Stata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 s
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- るるみ さどひら
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1 BR003
2 Stata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 sampsi 47 mwp-044 sdtest 54 mwp-043 signrank/signtest / 61 mwp-046 sktest / 65 mwp-048 swilk/sfrancia 67 mwp-049 ttest 70 mwp-041 StataCorp c 2011 Math c 2011 StataCorp LP Math web: master@math-koubou.jp
3 mwp-042 anova/oneway - anova oneway ANOVA ANOVA oneway 4. ANOVA anova ANOVA 7. ANOVA 8. ANOVA 1. 2 t mwp A, B, C t α 5% (1) A-B = 0.95 (2) A-C = 0.95 (3) B-C = = 0.86 (1), (2), (3) = % c Copyright Math c Copyright StataCorp LP (used with permission) 3
4 3 (ANOVA: analysis of variance) F (multiple comparison) 2. (1) (2) (3) (4) (repeated-measures) ANOVA 3. ANOVA oneway (factor) 1 ANOVA (one-way ANOVA) ANOVA anova, oneway oneway anova1.dta. use clear 24 (blood pressure). list if n <= 3 n >= 22, separator(3) * 1 bp drug drug 1, 2, 3, 4 4 drug *2 *1 Data Describe data List data *2 Stata 4
5 drug bp oneway α 5% Statistics Linear models and related ANOVA/MANOVA One-way ANOVA Main : Response variable: bp Factor variable: drug Multiple-comparison tests: Bonferroni Output: Produce summary table: 1 oneway - Main 5
6 . oneway bp drug, bonferroni tabulate Summary of bp drug Mean Std. Dev. Freq Total Analysis of Variance Source SS df MS F Prob > F Between groups Within groups Total Bartlett's test for equal variances: chi2(3) = Prob>chi2 = Comparison of bp by drug (Bonferroni) Row Mean Col Mean (1) ANOVA tabulate (frequency) 6 oneway anova (unbalanced data) ANOVA Analysis of Variance Source SS df MS F Prob > F Between groups Within groups Total Bartlett's test for equal variances: chi2(3) = Prob>chi2 =
7 SS (sum of squares) regress mwp-037 y (yi ȳ) 2 = (y i ŷ i ) 2 + (ŷ i ȳ) 2 (yi ȳ) 2 TSS (total sum of squares) (ŷi ȳ) 2 MSS (model sum of squares) (yi ŷ i ) 2 RSS (residual sum of squares) MSS (between groups) RSS (within groups) TSS (total) (df: degrees of freedom) MS (mean square) , F /54.16 = 3.92 F F p p < 0.05 ANOVA Bartlett ANOVA p 0.05 (2) ANOVA µ 1 = µ 2 = µ 3 = µ 4 bonferroni Bonferroni ANOVA Comparison of bp by drug (Bonferroni) Row Mean Col Mean M ij M ij µ i µ j 0 Bonferroni p µ 4 µ 2 Bonferroni Scheffe, Šidák 7
8 4. ANOVA anova ANOVA 7. ANOVA 8. ANOVA ANOVA 8
9 mwp-050 roc - ROC roc ROC roc roctab ROC roccomp AUC rocgold AUC 1. ROC AUC 2. roctab 3. roccomp rocgold 1. ROC AUC (ROC: receiver operating characteristic) / 2 roctab ROC (AUC: area under the curve) ROC AUC roc01.dta. use c Copyright Math c Copyright StataCorp LP (used with permission) 9
10 . list in 1/10 * 1 disease rating ROC refvar classvar 2 refvar (observation) / 0/1 0 1 roc01.dta disease refvar classvar roc01.dta rating classvar 1, 2, 3, 4, rating rating 2 rating4. generate rating4 = rating >= 4 * 2 rating >= 4 generate rating4 { rating4 = 0 if rating < 4 rating4 = 1 if rating 4 rating4 disease. tabulate rating4 disease, column * 3 *1 Data Describe data List data *2 Data Create or change data Create new variable *3 Statistics Summaries, tables, and tests Tables Two-way tables with measures of association 10
11 . tabulate rating4 disease, column Key frequency column percentage disease rating4 0 1 Total Total (frequency) (sensitivity) (specificity) 4 { = 78.85% = 91.67% (, ) 2 ROC 11
12 x (1 ) AUC 2. roctab 1 (1) 2 (2) (3) roctab ROC AUC (1) (2) (3) ROC (2) 6 (, ) 2 ROC roctab Statistics Epidemiology and related ROC analysis Nonparametric ROC analysis Main : Reference variable: disease Classification variable: rating Graph the ROC curve: Report the area under the ROC curve:. roctab disease rating, graph summary ROC Asymptotic Normal Obs Area Std. Err. [95% Conf. Interval]
13 AUC summary 95% CI 3. roccomp roccomp AUC (1) ROC (2) ROC 2 (1) wide (2) long wide, long [D] reshape (mwp-036 ) rocgold 13
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Analysis of Variance 2 two sample t test analysis of variance (ANOVA) CO 3 3 1 EFV1 µ 1 µ 2 µ 3 H 0 H 0 : µ 1 = µ 2 = µ 3 H A : Group 1 Group 2.. Group k population mean µ 1 µ µ κ SD σ 1 σ σ κ sample mean
Stata11 whitepapers mwp-037 regress - regress regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F(
mwp-037 regress - regress 1. 1.1 1.2 1.3 2. 3. 4. 5. 1. regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F( 2, 71) = 69.75 Model 1619.2877 2 809.643849 Prob > F = 0.0000 Residual
Stata 11 whitepaper mwp 4 mwp mwp-028 / 41 mwp mwp mwp-079 functions 72 mwp-076 insheet 89 mwp-030 recode 94 mwp-033 reshape wide
PS001 Stata 11 whitepaper mwp 4 mwp-027 23 mwp-028 / 41 mwp-001 51 mwp-078 62 mwp-079 functions 72 mwp-076 insheet 89 mwp-030 recode 94 mwp-033 reshape wide/long 100 mwp-036 ivregress 110 mwp-082 logistic/logit
Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestim
TS001 Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestimation 49 mwp-055 corrgram/ac/pac 56 mwp-009 dfgls
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卒業論文
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1 2 . sum Variable Obs Mean Std. Dev. Min Max ---------+----------------------------------------------------- var1 13.4923077.3545926.05 1.1 3 3 3 0.71 3 x 3 C 3 = 0.3579 2 1 0.71 2 x 0.29 x 3 C 2 = 0.4386
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1 2 λ 3 λ λ. correlate father mother first second (obs=20) father mother first second ---------+------------------------------------ father 1.0000 mother 0.2254 1.0000 first 0.7919 0.5841 1.0000 second
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11 OLS 2018 7 13 1 / 45 1. 2. 3. 2 / 45 n 2 ((y 1, x 1 ), (y 2, x 2 ),, (y n, x n )) linear regression model y i = β 0 + β 1 x i + u i, E(u i x i ) = 0, E(u i u j x i ) = 0 (i j), V(u i x i ) = σ 2, i
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87 6.1 AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t 2, V(y t y t 1, y t 2, ) = σ 2 3. Thus, y t y t 1,
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TS002 Stata 12 Stata VAR VEC whitepaper mwp 4 mwp-084 var VAR 14 mwp-004 varbasic VAR 26 mwp-005 svar VAR 33 mwp-007 vec intro VEC 51 mwp-008 vec VEC 80 mwp-063 VAR vargranger Granger 93 mwp-062 varlmar
Stata 11 Stata VAR VEC whitepaper mwp 4 mwp-084 var VAR 14 mwp-004 varbasic VAR 25 mwp-005 svar VAR 31 mwp-007 vec intro VEC 47 mwp-008 vec VEC 75 mwp
TS002 Stata 11 Stata VAR VEC whitepaper mwp 4 mwp-084 var VAR 14 mwp-004 varbasic VAR 25 mwp-005 svar VAR 31 mwp-007 vec intro VEC 47 mwp-008 vec VEC 75 mwp-063 VAR postestimation vargranger Granger 86
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13 2018 7 27 1 / 31 1. 2. 2 / 31 y i = β 0 + β X x i + β Z z i + β XZ x i z i + u i, E(u i x i, z i ) = 0, E(u i u j x i, z i ) = 0 (i j), V(u i x i, z i ) = σ 2, i = 1, 2,, n x i z i 1 3 / 31 y i = β
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: (EQS) /EQUATIONS V1 = 30*V F1 + E1; V2 = 25*V *F1 + E2; V3 = 16*V *F1 + E3; V4 = 10*V F2 + E4; V5 = 19*V99
218 6 219 6.11: (EQS) /EQUATIONS V1 = 30*V999 + 1F1 + E1; V2 = 25*V999 +.54*F1 + E2; V3 = 16*V999 + 1.46*F1 + E3; V4 = 10*V999 + 1F2 + E4; V5 = 19*V999 + 1.29*F2 + E5; V6 = 17*V999 + 2.22*F2 + E6; CALIS.
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5 Armitage x 1,, x n y i = 10x i + 3 y i = log x i {x i } {y i } 1.2 n i i x ij i j y ij, z ij i j 2 1 y = a x + b ( cm) x ij (i j )
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