Meta Analysis Portal by Jeehyoung Kim
메타 분석 뿐 아니라 통계공부에 정말 좋은 사이트
https://www.statsdirect.com/help/meta_analysis/incidence_rate.htm
메타 분석 뿐 아니라 통계공부에 정말 좋은 사이트
https://www.statsdirect.com/help/meta_analysis/incidence_rate.htm
초보자도 쉽게 하는 머신 러닝 / 예측 모형.
Machine Learning / Prediction Model
that makes it easy to begin.
example research Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models
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(강의 중 실습을 위해서 동일한 것을 여러 서버에 분산하여 배치함)
https://tinyurl.com/Bayes-Class1
= https://tinyurl.com/Bayes-Class1-0
= https://tinyurl.com/Bayes-Class1-1
= https://tinyurl.com/Bayes-Class1-2
https://tinyurl.com/Bayes-Class2
= https://tinyurl.com/Bayes-Class2-0
= https://tinyurl.com/Bayes-Class2-1
= https://tinyurl.com/Bayes-Class2-2
https://tinyurl.com/Neural-Networks-Prediction
https://tinyurl.com/confusion-matrix
>>Understand Sensitivity, Specificity https://tinyurl.com/2by2table
https://tinyurl.com/Prediction-kNN
https://tinyurl.com/Prediction-LVQ
https://tinyurl.com/Prediction-SOM
https://tinyurl.com/SVM-Prediction
https://tinyurl.com/Discriminant-Prediction
https://tinyurl.com/Decision-Tree-party
https://tinyurl.com/Decision-Tree-tree
https://tinyurl.com/Decision-Tree-rpart
https://tinyurl.com/Random-Forest2
https://tinyurl.com/Prediction-Bagging
https://tinyurl.com/Prediction-GBM
>> https://tinyurl.com/Exgboost
https://tinyurl.com/Prediction-ensemble/
>>prediction from Logistic cross validation
http://cafe.naver.com/easy2know/6632
https://tinyurl.com/calibration-plot = https://tinyurl.com/Model-Calibrations (from prediction)
https://tinyurl.com/calibration-plot2 (after logistic regression, raw data)
https://tinyurl.com/classifier-plot
https://tinyurl.com/ROC-pretty
>> https://tinyurl.com/Understand-ROC2
>> https://tinyurl.com/Understand-ROC
https://tinyurl.com/survival-Prediction
>> https://tinyurl.com/survival-Prediction2
>> https://tinyurl.com/Predict-survival
https://tinyurl.com/prediction-GLM
>> https://tinyurl.com/K-fold-val
https://tinyurl.com/prediction-BMA
https://tinyurl.com/SVM-Prediction
https://tinyurl.com/Prediction-GenBM
https://tinyurl.com/Prediction-PC-PLS
https://tinyurl.com/Predict-Nonlin-Reg
https://tinyurl.com/Prediction-RuleFit
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http://tinyurl.com/Taylor-diagram
https://tinyurl.com/matrix-scatterplot1
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https://tinyurl.com/MA-prediction-model
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https://tinyurl.com/NRI-predict
https://tinyurl.com/NRI-IDI-LR
https://tinyurl.com/Cox-Logistic-Nomogram
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Many data for excercise --> datasets at modeldata
Excel Data Visualization 엑셀 그래프 그리기 R Data Visualization vol 1 https://tinyurl.com/R-plot-I R Data Visualization vol 2 https://tinyurl.com/R-plot-II-2 단순한 변수 simple variables https://tinyurl.com/R-plot-II-3-4 다양한 변수 / 지도 many variables / map https://tinyurl.com/R-plot-II-5-6 시간 관련 / 통계 time related / statistics related https://tinyurl.com/R-plot-II-7-8 그외의 차트 / 반응형 차트 others / reactive chart 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] |
easier R than SPSS with Rcmdr |
Excel and Statistics, everybody should know. |
Sample Datasets https://tinyurl.com/data4edu |
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
>>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
>> Association Analysis 연관 분석
https://tinyurl.com/Asso-Anal
>>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
>>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
>> 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
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
>> 연관 분석 Association Analysis
https://tinyurl.com/Asso-Anal
>> Local Fisher Discriminant Analysis
https://tinyurl.com/FisherDiscriminant
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
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