docs/sea-plm.md
In eldafani/intsvy: International Assessment Data Manager
Examples with SEA-PLM 2019
library("tidyverse")
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── 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 errors
library("haven")
Read data
dir <- "/home/eldani/eldani/International LSA/SEA-PLM/Data/SEA-PLM_Regional_Students-sav"
student <- read_spss(file.path(dir, "SEA-PLM_Regional_Students.sav"))
dir <- "/home/eldani/eldani/International LSA/SEA-PLM/Data/SEA-PLM_Regional_Schools-sav"
school <- read_spss(file.path(dir, "SEA-PLM_Regional_Schools.sav"))
Merge data
sea <- left_join(student, school, by = c("CNT", "SchID"))
Create grouping variables
sea$Sex <- factor(sea$Gender, 1:2, labels = c("Female", "Male"))
sea$Language <- factor(sea$S_LANG, 0:1, labels = c("No", "Yes"))
sea$Location <- factor(sea$SC09Q01, 1:5, labels = c("Rural", rep("Urban", 4)))
Proficiency reading levels by CNT
intsvy.ben.pv(pvnames= paste0("PV", 1:5, "_R"), cutoff = c(304, 317),
by= c("CNT"), data=sea, config = sea_conf)
## CNT Benchmark Percentage Std. err.
## 1 KHM At or above 304 27.50 1.44
## 2 KHM At or above 317 11.02 1.01
## 3 LAO At or above 304 8.91 0.80
## 4 LAO At or above 317 2.50 0.41
## 5 MMR At or above 304 26.88 1.30
## 6 MMR At or above 317 10.74 0.78
## 7 MYS At or above 304 76.61 1.80
## 8 MYS At or above 317 58.26 2.06
## 9 PHL At or above 304 21.53 1.62
## 10 PHL At or above 317 9.56 1.21
## 11 VNM At or above 304 92.34 1.03
## 12 VNM At or above 317 81.88 1.42
Proficiency reading levels by CNT and sex
intsvy.ben.pv(pvnames= paste0("PV", 1:5, "_R"), cutoff = c(304, 317),
by= c("CNT", "Sex"), data=sea, config = sea_conf)
## CNT Sex Benchmark Percentage Std. err.
## 1 KHM Female At or above 304 32.68 1.85
## 2 KHM Female At or above 317 13.79 1.46
## 3 KHM Male At or above 304 22.04 1.33
## 4 KHM Male At or above 317 8.11 0.78
## 5 LAO Female At or above 304 10.50 1.12
## 6 LAO Female At or above 317 2.99 0.52
## 7 LAO Male At or above 304 7.42 0.96
## 8 LAO Male At or above 317 2.03 0.43
## 9 LAO <NA> At or above 304 0.00 0.00
## 10 LAO <NA> At or above 317 0.00 0.00
## 11 MMR Female At or above 304 29.59 1.50
## 12 MMR Female At or above 317 12.09 1.07
## 13 MMR Male At or above 304 24.41 1.59
## 14 MMR Male At or above 317 9.51 0.91
## 15 MYS Female At or above 304 82.41 1.60
## 16 MYS Female At or above 317 65.87 2.15
## 17 MYS Male At or above 304 70.66 2.29
## 18 MYS Male At or above 317 50.43 2.27
## 19 PHL Female At or above 304 25.10 1.95
## 20 PHL Female At or above 317 10.84 1.37
## 21 PHL Male At or above 304 18.07 1.88
## 22 PHL Male At or above 317 8.33 1.56
## 23 VNM Female At or above 304 93.44 1.03
## 24 VNM Female At or above 317 84.20 1.46
## 25 VNM Male At or above 304 91.31 1.31
## 26 VNM Male At or above 317 79.69 1.70
Mean reading achievement by groups
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_R"), by= c("CNT"), data=sea, config = sea_conf)
## CNT Freq Mean s.e. SD s.e
## 1 KHM 5396 290.12 0.82 21.88 0.43
## 2 LAO 4698 275.06 0.78 20.62 0.42
## 3 MMR 5707 291.73 0.78 19.98 0.37
## 4 MYS 4479 318.91 1.14 23.56 0.62
## 5 PHL 6083 287.72 0.91 20.59 0.59
## 6 VNM 4837 336.46 0.88 22.18 0.65
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_R"), by= c("CNT", "Location"), data=sea, config = sea_conf)
## CNT Location Freq Mean s.e. SD s.e
## 1 KHM Rural 2487 284.94 1.03 20.74 0.57
## 2 KHM Urban 2842 295.04 1.26 21.85 0.52
## 3 KHM <NA> 67 287.79 5.60 19.04 4.23
## 4 LAO Rural 3262 272.88 1.00 20.25 0.56
## 5 LAO Urban 1187 283.40 1.66 20.21 0.69
## 6 LAO <NA> 249 267.42 2.78 16.43 1.60
## 7 MMR Rural 2900 289.72 0.93 18.87 0.44
## 8 MMR Urban 2718 295.38 1.41 21.23 0.74
## 9 MMR <NA> 89 294.25 6.81 22.57 1.83
## 10 MYS Rural 1267 316.21 2.24 23.62 1.08
## 11 MYS Urban 3212 320.10 1.39 23.43 0.77
## 12 PHL Rural 2229 282.02 1.48 18.83 0.97
## 13 PHL Urban 3854 291.65 1.39 20.82 0.82
## 14 VNM Rural 2364 332.35 1.33 22.18 0.90
## 15 VNM Urban 2441 341.08 1.16 21.27 0.85
## 16 VNM <NA> 32 338.09 NaN 20.09 NaN
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_R"), by= c("CNT", "Sex"),
data=sea[!is.na(sea$Sex), ], config = sea_conf)
## CNT Sex Freq Mean s.e. SD s.e
## 1 KHM Female 2766 293.48 0.92 21.66 0.51
## 2 KHM Male 2630 286.59 0.87 21.54 0.44
## 3 LAO Female 2304 275.99 0.97 21.24 0.52
## 4 LAO Male 2393 274.20 0.81 19.98 0.49
## 5 MMR Female 2705 293.24 0.83 20.03 0.45
## 6 MMR Male 3002 290.35 0.87 19.83 0.44
## 7 MYS Female 2283 323.38 1.12 21.97 0.56
## 8 MYS Male 2196 314.33 1.31 24.24 0.78
## 9 PHL Female 3012 290.68 1.00 19.83 0.57
## 10 PHL Male 3071 284.85 1.10 20.90 0.93
## 11 VNM Female 2330 338.29 0.98 21.83 0.72
## 12 VNM Male 2507 334.73 0.99 22.36 0.74
intsvy.mean.pv(pvnames= paste0("PV", 1:5, "_R"), by= c("CNT", "Language"), data=sea, config = sea_conf)
## CNT Language Freq Mean s.e. SD s.e
## 1 KHM No 321 280.32 2.53 26.65 1.27
## 2 KHM Yes 5075 290.75 0.77 21.39 0.39
## 3 LAO No 1849 267.61 0.89 19.26 0.50
## 4 LAO Yes 2849 280.11 0.93 19.98 0.47
## 5 MMR No 1262 279.73 1.47 18.59 0.64
## 6 MMR Yes 4445 295.48 0.68 18.89 0.35
## 7 MYS No 903 309.16 1.77 24.78 0.80
## 8 MYS Yes 3576 321.36 1.24 22.59 0.80
## 9 PHL No 5656 287.72 0.87 20.09 0.49
## 10 PHL Yes 427 287.73 3.36 26.24 2.15
## 11 VNM No 432 317.12 3.28 26.03 1.72
## 12 VNM Yes 4405 338.62 0.67 20.61 0.35
Mean writing and math achievement by CNT
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_W"), by= c("CNT"), data=sea, config = sea_conf)
## CNT Freq Mean s.e. SD s.e
## 1 KHM 5396 284.82 1.01 27.24 0.46
## 2 LAO 4698 283.47 1.04 30.65 0.69
## 3 MMR 5707 298.48 0.89 20.10 0.55
## 4 MYS 4479 317.50 0.88 18.84 0.54
## 5 PHL 6083 288.28 1.13 27.73 0.55
## 6 VNM 4837 327.45 0.89 22.07 0.54
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_M"), by= c("CNT"), data=sea, config = sea_conf)
## CNT Freq Mean s.e. SD s.e
## 1 KHM 5396 289.41 0.82 20.74 0.49
## 2 LAO 4698 278.63 0.82 20.62 0.48
## 3 MMR 5707 287.92 0.61 17.23 0.33
## 4 MYS 4479 314.71 1.08 21.84 0.63
## 5 PHL 6083 287.88 0.84 19.99 0.52
## 6 VNM 4837 341.45 1.04 23.99 0.64
Regression of reading achievement on SES
intsvy.reg.pv(pvnames= paste0("PV", 1:5, "_R"), x= "SES", by= c("CNT"), data=sea, config = sea_conf)
## $KHM
## Estimate Std. Error t value
## (Intercept) 290.55 0.50 575.65
## SES 8.21 0.50 16.27
## R-squared 0.13 0.48 0.27
##
## $LAO
## Estimate Std. Error t value
## (Intercept) 276.02 0.53 522.57
## SES 9.00 0.50 18.12
## R-squared 0.18 0.49 0.36
##
## $MMR
## Estimate Std. Error t value
## (Intercept) 292.75 0.52 561.98
## SES 6.65 0.49 13.68
## R-squared 0.10 0.48 0.21
##
## $MYS
## Estimate Std. Error t value
## (Intercept) 319.25 0.64 495.28
## SES 7.87 0.64 12.23
## R-squared 0.11 0.64 0.17
##
## $PHL
## Estimate Std. Error t value
## (Intercept) 287.77 0.47 611.11
## SES 11.44 0.46 24.85
## R-squared 0.31 0.46 0.69
##
## $VNM
## Estimate Std. Error t value
## (Intercept) 337.08 0.51 655.34
## SES 9.48 0.55 17.30
## R-squared 0.17 0.51 0.35
Regression of math achievement on SES and sex
intsvy.reg.pv(pvnames= paste0("PV", 1:5, "_M"), x= c("SES", "Sex"), by= c("CNT"), data=sea, config = sea_conf)
## $KHM
## Estimate Std. Error t value
## (Intercept) 291.69 0.59 497.74
## SES 8.05 0.54 14.85
## SexMale -3.79 0.64 -5.95
## R-squared 0.15 0.53 0.28
##
## $LAO
## Estimate Std. Error t value
## (Intercept) 279.73 0.65 431.22
## SES 9.12 0.64 14.33
## SexMale -0.30 0.65 -0.46
## R-squared 0.18 0.63 0.29
##
## $MMR
## Estimate Std. Error t value
## (Intercept) 289.01 0.46 632.41
## SES 6.05 0.43 13.96
## SexMale -0.34 0.48 -0.71
## R-squared 0.11 0.43 0.26
##
## $MYS
## Estimate Std. Error t value
## (Intercept) 316.45 0.71 444.76
## SES 8.71 0.71 12.20
## SexMale -2.77 0.73 -3.76
## R-squared 0.16 0.68 0.24
##
## $PHL
## Estimate Std. Error t value
## (Intercept) 289.48 0.56 519.02
## SES 10.18 0.53 19.26
## SexMale -3.01 0.57 -5.26
## R-squared 0.27 0.53 0.51
##
## $VNM
## Estimate Std. Error t value
## (Intercept) 342.13 0.65 523.72
## SES 9.39 0.71 13.26
## SexMale -0.12 0.69 -0.17
## R-squared 0.15 0.64 0.23
eldafani/intsvy documentation built on March 18, 2024, 7:54 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Examples with SEA-PLM 2019
library("tidyverse")
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library("haven")
Read data
dir <- "/home/eldani/eldani/International LSA/SEA-PLM/Data/SEA-PLM_Regional_Students-sav"
student <- read_spss(file.path(dir, "SEA-PLM_Regional_Students.sav"))
dir <- "/home/eldani/eldani/International LSA/SEA-PLM/Data/SEA-PLM_Regional_Schools-sav"
school <- read_spss(file.path(dir, "SEA-PLM_Regional_Schools.sav"))
Merge data
sea <- left_join(student, school, by = c("CNT", "SchID"))
Create grouping variables
sea$Sex <- factor(sea$Gender, 1:2, labels = c("Female", "Male"))
sea$Language <- factor(sea$S_LANG, 0:1, labels = c("No", "Yes"))
sea$Location <- factor(sea$SC09Q01, 1:5, labels = c("Rural", rep("Urban", 4)))
Proficiency reading levels by CNT
intsvy.ben.pv(pvnames= paste0("PV", 1:5, "_R"), cutoff = c(304, 317),
by= c("CNT"), data=sea, config = sea_conf)
## CNT Benchmark Percentage Std. err.
## 1 KHM At or above 304 27.50 1.44
## 2 KHM At or above 317 11.02 1.01
## 3 LAO At or above 304 8.91 0.80
## 4 LAO At or above 317 2.50 0.41
## 5 MMR At or above 304 26.88 1.30
## 6 MMR At or above 317 10.74 0.78
## 7 MYS At or above 304 76.61 1.80
## 8 MYS At or above 317 58.26 2.06
## 9 PHL At or above 304 21.53 1.62
## 10 PHL At or above 317 9.56 1.21
## 11 VNM At or above 304 92.34 1.03
## 12 VNM At or above 317 81.88 1.42
Proficiency reading levels by CNT and sex
intsvy.ben.pv(pvnames= paste0("PV", 1:5, "_R"), cutoff = c(304, 317),
by= c("CNT", "Sex"), data=sea, config = sea_conf)
## CNT Sex Benchmark Percentage Std. err.
## 1 KHM Female At or above 304 32.68 1.85
## 2 KHM Female At or above 317 13.79 1.46
## 3 KHM Male At or above 304 22.04 1.33
## 4 KHM Male At or above 317 8.11 0.78
## 5 LAO Female At or above 304 10.50 1.12
## 6 LAO Female At or above 317 2.99 0.52
## 7 LAO Male At or above 304 7.42 0.96
## 8 LAO Male At or above 317 2.03 0.43
## 9 LAO <NA> At or above 304 0.00 0.00
## 10 LAO <NA> At or above 317 0.00 0.00
## 11 MMR Female At or above 304 29.59 1.50
## 12 MMR Female At or above 317 12.09 1.07
## 13 MMR Male At or above 304 24.41 1.59
## 14 MMR Male At or above 317 9.51 0.91
## 15 MYS Female At or above 304 82.41 1.60
## 16 MYS Female At or above 317 65.87 2.15
## 17 MYS Male At or above 304 70.66 2.29
## 18 MYS Male At or above 317 50.43 2.27
## 19 PHL Female At or above 304 25.10 1.95
## 20 PHL Female At or above 317 10.84 1.37
## 21 PHL Male At or above 304 18.07 1.88
## 22 PHL Male At or above 317 8.33 1.56
## 23 VNM Female At or above 304 93.44 1.03
## 24 VNM Female At or above 317 84.20 1.46
## 25 VNM Male At or above 304 91.31 1.31
## 26 VNM Male At or above 317 79.69 1.70
Mean reading achievement by groups
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_R"), by= c("CNT"), data=sea, config = sea_conf)
## CNT Freq Mean s.e. SD s.e
## 1 KHM 5396 290.12 0.82 21.88 0.43
## 2 LAO 4698 275.06 0.78 20.62 0.42
## 3 MMR 5707 291.73 0.78 19.98 0.37
## 4 MYS 4479 318.91 1.14 23.56 0.62
## 5 PHL 6083 287.72 0.91 20.59 0.59
## 6 VNM 4837 336.46 0.88 22.18 0.65
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_R"), by= c("CNT", "Location"), data=sea, config = sea_conf)
## CNT Location Freq Mean s.e. SD s.e
## 1 KHM Rural 2487 284.94 1.03 20.74 0.57
## 2 KHM Urban 2842 295.04 1.26 21.85 0.52
## 3 KHM <NA> 67 287.79 5.60 19.04 4.23
## 4 LAO Rural 3262 272.88 1.00 20.25 0.56
## 5 LAO Urban 1187 283.40 1.66 20.21 0.69
## 6 LAO <NA> 249 267.42 2.78 16.43 1.60
## 7 MMR Rural 2900 289.72 0.93 18.87 0.44
## 8 MMR Urban 2718 295.38 1.41 21.23 0.74
## 9 MMR <NA> 89 294.25 6.81 22.57 1.83
## 10 MYS Rural 1267 316.21 2.24 23.62 1.08
## 11 MYS Urban 3212 320.10 1.39 23.43 0.77
## 12 PHL Rural 2229 282.02 1.48 18.83 0.97
## 13 PHL Urban 3854 291.65 1.39 20.82 0.82
## 14 VNM Rural 2364 332.35 1.33 22.18 0.90
## 15 VNM Urban 2441 341.08 1.16 21.27 0.85
## 16 VNM <NA> 32 338.09 NaN 20.09 NaN
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_R"), by= c("CNT", "Sex"),
data=sea[!is.na(sea$Sex), ], config = sea_conf)
## CNT Sex Freq Mean s.e. SD s.e
## 1 KHM Female 2766 293.48 0.92 21.66 0.51
## 2 KHM Male 2630 286.59 0.87 21.54 0.44
## 3 LAO Female 2304 275.99 0.97 21.24 0.52
## 4 LAO Male 2393 274.20 0.81 19.98 0.49
## 5 MMR Female 2705 293.24 0.83 20.03 0.45
## 6 MMR Male 3002 290.35 0.87 19.83 0.44
## 7 MYS Female 2283 323.38 1.12 21.97 0.56
## 8 MYS Male 2196 314.33 1.31 24.24 0.78
## 9 PHL Female 3012 290.68 1.00 19.83 0.57
## 10 PHL Male 3071 284.85 1.10 20.90 0.93
## 11 VNM Female 2330 338.29 0.98 21.83 0.72
## 12 VNM Male 2507 334.73 0.99 22.36 0.74
intsvy.mean.pv(pvnames= paste0("PV", 1:5, "_R"), by= c("CNT", "Language"), data=sea, config = sea_conf)
## CNT Language Freq Mean s.e. SD s.e
## 1 KHM No 321 280.32 2.53 26.65 1.27
## 2 KHM Yes 5075 290.75 0.77 21.39 0.39
## 3 LAO No 1849 267.61 0.89 19.26 0.50
## 4 LAO Yes 2849 280.11 0.93 19.98 0.47
## 5 MMR No 1262 279.73 1.47 18.59 0.64
## 6 MMR Yes 4445 295.48 0.68 18.89 0.35
## 7 MYS No 903 309.16 1.77 24.78 0.80
## 8 MYS Yes 3576 321.36 1.24 22.59 0.80
## 9 PHL No 5656 287.72 0.87 20.09 0.49
## 10 PHL Yes 427 287.73 3.36 26.24 2.15
## 11 VNM No 432 317.12 3.28 26.03 1.72
## 12 VNM Yes 4405 338.62 0.67 20.61 0.35
Mean writing and math achievement by CNT
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_W"), by= c("CNT"), data=sea, config = sea_conf)
## CNT Freq Mean s.e. SD s.e
## 1 KHM 5396 284.82 1.01 27.24 0.46
## 2 LAO 4698 283.47 1.04 30.65 0.69
## 3 MMR 5707 298.48 0.89 20.10 0.55
## 4 MYS 4479 317.50 0.88 18.84 0.54
## 5 PHL 6083 288.28 1.13 27.73 0.55
## 6 VNM 4837 327.45 0.89 22.07 0.54
intsvy.mean.pv(pvnames=paste0("PV", 1:5, "_M"), by= c("CNT"), data=sea, config = sea_conf)
## CNT Freq Mean s.e. SD s.e
## 1 KHM 5396 289.41 0.82 20.74 0.49
## 2 LAO 4698 278.63 0.82 20.62 0.48
## 3 MMR 5707 287.92 0.61 17.23 0.33
## 4 MYS 4479 314.71 1.08 21.84 0.63
## 5 PHL 6083 287.88 0.84 19.99 0.52
## 6 VNM 4837 341.45 1.04 23.99 0.64
Regression of reading achievement on SES
intsvy.reg.pv(pvnames= paste0("PV", 1:5, "_R"), x= "SES", by= c("CNT"), data=sea, config = sea_conf)
## $KHM
## Estimate Std. Error t value
## (Intercept) 290.55 0.50 575.65
## SES 8.21 0.50 16.27
## R-squared 0.13 0.48 0.27
##
## $LAO
## Estimate Std. Error t value
## (Intercept) 276.02 0.53 522.57
## SES 9.00 0.50 18.12
## R-squared 0.18 0.49 0.36
##
## $MMR
## Estimate Std. Error t value
## (Intercept) 292.75 0.52 561.98
## SES 6.65 0.49 13.68
## R-squared 0.10 0.48 0.21
##
## $MYS
## Estimate Std. Error t value
## (Intercept) 319.25 0.64 495.28
## SES 7.87 0.64 12.23
## R-squared 0.11 0.64 0.17
##
## $PHL
## Estimate Std. Error t value
## (Intercept) 287.77 0.47 611.11
## SES 11.44 0.46 24.85
## R-squared 0.31 0.46 0.69
##
## $VNM
## Estimate Std. Error t value
## (Intercept) 337.08 0.51 655.34
## SES 9.48 0.55 17.30
## R-squared 0.17 0.51 0.35
Regression of math achievement on SES and sex
intsvy.reg.pv(pvnames= paste0("PV", 1:5, "_M"), x= c("SES", "Sex"), by= c("CNT"), data=sea, config = sea_conf)
## $KHM
## Estimate Std. Error t value
## (Intercept) 291.69 0.59 497.74
## SES 8.05 0.54 14.85
## SexMale -3.79 0.64 -5.95
## R-squared 0.15 0.53 0.28
##
## $LAO
## Estimate Std. Error t value
## (Intercept) 279.73 0.65 431.22
## SES 9.12 0.64 14.33
## SexMale -0.30 0.65 -0.46
## R-squared 0.18 0.63 0.29
##
## $MMR
## Estimate Std. Error t value
## (Intercept) 289.01 0.46 632.41
## SES 6.05 0.43 13.96
## SexMale -0.34 0.48 -0.71
## R-squared 0.11 0.43 0.26
##
## $MYS
## Estimate Std. Error t value
## (Intercept) 316.45 0.71 444.76
## SES 8.71 0.71 12.20
## SexMale -2.77 0.73 -3.76
## R-squared 0.16 0.68 0.24
##
## $PHL
## Estimate Std. Error t value
## (Intercept) 289.48 0.56 519.02
## SES 10.18 0.53 19.26
## SexMale -3.01 0.57 -5.26
## R-squared 0.27 0.53 0.51
##
## $VNM
## Estimate Std. Error t value
## (Intercept) 342.13 0.65 523.72
## SES 9.39 0.71 13.26
## SexMale -0.12 0.69 -0.17
## R-squared 0.15 0.64 0.23
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