Load packages and data set

Useful function - run first

## Correlation Function
Correlation <- function(data, Cor1, Cor2, title) {
  ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
  geom_point()+
  geom_smooth(method = lm)+
  ggtitle(title) +
  stat_cor(method = "pearson") +
  theme_light() 
}

Sweet Intensity

This is calculated using data from the Full Test (participants rated 5 total solutions twice within each session). The overall test-retest reliability for the sweet intensity measures is high (r = 0.90). The dose-response curve is similiar to previously published data (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8777740/).

## Reliability
Int.wide <- Int %>% 
  select(ID, Day, Sweet, Conc.mM, Sweet.Level) %>%
  group_by(ID, Day, Conc.mM, Sweet.Level) %>%
  summarize(Sweet.Int = mean(Sweet)) %>%
  pivot_wider(names_from = Day, values_from = Sweet.Int) %>%
  rename(Day.1 = "1", Day.2 = "2")
## `summarise()` has grouped output by 'ID', 'Day', 'Conc.mM'. You can override using the `.groups` argument.
Correlation(Int.wide, log10(Day.1), log10(Day.2), "Test retest reliability for sweet intensity measures")
## `geom_smooth()` using formula 'y ~ x'

## Dose-Response
Int.1 <- Int %>%
  filter(Day == 1) %>%
  group_by(Sweet.Level) %>%
  summarize(Sweet.Int = mean(Sweet), sd = sd(Sweet))
Int.1$Day <- 1

Int.2 <- Int %>%
  filter(Day == 2) %>%
  group_by(Sweet.Level) %>%
  summarise(Sweet.Int = mean(Sweet), sd = sd(Sweet), Conc.mM = (Conc.mM + 10))
## `summarise()` has grouped output by 'Sweet.Level'. You can override using the `.groups` argument.
Int.2$Day <- 2

Sweet.Int <- full_join(Int.1, Int.2) 
## Joining, by = c("Sweet.Level", "Sweet.Int", "sd", "Day")
Sweet.Int %>% ggplot(aes(x = Sweet.Level, y = Sweet.Int, color = as.factor(Day))) +
  geom_point() + 
  geom_line()+
  geom_errorbar(aes(ymin = Sweet.Int - sd/sqrt(21), ymax = Sweet.Int + sd/sqrt(21)), width = 0.1) +
  ylim(0, 100) +
  theme_light()

Sucrose and Sucralose sweetness intensity matching

The sucrose and sucralose concentrations were grabbed from a publication by Bobowski et. al (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647502/), who previously conducted the preference tracking procedure using sucrose and sweetness intensity matching sucralose. Our data suggest there may be a difference in sweetness intensity between the high concentration of sucrose and sucralose (p = 0.06), althought technically not statistically significant.

## Simple Sweet Sucrose vs Sucralose ANOVA
  SweetMatch <- aov(Sweet ~ as.factor(Sweetener) + as.factor(Sweet.Level), data = SS.Int)
  summary(SweetMatch)
##                         Df Sum Sq Mean Sq F value Pr(>F)    
## as.factor(Sweetener)     1    416     416   3.572 0.0605 .  
## as.factor(Sweet.Level)   1  32692   32692 280.409 <2e-16 ***
## Residuals              165  19237     117                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Post-hoc
  tukey.SweetMatch<-TukeyHSD(SweetMatch)
  tukey.SweetMatch
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Sweet ~ as.factor(Sweetener) + as.factor(Sweet.Level), data = SS.Int)
## 
## $`as.factor(Sweetener)`
##                      diff        lwr      upr     p adj
## Sucrose-Sucralose 3.14869 -0.1409176 6.438299 0.0605296
## 
## $`as.factor(Sweet.Level)`
##              diff       lwr      upr p adj
## Low-High -27.8994 -31.18901 -24.6098     0
## Simple Sweet Sucrose and Sucralose Sweetness Intensity Graph
SS.Int$Sweet.Level <- factor(SS.Int$Sweet.Level, levels=c("Low", "High"))
SS.Int$Sweetener <- factor(SS.Int$Sweetener, levels = c("Sucrose", "Sucralose"))
  
SS.Int %>%
  group_by(Sweetener, Sweet.Level) %>%
  summarize(meanSweet = mean(Sweet), SD = sd(Sweet)) %>%
  ggplot(aes(x = Sweet.Level, y = meanSweet, fill = Sweetener)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymin = meanSweet - SD/sqrt(21), ymax = meanSweet + SD/sqrt(21)), position = position_dodge(width=0.9), width = 0.3) +
  scale_fill_manual(values = c("#E57373", "#4527A0"))+
  ylim(0, 60)+
  theme_light()
## `summarise()` has grouped output by 'Sweetener'. You can override using the `.groups` argument.

Test-retest reliability

Below are the test-retest correlations of the sweet hedonic measures. The re-test correlation of the most liked concentration is r = 0.76, which is comparable to the retest correlation of the most preferred concentrations. For both versions of the Simple Sweet test, the higher concentrations (1050 mM sucrose and 1.5mM sucralose) have decent re-test reliability (sucrose: r = 0.73 and sucralose: r = 0.57). However, the retest correlation for the lower concentrations (90 mM sucrose and 0.13 mM sucralose) for both versions of the Simple Sweet test are not statistically significant. For the Single Solution tests, the retest reliability for sucrose is decent (r = 0.65), while the retest reliability for sucralose is not significant.

## Corrplot
SS.cor <- SS %>%
  select(Pref_1, Pref_2, MostLiked_1, MostLiked_2, SS.Sucrose_90mM_1, SS.Sucrose_90mM_2, SS.Sucrose_1050mM_1, SS.Sucrose_1050mM_2, SS.Sucralose_0.13mM_1, SS.Sucralose_0.13mM_2, SS.Sucralose_1.5mM_1, SS.Sucralose_1.5mM_2, Single.Sucrose_1, Single.Sucrose_2, Single.Sucralose_1, Single.Sucralose_2) %>%
  cor(.)

SS.cor.sig <- cor.mtest(SS.cor, conf.level = 0.95)

corrplot(SS.cor, p.mat = SS.cor.sig$p, method = 'color', diag = FALSE, type = 'upper',
         sig.level = c(0.001, 0.01, 0.05), pch.cex = 0.9,
         insig = 'label_sig', pch.col = 'grey20', order = 'AOE', tl.col = 'black')

## Most-liked Concentration
Correlation(SS, log10(MostLiked_1), log10(MostLiked_2), "Test re-test correlation for the most-liked concentration") 
## `geom_smooth()` using formula 'y ~ x'

## Preferred Concentration
Correlation(SS, log10(Pref_1), log10(Pref_2), "Test re-test correlation for the preferred concentration")
## `geom_smooth()` using formula 'y ~ x'

## Simple Sweet - Sucrose
Correlation(SS, SS.Sucrose_90mM_1, SS.Sucrose_90mM_2, "Test re-test correlation for the Simple Sweet - Sucrose test at 90mM")
## `geom_smooth()` using formula 'y ~ x'

Correlation(SS, SS.Sucrose_1050mM_1, SS.Sucrose_1050mM_2, "Test re-test correlation for the Simple Sweet - Sucrose test at 1050mM")
## `geom_smooth()` using formula 'y ~ x'

## Simple Sweet - Sucralose 
Correlation(SS, SS.Sucralose_0.13mM_1, SS.Sucralose_0.13mM_2, "Test re-test correlation for the Simple Sweet - Sucralose test at 0.13mM")
## `geom_smooth()` using formula 'y ~ x'

Correlation(SS, SS.Sucralose_1.5mM_1, SS.Sucralose_1.5mM_2, "Test re-test correlation for the Simple Sweet - Sucralose test at 1.5mM")
## `geom_smooth()` using formula 'y ~ x'

## Single Solution - Sucrose
Correlation(SS, Single.Sucrose_1, Single.Sucrose_2, "Test re-test correlation for the Simple Sweet - Sucrose test at 1050mM")
## `geom_smooth()` using formula 'y ~ x'

## Single Solution - Sucralose
Correlation(SS, Single.Sucralose_1, Single.Sucralose_2, "Test re-test correlation for the Simple Sweet - Sucrose test at 1.5mM")
## `geom_smooth()` using formula 'y ~ x'

Correlations between psychophysical measures

The correlation between the most liked and the preferred sucrose concentrations is high (r = 0.78), similiar to the data from our previous study. I also assessed the correlations between the two versions of the Simple Sweet test by correlating the slopes of the liking scores from each person. The two Simple Sweet versions are decently correlation (r = 0.51). I then correlated slopes of the Simple Sweet test with the preferred sucrose concentrations and found that the sucrose version (r = 0.64) is significantly correlated with the preferred sucralose concentration, while the sucralose version is not. Similiarly, Simple Sweet - Sucrose (r = 0.56) is significantly correlated with the most liked concentration, while Simple Sweet - Sucralose is not. The same trend is observed in the Single Solution tests. The liking score for the Single Solution Sucrose tests are significantly associated with both the preferred (r = 0.52) and the most liked (r = 0.64) concentrations, while the liking scores from the Single Solution Sucralose tests are not significantly associate with prefered and the most liked concentrations.

## Most-liked vs Preferred
Correlation(SS, log10(MostLiked), log10(Pref), "The relationship between the most liked and preferred sucrose concentration")
## `geom_smooth()` using formula 'y ~ x'

## Simple Sweet Sucralose vs Sucrose Slope
Correlation(SS, SS.Sucralose.Slope, SS.Sucrose.Slope, "The relationship between the slopes of the Simple Sweet - Sucrose and Sucralose tests")
## `geom_smooth()` using formula 'y ~ x'

## Single Sucralose vs Single Sucrose
Correlation(SS, Single.Sucralose, Single.Sucrose, "The relationship between the single solution Sucrose and Sucralose tests")
## `geom_smooth()` using formula 'y ~ x'

## Preference vs simplified procedures
  ## Simple Sweet - Sucrose
    Correlation(SS, log10(Pref), SS.Sucrose.Slope, "The relationship between the preferred sucrose concentration and Simple Sweet - Sucrose test")
## `geom_smooth()` using formula 'y ~ x'

  ## Simple Sweet - Sucralose
    Correlation(SS, log10(Pref), SS.Sucralose.Slope, "The relationship between the preferred sucrose concentration and Simple Sweet - Sucralose test")
## `geom_smooth()` using formula 'y ~ x'

  ## Single Solution - Sucrose
    Correlation(SS, log10(Pref), Single.Sucrose, "The relationship between the preferred sucrose concentration and Single Solution - Sucrose")
## `geom_smooth()` using formula 'y ~ x'

  ## Single Solution - Sucralose
    Correlation(SS, log10(Pref), Single.Sucralose, "The relationship between the preferred sucrose concentration and Single Solution - Sucralose")
## `geom_smooth()` using formula 'y ~ x'

  ## High concentration from Simple Sweet - Sucrose
    Correlation(SS, log10(Pref), SS.Sucrose_1050mM, "The relationship between the preferred sucrose concentration and the high concentraiton from the Simple Sweet - Sucrose test")
## `geom_smooth()` using formula 'y ~ x'

  ## High concentration from Simple Sweet - Sucralose
    Correlation(SS, log10(Pref), SS.Sucralose_1.5mM, "The relationship between the preferred sucrose concentration and the high concentraiton from the Simple Sweet - Sucralose test")    
## `geom_smooth()` using formula 'y ~ x'

## Most-liked vs simplified procedures
  ## Simple Sweet - Sucrose
    Correlation(SS, log10(MostLiked), SS.Sucrose.Slope, "The relationship between the most liked sucrose concentration and Simple Sweet - Sucrose test")
## `geom_smooth()` using formula 'y ~ x'

  ## Simple Sweet - Sucralose
    Correlation(SS, log10(MostLiked), SS.Sucralose.Slope, "The relationship between the most liked sucralose concentration and Simple Sweet - Sucralose test")
## `geom_smooth()` using formula 'y ~ x'

  ## Single Solution - Sucrose
    Correlation(SS, log10(MostLiked), Single.Sucrose, "The relationship between the most liked sucrose concentration and Single Solution - Sucrose")
## `geom_smooth()` using formula 'y ~ x'

  ## Single Solution - Sucralose
    Correlation(SS, log10(MostLiked), Single.Sucralose, "The relationship between the most liked sucralose concentration and Single Solution - Sucralose")
## `geom_smooth()` using formula 'y ~ x'

Correlations between sweet foods liking and psychophysical measures

Here comes the interesting part - correlating the Sweet Foods Liking (SFL) score from a validated survey by Val Duffy with the sweet hedonic measures. The most liked concentration is significantly correlated with the SFL score (r = 0.43), while the preferred sucrose concentration is not. Simple Sweet - Sucrose and Sucralose are both significantly correlated with the SFL score (r = 0.50 and r = 0.60, respectively). The Single Solution tests are also significantly correlated with the SFL scores (sucrose: r = 0.66, sucralose: r = 0.56)

## Sweet Foods Liking Questionnaire vs Psychophysical sweet tooth measures
  ## Most liked concentration
    Correlation(SS, Sweet.Liking, MostLiked, "The relationship between sweet foods liking and most liked sucrose concentration")
## `geom_smooth()` using formula 'y ~ x'

  ## Preferred concentration
    Correlation(SS, Sweet.Liking, Pref, "The relationship between sweet foods liking and preferred sucrose concentration")
## `geom_smooth()` using formula 'y ~ x'

  ## Simple Sweet - Sucrose
    Correlation(SS, Sweet.Liking, SS.Sucrose.Slope, "The relationship between sweet foods liking and Simple Sweet - Sucrose")
## `geom_smooth()` using formula 'y ~ x'

  ## Simple Sweet - Sucralose
    Correlation(SS, Sweet.Liking, SS.Sucralose.Slope, "The relationship between sweet foods liking and Simple Sweet - Sucralose")
## `geom_smooth()` using formula 'y ~ x'

  ## Single Solution - Sucrose
    Correlation(SS, Sweet.Liking, Single.Sucrose, "The relationship between sweet foods liking and Single Solution - Sucrose")
## `geom_smooth()` using formula 'y ~ x'

  ## Single Solution - Sucralose
    Correlation(SS, Sweet.Liking, Single.Sucralose, "The relationship between sweet foods liking and Single Solution - Sucralose")
## `geom_smooth()` using formula 'y ~ x'

  ## High Concentration Simple Sweet - Sucrose
    Correlation(SS, Sweet.Liking, SS.Sucrose_1050mM, "The relationship between sweet foods liking and the high concentraiton in the Simple Sweet - Sucrose test")
## `geom_smooth()` using formula 'y ~ x'

  ## High Concentration Simple Sweet - Sucralose
    Correlation(SS, Sweet.Liking, SS.Sucralose_1.5mM, "The relationship between sweet foods liking and the high concentraiton in the Simple Sweet - Sucralose test")
## `geom_smooth()` using formula 'y ~ x'

Differences across the strength of correlation between SFL and sweet hedonic measures

The strength of correlation between the SFL score and Single Solution - Sucrose test is significantly better than the correlation between SFL score and preferred concentration. The strength of correlation between the SFL score and high concentration from the Simple Sweet - Sucrose test is also significantly better than the correlation between SFL score and preferred concentration.

n <- 21

## Correlation coefficients between SFL scores and sweet hedonic measures
Pref <- 0.26
MostLiked <- 0.43
SSsucrose <- 0.5
SSsucralose <- 0.6
SingleSucrose <- 0.66
SingleSucralose <- 0.56
SSsucrose.High <- 0.66
SSsucralose.High <- 0.54

## SFL/Pref vs SFL/most liked
  cocor.dep.groups.overlap(Pref, MostLiked, 0.78, n) ## Not significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.26 and r.jh = 0.43
## Difference: r.jk - r.jh = -0.17
## Related correlation: r.kh = 0.78
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = -1.2585, p-value = 0.2082
##   Null hypothesis retained
## 
## hotelling1940: Hotelling's t (1940)
##   t = -1.2152, df = 18, p-value = 0.2400
##   Null hypothesis retained
## 
## williams1959: Williams' t (1959)
##   t = -1.2141, df = 18, p-value = 0.2404
##   Null hypothesis retained
## 
## olkin1967: Olkin's z (1967)
##   z = -1.2585, p-value = 0.2082
##   Null hypothesis retained
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = -1.1792, p-value = 0.2383
##   Null hypothesis retained
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = -1.2152, df = 18, p-value = 0.2400
##   Null hypothesis retained
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = -1.1721, p-value = 0.2412
##   Null hypothesis retained
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = -1.1679, p-value = 0.2428
##   Null hypothesis retained
##   95% confidence interval for r.jk - r.jh: -0.5190 0.1314
##   Null hypothesis retained (Interval includes 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = -1.1710, p-value = 0.2416
##   Null hypothesis retained
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.4699 0.1145
##   Null hypothesis retained (Interval includes 0)
## SFL/SS.Sucrose vs SFL/SS.Sucralose
  cocor.dep.groups.overlap(SSsucrose, SSsucralose, 0.67, n) ## Not significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.5 and r.jh = 0.6
## Difference: r.jk - r.jh = -0.1
## Related correlation: r.kh = 0.67
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = -0.6974, p-value = 0.4856
##   Null hypothesis retained
## 
## hotelling1940: Hotelling's t (1940)
##   t = -0.6619, df = 18, p-value = 0.5164
##   Null hypothesis retained
## 
## williams1959: Williams' t (1959)
##   t = -0.6572, df = 18, p-value = 0.5194
##   Null hypothesis retained
## 
## olkin1967: Olkin's z (1967)
##   z = -0.6974, p-value = 0.4856
##   Null hypothesis retained
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = -0.6526, p-value = 0.5140
##   Null hypothesis retained
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = -0.6619, df = 18, p-value = 0.5164
##   Null hypothesis retained
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = -0.6511, p-value = 0.5150
##   Null hypothesis retained
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = -0.6502, p-value = 0.5155
##   Null hypothesis retained
##   95% confidence interval for r.jk - r.jh: -0.5774 0.2897
##   Null hypothesis retained (Interval includes 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = -0.6503, p-value = 0.5155
##   Null hypothesis retained
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.4415 0.2133
##   Null hypothesis retained (Interval includes 0)
## SFL/Single.Sucrose vs SFL/Single.Sucralose
  cocor.dep.groups.overlap(SingleSucrose, SingleSucrose, 0.51, n) ## Not significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.66 and r.jh = 0.66
## Difference: r.jk - r.jh = 0
## Related correlation: r.kh = 0.51
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = 0.0000, p-value = 1.0000
##   Null hypothesis retained
## 
## hotelling1940: Hotelling's t (1940)
##   t = 0.0000, df = 18, p-value = 1.0000
##   Null hypothesis retained
## 
## williams1959: Williams' t (1959)
##   t = 0.0000, df = 18, p-value = 1.0000
##   Null hypothesis retained
## 
## olkin1967: Olkin's z (1967)
##   z = 0.0000, p-value = 1.0000
##   Null hypothesis retained
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = 0.0000, p-value = 1.0000
##   Null hypothesis retained
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = 0.0000, df = 18, p-value = 1.0000
##   Null hypothesis retained
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = 0.0000, p-value = 1.0000
##   Null hypothesis retained
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = 0.0000, p-value = 1.0000
##   Null hypothesis retained
##   95% confidence interval for r.jk - r.jh: -0.5482 0.5482
##   Null hypothesis retained (Interval includes 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = 0.0000, p-value = 1.0000
##   Null hypothesis retained
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.3373 0.3373
##   Null hypothesis retained (Interval includes 0)
## SFL/Pref vs SFL/other hedonic measures
  ## SFL/Pref vs SFL/SS.Sucrose
  cocor.dep.groups.overlap(Pref, SSsucrose, 0.64, n) ## Not significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.26 and r.jh = 0.5
## Difference: r.jk - r.jh = -0.24
## Related correlation: r.kh = 0.64
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = -1.4272, p-value = 0.1535
##   Null hypothesis retained
## 
## hotelling1940: Hotelling's t (1940)
##   t = -1.3913, df = 18, p-value = 0.1811
##   Null hypothesis retained
## 
## williams1959: Williams' t (1959)
##   t = -1.3865, df = 18, p-value = 0.1825
##   Null hypothesis retained
## 
## olkin1967: Olkin's z (1967)
##   z = -1.4272, p-value = 0.1535
##   Null hypothesis retained
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = -1.3419, p-value = 0.1796
##   Null hypothesis retained
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = -1.3913, df = 18, p-value = 0.1811
##   Null hypothesis retained
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = -1.3301, p-value = 0.1835
##   Null hypothesis retained
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = -1.3213, p-value = 0.1864
##   Null hypothesis retained
##   95% confidence interval for r.jk - r.jh: -0.7033 0.1369
##   Null hypothesis retained (Interval includes 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = -1.3271, p-value = 0.1845
##   Null hypothesis retained
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.6030 0.1098
##   Null hypothesis retained (Interval includes 0)
  ## SFL/Pref vs SFL/SS.Sucralose
  cocor.dep.groups.overlap(Pref, SSsucralose, 0.33, n) ## Not significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.26 and r.jh = 0.6
## Difference: r.jk - r.jh = -0.34
## Related correlation: r.kh = 0.33
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = -1.5791, p-value = 0.1143
##   Null hypothesis retained
## 
## hotelling1940: Hotelling's t (1940)
##   t = -1.5629, df = 18, p-value = 0.1355
##   Null hypothesis retained
## 
## williams1959: Williams' t (1959)
##   t = -1.5295, df = 18, p-value = 0.1435
##   Null hypothesis retained
## 
## olkin1967: Olkin's z (1967)
##   z = -1.5791, p-value = 0.1143
##   Null hypothesis retained
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = -1.4852, p-value = 0.1375
##   Null hypothesis retained
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = -1.5626, df = 18, p-value = 0.1355
##   Null hypothesis retained
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = -1.4700, p-value = 0.1416
##   Null hypothesis retained
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = -1.4557, p-value = 0.1455
##   Null hypothesis retained
##   95% confidence interval for r.jk - r.jh: -1.0020 0.1479
##   Null hypothesis retained (Interval includes 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = -1.4630, p-value = 0.1435
##   Null hypothesis retained
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.7903 0.1082
##   Null hypothesis retained (Interval includes 0)
  ## SFL/Pref vs SFL/Single.Sucrose
  cocor.dep.groups.overlap(Pref, SingleSucrose, 0.52, n) ## Significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.26 and r.jh = 0.66
## Difference: r.jk - r.jh = -0.4
## Related correlation: r.kh = 0.52
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = -2.1753, p-value = 0.0296
##   Null hypothesis rejected
## 
## hotelling1940: Hotelling's t (1940)
##   t = -2.3251, df = 18, p-value = 0.0320
##   Null hypothesis rejected
## 
## williams1959: Williams' t (1959)
##   t = -2.2955, df = 18, p-value = 0.0339
##   Null hypothesis rejected
## 
## olkin1967: Olkin's z (1967)
##   z = -2.1753, p-value = 0.0296
##   Null hypothesis rejected
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = -2.1384, p-value = 0.0325
##   Null hypothesis rejected
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = -2.3248, df = 18, p-value = 0.0320
##   Null hypothesis rejected
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = -2.0936, p-value = 0.0363
##   Null hypothesis rejected
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = -2.0578, p-value = 0.0396
##   Null hypothesis rejected
##   95% confidence interval for r.jk - r.jh: -1.0284 -0.0250
##   Null hypothesis rejected (Interval does not include 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = -2.0725, p-value = 0.0382
##   Null hypothesis rejected
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.8043 -0.0324
##   Null hypothesis rejected (Interval does not include 0)
  ## SFL/Pref vs SFL/Single.Sucralose
  cocor.dep.groups.overlap(Pref, SingleSucralose, 0.18, n) ## Not significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.26 and r.jh = 0.56
## Difference: r.jk - r.jh = -0.3
## Related correlation: r.kh = 0.18
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = -1.2534, p-value = 0.2101
##   Null hypothesis retained
## 
## hotelling1940: Hotelling's t (1940)
##   t = -1.2232, df = 18, p-value = 0.2370
##   Null hypothesis retained
## 
## williams1959: Williams' t (1959)
##   t = -1.1851, df = 18, p-value = 0.2514
##   Null hypothesis retained
## 
## olkin1967: Olkin's z (1967)
##   z = -1.2534, p-value = 0.2101
##   Null hypothesis retained
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = -1.1644, p-value = 0.2442
##   Null hypothesis retained
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = -1.2229, df = 18, p-value = 0.2371
##   Null hypothesis retained
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = -1.1571, p-value = 0.2472
##   Null hypothesis retained
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = -1.1500, p-value = 0.2502
##   Null hypothesis retained
##   95% confidence interval for r.jk - r.jh: -0.9918 0.2583
##   Null hypothesis retained (Interval includes 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = -1.1540, p-value = 0.2485
##   Null hypothesis retained
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.7892 0.2033
##   Null hypothesis retained (Interval includes 0)
  ## SFL/Pref vs SFL/High concentration from SS.Sucrose
  cocor.dep.groups.overlap(Pref, SSsucrose.High, 0.45, n) ## Significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.26 and r.jh = 0.66
## Difference: r.jk - r.jh = -0.4
## Related correlation: r.kh = 0.45
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = -2.0606, p-value = 0.0393
##   Null hypothesis rejected
## 
## hotelling1940: Hotelling's t (1940)
##   t = -2.1571, df = 18, p-value = 0.0448
##   Null hypothesis rejected
## 
## williams1959: Williams' t (1959)
##   t = -2.1200, df = 18, p-value = 0.0482
##   Null hypothesis rejected
## 
## olkin1967: Olkin's z (1967)
##   z = -2.0606, p-value = 0.0393
##   Null hypothesis rejected
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = -2.0016, p-value = 0.0453
##   Null hypothesis rejected
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = -2.1567, df = 18, p-value = 0.0448
##   Null hypothesis rejected
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = -1.9658, p-value = 0.0493
##   Null hypothesis rejected
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = -1.9348, p-value = 0.0530
##   Null hypothesis retained
##   95% confidence interval for r.jk - r.jh: -1.0603 0.0068
##   Null hypothesis retained (Interval includes 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = -1.9475, p-value = 0.0515
##   Null hypothesis retained
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.8204 -0.0074
##   Null hypothesis rejected (Interval does not include 0)
  ## SFL/Pref vs SFL/High concentration from SS.Sucralose
  cocor.dep.groups.overlap(Pref, SSsucralose.High, 0.071, n) ## Not significant
## 
##   Results of a comparison of two overlapping correlations based on dependent groups
## 
## Comparison between r.jk = 0.26 and r.jh = 0.54
## Difference: r.jk - r.jh = -0.28
## Related correlation: r.kh = 0.071
## Group size: n = 21
## Null hypothesis: r.jk is equal to r.jh
## Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
## Alpha: 0.05
## 
## pearson1898: Pearson and Filon's z (1898)
##   z = -1.0965, p-value = 0.2729
##   Null hypothesis retained
## 
## hotelling1940: Hotelling's t (1940)
##   t = -1.0736, df = 18, p-value = 0.2972
##   Null hypothesis retained
## 
## williams1959: Williams' t (1959)
##   t = -1.0292, df = 18, p-value = 0.3170
##   Null hypothesis retained
## 
## olkin1967: Olkin's z (1967)
##   z = -1.0965, p-value = 0.2729
##   Null hypothesis retained
## 
## dunn1969: Dunn and Clark's z (1969)
##   z = -1.0148, p-value = 0.3102
##   Null hypothesis retained
## 
## hendrickson1970: Hendrickson, Stanley, and Hills' (1970) modification of Williams' t (1959)
##   t = -1.0732, df = 18, p-value = 0.2973
##   Null hypothesis retained
## 
## steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
##   z = -1.0101, p-value = 0.3125
##   Null hypothesis retained
## 
## meng1992: Meng, Rosenthal, and Rubin's z (1992)
##   z = -1.0055, p-value = 0.3146
##   Null hypothesis retained
##   95% confidence interval for r.jk - r.jh: -0.9970 0.3209
##   Null hypothesis retained (Interval includes 0)
## 
## hittner2003: Hittner, May, and Silver's (2003) modification of Dunn and Clark's z (1969) using a backtransformed average Fisher's (1921) Z procedure
##   z = -1.0082, p-value = 0.3133
##   Null hypothesis retained
## 
## zou2007: Zou's (2007) confidence interval
##   95% confidence interval for r.jk - r.jh: -0.7965 0.2581
##   Null hypothesis retained (Interval includes 0)

Correlations between demographics sweet tooth measures

I looked into whether there are significant relationships between demographics data such as age and BMI with any of the sweet hedonic measures and SFL score. I did not find any meaningful relationships, so I decided to not include any of these variables as a covariate for the prediction models.

## Age
SS$Age <- as.numeric(SS$Age)

  ## Preference
    Correlation(SS, Age, Pref, "The relationship between age and the preferred sucrose concentraiton")
## `geom_smooth()` using formula 'y ~ x'

  ## Most Liked
    Correlation(SS, Age, MostLiked, "The relationship between age and the most liked sucrose concentraiton")
## `geom_smooth()` using formula 'y ~ x'

  ## Simple Sweet
    ## Sucrose
      Correlation(SS, Age, SS.Sucrose.Slope, "The relationship between age and Simple Sweet - Sucrose test")
## `geom_smooth()` using formula 'y ~ x'

    ## Sucralose
      Correlation(SS, Age, SS.Sucralose.Slope, "The relationship between age and Simple Sweet - Sucralose test")
## `geom_smooth()` using formula 'y ~ x'

  ## Single 
    ## Sucrose
      Correlation(SS, Age, Single.Sucrose, "The relationship between age and Single Solution - Sucrose test")
## `geom_smooth()` using formula 'y ~ x'

    ## Sucralose
      Correlation(SS, Age, Single.Sucralose, "The relationship between age and Single Solution - Sucralose test")
## `geom_smooth()` using formula 'y ~ x'

  ## Sweet Liking Survey
      Correlation(SS, Age, Sweet.Liking, "The relationship between age and sweet food liking")
## `geom_smooth()` using formula 'y ~ x'

## BMI
  ## Preference
    Correlation(SS, BMI, Pref, "The relationship between BMI and the preferred sucrose concentraiton")
## `geom_smooth()` using formula 'y ~ x'

  ## Most Liked
    Correlation(SS, BMI, MostLiked, "The relationship between BMI and the most liked sucrose concentraiton")
## `geom_smooth()` using formula 'y ~ x'

  ## Simple Sweet
    ## Sucrose
      Correlation(SS, BMI, SS.Sucrose.Slope, "The relationship between BMI and Simple Sweet - Sucrose test")
## `geom_smooth()` using formula 'y ~ x'

    ## Sucralose
      Correlation(SS, BMI, SS.Sucralose.Slope, "The relationship between BMI and Simple Sweet - Sucralose test")
## `geom_smooth()` using formula 'y ~ x'

  ## Single 
    ## Sucrose
      Correlation(SS, BMI, Single.Sucrose, "The relationship between BMI and Single Solution - Sucrose test")
## `geom_smooth()` using formula 'y ~ x'

    ## Sucralose
      Correlation(SS, BMI, Single.Sucralose, "The relationship between BMI and Single Solution - Sucralose test")
## `geom_smooth()` using formula 'y ~ x'

  ## Sweet Liking Survey
      Correlation(SS, BMI, Sweet.Liking, "The relationship between BMI and sweet food liking")
## `geom_smooth()` using formula 'y ~ x'

Prediciting the preferred concentration using other sweet hedonic measures

Similar to previous findings, the most liked concentrations significantly predicts the preferred concentrations (r2 = 0.64). The using multiple linear regression on the two liking score from the Simple Sweet - Sucrose can also predict the preferred concentrations (r2 = 0.25), although it did not capture as much variance as I’ve hoped for. Using the slopes of Simple Sweet - Sucrose test scores yield a slightly better model (r2 = 0.26). Simple Sweet - Sucralose did not significantly predict the preferred concentrations. The Single Solution tests also did not predict the preferred concentrations.

## Preference as the DV
Pref.M1 <- lm(Pref ~ MostLiked, data = SS)
summary(Pref.M1)
## 
## Call:
## lm(formula = Pref ~ MostLiked, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -225.40  -65.47  -36.70  102.30  306.39 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  19.7969    91.3250   0.217    0.831    
## MostLiked     0.7626     0.1249   6.104 7.21e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 157.4 on 19 degrees of freedom
## Multiple R-squared:  0.6623, Adjusted R-squared:  0.6445 
## F-statistic: 37.26 on 1 and 19 DF,  p-value: 7.206e-06
Pref.M2 <- lm(Pref ~ SS.Sucrose_90mM + SS.Sucrose_1050mM, data = SS)
summary(Pref.M2)
## 
## Call:
## lm(formula = Pref ~ SS.Sucrose_90mM + SS.Sucrose_1050mM, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -416.96 -172.13   -8.06  198.89  355.14 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        542.835    197.244   2.752   0.0131 *
## SS.Sucrose_90mM     -8.528      3.553  -2.400   0.0274 *
## SS.Sucrose_1050mM    5.161      2.513   2.054   0.0548 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 228.9 on 18 degrees of freedom
## Multiple R-squared:  0.3234, Adjusted R-squared:  0.2482 
## F-statistic: 4.302 on 2 and 18 DF,  p-value: 0.02971
Pref.M3 <- lm(Pref ~ SS.Sucrose.Slope, data = SS)
summary(Pref.M3)
## 
## Call:
## lm(formula = Pref ~ SS.Sucrose.Slope, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -401.88 -159.21   13.08  173.94  393.14 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        388.65      71.94   5.402 3.26e-05 ***
## SS.Sucrose.Slope  5935.36    2095.70   2.832   0.0106 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 227.2 on 19 degrees of freedom
## Multiple R-squared:  0.2968, Adjusted R-squared:  0.2598 
## F-statistic: 8.021 on 1 and 19 DF,  p-value: 0.01065
Pref.M4 <- lm(Pref ~ SS.Sucralose_0.13mM + SS.Sucralose_1.5mM, data = SS)
summary(Pref.M4)
## 
## Call:
## lm(formula = Pref ~ SS.Sucralose_0.13mM + SS.Sucralose_1.5mM, 
##     data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -317.55 -237.69   16.81  131.14  416.93 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         811.9513   202.0559   4.018 0.000806 ***
## SS.Sucralose_0.13mM  -6.7068     3.9193  -1.711 0.104214    
## SS.Sucralose_1.5mM   -0.2664     2.8048  -0.095 0.925375    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 257.2 on 18 degrees of freedom
## Multiple R-squared:  0.1459, Adjusted R-squared:  0.05102 
## F-statistic: 1.538 on 2 and 18 DF,  p-value: 0.2418
Pref.M5 <- lm(Pref ~ SS.Sucralose.Slope, data = SS)
summary(Pref.M5)
## 
## Call:
## lm(formula = Pref ~ SS.Sucralose.Slope, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -321.17 -237.07   -9.09  209.10  491.23 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         510.669     67.747   7.538 4.01e-07 ***
## SS.Sucralose.Slope    2.584      3.484   0.742    0.467    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 267.1 on 19 degrees of freedom
## Multiple R-squared:  0.02814,    Adjusted R-squared:  -0.02301 
## F-statistic: 0.5502 on 1 and 19 DF,  p-value: 0.4673
Pref.M6 <- lm(Pref ~ Single.Sucrose, data = SS)
summary(Pref.M6)
## 
## Call:
## lm(formula = Pref ~ Single.Sucrose, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -376.36 -162.25  -41.84  130.07  510.67 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     209.810    181.325   1.157   0.2616  
## Single.Sucrose    5.315      2.817   1.887   0.0745 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 248.6 on 19 degrees of freedom
## Multiple R-squared:  0.1578, Adjusted R-squared:  0.1135 
## F-statistic: 3.561 on 1 and 19 DF,  p-value: 0.07454
Pref.M7 <- lm(Pref ~ Single.Sucralose, data = SS)
summary(Pref.M7)
## 
## Call:
## lm(formula = Pref ~ Single.Sucralose, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -393.29 -192.71  -34.61  226.48  529.94 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      509.5004   182.8947   2.786   0.0118 *
## Single.Sucralose   0.5149     3.3264   0.155   0.8786  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 270.7 on 19 degrees of freedom
## Multiple R-squared:  0.001259,   Adjusted R-squared:  -0.05131 
## F-statistic: 0.02396 on 1 and 19 DF,  p-value: 0.8786

Predicting Sweet Food Liking using sweet hedonic measures

Before talking about the results from this section, I want to point out that Julie Mennella found significant correlations between the preferred sucrose concentrations and the sugar content of the participants’ favorite cereal (r = 0.13) and beverage (r = 0.11), hence showing criterion validity for the Monell Forced-Choice Paired Comparison Preference Tracking procedure (https://pubmed.ncbi.nlm.nih.gov/21227904/). My results are showing that the preferred sucrose concentration is not associated with SFL score with n = 21. However, the most liked sucrose concentration can predict the SFL score (r2 = 0.14). The Simple Sweet - Sucrose and Sucralose tests both predicted the SFL score (r2 = 0.38 and r = 0.31, respectively). The Single Solution Sucrose and Sucralose tests also predicted the SFL score (r2 = 0.41 and r2 = 0.28, respectively).

## Sweet Food Liking as the DV
SFL.M1 <- lm(Sweet.Liking ~ Pref, data = SS)
summary(SFL.M1)
## 
## Call:
## lm(formula = Sweet.Liking ~ Pref, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -57.791 -20.751  -0.678  20.031  52.520 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 25.87695   14.43005   1.793   0.0889 .
## Pref         0.02813    0.02425   1.160   0.2604  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28.64 on 19 degrees of freedom
## Multiple R-squared:  0.06614,    Adjusted R-squared:  0.01699 
## F-statistic: 1.346 on 1 and 19 DF,  p-value: 0.2604
SFL.M2 <- lm(Sweet.Liking ~ MostLiked, data = SS)
summary(SFL.M2)
## 
## Call:
## lm(formula = Sweet.Liking ~ MostLiked, data = SS)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43.25 -22.83  -0.28  17.86  48.19 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 10.91998   15.49804   0.705   0.4896  
## MostLiked    0.04436    0.02120   2.092   0.0501 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26.72 on 19 degrees of freedom
## Multiple R-squared:  0.1873, Adjusted R-squared:  0.1445 
## F-statistic: 4.378 on 1 and 19 DF,  p-value: 0.05007
SFL.M3 <- lm(Sweet.Liking ~ SS.Sucrose_90mM + SS.Sucrose_1050mM, data = SS)
summary(SFL.M3)
## 
## Call:
## lm(formula = Sweet.Liking ~ SS.Sucrose_90mM + SS.Sucrose_1050mM, 
##     data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.940 -11.784  -4.142  16.663  43.208 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -18.96589   19.61936  -0.967  0.34651   
## SS.Sucrose_90mM     0.06381    0.35341   0.181  0.85874   
## SS.Sucrose_1050mM   0.92041    0.24998   3.682  0.00171 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.77 on 18 degrees of freedom
## Multiple R-squared:  0.4406, Adjusted R-squared:  0.3785 
## F-statistic:  7.09 on 2 and 18 DF,  p-value: 0.005361
SFL.M4 <- lm(Sweet.Liking ~ SS.Sucrose.Slope, data = SS)
summary(SFL.M4)
## 
## Call:
## lm(formula = Sweet.Liking ~ SS.Sucrose.Slope, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.062 -17.206  -9.727  15.658  50.216 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        26.115      8.123   3.215  0.00456 **
## SS.Sucrose.Slope  596.970    236.626   2.523  0.02072 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.65 on 19 degrees of freedom
## Multiple R-squared:  0.2509, Adjusted R-squared:  0.2115 
## F-statistic: 6.365 on 1 and 19 DF,  p-value: 0.02072
SFL.M5 <- lm(Sweet.Liking ~ SS.Sucralose_0.13mM + SS.Sucralose_1.5mM, data = SS)
summary(SFL.M5)
## 
## Call:
## lm(formula = Sweet.Liking ~ SS.Sucralose_0.13mM + SS.Sucralose_1.5mM, 
##     data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.421 -24.281   1.578  18.184  42.269 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)          20.1129    18.9137   1.063  0.30166   
## SS.Sucralose_0.13mM  -0.5674     0.3669  -1.547  0.13938   
## SS.Sucralose_1.5mM    0.8173     0.2625   3.113  0.00601 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.08 on 18 degrees of freedom
## Multiple R-squared:  0.3747, Adjusted R-squared:  0.3052 
## F-statistic: 5.392 on 2 and 18 DF,  p-value: 0.01462
SFL.M6 <- lm(Sweet.Liking ~ SS.Sucralose.Slope, data = SS)
summary(SFL.M6)
## 
## Call:
## lm(formula = Sweet.Liking ~ SS.Sucralose.Slope, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.221 -17.327  -0.198  18.961  38.896 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         30.9115     6.0047   5.148 5.72e-05 ***
## SS.Sucralose.Slope   1.0140     0.3088   3.284  0.00391 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23.67 on 19 degrees of freedom
## Multiple R-squared:  0.362,  Adjusted R-squared:  0.3284 
## F-statistic: 10.78 on 1 and 19 DF,  p-value: 0.003908
SFL.M7 <- lm(Sweet.Liking ~ Single.Sucrose, data = SS)
summary(SFL.M7)
## 
## Call:
## lm(formula = Sweet.Liking ~ Single.Sucrose, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.053 -16.397  -3.673  24.009  39.715 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -18.3919    16.2339  -1.133  0.27134   
## Single.Sucrose   0.9663     0.2522   3.832  0.00112 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.26 on 19 degrees of freedom
## Multiple R-squared:  0.4359, Adjusted R-squared:  0.4062 
## F-statistic: 14.68 on 1 and 19 DF,  p-value: 0.001125
SFL.M8 <- lm(Sweet.Liking ~ Single.Sucralose, data = SS)
summary(SFL.M8)
## 
## Call:
## lm(formula = Sweet.Liking ~ Single.Sucralose, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.993 -16.254  -6.413  15.310  51.733 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -5.5663    16.5403  -0.337  0.74016   
## Single.Sucralose   0.8942     0.3008   2.973  0.00782 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.48 on 19 degrees of freedom
## Multiple R-squared:  0.3174, Adjusted R-squared:  0.2815 
## F-statistic: 8.836 on 1 and 19 DF,  p-value: 0.007822
SFL.M9 <- lm(Sweet.Liking ~ SS.Sucrose_1050mM, data = SS)
summary(SFL.M9)
## 
## Call:
## lm(formula = Sweet.Liking ~ SS.Sucrose_1050mM, data = SS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.496 -12.504  -4.612  16.195  44.581 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -16.9634    15.7661  -1.076  0.29542   
## SS.Sucrose_1050mM   0.9277     0.2403   3.861  0.00105 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.18 on 19 degrees of freedom
## Multiple R-squared:  0.4396, Adjusted R-squared:  0.4101 
## F-statistic: 14.91 on 1 and 19 DF,  p-value: 0.001053