#install.packages("flux")
#install.packages("effects")
#install.packages("gplots")
library(flux)
## Warning: package 'flux' was built under R version 3.2.5
## Loading required package: caTools
## This is flux 0.3-0
library(effects)
## Warning: package 'effects' was built under R version 3.2.5
library(gplots)
## Warning: package 'gplots' was built under R version 3.2.5
##
## Attaching package: 'gplots'
## Det følgende objekt er maskeret fra 'package:stats':
##
## lowess
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.2.5
Hood <- read.table(file="Hood.txt", header=TRUE, dec = ".")
head(Hood)
## Time Day V_CO2 V_CO2_sd V_O2 V_O2_sd RER EE DIT
## 1 -30 1 226.5 29.6 257.8 27.2 0.879 1792 0
## 2 30 1 237.6 35.5 282.5 39.9 0.841 1948 156
## 3 60 1 255.2 36.6 290.2 34.7 0.879 2022 230
## 4 90 1 231.5 32.1 289.2 35.4 0.801 1974 182
## 5 120 1 230.4 21.9 294.2 23.6 0.783 2000 208
## 6 150 1 232.7 26.6 284.1 26.9 0.819 1948 156
Hood$Day <- factor(Hood$Day)
Hood$Treat <- factor(Hood$Day, levels = 1:2, labels = c("Walnut", "Banana"))
Hood$EE0[1:6] <- with(subset(Hood, Hood$Day == 1), Hood$EE[1])
Hood$EE0[7:12] <- with(subset(Hood, Hood$Day == 2), Hood$EE[7])
Hood$EE-Hood$EE0
## [1] 0 156 230 182 208 156 0 764 925 959 964 682
Hood$DIT0[1:6] <- with(subset(Hood, Hood$Day == 1), Hood$DIT[1])
Hood$DIT0[7:12] <- with(subset(Hood, Hood$Day == 2), Hood$DIT[7])
Hood$RER0[1:6] <- with(subset(Hood, Hood$Day == 1), Hood$RER[1])
Hood$RER0[7:12] <- with(subset(Hood, Hood$Day == 2), Hood$RER[7])
VAS <- read.table(file="VAS2.txt", header=TRUE, dec = ".")
head(VAS)
## VAS_no. Day Time naus hung sat full much
## 1 1.0 1 -30 14 73 36 0 97
## 2 2.1 1 20 74 4 85 51 51
## 3 2.0 1 55 85 76 25 40 79
## 4 3.0 1 85 88 37 71 61 51
## 5 4.0 1 115 91 38 60 58 58
## 6 5.0 1 145 44 82 24 21 63
VAS$VAS_no. <- factor(VAS$VAS_no.)
VAS$Day <- factor(VAS$Day)
VAS$Treat <- factor(VAS$Day, levels = 1:2, labels = c("Walnut", "Banana"))
VAS2 <- read.table(file="VAS3.txt", header=TRUE, dec = ".")
head(VAS2)
## Day Treat naus.30 naus20 naus55 naus85 naus115 naus145 naus175 hung.30
## 1 1 Walnut 14 74 85 88 91 44 33 73
## 2 2 Banana 13 30 23 10 18 16 10 73
## hung20 hung55 hung85 hung115 hung145 hung175 sat.30 sat20 sat55 sat85
## 1 4 76 37 38 82 91 36 85 25 71
## 2 8 14 14 24 25 54 12 64 72 65
## sat115 sat145 sat175 full.30 full20 full55 full85 full115 full145
## 1 60 24 16 0 51 40 61 58 21
## 2 64 53 54 12 79 74 70 52 56
## full175 much.30 much20 much55 much85 much115 much145 much175
## 1 20 97 51 79 51 58 63 89
## 2 43 65 32 29 41 35 64 72
VAS2$Day <- factor(VAS2$Day)
Blood <- read.table(file="Blood.txt", header=TRUE, dec = ".")
Blood$Day <- factor(Blood$Day)
Blood$FFA <- Blood$FFA/1000
BloodWal <- subset(Blood, Blood$Day==1)
BloodBan <- subset(Blood, Blood$Day==2)
Blood2 <- read.table(file="Blood2.txt", header=TRUE, dec = ".")
Blood2$Day <- factor(Blood2$Day)
Blood2Wal <- subset(Blood2, Blood2$Day==1)
Blood2Ban <- subset(Blood2, Blood2$Day==2)
with(Hood, hist(EE))
with(Hood, qqnorm(EE))
with(Hood, qqline(EE))
with(Hood, hist(RER))
with(Hood, qqnorm(RER))
with(Hood, qqline(RER))
with(Hood, hist(DIT))
with(Hood, qqnorm(DIT))
with(Hood, qqline(DIT))
with(VAS, hist(naus))
with(VAS, qqnorm(naus))
with(VAS, qqline(naus))
with(VAS, hist(hung))
with(VAS, qqnorm(hung))
with(VAS, qqline(hung))
with(VAS, hist(sat))
with(VAS, qqnorm(sat))
with(VAS, qqline(sat))
with(VAS, hist(full))
with(VAS, qqnorm(full))
with(VAS, qqline(full))
with(VAS, hist(much))
with(VAS, qqnorm(much))
with(VAS, qqline(much))
with(BloodBan, hist(Glu))
with(BloodBan, qqnorm(Glu))
with(BloodBan, qqline(Glu))
with(BloodWal, hist(Glu))
with(BloodWal, qqnorm(Glu))
with(BloodWal, qqline(Glu))
with(BloodBan, hist(FFA))
with(BloodBan, qqnorm(FFA))
with(BloodBan, qqline(FFA))
with(BloodWal, hist(FFA))
with(BloodWal, qqnorm(FFA))
with(BloodWal, qqline(FFA))
with(BloodBan, hist(Pep))
with(BloodBan, qqnorm(Pep))
with(BloodBan, qqline(Pep))
with(BloodWal, hist(Pep))
with(BloodWal, qqnorm((Pep)^2))
with(BloodWal, qqline((Pep)^2))
with(Blood, hist(FFA))
with(Blood, qqnorm(FFA))
with(Blood, qqline(FFA))
with(Blood, hist(Pep))
with(Blood, qqnorm(Pep))
with(Blood, qqline(Pep))
Testing
DIT <- lm(DIT~Treat, Hood)
summary(DIT)
##
## Call:
## lm(formula = DIT ~ Treat, data = Hood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -715.67 -7.92 37.50 108.33 248.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 155.3 109.1 1.424 0.18478
## TreatBanana 560.3 154.2 3.633 0.00459 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 267.1 on 10 degrees of freedom
## Multiple R-squared: 0.569, Adjusted R-squared: 0.5259
## F-statistic: 13.2 on 1 and 10 DF, p-value: 0.004588
EE <- lm(EE~Treat, Hood)
summary(EE)
##
## Call:
## lm(formula = EE ~ Treat, data = Hood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -715.67 -7.92 37.50 108.33 248.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1947.3 109.1 17.857 6.48e-09 ***
## TreatBanana 587.3 154.2 3.808 0.00344 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 267.1 on 10 degrees of freedom
## Multiple R-squared: 0.5919, Adjusted R-squared: 0.5511
## F-statistic: 14.5 on 1 and 10 DF, p-value: 0.003438
RER <- lm(RER~Treat, Hood)
summary(RER)
##
## Call:
## lm(formula = RER ~ Treat, data = Hood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079000 -0.019167 0.007667 0.024000 0.045333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.83367 0.01640 50.818 2.1e-13 ***
## TreatBanana 0.09133 0.02320 3.937 0.00279 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04018 on 10 degrees of freedom
## Multiple R-squared: 0.6078, Adjusted R-squared: 0.5686
## F-statistic: 15.5 on 1 and 10 DF, p-value: 0.002789
naus <- lm(naus ~ Treat,VAS)
summary(naus)
##
## Call:
## lm(formula = naus ~ Treat, data = VAS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -47.286 -7.143 -0.143 12.821 29.714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.286 8.435 7.266 9.93e-06 ***
## TreatBanana -44.143 11.929 -3.701 0.00303 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.32 on 12 degrees of freedom
## Multiple R-squared: 0.533, Adjusted R-squared: 0.4941
## F-statistic: 13.69 on 1 and 12 DF, p-value: 0.003033
hung <- lm(hung ~ Treat,VAS)
summary(hung)
##
## Call:
## lm(formula = hung ~ Treat, data = VAS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.286 -18.536 -5.786 22.464 42.714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.29 10.61 5.401 0.00016 ***
## TreatBanana -27.00 15.00 -1.800 0.09701 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28.06 on 12 degrees of freedom
## Multiple R-squared: 0.2126, Adjusted R-squared: 0.147
## F-statistic: 3.24 on 1 and 12 DF, p-value: 0.09701
sat <- lm(sat ~ Treat,VAS)
summary(sat)
##
## Call:
## lm(formula = sat ~ Treat, data = VAS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.857 -17.536 4.143 13.571 39.714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.286 8.910 5.083 0.000269 ***
## TreatBanana 9.571 12.600 0.760 0.462146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.57 on 12 degrees of freedom
## Multiple R-squared: 0.04588, Adjusted R-squared: -0.03363
## F-statistic: 0.577 on 1 and 12 DF, p-value: 0.4621
full <- lm(full~ Treat,VAS)
summary(full)
##
## Call:
## lm(formula = full ~ Treat, data = VAS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.14 -14.18 2.50 17.93 25.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.857 8.655 4.143 0.00136 **
## TreatBanana 19.286 12.239 1.576 0.14108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.9 on 12 degrees of freedom
## Multiple R-squared: 0.1714, Adjusted R-squared: 0.1024
## F-statistic: 2.483 on 1 and 12 DF, p-value: 0.1411
much <- lm(much ~ Treat,VAS)
summary(much)
##
## Call:
## lm(formula = much ~ Treat, data = VAS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.29 -15.54 -7.00 16.46 27.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.714 6.936 10.051 3.39e-07 ***
## TreatBanana -21.429 9.809 -2.185 0.0495 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.35 on 12 degrees of freedom
## Multiple R-squared: 0.2846, Adjusted R-squared: 0.2249
## F-statistic: 4.773 on 1 and 12 DF, p-value: 0.04948
FFA <- lm(FFA ~ Treat,Blood)
summary(FFA)
##
## Call:
## lm(formula = FFA ~ Treat, data = Blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.06080 -0.04665 -0.01520 -0.00510 0.17420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06880 0.03419 2.013 0.07897 .
## TreatWalnut 0.22740 0.04835 4.704 0.00153 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07644 on 8 degrees of freedom
## Multiple R-squared: 0.7344, Adjusted R-squared: 0.7012
## F-statistic: 22.12 on 1 and 8 DF, p-value: 0.001534
Glu <- lm(Glu ~ Treat,Blood)
summary(Glu)
##
## Call:
## lm(formula = Glu ~ Treat, data = Blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.560 -0.161 -0.047 0.234 0.530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0000 0.1558 32.099 9.66e-10 ***
## TreatWalnut 0.3140 0.2203 1.425 0.192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3483 on 8 degrees of freedom
## Multiple R-squared: 0.2025, Adjusted R-squared: 0.1029
## F-statistic: 2.032 on 1 and 8 DF, p-value: 0.1919
Pep <- lm(Pep ~ Treat,Blood)
summary(Pep)
##
## Call:
## lm(formula = Pep ~ Treat, data = Blood)
##
## Residuals:
## Min 1Q Median 3Q Max
## -723.4 -43.6 6.9 229.3 437.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1088.4 167.5 6.498 0.000189 ***
## TreatWalnut -752.8 236.9 -3.178 0.013040 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 374.6 on 8 degrees of freedom
## Multiple R-squared: 0.558, Adjusted R-squared: 0.5027
## F-statistic: 10.1 on 1 and 8 DF, p-value: 0.01304