DAY 5- BAI TAP-14052025
NGUYEN VAN PHUC
2025-05-14
DAY 5- BÀI TẬP 14/05/2025-Phân tích theo thời gian
###Việc 1. Phân tích trước-sau cho 1 nhóm ### 1.1 Đọc dữ liệu vào R: Tải file Pre-Post study one group.csv lên R bằng Filefile.choose()“D:\R\DU LIEU THUC HANH TS ThACH GUI\Pre-Post study one group.csv” ### Đọc file vừa tải lên R
df=read.csv("D:\\R\\DU LIEU THUC HANH TS ThACH GUI\\Pre-Post study one group.csv")kiểm tra 6 dòng đầu file
head(df)## EmpID Before After
## 1 26 43 66
## 2 27 58 74
## 3 28 52 62
## 4 29 47 84
## 5 30 43 78
## 6 31 50 73###1.2 Đánh giá hiệu quả can thiệp bằng paired t-test
library(lessR)##
## lessR 4.4.3 feedback: gerbing@pdx.edu
## --------------------------------------------------------------
## > d <- Read("") Read data file, many formats available, e.g., Excel
## d is 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 from pivot tables
## Enter: browseVignettes("lessR")
##
## View lessR updates, now including time series forecasting
## Enter: news(package="lessR")
##
## Interactive data analysis
## Enter: interact()##
## Attaching package: 'lessR'## The following object is masked from 'package:base':
##
## sort_byttest(Before, After, paired = TRUE, data = df)##
##
## ------ Describe ------
##
## Difference: n.miss = 0, n = 25, mean = 19.640, sd = 11.597
##
##
## ------ Normality Assumption ------
##
## Null hypothesis is a normal distribution of Difference.
## Shapiro-Wilk normality test: W = 0.9687, p-value = 0.613
##
##
## ------ Infer ------
##
## t-cutoff for 95% range of variation: tcut = 2.064
## Standard Error of Mean: SE = 2.319
##
## Hypothesized Value H0: mu = 0
## Hypothesis Test of Mean: t-value = 8.468, df = 24, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 4.787
## 95% Confidence Interval for Mean: 14.853 to 24.427
##
##
## ------ Effect Size ------
##
## Distance of sample mean from hypothesized: 19.640
## Standardized Distance, Cohen's d: 1.694
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for 6.941
###1.4 GPT ###PROMPT 1: “Sử dụng kiểm định t bắt cặp (paired t-test)
trong gói lệnh ‘lessR’ để đánh giá thay đổi hormone trước (Before) và
sau (After) can thiệp”- Lổilệnh Paired(Before, After, data=df) ###PROMPT
2: “Tôi sử dụng gói lệnh ‘lessR’ version 4.4.2 (16 March 2025) không có
lệnh Paired (https://cran.r-project.org/web/packages/lessR/lessR.pdf).
Bạn hãy dùng lệnh ttest của gói ‘lessR’”
ttest(After, Before, paired = TRUE, data = df)##
##
## ------ Describe ------
##
## Difference: n.miss = 0, n = 25, mean = -19.640, sd = 11.597
##
##
## ------ Normality Assumption ------
##
## Null hypothesis is a normal distribution of Difference.
## Shapiro-Wilk normality test: W = 0.9687, p-value = 0.613
##
##
## ------ Infer ------
##
## t-cutoff for 95% range of variation: tcut = 2.064
## Standard Error of Mean: SE = 2.319
##
## Hypothesized Value H0: mu = 0
## Hypothesis Test of Mean: t-value = -8.468, df = 24, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 4.787
## 95% Confidence Interval for Mean: -24.427 to -14.853
##
##
## ------ Effect Size ------
##
## Distance of sample mean from hypothesized: -19.640
## Standardized Distance, Cohen's d: 1.694
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for 6.941
Finished- VIỆC 1
Việc 2. Phân tích trước-sau cho 2 nhóm
###2.1 Đọc dữ liệu file:file.choose() Link file “D:\R\DU LIEU THUC HANH TS ThACH GUI\Pre-Post test dataset.csv” df2 = read.csv(“D:\R\DU LIEU THUC HANH TS ThACH GUI\Pre-Post test dataset.csv”)
df2 = read.csv("D:\\R\\DU LIEU THUC HANH TS ThACH GUI\\Pre-Post test dataset.csv")
head(df2)## EmpID Treatment Before After
## 1 26 New 41 66
## 2 27 New 56 74
## 3 28 New 50 62
## 4 29 New 45 84
## 5 30 New 41 78
## 6 31 New 48 73###2.2 So sánh hiệu quả của 2 can thiệp bằng t-test
df2$Diff = df2$After - df2$Before
ttest(Diff ~ Treatment, data = df2)##
## Compare Diff across Treatment with levels New and Old
## Grouping Variable: Treatment
## Response Variable: Diff
##
##
## ------ Describe ------
##
## Diff for Treatment New: n.miss = 0, n = 25, mean = 22.040, sd = 11.238
## Diff for Treatment Old: n.miss = 0, n = 25, mean = 13.280, sd = 12.785
##
## Mean Difference of Diff: 8.760
##
## Weighted Average Standard Deviation: 12.036
##
##
## ------ 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 Diff.
## Group New Shapiro-Wilk normality test: W = 0.964, p-value = 0.502
## Group Old Shapiro-Wilk normality test: W = 0.954, p-value = 0.309
##
## Null hypothesis is equal variances of Diff, homogeneous.
## Variance Ratio test: F = 163.460/126.290 = 1.294, df = 24;24, p-value = 0.532
## Levene's test, Brown-Forsythe: t = -0.983, df = 48, p-value = 0.331
##
##
## ------ Infer ------
##
## --- Assume equal population variances of Diff for each Treatment
##
## t-cutoff for 95% range of variation: tcut = 2.011
## Standard Error of Mean Difference: SE = 3.404
##
## Hypothesis Test of 0 Mean Diff: t-value = 2.573, df = 48, p-value = 0.013
##
## Margin of Error for 95% Confidence Level: 6.845
## 95% Confidence Interval for Mean Difference: 1.915 to 15.605
##
##
## --- Do not assume equal population variances of Diff for each Treatment
##
## t-cutoff: tcut = 2.011
## Standard Error of Mean Difference: SE = 3.404
##
## Hypothesis Test of 0 Mean Diff: t = 2.573, df = 47.223, p-value = 0.013
##
## Margin of Error for 95% Confidence Level: 6.848
## 95% Confidence Interval for Mean Difference: 1.912 to 15.608
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of Diff for each Treatment
##
## Standardized Mean Difference of Diff, Cohen's d: 0.728
##
##
## ------ 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 Treatment New: 6.726
## Density bandwidth for Treatment Old: 7.655
###‘Pre-Post test dataset.csv’ được đọc vào R và đặt tên là df2. Sử dụng
gói lệnh ‘lessR’ để so sánh hiệu quả của 2 can thiệp New và Old (biến số
‘Treatment’) lên thay đổi trước (Before) và sau (After) bằng kiểm định t
bắt cặp. Diễn giải kết quả.” ###GPT: ####Để so sánh hiệu quả của hai can
thiệp New và Old (biến Treatment) lên sự thay đổi từ Before sang After
trong dữ liệu df2, bạn sẽ cần:
####Tính toán hiệu số After - Before cho từng cá nhân.
####Tách dữ liệu theo nhóm can thiệp.
####Thực hiện kiểm định t bắt cặp trong từng nhóm bằng ttest() của gói lessR.-lổi - chạy code dưới
df_sub <- subset(df2, Treatment == "New")
t.test(df_sub$After, df_sub$Before, paired = TRUE)##
## Paired t-test
##
## data: df_sub$After and df_sub$Before
## t = 9.8061, df = 24, p-value = 0.0000000007195
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 17.40123 26.67877
## sample estimates:
## mean difference
## 22.04####Nhóm “New”:
####ttest(After, Before, paired=TRUE, data = subset(df2, Treatment == “New”))-lổi
####Nhóm “Old”:
####ttest(After, Before, paired=TRUE, data = subset(df2, Treatment == “Old”)) ##2.2 GPT chưa chạy được
##2.3 So sánh hiệu quả của 2 can thiệp bằng phân tích hiệp biến
reg(After ~ Before + Treatment + Before*Treatment, data = df2)##
## >>> Treatment is not numeric. Converted to indicator variables.
## >>> Suggestion
## # Create an R markdown file for interpretative output with Rmd = "file_name"
## reg(After ~ Before + Treatment + Before * Treatment, data=df2, Rmd="eg")
##
##
## BACKGROUND
##
## Data Frame: df2
##
## Response Variable: After
## Predictor Variable 1: Before
## Predictor Variable 2: TreatmentOld
## Predictor Variable 3: Before.TreatmentOld
##
## Number of cases (rows) of data: 50
## Number of cases retained for analysis: 50
##
##
## BASIC ANALYSIS
##
## Estimate Std Err t-value p-value Lower 95% Upper 95%
## (Intercept) 90.468 12.149 7.446 0.000 66.012 114.923
## Before -0.360 0.239 -1.503 0.140 -0.842 0.122
## TreatmentOld -9.181 16.948 -0.542 0.591 -43.295 24.933
## Before.TreatmentOld -0.056 0.342 -0.163 0.871 -0.744 0.632
##
## Standard deviation of After: 9.7945
##
## Standard deviation of residuals: 7.8801 for df=46
## 95% range of residuals: 31.7235 = 2 * (2.013 * 7.8801)
##
## R-squared: 0.392 Adjusted R-squared: 0.353 PRESS R-squared: NA
##
## Null hypothesis of all 0 population slope coefficients:
## F-statistic: 9.901 df: 3 and 46 p-value: 0.000
##
## -- Analysis of Variance from Type II Sums of Squares
##
## df Sum Sq Mean Sq F-value p-value
## Before 1 319.163 319.163 5.249 0.026
## TreatmentOld 1 1724.303 1724.303 27.769 0.000
## Before.TreatmentOld 1 1.655 1.655 0.027 0.871
## Residuals 46 2856.382 62.095
##
## -- Test of Interaction
##
## Before:Treatment df: 1 df resid: 46 SS: 1.655 F: 0.027 p-value: 0.871
##
## -- Assume parallel lines, no interaction of Treatment with Before
##
## Level New: y^_After = 90.468 + -0.360(x_Before)
## Level Old: y^_After = 81.287 + -0.360(x_Before)
##
## -- Visualize Separately Computed Regression Lines
##
## Plot(Before, After, by=Treatment, fit="lm")
##
##
## K-FOLD CROSS-VALIDATION
##
##
## RELATIONS AMONG THE VARIABLES
##
## After Before TreatmentOld Before.TreatmentOld
## After 1.00 -0.16 -0.57 -0.60
## Before -0.16 1.00 -0.17 -0.04
## TreatmentOld -0.57 -0.17 1.00 0.98
## Before.TreatmentOld -0.60 -0.04 0.98 1.00
##
## Tolerance VIF
## Before 0.494 2.024
## TreatmentOld 0.017 57.821
## Before.TreatmentOld 0.018 56.198
##
## Before TreatmentOld Before.TreatmentOld R2adj X's
## 1 1 0 0.366 2
## 1 0 1 0.362 2
## 1 1 1 0.353 3
## 0 0 1 0.342 1
## 0 1 1 0.335 2
## 0 1 0 0.310 1
## 1 0 0 0.005 1
##
## [based on Thomas Lumley's leaps function from the leaps package]
##
##
## RESIDUALS AND INFLUENCE
##
## -- Data, Fitted, Residual, Studentized Residual, Dffits, Cook's Distance
## [sorted by Cook's Distance]
## [n_res_rows = 20, out of 50 rows of data, or do n_res_rows="all"]
## --------------------------------------------------------------------------------------
## Before TreatmentOld Before.TreatmentOld After fitted resid rstdnt dffits cooks
## 40 43 1 43 42 63.415 -21.415 -3.053 -0.800 0.136
## 19 64 0 0 77 67.437 9.563 1.381 0.718 0.126
## 50 60 1 60 46 56.349 -10.349 -1.466 -0.679 0.113
## 34 51 1 51 79 60.090 18.910 2.611 0.589 0.077
## 7 64 0 0 60 67.437 -7.437 -1.065 -0.554 0.076
## 32 44 1 44 79 62.999 16.001 2.172 0.527 0.064
## 1 41 0 0 66 75.714 -9.714 -1.325 -0.490 0.059
## 8 46 0 0 61 73.915 -12.915 -1.724 -0.425 0.043
## 26 51 1 51 74 60.090 13.910 1.857 0.419 0.042
## 46 47 1 47 49 61.752 -12.752 -1.685 -0.349 0.029
## 4 45 0 0 84 74.274 9.726 1.286 0.342 0.029
## 18 43 0 0 83 74.994 8.006 1.066 0.334 0.028
## 17 53 0 0 61 71.396 -10.396 -1.364 -0.302 0.022
## 16 43 0 0 82 74.994 7.006 0.930 0.292 0.021
## 10 47 0 0 83 73.555 9.445 1.237 0.284 0.020
## 3 50 0 0 62 72.475 -10.475 -1.370 -0.280 0.019
## 45 51 1 51 51 60.090 -9.090 -1.188 -0.268 0.018
## 30 42 1 42 70 63.830 6.170 0.811 0.231 0.013
## 36 50 1 50 53 60.505 -7.505 -0.973 -0.208 0.011
## 39 50 1 50 53 60.505 -7.505 -0.973 -0.208 0.011
##
##
## PREDICTION ERROR
##
## -- Data, Predicted, Standard Error of Prediction, 95% Prediction Intervals
## [sorted by lower bound of prediction interval]
## [to see all intervals add n_pred_rows="all"]
## ----------------------------------------------
##
## Before TreatmentOld Before.TreatmentOld After pred s_pred pi.lwr pi.upr width
## 50 60 1 60 46 56.349 8.549 39.142 73.557 34.415
## 35 59 1 59 59 56.765 8.469 39.718 73.811 34.093
## 47 58 1 58 60 57.180 8.395 40.282 74.078 33.796
## ...
## 48 49 1 49 62 60.921 8.040 44.738 77.104 32.365
## 41 48 1 48 60 61.337 8.036 45.161 77.512 32.352
## 27 47 1 47 65 61.752 8.040 45.568 77.936 32.368
## ...
## 44 36 1 36 67 66.324 8.555 49.103 83.545 34.442
## 7 64 0 0 60 67.437 8.678 49.970 84.905 34.935
## 19 64 0 0 77 67.437 8.678 49.970 84.905 34.935
##
## -------------------------------------------
## Plot 1: Distribution of Residuals
## Plot 2: Residuals vs Fitted Values
## Plot 3: Scatterplot and Least-Squares Lines
## -------------------------------------------##Việc 3. Ghi lại tất cả các hàm/lệnh trên và chia sẻ lên tài khoản rpubs: https://rpubs.com/PHUC/1309994 ## Finish