2021년 6월 1일 화요일

Continuous outcome	https://tinyurl.com/MetaA-cont = https://tinyurl.com/MetaA-cont1 = https://tinyurl.com/MetaA-cont2 = https://tinyurl.com/MetaA-cont3 >> https://tinyurl.com/MA-cont-sub
Binary outcome	https://tinyurl.com/MetaA-binary >> https://tinyurl.com/MA-bi-sub >> https://tinyurl.com/y5kbhywr OR RR convert
Incidence rates	https://tinyurl.com/MetaA-incidence >> https://tinyurl.com/MA-inc-sub
Regression	https://tinyurl.com/Meta-regression
single_means	https://tinyurl.com/MetaA-single-m
single_proportions	https://tinyurl.com/MetaA-single-p
correlation	https://tinyurl.com/Meta-corr

Survival data	https://tinyurl.com/MA-survival-se https://tinyurl.com/MA-survival-OmE https://tinyurl.com/MA-survival-HR https://tinyurl.com/MA-SurvCurve
NetWork MA	https://tinyurl.com/NetWork-MA4TE >> https://tinyurl.com/NetWork-MA4TE2 https://tinyurl.com/NetWork-MA4binary https://tinyurl.com/NetWork-MA4cont https://tinyurl.com/NetWork-MA4inci

Dose Response MA	https://tinyurl.com/Multi-DRMeta https://tinyurl.com/Multi-DRMeta-cont

Diagnosis related MA Observational Studies MA Meta-analysis of predicted models	https://tinyurl.com/MetaA-ROC https://tinyurl.com/MetaA-Diagnostic >> https://tinyurl.com/MA-Diagnostic-Forest https://tinyurl.com/MetaA-DiagBay https://tinyurl.com/MetaA-Moderators https://tinyurl.com/MA4Observational https://tinyurl.com/MA-prediction-model

Risk of Bias plot	https ://tinyurl.com/RoB-Plot https://tinyurl.com/RoB-Plot-2 >>https://tinyurl.com/Plot4ROB

메타 분석 뿐 아니라 통계공부에 정말 좋은 사이트

https://www.statsdirect.com/help/meta_analysis/incidence_rate.htm

자신의 데이터를 업로드하는 법

그래프, 차트를 다운로드 받고 추가 편집하는 법

후원을 원하시는 분은 아래 광고를 클릭해 주세요.

2021년 4월 28일 수요일

Machine Learning / Prediction Model that makes it easy to begin.

초보자도 쉽게 하는 머신 러닝 / 예측 모형.

Machine Learning / Prediction Model
that makes it easy to begin.

example file.zip

자신의 데이터를 업로드하는 법

그래프, 차트를 다운로드 받고 추가 편집하는 법

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

Baseline-Table

===================

강의록

==================================

베이지안 예측모형 Bayesian Prediction Model

(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

인공신경망 예측모형 Artificial Neural Network Model

https://tinyurl.com/Neural-Networks-Prediction

검토와 validation(1)

https://tinyurl.com/confusion-matrix

>>Understand Sensitivity, Specificity https://tinyurl.com/2by2table

K 근접법 예측모형 K -Nearest Neighbor Prediction Model

https://tinyurl.com/Prediction-kNN

>>Learning Vector Quantization(LVQ) Prediction Model

https://tinyurl.com/Prediction-LVQ

>>SOM(Self Organizing Map) Prediction Model

https://tinyurl.com/Prediction-SOM

SVM 예측모형 SVM Prediction Model

https://tinyurl.com/SVM-Prediction

>> Discriminant Prediction Model

https://tinyurl.com/Discriminant-Prediction

결정나무 예측모형 Decision Tree Prediction Model

https://tinyurl.com/Decision-Tree-party

https://tinyurl.com/Decision-Tree-tree

https://tinyurl.com/Decision-Tree-rpart

Random Forest 예측모형 Random forest Prediction Model

https://tinyurl.com/Random-Forest2

Bagging 예측모형 Bagging Prediction Model

https://tinyurl.com/Prediction-Bagging

Gradient boosting 예측모형 Gradient boosting Prediction Model

https://tinyurl.com/Prediction-GBM

>> Extreme Gradient boosting

>> https://tinyurl.com/Exgboost

딥러닝 예측모형 Deep Learning Prediction Model

https://tinyurl.com/Deep-NN

>>Ensemble Prediction Model

https://tinyurl.com/Prediction-ensemble/

====================

data for logisctic

로지스틱 회귀분석 예측모형 Logistic regression prediction model

https://tinyurl.com/Prediction-Logistic-Regression
>>https://tinyurl.com/ROC4table-model
>>https://tinyurl.com/Logistic-Comparison = https://tinyurl.com/Logistic-Comparison-II

>>prediction from Logistic cross validation

https://tinyurl.com/Prediction-Logistic-CV

>>k-fold cross validation with logistic regression
https://tinyurl.com/K-fold-cross

검토와 validation(2)

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

============================

생존 자료의 예측모형 Prediction model of survival data

https://tinyurl.com/survival-Prediction

>> https://tinyurl.com/survival-Prediction2

>> https://tinyurl.com/Predict-survival

============================

선형 회귀분석 예측모형 Linear regression prediction model

https://tinyurl.com/prediction-GLM

>> https://tinyurl.com/K-fold-val

https://tinyurl.com/LOWESS2

>> Prediction from Ridge & LASSO Regression

https://tinyurl.com/Prediction-Ridge-LASSO

Bayesian Model Averaging 예측모형 Bayesian Model Averaging Prediction Model

https://tinyurl.com/prediction-BMA

SVM 예측모형 SVM Prediction Model

https://tinyurl.com/SVM-Prediction

>>Generalized Boosted Regression Prediction Models

https://tinyurl.com/Prediction-GenBM

>>Prediction from Principal Component and Partial Least Squares Regression

https://tinyurl.com/Prediction-PC-PLS

>>Nonlinear Regression Prediction Models

https://tinyurl.com/Predict-Nonlin-Reg

>> Prediction from RuleFit

https://tinyurl.com/Prediction-RuleFit

------------

검토와 validation(3)

http://tinyurl.com/Taylor-diagram

https://tinyurl.com/matrix-scatterplot1

------------

예측모형의 메타분석 Meta-analysis of predicted models

https://tinyurl.com/MA-prediction-model

============================

NRI (Net Reclassification Improvement)

https://tinyurl.com/NRI-predict

>> NRI and IDI from Logistic Regression model

https://tinyurl.com/NRI-IDI-LR

노모그램(Cox/ Weibull/ Logistic)Nomogram

https://tinyurl.com/Cox-Logistic-Nomogram

=======================

Many data for excercise --> datasets at modeldata

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

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]
https://blog.naver.com/kjhnav/222456715592

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

Excel and Statistics, everybody should know.

모든 이가 알아야 하는 엑셀 통계 (E)

통계의 기초 개념들 모음

Sample Datasets https://tinyurl.com/data4edu

자신의 데이터를 업로드하는 법 /

그래프, 차트를 다운로드 받고 추가 편집하는 법

PART 1 왕초보 통계 (BASICS)

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

https://tinyurl.com/dot-violin-box-plot

= https://tinyurl.com/dot-violin-box

= https://tinyurl.com/dot-violin-box2

= https://tinyurl.com/t-test-and-plot

https://tinyurl.com/Beeswarm-Plot

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 *

http://tinyurl.com/matrix-scatterplot3

https://tinyurl.com/Correlogram

https://tinyurl.com/Grouped-Correlation-Plot

> https://tinyurl.com/network-1node

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

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

https://tinyurl.com/Table-plot

= https://tinyurl.com/Table-plot2

>>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

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

https://tinyurl.com/Multiple-Imputation-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

>> 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 *

https://tinyurl.com/duoplot

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

>>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

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

https://tinyurl.com/Discriminant-Prediction

6-2. Decision Tree 결정나무 *

https://tinyurl.com/Decision-Tree-party

https://tinyurl.com/Decision-Tree-tree

https://tinyurl.com/Decision-Tree-rpart

https://tinyurl.com/rpart-decision

6-3. Random Forest 예측모형 *

https://tinyurl.com/Random-Forest2

>> 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) *
https://tinyurl.com/Compa-sens-spec

7-2. Kappa and Agreement *
https://tinyurl.com/marimekko-mosaic-plot

>> 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)

>> 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