vd2
Lenguyen
2025-07-07
XÁC ĐỊNH MÔ HÌNH BMA
Đây là một ví dụ để thử với Rmarkdown. Nếu muốn viết nghiêng thì viết như thế này, còn nếu muốn tô đậm thì viết thế này tô đậm.
Trong phần này ta sẽ:
- nhập dữ liệu
- làm sạch
- tìm mô hình BMA
Phương trình thì viết như sau:
\(y = ax + b\)
nếu muốn căn giữa và xuống dòng thì làm như sau: \[ y = ax + b \]
Hoặc dẫn tài liệu tham khảo 1.
nếu muốn hiển thị đánh số thì làm như sau:
- Phân tích BMA
- Ước lượng mô hình
- Đánh giá kết quả
# Đọc dữ liệu từ file CSV
fa <- read.csv("D:/fage.csv")
# Xem cấu trúc và tóm tắt dữ liệu
str(fa)## 'data.frame': 100 obs. of 26 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ gender : int 2 2 2 2 2 1 2 2 2 1 ...
## $ age : int 24 19 24 24 24 21 23 23 24 23 ...
## $ level : int 2 2 3 2 2 2 3 2 2 2 ...
## $ job : int 2 1 3 4 4 1 3 3 3 2 ...
## $ mar : int 1 1 1 1 3 1 1 1 1 1 ...
## $ inc : int 72 60 240 72 120 36 72 72 84 60 ...
## $ spending : int 6 3 10 5 10 4 10 8 2 5 ...
## $ weight : int 57 47 48 43 42 55 75 49 55 74 ...
## $ height : int 160 155 163 150 162 175 164 155 160 180 ...
## $ skin : int 2 5 1 3 4 2 1 3 3 4 ...
## $ hair : int 4 1 2 2 2 1 1 2 1 1 ...
## $ style : int 1 2 1 7 3 1 1 3 7 3 ...
## $ life : int 2 6 1 7 6 6 1 7 6 3 ...
## $ fashion.age: int 25 15 24 20 15 30 21 18 24 18 ...
## $ DG1 : int 26 20 27 26 27 23 30 24 27 25 ...
## $ DG2 : int 25 22 22 22 22 23 25 23 24 23 ...
## $ DG3 : int 26 22 25 26 26 21 30 25 23 24 ...
## $ DG4 : int 24 22 26 23 30 27 29 30 33 23 ...
## $ DG5 : int 23 21 23 22 19 22 24 22 24 22 ...
## $ DG6 : int 24 24 24 25 25 27 25 26 26 21 ...
## $ DG7 : int 28 26 25 30 23 30 28 28 27 22 ...
## $ DG8 : int 24 23 27 30 20 24 27 25 35 25 ...
## $ DG9 : int 30 27 23 24 31 26 25 32 34 22 ...
## $ DG10 : int 22 21 25 24 20 23 27 25 24 21 ...
## $ fage : num 25.2 22.8 24.7 25.2 24.3 24.6 27 26 27.7 22.8 ...summary(fa)## id gender age level job
## Min. : 1.00 Min. :1.00 Min. :18.00 Min. :1.00 Min. :1.00
## 1st Qu.: 25.75 1st Qu.:1.00 1st Qu.:24.00 1st Qu.:1.00 1st Qu.:2.00
## Median : 50.50 Median :2.00 Median :31.00 Median :2.00 Median :2.00
## Mean : 50.50 Mean :1.59 Mean :38.45 Mean :1.84 Mean :2.32
## 3rd Qu.: 75.25 3rd Qu.:2.00 3rd Qu.:53.50 3rd Qu.:2.00 3rd Qu.:3.00
## Max. :100.00 Max. :2.00 Max. :75.00 Max. :3.00 Max. :5.00
## mar inc spending weight height
## Min. :1 Min. : 36.00 Min. : 2.0 Min. :40.00 Min. :150.0
## 1st Qu.:1 1st Qu.: 65.75 1st Qu.: 3.0 1st Qu.:49.75 1st Qu.:157.0
## Median :2 Median :120.00 Median : 4.0 Median :55.50 Median :160.0
## Mean :2 Mean :122.85 Mean : 5.1 Mean :55.56 Mean :162.5
## 3rd Qu.:3 3rd Qu.:170.00 3rd Qu.: 6.0 3rd Qu.:60.00 3rd Qu.:168.0
## Max. :3 Max. :300.00 Max. :25.0 Max. :75.00 Max. :180.0
## skin hair style life fashion.age
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00 Min. :15.00
## 1st Qu.:3.00 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:3.75 1st Qu.:23.00
## Median :3.00 Median :1.00 Median :3.00 Median :6.00 Median :31.00
## Mean :3.04 Mean :1.51 Mean :4.25 Mean :5.21 Mean :38.22
## 3rd Qu.:3.00 3rd Qu.:2.00 3rd Qu.:7.00 3rd Qu.:7.00 3rd Qu.:55.00
## Max. :5.00 Max. :4.00 Max. :7.00 Max. :8.00 Max. :75.00
## DG1 DG2 DG3 DG4 DG5
## Min. :19.00 Min. :20.00 Min. :17.0 Min. :19.00 Min. :19.00
## 1st Qu.:24.00 1st Qu.:26.50 1st Qu.:25.0 1st Qu.:24.75 1st Qu.:23.00
## Median :33.00 Median :34.50 Median :31.0 Median :32.00 Median :30.00
## Mean :39.39 Mean :39.68 Mean :38.8 Mean :38.19 Mean :37.80
## 3rd Qu.:53.00 3rd Qu.:53.50 3rd Qu.:52.0 3rd Qu.:50.25 3rd Qu.:55.25
## Max. :77.00 Max. :75.00 Max. :81.0 Max. :80.00 Max. :72.00
## DG6 DG7 DG8 DG9
## Min. :17.00 Min. :18.00 Min. :18.00 Min. :17.00
## 1st Qu.:22.75 1st Qu.:27.00 1st Qu.:25.00 1st Qu.:24.00
## Median :29.00 Median :36.00 Median :35.50 Median :35.00
## Mean :37.29 Mean :42.82 Mean :41.52 Mean :40.87
## 3rd Qu.:50.50 3rd Qu.:56.25 3rd Qu.:56.00 3rd Qu.:55.50
## Max. :80.00 Max. :82.00 Max. :90.00 Max. :80.00
## DG10 fage
## Min. :20.00 Min. :19.70
## 1st Qu.:25.00 1st Qu.:24.57
## Median :30.00 Median :34.20
## Mean :38.57 Mean :39.49
## 3rd Qu.:55.00 3rd Qu.:52.25
## Max. :80.00 Max. :76.70attach(fa)
names(fa)## [1] "id" "gender" "age" "level" "job"
## [6] "mar" "inc" "spending" "weight" "height"
## [11] "skin" "hair" "style" "life" "fashion.age"
## [16] "DG1" "DG2" "DG3" "DG4" "DG5"
## [21] "DG6" "DG7" "DG8" "DG9" "DG10"
## [26] "fage"#thiết laapk mô hình bma
library(BMA)## Warning: package 'BMA' was built under R version 4.3.3## Loading required package: survival## Warning: package 'survival' was built under R version 4.3.3## Loading required package: leaps## Warning: package 'leaps' was built under R version 4.3.3## Loading required package: robustbase## Warning: package 'robustbase' was built under R version 4.3.3##
## Attaching package: 'robustbase'## The following object is masked from 'package:survival':
##
## heart## Loading required package: inline## Warning: package 'inline' was built under R version 4.3.3## Loading required package: rrcov## Warning: package 'rrcov' was built under R version 4.3.3## Scalable Robust Estimators with High Breakdown Point (version 1.7-6)newdata =data.frame(fage,age,mar,level,hair,spending,life,style,gender)
newdata=na.omit(newdata)
yvar = newdata[,1]
xvars = newdata[,-1]
bma = bicreg(xvars, yvar, strict=FALSE, OR=20)
summary(bma)##
## Call:
## bicreg(x = xvars, y = yvar, strict = FALSE, OR = 20)
##
##
## 23 models were selected
## Best 5 models (cumulative posterior probability = 0.6339 ):
##
## p!=0 EV SD model 1 model 2 model 3 model 4
## Intercept 100.0 3.60835 2.08256 3.9913 2.7630 3.9967 2.8748
## age 100.0 0.91342 0.04210 0.9532 0.8859 0.9036 0.9415
## mar 53.5 0.66418 0.74549 . 1.2446 1.1376 .
## level 15.0 -0.14586 0.46941 . . . .
## hair 100.0 1.46974 0.36018 1.4306 1.5136 1.4510 1.4855
## spending 12.2 -0.01483 0.05745 . . . .
## life 10.3 0.01689 0.08033 . . . .
## style 42.3 0.11845 0.16605 . 0.2928 . 0.2649
## gender 92.0 -1.90661 0.88338 -2.0824 -2.1085 -2.3350 -1.8559
##
## nVar 3 5 4 4
## r2 0.965 0.968 0.966 0.966
## BIC -320.9122 -320.8641 -320.6347 -320.1314
## post prob 0.166 0.162 0.144 0.112
## model 5
## Intercept 6.8122
## age 0.8680
## mar 1.2971
## level -1.1854
## hair 1.4754
## spending .
## life .
## style .
## gender -2.0981
##
## nVar 5
## r2 0.967
## BIC -318.4936
## post prob 0.049nhận xét:
- 3 mô hình tối ưu
- chọn mô hình nào tùy bạn
imageplot.bma(bma)
nhận xét:
đây là biểu đồ bma
m=lm(fage~age,data=fa)
summary(m)##
## Call:
## lm(formula = fage ~ age, data = fa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4466 -1.9410 -0.2626 2.1135 12.1553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.10716 0.90106 2.339 0.0214 *
## age 0.97232 0.02133 45.584 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.732 on 98 degrees of freedom
## Multiple R-squared: 0.955, Adjusted R-squared: 0.9545
## F-statistic: 2078 on 1 and 98 DF, p-value: < 2.2e-16Tìm mô hình BMA theo phương pháp trung bình Bayes.↩︎