Day 2
Nguyen Ly
2026-01-07
Ngày 2: So ssánh 2 nhóm
df1=read.csv ("/Users/lynguyen/Documents/6 Dao tao/Tap_huan/R 2025/Tai_lieu_R/Bone data.csv")
dim(df1)## [1] 2162 9head(df1)summary (df1)## id sex age weight
## Min. : 1.0 Length:2162 Min. :57.00 Min. : 34.00
## 1st Qu.: 545.2 Class :character 1st Qu.:65.00 1st Qu.: 60.00
## Median :1093.5 Mode :character Median :69.00 Median : 69.00
## Mean :1094.7 Mean :70.65 Mean : 70.14
## 3rd Qu.:1639.8 3rd Qu.:75.00 3rd Qu.: 79.00
## Max. :2216.0 Max. :94.00 Max. :133.00
## NA's :1
## height prior.fx fnbmd smoking
## Min. :136.0 Min. :0.0000 Min. :0.2800 Min. :0.0000
## 1st Qu.:158.0 1st Qu.:0.0000 1st Qu.:0.7300 1st Qu.:0.0000
## Median :164.0 Median :0.0000 Median :0.8200 Median :0.0000
## Mean :164.9 Mean :0.1554 Mean :0.8287 Mean :0.4251
## 3rd Qu.:171.0 3rd Qu.:0.0000 3rd Qu.:0.9300 3rd Qu.:1.0000
## Max. :196.0 Max. :1.0000 Max. :1.5100 Max. :1.0000
## NA's :40
## fx
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.2521
## 3rd Qu.:1.0000
## Max. :1.0000
## việc 2 so sánh MĐX giữa nam và nữ
2.1 Histogram
library(lessR)##
## lessR 4.5 feedback: gerbing@pdx.edu
## --------------------------------------------------------------
## > d <- Read("") Read data file, many formats available, e.g., Excel
## d is the default data frame, data= in analysis routines optional
##
## Many examples of reading, writing, and manipulating data, graphics,
## testing means and proportions, regression, factor analysis,
## customization, forecasting, and aggregation to pivot tables.
## Enter: browseVignettes("lessR")
##
## View lessR updates, now including modern time series forecasting
## and many, new Plotly interactive visualizations output. Most
## visualization functions are now reorganized to three functions:
## Chart(): type="bar", "pie", "radar", "bubble", "treemap", "icicle"
## X(): type="histogram", "density", "vbs" and more
## XY(): type="scatter" for a scatterplot, or "contour", "smooth"
## Most previous function calls still work, such as:
## BarChart(), Histogram, and Plot().
## Enter: news(package="lessR"), or ?Chart, ?X, or ?XY
## There is also Flows() for Sankey flow diagrams, see ?Flows
##
## Interactive data analysis for constructing visualizations.
## Enter: interact()Histogram(fnbmd, data=df1,
fill = "blue")## lessR visualizations are now unified over just three core functions:
## - Chart() for pivot tables, such as bar charts. More info: ?Chart
## - X() for a single variable x, such as histograms. More info: ?X
## - XY() for scatterplots of two variables, x and y. More info: ?XY
##
## Histogram() is deprecated, though still working for now.
## Please use X(..., type = "histogram") going forward.## [Interactive plot from the Plotly R package (Sievert, 2020)]
## >>> Suggestions
## bin_width: set the width of each bin
## bin_start: set the start of the first bin
## bin_end: set the end of the last bin
## X(fnbmd, type="density") # smoothed curve + histogram
## X(fnbmd, type="vbs") # Violin/Box/Scatterplot (VBS) plot
##
## --- fnbmd ---
##
## n miss mean sd min mdn max
## 2122 40 0.829 0.155 0.280 0.820 1.510
##
##
##
## --- Outliers --- from the box plot: 33
##
## Small Large
## ----- -----
## 0.3 1.5
## 0.3 1.5
## 0.4 1.4
## 0.4 1.4
## 0.4 1.4
## 0.4 1.4
## 0.4 1.4
## 0.4 1.4
## 0.4 1.3
## 0.4 1.3
## 0.4 1.3
## 1.3
## 1.3
## 1.3
## 1.3
## 1.2
## 1.2
## 1.2
##
## + 15 more outliers
##
##
## Bin Width: 0.1
## Number of Bins: 14
##
## Bin Midpnt Count Prop Cumul.c Cumul.p
## ---------------------------------------------------
## 0.2 > 0.3 0.25 1 0.00 1 0.00
## 0.3 > 0.4 0.35 9 0.00 10 0.00
## 0.4 > 0.5 0.45 15 0.01 25 0.01
## 0.5 > 0.6 0.55 103 0.05 128 0.06
## 0.6 > 0.7 0.65 306 0.14 434 0.20
## 0.7 > 0.8 0.75 522 0.25 956 0.45
## 0.8 > 0.9 0.85 534 0.25 1490 0.70
## 0.9 > 1.0 0.95 371 0.17 1861 0.88
## 1.0 > 1.1 1.05 183 0.09 2044 0.96
## 1.1 > 1.2 1.15 48 0.02 2092 0.99
## 1.2 > 1.3 1.25 21 0.01 2113 1.00
## 1.3 > 1.4 1.35 6 0.00 2119 1.00
## 1.4 > 1.5 1.45 2 0.00 2121 1.00
## 1.5 > 1.6 1.55 1 0.00 2122 1.00
## 2.2 Ttest
library(lessR)
ttest(fnbmd ~sex, data=df1)##
## Compare fnbmd across sex with levels Male and Female
## Grouping Variable: sex
## Response Variable: fnbmd
##
##
## ------ Describe ------
##
## fnbmd for sex Male: n.miss = 23, n = 822, mean = 0.910, sd = 0.153
## fnbmd for sex Female: n.miss = 17, n = 1300, mean = 0.778, sd = 0.132
##
## Mean Difference of fnbmd: 0.132
##
## Weighted Average Standard Deviation: 0.141
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of fnbmd.
## Group Male: Sample mean assumed normal because n > 30, so no test needed.
## Group Female: Sample mean assumed normal because n > 30, so no test needed.
##
## Null hypothesis is equal variances of fnbmd, homogeneous.
## Variance Ratio test: F = 0.023/0.018 = 1.336, df = 821;1299, p-value = 0.000
## Levene's test, Brown-Forsythe: t = 3.449, df = 2120, p-value = 0.001
##
##
## ------ Infer ------
##
## --- Assume equal population variances of fnbmd for each sex
##
## t-cutoff for 95% range of variation: tcut = 1.961
## Standard Error of Mean Difference: SE = 0.006
##
## Hypothesis Test of 0 Mean Diff: t-value = 21.080, df = 2120, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 0.012
## 95% Confidence Interval for Mean Difference: 0.120 to 0.144
##
##
## --- Do not assume equal population variances of fnbmd for each sex
##
## t-cutoff: tcut = 1.961
## Standard Error of Mean Difference: SE = 0.006
##
## Hypothesis Test of 0 Mean Diff: t = 20.407, df = 1560.981, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 0.013
## 95% Confidence Interval for Mean Difference: 0.119 to 0.145
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of fnbmd for each sex
##
## Standardized Mean Difference of fnbmd, Cohen's d: 0.939
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for sex Male: 0.044
## Density bandwidth for sex Female: 0.034
## Việc 3 So sánh khác biệt về chỉ số RẺR giữa 2 nhóm ### 3.1 Giả sử
placebo <- c(105, 119, 100, 97, 96, 101, 94, 95, 98)
coffee <- c(96, 99, 94, 89, 96, 93, 88, 105, 88)3.2 Thực hiện phép kiểm định t tết về RER
ttest(placebo, coffee)##
## Compare Y across X with levels Group1 and Group2
## Grouping Variable: X
## Response Variable: Y
##
##
## ------ Describe ------
##
## Y for X Group1: n.miss = 0, n = 9, mean = 100.556, sd = 7.699
## Y for X Group2: n.miss = 0, n = 9, mean = 94.222, sd = 5.608
##
## Mean Difference of Y: 6.333
##
## Weighted Average Standard Deviation: 6.735
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of Y.
## Group Group1 Shapiro-Wilk normality test: W = 0.780, p-value = 0.012
## Group Group2 Shapiro-Wilk normality test: W = 0.924, p-value = 0.427
##
## Null hypothesis is equal variances of Y, homogeneous.
## Variance Ratio test: F = 59.278/31.444 = 1.885, df = 8;8, p-value = 0.389
## Levene's test, Brown-Forsythe: t = 0.230, df = 16, p-value = 0.821
##
##
## ------ Infer ------
##
## --- Assume equal population variances of Y for each X
##
## t-cutoff for 95% range of variation: tcut = 2.120
## Standard Error of Mean Difference: SE = 3.175
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.995, df = 16, p-value = 0.063
##
## Margin of Error for 95% Confidence Level: 6.731
## 95% Confidence Interval for Mean Difference: -0.397 to 13.064
##
##
## --- Do not assume equal population variances of Y for each X
##
## t-cutoff: tcut = 2.136
## Standard Error of Mean Difference: SE = 3.175
##
## Hypothesis Test of 0 Mean Diff: t = 1.995, df = 14.624, p-value = 0.065
##
## Margin of Error for 95% Confidence Level: 6.782
## 95% Confidence Interval for Mean Difference: -0.449 to 13.116
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of Y for each X
##
## Standardized Mean Difference of Y, Cohen's d: 0.940
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for X Group1: 5.642
## Density bandwidth for X Group2: 4.112
3.3 Thực hiện bootstrap để đánh giá sự kahcs biệt về RER giữa 2 nhóm
library(simpleboot)## Simple Bootstrap Routines (1.1-8)library(boot)
set.seed(123) # Để kết quả tái lập được
boot_result <- two.boot(placebo, coffee, FUN = mean, R = 10000)
boot.ci(boot_result, type = c("perc", "bca", "norm", "basic"))## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 10000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = boot_result, type = c("perc", "bca", "norm",
## "basic"))
##
## Intervals :
## Level Normal Basic
## 95% ( 0.383, 12.188 ) ( 0.111, 11.778 )
##
## Level Percentile BCa
## 95% ( 0.889, 12.556 ) ( 0.778, 12.444 )
## Calculations and Intervals on Original Scale# Bias-corrected and Accelerate bootstrap CI3.4 Sử dụng chat GPT
PROMPT 1: Tôi có dữ liệu của 18 người trong 2 nhóm như sau: placebo = 105, 119, 100, 97, 96, 101, 94, 95, 98 và coffee = 96, 99, 94, 89, 96, 93, 88, 105, 88. Bạn giúp viết lệnh R từ gói lệnh ‘simpleboot’ và ‘boot’ để phân tích sự khác biệt giữa 2 nhóm bằng phương pháp bootstrap và trình bày nhiều giá trị 95% CI (percentile, BCA, normal, basic) không?
Việc 4 So sanh ty le gay xuong giữa 2 nhóm
4.1 So sánh tỷ lệ gãy xương giữa nam và nữ
library(table1)##
## Attaching package: 'table1'## The following object is masked from 'package:lessR':
##
## label## The following objects are masked from 'package:base':
##
## units, units<-table1(~ fx + as.factor(fx) | sex, data = df1)| Female (N=1317) |
Male (N=845) |
Overall (N=2162) |
|
|---|---|---|---|
| fx | |||
| Mean (SD) | 0.304 (0.460) | 0.170 (0.376) | 0.252 (0.434) |
| Median [Min, Max] | 0 [0, 1.00] | 0 [0, 1.00] | 0 [0, 1.00] |
| as.factor(fx) | |||
| 0 | 916 (69.6%) | 701 (83.0%) | 1617 (74.8%) |
| 1 | 401 (30.4%) | 144 (17.0%) | 545 (25.2%) |
chisq.test(df1$fx, df1$sex)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: df1$fx and df1$sex
## X-squared = 48.363, df = 1, p-value = 3.542e-124.2 Sử dụng chatGPT
PROMPT: So sánh tỉ lệ gãy xương (fx) giữa nam và nữ (sex) ## Việc 5. Ghi lại tất cả các hàm/lệnh trên và chia sẻ lên tài khoản rpubs