t-test_summing_up
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| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| t-test_summing_up [2025/09/18 08:39] – [11] hkimscil | t-test_summing_up [2025/09/18 08:45] (current) – [t-test summing up] hkimscil | ||
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| Line 1: | Line 1: | ||
| ====== t-test summing up ====== | ====== t-test summing up ====== | ||
| < | < | ||
| - | |||
| rm(list=ls()) | rm(list=ls()) | ||
| Line 210: | Line 209: | ||
| dat <- stack(comb) | dat <- stack(comb) | ||
| head(dat) | head(dat) | ||
| + | tail(dat) | ||
| m.tot <- mean(s.all) | m.tot <- mean(s.all) | ||
| Line 216: | Line 216: | ||
| ss.tot <- ss(s.all) | ss.tot <- ss(s.all) | ||
| + | bet.s1 <- (m.tot - m.s1)^2 * length(s1) | ||
| + | bet.s2 <- (m.tot - m.s2)^2 * length(s2) | ||
| + | ss.bet <- bet.s1 + bet.s2 | ||
| ss.s1 <- ss(s1) | ss.s1 <- ss(s1) | ||
| ss.s2 <- ss(s2) | ss.s2 <- ss(s2) | ||
| + | ss.wit <- ss.s1+ss.s2 | ||
| - | df.tot <- length(s.all)-1 | + | ss.tot |
| - | df.s1 <- length(s1)-1 | + | |
| - | df.s2 <- length(s2)-1 | + | |
| - | + | ||
| - | ms.tot <- var(s.all) | + | |
| - | ms.tot | + | |
| - | ss.tot/df.tot | + | |
| - | + | ||
| - | var(s1) | + | |
| - | ss.s1 / df.s1 | + | |
| - | + | ||
| - | var(s2) | + | |
| - | ss.s2 / df.s2 | + | |
| - | + | ||
| - | ss.b.s1 <- length(s1) * ((m.tot - m.s1)^2) | + | |
| - | ss.b.s2 <- length(s2) * ((m.tot - m.s1)^2) | + | |
| - | ss.bet <- ss.b.s1+ss.b.s2 | + | |
| ss.bet | ss.bet | ||
| - | |||
| - | ss.wit <- ss.s1 + ss.s2 | ||
| ss.wit | ss.wit | ||
| + | ss.bet+ss.wit | ||
| - | ss.bet + ss.wit | + | df.tot <- length(s.all) - 1 |
| - | ss.tot | + | df.bet <- nlevels(dat$ind) - 1 |
| + | df.s1 <- length(s1)-1 | ||
| + | df.s2 <- length(s2)-1 | ||
| + | df.wit <- df.s1 + df.s2 | ||
| - | library(dplyr) | + | df.tot |
| - | # df.bet <- length(unique(dat)) - 1 | + | |
| - | df.bet <- nlevels(dat$ind) - 1 | + | |
| - | df.wit <- df.s1+df.s2 | + | |
| df.bet | df.bet | ||
| df.wit | df.wit | ||
| df.bet+df.wit | df.bet+df.wit | ||
| - | df.tot | + | |
| + | ss.tot/ | ||
| + | ms.tot <- ss.tot/df.tot | ||
| ms.bet <- ss.bet / df.bet | ms.bet <- ss.bet / df.bet | ||
| ms.wit <- ss.wit / df.wit | ms.wit <- ss.wit / df.wit | ||
| - | ms.bet | ||
| - | ms.wit | ||
| f.cal <- ms.bet / ms.wit | f.cal <- ms.bet / ms.wit | ||
| f.cal | f.cal | ||
| pf(f.cal, df.bet, df.wit, lower.tail = F) | pf(f.cal, df.bet, df.wit, lower.tail = F) | ||
| - | |||
| f.test <- aov(dat$values~ dat$ind, data = dat) | f.test <- aov(dat$values~ dat$ind, data = dat) | ||
| Line 273: | Line 259: | ||
| t.cal.ts | t.cal.ts | ||
| - | # this is anova after all. | + | # the above is anova after all. |
| m1 <- lm(dat$values~dat$ind, | m1 <- lm(dat$values~dat$ind, | ||
| Line 287: | Line 273: | ||
| sum.m1$fstatistic[1] | sum.m1$fstatistic[1] | ||
| ms.bet/ | ms.bet/ | ||
| - | |||
| </ | </ | ||
| ====== t-test summing up output ====== | ====== t-test summing up output ====== | ||
| Line 639: | Line 624: | ||
| - | ===== 8 ===== | + | ===== 8 t-test and anova ===== |
| <WRAP group> | <WRAP group> | ||
| <WRAP column half> | <WRAP column half> | ||
| Line 858: | Line 843: | ||
| <WRAP column half> | <WRAP column half> | ||
| ................................ | ................................ | ||
| + | * ss.tot 은 s1그룹과 s2그룹의 값을 한줄에 배열하여 구하는 종속변인의 (dat$values) ss값을 말한다. 즉, 독립변이 없을 때의 종속변인의 uncertainty를 말한다. | ||
| + | * ss.bet은 독립변인이 고려됨으로써 설명된 종속부분의 일부를 말한다. | ||
| + | * ss.tot = ss.bet + ss.wit | ||
| + | * total uncertainty (a) = treatment effect (b) + random effect | ||
| + | * b/a를 R square값이라고 부른다. | ||
| </ | </ | ||
| </ | </ | ||
t-test_summing_up.1758152366.txt.gz · Last modified: by hkimscil