RCT <- read.table(file="Analyzing RCT.txt", header=TRUE, dec = ",")
head(RCT)
## SP Oil Capsule Group Interv_length Compliance Age1 Height1 BMI1
## 1 4.373 1 1 11 56 1 29 1.775 23.55
## 2 4.342 1 1 11 56 3 24 1.770 26.24
## 3 4.335 1 1 11 55 3 27 1.690 22.55
## 4 4.374 1 1 11 62 3 20 1.780 25.44
## 5 4.344 1 1 11 56 3 24 1.755 20.88
## 6 4.316 1 1 11 63 3 23 1.795 23.34
## Weight1 WeightInc1_2 RBCn3PUFA_B1 RBCn3PUFA_B2 TG1_fast TG2_fast B1_LDLC
## 1 74.2 1 7.27 7.85 0.65 0.62 2.00
## 2 82.2 1 6.35 6.05 1.48 1.99 3.06
## 3 64.4 1 6.03 5.94 1.22 0.61 2.51
## 4 80.6 1 6.01 6.23 1.25 0.95 2.52
## 5 64.3 0 5.80 5.76 0.89 0.80 1.80
## 6 75.2 1 5.84 5.99 0.59 0.93 2.54
## B2_LDLC B1_HDLC B2_HDLC b2_crp HOMA_B1 HOMA_B2 SBP_B2 DBP_B2
## 1 1.95 1.31 1.31 0.24 2.04 3.12 125 63
## 2 3.47 1.30 1.35 2.56 2.67 3.10 121 69
## 3 1.78 1.30 1.43 0.72 1.16 0.80 114 63
## 4 2.47 1.31 1.54 1.77 2.40 2.79 129 63
## 5 1.83 1.18 1.21 0.60 0.54 0.66 114 63
## 6 2.93 1.23 1.25 0.24 1.43 1.33 117 67
RCT$Capsule <- factor(RCT$Capsule)
RCT$Oil<- factor(RCT$Oil)
Fisk <- subset(RCT, RCT$Capsule == 2)
Oliven <- subset(RCT, RCT$Capsule == 1)
Raps <- subset(RCT, RCT$Oil == 2)
Solsikke <- subset(RCT, RCT$Oil == 1)
hist(RCT$BMI1)
qqnorm(RCT$BMI1)
qqline(RCT$BMI1)
with(RCT, wilcox.test(RCT$Compliance~RCT$Capsule))
## Warning in wilcox.test.default(x = c(1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, :
## cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: RCT$Compliance by RCT$Capsule
## W = 481.5, p-value = 0.6684
## alternative hypothesis: true location shift is not equal to 0
t.test(Fisk$BMI1,Oliven$BMI1)
##
## Welch Two Sample t-test
##
## data: Fisk$BMI1 and Oliven$BMI1
## t = -1.868, df = 61.988, p-value = 0.06648
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.76344114 0.05970703
## sample estimates:
## mean of x mean of y
## 22.34419 23.19606
t.test(Raps$BMI1,Solsikke$BMI1)
##
## Welch Two Sample t-test
##
## data: Raps$BMI1 and Solsikke$BMI1
## t = 1.3839, df = 61.544, p-value = 0.1714
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2846669 1.5650971
## sample estimates:
## mean of x mean of y
## 23.11355 22.47333
m1 <- lm(BMI1~Capsule, RCT)
summary(m1)
##
## Call:
## lm(formula = BMI1 ~ Capsule, data = RCT)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0642 -1.2242 -0.2542 1.2169 4.2058
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.1961 0.3179 72.972 <2e-16 ***
## Capsule2 -0.8519 0.4567 -1.865 0.0669 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.826 on 62 degrees of freedom
## Multiple R-squared: 0.05313, Adjusted R-squared: 0.03785
## F-statistic: 3.479 on 1 and 62 DF, p-value: 0.0669
m2 <- lm(BMI1~Oil, RCT)
summary(m2)
##
## Call:
## lm(formula = BMI1 ~ Oil, data = RCT)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1933 -1.3135 -0.3934 1.0390 4.0367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.4733 0.3217 69.851 <2e-16 ***
## Oil2 0.6402 0.4623 1.385 0.171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.848 on 62 degrees of freedom
## Multiple R-squared: 0.03001, Adjusted R-squared: 0.01436
## F-statistic: 1.918 on 1 and 62 DF, p-value: 0.171