2021년 4월 26일 월요일

통계 포탈- 다양한 통계 방법을 쉽고 간단하게 무료로(Stat Portal)

Here is 

Excel Data Visualization  엑셀 그래프 그리기

R Data Visualization vol 1  https://tinyurl.com/R-plot-I 

R Data Visualization vol 2

R Data Visualization vol 3    https://tinyurl.com/R-data-Vis3 
R Data Visualization vol 4   R 데이터 시각화 4권 

Meta Analysis vol 2  MetaA-portal(2)

Machine Learning/Prediction Model   https://tinyurl.com/Machine-Learning-EZ

Sample Size Calculation   https://tinyurl.com/MY-sample-size 

역학 조사관을 위한  [통계] 
https://blog.naver.com/kjhnav/222456715592

easier R than SPSS with Rcmdr
SPSS보다 쉬운 R 코맨더 (E)

Excel and Statistics, everybody should know.

통계의 기초 개념들 모음  

Sample Datasets  https://tinyurl.com/data4edu



    PART 1 왕초보 통계 (BASICS)

     
    1-1. 수학 성적을 비교하라 (Comparison of continuous variables) *


     
    1-2. 합격률을 비교하라(Comparison of ratio/proportion ) *
    https://tinyurl.com/table2mosaic
     
    1-3. 샘플 수의 계산(Sample Size Calculation) *
    https://goo.gl/klfbW5 
     >> https://tinyurl.com/MY-sample-size

    1-4. Randomization
    https://tinyurl.com/Simple-Randomization
    https://tinyurl.com/Block-Randomization
    https://tinyurl.com/Random-Conceal 
    >> https://tinyurl.com/Adaptive-randomization

     
    1-5. Baseline Table *
    https://tinyurl.com/Baseline-Table  
    >> https://tinyurl.com/crosstable
    >>.https://tinyurl.com/D-Baseline 
    = https://tinyurl.com/D-Baseline2
    = https://tinyurl.com/D-Baseline3

      >>Explore Plot *
    https://tinyurl.com/plot4explore
    >> https://tinyurl.com/D-Explorer
    = https://tinyurl.com/D-Explorer2 
    = https://tinyurl.com/D-Explorer3

    >>Confidence Intervals
    https://tinyurl.com/C-Intervals


    1-6. Adverse Events *
    https://tinyurl.com/Adverse-Events-plot 

    1-7. Logistic Regression *
    https://tinyurl.com/Logistic-and-OR-plot
    =       https://tinyurl.com/Logistic-and-OR2 
     
    1-8. Sensitivity, Specificity *
    https://tinyurl.com/2by2table
    = https://tinyurl.com/2by2table-II



     

    PART 2 설문 조사 연구 (Survey Research)

     
    >>Likert Chart(IV) 
    >> https://tinyurl.com/LikertChart4

    2-1. Correlation 상관분석 *

    >>Repeated Measures Correlation *
    https://tinyurl.com/Repeated-Correlation

    2-2. Partial Correlation 편상관분석
    https://tinyurl.com/partial-Correlations  

    2-3. Canonical Correlation 정준 상관 분석
    https://tinyurl.com/Canonical-Correlation  
     
    2-4. Factor Analysis 요인 분석 주성분분석
    https://tinyurl.com/factor-analysis
    >> 주성분분석
    https://tinyurl.com/Plot-PCA
     
    >> Local Fisher Discriminant Analysis
    https://tinyurl.com/FisherDiscriminant

    2-5. Cluster Analysis 군집분석
    https://tinyurl.com/K-means-and-plot
    https://tinyurl.com/Partitioning-Around-Medoids
    https://tinyurl.com/Dendrograms2
    http://tinyurl.com/simple-Heatmap
    http://tinyurl.com/Heatmap2

    2-6. Cronbach alpha *
    https://tinyurl.com/Cronbach-al  
     
    2-7. Q method
    https://tinyurl.com/Q-methodology
    Q method.xlsx(https://tinyurl.com/Q-Cards
    https://youtu.be/vyQjxh4Vc64 






    PART 3 탐색적 분석 및 데이터 전처리 (Exploratory Analysis and Data Preparation)


    3-1. Table plot 초기 탐색(Initial Exploratory Analysis) *

    >>Parallel Coordinate Plot
    https://tinyurl.com/Parallel-Plot

    >>Alluvial Diagrams
    https://tinyurl.com/Alluvial-Diagram

    >>Combination Count Plot
    https://tinyurl.com/Combi-Plot2

    >>Correlation Funnel Plot
    https://tinyurl.com/Correlation-Funnel

    3-2. Outliers & Missing 이상값과 결측값 *
    https://tinyurl.com/Outliers-Mahalanobis
     
    >>See Missing data *
    https://tinyurl.com/4Missing

    >>How to treat missing data *
    https://tinyurl.com/missing-treat 

    3-3. Grapical Normality test 정규성 검정
    https://tinyurl.com/Histogram-QQ
     
    3-4. Homogeneity of Variance 등분산성 검정
    https://tinyurl.com/Homogeneity-Variance 

     3-5. Standardization 표준화
    https://tinyurl.com/easy-Standard  
     
    3-6. Tukey Ladder of Powers
    https://tinyurl.com/Ladder-Powers
     
    3-7. Box Cox Transformation
    https://tinyurl.com/BoxCox-Trans 
    >>  https://tinyurl.com/Box-Coxes  *
     
    3-8. Dummy 변수 만들기 (Create a dummy variable) *
    https://tinyurl.com/Create-dummy 
     
    3-9. Frequency data 바꾸기 *
    https://tinyurl.com/Original-freqency  
    추가>  https://tinyurl.com/categorical-pivot  

    추가> https://tinyurl.com/R-Pivot-table  *

    3-10. 두 데이터 차이 발견하기 (Discovering differences from two data ) *
    https://tinyurl.com/Find-Diff-in2  
    https://youtu.be/4awqs0z9Emw 
     
    3-11. 두 데이터 합치기 Merge *
    https://tinyurl.com/Data2Merge 
    . >> https://tinyurl.com/Data-Merge2

    3-12. 연속변수를 집단변수로(Replace continuous variables to nominal variables) *
    https://tinyurl.com/conti2ord  

    >> 연속변수를 등구간 연속변수로
    https://tinyurl.com/Tile-Plot   
     
    3-13. Wide data and Long data *
    https://tinyurl.com/wide-long  
     
    3-14. Matching tool *
    http://tinyurl.com/Matching4Cohort  
     
    3-15. Propensity Score Matching *
    https://tinyurl.com/PS-Matching  
    >>https://tinyurl.com/PS-Matching2 
    >> https://tinyurl.com/PS-Matching3

      >>3-16 Random Selection *
    >>  https://tinyurl.com/Rand-Sel  


    >> data transformation 
    다양한 형태의 데이터를 csv로 전환
    SPSS, Stata, SAS 데이터 등
    https://tinyurl.com/data-trans  




    PART 4 단변수 분석 (Univariable Analysis)


    4-1. Multiple Impute & t-test
     

    >>Bayesian t-test
    https://tinyurl.com/Bayes-inf

    >>  ANOVA alternatives *
    >> https://tinyurl.com/ANOVA-alter


    4-2. Multifactor ANOVA
    https://tinyurl.com/multifactor-ANOVA 
     

    >>Nonparametric Two-Way ANOVA
    https://tinyurl.com/Nonpara2ANOVA

    4-3. ANCOVA
    https://tinyurl.com/ANCOVA-plot 

     
    4-4. ANOVA, RM ANOVA, Friedman Test
    http://tinyurl.com/violins4explorers

    >> Multiple repeated data 
    여러 변수를 동시에, 사후 검정까지


    >> Durbin test   refer to wikipedia
    https://tinyurl.com/Durbin-test 

    4-5. (RM) ANOVA *
    https://tinyurl.com/Plot-with-error-bar
    = https://tinyurl.com/Plot-error-bar
    -> https://tinyurl.com/full-ANOVA
     

    >> Several Nonparametric K-Sample Tests
    https://tinyurl.com/Nonparametric-ss

    4-6. 비모수 다중 검정 (Nonparametric multiple test)
    https://tinyurl.com/Nonpa-mul-test  

    4-7. 카이제곱 적합도 검정 (Chi-Squared Goodness of Fit Test)  *
    http://tinyurl.com/Spie-Chart
     
    4-8. 카이제곱검정(I) (Chi-Squared Test)  *
    https://tinyurl.com/Iceburg-Plot
     
    4-9. 카이제곱검정(II) (Chi-Squared Test)
    https://tinyurl.com/castle-plot
     
    >> Barnard test
    https://tinyurl.com/Barnard2x2

    >>Barnard-Boschloo-Exact_test
    https://tinyurl.com/Barnard-Boschloo-test

    4-10. Mantel-Haenszel test(I)  *
    https://tinyurl.com/Mantel-Haenszel-plot 
     
    4-11. Mantel-Haenszel test(II)
    https://tinyurl.com/Cochran-Mantel-Haenszel  
      
    4-12. McNemar and Cochran Q  *
    https://tinyurl.com/McNemar-Cochran 
     
    4-13. Survival Analysis *
    https://tinyurl.com/compare-KM-curves
    = https://tinyurl.com/compare-KM
    = https://tinyurl.com/compare-KM2  

     > https://tinyurl.com/KMnTable
    = https://tinyurl.com/KMnNoRisk

    4-14. Restricted Mean Survival Time *
    https://tinyurl.com/RMST-RMTL

     4-15. Competing Risks  *
    https://tinyurl.com/Competing-Risks
    = https://tinyurl.com/Competing-Risks2
     >>https://tinyurl.com/Survival-Curves


    4-16. Matrix Correlations *
    http://tinyurl.com/matrix-scatterplot3

    >>Many Correlations *
    https://tinyurl.com/Peasy-Correlation 
    >> https://tinyurl.com/Many-Correlation


    4-17. Sequential Triangular Test  *
    https://tinyurl.com/Sequential-Triangular 
     
    4-18. N-of-1 trials
    https://tinyurl.com/Nof1trials  

     
    4-19. 기타 잡다한 통계 (Other miscellaneous statistics) *
    http://tinyurl.com/Many-tests 
        Shapiro-Wilk normality test, Cramer-von Mises normality test, Lilliefors (Kolmogorov-Smirnov) normality test, Pearson chi-square normality test, Shapiro-Francia normality test, Anderson Darling Test, Robust Jarque Bera test, Bartels Ratio test, Breusch-Godfrey test, Cochran-Armitage test for trend, Stuart-Maxwell test, Cochran’s Q test, Conover’s test of multiple comparisons, Kruskal-Wallis rank sum test, Log likelihood ratio (G-test) test, Jonckheere-Terpstra test, Kendall Tau A, Kendall Tau B, Moses of Extreme Reactions Nemenyi’s test of multiple comparisons, Page test for ordered alternatives, Friedman rank sum test, Runs Test for Randomness, Wald Wolfowitz runs test. Siegel-Tukey-test for equal variability, Mood two-sample test of scale, Ansari-Bradley test, Dependent-samples Sign-Test, Wilcoxon signed rank test, F test to compare two variances, Fligner-Killeen test of homogeneity of variances, Yuen Two Sample t-test, Yuen Paired t-test, Von Neumann Successive Difference Test

    4-20. Text Mining *
    https://tinyurl.com/Text-Miner  

     


    PART 5 다변수 분석 (Multivariable Analysis)


     5-0. VIF *

    5-1. Generalized LM *
    https://tinyurl.com/Generalized-LM

    >> Coefficients Plot
    http://tinyurl.com/Coefficients-Plot 
    >>https://tinyurl.com/Coefficients-Plot2

      >> regression table * 
    >> https://tinyurl.com/regression-table


    >>Poisson Regression 
    >> https://tinyurl.com/Poisson-R

    5-2. Residual Plots *
    https://tinyurl.com/residual-plots-linear-model 
    >> https://tinyurl.com/model-diagnostics
     
    >>Diagnostic Plot
    http://tinyurl.com/Diagnostic-Plot

    5-3. Calibration Plot *
    https://tinyurl.com/calibration-plot 

    >>Modified Hosmer-Lemeshow Test for Large Samples
    https://tinyurl.com/Modified-HL

    5-4. Logistic Comparison *
    https://tinyurl.com/Logistic-Comparison  
    = https://tinyurl.com/Logistic-Comparison-II

    5-5. Conditional Logistic R
    https://tinyurl.com/Conditional-Logistic-R  
     
    5-6. Multinomial Logistic R
    https://tinyurl.com/Multinomial-Logistic  

     
    5-7. Ordinal Logistic R
    https://tinyurl.com/Ordinal-Logistic  
    .
    5-8. Cox Regression *
    https://tinyurl.com/Cox-and-HR-plot
    = https://tinyurl.com/Cox-and-HR-plot2  

    >>Cox regression and Hazard ratio table Plot
    https://tinyurl.com/Cox-HR-plot 

    >> Stratified Cox regression
    https://tinyurl.com/Stratified-Cox

    >> Aalen's additive regression model for censored data
    >> https://tinyurl.com/Aalen-regression
     

    >>  Cohort Plot
    https://tinyurl.com/Cohort-Plot

    5-9. Many survival models
    http://tinyurl.com/many-survival  
     
    5-10. Nested survival analysis
    https://tinyurl.com/Nested-survival  
     
    5-11. Time dependent / Recurrent Survival
    https://tinyurl.com/Time-depend-Surv
     
    5-12. Nomogram *
    https://tinyurl.com/Cox-Logistic-Nomogram
     
    5-13. Poisson Regression
    https://tinyurl.com/Poisson-and-OR-plot
     
    5-14. Multiple Imputation
    https://tinyurl.com/Multiple-Imputaion-Multivar  
     
    5-15. Generalized Estimating Equation
    https://tinyurl.com/Spaghetti-Plot-for-longitudial

     >> Linear Mixed Effects Model
    https://tinyurl.com/EZ-LME

    5-16. MANOVA
    https://tinyurl.com/2way-MANOVA
    >>https://tinyurl.com/2w-MANOVA 

    https://tinyurl.com/Hotelling-Test 

    https://tinyurl.com/PERMANOVA 

    5-17. Dose-response analysis
    https://tinyurl.com/dose-response-curve 

    >> Principal Component and Partial Least Squares Regression
    https://tinyurl.com/Prediction-PC-PLS

     



    PART 6 결정나무와 판별분석 (Decision Tree & Discriminant Prediction)


    6-1. Discriminant Prediction 판별분석
     
    6-2. Decision Tree 결정나무 *
     
    6-3. Random Forest 예측모형 *

    >> 연관 분석 Association Analysis
    https://tinyurl.com/Asso-Anal

     >> Local Fisher Discriminant Analysis 
    https://tinyurl.com/FisherDiscriminant 



    PART 7 진단 관련 (Diagnosis related statistics)


    7-1. 민감도 특이도 비교(Sensitivity specificity comparison) *
      
    7-2. Kappa and Agreement *

    >> Gwet Scott agreement
    https://tinyurl.com/Gwet-Scott

    7-3. IntraClass Correlation *
    https://tinyurl.com/Bland-Altman
    = https://tinyurl.com/Bland-Altman2
    >> https://tinyurl.com/BA-plots  
    >> https://tinyurl.com/SimplyAgree


    7-4. ROC curve *
    https://tinyurl.com/ROC-pretty
    = https://tinyurl.com/ROC-pretty2 

    >> Understand ROC
      https://tinyurl.com/Understand-ROC  
     https://tinyurl.com/Understand-ROC2

    >> many ROC 그림만 그려줌
    https://tinyurl.com/many-ROC

    >> 여러 ROC 비교 그림있음
    https://tinyurl.com/moreROC2

     7-5. ROC from LR *
    https://tinyurl.com/ROC4table-model 

    >> Survival data ROC
    https://tinyurl.com/survivalROC

    >> Time-dependent ROC
    https://tinyurl.com/Time-ROC

    7-6. Confusion Matrix *
    https://tinyurl.com/confusion-matrix  

    >> Longitudinal Concordance Correlation
    https://tinyurl.com/Long-Conc





    PART 8 시간 관련 (Time related statistics)

    8-1. Seasonal Analysis *
    https://tinyurl.com/Seasonal-Plot
     
    8-2. Forecast Plot for ARIMA
    https://tinyurl.com/Forecast-best-ARIMA 

    8-3. Intervention Analysis *
    https://tinyurl.com/intervention-analysis

    8-4. Segmented Regression *
    https://tinyurl.com/segmented-Regression 
     
    8-5. Changepoint Line Chart *
    https://tinyurl.com/changingpoint
      
    >>Detecting Anomalies in Data
    https://tinyurl.com/Anomalies-Data

    8-6. Autocorrelation
    https://tinyurl.com/autocorrelation-gls 
     
    8-7. Trend Test *
    https://tinyurl.com/Trend-stat

    >> Landmark Analysis (아직 공부가 부족)
    https://tinyurl.com/Landmark-Analysis  

    >> curve fitting *
    https://tinyurl.com/fit2curve


    Group-Based Multivariate Trajectory Modeling
    Group-Based Multivariate Trajectory Modeling.R


    Non Parametric Trajectory Clustering
    https://tinyurl.com/traject-cluster







    Posted by at

    댓글 6개:

    1. Unknown2021년 9월 1일 오후 6:41

      오랜시간 노력하여 준비해주신 것을 선뜻 공유해주셔서 매우 감사드립니다.

      답글삭제
    2. Unknown2022년 3월 4일 오후 4:24

      안녕하세요 교수님. 3-15 propensity matching (2) 에서 가지고 있는 데이터를 활용하려고 하는데 여러번 시도하여도 "input string 1 is invalid in this locale"라는 문구가 나와서 여쭙습니다. 감사합니다.

      답글삭제
      답글
      1. Jeehyoung Kim2023년 7월 23일 오전 8:45

        혹시, 데이터가 잘 준비된 것이 맞을 까요? 데이터 준비에 대한 "자신의 데이터를 업로드하는 법" 읽어 보셨는지요?
        너무 늦게 답변드려 죄송합니다.

        삭제
    3. Jeehyoung Kim2023년 6월 25일 오후 10:17

      이 글의 주소는 https://tinyurl.com/stat-portal
      기억해 두시면 좋습니다.

      답글삭제
    4. oph2023년 8월 26일 오후 3:27

      분당서울대 강의 잘 들었습니다. 위에서 'R Data Visualization vol 4 R 데이터 시각화 4권' 메뉴에서 figure를 그리는 것으로 이해했습니다~ 클릭때마다 계속 새창이 열리는데 같은 창에서 열리면 더 편할 것 같습니다. 감사합니다.

      답글삭제
      답글
      1. Jeehyoung Kim2023년 8월 27일 오전 12:02

        제 생각에는 이것이 개인마다 선호도가 다를 것같습니다. 저는 새 창에서 열리는 것이 훨씬 좋거든요.
        여러 창이 열려서 불편하다면 닫는 단축키는 Ctrl W 입니다.

        삭제

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