Version 2013-01-12 21:27:15
Test \( \LaTeX \) mode on wiki?
library(coefplot2)
dat <- read.table("pulse.txt", header = TRUE, check.names = FALSE)
library(plyr)
dat <- rename(dat, c(PuBefor = "P1", PuAfter = "P2", Sex = "S", `Smokes?` = "K",
`Ran?` = "G"))
dat <- subset(dat, select = c(P1, P2, G, K, S))
summary(dat)
## P1 P2 G K S
## Min. : 48.0 Min. : 50 no :57 no :64 female:35
## 1st Qu.: 64.0 1st Qu.: 68 yes:35 yes:28 male :57
## Median : 71.0 Median : 76
## Mean : 72.9 Mean : 80
## 3rd Qu.: 80.0 3rd Qu.: 85
## Max. :100.0 Max. :140
## recode: G: 1=experimental, 2=control
dat <- transform(dat, G = 3 - as.numeric(G), K = 3 - as.numeric(K), S = 3 -
as.numeric(S))
summary(m1 <- lm(P2 ~ (P1 + G + S + K)^3, data = dat))
##
## Call:
## lm(formula = P2 ~ (P1 + G + S + K)^3, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.322 -3.359 0.511 2.508 27.461
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 131.7039 111.6678 1.18 0.24
## P1 -1.3445 1.5374 -0.87 0.38
## G -38.5096 51.1171 -0.75 0.45
## S 19.6285 62.9953 0.31 0.76
## K -79.8555 65.4931 -1.22 0.23
## P1:G 0.8671 0.6807 1.27 0.21
## P1:S 0.3258 0.7536 0.43 0.67
## P1:K 1.2570 0.9498 1.32 0.19
## G:S -26.2884 38.8808 -0.68 0.50
## G:K 21.9553 24.3581 0.90 0.37
## S:K 22.4863 31.6429 0.71 0.48
## P1:G:S 0.0113 0.3787 0.03 0.98
## P1:G:K -0.4236 0.3788 -1.12 0.27
## P1:S:K -0.2912 0.4085 -0.71 0.48
## G:S:K 1.5421 9.7982 0.16 0.88
##
## Residual standard error: 7.69 on 77 degrees of freedom
## Multiple R-squared: 0.829, Adjusted R-squared: 0.798
## F-statistic: 26.6 on 14 and 77 DF, p-value: <2e-16
convANOVA(anova(m1))
## Analysis of Variance Table
##
## Response: P2
## Df Sum Sq Mean Sq F value Pr(>F)
## Regression 14 22034 1574 26.6 <2e-16 ***
## Error 77 4556 59
## Total 91 26590
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
coefplot2(m1)