giang sinh
Lê Minh Thuận
2023-12-26
PHÂN TÍCH CÁC YẾU TỐ ĐÔNG MÁU
library(factoextra)## Warning: package 'factoextra' was built under R version 4.3.2## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyverse)## Warning: package 'stringr' was built under R version 4.3.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ✔ readr 2.1.4## ── 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(rstanarm)## Loading required package: Rcpp
## This is rstanarm version 2.26.1
## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores())library(BAS)## Warning: package 'BAS' was built under R version 4.3.2library(broom)
library(GGally)## Warning: package 'GGally' was built under R version 4.3.2## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(table1)##
## Attaching package: 'table1'
##
## The following objects are masked from 'package:base':
##
## units, units<-Gọi số liệu
dongmau = read.csv("E:\\OneDrive - UMP\\R - lenh 2016\\dongmau.csv", header=T)
head(dongmau)## ID MSBN HOTENBN gioi nam tuoi chandoan tcks tckpt tck1st
## 1 62 758008 TR?N TH? M? C\xdaC nu 1996 27 binhthuong 27.5 0.90 368.229
## 2 63 758756 PH?M TH? LAN H??NG nu 1991 32 binhthuong 35.7 1.16 337.741
## 3 74 715955 NGUY?N TH? T\xc1M nu 1958 65 binhthuong 33.5 1.12 226.970
## 4 75 710555 NGUY?N TH? NH? \xc1NH nu 1998 25 binhthuong 32.4 1.08 265.239
## 5 76 202001 LINH nu 2001 22 binhthuong 32.4 1.08 357.583
## 6 78 202020 B\xcdCH PH??NG nu 2000 23 binhthuong 29.0 0.97 299.362
## tck2nd tckDER
## 1 953.763 415.402
## 2 722.137 386.756
## 3 691.490 285.098
## 4 686.452 306.556
## 5 895.965 458.423
## 6 957.451 413.032attach(dongmau)
names(dongmau)## [1] "ID" "MSBN" "HOTENBN" "gioi" "nam" "tuoi"
## [7] "chandoan" "tcks" "tckpt" "tck1st" "tck2nd" "tckDER"Gọi thư viện
library(table1)
table1(~ tcks + tckpt + tck1st + tck2nd + tckDER | chandoan,
data = dongmau, transpose = F)| binhthuong (N=32) |
shocknhiemtrung (N=33) |
xogan (N=28) |
Overall (N=93) |
|
|---|---|---|---|---|
| tcks | ||||
| Mean (SD) | 31.1 (2.99) | 32.1 (2.84) | 31.6 (3.00) | 31.6 (2.94) |
| Median [Min, Max] | 31.2 [26.0, 36.7] | 32.3 [26.4, 36.7] | 31.4 [27.4, 38.3] | 31.7 [26.0, 38.3] |
| tckpt | ||||
| Mean (SD) | 1.02 (0.0973) | 1.06 (0.0963) | 1.04 (0.101) | 1.04 (0.0985) |
| Median [Min, Max] | 1.03 [0.850, 1.20] | 1.06 [0.870, 1.22] | 1.03 [0.890, 1.25] | 1.05 [0.850, 1.25] |
| tck1st | ||||
| Mean (SD) | 283 (66.0) | 394 (160) | 281 (133) | 322 (136) |
| Median [Min, Max] | 270 [188, 545] | 362 [127, 850] | 253 [112, 703] | 295 [112, 850] |
| tck2nd | ||||
| Mean (SD) | 776 (144) | 1070 (427) | 822 (314) | 895 (342) |
| Median [Min, Max] | 751 [487, 1030] | 1050 [294, 2310] | 762 [359, 1630] | 854 [294, 2310] |
| tckDER | ||||
| Mean (SD) | 349 (74.0) | 334 (146) | 299 (137) | 329 (123) |
| Median [Min, Max] | 344 [210, 592] | 322 [89.5, 809] | 291 [118, 695] | 322 [89.5, 809] |
# Phân tích
library(nnet);
require(brms)## Loading required package: brms## Warning: package 'brms' was built under R version 4.3.2## Loading 'brms' package (version 2.20.4). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').##
## Attaching package: 'brms'## The following objects are masked from 'package:rstanarm':
##
## dirichlet, exponential, get_y, lasso, ngrps## The following object is masked from 'package:stats':
##
## ar m.2 = multinom(tckDER ~ chandoan*gioi, data=dongmau)## # weights: 651 (552 variable)
## initial value 421.531753
## iter 10 value 260.126096
## iter 20 value 255.529734
## iter 30 value 255.507652
## final value 255.507626
## converged summary(m.2)## Warning in sqrt(diag(vc)): NaNs produced## Call:
## multinom(formula = tckDER ~ chandoan * gioi, data = dongmau)
##
## Coefficients:
## (Intercept) chandoanshocknhiemtrung chandoanxogan gioinu
## 103.975 -30.8582378 7.582747 -12.43298 1.337643
## 108.516 -30.8580446 7.582940 -12.43298 1.337836
## 118.037 -0.9740867 -14.112900 49.72905 -24.541018
## 119.016 -0.9740867 -14.112900 49.72905 -24.541018
## 138.995 -0.9740867 -14.112900 49.72905 -24.541018
## 154.745 -19.9765006 -14.399264 22.94473 14.455573
## 166.358 -0.9740867 -14.112900 49.72905 -24.541018
## 180.441 -19.9765008 -14.399264 22.94473 14.455573
## 182.349 -5.2749156 42.528930 -12.43184 -32.075827
## 199.119 -19.9765006 -14.399264 22.94473 14.455573
## 201.211 -19.9765005 -14.399264 22.94473 14.455573
## 209.545 -5.2771144 -26.760810 -16.82552 36.155279
## 210.187 -5.2749156 42.528930 -12.43184 -32.075827
## 223.049 -19.9765006 -14.399264 22.94473 14.455573
## 223.836 -30.8591048 7.581880 -12.43298 1.336776
## 229.404 -5.2749156 42.528930 -12.43184 -32.075827
## 236.528 -30.8590794 7.581905 -12.43298 1.336801
## 240.058 -30.8588916 7.582093 -12.43298 1.336989
## 248.788 -19.9765008 -14.399264 22.94473 14.455573
## 251.811 -5.2749156 42.528930 -12.43184 -32.075827
## 253.377 -0.9740867 -14.112900 49.72905 -24.541018
## 256.451 -5.2771144 -26.760810 -16.82552 36.155279
## 256.623 -19.9765006 -14.399264 22.94473 14.455573
## 259.216 -30.8590928 7.581892 -12.43298 1.336788
## 261.591 36.2670849 -24.173918 -20.81695 -30.187547
## 273.706 -30.8590225 7.581962 -12.43298 1.336858
## 274.221 -5.2749156 42.528930 -12.43184 -32.075827
## 277.902 -5.2771144 -26.760810 -16.82552 36.155279
## 285.098 -5.2771144 -26.760810 -16.82552 36.155279
## 285.398 36.2670849 -24.173918 -20.81695 -30.187547
## 287.85 -0.9740867 -14.112900 49.72905 -24.541018
## 290.612 -0.9740867 -14.112900 49.72905 -24.541018
## 290.641 -30.8590051 7.581980 -12.43298 1.336876
## 290.785 36.2670849 -24.173918 -20.81695 -30.187547
## 291.173 -19.9765008 -14.399264 22.94473 14.455573
## 291.557 -19.9765008 -14.399264 22.94473 14.455573
## 294.762 -5.2771144 -26.760810 -16.82552 36.155279
## 297.156 36.2670849 -24.173918 -20.81695 -30.187547
## 305.073 -0.9740867 -14.112900 49.72905 -24.541018
## 305.565 36.2670849 -24.173918 -20.81695 -30.187547
## 306.556 -5.2771145 -26.760810 -16.82552 36.155279
## 312.464 36.2670849 -24.173918 -20.81695 -30.187547
## 312.797 -5.2749156 42.528930 -12.43184 -32.075827
## 312.808 -30.8590696 7.581915 -12.43298 1.336811
## 314.154 -0.9740867 -14.112900 49.72905 -24.541018
## 321.9 -30.8585392 7.582446 -12.43298 1.337341
## 322.317 36.2670849 -24.173918 -20.81695 -30.187547
## 327.601 -19.9765009 -14.399264 22.94473 14.455573
## 328.973 -5.2749157 42.528930 -12.43184 -32.075827
## 330.333 -19.9765007 -14.399264 22.94473 14.455573
## 330.903 -5.2771144 -26.760810 -16.82552 36.155279
## 331.837 -0.9740867 -14.112900 49.72905 -24.541018
## 332.761 36.2670849 -24.173918 -20.81695 -30.187547
## 334.469 -0.9740867 -14.112900 49.72905 -24.541018
## 339.945 36.2670849 -24.173918 -20.81695 -30.187547
## 340.431 -30.8591480 7.581837 -12.43298 1.336733
## 347.8 36.2670849 -24.173918 -20.81695 -30.187547
## 349.491 -5.2771145 -26.760810 -16.82552 36.155279
## 350.981 36.2670849 -24.173918 -20.81695 -30.187547
## 356.414 -19.9765009 -14.399264 22.94473 14.455573
## 357.809 -5.2771144 -26.760810 -16.82552 36.155279
## 360.229 36.2670849 -24.173918 -20.81695 -30.187547
## 362.996 36.2670849 -24.173918 -20.81695 -30.187547
## 366.093 -5.2749156 42.528930 -12.43184 -32.075827
## 369.956 -30.8591615 7.581823 -12.43298 1.336719
## 374.278 36.2670849 -24.173918 -20.81695 -30.187547
## 377.571 -5.2749156 42.528930 -12.43184 -32.075827
## 378.873 -30.8587061 7.582279 -12.43298 1.337175
## 381.608 -5.2749156 42.528930 -12.43184 -32.075827
## 386.756 -5.2771145 -26.760810 -16.82552 36.155279
## 387.685 -5.2771145 -26.760810 -16.82552 36.155279
## 388.897 -5.2749156 42.528930 -12.43184 -32.075827
## 393.754 -30.8587078 7.582277 -12.43298 1.337173
## 398.638 -5.2749156 42.528930 -12.43184 -32.075827
## 401.292 -19.9765007 -14.399264 22.94473 14.455573
## 406.177 -5.2771144 -26.760810 -16.82552 36.155279
## 413.032 -5.2771145 -26.760810 -16.82552 36.155279
## 415.402 -5.2771145 -26.760810 -16.82552 36.155279
## 427.158 -5.2771144 -26.760810 -16.82552 36.155279
## 458.423 -5.2771144 -26.760810 -16.82552 36.155279
## 460.808 36.2670849 -24.173918 -20.81695 -30.187547
## 462.363 -30.8598983 7.581087 -12.43298 1.335982
## 465.134 -5.2749157 42.528930 -12.43184 -32.075827
## 476.553 -0.9740867 -14.112900 49.72905 -24.541018
## 476.743 -5.2749156 42.528930 -12.43184 -32.075827
## 496.612 -0.9740867 -14.112900 49.72905 -24.541018
## 563.558 -30.8585392 7.582446 -12.43298 1.337341
## 590.747 -19.9765007 -14.399264 22.94473 14.455573
## 592.374 36.2670849 -24.173918 -20.81695 -30.187547
## 594.636 -30.8588782 7.582107 -12.43298 1.337002
## 694.767 -0.9740867 -14.112900 49.72905 -24.541018
## 808.935 -30.8591166 7.581868 -12.43298 1.336764
## chandoanshocknhiemtrung:gioinu chandoanxogan:gioinu
## 103.975 21.938318 -5.999806
## 108.516 21.938511 -5.999806
## 118.037 -6.190533 -10.124581
## 119.016 -6.190533 -10.124581
## 138.995 -6.190533 -10.124581
## 154.745 -8.021063 39.111740
## 166.358 -6.190533 -10.124581
## 180.441 -8.021063 39.111740
## 182.349 -19.403167 -4.609902
## 199.119 -8.021063 39.111740
## 201.211 -8.021063 39.111740
## 209.545 -19.359712 -9.332335
## 210.187 -19.403167 -4.609902
## 223.049 -8.021063 39.111740
## 223.836 21.937451 -5.999806
## 229.404 -19.403167 -4.609902
## 236.528 21.937476 -5.999806
## 240.058 21.937664 -5.999806
## 248.788 -8.021063 39.111740
## 251.811 -19.403167 -4.609902
## 253.377 -6.190533 -10.124581
## 256.451 -19.359712 -9.332335
## 256.623 -8.021063 39.111740
## 259.216 21.937463 -5.999806
## 261.591 -6.750533 -4.809335
## 273.706 21.937533 -5.999806
## 274.221 -19.403167 -4.609902
## 277.902 -19.359712 -9.332335
## 285.098 -19.359712 -9.332335
## 285.398 -6.750533 -4.809335
## 287.85 -6.190533 -10.124581
## 290.612 -6.190533 -10.124581
## 290.641 21.937550 -5.999806
## 290.785 -6.750533 -4.809335
## 291.173 -8.021063 39.111740
## 291.557 -8.021063 39.111739
## 294.762 -19.359712 -9.332335
## 297.156 -6.750533 -4.809335
## 305.073 -6.190533 -10.124581
## 305.565 -6.750533 -4.809335
## 306.556 -19.359712 -9.332335
## 312.464 -6.750533 -4.809335
## 312.797 -19.403167 -4.609902
## 312.808 21.937486 -5.999806
## 314.154 -6.190533 -10.124581
## 321.9 21.938016 -5.999806
## 322.317 -6.750533 -4.809335
## 327.601 -8.021063 39.111739
## 328.973 -19.403167 -4.609902
## 330.333 -8.021063 39.111740
## 330.903 -19.359712 -9.332335
## 331.837 -6.190533 -10.124581
## 332.761 -6.750533 -4.809335
## 334.469 -6.190533 -10.124581
## 339.945 -6.750533 -4.809335
## 340.431 21.937408 -5.999806
## 347.8 -6.750533 -4.809335
## 349.491 -19.359712 -9.332335
## 350.981 -6.750533 -4.809335
## 356.414 -8.021063 39.111739
## 357.809 -19.359712 -9.332335
## 360.229 -6.750533 -4.809335
## 362.996 -6.750533 -4.809335
## 366.093 -19.403167 -4.609902
## 369.956 21.937394 -5.999806
## 374.278 -6.750533 -4.809335
## 377.571 -19.403167 -4.609902
## 378.873 21.937849 -5.999806
## 381.608 -19.403167 -4.609902
## 386.756 -19.359712 -9.332335
## 387.685 -19.359712 -9.332335
## 388.897 -19.403167 -4.609902
## 393.754 21.937848 -5.999806
## 398.638 -19.403167 -4.609902
## 401.292 -8.021063 39.111740
## 406.177 -19.359712 -9.332335
## 413.032 -19.359712 -9.332335
## 415.402 -19.359712 -9.332335
## 427.158 -19.359712 -9.332335
## 458.423 -19.359712 -9.332335
## 460.808 -6.750533 -4.809335
## 462.363 21.936657 -5.999806
## 465.134 -19.403167 -4.609902
## 476.553 -6.190533 -10.124581
## 476.743 -19.403167 -4.609902
## 496.612 -6.190533 -10.124581
## 563.558 21.938016 -5.999806
## 590.747 -8.021063 39.111740
## 592.374 -6.750533 -4.809335
## 594.636 21.937677 -5.999806
## 694.767 -6.190533 -10.124581
## 808.935 21.937439 -5.999806
##
## Std. Errors:
## (Intercept) chandoanshocknhiemtrung chandoanxogan gioinu
## 103.975 0.3531460 3.531460e-01 3.636027e-07 3.531460e-01
## 108.516 0.3530777 3.530777e-01 NaN 3.530777e-01
## 118.037 0.4818120 NaN 4.818120e-01 NaN
## 119.016 0.4818121 NaN 4.818121e-01 NaN
## 138.995 0.4818121 3.186153e-07 4.818121e-01 NaN
## 154.745 0.2409060 NaN 2.409060e-01 2.409060e-01
## 166.358 0.4818121 1.679264e-07 4.818121e-01 7.843537e-07
## 180.441 0.2409061 NaN 2.409061e-01 2.409061e-01
## 182.349 81.9128342 8.191283e+01 4.325677e-08 5.963200e+02
## 199.119 0.2409060 NaN 2.409060e-01 2.409060e-01
## 201.211 0.2409059 NaN 2.409059e-01 2.409059e-01
## 209.545 127.4306935 9.952473e+02 NaN 1.274307e+02
## 210.187 81.9128342 8.191283e+01 NaN 5.963200e+02
## 223.049 0.2409060 NaN 2.409060e-01 2.409060e-01
## 223.836 0.3534524 3.534524e-01 NaN 3.534524e-01
## 229.404 81.9128342 8.191283e+01 NaN 5.963200e+02
## 236.528 0.3534435 3.534435e-01 NaN 3.534435e-01
## 240.058 0.3533770 3.533770e-01 NaN 3.533770e-01
## 248.788 0.2409060 1.090026e-08 2.409060e-01 2.409060e-01
## 251.811 81.9128342 8.191283e+01 7.022077e-08 5.963200e+02
## 253.377 0.4818121 NaN 4.818121e-01 NaN
## 256.451 127.4306935 9.952473e+02 8.306431e-15 1.274307e+02
## 256.623 0.2409060 1.546108e-09 2.409060e-01 2.409060e-01
## 259.216 0.3534482 3.534482e-01 9.671325e-36 3.534482e-01
## 261.591 0.9682566 7.278633e-04 1.408067e-10 4.499183e-03
## 273.706 0.3534233 3.534233e-01 9.747323e-36 3.534233e-01
## 274.221 81.9128342 8.191283e+01 4.209798e-22 5.963200e+02
## 277.902 127.4306935 9.952473e+02 8.316488e-15 1.274307e+02
## 285.098 127.4306935 9.952473e+02 8.316488e-15 1.274307e+02
## 285.398 0.9682566 7.278633e-04 1.403363e-10 4.499183e-03
## 287.85 0.4818121 1.335723e-15 4.818121e-01 NaN
## 290.612 0.4818121 1.297858e-15 4.818121e-01 NaN
## 290.641 0.3534172 3.534172e-01 9.702387e-36 3.534172e-01
## 290.785 0.9682566 7.278633e-04 1.396786e-10 4.499183e-03
## 291.173 0.2409061 3.050634e-09 2.409061e-01 2.409061e-01
## 291.557 0.2409061 NaN 2.409061e-01 2.409061e-01
## 294.762 127.4306935 9.952473e+02 8.316488e-15 1.274307e+02
## 297.156 0.9682566 7.278633e-04 1.396562e-10 4.499183e-03
## 305.073 0.4818121 1.200739e-15 4.818121e-01 NaN
## 305.565 0.9682566 7.278633e-04 1.407439e-10 4.499183e-03
## 306.556 127.4306935 9.952474e+02 8.316487e-15 1.274307e+02
## 312.464 0.9682566 7.278633e-04 1.409333e-10 4.499183e-03
## 312.797 81.9128342 8.191283e+01 4.209799e-22 5.963200e+02
## 312.808 0.3534400 3.534400e-01 9.823492e-36 3.534400e-01
## 314.154 0.4818121 1.266621e-15 4.818121e-01 NaN
## 321.9 0.3532524 3.532524e-01 9.774661e-36 3.532524e-01
## 322.317 0.9682566 7.278633e-04 1.402801e-10 4.499183e-03
## 327.601 0.2409061 NaN 2.409061e-01 2.409061e-01
## 328.973 81.9128342 8.191283e+01 4.209799e-22 5.963200e+02
## 330.333 0.2409060 2.140449e-09 2.409060e-01 2.409060e-01
## 330.903 127.4306935 9.952474e+02 8.316488e-15 1.274307e+02
## 331.837 0.4818121 1.276445e-15 4.818121e-01 NaN
## 332.761 0.9682566 7.278633e-04 1.394856e-10 4.499183e-03
## 334.469 0.4818121 1.266440e-15 4.818121e-01 NaN
## 339.945 0.9682566 7.278633e-04 1.405054e-10 4.499183e-03
## 340.431 0.3534677 3.534677e-01 9.673573e-36 3.534677e-01
## 347.8 0.9682566 7.278633e-04 1.400102e-10 4.499183e-03
## 349.491 127.4306935 9.952474e+02 8.316487e-15 1.274307e+02
## 350.981 0.9682566 7.278633e-04 1.399485e-10 4.499183e-03
## 356.414 0.2409061 2.835271e-09 2.409061e-01 2.409061e-01
## 357.809 127.4306935 9.952474e+02 8.316487e-15 1.274307e+02
## 360.229 0.9682566 7.278633e-04 1.393340e-10 4.499183e-03
## 362.996 0.9682566 7.278633e-04 1.410873e-10 4.499183e-03
## 366.093 81.9128342 8.191283e+01 4.209799e-22 5.963200e+02
## 369.956 0.3534725 3.534725e-01 9.735290e-36 3.534725e-01
## 374.278 0.9682566 7.278633e-04 1.421789e-10 4.499183e-03
## 377.571 81.9128342 8.191283e+01 4.209798e-22 5.963200e+02
## 378.873 0.3533114 3.533114e-01 9.809990e-36 3.533114e-01
## 381.608 81.9128342 8.191283e+01 4.209798e-22 5.963200e+02
## 386.756 127.4306935 9.952475e+02 8.316486e-15 1.274307e+02
## 387.685 127.4306935 9.952474e+02 8.316487e-15 1.274307e+02
## 388.897 81.9128342 8.191283e+01 4.209799e-22 5.963200e+02
## 393.754 0.3533120 3.533120e-01 9.654381e-36 3.533120e-01
## 398.638 81.9128342 8.191283e+01 4.209800e-22 5.963200e+02
## 401.292 0.2409060 NaN 2.409060e-01 2.409060e-01
## 406.177 127.4306935 9.952473e+02 8.316488e-15 1.274307e+02
## 413.032 127.4306935 9.952474e+02 8.316487e-15 1.274307e+02
## 415.402 127.4306935 9.952474e+02 8.316487e-15 1.274307e+02
## 427.158 127.4306935 9.952473e+02 8.316488e-15 1.274307e+02
## 458.423 127.4306935 9.952474e+02 8.316487e-15 1.274307e+02
## 460.808 0.9682566 7.278633e-04 1.413468e-10 4.499183e-03
## 462.363 0.3537337 3.537337e-01 9.718498e-36 3.537337e-01
## 465.134 81.9128342 8.191283e+01 4.209774e-22 5.963200e+02
## 476.553 0.4818120 1.254393e-15 4.818120e-01 NaN
## 476.743 81.9128342 8.191283e+01 4.209798e-22 5.963200e+02
## 496.612 0.4818120 1.326032e-15 4.818120e-01 NaN
## 563.558 0.3532524 3.532524e-01 9.651496e-36 3.532524e-01
## 590.747 0.2409060 NaN 2.409060e-01 2.409060e-01
## 592.374 0.9682566 7.278633e-04 1.401594e-10 4.499183e-03
## 594.636 0.3533723 3.533723e-01 9.793705e-36 3.533723e-01
## 694.767 0.4818120 1.298093e-15 4.818120e-01 NaN
## 808.935 0.3534566 3.534566e-01 9.647063e-36 3.534566e-01
## chandoanshocknhiemtrung:gioinu chandoanxogan:gioinu
## 103.975 3.531460e-01 NaN
## 108.516 3.530777e-01 3.802164e-07
## 118.037 NaN NaN
## 119.016 NaN 3.555750e-07
## 138.995 NaN 2.417626e-07
## 154.745 4.384184e-07 2.409060e-01
## 166.358 1.058981e-06 3.281085e-07
## 180.441 3.477205e-07 2.409061e-01
## 182.349 5.963200e+02 5.065877e-07
## 199.119 NaN 2.409060e-01
## 201.211 1.519013e-07 2.409059e-01
## 209.545 9.952473e+02 5.464350e-07
## 210.187 5.963200e+02 2.791077e-07
## 223.049 NaN 2.409060e-01
## 223.836 3.534524e-01 NaN
## 229.404 5.963200e+02 1.337618e-07
## 236.528 3.534435e-01 1.073214e-07
## 240.058 3.533770e-01 NaN
## 248.788 NaN 2.409060e-01
## 251.811 5.963200e+02 2.795492e-08
## 253.377 NaN NaN
## 256.451 9.952473e+02 8.316488e-15
## 256.623 1.546108e-09 2.409060e-01
## 259.216 3.534482e-01 1.007757e-42
## 261.591 1.881733e-07 NaN
## 273.706 3.534233e-01 1.007496e-42
## 274.221 5.963200e+02 2.991790e-40
## 277.902 9.952473e+02 8.316488e-15
## 285.098 9.952473e+02 8.316488e-15
## 285.398 1.886031e-07 NaN
## 287.85 6.611985e-16 NaN
## 290.612 5.919626e-16 NaN
## 290.641 3.534172e-01 1.010449e-42
## 290.785 1.874701e-07 NaN
## 291.173 3.050634e-09 2.409061e-01
## 291.557 NaN 2.409061e-01
## 294.762 9.952473e+02 8.316488e-15
## 297.156 1.879866e-07 NaN
## 305.073 3.199305e-16 NaN
## 305.565 1.888124e-07 NaN
## 306.556 9.952474e+02 8.316487e-15
## 312.464 1.872901e-07 NaN
## 312.797 5.963200e+02 2.991951e-40
## 312.808 3.534400e-01 1.009434e-42
## 314.154 5.124377e-16 NaN
## 321.9 3.532524e-01 1.006465e-42
## 322.317 1.878346e-07 NaN
## 327.601 NaN 2.409061e-01
## 328.973 5.963200e+02 2.991741e-40
## 330.333 2.141033e-09 2.409060e-01
## 330.903 9.952474e+02 8.316488e-15
## 331.837 5.350971e-16 NaN
## 332.761 1.892069e-07 NaN
## 334.469 5.153291e-16 NaN
## 339.945 1.877003e-07 NaN
## 340.431 3.534677e-01 1.010557e-42
## 347.8 1.886623e-07 NaN
## 349.491 9.952474e+02 8.316487e-15
## 350.981 1.888949e-07 NaN
## 356.414 2.835271e-09 2.409061e-01
## 357.809 9.952474e+02 8.316487e-15
## 360.229 1.877701e-07 NaN
## 362.996 1.881891e-07 NaN
## 366.093 5.963200e+02 2.991880e-40
## 369.956 3.534725e-01 1.008483e-42
## 374.278 1.886682e-07 NaN
## 377.571 5.963200e+02 2.991829e-40
## 378.873 3.533114e-01 1.010781e-42
## 381.608 5.963200e+02 2.991866e-40
## 386.756 9.952475e+02 8.316486e-15
## 387.685 9.952474e+02 8.316487e-15
## 388.897 5.963200e+02 2.991866e-40
## 393.754 3.533120e-01 1.009878e-42
## 398.638 5.963200e+02 2.991878e-40
## 401.292 NaN 2.409060e-01
## 406.177 9.952473e+02 8.316488e-15
## 413.032 9.952474e+02 8.316487e-15
## 415.402 9.952474e+02 8.316487e-15
## 427.158 9.952473e+02 8.316488e-15
## 458.423 9.952474e+02 8.316487e-15
## 460.808 1.873184e-07 NaN
## 462.363 3.537337e-01 1.008684e-42
## 465.134 5.963200e+02 2.991950e-40
## 476.553 4.831781e-16 NaN
## 476.743 5.963200e+02 2.992066e-40
## 496.612 6.407326e-16 NaN
## 563.558 3.532524e-01 1.010073e-42
## 590.747 NaN 2.409060e-01
## 592.374 1.885252e-07 NaN
## 594.636 3.533723e-01 1.009872e-42
## 694.767 5.926583e-16 NaN
## 808.935 3.534566e-01 1.006794e-42
##
## Residual Deviance: 511.0153
## AIC: 1615.015gọi thư viện
library(bayesplot) ;## This is bayesplot version 1.10.0## - Online documentation and vignettes at mc-stan.org/bayesplot## - bayesplot theme set to bayesplot::theme_default()## * Does _not_ affect other ggplot2 plots## * See ?bayesplot_theme_set for details on theme setting##
## Attaching package: 'bayesplot'## The following object is masked from 'package:brms':
##
## rhatlibrary(ggplot2)
p = ggplot(dongmau, aes(x = tckDER, chandoan = chandoan))
p + geom_density(aes(fill = chandoan), alpha=0.5) + theme_light(base_size = 12)
library(rstan)## Warning: package 'rstan' was built under R version 4.3.2## Loading required package: StanHeaders##
## rstan version 2.32.3 (Stan version 2.26.1)## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)
## For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
## change `threads_per_chain` option:
## rstan_options(threads_per_chain = 1)## Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file##
## Attaching package: 'rstan'## The following object is masked from 'package:tidyr':
##
## extractlibrary(shinystan)## Loading required package: shiny## Warning: package 'shiny' was built under R version 4.3.2##
## This is shinystan version 2.6.0library(rstanarm)
library(brms)
set.seed(123)Không Intercept
prior1 = get_prior(tckDER ~ chandoan-1, family = gaussian, data = dongmau, iter = 1000) bf.2 = brm(data = dongmau, tckDER ~ chandoan-1, prior1, family = gaussian, chains = 2, iter = 1000)## Compiling Stan program...## Start sampling##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 3.3e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
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## Chain 1:
##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 7e-06 seconds
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## Chain 2: bf.2$fit## Inference for Stan model: anon_model.
## 2 chains, each with iter=1000; warmup=500; thin=1;
## post-warmup draws per chain=500, total post-warmup draws=1000.
##
## mean se_mean sd 2.5% 25% 50% 75%
## b_chandoanbinhthuong 348.00 0.70 22.81 303.30 333.50 348.09 362.44
## b_chandoanshocknhiemtrung 334.05 0.60 20.83 293.66 319.62 334.33 347.50
## b_chandoanxogan 299.07 0.80 25.55 245.56 282.05 299.00 315.10
## sigma 123.58 0.33 9.23 106.95 117.04 123.07 129.55
## lprior -5.75 0.00 0.11 -5.98 -5.82 -5.74 -5.68
## lp__ -581.00 0.07 1.55 -585.11 -581.88 -580.60 -579.83
## 97.5% n_eff Rhat
## b_chandoanbinhthuong 394.41 1071 1
## b_chandoanshocknhiemtrung 376.67 1220 1
## b_chandoanxogan 350.97 1011 1
## sigma 143.45 792 1
## lprior -5.56 796 1
## lp__ -579.10 461 1
##
## Samples were drawn using NUTS(diag_e) at Tue Dec 26 20:50:03 2023.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1). pairs(bf.2)
kiem dinh gia thuyet
hypothesis(bf.2, "chandoanxogan > chandoanbinhthuong", digits = 4)## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (chandoanxogan)-(... > 0 -48.93 33.71 -105.2 8.48 0.08
## Post.Prob Star
## 1 0.07
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities. hypothesis(bf.2, "chandoanshocknhiemtrung > chandoanbinhthuong", digits = 4)## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (chandoanshocknhi... > 0 -13.95 31.02 -65.31 33.92 0.49
## Post.Prob Star
## 1 0.33
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities. hypothesis(bf.2, "chandoanshocknhiemtrung > chandoanxogan", digits = 4)## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (chandoanshocknhi... > 0 34.98 33.26 -21.58 88.68 5.76
## Post.Prob Star
## 1 0.85
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.plot(bf.2, ignore_prior = T, theme = ggplot2::theme())
marginal_effects(bf.2, probs=c(0.05,0.95), conditions=chandoan)## Warning: Method 'marginal_effects' is deprecated. Please use
## 'conditional_effects' instead.## Warning: Argument 'probs' is deprecated. Please use 'prob' instead.## Warning: The following variables in 'conditions' are not part of the model:
## 'conditions'
Co Intercept tckDER chandoan
prior1b = get_prior(tckDER ~ chandoan*gioi*tuoi,
family = gaussian, data = dongmau)
bf.2b = brm(data = dongmau, tckDER ~ chandoan*gioi*tuoi,
prior1b, family = gaussian, chains = 2, iter = 1000)## Compiling Stan program...## Start sampling##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 4e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
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## Chain 1: 0.258 seconds (Sampling)
## Chain 1: 0.695 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 1.9e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
## Chain 2: Adjust your expectations accordingly!
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## Chain 2:
## Chain 2: Elapsed Time: 0.499 seconds (Warm-up)
## Chain 2: 0.16 seconds (Sampling)
## Chain 2: 0.659 seconds (Total)
## Chain 2: bf.2b$fit## Inference for Stan model: anon_model.
## 2 chains, each with iter=1000; warmup=500; thin=1;
## post-warmup draws per chain=500, total post-warmup draws=1000.
##
## mean se_mean sd 2.5% 25%
## b_Intercept 255.67 5.65 99.43 65.76 189.55
## b_chandoanshocknhiemtrung 76.96 9.33 164.81 -258.60 -28.27
## b_chandoanxogan -10.09 9.52 180.74 -355.92 -120.47
## b_gioinu 136.52 7.56 130.12 -115.73 47.35
## b_tuoi 2.30 0.14 2.24 -2.19 0.81
## b_chandoanshocknhiemtrung:gioinu -2.23 15.51 267.76 -494.20 -190.39
## b_chandoanxogan:gioinu -46.67 19.62 442.31 -913.83 -326.09
## b_chandoanshocknhiemtrung:tuoi -2.32 0.19 2.96 -7.99 -4.29
## b_chandoanxogan:tuoi -1.15 0.20 3.42 -8.19 -3.35
## b_gioinu:tuoi -3.52 0.18 3.08 -9.54 -5.56
## b_chandoanshocknhiemtrung:gioinu:tuoi 1.77 0.29 4.47 -6.90 -1.15
## b_chandoanxogan:gioinu:tuoi 1.71 0.34 6.98 -12.60 -2.79
## sigma 129.38 0.41 10.60 111.29 121.79
## lprior -11.40 0.00 0.13 -11.68 -11.48
## lp__ -590.39 0.17 3.01 -597.81 -592.13
## 50% 75% 97.5% n_eff Rhat
## b_Intercept 256.64 322.19 451.76 310 1.00
## b_chandoanshocknhiemtrung 80.39 183.68 415.02 312 1.00
## b_chandoanxogan -15.77 106.91 358.77 360 1.01
## b_gioinu 140.47 226.83 373.80 296 1.00
## b_tuoi 2.30 3.80 6.67 260 1.01
## b_chandoanshocknhiemtrung:gioinu -10.13 179.19 529.99 298 1.00
## b_chandoanxogan:gioinu -46.74 241.63 820.04 508 1.00
## b_chandoanshocknhiemtrung:tuoi -2.37 -0.32 4.05 239 1.01
## b_chandoanxogan:tuoi -1.01 1.01 5.41 288 1.01
## b_gioinu:tuoi -3.57 -1.39 2.60 278 1.00
## b_chandoanshocknhiemtrung:gioinu:tuoi 1.93 4.86 9.68 234 1.00
## b_chandoanxogan:gioinu:tuoi 1.76 6.26 14.98 416 1.00
## sigma 128.80 136.28 151.46 685 1.01
## lprior -11.40 -11.31 -11.19 670 1.00
## lp__ -589.94 -588.22 -585.69 315 1.00
##
## Samples were drawn using NUTS(diag_e) at Tue Dec 26 20:51:08 2023.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1). pairs(bf.2b)
hypothesis(bf.2b, "chandoanxogan > chandoanxogan:gioinu:tuoi", digits = 4)## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (chandoanxogan)-(... > 0 -11.8 177.18 -301.78 292.06 0.82
## Post.Prob Star
## 1 0.45
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities. plot(bf.2, ignore_prior = T, theme = ggplot2::theme())
marginal_effects(bf.2, probs=c(0.05,0.95),conditions=chandoan)## Warning: Method 'marginal_effects' is deprecated. Please use
## 'conditional_effects' instead.## Warning: Argument 'probs' is deprecated. Please use 'prob' instead.## Warning: The following variables in 'conditions' are not part of the model:
## 'conditions'