(matches("peninc")) %>%
summary()## peninc_1 peninc_2 peninc_3 peninc_4
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.0
## Median : 64.75 Median : 92.38 Median :119.50 Median :132.5
## Mean : 69.18 Mean : 82.07 Mean : 95.07 Mean :109.1
## 3rd Qu.:126.00 3rd Qu.:139.00 3rd Qu.:150.00 3rd Qu.:162.5
## Max. :400.00 Max. :400.00 Max. :320.00 Max. :400.0
## NA's :22 NA's :17 NA's :33 NA's :56# make new centered variables with "test" at the end
# usw <- usw %>%
# mutate_at(vars(starts_with("peninc")), - mean(., na.rm = T))
usw <- usw %>%
mutate(across(starts_with("peninc"), ~ . - mean(., na.rm = TRUE)))
# # rename
# usw <- usw %>%
# rename_at(vars(ends_with("test")), # select variables with "test"
# ~str_replace(., "peninc", "pinc") %>% # replace
# str_remove("_test")) # delete "_test"
#
# check results
usw %>%
select(matches("peninc")) %>%
summary()## peninc_1 peninc_2 peninc_3 peninc_4
## Min. :-69.183 Min. :-82.07 Min. :-95.07 Min. :-109.12
## 1st Qu.:-69.183 1st Qu.:-82.07 1st Qu.:-95.07 1st Qu.:-109.12
## Median : -4.433 Median : 10.31 Median : 24.43 Median : 23.38
## Mean : 0.000 Mean : 0.00 Mean : 0.00 Mean : 0.00
## 3rd Qu.: 56.817 3rd Qu.: 56.93 3rd Qu.: 54.93 3rd Qu.: 53.38
## Max. :330.817 Max. :317.93 Max. :224.93 Max. : 290.88
## NA's :22 NA's :17 NA's :33 NA's :56#------
# wave2-wave1
model <- 'peninc_2 ~ 1 + peninc_1' # define the model,0.826(p<0.05) / 25.029(p<0.05) / std.all=0.388
fit <- sem(model, data = usw) # run the model and save results
summary(fit, standardized = TRUE) # print summary of results## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 3
##
## Used Total
## Number of observations 6425 6459
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 0.826 0.008 100.748 0.000 0.826 0.783
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.092 0.577 0.159 0.874 0.092 0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2138.731 37.734 56.679 0.000 2138.731 0.388model <- 'peninc_3 ~ 1 + peninc_2' # define the model,0.786(p<0.05) / 30.549(p<0.05) / std.all=0.401
fit <- sem(model, data = usw) # run the model and save results
summary(fit, standardized = TRUE) # print summary of results## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 3
##
## Used Total
## Number of observations 6415 6459
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_3 ~
## peninc_2 0.786 0.008 97.986 0.000 0.786 0.774
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_3 0.023 0.596 0.039 0.969 0.023 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_3 2280.790 40.272 56.635 0.000 2280.790 0.401model <- 'peninc_4 ~ 1 + peninc_3' # define the model,0.785(p<0.05) / 34.668(p<0.05) / std.all=0.416
fit <- sem(model, data = usw) # run the model and save results
summary(fit, standardized = TRUE) # print summary of results## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 3
##
## Used Total
## Number of observations 6380 6459
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_4 ~
## peninc_3 0.785 0.008 94.705 0.000 0.785 0.764
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_4 0.222 0.627 0.355 0.723 0.222 0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_4 2504.554 44.344 56.480 0.000 2504.554 0.416# 1 lag
# wave2-wave1 + wave3-wave2
model <- 'peninc_2 ~ 1 + peninc_1
peninc_3 ~ 1 + peninc_2
peninc_4 ~ 1 + peninc_3'
fit <- sem(model, data = usw) # estimate model and save
summary(fit, standardized = TRUE) # print result## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 6352 6459
##
## Model Test User Model:
##
## Test statistic 590.953
## Degrees of freedom 3
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 0.826 0.008 100.371 0.000 0.826 0.783
## peninc_3 ~
## peninc_2 0.788 0.008 97.946 0.000 0.788 0.776
## peninc_4 ~
## peninc_3 0.788 0.008 95.158 0.000 0.788 0.767
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.065 0.580 0.112 0.911 0.065 0.001
## .peninc_3 -0.053 0.598 -0.089 0.929 -0.053 -0.001
## .peninc_4 0.222 0.626 0.355 0.723 0.222 0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2136.202 37.905 56.356 0.000 2136.202 0.387
## .peninc_3 2272.047 40.316 56.356 0.000 2272.047 0.398
## .peninc_4 2486.041 44.113 56.356 0.000 2486.041 0.412# 2 lags
# Three wave autoregressive + lag 2
model <- 'peninc_2 ~ 1 + peninc_1
peninc_3 ~ 1 + peninc_2 + peninc_1'
fit <- sem(model, data = usw)
summary(fit, standardized = TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Used Total
## Number of observations 6398 6459
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 0.826 0.008 100.691 0.000 0.826 0.783
## peninc_3 ~
## peninc_2 0.618 0.013 48.937 0.000 0.618 0.609
## peninc_1 0.227 0.013 17.034 0.000 0.227 0.212
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.082 0.578 0.143 0.887 0.082 0.001
## .peninc_3 0.025 0.584 0.044 0.965 0.025 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2136.038 37.766 56.560 0.000 2136.038 0.387
## .peninc_3 2179.600 38.536 56.560 0.000 2179.600 0.383model <- 'peninc_3 ~ 1 + peninc_2
peninc_4 ~ 1 + peninc_3 + peninc_2'
fit <- sem(model, data = usw)
summary(fit, standardized = TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Used Total
## Number of observations 6369 6459
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_3 ~
## peninc_2 0.788 0.008 98.008 0.000 0.788 0.775
## peninc_4 ~
## peninc_3 0.616 0.013 48.059 0.000 0.616 0.599
## peninc_2 0.225 0.013 17.311 0.000 0.225 0.216
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_3 -0.035 0.598 -0.058 0.954 -0.035 -0.000
## .peninc_4 0.207 0.611 0.339 0.735 0.207 0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_3 2274.429 40.304 56.431 0.000 2274.429 0.399
## .peninc_4 2378.076 42.141 56.431 0.000 2378.076 0.394# 3 wave autoregressive + corr
model <- 'peninc_2 ~ 1 + peninc_1
peninc_3 ~ 1 + peninc_2
peninc_1 ~~ peninc_3'
fit <- sem(model, data = usw)
summary(fit, standardized = TRUE)## lavaan 0.6.17 ended normally after 55 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 6398 6459
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 0.826 0.008 100.691 0.000 0.826 0.783
## peninc_3 ~
## peninc_2 0.618 0.013 48.937 0.000 0.618 0.609
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_3 ~~
## peninc_1 1125.343 68.997 16.310 0.000 1125.343 0.324
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.082 0.578 0.143 0.887 0.082 0.001
## .peninc_3 0.010 0.617 0.016 0.987 0.010 0.000
## peninc_1 -0.069 0.880 -0.078 0.938 -0.069 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2136.038 37.766 56.560 0.000 2136.038 0.387
## .peninc_3 2435.082 49.044 49.651 0.000 2435.082 0.428
## peninc_1 4956.897 87.640 56.560 0.000 4956.897 1.000#----加入间接效应和总效应
# add 间接效应
model <- 'peninc_2 ~ 1 + a*peninc_1
peninc_3 ~ 1 + b*peninc_2
ind := a*b' # new coefficient
fita <- sem(model, data = usw)
summary(fita, standardized = TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 6
##
## Used Total
## Number of observations 6398 6459
##
## Model Test User Model:
##
## Test statistic 283.762
## Degrees of freedom 1
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 (a) 0.826 0.008 100.691 0.000 0.826 0.783
## peninc_3 ~
## peninc_2 (b) 0.786 0.008 97.923 0.000 0.786 0.774
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.082 0.578 0.143 0.887 0.082 0.001
## .peninc_3 0.005 0.597 0.009 0.993 0.005 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2136.038 37.766 56.560 0.000 2136.038 0.387
## .peninc_3 2278.444 40.284 56.560 0.000 2278.444 0.400
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind 0.650 0.009 70.200 0.000 0.650 0.606# 加入间接+总效应
# Indirect and total effects
model <- 'peninc_2 ~ 1 + a*peninc_1
peninc_3 ~ 1 + b*peninc_2 + c*peninc_1
ind := a*b
total := c + (a*b)'
fitb <- sem(model, data = usw)
summary(fitb, standardized = TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Used Total
## Number of observations 6398 6459
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 (a) 0.826 0.008 100.691 0.000 0.826 0.783
## peninc_3 ~
## peninc_2 (b) 0.618 0.013 48.937 0.000 0.618 0.609
## peninc_1 (c) 0.227 0.013 17.034 0.000 0.227 0.212
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.082 0.578 0.143 0.887 0.082 0.001
## .peninc_3 0.025 0.584 0.044 0.965 0.025 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2136.038 37.766 56.560 0.000 2136.038 0.387
## .peninc_3 2179.600 38.536 56.560 0.000 2179.600 0.383
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind 0.511 0.012 44.014 0.000 0.511 0.477
## total 0.738 0.010 75.909 0.000 0.738 0.688anova(fita, fitb) # compare the two models##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fitb 0 134560 134607 0.00
## fita 1 134842 134882 283.76 283.76 0.21023 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#----
#----限制间接效应系数相等,fitc
model <- 'peninc_2 ~ 1 + a*peninc_1
peninc_3 ~ 1 + a*peninc_2
ind := a*a'
# estimate model and save
fitc <- sem(model, data = usw)
# print summary
summary(fitc, standardized = TRUE) ## lavaan 0.6.17 ended normally after 4 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 6
## Number of equality constraints 1
##
## Used Total
## Number of observations 6398 6459
##
## Model Test User Model:
##
## Test statistic 295.834
## Degrees of freedom 2
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 (a) 0.806 0.006 139.285 0.000 0.806 0.775
## peninc_3 ~
## peninc_2 (a) 0.806 0.006 139.285 0.000 0.806 0.777
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.082 0.578 0.143 0.887 0.082 0.001
## .peninc_3 0.005 0.597 0.009 0.993 0.005 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2138.098 37.802 56.560 0.000 2138.098 0.399
## .peninc_3 2280.548 40.321 56.560 0.000 2280.548 0.396
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind 0.650 0.009 69.642 0.000 0.650 0.601# compare 2 models
anova(fitc, fita) ##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fita 1 134842 134882 283.76
## fitc 2 134852 134885 295.83 12.072 0.041599 1 0.0005119 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Add wave4
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
# model 4 wave and calculate indirect effect of wave 1 on wave 4
model <- 'peninc_2 ~ 1 + a*peninc_1
peninc_3 ~ 1 + b*peninc_2
peninc_4 ~ 1 + c*peninc_3
ind := a*b*c'
fit1 <- sem(model, data = usw)
summary(fit1, standardized = TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 6352 6459
##
## Model Test User Model:
##
## Test statistic 590.953
## Degrees of freedom 3
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 (a) 0.826 0.008 100.371 0.000 0.826 0.783
## peninc_3 ~
## peninc_2 (b) 0.788 0.008 97.946 0.000 0.788 0.776
## peninc_4 ~
## peninc_3 (c) 0.788 0.008 95.158 0.000 0.788 0.767
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.065 0.580 0.112 0.911 0.065 0.001
## .peninc_3 -0.053 0.598 -0.089 0.929 -0.053 -0.001
## .peninc_4 0.222 0.626 0.355 0.723 0.222 0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2136.202 37.905 56.356 0.000 2136.202 0.387
## .peninc_3 2272.047 40.316 56.356 0.000 2272.047 0.398
## .peninc_4 2486.041 44.113 56.356 0.000 2486.041 0.412
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind 0.513 0.009 56.439 0.000 0.513 0.466# 验证时间稳定性
# is stability equal in time?
# stability 1 and 2
model <- 'peninc_2 ~ 1 + a*peninc_1
peninc_3 ~ 1 + a*peninc_2
peninc_4 ~ 1 + c*peninc_3
ind := a*a*c'
fit2 <- sem(model, data = usw)
summary(fit2, standardized = TRUE)## lavaan 0.6.17 ended normally after 9 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
## Number of equality constraints 1
##
## Used Total
## Number of observations 6352 6459
##
## Model Test User Model:
##
## Test statistic 602.033
## Degrees of freedom 4
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 (a) 0.807 0.006 139.119 0.000 0.807 0.776
## peninc_3 ~
## peninc_2 (a) 0.807 0.006 139.119 0.000 0.807 0.778
## peninc_4 ~
## peninc_3 (c) 0.788 0.008 95.696 0.000 0.788 0.768
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.059 0.580 0.101 0.919 0.059 0.001
## .peninc_3 -0.050 0.598 -0.083 0.934 -0.050 -0.001
## .peninc_4 0.222 0.626 0.355 0.723 0.222 0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2138.112 37.939 56.356 0.000 2138.112 0.398
## .peninc_3 2273.980 40.350 56.356 0.000 2273.980 0.394
## .peninc_4 2486.041 44.113 56.356 0.000 2486.041 0.410
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind 0.513 0.009 56.266 0.000 0.513 0.462# all 3 stabilities
model <- 'peninc_2 ~ 1 + a*peninc_1
peninc_3 ~ 1 + a*peninc_2
peninc_4 ~ 1 + a*peninc_3
ind := a*a*a'
fit3 <- sem(model, data = usw)
summary(fit3, standardized = TRUE)## lavaan 0.6.17 ended normally after 12 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
## Number of equality constraints 2
##
## Used Total
## Number of observations 6352 6459
##
## Model Test User Model:
##
## Test statistic 605.430
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 (a) 0.801 0.005 168.195 0.000 0.801 0.773
## peninc_3 ~
## peninc_2 (a) 0.801 0.005 168.195 0.000 0.801 0.775
## peninc_4 ~
## peninc_3 (a) 0.801 0.005 168.195 0.000 0.801 0.771
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.057 0.580 0.098 0.922 0.057 0.001
## .peninc_3 -0.051 0.598 -0.085 0.932 -0.051 -0.001
## .peninc_4 0.224 0.626 0.358 0.720 0.224 0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2139.470 37.963 56.356 0.000 2139.470 0.402
## .peninc_3 2272.932 40.332 56.356 0.000 2272.932 0.400
## .peninc_4 2486.937 44.129 56.356 0.000 2486.937 0.406
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind 0.514 0.009 56.065 0.000 0.514 0.462anova(fit1, fit2)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit1 3 201550 201611 590.95
## fit2 4 201559 201613 602.03 11.08 0.039836 1 0.0008727 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit1, fit3)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit1 3 201550 201611 590.95
## fit3 5 201560 201608 605.43 14.477 0.031339 2 0.0007183 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit1, fit2, fit3)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit1 3 201550 201611 590.95
## fit2 4 201559 201613 602.03 11.0799 0.039836 1 0.0008727 ***
## fit3 5 201560 201608 605.43 3.3973 0.019427 1 0.0653047 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# ----check 2 lags是否有效，即1-3/2-4是否有间接效应
# investigate if there is lag 2 effects and calc indirect effects
model <- 'peninc_2 ~ 1 + a*peninc_1
peninc_3 ~ 1 + b*peninc_2 + d*peninc_1
peninc_4 ~ 1 + c*peninc_3 + e*peninc_2
ind := a*b*c + d*c + a*e'
fit4 <- sem(model, data = usw)
summary(fit4, standardized = TRUE)## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 11
##
## Used Total
## Number of observations 6352 6459
##
## Model Test User Model:
##
## Test statistic 23.417
## Degrees of freedom 1
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 (a) 0.826 0.008 100.371 0.000 0.826 0.783
## peninc_3 ~
## peninc_2 (b) 0.620 0.013 49.020 0.000 0.620 0.611
## peninc_1 (d) 0.226 0.013 16.912 0.000 0.226 0.211
## peninc_4 ~
## peninc_3 (c) 0.618 0.013 48.127 0.000 0.618 0.601
## peninc_2 (e) 0.224 0.013 17.158 0.000 0.224 0.214
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.065 0.580 0.112 0.911 0.065 0.001
## .peninc_3 -0.016 0.585 -0.027 0.979 -0.016 -0.000
## .peninc_4 0.229 0.612 0.375 0.708 0.229 0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2136.202 37.905 56.356 0.000 2136.202 0.387
## .peninc_3 2174.152 38.579 56.356 0.000 2174.152 0.381
## .peninc_4 2375.924 42.159 56.356 0.000 2375.924 0.394
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind 0.641 0.010 64.497 0.000 0.641 0.581anova(fit1, fit4) # fit4 is better##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit4 1 200986 201061 23.417
## fit1 3 201550 201611 590.953 567.54 0.21099 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# ----最大似然估计处理缺失值
# make variables that tells us if something is missing
usw <- usw %>%
mutate_at(vars(matches("peninc")),
list("miss" = ~is.na(.)))
usw %>%
count(peninc_1_miss, peninc_2_miss, peninc_3_miss, peninc_4_miss) %>%
arrange(desc(n)) # arrange by n in descending order## # A tibble: 11 × 5
## peninc_1_miss peninc_2_miss peninc_3_miss peninc_4_miss n
## <lgl> <lgl> <lgl> <lgl> <int>
## 1 FALSE FALSE FALSE FALSE 6352
## 2 FALSE FALSE FALSE TRUE 46
## 3 FALSE FALSE TRUE FALSE 19
## 4 TRUE FALSE FALSE FALSE 17
## 5 FALSE FALSE TRUE TRUE 8
## 6 FALSE TRUE FALSE FALSE 8
## 7 FALSE TRUE TRUE FALSE 3
## 8 TRUE TRUE FALSE FALSE 3
## 9 FALSE TRUE TRUE TRUE 1
## 10 TRUE TRUE TRUE FALSE 1
## 11 TRUE TRUE TRUE TRUE 1# ----最大似然估计+fit4
model <- 'peninc_2 ~ 1 + a*peninc_1
peninc_3 ~ 1 + b*peninc_2 + d*peninc_1
peninc_4 ~ 1 + c*peninc_3 + e*peninc_2
ind := a*b*c + d*c + a*e' # calculate the indirect effect
# estimate with FIML
fit6 <- sem(model, data = usw, missing = "ML") ## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 22 cases were deleted due to missing values in
## exogenous variable(s), while fixed.x = TRUE.# look at results
summary(fit6, standardized = TRUE) ## lavaan 0.6.17 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 11
##
## Used Total
## Number of observations 6437 6459
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 23.055
## Degrees of freedom 1
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## peninc_2 ~
## peninc_1 (a) 0.826 0.008 100.775 0.000 0.826 0.782
## peninc_3 ~
## peninc_2 (b) 0.618 0.013 48.933 0.000 0.618 0.608
## peninc_1 (d) 0.227 0.013 17.078 0.000 0.227 0.212
## peninc_4 ~
## peninc_3 (c) 0.618 0.013 48.126 0.000 0.618 0.600
## peninc_2 (e) 0.223 0.013 17.091 0.000 0.223 0.213
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 0.105 0.577 0.181 0.856 0.105 0.001
## .peninc_3 0.006 0.583 0.010 0.992 0.006 0.000
## .peninc_4 0.244 0.611 0.399 0.690 0.244 0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_2 2138.297 37.719 56.691 0.000 2138.297 0.388
## .peninc_3 2180.243 38.543 56.566 0.000 2180.243 0.383
## .peninc_4 2382.347 42.228 56.416 0.000 2382.347 0.396
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind 0.640 0.010 64.596 0.000 0.640 0.580anova(fit4, fit6) # fit4 is better## Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan WARNING:
## Some restricted models fit better than less restricted models;
## either these models are not nested, or the less restricted model
## failed to reach a global optimum. Smallest difference =
## -0.361343680249085## Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan
## WARNING: some models have the same degrees of freedom##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit4 1 200986 201061 23.417
## fit6 1 202691 202766 23.055 -0.36134 0 0# Cross-lagged models
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
# make new centered variables with "test" at the end
usw <- usw %>%
mutate_at(vars(starts_with("totincome")),
list("totest" = ~ . - mean(., na.rm = T)))
# rename new variables
usw <- usw %>%
rename_at(vars(ends_with("totest")), # select variables with "test"
~str_replace(., "totincome", "totinc") %>% # replace
str_remove("_totest")) # delete "_test"
# check new vars
usw %>%
select(matches("totinc")) %>% # 以totinc_1,totinc_2,totinc_3,totinc_4为准
summary()## totincome_1 totincome_2 totincome_3 totincome_4
## Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 301.4 1st Qu.: 302.5 1st Qu.: 317.3 1st Qu.: 324.7
## Median : 466.7 Median : 484.5 Median : 480.8 Median : 499.9
## Mean : 571.0 Mean : 577.4 Mean : 575.7 Mean : 605.4
## 3rd Qu.: 729.0 3rd Qu.: 734.2 3rd Qu.: 728.6 3rd Qu.: 754.2
## Max. :3000.0 Max. :3000.0 Max. :3000.0 Max. :3000.0
## NA's :94 NA's :105 NA's :76 NA's :92
## totinc_1 totinc_2 totinc_3 totinc_4
## Min. :-571.0 Min. :-577.41 Min. :-575.7 Min. :-605.4
## 1st Qu.:-269.6 1st Qu.:-274.89 1st Qu.:-258.4 1st Qu.:-280.7
## Median :-104.3 Median : -92.92 Median : -94.9 Median :-105.5
## Mean : 0.0 Mean : 0.00 Mean : 0.0 Mean : 0.0
## 3rd Qu.: 158.0 3rd Qu.: 156.82 3rd Qu.: 152.9 3rd Qu.: 148.8
## Max. :2429.0 Max. :2422.59 Max. :2424.3 Max. :2394.6
## NA's :94 NA's :105 NA's :76 NA's :92# nice correlation matrix（pcsc=totinc）
usw %>%
select(matches("totinc")) %>% # select vars. that have this in name
na.omit() %>% # omit missing from data
cor() %>% # do correlations
round(2) # round to 2 decimal points## totincome_1 totincome_2 totincome_3 totincome_4 totinc_1 totinc_2
## totincome_1 1.00 0.66 0.62 0.58 1.00 0.66
## totincome_2 0.66 1.00 0.68 0.63 0.66 1.00
## totincome_3 0.62 0.68 1.00 0.70 0.62 0.68
## totincome_4 0.58 0.63 0.70 1.00 0.58 0.63
## totinc_1 1.00 0.66 0.62 0.58 1.00 0.66
## totinc_2 0.66 1.00 0.68 0.63 0.66 1.00
## totinc_3 0.62 0.68 1.00 0.70 0.62 0.68
## totinc_4 0.58 0.63 0.70 1.00 0.58 0.63
## totinc_3 totinc_4
## totincome_1 0.62 0.58
## totincome_2 0.68 0.63
## totincome_3 1.00 0.70
## totincome_4 0.70 1.00
## totinc_1 0.62 0.58
## totinc_2 0.68 0.63
## totinc_3 1.00 0.70
## totinc_4 0.70 1.00# ------
# Cross-lagged model
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1
totinc_3 ~ 1 + totinc_2 + peninc_2
totinc_4 ~ 1 + totinc_3 + peninc_3
peninc_2 ~ 1 + peninc_1 + totinc_1
peninc_3 ~ 1 + peninc_2 + totinc_2
peninc_4 ~ 1 + peninc_3 + totinc_3
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m1 <- sem(model, data = usw, missing = "ML") # estimate model## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1662summary(m1, standardized = TRUE) # print results## lavaan 0.6.17 ended normally after 175 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
##
## Used Total
## Number of observations 6458 6459
## Number of missing patterns 25
##
## Model Test User Model:
##
## Test statistic 1944.482
## Degrees of freedom 12
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.643 0.009 68.562 0.000 0.643 0.653
## peninc_1 -0.327 0.056 -5.869 0.000 -0.327 -0.056
## totinc_3 ~
## totinc_2 0.657 0.009 72.107 0.000 0.657 0.669
## peninc_2 -0.304 0.051 -6.015 0.000 -0.304 -0.056
## totinc_4 ~
## totinc_3 0.752 0.010 76.803 0.000 0.752 0.691
## peninc_3 -0.379 0.053 -7.199 0.000 -0.379 -0.065
## peninc_2 ~
## peninc_1 0.820 0.008 99.152 0.000 0.820 0.778
## totinc_1 -0.007 0.001 -5.105 0.000 -0.007 -0.040
## peninc_3 ~
## peninc_2 0.783 0.008 97.128 0.000 0.783 0.770
## totinc_2 -0.007 0.001 -4.535 0.000 -0.007 -0.036
## peninc_4 ~
## peninc_3 0.783 0.008 94.305 0.000 0.783 0.761
## totinc_3 -0.008 0.002 -5.011 0.000 -0.008 -0.040
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_1 ~~
## peninc_1 -1183.564 368.423 -3.213 0.001 -1183.564 -0.041
## .totinc_2 ~~
## .peninc_2 1524.405 181.424 8.402 0.000 1524.405 0.107
## .totinc_3 ~~
## .peninc_3 1498.553 180.554 8.300 0.000 1498.553 0.105
## .totinc_4 ~~
## .peninc_4 1349.727 198.307 6.806 0.000 1349.727 0.086
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 0.978 3.880 0.252 0.801 0.978 0.002
## .totinc_3 -1.033 3.737 -0.276 0.782 -1.033 -0.003
## .totinc_4 -0.612 3.959 -0.155 0.877 -0.612 -0.001
## .peninc_2 0.072 0.576 0.125 0.900 0.072 0.001
## .peninc_3 0.028 0.596 0.047 0.963 0.028 0.000
## .peninc_4 0.200 0.625 0.320 0.749 0.200 0.003
## totinc_1 0.448 5.211 0.086 0.932 0.448 0.001
## peninc_1 -0.056 0.873 -0.064 0.949 -0.056 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 95709.409 1710.162 55.965 0.000 95709.409 0.568
## .totinc_3 88964.096 1589.855 55.957 0.000 88964.096 0.546
## .totinc_4 99509.178 1769.637 56.231 0.000 99509.178 0.515
## .peninc_2 2138.311 37.879 56.451 0.000 2138.311 0.391
## .peninc_3 2283.635 40.496 56.391 0.000 2283.635 0.404
## .peninc_4 2496.473 44.180 56.507 0.000 2496.473 0.417
## totinc_1 173653.848 3079.426 56.392 0.000 173653.848 1.000
## peninc_1 4911.015 86.708 56.639 0.000 4911.015 1.000# ------
# 控制混杂因素-控制时间常量：gender / age
# # Cross-lagged model with controls for sex：系数差异较小
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1 + gndr_f
totinc_3 ~ 1 + totinc_2 + peninc_2 + gndr_f
totinc_4 ~ 1 + totinc_3 + peninc_3 + gndr_f
peninc_2 ~ 1 + peninc_1 + totinc_1 + gndr_f
peninc_3 ~ 1 + peninc_2 + totinc_2 + gndr_f
peninc_4 ~ 1 + peninc_3 + totinc_3 + gndr_f
peninc_1 ~ 1 + gndr_f
totinc_1 ~ 1 + gndr_f
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m2 <- sem(model, data = usw, missing = "ML") # estimate model
summary(m2, standardized = TRUE) # print results## lavaan 0.6.17 ended normally after 198 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Number of observations 6459
## Number of missing patterns 26
##
## Model Test User Model:
##
## Test statistic 1903.220
## Degrees of freedom 12
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.641 0.009 68.211 0.000 0.641 0.650
## peninc_1 -0.330 0.056 -5.941 0.000 -0.330 -0.056
## gndr_f -25.171 7.823 -3.218 0.001 -25.171 -0.030
## totinc_3 ~
## totinc_2 0.654 0.009 71.716 0.000 0.654 0.666
## peninc_2 -0.317 0.050 -6.285 0.000 -0.317 -0.058
## gndr_f -29.982 7.543 -3.975 0.000 -29.982 -0.037
## totinc_4 ~
## totinc_3 0.749 0.010 75.928 0.000 0.749 0.686
## peninc_3 -0.401 0.053 -7.559 0.000 -0.401 -0.068
## gndr_f -31.295 8.069 -3.878 0.000 -31.295 -0.035
## peninc_2 ~
## peninc_1 0.819 0.008 99.490 0.000 0.819 0.776
## totinc_1 -0.008 0.001 -5.664 0.000 -0.008 -0.045
## gndr_f -7.411 1.160 -6.389 0.000 -7.411 -0.050
## peninc_3 ~
## peninc_2 0.779 0.008 97.117 0.000 0.779 0.766
## totinc_2 -0.008 0.001 -5.272 0.000 -0.008 -0.042
## gndr_f -9.432 1.199 -7.866 0.000 -9.432 -0.062
## peninc_4 ~
## peninc_3 0.773 0.008 93.138 0.000 0.773 0.750
## totinc_3 -0.009 0.002 -6.099 0.000 -0.009 -0.049
## gndr_f -13.679 1.264 -10.825 0.000 -13.679 -0.087
## peninc_1 ~
## gndr_f -2.062 1.763 -1.170 0.242 -2.062 -0.015
## totinc_1 ~
## gndr_f -74.371 10.484 -7.094 0.000 -74.371 -0.088
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_1 ~~
## .totinc_1 -3824.036 367.140 -10.416 0.000 -3824.036 -0.131
## .totinc_2 ~~
## .peninc_2 1118.849 177.917 6.289 0.000 1118.849 0.079
## .totinc_3 ~~
## .peninc_3 1016.708 176.415 5.763 0.000 1016.708 0.072
## .totinc_4 ~~
## .peninc_4 1643.351 199.444 8.240 0.000 1643.351 0.105
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 40.276 12.801 3.146 0.002 40.276 0.098
## .totinc_3 45.809 12.345 3.711 0.000 45.809 0.113
## .totinc_4 48.260 13.201 3.656 0.000 48.260 0.109
## .peninc_2 11.641 1.899 6.130 0.000 11.641 0.157
## .peninc_3 14.751 1.963 7.516 0.000 14.751 0.196
## .peninc_4 21.543 2.067 10.423 0.000 21.543 0.277
## .peninc_1 3.165 2.887 1.096 0.273 3.165 0.045
## .totinc_1 116.559 17.164 6.791 0.000 116.559 0.279
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 95069.385 1689.728 56.263 0.000 95069.385 0.561
## .totinc_3 88258.949 1568.323 56.276 0.000 88258.949 0.540
## .totinc_4 99804.060 1785.376 55.901 0.000 99804.060 0.512
## .peninc_2 2114.466 37.262 56.746 0.000 2114.466 0.384
## .peninc_3 2249.574 39.668 56.711 0.000 2249.574 0.396
## .peninc_4 2464.171 43.870 56.170 0.000 2464.171 0.409
## .peninc_1 4935.174 86.711 56.915 0.000 4935.174 1.000
## .totinc_1 173194.647 3056.464 56.665 0.000 173194.647 0.992# # gender + age
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1 + gndr_f + age
totinc_3 ~ 1 + totinc_2 + peninc_2 + gndr_f + age
totinc_4 ~ 1 + totinc_3 + peninc_3 + gndr_f + age
peninc_2 ~ 1 + peninc_1 + totinc_1 + gndr_f + age
peninc_3 ~ 1 + peninc_2 + totinc_2 + gndr_f + age
peninc_4 ~ 1 + peninc_3 + totinc_3 + gndr_f + age
peninc_1 ~ 1 + gndr_f + age
totinc_1 ~ 1 + gndr_f + age
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m3 <- sem(model, data = usw, missing = "ML") # estimate model
summary(m3, standardized = TRUE) # print results## lavaan 0.6.17 ended normally after 195 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 6459
## Number of missing patterns 26
##
## Model Test User Model:
##
## Test statistic 1531.764
## Degrees of freedom 12
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.633 0.010 66.397 0.000 0.633 0.642
## peninc_1 -0.092 0.075 -1.221 0.222 -0.092 -0.016
## gndr_f -26.273 7.822 -3.359 0.001 -26.273 -0.032
## age -2.824 0.608 -4.646 0.000 -2.824 -0.061
## totinc_3 ~
## totinc_2 0.645 0.009 69.475 0.000 0.645 0.657
## peninc_2 -0.106 0.067 -1.586 0.113 -0.106 -0.020
## gndr_f -29.800 7.538 -3.953 0.000 -29.800 -0.037
## age -2.739 0.574 -4.769 0.000 -2.739 -0.060
## totinc_4 ~
## totinc_3 0.743 0.010 74.177 0.000 0.743 0.683
## peninc_3 -0.291 0.068 -4.280 0.000 -0.291 -0.050
## gndr_f -30.620 8.040 -3.808 0.000 -30.620 -0.034
## age -1.537 0.592 -2.596 0.009 -1.537 -0.031
## peninc_2 ~
## peninc_1 0.654 0.011 60.607 0.000 0.654 0.620
## totinc_1 -0.003 0.001 -1.894 0.058 -0.003 -0.015
## gndr_f -6.641 1.118 -5.937 0.000 -6.641 -0.044
## age 1.957 0.087 22.447 0.000 1.957 0.233
## peninc_3 ~
## peninc_2 0.645 0.010 62.086 0.000 0.645 0.634
## totinc_2 -0.002 0.001 -1.509 0.131 -0.002 -0.012
## gndr_f -9.530 1.167 -8.170 0.000 -9.530 -0.063
## age 1.738 0.089 19.526 0.000 1.738 0.204
## peninc_4 ~
## peninc_3 0.657 0.010 63.223 0.000 0.657 0.639
## totinc_3 -0.004 0.002 -2.588 0.010 -0.004 -0.021
## gndr_f -14.406 1.230 -11.712 0.000 -14.406 -0.092
## age 1.599 0.091 17.622 0.000 1.599 0.182
## peninc_1 ~
## gndr_f -0.058 1.286 -0.045 0.964 -0.058 -0.000
## age 5.448 0.072 75.619 0.000 5.448 0.686
## totinc_1 ~
## gndr_f -78.122 10.237 -7.631 0.000 -78.122 -0.093
## age -10.496 0.573 -18.318 0.000 -10.496 -0.222
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_1 ~~
## .totinc_1 148.786 260.004 0.572 0.567 148.786 0.007
## .totinc_2 ~~
## .peninc_2 1501.106 172.393 8.707 0.000 1501.106 0.110
## .totinc_3 ~~
## .peninc_3 1412.268 172.587 8.183 0.000 1412.268 0.103
## .totinc_4 ~~
## .peninc_4 1150.067 190.482 6.038 0.000 1150.067 0.076
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 225.939 41.972 5.383 0.000 225.939 0.548
## .totinc_3 223.826 39.341 5.689 0.000 223.826 0.553
## .totinc_4 147.286 40.326 3.652 0.000 147.286 0.334
## .peninc_2 -117.002 6.018 -19.440 0.000 -117.002 -1.575
## .peninc_3 -98.239 6.096 -16.114 0.000 -98.239 -1.301
## .peninc_4 -81.359 6.177 -13.172 0.000 -81.359 -1.048
## .peninc_1 -354.843 5.181 -68.484 0.000 -354.843 -5.042
## .totinc_1 806.162 41.198 19.568 0.000 806.162 1.927
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 94960.325 1691.426 56.142 0.000 94960.325 0.558
## .totinc_3 88152.182 1569.760 56.156 0.000 88152.182 0.538
## .totinc_4 98970.584 1756.850 56.334 0.000 98970.584 0.509
## .peninc_2 1964.783 34.691 56.636 0.000 1964.783 0.356
## .peninc_3 2129.239 37.641 56.567 0.000 2129.239 0.373
## .peninc_4 2333.177 41.208 56.619 0.000 2333.177 0.387
## .peninc_1 2621.687 46.203 56.742 0.000 2621.687 0.529
## .totinc_1 165014.665 2920.722 56.498 0.000 165014.665 0.943# 控制时间变量：health / tenure
# health
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1 + health_2
totinc_3 ~ 1 + totinc_2 + peninc_2 + health_3
totinc_4 ~ 1 + totinc_3 + peninc_3 + health_4
peninc_2 ~ 1 + peninc_1 + totinc_1 + health_2
peninc_3 ~ 1 + peninc_2 + totinc_2 + health_3
peninc_4 ~ 1 + peninc_3 + totinc_3 + health_4
peninc_1 ~ 1 + health_1
totinc_1 ~ 1 + health_1
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m4 <- sem(model, data = usw, missing = "ML")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 408 cases were deleted due to missing values in
## exogenous variable(s), while fixed.x = TRUE.summary(m4, standardized = TRUE)## lavaan 0.6.17 ended normally after 175 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Used Total
## Number of observations 6051 6459
## Number of missing patterns 24
##
## Model Test User Model:
##
## Test statistic 1933.767
## Degrees of freedom 36
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.645 0.010 65.127 0.000 0.645 0.650
## peninc_1 -0.284 0.057 -5.016 0.000 -0.284 -0.049
## health_2 -25.592 3.870 -6.614 0.000 -25.592 -0.066
## totinc_3 ~
## totinc_2 0.651 0.009 68.819 0.000 0.651 0.662
## peninc_2 -0.257 0.052 -4.980 0.000 -0.257 -0.047
## health_3 -31.500 3.628 -8.681 0.000 -31.500 -0.084
## totinc_4 ~
## totinc_3 0.749 0.010 72.641 0.000 0.749 0.685
## peninc_3 -0.354 0.054 -6.508 0.000 -0.354 -0.060
## health_4 -20.671 3.843 -5.380 0.000 -20.671 -0.051
## peninc_2 ~
## peninc_1 0.821 0.009 96.310 0.000 0.821 0.779
## totinc_1 -0.008 0.001 -5.035 0.000 -0.008 -0.042
## health_2 -0.449 0.582 -0.771 0.441 -0.449 -0.006
## peninc_3 ~
## peninc_2 0.781 0.008 93.496 0.000 0.781 0.769
## totinc_2 -0.007 0.002 -4.474 0.000 -0.007 -0.037
## health_3 -0.396 0.588 -0.674 0.501 -0.396 -0.006
## peninc_4 ~
## peninc_3 0.784 0.009 91.976 0.000 0.784 0.764
## totinc_3 -0.007 0.002 -4.376 0.000 -0.007 -0.037
## health_4 -0.423 0.603 -0.701 0.483 -0.423 -0.006
## peninc_1 ~
## health_1 5.931 0.848 6.991 0.000 5.931 0.090
## totinc_1 ~
## health_1 -87.754 4.896 -17.925 0.000 -87.754 -0.226
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_1 ~~
## .totinc_1 -975.722 365.925 -2.666 0.008 -975.722 -0.035
## .totinc_2 ~~
## .peninc_2 1503.293 183.761 8.181 0.000 1503.293 0.107
## .totinc_3 ~~
## .peninc_3 1511.520 183.480 8.238 0.000 1511.520 0.108
## .totinc_4 ~~
## .peninc_4 1289.553 201.893 6.387 0.000 1289.553 0.083
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 71.866 11.178 6.429 0.000 71.866 0.176
## .totinc_3 86.687 10.693 8.107 0.000 86.687 0.215
## .totinc_4 59.236 11.615 5.100 0.000 59.236 0.135
## .peninc_2 1.196 1.680 0.712 0.477 1.196 0.016
## .peninc_3 1.490 1.732 0.860 0.390 1.490 0.020
## .peninc_4 1.427 1.823 0.783 0.434 1.427 0.019
## .peninc_1 -15.407 2.434 -6.330 0.000 -15.407 -0.219
## .totinc_1 232.516 14.052 16.547 0.000 232.516 0.563
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 92770.341 1711.088 54.217 0.000 92770.341 0.554
## .totinc_3 86257.485 1590.107 54.246 0.000 86257.485 0.533
## .totinc_4 98976.357 1812.914 54.595 0.000 98976.357 0.511
## .peninc_2 2125.665 38.863 54.697 0.000 2125.665 0.388
## .peninc_3 2286.730 41.822 54.678 0.000 2286.730 0.405
## .peninc_4 2456.337 44.753 54.886 0.000 2456.337 0.413
## .peninc_1 4898.904 89.311 54.852 0.000 4898.904 0.992
## .totinc_1 161594.956 2959.749 54.598 0.000 161594.956 0.949# tenure
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1 + tenure_2
totinc_3 ~ 1 + totinc_2 + peninc_2 + tenure_3
totinc_4 ~ 1 + totinc_3 + peninc_3 + tenure_4
peninc_2 ~ 1 + peninc_1 + totinc_1 + tenure_2
peninc_3 ~ 1 + peninc_2 + totinc_2 + tenure_3
peninc_4 ~ 1 + peninc_3 + totinc_3 + tenure_4
peninc_1 ~ 1 + tenure_1
totinc_1 ~ 1 + tenure_2
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m5 <- sem(model, data = usw, missing = "ML")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 89 cases were deleted due to missing values in
## exogenous variable(s), while fixed.x = TRUE.summary(m5, standardized = TRUE)## lavaan 0.6.17 ended normally after 166 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Used Total
## Number of observations 6370 6459
## Number of missing patterns 21
##
## Model Test User Model:
##
## Test statistic 2072.272
## Degrees of freedom 36
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.636 0.010 66.263 0.000 0.636 0.643
## peninc_1 -0.366 0.056 -6.533 0.000 -0.366 -0.062
## tenure_2 -26.938 3.752 -7.180 0.000 -26.938 -0.069
## totinc_3 ~
## totinc_2 0.646 0.009 69.381 0.000 0.646 0.658
## peninc_2 -0.329 0.051 -6.472 0.000 -0.329 -0.060
## tenure_3 -27.125 3.611 -7.512 0.000 -27.125 -0.071
## totinc_4 ~
## totinc_3 0.740 0.010 73.889 0.000 0.740 0.682
## peninc_3 -0.396 0.053 -7.483 0.000 -0.396 -0.068
## tenure_4 -19.545 3.800 -5.143 0.000 -19.545 -0.048
## peninc_2 ~
## peninc_1 0.816 0.008 97.473 0.000 0.816 0.773
## totinc_1 -0.008 0.001 -5.805 0.000 -0.008 -0.047
## tenure_2 -2.820 0.562 -5.021 0.000 -2.820 -0.040
## peninc_3 ~
## peninc_2 0.779 0.008 95.939 0.000 0.779 0.765
## totinc_2 -0.008 0.001 -5.549 0.000 -0.008 -0.045
## tenure_3 -3.575 0.578 -6.188 0.000 -3.575 -0.050
## peninc_4 ~
## peninc_3 0.779 0.008 93.239 0.000 0.779 0.757
## totinc_3 -0.009 0.002 -5.739 0.000 -0.009 -0.047
## tenure_4 -2.534 0.601 -4.215 0.000 -2.534 -0.035
## peninc_1 ~
## tenure_1 -6.777 0.831 -8.159 0.000 -6.777 -0.102
## totinc_1 ~
## tenure_2 -71.104 4.865 -14.615 0.000 -71.104 -0.181
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_1 ~~
## .totinc_1 -1379.329 360.886 -3.822 0.000 -1379.329 -0.049
## .totinc_2 ~~
## .peninc_2 1531.733 181.634 8.433 0.000 1531.733 0.108
## .totinc_3 ~~
## .peninc_3 1417.523 180.382 7.858 0.000 1417.523 0.100
## .totinc_4 ~~
## .peninc_4 1361.183 198.544 6.856 0.000 1361.183 0.087
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 43.572 7.083 6.152 0.000 43.572 0.106
## .totinc_3 41.686 6.706 6.216 0.000 41.686 0.103
## .totinc_4 28.745 6.997 4.108 0.000 28.745 0.066
## .peninc_2 4.420 1.059 4.174 0.000 4.420 0.060
## .peninc_3 5.575 1.072 5.199 0.000 5.575 0.074
## .peninc_4 4.132 1.108 3.730 0.000 4.132 0.053
## .peninc_1 10.558 1.598 6.607 0.000 10.558 0.151
## .totinc_1 114.148 9.242 12.351 0.000 114.148 0.275
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 94564.286 1702.316 55.550 0.000 94564.286 0.561
## .totinc_3 88206.109 1585.674 55.627 0.000 88206.109 0.542
## .totinc_4 98979.592 1766.561 56.030 0.000 98979.592 0.515
## .peninc_2 2134.333 38.105 56.012 0.000 2134.333 0.391
## .peninc_3 2272.676 40.549 56.047 0.000 2272.676 0.402
## .peninc_4 2495.795 44.325 56.307 0.000 2495.795 0.416
## .peninc_1 4844.322 86.038 56.305 0.000 4844.322 0.990
## .totinc_1 166763.122 2974.756 56.059 0.000 166763.122 0.967# famtype
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1 + famtype_2
totinc_3 ~ 1 + totinc_2 + peninc_2 + famtype_3
totinc_4 ~ 1 + totinc_3 + peninc_3 + famtype_4
peninc_2 ~ 1 + peninc_1 + totinc_1 + famtype_2
peninc_3 ~ 1 + peninc_2 + totinc_2 + famtype_3
peninc_4 ~ 1 + peninc_3 + totinc_3 + famtype_4
peninc_1 ~ 1 + famtype_1
totinc_1 ~ 1 + famtype_1
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m6 <- sem(model, data = usw, missing = "ML")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 1 cases were deleted due to missing values in
## exogenous variable(s), while fixed.x = TRUE.summary(m6, standardized = TRUE)## lavaan 0.6.17 ended normally after 146 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Used Total
## Number of observations 6458 6459
## Number of missing patterns 26
##
## Model Test User Model:
##
## Test statistic 1978.077
## Degrees of freedom 36
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.623 0.010 65.031 0.000 0.623 0.634
## peninc_1 -0.206 0.057 -3.619 0.000 -0.206 -0.035
## famtype_2 13.902 1.518 9.158 0.000 13.902 0.091
## totinc_3 ~
## totinc_2 0.634 0.009 68.223 0.000 0.634 0.647
## peninc_2 -0.179 0.051 -3.482 0.000 -0.179 -0.033
## famtype_3 15.220 1.436 10.598 0.000 15.220 0.103
## totinc_4 ~
## totinc_3 0.733 0.010 72.806 0.000 0.733 0.674
## peninc_3 -0.292 0.054 -5.457 0.000 -0.292 -0.050
## famtype_4 11.266 1.510 7.461 0.000 11.266 0.071
## peninc_2 ~
## peninc_1 0.810 0.008 95.559 0.000 0.810 0.769
## totinc_1 -0.005 0.001 -3.824 0.000 -0.005 -0.031
## famtype_2 -1.128 0.227 -4.968 0.000 -1.128 -0.041
## peninc_3 ~
## peninc_2 0.772 0.008 93.610 0.000 0.772 0.760
## totinc_2 -0.005 0.001 -3.044 0.002 -0.005 -0.025
## famtype_3 -1.346 0.231 -5.829 0.000 -1.346 -0.049
## peninc_4 ~
## peninc_3 0.776 0.008 91.612 0.000 0.776 0.755
## totinc_3 -0.006 0.002 -3.986 0.000 -0.006 -0.033
## famtype_4 -0.823 0.240 -3.435 0.001 -0.823 -0.029
## peninc_1 ~
## famtype_1 -7.127 0.312 -22.828 0.000 -7.127 -0.273
## totinc_1 ~
## famtype_1 40.737 1.866 21.832 0.000 40.737 0.263
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_1 ~~
## .totinc_1 -585.868 341.668 -1.715 0.086 -585.868 -0.021
## .totinc_2 ~~
## .peninc_2 1467.954 178.716 8.214 0.000 1467.954 0.104
## .totinc_3 ~~
## .peninc_3 1498.011 177.533 8.438 0.000 1498.011 0.107
## .totinc_4 ~~
## .peninc_4 1257.915 196.303 6.408 0.000 1257.915 0.080
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 -73.512 8.976 -8.190 0.000 -73.512 -0.179
## .totinc_3 -81.043 8.387 -9.663 0.000 -81.043 -0.201
## .totinc_4 -58.802 8.720 -6.744 0.000 -58.802 -0.134
## .peninc_2 6.126 1.345 4.556 0.000 6.126 0.083
## .peninc_3 7.101 1.350 5.260 0.000 7.101 0.094
## .peninc_4 4.454 1.385 3.215 0.001 4.454 0.058
## .peninc_1 38.806 1.899 20.439 0.000 38.806 0.552
## .totinc_1 -221.457 11.335 -19.538 0.000 -221.457 -0.530
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 94253.688 1679.823 56.109 0.000 94253.688 0.558
## .totinc_3 87212.371 1555.213 56.077 0.000 87212.371 0.537
## .totinc_4 98351.381 1746.584 56.311 0.000 98351.381 0.513
## .peninc_2 2125.478 37.556 56.596 0.000 2125.478 0.387
## .peninc_3 2267.452 40.123 56.512 0.000 2267.452 0.401
## .peninc_4 2488.049 43.966 56.590 0.000 2488.049 0.416
## .peninc_1 4571.005 80.582 56.725 0.000 4571.005 0.925
## .totinc_1 162574.776 2878.885 56.471 0.000 162574.776 0.931# marstat
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1 + marstat_2
totinc_3 ~ 1 + totinc_2 + peninc_2 + marstat_3
totinc_4 ~ 1 + totinc_3 + peninc_3 + marstat_4
peninc_2 ~ 1 + peninc_1 + totinc_1 + marstat_2
peninc_3 ~ 1 + peninc_2 + totinc_2 + marstat_3
peninc_4 ~ 1 + peninc_3 + totinc_3 + marstat_4
peninc_1 ~ 1 + marstat_1
totinc_1 ~ 1 + marstat_1
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m7 <- sem(model, data = usw, missing = "ML")
summary(m7, standardized = TRUE)## lavaan 0.6.17 ended normally after 155 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Number of observations 6459
## Number of missing patterns 26
##
## Model Test User Model:
##
## Test statistic 2084.421
## Degrees of freedom 36
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.599 0.010 61.461 0.000 0.599 0.608
## peninc_1 -0.192 0.056 -3.454 0.001 -0.192 -0.033
## marstat_2 -40.267 2.786 -14.452 0.000 -40.267 -0.144
## totinc_3 ~
## totinc_2 0.609 0.010 63.830 0.000 0.609 0.622
## peninc_2 -0.172 0.051 -3.407 0.001 -0.172 -0.032
## marstat_3 -39.153 2.685 -14.580 0.000 -39.153 -0.144
## totinc_4 ~
## totinc_3 0.709 0.010 68.214 0.000 0.709 0.651
## peninc_3 -0.271 0.053 -5.137 0.000 -0.271 -0.047
## marstat_4 -33.526 2.856 -11.738 0.000 -33.526 -0.114
## peninc_2 ~
## peninc_1 0.815 0.008 97.350 0.000 0.815 0.773
## totinc_1 -0.005 0.001 -3.715 0.000 -0.005 -0.031
## marstat_2 1.513 0.422 3.587 0.000 1.513 0.030
## peninc_3 ~
## peninc_2 0.779 0.008 95.168 0.000 0.779 0.766
## totinc_2 -0.005 0.002 -3.203 0.001 -0.005 -0.027
## marstat_3 1.335 0.436 3.061 0.002 1.335 0.026
## peninc_4 ~
## peninc_3 0.779 0.008 92.541 0.000 0.779 0.758
## totinc_3 -0.006 0.002 -3.695 0.000 -0.006 -0.032
## marstat_4 1.258 0.456 2.756 0.006 1.258 0.024
## peninc_1 ~
## marstat_1 10.014 0.588 17.038 0.000 10.014 0.207
## totinc_1 ~
## marstat_1 -97.470 3.363 -28.983 0.000 -97.470 -0.340
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_1 ~~
## .totinc_1 -602.344 338.752 -1.778 0.075 -602.344 -0.022
## .totinc_2 ~~
## .peninc_2 1496.072 177.317 8.437 0.000 1496.072 0.107
## .totinc_3 ~~
## .peninc_3 1452.430 176.460 8.231 0.000 1452.430 0.104
## .totinc_4 ~~
## .peninc_4 1267.517 195.272 6.491 0.000 1267.517 0.081
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 77.838 6.562 11.862 0.000 77.838 0.189
## .totinc_3 75.533 6.416 11.772 0.000 75.533 0.187
## .totinc_4 66.310 6.914 9.591 0.000 66.310 0.151
## .peninc_2 -2.813 0.990 -2.841 0.005 -2.813 -0.038
## .peninc_3 -2.583 1.040 -2.483 0.013 -2.583 -0.034
## .peninc_4 -2.305 1.103 -2.091 0.037 -2.305 -0.030
## .peninc_1 -18.907 1.399 -13.513 0.000 -18.907 -0.269
## .totinc_1 184.197 8.029 22.942 0.000 184.197 0.441
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 92491.410 1649.130 56.085 0.000 92491.410 0.547
## .totinc_3 85936.217 1532.159 56.088 0.000 85936.217 0.529
## .totinc_4 97288.908 1727.436 56.320 0.000 97288.908 0.506
## .peninc_2 2129.671 37.637 56.584 0.000 2129.671 0.387
## .peninc_3 2275.768 40.263 56.522 0.000 2275.768 0.401
## .peninc_4 2489.312 43.984 56.596 0.000 2489.312 0.415
## .peninc_1 4726.721 83.321 56.729 0.000 4726.721 0.957
## .totinc_1 154423.755 2735.494 56.452 0.000 154423.755 0.885# ----- add more confunders:health + tenure + famtype + marstat
# marstat
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1 + health_2 + tenure_2 + famtype_2 + marstat_2
totinc_3 ~ 1 + totinc_2 + peninc_2 + health_3 + tenure_3 + famtype_3 + marstat_3
totinc_4 ~ 1 + totinc_3 + peninc_3 + health_4 + tenure_4 + famtype_4 + marstat_4
peninc_2 ~ 1 + peninc_1 + totinc_1 + health_2 + tenure_2 + famtype_2 + marstat_2
peninc_3 ~ 1 + peninc_2 + totinc_2 + health_3 + tenure_3 + famtype_3 + marstat_3
peninc_4 ~ 1 + peninc_3 + totinc_3 + health_4 + tenure_4 + famtype_4 + marstat_4
peninc_1 ~ 1 + health_1 + tenure_1 + famtype_1 + marstat_1
totinc_1 ~ 1 + health_1 + tenure_1 + famtype_1 + marstat_1
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m8 <- sem(model, data = usw, missing = "ML")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 459 cases were deleted due to missing values in
## exogenous variable(s), while fixed.x = TRUE.summary(m8, standardized = TRUE)## lavaan 0.6.17 ended normally after 330 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 64
##
## Used Total
## Number of observations 6000 6459
## Number of missing patterns 21
##
## Model Test User Model:
##
## Test statistic 2114.691
## Degrees of freedom 108
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.600 0.010 58.076 0.000 0.600 0.604
## peninc_1 -0.177 0.058 -3.060 0.002 -0.177 -0.030
## health_2 -21.264 3.862 -5.506 0.000 -21.264 -0.055
## tenure_2 -13.008 3.906 -3.331 0.001 -13.008 -0.033
## famtype_2 2.060 1.968 1.046 0.295 2.060 0.014
## marstat_2 -34.186 3.696 -9.249 0.000 -34.186 -0.124
## totinc_3 ~
## totinc_2 0.604 0.010 60.635 0.000 0.604 0.614
## peninc_2 -0.139 0.053 -2.638 0.008 -0.139 -0.025
## health_3 -27.753 3.617 -7.672 0.000 -27.753 -0.074
## tenure_3 -12.383 3.736 -3.314 0.001 -12.383 -0.033
## famtype_3 3.112 1.878 1.657 0.097 3.112 0.021
## marstat_3 -32.671 3.583 -9.119 0.000 -32.671 -0.122
## totinc_4 ~
## totinc_3 0.702 0.011 63.923 0.000 0.702 0.644
## peninc_3 -0.257 0.055 -4.632 0.000 -0.257 -0.044
## health_4 -17.523 3.851 -4.550 0.000 -17.523 -0.043
## tenure_4 -9.508 3.996 -2.379 0.017 -9.508 -0.023
## famtype_4 0.595 2.016 0.295 0.768 0.595 0.004
## marstat_4 -30.721 3.876 -7.927 0.000 -30.721 -0.105
## peninc_2 ~
## peninc_1 0.804 0.009 90.807 0.000 0.804 0.763
## totinc_1 -0.006 0.002 -4.018 0.000 -0.006 -0.035
## health_2 -0.035 0.590 -0.059 0.953 -0.035 -0.000
## tenure_2 -3.239 0.598 -5.415 0.000 -3.239 -0.046
## famtype_2 -0.906 0.301 -3.009 0.003 -0.906 -0.033
## marstat_2 0.929 0.566 1.640 0.101 0.929 0.019
## peninc_3 ~
## peninc_2 0.763 0.009 88.488 0.000 0.763 0.753
## totinc_2 -0.006 0.002 -3.694 0.000 -0.006 -0.033
## health_3 0.280 0.594 0.472 0.637 0.280 0.004
## tenure_3 -4.022 0.614 -6.546 0.000 -4.022 -0.057
## famtype_3 -1.409 0.309 -4.560 0.000 -1.409 -0.052
## marstat_3 0.203 0.590 0.344 0.731 0.203 0.004
## peninc_4 ~
## peninc_3 0.771 0.009 87.739 0.000 0.771 0.752
## totinc_3 -0.006 0.002 -3.473 0.001 -0.006 -0.032
## health_4 0.010 0.611 0.017 0.986 0.010 0.000
## tenure_4 -3.037 0.633 -4.795 0.000 -3.037 -0.042
## famtype_4 -0.778 0.320 -2.431 0.015 -0.778 -0.028
## marstat_4 0.833 0.616 1.353 0.176 0.833 0.016
## peninc_1 ~
## health_1 6.457 0.828 7.795 0.000 6.457 0.097
## tenure_1 -11.117 0.859 -12.939 0.000 -11.117 -0.166
## famtype_1 -5.984 0.427 -14.004 0.000 -5.984 -0.231
## marstat_1 3.661 0.807 4.539 0.000 3.661 0.077
## totinc_1 ~
## health_1 -69.840 4.720 -14.797 0.000 -69.840 -0.180
## tenure_1 -27.287 4.891 -5.579 0.000 -27.287 -0.069
## famtype_1 11.617 2.430 4.780 0.000 11.617 0.076
## marstat_1 -72.037 4.583 -15.718 0.000 -72.037 -0.258
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .peninc_1 ~~
## .totinc_1 -487.167 326.910 -1.490 0.136 -487.167 -0.019
## .totinc_2 ~~
## .peninc_2 1498.703 179.629 8.343 0.000 1498.703 0.109
## .totinc_3 ~~
## .peninc_3 1447.037 178.777 8.094 0.000 1447.037 0.106
## .totinc_4 ~~
## .peninc_4 1223.017 198.856 6.150 0.000 1223.017 0.080
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 135.517 19.258 7.037 0.000 135.517 0.331
## .totinc_3 144.918 18.407 7.873 0.000 144.918 0.359
## .totinc_4 123.387 19.823 6.225 0.000 123.387 0.281
## .peninc_2 8.126 2.947 2.757 0.006 8.126 0.110
## .peninc_3 12.773 3.026 4.221 0.000 12.773 0.170
## .peninc_4 7.172 3.146 2.280 0.023 7.172 0.093
## .peninc_1 26.365 4.151 6.352 0.000 26.365 0.375
## .totinc_1 304.230 23.593 12.895 0.000 304.230 0.737
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 89161.791 1648.517 54.086 0.000 89161.791 0.531
## .totinc_3 82917.443 1531.454 54.143 0.000 82917.443 0.510
## .totinc_4 96441.062 1769.381 54.506 0.000 96441.062 0.499
## .peninc_2 2109.043 38.652 54.565 0.000 2109.043 0.384
## .peninc_3 2256.545 41.351 54.570 0.000 2256.545 0.399
## .peninc_4 2441.636 44.555 54.800 0.000 2441.636 0.411
## .peninc_1 4441.803 81.206 54.698 0.000 4441.803 0.897
## .totinc_1 142614.516 2620.454 54.424 0.000 142614.516 0.837# Investigating differences in coefficients
#------------------------------------------------------------------------------
# 探究交叉滞后模型的时间平稳性
# Cross-lagged model with equal cross-effects w1
model <- 'totinc_2 ~ 1 + totinc_1 + a*peninc_1
totinc_3 ~ 1 + totinc_2 + peninc_2
totinc_4 ~ 1 + totinc_3 + peninc_3
peninc_2 ~ 1 + peninc_1 + a*totinc_1
peninc_3 ~ 1 + peninc_2 + totinc_2
peninc_4 ~ 1 + peninc_3 + totinc_3
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m11 <- sem(model, data = usw, missing = "ML") # estimate model## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1662## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -8.767880e-12) is smaller than zero. This may be a symptom that
## the model is not identified.summary(m11, standardized = TRUE) # print results## lavaan 0.6.17 ended normally after 168 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
## Number of equality constraints 1
##
## Used Total
## Number of observations 6458 6459
## Number of missing patterns 25
##
## Model Test User Model:
##
## Test statistic 1977.070
## Degrees of freedom 13
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.651 0.009 69.871 0.000 0.651 0.658
## peninc_1 (a) -0.007 0.001 -5.161 0.000 -0.007 -0.001
## totinc_3 ~
## totinc_2 0.657 0.009 72.095 0.000 0.657 0.671
## peninc_2 -0.306 0.051 -6.044 0.000 -0.306 -0.056
## totinc_4 ~
## totinc_3 0.752 0.010 76.800 0.000 0.752 0.691
## peninc_3 -0.379 0.053 -7.201 0.000 -0.379 -0.065
## peninc_2 ~
## peninc_1 0.825 0.008 100.006 0.000 0.825 0.779
## totinc_1 (a) -0.007 0.001 -5.161 0.000 -0.007 -0.040
## peninc_3 ~
## peninc_2 0.783 0.008 97.130 0.000 0.783 0.772
## totinc_2 -0.007 0.001 -4.522 0.000 -0.007 -0.036
## peninc_4 ~
## peninc_3 0.783 0.008 94.302 0.000 0.783 0.762
## totinc_3 -0.008 0.002 -5.009 0.000 -0.008 -0.040
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_1 ~~
## peninc_1 -1192.857 366.971 -3.251 0.001 -1192.857 -0.041
## .totinc_2 ~~
## .peninc_2 1525.628 179.403 8.504 0.000 1525.628 0.106
## .totinc_3 ~~
## .peninc_3 1503.888 177.749 8.461 0.000 1503.888 0.105
## .totinc_4 ~~
## .peninc_4 1356.343 196.855 6.890 0.000 1356.343 0.086
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 0.909 3.889 0.234 0.815 0.909 0.002
## .totinc_3 -1.010 3.738 -0.270 0.787 -1.010 -0.003
## .totinc_4 -0.612 3.959 -0.155 0.877 -0.612 -0.001
## .peninc_2 0.073 0.576 0.126 0.900 0.073 0.001
## .peninc_3 0.027 0.596 0.046 0.963 0.027 0.000
## .peninc_4 0.200 0.625 0.319 0.749 0.200 0.003
## totinc_1 0.490 5.211 0.094 0.925 0.490 0.001
## peninc_1 -0.055 0.873 -0.064 0.949 -0.055 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 96182.748 3.364 28591.056 0.000 96182.748 0.567
## .totinc_3 88977.133 3.592 24774.238 0.000 88977.133 0.547
## .totinc_4 99516.786 2.706 36777.763 0.000 99516.786 0.516
## .peninc_2 2138.157 37.869 56.462 0.000 2138.157 0.389
## .peninc_3 2283.929 40.502 56.391 0.000 2283.929 0.403
## .peninc_4 2496.662 44.185 56.505 0.000 2496.662 0.417
## totinc_1 173639.896 3.775 45994.622 0.000 173639.896 1.000
## peninc_1 4910.708 86.688 56.648 0.000 4910.708 1.000anova(m1, m11) # m11 更好##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## m1 12 644795 645012 1944.5
## m11 13 644825 645035 1977.1 32.588 0.069938 1 1.139e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Cross-lagged model with equal cross-effects w1
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1
totinc_3 ~ 1 + totinc_2 + b*peninc_2
totinc_4 ~ 1 + totinc_3 + peninc_3
peninc_2 ~ 1 + peninc_1 + totinc_1
peninc_3 ~ 1 + peninc_2 + b*totinc_2
peninc_4 ~ 1 + peninc_3 + totinc_3
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m12 <- sem(model, data = usw, missing = "ML") # estimate model## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1662## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -2.137970e-11) is smaller than zero. This may be a symptom that
## the model is not identified.summary(m12, standardized = TRUE) # print results## lavaan 0.6.17 ended normally after 170 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
## Number of equality constraints 1
##
## Used Total
## Number of observations 6458 6459
## Number of missing patterns 25
##
## Model Test User Model:
##
## Test statistic 1978.822
## Degrees of freedom 13
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.643 0.009 68.552 0.000 0.643 0.653
## peninc_1 -0.328 0.056 -5.899 0.000 -0.328 -0.056
## totinc_3 ~
## totinc_2 0.662 0.009 72.820 0.000 0.662 0.673
## peninc_2 (b) -0.007 0.001 -4.654 0.000 -0.007 -0.001
## totinc_4 ~
## totinc_3 0.753 0.010 76.808 0.000 0.753 0.693
## peninc_3 -0.380 0.053 -7.216 0.000 -0.380 -0.065
## peninc_2 ~
## peninc_1 0.820 0.008 99.147 0.000 0.820 0.777
## totinc_1 -0.007 0.001 -5.111 0.000 -0.007 -0.040
## peninc_3 ~
## peninc_2 0.788 0.008 97.967 0.000 0.788 0.772
## totinc_2 (b) -0.007 0.001 -4.654 0.000 -0.007 -0.037
## peninc_4 ~
## peninc_3 0.783 0.008 94.306 0.000 0.783 0.763
## totinc_3 -0.008 0.002 -5.002 0.000 -0.008 -0.040
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_1 ~~
## peninc_1 -1189.942 367.009 -3.242 0.001 -1189.942 -0.041
## .totinc_2 ~~
## .peninc_2 1527.496 178.551 8.555 0.000 1527.496 0.107
## .totinc_3 ~~
## .peninc_3 1501.204 178.635 8.404 0.000 1501.204 0.105
## .totinc_4 ~~
## .peninc_4 1354.864 196.848 6.883 0.000 1354.864 0.086
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 0.999 3.880 0.258 0.797 0.999 0.002
## .totinc_3 -1.068 3.747 -0.285 0.776 -1.068 -0.003
## .totinc_4 -0.596 3.959 -0.151 0.880 -0.596 -0.001
## .peninc_2 0.072 0.576 0.126 0.900 0.072 0.001
## .peninc_3 0.029 0.596 0.048 0.962 0.029 0.000
## .peninc_4 0.199 0.625 0.319 0.750 0.199 0.003
## totinc_1 0.459 5.211 0.088 0.930 0.459 0.001
## peninc_1 -0.056 0.873 -0.064 0.949 -0.056 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 95716.394 3.386 28267.245 0.000 95716.394 0.568
## .totinc_3 89436.826 3.557 25141.831 0.000 89436.826 0.547
## .totinc_4 99508.187 2.702 36833.172 0.000 99508.187 0.516
## .peninc_2 2138.504 37.882 56.452 0.000 2138.504 0.391
## .peninc_3 2283.418 40.484 56.403 0.000 2283.418 0.401
## .peninc_4 2496.659 44.185 56.505 0.000 2496.659 0.417
## totinc_1 173653.479 3.781 45931.788 0.000 173653.479 1.000
## peninc_1 4910.864 86.692 56.647 0.000 4910.864 1.000anova(m1, m12) # m12 better##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## m1 12 644795 645012 1944.5
## m12 13 644827 645037 1978.8 34.34 0.071851 1 4.627e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Cross-lagged model with equal cross-effects w1
model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1
totinc_3 ~ 1 + totinc_2 + peninc_2
totinc_4 ~ 1 + totinc_3 + c*peninc_3
peninc_2 ~ 1 + peninc_1 + totinc_1
peninc_3 ~ 1 + peninc_2 + totinc_2
peninc_4 ~ 1 + peninc_3 + c*totinc_3
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m13 <- sem(model, data = usw, missing = "ML") # estimate model## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1662## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.446734e-11) is smaller than zero. This may be a symptom that
## the model is not identified.summary(m13, standardized = TRUE) # print results## lavaan 0.6.17 ended normally after 197 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
## Number of equality constraints 1
##
## Used Total
## Number of observations 6458 6459
## Number of missing patterns 25
##
## Model Test User Model:
##
## Test statistic 1931.594
## Degrees of freedom 13
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 0.643 0.009 68.740 0.000 0.643 0.653
## peninc_1 -0.325 0.056 -5.850 0.000 -0.325 -0.055
## totinc_3 ~
## totinc_2 0.657 0.009 72.316 0.000 0.657 0.670
## peninc_2 -0.305 0.050 -6.046 0.000 -0.305 -0.056
## totinc_4 ~
## totinc_3 0.758 0.010 77.020 0.000 0.758 0.694
## peninc_3 (c) -0.008 0.002 -5.154 0.000 -0.008 -0.001
## peninc_2 ~
## peninc_1 0.820 0.008 99.394 0.000 0.820 0.777
## totinc_1 -0.007 0.001 -5.098 0.000 -0.007 -0.040
## peninc_3 ~
## peninc_2 0.783 0.008 97.394 0.000 0.783 0.770
## totinc_2 -0.007 0.001 -4.545 0.000 -0.007 -0.036
## peninc_4 ~
## peninc_3 0.789 0.008 94.998 0.000 0.789 0.762
## totinc_3 (c) -0.008 0.002 -5.154 0.000 -0.008 -0.041
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_1 ~~
## peninc_1 -3693.653 363.137 -10.172 0.000 -3693.653 -0.126
## .totinc_2 ~~
## .peninc_2 1150.915 177.593 6.481 0.000 1150.915 0.081
## .totinc_3 ~~
## .peninc_3 1082.516 176.690 6.127 0.000 1082.516 0.076
## .totinc_4 ~~
## .peninc_4 1805.935 199.738 9.041 0.000 1805.935 0.113
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 0.981 3.870 0.254 0.800 0.981 0.002
## .totinc_3 -0.977 3.727 -0.262 0.793 -0.977 -0.002
## .totinc_4 -0.551 3.987 -0.138 0.890 -0.551 -0.001
## .peninc_2 0.073 0.575 0.127 0.899 0.073 0.001
## .peninc_3 0.028 0.594 0.047 0.963 0.028 0.000
## .peninc_4 0.203 0.627 0.324 0.746 0.203 0.003
## totinc_1 0.508 5.222 0.097 0.922 0.508 0.001
## peninc_1 -0.056 0.875 -0.064 0.949 -0.056 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 95216.769 1.984 47998.310 0.000 95216.769 0.562
## .totinc_3 88461.712 1.908 46366.306 0.000 88461.712 0.541
## .totinc_4 100917.903 4.462 22616.002 0.000 100917.903 0.518
## .peninc_2 2127.476 37.483 56.758 0.000 2127.476 0.387
## .peninc_3 2271.068 40.039 56.722 0.000 2271.068 0.400
## .peninc_4 2512.049 44.760 56.122 0.000 2512.049 0.413
## totinc_1 174436.864 6.919 25211.475 0.000 174436.864 1.000
## peninc_1 4932.880 86.611 56.954 0.000 4932.880 1.000anova(m1, m13) # m1 better## Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan WARNING:
## Some restricted models fit better than less restricted models;
## either these models are not nested, or the less restricted model
## failed to reach a global optimum. Smallest difference =
## -12.8881701886439##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## m1 12 644795 645012 1944.5
## m13 13 644780 644990 1931.6 -12.888 0 1 1# ------
# Cross-lagged model with stability on physical,时间稳定性
model <- 'totinc_2 ~ 1 + a*totinc_1 + peninc_1
totinc_3 ~ 1 + a*totinc_2 + peninc_2
totinc_4 ~ 1 + a*totinc_3 + peninc_3
peninc_2 ~ 1 + peninc_1 + totinc_1
peninc_3 ~ 1 + peninc_2 + totinc_2
peninc_4 ~ 1 + peninc_3 + totinc_3
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m21 <- sem(model, data = usw, missing = "ML")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1662## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -2.910085e-13) is smaller than zero. This may be a symptom that
## the model is not identified.summary(m21, standardized = TRUE)## lavaan 0.6.17 ended normally after 204 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
## Number of equality constraints 2
##
## Used Total
## Number of observations 6458 6459
## Number of missing patterns 25
##
## Model Test User Model:
##
## Test statistic 1957.870
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 (a) 0.681 0.005 125.499 0.000 0.681 0.674
## peninc_1 -0.293 0.055 -5.301 0.000 -0.293 -0.049
## totinc_3 ~
## totinc_2 (a) 0.681 0.005 125.499 0.000 0.681 0.692
## peninc_2 -0.291 0.050 -5.784 0.000 -0.291 -0.052
## totinc_4 ~
## totinc_3 (a) 0.681 0.005 125.499 0.000 0.681 0.661
## peninc_3 -0.409 0.053 -7.724 0.000 -0.409 -0.072
## peninc_2 ~
## peninc_1 0.820 0.008 99.439 0.000 0.820 0.777
## totinc_1 -0.007 0.001 -4.775 0.000 -0.007 -0.037
## peninc_3 ~
## peninc_2 0.783 0.008 97.418 0.000 0.783 0.771
## totinc_2 -0.006 0.001 -4.346 0.000 -0.006 -0.035
## peninc_4 ~
## peninc_3 0.782 0.008 93.943 0.000 0.782 0.759
## totinc_3 -0.009 0.002 -5.814 0.000 -0.009 -0.048
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_1 ~~
## peninc_1 -3702.148 363.041 -10.198 0.000 -3702.148 -0.126
## .totinc_2 ~~
## .peninc_2 1159.235 177.994 6.513 0.000 1159.235 0.081
## .totinc_3 ~~
## .peninc_3 1082.943 176.883 6.122 0.000 1082.943 0.076
## .totinc_4 ~~
## .peninc_4 1800.730 199.885 9.009 0.000 1800.730 0.113
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 0.956 3.874 0.247 0.805 0.956 0.002
## .totinc_3 -1.065 3.730 -0.285 0.775 -1.065 -0.003
## .totinc_4 -0.532 3.988 -0.133 0.894 -0.532 -0.001
## .peninc_2 0.073 0.575 0.127 0.899 0.073 0.001
## .peninc_3 0.028 0.594 0.048 0.962 0.028 0.000
## .peninc_4 0.201 0.627 0.321 0.748 0.201 0.003
## totinc_1 0.518 5.221 0.099 0.921 0.518 0.001
## peninc_1 -0.056 0.875 -0.064 0.949 -0.056 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 95438.292 2.007 47544.027 0.000 95438.292 0.535
## .totinc_3 88548.947 1.907 46427.479 0.000 88548.947 0.512
## .totinc_4 100988.493 4.442 22733.067 0.000 100988.493 0.550
## .peninc_2 2127.613 37.488 56.755 0.000 2127.613 0.387
## .peninc_3 2270.914 40.034 56.725 0.000 2270.914 0.400
## .peninc_4 2512.080 44.759 56.125 0.000 2512.080 0.416
## totinc_1 174403.495 6.955 25075.284 0.000 174403.495 1.000
## peninc_1 4933.175 86.621 56.951 0.000 4933.175 1.000anova(m1, m21) # m1 better,说明在养老金收入中，前后效应不同##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## m1 12 644795 645012 1944.5
## m21 14 644804 645007 1957.9 13.388 0.029693 2 0.001239 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Cross-lagged model with stability physical and mental
model <- 'totinc_2 ~ 1 + a*totinc_1 + peninc_1
totinc_3 ~ 1 + a*totinc_2 + peninc_2
totinc_4 ~ 1 + a*totinc_3 + peninc_3
peninc_2 ~ 1 + b*peninc_1 + totinc_1
peninc_3 ~ 1 + b*peninc_2 + totinc_2
peninc_4 ~ 1 + b*peninc_3 + totinc_3
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m22 <- sem(model, data = usw, missing = "ML")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1662## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.482436e-15) is close to zero. This may be a symptom that the
## model is not identified.summary(m22, standardized = TRUE)## lavaan 0.6.17 ended normally after 172 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
## Number of equality constraints 4
##
## Used Total
## Number of observations 6458 6459
## Number of missing patterns 25
##
## Model Test User Model:
##
## Test statistic 2034.223
## Degrees of freedom 16
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 (a) 0.682 0.005 125.413 0.000 0.682 0.674
## peninc_1 -0.312 0.055 -5.660 0.000 -0.312 -0.052
## totinc_3 ~
## totinc_2 (a) 0.682 0.005 125.413 0.000 0.682 0.692
## peninc_2 -0.283 0.050 -5.636 0.000 -0.283 -0.050
## totinc_4 ~
## totinc_3 (a) 0.682 0.005 125.413 0.000 0.682 0.664
## peninc_3 -0.402 0.053 -7.640 0.000 -0.402 -0.071
## peninc_2 ~
## peninc_1 (b) 0.795 0.005 167.687 0.000 0.795 0.768
## totinc_1 -0.007 0.001 -5.143 0.000 -0.007 -0.041
## peninc_3 ~
## peninc_2 (b) 0.795 0.005 167.687 0.000 0.795 0.769
## totinc_2 -0.006 0.001 -4.131 0.000 -0.006 -0.034
## peninc_4 ~
## peninc_3 (b) 0.795 0.005 167.687 0.000 0.795 0.765
## totinc_3 -0.009 0.002 -5.502 0.000 -0.009 -0.045
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_1 ~~
## peninc_1 -1194.837 366.879 -3.257 0.001 -1194.837 -0.041
## .totinc_2 ~~
## .peninc_2 1528.583 179.197 8.530 0.000 1528.583 0.107
## .totinc_3 ~~
## .peninc_3 1510.557 178.003 8.486 0.000 1510.557 0.106
## .totinc_4 ~~
## .peninc_4 1347.466 198.556 6.786 0.000 1347.466 0.085
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 0.966 3.884 0.249 0.804 0.966 0.002
## .totinc_3 -1.092 3.741 -0.292 0.770 -1.092 -0.003
## .totinc_4 -0.537 3.975 -0.135 0.893 -0.537 -0.001
## .peninc_2 0.071 0.577 0.124 0.902 0.071 0.001
## .peninc_3 0.027 0.596 0.046 0.963 0.027 0.000
## .peninc_4 0.204 0.625 0.327 0.744 0.204 0.003
## totinc_1 0.464 5.209 0.089 0.929 0.464 0.001
## peninc_1 -0.056 0.873 -0.064 0.949 -0.056 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 95933.459 3.391 28291.343 0.000 95933.459 0.540
## .totinc_3 89063.720 3.615 24634.247 0.000 89063.720 0.516
## .totinc_4 100354.596 2.650 37872.782 0.000 100354.596 0.552
## .peninc_2 2141.473 37.951 56.427 0.000 2141.473 0.406
## .peninc_3 2284.815 40.523 56.383 0.000 2284.815 0.406
## .peninc_4 2497.174 44.193 56.506 0.000 2497.174 0.410
## totinc_1 173599.827 3.757 46206.874 0.000 173599.827 1.000
## peninc_1 4911.010 86.693 56.648 0.000 4911.010 1.000anova(m1,m21,m22) # m1 better,说明两个变量的各自前后效应不同##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## m1 12 644795 645012 1944.5
## m21 14 644804 645007 1957.9 13.388 0.029693 2 0.001239 **
## m22 16 644877 645066 2034.2 76.354 0.075873 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#------------------------------------------------------------------------------
# 加入随机效应：模型测试时间过长
# library(blavaan)
# # cross-lagged model with random intercept for mental health
# model <- 'totinc_2 ~ 1 + totinc_1 + peninc_1
# totinc_3 ~ 1 + totinc_2 + peninc_2
# totinc_4 ~ 1 + totinc_3 + peninc_3
#
# peninc_2 ~ 1 + peninc_1 + totinc_1
# peninc_3 ~ 1 + peninc_2 + totinc_2
# peninc_4 ~ 1 + peninc_3 + totinc_3
#
# totinc_1 ~~ peninc_1
# totinc_2 ~~ peninc_2
# totinc_3 ~~ peninc_3
# totinc_4 ~~ peninc_4
#
# rm =~ 1*peninc_1 + 1*peninc_2 + 1*peninc_3 + 1*peninc_4
# pm =~ 1*totinc_1 + 1*totinc_2 + 1*totinc_3 + 1*totinc_4'
#
# m31 <- bsem(model, data = usw, missing = "ML")
# summary(m31, standardized = TRUE)
#
# anova(m1, m31)
#------------------------------------------------------------------------------
## Optional: stationarity and equilibrium
# model <- 'totinc_2 ~ 1 + a*totinc_1 + peninc_1
# totinc_3 ~ 1 + a*totinc_2 + peninc_2
# totinc_4 ~ 1 + a*totinc_3 + peninc_3
#
# peninc_2 ~ 1 + peninc_1 + totinc_1
# peninc_3 ~ 1 + peninc_2 + totinc_2
# peninc_4 ~ 1 + peninc_3 + totinc_3
#
# totinc_1 ~~ peninc_1
# totinc_2 ~~ peninc_2
# totinc_3 ~~ peninc_3
# totinc_4 ~~ peninc_4'
# m21 <- sem(model, data = usw, missing = "ML")
# summary(m21, standardized = TRUE)
# stationarity of the effect of mental on physical
model <- 'totinc_2 ~ 1 + b*totinc_1 + c*peninc_1
totinc_3 ~ 1 + b*totinc_2 + c*peninc_2
totinc_4 ~ 1 + b*totinc_3 + c*peninc_3
peninc_2 ~ 1 + peninc_1 + totinc_1
peninc_3 ~ 1 + peninc_2 + totinc_2
peninc_4 ~ 1 + peninc_3 + totinc_3
totinc_1 ~~ peninc_1
totinc_2 ~~ peninc_2
totinc_3 ~~ peninc_3
totinc_4 ~~ peninc_4'
m31 <- sem(model, data = usw, missing = "ML")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1662## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.888412e-13) is smaller than zero. This may be a symptom that
## the model is not identified.summary(m31, standardized = TRUE)## lavaan 0.6.17 ended normally after 167 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
## Number of equality constraints 4
##
## Used Total
## Number of observations 6458 6459
## Number of missing patterns 25
##
## Model Test User Model:
##
## Test statistic 2023.467
## Degrees of freedom 16
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_2 ~
## totinc_1 (b) 0.681 0.005 125.432 0.000 0.681 0.674
## peninc_1 (c) -0.331 0.030 -10.875 0.000 -0.331 -0.055
## totinc_3 ~
## totinc_2 (b) 0.681 0.005 125.432 0.000 0.681 0.691
## peninc_2 (c) -0.331 0.030 -10.875 0.000 -0.331 -0.059
## totinc_4 ~
## totinc_3 (b) 0.681 0.005 125.432 0.000 0.681 0.664
## peninc_3 (c) -0.331 0.030 -10.875 0.000 -0.331 -0.058
## peninc_2 ~
## peninc_1 0.820 0.008 99.529 0.000 0.820 0.778
## totinc_1 -0.007 0.001 -4.685 0.000 -0.007 -0.037
## peninc_3 ~
## peninc_2 0.783 0.008 97.400 0.000 0.783 0.770
## totinc_2 -0.006 0.001 -4.267 0.000 -0.006 -0.035
## peninc_4 ~
## peninc_3 0.783 0.008 94.601 0.000 0.783 0.760
## totinc_3 -0.009 0.002 -5.618 0.000 -0.009 -0.047
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## totinc_1 ~~
## peninc_1 -1192.292 366.873 -3.250 0.001 -1192.292 -0.041
## .totinc_2 ~~
## .peninc_2 1532.777 178.954 8.565 0.000 1532.777 0.107
## .totinc_3 ~~
## .peninc_3 1506.266 177.952 8.464 0.000 1506.266 0.106
## .totinc_4 ~~
## .peninc_4 1348.046 198.552 6.789 0.000 1348.046 0.085
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 0.975 3.885 0.251 0.802 0.975 0.002
## .totinc_3 -1.086 3.741 -0.290 0.772 -1.086 -0.003
## .totinc_4 -0.537 3.976 -0.135 0.893 -0.537 -0.001
## .peninc_2 0.072 0.576 0.125 0.901 0.072 0.001
## .peninc_3 0.027 0.596 0.045 0.964 0.027 0.000
## .peninc_4 0.201 0.625 0.322 0.747 0.201 0.003
## totinc_1 0.452 5.209 0.087 0.931 0.452 0.001
## peninc_1 -0.056 0.873 -0.064 0.949 -0.056 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .totinc_2 95942.313 3.397 28246.545 0.000 95942.313 0.540
## .totinc_3 89065.212 3.599 24746.380 0.000 89065.212 0.516
## .totinc_4 100391.823 2.645 37952.921 0.000 100391.823 0.552
## .peninc_2 2138.542 37.883 56.451 0.000 2138.542 0.392
## .peninc_3 2283.925 40.502 56.390 0.000 2283.925 0.404
## .peninc_4 2496.239 44.171 56.514 0.000 2496.239 0.417
## totinc_1 173601.899 3.765 46109.858 0.000 173601.899 1.000
## peninc_1 4910.832 86.688 56.650 0.000 4910.832 1.000anova(m21, m31) # m21:只限制了1lag的系数 m31:限制了2lags的系数,结果一个aic大一个bic小##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## m21 14 644804 645007 1957.9
## m31 16 644866 645056 2023.5 65.597 0.070171 2 5.698e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 两波的稳定性是否相同有待继续研究
#------------------------------------------------------------------------------
# 研究社会养老金收入如何随时间变化
# 潜在增长模型
library(lme4)## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack# 1-----检查养老金收入的分布和相关性矩阵-----
# nice correlation matrix
# 删除peninc_1_miss, peninc_2miss, peninc_3_miss, peninc_4_miss
usw <- usw %>%
select(-grep("_miss", names(usw)))
usw %>%
select(matches("peninc")) %>% # select vars. that have this in name
na.omit() %>% # omit missing from data
cor() %>% # do correlations
round(2) # round to 2 decimal points## peninc_1 peninc_2 peninc_3 peninc_4
## peninc_1 1.00 0.78 0.69 0.60
## peninc_2 0.78 1.00 0.78 0.68
## peninc_3 0.69 0.78 1.00 0.77
## peninc_4 0.60 0.68 0.77 1.00# summaries of peninc
usw %>%
select(matches("peninc")) %>% # select vars. that have this in name
summary()## peninc_1 peninc_2 peninc_3 peninc_4
## Min. :-69.183 Min. :-82.07 Min. :-95.07 Min. :-109.12
## 1st Qu.:-69.183 1st Qu.:-82.07 1st Qu.:-95.07 1st Qu.:-109.12
## Median : -4.433 Median : 10.31 Median : 24.43 Median : 23.38
## Mean : 0.000 Mean : 0.00 Mean : 0.00 Mean : 0.00
## 3rd Qu.: 56.817 3rd Qu.: 56.93 3rd Qu.: 54.93 3rd Qu.: 53.38
## Max. :330.817 Max. :317.93 Max. :224.93 Max. : 290.88
## NA's :22 NA's :17 NA's :33 NA's :56#------------------------------------------------------------------------------
# 总收入的潜在增长模型
# nice correlation matrix
usw %>%
select(matches("logincome")) %>% # select vars. that have this in name
na.omit() %>% # omit missing from data
cor() %>% # do correlations
round(2) # round to 2 decimal points## logincome_1 logincome_2 logincome_3 logincome_4
## logincome_1 1.00 0.65 0.64 0.58
## logincome_2 0.65 1.00 0.66 0.60
## logincome_3 0.64 0.66 1.00 0.69
## logincome_4 0.58 0.60 0.69 1.00# summaries of logincome
usw %>%
select(matches("logincome")) %>% # select vars. that have this in name
summary()## logincome_1 logincome_2 logincome_3 logincome_4
## Min. :2.303 Min. :2.303 Min. :2.303 Min. :2.303
## 1st Qu.:5.741 1st Qu.:5.745 1st Qu.:5.791 1st Qu.:5.813
## Median :6.167 Median :6.204 Median :6.196 Median :6.234
## Mean :6.141 Mean :6.152 Mean :6.171 Mean :6.212
## 3rd Qu.:6.605 3rd Qu.:6.612 3rd Qu.:6.605 3rd Qu.:6.639
## Max. :8.010 Max. :8.010 Max. :8.010 Max. :8.010
## NA's :94 NA's :105 NA's :76 NA's :92# ------
# first LGM
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 +
1*logincome_4
s =~ 0*logincome_1 + 1*logincome_2 + 2*logincome_3 +
3*logincome_4'
lgm1 <- growth(model, data = usw)
summary(lgm1, standardized = TRUE)## lavaan 0.6.17 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 6182 6459
##
## Model Test User Model:
##
## Test statistic 26.209
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.587 0.836
## logincome_2 1.000 0.587 0.834
## logincome_3 1.000 0.587 0.896
## logincome_4 1.000 0.587 0.883
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.112 0.159
## logincome_3 2.000 0.224 0.342
## logincome_4 3.000 0.336 0.505
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.024 0.002 -10.318 0.000 -0.368 -0.368
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.139 0.009 716.825 0.000 10.449 10.449
## s 0.020 0.003 7.964 0.000 0.182 0.182
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.149 0.005 27.150 0.000 0.149 0.301
## .logincome_2 0.187 0.004 44.382 0.000 0.187 0.377
## .logincome_3 0.131 0.003 40.169 0.000 0.131 0.305
## .logincome_4 0.130 0.005 27.592 0.000 0.130 0.293
## i 0.345 0.009 39.814 0.000 1.000 1.000
## s 0.013 0.001 11.990 0.000 1.000 1.000# make wave0 variable
usl <- mutate(usl, wave0 = as.numeric(wave) - 1)
# MLM empty model with random effect for time
mm1 <- lmer(data = usl,
logincome ~ 1 + wave0 +
(1 + wave0 | pid))
summary(mm1) # 很奇怪，运行不出来结果## Linear mixed model fit by REML ['lmerMod']
## Formula: logincome ~ 1 + wave0 + (1 + wave0 | pid)
## Data: usl
##
## REML criterion at convergence: 40650.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.6583 -0.3120 0.0179 0.3616 4.5272
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pid (Intercept) 0.3505 0.5920
## wave0 0.0106 0.1029 -0.40
## Residual 0.1556 0.3944
## Number of obs: 25469, groups: pid, 6455
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.136666 0.008462 725.206
## wave0 0.021648 0.002564 8.444
##
## Correlation of Fixed Effects:
## (Intr)
## wave0 -0.514model <- 'i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s =~ 0*logincome_1 + 1*logincome_2 + 2*logincome_3 + 3*logincome_4
logincome_1 ~~ a*logincome_1
logincome_2 ~~ a*logincome_2
logincome_3 ~~ a*logincome_3
logincome_4 ~~ a*logincome_4'
lgm2 <- growth(model, data = usw)
summary(lgm2, standardized = TRUE)## lavaan 0.6.17 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
## Number of equality constraints 3
##
## Used Total
## Number of observations 6182 6459
##
## Model Test User Model:
##
## Test statistic 164.548
## Degrees of freedom 8
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.590 0.833
## logincome_2 1.000 0.590 0.866
## logincome_3 1.000 0.590 0.882
## logincome_4 1.000 0.590 0.878
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.101 0.148
## logincome_3 2.000 0.202 0.302
## logincome_4 3.000 0.303 0.452
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.024 0.002 -11.622 0.000 -0.396 -0.396
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.139 0.009 715.313 0.000 10.404 10.404
## s 0.020 0.003 7.901 0.000 0.201 0.201
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincom_1 (a) 0.153 0.002 78.626 0.000 0.153 0.305
## .logincom_2 (a) 0.153 0.002 78.626 0.000 0.153 0.330
## .logincom_3 (a) 0.153 0.002 78.626 0.000 0.153 0.342
## .logincom_4 (a) 0.153 0.002 78.626 0.000 0.153 0.339
## i 0.348 0.008 41.933 0.000 1.000 1.000
## s 0.010 0.001 12.304 0.000 1.000 1.000anova(lgm1, lgm2)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## lgm1 5 38877 38937 26.209
## lgm2 8 39009 39049 164.548 138.34 0.085425 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# predict the two latent variables
pred_lgm <- predict(lgm1)
# the top of the predicted scores
head(pred_lgm)## i s
## [1,] 6.209939 0.002191682
## [2,] 6.460516 0.007714084
## [3,] 6.154526 0.029687838
## [4,] 5.998372 0.030518848
## [5,] 5.932325 0.009753420
## [6,] 6.417566 -0.055924716# average of the intercepts (first column)
mean(pred_lgm[, 1]) ## [1] 6.138753# average of the slope (second column)
mean(pred_lgm[, 2])## [1] 0.02044986map(0:3, # what to loop over, in this case numbers 0 to 3
function(x) pred_lgm[, 1] + x * pred_lgm[, 2]) # formula to use## [[1]]
## [1] 6.209939 6.460516 6.154526 5.998372 5.932325 6.417566 5.901809 6.653539
## [9] 6.421996 6.339451 6.687403 6.400949 7.071825 5.614518 6.430314 5.981917
## [17] 5.990872 6.824932 5.857794 6.888851 6.174096 5.638213 6.976784 5.503427
## [25] 6.407084 6.068671 6.277843 6.439080 6.319615 5.723634 6.318223 6.539129
## [33] 5.996382 5.630873 5.866552 6.032682 6.158300 7.014781 5.577859 6.222741
## [41] 6.743701 6.675766 6.361153 6.206791 5.805008 6.157566 5.350674 5.715025
## [49] 5.735583 5.251255 6.421772 6.657176 6.493042 5.622855 5.479824 5.775863
## [57] 7.064994 6.003269 6.817455 6.172405 6.974897 6.737322 5.211327 6.694037
## [65] 5.853510 5.791683 6.170783 5.472509 6.111545 5.992576 6.038643 6.372558
## [73] 5.387781 6.409199 6.507788 6.590764 5.782901 5.811402 6.663581 6.526178
## [81] 5.631203 5.685102 6.245628 5.496651 5.901898 5.869774 5.812499 6.429012
## [89] 7.083184 5.638213 6.201844 6.526467 6.953359 6.382196 5.943453 5.557227
## [97] 6.327291 6.587300 6.637347 6.494925 5.798099 5.658602 5.176876 6.871508
## [105] 6.002966 6.640452 5.694979 5.729754 5.279000 6.268672 6.334418 6.266468
## [113] 6.144449 5.954603 6.205226 5.507648 6.781329 6.098057 7.101137 6.197711
## [121] 5.613476 6.290118 6.452310 5.464806 5.959251 5.232713 6.974897 5.984998
## [129] 5.717744 6.215058 6.375932 6.404158 6.227137 6.854290 5.852539 5.161413
## [137] 5.422331 5.912736 6.090159 5.667869 5.516671 6.234015 6.332054 6.588847
## [145] 5.822746 5.184169 5.875653 6.437653 6.290118 5.724112 5.515425 6.489586
## [153] 6.623414 6.057737 5.836486 5.886443 6.319615 5.153187 6.436862 6.126683
## [161] 5.618902 6.456719 5.765652 6.093717 5.385487 5.933442 6.099758 5.436008
## [169] 5.911731 5.915394 6.283465 6.215774 6.810073 5.269589 6.791737 5.274709
## [177] 6.191535 6.032470 6.388086 5.478915 6.525026 6.712980 5.915394 5.353737
## [185] 5.670602 6.546741 5.647264 6.017469 5.927114 5.919481 6.245628 6.085096
## [193] 5.371699 5.460155 6.316643 5.204613 6.555633 6.242170 5.936121 5.519940
## [201] 6.525816 7.046840 5.944278 4.750011 6.052493 5.821968 5.630985 6.110223
## [209] 6.294848 5.614533 5.801793 5.811864 5.855036 6.675766 5.001948 6.597368
## [217] 6.038643 6.481730 6.090167 6.584398 6.172405 5.882715 6.401167 4.812208
## [225] 6.575363 5.945202 6.305019 5.996382 6.388075 5.803340 5.996914 6.017041
## [233] 5.352177 5.045969 6.790891 5.352193 6.264313 6.973523 6.044200 5.461856
## [241] 5.356309 6.780687 6.850494 5.599799 5.796793 6.577981 5.990421 5.781984
## [249] 6.324939 6.072233 5.827927 7.296143 6.218740 5.522743 5.575871 6.271451
## [257] 6.354752 6.464704 5.899044 6.269446 5.913071 6.556204 6.269928 5.367052
## [265] 6.760067 5.268809 5.553859 5.795513 6.582352 5.961440 6.640452 5.007061
## [273] 5.148043 5.694979 6.591519 6.494925 5.919481 6.144225 6.294236 5.531066
## [281] 6.337600 5.765408 6.051792 6.244171 6.075953 6.742557 6.632459 5.806817
## [289] 6.551910 5.884899 5.507267 6.303501 7.299110 5.948570 7.168180 6.721451
## [297] 5.670829 6.738392 6.615089 6.360382 6.451960 6.452310 6.241807 5.739170
## [305] 5.946975 6.205865 5.233946 6.166107 6.654414 5.357211 5.901420 6.871508
## [313] 5.721715 5.999757 6.490877 6.585642 5.750583 6.850494 6.488790 6.641726
## [321] 6.912068 6.179348 6.034626 6.054357 5.906296 6.730813 5.677661 5.628566
## [329] 6.607565 6.334447 6.375932 6.145098 7.030693 5.564100 6.730813 6.546741
## [337] 6.712980 5.835541 4.014394 6.286688 6.430720 6.511210 5.503399 5.670559
## [345] 6.316741 5.483262 6.277843 6.156795 5.640336 5.978790 6.381601 7.324052
## [353] 5.676932 6.667655 6.119760 6.574869 6.707007 6.009261 6.377736 6.687882
## [361] 5.403563 6.751623 6.201882 5.944278 6.538183 6.247954 6.922312 5.781257
## [369] 6.838558 6.632017 6.263508 5.937850 7.015591 6.294236 6.677022 5.911152
## [377] 6.508521 6.703208 5.856356 6.298434 6.662673 6.023917 6.068350 5.575282
## [385] 5.681246 5.832677 6.628345 6.057737 5.723527 6.505467 6.205865 6.164039
## [393] 6.723509 6.171883 5.701992 6.663581 6.354752 6.322814 6.111000 5.906296
## [401] 6.215058 7.479415 6.540188 6.241807 6.572421 5.956427 5.231938 5.264560
## [409] 5.442662 6.942939 6.446392 6.158687 7.221099 5.425751 5.082541 6.654414
## [417] 6.538183 6.393366 6.327291 6.432081 5.185872 7.071825 6.506955 6.159613
## [425] 5.503363 6.445007 5.565892 6.027834 5.806088 6.019719 5.759548 6.152032
## [433] 6.588847 5.316709 5.247516 5.582623 5.984998 5.992884 5.800210 6.269704
## [441] 5.284764 5.901898 6.234015 7.118107 5.666447 6.485027 6.580510 6.299922
## [449] 5.860317 7.101137 6.641726 5.960351 5.544637 6.545127 5.599128 6.491892
## [457] 6.050693 6.638638 6.662673 6.220311 6.158300 4.084214 5.561825 6.646448
## [465] 6.532661 5.794264 6.103019 6.843880 7.168180 5.863217 4.883167 6.381601
## [473] 5.205267 6.738392 6.569815 5.811986 6.064426 6.053116 5.220552 5.710614
## [481] 5.940684 6.348759 6.473988 5.964087 5.363817 6.294342 5.959251 6.042659
## [489] 5.912438 5.918702 6.071509 6.292143 6.488425 5.992884 6.520811 5.742724
## [497] 5.247651 5.763787 6.048152 5.575690 6.322420 5.419692 6.056863 6.790058
## [505] 6.032470 5.353025 6.361153 7.083184 6.770254 6.018963 5.667595 5.032473
## [513] 5.996914 4.978177 5.617735 6.340078 6.429012 6.176146 5.878181 5.695469
## [521] 6.141655 6.032716 6.303501 6.901167 6.206791 6.493281 5.876012 6.388075
## [529] 6.221481 6.126683 6.397242 5.115405 6.332976 6.005502 6.888851 5.997238
## [537] 5.957387 6.092446 5.650158 6.294342 5.444748 6.051459 5.996188 6.036910
## [545] 6.976784 5.185651 5.565892 5.635553 6.605756 6.472433 5.708830 6.332976
## [553] 4.713175 5.943453 6.087487 6.677022 6.314712 5.423769 7.033941 5.045238
## [561] 6.759049 5.322710 6.625851 5.448827 5.526856 5.962639 6.815078 5.763787
## [569] 6.739734 5.629847 5.216471 6.491637 6.526067 6.409199 5.228077 5.845304
## [577] 5.798099 6.036910 6.023042 6.478435 6.480379 6.456602 6.519546 5.811402
## [585] 6.355554 6.445007 5.951955 5.789438 6.586136 6.372558 5.906379 5.945202
## [593] 5.721101 6.529620 6.140714 5.860515 6.627714 5.739186 6.771350 5.895353
## [601] 5.987694 5.442599 6.155126 6.590739 6.269704 5.416136 5.940684 6.585642
## [609] 5.262667 5.801793 5.706235 6.263670 6.525816 5.750332 6.592884 6.858319
## [617] 5.876912 6.016305 7.479415 6.040529 5.564926 6.437653 5.817960 6.752421
## [625] 6.687403 7.014781 6.688598 6.636840 6.751623 6.334447 6.147704 5.166873
## [633] 5.739170 6.260843 5.978790 5.996188 5.982087 5.630873 7.292713 5.860823
## [641] 6.283465 6.851219 5.755852 6.271451 6.525026 6.781329 6.135634 5.747057
## [649] 5.911649 5.632829 6.441393 6.342430 5.792373 5.964018 5.360286 6.077092
## [657] 5.805008 6.360346 5.883503 5.831792 5.600198 6.558863 5.960351 5.849176
## [665] 5.199434 6.790058 6.582091 5.899044 6.098278 6.421610 4.912062 5.391149
## [673] 6.090167 5.473337 5.280904 6.292370 5.644927 6.090159 6.135634 5.145904
## [681] 6.370833 6.072235 6.461394 6.815078 5.704350 6.176456 5.611435 6.085862
## [689] 6.892288 6.095463 6.017469 5.927114 6.373214 5.721248 5.707167 5.997476
## [697] 6.708004 6.042925 5.494159 5.842973 5.784290 6.210393 6.400393 6.345861
## [705] 5.277322 6.228203 6.051814 5.396561 5.715662 6.371079 6.628856 7.292713
## [713] 6.615089 6.258113 6.017041 5.324187 5.716420 6.742557 5.962754 5.932345
## [721] 5.519940 5.636550 6.697395 5.627137 5.640317 6.033461 6.551910 6.391190
## [729] 5.612854 5.145314 6.687581 5.795513 6.539129 6.382196 6.170783 6.120871
## [737] 5.246166 5.932325 5.928143 6.687882 6.582091 6.269145 5.484123 6.324939
## [745] 6.242170 6.222081 6.353125 6.912068 6.078883 6.216397 5.755655 5.697104
## [753] 6.154899 6.668984 6.415036 6.298434 6.638638 6.578018 6.152032 6.431599
## [761] 6.497355 6.000123 6.798189 7.387964 6.824932 5.790566 5.578815 5.806817
## [769] 6.891790 5.992568 6.473780 5.784290 6.678592 6.851219 5.475356 5.771793
## [777] 6.147704 5.575364 5.887690 6.371079 6.126797 6.572421 5.934602 6.064426
## [785] 6.310461 5.798466 6.377736 5.561150 5.706235 6.286688 6.279015 6.179684
## [793] 6.778238 6.632459 6.617164 6.023917 5.887714 5.269811 5.732298 6.674327
## [801] 6.507316 6.179684 6.739734 6.493281 6.213013 5.232043 6.165543 6.310461
## [809] 6.707007 6.258113 5.074782 6.054357 6.213013 6.668984 6.582352 5.825010
## [817] 5.953011 6.292370 5.329773 6.023042 5.811864 5.638754 6.120871 5.800694
## [825] 5.812499 6.322303 7.299110 7.221099 5.431086 6.110223 5.794855 6.213605
## [833] 5.762937 6.381774 6.417072 5.831549 6.063158 5.308802 5.592619 5.861692
## [841] 6.597387 5.611215 6.198750 6.463709 5.149458 6.810073 6.753127 6.838558
## [849] 5.618477 6.351266 5.577859 5.811153 6.516217 5.333757 5.968048 6.648388
## [857] 5.382775 5.744768 6.405495 6.322420 5.716634 6.520811 5.794672 6.436862
## [865] 6.126091 6.516217 6.303676 5.673265 5.775622 6.646448 5.231746 5.857794
## [873] 4.860674 6.152710 6.282171 5.919012 6.140557 6.040428 5.911731 6.067200
## [881] 6.695170 6.654847 5.809481 5.753429 6.497355 5.428262 6.770254 5.435243
## [889] 6.127923 5.749574 6.636840 6.184666 5.794338 6.240383 6.044200 5.789433
## [897] 6.865411 6.002186 5.529969 6.699932 6.535334 5.745642 6.551632 5.614619
## [905] 6.837250 5.787476 5.840972 5.446878 5.759792 6.525760 6.833957 5.782047
## [913] 6.075589 5.602480 6.129106 5.773963 6.480379 5.354316 6.201844 6.427997
## [921] 5.716768 5.752112 6.461669 4.991375 6.678351 5.721101 6.569815 6.091296
## [929] 5.949716 6.305019 5.866649 7.240637 6.003269 4.925902 6.366882 6.342430
## [937] 5.645848 6.505467 6.490877 6.511210 6.126091 6.144020 5.247977 6.461669
## [945] 5.927539 5.263505 6.164428 6.577981 5.866649 6.526178 6.358238 6.763226
## [953] 5.953065 5.957867 6.295264 5.742724 5.747057 5.428206 5.744768 6.072235
## [961] 6.759049 5.358022 6.834731 5.779034 6.623397 5.876012 5.151571 5.548592
## [969] 6.348033 6.459083 6.667655 5.381706 5.717744 5.805708 5.953439 6.458424
## [977] 6.198891 5.825464 6.019719 5.286325 5.428064 5.690969 6.433261 6.154192
## [985] 6.176706 5.459166 4.963634 6.002186 7.089331 6.295264 6.154899 6.340078
## [993] 6.723509 7.030693 6.607565 6.488790 6.071509 5.934602 5.128488 6.245761
## [1001] 5.933442 6.695427 6.198750 6.212643 6.610153 5.827620 6.778238 6.095463
## [1009] 5.232713 6.412617 5.227205 5.965656 5.471695 4.559447 5.656507 6.412617
## [1017] 6.473988 6.185521 5.604937 6.679016 6.570414 6.063158 6.901167 6.240383
## [1025] 6.626971 6.560585 6.682710 5.810982 5.426688 6.753127 6.266468 5.799498
## [1033] 6.699932 5.059313 5.869774 6.176706 5.553859 6.345949 5.901420 6.009261
## [1041] 5.787476 6.430720 6.695427 6.570414 6.175119 6.525760 5.494209 5.503427
## [1049] 5.904055 5.634334 5.930452 5.878181 6.162223 6.217195 6.427997 5.987694
## [1057] 5.296682 5.857788 5.949716 6.217195 6.678592 5.939332 6.432081 5.574643
## [1065] 6.431599 7.227100 5.411294 5.773448 6.337600 5.962813 6.493958 5.452478
## [1073] 6.224944 5.708905 6.858319 6.041456 6.118578 5.371659 5.825464 6.155126
## [1081] 6.694037 6.169058 5.266408 6.260843 5.661023 7.089331 6.008595 6.206053
## [1089] 5.998136 7.296143 6.719006 5.482934 5.874186 6.071203 6.697395 6.400393
## [1097] 5.433547 6.358238 6.152534 6.007007 6.159741 6.833957 6.451960 5.179769
## [1105] 5.435147 6.560585 6.040529 5.271499 6.072939 5.666447 5.941486 6.464704
## [1113] 6.224944 5.917666 6.393366 6.068671 6.024967 5.835541 6.085096 5.580023
## [1121] 5.613476 6.461394 5.850670 5.700193 6.627714 5.693786 6.154526 6.417566
## [1129] 5.016107 6.355554 6.031247 6.220161 6.021732 6.687581 6.526067 6.857754
## [1137] 6.837250 6.592834 6.313317 5.754599 6.129106 6.491637 6.375565 6.031247
## [1145] 6.049553 6.164039 5.640317 6.239822 5.329000 6.371588 5.226222 6.034767
## [1153] 5.205590 6.610153 6.090575 6.210393 6.359438 5.860515 6.721451 5.844747
## [1161] 6.973523 6.509117 6.637347 5.840705 5.647523 6.491892 6.695170 5.827020
## [1169] 6.049553 5.948570 6.141655 5.708830 6.922312 5.960892 6.724421 5.182400
## [1177] 6.314712 5.945092 5.647264 5.280413 5.866552 6.839530 6.206053 6.489105
## [1185] 6.373214 5.313388 6.229149 5.817960 5.602480 5.737775 5.883503 5.887714
## [1193] 6.215774 6.152534 5.312591 7.240637 7.033941 5.962639 5.863217 6.509117
## [1201] 5.754599 6.332054 6.628345 6.175119 5.599528 5.911984 5.415012 5.842973
## [1209] 5.825010 5.384000 5.145314 6.743701 6.361687 5.631186 6.573729 5.110336
## [1217] 7.387964 5.997061 5.264402 6.174096 6.489105 6.648388 6.626971 6.618151
## [1225] 5.995044 7.572399 5.638754 5.599128 6.292143 6.176456 6.458424 5.636195
## [1233] 6.198891 5.860823 5.707567 6.191535 6.339747 6.034626 6.092446 6.157566
## [1241] 6.339747 5.548868 5.299576 5.345213 5.882715 5.455958 5.997061 5.521428
## [1249] 6.591519 6.231297 6.227137 6.397242 6.674327 6.366882 6.628856 6.506955
## [1257] 6.493958 6.506937 5.614518 6.181962 6.034994 6.313317 5.337148 6.249509
## [1265] 6.040428 5.622435 4.851397 5.219124 5.342064 6.891790 6.181962 6.145098
## [1273] 5.634139 5.293595 5.951955 6.356904 6.574869 5.271950 5.657561 6.118578
## [1281] 6.221481 6.166107 6.216780 5.602511 6.551632 5.679960 6.488425 5.917489
## [1289] 6.688598 6.020733 6.857754 5.991815 6.316643 5.788033 5.982997 6.535334
## [1297] 6.228353 6.892288 6.555633 5.425588 5.366601 5.397512 6.203036 5.622855
## [1305] 5.093658 6.417072 5.163625 5.487099 6.197711 5.879568 5.884645 5.378297
## [1313] 5.701992 5.756057 5.505817 5.512013 5.669379 5.906379 6.334539 6.578018
## [1321] 6.214595 6.222741 5.825275 5.945092 6.087487 5.538280 6.584398 6.111545
## [1329] 5.680168 6.421772 5.710614 5.965656 6.441393 5.765316 5.299017 6.115336
## [1337] 6.410072 6.771350 6.045035 5.715662 6.489586 6.538263 6.790891 6.865411
## [1345] 5.911152 5.418146 6.201882 6.526467 6.190766 5.883395 5.436181 5.503399
## [1353] 6.222081 6.072233 6.415036 6.771826 5.877976 5.629222 6.400112 6.008595
## [1361] 4.363537 6.388610 6.299922 6.747149 5.993495 6.368227 5.717492 5.551745
## [1369] 5.649638 6.132653 5.856356 7.438726 5.628566 4.959101 5.355903 5.703766
## [1377] 6.708004 5.794855 6.482269 6.282171 6.209939 6.220311 6.459083 5.617451
## [1385] 6.737322 5.946975 6.507788 6.375565 7.118107 6.597387 5.911798 5.741242
## [1393] 6.189531 6.026129 5.906233 6.381774 5.850670 6.316741 6.558863 5.531736
## [1401] 5.703766 5.269920 6.650126 5.503625 6.623414 5.317454 5.723634 5.863545
## [1409] 4.188307 6.111000 6.724421 6.391190 7.324052 6.625851 6.176146 5.752703
## [1417] 6.556204 5.827927 5.387967 5.938137 7.227100 5.992576 5.729754 5.472616
## [1425] 5.611549 6.472247 5.383660 6.473780 6.041456 5.509004 6.463709 6.401167
## [1433] 5.112032 6.617743 6.135448 6.231297 6.592834 6.405495 5.974761 6.281400
## [1441] 6.527466 6.378531 5.879568 5.799498 5.060078 6.228353 6.035207 6.345949
## [1449] 6.545127 6.214595 6.378531 6.228203 6.140714 6.005658 5.056032 6.360346
## [1457] 5.732678 6.161081 6.318223 5.708905 6.953359 5.640285 5.863545 5.921364
## [1465] 6.439080 6.159613 6.368227 7.438726 6.056139 6.042925 6.617164 6.752421
## [1473] 6.205226 5.329255 6.159741 6.597368 6.345861 5.182981 5.721715 5.328614
## [1481] 5.398249 6.472247 6.033461 6.155347 5.695695 6.386639 5.492241 5.186994
## [1489] 5.058937 6.632017 5.697104 5.463706 5.673265 6.247954 5.168952 6.246067
## [1497] 6.198832 6.131108 6.337194 5.986019 6.815026 6.405162 5.938720 6.908231
## [1505] 7.068124 5.788205 5.869482 6.368134 6.867885 6.019851 6.744433 5.628056
## [1513] 5.574681 5.789444 5.336704 5.569560 5.881914 6.099022 5.352443 5.562693
## [1521] 6.372822 5.121106 6.150700 5.916789 5.756059 5.827477 5.815379 6.418814
## [1529] 6.123696 6.430314 6.396139 6.436305 6.378201 5.962403 6.536325 6.281076
## [1537] 5.919868 6.122248 5.346721 6.691261 5.859379 7.214808 6.274274 6.259469
## [1545] 5.581712 6.390967 5.928058 5.692112 7.013564 5.998372 5.290995 5.132995
## [1553] 6.935293 6.263790 6.292369 6.890348 6.265361 6.007491 6.807155 5.697504
## [1561] 6.452090 5.789532 5.214329 6.519207 6.248426 5.652968 7.013564 5.758076
## [1569] 6.292369 5.633511 5.651061 6.165131 5.789711 6.143884 5.336228 6.023244
## [1577] 5.435619 5.925845 6.198832 6.998246 6.437063 6.388010 5.869482 5.906168
## [1585] 6.519207 6.277631 6.339451 6.304568 6.253991 6.580420 6.820114 5.470638
## [1593] 5.384169 5.837622 5.445407 4.942624 6.274986 6.368134 5.943227 5.512013
## [1601] 5.749701 6.161158 6.740289 5.015514 6.103701 5.179192 5.426656 6.839270
## [1609] 6.362184 5.982395 6.295475 6.448124 5.439803 5.848089 6.370578 5.986019
## [1617] 5.788205 5.082033 6.890348 6.740289 5.957544 5.608160 6.277456 5.147341
## [1625] 5.908567 6.935293 6.908231 6.390967 5.877836 6.983120 6.675533 5.224849
## [1633] 5.957544 5.154090 6.673916 5.496462 6.021430 6.215942 5.252186 5.581712
## [1641] 6.333306 5.623485 6.744433 6.087832 7.380555 6.347671 6.415652 6.021646
## [1649] 6.983120 5.804964 5.784750 6.167310 5.913966 5.790328 6.448124 6.831860
## [1657] 6.072093 5.798038 5.938720 5.712674 6.179254 5.934980 5.337234 6.438992
## [1665] 6.998246 5.286409 6.720079 5.780311 5.906451 6.256229 6.031791 6.450591
## [1673] 6.418814 6.721041 5.727962 5.852373 6.995179 6.033151 5.321575 5.506364
## [1681] 6.587393 6.074978 5.976002 5.206982 5.964168 5.955658 6.141360 5.906451
## [1689] 5.365036 5.931948 6.219967 5.349936 6.168331 6.261292 5.616234 5.811999
## [1697] 5.563661 5.756672 5.752347 6.086521 5.206432 6.807155 6.482845 6.455733
## [1705] 6.237105 6.436305 5.758076 4.992690 4.957611 6.141360 5.714033 5.257521
## [1713] 5.502103 6.025938 6.259469 6.820114 5.302377 5.847250 5.716350 5.739997
## [1721] 6.087832 5.964168 5.932219 5.943675 5.188849 5.925845 5.568125 5.234288
## [1729] 5.389568 6.349021 6.264938 6.263790 5.654292 5.673820 5.607977 5.898868
## [1737] 5.425106 5.851537 6.623490 6.333306 5.804553 7.075280 6.165131 5.651061
## [1745] 5.966012 6.145511 6.480458 6.205838 6.434385 5.762310 5.289510 6.099817
## [1753] 5.532796 5.714033 6.304568 6.362184 6.446944 7.120053 6.405162 5.886923
## [1761] 5.751387 6.655416 6.219967 5.790509 6.037164 6.000003 5.851537 6.392909
## [1769] 7.169390 6.434385 5.943227 5.835686 6.135597 6.122735 6.557143 6.573134
## [1777] 5.850528 5.894785 6.655416 6.000743 6.446375 6.659682 6.618448 5.858527
## [1785] 6.357661 5.951209 6.048287 5.420427 6.256229 5.882984 5.599015 6.243358
## [1793] 5.562693 5.623799 6.277631 5.435598 6.389143 6.251471 5.789711 6.386976
## [1801] 5.712674 5.129113 5.374672 5.398128 6.791833 6.617068 6.376466 6.617068
## [1809] 6.437063 6.355912 6.378201 6.446560 5.709196 6.167310 5.364889 6.145511
## [1817] 5.568287 6.250380 5.932219 5.900736 6.021646 6.065290 5.919868 6.841473
## [1825] 5.657360 6.679686 5.784128 6.057420 5.822195 6.074978 5.706369 5.820028
## [1833] 6.721041 6.309778 5.016096 6.837461 6.388010 6.213550 6.557143 5.769428
## [1841] 6.099817 6.069566 5.529980 5.934980 5.186017 6.265361 6.334059 6.000003
## [1849] 5.087192 5.175836 5.541808 6.452090 6.446375 6.700643 5.515252 7.198494
## [1857] 5.018124 6.349021 5.663659 6.679686 5.057190 5.697504 6.031791 5.715476
## [1865] 5.739997 5.804257 5.736763 6.246067 5.752347 6.355912 6.900576 5.666259
## [1873] 5.500480 5.879951 5.930161 6.630740 6.205838 4.545014 6.536325 5.202902
## [1881] 6.635393 5.914075 6.248426 5.873147 6.438992 6.310534 6.440916 5.555015
## [1889] 5.766085 5.385373 6.236257 6.072093 5.419007 5.822731 6.310534 5.376124
## [1897] 5.928388 6.431997 7.380555 6.099022 5.606513 5.644203 5.832474 5.804257
## [1905] 5.617768 5.523681 6.005207 5.229136 6.006357 5.794774 5.955135 5.345176
## [1913] 5.563661 5.914075 6.160253 5.926200 7.075280 6.236257 6.087508 6.277456
## [1921] 6.421996 5.926200 6.252498 5.402238 6.839270 5.616234 6.251471 6.841473
## [1929] 6.483982 5.404717 5.441939 6.673916 5.864850 6.131108 5.820312 6.296952
## [1937] 5.849107 6.308963 5.906168 6.143884 6.765224 5.615292 5.226712 6.448601
## [1945] 6.659682 6.048287 5.843976 6.440916 6.052891 5.324541 6.630740 6.267529
## [1953] 7.714620 5.792079 5.794774 5.500480 6.216094 5.928388 6.213550 5.623485
## [1961] 5.762310 5.749701 5.863524 5.766085 6.259301 5.769711 6.012023 5.755245
## [1969] 6.606227 6.264938 6.093783 5.077183 5.928058 5.798307 5.864850 5.706369
## [1977] 6.165595 7.169390 7.198494 6.165595 5.294644 6.036597 6.185132 5.446103
## [1985] 6.448601 5.799683 6.003680 5.470090 5.531581 5.985103 6.386976 5.824376
## [1993] 6.031803 7.120053 6.357661 6.160253 5.858527 5.034059 5.749436 6.786820
## [2001] 6.867885 6.623490 5.304767 6.200805 6.450591 5.894785 5.820312 6.296685
## [2009] 6.392348 6.210428 6.342302 5.947838 6.215942 6.052891 6.342302 6.274274
## [2017] 6.252498 5.990480 6.253991 6.087508 5.831130 7.068124 6.445308 5.341508
## [2025] 6.022496 5.274918 6.415652 6.700643 5.811999 6.003680 5.943675 6.337194
## [2033] 6.250380 6.392909 5.951209 5.193262 6.295475 5.982395 5.944699 5.789532
## [2041] 6.185132 5.663659 6.791833 5.852373 5.812882 6.720079 6.431997 7.214808
## [2049] 5.780311 6.324570 5.837622 5.087192 6.482845 5.967129 6.161158 6.135597
## [2057] 6.007036 5.702441 5.824376 5.215628 6.691261 6.370578 6.587393 6.580420
## [2065] 6.296952 6.815026 5.433257 6.033151 5.420867 6.396139 6.265700 5.782272
## [2073] 7.714620 6.635393 5.343384 6.337272 6.831860 5.430826 5.503070 6.168331
## [2081] 5.736470 5.789444 6.900576 6.122735 6.675533 6.107894 5.931948 6.366022
## [2089] 5.766674 6.261292 5.391160 5.935560 6.404235 5.874789 5.770411 6.354339
## [2097] 6.431110 6.276902 5.194196 6.047666 5.637092 6.367971 5.754461 6.971132
## [2105] 5.939569 6.628683 4.942770 6.768580 5.506197 6.700347 5.584835 6.814804
## [2113] 6.069435 6.536016 6.143968 7.041044 6.000886 5.942803 6.285368 6.529781
## [2121] 6.238794 6.144274 5.847420 6.252019 5.629492 5.521336 5.825667 6.478909
## [2129] 6.476398 5.819335 5.796621 5.818186 6.449053 5.937297 5.179126 4.796652
## [2137] 6.411000 5.116333 5.977007 5.300930 6.551677 6.333690 6.234721 6.088557
## [2145] 6.037360 5.948775 5.439585 6.402102 5.490881 6.830113 6.382306 5.788400
## [2153] 6.652607 6.423176 6.234721 6.424133 5.603779 5.629492 5.965743 6.079753
## [2161] 5.392368 6.950824 5.868955 6.363011 6.183297 6.912170 5.380874 3.760368
## [2169] 5.232450 5.996678 6.215804 6.077360 6.078661 5.792220 6.286649 5.870669
## [2177] 6.776115 7.048784 4.810976 5.596789 5.451145 5.351912 5.669016 5.432712
## [2185] 5.942803 5.915359 6.181572 6.509502 6.227700 5.327120 5.968834 6.527349
## [2193] 6.474272 5.554148 6.303041 6.436450 5.884827 4.727443 6.097654 5.713066
## [2201] 6.841422 5.676408 5.988961 6.766237 6.297357 6.547944 6.135634 5.510474
## [2209] 6.593162 6.739326 6.009746 6.843195 6.643822 6.178567 6.668096 5.246356
## [2217] 5.995248 5.941429 6.986099 6.758137 5.948513 6.299153 6.693315 6.131419
## [2225] 5.935560 6.754660 6.232642 6.718353 6.252019 5.792134 6.344657 5.677170
## [2233] 5.853933 6.044627 6.502420 6.380297 5.941057 5.937297 6.215334 5.796621
## [2241] 5.557929 6.589410 6.971199 6.545646 5.347785 6.650365 6.048857 5.769968
## [2249] 5.941057 5.608418 5.345696 6.331429 6.098627 6.187667 6.791104 5.492242
## [2257] 5.880841 7.170064 6.027020 5.680824 6.190397 5.231908 5.697151 5.716340
## [2265] 4.965838 5.730662 5.825649 5.792220 6.432373 5.944745 6.639982 6.240390
## [2273] 5.014601 5.848608 6.092773 5.786924 6.784050 6.503454 5.705522 7.132998
## [2281] 6.589410 6.426674 6.458144 6.316585 6.371099 6.567424 6.135317 4.793961
## [2289] 5.740334 5.536973 5.093723 6.139421 5.450893 6.551677 6.400483 5.750028
## [2297] 6.444780 6.163844 5.896071 6.573453 5.477467 5.575671 5.927866 6.306795
## [2305] 6.097654 5.669016 6.060871 5.965418 5.840640 5.290936 6.814804 5.863896
## [2313] 5.840606 6.068083 5.811929 6.096044 5.768243 6.397898 5.508823 6.063942
## [2321] 5.596361 5.642461 6.367326 6.222249 6.449159 5.522080 5.897181 6.316561
## [2329] 5.508351 6.643822 6.013421 7.409263 5.754153 5.579724 6.213637 6.600794
## [2337] 6.426674 6.295781 6.145417 6.647055 5.791302 6.270324 6.571206 6.448947
## [2345] 6.369517 6.455049 6.333690 6.076039 5.505261 5.888597 5.608365 5.269447
## [2353] 6.865434 5.313314 6.263580 5.548214 6.029372 6.025927 6.645075 5.867927
## [2361] 5.521282 5.331045 6.255881 5.791852 5.206172 5.246637 5.505041 4.942770
## [2369] 6.271668 4.957668 6.507682 5.646315 5.112325 6.273357 6.275173 6.152076
## [2377] 6.431110 6.115245 6.308497 5.776404 5.903047 6.892781 5.509053 5.783044
## [2385] 5.404671 6.017722 6.123150 5.830255 6.385282 5.197270 6.235184 5.728335
## [2393] 5.873009 6.410690 6.427994 6.254836 5.083065 6.270324 5.750916 6.256099
## [2401] 6.238794 6.244570 6.675114 5.810637 7.141132 6.115602 6.106235 6.273357
## [2409] 6.326122 6.032241 5.286052 6.533860 5.621472 6.210177 5.267240 6.147884
## [2417] 6.429789 6.262504 7.812772 5.510807 6.485182 5.908790 7.129263 6.405824
## [2425] 4.529993 5.902594 6.976829 6.523608 6.087594 6.815002 6.002152 5.658907
## [2433] 7.141132 5.206180 5.927359 6.099149 6.382306 6.077360 5.631848 5.769836
## [2441] 6.364968 6.268298 5.194196 5.193693 6.297357 6.571339 6.650365 6.381237
## [2449] 6.103912 5.504664 6.115602 5.913139 5.166687 6.932344 6.186181 5.350981
## [2457] 5.579653 6.331429 6.840781 5.811093 6.326122 5.199458 5.201981 6.021518
## [2465] 6.627440 6.474272 6.638298 6.051460 6.782009 5.303101 6.403296 6.298372
## [2473] 6.339768 6.091271 5.956796 6.327738 6.342496 6.060319 6.892781 6.442437
## [2481] 5.658131 5.646315 6.443471 6.053151 6.477416 5.846879 5.353242 6.421073
## [2489] 6.655811 6.081134 5.670145 6.123528 5.783312 6.093254 6.251831 5.466301
## [2497] 6.367971 6.314414 6.332442 6.190364 6.087594 6.693315 6.355932 6.063611
## [2505] 5.996415 6.312133 6.923340 6.501183 6.149860 7.130328 5.508823 6.081173
## [2513] 6.213937 5.405662 6.190490 6.773821 4.977491 6.133466 5.427691 6.725060
## [2521] 5.681288 5.579248 5.574537 4.337502 5.851047 6.718445 6.485653 6.252735
## [2529] 5.428302 6.739326 5.510474 6.726368 6.249431 5.988961 5.564533 6.249431
## [2537] 6.758137 5.759043 5.672963 6.557606 6.880951 6.210177 5.783312 6.206900
## [2545] 5.791852 6.013158 5.519669 6.093582 5.917874 6.673635 6.102687 5.769587
## [2553] 6.015685 6.425300 6.159121 6.718445 5.622779 5.697151 6.762272 5.840606
## [2561] 6.069435 6.387808 6.062853 6.896313 5.810847 6.400483 6.002544 5.468245
## [2569] 5.297128 5.974692 6.081134 6.079753 5.579724 6.060319 6.067222 6.417582
## [2577] 6.871159 5.976384 6.268667 6.628683 6.204981 5.891347 5.783138 6.724969
## [2585] 6.114870 6.160313 6.073113 5.275005 6.383558 6.314414 5.714539 5.034378
## [2593] 6.333772 6.130151 6.659931 6.051598 6.571206 5.584835 6.106587 6.449159
## [2601] 5.496454 5.768413 5.840208 5.613737 5.247008 6.285668 6.052374 6.563792
## [2609] 6.065244 6.488029 6.120920 6.095977 6.072310 6.192072 6.655811 5.399271
## [2617] 7.019538 5.372206 5.411244 5.867360 6.362618 5.912977 5.447482 6.172716
## [2625] 5.148675 6.281909 7.010823 5.897119 5.983178 6.423176 5.699011 5.360553
## [2633] 5.903047 6.274795 6.100741 5.607136 6.595545 6.111152 6.383558 5.158660
## [2641] 7.354266 6.785681 6.013158 6.164824 5.844409 6.233238 6.865434 5.521336
## [2649] 5.191208 6.432373 5.445003 5.662413 5.396155 6.401760 6.507682 6.430005
## [2657] 5.814644 5.883296 6.685645 5.317896 5.911478 6.357593 6.312133 5.818442
## [2665] 6.070183 6.600794 6.676965 6.430005 5.468710 5.777030 6.105889 6.287406
## [2673] 5.683487 5.584186 6.241587 5.860118 5.577477 6.718353 5.535200 6.971199
## [2681] 6.782773 7.337850 5.474613 5.716927 6.388052 6.557532 6.227700 5.625792
## [2689] 6.053151 6.240347 5.774203 5.866525 6.323050 5.997265 6.497544 6.673635
## [2697] 6.069705 6.370788 5.016702 5.330006 6.889648 7.069136 6.147246 6.044915
## [2705] 6.424133 6.187667 7.023794 6.971132 6.759473 6.018800 6.172716 5.218941
## [2713] 6.547021 6.831941 5.766262 6.477416 5.818186 5.322463 6.478524 6.442437
## [2721] 3.760368 5.848608 5.822903 5.459207 6.151473 5.298746 6.292618 5.396155
## [2729] 6.225342 5.308062 6.027020 5.411092 6.880951 6.184056 5.892801 4.855647
## [2737] 5.970107 7.812772 7.189968 6.029372 6.843195 5.550217 5.584879 6.333772
## [2745] 5.583251 6.420177 5.125496 6.403296 6.078661 6.673819 6.147884 6.227578
## [2753] 5.996400 5.518806 5.897181 6.055137 6.342496 6.227578 6.855284 7.024361
## [2761] 6.533860 6.384992 6.222249 6.814367 6.105889 6.659931 6.063942 6.223522
## [2769] 6.640280 5.180746 5.148967 6.640632 5.406731 6.568985 5.782974 6.612369
## [2777] 6.735940 5.186220 6.224679 6.241587 6.259383 5.223411 6.675114 6.534234
## [2785] 6.198559 6.355932 5.836978 6.697858 5.576390 5.990872 6.106587 7.681524
## [2793] 6.574959 6.715065 6.213637 5.513503 6.244570 6.000886 6.098627 6.645075
## [2801] 5.778391 6.171711 6.382134 5.825649 6.148576 5.883568 6.263580 5.560241
## [2809] 6.021518 4.600080 5.737571 5.513260 5.321256 6.889204 7.081661 6.754970
## [2817] 5.196353 5.921682 6.164824 5.941429 5.716340 5.727751 6.726368 5.617964
## [2825] 5.958333 6.627728 5.467870 7.184560 6.814367 6.509502 6.415179 6.896313
## [2833] 5.714698 6.048935 5.948513 5.274913 5.574537 5.783463 5.958671 5.443913
## [2841] 5.695280 5.542036 5.272424 5.553702 6.373376 6.701070 5.818498 5.892801
## [2849] 6.529594 6.343347 5.878773 7.021666 6.286649 6.183514 6.120920 6.190364
## [2857] 6.095977 6.134989 6.781940 4.964613 5.150701 5.979678 6.848663 5.306419
## [2865] 5.840208 7.021666 5.881883 6.329837 6.437056 7.010823 6.436450 6.427994
## [2873] 6.555779 6.523608 5.753721 5.523232 6.017485 6.153613 5.550737 6.155247
## [2881] 6.193349 6.768614 5.236393 5.852081 6.692574 5.520037 5.826201 5.927934
## [2889] 6.241876 7.133795 5.583396 5.500404 6.276902 5.726351 6.873295 5.379508
## [2897] 6.268667 5.706299 6.773821 6.735940 6.415179 6.503454 6.503066 5.608365
## [2905] 5.212013 6.373098 6.627777 5.399513 6.215804 6.501183 5.313314 6.206968
## [2913] 6.896803 6.595545 5.460633 5.614534 6.420177 5.282123 5.173817 6.403217
## [2921] 5.843150 6.458144 5.702632 5.832315 6.209363 5.697700 5.901809 6.529781
## [2929] 6.072310 5.138382 5.637092 5.511364 6.746984 5.139240 6.198984 6.186181
## [2937] 7.645396 6.131419 5.974692 6.085563 5.925347 6.111152 5.650386 6.139421
## [2945] 5.867927 6.360097 6.417212 6.512878 5.857616 6.532426 6.062035 5.977239
## [2953] 7.122899 6.834931 6.092773 6.639982 5.730662 5.811093 6.652607 6.191110
## [2961] 5.769836 5.855979 6.073375 5.944745 6.160313 6.373376 5.720022 6.557532
## [2969] 5.736854 6.392040 6.156317 5.725791 5.976103 5.340686 6.166384 6.163844
## [2977] 6.896803 6.186972 5.972165 6.664303 6.139882 6.468944 7.184560 6.292174
## [2985] 6.281909 5.794186 6.080861 5.454716 6.673819 5.198029 5.939569 6.568985
## [2993] 6.225417 6.647055 5.973927 6.455049 6.640280 5.534173 5.938841 6.561662
## [3001] 5.397228 6.516658 6.226136 5.825667 5.788400 6.100741 6.148576 6.298784
## [3009] 6.017485 6.986099 5.166694 6.375890 6.502420 5.517681 5.579233 6.298784
## [3017] 5.438631 6.576053 6.448947 6.547982 5.527568 6.437056 6.023774 6.932344
## [3025] 6.055137 6.204981 5.672963 6.527349 5.234284 6.664303 5.560241 6.069705
## [3033] 5.823468 6.598540 6.425300 6.018800 5.115899 6.984355 6.122659 6.388052
## [3041] 6.256099 6.113833 6.874236 5.822903 6.653539 6.444780 6.037360 6.848663
## [3049] 5.679791 6.285368 6.380297 5.995901 6.181572 5.468272 5.329381 5.410879
## [3057] 5.828708 6.362618 6.177086 6.534234 6.561662 6.062853 5.836978 5.468245
## [3065] 5.713294 6.768614 5.944727 5.573474 6.571339 6.427199 6.798413 6.143968
## [3073] 6.297514 5.579301 5.622779 5.658419 5.580311 5.681288 6.984355 6.285294
## [3081] 6.153613 6.374023 7.681524 5.087723 6.277102 6.303041 7.081661 6.190490
## [3089] 6.431669 6.193557 6.476398 5.754461 5.779803 6.106235 6.745583 6.081173
## [3097] 5.883296 6.155757 6.130151 6.159121 5.229732 5.592950 7.184640 5.298575
## [3105] 5.583236 5.288885 5.625766 6.074235 6.802998 6.692574 5.960458 6.923340
## [3113] 6.255519 5.816343 6.122659 5.187986 5.802415 5.638835 5.318295 6.815002
## [3121] 5.523232 5.986511 6.536016 5.750028 5.754153 6.840781 6.737551 6.204588
## [3129] 6.672861 5.898724 5.497153 4.709025 7.189968 6.576053 6.057801 6.217686
## [3137] 5.811447 5.877551 5.584433 6.529594 6.268298 6.107525 6.103912 5.581073
## [3145] 6.151473 6.766237 4.974582 6.323050 6.191110 6.532426 6.781940 5.118144
## [3153] 6.607213 5.680407 7.078552 5.977239 6.465578 6.099149 5.512858 5.852081
## [3161] 6.255881 6.582549 6.818169 6.431669 7.122899 5.818442 6.060995 6.427199
## [3169] 5.777030 6.186972 6.285064 6.065003 5.944727 5.524350 6.060871 6.159055
## [3177] 5.810964 6.366240 6.047666 6.049262 6.067135 4.977491 5.774203 6.798413
## [3185] 6.309210 5.584186 6.354339 6.277221 6.031265 6.051598 7.414456 7.078552
## [3193] 6.015685 6.443471 6.022123 6.235184 5.817258 6.584972 6.818169 5.094240
## [3201] 6.261916 6.057801 6.435304 6.364968 6.831941 5.129711 6.225417 6.259736
## [3209] 6.107525 6.547944 6.175237 6.248801 5.969423 6.582549 6.404235 5.905767
## [3217] 5.618133 6.293713 7.041044 6.873295 6.373839 5.815788 6.193557 5.803851
## [3225] 7.170064 5.915359 6.262504 5.140874 5.483198 5.975342 5.414046 6.874236
## [3233] 5.707695 5.019177 6.184056 6.123528 5.561769 5.662033 6.600876 6.293713
## [3241] 7.024361 6.468944 6.711844 7.092445 6.950824 5.426654 5.960458 6.335912
## [3249] 6.004440 6.592073 6.889648 7.019538 6.204588 6.924668 6.871159 5.642332
## [3257] 6.754660 6.123150 6.059098 6.391918 6.003795 6.411000 5.864491 6.460516
## [3265] 5.864525 5.981917 5.214457 6.093254 6.391918 6.547021 6.512878 6.737551
## [3273] 6.207780 6.031055 5.788269 6.769667 5.874789 6.624951 5.860118 6.208341
## [3281] 6.266553 6.068083 6.113833 5.255074 6.678262 5.844409 5.296426 6.769079
## [3289] 6.163657 6.144274 6.360097 6.746984 6.665806 5.268681 5.913139 6.134989
## [3297] 6.627440 7.294876 6.435304 5.850935 6.221493 5.727751 5.958333 5.826647
## [3305] 6.261916 5.901257 6.044915 6.745583 7.069136 5.558052 5.759542 6.574193
## [3313] 5.853933 4.543863 6.005285 6.516658 6.248622 6.032950 6.155247 6.607213
## [3321] 6.024859 5.853920 5.954234 5.867360 6.178567 6.096044 5.397228 6.002152
## [3329] 5.091897 6.384992 6.062035 6.072190 6.097188 6.285668 6.450170 5.321256
## [3337] 6.387808 4.903054 6.417582 5.494877 5.913790 6.478909 5.570281 6.924668
## [3345] 7.132998 6.678262 5.562722 6.232642 6.400066 5.347774 7.184640 5.680102
## [3353] 6.061072 6.462822 6.240390 5.413951 4.719888 6.299153 6.478524 6.450170
## [3361] 7.409263 5.917874 4.984699 5.506197 6.343347 5.180615 6.286310 6.312406
## [3369] 5.136315 6.329837 5.706871 7.337850 6.624951 6.024196 6.612369 6.251831
## [3377] 6.158051 5.550208 6.273085 6.725060 6.273085 7.012733 5.586211 5.553081
## [3385] 5.853802 5.283675 6.507718 5.551712 7.012733 6.676965 6.468549 6.685645
## [3393] 4.948497 5.977007 7.048784 6.292618 6.031265 6.555779 6.286310 6.448188
## [3401] 6.382134 6.640632 6.023774 6.400066 6.297514 6.183297 6.025927 6.593162
## [3409] 5.769968 5.197035 6.347246 6.627777 5.996400 6.598540 6.266553 6.298372
## [3417] 6.723673 6.121011 6.449053 6.308497 6.403217 6.375890 5.524798 6.823901
## [3425] 6.022123 5.967847 6.184962 5.770411 6.463122 6.638298 6.700347 6.102687
## [3433] 6.041360 6.032950 6.085563 5.811929 6.049364 5.744557 5.479070 6.429789
## [3441] 5.826647 6.507718 6.277102 5.847420 6.784050 5.866721 6.567424 6.080861
## [3449] 6.040683 5.997265 6.488029 6.724969 6.462822 5.720022 6.184962 6.715065
## [3457] 5.817258 5.768677 5.514925 6.207134 5.392681 6.545646 5.795077 5.695469
## [3465] 6.672861 6.287256 6.175448 6.174363 5.227141 6.163657 5.878773 5.898463
## [3473] 6.091625 5.580311 5.344699 6.889204 6.116603 6.824559 6.371099 5.316129
## [3481] 7.023794 6.252735 5.970107 7.414456 6.759473 6.584972 6.802998 6.248622
## [3489] 6.855284 6.006912 6.768580 5.399271 5.975342 6.190397 5.745353 6.417212
## [3497] 6.013421 5.885124 6.103335 6.149860 6.824559 6.067135 6.134943 5.824021
## [3505] 5.767310 6.274795 5.890446 6.309210 5.296513 5.799890 5.597622 5.344283
## [3513] 5.667872 6.335912 5.953433 5.294059 5.682238 5.832315 5.333589 6.060995
## [3521] 5.897119 5.728335 5.380973 5.674196 6.557606 6.602468 6.392933 6.830113
## [3529] 5.964967 5.773781 6.327738 5.651645 5.968834 6.255519 6.112511 5.577215
## [3537] 7.645396 5.883568 7.294876 5.204820 5.738062 5.559543 6.485653 6.225342
## [3545] 6.135317 6.154506 6.754970 6.268672 6.285294 6.995179 6.493042 6.782773
## [3553] 6.065244 6.072190 6.617743 5.821968 6.590764 5.992568 6.798189 6.020733
## [3561] 6.244171 6.392040 5.828708 5.542036 6.823901 6.580510 7.354266 6.091625
## [3569] 6.682710 6.316585 6.207780 6.374023 6.719006 5.927934 5.913966 5.750916
## [3577] 6.036597 6.760067 6.769667 6.889414 6.117719 6.145052 5.594205 6.650522
## [3585] 6.144359 5.546877 4.572528 6.518276 6.358356 5.612540 6.079535 6.221324
## [3593] 6.330384 6.274282 5.560844 6.904016 6.280843 5.378378 6.727827 6.182993
## [3601] 6.192303 6.373686 5.832463 6.052881 5.989665 6.250941 6.713759 6.818137
## [3609] 6.274282 6.990949 6.077715 6.582114 5.694383 6.902413 6.745902 6.079535
## [3617] 6.697946 6.636975 5.182360 5.870084 5.261544 6.786711 5.842645 6.780022
## [3625] 6.096572 5.836634 6.889689 6.765971 6.072207 6.639662 6.356631 7.421711
## [3633] 5.333089 6.495037 4.982324 5.677202 5.178050 5.468481 5.253022 6.802510
## [3641] 6.627181 5.929444 5.981529 6.484931 6.886390 5.994036 6.517543 6.371603
## [3649] 6.241163 6.623308 6.697946 6.357452 6.647900 5.487300 6.138824 5.405066
## [3657] 6.683702 5.804800 6.919119 5.893253 5.813752 6.250318 6.993820 5.453261
## [3665] 5.870084 5.859406 6.401985 5.969228 6.082173 5.475440 6.605364 5.521220
## [3673] 4.646545 7.461358 6.937309 5.225783 6.043633 6.518276 5.706240 5.981529
## [3681] 6.452123 6.379043 6.703708 6.283042 6.399137 5.637336 5.032687 6.873686
## [3689] 6.379862 6.825566 6.374799 5.981083 5.275959 6.649469 5.658412 6.144359
## [3697] 6.381515 7.073045 6.283216 6.484945 5.631110 5.323768 6.847302 6.629906
## [3705] 6.344035 5.405066 5.737440 5.525103 6.001003 6.462663 6.696763 6.379043
## [3713] 5.567053 6.829015 6.703708 6.372589 5.940709 6.392310 5.847735 6.118106
## [3721] 6.308764 6.620875 6.073951 6.476950 6.593552 5.693364 6.233514 6.484847
## [3729] 6.197471 6.093235 6.593351 6.299601 6.615163 6.021286 5.852943 6.623685
## [3737] 6.088221 6.780022 6.618119 6.442815 6.348929 6.618603 5.072697 5.768918
## [3745] 6.035459 6.117323 5.730892 6.727827 6.388937 6.025975 5.760827 6.275364
## [3753] 6.283692 5.206077 6.961420 6.658111 5.688153 6.064893 6.056730 6.379862
## [3761] 5.473787 5.032687 6.819023 6.304102 6.805985 6.372589 5.594205 6.613985
## [3769] 6.099550 6.296588 6.175670 5.811102 6.215382 7.554268 6.096572 6.344035
## [3777] 6.296588 6.118106 6.189830 5.989665 6.195011 6.399980 5.842645 5.565072
## [3785] 5.691719 5.198101 5.225823 5.597565 5.940709 6.462663 6.396463 6.327768
## [3793] 6.415883 7.479608 6.568479 5.871538 6.025302 5.637336 7.176060 6.041894
## [3801] 5.893253 6.304102 6.209645 5.827308 6.476950 6.466843 6.544067 6.647900
## [3809] 6.399423 6.766125 6.472869 6.327672 5.868933 6.113751 6.671006 6.700828
## [3817] 6.283616 6.219471 6.699153 5.810094 6.047626 6.299601 5.691719 6.333141
## [3825] 6.435428 6.470131 5.378378 6.568479 6.283616 6.700828 5.997395 6.712982
## [3833] 6.443313 5.936995 6.343967 6.472038 4.986254 5.813829 5.831031 6.623685
## [3841] 5.735009 6.561510 6.420680 6.484945 6.387506 6.945865 5.626501 6.034252
## [3849] 6.316482 5.532534 4.977290 6.520250 5.124298 6.937309 5.607714 5.737440
## [3857] 5.658412 6.474844 6.333141 5.161668 6.459502 6.393546 6.993820 6.282292
## [3865] 6.076051 6.327938 6.802510 6.730227 5.641706 6.972827 6.671006 6.021484
## [3873] 7.221788 6.902413 5.310736 5.973851 5.598291 6.327768 5.991013 6.582114
## [3881] 6.034252 6.883168 6.605364 6.723515 6.278242 5.323768 5.447994 4.694913
## [3889] 4.770108 6.658111 6.818137 4.957749 7.078981 6.910005 6.825566 5.501597
## [3897] 6.133914 5.956389 4.913823 6.626815 6.683702 6.817339 6.041894 5.417596
## [3905] 5.659205 6.756162 6.494088 6.300677 6.308925 5.721448 4.709962 6.066147
## [3913] 5.136193 6.275364 6.829015 6.435428 6.346462 6.462487 5.888958 4.858944
## [3921] 6.889689 4.723912 6.033303 6.470131 7.244802 5.517787 5.032297 6.495037
## [3929] 6.282292 6.099137 6.776706 6.544067 6.817339 6.972827 5.583887 6.776706
## [3937] 6.566469 5.181912 5.953182 6.221324 5.846831 6.705764 6.000690 6.883168
## [3945] 7.452575 4.902503 6.280843 6.484893 6.072207 5.973851 6.696763 6.290614
## [3953] 6.819023 6.358356 6.990949 6.626512 6.157442 5.833856 4.942256 5.809930
## [3961] 6.040212 5.583887 7.073045 5.981083 6.257447 6.593351 6.249592 4.787818
## [3969] 3.564336 5.607714 6.132835 6.299977 6.033303 5.609891 5.121121 7.176060
## [3977] 7.513036 6.853880 6.796788 5.805637 5.768918 4.913823 5.045632 6.415883
## [3985] 6.618603 6.399423 5.595805 6.482358 6.756162 6.520250 6.052881 6.117323
## [3993] 6.356631 7.112110 5.659205 6.381515 6.618127 6.672613 6.036733 6.620875
## [4001] 7.078981 6.012304 6.388937 5.699787 6.902276 6.215382 5.728390 5.956389
## [4009] 5.016192 6.197471 5.384618 6.358332 6.487459 6.299977 6.713308 6.602361
## [4017] 6.225296 5.206077 5.969228 6.766125 6.295798 6.087653 5.054531 5.991013
## [4025] 5.888912 6.765665 6.566469 6.347562 6.330384 5.444418 6.890820 6.786711
## [4033] 7.513036 5.284812 6.626512 6.649469 5.872930 6.104507 6.076051 5.990451
## [4041] 6.705764 6.001003 4.957749 5.147418 6.730227 6.636975 6.125279 6.873686
## [4049] 6.148858 6.300677 5.871538 6.942510 5.559113 6.496342 6.308925 6.815593
## [4057] 6.443313 6.138824 6.128451 5.728390 6.203583 6.949550 5.987086 6.056730
## [4065] 6.560836 6.295798 6.488613 7.452575 5.775100 5.560844 6.117719 6.187848
## [4073] 6.036733 6.084097 5.802270 6.278242 6.805985 6.257447 6.102003 6.125450
## [4081] 6.901679 5.805637 5.929444 6.132835 6.123136 5.558362 6.961420 6.188772
## [4089] 6.639662 6.462487 6.077715 6.399137 6.966327 7.243814 5.072697 6.387506
## [4097] 6.290614 4.781644 6.124085 6.650522 5.494889 6.012304 5.438619 6.125279
## [4105] 6.021286 6.626993 5.165854 6.458424 6.093235 5.832896 7.421711 6.080887
## [4113] 5.672980 6.316482 6.209645 6.484931 6.815593 6.886390 6.517543 6.910005
## [4121] 6.346462 6.146334 6.713759 5.065969 6.713308 6.656312 6.232274 4.759342
## [4129] 5.888958 6.080887 6.560836 6.283042 6.172255 6.617146 6.148858 6.147316
## [4137] 6.175670 6.254211 6.558360 5.455524 5.547409 6.147316 6.627181 6.125450
## [4145] 6.647773 6.949550 6.766774 6.087653 6.618127 5.884468 6.845536 6.765971
## [4153] 6.647773 6.602802 6.221838 6.017613 5.987086 6.358332 6.228130 5.702874
## [4161] 6.613985 4.964766 6.000934 6.214879 5.551748 5.892316 6.618119 6.225296
## [4169] 5.875103 5.813752 6.128451 6.765665 6.602802 6.241163 4.998356 6.883947
## [4177] 6.903137 4.787398 5.002800 6.901679 5.836634 5.040371 7.479608 6.626815
## [4185] 6.593552 6.269352 6.189830 7.516928 5.990451 5.197538 6.903137 6.845536
## [4193] 6.484893 6.219471 4.781644 5.182360 6.945865 7.112110 5.449441 5.936995
## [4201] 6.099137 5.434875 6.357452 5.223354 5.693364 5.586047 6.466843 6.396463
## [4209] 5.997395 6.566762 6.220478 5.832463 6.043633 6.966327 6.443104 6.836318
## [4217] 4.461420 6.494088 6.474844 6.904016 5.188185 5.220662 6.025302 5.888912
## [4225] 6.471708 6.000359 6.699153 6.308764 5.751358 6.957760 6.106427 6.066147
## [4233] 6.472869 5.368662 6.826001 6.494390 6.571494 6.548316 4.642988 6.105135
## [4241] 6.035459 6.626993 6.064893 5.465354 6.157442 6.617146 6.124085 6.957760
## [4249] 5.953182 5.250822 6.847302 5.677202 5.846831 5.813829 6.484847 7.221788
## [4257] 5.872930 6.420680 4.572528 5.810094 6.220478 6.373686 6.571494 5.546877
## [4265] 6.347562 6.452123 6.672613 6.123136 6.343967 5.559113 6.472038 6.228130
## [4273] 6.021484 6.942510 6.348929 6.458424 6.726381 6.074347 6.454627 6.853893
## [4281] 6.553748 5.846518 6.075125 5.513242 6.203108 3.849641 6.204607 7.423502
## [4289] 6.385303 6.214046 6.002971 6.342637 5.441341 6.278245 6.503773 6.262501
## [4297] 6.259454 5.754172 6.133942 6.906797 6.442297 5.632685 5.829954 6.108664
## [4305] 6.368466 6.410452 5.406096 6.310372 5.489406 6.400396 6.046968 4.908738
## [4313] 3.922675 6.032471 6.594049 6.387440 6.108664 6.113532 7.687605 6.109495
## [4321] 6.383398 6.829386 6.725937 6.128232 4.855364 5.003094 6.405804 6.288420
## [4329] 5.818615 5.796108 6.147211 6.568128 5.965715 6.309386 5.623963 6.365639
## [4337] 6.316015 6.214046 7.020003 5.610217 5.455948 7.047310 4.861775 4.718390
## [4345] 6.186539 6.805135 7.065369 3.718206 6.490518 6.893877 5.619240 6.675599
## [4353] 6.032471 6.342720 6.342999 6.142907 5.975511 5.819921 7.145052 5.636870
## [4361] 6.478389 4.651326 6.707758 4.738724 7.085423 6.538274 5.508190 6.642354
## [4369] 6.407865 5.849448 5.788686 7.412753 6.072577 6.689788 5.827198 6.443562
## [4377] 7.412753 6.205495 6.181426 6.108994 6.815881 5.609566 6.207890 6.464110
## [4385] 6.265252 5.640099 6.703537 6.211207 6.464884 6.741756 6.123572 6.219082
## [4393] 6.191527 6.392511 5.631116 6.206950 6.650204 5.741123 6.270276 5.338865
## [4401] 6.091149 6.482034 5.133641 6.216429 5.665593 6.005732 5.864397 5.728417
## [4409] 5.663322 5.558003 5.769446 5.875229 6.313029 6.181169 6.315359 6.005677
## [4417] 6.748011 6.943198 6.581449 5.653698 5.791570 6.288420 5.884933 5.682787
## [4425] 5.759193 5.989565 6.862711 6.506503 6.275670 5.572156 5.740097 5.826443
## [4433] 5.533136 5.525712 6.051732 6.205363 6.532295 7.265111 6.005732 5.985831
## [4441] 6.492926 5.868661 5.344507 6.292506 5.764437 5.777789 6.651178 5.711699
## [4449] 5.602469 6.306835 6.404468 6.932943 6.456723 5.786160 6.052019 6.754240
## [4457] 6.748011 6.581449 5.724418 6.204933 6.008958 5.399751 6.182285 6.777716
## [4465] 6.684346 6.494963 6.009275 6.794834 6.915437 6.199717 5.641406 6.100688
## [4473] 6.091566 5.790073 6.976693 7.317629 5.417930 7.018850 5.640099 6.726722
## [4481] 6.627190 5.720306 7.423502 6.032225 6.762337 6.398266 6.597202 6.911049
## [4489] 5.956653 5.760700 6.881047 5.447613 7.085930 6.836492 5.670964 6.000537
## [4497] 6.243582 5.888453 4.510186 6.883244 5.331520 6.495338 6.207755 6.216429
## [4505] 6.000537 6.519786 6.961897 6.801637 6.120627 6.293443 6.937818 5.694501
## [4513] 6.726381 6.462700 5.814089 6.223422 6.915437 5.998457 5.369720 7.214361
## [4521] 6.042815 5.893273 5.993723 6.161416 6.487490 5.859390 6.059822 6.308953
## [4529] 6.707758 5.055341 6.330398 5.138454 6.460382 6.752154 6.236623 6.769118
## [4537] 6.718876 6.464703 5.781675 5.652976 5.884481 5.278290 6.369651 6.668501
## [4545] 6.304928 4.878013 5.421423 6.396859 5.914650 6.570272 6.232713 5.864133
## [4553] 5.059277 5.918444 6.331098 5.577203 6.204933 6.241233 5.672271 6.378107
## [4561] 6.004928 5.741123 6.358484 7.687605 6.316015 6.024889 6.243582 7.047310
## [4569] 5.831198 6.589580 5.514534 5.746255 5.368417 6.229418 6.721915 5.518908
## [4577] 6.572185 6.881047 6.233415 6.447716 6.767674 6.310426 6.642354 5.050798
## [4585] 6.585009 6.553494 6.621163 5.733624 6.469712 6.686868 6.013981 5.435655
## [4593] 5.825486 6.334226 5.041570 5.174249 5.385915 6.112525 5.622023 6.354728
## [4601] 6.733146 6.530742 5.630599 6.057698 6.146103 6.635648 6.678804 6.260673
## [4609] 6.420860 6.687989 6.232528 6.552059 6.767104 6.202502 6.281322 6.214659
## [4617] 6.283032 6.726722 6.440492 5.874245 6.223529 6.254608 6.747277 6.262501
## [4625] 6.650542 5.779776 7.491639 6.199717 6.316849 6.389458 5.989565 6.374087
## [4633] 6.148250 5.825486 6.658377 6.135922 6.635504 6.833919 6.232528 7.317629
## [4641] 5.357418 6.186955 5.746255 6.531793 6.290960 6.003554 5.340408 6.329353
## [4649] 7.145052 5.266137 6.119797 6.281322 6.039161 6.134920 5.489236 5.681429
## [4657] 6.460382 6.631265 6.368949 6.604019 5.868661 6.055664 5.577203 6.139549
## [4665] 6.997422 6.527859 6.490573 5.700330 6.684962 7.247071 6.307041 6.380078
## [4673] 6.568810 5.332864 6.902286 5.964705 5.671612 6.668501 5.832985 6.501153
## [4681] 5.786329 6.215309 6.682593 6.191008 6.572185 6.685306 5.657351 6.320966
## [4689] 5.807488 6.564351 6.197455 6.303923 6.203066 5.858795 5.883833 6.260673
## [4697] 5.640481 6.925889 5.767785 6.635648 6.398266 6.030287 6.580344 6.432431
## [4705] 6.430909 6.214733 7.223950 6.313585 6.035361 6.166768 6.205009 5.475343
## [4713] 6.605336 5.884245 6.282672 6.596091 5.927133 6.823650 5.218235 6.527927
## [4721] 6.842306 6.394337 5.543164 6.245687 5.897455 6.278245 6.089961 6.597942
## [4729] 6.814059 5.104037 6.043233 5.970791 6.695844 6.306722 5.245616 6.932756
## [4737] 6.375849 6.207890 6.478340 6.375849 5.799029 6.368466 6.932943 6.568128
## [4745] 6.718876 6.627190 6.179256 6.142008 5.913037 6.387310 6.310426 6.122399
## [4753] 6.491653 6.501775 5.941316 6.398954 6.571488 5.936459 6.611110 6.436809
## [4761] 6.059822 6.420860 6.312541 5.315892 6.464633 6.316849 6.365639 5.781675
## [4769] 6.284922 5.227823 5.923256 6.605509 5.654452 6.196422 6.464884 5.929786
## [4777] 6.478340 6.503773 5.887769 6.348496 6.330398 6.628169 5.784616 5.814028
## [4785] 6.458233 6.129362 5.740898 6.052219 6.682593 6.182285 5.696797 7.457765
## [4793] 6.725937 6.394337 6.863969 7.088932 6.807799 6.610482 6.283032 5.913037
## [4801] 5.354645 6.162083 6.506503 6.085551 6.591444 5.864397 6.686550 5.249902
## [4809] 6.106678 6.091905 6.769118 6.763332 5.611694 6.271912 5.576842 6.166768
## [4817] 7.457765 6.612605 6.721915 6.009720 6.501775 7.500821 5.148944 5.784164
## [4825] 6.331098 6.902286 6.641089 6.279512 4.645448 5.558003 5.894720 6.571488
## [4833] 5.761495 5.450515 5.911048 6.736860 5.410770 5.998275 5.437465 5.689140
## [4841] 6.997422 5.690948 5.770742 6.527859 6.149184 5.508023 5.587354 6.088257
## [4849] 6.601876 5.697161 6.191527 6.932756 6.054471 6.246246 6.925889 6.407740
## [4857] 6.443562 6.487008 6.934921 5.905514 7.066153 5.605709 5.406000 6.068580
## [4865] 5.245272 6.339435 6.039579 6.921632 5.741113 6.862711 6.305791 6.358484
## [4873] 6.148250 6.304928 6.583645 6.814059 7.062426 6.315976 6.163990 5.978923
## [4881] 6.441485 4.754684 6.970909 3.987653 6.392511 5.001873 5.671455 6.236623
## [4889] 6.278331 6.211207 5.510676 5.439086 6.065274 6.161416 7.491639 6.332204
## [4897] 6.027634 5.600303 6.394513 6.815881 6.760919 5.661018 5.849448 5.324693
## [4905] 6.463422 5.513973 6.631265 5.603911 6.172103 6.823650 5.657825 6.454627
## [4913] 5.998275 4.383608 6.628169 6.833919 5.431789 6.796570 6.490045 5.827946
## [4921] 5.897455 5.808689 6.219082 6.847577 6.257449 5.528554 7.173389 6.205009
## [4929] 5.776263 6.407740 6.350756 5.075442 6.726768 6.223422 6.937818 7.750035
## [4937] 6.743692 6.030287 6.145853 6.591444 5.508640 7.085423 6.222600 6.143746
## [4945] 5.691611 5.956985 5.570740 6.741756 5.916921 6.205363 6.133942 5.443362
## [4953] 5.859448 2.675754 6.533358 6.345657 6.222600 6.073480 6.786796 6.432647
## [4961] 6.715056 5.911048 6.236133 6.313585 6.263302 6.334226 6.370248 6.767674
## [4969] 5.897244 6.179227 5.813855 5.576842 6.490045 5.467527 5.154913 7.299905
## [4977] 6.440492 6.703537 6.244586 6.123300 5.609566 5.107135 6.359648 5.819921
## [4985] 6.680065 6.652520 5.676417 6.166242 6.009275 6.066204 6.418101 6.777716
## [4993] 6.197455 7.496292 6.536167 6.054471 6.491653 5.798310 5.250804 6.123572
## [5001] 6.527927 6.122399 6.310372 6.190776 6.538784 6.389458 5.502603 5.650618
## [5009] 6.726287 6.320966 6.456723 5.918014 5.831198 6.478389 5.903065 6.381349
## [5017] 7.023439 6.570272 6.190776 6.743692 5.893273 5.859390 5.802665 5.672271
## [5025] 6.168171 5.801772 6.611110 5.533136 5.936459 6.760919 6.538784 5.286508
## [5033] 5.672871 6.842306 6.230022 5.866254 6.289284 5.998457 6.305791 4.673319
## [5041] 6.526681 6.312541 6.301350 5.832199 7.750035 6.684455 6.987576 6.085543
## [5049] 6.533358 6.813808 6.341041 5.991624 6.088257 6.405561 6.359648 5.973370
## [5057] 5.436983 7.147332 6.726287 5.713877 5.939741 6.594049 6.030379 6.004928
## [5065] 5.793860 6.110535 5.602469 6.577564 5.499084 5.767655 6.229418 5.806526
## [5073] 5.741820 5.947414 6.829332 5.782821 5.972244 6.102805 6.379009 6.374529
## [5081] 6.089961 5.922872 6.074347 5.333335 5.693133 6.575252 5.918014 5.802665
## [5089] 5.961089 6.053683 5.727962 6.375649 5.034532 6.142008 5.760700 6.976693
## [5097] 4.627586 7.261314 5.436983 6.082311 6.432647 7.074125 7.327408 5.264114
## [5105] 5.941481 6.458259 5.610170 6.102805 5.455273 6.961897 5.799726 6.160207
## [5113] 6.564351 6.165168 6.585009 6.341041 5.301143 5.858795 5.711699 6.612493
## [5121] 5.888453 6.921632 5.917693 5.807488 6.737168 5.493396 6.597942 6.033427
## [5129] 6.109388 6.494963 7.205359 5.630439 6.684962 6.610482 5.797488 6.233502
## [5137] 6.112525 6.405561 6.675443 6.519603 5.929786 5.916151 6.430909 6.487008
## [5145] 5.577903 6.498021 6.108994 7.018850 4.323285 6.271912 5.764437 6.101983
## [5153] 5.696100 6.394951 6.127301 5.373657 6.733146 6.553494 6.165168 6.144575
## [5161] 6.282672 6.384697 5.010650 6.101983 6.385303 6.775832 6.369651 6.528366
## [5169] 6.143572 6.966087 5.235055 5.237617 6.386214 6.805135 6.294275 6.313029
## [5177] 6.553748 7.133949 6.057157 6.293443 6.531793 5.947414 5.155820 4.964082
## [5185] 6.725976 7.192903 6.163866 6.019751 5.378480 5.512194 6.400396 6.482034
## [5193] 6.801637 5.694501 6.179256 5.287605 6.037033 6.270276 6.970909 5.846518
## [5201] 4.901059 6.205907 5.514584 6.287567 6.836492 6.301350 5.716211 6.339435
## [5209] 5.684988 7.223950 5.626001 4.731313 6.003554 6.236133 6.205518 5.696797
## [5217] 6.650542 5.903065 6.408166 6.922644 5.132416 5.493396 5.874348 6.463122
## [5225] 7.088932 6.290391 6.215309 5.483085 6.223529 6.233415 5.369279 5.568221
## [5233] 6.129549 6.144575 5.047864 5.604383 6.085543 5.670658 6.308953 6.421540
## [5241] 6.086520 6.407865 6.747277 5.344343 6.254739 6.900567 6.684346 7.023439
## [5249] 6.726768 5.626001 5.524634 5.664861 6.308313 6.204607 5.460804 6.242199
## [5257] 5.689140 6.599282 6.345657 5.788686 5.858708 6.287594 6.695844 5.923256
## [5265] 6.008958 6.807038 6.136578 5.747157 6.492926 6.265252 6.767104 6.292506
## [5273] 6.394951 6.042815 6.687989 5.350619 7.085930 5.798310 5.489236 6.273954
## [5281] 6.513310 6.501153 6.483676 5.517806 6.476236 5.815870 5.439086 6.002971
## [5289] 6.725976 6.167699 5.508023 5.543656 5.277310 6.011045 5.410770 5.779016
## [5297] 5.653698 5.730352 6.049298 6.342720 5.815870 6.315359 6.577564 7.062426
## [5305] 6.175362 6.458233 7.327408 5.579024 5.936970 6.934921 6.737168 6.580344
## [5313] 5.767550 7.261314 6.813808 4.603677 6.823048 6.101800 6.068340 5.676019
## [5321] 6.254608 6.303923 5.916921 6.421540 6.306722 5.504986 5.417735 6.354275
## [5329] 5.607374 6.594626 6.987576 6.464703 6.082311 6.266377 6.587777 4.702415
## [5337] 6.307041 6.391209 5.938513 6.568810 5.707141 5.952933 6.612605 6.675443
## [5345] 6.067833 5.179046 6.142195 6.210925 6.085551 6.135922 6.381349 6.796570
## [5353] 5.181203 6.167699 6.528366 7.173389 6.191008 6.410452 5.713877 5.525375
## [5361] 5.594061 5.979783 6.306604 5.716211 6.084960 6.203108 6.436809 6.391209
## [5369] 6.306835 6.839376 5.572156 6.100688 5.741113 6.244586 6.141512 6.033157
## [5377] 6.215436 5.897244 7.330188 7.299905 6.932671 5.959960 5.995563 6.575252
## [5385] 5.253638 5.198471 6.932671 6.410844 6.203066 6.320194 6.693060 5.556463
## [5393] 5.608647 6.380078 6.123300 6.495338 5.667732 6.754240 6.146103 6.128232
## [5401] 6.205518 6.605336 6.186955 5.800633 6.078262 7.500821 5.730413 6.052219
## [5409] 6.692884 6.091905 6.464633 6.526681 7.192903 6.329353 6.432431 6.463422
## [5417] 6.601876 6.308313 5.542696 6.538274 6.245687 5.639452 6.306604 6.692884
## [5425] 6.635504 6.078262 6.752154 5.506306 5.894720 6.068580 6.218704 5.227212
## [5433] 5.392922 5.794517 6.762337 7.496292 5.525375 5.551244 5.956681 6.005580
## [5441] 6.675599 6.583645 5.791570 5.532030 6.483676 6.587777 5.599989 6.807799
## [5449] 5.879846 7.065369 6.383398 6.163866 6.040459 6.440518 5.554915 6.127301
## [5457] 6.469712 6.205495 6.242199 6.257449 6.410844 5.858708 6.621163 6.368949
## [5465] 5.516575 5.696100 6.593344 5.186841 6.689788 6.839376 6.386214 6.498021
## [5473] 6.405804 5.245063 6.387310 6.129362 6.214659 5.827946 5.767550 5.904520
## [5481] 6.641089 6.086520 5.936970 6.067833 6.289284 6.684455 6.394513 5.985831
## [5489] 7.265111 6.458259 6.883244 6.447716 7.214361 6.408166 6.853893 6.847577
## [5497] 6.807038 6.207755 6.027634 5.454652 5.849063 6.682197 6.490518 6.596091
## [5505] 5.864900 5.487563 6.678804 5.510676 6.210925 5.593596 5.923836 5.603911
## [5513] 5.393149 7.202917 6.476236 6.354275 5.927133 5.720267 6.490573 6.736860
## [5521] 5.728417 5.982888 6.022844 6.068340 5.903792 6.387440 6.060516 7.247071
## [5529] 6.604019 6.966087 6.320194 5.806526 5.970791 6.141512 6.005677 5.800633
## [5537] 5.829870 6.680065 6.863969 6.441485 6.593344 6.829332 6.900567 6.376466
## [5545] 6.017613 6.747149 6.922644 6.106427 6.976829 6.076211 6.040212 6.295781
## [5553] 6.049262 6.558360 7.554268 6.883947 6.497544 6.005502 6.853880 6.084097
## [5561] 6.615163 6.065274 6.989112 6.664976 6.981138 6.802612 6.558512 6.387869
## [5569] 6.544017 7.014219 6.708554 6.496472 6.375363 6.103788 6.688154 6.042550
## [5577] 6.488691 6.421181 6.935876 6.995749 7.044711 6.658145 7.102279 6.866701
## [5585] 6.183683 5.346877 6.133986 6.140572 6.459397 6.018994 6.989112 6.154730
## [5593] 6.580913 6.570078 6.635451 6.321550 6.496472 6.670270 6.639146 6.340200
## [5601] 5.944664 6.629695 7.748917 5.301496 7.058163 6.771141 6.484684 5.931281
## [5609] 6.131593 4.818644 6.935876 4.768668 6.786932 5.672753 6.580913 6.703672
## [5617] 5.622253 6.385590 6.542888 6.471712 6.733997 6.541854 7.086272 6.584745
## [5625] 6.364997 6.702862 6.647180 6.635451 6.737848 5.226553 7.014219 6.990248
## [5633] 6.534132 5.948881 5.975377 5.772340 6.194010 7.710897 6.321550 6.529072
## [5641] 5.230460 6.853676 7.041072 6.062778 5.922688 5.308176 6.388321 6.226868
## [5649] 6.073444 6.623650 6.995749 6.578979 6.471071 6.010786 6.268073 5.449977
## [5657] 6.636056 6.840618 6.758960 6.459124 5.129295 6.408039 6.328482 5.805206
## [5665] 6.489190 6.833573 6.211249 6.067572 6.855141 6.981541 5.481674 6.570078
## [5673] 6.459124 4.744606 6.722315 6.444591 6.484684 6.667576 6.320468 5.962743
## [5681] 6.463752 6.719016 5.561635 5.563105 6.203318 5.218702 6.217925 6.387357
## [5689] 6.795843 5.860546 5.953354 6.637948 6.705402 5.932139 6.981138 6.707651
## [5697] 6.160723 5.490461 6.722315 6.651576 6.225557 6.840618 6.316915 5.997105
## [5705] 6.321678 6.501114 6.491872 6.085336 6.544017 6.859802 5.207613 6.385590
## [5713] 7.409315 5.003809 5.229129 6.326015 7.670937 5.961769 5.162142 5.633420
## [5721] 4.954534 6.373029 5.866254 6.336002 6.489190 5.235800 6.258490 5.566460
## [5729] 6.737848 5.545690 5.870630 5.861121 6.654617 6.299989 5.838631 6.446263
## [5737] 7.417229 5.879819 6.860397 5.976460 6.067572 5.931281 6.883940 5.195389
## [5745] 5.860546 6.640669 6.810977 5.743740 6.481029 5.530976 5.542049 5.944664
## [5753] 5.264798 6.202986 6.121748 6.707673 5.320763 6.333719 6.157301 4.624882
## [5761] 5.544958 6.745856 5.879181 6.264123 6.359231 6.354909 6.194010 6.361542
## [5769] 6.778176 6.160589 4.411071 6.373498 6.425568 6.631220 6.316915 6.517385
## [5777] 5.705924 4.999260 6.268462 6.615264 7.102279 5.975843 6.110098 6.579584
## [5785] 6.444591 6.471071 6.784741 6.481029 6.229599 6.976407 6.588943 5.972872
## [5793] 6.013239 7.812772 5.723756 6.435697 6.558872 7.748917 6.211249 6.575815
## [5801] 6.745902 6.453663 6.411190 7.358738 6.320468 6.109072 5.965236 7.812772
## [5809] 6.756662 6.094720 5.084877 6.756662 6.399668 6.647180 6.198747 6.758960
## [5817] 7.710897 6.402460 6.275469 6.795843 6.990248 7.283792 7.041811 6.500872
## [5825] 5.423554 6.408039 6.156115 7.518081 6.370442 6.371681 5.077189 6.584745
## [5833] 5.331137 6.508742 6.651021 6.018994 5.965128 5.961769 5.785708 6.729280
## [5841] 7.784742 5.481674 6.542888 4.926293 7.358738 5.084877 6.802612 6.710542
## [5849] 6.420360 6.675040 3.982149 6.435235 6.342282 6.517385 6.778176 5.253838
## [5857] 6.626037 6.100130 5.919123 6.396657 6.202986 6.664976 6.927370 6.498840
## [5865] 5.537464 5.908252 6.973077 6.651576 5.540594 5.975377 7.049087 5.544958
## [5873] 6.333561 5.490020 5.984828 6.517665 5.540594 4.653178 7.049087 5.218702
## [5881] 6.261960 6.787736 6.753362 6.696549 6.590794 6.321678 6.639146 5.155642
## [5889] 6.060002 5.465886 5.155642 6.457289 5.840810 7.264699 6.024074 5.436698
## [5897] 6.261960 5.793738 5.004080 4.690208 4.860351 5.655241 6.342282 6.637948
## [5905] 6.333508 5.331137 6.493461 6.340200 6.786796 5.926143 6.670270 6.025399
## [5913] 5.586518 6.457289 5.743740 6.491872 6.753362 6.741970 6.775832 6.333489
## [5921] 6.211050 6.156115 6.833573 5.672350 4.651906 6.333719 6.387869 4.644298
## [5929] 5.879819 6.524496 7.208261 6.196694 6.498840 6.668077 6.891227 6.771141
## [5937] 7.264699 7.019009 6.361800 6.705402 6.341123 4.045429 6.379391 6.551569
## [5945] 6.375363 6.656142 6.723083 5.672753 6.370531 6.217925 6.131593 6.696549
## [5953] 6.860397 5.834749 7.058163 6.703672 7.086272 5.566460 6.327612 6.257241
## [5961] 5.655888 6.281225 6.810977 6.710542 6.241709 6.853676 5.964273 4.490300
## [5969] 6.638649 6.638649 6.050630 6.196694 6.359231 5.997105 5.432411 6.508742
## [5977] 6.626037 6.784741 5.745844 5.953354 6.050630 6.527017 6.213513 7.063586
## [5985] 5.423554 6.225557 5.979044 6.579584 4.720783 6.425568 6.198747 5.449977
## [5993] 6.311688 6.506958 6.658145 6.488691 5.787743 6.707673 4.567756 6.257241
## [6001] 6.745856 5.984244 6.167247 6.923570 6.506958 7.670937 7.063586 6.266238
## [6009] 6.157301 6.733997 6.051025 6.463752 6.040919 6.183444 5.822051 6.981541
## [6017] 5.875710 6.367326 7.041811 6.686495 7.001070 5.916886 6.327612 6.613852
## [6025] 3.519642 6.044748 7.001070 6.615264 6.229599 7.228689 6.241709 6.396657
## [6033] 6.551569 6.667576 5.618254 5.873579 6.189510 6.654617 6.907404 4.540798
## [6041] 6.154730 6.859802 6.558512 6.103788 7.518081 6.299989 4.379811 5.842443
## [6049] 6.379391 6.459397 6.538244 7.472968 5.274611 6.326015 6.886719 5.805206
## [6057] 7.041072 5.923528 6.973077 5.919932 6.446263 5.365736 5.551923 6.387357
## [6065] 6.333489 6.453663 7.044711 5.793738 6.750068 6.471712 5.082057 6.686495
## [6073] 5.730595 5.730595 6.651021 6.588943 6.388321 6.661301 6.787736 5.493730
## [6081] 6.501114 7.243814 6.723083 6.750068 6.578979 6.636056 6.369711 6.613852
## [6089] 6.435235 5.908252 5.395719 5.919932 6.275969 6.575815 6.333508 5.879181
## [6097] 6.719016 5.567494 6.656142 4.883726 5.973422 5.586518 5.581647 4.548371
## [6105] 5.301496 7.417229 6.275969 7.019009 6.324532 7.228689 5.705924 6.623650
## [6113] 6.140572 5.501247 6.540162 5.891495 4.953740 6.558872 6.435697 6.447610
## [6121] 6.923570 6.534132 6.976407 6.268073 6.629695 6.099865 5.500197 6.007995
## [6129] 6.650105 6.402460 6.866701 6.729280 5.639053 6.324532 6.974565 6.786932
## [6137] 6.661301 6.094720 5.923528 5.788861 5.784435 6.574421 6.284689 6.281225
## [6145] 6.189510 6.258490 6.640669 6.371681 6.373498 5.500197 6.264123 4.880224
## [6153] 6.104990 6.013239 6.675040 6.530742 6.167247 6.062778 5.695796 6.493461
## [6161] 4.361456 6.541854 6.886719 6.110098 6.927370 6.133986 6.408634 6.183444
## [6169] 6.051025 6.211050 6.650105 7.208261 6.160589 5.795811 6.498560 5.250047
## [6177] 6.860790 6.860790 6.498560 5.489814 6.292174 6.254836
##
## [[2]]
## [1] 6.212131 6.468230 6.184214 6.028891 5.942079 6.361642 5.969594 6.669775
## [9] 6.459122 6.325929 6.711321 6.401986 7.073576 5.643170 6.399416 5.990337
## [17] 6.006492 6.842990 5.926365 6.847590 6.123175 5.667818 6.866390 5.605067
## [25] 6.415130 6.054453 6.232788 6.452476 6.350702 5.763728 6.353910 6.513563
## [33] 6.005152 5.662977 5.915440 6.011970 6.178479 7.041336 5.577739 6.190225
## [41] 6.785498 6.692039 6.268835 6.237541 5.833798 6.205155 5.433381 5.739455
## [49] 5.762666 5.303682 6.450046 6.581069 6.406917 5.637205 5.517765 5.785482
## [57] 7.109832 6.044677 6.745592 6.294575 7.087806 6.803837 5.295317 6.726675
## [65] 5.899155 5.825314 6.058879 5.447834 6.157291 5.903120 6.087844 6.471941
## [73] 5.398590 6.584312 6.519933 6.565541 5.808241 5.886089 6.692224 6.556328
## [81] 5.720503 5.745068 6.325998 5.559751 5.985403 5.869740 5.836089 6.422609
## [89] 6.938378 5.667818 6.183843 6.573012 6.890943 6.470383 5.980468 5.647288
## [97] 6.362825 6.663346 6.648015 6.497218 5.862963 5.667513 5.244641 7.008304
## [105] 6.021371 6.648045 5.696237 5.800972 5.321663 6.303027 6.318985 6.260683
## [113] 6.118814 5.969797 6.235019 5.520654 6.785459 6.147585 7.050174 6.225049
## [121] 5.644123 6.264924 6.467971 5.496341 5.984396 5.359690 7.087806 6.026001
## [129] 5.823150 6.241720 6.469004 6.561852 6.315330 6.859570 5.948839 5.230684
## [137] 5.439617 5.933513 6.094768 5.699470 5.558914 6.255642 6.301435 6.576604
## [145] 5.860348 5.212695 5.919176 6.439217 6.264924 5.753249 5.547904 6.449179
## [153] 6.726946 6.190759 5.960581 5.877472 6.350702 5.192127 6.452636 6.102093
## [161] 5.653394 6.452100 5.815227 6.246857 5.422359 5.960697 6.129110 5.460865
## [169] 6.102544 5.913959 6.424922 6.202257 6.830857 5.284511 6.679708 5.158928
## [177] 6.235683 6.064363 6.315848 5.441892 6.604177 6.775764 5.913959 5.372013
## [185] 5.740852 6.534774 5.690602 6.033579 5.941553 5.933353 6.325998 6.101415
## [193] 5.376357 5.629209 6.361483 5.234530 6.565636 6.269608 6.006350 5.573006
## [201] 6.483642 7.119491 6.333476 4.980929 6.086593 5.907045 5.584164 6.169857
## [209] 6.251281 5.604408 5.802014 5.835725 5.970468 6.692039 5.073076 6.624546
## [217] 6.087844 6.488962 6.143528 6.497549 6.294575 5.919356 6.454004 5.092278
## [225] 6.597143 5.986942 6.319541 6.005152 6.411792 5.911011 6.041942 6.053916
## [233] 5.385238 5.085474 6.773822 5.484398 6.281796 6.989809 6.064484 5.501649
## [241] 5.459699 6.685748 6.833711 5.566852 5.918339 6.596686 5.972175 5.784483
## [249] 6.414204 6.092710 5.886897 7.303794 6.234456 5.526270 5.553313 6.294577
## [257] 6.433309 6.466030 5.938392 6.257844 5.882129 6.526864 6.228160 5.399118
## [265] 6.747724 5.203681 5.717599 5.805010 6.601572 5.996627 6.648045 5.061506
## [273] 5.178775 5.696237 6.635254 6.497218 5.933353 6.110269 6.345113 5.546269
## [281] 6.365349 5.805193 6.120918 6.212900 6.070897 6.783203 6.640074 5.838261
## [289] 6.549051 5.866246 5.604602 6.334401 7.306451 5.966530 7.151037 6.719993
## [297] 5.657101 6.739184 6.632928 6.380736 6.530442 6.467971 6.236622 5.757383
## [305] 6.029462 6.236619 5.271417 6.248542 6.599452 5.346176 5.842657 7.008304
## [313] 5.751533 6.026385 6.482833 6.592962 5.777235 6.833711 6.504450 6.666469
## [321] 7.001057 6.199460 6.064511 6.090057 5.929996 6.753679 5.672155 5.728435
## [329] 6.583976 6.371084 6.469004 6.186581 7.013351 5.618137 6.753679 6.534774
## [337] 6.775764 5.867522 4.256472 6.285989 6.479143 6.549583 5.751085 5.715331
## [345] 6.351716 5.497669 6.232788 6.163646 5.678598 5.923800 6.390490 7.375928
## [353] 5.701547 6.684950 6.029618 6.572708 6.693172 5.894007 6.397590 6.829268
## [361] 5.384787 6.759942 6.202119 6.333476 6.583671 6.309561 6.894611 5.800437
## [369] 6.859166 6.636425 6.114840 5.945024 6.995998 6.345113 6.690086 5.913895
## [377] 6.554758 6.888986 5.869960 6.322643 6.668566 6.048530 6.109095 5.605084
## [385] 5.721952 5.853721 6.596699 6.190759 5.754684 6.272533 6.236619 6.151077
## [393] 6.790360 6.158733 5.909104 6.692224 6.433309 6.239265 6.113965 5.929996
## [401] 6.241720 7.479290 6.573529 6.236622 6.564141 5.905876 4.898942 5.234843
## [409] 5.459005 6.997383 6.337737 6.109255 7.124382 5.471913 5.097994 6.599452
## [417] 6.583671 6.395657 6.362825 6.454974 5.217875 7.073576 6.522412 6.180111
## [425] 5.588613 6.445860 5.553143 6.064266 5.819996 6.039000 5.787921 6.177837
## [433] 6.576604 5.345169 5.283968 5.508330 6.026001 5.955659 5.804011 6.257120
## [441] 5.362133 5.985403 6.255642 7.091958 5.709766 6.476707 6.610256 6.331191
## [449] 5.859721 7.050174 6.666469 5.988904 5.574625 6.534016 5.623603 6.435788
## [457] 6.082107 6.658968 6.668566 6.256617 6.178479 4.374012 5.581346 6.705339
## [465] 6.549221 5.768508 6.118427 6.848806 7.151037 5.866235 4.867808 6.390490
## [473] 5.282946 6.739184 6.608524 5.902815 6.101388 6.008141 5.240979 5.741716
## [481] 6.053604 6.377208 6.496609 6.001876 5.335300 6.281716 5.984396 6.041814
## [489] 5.909649 5.947297 6.156579 6.292222 6.459338 5.955659 6.591415 5.791613
## [497] 5.296619 5.849247 6.102759 5.559709 6.299358 5.415193 6.095929 6.825895
## [505] 6.064363 5.402755 6.268835 6.938378 6.791103 5.971374 5.722109 5.071917
## [513] 6.041942 5.048402 5.555481 6.382076 6.422609 6.217466 5.936207 5.725590
## [521] 6.099448 6.130273 6.334401 6.965993 6.237541 6.605672 5.901117 6.411792
## [529] 6.254135 6.102093 6.441667 5.154245 6.372952 6.015788 6.847590 5.983720
## [537] 5.993711 6.106452 5.680123 6.281716 5.453303 6.055953 6.091857 6.034882
## [545] 6.866390 5.232378 5.553143 5.776303 6.646779 6.408653 5.761644 6.372952
## [553] 4.883411 5.980468 6.059921 6.690086 6.260698 5.440241 6.990547 5.125689
## [561] 6.934524 5.426571 6.746750 5.565634 5.553858 6.038321 6.834694 5.849247
## [569] 6.742725 5.660226 5.222755 6.506901 6.576916 6.584312 5.265103 5.878883
## [577] 5.862963 6.034882 6.087656 6.351060 6.536777 6.493834 6.391579 5.886089
## [585] 6.419342 6.445860 5.892334 5.821668 6.556495 6.471941 6.010176 5.986942
## [593] 5.770783 6.385028 6.162044 5.875940 6.647819 5.779214 6.742953 5.888757
## [601] 6.014503 5.465547 6.223832 6.609797 6.257120 5.448944 6.053604 6.592962
## [609] 5.320727 5.802014 5.756464 6.318529 6.483642 5.782125 6.582176 6.826499
## [617] 5.951765 6.032780 7.479290 6.092165 5.517470 6.439217 5.847275 6.713697
## [625] 6.711321 7.041336 6.672471 6.602658 6.759942 6.371084 6.164653 5.200128
## [633] 5.757383 6.244156 5.923800 6.091857 5.999380 5.662977 7.271516 5.937767
## [641] 6.424922 6.861275 5.769256 6.294577 6.604177 6.785459 6.175379 5.830256
## [649] 5.911947 5.614511 6.435899 6.380662 5.843307 5.895844 5.396202 6.080558
## [657] 5.833798 6.337324 5.913661 5.860232 5.670381 6.575556 5.988904 5.857578
## [665] 5.234462 6.825895 6.585948 5.938392 6.035649 6.433949 4.963094 5.469247
## [673] 6.143528 5.491905 5.312040 6.303397 5.697895 6.094768 6.175379 5.271804
## [681] 6.366162 6.097013 6.520552 6.834694 5.734212 6.243126 5.587165 6.138335
## [689] 6.930597 6.113331 6.033579 5.941553 6.384591 5.666893 5.756593 6.014737
## [697] 6.620910 6.072549 5.517836 5.895718 5.815242 6.167822 6.461961 6.356371
## [705] 5.310899 6.224550 5.973262 5.431075 5.727102 6.416844 6.639280 7.271516
## [713] 6.632928 6.321314 6.053916 5.406141 5.756211 6.783203 5.918021 5.998219
## [721] 5.573006 5.606223 6.696735 5.614009 5.648675 6.052047 6.549051 6.449867
## [729] 5.646557 5.434835 6.703785 5.805010 6.513563 6.470383 6.058879 6.123865
## [737] 5.279620 5.942079 5.911426 6.829268 6.585948 6.197084 5.484930 6.414204
## [745] 6.269608 6.154403 6.388317 7.001057 6.150620 6.181847 5.719186 5.735229
## [753] 6.268618 6.635977 6.401801 6.322643 6.658968 6.580110 6.177837 6.451361
## [761] 6.536670 6.021570 6.784944 7.339115 6.842990 5.752082 5.538146 5.838261
## [769] 6.925047 6.045225 6.454708 5.815242 6.675688 6.861275 5.519549 5.787955
## [777] 6.164653 5.603545 5.878502 6.416844 6.159512 6.564141 5.966924 6.101388
## [785] 6.342788 5.952396 6.397590 5.596509 5.756464 6.285989 6.310423 6.224921
## [793] 6.884454 6.640074 6.657075 6.048530 5.864488 5.170789 5.750764 6.721005
## [801] 6.528128 6.224921 6.742725 6.605672 6.219269 5.230781 6.161437 6.342788
## [809] 6.693172 6.321314 5.106614 6.090057 6.219269 6.635977 6.601572 5.950115
## [817] 5.992086 6.303397 5.377585 6.087656 5.835725 5.694072 6.123865 5.751802
## [825] 5.836089 6.385895 7.306451 7.124382 5.582072 6.169857 5.811678 6.114475
## [833] 5.834060 6.416360 6.445074 5.770502 6.089751 5.335776 5.615681 5.892601
## [841] 6.601154 5.646591 6.173002 6.483586 5.182414 6.830857 6.829678 6.859166
## [849] 5.634715 6.278180 5.577739 5.806426 6.560271 5.357819 5.946590 6.674265
## [857] 5.338133 5.780364 6.438764 6.299358 5.734199 6.591415 5.763137 6.452636
## [865] 6.141322 6.560271 6.274385 5.656213 5.779060 6.705339 5.309315 5.926365
## [873] 4.990660 6.154854 6.285906 5.957219 6.213757 6.064655 6.102544 6.076412
## [881] 6.646199 6.642711 5.761405 5.742939 6.536670 5.500395 6.791103 5.471705
## [889] 6.153143 5.761369 6.602658 6.253429 5.825351 6.267035 6.064484 5.798831
## [897] 6.870158 6.005732 5.562597 6.716623 6.561124 5.766714 6.544953 5.628479
## [905] 6.810058 5.778012 5.868646 5.479589 5.765358 6.582410 6.875478 5.681436
## [913] 6.161481 5.703497 6.178290 5.719054 6.536777 5.391128 6.183843 6.440837
## [921] 5.765639 5.787199 6.500692 5.019748 6.696840 5.770783 6.608524 6.144731
## [929] 5.969255 6.319541 5.936103 7.128399 6.044677 5.030615 6.464828 6.380662
## [937] 5.681402 6.272533 6.482833 6.549583 6.141322 6.150145 5.292360 6.500692
## [945] 5.922651 5.265310 6.122043 6.596686 5.936103 6.556328 6.404397 6.734549
## [953] 6.056579 5.891935 6.288811 5.791613 5.830256 5.508708 5.780364 6.097013
## [961] 6.934524 5.358019 6.813012 5.808930 6.420202 5.901117 5.188011 5.593880
## [969] 6.321289 6.469857 6.684950 5.423548 5.823150 5.806104 5.966468 6.489751
## [977] 6.193358 5.856791 6.039000 5.266538 5.469658 5.783321 6.480845 6.196677
## [985] 6.144312 5.489244 5.022536 6.005732 6.987285 6.288811 6.268618 6.382076
## [993] 6.790360 7.013351 6.583976 6.504450 6.156579 5.966924 5.171671 6.281662
## [1001] 5.960697 6.750520 6.173002 6.101694 6.594572 5.813790 6.884454 6.113331
## [1009] 5.359690 6.473113 5.263937 5.992348 5.547961 4.636203 5.528869 6.473113
## [1017] 6.496609 6.151975 5.642611 6.617045 6.594817 6.089751 6.965993 6.267035
## [1025] 6.658260 6.589774 6.706370 5.886918 5.431356 6.829678 6.260683 5.907727
## [1033] 6.716623 5.098393 5.869740 6.144312 5.717599 6.358137 5.842657 5.894007
## [1041] 5.778012 6.479143 6.750520 6.594817 6.180707 6.582410 5.537102 5.605067
## [1049] 5.852342 5.670629 5.962871 5.936207 6.141804 6.282038 6.440837 6.014503
## [1057] 5.348618 5.886384 5.969255 6.282038 6.675688 5.896710 6.454974 5.562532
## [1065] 6.451361 7.212983 5.445845 5.822783 6.365349 5.962895 6.524218 5.470249
## [1073] 6.417415 5.733013 6.826499 6.008258 6.137155 5.367969 5.856791 6.223832
## [1081] 6.726675 6.083738 5.313693 6.244156 5.661415 6.987285 6.018764 6.316873
## [1089] 6.039065 7.303794 6.814700 5.493280 6.023541 6.083139 6.696735 6.461961
## [1097] 5.458722 6.404397 6.208259 6.040916 6.059203 6.875478 6.530442 5.226041
## [1105] 5.431042 6.589774 6.092165 5.321075 6.111004 5.709766 6.097271 6.466030
## [1113] 6.417415 5.887767 6.395657 6.054453 6.005908 5.867522 6.101415 5.588497
## [1121] 5.644123 6.520552 5.882693 5.715177 6.647819 5.921090 6.184214 6.361642
## [1129] 5.117559 6.419342 6.085234 6.246200 5.945914 6.703785 6.576916 6.792881
## [1137] 6.810058 6.620643 6.245262 5.790633 6.178290 6.506901 6.420467 6.085234
## [1145] 6.067146 6.151077 5.648675 6.238823 5.359972 6.332804 5.271048 6.061894
## [1153] 5.280138 6.594572 6.025801 6.167822 6.312353 5.875940 6.719993 5.835444
## [1161] 6.989809 6.502144 6.648015 5.786431 5.677976 6.435788 6.646199 5.859941
## [1169] 6.067146 5.966530 6.099448 5.761644 6.894611 5.984658 6.680370 5.218489
## [1177] 6.260698 6.045759 5.690602 5.302272 5.915440 6.919079 6.316873 6.547027
## [1185] 6.384591 5.426859 6.250064 5.847275 5.703497 5.739556 5.913661 5.864488
## [1193] 6.202257 6.208259 5.363507 7.128399 6.990547 6.038321 5.866235 6.502144
## [1201] 5.790633 6.301435 6.596699 6.180707 5.681424 5.928804 5.415386 5.895718
## [1209] 5.950115 5.446022 5.434835 6.785498 6.303283 5.684499 6.567951 5.034350
## [1217] 7.339115 6.069330 5.366568 6.123175 6.547027 6.674265 6.658260 6.572113
## [1225] 6.002469 7.529199 5.694072 5.623603 6.292222 6.243126 6.489751 5.642358
## [1233] 6.193358 5.937767 5.727763 6.235683 6.365615 6.064511 6.106452 6.205155
## [1241] 6.365615 5.609925 5.399581 5.363637 5.919356 5.453862 6.069330 5.628362
## [1249] 6.635254 6.229988 6.315330 6.441667 6.721005 6.464828 6.639280 6.522412
## [1257] 6.524218 6.462372 5.643170 6.169039 6.064786 6.245262 5.368974 6.242138
## [1265] 6.064655 5.626879 4.968291 5.259231 5.406437 6.925047 6.169039 6.186581
## [1273] 5.662285 5.247661 5.892334 6.377560 6.572708 5.303572 5.682999 6.137155
## [1281] 6.254135 6.248542 6.218546 5.637893 6.544953 5.666035 6.459338 5.934820
## [1289] 6.672471 6.041205 6.792881 5.985090 6.361483 5.851802 6.043832 6.561124
## [1297] 6.325956 6.930597 6.565636 5.427244 5.401527 5.471071 6.220203 5.637205
## [1305] 5.186517 6.445074 5.265876 5.525913 6.225049 5.801429 5.955831 5.574552
## [1313] 5.909104 5.813557 5.544501 5.510034 5.676950 6.010176 6.312198 6.580110
## [1321] 6.177645 6.190225 5.889622 6.045759 6.059921 5.626318 6.497549 6.157291
## [1329] 5.706117 6.450046 5.741716 5.992348 6.435899 5.788723 5.341360 6.072710
## [1337] 6.448338 6.742953 6.073598 5.727102 6.449179 6.563266 6.773822 6.870158
## [1345] 5.913895 5.477257 6.202119 6.573012 6.218242 5.934849 5.497199 5.751085
## [1353] 6.154403 6.092710 6.401801 6.735382 5.880126 5.660555 6.351820 6.018764
## [1361] 4.606440 6.405859 6.331191 6.800275 6.095013 6.321612 5.747050 5.566959
## [1369] 5.620130 6.177898 5.869960 7.489213 5.728435 4.991605 5.336372 5.753913
## [1377] 6.620910 5.811678 6.462848 6.285906 6.212131 6.256617 6.469857 5.648406
## [1385] 6.803837 6.029462 6.519933 6.420467 7.091958 6.601154 5.896546 5.731321
## [1393] 6.121999 6.026673 5.874277 6.416360 5.882693 6.351716 6.575556 5.536297
## [1401] 5.753913 5.366377 6.605398 5.558752 6.726946 5.304693 5.763728 5.931115
## [1409] 4.509986 6.113965 6.680370 6.449867 7.375928 6.746750 6.217466 5.806187
## [1417] 6.526864 5.886897 5.419330 6.013525 7.212983 5.903120 5.800972 5.434813
## [1425] 5.632705 6.483147 5.305131 6.454708 6.008258 5.544450 6.483586 6.454004
## [1433] 5.154480 6.674440 6.147330 6.229988 6.620643 6.438764 5.919058 6.293026
## [1441] 6.449243 6.435900 5.801429 5.907727 5.107313 6.325956 6.070105 6.358137
## [1449] 6.534016 6.177645 6.435900 6.224550 6.162044 6.037271 5.134452 6.337324
## [1457] 5.801225 6.193663 6.353910 5.733013 6.890943 5.669644 5.931115 5.879396
## [1465] 6.452476 6.180111 6.321612 7.489213 6.164290 6.072549 6.657075 6.713697
## [1473] 6.235019 5.383663 6.059203 6.624546 6.356371 5.214003 5.751533 5.445466
## [1481] 5.378989 6.483147 6.052047 6.204781 5.740790 6.446804 5.506133 5.409611
## [1489] 5.097817 6.636425 5.735229 5.424306 5.656213 6.309561 5.194048 6.301287
## [1497] 6.227113 6.094405 6.228631 5.985776 6.832746 6.437735 6.063333 6.944710
## [1505] 7.087213 5.829665 5.954309 6.414368 6.929308 6.052319 6.728670 5.809135
## [1513] 5.603701 5.791881 5.404847 5.581312 5.913532 6.163540 5.379113 5.632150
## [1521] 6.462083 5.161867 6.203721 5.936226 5.771945 5.858978 5.811192 6.352813
## [1529] 6.129301 6.399416 6.408568 6.469584 6.432855 5.937626 6.600178 6.274553
## [1537] 5.953767 6.146754 5.422907 6.656622 5.857363 7.227617 6.334569 6.324624
## [1545] 5.623765 6.440461 5.958801 5.683830 7.018252 6.028891 5.321330 5.141089
## [1553] 6.954759 6.309744 6.316058 6.904992 6.216076 6.022737 6.791223 5.660893
## [1561] 6.525835 5.763793 5.238084 6.549111 6.246663 5.726107 7.018252 6.099369
## [1569] 6.316058 5.695595 5.725385 6.118607 5.798001 6.176698 5.396397 6.058529
## [1577] 5.461024 5.935268 6.227113 6.952296 6.478541 6.347077 5.954309 5.928592
## [1585] 6.549111 6.313789 6.325929 6.316211 6.244608 6.600180 6.850876 5.491799
## [1593] 5.446836 5.895288 5.468447 4.985008 6.310006 6.414368 5.945297 5.581163
## [1601] 5.811056 6.182710 6.703071 5.107183 6.117549 5.226879 5.392953 6.871839
## [1609] 6.355529 6.017283 6.247633 6.444842 5.492810 5.780386 6.428568 5.985776
## [1617] 5.829665 5.124329 6.904992 6.703071 5.985556 5.686934 6.304932 5.187394
## [1625] 5.760996 6.954759 6.944710 6.440461 5.912691 7.005184 6.687524 5.255962
## [1633] 5.985556 5.170751 6.777752 5.516597 6.043103 6.229410 5.256765 5.623765
## [1641] 6.344796 5.738363 6.728670 6.149382 7.361153 6.339932 6.441659 6.165423
## [1649] 7.005184 5.834890 5.780110 6.233367 5.823626 5.827229 6.444842 6.851107
## [1657] 6.096621 5.824104 6.063333 5.755737 6.068623 5.965092 5.324981 6.437863
## [1665] 6.952296 5.324081 6.718447 5.800757 5.942042 6.207304 6.062960 6.477338
## [1673] 6.352813 6.659261 5.724255 5.920837 6.994200 6.048226 5.357927 5.550163
## [1681] 6.615896 6.135232 5.990778 5.264517 5.984197 5.982848 6.137955 5.942042
## [1689] 5.372139 6.027592 6.273591 5.340739 6.172904 6.195026 5.658778 5.859185
## [1697] 5.501601 5.787098 5.804794 6.143173 5.239466 6.791223 6.508206 6.520436
## [1705] 6.259415 6.469584 6.099369 5.083822 5.049189 6.137955 5.810545 5.352283
## [1713] 5.535859 6.000792 6.324624 6.850876 5.264563 5.872164 5.694724 5.806188
## [1721] 6.149382 5.984197 5.937540 5.976774 5.230119 5.935268 5.514769 5.251409
## [1729] 5.381836 6.460839 6.236451 6.309744 5.687721 5.691980 5.644233 5.907111
## [1737] 5.452108 5.898160 6.622470 6.344796 5.813110 7.051227 6.118607 5.725385
## [1745] 6.000250 6.172648 6.458401 6.242011 6.447815 5.903465 5.414760 6.112661
## [1753] 5.651725 5.810545 6.316211 6.355529 6.418993 7.098539 6.437735 5.780003
## [1761] 5.806812 6.667558 6.273591 5.866960 5.975562 6.060248 5.898160 6.315096
## [1769] 7.153988 6.447815 5.945297 5.939283 6.204012 6.137825 6.556288 6.556250
## [1777] 5.890057 5.928846 6.667558 5.979096 6.471255 6.648149 6.588090 5.948140
## [1785] 6.384203 6.035598 6.070976 5.473282 6.207304 5.913482 5.612262 6.242979
## [1793] 5.632150 5.657864 6.313789 5.480412 6.374009 6.287886 5.798001 6.426068
## [1801] 5.755737 5.163586 5.432893 5.429070 6.836793 6.609755 6.440914 6.609755
## [1809] 6.478541 6.300237 6.432855 6.276645 5.682695 6.233367 5.443324 6.172648
## [1817] 5.672395 6.270144 5.937540 5.889843 6.165423 6.084026 5.953767 6.869407
## [1825] 5.584423 6.711065 5.849488 6.034887 5.846622 6.135232 5.716574 5.853743
## [1833] 6.659261 6.345572 5.082583 6.863954 6.347077 6.178520 6.556288 5.805535
## [1841] 6.112661 6.082417 5.553642 5.965092 5.217520 6.216076 6.331831 6.060248
## [1849] 5.248424 5.188079 5.593484 6.525835 6.471255 6.727508 5.564720 7.183453
## [1857] 4.980932 6.460839 5.691683 6.711065 5.158327 5.660893 6.062960 5.710178
## [1865] 5.806188 5.851952 5.767859 6.301287 5.804794 6.300237 6.949877 5.686649
## [1873] 5.531685 5.866352 5.908643 6.670733 6.242011 4.588506 6.600178 5.240306
## [1881] 6.663018 5.991041 6.246663 5.859670 6.437863 6.393766 6.469020 5.580629
## [1889] 5.837268 5.415987 6.236220 6.096621 5.496183 5.811636 6.393766 5.517812
## [1897] 5.961996 6.416572 7.361153 6.163540 5.653622 5.700311 5.864836 5.851952
## [1905] 5.662396 5.761011 5.992199 5.267045 6.083035 5.804270 5.937895 5.331900
## [1913] 5.501601 5.991041 6.193418 6.090868 7.051227 6.236220 6.121944 6.304932
## [1921] 6.459122 6.090868 6.262095 5.502802 6.871839 5.658778 6.287886 6.869407
## [1929] 6.513472 5.490382 5.422210 6.777752 5.862955 6.094405 5.823233 6.414124
## [1937] 5.846640 6.280436 5.928592 6.176698 6.777548 5.670639 5.288381 6.477375
## [1945] 6.648149 6.070976 5.879525 6.469020 6.073790 5.348832 6.670733 6.285604
## [1953] 7.624595 5.818850 5.804270 5.531685 6.238100 5.961996 6.178520 5.738363
## [1961] 5.903465 5.811056 5.773342 5.837268 6.282601 5.815236 6.034130 5.785429
## [1969] 6.527416 6.236451 6.148838 5.129321 5.958801 5.804848 5.862955 5.716574
## [1977] 6.191993 7.153988 7.183453 6.191993 5.329177 6.015144 6.231258 5.471518
## [1985] 6.477375 5.830290 6.068705 5.581701 5.462270 6.033019 6.426068 5.872729
## [1993] 6.083357 7.098539 6.384203 6.193418 5.948140 4.964012 5.790172 6.723110
## [2001] 6.929308 6.622470 5.243233 6.195809 6.477338 5.928846 5.823233 6.393166
## [2009] 6.404586 6.160159 6.375407 5.932931 6.229410 6.073790 6.375407 6.334569
## [2017] 6.262095 5.964683 6.244608 6.121944 5.854661 7.087213 6.466829 5.353029
## [2025] 6.057461 5.304832 6.441659 6.727508 5.859185 6.068705 5.976774 6.228631
## [2033] 6.270144 6.315096 6.035598 5.246663 6.247633 6.017283 5.950321 5.763793
## [2041] 6.231258 5.691683 6.836793 5.920837 5.816512 6.718447 6.416572 7.227617
## [2049] 5.800757 6.275007 5.895288 5.248424 6.508206 5.992941 6.182710 6.204012
## [2057] 6.032462 5.737514 5.872729 5.243611 6.656622 6.428568 6.615896 6.600180
## [2065] 6.414124 6.832746 5.514161 6.048226 5.470955 6.408568 6.322608 5.743384
## [2073] 7.624595 6.663018 5.409667 6.304866 6.851107 5.470955 5.580970 6.172904
## [2081] 5.887227 5.791881 6.949877 6.137825 6.687524 6.128652 6.027592 6.241991
## [2089] 5.769592 6.195026 5.433618 5.998380 6.445001 5.915395 5.782396 6.288361
## [2097] 6.381208 6.264638 5.231151 6.055977 5.696128 6.398545 5.766799 6.987960
## [2105] 5.999594 6.590301 5.209282 6.715553 5.538912 6.682307 5.626887 6.614543
## [2113] 6.107685 6.561914 6.162971 7.055259 6.062336 5.950394 6.285506 6.588242
## [2121] 6.247239 6.103245 5.907410 6.262625 5.683146 5.582752 5.847260 6.474224
## [2129] 6.531313 5.833810 5.844708 5.845450 6.560578 5.966576 5.329591 4.903149
## [2137] 6.328521 5.199072 6.031323 5.316574 6.556242 6.285422 6.257403 6.131514
## [2145] 6.066456 5.992918 5.478889 6.372450 5.507777 6.768196 6.431702 5.806170
## [2153] 6.669880 6.481179 6.257403 6.446957 5.599231 5.683146 5.983563 6.094710
## [2161] 5.390069 6.965900 5.765853 6.439814 6.164206 6.898400 5.375457 4.194648
## [2169] 5.312746 5.980639 6.211384 6.112193 6.094576 5.805721 6.314745 5.862487
## [2177] 6.750194 7.063318 4.896595 5.559891 5.491755 5.367094 5.700091 5.557811
## [2185] 5.950394 5.893520 6.179655 6.531053 6.264539 5.362848 6.004236 6.557092
## [2193] 6.496889 5.574623 6.368662 6.459779 5.926734 4.827470 6.140756 5.731945
## [2201] 6.848115 5.738126 6.022708 6.788843 6.324734 6.573591 6.161739 5.546225
## [2209] 6.618131 6.819156 5.994867 6.778035 6.681865 6.199736 6.607909 5.303521
## [2217] 5.960648 5.976146 7.000881 6.754123 6.045518 6.396105 6.736772 6.154477
## [2225] 5.998380 6.693631 6.279310 6.734640 6.262625 5.824374 6.244232 5.763575
## [2233] 5.892234 6.090753 6.393224 6.366155 5.960604 5.966576 6.203122 5.844708
## [2241] 5.542765 6.625353 7.157754 6.623951 5.519262 6.649599 6.052547 5.791626
## [2249] 5.960604 5.675179 5.403066 6.356655 6.144810 6.205046 6.775142 5.564939
## [2257] 5.819008 7.143605 6.013926 5.756364 6.218483 5.249165 5.763221 5.691963
## [2265] 4.994268 5.766922 5.934644 5.805721 6.444289 5.989982 6.728753 6.244546
## [2273] 5.054737 5.890032 6.090480 5.696512 6.837565 6.546544 5.765006 7.217509
## [2281] 6.625353 6.434221 6.521055 6.263840 6.386170 6.587956 6.160856 4.839743
## [2289] 5.856035 5.581193 5.173802 6.177308 5.424776 6.556242 6.383410 5.733267
## [2297] 6.371860 6.193366 5.947146 6.593557 5.418553 5.553307 5.896764 6.346657
## [2305] 6.140756 5.700091 6.130270 6.000080 5.841712 5.314174 6.614543 5.880195
## [2313] 5.863434 6.109984 5.830440 6.123428 5.769465 6.414872 5.623811 6.067403
## [2321] 5.516554 5.624600 6.319871 6.171063 6.470939 5.583425 5.925421 6.357749
## [2329] 5.556210 6.681865 6.074513 7.404975 5.831078 5.657201 6.247574 6.613306
## [2337] 6.434221 6.314674 6.151740 6.668227 5.818446 6.264349 6.630635 6.454635
## [2345] 6.440012 6.450836 6.285422 6.107237 5.415082 5.917416 5.756071 5.255273
## [2353] 6.911396 5.386765 6.367525 5.583284 6.086684 6.052158 6.684975 5.889080
## [2361] 5.492249 5.398984 6.247234 5.816582 5.239394 5.351244 5.686956 5.209282
## [2369] 6.120228 4.998896 6.544636 5.676052 5.228958 6.266224 6.161935 6.183622
## [2377] 6.381208 6.139464 6.307581 5.737577 5.907774 6.901574 5.521289 5.734032
## [2385] 5.389991 6.037045 6.142625 6.037319 6.596176 5.397978 6.297420 5.796140
## [2393] 5.926464 6.440923 6.400436 6.298809 5.120039 6.264349 5.774623 6.265004
## [2401] 6.247239 6.265784 6.703288 5.844783 7.116379 6.157542 6.121229 6.266224
## [2409] 6.351330 6.025562 5.385067 6.495741 5.601878 6.264969 5.356697 6.215539
## [2417] 6.456627 6.245686 7.811117 5.545292 6.720413 5.925716 7.064324 6.434863
## [2425] 4.647255 5.939009 6.957627 6.544122 6.155321 6.818737 6.039359 5.712649
## [2433] 7.116379 5.270252 5.852396 6.127299 6.431702 6.112193 5.659938 5.785347
## [2441] 6.408670 6.277549 5.231151 5.233799 6.324734 6.635384 6.649599 6.407882
## [2449] 6.144588 5.515337 6.157542 5.877960 5.217502 6.921619 6.184135 5.412336
## [2457] 5.620028 6.356655 6.883466 5.881842 6.351330 5.246255 5.229875 6.058905
## [2465] 6.559246 6.496889 6.675236 6.120756 6.780111 5.370188 6.386759 6.343572
## [2473] 6.353563 6.108591 5.956005 6.267947 6.350089 6.085834 6.901574 6.442786
## [2481] 5.684332 5.676052 6.435342 6.088244 6.472176 5.983243 5.454820 6.443629
## [2489] 6.643796 6.063184 5.627354 6.092216 5.810704 6.129647 6.283354 5.503110
## [2497] 6.398545 6.312817 6.211440 6.223277 6.155321 6.736772 6.422840 6.094516
## [2505] 6.026285 6.333305 6.934822 6.477203 6.172925 7.144997 5.623811 6.127123
## [2513] 6.199772 5.454896 6.217752 6.717627 5.359365 6.109476 5.502209 6.747459
## [2521] 5.772014 5.570486 5.591655 4.674143 5.870514 6.598459 6.568971 6.271020
## [2529] 5.549141 6.819156 5.546225 6.751470 6.266256 6.022708 5.510963 6.266256
## [2537] 6.754123 5.800885 5.704857 6.614004 6.830315 6.264969 5.810704 6.136858
## [2545] 5.816582 6.021021 5.586628 6.135236 5.941313 6.674729 6.109505 5.800105
## [2553] 6.024885 6.451911 6.176614 6.598459 5.761594 5.763221 6.581821 5.863434
## [2561] 6.107685 6.266474 6.132146 6.832642 5.809857 6.383410 5.998322 5.684068
## [2569] 5.438212 6.049483 6.063184 6.094710 5.657201 6.085834 6.091866 6.450093
## [2577] 6.808903 6.010046 6.234688 6.590301 6.243180 5.868065 5.796819 6.734192
## [2585] 6.114608 6.091153 6.205540 5.314706 6.374667 6.312817 5.760590 5.006725
## [2593] 6.330029 6.155203 6.652245 6.125377 6.630635 5.626887 6.127661 6.470939
## [2601] 5.514756 5.786581 5.869818 5.577718 5.322515 6.307508 6.038871 6.603051
## [2609] 6.116125 6.541609 6.126370 6.148778 6.065267 6.204572 6.643796 5.481461
## [2617] 7.040469 5.421549 5.469576 5.901138 6.362692 5.950336 5.474597 6.177976
## [2625] 5.184878 6.238259 7.024636 5.922129 5.869854 6.481179 5.704587 5.399218
## [2633] 5.907774 6.204767 6.125232 5.621098 6.634072 6.094323 6.374667 5.177370
## [2641] 7.375574 6.826369 6.021021 6.188563 5.916736 6.271940 6.911396 5.582752
## [2649] 5.211490 6.444289 5.466813 5.720729 5.354214 6.409545 6.544636 6.396648
## [2657] 6.073076 5.947174 6.707265 5.370987 5.890699 6.295576 6.333305 5.883873
## [2665] 5.990734 6.613306 6.661297 6.396648 5.549075 5.844643 6.125656 6.328885
## [2673] 5.754143 5.611933 6.261176 5.898457 5.612197 6.734640 5.560480 7.157754
## [2681] 6.773262 7.404052 5.532701 5.761150 6.497620 6.565891 6.264539 5.749980
## [2689] 6.088244 6.234760 5.759769 5.900757 6.348345 6.051191 6.513100 6.674729
## [2697] 6.079475 6.318688 5.157914 5.365922 6.945769 7.105935 6.118405 6.064561
## [2705] 6.446957 6.205046 6.979655 6.987960 6.751444 6.061255 6.177976 5.244914
## [2713] 6.562233 6.853893 5.807779 6.472176 5.845450 5.338498 6.450497 6.442786
## [2721] 4.194648 5.890032 5.847248 5.418757 6.192349 5.372486 6.390914 5.354214
## [2729] 6.198356 5.311066 6.013926 5.452253 6.830315 6.254565 5.939952 4.815865
## [2737] 5.968578 7.811117 7.128653 6.086684 6.778035 5.608980 5.638965 6.330029
## [2745] 5.703503 6.302410 5.225192 6.386759 6.094576 6.720861 6.215539 6.254088
## [2753] 6.015120 5.554499 5.925421 6.097962 6.350089 6.254088 6.857277 6.986360
## [2761] 6.495741 6.422791 6.171063 6.841515 6.125656 6.652245 6.067403 6.139387
## [2769] 6.624173 5.150808 5.195173 6.649249 5.405677 6.525399 5.758614 6.632087
## [2777] 6.804633 5.239870 6.207992 6.261176 6.281809 5.220197 6.703288 6.556004
## [2785] 6.229474 6.422840 5.882258 6.706546 5.557321 6.006492 6.127661 7.654223
## [2793] 6.513787 6.735744 6.247574 5.533537 6.265784 6.062336 6.144810 6.684975
## [2801] 5.787959 6.080847 6.408397 5.934644 6.164884 6.002425 6.367525 5.554800
## [2809] 6.058905 4.973137 5.758264 5.554709 5.354604 6.846776 7.029727 6.747086
## [2817] 5.285498 5.928488 6.188563 5.976146 5.691963 5.888426 6.751470 5.648309
## [2825] 6.008840 6.589130 5.506852 7.219460 6.841515 6.531053 6.387053 6.832642
## [2833] 5.747023 6.068918 6.045518 5.279694 5.591655 5.816627 6.056337 5.544953
## [2841] 5.701390 5.554882 5.309127 5.574928 6.411176 6.680602 5.782290 5.939952
## [2849] 6.518179 6.411326 5.901348 7.188812 6.314745 6.260221 6.126370 6.223277
## [2857] 6.148778 6.219204 6.735875 4.947122 5.182087 6.000854 6.811656 5.367728
## [2865] 5.869818 7.188812 5.878995 6.371070 6.390120 7.024636 6.459779 6.400436
## [2873] 6.596693 6.544122 5.821152 5.554819 5.996931 6.142062 5.590539 6.345931
## [2881] 6.021866 6.785281 5.290444 5.916420 6.678921 5.582218 5.853327 5.996069
## [2889] 6.218099 7.119218 5.637629 5.489457 6.264638 5.803588 6.921224 5.382551
## [2897] 6.234688 5.656032 6.717627 6.804633 6.387053 6.546544 6.458345 5.756071
## [2905] 5.276535 6.238982 6.646077 5.431653 6.211384 6.477203 5.386765 6.244199
## [2913] 6.832616 6.634072 5.448237 5.649086 6.302410 5.322898 5.203591 6.443666
## [2921] 5.877626 6.521055 5.735868 5.851499 6.216886 5.703713 5.969594 6.588242
## [2929] 6.065267 5.094208 5.696128 5.583255 6.691143 5.177295 6.268246 6.184135
## [2937] 7.611655 6.154477 6.049483 6.121991 5.973234 6.094323 5.788547 6.177308
## [2945] 5.889080 6.397695 6.353578 6.496373 5.865427 6.540621 6.096903 5.960434
## [2953] 7.159327 6.824214 6.090480 6.728753 5.766922 5.881842 6.669880 6.155224
## [2961] 5.785347 5.832181 6.070266 5.989982 6.091153 6.411176 5.711783 6.565891
## [2969] 5.815937 6.441497 6.243195 5.772597 6.060971 5.363216 6.191976 6.193366
## [2977] 6.832616 6.179156 5.992166 6.695495 6.046613 6.469136 7.219460 6.344841
## [2985] 6.238259 5.898565 6.117134 5.407589 6.720861 5.271335 5.999594 6.525399
## [2993] 6.245682 6.668227 5.987352 6.450836 6.624173 5.570151 5.880170 6.572806
## [3001] 5.474615 6.519214 6.176895 5.847260 5.806170 6.125232 6.164884 6.327193
## [3009] 5.996931 7.000881 5.201465 6.347798 6.393224 5.599496 5.558706 6.327193
## [3017] 5.469916 6.592918 6.454635 6.539604 5.564013 6.390120 6.028590 6.921619
## [3025] 6.097962 6.243180 5.704857 6.557092 5.277040 6.695495 5.554800 6.079475
## [3033] 5.851554 6.612692 6.451911 6.061255 5.192815 6.952017 6.132565 6.497620
## [3041] 6.265004 6.143817 6.888556 5.847248 6.669775 6.371860 6.066456 6.811656
## [3049] 5.683889 6.285506 6.366155 6.033674 6.179655 5.476307 5.427757 5.385954
## [3057] 5.997351 6.362692 6.208990 6.556004 6.572806 6.132146 5.882258 5.684068
## [3065] 5.748183 6.785281 5.964434 5.634135 6.635384 6.458545 6.818613 6.162971
## [3073] 6.311577 5.630619 5.761594 5.692882 5.574942 5.772014 6.952017 6.323045
## [3081] 6.142062 6.478213 7.654223 5.117065 6.298434 6.368662 7.029727 6.217752
## [3089] 6.352948 6.184503 6.531313 5.766799 5.824058 6.121229 6.764639 6.127123
## [3097] 5.947174 6.172278 6.155203 6.176614 5.307731 5.491097 7.116350 5.322259
## [3105] 5.653083 5.327430 5.631249 6.179344 6.839300 6.678921 5.973631 6.934822
## [3113] 6.306357 5.855219 6.132565 5.223323 5.882917 5.654144 5.421648 6.818737
## [3121] 5.554819 6.040092 6.561914 5.733267 5.831078 6.883466 6.694447 6.244388
## [3129] 6.694892 5.932821 5.549089 4.696540 7.128653 6.592918 6.063790 6.250555
## [3137] 5.945799 5.909247 5.626890 6.518179 6.277549 6.081357 6.144588 5.587371
## [3145] 6.192349 6.788843 5.012080 6.348345 6.155224 6.540621 6.735875 5.045283
## [3153] 6.659358 5.684301 7.103107 5.960434 6.522499 6.127299 5.546419 5.916420
## [3161] 6.247234 6.505210 6.776480 6.352948 7.159327 5.883873 6.088760 6.458545
## [3169] 5.844643 6.179156 6.186425 5.942398 5.964434 5.506379 6.130270 6.145499
## [3177] 5.893158 6.248924 6.055977 6.041283 6.113154 5.359365 5.759769 6.818613
## [3185] 6.261707 5.611933 6.288361 6.463652 6.047581 6.125377 7.434158 7.103107
## [3193] 6.024885 6.435342 6.067773 6.297420 5.859220 6.605303 6.776480 5.199846
## [3201] 6.220381 6.063790 6.410217 6.408670 6.853893 5.218889 6.245682 6.290100
## [3209] 6.081357 6.573591 6.208327 6.199919 6.017257 6.505210 6.445001 5.948098
## [3217] 5.615052 6.305497 7.055259 6.921224 6.445015 5.880309 6.184503 5.779593
## [3225] 7.143605 5.893520 6.245686 5.177763 5.512313 6.004648 5.442435 6.888556
## [3233] 5.732554 5.130820 6.254565 6.092216 5.596399 5.723985 6.685133 6.305497
## [3241] 6.986360 6.469136 6.754920 7.051192 6.965900 5.472233 5.973631 6.325565
## [3249] 6.010695 6.550306 6.945769 7.040469 6.244388 6.953380 6.808903 5.641005
## [3257] 6.693631 6.142625 6.089048 6.400671 6.030921 6.328521 5.890010 6.468230
## [3265] 5.844060 5.990337 5.253950 6.129647 6.400671 6.562233 6.496373 6.694447
## [3273] 6.236154 6.062450 5.803580 6.804597 5.915395 6.639039 5.898457 6.057180
## [3281] 6.291196 6.109984 6.143817 5.227468 6.613125 5.916736 5.370217 6.817645
## [3289] 6.353327 6.103245 6.397695 6.691143 6.686510 5.328974 5.877960 6.219204
## [3297] 6.559246 7.327770 6.410217 5.880004 6.169961 5.888426 6.008840 5.849609
## [3305] 6.220381 5.961144 6.064561 6.764639 7.105935 5.712554 5.799325 6.671093
## [3313] 5.892234 4.733664 6.033748 6.519214 6.265537 6.076681 6.345931 6.659358
## [3321] 5.988681 5.867955 6.030767 5.901138 6.199736 6.123428 5.474615 6.039359
## [3329] 5.140267 6.422791 6.096903 6.120529 6.132371 6.307508 6.508417 5.354604
## [3337] 6.266474 5.101240 6.450093 5.554547 5.947731 6.474224 5.548579 6.953380
## [3345] 7.217509 6.613125 5.493832 6.279310 6.405361 5.381955 7.116350 5.707278
## [3353] 6.092225 6.483524 6.244546 5.440486 4.893603 6.396105 6.450497 6.508417
## [3361] 7.404975 5.941313 5.115799 5.538912 6.411326 5.217524 6.274130 6.327848
## [3369] 5.185148 6.371070 5.761427 7.404052 6.639039 6.088607 6.632087 6.283354
## [3377] 6.281325 5.586382 6.243267 6.747459 6.243267 7.003959 5.615172 5.505049
## [3385] 5.864416 5.320511 6.474394 5.584236 7.003959 6.661297 6.514015 6.707265
## [3393] 5.076934 6.031323 7.063318 6.390914 6.047581 6.596693 6.274130 6.239092
## [3401] 6.408397 6.649249 6.028590 6.405361 6.311577 6.164206 6.052158 6.618131
## [3409] 5.791626 5.238555 6.331665 6.646077 6.015120 6.612692 6.291196 6.343572
## [3417] 6.660016 6.052612 6.560578 6.307581 6.443666 6.347798 5.558895 6.837337
## [3425] 6.067773 5.981735 6.216810 5.782396 6.461284 6.675236 6.682307 6.109505
## [3433] 6.100963 6.076681 6.121991 5.830440 6.069876 5.742107 5.513546 6.456627
## [3441] 5.849609 6.474394 6.298434 5.907410 6.837565 5.865368 6.587956 6.117134
## [3449] 6.029014 6.051191 6.541609 6.734192 6.483524 5.711783 6.216810 6.735744
## [3457] 5.859220 5.826608 5.461343 6.108399 5.433680 6.623951 5.880048 5.727656
## [3465] 6.694892 6.283238 6.101651 6.176928 5.303415 6.353327 5.901348 5.948387
## [3473] 6.085234 5.574942 5.376022 6.846776 6.076435 6.806093 6.386170 5.344788
## [3481] 6.979655 6.271020 5.968578 7.434158 6.751444 6.605303 6.839300 6.265537
## [3489] 6.857277 5.991495 6.715553 5.481461 6.004648 6.218483 5.746275 6.353578
## [3497] 6.074513 5.935435 6.129876 6.172925 6.806093 6.113154 6.071942 5.867774
## [3505] 5.886315 6.204767 5.922902 6.261707 5.343719 5.776361 5.627672 5.404554
## [3513] 5.743737 6.325565 5.970797 5.326136 5.725247 5.851499 5.367384 6.088760
## [3521] 5.922129 5.796140 5.359033 5.777461 6.614004 6.559702 6.394168 6.768196
## [3529] 5.989808 5.802722 6.267947 5.676585 6.004236 6.306357 6.124071 5.642167
## [3537] 7.611655 6.002425 7.327770 5.232576 5.733653 5.566633 6.568971 6.198356
## [3545] 6.160856 6.142062 6.747086 6.303027 6.323045 6.994200 6.406917 6.773262
## [3553] 6.116125 6.120529 6.674440 5.907045 6.565541 6.045225 6.784944 6.041205
## [3561] 6.212900 6.441497 5.997351 5.554882 6.837337 6.610256 7.375574 6.085234
## [3569] 6.706370 6.263840 6.236154 6.478213 6.814700 5.996069 5.823626 5.774623
## [3577] 6.015144 6.747724 6.804597 6.871042 6.114454 6.134510 5.631658 6.669512
## [3585] 6.241423 5.599173 4.464448 6.525456 6.387777 5.541399 6.033115 6.219221
## [3593] 6.353618 6.345406 5.652994 6.850963 6.269648 5.495308 6.677150 6.235440
## [3601] 6.078265 6.487577 5.797651 6.046312 6.103405 6.247384 6.751834 6.854609
## [3609] 6.345406 6.984824 6.071600 6.671871 5.907869 6.874902 6.731257 6.033115
## [3617] 6.692550 6.614121 5.208570 5.889793 5.277456 6.762014 5.920436 6.876164
## [3625] 6.157571 5.834365 6.868768 6.792249 5.980233 6.625082 6.369408 7.413208
## [3633] 5.407685 6.544876 5.125998 5.722615 5.405347 5.488898 5.239514 6.836668
## [3641] 6.474897 6.008248 5.981804 6.433075 6.840680 5.942221 6.542669 6.441096
## [3649] 6.195339 6.664302 6.692550 6.442204 6.750937 5.505676 6.234935 5.671527
## [3657] 6.658994 5.860215 6.968088 5.935525 5.871987 6.192196 6.997501 5.352779
## [3665] 5.889793 5.912872 6.442382 6.015813 5.975970 5.418106 6.665381 5.544276
## [3673] 4.845467 7.392029 6.956448 5.051546 5.967405 6.525456 5.338454 5.981804
## [3681] 6.528165 6.353533 6.728138 6.255343 6.435730 5.730698 5.438195 6.758510
## [3689] 6.494724 6.827166 6.304041 6.006232 5.361220 6.627931 5.702004 6.241423
## [3697] 6.366829 7.048172 6.234683 6.501137 5.707452 5.397038 6.831727 6.586766
## [3705] 6.377198 5.671527 5.747336 5.524440 5.952944 6.452008 6.712450 6.353533
## [3713] 5.598874 6.784627 6.728138 6.311518 5.927832 6.294374 5.831210 6.216858
## [3721] 6.371008 6.629284 5.974156 6.512343 6.601271 5.854935 6.172703 6.451924
## [3729] 6.147540 6.147746 6.560011 6.354297 6.673927 6.047522 5.944437 6.665612
## [3737] 6.141746 6.876164 6.607913 6.419150 6.314312 6.550270 5.131073 5.862117
## [3745] 5.957232 6.143819 5.728449 6.677150 6.368232 6.056627 5.719823 6.255988
## [3753] 6.354364 4.898849 6.953945 6.628445 5.652531 6.159885 6.037516 6.494724
## [3761] 5.508028 5.438195 6.772653 6.043980 6.867959 6.311518 5.631658 6.670532
## [3769] 6.087881 6.217145 6.182892 5.886013 6.219310 7.519302 6.157571 6.377198
## [3777] 6.217145 6.216858 6.159343 6.103405 6.197001 6.363306 5.920436 5.670767
## [3785] 5.781247 5.278254 5.121830 5.562018 5.927832 6.452008 6.471963 6.368894
## [3793] 6.266019 7.421492 6.562723 5.975349 6.147300 5.730698 7.180796 6.086124
## [3801] 5.935525 6.043980 6.246168 5.819957 6.512343 6.448555 6.512169 6.750937
## [3809] 6.394265 6.742394 6.331049 6.384507 5.910503 6.110437 6.581416 6.686739
## [3817] 6.272326 6.251669 6.725723 5.833221 6.064180 6.354297 5.781247 6.305383
## [3825] 6.457103 6.438611 5.495308 6.562723 6.272326 6.686739 5.983947 6.729118
## [3833] 6.486266 6.023275 6.351191 6.497304 4.922529 5.829086 5.807406 6.665612
## [3841] 5.825773 6.589606 6.407461 6.501137 6.407434 6.779216 5.759015 6.065601
## [3849] 6.342441 5.630121 5.093831 6.347827 5.153949 6.956448 5.357267 5.747336
## [3857] 5.702004 6.388108 6.305383 5.321223 6.438020 6.392304 6.997501 6.301722
## [3865] 5.958234 6.434261 6.836668 6.643244 5.590481 6.971356 6.581416 6.058587
## [3873] 7.257639 6.874902 5.283818 5.946618 5.560587 6.368894 5.991285 6.671871
## [3881] 6.065601 6.846951 6.665381 6.726681 6.205298 5.397038 5.548011 4.779196
## [3889] 5.002943 6.628445 6.854609 5.118949 7.073058 6.932068 6.827166 5.391958
## [3897] 6.072901 5.974441 5.046257 6.641644 6.658994 6.697105 6.086124 5.514456
## [3905] 5.626222 6.762768 6.552341 6.337520 6.275804 5.747253 4.730155 6.132121
## [3913] 5.171933 6.255988 6.784627 6.457103 6.435557 6.498568 5.582786 4.893961
## [3921] 6.868768 4.827843 5.902199 6.438611 7.130559 5.598556 4.862943 6.544876
## [3929] 6.301722 6.133397 6.809797 6.512169 6.697105 6.971356 5.754372 6.809797
## [3937] 6.686278 5.323157 6.067155 6.219221 5.809558 6.668975 6.035989 6.846951
## [3945] 7.526496 4.945266 6.269648 6.497049 5.980233 5.946618 6.712450 6.337510
## [3953] 6.772653 6.387777 6.984824 6.624496 6.116055 5.789672 5.102180 5.717854
## [3961] 6.000993 5.754372 7.048172 6.006232 6.282863 6.560011 6.180615 4.836023
## [3969] 3.722601 5.357267 6.118869 6.412490 5.902199 5.613653 5.255093 7.180796
## [3977] 7.511703 6.824747 6.771600 5.834589 5.862117 5.046257 4.892499 6.266019
## [3985] 6.550270 6.394265 5.665333 6.468580 6.762768 6.347827 6.046312 6.143819
## [3993] 6.369408 7.123425 5.626222 6.366829 6.579941 6.576109 6.126724 6.629284
## [4001] 7.073058 6.088102 6.368232 5.757204 6.978693 6.219310 5.798240 5.974441
## [4009] 5.093982 6.147540 5.532580 6.410913 6.069017 6.412490 6.743020 6.590152
## [4017] 6.199089 4.898849 6.015813 6.742394 6.332067 6.061816 5.064387 5.991285
## [4025] 5.895041 6.812983 6.686278 6.395525 6.353618 5.472641 6.977040 6.762014
## [4033] 7.511703 5.418703 6.624496 6.627931 5.911113 6.100793 5.958234 6.040819
## [4041] 6.668975 5.952944 5.118949 5.183717 6.643244 6.614121 6.157117 6.758510
## [4049] 6.115372 6.337520 5.975349 6.922287 5.609735 6.550449 6.275804 6.849371
## [4057] 6.486266 6.234935 6.122744 5.798240 6.242969 6.949378 5.996632 6.037516
## [4065] 6.571721 6.332067 6.511648 7.526496 5.768825 5.652994 6.114454 6.076950
## [4073] 6.126724 6.105233 5.778487 6.205298 6.867959 6.282863 6.140322 6.197854
## [4081] 6.841400 5.834589 6.008248 6.118869 6.168996 5.713217 6.953945 6.214370
## [4089] 6.625082 6.498568 6.071600 6.435730 6.992433 7.271242 5.131073 6.407434
## [4097] 6.337510 4.899954 6.167184 6.669512 5.556484 6.088102 5.488997 6.157117
## [4105] 6.047522 6.644695 5.362735 6.528243 6.147746 5.832717 7.413208 6.094507
## [4113] 5.750196 6.342441 6.246168 6.433075 6.849371 6.840680 6.542669 6.932068
## [4121] 6.435557 5.731276 6.751834 5.176837 6.743020 6.702608 6.257303 4.768932
## [4129] 5.582786 6.094507 6.571721 6.255343 6.157524 6.615287 6.115372 6.162651
## [4137] 6.182892 6.231077 6.624867 5.495745 5.666698 6.162651 6.474897 6.197854
## [4145] 6.592701 6.949378 6.727581 6.061816 6.579941 5.848632 6.800807 6.792249
## [4153] 6.592701 6.478946 6.213783 6.054108 5.996632 6.410913 5.949422 5.717209
## [4161] 6.670532 5.013690 6.050976 6.097946 5.319835 5.823102 6.607913 6.199089
## [4169] 5.888448 5.871987 6.122744 6.812983 6.478946 6.195339 5.125605 6.917956
## [4177] 6.840959 4.897963 4.977362 6.841400 5.834365 5.176659 7.421492 6.641644
## [4185] 6.601271 6.193229 6.159343 7.544549 6.040819 5.236467 6.840959 6.800807
## [4193] 6.497049 6.251669 4.899954 5.208570 6.779216 7.123425 5.475269 6.023275
## [4201] 6.133397 5.473350 6.442204 5.110980 5.854935 5.665915 6.448555 6.471963
## [4209] 5.983947 6.565015 6.345188 5.797651 5.967405 6.992433 6.368245 6.880814
## [4217] 4.510196 6.552341 6.388108 6.850963 5.113996 5.281369 6.147300 5.895041
## [4225] 6.498633 6.025792 6.725723 6.371008 5.751711 6.963892 6.106770 6.132121
## [4233] 6.331049 5.408775 6.832542 6.447864 6.616603 6.416195 4.728541 6.173736
## [4241] 5.957232 6.644695 6.159885 5.474045 6.116055 6.615287 6.167184 6.963892
## [4249] 6.067155 5.228458 6.831727 5.722615 5.809558 5.829086 6.451924 7.257639
## [4257] 5.911113 6.407461 4.464448 5.833221 6.345188 6.487577 6.616603 5.599173
## [4265] 6.395525 6.528165 6.576109 6.168996 6.351191 5.609735 6.497304 5.949422
## [4273] 6.058587 6.922287 6.314312 6.528243 6.629689 6.032187 6.459286 6.868070
## [4281] 6.589320 5.875258 6.096978 5.603335 6.244375 4.191338 6.304879 7.453667
## [4289] 6.420468 6.246594 6.020386 6.389524 5.406978 6.337512 6.548841 6.288213
## [4297] 6.271401 5.759336 6.149435 6.940161 6.392762 5.674383 5.948023 6.244635
## [4305] 6.359487 6.377321 5.388354 6.321157 5.581439 6.377347 6.041688 5.006185
## [4313] 4.338625 6.051298 6.603101 6.411994 6.244635 6.098467 7.710270 6.064699
## [4321] 6.440874 6.853321 6.765542 6.142630 4.947890 4.982786 6.360074 6.280167
## [4329] 5.846225 5.840901 6.142938 6.537700 6.018329 6.319737 5.506808 6.334097
## [4337] 6.317604 6.246594 6.976944 5.646784 5.555916 7.092915 4.892996 4.788937
## [4345] 6.185574 6.859793 7.092475 4.065711 6.427055 6.983096 5.697977 6.725514
## [4353] 6.051298 6.251898 6.405845 6.217106 5.992996 5.979202 7.143016 5.681157
## [4361] 6.503433 4.653679 6.719984 4.849553 7.067561 6.531927 5.541965 6.675835
## [4369] 6.428786 5.859612 5.820276 7.385495 6.125418 6.688260 5.899716 6.539383
## [4377] 7.385495 6.232312 6.197656 6.147146 6.829801 5.676903 6.239647 6.530562
## [4385] 6.276144 5.631646 6.733822 6.237510 6.439159 6.708084 6.064219 6.228559
## [4393] 6.194371 6.410712 5.820465 6.181861 6.750660 5.802087 6.405386 5.339806
## [4401] 6.066016 6.444174 5.166846 6.287652 5.652279 6.017625 5.900165 5.752659
## [4409] 5.652511 5.441973 5.772065 5.882616 6.333864 6.177902 6.335542 6.061961
## [4417] 6.737577 6.946981 6.595796 5.584765 5.821711 6.280167 5.911190 5.680690
## [4425] 5.749493 6.072683 6.947957 6.524873 6.318827 5.515538 5.757335 5.921760
## [4433] 5.581089 5.566748 6.083529 6.274124 6.504015 7.292201 6.017625 6.008375
## [4441] 6.529139 5.959420 5.445787 6.361902 5.859705 5.796567 6.609327 5.730156
## [4449] 5.633842 6.354772 6.211590 6.888757 6.485777 5.819239 6.039995 6.772829
## [4457] 6.737577 6.595796 5.693660 6.280041 5.999007 5.414089 6.225573 6.807386
## [4465] 6.643705 6.530427 6.018975 6.770291 6.902009 6.278819 5.645448 6.121915
## [4473] 6.123752 5.866092 7.020230 7.250163 5.375569 6.980027 5.631646 6.690987
## [4481] 6.624018 5.717735 7.453667 6.026899 6.778575 6.406804 6.631461 7.013610
## [4489] 5.970380 5.786597 6.857517 5.459495 7.053568 6.963888 5.724758 6.041458
## [4497] 6.292912 5.931238 4.756632 7.006580 5.326137 6.493492 6.253999 6.287652
## [4505] 6.041458 6.485941 6.937555 6.643676 6.134257 6.328405 6.954862 5.675911
## [4513] 6.629689 6.494137 5.808319 6.271583 6.902009 6.039413 5.393435 7.239366
## [4521] 6.053310 5.939286 6.011030 6.134428 6.402804 5.989480 6.089423 6.272847
## [4529] 6.719984 5.218895 6.279121 5.177931 6.577566 6.716010 6.234705 6.725460
## [4537] 6.729953 6.486254 5.829480 5.677834 5.854167 5.202157 6.397290 6.690231
## [4545] 6.324057 4.975235 5.536930 6.420518 6.014575 6.612763 6.247192 5.900106
## [4553] 5.111089 5.945065 6.352750 5.722295 6.280041 6.161341 5.703311 6.393035
## [4561] 6.064443 5.802087 6.387393 7.710270 6.317604 6.069456 6.292912 7.092915
## [4569] 5.887536 6.622550 5.487802 5.763174 5.348323 6.269721 6.708403 5.562659
## [4577] 6.648725 6.857517 6.257418 6.477919 6.807355 6.338553 6.675835 5.069336
## [4585] 6.630327 6.556191 6.638367 5.822063 6.675784 6.761485 6.071075 5.434858
## [4593] 5.865472 6.314685 5.164690 5.288043 5.456649 6.251859 5.681303 6.431321
## [4601] 6.730676 6.580981 5.629931 6.069807 6.114066 6.570314 6.815126 6.284204
## [4609] 6.405369 6.695458 6.259804 6.542438 6.800461 6.203622 6.342287 6.201647
## [4617] 6.306040 6.690987 6.421813 5.900792 6.310006 6.283284 6.802999 6.288213
## [4625] 6.657366 5.834571 7.519636 6.278819 6.364414 6.462598 6.072683 6.387354
## [4633] 6.153047 5.865472 6.650645 6.177707 6.624574 6.835042 6.259804 7.250163
## [4641] 5.336636 6.223676 5.763174 6.545253 6.329855 6.032060 5.358339 6.332181
## [4649] 7.143016 5.361133 6.157716 6.342287 6.087150 6.084214 5.518031 5.665161
## [4657] 6.577566 6.644135 6.381210 6.640785 5.959420 6.105433 5.722295 6.191901
## [4665] 6.976130 6.508754 6.553032 5.754401 6.700414 7.232405 6.269478 6.360837
## [4673] 6.590529 5.333115 6.945930 5.971949 5.725881 6.690231 5.762770 6.521384
## [4681] 5.779136 6.228821 6.713794 6.223248 6.648725 6.718050 5.544517 6.341135
## [4689] 5.822743 6.573657 6.191223 6.428463 6.220162 5.875202 5.823483 6.284204
## [4697] 5.634753 6.940163 5.777996 6.570314 6.406804 6.049227 6.584757 6.437402
## [4705] 6.488992 6.213486 7.236480 6.332567 6.024563 6.190697 6.226003 5.467208
## [4713] 6.563200 5.850611 6.291906 6.601684 5.936074 6.785643 5.283442 6.564308
## [4721] 6.811488 6.340926 5.547491 6.355245 5.956853 6.337512 6.120922 6.565884
## [4729] 6.791782 5.229789 6.069570 6.118877 6.676963 6.340306 5.278094 6.903517
## [4737] 6.382286 6.239647 6.506922 6.382286 5.830869 6.359487 6.888757 6.537700
## [4745] 6.729953 6.624018 6.194747 6.186160 5.958457 6.371671 6.338553 6.219682
## [4753] 6.516772 6.482980 5.945346 6.424062 6.578064 5.946223 6.617635 6.409323
## [4761] 6.089423 6.405369 6.321765 5.383918 6.521488 6.364414 6.334097 5.829480
## [4769] 6.438818 5.225791 5.928751 6.651080 5.715655 6.134054 6.439159 5.953138
## [4777] 6.506922 6.548841 5.965078 6.336236 6.279121 6.673868 5.792108 5.685986
## [4785] 6.180330 6.182848 5.722845 6.083176 6.713794 6.225573 5.782844 7.428480
## [4793] 6.765542 6.340926 6.776275 6.977939 6.824061 6.608824 6.306040 5.958457
## [4801] 5.531398 6.192504 6.524873 6.122478 6.584748 5.900165 6.691680 5.308719
## [4809] 6.142225 6.060813 6.725460 6.722148 5.610623 6.305680 5.541419 6.190697
## [4817] 7.428480 6.656342 6.708403 6.008396 6.482980 7.561135 5.185111 5.809077
## [4825] 6.352750 6.945930 6.661853 6.265625 4.703622 5.441973 5.922478 6.578064
## [4833] 5.700264 5.644808 5.934200 6.754921 5.535150 6.053525 5.475472 5.732250
## [4841] 6.976130 5.696030 5.787772 6.508754 6.119780 5.584780 5.570582 6.141150
## [4849] 6.651713 5.685220 6.194371 6.903517 6.075942 6.257990 6.940163 6.411715
## [4857] 6.539383 6.509897 6.965107 5.919550 7.065773 5.637575 5.472125 6.085198
## [4865] 5.319421 6.358206 6.068667 6.813058 5.776427 6.947957 6.316982 6.387393
## [4873] 6.153047 6.324057 6.589630 6.791782 6.991990 6.351738 6.127282 6.029400
## [4881] 6.396075 4.779458 7.001353 4.256146 6.410712 5.132649 5.703060 6.234705
## [4889] 6.200600 6.237510 5.533200 5.483018 6.103608 6.134428 7.519636 6.297741
## [4897] 6.117525 5.605883 6.402425 6.829801 6.766259 5.605635 5.859612 5.372824
## [4905] 6.506929 5.616952 6.644135 5.645998 6.190667 6.785643 5.682636 6.459286
## [4913] 6.053525 4.537229 6.673868 6.835042 5.526147 6.724876 6.521583 5.867956
## [4921] 5.956853 5.653556 6.228559 6.833317 6.312441 5.553986 7.084100 6.226003
## [4929] 5.799082 6.411715 6.375442 5.042278 6.774212 6.271583 6.954862 7.762421
## [4937] 6.775657 6.049227 6.128478 6.584748 5.523602 7.067561 6.254937 6.175739
## [4945] 5.716408 5.930999 5.697129 6.708084 5.915292 6.274124 6.149435 5.488549
## [4953] 5.878468 2.721445 6.579576 6.375648 6.254937 6.095442 6.858102 6.430168
## [4961] 6.683867 5.934200 6.239823 6.332567 6.230548 6.314685 6.336954 6.807355
## [4969] 5.554471 6.074250 5.652054 5.541419 6.521583 5.539043 5.252243 7.306845
## [4977] 6.421813 6.733822 6.258007 6.150401 5.676903 4.968396 6.383833 5.979202
## [4985] 6.633777 6.637476 5.703927 6.167925 6.018975 6.140297 6.444765 6.807386
## [4993] 6.191223 7.395112 6.593201 6.075942 6.516772 5.824156 5.273788 6.064219
## [5001] 6.564308 6.219682 6.321157 6.217846 6.599166 6.462598 5.627802 5.665922
## [5009] 6.757656 6.341135 6.485777 5.954507 5.887536 6.503433 5.953526 6.406013
## [5017] 7.165777 6.612763 6.217846 6.775657 5.939286 5.989480 5.991863 5.703311
## [5025] 6.224484 5.840243 6.617635 5.581089 5.946223 6.766259 6.599166 5.353109
## [5033] 5.721227 6.811488 6.193191 5.973329 6.274598 6.039413 6.316982 4.871211
## [5041] 6.528589 6.321765 6.321344 5.935093 7.762421 6.691494 6.994842 6.137528
## [5049] 6.579576 6.814046 6.394708 5.941936 6.141150 6.464295 6.383833 5.945871
## [5057] 5.593255 7.120268 6.757656 5.752878 5.911804 6.603101 6.004678 6.064443
## [5065] 5.751867 6.036941 5.633842 6.551119 5.531124 5.781384 6.269721 5.796771
## [5073] 5.791184 5.861160 6.910872 5.800680 5.918229 6.142222 6.318131 6.425187
## [5081] 6.120922 5.946855 6.032187 5.365262 5.605609 6.605921 5.954507 5.991863
## [5089] 5.993451 6.098350 5.759948 6.391866 4.774222 6.186160 5.786597 7.020230
## [5097] 4.747065 7.161413 5.593255 6.129853 6.430168 7.076804 7.321265 5.315731
## [5105] 5.947697 6.485835 5.621286 6.142222 5.488174 6.937555 5.793625 6.148081
## [5113] 6.573657 6.186867 6.630327 6.394708 5.323175 5.875202 5.730156 6.590986
## [5121] 5.931238 6.813058 6.077981 5.822743 6.777992 5.601285 6.565884 5.981417
## [5129] 6.164817 6.530427 7.132113 5.650046 6.700414 6.608824 5.764462 6.251679
## [5137] 6.251859 6.464295 6.668914 6.355563 5.953138 5.960425 6.488992 6.509897
## [5145] 5.627376 6.539543 6.147146 6.980027 4.557020 6.305680 5.859705 6.095475
## [5153] 5.699310 6.436546 6.156172 5.397566 6.730676 6.556191 6.186867 6.154374
## [5161] 6.291906 6.302972 4.990370 6.095475 6.420468 6.836439 6.397290 6.664634
## [5169] 6.225383 6.864971 5.271778 5.266790 6.451715 6.859793 6.307361 6.333864
## [5177] 6.589320 7.124297 6.129949 6.328405 6.545253 5.861160 5.256861 5.061863
## [5185] 6.780589 7.196650 6.193831 5.974650 5.411333 5.633489 6.377347 6.444174
## [5193] 6.643676 5.675911 6.194747 5.356022 6.018596 6.405386 7.001353 5.875258
## [5201] 4.945268 6.254572 5.500708 6.362531 6.963888 6.321344 5.745720 6.358206
## [5209] 5.702787 7.236480 5.681058 4.830291 6.032060 6.239823 6.149086 5.782844
## [5217] 6.657366 5.953526 6.492123 6.831847 5.240270 5.601285 5.929300 6.420393
## [5225] 6.977939 6.238451 6.228821 5.513783 6.310006 6.257418 5.373571 5.606512
## [5233] 6.114631 6.154374 5.169114 5.629248 6.137528 5.763379 6.272847 6.442596
## [5241] 6.154360 6.428786 6.802999 5.331608 6.278060 6.793185 6.643705 7.165777
## [5249] 6.774212 5.681058 5.525611 5.624518 6.318938 6.304879 5.539094 6.261168
## [5257] 5.732250 6.586156 6.375648 5.820276 5.898267 6.271760 6.676963 5.928751
## [5265] 5.999007 6.763469 6.165239 5.723168 6.529139 6.276144 6.800461 6.361902
## [5273] 6.436546 6.053310 6.695458 5.380965 7.053568 5.824156 5.518031 6.236792
## [5281] 6.538179 6.521384 6.507262 5.557433 6.454383 5.848204 5.483018 6.020386
## [5289] 6.780589 6.294894 5.584780 5.530959 5.298391 5.925015 5.535150 5.820301
## [5297] 5.584765 5.888204 6.090548 6.251898 5.848204 6.335542 6.551119 6.991990
## [5305] 6.229828 6.180330 7.321265 5.609803 5.950781 6.965107 6.777992 6.584757
## [5313] 5.876214 7.161413 6.814046 4.654476 6.787648 6.067487 5.995096 5.724032
## [5321] 6.283284 6.428463 5.915292 6.442596 6.340306 5.591208 5.447316 6.334712
## [5329] 5.665704 6.401549 6.994842 6.486254 6.129853 6.249902 6.631231 4.787562
## [5337] 6.269478 6.420755 5.917663 6.590529 5.612551 5.920476 6.656342 6.668914
## [5345] 6.049732 5.241025 6.152363 6.322888 6.122478 6.177707 6.406013 6.724876
## [5353] 5.199074 6.294894 6.664634 7.084100 6.223248 6.377321 5.752878 5.482600
## [5361] 5.633340 5.981312 6.389241 5.745720 6.071529 6.244375 6.409323 6.420755
## [5369] 6.354772 6.853656 5.515538 6.121915 5.776427 6.258007 6.161786 5.760717
## [5377] 6.315908 5.554471 7.223847 7.306845 6.811472 5.992004 6.043699 6.605921
## [5385] 5.240935 5.232351 6.811472 6.432925 6.220162 6.326439 6.693029 5.463363
## [5393] 5.764103 6.360837 6.150401 6.493492 5.717349 6.772829 6.114066 6.142630
## [5401] 6.149086 6.563200 6.223676 5.821033 6.164747 7.561135 5.730240 6.083176
## [5409] 6.722445 6.060813 6.521488 6.528589 7.196650 6.332181 6.437402 6.506929
## [5417] 6.651713 6.318938 5.574523 6.531927 6.355245 5.671244 6.389241 6.722445
## [5425] 6.624574 6.164747 6.716010 5.527441 5.922478 6.085198 6.079655 5.268192
## [5433] 5.428431 5.822229 6.778575 7.395112 5.482600 5.698906 6.038605 6.107259
## [5441] 6.725514 6.589630 5.821711 5.522923 6.507262 6.631231 5.637423 6.824061
## [5449] 5.862842 7.092475 6.440874 6.193831 5.982251 6.388485 5.584775 6.156172
## [5457] 6.675784 6.232312 6.261168 6.312441 6.432925 5.898267 6.638367 6.381210
## [5465] 5.490927 5.699310 6.565806 5.223642 6.688260 6.853656 6.451715 6.539543
## [5473] 6.360074 5.265466 6.371671 6.182848 6.201647 5.867956 5.876214 5.921461
## [5481] 6.661853 6.154360 5.950781 6.049732 6.274598 6.691494 6.402425 6.008375
## [5489] 7.292201 6.485835 7.006580 6.477919 7.239366 6.492123 6.868070 6.833317
## [5497] 6.763469 6.253999 6.117525 5.490482 5.987989 6.668465 6.427055 6.601684
## [5505] 5.909805 5.563887 6.815126 5.533200 6.322888 5.465573 5.868521 5.645998
## [5513] 5.490455 7.166507 6.454383 6.334712 5.936074 5.786114 6.553032 6.754921
## [5521] 5.752659 6.015409 5.986026 5.995096 5.877517 6.411994 6.103464 7.232405
## [5529] 6.640785 6.864971 6.326439 5.796771 6.118877 6.161786 6.061961 5.821033
## [5537] 5.827621 6.633777 6.776275 6.396075 6.565806 6.910872 6.793185 6.440914
## [5545] 6.054108 6.800275 6.831847 6.106770 6.957627 5.928648 6.000993 6.314674
## [5553] 6.041283 6.624867 7.519302 6.917956 6.513100 6.015788 6.824747 6.105233
## [5561] 6.673927 6.103608 6.955836 6.606740 6.978521 6.778117 6.584003 6.529242
## [5569] 6.512525 7.073210 6.611963 6.514784 6.423665 6.116495 6.750188 6.065850
## [5577] 6.316873 6.516465 6.964909 6.951321 7.079263 6.700817 7.029525 6.848110
## [5585] 6.102670 5.399936 6.158669 6.173493 6.635900 6.085561 6.955836 6.257418
## [5593] 6.565321 6.570593 6.654125 6.378182 6.514784 6.607913 6.642373 6.375745
## [5601] 5.972385 6.586266 7.739791 5.360180 7.034157 6.801985 6.472504 5.979099
## [5609] 6.143498 4.946878 6.964909 4.797649 6.794899 5.568814 6.565321 6.686373
## [5617] 5.647202 6.434437 6.539541 6.517427 6.769659 6.533806 7.139755 6.617871
## [5625] 6.391226 6.783467 6.676077 6.654125 6.760552 5.140751 7.073210 7.022132
## [5633] 6.535237 5.965808 5.970650 5.863581 6.165821 7.692627 6.378182 6.562232
## [5641] 5.347749 6.581757 7.008072 6.081535 5.612558 5.472859 6.452035 6.171939
## [5649] 6.062497 6.541111 6.951321 6.509637 6.482949 6.065981 6.227015 5.674305
## [5657] 6.725814 6.825433 6.780783 6.391724 5.153773 6.462567 6.431631 5.755368
## [5665] 6.494579 6.848253 6.189115 6.098866 6.854074 7.042369 5.698322 6.570593
## [5673] 6.391724 5.060391 6.746246 6.467954 6.472504 6.661965 6.331527 5.943206
## [5681] 6.524950 6.765356 5.603797 5.581246 6.273293 5.309824 6.239540 6.410829
## [5689] 6.805275 5.795027 6.024145 6.658900 6.617422 5.910329 6.978521 6.664068
## [5697] 6.073389 5.624041 6.746246 6.668454 6.275448 6.825433 6.426562 5.856252
## [5705] 6.293882 6.473825 6.534238 5.973414 6.512525 6.852369 5.226729 6.434437
## [5713] 7.355267 5.031092 5.289276 6.393914 7.701512 5.855400 4.704568 5.657612
## [5721] 5.113593 6.391690 5.973329 6.433805 6.494579 5.215539 6.331891 5.639092
## [5729] 6.760552 5.604319 5.821448 5.861362 6.667763 6.265291 5.821740 6.450106
## [5737] 7.516171 5.907955 6.852817 5.988164 6.098866 5.979099 6.947048 5.278788
## [5745] 5.795027 6.658763 6.807531 5.851461 6.466112 5.537801 5.469653 5.972385
## [5753] 5.191685 6.214037 6.036658 6.717844 5.301639 6.200255 6.171328 4.714367
## [5761] 5.581846 6.735691 6.018348 6.155132 6.398977 6.377157 6.165821 6.461903
## [5769] 6.791662 6.219262 4.708784 6.567766 6.489584 6.701475 6.426562 6.567703
## [5777] 5.789970 5.051945 6.281668 6.619205 7.029525 5.993824 5.889315 6.601076
## [5785] 6.467954 6.482949 6.769776 6.466112 6.072885 6.987673 6.602458 6.050582
## [5793] 6.030646 7.811117 5.891552 6.445507 6.551714 7.739791 6.189115 6.555511
## [5801] 6.731257 6.409332 6.472330 7.413905 6.331527 6.126495 5.989360 7.811117
## [5809] 6.769113 6.121767 5.191008 6.769113 6.377970 6.676077 6.165210 6.780783
## [5817] 7.692627 6.450128 6.296472 6.805275 7.022132 7.277641 6.987359 6.423573
## [5825] 5.526268 6.462567 6.139887 7.553193 6.372103 6.401495 5.110082 6.617871
## [5833] 5.389873 6.519002 6.821245 6.085561 6.072113 5.855400 6.017238 6.781846
## [5841] 7.790216 5.698322 6.539541 5.098127 7.413905 5.191008 6.778117 6.700049
## [5849] 6.400277 6.674700 4.376978 6.485607 6.369265 6.567703 6.791662 5.100549
## [5857] 6.653932 6.118099 5.979345 6.441278 6.214037 6.606740 6.906423 6.485705
## [5865] 5.632128 5.969570 6.993119 6.668454 5.555640 5.970650 6.972878 5.581846
## [5873] 6.334059 5.410451 5.968109 6.514437 5.555640 4.586738 6.972878 5.309824
## [5881] 6.280924 6.833934 6.757025 6.685090 6.711477 6.293882 6.642373 5.272790
## [5889] 6.169062 5.537139 5.272790 6.476406 5.874997 7.285042 6.167848 5.537736
## [5897] 6.280924 5.830939 5.054783 4.818354 4.954852 5.717367 6.369265 6.658900
## [5905] 6.370905 5.389873 6.524974 6.375745 6.858102 5.919584 6.607913 6.055657
## [5913] 5.646513 6.476406 5.851461 6.534238 6.757025 6.743382 6.836439 6.341957
## [5921] 6.255702 6.139887 6.848253 5.712066 4.716946 6.200255 6.529242 4.707917
## [5929] 5.907955 6.543515 7.250208 6.254694 6.485705 6.720303 6.867357 6.801985
## [5937] 7.285042 7.012920 6.355355 6.617422 6.257819 4.287188 6.403144 6.594723
## [5945] 6.423665 6.662438 6.610927 5.568814 6.359754 6.239540 6.143498 6.685090
## [5953] 6.852817 5.859005 7.034157 6.686373 7.139755 5.639092 6.221113 6.041761
## [5961] 5.590348 6.259539 6.807531 6.700049 6.200110 6.581757 5.870871 4.712423
## [5969] 6.656901 6.656901 6.025988 6.254694 6.398977 5.856252 5.291273 6.519002
## [5977] 6.653932 6.769776 5.735847 6.024145 6.025988 6.566648 6.226127 7.077951
## [5985] 5.526268 6.275448 6.142230 6.601076 4.814214 6.489584 6.165210 5.674305
## [5993] 6.429121 6.489431 6.700817 6.316873 5.829282 6.717844 4.722442 6.041761
## [6001] 6.735691 6.009787 6.128328 6.947565 6.489431 7.701512 7.077951 6.286024
## [6009] 6.171328 6.769659 6.131279 6.524950 6.136859 6.115288 5.896224 7.042369
## [6017] 5.899751 6.319871 6.987359 6.687231 6.882176 5.909243 6.221113 6.704596
## [6025] 3.727691 6.096159 6.882176 6.619205 6.072885 7.223607 6.200110 6.441278
## [6033] 6.594723 6.661965 5.757986 5.708414 6.142045 6.667763 6.992040 4.563453
## [6041] 6.257418 6.852369 6.584003 6.116495 7.553193 6.265291 4.615899 5.840933
## [6049] 6.403144 6.635900 6.782708 7.539928 5.267101 6.393914 6.922320 5.755368
## [6057] 7.008072 5.993366 6.993119 5.921096 6.450106 5.426436 5.448361 6.410829
## [6065] 6.341957 6.409332 7.079263 5.830939 6.617685 6.517427 5.060083 6.687231
## [6073] 5.709805 5.709805 6.821245 6.602458 6.452035 6.684521 6.833934 5.427583
## [6081] 6.473825 7.271242 6.610927 6.617685 6.509637 6.725814 6.373284 6.704596
## [6089] 6.485607 5.969570 5.413952 5.921096 6.252815 6.555511 6.370905 6.018348
## [6097] 6.765356 5.629828 6.662438 4.768083 5.956881 5.646513 5.341137 4.580647
## [6105] 5.360180 7.516171 6.252815 7.012920 6.273428 7.223607 5.789970 6.541111
## [6113] 6.173493 5.549959 6.535495 5.915027 4.986701 6.551714 6.445507 6.490004
## [6121] 6.947565 6.535237 6.987673 6.227015 6.586266 6.062749 5.593752 5.953957
## [6129] 6.746097 6.450128 6.848110 6.781846 5.696292 6.273428 6.960037 6.794899
## [6137] 6.684521 6.121767 5.993366 5.756046 5.766778 6.690949 6.263792 6.259539
## [6145] 6.142045 6.331891 6.658763 6.401495 6.567766 5.593752 6.155132 4.995328
## [6153] 6.008009 6.030646 6.674700 6.580981 6.128328 6.081535 5.721235 6.524974
## [6161] 4.331288 6.533806 6.922320 5.889315 6.906423 6.158669 6.095455 6.115288
## [6169] 6.131279 6.255702 6.746097 7.250208 6.219262 5.757601 6.459431 5.469934
## [6177] 6.812159 6.812159 6.459431 5.583098 6.344841 6.298809
##
## [[3]]
## [1] 6.214323 6.475944 6.213902 6.059410 5.951832 6.305717 6.037380 6.686011
## [9] 6.496249 6.312407 6.735238 6.403024 7.075326 5.671822 6.368519 5.998758
## [17] 6.022112 6.861048 5.994936 6.806328 6.072255 5.697422 6.755997 5.706708
## [25] 6.423176 6.040236 6.187733 6.465871 6.381788 5.803821 6.389597 6.487997
## [33] 6.013922 5.695081 5.964327 5.991259 6.198657 7.067891 5.577619 6.157709
## [41] 6.827295 6.708312 6.176517 6.268291 5.862587 6.252744 5.516088 5.763885
## [49] 5.789749 5.356109 6.478320 6.504962 6.320793 5.651554 5.555706 5.795100
## [57] 7.154670 6.086086 6.673730 6.416746 7.200715 6.870352 5.379308 6.759314
## [65] 5.944800 5.858945 5.946976 5.423160 6.203038 5.813664 6.137044 6.571323
## [73] 5.409398 6.759425 6.532078 6.540318 5.833581 5.960777 6.720867 6.586479
## [81] 5.809803 5.805034 6.406367 5.622852 6.068909 5.869706 5.859679 6.416205
## [89] 6.793572 5.697422 6.165843 6.619558 6.828527 6.558571 6.017482 5.737349
## [97] 6.398359 6.739392 6.658684 6.499511 5.927826 5.676424 5.312405 7.145100
## [105] 6.039776 6.655639 5.697496 5.872190 5.364326 6.337381 6.303552 6.254897
## [113] 6.093180 5.984990 6.264811 5.533660 6.789589 6.197114 6.999210 6.252386
## [121] 5.674771 6.239729 6.483632 5.527877 6.009542 5.486666 7.200715 6.067003
## [129] 5.928557 6.268381 6.562075 6.719546 6.403523 6.864850 6.045139 5.299954
## [137] 5.456903 5.954289 6.099377 5.731070 5.601158 6.277269 6.270816 6.564361
## [145] 5.897950 5.241221 5.962698 6.440781 6.239729 5.782385 5.580382 6.408772
## [153] 6.830478 6.323780 6.084675 5.868502 6.381788 5.231067 6.468410 6.077502
## [161] 5.687887 6.447481 5.864802 6.399996 5.459230 5.987951 6.158461 5.485722
## [169] 6.293357 5.912523 6.566379 6.188739 6.851641 5.299433 6.567679 5.043147
## [177] 6.279831 6.096257 6.243611 5.404868 6.683327 6.838548 5.912523 5.390289
## [185] 5.811102 6.522807 5.733940 6.049689 5.955991 5.947226 6.406367 6.117734
## [193] 5.381015 5.798262 6.406323 5.264447 6.575640 6.297047 6.076579 5.626071
## [201] 6.441468 7.192142 6.722674 5.211847 6.120693 5.992122 5.537343 6.229491
## [209] 6.207714 5.594283 5.802236 5.859585 6.085899 6.708312 5.144204 6.651725
## [217] 6.137044 6.496193 6.196888 6.410700 6.416746 5.955997 6.506840 5.372347
## [225] 6.618924 6.028683 6.334064 6.013922 6.435510 6.018682 6.086969 6.090791
## [233] 5.418298 5.124978 6.756753 5.616603 6.299278 7.006095 6.084768 5.541442
## [241] 5.563089 6.590810 6.816927 5.533905 6.039885 6.615391 5.953928 5.786981
## [249] 6.503470 6.113186 5.945868 7.311445 6.250172 5.529797 5.530755 6.317704
## [257] 6.511866 6.467357 5.977739 6.246242 5.851187 6.497525 6.186393 5.431184
## [265] 6.735381 5.138554 5.881340 5.814507 6.620792 6.031814 6.655639 5.115952
## [273] 5.209507 5.697496 6.678989 6.499511 5.947226 6.076313 6.395990 5.561473
## [281] 6.393097 5.844978 6.190043 6.181630 6.065841 6.823849 6.647689 5.869704
## [289] 6.546191 5.847593 5.701938 6.365300 7.313792 5.984490 7.133895 6.718534
## [297] 5.643373 6.739977 6.650768 6.401091 6.608923 6.483632 6.231436 5.775596
## [305] 6.111948 6.267374 5.308887 6.330978 6.544489 5.335141 5.783895 7.145100
## [313] 5.781351 6.053013 6.474790 6.600281 5.803886 6.816927 6.520110 6.691212
## [321] 7.090046 6.219572 6.094395 6.125758 5.953696 6.776546 5.666649 5.828305
## [329] 6.560386 6.407720 6.562075 6.228065 6.996009 5.672174 6.776546 6.522807
## [337] 6.838548 5.899502 4.498551 6.285290 6.527566 6.587955 5.998771 5.760102
## [345] 6.386691 5.512076 6.187733 6.170497 5.716861 5.868809 6.399378 7.427804
## [353] 5.726163 6.702246 5.939475 6.570547 6.679337 5.778753 6.417444 6.970654
## [361] 5.366012 6.768260 6.202355 6.722674 6.629158 6.371167 6.866911 5.819617
## [369] 6.879774 6.640833 5.966172 5.952199 6.976406 6.395990 6.703151 5.916638
## [377] 6.600994 7.074764 5.883565 6.346851 6.674459 6.073143 6.149840 5.634886
## [385] 5.762658 5.874765 6.565053 6.323780 5.785841 6.039598 6.267374 6.138116
## [393] 6.857211 6.145583 6.116215 6.720867 6.511866 6.155716 6.116931 5.953696
## [401] 6.268381 7.479165 6.606871 6.231436 6.555860 5.855325 4.565945 5.205125
## [409] 5.475349 7.051826 6.229083 6.059823 7.027664 5.518075 5.113447 6.544489
## [417] 6.629158 6.397948 6.398359 6.477867 5.249878 7.075326 6.537870 6.200609
## [425] 5.673864 6.446713 5.540393 6.100699 5.833903 6.058281 5.816295 6.203643
## [433] 6.564361 5.373629 5.320420 5.434037 6.067003 5.918434 5.807813 6.244536
## [441] 5.439502 6.068909 6.277269 7.065809 5.753085 6.468388 6.640002 6.362459
## [449] 5.859126 6.999210 6.691212 6.017456 5.604614 6.522904 5.648079 6.379684
## [457] 6.113522 6.679298 6.674459 6.292924 6.198657 4.663810 5.600868 6.764230
## [465] 6.565781 5.742753 6.133835 6.853732 7.133895 5.869254 4.852450 6.399378
## [473] 5.360625 6.739977 6.647232 5.993644 6.138350 5.963165 5.261405 5.772819
## [481] 6.166523 6.405657 6.519229 6.039664 5.306783 6.269089 6.009542 6.040969
## [489] 5.906860 5.975892 6.241648 6.292300 6.430251 5.918434 6.662019 5.840502
## [497] 5.345587 5.934708 6.157365 5.543729 6.276295 5.410695 6.134995 6.861732
## [505] 6.096257 5.452485 6.176517 6.793572 6.811951 5.923786 5.776623 5.111360
## [513] 6.086969 5.118627 5.493227 6.424074 6.416205 6.258785 5.994233 5.755711
## [521] 6.057241 6.227829 6.365300 7.030818 6.268291 6.718063 5.926221 6.435510
## [529] 6.286790 6.077502 6.486092 5.193085 6.412927 6.026075 6.806328 5.970203
## [537] 6.030035 6.120458 5.710089 6.269089 5.461859 6.060446 6.187526 6.032855
## [545] 6.755997 5.279104 5.540393 5.917053 6.687802 6.344872 5.814459 6.412927
## [553] 5.053647 6.017482 6.032355 6.703151 6.206685 5.456712 6.947153 5.206141
## [561] 7.109999 5.530432 6.867650 5.682441 5.580860 6.114004 6.854310 5.934708
## [569] 6.745715 5.690606 5.229039 6.522166 6.627766 6.759425 5.302130 5.912462
## [577] 5.927826 6.032855 6.152271 6.223685 6.593174 6.531065 6.263612 5.960777
## [585] 6.483130 6.446713 5.832714 5.853898 6.526855 6.571323 6.113974 6.028683
## [593] 5.820465 6.240436 6.183374 5.891366 6.667923 5.819241 6.714556 5.882161
## [601] 6.041313 5.488496 6.292538 6.628855 6.244536 5.481752 6.166523 6.600281
## [609] 5.378787 5.802236 5.806692 6.373389 6.441468 5.813919 6.571468 6.794679
## [617] 6.026617 6.049255 7.479165 6.143801 5.470013 6.440781 5.876590 6.674973
## [625] 6.735238 7.067891 6.656344 6.568477 6.768260 6.407720 6.181602 5.233382
## [633] 5.775596 6.227469 5.868809 6.187526 6.016673 5.695081 7.250320 6.014711
## [641] 6.566379 6.871330 5.782661 6.317704 6.683327 6.789589 6.215124 5.913455
## [649] 5.912245 5.596193 6.430404 6.418895 5.894240 5.827670 5.432117 6.084023
## [657] 5.862587 6.314302 5.943818 5.888672 5.740564 6.592249 6.017456 5.865979
## [665] 5.269491 6.861732 6.589804 5.977739 5.973019 6.446289 5.014126 5.547345
## [673] 6.196888 5.510473 5.343177 6.314424 5.750863 6.099377 6.215124 5.397703
## [681] 6.361492 6.121791 6.579710 6.854310 5.764073 6.309797 5.562895 6.190807
## [689] 6.968906 6.131200 6.049689 5.955991 6.395968 5.612538 5.806018 6.031998
## [697] 6.533817 6.102173 5.541512 5.948464 5.846194 6.125251 6.523530 6.366881
## [705] 5.344476 6.220897 5.894709 5.465588 5.738543 6.462610 6.649704 7.250320
## [713] 6.650768 6.384514 6.090791 5.488096 5.796002 6.823849 5.873287 6.064092
## [721] 5.626071 5.575897 6.696075 5.600880 5.657032 6.070633 6.546191 6.508544
## [729] 5.680261 5.724357 6.719988 5.814507 6.487997 6.558571 5.946976 6.126859
## [737] 5.313074 5.951832 5.894709 6.970654 6.589804 6.125022 5.485736 6.503470
## [745] 6.297047 6.086725 6.423510 7.090046 6.222356 6.147297 5.682717 5.773354
## [753] 6.382337 6.602970 6.388565 6.346851 6.679298 6.582203 6.203643 6.471124
## [761] 6.575985 6.043017 6.771699 7.290267 6.861048 5.713598 5.497478 5.869704
## [769] 6.958303 6.097882 6.435635 5.846194 6.672783 6.871330 5.563741 5.804118
## [777] 6.181602 5.631726 5.869315 6.462610 6.192227 6.555860 5.999246 6.138350
## [785] 6.375115 6.106326 6.417444 5.631868 5.806692 6.285290 6.341830 6.270157
## [793] 6.990671 6.647689 6.696986 6.073143 5.841262 5.071766 5.769230 6.767684
## [801] 6.548940 6.270157 6.745715 6.718063 6.225525 5.229518 6.157331 6.375115
## [809] 6.679337 6.384514 5.138447 6.125758 6.225525 6.602970 6.620792 6.075219
## [817] 6.031160 6.314424 5.425397 6.152271 5.859585 5.749390 6.126859 5.702911
## [825] 5.859679 6.449487 7.313792 7.027664 5.733059 6.229491 5.828500 6.015345
## [833] 5.905183 6.450947 6.473076 5.709456 6.116345 5.362750 5.638743 5.923510
## [841] 6.604922 5.681968 6.147253 6.503462 5.215371 6.851641 6.906230 6.879774
## [849] 5.650952 6.205094 5.577619 5.801699 6.604324 5.381880 5.925131 6.700142
## [857] 5.293492 5.815961 6.472034 6.276295 5.751765 6.662019 5.731602 6.468410
## [865] 6.156552 6.604324 6.245094 5.639160 5.782498 6.764230 5.386884 5.994936
## [873] 5.120646 6.156999 6.289641 5.995425 6.286958 6.088883 6.293357 6.085625
## [881] 6.597228 6.630575 5.713329 5.732449 6.575985 5.572528 6.811951 5.508168
## [889] 6.178364 5.773163 6.568477 6.322192 5.856364 6.293686 6.084768 5.808229
## [897] 6.874904 6.009278 5.595226 6.733315 6.586915 5.787785 6.538275 5.642339
## [905] 6.782866 5.768549 5.896319 5.512300 5.770923 6.639061 6.916999 5.580825
## [913] 6.247373 5.804514 6.227475 5.664145 6.593174 5.427940 6.165843 6.453677
## [921] 5.814510 5.822287 6.539714 5.048121 6.715328 5.820465 6.647232 6.198166
## [929] 5.988795 6.334064 6.005557 7.016160 6.086086 5.135329 6.562774 6.418895
## [937] 5.716956 6.039598 6.474790 6.587955 6.156552 6.156269 5.336743 6.539714
## [945] 5.917763 5.267115 6.079658 6.615391 6.005557 6.586479 6.450557 6.705871
## [953] 6.160094 5.826004 6.282358 5.840502 5.913455 5.589211 5.815961 6.121791
## [961] 7.109999 5.358016 6.791293 5.838826 6.217007 5.926221 5.224452 5.639169
## [969] 6.294545 6.480631 6.702246 5.465390 5.928557 5.806500 5.979497 6.521079
## [977] 6.187824 5.888117 6.058281 5.246752 5.511252 5.875674 6.528429 6.239162
## [985] 6.111919 5.519322 5.081438 6.009278 6.885238 6.282358 6.382337 6.424074
## [993] 6.857211 6.996009 6.560386 6.520110 6.241648 5.999246 5.214853 6.317562
## [1001] 5.987951 6.805613 6.147253 5.990744 6.578991 5.799960 6.990671 6.131200
## [1009] 5.486666 6.533609 5.300669 6.019039 5.624226 4.712958 5.401230 6.533609
## [1017] 6.519229 6.118430 5.680285 6.555075 6.619220 6.116345 7.030818 6.293686
## [1025] 6.689550 6.618963 6.730030 5.962853 5.436024 6.906230 6.254897 6.015955
## [1033] 6.733315 5.137473 5.869706 6.111919 5.881340 6.370324 5.783895 5.778753
## [1041] 5.768549 6.527566 6.805613 6.619220 6.186296 6.639061 5.579996 5.706708
## [1049] 5.800630 5.706925 5.995291 5.994233 6.121385 6.346881 6.453677 6.041313
## [1057] 5.400553 5.914980 5.988795 6.346881 6.672783 5.854088 6.477867 5.550421
## [1065] 6.471124 7.198866 5.480395 5.872118 6.393097 5.962977 6.554478 5.488020
## [1073] 6.609886 5.757122 6.794679 5.975059 6.155732 5.364278 5.888117 6.292538
## [1081] 6.759314 5.998418 5.360979 6.227469 5.661806 6.885238 6.028932 6.427694
## [1089] 6.079995 7.311445 6.910394 5.503626 6.172896 6.095075 6.696075 6.523530
## [1097] 5.483898 6.450557 6.263983 6.074825 5.958664 6.916999 6.608923 5.272313
## [1105] 5.426937 6.618963 6.143801 5.370650 6.149069 5.753085 6.253056 6.467357
## [1113] 6.609886 5.857868 6.397948 6.040236 5.986849 5.899502 6.117734 5.596971
## [1121] 5.674771 6.579710 5.914716 5.730160 6.667923 6.148393 6.213902 6.305717
## [1129] 5.219010 6.483130 6.139221 6.272239 5.870096 6.719988 6.627766 6.728009
## [1137] 6.782866 6.648451 6.177207 5.826666 6.227475 6.522166 6.465368 6.139221
## [1145] 6.084738 6.138116 5.657032 6.237824 5.390944 6.294020 5.315875 6.089021
## [1153] 5.354686 6.578991 5.961027 6.125251 6.265268 5.891366 6.718534 5.826142
## [1161] 7.006095 6.495172 6.658684 5.732157 5.708430 6.379684 6.597228 5.892862
## [1169] 6.084738 5.984490 6.057241 5.814459 6.866911 6.008425 6.636319 5.254579
## [1177] 6.206685 6.146427 5.733940 5.324131 5.964327 6.998629 6.427694 6.604948
## [1185] 6.395968 5.540330 6.270980 5.876590 5.804514 5.741337 5.943818 5.841262
## [1193] 6.188739 6.263983 5.414423 7.016160 6.947153 6.114004 5.869254 6.495172
## [1201] 5.826666 6.270816 6.565053 6.186296 5.763321 5.945625 5.415760 5.948464
## [1209] 6.075219 5.508045 5.724357 6.827295 6.244879 5.737812 6.562174 4.958365
## [1217] 7.290267 6.141599 5.468734 6.072255 6.604948 6.700142 6.689550 6.526075
## [1225] 6.009895 7.485998 5.749390 5.648079 6.292300 6.309797 6.521079 5.648522
## [1233] 6.187824 6.014711 5.747959 6.279831 6.391482 6.094395 6.120458 6.252744
## [1241] 6.391482 5.670982 5.499587 5.382061 5.955997 5.451766 6.141599 5.735297
## [1249] 6.678989 6.228679 6.403523 6.486092 6.767684 6.562774 6.649704 6.537870
## [1257] 6.554478 6.417807 5.671822 6.156116 6.094577 6.177207 5.400800 6.234767
## [1265] 6.088883 5.631324 5.085185 5.299337 5.470811 6.958303 6.156116 6.228065
## [1273] 5.690431 5.201726 5.832714 6.398215 6.570547 5.335194 5.708437 6.155732
## [1281] 6.286790 6.330978 6.220311 5.673275 6.538275 5.652110 6.430251 5.952151
## [1289] 6.656344 6.061677 6.728009 5.978365 6.406323 5.915570 6.104666 6.586915
## [1297] 6.423558 6.968906 6.575640 5.428900 5.436453 5.544630 6.237369 5.651554
## [1305] 5.279376 6.473076 5.368127 5.564726 6.252386 5.723290 6.027016 5.770808
## [1313] 6.116215 5.871056 5.583185 5.508054 5.684520 6.113974 6.289857 6.582203
## [1321] 6.140695 6.157709 5.953969 6.146427 6.032355 5.714355 6.410700 6.203038
## [1329] 5.732067 6.478320 5.772819 6.019039 6.430404 5.812130 5.383702 6.030083
## [1337] 6.486604 6.714556 6.102162 5.738543 6.408772 6.588269 6.756753 6.874904
## [1345] 5.916638 5.536368 6.202355 6.619558 6.245718 5.986303 5.558217 5.998771
## [1353] 6.086725 6.113186 6.388565 6.698939 5.882276 5.691888 6.303528 6.028932
## [1361] 4.849343 6.423109 6.362459 6.853401 6.196531 6.274997 5.776607 5.582174
## [1369] 5.590623 6.223143 5.883565 7.539700 5.828305 5.024109 5.316842 5.804061
## [1377] 6.533817 5.828500 6.443426 6.289641 6.214323 6.292924 6.480631 5.679361
## [1385] 6.870352 6.111948 6.532078 6.465368 7.065809 6.604922 5.881295 5.721399
## [1393] 6.054467 6.027217 5.842321 6.450947 5.914716 6.386691 6.592249 5.540858
## [1401] 5.804061 5.462835 6.560671 5.613880 6.830478 5.291932 5.803821 5.998685
## [1409] 4.831665 6.116931 6.636319 6.508544 7.427804 6.867650 6.258785 5.859672
## [1417] 6.497525 5.945868 5.450692 6.088913 7.198866 5.813664 5.872190 5.397011
## [1425] 5.653861 6.494047 5.226602 6.435635 5.975059 5.579895 6.503462 6.506840
## [1433] 5.196928 6.731137 6.159212 6.228679 6.648451 6.472034 5.863356 6.304653
## [1441] 6.371020 6.493268 5.723290 6.015955 5.154548 6.423558 6.105004 6.370324
## [1449] 6.522904 6.140695 6.493268 6.220897 6.183374 6.068884 5.212871 6.314302
## [1457] 5.869772 6.226245 6.389597 5.757122 6.828527 5.699002 5.998685 5.837428
## [1465] 6.465871 6.200609 6.274997 7.539700 6.272441 6.102173 6.696986 6.674973
## [1473] 6.264811 5.438071 5.958664 6.651725 6.366881 5.245025 5.781351 5.562318
## [1481] 5.359728 6.494047 6.070633 6.254215 5.785884 6.506969 5.520025 5.632228
## [1489] 5.136697 6.640833 5.773354 5.384906 5.639160 6.371167 5.219144 6.356507
## [1497] 6.255395 6.057702 6.120067 5.985532 6.850466 6.470307 6.187945 6.981189
## [1505] 7.106302 5.871126 6.039135 6.460602 6.990730 6.084787 6.712906 5.990214
## [1513] 5.632721 5.794319 5.472991 5.593064 5.945150 6.228058 5.405782 5.701607
## [1521] 6.551344 5.202628 6.256742 5.955664 5.787832 5.890478 5.807004 6.286811
## [1529] 6.134906 6.368519 6.420997 6.502864 6.487509 5.912848 6.664032 6.268029
## [1537] 5.987666 6.171261 5.499093 6.621982 5.855348 7.240427 6.394863 6.389778
## [1545] 5.665818 6.489956 5.989544 5.675547 7.022940 6.059410 5.351664 5.149184
## [1553] 6.974224 6.355698 6.339747 6.919635 6.166790 6.037983 6.775291 5.624281
## [1561] 6.599580 5.738055 5.261839 6.579015 6.244901 5.799245 7.022940 6.440662
## [1569] 6.339747 5.757679 5.799709 6.072084 5.806291 6.209511 5.456566 6.093815
## [1577] 5.486428 5.944691 6.255395 6.906346 6.520019 6.306144 6.039135 5.951017
## [1585] 6.579015 6.349948 6.312407 6.327854 6.235225 6.619939 6.881638 5.512959
## [1593] 5.509503 5.952954 5.491487 5.027392 6.345026 6.460602 5.947366 5.650314
## [1601] 5.872412 6.204262 6.665853 5.198851 6.131398 5.274566 5.359251 6.904408
## [1609] 6.348875 6.052170 6.199790 6.441561 5.545816 5.712683 6.486558 5.985532
## [1617] 5.871126 5.166625 6.919635 6.665853 6.013568 5.765707 6.332409 5.227447
## [1625] 5.613424 6.974224 6.981189 6.489956 5.947547 7.027248 6.699515 5.287076
## [1633] 6.013568 5.187412 6.881589 5.536731 6.064776 6.242878 5.261343 5.665818
## [1641] 6.356285 5.853241 6.712906 6.210931 7.341751 6.332193 6.467667 6.309200
## [1649] 7.027248 5.864816 5.775470 6.299423 5.733287 5.864130 6.441561 6.870355
## [1657] 6.121150 5.850171 6.187945 5.798801 5.957992 5.995205 5.312727 6.436735
## [1665] 6.906346 5.361752 6.716815 5.821203 5.977632 6.158378 6.094129 6.504086
## [1673] 6.286811 6.597482 5.720547 5.989301 6.993221 6.063301 5.394278 5.593963
## [1681] 6.644400 6.195486 6.005555 5.322052 6.004227 6.010038 6.134549 5.977632
## [1689] 5.379242 6.123237 6.327215 5.331542 6.177476 6.128760 5.701321 5.906370
## [1697] 5.439541 5.817523 5.857240 6.199825 5.272500 6.775291 6.533567 6.585139
## [1705] 6.281726 6.502864 6.440662 5.174953 5.140767 6.134549 5.907056 5.447044
## [1713] 5.569615 5.975646 6.389778 6.881638 5.226749 5.897079 5.673098 5.872379
## [1721] 6.210931 6.004227 5.942861 6.009872 5.271388 5.944691 5.461412 5.268529
## [1729] 5.374105 6.572658 6.207965 6.355698 5.721149 5.710141 5.680488 5.915355
## [1737] 5.479110 5.944783 6.621450 6.356285 5.821667 7.027175 6.072084 5.799709
## [1745] 6.034488 6.199784 6.436345 6.278184 6.461245 6.044621 5.540010 6.125505
## [1753] 5.770655 5.907056 6.327854 6.348875 6.391042 7.077024 6.470307 5.673083
## [1761] 5.862237 6.679700 6.327215 5.943411 5.913959 6.120493 5.944783 6.237284
## [1769] 7.138585 6.461245 5.947366 6.042881 6.272428 6.152916 6.555434 6.539366
## [1777] 5.929586 5.962907 6.679700 5.957449 6.496136 6.636616 6.557731 6.037754
## [1785] 6.410746 6.119986 6.093666 5.526138 6.158378 5.943979 5.625509 6.242600
## [1793] 5.701607 5.691930 6.349948 5.525226 6.358874 6.324300 5.806291 6.465161
## [1801] 5.798801 5.198060 5.491113 5.460012 6.881753 6.602443 6.505363 6.602443
## [1809] 6.520019 6.244561 6.487509 6.106731 5.656195 6.299423 5.521759 6.199784
## [1817] 5.776503 6.289908 5.942861 5.878950 6.309200 6.102763 5.987666 6.897340
## [1825] 5.511486 6.742444 5.914848 6.012353 5.871049 6.195486 5.726780 5.887458
## [1833] 6.597482 6.381366 5.149070 6.890448 6.306144 6.143491 6.555434 5.841642
## [1841] 6.125505 6.095267 5.577304 5.995205 5.249024 6.166790 6.329604 6.120493
## [1849] 5.409657 5.200321 5.645159 6.599580 6.496136 6.754373 5.614187 7.168411
## [1857] 4.943741 6.572658 5.719708 6.742444 5.259465 5.624281 6.094129 5.704880
## [1865] 5.872379 5.899646 5.798954 6.356507 5.857240 6.244561 6.999178 5.707039
## [1873] 5.562889 5.852752 5.887126 6.710727 6.278184 4.631998 6.664032 5.277709
## [1881] 6.690643 6.068008 6.244901 5.846192 6.436735 6.476998 6.497123 5.606243
## [1889] 5.908451 5.446602 6.236183 6.121150 5.573359 5.800541 6.476998 5.659500
## [1897] 5.995604 6.401146 7.341751 6.228058 5.700730 5.756420 5.897198 5.899646
## [1905] 5.707023 5.998342 5.979190 5.304955 6.159713 5.813767 5.920655 5.318625
## [1913] 5.439541 6.068008 6.226583 6.255536 7.027175 6.236183 6.156380 6.332409
## [1921] 6.496249 6.255536 6.271691 5.603365 6.904408 5.701321 6.324300 6.897340
## [1929] 6.542963 5.576046 5.402480 6.881589 5.861061 6.057702 5.826153 6.531296
## [1937] 5.844173 6.251909 5.951017 6.209511 6.789872 5.725986 5.350051 6.506149
## [1945] 6.636616 6.093666 5.915075 6.497123 6.094688 5.373124 6.710727 6.303680
## [1953] 7.534570 5.845621 5.813767 5.562889 6.260105 5.995604 6.143491 5.853241
## [1961] 6.044621 5.872412 5.683159 5.908451 6.305900 5.860761 6.056236 5.815614
## [1969] 6.448604 6.207965 6.203893 5.181458 5.989544 5.811389 5.861061 5.726780
## [1977] 6.218391 7.138585 7.168411 6.218391 5.363710 5.993691 6.277384 5.496932
## [1985] 6.506149 5.860896 6.133730 5.693312 5.392960 6.080936 6.465161 5.921081
## [1993] 6.134911 7.077024 6.410746 6.226583 6.037754 4.893966 5.830908 6.659399
## [2001] 6.990730 6.621450 5.181699 6.190812 6.504086 5.962907 5.826153 6.489647
## [2009] 6.416823 6.109890 6.408511 5.918024 6.242878 6.094688 6.408511 6.394863
## [2017] 6.271691 5.938885 6.235225 6.156380 5.878191 7.106302 6.488350 5.364551
## [2025] 6.092426 5.334746 6.467667 6.754373 5.906370 6.133730 6.009872 6.120067
## [2033] 6.289908 6.237284 6.119986 5.300064 6.199790 6.052170 5.955943 5.738055
## [2041] 6.277384 5.719708 6.881753 5.989301 5.820142 6.716815 6.401146 7.240427
## [2049] 5.821203 6.225444 5.952954 5.409657 6.533567 6.018752 6.204262 6.272428
## [2057] 6.057889 5.772587 5.921081 5.271593 6.621982 6.486558 6.644400 6.619939
## [2065] 6.531296 6.850466 5.595066 6.063301 5.521043 6.420997 6.379517 5.704495
## [2073] 7.534570 6.690643 5.475950 6.272461 6.870355 5.511083 5.658871 6.177476
## [2081] 6.037984 5.794319 6.999178 6.152916 6.699515 6.149410 6.123237 6.117961
## [2089] 5.772511 6.128760 5.476075 6.061200 6.485766 5.956002 5.794382 6.222383
## [2097] 6.331305 6.252374 5.268105 6.064289 5.755165 6.429119 5.779137 7.004787
## [2105] 6.059619 6.551918 5.475794 6.662525 5.571628 6.664268 5.668939 6.414283
## [2113] 6.145935 6.587813 6.181974 7.069474 6.123786 5.957985 6.285644 6.646702
## [2121] 6.255684 6.062216 5.967400 6.273232 5.736800 5.644168 5.868854 6.469539
## [2129] 6.586228 5.848285 5.892794 5.872714 6.672103 5.995855 5.480056 5.009646
## [2137] 6.246042 5.281811 6.085640 5.332218 6.560806 6.237155 6.280086 6.174472
## [2145] 6.095552 6.037060 5.518194 6.342798 5.524673 6.706278 6.481098 5.823940
## [2153] 6.687153 6.539182 6.280086 6.469781 5.594684 5.736800 6.001382 6.109667
## [2161] 5.387769 6.980976 5.662751 6.516617 6.145115 6.884631 5.370039 4.628928
## [2169] 5.393042 5.964599 6.206965 6.147025 6.110491 5.819223 6.342841 5.854304
## [2177] 6.724273 7.077852 4.982213 5.522993 5.532365 5.382275 5.731166 5.682911
## [2185] 5.957985 5.871681 6.177738 6.552603 6.301378 5.398575 6.039639 6.586836
## [2193] 6.519506 5.595099 6.434282 6.483108 5.968642 4.927497 6.183859 5.750824
## [2201] 6.854809 5.799844 6.056456 6.811450 6.352110 6.599238 6.187845 5.581976
## [2209] 6.643101 6.898986 5.979989 6.712875 6.719907 6.220905 6.547722 5.360686
## [2217] 5.926047 6.010862 7.015664 6.750109 6.142524 6.493058 6.780229 6.177536
## [2225] 6.061200 6.632602 6.325979 6.750926 6.273232 5.856615 6.143807 5.849980
## [2233] 5.930535 6.136878 6.284029 6.352013 5.980152 5.995855 6.190910 5.892794
## [2241] 5.527602 6.661297 7.344308 6.702255 5.690739 6.648832 6.056237 5.813285
## [2249] 5.980152 5.741940 5.460436 6.381881 6.190992 6.222425 6.759181 5.637637
## [2257] 5.757175 7.117146 6.000831 5.831904 6.246569 5.266423 5.829291 5.667586
## [2265] 5.022698 5.803182 6.043639 5.819223 6.456205 6.035220 6.817523 6.248703
## [2273] 5.094873 5.931457 6.088188 5.606100 6.891081 6.589633 5.824490 7.302020
## [2281] 6.661297 6.441768 6.583966 6.211095 6.401240 6.608488 6.186394 4.885525
## [2289] 5.971735 5.625414 5.253880 6.215195 5.398660 6.560806 6.366337 5.716506
## [2297] 6.298941 6.222888 5.998222 6.613662 5.359640 5.530944 5.865663 6.386519
## [2305] 6.183859 5.731166 6.199669 6.034742 5.842784 5.337412 6.414283 5.896495
## [2313] 5.886263 6.151884 5.848950 6.150813 5.770687 6.431846 5.738799 6.070864
## [2321] 5.436747 5.606739 6.272415 6.119878 6.492720 5.644769 5.953660 6.398938
## [2329] 5.604069 6.719907 6.135606 7.400688 5.908004 5.734678 6.281510 6.625817
## [2337] 6.441768 6.333568 6.158063 6.689399 5.845591 6.258374 6.690063 6.460323
## [2345] 6.510506 6.446623 6.237155 6.138435 5.324904 5.946235 5.903777 5.241099
## [2353] 6.957357 5.460216 6.471470 5.618353 6.143997 6.078389 6.724874 5.910234
## [2361] 5.463217 5.466924 6.238586 5.841313 5.272616 5.455850 5.868871 5.475794
## [2369] 5.968788 5.040125 6.581590 5.705789 5.345590 6.259090 6.048698 6.215169
## [2377] 6.331305 6.163684 6.306666 5.698750 5.912502 6.910367 5.533525 5.685021
## [2385] 5.375312 6.056367 6.162101 6.244383 6.807070 5.598686 6.359657 5.863946
## [2393] 5.979919 6.471156 6.372879 6.342783 5.157012 6.258374 5.798330 6.273909
## [2401] 6.255684 6.286997 6.731463 5.878930 7.091627 6.199482 6.136223 6.259090
## [2409] 6.376539 6.018884 5.484081 6.457622 5.582283 6.319760 5.446154 6.283194
## [2417] 6.483465 6.228868 7.809463 5.579777 6.955645 5.942642 6.999385 6.463902
## [2425] 4.764517 5.975424 6.938424 6.564636 6.223049 6.822473 6.076566 5.766391
## [2433] 7.091627 5.334323 5.777432 6.155448 6.481098 6.147025 5.688027 5.800858
## [2441] 6.452371 6.286800 5.268105 5.273904 6.352110 6.699430 6.648832 6.434528
## [2449] 6.185264 5.526011 6.199482 5.842782 5.268317 6.910893 6.182089 5.473692
## [2457] 5.660403 6.381881 6.926150 5.952590 6.376539 5.293053 5.257769 6.096292
## [2465] 6.491051 6.519506 6.712174 6.190051 6.778214 5.437275 6.370222 6.388772
## [2473] 6.367359 6.125910 5.955215 6.208156 6.357682 6.111349 6.910367 6.443135
## [2481] 5.710532 5.705789 6.427213 6.123338 6.466936 6.119608 5.556397 6.466186
## [2489] 6.631781 6.045235 5.584562 6.060904 5.838097 6.166040 6.314877 5.539919
## [2497] 6.429119 6.311221 6.090438 6.256189 6.223049 6.780229 6.489749 6.125421
## [2505] 6.056155 6.354478 6.946304 6.453222 6.195990 7.159667 5.738799 6.173073
## [2513] 6.185607 5.504129 6.245014 6.661432 5.741240 6.085485 5.576728 6.769859
## [2521] 5.862740 5.561724 5.608774 5.010783 5.889982 6.478474 6.652289 6.289306
## [2529] 5.669979 6.898986 5.581976 6.776572 6.283080 6.056456 5.457394 6.283080
## [2537] 6.750109 5.842728 5.736752 6.670402 6.779680 6.319760 5.838097 6.066815
## [2545] 5.841313 6.028883 5.653586 6.176891 5.964751 6.675823 6.116323 5.830624
## [2553] 6.034085 6.478523 6.194106 6.478474 5.900410 5.829291 6.401370 5.886263
## [2561] 6.145935 6.145140 6.201439 6.768971 5.808867 6.366337 5.994100 5.899891
## [2569] 5.579297 6.124274 6.045235 6.109667 5.734678 6.111349 6.116510 6.482605
## [2577] 6.746648 6.043708 6.200710 6.551918 6.281379 5.844782 5.810499 6.743414
## [2585] 6.114346 6.021994 6.337967 5.354408 6.365776 6.311221 5.806641 4.979073
## [2593] 6.326286 6.180256 6.644559 6.199156 6.690063 5.668939 6.148736 6.492720
## [2601] 5.533058 5.804750 5.899428 5.541700 5.398022 6.329347 6.025369 6.642310
## [2609] 6.167006 6.595188 6.131820 6.201579 6.058224 6.217072 6.631781 5.563650
## [2617] 7.061399 5.470891 5.527908 5.934915 6.362766 5.987695 5.501712 6.183237
## [2625] 5.221080 6.194609 7.038449 5.947138 5.756530 6.539182 5.710162 5.437882
## [2633] 5.912502 6.134738 6.149723 5.635061 6.672598 6.077494 6.365776 5.196080
## [2641] 7.396881 6.867057 6.028883 6.212302 5.989064 6.310643 6.957357 5.644168
## [2649] 5.231773 6.456205 5.488624 5.779046 5.312273 6.417329 6.581590 6.363291
## [2657] 6.331509 6.011051 6.728885 5.424078 5.869919 6.233559 6.354478 5.949305
## [2665] 5.911284 6.625817 6.645629 6.363291 5.629439 5.912256 6.145422 6.370363
## [2673] 5.824798 5.639680 6.280764 5.936797 5.646918 6.750926 5.585760 7.344308
## [2681] 6.763751 7.470253 5.590789 5.805372 6.607187 6.574249 6.301378 5.874169
## [2689] 6.123338 6.229173 5.745335 5.934988 6.373641 6.105116 6.528656 6.675823
## [2697] 6.089246 6.266589 5.299125 5.401837 7.001891 7.142734 6.089563 6.084208
## [2705] 6.469781 6.222425 6.935516 7.004787 6.743416 6.103709 6.183237 5.270887
## [2713] 6.577446 6.875846 5.849296 6.466936 5.872714 5.354534 6.422470 6.443135
## [2721] 4.628928 5.931457 5.871593 5.378306 6.233225 5.446226 6.489210 5.312273
## [2729] 6.171369 5.314070 6.000831 5.493415 6.779680 6.325074 5.987104 4.776082
## [2737] 5.967049 7.809463 7.067338 6.143997 6.712875 5.667743 5.693050 6.326286
## [2745] 5.823754 6.184644 5.324889 6.370222 6.110491 6.767904 6.283194 6.280598
## [2753] 6.033841 5.590191 5.953660 6.140788 6.357682 6.280598 6.859271 6.948359
## [2761] 6.457622 6.460591 6.119878 6.868664 6.145422 6.644559 6.070864 6.055253
## [2769] 6.608067 5.120870 5.241378 6.657866 5.404623 6.481814 5.734254 6.651805
## [2777] 6.873326 5.293521 6.191305 6.280764 6.304236 5.216983 6.731463 6.577773
## [2785] 6.260388 6.489749 5.927538 6.715235 5.538252 6.022112 6.148736 7.626922
## [2793] 6.452615 6.756422 6.281510 5.553572 6.286997 6.123786 6.190992 6.724874
## [2801] 5.797527 5.989982 6.434661 6.043639 6.181192 6.121281 6.471470 5.549358
## [2809] 6.096292 5.346194 5.778957 5.596159 5.387952 6.804348 6.977793 6.739203
## [2817] 5.374644 5.935293 6.212302 6.010862 5.667586 6.049101 6.776572 5.678654
## [2825] 6.059346 6.550531 5.545834 7.254359 6.868664 6.552603 6.358926 6.768971
## [2833] 5.779348 6.088901 6.142524 5.284475 5.608774 5.849791 6.154002 5.645992
## [2841] 5.707500 5.567728 5.345830 5.596154 6.448976 6.660134 5.746082 5.987104
## [2849] 6.506763 6.479305 5.923924 7.355958 6.342841 6.336929 6.131820 6.256189
## [2857] 6.201579 6.303418 6.689809 4.929632 5.213473 6.022030 6.774648 5.429037
## [2865] 5.899428 7.355958 5.876108 6.412303 6.343184 7.038449 6.483108 6.372879
## [2873] 6.637607 6.564636 5.888583 5.586405 5.976377 6.130511 5.630342 6.536616
## [2881] 5.850383 6.801949 5.344495 5.980759 6.665267 5.644399 5.880453 6.064203
## [2889] 6.194322 7.104642 5.691863 5.478510 6.252374 5.880824 6.969153 5.385594
## [2897] 6.200710 5.605766 6.661432 6.873326 6.358926 6.589633 6.413625 5.903777
## [2905] 5.341057 6.104866 6.664378 5.463794 6.206965 6.453222 5.460216 6.281431
## [2913] 6.768429 6.672598 5.435842 5.683639 6.184644 5.363673 5.233366 6.484115
## [2921] 5.912103 6.583966 5.769105 5.870683 6.224410 5.709726 6.037380 6.646702
## [2929] 6.058224 5.050033 5.755165 5.655146 6.635302 5.215350 6.337509 6.182089
## [2937] 7.577914 6.177536 6.124274 6.158418 6.021121 6.077494 5.926707 6.215195
## [2945] 5.910234 6.435292 6.289945 6.479867 5.873238 6.548816 6.131771 5.943630
## [2953] 7.195756 6.813496 6.088188 6.817523 5.803182 5.952590 6.687153 6.119338
## [2961] 5.800858 5.808384 6.067158 6.035220 6.021994 6.448976 5.703544 6.574249
## [2969] 5.895021 6.490953 6.330073 5.819403 6.145838 5.385746 6.217568 6.222888
## [2977] 6.768429 6.171339 6.012168 6.726687 5.953344 6.469327 7.254359 6.397508
## [2985] 6.194609 6.002944 6.153407 5.360462 6.767904 5.344642 6.059619 6.481814
## [2993] 6.265948 6.689399 6.000777 6.446623 6.608067 5.606129 5.821499 6.583951
## [3001] 5.552002 6.521770 6.127654 5.868854 5.823940 6.149723 6.181192 6.355602
## [3009] 5.976377 7.015664 5.236235 6.319705 6.284029 5.681311 5.538178 6.355602
## [3017] 5.501201 6.609783 6.460323 6.531226 5.600458 6.343184 6.033407 6.910893
## [3025] 6.140788 6.281379 5.736752 6.586836 5.319795 6.726687 5.549358 6.089246
## [3033] 5.879640 6.626844 6.478523 6.103709 5.269732 6.919679 6.142471 6.607187
## [3041] 6.273909 6.173800 6.902877 5.871593 6.686011 6.298941 6.095552 6.774648
## [3049] 5.687988 6.285644 6.352013 6.071447 6.177738 5.484341 5.526133 5.361029
## [3057] 6.165993 6.362766 6.240894 6.577773 6.583951 6.201439 5.927538 5.899891
## [3065] 5.783071 6.801949 5.984140 5.694796 6.699430 6.489891 6.838812 6.181974
## [3073] 6.325640 5.681938 5.900410 5.727346 5.569572 5.862740 6.919679 6.360795
## [3081] 6.130511 6.582404 7.626922 5.146406 6.319766 6.434282 6.977793 6.245014
## [3089] 6.274227 6.175449 6.586228 5.779137 5.868312 6.136223 6.783695 6.173073
## [3097] 6.011051 6.188800 6.180256 6.194106 5.385730 5.389244 7.048060 5.345943
## [3105] 5.722930 5.365975 5.636733 6.284453 6.875601 6.665267 5.986804 6.946304
## [3113] 6.357195 5.894095 6.142471 5.258659 5.963419 5.669454 5.525001 6.822473
## [3121] 5.586405 6.093673 6.587813 5.716506 5.908004 6.926150 6.651343 6.284189
## [3129] 6.716924 5.966919 5.601026 4.684055 7.067338 6.609783 6.069779 6.283423
## [3137] 6.080150 5.940942 5.669346 6.506763 6.286800 6.055189 6.185264 5.593669
## [3145] 6.233225 6.811450 5.049577 6.373641 6.119338 6.548816 6.689809 4.972422
## [3153] 6.711503 5.688195 7.127662 5.943630 6.579420 6.155448 5.579979 5.980759
## [3161] 6.238586 6.427870 6.734792 6.274227 7.195756 5.949305 6.116525 6.489891
## [3169] 5.912256 6.171339 6.087785 5.819794 5.984140 5.488408 6.199669 6.131942
## [3177] 5.975352 6.131609 6.064289 6.033304 6.159174 5.741240 5.745335 6.838812
## [3185] 6.214204 5.639680 6.222383 6.650083 6.063897 6.199156 7.453861 7.127662
## [3193] 6.034085 6.427213 6.113423 6.359657 5.901183 6.625635 6.734792 5.305453
## [3201] 6.178846 6.069779 6.385130 6.452371 6.875846 5.308067 6.265948 6.320464
## [3209] 6.055189 6.599238 6.241417 6.151037 6.065090 6.427870 6.485766 5.990430
## [3217] 5.611971 6.317280 7.069474 6.969153 6.516190 5.944829 6.175449 5.755334
## [3225] 7.117146 5.871681 6.228868 5.214653 5.541428 6.033954 5.470825 6.902877
## [3233] 5.757412 5.242463 6.325074 6.060904 5.631029 5.785937 6.769390 6.317280
## [3241] 6.948359 6.469327 6.797997 7.009938 6.980976 5.517811 5.986804 6.315217
## [3249] 6.016950 6.508539 7.001891 7.061399 6.284189 6.982092 6.746648 5.639679
## [3257] 6.632602 6.162101 6.118998 6.409423 6.058047 6.246042 5.915530 6.475944
## [3265] 5.823595 5.998758 5.293442 6.166040 6.409423 6.577446 6.479867 6.651343
## [3273] 6.264528 6.093846 5.818891 6.839527 5.956002 6.653127 5.936797 5.906019
## [3281] 6.315838 6.151884 6.173800 5.199861 6.547988 5.989064 5.444008 6.866211
## [3289] 6.542997 6.062216 6.435292 6.635302 6.707213 5.389266 5.842782 6.303418
## [3297] 6.491051 7.360664 6.385130 5.909072 6.118428 6.049101 6.059346 5.872571
## [3305] 6.178846 6.021031 6.084208 6.783695 7.142734 5.867056 5.839109 6.767994
## [3313] 5.930535 4.923466 6.062211 6.521770 6.282451 6.120412 6.536616 6.711503
## [3321] 5.952502 5.881989 6.107300 5.934915 6.220905 6.150813 5.552002 6.076566
## [3329] 5.188637 6.460591 6.131771 6.168867 6.167554 6.329347 6.566665 5.387952
## [3337] 6.145140 5.299426 6.482605 5.614218 5.981672 6.469539 5.526877 6.982092
## [3345] 7.302020 6.547988 5.424942 6.325979 6.410656 5.416136 7.048060 5.734454
## [3353] 6.123378 6.504226 6.248703 5.467020 5.067318 6.493058 6.422470 6.566665
## [3361] 7.400688 5.964751 5.246899 5.571628 6.479305 5.254432 6.261950 6.343290
## [3369] 5.233981 6.412303 5.815984 7.470253 6.653127 6.153018 6.651805 6.314877
## [3377] 6.404599 5.622556 6.213449 6.769859 6.213449 6.995184 5.644133 5.457017
## [3385] 5.875031 5.357347 6.441070 5.616761 6.995184 6.645629 6.559482 6.728885
## [3393] 5.205372 6.085640 7.077852 6.489210 6.063897 6.637607 6.261950 6.029995
## [3401] 6.434661 6.657866 6.033407 6.410656 6.325640 6.145115 6.078389 6.643101
## [3409] 5.813285 5.280075 6.316083 6.664378 6.033841 6.626844 6.315838 6.388772
## [3417] 6.596359 5.984214 6.672103 6.306666 6.484115 6.319705 5.592992 6.850773
## [3425] 6.113423 5.995623 6.248658 5.794382 6.459446 6.712174 6.664268 6.116323
## [3433] 6.160567 6.120412 6.158418 5.848950 6.090389 5.739657 5.548021 6.483465
## [3441] 5.872571 6.441070 6.319766 5.967400 6.891081 5.864016 6.608488 6.153407
## [3449] 6.017346 6.105116 6.595188 6.743414 6.504226 5.703544 6.248658 6.756422
## [3457] 5.901183 5.884539 5.407760 6.009663 5.474679 6.702255 5.965019 5.759843
## [3465] 6.716924 6.279219 6.027853 6.179494 5.379689 6.542997 5.923924 5.998312
## [3473] 6.078842 5.569572 5.407345 6.804348 6.036268 6.787628 6.401240 5.373446
## [3481] 6.935516 6.289306 5.967049 7.453861 6.743416 6.625635 6.875601 6.282451
## [3489] 6.859271 5.976078 6.662525 5.563650 6.033954 6.246569 5.747198 6.289945
## [3497] 6.135606 5.985746 6.156417 6.195990 6.787628 6.159174 6.008940 5.911527
## [3505] 6.005319 6.134738 5.955357 6.214204 5.390924 5.752832 5.657722 5.464824
## [3513] 5.819602 6.315217 5.988162 5.358214 5.768257 5.870683 5.401180 6.116525
## [3521] 5.947138 5.863946 5.337093 5.880726 6.670402 6.516936 6.395404 6.706278
## [3529] 6.014649 5.831663 6.208156 5.701525 6.039639 6.357195 6.135631 5.707120
## [3537] 7.577914 6.121281 7.360664 5.260332 5.729244 5.573722 6.652289 6.171369
## [3545] 6.186394 6.129618 6.739203 6.337381 6.360795 6.993221 6.320793 6.763751
## [3553] 6.167006 6.168867 6.731137 5.992122 6.540318 6.097882 6.771699 6.061677
## [3561] 6.181630 6.490953 6.165993 5.567728 6.850773 6.640002 7.396881 6.078842
## [3569] 6.730030 6.211095 6.264528 6.582404 6.910394 6.064203 5.733287 5.798330
## [3577] 5.993691 6.735381 6.839527 6.852669 6.111189 6.123968 5.669111 6.688502
## [3585] 6.338486 5.651469 4.356368 6.532636 6.417199 5.470258 5.986696 6.217118
## [3593] 6.376852 6.416531 5.745143 6.797910 6.258452 5.612237 6.626473 6.287887
## [3601] 5.964226 6.601468 5.762839 6.039744 6.217144 6.243827 6.789908 6.891081
## [3609] 6.416531 6.978698 6.065484 6.761628 6.121355 6.847391 6.716613 5.986696
## [3617] 6.687155 6.591267 5.234780 5.909503 5.293368 6.737317 5.998228 6.972306
## [3625] 6.218570 5.832096 6.847847 6.818528 5.888258 6.610502 6.382185 7.404706
## [3633] 5.482281 6.594715 5.269671 5.768029 5.632644 5.509315 5.226006 6.870827
## [3641] 6.322614 6.087053 5.982079 6.381218 6.794970 5.890406 6.567795 6.510588
## [3649] 6.149515 6.705296 6.687155 6.526955 6.853974 5.524051 6.331046 5.937988
## [3657] 6.634287 5.915630 7.017058 5.977797 5.930221 6.134074 7.001182 5.252298
## [3665] 5.909503 5.966338 6.482779 6.062398 5.869767 5.360773 6.725398 5.567331
## [3673] 5.044388 7.322700 6.975586 4.877309 5.891176 6.532636 4.970668 5.982079
## [3681] 6.604206 6.328023 6.752569 6.227644 6.472324 5.824060 5.843703 6.643334
## [3689] 6.609586 6.828767 6.233282 6.031382 5.446481 6.606393 5.745595 6.338486
## [3697] 6.352143 7.023298 6.186151 6.517329 5.783793 5.470308 6.816152 6.543626
## [3705] 6.410362 5.937988 5.757231 5.523776 5.904886 6.441353 6.728137 6.328023
## [3713] 5.630696 6.740238 6.752569 6.250447 5.914954 6.196438 5.814684 6.315611
## [3721] 6.433252 6.637693 5.874361 6.547735 6.608991 6.016506 6.111892 6.419002
## [3729] 6.097608 6.202258 6.526670 6.408993 6.732691 6.073757 6.035930 6.707538
## [3737] 6.195270 6.972306 6.597707 6.395484 6.279695 6.481938 5.189450 5.955316
## [3745] 5.879006 6.170316 5.726005 6.626473 6.347527 6.087278 5.678818 6.236613
## [3753] 6.425037 4.591622 6.946469 6.598779 5.616909 6.254878 6.018301 6.609586
## [3761] 5.542268 5.843703 6.726283 5.783858 6.929932 6.250447 5.669111 6.727078
## [3769] 6.076211 6.137702 6.190115 5.960924 6.223238 7.484335 6.218570 6.410362
## [3777] 6.137702 6.315611 6.128856 6.217144 6.198991 6.326631 5.998228 5.776463
## [3785] 5.870775 5.358406 5.017837 5.526471 5.914954 6.441353 6.547462 6.410021
## [3793] 6.116154 7.363376 6.556966 6.079160 6.269299 5.824060 7.185532 6.130355
## [3801] 5.977797 5.783858 6.282691 5.812605 6.547735 6.430267 6.480271 6.853974
## [3809] 6.389108 6.718663 6.189230 6.441342 5.952073 6.107123 6.491827 6.672651
## [3817] 6.261036 6.283867 6.752293 5.856348 6.080735 6.408993 5.870775 6.277624
## [3825] 6.478777 6.407092 5.612237 6.556966 6.261036 6.672651 5.970498 6.745253
## [3833] 6.529220 6.109556 6.358414 6.522570 4.858803 5.844343 5.783781 6.707538
## [3841] 5.916537 6.617702 6.394242 6.517329 6.427362 6.612566 5.891529 6.096950
## [3849] 6.368400 5.727707 5.210373 6.175404 5.183600 6.975586 5.106820 5.757231
## [3857] 5.745595 6.301373 6.277624 5.480777 6.416539 6.391061 7.001182 6.321152
## [3865] 5.840417 6.540585 6.870827 6.556261 5.539255 6.969886 6.491827 6.095691
## [3873] 7.293490 6.847391 5.256900 5.919386 5.522884 6.410021 5.991557 6.761628
## [3881] 6.096950 6.810733 6.725398 6.729846 6.132355 5.470308 5.648028 4.863479
## [3889] 5.235779 6.598779 6.891081 5.280148 7.067135 6.954131 6.828767 5.282318
## [3897] 6.011888 5.992493 5.178692 6.656472 6.634287 6.576871 6.130355 5.611316
## [3905] 5.593240 6.769375 6.610595 6.374363 6.242683 5.773059 4.750348 6.198095
## [3913] 5.207672 6.236613 6.740238 6.478777 6.524653 6.534648 5.276613 4.928979
## [3921] 6.847847 4.931774 5.771095 6.407092 7.016316 5.679324 4.693589 6.594715
## [3929] 6.321152 6.167657 6.842889 6.480271 6.576871 6.969886 5.924857 6.842889
## [3937] 6.806088 5.464402 6.181128 6.217118 5.772286 6.632185 6.071288 6.810733
## [3945] 7.600417 4.988028 6.258452 6.509205 5.888258 5.919386 6.728137 6.384407
## [3953] 6.726283 6.417199 6.978698 6.622480 6.074667 5.745487 5.262104 5.625778
## [3961] 5.961773 5.924857 7.023298 6.031382 6.308279 6.526670 6.111639 4.884228
## [3969] 3.880866 5.106820 6.104904 6.525004 5.771095 5.617414 5.389065 7.185532
## [3977] 7.510371 6.795613 6.746413 5.863542 5.955316 5.178692 4.739366 6.116154
## [3985] 6.481938 6.389108 5.734861 6.454802 6.769375 6.175404 6.039744 6.170316
## [3993] 6.382185 7.134739 5.593240 6.352143 6.541754 6.479605 6.216716 6.637693
## [4001] 7.067135 6.163900 6.347527 5.814620 7.055109 6.223238 5.868090 5.992493
## [4009] 5.171771 6.097608 5.680542 6.463494 5.650575 6.525004 6.772733 6.577944
## [4017] 6.172883 4.591622 6.062398 6.718663 6.368336 6.035980 5.074242 5.991557
## [4025] 5.901169 6.860302 6.806088 6.443488 6.376852 5.500863 7.063259 6.737317
## [4033] 7.510371 5.552593 6.622480 6.606393 5.949296 6.097079 5.840417 6.091186
## [4041] 6.632185 5.904886 5.280148 5.220016 6.556261 6.591267 6.188954 6.643334
## [4049] 6.081885 6.374363 6.079160 6.902064 5.660357 6.604556 6.242683 6.883148
## [4057] 6.529220 6.331046 6.117038 5.868090 6.282355 6.949206 6.006179 6.018301
## [4065] 6.582605 6.368336 6.534684 7.600417 5.762550 5.745143 6.111189 5.966052
## [4073] 6.216716 6.126370 5.754703 6.132355 6.929932 6.308279 6.178640 6.270258
## [4081] 6.781121 5.863542 6.087053 6.104904 6.214856 5.868072 6.946469 6.239968
## [4089] 6.610502 6.534648 6.065484 6.472324 7.018539 7.298670 5.189450 6.427362
## [4097] 6.384407 5.018264 6.210282 6.688502 5.618080 6.163900 5.539375 6.188954
## [4105] 6.073757 6.662397 5.559617 6.598061 6.202258 5.832538 7.404706 6.108126
## [4113] 5.827412 6.368400 6.282691 6.381218 6.883148 6.794970 6.567795 6.954131
## [4121] 6.524653 5.316218 6.789908 5.287705 6.772733 6.748903 6.282332 4.778521
## [4129] 5.276613 6.108126 6.582605 6.227644 6.142792 6.613428 6.081885 6.177985
## [4137] 6.190115 6.207943 6.691375 5.535965 5.785987 6.177985 6.322614 6.270258
## [4145] 6.537629 6.949206 6.688389 6.035980 6.541754 5.812796 6.756078 6.818528
## [4153] 6.537629 6.355091 6.205728 6.090603 6.006179 6.463494 5.670713 5.731543
## [4161] 6.727078 5.062614 6.101017 5.981013 5.087922 5.753888 6.597707 6.172883
## [4169] 5.901793 5.930221 6.117038 6.860302 6.355091 6.149515 5.252854 6.951965
## [4177] 6.778781 5.008528 4.951924 6.781121 5.832096 5.312946 7.363376 6.656472
## [4185] 6.608991 6.117106 6.128856 7.572169 6.091186 5.275397 6.778781 6.756078
## [4193] 6.509205 6.283867 5.018264 5.234780 6.612566 7.134739 5.501096 6.109556
## [4201] 6.167657 5.511824 6.526955 4.998605 6.016506 5.745783 6.430267 6.547462
## [4209] 5.970498 6.563268 6.469897 5.762839 5.891176 7.018539 6.293386 6.925310
## [4217] 4.558971 6.610595 6.301373 6.797910 5.039807 5.342075 6.269299 5.901169
## [4225] 6.525559 6.051224 6.752293 6.433252 5.752064 6.970024 6.107114 6.198095
## [4233] 6.189230 5.448888 6.839083 6.401338 6.661712 6.284074 4.814095 6.242337
## [4241] 5.879006 6.662397 6.254878 5.482735 6.074667 6.613428 6.210282 6.970024
## [4249] 6.181128 5.206095 6.816152 5.768029 5.772286 5.844343 6.419002 7.293490
## [4257] 5.949296 6.394242 4.356368 5.856348 6.469897 6.601468 6.661712 5.651469
## [4265] 6.443488 6.604206 6.479605 6.214856 6.358414 5.660357 6.522570 5.670713
## [4273] 6.095691 6.902064 6.279695 6.598061 6.532997 5.990027 6.463945 6.882246
## [4281] 6.624892 5.903998 6.118830 5.693428 6.285641 4.533035 6.405152 7.483833
## [4289] 6.455632 6.279141 6.037802 6.436412 5.372614 6.396780 6.593909 6.313925
## [4297] 6.283348 5.764500 6.164929 6.973525 6.343227 5.716081 6.066091 6.380606
## [4305] 6.350507 6.344190 5.370612 6.331942 5.673472 6.354298 6.036408 5.103633
## [4313] 4.754574 6.070124 6.612154 6.436548 6.380606 6.083402 7.732934 6.019903
## [4321] 6.498349 6.877256 6.805148 6.157029 5.040416 4.962477 6.314345 6.271913
## [4329] 5.873836 5.885695 6.138666 6.507271 6.070943 6.330089 5.389654 6.302554
## [4337] 6.319193 6.279141 6.933886 5.683351 5.655884 7.138521 4.924216 4.859484
## [4345] 6.184609 6.914451 7.119581 4.413215 6.363592 7.072316 5.776713 6.775429
## [4353] 6.070124 6.161076 6.468692 6.291306 6.010482 6.138483 7.140979 5.725443
## [4361] 6.528477 4.656033 6.732211 4.960381 7.049699 6.525579 5.575740 6.709317
## [4369] 6.449707 5.869775 5.851866 7.358237 6.178259 6.686732 5.972234 6.635205
## [4377] 7.358237 6.259129 6.213887 6.185298 6.843720 5.744241 6.271404 6.597014
## [4385] 6.287035 5.623193 6.764107 6.263813 6.413433 6.674413 6.004865 6.238036
## [4393] 6.197215 6.428914 6.009815 6.156773 6.851117 5.863051 6.540497 5.340747
## [4401] 6.040883 6.406313 5.200052 6.358876 5.638966 6.029518 5.935933 5.776901
## [4409] 5.641699 5.325942 5.774683 5.890004 6.354699 6.174636 6.355725 6.118244
## [4417] 6.727142 6.950763 6.610143 5.515832 5.851852 6.271913 5.937448 5.678593
## [4425] 5.739794 6.155800 7.033202 6.543243 6.361985 5.458921 5.774574 6.017077
## [4433] 5.629041 5.607785 6.115325 6.342885 6.475735 7.319292 6.029518 6.030920
## [4441] 6.565353 6.050179 5.547067 6.431299 5.954973 5.815346 6.567476 5.748613
## [4449] 5.665214 6.402709 6.018712 6.844571 6.514830 5.852318 6.027970 6.791417
## [4457] 6.727142 6.610143 5.662902 6.355149 5.989056 5.428427 6.268862 6.837056
## [4465] 6.603063 6.565891 6.028675 6.745748 6.888580 6.357921 5.649490 6.143141
## [4473] 6.155938 5.942111 7.063766 7.182697 5.333209 6.941204 5.623193 6.655252
## [4481] 6.620845 5.715163 7.483833 6.021573 6.794812 6.415342 6.665720 7.116172
## [4489] 5.984106 5.812495 6.833988 5.471376 7.021205 7.091284 5.778553 6.082380
## [4497] 6.342243 5.974023 5.003078 7.129915 5.320755 6.491645 6.300244 6.358876
## [4505] 6.082380 6.452096 6.913213 6.485714 6.147888 6.363366 6.971905 5.657321
## [4513] 6.532997 6.525574 5.802550 6.319744 6.888580 6.080369 5.417149 7.264371
## [4521] 6.063804 5.985300 6.028337 6.107439 6.318117 6.119570 6.119024 6.236741
## [4529] 6.732211 5.382450 6.227844 5.217408 6.694751 6.679866 6.232787 6.681802
## [4537] 6.741031 6.507804 5.877286 5.702693 5.823854 5.126024 6.424929 6.711961
## [4545] 6.343187 5.072457 5.652436 6.444177 6.114500 6.655254 6.261671 5.936078
## [4553] 5.162901 5.971685 6.374401 5.867387 6.355149 6.081448 5.734350 6.407962
## [4561] 6.123959 5.863051 6.416302 7.732934 6.319193 6.114023 6.342243 7.138521
## [4569] 5.943875 6.655520 5.461070 5.780094 5.328229 6.310023 6.694891 5.606410
## [4577] 6.725265 6.833988 6.281422 6.508122 6.847036 6.366679 6.709317 5.087874
## [4585] 6.675644 6.558889 6.655571 5.910503 6.881857 6.836102 6.128169 5.434060
## [4593] 5.905458 6.295143 5.287810 5.401837 5.527382 6.391193 5.740583 6.507914
## [4601] 6.728206 6.631219 5.629262 6.081916 6.082029 6.504980 6.951448 6.307736
## [4609] 6.389878 6.702927 6.287080 6.532817 6.833819 6.204743 6.403252 6.188635
## [4617] 6.329047 6.655252 6.403135 5.927340 6.396482 6.311960 6.858720 6.313925
## [4625] 6.664190 5.889366 7.547634 6.357921 6.411979 6.535737 6.155800 6.400621
## [4633] 6.157844 5.905458 6.642914 6.219493 6.613643 6.836164 6.287080 7.182697
## [4641] 5.315853 6.260397 5.780094 6.558712 6.368750 6.060566 5.376270 6.335009
## [4649] 7.140979 5.456128 6.195635 6.403252 6.135139 6.033508 5.546825 5.648893
## [4657] 6.694751 6.657005 6.393472 6.677550 6.050179 6.155201 5.867387 6.244254
## [4665] 6.954839 6.489649 6.615492 5.808472 6.715866 7.217739 6.231915 6.341596
## [4673] 6.612248 5.333365 6.989574 5.979194 5.780149 6.711961 5.692554 6.541614
## [4681] 5.771944 6.242333 6.744995 6.255488 6.725265 6.750794 5.431682 6.361304
## [4689] 5.837998 6.582962 6.184990 6.553002 6.237258 5.891608 5.763132 6.307736
## [4697] 5.629024 6.954438 5.788207 6.504980 6.415342 6.068167 6.589170 6.442372
## [4705] 6.547076 6.212239 7.249011 6.351549 6.013765 6.214627 6.246997 5.459073
## [4713] 6.521063 5.816977 6.301140 6.607277 5.945014 6.747636 5.348649 6.600690
## [4721] 6.780670 6.287515 5.551817 6.464802 6.016251 6.396780 6.151883 6.533827
## [4729] 6.769504 5.355540 6.095908 6.266964 6.658082 6.373890 5.310571 6.874277
## [4737] 6.388723 6.271404 6.535505 6.388723 5.862710 6.350507 6.844571 6.507271
## [4745] 6.741031 6.620845 6.210239 6.230313 6.003877 6.356031 6.366679 6.316965
## [4753] 6.541892 6.464186 5.949375 6.449169 6.584640 5.955986 6.624160 6.381837
## [4761] 6.119024 6.389878 6.330988 5.451943 6.578342 6.411979 6.302554 5.877286
## [4769] 6.592715 5.223758 5.934247 6.696650 5.776857 6.071686 6.413433 5.976489
## [4777] 6.535505 6.593909 6.042388 6.323975 6.227844 6.719568 5.799600 5.557944
## [4785] 5.902428 6.236334 5.704793 6.114133 6.744995 6.268862 5.868891 7.399195
## [4793] 6.805148 6.287515 6.688580 6.866946 6.840324 6.607165 6.329047 6.003877
## [4801] 5.708151 6.222925 6.543243 6.159405 6.578052 5.935933 6.696811 5.367536
## [4809] 6.177772 6.029721 6.681802 6.680964 5.609552 6.339447 5.505995 6.214627
## [4817] 7.399195 6.700078 6.694891 6.007072 6.464186 7.621449 5.221278 5.833991
## [4825] 6.374401 6.989574 6.682617 6.251739 4.761797 5.325942 5.950236 6.584640
## [4833] 5.639033 5.839102 5.957352 6.772983 5.659531 6.108776 5.513479 5.775360
## [4841] 6.954839 5.701111 5.804801 6.489649 6.090377 5.661538 5.553810 6.194043
## [4849] 6.701550 5.673280 6.197215 6.874277 6.097413 6.269734 6.954438 6.415690
## [4857] 6.635205 6.532787 6.995293 5.933586 7.065394 5.669441 5.538250 6.101817
## [4865] 5.393570 6.376976 6.097755 6.704485 5.811741 7.033202 6.328172 6.416302
## [4873] 6.157844 6.343187 6.595615 6.769504 6.921553 6.387500 6.090574 6.079877
## [4881] 6.350665 4.804232 7.031797 4.524640 6.428914 5.263426 5.734665 6.232787
## [4889] 6.122870 6.263813 5.555723 5.526950 6.141943 6.107439 7.547634 6.263278
## [4897] 6.207415 5.611463 6.410337 6.843720 6.771598 5.550253 5.869775 5.420956
## [4905] 6.550436 5.719931 6.657005 5.688084 6.209231 6.747636 5.707447 6.463945
## [4913] 6.108776 4.690851 6.719568 6.836164 5.620505 6.653183 6.553122 5.907966
## [4921] 6.016251 5.498424 6.238036 6.819056 6.367433 5.579418 6.994811 6.246997
## [4929] 5.821900 6.415690 6.400127 5.009114 6.821655 6.319744 6.971905 7.774807
## [4937] 6.807622 6.068167 6.111103 6.578052 5.538564 7.049699 6.287274 6.207732
## [4945] 5.741206 5.905013 5.823518 6.674413 5.913663 6.342885 6.164929 5.533737
## [4953] 5.897487 2.767136 6.625795 6.405640 6.287274 6.117405 6.929408 6.427689
## [4961] 6.652678 5.957352 6.243514 6.351549 6.197795 6.295143 6.303660 6.847036
## [4969] 5.211699 5.969272 5.490253 5.505995 6.553122 5.610559 5.349572 7.313784
## [4977] 6.403135 6.764107 6.271427 6.177501 5.744241 4.829657 6.408018 6.138483
## [4985] 6.587490 6.622432 5.731436 6.169607 6.028675 6.214389 6.471429 6.837056
## [4993] 6.184990 7.293933 6.650235 6.097413 6.541892 5.850002 5.296772 6.004865
## [5001] 6.600690 6.316965 6.331942 6.244916 6.659547 6.535737 5.753000 5.681226
## [5009] 6.789025 6.361304 6.514830 5.991000 5.943875 6.528477 6.003987 6.430678
## [5017] 7.308116 6.655254 6.244916 6.807622 5.985300 6.119570 6.181061 5.734350
## [5025] 6.280798 5.878714 6.624160 5.629041 5.955986 6.771598 6.659547 5.419709
## [5033] 5.769584 6.780670 6.156360 6.080405 6.259911 6.080369 6.328172 5.069104
## [5041] 6.530497 6.330988 6.341339 6.037988 7.774807 6.698533 7.002108 6.189514
## [5049] 6.625795 6.814285 6.448374 5.892247 6.194043 6.523029 6.408018 5.918372
## [5057] 5.749527 7.093204 6.789025 5.791878 5.883867 6.612154 5.978977 6.123959
## [5065] 5.709875 5.963347 5.665214 6.524674 5.563165 5.795113 6.310023 5.787015
## [5073] 5.840549 5.774905 6.992411 5.818538 5.864214 6.181638 6.257253 6.475845
## [5081] 6.151883 5.970838 5.990027 5.397190 5.518084 6.636590 5.991000 6.181061
## [5089] 6.025813 6.143016 5.791934 6.408084 4.513912 6.230313 5.812495 7.063766
## [5097] 4.866544 7.061513 5.749527 6.177394 6.427689 7.079482 7.315123 5.367349
## [5105] 5.953913 6.513411 5.632403 6.181638 5.521075 6.913213 5.787523 6.135955
## [5113] 6.582962 6.208565 6.675644 6.448374 5.345206 5.891608 5.748613 6.569478
## [5121] 5.974023 6.704485 6.238270 5.837998 6.818816 5.709174 6.533827 5.929408
## [5129] 6.220245 6.565891 7.058867 5.669653 6.715866 6.607165 5.731436 6.269857
## [5137] 6.391193 6.523029 6.662385 6.191524 5.976489 6.004699 6.547076 6.532787
## [5145] 5.676850 6.581065 6.185298 6.941204 4.790755 6.339447 5.954973 6.088967
## [5153] 5.702520 6.478141 6.185043 5.421475 6.728206 6.558889 6.208565 6.164173
## [5161] 6.301140 6.221247 4.970091 6.088967 6.455632 6.897047 6.424929 6.800903
## [5169] 6.307195 6.763856 5.308502 5.295963 6.517217 6.914451 6.320448 6.354699
## [5177] 6.624892 7.114645 6.202742 6.363366 6.558712 5.774905 5.357903 5.159643
## [5185] 6.835201 7.200397 6.223796 5.929549 5.444186 5.754785 6.354298 6.406313
## [5193] 6.485714 5.657321 6.210239 5.424440 6.000159 6.540497 7.031797 5.903998
## [5201] 4.989477 6.303238 5.486833 6.437495 7.091284 6.341339 5.775229 6.376976
## [5209] 5.720586 7.249011 5.736115 4.929270 6.060566 6.243514 6.092653 5.868891
## [5217] 6.664190 6.003987 6.576081 6.741050 5.348125 5.709174 5.984253 6.377663
## [5225] 6.866946 6.186510 6.242333 5.544480 6.396482 6.281422 5.377864 5.644803
## [5233] 6.099713 6.164173 5.290365 5.654114 6.189514 5.856101 6.236741 6.463652
## [5241] 6.222200 6.449707 6.858720 5.318873 6.301381 6.685803 6.603063 7.308116
## [5249] 6.821655 5.736115 5.526588 5.584175 6.329562 6.405152 5.617383 6.280136
## [5257] 5.775360 6.573031 6.405640 5.851866 5.937826 6.255927 6.658082 5.934247
## [5265] 5.989056 6.719901 6.193899 5.699179 6.565353 6.287035 6.833819 6.431299
## [5273] 6.478141 6.063804 6.702927 5.411311 7.021205 5.850002 5.546825 6.199630
## [5281] 6.563048 6.541614 6.530847 5.597060 6.432529 5.880538 5.526950 6.037802
## [5289] 6.835201 6.422088 5.661538 5.518262 5.319471 5.838985 5.659531 5.861585
## [5297] 5.515832 6.046056 6.131798 6.161076 5.880538 6.355725 6.524674 6.921553
## [5305] 6.284294 5.902428 7.315123 5.640582 5.964591 6.995293 6.818816 6.589170
## [5313] 5.984878 7.061513 6.814285 4.705275 6.752248 6.033174 5.921853 5.772046
## [5321] 6.311960 6.553002 5.913663 6.463652 6.373890 5.677430 5.476897 6.315148
## [5329] 5.724034 6.208472 7.002108 6.507804 6.177394 6.233427 6.674685 4.872709
## [5337] 6.231915 6.450301 5.896814 6.612248 5.517962 5.888019 6.700078 6.662385
## [5345] 6.031631 5.303004 6.162531 6.434852 6.159405 6.219493 6.430678 6.653183
## [5353] 5.216945 6.422088 6.800903 6.994811 6.255488 6.344190 5.791878 5.439825
## [5361] 5.672620 5.982840 6.471879 5.775229 6.058099 6.285641 6.381837 6.450301
## [5369] 6.402709 6.867937 5.458921 6.143141 5.811741 6.271427 6.182059 5.488277
## [5377] 6.416380 5.211699 7.117506 7.313784 6.690273 6.024047 6.091834 6.636590
## [5385] 5.228231 5.266232 6.690273 6.455007 6.237258 6.332684 6.692998 5.370263
## [5393] 5.919558 6.341596 6.177501 6.491645 5.766967 6.791417 6.082029 6.157029
## [5401] 6.092653 6.521063 6.260397 5.841433 6.251232 7.621449 5.730067 6.114133
## [5409] 6.752005 6.029721 6.578342 6.530497 7.200397 6.335009 6.442372 6.550436
## [5417] 6.701550 6.329562 5.606349 6.525579 6.464802 5.703035 6.471879 6.752005
## [5425] 6.613643 6.251232 6.679866 5.548577 5.950236 6.101817 5.940605 5.309172
## [5433] 5.463941 5.849941 6.794812 7.293933 5.439825 5.846568 6.120528 6.208937
## [5441] 6.775429 6.595615 5.851852 5.513816 6.530847 6.674685 5.674856 6.840324
## [5449] 5.845837 7.119581 6.498349 6.223796 5.924044 6.336451 5.614636 6.185043
## [5457] 6.881857 6.259129 6.280136 6.367433 6.455007 5.937826 6.655571 6.393472
## [5465] 5.465280 5.702520 6.538269 5.260442 6.686732 6.867937 6.517217 6.581065
## [5473] 6.314345 5.285870 6.356031 6.236334 6.188635 5.907966 5.984878 5.938402
## [5481] 6.682617 6.222200 5.964591 6.031631 6.259911 6.698533 6.410337 6.030920
## [5489] 7.319292 6.513411 7.129915 6.508122 7.264371 6.576081 6.882246 6.819056
## [5497] 6.719901 6.300244 6.207415 5.526312 6.126915 6.654734 6.363592 6.607277
## [5505] 5.954711 5.640211 6.951448 5.555723 6.434852 5.337550 5.813206 5.688084
## [5513] 5.587762 7.130096 6.432529 6.315148 5.945014 5.851961 6.615492 6.772983
## [5521] 5.776901 6.047931 5.949209 5.921853 5.851241 6.436548 6.146413 7.217739
## [5529] 6.677550 6.763856 6.332684 5.787015 6.266964 6.182059 6.118244 5.841433
## [5537] 5.825372 6.587490 6.688580 6.350665 6.538269 6.992411 6.685803 6.505363
## [5545] 6.090603 6.853401 6.741050 6.107114 6.938424 5.781086 5.961773 6.333568
## [5553] 6.033304 6.691375 7.484335 6.951965 6.528656 6.026075 6.795613 6.126370
## [5561] 6.732691 6.141943 6.922560 6.548503 6.975904 6.753622 6.609494 6.670615
## [5569] 6.481032 7.132202 6.515372 6.533096 6.471967 6.129201 6.812222 6.089151
## [5577] 6.145055 6.611749 6.993942 6.906892 7.113815 6.743490 6.956771 6.829520
## [5585] 6.021656 5.452996 6.183351 6.206414 6.812404 6.152128 6.922560 6.360106
## [5593] 6.549729 6.571108 6.672800 6.434814 6.533096 6.545556 6.645600 6.411290
## [5601] 6.000106 6.542836 7.730664 5.418865 7.010150 6.832829 6.460324 6.026917
## [5609] 6.155403 5.075111 6.993942 4.826631 6.802866 5.464875 6.549729 6.669075
## [5617] 5.672152 6.483284 6.536194 6.563142 6.805320 6.525759 7.193237 6.650998
## [5625] 6.417456 6.864072 6.704975 6.672800 6.783256 5.054949 7.132202 7.054017
## [5633] 6.536341 5.982736 5.965922 5.954821 6.137632 7.674358 6.434814 6.595393
## [5641] 5.465038 6.309837 6.975072 6.100293 5.302427 5.637542 6.515750 6.117010
## [5649] 6.051551 6.458572 6.906892 6.440295 6.494827 6.121177 6.185958 5.898633
## [5657] 6.815571 6.810248 6.802606 6.324324 5.178250 6.517094 6.534779 5.705529
## [5665] 6.499968 6.862934 6.166982 6.130160 6.853007 7.103198 5.914969 6.571108
## [5673] 6.324324 5.376175 6.770178 6.491316 6.460324 6.656354 6.342587 5.923669
## [5681] 6.586149 6.811697 5.645959 5.599386 6.343267 5.400945 6.261155 6.434301
## [5689] 6.814706 5.729508 6.094935 6.679851 6.529443 5.888519 6.975904 6.620485
## [5697] 5.986056 5.757620 6.770178 6.685333 6.325338 6.810248 6.536209 5.715399
## [5705] 6.266086 6.446536 6.576605 5.861492 6.481032 6.844936 5.245845 6.483284
## [5713] 7.301219 5.058374 5.349423 6.461812 7.732087 5.749031 4.246995 5.681805
## [5721] 5.272652 6.410351 6.080405 6.531609 6.499968 5.195279 6.405291 5.711724
## [5729] 6.783256 5.662949 5.772266 5.861603 6.680908 6.230592 5.804850 6.453949
## [5737] 7.615113 5.936092 6.845238 5.999867 6.130160 6.026917 7.010156 5.362187
## [5745] 5.729508 6.676856 6.804086 5.959182 6.451195 5.544626 5.397258 6.000106
## [5753] 5.118572 6.225087 5.951568 6.728015 5.282515 6.066792 6.185355 4.803853
## [5761] 5.618733 6.725526 6.157515 6.046141 6.438722 6.399406 6.137632 6.562264
## [5769] 6.805148 6.277934 5.006497 6.762034 6.553600 6.771729 6.536209 6.618021
## [5777] 5.874016 5.104629 6.294874 6.623147 6.956771 6.011804 5.668532 6.622568
## [5785] 6.491316 6.494827 6.754812 6.451195 5.916170 6.998939 6.615972 6.128291
## [5793] 6.048053 7.809463 6.059348 6.455317 6.544555 7.730664 6.166982 6.535206
## [5801] 6.716613 6.365002 6.533469 7.469073 6.342587 6.143917 6.013484 7.809463
## [5809] 6.781564 6.148815 5.297138 6.781564 6.356272 6.704975 6.131674 6.802606
## [5817] 7.674358 6.497797 6.317476 6.814706 7.054017 7.271489 6.932907 6.346274
## [5825] 5.628982 6.517094 6.123658 7.588305 6.373764 6.431309 5.142975 6.650998
## [5833] 5.448608 6.529262 6.991469 6.152128 6.179098 5.749031 6.248769 6.834412
## [5841] 7.795690 5.914969 6.536194 5.269961 7.469073 5.297138 6.753622 6.689556
## [5849] 6.380194 6.674359 4.771807 6.535978 6.396249 6.618021 6.805148 4.947261
## [5857] 6.681828 6.136068 6.039567 6.485899 6.225087 6.548503 6.885475 6.472571
## [5865] 5.726792 6.030888 7.013162 6.685333 5.570686 5.965922 6.896669 5.618733
## [5873] 6.334558 5.330882 5.951391 6.511208 5.570686 4.520298 6.896669 5.400945
## [5881] 6.299889 6.880133 6.760688 6.673632 6.832160 6.266086 6.645600 5.389938
## [5889] 6.278121 5.608393 5.389938 6.495523 5.909184 7.305384 6.311621 5.638773
## [5897] 6.299889 5.868140 5.105486 4.946499 5.049352 5.779494 6.396249 6.679851
## [5905] 6.408303 5.448608 6.556487 6.411290 6.929408 5.913024 6.545556 6.085914
## [5913] 5.706507 6.495523 5.959182 6.576605 6.760688 6.744793 6.897047 6.350424
## [5921] 6.300354 6.123658 6.862934 5.751781 4.781986 6.066792 6.670615 4.771536
## [5929] 5.936092 6.562534 7.292155 6.312693 6.472571 6.772529 6.843487 6.832829
## [5937] 7.305384 7.006831 6.348909 6.529443 6.174515 4.528948 6.426897 6.637878
## [5945] 6.471967 6.668735 6.498771 5.464875 6.348978 6.261155 6.155403 6.673632
## [5953] 6.845238 5.883261 7.010150 6.669075 7.193237 5.711724 6.114613 5.826281
## [5961] 5.524808 6.237854 6.804086 6.689556 6.158510 6.309837 5.777469 4.934546
## [5969] 6.675153 6.675153 6.001346 6.312693 6.438722 5.715399 5.150136 6.529262
## [5977] 6.681828 6.754812 5.725850 6.094935 6.001346 6.606278 6.238740 7.092316
## [5985] 5.628982 6.325338 6.305417 6.622568 4.907646 6.553600 6.131674 5.898633
## [5993] 6.546554 6.471903 6.743490 6.145055 5.870820 6.728015 4.877128 5.826281
## [6001] 6.725526 6.035331 6.089410 6.971560 6.471903 7.732087 7.092316 6.305809
## [6009] 6.185355 6.805320 6.211533 6.586149 6.232798 6.047131 5.970397 7.103198
## [6017] 5.923793 6.272415 6.932907 6.687968 6.763283 5.901600 6.114613 6.795340
## [6025] 3.935740 6.147571 6.763283 6.623147 5.916170 7.218524 6.158510 6.485899
## [6033] 6.637878 6.656354 5.897718 5.543249 6.094581 6.680908 7.076676 4.586108
## [6041] 6.360106 6.844936 6.609494 6.129201 7.588305 6.230592 4.851987 5.839423
## [6049] 6.426897 6.812404 7.027171 7.606888 5.259591 6.461812 6.957921 5.705529
## [6057] 6.975072 6.063204 7.013162 5.922260 6.453949 5.487137 5.344799 6.434301
## [6065] 6.350424 6.365002 7.113815 5.868140 6.485302 6.563142 5.038109 6.687968
## [6073] 5.689016 5.689016 6.991469 6.615972 6.515750 6.707741 6.880133 5.361437
## [6081] 6.446536 7.298670 6.498771 6.485302 6.440295 6.815571 6.376857 6.795340
## [6089] 6.535978 6.030888 5.432184 5.922260 6.229661 6.535206 6.408303 6.157515
## [6097] 6.811697 5.692162 6.668735 4.652441 5.940341 5.706507 5.100626 4.612923
## [6105] 5.418865 7.615113 6.229661 7.006831 6.222325 7.218524 5.874016 6.458572
## [6113] 6.206414 5.598672 6.530827 5.938560 5.019662 6.544555 6.455317 6.532398
## [6121] 6.971560 6.536341 6.998939 6.185958 6.542836 6.025633 5.687307 5.899919
## [6129] 6.842088 6.497797 6.829520 6.834412 5.753530 6.222325 6.945509 6.802866
## [6137] 6.707741 6.148815 6.063204 5.723232 5.749121 6.807476 6.242895 6.237854
## [6145] 6.094581 6.405291 6.676856 6.431309 6.762034 5.687307 6.046141 5.110431
## [6153] 5.911028 6.048053 6.674359 6.631219 6.089410 6.100293 5.746674 6.556487
## [6161] 4.301121 6.525759 6.957921 5.668532 6.885475 6.183351 5.782276 6.047131
## [6169] 6.211533 6.300354 6.842088 7.292155 6.277934 5.719392 6.420302 5.689821
## [6177] 6.763529 6.763529 6.420302 5.676382 6.397508 6.342783
##
## [[4]]
## [1] 6.216514 6.483658 6.243590 6.089928 5.961585 6.249792 6.105166 6.702247
## [9] 6.533376 6.298885 6.759155 6.404062 7.077077 5.700475 6.337621 6.007179
## [17] 6.037732 6.879106 6.063507 6.765066 6.021335 5.727027 6.645603 5.808349
## [25] 6.431222 6.026018 6.142678 6.479267 6.412875 5.843914 6.425283 6.462432
## [33] 6.022692 5.727186 6.013215 5.970548 6.218836 7.094445 5.577499 6.125193
## [41] 6.869092 6.724586 6.084199 6.299041 5.891377 6.300333 5.598795 5.788316
## [49] 5.816832 5.408536 6.506593 6.428855 6.234668 5.665903 5.593647 5.804719
## [57] 7.199508 6.127495 6.601867 6.538916 7.313625 6.936867 5.463299 6.791953
## [65] 5.990446 5.892576 5.835072 5.398485 6.248784 5.724207 6.186245 6.670705
## [73] 5.420207 6.934538 6.544223 6.515095 5.858921 6.035465 6.749511 6.616630
## [81] 5.899103 5.865000 6.486737 5.685952 6.152415 5.869672 5.883268 6.409802
## [89] 6.648767 5.727027 6.147842 6.666104 6.766111 6.646758 6.054497 5.827409
## [97] 6.433893 6.815439 6.669352 6.501805 5.992690 5.685334 5.380170 7.281896
## [105] 6.058182 6.663233 5.698754 5.943408 5.406990 6.371736 6.288119 6.249111
## [113] 6.067546 6.000183 6.294603 5.546665 6.793719 6.246642 6.948247 6.279724
## [121] 5.705418 6.214535 6.499292 5.559412 6.034688 5.613643 7.313625 6.108006
## [129] 6.033964 6.295043 6.655147 6.877240 6.491715 6.870131 6.141439 5.369225
## [137] 5.474189 5.975066 6.103986 5.762671 5.643401 6.298896 6.240198 6.552117
## [145] 5.935552 5.269747 6.006220 6.442346 6.214535 5.811522 5.612860 6.368365
## [153] 6.934010 6.456802 6.208769 5.859532 6.412875 5.270006 6.484183 6.052912
## [161] 5.722379 6.442862 5.914377 6.553136 5.496102 6.015206 6.187813 5.510579
## [169] 6.484170 5.911088 6.707836 6.175221 6.872426 5.314355 6.455650 4.927366
## [177] 6.323979 6.128150 6.171374 5.367845 6.762478 6.901333 5.911088 5.408566
## [185] 5.881351 6.510840 5.777278 6.065798 5.970430 5.961098 6.486737 6.134053
## [193] 5.385672 5.967316 6.451164 5.294364 6.585643 6.324486 6.146808 5.679136
## [201] 6.399294 7.264792 7.111872 5.442765 6.154793 6.077199 5.490522 6.289125
## [209] 6.164147 5.584158 5.802457 5.883446 6.201331 6.724586 5.215332 6.678904
## [217] 6.186245 6.503424 6.250249 6.323850 6.538916 5.992638 6.559677 5.652416
## [225] 6.640704 6.070423 6.348587 6.022692 6.459228 6.126354 6.131997 6.127666
## [233] 5.451358 5.164483 6.739684 5.748807 6.316761 7.022381 6.105053 5.581234
## [241] 5.666479 6.495871 6.800144 5.500958 6.161430 6.634096 5.935682 5.789480
## [249] 6.592736 6.133662 6.004839 7.319096 6.265888 5.533325 5.508197 6.340831
## [257] 6.590423 6.468684 6.017087 6.234640 5.820246 6.468185 6.144625 5.463250
## [265] 6.723038 5.073427 6.045080 5.824004 6.640012 6.067001 6.663233 5.170397
## [273] 5.240239 5.698754 6.722724 6.501805 5.961098 6.042356 6.446867 5.576676
## [281] 6.420846 5.884764 6.259168 6.150359 6.060785 6.864494 6.655304 5.901148
## [289] 6.543332 5.828941 5.799273 6.396200 7.321134 6.002450 7.116752 6.717076
## [297] 5.629645 6.740770 6.668607 6.421445 6.687404 6.499292 6.226251 5.793809
## [305] 6.194435 6.298128 5.346358 6.413413 6.489527 5.324105 5.725132 7.281896
## [313] 5.811169 6.079641 6.466747 6.607601 5.830537 6.800144 6.535769 6.715955
## [321] 7.179036 6.239683 6.124280 6.161458 5.977395 6.799413 5.661144 5.928174
## [329] 6.536797 6.444356 6.655147 6.269548 6.978666 5.726212 6.799413 6.510840
## [337] 6.901333 5.931483 4.740629 6.284591 6.575989 6.626328 6.246458 5.804874
## [345] 6.421666 5.526483 6.142678 6.177348 5.755123 5.813819 6.408266 7.479680
## [353] 5.750778 6.719541 5.849332 6.568385 6.665502 5.663498 6.437299 7.112040
## [361] 5.347236 6.776578 6.202592 7.111872 6.674645 6.432774 6.839210 5.838797
## [369] 6.900382 6.645241 5.817504 5.959374 6.956813 6.446867 6.716215 5.919381
## [377] 6.647231 7.260541 5.897170 6.371060 6.680352 6.097756 6.190586 5.664688
## [385] 5.803364 5.895810 6.533406 6.456802 5.816998 5.806664 6.298128 6.125154
## [393] 6.924062 6.132433 6.323327 6.749511 6.590423 6.072167 6.119896 5.977395
## [401] 6.295043 7.479040 6.640212 6.226251 6.547580 5.804774 4.232949 5.175408
## [409] 5.491692 7.106270 6.120428 6.010391 6.930946 5.564237 5.128899 6.489527
## [417] 6.674645 6.400239 6.433893 6.500761 5.281881 7.077077 6.553328 6.221107
## [425] 5.759114 6.447566 5.527644 6.137131 5.847811 6.077562 5.844669 6.229449
## [433] 6.552117 5.402088 5.356872 5.359744 6.108006 5.881210 5.811614 6.231952
## [441] 5.516871 6.152415 6.298896 7.039660 5.796405 6.460068 6.669747 6.393728
## [449] 5.858530 6.948247 6.715955 6.046009 5.634602 6.511793 5.672554 6.323581
## [457] 6.144937 6.699628 6.680352 6.329230 6.218836 4.953608 5.620390 6.823122
## [465] 6.582341 5.716997 6.149244 6.858657 7.116752 5.872272 4.837091 6.408266
## [473] 5.438304 6.740770 6.685941 6.084473 6.175312 5.918190 5.281831 5.803922
## [481] 6.279443 6.434106 6.541850 6.077452 5.278266 6.256463 6.034688 6.040124
## [489] 5.904070 6.004487 6.326718 6.292378 6.401165 5.881210 6.732623 5.889391
## [497] 5.394554 6.020169 6.211971 5.527748 6.253233 5.406196 6.174061 6.897568
## [505] 6.128150 5.502215 6.084199 6.648767 6.832800 5.876198 5.831137 5.150803
## [513] 6.131997 5.188853 5.430973 6.466072 6.409802 6.300105 6.052259 5.785831
## [521] 6.015034 6.325385 6.396200 7.095644 6.299041 6.830454 5.951325 6.459228
## [529] 6.319445 6.052912 6.530517 5.231925 6.452903 6.036361 6.765066 5.956685
## [537] 6.066359 6.134465 5.740054 6.256463 5.470414 6.064940 6.283195 6.030827
## [545] 6.645603 5.325831 5.527644 6.057804 6.728825 6.281092 5.867274 6.452903
## [553] 5.223883 6.054497 6.004789 6.716215 6.152671 5.473183 6.903758 5.286592
## [561] 7.285474 5.634292 6.988549 5.799248 5.607862 6.189686 6.873926 6.020169
## [569] 6.748705 5.720986 5.235323 6.537431 6.678615 6.934538 5.339156 5.946041
## [577] 5.992690 6.030827 6.216885 6.096310 6.649572 6.568297 6.135645 6.035465
## [585] 6.546919 6.447566 5.773094 5.886128 6.497214 6.670705 6.217771 6.070423
## [593] 5.870147 6.095843 6.204704 5.906791 6.688028 5.859269 6.686159 5.875565
## [601] 6.068122 5.511444 6.361245 6.647913 6.231952 5.514559 6.279443 6.607601
## [609] 5.436847 5.802457 5.856920 6.428249 6.399294 5.845713 6.560760 6.762858
## [617] 6.101469 6.065730 7.479040 6.195437 5.422557 6.442346 5.905904 6.636249
## [625] 6.759155 7.094445 6.640217 6.534295 6.776578 6.444356 6.198551 5.266637
## [633] 5.793809 6.210782 5.813819 6.283195 6.033966 5.727186 7.229124 6.091655
## [641] 6.707836 6.881385 5.796066 6.340831 6.762478 6.793719 6.254869 5.996654
## [649] 5.912544 5.577875 6.424909 6.457127 5.945174 5.759496 5.468033 6.087488
## [657] 5.891377 6.291280 5.973976 5.917112 5.810747 6.608942 6.046009 5.874381
## [665] 5.304519 6.897568 6.593661 6.017087 5.910389 6.458628 5.065158 5.625443
## [673] 6.250249 5.529041 5.374314 6.325451 5.803831 6.103986 6.254869 5.523602
## [681] 6.356821 6.146569 6.638868 6.873926 5.793935 6.376467 5.538625 6.243279
## [689] 7.007215 6.149069 6.065798 5.970430 6.407346 5.558183 5.855444 6.049259
## [697] 6.446723 6.131798 5.565189 6.001210 5.877146 6.082680 6.585098 6.377391
## [705] 5.378053 6.217245 5.816156 5.500102 5.749983 6.508375 6.660128 7.229124
## [713] 6.668607 6.447715 6.127666 5.570051 5.835793 6.864494 5.828553 6.129966
## [721] 5.679136 5.545570 6.695414 5.587752 5.665390 6.089219 6.543332 6.567221
## [729] 5.713964 6.013879 6.736192 5.824004 6.462432 6.646758 5.835072 6.129853
## [737] 5.346527 5.961585 5.877993 7.112040 6.593661 6.052961 5.486543 6.592736
## [745] 6.324486 6.019047 6.458702 7.179036 6.294092 6.112747 5.646248 5.811479
## [753] 6.496056 6.569963 6.375329 6.371060 6.699628 6.584295 6.229449 6.490886
## [761] 6.615300 6.064464 6.758455 7.241418 6.879106 5.675115 5.456810 5.901148
## [769] 6.991560 6.150538 6.416563 5.877146 6.669878 6.881385 5.607934 5.820281
## [777] 6.198551 5.659907 5.860127 6.508375 6.224943 6.547580 6.031568 6.175312
## [785] 6.407442 6.260256 6.437299 5.667227 5.856920 6.284591 6.373237 6.315394
## [793] 7.096887 6.655304 6.736897 6.097756 5.818036 4.972744 5.787695 6.814362
## [801] 6.569752 6.315394 6.748705 6.830454 6.231781 5.228255 6.153224 6.407442
## [809] 6.665502 6.447715 5.170279 6.161458 6.231781 6.569963 6.640012 6.200323
## [817] 6.070234 6.325451 5.473209 6.216885 5.883446 5.804708 6.129853 5.654020
## [825] 5.883268 6.513079 7.321134 6.930946 5.884045 6.289125 5.845322 5.916215
## [833] 5.976306 6.485534 6.501077 5.648409 6.142938 5.389725 5.661804 5.954418
## [841] 6.608690 5.717344 6.121505 6.523339 5.248327 6.872426 6.982781 6.900382
## [849] 5.667190 6.132009 5.577499 5.796972 6.648378 5.405942 5.903673 6.726018
## [857] 5.248851 5.851558 6.505303 6.253233 5.769330 6.732623 5.700066 6.484183
## [865] 6.171782 6.648378 6.215803 5.622108 5.785936 6.823122 5.464453 6.063507
## [873] 5.250631 6.159143 6.293376 6.033631 6.360158 6.113110 6.484170 6.094837
## [881] 6.548257 6.618439 5.665253 5.721959 6.615300 5.644661 6.832800 5.544631
## [889] 6.203585 5.784958 6.534295 6.390955 5.887377 6.320338 6.105053 5.817627
## [897] 6.879650 6.012825 5.627854 6.750007 6.612705 5.808857 6.531596 5.656199
## [905] 6.755674 5.759085 5.923992 5.545012 5.776488 6.695712 6.958519 5.480214
## [913] 6.333264 5.905531 6.276659 5.609237 6.649572 5.464752 6.147842 6.466517
## [921] 5.863381 5.857375 6.578737 5.076493 6.733816 5.870147 6.685941 6.251601
## [929] 6.008335 6.348587 6.075011 6.903922 6.127495 5.240042 6.660721 6.457127
## [937] 5.752510 5.806664 6.466747 6.626328 6.171782 6.162393 5.381126 6.578737
## [945] 5.912875 5.268920 6.037274 6.634096 6.075011 6.616630 6.496716 6.677194
## [953] 6.263609 5.760072 6.275905 5.889391 5.996654 5.669714 5.851558 6.146569
## [961] 7.285474 5.358013 6.769574 5.868721 6.013812 5.951325 5.260893 5.684458
## [969] 6.267801 6.491405 6.719541 5.507232 6.033964 5.806895 5.992527 6.552406
## [977] 6.182290 5.919444 6.077562 5.226965 5.552847 5.968026 6.576013 6.281647
## [985] 6.079525 5.549399 5.140340 6.012825 6.783192 6.275905 6.496056 6.466072
## [993] 6.924062 6.978666 6.536797 6.535769 6.326718 6.031568 5.258035 6.353463
## [1001] 6.015206 6.860705 6.121505 5.879795 6.563409 5.786131 7.096887 6.149069
## [1009] 5.613643 6.594105 5.337401 6.045731 5.700491 4.789713 5.273592 6.594105
## [1017] 6.541850 6.084885 5.717960 6.493104 6.643622 6.142938 7.095644 6.320338
## [1025] 6.720839 6.648153 6.753690 6.038788 5.440692 6.982781 6.249111 6.124184
## [1033] 6.750007 5.176553 5.869672 6.079525 6.045080 6.382512 5.725132 5.663498
## [1041] 5.759085 6.575989 6.860705 6.643622 6.191884 6.695712 5.622889 5.808349
## [1049] 5.748917 5.743220 6.027710 6.052259 6.100967 6.411724 6.466517 6.068122
## [1057] 5.452489 5.943576 6.008335 6.411724 6.669878 5.811466 6.500761 5.538310
## [1065] 6.490886 7.184750 5.514945 5.921452 6.420846 5.963059 6.584739 5.505790
## [1073] 6.802357 5.781230 6.762858 5.941861 6.174309 5.360588 5.919444 6.361245
## [1081] 6.791953 5.913097 5.408264 6.210782 5.662197 6.783192 6.039100 6.538514
## [1089] 6.120924 7.319096 7.006089 5.513972 6.322252 6.107010 6.695414 6.585098
## [1097] 5.509073 6.496716 6.319707 6.108734 5.858126 6.958519 6.687404 5.318584
## [1105] 5.422832 6.648153 6.195437 5.420226 6.187134 5.796405 6.408841 6.468684
## [1113] 6.802357 5.827969 6.400239 6.026018 5.967790 5.931483 6.134053 5.605446
## [1121] 5.705418 6.638868 5.946739 5.745144 6.688028 6.375697 6.243590 6.249792
## [1129] 5.320462 6.546919 6.193208 6.298277 5.794278 6.736192 6.678615 6.663136
## [1137] 6.755674 6.676260 6.109153 5.862699 6.276659 6.537431 6.510270 6.193208
## [1145] 6.102330 6.125154 5.665390 6.236826 5.421915 6.255237 5.360701 6.116148
## [1153] 5.429234 6.563409 5.896253 6.082680 6.218183 5.906791 6.717076 5.816839
## [1161] 7.022381 6.488199 6.669352 5.677883 5.738884 6.323581 6.548257 5.925783
## [1169] 6.102330 6.002450 6.015034 5.867274 6.839210 6.032192 6.592268 5.290668
## [1177] 6.152671 6.247094 5.777278 5.345990 6.013215 7.078178 6.538514 6.662870
## [1185] 6.407346 5.653800 6.291895 5.905904 5.905531 5.743117 5.973976 5.818036
## [1193] 6.175221 6.319707 5.465339 6.903922 6.903758 6.189686 5.872272 6.488199
## [1201] 5.862699 6.240198 6.533406 6.191884 5.845217 5.962445 5.416134 6.001210
## [1209] 6.200323 5.570067 6.013879 6.869092 6.186475 5.791125 6.556397 4.882379
## [1217] 7.241418 6.213868 5.570900 6.021335 6.662870 6.726018 6.720839 6.480038
## [1225] 6.017321 7.442798 5.804708 5.672554 6.292378 6.376467 6.552406 5.654685
## [1233] 6.182290 6.091655 5.768155 6.323979 6.417350 6.124280 6.134465 6.300333
## [1241] 6.417350 5.732039 5.599592 5.400485 5.992638 5.449669 6.213868 5.842232
## [1249] 6.722724 6.227370 6.491715 6.530517 6.814362 6.660721 6.660128 6.553328
## [1257] 6.584739 6.373242 5.700475 6.143193 6.124369 6.109153 5.432626 6.227397
## [1265] 6.113110 5.635768 5.202079 5.339443 5.535184 6.991560 6.143193 6.269548
## [1273] 5.718577 5.155791 5.773094 6.418871 6.568385 5.366816 5.733875 6.174309
## [1281] 6.319445 6.413413 6.222076 5.708657 6.531596 5.638185 6.401165 5.969482
## [1289] 6.640217 6.082149 6.663136 5.971640 6.451164 5.979339 6.165501 6.612705
## [1297] 6.521160 7.007215 6.585643 5.430556 5.471379 5.618188 6.254536 5.665903
## [1305] 5.372235 6.501077 5.470377 5.603540 6.279724 5.645151 6.098201 5.967063
## [1313] 6.323327 5.928556 5.621869 5.506075 5.692090 6.217771 6.267516 6.584295
## [1321] 6.103745 6.125193 6.018316 6.247094 6.004789 5.802393 6.323850 6.248784
## [1329] 5.758016 6.506593 5.803922 6.045731 6.424909 5.835537 5.426045 5.987457
## [1337] 6.524870 6.686159 6.130725 5.749983 6.368365 6.613272 6.739684 6.879650
## [1345] 5.919381 5.595479 6.202592 6.666104 6.273194 6.037758 5.619234 6.246458
## [1353] 6.019047 6.133662 6.375329 6.662496 5.884425 5.723221 6.255236 6.039100
## [1361] 5.092246 6.440358 6.393728 6.906528 6.298050 6.228382 5.806165 5.597388
## [1369] 5.561116 6.268388 5.897170 7.590187 5.928174 5.056612 5.297311 5.854208
## [1377] 6.446723 5.845322 6.424005 6.293376 6.216514 6.329230 6.491405 5.710315
## [1385] 6.936867 6.194435 6.544223 6.510270 7.039660 6.608690 5.866043 5.711477
## [1393] 5.986935 6.027762 5.810365 6.485534 5.946739 6.421666 6.608942 5.545420
## [1401] 5.854208 5.559292 6.515943 5.669007 6.934010 5.279171 5.843914 6.066255
## [1409] 5.153344 6.119896 6.592268 6.567221 7.479680 6.988549 6.300105 5.913156
## [1417] 6.468185 6.004839 5.482055 6.164302 7.184750 5.724207 5.943408 5.359208
## [1425] 5.675018 6.504948 5.148073 6.416563 5.941861 5.615340 6.523339 6.559677
## [1433] 5.239377 6.787835 6.171093 6.227370 6.676260 6.505303 5.807653 6.316279
## [1441] 6.292797 6.550637 5.645151 6.124184 5.201783 6.521160 6.139902 6.382512
## [1449] 6.511793 6.103745 6.550637 6.217245 6.204704 6.100498 5.291291 6.291280
## [1457] 5.938319 6.258827 6.425283 5.781230 6.766111 5.728360 6.066255 5.795460
## [1465] 6.479267 6.221107 6.228382 7.590187 6.380592 6.131798 6.736897 6.636249
## [1473] 6.294603 5.492479 5.858126 6.678904 6.377391 5.276048 5.811169 5.679170
## [1481] 5.340467 6.504948 6.089219 6.303648 5.830979 6.567134 5.533918 5.854845
## [1489] 5.175577 6.645241 5.811479 5.345506 5.622108 6.432774 5.244240 6.411728
## [1497] 6.283676 6.020999 6.011503 5.985289 6.868187 6.502880 6.312558 7.017667
## [1505] 7.125391 5.912586 6.123962 6.506837 7.052152 6.117255 6.697142 6.171293
## [1513] 5.661741 5.796756 5.541134 5.604816 5.976767 6.292576 5.432452 5.771065
## [1521] 6.640604 5.243390 6.309763 5.975101 5.803718 5.921979 5.802817 6.220809
## [1529] 6.140511 6.337621 6.433426 6.536143 6.542162 5.888071 6.727885 6.261506
## [1537] 6.021566 6.195768 5.575279 6.587343 5.853332 7.253236 6.455158 6.454933
## [1545] 5.707871 6.539451 6.020287 5.667264 7.027629 6.089928 5.381999 5.157278
## [1553] 6.993690 6.401653 6.363436 6.934278 6.117505 6.053228 6.759359 5.587670
## [1561] 6.673325 5.712316 5.285594 6.608919 6.243138 5.872384 7.027629 6.781955
## [1569] 6.363436 5.819763 5.874033 6.025561 5.814581 6.242324 5.516735 6.129100
## [1577] 5.511833 5.954114 6.283676 6.860396 6.561497 6.265212 6.123962 5.973441
## [1585] 6.608919 6.386106 6.298885 6.339498 6.225842 6.639698 6.912399 5.534120
## [1593] 5.572170 6.010620 5.514527 5.069776 6.380046 6.506837 5.949436 5.719464
## [1601] 5.933767 6.225813 6.628635 5.290519 6.145247 5.322254 5.325548 6.936977
## [1609] 6.342220 6.087057 6.151947 6.438280 5.598823 5.644980 6.544548 5.985289
## [1617] 5.912586 5.208922 6.934278 6.628635 6.041579 5.844481 6.359886 5.267500
## [1625] 5.465853 6.993690 7.017667 6.539451 5.982402 7.049312 6.711507 5.318189
## [1633] 6.041579 5.204073 6.985425 5.556865 6.086449 6.256346 5.265921 5.707871
## [1641] 6.367775 5.968119 6.697142 6.272480 7.322349 6.324454 6.493674 6.452977
## [1649] 7.049312 5.894742 5.770830 6.365480 5.642947 5.901032 6.438280 6.889602
## [1657] 6.145678 5.876237 6.312558 5.841864 5.847361 6.025317 5.300473 6.435606
## [1665] 6.860396 5.399423 6.715183 5.841649 6.013223 6.109452 6.125298 6.530833
## [1673] 6.220809 6.535702 5.716840 6.057765 6.992242 6.078376 5.430630 5.637762
## [1681] 6.672904 6.255739 6.020332 5.379588 6.024256 6.037229 6.131143 6.013223
## [1689] 5.386345 6.218882 6.380839 5.322345 6.182049 6.062493 5.743865 5.953556
## [1697] 5.377481 5.847949 5.909687 6.256477 5.305534 6.759359 6.558928 6.649842
## [1705] 6.304036 6.536143 6.781955 5.266085 5.232346 6.131143 6.003567 5.541806
## [1713] 5.603371 5.950500 6.454933 6.912399 5.188935 5.921993 5.651473 5.938569
## [1721] 6.272480 6.024256 5.948182 6.042970 5.312658 5.954114 5.408056 5.285650
## [1729] 5.366373 6.684476 6.179478 6.401653 5.754578 5.728301 5.716744 5.923599
## [1737] 5.506113 5.991406 6.620429 6.367775 5.830224 7.003122 6.025561 5.874033
## [1745] 6.068726 6.226921 6.414289 6.314357 6.474676 6.185777 5.665260 6.138349
## [1753] 5.889584 6.003567 6.339498 6.342220 6.363090 7.055509 6.502880 5.566163
## [1761] 5.917661 6.691841 6.380839 6.019863 5.852356 6.180739 5.991406 6.159471
## [1769] 7.123183 6.474676 5.949436 6.146479 6.340844 6.168007 6.554580 6.522482
## [1777] 5.969116 5.996968 6.691841 5.935801 6.521016 6.625084 6.527372 6.127367
## [1785] 6.437288 6.204374 6.116355 5.578994 6.109452 5.974476 5.638756 6.242220
## [1793] 5.771065 5.725995 6.386106 5.570039 6.343739 6.360715 5.814581 6.504253
## [1801] 5.841864 5.232534 5.549334 5.490955 6.926714 6.595130 6.569811 6.595130
## [1809] 6.561497 6.188886 6.542162 5.936816 5.629694 6.365480 5.600193 6.226921
## [1817] 5.880610 6.309672 5.948182 5.868057 6.452977 6.121499 6.021566 6.925273
## [1825] 5.438549 6.773823 5.980207 5.989820 5.895476 6.255739 5.736985 5.921173
## [1833] 6.535702 6.417160 5.215557 6.916941 6.265212 6.108462 6.554580 5.877749
## [1841] 6.138349 6.108118 5.600965 6.025317 5.280527 6.117505 6.327376 6.180739
## [1849] 5.570889 5.212563 5.696834 6.673325 6.521016 6.781238 5.663655 7.153370
## [1857] 4.906549 6.684476 5.747732 6.773823 5.360602 5.587670 6.125298 5.699581
## [1865] 5.938569 5.947340 5.830050 6.411728 5.909687 6.188886 7.048479 5.727430
## [1873] 5.594094 5.839153 5.865608 6.750720 6.314357 4.675490 6.727885 5.315113
## [1881] 6.718268 6.144974 6.243138 5.832715 6.435606 6.560229 6.525227 5.631857
## [1889] 5.979635 5.477217 6.236146 6.145678 5.650535 5.789446 6.560229 5.801187
## [1897] 6.029212 6.385721 7.322349 6.292576 5.747839 5.812528 5.929560 5.947340
## [1905] 5.751651 6.235672 5.966182 5.342864 6.236390 5.823263 5.903415 5.305350
## [1913] 5.377481 6.144974 6.259748 6.420204 7.003122 6.236146 6.190816 6.359886
## [1921] 6.533376 6.420204 6.281287 5.703929 6.936977 5.743865 6.360715 6.925273
## [1929] 6.572454 5.661711 5.382751 6.985425 5.859166 6.020999 5.829074 6.648468
## [1937] 5.841706 6.223381 5.973441 6.242324 6.802196 5.781333 5.411721 6.534922
## [1945] 6.625084 6.116355 5.950625 6.525227 6.115586 5.397415 6.750720 6.321755
## [1953] 7.444545 5.872391 5.823263 5.594094 6.282111 6.029212 6.108462 5.968119
## [1961] 6.185777 5.933767 5.592977 5.979635 6.329200 5.906286 6.078342 5.845798
## [1969] 6.369792 6.179478 6.258948 5.233596 6.020287 5.817930 5.859166 5.736985
## [1977] 6.244790 7.123183 7.153370 6.244790 5.398243 5.972237 6.323511 5.522347
## [1985] 6.534922 5.891503 6.198755 5.804923 5.323649 6.128852 6.504253 5.969434
## [1993] 6.186466 7.055509 6.437288 6.259748 6.127367 4.823919 5.871645 6.595688
## [2001] 7.052152 6.620429 5.120166 6.185816 6.530833 5.996968 5.829074 6.586128
## [2009] 6.429061 6.059622 6.441615 5.903116 6.256346 6.115586 6.441615 6.455158
## [2017] 6.281287 5.913087 6.225842 6.190816 5.901722 7.125391 6.509871 5.376073
## [2025] 6.127391 5.364660 6.493674 6.781238 5.953556 6.198755 6.042970 6.011503
## [2033] 6.309672 6.159471 6.204374 5.353465 6.151947 6.087057 5.961564 5.712316
## [2041] 6.323511 5.747732 6.926714 6.057765 5.823771 6.715183 6.385721 7.253236
## [2049] 5.841649 6.175882 6.010620 5.570889 6.558928 6.044563 6.225813 6.340844
## [2057] 6.083315 5.807660 5.969434 5.299575 6.587343 6.544548 6.672904 6.639698
## [2065] 6.648468 6.868187 5.675970 6.078376 5.571130 6.433426 6.436426 5.665607
## [2073] 7.444545 6.718268 5.542234 6.240056 6.889602 5.551212 5.736771 6.182049
## [2081] 6.188740 5.796756 7.048479 6.168007 6.711507 6.170168 6.218882 5.993930
## [2089] 5.775429 6.062493 5.518533 6.124019 6.526532 5.996608 5.806367 6.156406
## [2097] 6.281403 6.240110 5.305060 6.072601 5.814202 6.459693 5.791475 7.021614
## [2105] 6.119644 6.513535 5.742307 6.609498 5.604343 6.646228 5.710990 6.214023
## [2113] 6.184186 6.613711 6.200977 7.083689 6.185236 5.965576 6.285781 6.705162
## [2121] 6.264129 6.021187 6.027391 6.283839 5.790455 5.705584 5.890448 6.464854
## [2129] 6.641143 5.862759 5.940880 5.899978 6.783628 6.025134 5.630521 5.116143
## [2137] 6.163563 5.364550 6.139956 5.347862 6.565371 6.188887 6.302768 6.217430
## [2145] 6.124647 6.081203 5.557498 6.313146 5.541569 6.644361 6.530494 5.841709
## [2153] 6.704425 6.597186 6.302768 6.492605 5.590136 5.790455 6.019202 6.124623
## [2161] 5.385470 6.996053 5.559650 6.593421 6.126023 6.870861 5.364622 5.063207
## [2169] 5.473338 5.948559 6.202545 6.181858 6.126406 5.832724 6.370937 5.846121
## [2177] 6.698352 7.092386 5.067832 5.486095 5.572975 5.397457 5.762240 5.808011
## [2185] 5.965576 5.849842 6.175821 6.574154 6.338216 5.434303 6.075041 6.616579
## [2193] 6.542123 5.615575 6.499902 6.506438 6.010549 5.027524 6.226961 5.769702
## [2201] 6.861502 5.861562 6.090204 6.834057 6.379487 6.624885 6.213950 5.617727
## [2209] 6.668070 6.978816 5.965110 6.647716 6.757950 6.242074 6.487535 5.417851
## [2217] 5.891446 6.045578 7.030446 6.746095 6.239529 6.590010 6.823686 6.200594
## [2225] 6.124019 6.571573 6.372648 6.767213 6.283839 5.888856 6.043382 5.936385
## [2233] 5.968837 6.183003 6.174834 6.337870 5.999699 6.025134 6.178697 5.940880
## [2241] 5.512438 6.697240 7.530863 6.780560 5.862217 6.648065 6.059927 5.834943
## [2249] 5.999699 5.808701 5.517806 6.407108 6.237175 6.239803 6.743219 5.710335
## [2257] 5.695341 7.090687 5.987737 5.907445 6.274654 5.283681 5.895361 5.643209
## [2265] 5.051129 5.839442 6.152635 5.832724 6.468120 6.080457 6.906294 6.252860
## [2273] 5.135009 5.972881 6.085895 5.515688 6.944596 6.632722 5.883975 7.386532
## [2281] 6.697240 6.449314 6.646876 6.158350 6.416311 6.629019 6.211932 4.931307
## [2289] 6.087435 5.669635 5.333959 6.253082 5.372543 6.565371 6.349265 5.699745
## [2297] 6.226021 6.252409 6.049297 6.633766 5.300726 5.508581 5.834561 6.426380
## [2305] 6.226961 5.762240 6.269069 6.069404 5.843856 5.360649 6.214023 5.912794
## [2313] 5.909091 6.193785 5.867461 6.178197 5.771909 6.448819 5.853787 6.074325
## [2321] 5.356940 5.588878 6.224960 6.068692 6.514500 5.706114 5.981899 6.440127
## [2329] 5.651928 6.757950 6.196698 7.396400 5.984930 5.812155 6.315447 6.638329
## [2337] 6.449314 6.352461 6.164387 6.710571 5.872735 6.252399 6.749492 6.466011
## [2345] 6.581001 6.442410 6.188887 6.169633 5.234726 5.975054 6.051483 5.226924
## [2353] 7.003319 5.533667 6.575414 5.653423 6.201309 6.104620 6.764774 5.931387
## [2361] 5.434184 5.534863 6.229938 5.866043 5.305838 5.560456 6.050785 5.742307
## [2369] 5.817348 5.081354 6.618544 5.735526 5.462223 6.251957 5.935461 6.246715
## [2377] 6.281403 6.187903 6.305750 5.659922 5.917229 6.919159 5.545761 5.636009
## [2385] 5.360633 6.075690 6.181577 6.451448 7.017965 5.799393 6.421893 5.931752
## [2393] 6.033373 6.501388 6.345322 6.386756 5.193986 6.252399 5.822037 6.282815
## [2401] 6.264129 6.308211 6.759637 5.913077 7.066875 6.241421 6.151217 6.251957
## [2409] 6.401747 6.012205 5.583095 6.419503 5.562688 6.374551 5.535610 6.350849
## [2417] 6.510302 6.212051 7.807808 5.614262 7.190876 5.959569 6.934447 6.492941
## [2425] 4.881779 6.011839 6.919222 6.585150 6.290776 6.826209 6.113774 5.820132
## [2433] 7.066875 5.398394 5.702469 6.183598 6.530494 6.181858 5.716116 5.816369
## [2441] 6.496072 6.296051 5.305060 5.314010 6.379487 6.763475 6.648065 6.461173
## [2449] 6.225939 5.536684 6.241421 5.807603 5.319132 6.900168 6.180043 5.535047
## [2457] 5.700777 6.407108 6.968835 6.023339 6.401747 5.339850 5.285664 6.133679
## [2465] 6.422857 6.542123 6.749112 6.259346 6.776316 5.504361 6.353686 6.433972
## [2473] 6.381154 6.143230 5.954425 6.148365 6.365276 6.136864 6.919159 6.443484
## [2481] 5.736733 5.735526 6.419084 6.158431 6.461696 6.255972 5.657975 6.488743
## [2489] 6.619766 6.027286 5.541771 6.029592 5.865490 6.202433 6.346401 5.576727
## [2497] 6.459693 6.309624 5.969436 6.289101 6.290776 6.823686 6.556658 6.156325
## [2505] 6.086025 6.375650 6.957786 6.429242 6.219055 7.174336 5.853787 6.219023
## [2513] 6.171443 5.553363 6.272277 6.605238 6.123114 6.061494 5.651247 6.792259
## [2521] 5.953466 5.552962 5.625892 5.347424 5.909449 6.358488 6.735608 6.307591
## [2529] 5.790817 6.978816 5.617727 6.801674 6.299905 6.090204 5.403824 6.299905
## [2537] 6.746095 5.884570 5.768647 6.726800 6.729045 6.374551 5.865490 5.996773
## [2545] 5.866043 6.036746 5.720545 6.218546 5.988190 6.676917 6.123141 5.861142
## [2553] 6.043285 6.505134 6.211599 6.358488 6.039225 5.895361 6.220920 5.909091
## [2561] 6.184186 6.023806 6.270732 6.705301 5.807877 6.349265 5.989877 6.115714
## [2569] 5.720381 6.199065 6.027286 6.124623 5.812155 6.136864 6.141154 6.515117
## [2577] 6.684392 6.077370 6.166731 6.513535 6.319578 5.821499 5.824179 6.752637
## [2585] 6.114085 5.952834 6.470394 5.394110 6.356885 6.309624 5.852693 4.951421
## [2593] 6.322544 6.205308 6.636874 6.272935 6.749492 5.710990 6.169810 6.514500
## [2601] 5.551360 5.822919 5.929039 5.505681 5.473530 6.351186 6.011866 6.681568
## [2609] 6.217887 6.648768 6.137269 6.254380 6.051181 6.229572 6.619766 5.645839
## [2617] 7.082330 5.520234 5.586239 5.968693 6.362841 6.025054 5.528827 6.188498
## [2625] 5.257283 6.150959 7.052263 5.972148 5.643206 6.597186 5.715738 5.476547
## [2633] 5.917229 6.064710 6.174215 5.649023 6.711125 6.060665 6.356885 5.214790
## [2641] 7.418189 6.907745 6.036746 6.236041 6.061392 6.349345 7.003319 5.705584
## [2649] 5.252055 6.468120 5.510435 5.837362 5.270332 6.425113 6.618544 6.329934
## [2657] 6.589942 6.074929 6.750505 5.477170 5.849139 6.171541 6.375650 6.014736
## [2665] 5.831835 6.638329 6.629962 6.329934 5.709803 5.979869 6.165189 6.411841
## [2673] 5.895454 5.667426 6.300352 5.975137 5.681639 6.767213 5.611040 7.530863
## [2681] 6.754240 7.536454 5.648878 5.849595 6.716754 6.582607 6.338216 5.998357
## [2689] 6.158431 6.223585 5.730901 5.969219 6.398936 6.159042 6.544212 6.676917
## [2697] 6.099017 6.214490 5.440336 5.437752 7.058013 7.179532 6.060722 6.103854
## [2705] 6.492605 6.239803 6.891377 7.021614 6.735387 6.146163 6.188498 5.296860
## [2713] 6.592658 6.897799 5.890812 6.461696 5.899978 5.370569 6.394443 6.443484
## [2721] 5.063207 5.972881 5.895937 5.337856 6.274101 5.519966 6.587506 5.270332
## [2729] 6.144382 5.317074 5.987737 5.534576 6.729045 6.395583 6.034256 4.736300
## [2737] 5.965520 7.807808 7.006023 6.201309 6.647716 5.726507 5.747136 6.322544
## [2745] 5.944005 6.066877 5.424586 6.353686 6.126406 6.814947 6.350849 6.307108
## [2753] 6.052561 5.625883 5.981899 6.183614 6.365276 6.307108 6.861264 6.910358
## [2761] 6.419503 6.498391 6.068692 6.895812 6.165189 6.636874 6.074325 5.971119
## [2769] 6.591961 5.090933 5.287584 6.666483 5.403569 6.438228 5.709894 6.671523
## [2777] 6.942019 5.347171 6.174618 6.300352 6.326662 5.213769 6.759637 6.599543
## [2785] 6.291302 6.556658 5.972818 6.723923 5.519183 6.037732 6.169810 7.599621
## [2793] 6.391444 6.777100 6.315447 5.573606 6.308211 6.185236 6.237175 6.764774
## [2801] 5.807094 5.899117 6.460925 6.152635 6.197501 6.240138 6.575414 5.543917
## [2809] 6.133679 5.719251 5.799649 5.637608 5.421299 6.761920 6.925860 6.731320
## [2817] 5.463789 5.942099 6.236041 6.045578 5.643209 6.209777 6.801674 5.709000
## [2825] 6.109853 6.511933 5.584816 7.289258 6.895812 6.574154 6.330799 6.705301
## [2833] 5.811673 6.108884 6.239529 5.289257 5.625892 5.882955 6.251668 5.747031
## [2841] 5.713610 5.580574 5.382533 5.617380 6.486777 6.639665 5.709874 6.034256
## [2849] 6.495347 6.547284 5.946499 7.523104 6.370937 6.413636 6.137269 6.289101
## [2857] 6.254380 6.387633 6.643743 4.912141 5.244858 6.043206 6.737641 5.490346
## [2865] 5.929039 7.523104 5.873220 6.453536 6.296249 7.052263 6.506438 6.345322
## [2873] 6.678521 6.585150 5.956014 5.617991 5.955823 6.118959 5.670144 6.727300
## [2881] 5.678900 6.818616 5.398546 6.045098 6.651614 5.706579 5.907578 6.132338
## [2889] 6.170544 7.090065 5.746096 5.467563 6.240110 5.958061 7.017082 5.388637
## [2897] 6.166731 5.555499 6.605238 6.942019 6.330799 6.632722 6.368904 6.051483
## [2905] 5.405580 5.970751 6.682679 5.495934 6.202545 6.429242 5.533667 6.318662
## [2913] 6.704242 6.711125 5.423446 5.718191 6.066877 5.404448 5.263141 6.524564
## [2921] 5.946580 6.646876 5.802341 5.889867 6.231934 5.715738 6.105166 6.705162
## [2929] 6.051181 5.005859 5.814202 5.727037 6.579461 5.253406 6.406771 6.180043
## [2937] 7.544173 6.200594 6.199065 6.194846 6.069008 6.060665 6.064868 6.253082
## [2945] 5.931387 6.472889 6.226312 6.463362 5.881048 6.557011 6.166639 5.926825
## [2953] 7.232184 6.802779 6.085895 6.906294 5.839442 6.023339 6.704425 6.083452
## [2961] 5.816369 5.784586 6.064049 6.080457 5.952834 6.486777 5.695305 6.582607
## [2969] 5.974104 6.540409 6.416951 5.866209 6.230706 5.408276 6.243160 6.252409
## [2977] 6.704242 6.163522 6.032169 6.757879 5.860076 6.469518 7.289258 6.450175
## [2985] 6.150959 6.107323 6.189680 5.313336 6.814947 5.417948 6.119644 6.438228
## [2993] 6.286213 6.710571 6.014202 6.442410 6.591961 5.642107 5.762827 6.595095
## [3001] 5.629389 6.524327 6.078413 5.890448 5.841709 6.174215 6.197501 6.384011
## [3009] 5.955823 7.030446 5.271006 6.291613 6.174834 5.763126 5.517650 6.384011
## [3017] 5.532486 6.626648 6.466011 6.522848 5.636903 6.296249 6.038223 6.900168
## [3025] 6.183614 6.319578 5.768647 6.616579 5.362550 6.757879 5.543917 6.099017
## [3033] 5.907725 6.640996 6.505134 6.146163 5.346648 6.887340 6.152377 6.716754
## [3041] 6.282815 6.203784 6.917197 5.895937 6.702247 6.226021 6.124647 6.737641
## [3049] 5.692087 6.285781 6.337870 6.109220 6.175821 5.492376 5.624509 5.336103
## [3057] 6.334636 6.362841 6.272798 6.599543 6.595095 6.270732 5.972818 6.115714
## [3065] 5.817959 6.818616 6.003846 5.755457 6.763475 6.521238 6.859011 6.200977
## [3073] 6.339704 5.733257 6.039225 5.761809 5.564202 5.953466 6.887340 6.398546
## [3081] 6.118959 6.686595 7.599621 5.175747 6.341097 6.499902 6.925860 6.272277
## [3089] 6.195506 6.166394 6.641143 5.791475 5.912567 6.151217 6.802751 6.219023
## [3097] 6.074929 6.205321 6.205308 6.211599 5.463729 5.287392 6.979769 5.369627
## [3105] 5.792777 5.404520 5.642217 6.389562 6.911902 6.651614 5.999978 6.957786
## [3113] 6.408033 5.932971 6.152377 5.293995 6.043920 5.684764 5.628354 6.826209
## [3121] 5.617991 6.147254 6.613711 5.699745 5.984930 6.968835 6.608239 6.323989
## [3129] 6.738956 6.001017 5.652963 4.671571 7.006023 6.626648 6.075769 6.316292
## [3137] 6.214502 5.972638 5.711802 6.495347 6.296051 6.029021 6.225939 5.599967
## [3145] 6.274101 6.834057 5.087075 6.398936 6.083452 6.557011 6.643743 4.899560
## [3153] 6.763648 5.692089 7.152218 5.926825 6.636341 6.183598 5.613540 6.045098
## [3161] 6.229938 6.350530 6.693103 6.195506 7.232184 6.014736 6.144290 6.521238
## [3169] 5.979869 6.163522 5.989146 5.697190 6.003846 5.470437 6.269069 6.118386
## [3177] 6.057546 6.014293 6.072601 6.025325 6.205193 6.123114 5.730901 6.859011
## [3185] 6.166701 5.667426 6.156406 6.836513 6.080213 6.272935 7.473564 7.152218
## [3193] 6.043285 6.419084 6.159073 6.421893 5.943145 6.645967 6.693103 5.411059
## [3201] 6.137312 6.075769 6.360042 6.496072 6.897799 5.397245 6.286213 6.350829
## [3209] 6.029021 6.624885 6.274507 6.102155 6.112924 6.350530 6.526532 6.032761
## [3217] 5.608890 6.329064 7.083689 7.017082 6.587366 6.009350 6.166394 5.731076
## [3225] 7.090687 5.849842 6.212051 5.251542 5.570542 6.063261 5.499214 6.917197
## [3233] 5.782271 5.354106 6.395583 6.029592 5.665660 5.847889 6.853647 6.329064
## [3241] 6.910358 6.469518 6.841074 6.968685 6.996053 5.563390 5.999978 6.304870
## [3249] 6.023205 6.466772 7.058013 7.082330 6.323989 7.010804 6.684392 5.638352
## [3257] 6.571573 6.181577 6.148948 6.418175 6.085174 6.163563 5.941049 6.483658
## [3265] 5.803130 6.007179 5.332935 6.202433 6.418175 6.592658 6.463362 6.608239
## [3273] 6.292902 6.125241 5.834202 6.874457 5.996608 6.667216 5.975137 5.754858
## [3281] 6.340481 6.193785 6.203784 5.172254 6.482851 6.061392 5.517799 6.914776
## [3289] 6.732667 6.021187 6.472889 6.579461 6.727916 5.449559 5.807603 6.387633
## [3297] 6.422857 7.393557 6.360042 5.938141 6.066896 6.209777 6.109853 5.895533
## [3305] 6.137312 6.080917 6.103854 6.802751 7.179532 6.021557 5.878893 6.864894
## [3313] 5.968837 5.113267 6.090675 6.524327 6.299366 6.164143 6.727300 6.763648
## [3321] 5.916323 5.896023 6.183833 5.968693 6.242074 6.178197 5.629389 6.113774
## [3329] 5.237006 6.498391 6.166639 6.217206 6.202736 6.351186 6.624912 5.421299
## [3337] 6.023806 5.497612 6.515117 5.673888 6.015613 6.464854 5.505175 7.010804
## [3345] 7.386532 6.482851 5.356052 6.372648 6.415951 5.450317 6.979769 5.761631
## [3353] 6.154532 6.524928 6.252860 5.493555 5.241033 6.590010 6.394443 6.624912
## [3361] 7.396400 5.988190 5.377999 5.604343 6.547284 5.291340 6.249769 6.358732
## [3369] 5.282814 6.453536 5.870540 7.536454 6.667216 6.217429 6.671523 6.346401
## [3377] 6.527873 5.658729 6.183631 6.792259 6.183631 6.986409 5.673094 5.408985
## [3385] 5.885646 5.394183 6.407746 5.649285 6.986409 6.629962 6.604948 6.750505
## [3393] 5.333809 6.139956 7.092386 6.587506 6.080213 6.678521 6.249769 5.820899
## [3401] 6.460925 6.666483 6.038223 6.415951 6.339704 6.126023 6.104620 6.668070
## [3409] 5.834943 5.321594 6.300502 6.682679 6.052561 6.640996 6.340481 6.433972
## [3417] 6.532701 5.915815 6.783628 6.305750 6.524564 6.291613 5.627088 6.864209
## [3425] 6.159073 6.009511 6.280507 5.806367 6.457608 6.749112 6.646228 6.123141
## [3433] 6.220170 6.164143 6.194846 5.867461 6.110901 5.737208 5.582496 6.510302
## [3441] 5.895533 6.407746 6.341097 6.027391 6.944596 5.862664 6.629019 6.189680
## [3449] 6.005678 6.159042 6.648768 6.752637 6.524928 5.695305 6.280507 6.777100
## [3457] 5.943145 5.942471 5.354178 5.910928 5.515678 6.780560 6.049989 5.792031
## [3465] 6.738956 6.275201 5.954056 6.182060 5.455964 6.732667 5.946499 6.048236
## [3473] 6.072451 5.564202 5.438668 6.761920 5.996100 6.769163 6.416311 5.402104
## [3481] 6.891377 6.307591 5.965520 7.473564 6.735387 6.645967 6.911902 6.299366
## [3489] 6.861264 5.960661 6.609498 5.645839 6.063261 6.274654 5.748120 6.226312
## [3497] 6.196698 6.036057 6.182959 6.219055 6.769163 6.205193 5.945938 5.955279
## [3505] 6.124324 6.064710 5.987813 6.166701 5.438130 5.729303 5.687772 5.525095
## [3513] 5.895466 6.304870 6.005526 5.390292 5.811267 5.889867 5.434975 6.144290
## [3521] 5.972148 5.931752 5.315154 5.983992 6.726800 6.474170 6.396639 6.644361
## [3529] 6.039490 5.860605 6.148365 5.726465 6.075041 6.408033 6.147191 5.772072
## [3537] 7.544173 6.240138 7.393557 5.288087 5.724834 5.580812 6.735608 6.144382
## [3545] 6.211932 6.117174 6.731320 6.371736 6.398546 6.992242 6.234668 6.754240
## [3553] 6.217887 6.217206 6.787835 6.077199 6.515095 6.150538 6.758455 6.082149
## [3561] 6.150359 6.540409 6.334636 5.580574 6.864209 6.669747 7.418189 6.072451
## [3569] 6.753690 6.158350 6.292902 6.686595 7.006089 6.132338 5.642947 5.822037
## [3577] 5.972237 6.723038 6.874457 6.834297 6.107923 6.113427 5.706564 6.707493
## [3585] 6.435550 5.703764 4.248289 6.539816 6.446621 5.399118 5.940276 6.215016
## [3593] 6.400086 6.487655 5.837292 6.744857 6.247257 5.729167 6.575796 6.340335
## [3601] 5.850188 6.715359 5.728027 6.033175 6.330884 6.240269 6.827982 6.927553
## [3609] 6.487655 6.972573 6.059369 6.851384 6.334841 6.819880 6.701968 5.940276
## [3617] 6.681759 6.568413 5.260991 5.929212 5.309279 6.712621 6.076019 7.068448
## [3625] 6.279570 5.829827 6.826925 6.844806 5.796283 6.595921 6.394962 7.396203
## [3633] 5.556878 6.644554 5.413345 5.813442 5.859941 5.529732 5.212499 6.904985
## [3641] 6.170331 6.165858 5.982355 6.329361 6.749260 5.838591 6.592921 6.580080
## [3649] 6.103691 6.746290 6.681759 6.611707 6.957011 5.542427 6.427156 6.204450
## [3657] 6.609579 5.971045 7.066028 6.020069 5.988455 6.075952 7.004862 5.151816
## [3665] 5.929212 6.019804 6.523175 6.108983 5.763563 5.303439 6.785415 5.590387
## [3673] 5.243309 7.253370 6.994724 4.703071 5.814948 6.539816 4.602882 5.982355
## [3681] 6.680248 6.302512 6.777000 6.199945 6.508917 5.917422 6.249211 6.528158
## [3689] 6.724448 6.830368 6.162524 6.056532 5.531742 6.584854 5.789187 6.435550
## [3697] 6.337457 6.998424 6.137618 6.533520 5.860135 5.543578 6.800577 6.500485
## [3705] 6.443525 6.204450 5.767127 5.523113 5.856827 6.430698 6.743823 6.302512
## [3713] 5.662517 6.695850 6.777000 6.189377 5.902076 6.098501 5.798159 6.414364
## [3721] 6.495496 6.646101 5.774565 6.583128 6.616710 6.178077 6.051081 6.386079
## [3729] 6.047677 6.256769 6.493330 6.463688 6.791455 6.099993 6.127424 6.749465
## [3737] 6.248794 7.068448 6.587500 6.371819 6.245078 6.413605 5.247827 6.048515
## [3745] 5.800779 6.196812 5.723562 6.575796 6.326822 6.117929 5.637813 6.217237
## [3753] 6.495709 4.284395 6.938994 6.569114 5.581287 6.349870 5.999086 6.724448
## [3761] 5.576508 6.249211 6.679913 5.523737 6.991906 6.189377 5.706564 6.783624
## [3769] 6.064542 6.058259 6.197338 6.035835 6.227165 7.449369 6.279570 6.443525
## [3777] 6.058259 6.414364 6.098369 6.330884 6.200981 6.289956 6.076019 5.882158
## [3785] 5.960303 5.438558 4.913845 5.490924 5.902076 6.430698 6.622962 6.451148
## [3793] 5.966289 7.305260 6.551210 6.182971 6.391297 5.917422 7.190268 6.174586
## [3801] 6.020069 5.523737 6.319214 5.805253 6.583128 6.411979 6.448373 6.957011
## [3809] 6.383950 6.694932 6.047410 6.498176 5.993643 6.103809 6.402237 6.658562
## [3817] 6.249747 6.316065 6.778862 5.879475 6.097290 6.463688 5.960303 6.249866
## [3825] 6.500452 6.375573 5.729167 6.551210 6.249747 6.658562 5.957049 6.761389
## [3833] 6.572173 6.195837 6.365638 6.547836 4.795077 5.859600 5.760156 6.749465
## [3841] 6.007300 6.645798 6.381023 6.533520 6.447290 6.445916 6.024043 6.128299
## [3849] 6.394360 5.825294 5.326914 6.002981 5.213252 6.994724 4.856372 5.767127
## [3857] 5.789187 6.214638 6.249866 5.640332 6.395057 6.389818 7.004862 6.340581
## [3865] 5.722601 6.646908 6.904985 6.469278 5.488029 6.968416 6.402237 6.132794
## [3873] 7.329342 6.819880 5.229981 5.892153 5.485181 6.451148 5.991829 6.851384
## [3881] 6.128299 6.774516 6.785415 6.733011 6.059412 5.543578 5.748045 4.947762
## [3889] 5.468614 6.569114 6.927553 5.441347 7.061212 6.976194 6.830368 5.172679
## [3897] 5.950875 6.010545 5.311126 6.671301 6.609579 6.456637 6.174586 5.708176
## [3905] 5.560257 6.775981 6.668849 6.411206 6.209563 5.798864 4.770541 6.264069
## [3913] 5.243411 6.217237 6.695850 6.500452 6.613748 6.570729 4.970440 4.963996
## [3921] 6.826925 5.035705 5.639990 6.375573 6.902073 5.760092 4.524235 6.644554
## [3929] 6.340581 6.201917 6.875980 6.448373 6.456637 6.968416 6.095343 6.875980
## [3937] 6.925897 5.605648 6.295101 6.215016 5.735014 6.595395 6.106587 6.774516
## [3945] 7.674338 5.030790 6.247257 6.521360 5.796283 5.892153 6.743823 6.431304
## [3953] 6.679913 6.446621 6.972573 6.620464 6.033280 5.701303 5.422028 5.533702
## [3961] 5.922554 6.095343 6.998424 6.056532 6.333695 6.493330 6.042663 4.932434
## [3969] 4.039131 4.856372 6.090939 6.637517 5.639990 5.621175 5.523037 7.190268
## [3977] 7.509038 6.766480 6.721225 5.892494 6.048515 5.311126 4.586233 5.966289
## [3985] 6.413605 6.383950 5.804389 6.441023 6.775981 6.002981 6.033175 6.196812
## [3993] 6.394962 7.146054 5.560257 6.337457 6.503568 6.383101 6.306707 6.646101
## [4001] 7.061212 6.239698 6.326822 5.872037 7.131526 6.227165 5.937941 6.010545
## [4009] 5.249561 6.047677 5.828505 6.516076 5.232133 6.637517 6.802446 6.565735
## [4017] 6.146676 4.284395 6.108983 6.694932 6.404604 6.010143 5.084098 5.991829
## [4025] 5.907297 6.907620 6.925897 6.491450 6.400086 5.529086 7.149479 6.712621
## [4033] 7.509038 5.686483 6.620464 6.584854 5.987479 6.093365 5.722601 6.141553
## [4041] 6.595395 5.856827 5.441347 5.256315 6.469278 6.568413 6.220792 6.528158
## [4049] 6.048398 6.411206 6.182971 6.881841 5.710978 6.658663 6.209563 6.916926
## [4057] 6.572173 6.427156 6.111332 5.937941 6.321741 6.949034 6.015726 5.999086
## [4065] 6.593490 6.404604 6.557719 7.674338 5.756275 5.837292 6.107923 5.855155
## [4073] 6.306707 6.147506 5.730920 6.059412 6.991906 6.333695 6.216959 6.342662
## [4081] 6.720842 5.892494 6.165858 6.090939 6.260716 6.022927 6.938994 6.265565
## [4089] 6.595921 6.570729 6.059369 6.508917 7.044644 7.326098 5.247827 6.447290
## [4097] 6.431304 5.136574 6.253381 6.707493 5.679675 6.239698 5.589753 6.220792
## [4105] 6.099993 6.680099 5.756499 6.667880 6.256769 5.832359 7.396203 6.121746
## [4113] 5.904628 6.394360 6.319214 6.329361 6.916926 6.749260 6.592921 6.976194
## [4121] 6.613748 4.901161 6.827982 5.398573 6.802446 6.795199 6.307362 4.788111
## [4129] 4.970440 6.121746 6.593490 6.199945 6.128060 6.611569 6.048398 6.193320
## [4137] 6.197338 6.184809 6.757882 5.576186 5.905276 6.193320 6.170331 6.342662
## [4145] 6.482556 6.949034 6.649196 6.010143 6.503568 5.776961 6.711348 6.844806
## [4153] 6.482556 6.231235 6.197673 6.127099 6.015726 6.516076 5.392005 5.745878
## [4161] 6.783624 5.111538 6.151059 5.864079 4.856008 5.684674 6.587500 6.146676
## [4169] 5.915137 5.988455 6.111332 6.907620 6.231235 6.103691 5.380103 6.985974
## [4177] 6.716603 5.119093 4.926486 6.720842 5.829827 5.449233 7.305260 6.671301
## [4185] 6.616710 6.040983 6.098369 7.599789 6.141553 5.314327 6.716603 6.711348
## [4193] 6.521360 6.316065 5.136574 5.260991 6.445916 7.146054 5.526924 6.195837
## [4201] 6.201917 5.550299 6.611707 4.886231 6.178077 5.825651 6.411979 6.622962
## [4209] 5.957049 6.561522 6.594607 5.728027 5.814948 7.044644 6.218526 6.969806
## [4217] 4.607747 6.668849 6.214638 6.744857 4.965618 5.402781 6.391297 5.907297
## [4225] 6.552484 6.076657 6.778862 6.495496 5.752417 6.976156 6.107458 6.264069
## [4233] 6.047410 5.489001 6.845624 6.354812 6.706821 6.151953 4.899649 6.310937
## [4241] 5.800779 6.680099 6.349870 5.491426 6.033280 6.611569 6.253381 6.976156
## [4249] 6.295101 5.183731 6.800577 5.813442 5.735014 5.859600 6.386079 7.329342
## [4257] 5.987479 6.381023 4.248289 5.879475 6.594607 6.715359 6.706821 5.703764
## [4265] 6.491450 6.680248 6.383101 6.260716 6.365638 5.710978 6.547836 5.392005
## [4273] 6.132794 6.881841 6.245078 6.667880 6.436305 5.947867 6.468604 6.896422
## [4281] 6.660464 5.932738 6.140683 5.783521 6.326908 4.874731 6.505424 7.513998
## [4289] 6.490797 6.311689 6.055217 6.483300 5.338251 6.456047 6.638978 6.339637
## [4297] 6.295295 5.769664 6.180423 7.006890 6.293692 5.757779 6.184159 6.516577
## [4305] 6.341528 6.311060 5.352870 6.342727 5.765505 6.331249 6.031128 5.201080
## [4313] 5.170523 6.088950 6.621207 6.461103 6.516577 6.068336 7.755599 5.975106
## [4321] 6.555824 6.901191 6.844753 6.171427 5.132942 4.942169 6.268616 6.263660
## [4329] 5.901447 5.930488 6.134393 6.476842 6.123556 6.340440 5.272499 6.271011
## [4337] 6.320782 6.311689 6.890827 5.719918 5.755852 7.184126 4.955437 4.930032
## [4345] 6.183644 6.969109 7.146687 4.760719 6.300128 7.161536 5.855450 6.825344
## [4353] 6.088950 6.070254 6.531539 6.365505 6.027967 6.297764 7.138943 5.769730
## [4361] 6.553520 4.658386 6.744437 5.071209 7.031836 6.519232 5.609516 6.742799
## [4369] 6.470627 5.879939 5.883457 7.330979 6.231100 6.685204 6.044752 6.731027
## [4377] 7.330979 6.285947 6.230117 6.223451 6.857640 5.811578 6.303161 6.663467
## [4385] 6.297927 5.614740 6.794391 6.290116 6.387708 6.640741 5.945512 6.247514
## [4393] 6.200059 6.447115 6.199164 6.131685 6.951573 5.924016 6.675607 5.341688
## [4401] 6.015751 6.368453 5.233257 6.430099 5.625652 6.041411 5.971700 5.801143
## [4409] 5.630888 5.209912 5.777301 5.897391 6.375534 6.171369 6.375908 6.174527
## [4417] 6.716708 6.954545 6.624491 5.446899 5.881993 6.263660 5.963706 5.676496
## [4425] 5.730094 6.238918 7.118448 6.561613 6.405142 5.402303 5.791813 6.112394
## [4433] 5.676993 5.648822 6.147121 6.411645 6.447455 7.346382 6.041411 6.053464
## [4441] 6.601566 6.140939 5.648348 6.500695 6.050241 5.834124 6.525625 5.767070
## [4449] 5.696587 6.450646 5.825834 6.800385 6.543884 5.885397 6.015945 6.810005
## [4457] 6.716708 6.624491 5.632144 6.430258 5.979105 5.442766 6.312150 6.866725
## [4465] 6.562421 6.601355 6.038376 6.721205 6.875152 6.437024 5.653532 6.164367
## [4473] 6.188124 6.018129 7.107303 7.115231 5.290849 6.902381 5.614740 6.619517
## [4481] 6.617673 5.712592 7.513998 6.016248 6.811049 6.423880 6.699978 7.218733
## [4489] 5.997833 5.838393 6.810459 5.483258 6.988842 7.218680 5.832348 6.123301
## [4497] 6.391574 6.016808 5.249525 7.253250 5.315373 6.489799 6.346489 6.430099
## [4505] 6.123301 6.418251 6.888872 6.327753 6.161518 6.398327 6.988949 5.638731
## [4513] 6.436305 6.557011 5.796781 6.367905 6.875152 6.121325 5.440864 7.289375
## [4521] 6.074298 6.031314 6.045645 6.080451 6.233431 6.249661 6.148625 6.200634
## [4529] 6.744437 5.546005 6.176568 5.256886 6.811935 6.643721 6.230869 6.638144
## [4537] 6.752109 6.529355 5.925091 5.727551 5.793540 5.049891 6.452568 6.733692
## [4545] 6.362316 5.169678 5.767943 6.467836 6.214426 6.697745 6.276150 5.972050
## [4553] 5.214713 5.998305 6.396052 6.012479 6.430258 6.001556 5.765389 6.422890
## [4561] 6.183474 5.924016 6.445212 7.755599 6.320782 6.158590 6.391574 7.184126
## [4569] 6.000213 6.688490 5.434338 5.797013 5.308136 6.350326 6.681379 5.650161
## [4577] 6.801805 6.810459 6.305425 6.538324 6.886716 6.394806 6.742799 5.106412
## [4585] 6.720961 6.561587 6.672776 5.998943 7.087929 6.910720 6.185263 5.433263
## [4593] 5.945444 6.275602 5.410930 5.515631 5.598115 6.530528 5.799863 6.584508
## [4601] 6.725736 6.681458 5.628594 6.094026 6.049991 6.439646 7.087769 6.331267
## [4609] 6.374386 6.710396 6.314356 6.523196 6.867176 6.205864 6.464217 6.175623
## [4617] 6.352055 6.619517 6.384456 5.953887 6.482958 6.340636 6.914442 6.339637
## [4625] 6.671014 5.944161 7.575631 6.437024 6.459544 6.608876 6.238918 6.413888
## [4633] 6.162641 5.945444 6.635182 6.261278 6.602712 6.837286 6.314356 7.115231
## [4641] 5.295071 6.297118 5.797013 6.572172 6.407645 6.089072 5.394201 6.337837
## [4649] 7.138943 5.551124 6.233554 6.464217 6.183128 5.982802 5.575620 5.632626
## [4657] 6.811935 6.669875 6.405733 6.714315 6.140939 6.204970 6.012479 6.296606
## [4665] 6.933548 6.470544 6.677951 5.862543 6.731318 7.203074 6.194353 6.322355
## [4673] 6.633967 5.333616 7.033219 5.986438 5.834418 6.733692 5.622339 6.561845
## [4681] 5.764752 6.255846 6.776196 6.287729 6.801805 6.783538 5.318847 6.381473
## [4689] 5.853253 6.592268 6.178757 6.677541 6.254354 5.908015 5.702782 6.331267
## [4697] 5.623296 6.968713 5.798419 6.439646 6.423880 6.087107 6.593583 6.447342
## [4705] 6.605160 6.210992 7.261542 6.370532 6.002967 6.238556 6.267992 5.450939
## [4713] 6.478927 5.783343 6.310374 6.612870 5.953954 6.709629 5.413857 6.637072
## [4721] 6.749851 6.234104 5.556144 6.574359 6.075649 6.456047 6.182844 6.501770
## [4729] 6.747226 5.481292 6.122245 6.415050 6.639201 6.407473 5.343049 6.845038
## [4737] 6.395161 6.303161 6.564087 6.395161 5.894550 6.341528 6.800385 6.476842
## [4745] 6.752109 6.617673 6.225730 6.274466 6.049298 6.340391 6.394806 6.414248
## [4753] 6.567012 6.445391 5.953405 6.474276 6.591215 5.965750 6.630685 6.354351
## [4761] 6.148625 6.374386 6.340212 5.519968 6.635197 6.459544 6.271011 5.925091
## [4769] 6.746612 5.221725 5.939743 6.742221 5.838060 6.009317 6.387708 5.999840
## [4777] 6.564087 6.638978 6.119697 6.311715 6.176568 6.765267 5.807092 5.429903
## [4785] 5.624526 6.289820 5.686740 6.145091 6.776196 6.312150 5.954937 7.369909
## [4793] 6.844753 6.234104 6.600886 6.755953 6.856587 6.605507 6.352055 6.049298
## [4801] 5.884904 6.253345 6.561613 6.196332 6.571355 5.971700 6.701941 5.426354
## [4809] 6.213319 5.998629 6.638144 6.639781 5.608481 6.373215 5.470571 6.238556
## [4817] 7.369909 6.743815 6.681379 6.005748 6.445391 7.681764 5.257445 5.858905
## [4825] 6.396052 7.033219 6.703380 6.237852 4.819971 5.209912 5.977994 6.591215
## [4833] 5.577802 6.033395 5.980504 6.791044 5.783911 6.164026 5.551486 5.818471
## [4841] 6.933548 5.706193 5.821831 6.470544 6.060973 5.738296 5.537038 6.246936
## [4849] 6.751387 5.661339 6.200059 6.845038 6.118884 6.281479 6.968713 6.419665
## [4857] 6.731027 6.555676 7.025480 5.947622 7.065014 5.701306 5.604375 6.118436
## [4865] 5.467720 6.395746 6.126843 6.595911 5.847055 7.118448 6.339362 6.445212
## [4873] 6.162641 6.362316 6.601599 6.747226 6.851117 6.423262 6.053865 6.130354
## [4881] 6.305256 4.829005 7.062241 4.793133 6.447115 5.394203 5.766270 6.230869
## [4889] 6.045139 6.290116 5.578246 5.570882 6.180277 6.080451 7.575631 6.228814
## [4897] 6.297306 5.617044 6.418249 6.857640 6.776937 5.494870 5.879939 5.469088
## [4905] 6.593943 5.822910 6.669875 5.730171 6.227795 6.709629 5.732257 6.468604
## [4913] 6.164026 4.844473 6.765267 6.837286 5.714863 6.581489 6.584660 5.947976
## [4921] 6.075649 5.343292 6.247514 6.804796 6.422425 5.604850 6.905522 6.267992
## [4929] 5.844719 6.419665 6.424813 4.975950 6.869099 6.367905 6.988949 7.787193
## [4937] 6.839586 6.087107 6.093728 6.571355 5.553526 7.031836 6.319611 6.239725
## [4945] 5.766003 5.879027 5.949907 6.640741 5.912034 6.411645 6.180423 5.578925
## [4953] 5.916507 2.812827 6.672013 6.435631 6.319611 6.139367 7.000714 6.425209
## [4961] 6.621489 5.980504 6.247204 6.370532 6.165042 6.275602 6.270366 6.886716
## [4969] 4.868926 5.864295 5.328452 5.470571 6.584660 5.682075 5.446902 7.320723
## [4977] 6.384456 6.794391 6.284848 6.204602 5.811578 4.690918 6.432204 6.297764
## [4985] 6.541203 6.607389 5.758945 6.171290 6.038376 6.288482 6.498093 6.866725
## [4993] 6.178757 7.192754 6.707270 6.118884 6.567012 5.875847 5.319756 5.945512
## [5001] 6.637072 6.414248 6.342727 6.271985 6.719929 6.608876 5.878198 5.696530
## [5009] 6.820393 6.381473 6.543884 6.027493 6.000213 6.553520 6.054448 6.455342
## [5017] 7.450455 6.697745 6.271985 6.839586 6.031314 6.249661 6.370260 5.765389
## [5025] 6.337111 5.917185 6.630685 5.676993 5.965750 6.776937 6.719929 5.486310
## [5033] 5.817940 6.749851 6.119529 6.187481 6.245225 6.121325 6.339362 5.266997
## [5041] 6.532405 6.340212 6.361334 6.140883 7.787193 6.705573 7.009373 6.241499
## [5049] 6.672013 6.814524 6.502041 5.842558 6.246936 6.581764 6.432204 5.890873
## [5057] 5.905799 7.066140 6.820393 5.830879 5.855930 6.621207 5.953276 6.183474
## [5065] 5.667883 5.889752 5.696587 6.498229 5.595205 5.808842 6.350326 5.777259
## [5073] 5.889913 5.688651 7.073951 5.836397 5.810200 6.221054 6.196376 6.526502
## [5081] 6.182844 5.994821 5.947867 5.429117 5.430560 6.667259 6.027493 6.370260
## [5089] 6.058175 6.187682 5.823919 6.424301 4.253602 6.274466 5.838393 7.107303
## [5097] 4.986023 6.961612 5.905799 6.224935 6.425209 7.082161 7.308980 5.418966
## [5105] 5.960129 6.540987 5.643520 6.221054 5.553976 6.888872 5.781421 6.123830
## [5113] 6.592268 6.230264 6.720961 6.502041 5.367238 5.908015 5.767070 6.547971
## [5121] 6.016808 6.595911 6.398559 5.853253 6.859640 5.817063 6.501770 5.877398
## [5129] 6.275674 6.601355 6.985621 5.689260 6.731318 6.605507 5.698410 6.288034
## [5137] 6.530528 6.581764 6.655857 6.027484 5.999840 6.048973 6.605160 6.555676
## [5145] 5.726323 6.622586 6.223451 6.902381 5.024490 6.373215 6.050241 6.082459
## [5153] 5.705731 6.519736 6.213914 5.445383 6.725736 6.561587 6.230264 6.173972
## [5161] 6.310374 6.139522 4.949811 6.082459 6.490797 6.957654 6.452568 6.937171
## [5169] 6.389006 6.662741 5.345225 5.325136 6.582718 6.969109 6.333535 6.375534
## [5177] 6.660464 7.104994 6.275534 6.398327 6.572172 5.688651 5.458944 5.257424
## [5185] 6.889813 7.204143 6.253761 5.884447 5.477038 5.876080 6.331249 6.368453
## [5193] 6.327753 5.638731 6.225730 5.492857 5.981722 6.675607 7.062241 5.932738
## [5201] 5.033687 6.351904 5.472957 6.512460 7.218680 6.361334 5.804738 6.395746
## [5209] 5.738385 7.261542 5.791171 5.028249 6.089072 6.247204 6.036221 5.954937
## [5217] 6.671014 6.054448 6.660038 6.650253 5.455979 5.817063 6.039206 6.334933
## [5225] 6.755953 6.134569 6.255846 5.575178 6.482958 6.305425 5.382156 5.683095
## [5233] 6.084795 6.173972 5.411615 5.678980 6.241499 5.948822 6.200634 6.484708
## [5241] 6.290041 6.470627 6.914442 5.306138 6.324702 6.578421 6.562421 7.450455
## [5249] 6.869099 5.791171 5.527566 5.543832 6.340186 6.505424 5.695672 6.299105
## [5257] 5.818471 6.559906 6.435631 5.883457 5.977384 6.240093 6.639201 5.939743
## [5265] 5.979105 6.676332 6.222559 5.675190 6.601566 6.297927 6.867176 6.500695
## [5273] 6.519736 6.074298 6.710396 5.441656 6.988842 5.875847 5.575620 6.162467
## [5281] 6.587918 6.561845 6.554433 5.636687 6.410676 5.912872 5.570882 6.055217
## [5289] 6.889813 6.549282 5.738296 5.505565 5.340552 5.752955 5.783911 5.902870
## [5297] 5.446899 6.203908 6.173047 6.070254 5.912872 6.375908 6.498229 6.851117
## [5305] 6.338759 5.624526 7.308980 5.671360 5.978402 7.025480 6.859640 6.593583
## [5313] 6.093542 6.961612 6.814524 4.756074 6.716847 5.998860 5.848609 5.820060
## [5321] 6.340636 6.677541 5.912034 6.484708 6.407473 5.763653 5.506479 6.295584
## [5329] 5.782364 6.015395 7.009373 6.529355 6.224935 6.216952 6.718140 4.957856
## [5337] 6.194353 6.479848 5.875964 6.633967 5.423372 5.855562 6.743815 6.655857
## [5345] 6.013531 5.364983 6.172699 6.546816 6.196332 6.261278 6.455342 6.581489
## [5353] 5.234816 6.549282 6.937171 6.905522 6.287729 6.311060 5.830879 5.397050
## [5361] 5.711899 5.984368 6.554516 5.804738 6.044668 6.326908 6.354351 6.479848
## [5369] 6.450646 6.882217 5.402303 6.164367 5.847055 6.284848 6.202333 5.215837
## [5377] 6.516852 4.868926 7.011166 7.320723 6.569074 6.056091 6.139970 6.667259
## [5385] 5.215528 5.300112 6.569074 6.477088 6.254354 6.338929 6.692967 5.277163
## [5393] 6.075014 6.322355 6.204602 6.489799 5.816584 6.810005 6.049991 6.171427
## [5401] 6.036221 6.478927 6.297118 5.861832 6.337717 7.681764 5.729894 6.145091
## [5409] 6.781566 5.998629 6.635197 6.532405 7.204143 6.337837 6.447342 6.593943
## [5417] 6.751387 6.340186 5.638176 6.519232 6.574359 5.734826 6.554516 6.781566
## [5425] 6.602712 6.337717 6.643721 5.569712 5.977994 6.118436 5.801555 5.350152
## [5433] 5.499451 5.877653 6.811049 7.192754 5.397050 5.994230 6.202452 6.310615
## [5441] 6.825344 6.601599 5.881993 5.504709 6.554433 6.718140 5.712290 6.856587
## [5449] 5.828833 7.146687 6.555824 6.253761 5.865837 6.284418 5.644496 6.213914
## [5457] 7.087929 6.285947 6.299105 6.422425 6.477088 5.977384 6.672776 6.405733
## [5465] 5.439632 5.705731 6.510731 5.297242 6.685204 6.882217 6.582718 6.622586
## [5473] 6.268616 5.306273 6.340391 6.289820 6.175623 5.947976 6.093542 5.955343
## [5481] 6.703380 6.290041 5.978402 6.013531 6.245225 6.705573 6.418249 6.053464
## [5489] 7.346382 6.540987 7.253250 6.538324 7.289375 6.660038 6.896422 6.804796
## [5497] 6.676332 6.346489 6.297306 5.562142 6.265842 6.641003 6.300128 6.612870
## [5505] 5.999616 5.716535 7.087769 5.578246 6.546816 5.209527 5.757890 5.730171
## [5513] 5.685068 7.093685 6.410676 6.295584 5.953954 5.917808 6.677951 6.791044
## [5521] 5.801143 6.080452 5.912391 5.848609 5.824966 6.461103 6.189362 7.203074
## [5529] 6.714315 6.662741 6.338929 5.777259 6.415050 6.202333 6.174527 5.861832
## [5537] 5.823122 6.541203 6.600886 6.305256 6.510731 7.073951 6.578421 6.569811
## [5545] 6.127099 6.906528 6.650253 6.107458 6.919222 5.633523 5.922554 6.352461
## [5553] 6.025325 6.757882 7.449369 6.985974 6.544212 6.036361 6.766480 6.147506
## [5561] 6.791455 6.180277 6.889284 6.490267 6.973287 6.729127 6.634985 6.811988
## [5569] 6.449540 7.191193 6.418781 6.551408 6.520270 6.141908 6.874256 6.112451
## [5577] 5.973236 6.707034 7.022975 6.862463 7.148367 6.786162 6.884017 6.810930
## [5585] 5.940642 5.506056 6.208034 6.239336 6.988907 6.218696 6.889284 6.462794
## [5593] 6.534137 6.571623 6.691474 6.491446 6.551408 6.483200 6.648828 6.446836
## [5601] 6.027828 6.499407 7.721538 5.477550 6.986144 6.863673 6.448144 6.074735
## [5609] 6.167309 5.203345 7.022975 4.855612 6.810832 5.360936 6.534137 6.651776
## [5617] 5.697102 6.532131 6.532846 6.608857 6.840981 6.517711 7.246720 6.684124
## [5625] 6.443685 6.944678 6.733873 6.691474 6.805960 4.969147 7.191193 7.085901
## [5633] 6.537446 5.999663 5.961194 6.046062 6.109443 7.656088 6.491446 6.628554
## [5641] 5.582328 6.037917 6.942072 6.119050 4.992297 5.802225 6.579464 6.062081
## [5649] 6.040604 6.376033 6.862463 6.370954 6.506705 6.176372 6.144901 6.122962
## [5657] 6.905329 6.795063 6.824429 6.256924 5.202728 6.571622 6.637928 5.655691
## [5665] 6.505357 6.877615 6.144849 6.161453 6.851940 7.164027 6.131617 6.571623
## [5673] 6.256924 5.691960 6.794109 6.514678 6.448144 6.650744 6.353647 5.904133
## [5681] 6.647347 6.858037 5.688121 5.617527 6.413242 5.492067 6.282770 6.457774
## [5689] 6.824137 5.663990 6.165726 6.700802 6.441464 5.866710 6.973287 6.576902
## [5697] 5.898723 5.891200 6.794109 6.702211 6.375229 6.795063 6.645856 5.574546
## [5705] 6.238289 6.419246 6.618972 5.749571 6.449540 6.837503 5.264962 6.532131
## [5713] 7.247171 5.085656 5.409570 6.529711 7.762662 5.642662 3.789421 5.705997
## [5721] 5.431711 6.429012 6.187481 6.629413 6.505357 5.175018 6.478692 5.784355
## [5729] 6.805960 5.721578 5.723085 5.861845 6.694054 6.195894 5.787959 6.457792
## [5737] 7.714055 5.964228 6.837658 6.011570 6.161453 6.074735 7.073265 5.445586
## [5745] 5.663990 6.694949 6.800641 6.066903 6.436277 5.551451 5.324862 6.027828
## [5753] 5.045459 6.236138 5.866478 6.738186 5.263391 5.933329 6.199382 4.893339
## [5761] 5.655621 6.715361 6.296681 5.937150 6.478468 6.421654 6.109443 6.662624
## [5769] 6.818633 6.336607 5.304210 6.956302 6.617616 6.841984 6.645856 6.668338
## [5777] 5.958062 5.157314 6.308079 6.627088 6.884017 6.029784 5.447750 6.644061
## [5785] 6.514678 6.506705 6.739847 6.436277 5.759455 7.010206 6.629487 6.206001
## [5793] 6.065460 7.807808 6.227144 6.465126 6.537397 7.721538 6.144849 6.514901
## [5801] 6.701968 6.320672 6.594608 7.524241 6.353647 6.161340 6.037608 7.807808
## [5809] 6.794015 6.175862 5.403269 6.794015 6.334574 6.733873 6.098138 6.824429
## [5817] 7.656088 6.545466 6.338480 6.824137 7.085901 7.265337 6.878455 6.268975
## [5825] 5.731697 6.571622 6.107429 7.623416 6.375425 6.461122 5.175868 6.684124
## [5833] 5.507344 6.539522 7.161694 6.218696 6.286083 5.642662 6.480299 6.886978
## [5841] 7.801164 6.131617 6.532846 5.441795 7.524241 5.403269 6.729127 6.679063
## [5849] 6.360111 6.674019 5.166637 6.586349 6.423232 6.668338 6.818633 4.793973
## [5857] 6.709723 6.154037 6.099789 6.530520 6.236138 6.490267 6.864528 6.459436
## [5865] 5.821456 6.092206 7.033204 6.702211 5.585732 5.961194 6.820460 5.655621
## [5873] 6.335056 5.251313 5.934672 6.507980 5.585732 4.453859 6.820460 5.492067
## [5881] 6.318853 6.926332 6.764351 6.662173 6.952843 6.238289 6.648828 5.507086
## [5889] 6.387181 5.679647 5.507086 6.514640 5.943371 7.325727 6.455395 5.739810
## [5897] 6.318853 5.905341 5.156189 5.074645 5.143853 5.841620 6.423232 6.700802
## [5905] 6.445701 5.507344 6.588001 6.446836 7.000714 5.906465 6.483200 6.116172
## [5913] 5.766502 6.514640 6.066903 6.618972 6.764351 6.746204 6.957654 6.358892
## [5921] 6.345006 6.107429 6.877615 5.791497 4.847027 5.933329 6.811988 4.835155
## [5929] 5.964228 6.581554 7.334103 6.370692 6.459436 6.824754 6.819617 6.863673
## [5937] 7.325727 7.000742 6.342464 6.441464 6.091211 4.770707 6.450651 6.681032
## [5945] 6.520270 6.675031 6.386614 5.360936 6.338202 6.282770 6.167309 6.662173
## [5953] 6.837658 5.907517 6.986144 6.651776 7.246720 5.784355 6.008114 5.610800
## [5961] 5.459268 6.216168 6.800641 6.679063 6.116910 6.037917 5.684067 5.156669
## [5969] 6.693404 6.693404 5.976703 6.370692 6.478468 5.574546 5.008998 6.539522
## [5977] 6.709723 6.739847 5.715853 6.165726 5.976703 6.645909 6.251354 7.106681
## [5985] 5.731697 6.375229 6.468603 6.644061 5.001078 6.617616 6.098138 6.122962
## [5993] 6.663987 6.454376 6.786162 5.973236 5.912359 6.738186 5.031814 5.610800
## [6001] 6.715361 6.060874 6.050491 6.995555 6.454376 7.762662 7.106681 6.325595
## [6009] 6.199382 6.840981 6.291788 6.647347 6.328737 5.978975 6.044570 7.164027
## [6017] 5.947834 6.224960 6.878455 6.688704 6.644390 5.893957 6.008114 6.886084
## [6025] 4.143790 6.198982 6.644390 6.627088 5.759455 7.213441 6.116910 6.530520
## [6033] 6.681032 6.650744 6.037450 5.378084 6.047116 6.694054 7.161312 4.608763
## [6041] 6.462794 6.837503 6.634985 6.141908 7.623416 6.195894 5.088075 5.837913
## [6049] 6.450651 6.988907 7.271634 7.673848 5.252081 6.529711 6.993522 5.655691
## [6057] 6.942072 6.133042 7.033204 5.923424 6.457792 5.547838 5.241237 6.457774
## [6065] 6.358892 6.320672 7.148367 5.905341 6.352919 6.608857 5.016134 6.688704
## [6073] 5.668226 5.668226 7.161694 6.629487 6.579464 6.730961 6.926332 5.295291
## [6081] 6.419246 7.326098 6.386614 6.352919 6.370954 6.905329 6.380429 6.886084
## [6089] 6.586349 6.092206 5.450417 5.923424 6.206507 6.514901 6.445701 6.296681
## [6097] 6.858037 5.754497 6.675031 4.536798 5.923800 5.766502 4.860115 4.645199
## [6105] 5.477550 7.714055 6.206507 7.000742 6.171222 7.213441 5.958062 6.376033
## [6113] 6.239336 5.647384 6.526160 5.962092 5.052623 6.537397 6.465126 6.574792
## [6121] 6.995555 6.537446 7.010206 6.144901 6.499407 5.988518 5.780862 5.845882
## [6129] 6.938079 6.545466 6.810930 6.886978 5.810769 6.171222 6.930981 6.810832
## [6137] 6.730961 6.175862 6.133042 5.690417 5.731463 6.924004 6.221997 6.216168
## [6145] 6.047116 6.478692 6.694949 6.461122 6.956302 5.780862 5.937150 5.225535
## [6153] 5.814047 6.065460 6.674019 6.681458 6.050491 6.119050 5.772113 6.588001
## [6161] 4.270954 6.517711 6.993522 5.447750 6.864528 6.208034 5.469097 5.978975
## [6169] 6.291788 6.345006 6.938079 7.334103 6.336607 5.681182 6.381172 5.909708
## [6177] 6.714898 6.714898 6.381172 5.769666 6.450175 6.386756# create long data for each individual
pred_lgm_long <- map(0:3, # loop over time
function(x) pred_lgm[, 1] + x * pred_lgm[, 2]) %>%
reduce(cbind) %>% # bring together the wave predictions
as.data.frame() %>% # make data frame
setNames(str_c("Wave", 1:4)) %>% # give names to variables
mutate(id = row_number()) %>% # make unique id
gather(-id, key = wave, value = pred) # make long format
# make graph (takes a minute to plot)
pred_lgm_long %>%
ggplot(aes(wave, pred, group = id)) + # what variables to plot?
geom_line(alpha = 0.01) + # add a transparent line for each person
stat_summary( # add average line
aes(group = 1),
fun = mean,
geom = "line",
size = 1.5,
color = "red"
) +
theme_bw() + # makes graph look nicer
labs(y = "Predicted logincome", # labels
x = "Wave")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# recode sex
usw <- mutate(usw, female = gndr - 1)
# check result
count(usw, gndr, female)## # A tibble: 2 × 3
## gndr female n
## <int> <dbl> <int>
## 1 1 0 2834
## 2 2 1 3625# LGM with time constant variables
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s =~ 0*logincome_1 + 1*logincome_2 + 2*logincome_3 + 3*logincome_4
i ~ female
s ~ female'
lgm3 <- growth(model, data = usw)
summary(lgm3, standardized = TRUE)## lavaan 0.6.17 ended normally after 50 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 11
##
## Used Total
## Number of observations 6182 6459
##
## Model Test User Model:
##
## Test statistic 26.627
## Degrees of freedom 7
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.588 0.836
## logincome_2 1.000 0.588 0.834
## logincome_3 1.000 0.588 0.896
## logincome_4 1.000 0.588 0.883
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.112 0.159
## logincome_3 2.000 0.224 0.342
## logincome_4 3.000 0.337 0.506
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~
## female -0.148 0.017 -8.619 0.000 -0.251 -0.125
## s ~
## female -0.004 0.005 -0.852 0.394 -0.039 -0.019
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .i ~~
## .s -0.024 0.002 -10.436 0.000 -0.373 -0.373
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .i 6.221 0.013 485.738 0.000 10.589 10.589
## .s 0.023 0.004 5.931 0.000 0.204 0.204
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.148 0.005 27.148 0.000 0.148 0.301
## .logincome_2 0.187 0.004 44.405 0.000 0.187 0.377
## .logincome_3 0.131 0.003 40.231 0.000 0.131 0.305
## .logincome_4 0.130 0.005 27.616 0.000 0.130 0.293
## .i 0.340 0.009 39.618 0.000 0.984 0.984
## .s 0.013 0.001 12.019 0.000 1.000 1.000library(lme4)
mm3 <- lmer(data = usl, logincome ~ 1 + wave0 + gndr_f + gndr_f:wave0 +
(1 + wave0 | pid))
summary(mm3)## Linear mixed model fit by REML ['lmerMod']
## Formula: logincome ~ 1 + wave0 + gndr_f + gndr_f:wave0 + (1 + wave0 | pid)
## Data: usl
##
## REML criterion at convergence: 40549.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.6808 -0.3139 0.0164 0.3620 4.5020
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pid (Intercept) 0.34474 0.5871
## wave0 0.01061 0.1030 -0.40
## Residual 0.15558 0.3944
## Number of obs: 25469, groups: pid, 6455
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.222880 0.012684 490.595
## wave0 0.022835 0.003865 5.908
## gndr_fFemale -0.153736 0.016943 -9.074
## wave0:gndr_fFemale -0.002093 0.005165 -0.405
##
## Correlation of Fixed Effects:
## (Intr) wave0 gndr_F
## wave0 -0.517
## gndr_fFemal -0.749 0.387
## wv0:gndr_fF 0.387 -0.748 -0.517# Practical 2. Other LGM
## Investigating time varying variables
usw2 <- mutate_at(usw, vars(starts_with("health_")), ~. - 1)## Warning: There were 4 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `health_f_1 = (structure(function (..., .x = ..1, .y = ..2, . =
## ..1) ...`.
## Caused by warning in `Ops.factor()`:
## ! '-' not meaningful for factors
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining warnings.count(usw2, health_1)## # A tibble: 6 × 2
## health_1 n
## <dbl> <int>
## 1 0 868
## 2 1 1995
## 3 2 2043
## 4 3 1060
## 5 4 322
## 6 NA 171# Controlling for satisfaction
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s =~ 0*logincome_1 + 1*logincome_2 + 2*logincome_3 + 3*logincome_4
logincome_1 ~ health_1
logincome_2 ~ health_2
logincome_3 ~ health_3
logincome_4 ~ health_4'
lgm4 <- growth(model, data = usw2)
summary(lgm4, standardized = TRUE)## lavaan 0.6.17 ended normally after 55 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 13
##
## Used Total
## Number of observations 5815 6459
##
## Model Test User Model:
##
## Test statistic 445.727
## Degrees of freedom 17
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.576 0.833
## logincome_2 1.000 0.576 0.827
## logincome_3 1.000 0.576 0.885
## logincome_4 1.000 0.576 0.871
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.113 0.163
## logincome_3 2.000 0.226 0.348
## logincome_4 3.000 0.339 0.513
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## logincome_1 ~
## health_1 -0.056 0.006 -9.245 0.000 -0.056 -0.086
## logincome_2 ~
## health_2 -0.053 0.005 -10.442 0.000 -0.053 -0.080
## logincome_3 ~
## health_3 -0.056 0.005 -11.761 0.000 -0.056 -0.092
## logincome_4 ~
## health_4 -0.043 0.006 -7.856 0.000 -0.043 -0.071
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.024 0.002 -9.936 0.000 -0.361 -0.361
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.232 0.013 475.259 0.000 10.827 10.827
## s 0.018 0.005 3.493 0.000 0.157 0.157
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.143 0.005 25.963 0.000 0.143 0.299
## .logincome_2 0.184 0.004 43.065 0.000 0.184 0.380
## .logincome_3 0.131 0.003 39.088 0.000 0.131 0.309
## .logincome_4 0.130 0.005 26.807 0.000 0.130 0.296
## i 0.331 0.009 38.428 0.000 1.000 1.000
## s 0.013 0.001 11.983 0.000 1.000 1.000# Relationship between log income and satisfaction
model <- ' i_inc =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s_inc =~ 0*logincome_1 + 1*logincome_2 + 2*logincome_3 + 3*logincome_4
i_heal =~ 1*health_1 + 1*health_2 + 1*health_3 + 1*health_4
s_heal =~ 0*health_1 + 1*health_2 + 2*health_3 + 3*health_4'
lgm5 <- growth(model, data = usw)
summary(lgm5, standardized = TRUE)## lavaan 0.6.17 ended normally after 87 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 22
##
## Used Total
## Number of observations 5815 6459
##
## Model Test User Model:
##
## Test statistic 75.676
## Degrees of freedom 22
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i_inc =~
## logincome_1 1.000 0.590 0.844
## logincome_2 1.000 0.590 0.838
## logincome_3 1.000 0.590 0.897
## logincome_4 1.000 0.590 0.884
## s_inc =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.114 0.161
## logincome_3 2.000 0.227 0.345
## logincome_4 3.000 0.341 0.510
## i_heal =~
## health_1 1.000 0.882 0.830
## health_2 1.000 0.882 0.838
## health_3 1.000 0.882 0.829
## health_4 1.000 0.882 0.804
## s_heal =~
## health_1 0.000 0.000 0.000
## health_2 1.000 0.135 0.128
## health_3 2.000 0.270 0.254
## health_4 3.000 0.405 0.370
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i_inc ~~
## s_inc -0.024 0.002 -10.244 0.000 -0.364 -0.364
## i_heal -0.168 0.009 -18.306 0.000 -0.323 -0.323
## s_heal -0.001 0.003 -0.519 0.604 -0.017 -0.017
## s_inc ~~
## i_heal 0.005 0.003 1.782 0.075 0.047 0.047
## s_heal 0.000 0.001 0.147 0.883 0.008 0.008
## i_heal ~~
## s_heal -0.020 0.005 -3.816 0.000 -0.167 -0.167
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i_inc 6.136 0.009 695.914 0.000 10.393 10.393
## s_inc 0.021 0.003 8.136 0.000 0.188 0.188
## i_heal 2.659 0.013 200.405 0.000 3.016 3.016
## s_heal 0.055 0.004 13.886 0.000 0.407 0.407
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.141 0.005 25.882 0.000 0.141 0.288
## .logincome_2 0.183 0.004 43.155 0.000 0.183 0.369
## .logincome_3 0.131 0.003 39.267 0.000 0.131 0.301
## .logincome_4 0.128 0.005 26.750 0.000 0.128 0.287
## .health_1 0.350 0.012 28.963 0.000 0.350 0.310
## .health_2 0.350 0.009 40.672 0.000 0.350 0.317
## .health_3 0.360 0.009 40.719 0.000 0.360 0.318
## .health_4 0.379 0.013 29.746 0.000 0.379 0.316
## i_inc 0.349 0.009 39.192 0.000 1.000 1.000
## s_inc 0.013 0.001 12.186 0.000 1.000 1.000
## i_heal 0.777 0.020 38.975 0.000 1.000 1.000
## s_heal 0.018 0.002 7.457 0.000 1.000 1.000pred_lgm5 <- predict(lgm5) %>%
as_tibble()
qplot(pred_lgm5$s_inc)## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Don't know how to automatically pick scale for object of type
## <lavaan.matrix/matrix>. Defaulting to continuous.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(pred_lgm5$s_heal)## Don't know how to automatically pick scale for object of type
## <lavaan.matrix/matrix>. Defaulting to continuous.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(pred_lgm5$s_inc, pred_lgm5$s_heal)## Don't know how to automatically pick scale for object of type
## <lavaan.matrix/matrix>. Defaulting to continuous.
## Don't know how to automatically pick scale for object of type
## <lavaan.matrix/matrix>. Defaulting to continuous.
## Treating time flexibly------
######## change the intercept
summary(lgm1, standardized = TRUE)## lavaan 0.6.17 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 6182 6459
##
## Model Test User Model:
##
## Test statistic 26.209
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.587 0.836
## logincome_2 1.000 0.587 0.834
## logincome_3 1.000 0.587 0.896
## logincome_4 1.000 0.587 0.883
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.112 0.159
## logincome_3 2.000 0.224 0.342
## logincome_4 3.000 0.336 0.505
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.024 0.002 -10.318 0.000 -0.368 -0.368
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.139 0.009 716.825 0.000 10.449 10.449
## s 0.020 0.003 7.964 0.000 0.182 0.182
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.149 0.005 27.150 0.000 0.149 0.301
## .logincome_2 0.187 0.004 44.382 0.000 0.187 0.377
## .logincome_3 0.131 0.003 40.169 0.000 0.131 0.305
## .logincome_4 0.130 0.005 27.592 0.000 0.130 0.293
## i 0.345 0.009 39.814 0.000 1.000 1.000
## s 0.013 0.001 11.990 0.000 1.000 1.000# Changing the intercept at the end of the study
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s =~ -3*logincome_1 + -2*logincome_2 + -1*logincome_3 + 0*logincome_4'
lgm6 <- growth(model, data = usw)
summary(lgm6, standardized = TRUE)## lavaan 0.6.17 ended normally after 58 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 6182 6459
##
## Model Test User Model:
##
## Test statistic 26.209
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.559 0.796
## logincome_2 1.000 0.559 0.794
## logincome_3 1.000 0.559 0.853
## logincome_4 1.000 0.559 0.841
## s =~
## logincome_1 -3.000 -0.336 -0.479
## logincome_2 -2.000 -0.224 -0.318
## logincome_3 -1.000 -0.112 -0.171
## logincome_4 0.000 0.000 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s 0.013 0.002 6.427 0.000 0.215 0.215
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.200 0.008 765.940 0.000 11.084 11.084
## s 0.020 0.003 7.964 0.000 0.182 0.182
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.149 0.005 27.150 0.000 0.149 0.301
## .logincome_2 0.187 0.004 44.382 0.000 0.187 0.377
## .logincome_3 0.131 0.003 40.169 0.000 0.131 0.305
## .logincome_4 0.130 0.005 27.592 0.000 0.130 0.293
## i 0.313 0.008 40.827 0.000 1.000 1.000
## s 0.013 0.001 11.990 0.000 1.000 1.000# Changing the intercept at the end of the study
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s =~ -1.5*logincome_1 + -0.5*logincome_2 + 0.5*logincome_3 + 1.5*logincome_4'
lgm7 <- growth(model, data = usw)
summary(lgm7, standardized = TRUE)## lavaan 0.6.17 ended normally after 65 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 6182 6459
##
## Model Test User Model:
##
## Test statistic 26.209
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.548 0.780
## logincome_2 1.000 0.548 0.778
## logincome_3 1.000 0.548 0.837
## logincome_4 1.000 0.548 0.824
## s =~
## logincome_1 -1.500 -0.168 -0.239
## logincome_2 -0.500 -0.056 -0.080
## logincome_3 0.500 0.056 0.086
## logincome_4 1.500 0.168 0.253
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.005 0.002 -3.409 0.001 -0.087 -0.087
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.169 0.007 834.954 0.000 11.250 11.250
## s 0.020 0.003 7.964 0.000 0.182 0.182
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.149 0.005 27.150 0.000 0.149 0.301
## .logincome_2 0.187 0.004 44.382 0.000 0.187 0.377
## .logincome_3 0.131 0.003 40.169 0.000 0.131 0.305
## .logincome_4 0.130 0.005 27.592 0.000 0.130 0.293
## i 0.301 0.006 49.316 0.000 1.000 1.000
## s 0.013 0.001 11.990 0.000 1.000 1.000# Include square in growth
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s =~ 0*logincome_1 + 1*logincome_2 + 2*logincome_3 + 3*logincome_4
sq =~ 0*logincome_1 + 1*logincome_2 + 4*logincome_3 + 9*logincome_4'
lgm8 <- growth(model, data = usw)
summary(lgm8, standardized = TRUE)## lavaan 0.6.17 ended normally after 73 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 13
##
## Used Total
## Number of observations 6182 6459
##
## Model Test User Model:
##
## Test statistic 1.364
## Degrees of freedom 1
## P-value (Chi-square) 0.243
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.603 0.863
## logincome_2 1.000 0.603 0.848
## logincome_3 1.000 0.603 0.917
## logincome_4 1.000 0.603 0.910
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.242 0.340
## logincome_3 2.000 0.484 0.736
## logincome_4 3.000 0.726 1.095
## sq =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.062 0.087
## logincome_3 4.000 0.249 0.378
## logincome_4 9.000 0.559 0.844
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.045 0.017 -2.690 0.007 -0.305 -0.305
## sq 0.004 0.004 1.117 0.264 0.120 0.120
## s ~~
## sq -0.013 0.004 -3.088 0.002 -0.864 -0.864
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.146 0.009 695.173 0.000 10.197 10.197
## s -0.003 0.008 -0.406 0.685 -0.013 -0.013
## sq 0.008 0.002 3.182 0.001 0.127 0.127
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.125 0.016 8.050 0.000 0.125 0.256
## .logincome_2 0.185 0.006 32.243 0.000 0.185 0.367
## .logincome_3 0.123 0.005 24.167 0.000 0.123 0.285
## .logincome_4 0.124 0.015 8.429 0.000 0.124 0.283
## i 0.363 0.017 21.751 0.000 1.000 1.000
## s 0.058 0.018 3.268 0.001 1.000 1.000
## sq 0.004 0.001 3.248 0.001 1.000 1.000# predict scores
pred_lgm2 <- predict(lgm8)
# create long data for each individual
pred_lgm2_long <- map(0:3, # loop over time
function(x) pred_lgm2[, 1] +
x * pred_lgm2[, 2] +
x^2 * pred_lgm2[, 3]) %>%
reduce(cbind) %>% # bring together the wave predictions
as.data.frame() %>% # make data frame
setNames(str_c("Wave", 1:4)) %>% # give names to variables
mutate(id = row_number()) %>% # make unique id
gather(-id, key = wave, value = pred) # make long format
# make graph (takes a minute to plot)
pred_lgm2_long %>%
ggplot(aes(wave, pred, group = id)) + # what variables to plot?
geom_line(alpha = 0.01) + # add a transparent line for each person
stat_summary( # add average line
aes(group = 1),
fun = mean,
geom = "line",
size = 1.5,
color = "red",
alpha = 0.5
) +
stat_summary(data = pred_lgm_long, # add average from linear model
aes(group = 1),
fun = mean,
geom = "line",
size = 1.5,
color = "blue",
alpha = 0.5
) +
theme_bw() + # makes graph look nicer
labs(y = "Predicted logincome", # labels
x = "Wave")
anova(lgm1, lgm8)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## lgm8 1 38860 38947 1.3639
## lgm1 5 38877 38937 26.2093 24.845 0.029034 4 5.404e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Alternative change estimation
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s =~ 0*logincome_1 + logincome_2 + logincome_3 + 1*logincome_4'
lgm9 <- growth(model, data = usw)
summary(lgm9, standardized = TRUE)## lavaan 0.6.17 ended normally after 69 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 11
##
## Used Total
## Number of observations 6182 6459
##
## Model Test User Model:
##
## Test statistic 12.326
## Degrees of freedom 3
## P-value (Chi-square) 0.006
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.570 0.809
## logincome_2 1.000 0.570 0.812
## logincome_3 1.000 0.570 0.863
## logincome_4 1.000 0.570 0.861
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 0.089 0.054 1.652 0.099 0.030 0.042
## logincome_3 0.461 0.064 7.167 0.000 0.154 0.234
## logincome_4 1.000 0.335 0.506
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.052 0.007 -7.306 0.000 -0.271 -0.271
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.145 0.008 733.061 0.000 10.781 10.781
## s 0.061 0.007 8.336 0.000 0.183 0.183
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.171 0.006 27.931 0.000 0.171 0.345
## .logincome_2 0.176 0.005 33.328 0.000 0.176 0.358
## .logincome_3 0.135 0.004 36.516 0.000 0.135 0.310
## .logincome_4 0.105 0.014 7.748 0.000 0.105 0.240
## i 0.325 0.008 38.752 0.000 1.000 1.000
## s 0.112 0.015 7.319 0.000 1.000 1.000anova(lgm1, lgm8, lgm9)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## lgm8 1 38860 38947 1.3639
## lgm9 3 38867 38941 12.3264 10.963 0.026924 2 0.0041642 **
## lgm1 5 38877 38937 26.2093 13.883 0.031002 2 0.0009669 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# dealing with data collected at different time spans
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_4
s =~ 0*logincome_1 + 1*logincome_2 + 3*logincome_4'
lgm10 <- growth(model, data = usw)
summary(lgm10, standardized = TRUE)## lavaan 0.6.17 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
##
## Used Total
## Number of observations 6219 6459
##
## Model Test User Model:
##
## Test statistic 2.505
## Degrees of freedom 1
## P-value (Chi-square) 0.113
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.594 0.848
## logincome_2 1.000 0.594 0.834
## logincome_4 1.000 0.594 0.895
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.118 0.166
## logincome_4 3.000 0.354 0.534
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.027 0.003 -9.909 0.000 -0.388 -0.388
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.141 0.009 714.661 0.000 10.343 10.343
## s 0.022 0.003 8.337 0.000 0.184 0.184
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.138 0.007 19.317 0.000 0.138 0.281
## .logincome_2 0.195 0.005 36.748 0.000 0.195 0.384
## .logincome_4 0.126 0.013 9.498 0.000 0.126 0.285
## i 0.353 0.009 38.270 0.000 1.000 1.000
## s 0.014 0.002 6.342 0.000 1.000 1.000# 控制上一波的健康程度
# Controlling for satisfaction at previous wave
model <- ' i =~ 1*logincome_1 + 1*logincome_2 + 1*logincome_3 + 1*logincome_4
s =~ 0*logincome_1 + 1*logincome_2 + 2*logincome_3 + 3*logincome_4
logincome_2 ~ health_1
logincome_3 ~ health_2
logincome_4 ~ health_3'
lgm11 <- growth(model, data = usw)
summary(lgm11, standardized = TRUE)## lavaan 0.6.17 ended normally after 52 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 12
##
## Used Total
## Number of observations 5907 6459
##
## Model Test User Model:
##
## Test statistic 487.002
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## logincome_1 1.000 0.588 0.841
## logincome_2 1.000 0.588 0.841
## logincome_3 1.000 0.588 0.904
## logincome_4 1.000 0.588 0.891
## s =~
## logincome_1 0.000 0.000 0.000
## logincome_2 1.000 0.114 0.163
## logincome_3 2.000 0.228 0.351
## logincome_4 3.000 0.342 0.518
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## logincome_2 ~
## health_1 -0.020 0.003 -6.968 0.000 -0.020 -0.030
## logincome_3 ~
## health_2 -0.036 0.004 -9.092 0.000 -0.036 -0.058
## logincome_4 ~
## health_3 -0.043 0.005 -7.870 0.000 -0.043 -0.070
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s -0.027 0.002 -11.101 0.000 -0.396 -0.396
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 6.143 0.009 678.928 0.000 10.447 10.447
## s 0.061 0.006 10.736 0.000 0.533 0.533
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .logincome_1 0.143 0.005 26.120 0.000 0.143 0.293
## .logincome_2 0.182 0.004 43.186 0.000 0.182 0.373
## .logincome_3 0.130 0.003 39.418 0.000 0.130 0.308
## .logincome_4 0.130 0.005 27.241 0.000 0.130 0.299
## i 0.346 0.009 39.253 0.000 1.000 1.000
## s 0.013 0.001 12.236 0.000 1.000 1.000Including Plots
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