Social Media
May
2023-03-17
- Load packages and data set
- Cronbach’s alpha for food groups
- Scores decoding
- QQ plots
- Participant charateristics
- Correlations - demographics
- Correlations - sweet liking
- Correlations - sweet liking: females only
- Correlations - sugar intake
- Correlations - sugar intake: females only
- Correlations - SSB
- Correlations - fat sHEI score
- Correlations - fat sHEI score: females only
- Correlations - sweet + fat sHEI score
- Correlations - sweet + fat sHEI score: females only
- Correlations - sHEI
- Correlations - sHEI: females only
- Correlations - body appreciation
- Correlations - body appreciation: female only
- Partial correlation - controlling for age
- Models: interactions - sugar intake
- Models: mediation - sugar intake
- Models: interactions - sugar intake (females)
- Models: mediation - sugar intake (females)
- Models: interactions - sHEI
- Models: mediation - sHEI
- Models: interactions - sHEI (females)
- Models: mediation - sHEI (females)
- AEBQ emotional overeating analysis
- TFEQ emotional analysis
- TFEQ restraint analysis
- Liking hierarchial clustering
- sHEI hierarchial clustering
- Behavior hierarchial clustering
- AChemS poster graphs
- Social media poster graphs
setwd(“/Users/maycheung/Documents/Research/After BC/Social Media/Results”)
Load packages and data set
Cronbach’s alpha for food groups
## Vegetable
full.df %>%
select(starts_with("veg"))%>%
select(!"vegetable.day") %>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 6
## Sample units: 142
## alpha: 0.76## Fruit
full.df %>%
select(starts_with("fruit"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 6
## Sample units: 143
## alpha: 0.688## Salty/fat
full.df %>%
select(starts_with("saltyfat"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 8
## Sample units: 127
## alpha: 0.671## Alcohol
full.df %>%
select(starts_with("alcohol"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 122
## alpha: 0.777## Whole grain
full.df %>%
select(starts_with("wg"))%>%
select(!c("wg.day", "wg.freq"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 4
## Sample units: 142
## alpha: 0.76## Carbs
full.df %>%
select(starts_with("carbs"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 143
## alpha: 0.536## Healthy fats
full.df %>%
select(starts_with("healthyfat"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 4
## Sample units: 141
## alpha: 0.64## Sweets
full.df %>%
select(starts_with("sweets"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 144
## alpha: 0.74## Sugar-sweetened beverages and sweets
full.df %>%
select(starts_with(c("ssb", "sweet")))%>%
select(!c("ssb.day", "ssb.freq")) %>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 9
## Sample units: 128
## alpha: 0.666## Unhealthy fat
full.df %>%
select(starts_with("unhealthyfat"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 144
## alpha: 0.529## Protein
full.df %>%
select(starts_with("protein"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 114
## alpha: 0.528## Spicy
full.df %>%
select(starts_with("spicy"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 3
## Sample units: 137
## alpha: 0.694Scores decoding
## Sweet liking score
liking.df <- full.df %>%
select(starts_with(c("ID", "ssb", "sweets", "veg", "fruit", "saltyfat", "fatprotein", "alcohol", "wg", "carbs", "healthyfat", "unhealthyfat", "protein", "spicy")))%>%
select(!c("ssb.day", "ssb.freq", "vegetable.day", "fruit.day", "wg.day", "wg.freq")) %>%
mutate(sweet.liking = rowMeans(select(., "ssb.soda":"sweets.chocolate"), na.rm = TRUE),
veg.liking = rowMeans(select(., "veg.greens":"veg.carrots"), na.rm = TRUE),
fruit.liking = rowMeans(select(., "fruit.citrus":"fruit.melon"), na.rm = TRUE),
saltyfat.liking = rowMeans(select(., "saltyfat.snacks":"saltyfat.delimeats"), na.rm = TRUE),
fatprotein.liking = fatprotein.friedmeats,
alcohol.liking = rowMeans(select(., "alcohol.beer":"alcohol.cocktails"), na.rm = TRUE),
wg.liking = rowMeans(select(., "wg.bread":"wg.brownrice"), na.rm = TRUE),
carbs.liking = rowMeans(select(., "carbs.rice":"carbs.cereal"), na.rm = TRUE),
healthyfat.liking = rowMeans(select(., "healthyfat.nuts":"healthyfat.avocado"), na.rm = TRUE),
unhealthyfat.liking = rowMeans(select(., "unhealthyfat.cheese":"unhealthyfat.oil"), na.rm = TRUE),
protein.liking = rowMeans(select(., "protein.chicken":"protein.fish"), na.rm = TRUE),
spicy.liking = rowMeans(select(., "spicy.hotsauce":"spicy.mustard"), na.rm = TRUE)) %>%
select(ends_with(".liking"))
## Body appreciation scale
BAS.df <- full.df %>%
select("ID", starts_with("BAS")) %>%
mutate(BAS = rowSums(select(., 2:11), na.rm = TRUE))
## Three factor questionnaire
TFQ.df <- full.df %>%
select("ID", starts_with("TFQ")) %>%
mutate(uncontrolled = rowSums(select(., "TFQ.03", "TFQ.06", "TFQ.08", "TFQ.09", "TFQ.12", "TFQ.13", "TFQ.15", "TFQ.19", "TFQ.20"), na.rm = TRUE)) %>%
mutate(restraint = rowSums(select(., "TFQ.01", "TFQ.05", "TFQ.11", "TFQ.17", "TFQ.18", "TFQ.21"), na.rm = TRUE)) %>%
mutate(emotional = rowSums(select(., "TFQ.02", "TFQ.04", "TFQ.07", "TFQ.10", "TFQ.14", "TFQ.16"), na.rm = TRUE))
## Adult eating behavior questionnaire
AEBQ.df <- full.df %>%
select("ID", starts_with("AEBQ")) %>%
mutate(EF = rowSums(select(., "AEBQ.01", "AEBQ.03", "AEBQ.04"), na.rm = TRUE)) %>%
mutate(EOE = rowSums(select(., "AEBQ.05", "AEBQ.08", "AEBQ.10", "AEBQ.16"), na.rm = TRUE)) %>%
mutate(EUE = rowSums(select(., "AEBQ.15", "AEBQ.20", "AEBQ.27", "AEBQ.35"), na.rm = TRUE)) %>%
mutate(FF = rowSums(select(., "AEBQ.02", "AEBQ.07", "AEBQ.12", "AEBQ.19", "AEBQ.24"), na.rm = TRUE)) %>%
mutate(FR = rowSums(select(., "AEBQ.13", "AEBQ.22", "AEBQ.33"), na.rm = TRUE)) %>%
mutate(SE = rowSums(select(., "AEBQ.14", "AEBQ.25", "AEBQ.26", "AEBQ.29"), na.rm = TRUE)) %>%
mutate(H = rowSums(select(., "AEBQ.06", "AEBQ.09", "AEBQ.28", "AEBQ.32", "AEBQ.34"), na.rm = TRUE)) %>%
mutate(SR = rowSums(select(., "AEBQ.11", "AEBQ.23", "AEBQ.30", "AEBQ.31"), na.rm = TRUE))
## BMI
BMI.df <- full.df %>%
select("ID", "weight", "height") %>%
mutate(BMI = weight/(height^2)*703)
## sHEI score
HEI.df <- full.df %>%
select("ID", "sex", ends_with(c(".day", ".freq", ".amount"))) %>%
mutate(fruit.1 = case_when(
fruit.day == 1 ~ 0,
fruit.day == 2 ~ 2,
fruit.day == 3 ~ 3.5,
fruit.day >= 4 ~ 5)) %>%
mutate(fruit.2 = case_when (
juice.day == 1 ~ 0,
juice.day == 2 ~ 2,
juice.day == 3 ~ 3.5,
juice.day >= 4 ~ 5)) %>%
mutate(tfruitHEI = case_when(
fruit.1 + fruit.2 <5 ~ 0,
fruit.1 + fruit.2 >= 5 ~ 5)) %>%
mutate(wfruitHEI = case_when(
fruit.day == 1 ~ 0,
fruit.day == 2 ~ 2.5,
fruit.day >= 3 ~ 5)) %>%
mutate(vegHEI = case_when(
greens.day == 1 ~ 1.6,
greens.day == 2 & starchy.day >= 2 ~ 2.46,
greens.day >= 2 & starchy.day >= 2 ~ 3.24,
greens.day >= 2 & starchy.day == 1 ~ 3.56)) %>%
mutate(bean.1 = case_when(
greens.day == 1 ~ 0,
greens.day >= 2 ~5)) %>%
mutate(bean.2 = case_when(
beans.day == 1 ~ 0,
beans.day >= 2 ~ 5)) %>%
mutate(beanHEI = case_when(
bean.1 + bean.2 < 5 ~ 0,
bean.1 + bean.2 >= 5 ~ 5)) %>%
mutate(wgHEI = case_when(
wg.day == 1 ~ 0.51,
sex == 1 & wg.day >= 2 ~ 2.97,
sex == 2 & wg.day >= 2 & wg.day <= 3 ~ 5.20,
sex == 2 & wg.day >= 4 ~ 6.94)) %>%
mutate(dairyHEI = case_when(
sex == 1 & milk.day <= 3 ~ 3.22,
sex == 2 & milk.day <= 3 & lfmilk.day == 1 ~ 3.32,
sex == 2 & milk.day <= 3 & lfmilk.day >= 2 ~ 4.81,
milk.day >= 4 ~ 6.51)) %>%
mutate(tproteinHEI = case_when(
sex == 1 & seafood.freq <= 4 ~ 4.11,
sex == 1 & seafood.freq >= 5 ~ 4.98,
sex == 1 & is.na(seafood.freq) ~ 4.11,
sex == 2 ~ 4.97)) %>%
mutate(sfpproteinHEI = case_when(
sex == 1 & nuts.day <= 2 ~ 0.49,
sex == 2 & nuts.day <= 2 ~ 1.50,
nuts.day >= 3 ~ 4.20)) %>%
mutate(farHEI = case_when(
milk.day >= 4 ~ 2.56,
fat.amount >= 2 & fat.amount <= 3 & milk.freq >= 1 & lfmilk.freq <= 2 ~ 2.63,
fat.amount >= 2 & fat.amount <= 3 & is.na(milk.freq) | is.na(lfmilk.freq) ~ 2.63,
fat.amount >= 2 & fat.amount <= 3 & milk.freq >= 1 & lfmilk.freq >= 3 ~ 4.54,
fat.amount == 1 & milk.freq >= 1 ~ 5.93,
fat.amount == 1 & is.na(milk.freq) ~ 5.93)) %>%
mutate(grainHEI = case_when(
greens.day == 1 ~ 2.13,
grains.day >= 3 & seafood.day >= 2 & greens.day >= 2 ~ 2.27,
grains.day >= 3 & nuts.day >= 1 & nuts.day <= 2 & seafood.day == 1 & greens.day >= 2 ~ 4.73,
grains.day >= 3 & nuts.day >= 3 & seafood.day == 1 & greens.day >= 2 ~ 8.45,
grains.day >= 1 & grains.day <= 2 & greens.day >= 2 ~ 9.25)) %>%
mutate(sodiumHEI = case_when(
fruit.day >= 1 & fruit.day <= 2 & grains.day >= 3 & water.amount == 3 ~ 0.70,
fruit.day >= 3 & grains.day >= 3 & water.amount == 3 ~ 2.30,
grains.day >= 3 & water.amount >= 1 & water.amount <= 2 ~ 4.94,
grains.day >= 1 & grains.day <= 2 ~ 6.07)) %>%
mutate(ssb.day.cal = case_when(
ssb.day == 1 ~ 0,
ssb.day == 2 ~ 156,
ssb.day == 3 ~ 312,
ssb.day == 4 ~ 468,
ssb.day == 5 ~ 624,
ssb.day == 6 ~ 780,
ssb.day == 7 ~ 936),
sugar.cal = case_when(
sugars.amount == 1 ~ 130,
sugars.amount == 2 ~ 260,
sugars.amount == 3 ~ 520)) %>%
mutate(sugar.intake = (ssb.day.cal + sugar.cal)) %>%
mutate(sugarHEI = case_when(
sugar.intake <= 130 ~ 10,
sugar.intake > 130 & sugar.intake < 520 ~ 5,
sugar.intake >= 520 ~ 0)) %>%
mutate(fatHEI = case_when(
ssb.day >= 3 ~ 1.82,
ssb.day <= 2 & grains.day <= 2 ~ 3.20,
ssb.day <= 2 & grains.day >= 3 & nuts.day <= 2 ~ 4.64,
ssb.day <= 2 & grains.day >= 3 & nuts.day >= 3 ~ 6.56)) %>%
select("ID", "sugar.intake", "sugars.amount",ends_with("HEI")) %>%
mutate(sHEI = rowSums(select(., "tfruitHEI":"fatHEI"), na.rm = TRUE)) %>%
mutate(SuFatHEI = rowSums(select(., c("sugarHEI", "fatHEI"))))
## Social media usage
SM.df <- full.df %>%
select("ID", "instagram")
## Putting scores together
EB.df <- bind_cols(select(full.df, 1:17, "ssb.day", "sugars.amount"), select(BMI.df, "BMI"), select(liking.df, "sweet.liking"), select(HEI.df, c("sugar.intake", "sugarHEI", "fatHEI", "SuFatHEI", "sHEI")), select(BAS.df, "BAS"), select(TFQ.df, c("uncontrolled", "restraint", "emotional")), select(AEBQ.df, c("EF", "EOE", "EUE", "FF", "FR", "SE", "H", "SR")), select(SM.df, "instagram")) %>%
mutate(emo = rowSums(select(., c("emotional", "EOE"))))
## EB: females only
EB.fem.df <- EB.df %>% filter(sex == "2")
## EB: males only
EB.mal.df <- EB.df %>% filter(sex == "1")QQ plots
## Distribution
qqfunc <- function(data, sample, title) {
ggplot(data = data, aes(sample = {{sample}})) +
stat_qq() +
stat_qq_line() +
ggtitle(title) +
theme_light()
}
## Age qqplot
qqfunc(EB.df, age, "QQ plot - Age")
## BMI qqplot
qqfunc(EB.df, BMI, "QQ plot - BMI")
## Sweet liking qqplot
qqfunc(EB.df, sweet.liking, "QQ plot - Sweet Liking")
## Sweet intake qqplot
qqfunc(EB.df, sugar.intake, "QQ plot - Sugar Intake")
## Sugar and fat intake score qqplot
qqfunc(EB.df, SuFatHEI, "QQ plot - Emotional Eating")
## sHEI
qqfunc(EB.df, sHEI, "QQ plot - sHEI")
## BAS
qqfunc(EB.df, BAS, "QQ plot - BAS")
## Uncontrolled eating
qqfunc(EB.df, uncontrolled, "QQ plot - Uncontrolled Eating")
## Emotional eating
qqfunc(EB.df, emotional, "QQ plot - Emotional Eating")
Participant charateristics
## summary stat
EB.df %>%
get_summary_stats()## # A tibble: 36 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 age 145 18 68 24 21 28 7 4.45 27.2 9.64 0.801
## 2 height 145 53 76 65 63 68 5 4.08 65.3 3.90 0.324
## 3 weight 145 80 320 145 126 175 49 34.1 154. 38.5 3.20
## 4 sex 145 1 2 2 1 2 1 0 1.70 0.461 0.038
## 5 race 141 1 7 4 3 5 2 1.48 4.25 1.50 0.126
## 6 ethnici… 145 1 2 2 2 2 0 0 1.75 0.434 0.036
## 7 us.born 145 1 2 1 1 2 1 0 1.34 0.477 0.04
## 8 agetous 145 1 7 1 1 4 3 0 2.44 2.20 0.183
## 9 timeinus 145 1 8 1 1 3 2 0 2.32 2.19 0.182
## 10 educati… 145 5 12 9 6 9 3 1.48 7.96 1.95 0.162
## # … with 26 more rows, and 1 more variable: ci <dbl>EB.df %>%
count(race) ## race n
## 1 1 6
## 2 2 2
## 3 3 36
## 4 4 48
## 5 5 27
## 6 7 22
## 7 NA 4EB.df %>%
count(ethnicity) ## ethnicity n
## 1 1 36
## 2 2 109EB.df %>%
count(sex) ## sex n
## 1 1 44
## 2 2 101EB.df %>%
group_by(race) %>%
select(race, sweet.liking, sugar.intake) %>%
get_summary_stats()## # A tibble: 14 × 14
## race variable n min max median q1 q3 iqr mad mean
## <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 sweet.liking 6 7.44 42.3 29.3 27.7 31.5 3.83 3.71 28.0
## 2 1 sugar.intake 6 130 728 416 299 416 117 116. 394.
## 3 2 sweet.liking 2 46.4 73.6 60 53.2 66.8 13.6 20.1 60
## 4 2 sugar.intake 2 260 1144 702 481 923 442 655. 702
## 5 3 sweet.liking 36 -22.9 85 32.6 18.1 49.0 30.9 22.8 31.3
## 6 3 sugar.intake 36 130 1300 416 260 520 260 231. 405.
## 7 4 sweet.liking 48 -40.1 77.8 37.8 12.6 50.9 38.4 24.7 32.0
## 8 4 sugar.intake 48 130 1144 260 260 520 260 193. 387.
## 9 5 sweet.liking 27 -12 66.9 40.1 22.2 51.8 29.7 22.7 35.1
## 10 5 sugar.intake 27 130 754 260 260 416 156 193. 351.
## 11 7 sweet.liking 22 -62.1 92 40.3 22.1 53.9 31.9 25.2 36.5
## 12 7 sugar.intake 22 130 1300 416 260 572 312 231. 488.
## 13 NA sweet.liking 4 15.1 39.1 27.2 18.7 35.6 16.9 14.3 27.1
## 14 NA sugar.intake 4 130 832 338 228. 520 292. 212. 410.
## # … with 3 more variables: sd <dbl>, se <dbl>, ci <dbl>## differences by race
race.liking.aov <- aov(sweet.liking ~ race, EB.df)
summary(race.liking.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## race 1 321 320.7 0.484 0.488
## Residuals 139 92174 663.1
## 4 observations deleted due to missingnessrace.intake.aov <- aov(sugar.intake ~ race, EB.df)
summary(race.intake.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## race 1 29015 29015 0.458 0.5
## Residuals 139 8803573 63335
## 4 observations deleted due to missingnessrace.sHEI.aov <- aov(sHEI ~ race, EB.df)
summary(race.sHEI.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## race 1 160 159.98 1.887 0.172
## Residuals 139 11786 84.79
## 4 observations deleted due to missingness## Models
sugar.M1 <- lm(sugar.intake ~ sweet.liking, EB.df)
summary(sugar.M1)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -428.62 -144.70 -21.13 83.98 743.90
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.0349 30.6945 8.309 6.75e-14 ***
## sweet.liking 4.5387 0.7354 6.172 6.58e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 224.3 on 143 degrees of freedom
## Multiple R-squared: 0.2103, Adjusted R-squared: 0.2048
## F-statistic: 38.09 on 1 and 143 DF, p-value: 6.582e-09sugar.M2 <- lm(sugar.intake ~ sweet.liking + sweet.liking*race + race, EB.df)
summary(sugar.M2)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * race +
## race, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -439.44 -152.86 -16.04 77.66 782.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 326.3580 95.8449 3.405 0.000868 ***
## sweet.liking 1.7593 2.3184 0.759 0.449242
## race -15.9746 21.0991 -0.757 0.450275
## sweet.liking:race 0.6138 0.4927 1.246 0.215012
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 223.8 on 137 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.2232, Adjusted R-squared: 0.2062
## F-statistic: 13.12 on 3 and 137 DF, p-value: 1.396e-07sHEI.M1 <- lm(sHEI ~ sweet.liking, EB.df)
summary(sHEI.M1)##
## Call:
## lm(formula = sHEI ~ sweet.liking, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.0825 -5.5327 0.7146 5.4522 20.4391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.69812 1.20984 41.905 < 2e-16 ***
## sweet.liking -0.10777 0.02899 -3.718 0.000288 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.841 on 143 degrees of freedom
## Multiple R-squared: 0.08814, Adjusted R-squared: 0.08177
## F-statistic: 13.82 on 1 and 143 DF, p-value: 0.0002876sHEI.M2 <- lm(sHEI ~ sweet.liking + sweet.liking*race + race, EB.df)
summary(sHEI.M2)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * race + race,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.0646 -5.6944 0.8049 6.0326 19.1088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.31229 3.77252 12.806 <2e-16 ***
## sweet.liking 0.03538 0.09125 0.388 0.699
## race 0.44579 0.83047 0.537 0.592
## sweet.liking:race -0.03081 0.01939 -1.589 0.114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.809 on 137 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.1102, Adjusted R-squared: 0.09067
## F-statistic: 5.653 on 3 and 137 DF, p-value: 0.001109Correlations - demographics
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - the purpose is to determine covariates
## Age X BMI
corrfunc(EB.df, age, BMI, "Correlation - Age X BMI")## `geom_smooth()` using formula = 'y ~ x'
## Age X personal income
corrfunc(EB.df, age, income.personal, "Correlation - Age X Personal Income") ## don't use personal income as a covariate - use age instead to avoid multicolinearity## `geom_smooth()` using formula = 'y ~ x'
## Age X household income
corrfunc(EB.df, age, income.household, "Correlation - Age X Household Income")## `geom_smooth()` using formula = 'y ~ x'
## Age X instagram usage
corrfunc(EB.df, age, instagram, "Correlation - Age X Instagram Usage")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sweet liking
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugar intake and traits
## Age X sweet liking
corrfunc(EB.df, age, sweet.liking, "Correlation - Age X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Sex X sweet liking
corrfunc(EB.df, sex, sweet.liking, "Correlation - Sex X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sweet liking
corrfunc(EB.df, BMI, sweet.liking, "Correlation - BMI X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Race X sweet liking
corrfunc(EB.df, race, sweet.liking, "Correlation - Race X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
## Household income X sweet liking
corrfunc(EB.df, income.household, sweet.liking, "Correlation - Income X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X sweet liking
corrfunc(EB.df, instagram, sweet.liking, "Correlation - Instagram Usage X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X sweet liking
corrfunc(EB.df, sugar.intake, sweet.liking, "Correlation - Sugar Intake X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sweet liking
corrfunc(EB.df, ssb.day, sweet.liking, "Correlation - SSB X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sweet liking
corrfunc(EB.df, emotional, sweet.liking, "Correlation - Emotional Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sweet liking
corrfunc(EB.df, uncontrolled, sweet.liking, "Correlation - Uncontrolled Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sweet liking
corrfunc(EB.df, restraint, sweet.liking, "Correlation - Dietary Restraint X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sweet liking
corrfunc(EB.df, EF, sweet.liking, "Correlation - Enjoyment of Food X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sweet liking
corrfunc(EB.df, EOE, sweet.liking, "Correlation - Emotional Over-Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sweet liking
corrfunc(EB.df, EUE, sweet.liking, "Correlation - Emotional Under-Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sweet liking
corrfunc(EB.df, FF, sweet.liking, "Correlation - Food fussiness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sweet liking
corrfunc(EB.df, FR, sweet.liking, "Correlation - Food Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sweet liking
corrfunc(EB.df, SE, sweet.liking, "Correlation - Slowness in Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sweet liking
corrfunc(EB.df, H, sweet.liking, "Correlation - Hunger X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sweet liking
corrfunc(EB.df, SR, sweet.liking, "Correlation - Satiety Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sweet liking
corrfunc(EB.df, restraint, emotional, "Correlation - Satiety Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sweet liking: females only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugar intake and traits
## Age X sweet liking
corrfunc(EB.fem.df, age, sweet.liking, "Correlation - Age X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sweet liking
corrfunc(EB.fem.df, BMI, sweet.liking, "Correlation - BMI X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Race X sweet liking
corrfunc(EB.fem.df, race, sweet.liking, "Correlation - Race X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 3 rows containing non-finite values (`stat_cor()`).## Warning: Removed 3 rows containing missing values (`geom_point()`).
## Household income X sweet liking
corrfunc(EB.fem.df, income.household, sweet.liking, "Correlation - Income X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X sweet liking
corrfunc(EB.fem.df, instagram, sweet.liking, "Correlation - Instagram Usage X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X sweet liking
corrfunc(EB.fem.df, sugar.intake, sweet.liking, "Correlation - Sugar Intake X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sweet liking
corrfunc(EB.fem.df, ssb.day, sweet.liking, "Correlation - SSB X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sweet liking
corrfunc(EB.fem.df, emotional, sweet.liking, "Correlation - Emotional Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sweet liking
corrfunc(EB.fem.df, uncontrolled, sweet.liking, "Correlation - Uncontrolled Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sweet liking
corrfunc(EB.fem.df, restraint, sweet.liking, "Correlation - Dietary Restraint X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sweet liking
corrfunc(EB.fem.df, EF, sweet.liking, "Correlation - Enjoyment of Food X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sweet liking
corrfunc(EB.fem.df, EOE, sweet.liking, "Correlation - Emotional Over-Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sweet liking
corrfunc(EB.fem.df, EUE, sweet.liking, "Correlation - Emotional Under-Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sweet liking
corrfunc(EB.fem.df, FF, sweet.liking, "Correlation - Food fussiness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sweet liking
corrfunc(EB.fem.df, FR, sweet.liking, "Correlation - Food Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sweet liking
corrfunc(EB.fem.df, SE, sweet.liking, "Correlation - Slowness in Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sweet liking
corrfunc(EB.fem.df, H, sweet.liking, "Correlation - Hunger X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sweet liking
corrfunc(EB.fem.df, SR, sweet.liking, "Correlation - Satiety Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sweet liking
corrfunc(EB.fem.df, emo, sweet.liking, "Correlation - Emo X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sugar intake
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugar intake and traits
## Age X sugar intake
corrfunc(EB.df, age, sugar.intake, "Correlation - Age X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Sex X sugar intake
corrfunc(EB.df, sex, sugar.intake, "Correlation - Sex X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sugar intake
corrfunc(EB.df, BMI, sugar.intake, "Correlation - BMI X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Race X sweet liking
corrfunc(EB.df, race, sugar.intake, "Correlation - Race X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
## Household income X sweet liking
corrfunc(EB.df, income.household, sugar.intake, "Correlation - Income X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X sweet liking
corrfunc(EB.df, instagram, sugar.intake, "Correlation - Instagram Usage X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X sweet liking
corrfunc(EB.df, sugar.intake, sweet.liking, "Correlation - Sweet Liking X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sugar intake
corrfunc(EB.df, ssb.day, sugar.intake, "Correlation - SSB X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sugar intake
corrfunc(EB.df, emotional, sugar.intake, "Correlation - Emotional Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sugar intake
corrfunc(EB.df, uncontrolled, sugar.intake, "Correlation - Uncontrolled Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sugar intake
corrfunc(EB.df, restraint, sugar.intake, "Correlation - Dietary Restraint X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sugar intake
corrfunc(EB.df, EF, sugar.intake, "Correlation - Enjoyment of Food X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sugar intake
corrfunc(EB.df, EOE, sugar.intake, "Correlation - Emotional Over-Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sugar intake
corrfunc(EB.df, EUE, sugar.intake, "Correlation - Emotional Under-Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sugar intake
corrfunc(EB.df, FF, sugar.intake, "Correlation - Food fussiness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sugar intake
corrfunc(EB.df, FR, sugar.intake, "Correlation - Food Responsiveness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sugar intake
corrfunc(EB.df, SE, sugar.intake, "Correlation - Slowness in Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sugar intake
corrfunc(EB.df, H, sugar.intake, "Correlation - Hunger X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sugar intake
corrfunc(EB.df, SR, sugar.intake, "Correlation - Satiety Responsiveness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sweet liking
corrfunc(EB.df, emo, sugar.intake, "Correlation - Emo X Sweet Intake")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sugar intake: females only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugar intake and traits
## Age X sugar intake
corrfunc(EB.fem.df, age, sugar.intake, "Correlation - Age X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sugar intake
corrfunc(EB.fem.df, BMI, sugar.intake, "Correlation - BMI X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Race X sweet liking
corrfunc(EB.fem.df, race, sugar.intake, "Correlation - Race X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 3 rows containing non-finite values (`stat_cor()`).## Warning: Removed 3 rows containing missing values (`geom_point()`).
## Household income X sweet liking
corrfunc(EB.fem.df, income.household, sugar.intake, "Correlation - Income X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X sweet liking
corrfunc(EB.fem.df, instagram, sugar.intake, "Correlation - Instagram Usage X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X sugar intake
corrfunc(EB.fem.df, sweet.liking, sugar.intake, "Correlation - Sweet Liking X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sugar intake
corrfunc(EB.fem.df, emotional, sugar.intake, "Correlation - Emotional Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sugar intake
corrfunc(EB.fem.df, uncontrolled, sugar.intake, "Correlation - Uncontrolled Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sugar intake
corrfunc(EB.fem.df, restraint, sugar.intake, "Correlation - Dietary Restraint X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sugar intake
corrfunc(EB.fem.df, EF, sugar.intake, "Correlation - Enjoyment of Food X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sugar intake
corrfunc(EB.fem.df, EOE, sugar.intake, "Correlation - Emotional Over-Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sugar intake
corrfunc(EB.fem.df, EUE, sugar.intake, "Correlation - Emotional Under-Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sugar intake
corrfunc(EB.fem.df, FF, sugar.intake, "Correlation - Food fussiness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sugar intake
corrfunc(EB.fem.df, FR, sugar.intake, "Correlation - Food Responsiveness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sugar intake
corrfunc(EB.fem.df, SE, sugar.intake, "Correlation - Slowness in Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sugar intake
corrfunc(EB.fem.df, H, sugar.intake, "Correlation - Hunger X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sugar intake
corrfunc(EB.fem.df, SR, sugar.intake, "Correlation - Satiety Responsiveness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sugar intake
corrfunc(EB.fem.df, emo, sugar.intake, "Correlation - Emo X Sweet Intake")## `geom_smooth()` using formula = 'y ~ x'
Correlations - SSB
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - SSB and traits
## Age X SSB
corrfunc(EB.df, age, ssb.day, "Correlation - Age X SSB")## `geom_smooth()` using formula = 'y ~ x'
## BMI X SSB
corrfunc(EB.df, BMI, ssb.day, "Correlation - BMI X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Race X SSB
corrfunc(EB.df, race, ssb.day, "Correlation - Race X SSB")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
## Household income X SSB
corrfunc(EB.df, income.household, ssb.day, "Correlation - Income X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X SSB
corrfunc(EB.df, instagram, ssb.day, "Correlation - Instagram Usage X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X SSB
corrfunc(EB.df, ssb.day, sweet.liking, "Correlation - Sugar Intake X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X SSB
corrfunc(EB.df, emotional, ssb.day, "Correlation - Emotional Eating X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X SSB
corrfunc(EB.df, uncontrolled, ssb.day, "Correlation - Uncontrolled Eating X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X SSB
corrfunc(EB.df, restraint, ssb.day, "Correlation - Dietary Restraint X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X SSB
corrfunc(EB.df, EF, ssb.day, "Correlation - Enjoyment of Food X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X SSB
corrfunc(EB.df, EOE, ssb.day, "Correlation - Emotional Over-Eating X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X SSB
corrfunc(EB.df, EUE, ssb.day, "Correlation - Emotional Under-Eating X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X SSB
corrfunc(EB.df, FF, ssb.day, "Correlation - Food fussiness X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X SSB
corrfunc(EB.df, FR, ssb.day, "Correlation - Food Responsiveness X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X SSB
corrfunc(EB.df, SE, ssb.day, "Correlation - Slowness in Eating X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X SSB
corrfunc(EB.df, H, ssb.day, "Correlation - Hunger X SSB")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X SSB
corrfunc(EB.df, SR, ssb.day, "Correlation - Satiety Responsiveness X SSB")## `geom_smooth()` using formula = 'y ~ x'
Correlations - fat sHEI score
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - fatHEI and traits
## Age X fatHEI
corrfunc(EB.df, age, fatHEI, "Correlation - Age X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## BMI X fatHEI
corrfunc(EB.df, BMI, fatHEI, "Correlation - BMI X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Race X fatHEI
corrfunc(EB.df, race, fatHEI, "Correlation - Race X Fat Score")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
## Household income X fatHEI
corrfunc(EB.df, income.household, fatHEI, "Correlation - Income X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X fatHEI
corrfunc(EB.df, instagram, fatHEI, "Correlation - Instagram Usage X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X fatHEI
corrfunc(EB.df, sweet.liking, fatHEI, "Correlation - Sweet Liking X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X fatHEI
corrfunc(EB.df, emotional, fatHEI, "Correlation - Emotional Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X fatHEI
corrfunc(EB.df, uncontrolled, fatHEI, "Correlation - Uncontrolled Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X fatHEI
corrfunc(EB.df, restraint, fatHEI, "Correlation - Dietary Restraint X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X fatHEI
corrfunc(EB.df, EF, fatHEI, "Correlation - Enjoyment of Food X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X fatHEI
corrfunc(EB.df, EOE, fatHEI, "Correlation - Emotional Over-Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X fatHEI
corrfunc(EB.df, EUE, fatHEI, "Correlation - Emotional Under-Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X fatHEI
corrfunc(EB.df, FF, fatHEI, "Correlation - Food fussiness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X fatHEI
corrfunc(EB.df, FR, fatHEI, "Correlation - Food Responsiveness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X fatHEI
corrfunc(EB.df, SE, fatHEI, "Correlation - Slowness in Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X fatHEI
corrfunc(EB.df, H, fatHEI, "Correlation - Hunger X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X fatHEI
corrfunc(EB.df, SR, fatHEI, "Correlation - Satiety Responsiveness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X fatHEI
corrfunc(EB.df, emo, fatHEI, "Correlation - Emo X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
Correlations - fat sHEI score: females only
## Correlation Function
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - fatHEI and traits
## Age X fatHEI
corrfunc(EB.fem.df, age, fatHEI, "Correlation - Age X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## BMI X fatHEI
corrfunc(EB.fem.df, BMI, fatHEI, "Correlation - BMI X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Household income X fatHEI
corrfunc(EB.fem.df, income.household, fatHEI, "Correlation - Income X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X fatHEI
corrfunc(EB.fem.df, instagram, fatHEI, "Correlation - Instagram Usage X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X fatHEI
corrfunc(EB.fem.df, sweet.liking, fatHEI, "Correlation - Sweet Liking X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X fatHEI
corrfunc(EB.fem.df, sweet.liking, fatHEI, "Correlation - Sweet Liking X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X fatHEI
corrfunc(EB.fem.df, emotional, fatHEI, "Correlation - Emotional Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X fatHEI
corrfunc(EB.fem.df, uncontrolled, fatHEI, "Correlation - Uncontrolled Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X fatHEI
corrfunc(EB.fem.df, restraint, fatHEI, "Correlation - Dietary Restraint X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X fatHEI
corrfunc(EB.fem.df, EF, fatHEI, "Correlation - Enjoyment of Food X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X fatHEI
corrfunc(EB.fem.df, EOE, fatHEI, "Correlation - Emotional Over-Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X fatHEI
corrfunc(EB.fem.df, EUE, fatHEI, "Correlation - Emotional Under-Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X fatHEI
corrfunc(EB.fem.df, FF, fatHEI, "Correlation - Food fussiness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X fatHEI
corrfunc(EB.fem.df, FR, fatHEI, "Correlation - Food Responsiveness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X fatHEI
corrfunc(EB.fem.df, SE, fatHEI, "Correlation - Slowness in Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X fatHEI
corrfunc(EB.fem.df, H, fatHEI, "Correlation - Hunger X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X fatHEI
corrfunc(EB.fem.df, SR, fatHEI, "Correlation - Satiety Responsiveness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X fatHEI
corrfunc(EB.fem.df, emo, fatHEI, "Correlation - Emo X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sweet + fat sHEI score
## Correlation Function
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugarHEI + fatHEI and traits
## Age X (sugarHEI + fatHEI)
corrfunc(EB.df, age, (sugarHEI + fatHEI), "Correlation - Age X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sex X (sugarHEI + fatHEI)
corrfunc(EB.df, sex, (sugarHEI + fatHEI), "Correlation - Sex X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## BMI X (sugarHEI + fatHEI)
corrfunc(EB.df, BMI, (sugarHEI + fatHEI), "Correlation - BMI X Sweet +Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Household income X (sugarHEI + fatHEI)
corrfunc(EB.df, income.household, (sugarHEI + fatHEI), "Correlation - Income X Sweet +Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X (sugarHEI + fatHEI)
corrfunc(EB.df, instagram, (sugarHEI + fatHEI), "Correlation - Instagram Usage X Sweet +Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X (sugarHEI + fatHEI)
corrfunc(EB.df, sweet.liking, (sugarHEI + fatHEI), "Correlation - Sweet Liking X Sweet +Fat Score") ## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X (sugarHEI + fatHEI)
corrfunc(EB.df, emotional, (sugarHEI + fatHEI), "Correlation - Emotional Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X (sugarHEI + fatHEI)
corrfunc(EB.df, uncontrolled, (sugarHEI + fatHEI), "Correlation - Uncontrolled Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X (sugarHEI + fatHEI)
corrfunc(EB.df, restraint, (sugarHEI + fatHEI), "Correlation - Dietary Restraint X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X (sugarHEI + fatHEI)
corrfunc(EB.df, EF, (sugarHEI + fatHEI), "Correlation - Enjoyment of Food X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X (sugarHEI + fatHEI)
corrfunc(EB.df, EOE, (sugarHEI + fatHEI), "Correlation - Emotional Over-Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X (sugarHEI + fatHEI)
corrfunc(EB.df, EUE, (sugarHEI + fatHEI), "Correlation - Emotional Under-Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X (sugarHEI + fatHEI)
corrfunc(EB.df, FF, (sugarHEI + fatHEI), "Correlation - Food fussiness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X (sugarHEI + fatHEI)
corrfunc(EB.df, FR, (sugarHEI + fatHEI), "Correlation - Food Responsiveness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X (sugarHEI + fatHEI)
corrfunc(EB.df, SE, (sugarHEI + fatHEI), "Correlation - Slowness in Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X (sugarHEI + fatHEI)
corrfunc(EB.df, H, (sugarHEI + fatHEI), "Correlation - Hunger X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X (sugarHEI + fatHEI)
corrfunc(EB.df, SR, (sugarHEI + fatHEI), "Correlation - Satiety Responsiveness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X (sugarHEI + fatHEI)
corrfunc(EB.df, emo, (sugarHEI + fatHEI), "Correlation - Emo X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sweet + fat sHEI score: females only
## Correlation Function
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugarHEI + fatHEI and traits
## Age X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, age, (sugarHEI + fatHEI), "Correlation - Age X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## BMI X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, BMI, (sugarHEI + fatHEI), "Correlation - BMI X Sweet +Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Household income X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, income.household, (sugarHEI + fatHEI), "Correlation - Income X Sweet +Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, instagram, (sugarHEI + fatHEI), "Correlation - Instagram Usage X Sweet +Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, sweet.liking, (sugarHEI + fatHEI), "Correlation - Sweet Liking X Sweet +Fat Score") ## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, sweet.liking, (sugarHEI + fatHEI), "Correlation - Sweet Liking X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, emotional, (sugarHEI + fatHEI), "Correlation - Emotional Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, uncontrolled, (sugarHEI + fatHEI), "Correlation - Uncontrolled Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, restraint, (sugarHEI + fatHEI), "Correlation - Dietary Restraint X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, EF, (sugarHEI + fatHEI), "Correlation - Enjoyment of Food X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, EOE, (sugarHEI + fatHEI), "Correlation - Emotional Over-Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, EUE, (sugarHEI + fatHEI), "Correlation - Emotional Under-Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, FF, (sugarHEI + fatHEI), "Correlation - Food fussiness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, FR, (sugarHEI + fatHEI), "Correlation - Food Responsiveness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, SE, (sugarHEI + fatHEI), "Correlation - Slowness in Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, H, (sugarHEI + fatHEI), "Correlation - Hunger X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, SR, (sugarHEI + fatHEI), "Correlation - Satiety Responsiveness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, emo, (sugarHEI + fatHEI), "Correlation - Emo X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sHEI
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sHEI and traits
## Age X sHEI
corrfunc(EB.df, age, sHEI, "Correlation - Age X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sHEI
corrfunc(EB.df, BMI, sHEI, "Correlation - BMI X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Household income X sHEI
corrfunc(EB.df, income.household, sHEI, "Correlation - Income X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X sHEI
corrfunc(EB.df, instagram, sHEI, "Correlation - Instagram Usage X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X sHEI
corrfunc(EB.df, sweet.liking, sHEI, "Correlation - Sweet Liking X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## SSB X sugar intake
corrfunc(EB.df, ssb.day, sHEI, "Correlation - SSB X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sHEI
corrfunc(EB.df, emotional, sHEI, "Correlation - Emotional Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sHEI
corrfunc(EB.df, uncontrolled, sHEI, "Correlation - Uncontrolled Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sHEI
corrfunc(EB.df, restraint, sHEI, "Correlation - Dietary Restraint X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sHEI
corrfunc(EB.df, EF, sHEI, "Correlation - Enjoyment of Food X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sHEI
corrfunc(EB.df, EOE, sHEI, "Correlation - Emotional Over-Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.df, EUE, sHEI, "Correlation - Emotional Under-Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sHEI
corrfunc(EB.df, FF, sHEI, "Correlation - Food fussiness X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sHEI
corrfunc(EB.df, FR, sHEI, "Correlation - Food Responsiveness X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sHEI
corrfunc(EB.df, SE, sHEI, "Correlation - Slowness in Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sHEI
corrfunc(EB.df, H, sHEI, "Correlation - Hunger X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sHEI
corrfunc(EB.df, SR, sHEI, "Correlation - Satiety Responsiveness X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sHEI
corrfunc(EB.df, emo, sHEI, "Correlation - Emo X sHEI")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sHEI: females only
## Correlation Function
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sHEI and traits
## Age X sHEI
corrfunc(EB.fem.df, age, sHEI, "Correlation - Age X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sHEI
corrfunc(EB.fem.df, BMI, sHEI, "Correlation - BMI X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Household income X sHEI
corrfunc(EB.fem.df, income.household, sHEI, "Correlation - Income X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X sHEI
corrfunc(EB.fem.df, instagram, sHEI, "Correlation - Instagram Usage X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X sHEI
corrfunc(EB.fem.df, sweet.liking, sHEI, "Correlation - Sweet Liking X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X sHEI
corrfunc(EB.fem.df, sweet.liking, sHEI, "Correlation - Sweet Liking X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sHEI
corrfunc(EB.fem.df, ssb.day, sHEI, "Correlation - SSB X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sHEI
corrfunc(EB.fem.df, emotional, sHEI, "Correlation - Emotional Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sHEI
corrfunc(EB.fem.df, uncontrolled, sHEI, "Correlation - Uncontrolled Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sHEI
corrfunc(EB.fem.df, restraint, sHEI, "Correlation - Dietary Restraint X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sHEI
corrfunc(EB.fem.df, EF, sHEI, "Correlation - Enjoyment of Food X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sHEI
corrfunc(EB.fem.df, EOE, sHEI, "Correlation - Emotional Over-Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.fem.df, EUE, sHEI, "Correlation - Emotional Under-Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sHEI
corrfunc(EB.fem.df, FF, sHEI, "Correlation - Food fussiness X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sHEI
corrfunc(EB.fem.df, FR, sHEI, "Correlation - Food Responsiveness X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.fem.df, SE, sHEI, "Correlation - Slowness in Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.fem.df, H, sHEI, "Correlation - Hunger X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.fem.df, SR, sHEI, "Correlation - Satiety Responsiveness X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sHEI
corrfunc(EB.fem.df, emo, sHEI, "Correlation - Emo X sHEI")## `geom_smooth()` using formula = 'y ~ x'
Correlations - body appreciation
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - BAS and traits
## Age X BAS
corrfunc(EB.df, age, BAS, "Correlation - Age X BAS")## `geom_smooth()` using formula = 'y ~ x'
## BMI X BAS
corrfunc(EB.df, BMI, BAS, "Correlation - BMI X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Household income X BAS
corrfunc(EB.df, income.household, BAS, "Correlation - Income X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X BAS
corrfunc(EB.df, instagram, BAS, "Correlation - Instagram Usage X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X BAS
corrfunc(EB.df, sweet.liking, BAS, "Correlation - Sweet Liking X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X BAS
corrfunc(EB.df, BAS, sweet.liking, "Correlation - Sweet Liking X BAS")## `geom_smooth()` using formula = 'y ~ x'
## SSB X BAS
corrfunc(EB.df, ssb.day, BAS, "Correlation - SSB X BAS") ## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X BAS
corrfunc(EB.df, BAS, sugar.intake, "Correlation - Sugar Intake X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X BAS
corrfunc(EB.df, BAS, emotional, "Correlation - Emotional Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X BAS
corrfunc(EB.df, BAS, uncontrolled, "Correlation - Uncontrolled Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X BAS
corrfunc(EB.df, BAS, restraint, "Correlation - Dietary Restraint X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X BAS
corrfunc(EB.df, BAS, EF, "Correlation - Enjoyment of Food X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X BAS
corrfunc(EB.df, BAS, EOE, "Correlation - Emotional Over-Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X BAS
corrfunc(EB.df, BAS, EUE, "Correlation - Emotional Under-Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X BAS
corrfunc(EB.df, BAS, FF, "Correlation - Food fussiness X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X BAS
corrfunc(EB.df, BAS, FR, "Correlation - Food Responsiveness X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X BAS
corrfunc(EB.df, BAS, SE, "Correlation - Slowness in Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X BAS
corrfunc(EB.df, BAS, H, "Correlation - Hunger X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X BAS
corrfunc(EB.df, BAS, SR, "Correlation - Satiety Responsiveness X BAS") ## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X BAS
corrfunc(EB.df, BAS, emo, "Correlation - Emo X BAS")## `geom_smooth()` using formula = 'y ~ x'
Correlations - body appreciation: female only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_jitter()+
geom_smooth(method = lm)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - BAS and traits
## Age X BMI
corrfunc(EB.fem.df, age, BMI, "Correlation - Age X BMI")## `geom_smooth()` using formula = 'y ~ x'
## Age X BAS
corrfunc(EB.fem.df, age, BAS, "Correlation - Age X BAS")## `geom_smooth()` using formula = 'y ~ x'
## BMI X BAS
corrfunc(EB.fem.df, BMI, BAS, "Correlation - BMI X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Household income X BAS
corrfunc(EB.fem.df, income.household, BAS, "Correlation - Income X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Instagram usage X BAS
corrfunc(EB.fem.df, instagram, BAS, "Correlation - Instagram Usage X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X BAS
corrfunc(EB.fem.df, sweet.liking, BAS, "Correlation - Sweet Liking X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X BAS
corrfunc(EB.fem.df, BAS, sweet.liking, "Correlation - Sweet Liking X BAS")## `geom_smooth()` using formula = 'y ~ x'
## SSB X BAS
corrfunc(EB.fem.df, ssb.day, BAS, "Correlation - SSB X BAS") ## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X BAS
corrfunc(EB.fem.df, BAS, sugar.intake, "Correlation - Sugar Intake X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X BAS
corrfunc(EB.fem.df, BAS, emotional, "Correlation - Emotional Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X BAS
corrfunc(EB.fem.df, BAS, uncontrolled, "Correlation - Uncontrolled Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X BAS
corrfunc(EB.fem.df, BAS, restraint, "Correlation - Dietary Restraint X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X BAS
corrfunc(EB.fem.df, BAS, EF, "Correlation - Enjoyment of Food X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X BAS
corrfunc(EB.fem.df, BAS, EOE, "Correlation - Emotional Over-Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X BAS
corrfunc(EB.fem.df, BAS, EUE, "Correlation - Emotional Under-Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X BAS
corrfunc(EB.fem.df, BAS, FF, "Correlation - Food fussiness X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X BAS
corrfunc(EB.fem.df, BAS, FR, "Correlation - Food Responsiveness X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X BAS
corrfunc(EB.fem.df, BAS, SE, "Correlation - Slowness in Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X BAS
corrfunc(EB.fem.df, BAS, H, "Correlation - Hunger X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X BAS
corrfunc(EB.fem.df, BAS, SR, "Correlation - Satiety Responsiveness X BAS") ## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X BAS
corrfunc(EB.fem.df, BAS, emo, "Correlation - Emo X BAS")## `geom_smooth()` using formula = 'y ~ x'
Partial correlation - controlling for age
EB.df %>%
ggplot(aes(age, BMI)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## age and BMI are not correlationed - enter both as covariates## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking and sugar intake
pcor.test(EB.df$sweet.liking, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.4637036 6.185701e-09 6.192634 145 3 pearson## Emotional eating
pcor.test(EB.df$emotional, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.04176439 0.6216617 -0.4945945 145 3 pearsonpcor.test(EB.df$emotional, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.03173641 0.7077089 -0.3756996 145 3 pearsonpcor.test(EB.df$emotional, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.09451567 0.2632101 1.123353 145 3 pearsonpcor.test(EB.df$emotional, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.05741082 0.4973646 0.6804163 145 3 pearsonpcor.test(EB.df$emotional, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.04417137 0.6016946 0.5231533 145 3 pearsonpcor.test(EB.df$emotional, EB.df$instagram, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.03343014 0.6928757 -0.395772 145 3 pearson## Uncontrolled eating
pcor.test(EB.df$uncontrolled, EB.df$sweet.liking, EB.df[,c("age", "sex")])## estimate p.value statistic n gp Method
## 1 0.167956 0.04495225 2.023106 145 2 pearsonpcor.test(EB.df$uncontrolled, EB.df$sugar.intake, EB.df[,c("age", "sex")])## estimate p.value statistic n gp Method
## 1 0.04095831 0.6271832 0.4867615 145 2 pearsonpcor.test(EB.df$uncontrolled, EB.df$fatHEI, EB.df[,c("age", "sex")])## estimate p.value statistic n gp Method
## 1 0.05675599 0.5007633 0.6750281 145 2 pearsonpcor.test(EB.df$uncontrolled, EB.df$SuFatHEI, EB.df[,c("age", "sex")])## estimate p.value statistic n gp Method
## 1 -0.001751895 0.9834325 -0.02080264 145 2 pearsonpcor.test(EB.df$uncontrolled, EB.df$sHEI, EB.df[,c("age", "sex")])## estimate p.value statistic n gp Method
## 1 -0.01453138 0.8632378 -0.1725688 145 2 pearsonpcor.test(EB.df$uncontrolled, EB.df$instagram, EB.df[,c("age", "sex")])## estimate p.value statistic n gp Method
## 1 0.06018715 0.4751873 0.7159808 145 2 pearson## Dietary restraint
pcor.test(EB.df$restraint, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1803144 0.03176655 -2.169062 145 3 pearsonpcor.test(EB.df$restraint, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1466795 0.08153072 -1.754512 145 3 pearsonpcor.test(EB.df$restraint, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.008913662 0.9161522 -0.1054721 145 3 pearsonpcor.test(EB.df$restraint, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.1674257 0.04642004 2.00937 145 3 pearsonpcor.test(EB.df$restraint, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.3046779 0.0002272581 3.784952 145 3 pearsonpcor.test(EB.df$restraint, EB.df$instagram, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.07301537 0.3878398 -0.8662417 145 3 pearson## EOE
pcor.test(EB.df$EOE, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.03626836 0.668281 -0.4294156 145 3 pearsonpcor.test(EB.df$EOE, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.01481488 0.8610884 0.1753113 145 3 pearsonpcor.test(EB.df$EOE, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.1035156 0.2202271 1.231429 145 3 pearsonpcor.test(EB.df$EOE, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.01261463 0.8815553 0.1492701 145 3 pearsonpcor.test(EB.df$EOE, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.01933191 0.819373 0.2287809 145 3 pearsonpcor.test(EB.df$EOE, EB.df$instagram, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.0007297134 0.9931234 0.008634087 145 3 pearson## BAS
pcor.test(EB.df$BAS, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.02288683 0.7868888 0.2708716 145 3 pearsonpcor.test(EB.df$BAS, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.05415029 0.5221455 -0.6416563 145 3 pearsonpcor.test(EB.df$BAS, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.09037246 0.2848066 1.073695 145 3 pearsonpcor.test(EB.df$BAS, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.06841025 0.4185467 0.8113417 145 3 pearsonpcor.test(EB.df$BAS, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.02678668 0.7516719 -0.3170581 145 3 pearsonpcor.test(EB.df$BAS, EB.df$instagram, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.1435251 0.0883781 1.715978 145 3 pearson## BAS with eating behavior traits
pcor.test(EB.df$BAS, EB.df$emotional, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1789932 0.03306102 -2.152641 145 3 pearsonpcor.test(EB.df$BAS, EB.df$uncontrolled, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.00124547 0.9882633 -0.01473661 145 3 pearsonpcor.test(EB.df$BAS, EB.df$restraint, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1401252 0.096264 -1.674504 145 3 pearsonpcor.test(EB.df$BAS, EB.df$EOE, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1201339 0.1544262 -1.431813 145 3 pearsonModels: interactions - sugar intake
summary(glm(sugar.intake ~ sweet.liking, data = EB.df)) ## sweet liking predicts sugar intake##
## Call:
## glm(formula = sugar.intake ~ sweet.liking, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -428.62 -144.70 -21.13 83.98 743.90
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.0349 30.6945 8.309 6.75e-14 ***
## sweet.liking 4.5387 0.7354 6.172 6.58e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50315.42)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7195106 on 143 degrees of freedom
## AIC: 1985.3
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ age, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ age, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -298.34 -158.49 -67.39 99.05 891.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 472.656 62.710 7.537 5.03e-12 ***
## age -2.462 2.171 -1.134 0.259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63150.14)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 9030470 on 143 degrees of freedom
## AIC: 2018.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ as.factor(sex), data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ as.factor(sex), data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -306.7 -132.1 -106.1 127.9 863.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 436.68 37.93 11.514 <2e-16 ***
## as.factor(sex)2 -44.62 45.44 -0.982 0.328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63291.21)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 9050643 on 143 degrees of freedom
## AIC: 2018.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*sex, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -455.08 -143.24 -21.23 82.09 717.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 267.4872 129.3488 2.068 0.0405 *
## sweet.liking 5.3396 2.9944 1.783 0.0767 .
## sex -7.2400 71.6306 -0.101 0.9196
## sweet.liking:sex -0.4833 1.6750 -0.289 0.7734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50872.52)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7173025 on 141 degrees of freedom
## AIC: 1988.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*emotional, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * emotional, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -389.31 -153.39 -22.32 80.16 766.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 252.8874 101.0340 2.503 0.0135 *
## sweet.liking 5.7798 2.2526 2.566 0.0113 *
## emotional 0.2327 8.2130 0.028 0.9774
## sweet.liking:emotional -0.1006 0.1799 -0.559 0.5771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50612.75)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7136398 on 141 degrees of freedom
## AIC: 1988.1
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*uncontrolled, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * uncontrolled, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -418.56 -154.15 -23.36 80.52 745.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 303.33438 126.66675 2.395 0.0179 *
## sweet.liking 4.07112 3.20964 1.268 0.2067
## uncontrolled -2.75581 6.92042 -0.398 0.6911
## sweet.liking:uncontrolled 0.02893 0.16551 0.175 0.8615
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50949.84)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7183927 on 141 degrees of freedom
## AIC: 1989
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*restraint, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * restraint, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -378.39 -151.75 -18.89 79.52 793.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 241.2727 128.8720 1.872 0.0633 .
## sweet.liking 7.2804 3.0659 2.375 0.0189 *
## restraint 0.8653 8.5132 0.102 0.9192
## sweet.liking:restraint -0.1955 0.2056 -0.951 0.3433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50247.29)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7084868 on 141 degrees of freedom
## AIC: 1987
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*EF, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * EF, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -412.64 -141.78 -13.09 88.03 742.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 462.95934 156.04063 2.967 0.00353 **
## sweet.liking 3.65575 4.21416 0.867 0.38715
## EF -17.04104 12.47093 -1.366 0.17397
## sweet.liking:EF 0.08549 0.32854 0.260 0.79508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 49817.07)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7024207 on 141 degrees of freedom
## AIC: 1985.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*EOE, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * EOE, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -400.51 -143.86 -21.48 85.17 731.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 200.2259 88.0437 2.274 0.02447 *
## sweet.liking 6.3857 2.0242 3.155 0.00196 **
## EOE 5.7606 8.6213 0.668 0.50511
## sweet.liking:EOE -0.1910 0.1959 -0.975 0.33117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50650.27)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7141688 on 141 degrees of freedom
## AIC: 1988.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*EUE, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * EUE, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -435.20 -167.66 -11.61 85.37 748.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 140.3413 113.2442 1.239 0.21730
## sweet.liking 6.5142 2.4473 2.662 0.00867 **
## EUE 9.3099 8.8562 1.051 0.29495
## sweet.liking:EUE -0.1624 0.1933 -0.840 0.40219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50628.8)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7138661 on 141 degrees of freedom
## AIC: 1988.1
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*FF, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * FF, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -415.88 -154.79 -18.68 87.34 709.24
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 187.60815 92.89357 2.020 0.0453 *
## sweet.liking 4.07855 2.23015 1.829 0.0695 .
## FF 6.65384 9.02818 0.737 0.4623
## sweet.liking:FF 0.04249 0.21796 0.195 0.8457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50093.54)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7063189 on 141 degrees of freedom
## AIC: 1986.6
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*FR, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * FR, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -478.48 -143.18 -24.08 72.28 720.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 410.7735 149.7694 2.743 0.00688 **
## sweet.liking 0.1884 3.6390 0.052 0.95878
## FR -15.8837 15.0952 -1.052 0.29449
## sweet.liking:FR 0.4351 0.3560 1.222 0.22368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50493.36)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7119563 on 141 degrees of freedom
## AIC: 1987.7
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*SE, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * SE, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -403.75 -159.67 -28.07 89.17 739.24
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 204.53880 90.92720 2.249 0.0260 *
## sweet.liking 4.70084 2.24667 2.092 0.0382 *
## SE 4.30313 7.12198 0.604 0.5467
## sweet.liking:SE -0.00909 0.17842 -0.051 0.9594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50737.33)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7153964 on 141 degrees of freedom
## AIC: 1988.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*H, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * H, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -414.60 -156.72 -14.84 74.58 710.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 438.3106 145.0530 3.022 0.00299 **
## sweet.liking -2.0291 3.6852 -0.551 0.58278
## H -12.1856 9.5124 -1.281 0.20229
## sweet.liking:H 0.4234 0.2345 1.805 0.07318 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 49803.47)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7022290 on 141 degrees of freedom
## AIC: 1985.7
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*SR, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * SR, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -417.25 -157.90 -17.47 87.63 741.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.7642 106.0225 0.658 0.51160
## sweet.liking 6.9994 2.3267 3.008 0.00311 **
## SR 16.1048 8.8302 1.824 0.07030 .
## sweet.liking:SR -0.2060 0.1971 -1.045 0.29783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 49674.37)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7004086 on 141 degrees of freedom
## AIC: 1985.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*BAS, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * BAS, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -423.65 -164.52 -18.44 86.07 743.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 335.55312 152.89656 2.195 0.0298 *
## sweet.liking 2.60181 3.78304 0.688 0.4927
## BAS -2.15073 4.01929 -0.535 0.5934
## sweet.liking:BAS 0.05153 0.09902 0.520 0.6036
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50918.24)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7179472 on 141 degrees of freedom
## AIC: 1988.9
##
## Number of Fisher Scoring iterations: 2Models: mediation - sugar intake
summary(glm(sugar.intake ~ sweet.liking, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -428.62 -144.70 -21.13 83.98 743.90
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.0349 30.6945 8.309 6.75e-14 ***
## sweet.liking 4.5387 0.7354 6.172 6.58e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50315.42)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7195106 on 143 degrees of freedom
## AIC: 1985.3
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ age, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ age, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -298.34 -158.49 -67.39 99.05 891.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 472.656 62.710 7.537 5.03e-12 ***
## age -2.462 2.171 -1.134 0.259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63150.14)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 9030470 on 143 degrees of freedom
## AIC: 2018.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ as.factor(sex), data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ as.factor(sex), data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -306.7 -132.1 -106.1 127.9 863.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 436.68 37.93 11.514 <2e-16 ***
## as.factor(sex)2 -44.62 45.44 -0.982 0.328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63291.21)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 9050643 on 143 degrees of freedom
## AIC: 2018.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + sex, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -444.27 -145.67 -21.19 78.50 728.23
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 297.3113 77.5160 3.835 0.000188 ***
## sweet.liking 4.5025 0.7396 6.088 1.01e-08 ***
## sex -24.2120 40.7481 -0.594 0.553331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50544.09)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7177260 on 142 degrees of freedom
## AIC: 1986.9
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + emotional, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + emotional, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -390.18 -152.64 -21.70 77.57 756.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 299.4326 57.0763 5.246 5.53e-07 ***
## sweet.liking 4.5905 0.7379 6.221 5.22e-09 ***
## emotional -3.7526 4.0664 -0.923 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50367.68)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7152211 on 142 degrees of freedom
## AIC: 1986.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + uncontrolled, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + uncontrolled, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -405.77 -150.85 -22.69 80.75 748.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 285.7837 76.9391 3.714 0.000292 ***
## sweet.liking 4.6161 0.7585 6.086 1.02e-08 ***
## uncontrolled -1.7813 4.0848 -0.436 0.663447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50601.99)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7185483 on 142 degrees of freedom
## AIC: 1987.1
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + restraint, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + restraint, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -403.36 -136.30 -24.59 80.75 769.12
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 337.9515 79.1705 4.269 3.58e-05 ***
## sweet.liking 4.4508 0.7387 6.025 1.38e-08 ***
## restraint -5.6836 5.0033 -1.136 0.258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50213.43)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7130308 on 142 degrees of freedom
## AIC: 1985.9
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + EF, data = EB.df)) ## Enjoyment of food = partial mediator##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + EF, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -419.25 -140.48 -13.68 88.87 742.79
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 432.0652 100.9208 4.281 3.40e-05 ***
## sweet.liking 4.7353 0.7371 6.424 1.87e-09 ***
## EF -14.5358 7.9006 -1.840 0.0679 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 49490)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7027580 on 142 degrees of freedom
## AIC: 1983.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + EOE, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + EOE, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -415.80 -149.17 -24.58 84.02 740.93
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 268.8783 52.8578 5.087 1.13e-06 ***
## sweet.liking 4.5479 0.7383 6.160 7.07e-09 ***
## EOE -1.4358 4.4559 -0.322 0.748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50632.74)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7189849 on 142 degrees of freedom
## AIC: 1987.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + EUE, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + EUE, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -412.97 -160.67 -12.73 84.91 738.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 217.4417 66.2992 3.280 0.00131 **
## sweet.liking 4.5536 0.7373 6.176 6.53e-09 ***
## EUE 2.9918 4.6742 0.640 0.52316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50523.99)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7174406 on 142 degrees of freedom
## AIC: 1986.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + FF, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + FF, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -416.93 -155.16 -18.61 88.23 714.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 173.6344 58.8842 2.949 0.00373 **
## sweet.liking 4.4891 0.7319 6.133 8.09e-09 ***
## FF 8.1145 5.0197 1.617 0.10820
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 49754.17)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7065093 on 142 degrees of freedom
## AIC: 1984.6
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + FR, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + FR, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -427.07 -142.50 -21.74 83.99 744.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.8302 83.6597 3.094 0.00238 **
## sweet.liking 4.5426 0.7424 6.119 8.69e-09 ***
## FR -0.4012 8.2231 -0.049 0.96115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50668.91)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7194985 on 142 degrees of freedom
## AIC: 1987.3
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + SE, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + SE, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -401.28 -159.80 -27.24 88.59 739.11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 207.9862 60.5209 3.437 0.000773 ***
## sweet.liking 4.5928 0.7383 6.221 5.23e-09 ***
## SE 4.0208 4.4565 0.902 0.368470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50380.96)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7154096 on 142 degrees of freedom
## AIC: 1986.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + H, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + H, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -425.91 -159.11 -20.11 83.71 739.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 220.7050 81.3173 2.714 0.00747 **
## sweet.liking 4.4879 0.7458 6.018 1.43e-08 ***
## H 2.3344 5.1182 0.456 0.64901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50595.63)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7184580 on 142 degrees of freedom
## AIC: 1987
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + SR, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + SR, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -424.09 -162.81 -18.60 86.62 746.78
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 155.2744 67.4350 2.303 0.0228 *
## sweet.liking 4.6932 0.7368 6.369 2.47e-09 ***
## SR 8.6629 5.2223 1.659 0.0994 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 49706.54)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7058328 on 142 degrees of freedom
## AIC: 1984.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + BAS, data = EB.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + BAS, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -429.20 -151.84 -19.01 84.75 745.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 271.0021 89.1616 3.039 0.00282 **
## sweet.liking 4.5324 0.7386 6.136 7.96e-09 ***
## BAS -0.4403 2.3076 -0.191 0.84893
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 50656.77)
##
## Null deviance: 9111669 on 144 degrees of freedom
## Residual deviance: 7193261 on 142 degrees of freedom
## AIC: 1987.2
##
## Number of Fisher Scoring iterations: 2Models: interactions - sugar intake (females)
summary(glm(sugar.intake ~ sweet.liking, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -365.46 -128.32 -13.41 76.08 680.61
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 253.0073 30.1417 8.394 3.41e-13 ***
## sweet.liking 4.3730 0.7352 5.948 4.11e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36559.28)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3619368 on 99 degrees of freedom
## AIC: 1351.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*emotional, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * emotional, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -367.97 -123.50 -11.55 71.45 681.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 300.9874 120.5545 2.497 0.0142 *
## sweet.liking 2.7622 2.7963 0.988 0.3257
## emotional -3.7997 9.3349 -0.407 0.6849
## sweet.liking:emotional 0.1250 0.2109 0.593 0.5548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 37154.63)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3603999 on 97 degrees of freedom
## AIC: 1355.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*uncontrolled, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * uncontrolled, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -369.09 -128.17 -13.61 66.84 696.39
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 186.805552 133.443052 1.400 0.165
## sweet.liking 4.276941 3.450916 1.239 0.218
## uncontrolled 3.903112 7.353805 0.531 0.597
## sweet.liking:uncontrolled -0.002623 0.178314 -0.015 0.988
##
## (Dispersion parameter for gaussian family taken to be 37030.57)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3591966 on 97 degrees of freedom
## AIC: 1355
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*restraint, data = EB.fem.df)) ##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * restraint, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -295.97 -130.04 -3.50 63.38 650.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 144.7288 133.5750 1.084 0.28127
## sweet.liking 8.6519 3.0686 2.819 0.00583 **
## restraint 6.9620 8.5517 0.814 0.41757
## sweet.liking:restraint -0.2960 0.2011 -1.472 0.14423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36357.57)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3526684 on 97 degrees of freedom
## AIC: 1353.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*EF, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * EF, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -365.59 -123.64 -6.44 71.77 682.21
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 533.4244 180.5810 2.954 0.00394 **
## sweet.liking -0.5422 4.8207 -0.112 0.91068
## EF -22.3087 14.1285 -1.579 0.11760
## sweet.liking:EF 0.3883 0.3752 1.035 0.30323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36270.05)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3518195 on 97 degrees of freedom
## AIC: 1352.9
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*EOE, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * EOE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -375.96 -117.27 -14.02 61.70 674.09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 256.22628 100.47595 2.550 0.0123 *
## sweet.liking 3.46166 2.45794 1.408 0.1622
## EOE -0.24632 9.20872 -0.027 0.9787
## sweet.liking:EOE 0.08464 0.22142 0.382 0.7031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 37122.55)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3600887 on 97 degrees of freedom
## AIC: 1355.3
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*EUE, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * EUE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -342.95 -122.16 -10.43 61.95 656.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.9026 121.8508 1.838 0.0692 .
## sweet.liking 6.0793 3.0107 2.019 0.0462 *
## EUE 2.3395 9.5326 0.245 0.8066
## sweet.liking:EUE -0.1304 0.2289 -0.570 0.5701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 37111.82)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3599846 on 97 degrees of freedom
## AIC: 1355.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*FF, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * FF, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -396.93 -138.32 -7.73 68.76 603.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 259.1996 91.9436 2.819 0.00584 **
## sweet.liking 1.8091 2.3029 0.786 0.43403
## FF -0.9628 8.7318 -0.110 0.91243
## sweet.liking:FF 0.2497 0.2186 1.142 0.25615
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36099.12)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3501615 on 97 degrees of freedom
## AIC: 1352.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*FR, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * FR, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -363.65 -126.91 -18.46 74.33 685.68
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 309.5826 156.9059 1.973 0.0513 .
## sweet.liking 2.8740 4.0319 0.713 0.4777
## FR -5.7171 15.7051 -0.364 0.7166
## sweet.liking:FR 0.1477 0.3882 0.380 0.7045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 37254.56)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3613692 on 97 degrees of freedom
## AIC: 1355.6
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*SE, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * SE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -362.67 -125.80 -12.01 76.15 681.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 261.48135 101.62517 2.573 0.0116 *
## sweet.liking 3.71284 2.63810 1.407 0.1625
## SE -0.58569 7.48283 -0.078 0.9378
## sweet.liking:SE 0.05209 0.19609 0.266 0.7911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 37269.29)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3615121 on 97 degrees of freedom
## AIC: 1355.7
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*H, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * H, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -380.46 -124.13 -9.36 70.21 706.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 388.45989 186.22951 2.086 0.0396 *
## sweet.liking 0.09682 4.64961 0.021 0.9834
## H -8.74406 11.96301 -0.731 0.4666
## sweet.liking:H 0.26842 0.28963 0.927 0.3564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36970.52)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3586141 on 97 degrees of freedom
## AIC: 1354.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*SR, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * SR, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -380.78 -124.71 -13.86 88.78 680.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 81.9238 125.5712 0.652 0.5157
## sweet.liking 6.8044 2.9700 2.291 0.0241 *
## SR 14.6768 10.4186 1.409 0.1621
## sweet.liking:SR -0.2066 0.2432 -0.849 0.3978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36449.62)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3535613 on 97 degrees of freedom
## AIC: 1353.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking*BAS, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking * BAS, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -348.43 -130.52 -15.86 73.08 671.23
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 312.44997 140.58698 2.222 0.0286 *
## sweet.liking 2.49376 3.57386 0.698 0.4870
## BAS -1.57921 3.69182 -0.428 0.6698
## sweet.liking:BAS 0.05120 0.09522 0.538 0.5920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 37202.18)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3608612 on 97 degrees of freedom
## AIC: 1355.5
##
## Number of Fisher Scoring iterations: 2Models: mediation - sugar intake (females)
summary(glm(sugar.intake ~ sweet.liking, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -365.46 -128.32 -13.41 76.08 680.61
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 253.0073 30.1417 8.394 3.41e-13 ***
## sweet.liking 4.3730 0.7352 5.948 4.11e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36559.28)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3619368 on 99 degrees of freedom
## AIC: 1351.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + emotional, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + emotional, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -366.59 -126.13 -13.03 73.18 680.64
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 239.5970 61.4494 3.899 0.000177 ***
## sweet.liking 4.3597 0.7406 5.887 5.53e-08 ***
## emotional 1.0907 4.3486 0.251 0.802486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36908.64)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3617047 on 98 degrees of freedom
## AIC: 1353.7
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + uncontrolled, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + uncontrolled, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -369.09 -128.00 -13.74 66.82 696.74
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 188.3649 80.6326 2.336 0.0215 *
## sweet.liking 4.2274 0.7552 5.598 1.98e-07 ***
## uncontrolled 3.8169 4.4149 0.865 0.3894
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36652.79)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3591974 on 98 degrees of freedom
## AIC: 1353
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + restraint, data = EB.fem.df)) ##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + restraint, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -340.81 -123.73 -21.16 68.37 674.23
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 300.0908 82.3638 3.643 0.000432 ***
## sweet.liking 4.2720 0.7556 5.654 1.55e-07 ***
## restraint -3.1493 5.1245 -0.615 0.540267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36790.54)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3605473 on 98 degrees of freedom
## AIC: 1353.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + EF, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + EF, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -390.90 -116.55 -4.18 76.85 685.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 377.5487 99.6784 3.788 0.000262 ***
## sweet.liking 4.3893 0.7327 5.991 3.46e-08 ***
## EF -10.0010 7.6325 -1.310 0.193149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36296.42)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3557049 on 98 degrees of freedom
## AIC: 1352
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + EOE, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + EOE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -372.32 -117.64 -14.77 64.73 676.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.5437 56.5498 3.971 0.000137 ***
## sweet.liking 4.3575 0.7381 5.904 5.11e-08 ***
## EOE 2.7826 4.6715 0.596 0.552775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36799.1)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3606312 on 98 degrees of freedom
## AIC: 1353.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + EUE, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + EUE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -355.12 -117.51 -15.73 69.88 670.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 280.9879 69.1122 4.066 9.68e-05 ***
## sweet.liking 4.4176 0.7448 5.931 4.53e-08 ***
## EUE -2.2667 5.0335 -0.450 0.653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36856.06)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3611894 on 98 degrees of freedom
## AIC: 1353.6
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + FF, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + FF, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -381.18 -139.57 -14.18 78.43 643.81
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 179.8685 60.3451 2.981 0.00363 **
## sweet.liking 4.3030 0.7334 5.867 6.03e-08 ***
## FF 7.1337 5.1071 1.397 0.16562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36211.39)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3548716 on 98 degrees of freedom
## AIC: 1351.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + FR, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + FR, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -365.47 -128.18 -14.55 75.59 679.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 260.2602 87.9848 2.958 0.00388 **
## sweet.liking 4.3812 0.7448 5.882 5.63e-08 ***
## FR -0.7696 8.7656 -0.088 0.93021
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36929.43)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3619084 on 98 degrees of freedom
## AIC: 1353.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + SE, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + SE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -364.69 -126.35 -17.31 75.87 680.37
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 240.8813 65.3710 3.685 0.000375 ***
## sweet.liking 4.3852 0.7411 5.917 4.82e-08 ***
## SE 0.9702 4.6350 0.209 0.834634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36915.83)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3617751 on 98 degrees of freedom
## AIC: 1353.7
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + H, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + H, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -368.52 -128.94 -12.30 75.48 684.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 236.4636 88.1571 2.682 0.00858 **
## sweet.liking 4.3497 0.7479 5.816 7.57e-08 ***
## H 1.0992 5.5010 0.200 0.84203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36917.29)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3617894 on 98 degrees of freedom
## AIC: 1353.7
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + SR, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + SR, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -397.59 -143.77 -14.12 78.07 685.35
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 168.0650 73.9410 2.273 0.0252 *
## sweet.liking 4.3600 0.7331 5.947 4.22e-08 ***
## SR 7.3620 5.8553 1.257 0.2116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36346.02)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3561910 on 98 degrees of freedom
## AIC: 1352.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sugar.intake ~ sweet.liking + BAS, data = EB.fem.df))##
## Call:
## glm(formula = sugar.intake ~ sweet.liking + BAS, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -365.45 -128.32 -13.41 76.08 680.61
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.530e+02 8.644e+01 2.926 0.00426 **
## sweet.liking 4.373e+00 7.445e-01 5.873 5.86e-08 ***
## BAS 1.194e-03 2.226e+00 0.001 0.99957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 36932.33)
##
## Null deviance: 4912740 on 100 degrees of freedom
## Residual deviance: 3619368 on 98 degrees of freedom
## AIC: 1353.8
##
## Number of Fisher Scoring iterations: 2Models: interactions - sHEI
## Sugar intake models
summary(glm(sHEI ~ sweet.liking, data = EB.df)) ## sweet liking predicts sHEI##
## Call:
## glm(formula = sHEI ~ sweet.liking, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -26.0825 -5.5327 0.7146 5.4522 20.4391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.69812 1.20984 41.905 < 2e-16 ***
## sweet.liking -0.10777 0.02899 -3.718 0.000288 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 78.16872)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 11178 on 143 degrees of freedom
## AIC: 1047.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*sex, data = EB.df)) ## sex has a main and interaction effect on sHEI##
## Call:
## glm(formula = sHEI ~ sweet.liking * sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.588 -6.125 1.026 5.473 18.731
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.85280 4.81762 6.819 2.47e-10 ***
## sweet.liking 0.13584 0.11153 1.218 0.2252
## sex 10.18966 2.66790 3.819 0.0002 ***
## sweet.liking:sex -0.13599 0.06239 -2.180 0.0309 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 70.57063)
##
## Null deviance: 12258.6 on 144 degrees of freedom
## Residual deviance: 9950.5 on 141 degrees of freedom
## AIC: 1034.6
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*emotional + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * emotional + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.722 -6.204 1.266 5.614 18.349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.9141317 4.4684618 8.932 2.15e-15 ***
## sweet.liking -0.0903780 0.0857407 -1.054 0.29366
## emotional 0.1347469 0.3149102 0.428 0.66939
## sex 5.2662373 1.5693664 3.356 0.00102 **
## sweet.liking:emotional -0.0009201 0.0068586 -0.134 0.89347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 73.25502)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10256 on 140 degrees of freedom
## AIC: 1041
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*uncontrolled + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * uncontrolled + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.067 -6.050 1.211 5.936 18.984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.272617 5.724694 7.734 1.86e-12 ***
## sweet.liking -0.216338 0.121452 -1.781 0.07704 .
## uncontrolled -0.174120 0.262847 -0.662 0.50878
## sex 5.489604 1.569638 3.497 0.00063 ***
## sweet.liking:uncontrolled 0.006124 0.006264 0.978 0.32990
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.95042)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10213 on 140 degrees of freedom
## AIC: 1040.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*restraint + sex, data = EB.df)) ##
## Call:
## glm(formula = sHEI ~ sweet.liking * restraint + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -23.0969 -6.4349 -0.0074 5.9223 15.9122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.391776 5.214434 6.404 2.14e-09 ***
## sweet.liking -0.141968 0.114296 -1.242 0.216271
## restraint 0.484235 0.315367 1.535 0.126927
## sex 5.877722 1.525014 3.854 0.000176 ***
## sweet.liking:restraint 0.003617 0.007701 0.470 0.639304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 68.15472)
##
## Null deviance: 12258.6 on 144 degrees of freedom
## Residual deviance: 9541.7 on 140 degrees of freedom
## AIC: 1030.6
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*EF + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * EF + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.6639 -5.8328 0.8237 6.0361 18.9135
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.97580 6.30666 6.814 2.59e-10 ***
## sweet.liking -0.25247 0.16443 -1.535 0.126949
## EF -0.19060 0.48067 -0.397 0.692314
## sex 5.78282 1.58730 3.643 0.000378 ***
## sweet.liking:EF 0.01197 0.01286 0.931 0.353579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.88162)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10203 on 140 degrees of freedom
## AIC: 1040.3
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*EOE + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * EOE + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.637 -5.676 1.227 5.461 18.233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.791271 4.022743 9.394 < 2e-16 ***
## sweet.liking -0.015559 0.076686 -0.203 0.83951
## EOE 0.407930 0.330789 1.233 0.21957
## sex 5.158724 1.580900 3.263 0.00138 **
## sweet.liking:EOE -0.008833 0.007434 -1.188 0.23676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.64555)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10170 on 140 degrees of freedom
## AIC: 1039.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*EUE + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * EUE + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.8145 -5.8060 0.8636 6.0426 18.4180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.239268 5.166344 8.369 5.34e-14 ***
## sweet.liking -0.061740 0.093556 -0.660 0.510387
## EUE -0.277015 0.331725 -0.835 0.405099
## sex 6.291024 1.591628 3.953 0.000122 ***
## sweet.liking:EUE -0.003200 0.007367 -0.434 0.664711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 70.87507)
##
## Null deviance: 12258.6 on 144 degrees of freedom
## Residual deviance: 9922.5 on 140 degrees of freedom
## AIC: 1036.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*FF + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * FF + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -19.319 -5.669 1.077 5.475 23.546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.0947248 4.2751750 10.548 < 2e-16 ***
## sweet.liking -0.1032755 0.0838762 -1.231 0.220281
## FF -0.4718328 0.3398695 -1.388 0.167258
## sex 5.9131712 1.5393229 3.841 0.000185 ***
## sweet.liking:FF 0.0007319 0.0081900 0.089 0.928918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 70.70599)
##
## Null deviance: 12258.6 on 144 degrees of freedom
## Residual deviance: 9898.8 on 140 degrees of freedom
## AIC: 1035.9
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*FR + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * FR + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.9429 -5.5685 0.8191 5.8961 18.8911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.362792 6.386489 6.790 2.94e-10 ***
## sweet.liking -0.078765 0.138551 -0.568 0.570612
## FR -0.225867 0.574311 -0.393 0.694709
## sex 5.411540 1.549657 3.492 0.000642 ***
## sweet.liking:FR -0.001805 0.013547 -0.133 0.894202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 73.0102)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10221 on 140 degrees of freedom
## AIC: 1040.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*SE + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * SE + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.643 -6.222 1.244 5.760 19.181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.216493 4.073495 10.118 < 2e-16 ***
## sweet.liking -0.026541 0.084072 -0.316 0.753
## SE -0.157819 0.270399 -0.584 0.560
## sex 6.429815 1.600435 4.018 9.56e-05 ***
## sweet.liking:SE -0.006430 0.006672 -0.964 0.337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 70.90685)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 9927 on 140 degrees of freedom
## AIC: 1036.3
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*H + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * H + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.655 -5.995 1.221 6.007 18.311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.880233 5.973458 5.672 7.8e-08 ***
## sweet.liking 0.113308 0.140340 0.807 0.420815
## H 0.497655 0.363462 1.369 0.173127
## sex 5.363934 1.554694 3.450 0.000741 ***
## sweet.liking:H -0.013873 0.008932 -1.553 0.122632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.21918)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10111 on 140 degrees of freedom
## AIC: 1039
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*SR + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * SR + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.182 -6.574 1.020 6.183 18.870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.382561 4.875678 8.077 2.76e-13 ***
## sweet.liking -0.019597 0.090306 -0.217 0.828520
## SR 0.056173 0.337528 0.166 0.868064
## sex 6.113931 1.629721 3.752 0.000257 ***
## sweet.liking:SR -0.007396 0.007642 -0.968 0.334829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.49715)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10150 on 140 degrees of freedom
## AIC: 1039.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*BAS + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * BAS + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.388 -5.882 1.240 5.619 18.645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.088559 6.369796 6.922 1.48e-10 ***
## sweet.liking -0.130443 0.144269 -0.904 0.367463
## BAS -0.073707 0.152520 -0.483 0.629665
## sex 5.338526 1.578302 3.382 0.000932 ***
## sweet.liking:BAS 0.000800 0.003785 0.211 0.832887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 73.29628)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10261 on 140 degrees of freedom
## AIC: 1041.1
##
## Number of Fisher Scoring iterations: 2Models: mediation - sHEI
summary(glm(sHEI ~ sweet.liking, data = EB.df)) ##
## Call:
## glm(formula = sHEI ~ sweet.liking, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -26.0825 -5.5327 0.7146 5.4522 20.4391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.69812 1.20984 41.905 < 2e-16 ***
## sweet.liking -0.10777 0.02899 -3.718 0.000288 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 78.16872)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 11178 on 143 degrees of freedom
## AIC: 1047.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + sex, data = EB.df)) ## sex has a main on sHEI##
## Call:
## glm(formula = sHEI ~ sweet.liking + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.280 -5.745 1.175 5.761 18.829
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.24435 2.93447 14.05 < 2e-16 ***
## sweet.liking -0.09968 0.02800 -3.56 0.000504 ***
## sex 5.41426 1.54258 3.51 0.000601 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.43507)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10286 on 142 degrees of freedom
## AIC: 1037.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + emotional + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + emotional + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.763 -6.199 1.281 5.633 18.401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.31826 3.28897 12.259 < 2e-16 ***
## sweet.liking -0.10124 0.02817 -3.594 0.000449 ***
## emotional 0.09809 0.15600 0.629 0.530522
## sex 5.28000 1.56055 3.383 0.000927 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.74483)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10257 on 141 degrees of freedom
## AIC: 1039
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + uncontrolled + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + uncontrolled + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.258 -5.899 1.283 5.751 18.926
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.60392 4.32279 9.393 < 2e-16 ***
## sweet.liking -0.10099 0.02882 -3.504 0.000615 ***
## uncontrolled 0.03181 0.15722 0.202 0.839934
## sex 5.46652 1.56922 3.484 0.000659 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.92762)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10283 on 141 degrees of freedom
## AIC: 1039.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + restraint + sex, data = EB.df)) ## restraint has a positive effect on sHEI, after adjusting for sex##
## Call:
## glm(formula = sHEI ~ sweet.liking + restraint + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -23.1734 -6.3698 0.1704 5.7717 15.8014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.84599 4.03347 7.895 7.37e-13 ***
## sweet.liking -0.08984 0.02725 -3.297 0.001236 **
## restraint 0.60428 0.18424 3.280 0.001309 **
## sex 5.74795 1.49563 3.843 0.000183 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 67.77799)
##
## Null deviance: 12258.6 on 144 degrees of freedom
## Residual deviance: 9556.7 on 141 degrees of freedom
## AIC: 1028.8
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + EF + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + EF + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.997 -5.905 1.419 5.626 18.902
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.25001 4.87112 8.058 2.98e-13 ***
## sweet.liking -0.10171 0.02835 -3.588 0.000459 ***
## EF 0.15603 0.30374 0.514 0.608259
## sex 5.46811 1.55014 3.528 0.000567 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.81252)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10267 on 141 degrees of freedom
## AIC: 1039.2
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + EOE + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + EOE + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.952 -5.993 1.197 5.567 18.567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.78061 3.14353 12.973 < 2e-16 ***
## sweet.liking -0.10035 0.02812 -3.568 0.000492 ***
## EOE 0.07246 0.17262 0.420 0.675283
## sex 5.27971 1.57992 3.342 0.001066 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.85774)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10273 on 141 degrees of freedom
## AIC: 1039.3
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + EUE + sex, data = EB.df)) ## emotional undereating has a negative effect on sHEI, after adjusting for sex##
## Call:
## glm(formula = sHEI ~ sweet.liking + EUE + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.924 -5.875 0.901 6.032 18.440
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.94850 3.33773 13.467 < 2e-16 ***
## sweet.liking -0.10055 0.02762 -3.641 0.000381 ***
## EUE -0.39825 0.17872 -2.228 0.027442 *
## sex 6.15878 1.55773 3.954 0.000121 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 70.46723)
##
## Null deviance: 12258.6 on 144 degrees of freedom
## Residual deviance: 9935.9 on 141 degrees of freedom
## AIC: 1034.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + FF + sex, data = EB.df)) ## food fussiness has a negative effect on sHEI, after adjusting for sex##
## Call:
## glm(formula = sHEI ~ sweet.liking + FF + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -19.342 -5.778 1.122 5.422 23.500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.8501 3.2725 13.705 < 2e-16 ***
## sweet.liking -0.0962 0.0276 -3.485 0.000656 ***
## FF -0.4467 0.1904 -2.346 0.020374 *
## sex 5.9157 1.5336 3.857 0.000174 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 70.20853)
##
## Null deviance: 12258.6 on 144 degrees of freedom
## Residual deviance: 9899.4 on 141 degrees of freedom
## AIC: 1033.9
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + FR + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + FR + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.9244 -5.5991 0.7548 5.8577 18.9056
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.00562 4.16963 10.554 < 2e-16 ***
## sweet.liking -0.09684 0.02818 -3.437 0.000774 ***
## FR -0.29009 0.31106 -0.933 0.352628
## sex 5.40438 1.54332 3.502 0.000619 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.50159)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10223 on 141 degrees of freedom
## AIC: 1038.6
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + SE + sex, data = EB.df)) ## slowness in eating has a negative effect on sHEI, after adjusting for sex##
## Call:
## glm(formula = sHEI ~ sweet.liking + SE + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.773 -6.438 1.583 6.280 19.141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.70959 3.14569 13.895 < 2e-16 ***
## sweet.liking -0.10302 0.02774 -3.713 0.000294 ***
## SE -0.35625 0.17522 -2.033 0.043909 *
## sex 6.38980 1.59949 3.995 0.000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 70.87097)
##
## Null deviance: 12258.6 on 144 degrees of freedom
## Residual deviance: 9992.8 on 141 degrees of freedom
## AIC: 1035.3
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + H + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + H + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.332 -5.871 1.201 5.772 18.758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.96978 3.87254 10.580 < 2e-16 ***
## sweet.liking -0.10018 0.02847 -3.519 0.000583 ***
## H 0.02141 0.19614 0.109 0.913221
## sex 5.39116 1.56236 3.451 0.000738 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.94263)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10285 on 141 degrees of freedom
## AIC: 1039.4
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + SR + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + SR + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.580 -6.393 1.420 6.356 18.903
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.81077 3.34943 12.782 < 2e-16 ***
## sweet.liking -0.10263 0.02817 -3.644 0.000377 ***
## SR -0.20142 0.20750 -0.971 0.333360
## sex 5.84557 1.60560 3.641 0.000381 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.46454)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10217 on 141 degrees of freedom
## AIC: 1038.5
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + BAS + sex, data = EB.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + BAS + sex, data = EB.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -22.402 -5.989 1.334 5.659 18.681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.16810 4.63341 9.317 < 2e-16 ***
## sweet.liking -0.10053 0.02811 -3.576 0.000478 ***
## BAS -0.04747 0.08833 -0.537 0.591831
## sex 5.29825 1.56144 3.393 0.000897 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72.79968)
##
## Null deviance: 12259 on 144 degrees of freedom
## Residual deviance: 10265 on 141 degrees of freedom
## AIC: 1039.2
##
## Number of Fisher Scoring iterations: 2Models: interactions - sHEI (females)
summary(glm(sHEI ~ sweet.liking, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.8804 -6.1254 0.3665 5.8845 18.7310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.23213 1.26330 42.137 < 2e-16 ***
## sweet.liking -0.13614 0.03081 -4.418 2.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.2206)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6357.8 on 99 degrees of freedom
## AIC: 711
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*emotional, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * emotional, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.2546 -5.5000 0.7599 5.7375 18.4182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.496559 5.050215 10.593 <2e-16 ***
## sweet.liking -0.179847 0.117141 -1.535 0.128
## emotional -0.018788 0.391053 -0.048 0.962
## sweet.liking:emotional 0.003314 0.008836 0.375 0.708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 65.20279)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6324.7 on 97 degrees of freedom
## AIC: 714.47
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*uncontrolled, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * uncontrolled, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.0654 -5.9020 0.5373 5.6834 18.8719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.895137 5.610494 9.606 9.51e-16 ***
## sweet.liking -0.178991 0.145091 -1.234 0.220
## uncontrolled -0.034282 0.309184 -0.111 0.912
## sweet.liking:uncontrolled 0.002193 0.007497 0.293 0.770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 65.4592)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6349.5 on 97 degrees of freedom
## AIC: 714.87
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*restraint, data = EB.fem.df)) ##
## Call:
## glm(formula = sHEI ~ sweet.liking * restraint, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.5697 -6.1949 -0.5687 5.6351 16.6047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.422393 5.484792 8.464 2.75e-13 ***
## sweet.liking -0.153951 0.126003 -1.222 0.225
## restraint 0.457740 0.351144 1.304 0.195
## sweet.liking:restraint 0.002371 0.008257 0.287 0.775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 61.30063)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 5946.2 on 97 degrees of freedom
## AIC: 708.24
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*EF, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * EF, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.9419 -6.0739 0.0535 5.6501 18.7424
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.643540 7.673203 7.121 1.88e-10 ***
## sweet.liking -0.192326 0.204838 -0.939 0.350
## EF -0.110954 0.600344 -0.185 0.854
## sweet.liking:EF 0.004421 0.015943 0.277 0.782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 65.48737)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6352.3 on 97 degrees of freedom
## AIC: 714.91
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*EOE, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * EOE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.8270 -5.5524 0.7952 4.9135 17.8094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.307647 4.189310 11.770 <2e-16 ***
## sweet.liking -0.090625 0.102483 -0.884 0.379
## EOE 0.380093 0.383954 0.990 0.325
## sweet.liking:EOE -0.004417 0.009232 -0.478 0.633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.53549)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6259.9 on 97 degrees of freedom
## AIC: 713.43
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*EUE, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * EUE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -17.0576 -5.7092 0.1056 5.6263 18.4528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.596433 5.044195 11.815 <2e-16 ***
## sweet.liking -0.190934 0.124632 -1.532 0.129
## EUE -0.514902 0.394615 -1.305 0.195
## sweet.liking:EUE 0.004832 0.009474 0.510 0.611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63.59733)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6168.9 on 97 degrees of freedom
## AIC: 711.95
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*FF, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * FF, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.0429 -5.5550 0.6768 4.9658 21.9539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.077508 3.878199 14.718 <2e-16 ***
## sweet.liking -0.158097 0.097135 -1.628 0.107
## FF -0.378647 0.368308 -1.028 0.306
## sweet.liking:FF 0.002491 0.009219 0.270 0.788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.22645)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6230.0 on 97 degrees of freedom
## AIC: 712.95
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*FR, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * FR, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.8794 -6.0701 0.3254 5.6197 18.7387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.231794 6.577081 8.094 1.7e-12 ***
## sweet.liking -0.102989 0.169005 -0.609 0.544
## FR -0.006038 0.658317 -0.009 0.993
## sweet.liking:FR -0.003133 0.016274 -0.192 0.848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 65.4587)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6349.5 on 97 degrees of freedom
## AIC: 714.87
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*SE, data = EB.fem.df)) ## slowness in eating has a negative effect in sHEI##
## Call:
## glm(formula = sHEI ~ sweet.liking * SE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.405 -6.494 1.015 5.766 18.985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.785040 4.179255 14.305 <2e-16 ***
## sweet.liking -0.209814 0.108490 -1.934 0.0560 .
## SE -0.514791 0.307725 -1.673 0.0976 .
## sweet.liking:SE 0.005363 0.008064 0.665 0.5076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63.02985)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6113.9 on 97 degrees of freedom
## AIC: 711.05
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*H, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * H, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.9672 -5.6441 0.6846 5.7163 17.0613
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.75789 7.68888 5.041 2.15e-06 ***
## sweet.liking 0.13802 0.19197 0.719 0.4739
## H 0.94485 0.49392 1.913 0.0587 .
## sweet.liking:H -0.01770 0.01196 -1.480 0.1421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63.02085)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6113.0 on 97 degrees of freedom
## AIC: 711.03
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*SR, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * SR, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.5336 -5.9735 0.7721 5.6429 18.8190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.128125 5.305399 10.768 <2e-16 ***
## sweet.liking -0.186837 0.125485 -1.489 0.140
## SR -0.334514 0.440188 -0.760 0.449
## sweet.liking:SR 0.004311 0.010275 0.420 0.676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 65.06524)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6311.3 on 97 degrees of freedom
## AIC: 714.26
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking*BAS, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking * BAS, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.8346 -5.8190 -0.2086 5.4896 18.2315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.864783 5.790891 9.992 <2e-16 ***
## sweet.liking -0.089400 0.147210 -0.607 0.545
## BAS -0.128963 0.152069 -0.848 0.398
## sweet.liking:BAS -0.001467 0.003922 -0.374 0.709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63.1203)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6122.7 on 97 degrees of freedom
## AIC: 711.19
##
## Number of Fisher Scoring iterations: 2Models: mediation - sHEI (females)
## Sugar intake models
summary(glm(sHEI ~ sweet.liking, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.8804 -6.1254 0.3665 5.8845 18.7310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.23213 1.26330 42.137 < 2e-16 ***
## sweet.liking -0.13614 0.03081 -4.418 2.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.2206)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6357.8 on 99 degrees of freedom
## AIC: 711
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + emotional, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + emotional, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.7267 -5.5986 0.9343 5.7686 18.2487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 51.86880 2.57143 20.171 < 2e-16 ***
## sweet.liking -0.13749 0.03099 -4.436 2.4e-05 ***
## emotional 0.11088 0.18197 0.609 0.544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.63105)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6333.8 on 98 degrees of freedom
## AIC: 712.62
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + uncontrolled, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + uncontrolled, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.8972 -5.9994 0.4448 5.7709 18.8463
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.59113 3.39162 15.506 < 2e-16 ***
## sweet.liking -0.13758 0.03176 -4.331 3.58e-05 ***
## uncontrolled 0.03785 0.18570 0.204 0.839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.84842)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6355.1 on 98 degrees of freedom
## AIC: 712.96
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + restraint, data = EB.fem.df)) ## restraint has a positive impact on sHEI in women, but not in men##
## Call:
## glm(formula = sHEI ~ sweet.liking + restraint, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.4702 -6.1595 -0.4402 5.7638 16.0955
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.1778 3.3462 13.501 < 2e-16 ***
## sweet.liking -0.1189 0.0307 -3.872 0.000195 ***
## restraint 0.5387 0.2082 2.588 0.011131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 60.7267)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 5951.2 on 98 degrees of freedom
## AIC: 706.32
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + EF, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + EF, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.8669 -6.0815 0.2949 5.8711 18.7456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.86909 4.21398 12.546 < 2e-16 ***
## sweet.liking -0.13619 0.03097 -4.397 2.79e-05 ***
## EF 0.02915 0.32267 0.090 0.928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.87051)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6357.3 on 98 degrees of freedom
## AIC: 712.99
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + EOE, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + EOE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.4884 -5.6223 0.8391 4.8232 17.9297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.96103 2.35883 21.604 < 2e-16 ***
## sweet.liking -0.13737 0.03079 -4.462 2.17e-05 ***
## EOE 0.22202 0.19486 1.139 0.257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.0277)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6274.7 on 98 degrees of freedom
## AIC: 711.67
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + EUE, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + EUE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -17.0716 -5.8572 0.2833 5.7670 18.4153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.48112 2.86005 20.098 < 2e-16 ***
## sweet.liking -0.12936 0.03082 -4.197 5.95e-05 ***
## EUE -0.34422 0.20830 -1.652 0.102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63.11718)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6185.5 on 98 degrees of freedom
## AIC: 710.22
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + FF, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + FF, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -14.8040 -5.6798 0.7653 4.7766 21.8474
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 56.28610 2.52937 22.253 < 2e-16 ***
## sweet.liking -0.13322 0.03074 -4.334 3.56e-05 ***
## FF -0.29788 0.21407 -1.392 0.167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63.61891)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6234.7 on 98 degrees of freedom
## AIC: 711.02
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + FR, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + FR, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.0454 -6.2582 0.3243 5.5617 18.7606
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.2780 3.6860 14.725 < 2e-16 ***
## sweet.liking -0.1350 0.0312 -4.325 3.67e-05 ***
## FR -0.1110 0.3672 -0.302 0.763
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.81551)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6351.9 on 98 degrees of freedom
## AIC: 712.91
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + SE, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + SE, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -17.0885 -6.6838 0.6558 5.6732 19.0385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.66408 2.69347 21.409 < 2e-16 ***
## sweet.liking -0.14059 0.03053 -4.604 1.24e-05 ***
## SE -0.35460 0.19098 -1.857 0.0663 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 62.67118)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6141.8 on 98 degrees of freedom
## AIC: 709.51
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + H, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + H, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.6699 -5.6488 0.5955 5.0511 17.7447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.77979 3.66443 13.312 < 2e-16 ***
## sweet.liking -0.14240 0.03109 -4.580 1.37e-05 ***
## H 0.29583 0.22866 1.294 0.199
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 63.78643)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6251.1 on 98 degrees of freedom
## AIC: 711.29
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + SR, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + SR, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.6309 -6.0518 0.7378 5.4056 18.8057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.33023 3.11529 17.761 < 2e-16 ***
## sweet.liking -0.13582 0.03089 -4.397 2.79e-05 ***
## SR -0.18184 0.24669 -0.737 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 64.5182)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6322.8 on 98 degrees of freedom
## AIC: 712.44
##
## Number of Fisher Scoring iterations: 2summary(glm(sHEI ~ sweet.liking + BAS, data = EB.fem.df))##
## Call:
## glm(formula = sHEI ~ sweet.liking + BAS, data = EB.fem.df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -15.9028 -5.6117 -0.0486 5.7217 18.1770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.56971 3.55777 16.744 < 2e-16 ***
## sweet.liking -0.14326 0.03064 -4.675 9.4e-06 ***
## BAS -0.17426 0.09162 -1.902 0.0601 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 62.56638)
##
## Null deviance: 7611.4 on 100 degrees of freedom
## Residual deviance: 6131.5 on 98 degrees of freedom
## AIC: 709.34
##
## Number of Fisher Scoring iterations: 2AEBQ emotional overeating analysis
## Emotional overeating
EB.df %>%
summary()## ID age height weight
## Length:145 Min. :18.00 Min. :53.00 Min. : 80
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126
## Mode :character Median :24.00 Median :65.00 Median :145
## Mean :27.23 Mean :65.32 Mean :154
## 3rd Qu.:28.00 3rd Qu.:68.00 3rd Qu.:175
## Max. :68.00 Max. :76.00 Max. :320
##
## sex sex.other race race.other ethnicity
## Min. :1.000 Min. : NA Min. :1.000 Min. : NA Min. :1.000
## 1st Qu.:1.000 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.000
## Median :2.000 Median : NA Median :4.000 Median : NA Median :2.000
## Mean :1.697 Mean :NaN Mean :4.248 Mean :NaN Mean :1.752
## 3rd Qu.:2.000 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.000
## Max. :2.000 Max. : NA Max. :7.000 Max. : NA Max. :2.000
## NA's :145 NA's :4 NA's :145
## us.born birthplace agetous timeinus education
## Min. :1.000 Min. : NA Min. :1.000 Min. :1.000 Min. : 5.000
## 1st Qu.:1.000 1st Qu.: NA 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 6.000
## Median :1.000 Median : NA Median :1.000 Median :1.000 Median : 9.000
## Mean :1.345 Mean :NaN Mean :2.441 Mean :2.324 Mean : 7.959
## 3rd Qu.:2.000 3rd Qu.: NA 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.: 9.000
## Max. :2.000 Max. : NA Max. :7.000 Max. :8.000 Max. :12.000
## NA's :145
## income.personal income.household exercise ssb.day
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :5.000 Median :3.000 Median :1.000
## Mean :3.545 Mean :5.269 Mean :3.352 Mean :1.876
## 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :8.000 Max. :8.000 Max. :8.000 Max. :7.000
##
## sugars.amount BMI sweet.liking sugar.intake
## Min. :1.000 Min. :16.16 Min. :-62.11 Min. : 130.0
## 1st Qu.:2.000 1st Qu.:21.13 1st Qu.: 18.50 1st Qu.: 260.0
## Median :2.000 Median :24.54 Median : 36.44 Median : 286.0
## Mean :1.931 Mean :25.39 Mean : 33.17 Mean : 405.6
## 3rd Qu.:2.000 3rd Qu.:28.32 3rd Qu.: 51.22 3rd Qu.: 520.0
## Max. :3.000 Max. :55.78 Max. : 92.00 Max. :1300.0
##
## sugarHEI fatHEI SuFatHEI sHEI
## Min. : 0.000 Min. :1.820 Min. : 1.820 Min. :21.45
## 1st Qu.: 0.000 1st Qu.:3.200 1st Qu.: 4.640 1st Qu.:41.42
## Median : 5.000 Median :4.640 Median : 8.200 Median :47.62
## Mean : 4.379 Mean :3.932 Mean : 8.311 Mean :47.12
## 3rd Qu.: 5.000 3rd Qu.:4.640 3rd Qu.: 9.640 3rd Qu.:54.13
## Max. :10.000 Max. :6.560 Max. :16.560 Max. :68.00
##
## BAS uncontrolled restraint emotional EF
## Min. :10.00 Min. : 9.0 Min. : 6.00 Min. : 6.00 Min. : 3.00
## 1st Qu.:31.00 1st Qu.:16.0 1st Qu.:12.00 1st Qu.: 9.00 1st Qu.:12.00
## Median :36.00 Median :19.0 Median :14.00 Median :12.00 Median :13.00
## Mean :35.79 Mean :18.7 Mean :14.08 Mean :12.29 Mean :12.63
## 3rd Qu.:42.00 3rd Qu.:22.0 3rd Qu.:17.00 3rd Qu.:15.00 3rd Qu.:15.00
## Max. :50.00 Max. :34.0 Max. :24.00 Max. :24.00 Max. :15.00
##
## EOE EUE FF FR
## Min. : 4.000 Min. : 4.0 Min. : 5.00 Min. : 3.000
## 1st Qu.: 7.000 1st Qu.:10.0 1st Qu.: 7.00 1st Qu.: 8.000
## Median : 9.000 Median :12.0 Median :10.00 Median :10.000
## Mean : 9.855 Mean :12.4 Mean :10.23 Mean : 9.786
## 3rd Qu.:13.000 3rd Qu.:16.0 3rd Qu.:13.00 3rd Qu.:11.000
## Max. :20.000 Max. :20.0 Max. :21.00 Max. :15.000
##
## SE H SR instagram
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. :1.000
## 1st Qu.: 8.00 1st Qu.:13.00 1st Qu.: 9.00 1st Qu.:5.000
## Median :11.00 Median :15.00 Median :11.00 Median :6.000
## Mean :11.26 Mean :15.43 Mean :10.92 Mean :5.428
## 3rd Qu.:15.00 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:7.000
## Max. :20.00 Max. :24.00 Max. :19.00 Max. :7.000
##
## emo
## Min. :10.00
## 1st Qu.:16.00
## Median :22.00
## Mean :22.14
## 3rd Qu.:28.00
## Max. :42.00
## EB.df <- EB.df %>%
mutate(EOE.level = case_when(
EOE < 9 ~ "Low",
EOE >= 9 ~ "High"))
EB.df %>%
group_by(EOE.level) %>%
count()## # A tibble: 2 × 2
## # Groups: EOE.level [2]
## EOE.level n
## <chr> <int>
## 1 High 82
## 2 Low 63EB.df %>% ggplot(aes(sweet.liking, sugar.intake, color = EOE.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
## Emotional overeating: females only
EB.fem.df %>%
summary()## ID age height weight sex
## Length:101 Min. :18.00 Min. :53.00 Min. : 80.0 Min. :2
## Class :character 1st Qu.:21.00 1st Qu.:62.00 1st Qu.:124.0 1st Qu.:2
## Mode :character Median :24.00 Median :64.00 Median :138.0 Median :2
## Mean :26.57 Mean :63.81 Mean :144.6 Mean :2
## 3rd Qu.:28.00 3rd Qu.:66.00 3rd Qu.:160.0 3rd Qu.:2
## Max. :63.00 Max. :71.00 Max. :320.0 Max. :2
##
## sex.other race race.other ethnicity us.born
## Min. : NA Min. :1.000 Min. : NA Min. :1.000 Min. :1.000
## 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.000 1st Qu.:1.000
## Median : NA Median :4.000 Median : NA Median :2.000 Median :1.000
## Mean :NaN Mean :4.306 Mean :NaN Mean :1.752 Mean :1.327
## 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.000 3rd Qu.:2.000
## Max. : NA Max. :7.000 Max. : NA Max. :2.000 Max. :2.000
## NA's :101 NA's :3 NA's :101
## birthplace agetous timeinus education income.personal
## Min. : NA Min. :1.000 Min. :1.000 Min. : 5.000 Min. :1.000
## 1st Qu.: NA 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 6.000 1st Qu.:2.000
## Median : NA Median :1.000 Median :1.000 Median : 8.000 Median :3.000
## Mean :NaN Mean :2.307 Mean :2.396 Mean : 7.782 Mean :3.248
## 3rd Qu.: NA 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.: 9.000 3rd Qu.:4.000
## Max. : NA Max. :7.000 Max. :8.000 Max. :12.000 Max. :8.000
## NA's :101
## income.household exercise ssb.day sugars.amount
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.00
## Median :5.000 Median :3.000 Median :1.000 Median :2.00
## Mean :5.139 Mean :3.168 Mean :1.772 Mean :1.96
## 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :8.000 Max. :8.000 Max. :7.000 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI
## Min. :16.16 Min. :-62.11 Min. : 130.0 Min. : 0.000
## 1st Qu.:21.03 1st Qu.: 16.44 1st Qu.: 260.0 1st Qu.: 5.000
## Median :24.36 Median : 36.89 Median : 286.0 Median : 5.000
## Mean :25.07 Mean : 31.80 Mean : 392.1 Mean : 4.356
## 3rd Qu.:27.92 3rd Qu.: 51.11 3rd Qu.: 442.0 3rd Qu.: 5.000
## Max. :55.06 Max. : 85.00 Max. :1196.0 Max. :10.000
##
## fatHEI SuFatHEI sHEI BAS
## Min. :1.820 Min. : 1.82 Min. :29.55 Min. :10.00
## 1st Qu.:3.200 1st Qu.: 6.82 1st Qu.:42.23 1st Qu.:30.00
## Median :4.640 Median : 8.20 Median :49.71 Median :35.00
## Mean :4.024 Mean : 8.38 Mean :48.90 Mean :35.07
## 3rd Qu.:4.640 3rd Qu.: 9.64 3rd Qu.:55.63 3rd Qu.:42.00
## Max. :6.560 Max. :16.56 Max. :68.00 Max. :50.00
##
## uncontrolled restraint emotional EF EOE
## Min. : 9.00 Min. : 6.00 Min. : 6.00 Min. : 3.0 Min. : 4.00
## 1st Qu.:15.00 1st Qu.:11.00 1st Qu.: 9.00 1st Qu.:11.0 1st Qu.: 7.00
## Median :18.00 Median :14.00 Median :13.00 Median :13.0 Median :11.00
## Mean :18.15 Mean :13.93 Mean :12.68 Mean :12.5 Mean :10.41
## 3rd Qu.:21.00 3rd Qu.:17.00 3rd Qu.:16.00 3rd Qu.:15.0 3rd Qu.:13.00
## Max. :32.00 Max. :22.00 Max. :24.00 Max. :15.0 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.000 Min. : 4.0
## 1st Qu.:10.00 1st Qu.: 8.00 1st Qu.: 8.000 1st Qu.: 9.0
## Median :13.00 Median :10.00 Median :10.000 Median :12.0
## Mean :12.97 Mean :10.56 Mean : 9.762 Mean :12.1
## 3rd Qu.:16.00 3rd Qu.:13.00 3rd Qu.:11.000 3rd Qu.:16.0
## Max. :20.00 Max. :21.00 Max. :15.000 Max. :20.0
##
## H SR instagram emo
## Min. : 5.00 Min. : 4.00 Min. :1.000 Min. :10.00
## 1st Qu.:14.00 1st Qu.: 9.00 1st Qu.:5.000 1st Qu.:16.00
## Median :16.00 Median :12.00 Median :6.000 Median :23.00
## Mean :15.72 Mean :11.59 Mean :5.663 Mean :23.09
## 3rd Qu.:18.00 3rd Qu.:14.00 3rd Qu.:7.000 3rd Qu.:29.00
## Max. :23.00 Max. :19.00 Max. :7.000 Max. :42.00
## EB.fem.df <- EB.fem.df %>%
mutate(EOE.level = case_when(
EOE < 11 ~ "Low",
EOE >= 11 ~ "High"))
EB.fem.df %>%
group_by(EOE.level) %>%
count()## # A tibble: 2 × 2
## # Groups: EOE.level [2]
## EOE.level n
## <chr> <int>
## 1 High 51
## 2 Low 50EB.fem.df %>% ggplot(aes(sweet.liking, sugar.intake, color = EOE.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
TFEQ emotional analysis
## TFEQ Emotional
EB.df %>%
summary()## ID age height weight
## Length:145 Min. :18.00 Min. :53.00 Min. : 80
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126
## Mode :character Median :24.00 Median :65.00 Median :145
## Mean :27.23 Mean :65.32 Mean :154
## 3rd Qu.:28.00 3rd Qu.:68.00 3rd Qu.:175
## Max. :68.00 Max. :76.00 Max. :320
##
## sex sex.other race race.other ethnicity
## Min. :1.000 Min. : NA Min. :1.000 Min. : NA Min. :1.000
## 1st Qu.:1.000 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.000
## Median :2.000 Median : NA Median :4.000 Median : NA Median :2.000
## Mean :1.697 Mean :NaN Mean :4.248 Mean :NaN Mean :1.752
## 3rd Qu.:2.000 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.000
## Max. :2.000 Max. : NA Max. :7.000 Max. : NA Max. :2.000
## NA's :145 NA's :4 NA's :145
## us.born birthplace agetous timeinus education
## Min. :1.000 Min. : NA Min. :1.000 Min. :1.000 Min. : 5.000
## 1st Qu.:1.000 1st Qu.: NA 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 6.000
## Median :1.000 Median : NA Median :1.000 Median :1.000 Median : 9.000
## Mean :1.345 Mean :NaN Mean :2.441 Mean :2.324 Mean : 7.959
## 3rd Qu.:2.000 3rd Qu.: NA 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.: 9.000
## Max. :2.000 Max. : NA Max. :7.000 Max. :8.000 Max. :12.000
## NA's :145
## income.personal income.household exercise ssb.day
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :5.000 Median :3.000 Median :1.000
## Mean :3.545 Mean :5.269 Mean :3.352 Mean :1.876
## 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :8.000 Max. :8.000 Max. :8.000 Max. :7.000
##
## sugars.amount BMI sweet.liking sugar.intake
## Min. :1.000 Min. :16.16 Min. :-62.11 Min. : 130.0
## 1st Qu.:2.000 1st Qu.:21.13 1st Qu.: 18.50 1st Qu.: 260.0
## Median :2.000 Median :24.54 Median : 36.44 Median : 286.0
## Mean :1.931 Mean :25.39 Mean : 33.17 Mean : 405.6
## 3rd Qu.:2.000 3rd Qu.:28.32 3rd Qu.: 51.22 3rd Qu.: 520.0
## Max. :3.000 Max. :55.78 Max. : 92.00 Max. :1300.0
##
## sugarHEI fatHEI SuFatHEI sHEI
## Min. : 0.000 Min. :1.820 Min. : 1.820 Min. :21.45
## 1st Qu.: 0.000 1st Qu.:3.200 1st Qu.: 4.640 1st Qu.:41.42
## Median : 5.000 Median :4.640 Median : 8.200 Median :47.62
## Mean : 4.379 Mean :3.932 Mean : 8.311 Mean :47.12
## 3rd Qu.: 5.000 3rd Qu.:4.640 3rd Qu.: 9.640 3rd Qu.:54.13
## Max. :10.000 Max. :6.560 Max. :16.560 Max. :68.00
##
## BAS uncontrolled restraint emotional EF
## Min. :10.00 Min. : 9.0 Min. : 6.00 Min. : 6.00 Min. : 3.00
## 1st Qu.:31.00 1st Qu.:16.0 1st Qu.:12.00 1st Qu.: 9.00 1st Qu.:12.00
## Median :36.00 Median :19.0 Median :14.00 Median :12.00 Median :13.00
## Mean :35.79 Mean :18.7 Mean :14.08 Mean :12.29 Mean :12.63
## 3rd Qu.:42.00 3rd Qu.:22.0 3rd Qu.:17.00 3rd Qu.:15.00 3rd Qu.:15.00
## Max. :50.00 Max. :34.0 Max. :24.00 Max. :24.00 Max. :15.00
##
## EOE EUE FF FR
## Min. : 4.000 Min. : 4.0 Min. : 5.00 Min. : 3.000
## 1st Qu.: 7.000 1st Qu.:10.0 1st Qu.: 7.00 1st Qu.: 8.000
## Median : 9.000 Median :12.0 Median :10.00 Median :10.000
## Mean : 9.855 Mean :12.4 Mean :10.23 Mean : 9.786
## 3rd Qu.:13.000 3rd Qu.:16.0 3rd Qu.:13.00 3rd Qu.:11.000
## Max. :20.000 Max. :20.0 Max. :21.00 Max. :15.000
##
## SE H SR instagram
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. :1.000
## 1st Qu.: 8.00 1st Qu.:13.00 1st Qu.: 9.00 1st Qu.:5.000
## Median :11.00 Median :15.00 Median :11.00 Median :6.000
## Mean :11.26 Mean :15.43 Mean :10.92 Mean :5.428
## 3rd Qu.:15.00 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:7.000
## Max. :20.00 Max. :24.00 Max. :19.00 Max. :7.000
##
## emo EOE.level
## Min. :10.00 Length:145
## 1st Qu.:16.00 Class :character
## Median :22.00 Mode :character
## Mean :22.14
## 3rd Qu.:28.00
## Max. :42.00
## EB.df <- EB.df %>%
mutate(emo.level = case_when(
emotional <= 12 ~ "Low",
emotional > 12 ~ "High"))
EB.df %>%
group_by(emo.level) %>%
count()## # A tibble: 2 × 2
## # Groups: emo.level [2]
## emo.level n
## <chr> <int>
## 1 High 69
## 2 Low 76EB.df %>% ggplot(aes(sweet.liking, sugar.intake, color = emo.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
## TFEQ Emotional: females only
EB.fem.df %>%
summary()## ID age height weight sex
## Length:101 Min. :18.00 Min. :53.00 Min. : 80.0 Min. :2
## Class :character 1st Qu.:21.00 1st Qu.:62.00 1st Qu.:124.0 1st Qu.:2
## Mode :character Median :24.00 Median :64.00 Median :138.0 Median :2
## Mean :26.57 Mean :63.81 Mean :144.6 Mean :2
## 3rd Qu.:28.00 3rd Qu.:66.00 3rd Qu.:160.0 3rd Qu.:2
## Max. :63.00 Max. :71.00 Max. :320.0 Max. :2
##
## sex.other race race.other ethnicity us.born
## Min. : NA Min. :1.000 Min. : NA Min. :1.000 Min. :1.000
## 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.000 1st Qu.:1.000
## Median : NA Median :4.000 Median : NA Median :2.000 Median :1.000
## Mean :NaN Mean :4.306 Mean :NaN Mean :1.752 Mean :1.327
## 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.000 3rd Qu.:2.000
## Max. : NA Max. :7.000 Max. : NA Max. :2.000 Max. :2.000
## NA's :101 NA's :3 NA's :101
## birthplace agetous timeinus education income.personal
## Min. : NA Min. :1.000 Min. :1.000 Min. : 5.000 Min. :1.000
## 1st Qu.: NA 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 6.000 1st Qu.:2.000
## Median : NA Median :1.000 Median :1.000 Median : 8.000 Median :3.000
## Mean :NaN Mean :2.307 Mean :2.396 Mean : 7.782 Mean :3.248
## 3rd Qu.: NA 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.: 9.000 3rd Qu.:4.000
## Max. : NA Max. :7.000 Max. :8.000 Max. :12.000 Max. :8.000
## NA's :101
## income.household exercise ssb.day sugars.amount
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.00
## Median :5.000 Median :3.000 Median :1.000 Median :2.00
## Mean :5.139 Mean :3.168 Mean :1.772 Mean :1.96
## 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :8.000 Max. :8.000 Max. :7.000 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI
## Min. :16.16 Min. :-62.11 Min. : 130.0 Min. : 0.000
## 1st Qu.:21.03 1st Qu.: 16.44 1st Qu.: 260.0 1st Qu.: 5.000
## Median :24.36 Median : 36.89 Median : 286.0 Median : 5.000
## Mean :25.07 Mean : 31.80 Mean : 392.1 Mean : 4.356
## 3rd Qu.:27.92 3rd Qu.: 51.11 3rd Qu.: 442.0 3rd Qu.: 5.000
## Max. :55.06 Max. : 85.00 Max. :1196.0 Max. :10.000
##
## fatHEI SuFatHEI sHEI BAS
## Min. :1.820 Min. : 1.82 Min. :29.55 Min. :10.00
## 1st Qu.:3.200 1st Qu.: 6.82 1st Qu.:42.23 1st Qu.:30.00
## Median :4.640 Median : 8.20 Median :49.71 Median :35.00
## Mean :4.024 Mean : 8.38 Mean :48.90 Mean :35.07
## 3rd Qu.:4.640 3rd Qu.: 9.64 3rd Qu.:55.63 3rd Qu.:42.00
## Max. :6.560 Max. :16.56 Max. :68.00 Max. :50.00
##
## uncontrolled restraint emotional EF EOE
## Min. : 9.00 Min. : 6.00 Min. : 6.00 Min. : 3.0 Min. : 4.00
## 1st Qu.:15.00 1st Qu.:11.00 1st Qu.: 9.00 1st Qu.:11.0 1st Qu.: 7.00
## Median :18.00 Median :14.00 Median :13.00 Median :13.0 Median :11.00
## Mean :18.15 Mean :13.93 Mean :12.68 Mean :12.5 Mean :10.41
## 3rd Qu.:21.00 3rd Qu.:17.00 3rd Qu.:16.00 3rd Qu.:15.0 3rd Qu.:13.00
## Max. :32.00 Max. :22.00 Max. :24.00 Max. :15.0 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.000 Min. : 4.0
## 1st Qu.:10.00 1st Qu.: 8.00 1st Qu.: 8.000 1st Qu.: 9.0
## Median :13.00 Median :10.00 Median :10.000 Median :12.0
## Mean :12.97 Mean :10.56 Mean : 9.762 Mean :12.1
## 3rd Qu.:16.00 3rd Qu.:13.00 3rd Qu.:11.000 3rd Qu.:16.0
## Max. :20.00 Max. :21.00 Max. :15.000 Max. :20.0
##
## H SR instagram emo
## Min. : 5.00 Min. : 4.00 Min. :1.000 Min. :10.00
## 1st Qu.:14.00 1st Qu.: 9.00 1st Qu.:5.000 1st Qu.:16.00
## Median :16.00 Median :12.00 Median :6.000 Median :23.00
## Mean :15.72 Mean :11.59 Mean :5.663 Mean :23.09
## 3rd Qu.:18.00 3rd Qu.:14.00 3rd Qu.:7.000 3rd Qu.:29.00
## Max. :23.00 Max. :19.00 Max. :7.000 Max. :42.00
##
## EOE.level
## Length:101
## Class :character
## Mode :character
##
##
##
## EB.fem.df <- EB.fem.df %>%
mutate(emo.level = case_when(
emotional <= 12 ~ "Low",
emotional > 12 ~ "High"))
EB.fem.df %>%
group_by(emo.level) %>%
count()## # A tibble: 2 × 2
## # Groups: emo.level [2]
## emo.level n
## <chr> <int>
## 1 High 54
## 2 Low 47EB.fem.df %>% ggplot(aes(sweet.liking, sugar.intake, color = emo.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
TFEQ restraint analysis
## TFEQ Emotional
EB.df %>%
summary()## ID age height weight
## Length:145 Min. :18.00 Min. :53.00 Min. : 80
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126
## Mode :character Median :24.00 Median :65.00 Median :145
## Mean :27.23 Mean :65.32 Mean :154
## 3rd Qu.:28.00 3rd Qu.:68.00 3rd Qu.:175
## Max. :68.00 Max. :76.00 Max. :320
##
## sex sex.other race race.other ethnicity
## Min. :1.000 Min. : NA Min. :1.000 Min. : NA Min. :1.000
## 1st Qu.:1.000 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.000
## Median :2.000 Median : NA Median :4.000 Median : NA Median :2.000
## Mean :1.697 Mean :NaN Mean :4.248 Mean :NaN Mean :1.752
## 3rd Qu.:2.000 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.000
## Max. :2.000 Max. : NA Max. :7.000 Max. : NA Max. :2.000
## NA's :145 NA's :4 NA's :145
## us.born birthplace agetous timeinus education
## Min. :1.000 Min. : NA Min. :1.000 Min. :1.000 Min. : 5.000
## 1st Qu.:1.000 1st Qu.: NA 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 6.000
## Median :1.000 Median : NA Median :1.000 Median :1.000 Median : 9.000
## Mean :1.345 Mean :NaN Mean :2.441 Mean :2.324 Mean : 7.959
## 3rd Qu.:2.000 3rd Qu.: NA 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.: 9.000
## Max. :2.000 Max. : NA Max. :7.000 Max. :8.000 Max. :12.000
## NA's :145
## income.personal income.household exercise ssb.day
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :5.000 Median :3.000 Median :1.000
## Mean :3.545 Mean :5.269 Mean :3.352 Mean :1.876
## 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :8.000 Max. :8.000 Max. :8.000 Max. :7.000
##
## sugars.amount BMI sweet.liking sugar.intake
## Min. :1.000 Min. :16.16 Min. :-62.11 Min. : 130.0
## 1st Qu.:2.000 1st Qu.:21.13 1st Qu.: 18.50 1st Qu.: 260.0
## Median :2.000 Median :24.54 Median : 36.44 Median : 286.0
## Mean :1.931 Mean :25.39 Mean : 33.17 Mean : 405.6
## 3rd Qu.:2.000 3rd Qu.:28.32 3rd Qu.: 51.22 3rd Qu.: 520.0
## Max. :3.000 Max. :55.78 Max. : 92.00 Max. :1300.0
##
## sugarHEI fatHEI SuFatHEI sHEI
## Min. : 0.000 Min. :1.820 Min. : 1.820 Min. :21.45
## 1st Qu.: 0.000 1st Qu.:3.200 1st Qu.: 4.640 1st Qu.:41.42
## Median : 5.000 Median :4.640 Median : 8.200 Median :47.62
## Mean : 4.379 Mean :3.932 Mean : 8.311 Mean :47.12
## 3rd Qu.: 5.000 3rd Qu.:4.640 3rd Qu.: 9.640 3rd Qu.:54.13
## Max. :10.000 Max. :6.560 Max. :16.560 Max. :68.00
##
## BAS uncontrolled restraint emotional EF
## Min. :10.00 Min. : 9.0 Min. : 6.00 Min. : 6.00 Min. : 3.00
## 1st Qu.:31.00 1st Qu.:16.0 1st Qu.:12.00 1st Qu.: 9.00 1st Qu.:12.00
## Median :36.00 Median :19.0 Median :14.00 Median :12.00 Median :13.00
## Mean :35.79 Mean :18.7 Mean :14.08 Mean :12.29 Mean :12.63
## 3rd Qu.:42.00 3rd Qu.:22.0 3rd Qu.:17.00 3rd Qu.:15.00 3rd Qu.:15.00
## Max. :50.00 Max. :34.0 Max. :24.00 Max. :24.00 Max. :15.00
##
## EOE EUE FF FR
## Min. : 4.000 Min. : 4.0 Min. : 5.00 Min. : 3.000
## 1st Qu.: 7.000 1st Qu.:10.0 1st Qu.: 7.00 1st Qu.: 8.000
## Median : 9.000 Median :12.0 Median :10.00 Median :10.000
## Mean : 9.855 Mean :12.4 Mean :10.23 Mean : 9.786
## 3rd Qu.:13.000 3rd Qu.:16.0 3rd Qu.:13.00 3rd Qu.:11.000
## Max. :20.000 Max. :20.0 Max. :21.00 Max. :15.000
##
## SE H SR instagram
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. :1.000
## 1st Qu.: 8.00 1st Qu.:13.00 1st Qu.: 9.00 1st Qu.:5.000
## Median :11.00 Median :15.00 Median :11.00 Median :6.000
## Mean :11.26 Mean :15.43 Mean :10.92 Mean :5.428
## 3rd Qu.:15.00 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:7.000
## Max. :20.00 Max. :24.00 Max. :19.00 Max. :7.000
##
## emo EOE.level emo.level
## Min. :10.00 Length:145 Length:145
## 1st Qu.:16.00 Class :character Class :character
## Median :22.00 Mode :character Mode :character
## Mean :22.14
## 3rd Qu.:28.00
## Max. :42.00
## EB.df <- EB.df %>%
mutate(res.level = case_when(
restraint <= 14 ~ "Low",
restraint > 14 ~ "High"))
EB.df %>%
group_by(res.level) %>%
count()## # A tibble: 2 × 2
## # Groups: res.level [2]
## res.level n
## <chr> <int>
## 1 High 64
## 2 Low 81EB.df %>% ggplot(aes(sweet.liking, sugar.intake, color = res.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
## TFEQ Emotional: females only
EB.fem.df %>%
summary()## ID age height weight sex
## Length:101 Min. :18.00 Min. :53.00 Min. : 80.0 Min. :2
## Class :character 1st Qu.:21.00 1st Qu.:62.00 1st Qu.:124.0 1st Qu.:2
## Mode :character Median :24.00 Median :64.00 Median :138.0 Median :2
## Mean :26.57 Mean :63.81 Mean :144.6 Mean :2
## 3rd Qu.:28.00 3rd Qu.:66.00 3rd Qu.:160.0 3rd Qu.:2
## Max. :63.00 Max. :71.00 Max. :320.0 Max. :2
##
## sex.other race race.other ethnicity us.born
## Min. : NA Min. :1.000 Min. : NA Min. :1.000 Min. :1.000
## 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.000 1st Qu.:1.000
## Median : NA Median :4.000 Median : NA Median :2.000 Median :1.000
## Mean :NaN Mean :4.306 Mean :NaN Mean :1.752 Mean :1.327
## 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.000 3rd Qu.:2.000
## Max. : NA Max. :7.000 Max. : NA Max. :2.000 Max. :2.000
## NA's :101 NA's :3 NA's :101
## birthplace agetous timeinus education income.personal
## Min. : NA Min. :1.000 Min. :1.000 Min. : 5.000 Min. :1.000
## 1st Qu.: NA 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 6.000 1st Qu.:2.000
## Median : NA Median :1.000 Median :1.000 Median : 8.000 Median :3.000
## Mean :NaN Mean :2.307 Mean :2.396 Mean : 7.782 Mean :3.248
## 3rd Qu.: NA 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.: 9.000 3rd Qu.:4.000
## Max. : NA Max. :7.000 Max. :8.000 Max. :12.000 Max. :8.000
## NA's :101
## income.household exercise ssb.day sugars.amount
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.00
## Median :5.000 Median :3.000 Median :1.000 Median :2.00
## Mean :5.139 Mean :3.168 Mean :1.772 Mean :1.96
## 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :8.000 Max. :8.000 Max. :7.000 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI
## Min. :16.16 Min. :-62.11 Min. : 130.0 Min. : 0.000
## 1st Qu.:21.03 1st Qu.: 16.44 1st Qu.: 260.0 1st Qu.: 5.000
## Median :24.36 Median : 36.89 Median : 286.0 Median : 5.000
## Mean :25.07 Mean : 31.80 Mean : 392.1 Mean : 4.356
## 3rd Qu.:27.92 3rd Qu.: 51.11 3rd Qu.: 442.0 3rd Qu.: 5.000
## Max. :55.06 Max. : 85.00 Max. :1196.0 Max. :10.000
##
## fatHEI SuFatHEI sHEI BAS
## Min. :1.820 Min. : 1.82 Min. :29.55 Min. :10.00
## 1st Qu.:3.200 1st Qu.: 6.82 1st Qu.:42.23 1st Qu.:30.00
## Median :4.640 Median : 8.20 Median :49.71 Median :35.00
## Mean :4.024 Mean : 8.38 Mean :48.90 Mean :35.07
## 3rd Qu.:4.640 3rd Qu.: 9.64 3rd Qu.:55.63 3rd Qu.:42.00
## Max. :6.560 Max. :16.56 Max. :68.00 Max. :50.00
##
## uncontrolled restraint emotional EF EOE
## Min. : 9.00 Min. : 6.00 Min. : 6.00 Min. : 3.0 Min. : 4.00
## 1st Qu.:15.00 1st Qu.:11.00 1st Qu.: 9.00 1st Qu.:11.0 1st Qu.: 7.00
## Median :18.00 Median :14.00 Median :13.00 Median :13.0 Median :11.00
## Mean :18.15 Mean :13.93 Mean :12.68 Mean :12.5 Mean :10.41
## 3rd Qu.:21.00 3rd Qu.:17.00 3rd Qu.:16.00 3rd Qu.:15.0 3rd Qu.:13.00
## Max. :32.00 Max. :22.00 Max. :24.00 Max. :15.0 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.000 Min. : 4.0
## 1st Qu.:10.00 1st Qu.: 8.00 1st Qu.: 8.000 1st Qu.: 9.0
## Median :13.00 Median :10.00 Median :10.000 Median :12.0
## Mean :12.97 Mean :10.56 Mean : 9.762 Mean :12.1
## 3rd Qu.:16.00 3rd Qu.:13.00 3rd Qu.:11.000 3rd Qu.:16.0
## Max. :20.00 Max. :21.00 Max. :15.000 Max. :20.0
##
## H SR instagram emo
## Min. : 5.00 Min. : 4.00 Min. :1.000 Min. :10.00
## 1st Qu.:14.00 1st Qu.: 9.00 1st Qu.:5.000 1st Qu.:16.00
## Median :16.00 Median :12.00 Median :6.000 Median :23.00
## Mean :15.72 Mean :11.59 Mean :5.663 Mean :23.09
## 3rd Qu.:18.00 3rd Qu.:14.00 3rd Qu.:7.000 3rd Qu.:29.00
## Max. :23.00 Max. :19.00 Max. :7.000 Max. :42.00
##
## EOE.level emo.level
## Length:101 Length:101
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## EB.fem.df <- EB.fem.df %>%
mutate(res.level = case_when(
restraint < 14 ~ "Low",
restraint >= 14 ~ "High"))
EB.fem.df %>%
group_by(res.level) %>%
count()## # A tibble: 2 × 2
## # Groups: res.level [2]
## res.level n
## <chr> <int>
## 1 High 54
## 2 Low 47EB.fem.df %>% ggplot(aes(sweet.liking, sugar.intake, color = res.level)) +
geom_point() +
geom_smooth(method = lm)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
Liking hierarchial clustering
liking.df[is.na(liking.df)] <- 0 ## replace "NA"s with 0s
res.dist <- dist(x = liking.df, method = "euclidean") ## calculate distance
Likingclust <- hclust(res.dist, method = "ward.D") ## create clusters
plot(Likingclust) ## create dendrogram to visualize clusters
fviz_nbclust(liking.df, FUNcluster = hcut, method = "wss") ## identify number of clusters
Likinggroup <- cutree(Likingclust, k = 3) ## break clusters into desirable number of groups
km.res <- kmeans(liking.df, 3, nstart = 25)
fviz_cluster(km.res, data = liking.df, ggtheme = theme_light())
## calculate mean score for each cluster
tapply(liking.df$sweet.liking, Likinggroup, mean)## 1 2 3
## 43.91852 30.84661 15.17101tapply(liking.df$unhealthyfat.liking, Likinggroup, mean)## 1 2 3
## 37.69778 28.98214 -2.38750tapply(liking.df$alcohol.liking, Likinggroup, mean)## 1 2 3
## 22.736667 5.669048 -13.178125tapply(liking.df$saltyfat.liking, Likinggroup, mean)## 1 2 3
## 55.54984 43.93727 -0.78125## identify individuals within each cluster
Likeclust1 <- subset(liking.df, Likinggroup == 1)
Likeclust2 <- subset(liking.df, Likinggroup == 2)
Likeclust3 <- subset(liking.df, Likinggroup == 3)
Likeclust4 <- subset(liking.df, Likinggroup == 4)sHEI hierarchial clustering
HEI.clust.df <- select(HEI.df, -c("ID", "sugar.intake"))
HEI.clust.df[is.na(HEI.clust.df)] <- 0 ## replace "NA"s with 0s
res.dist <- dist(x = HEI.clust.df[1:13], method = "euclidean") ## calculate distance
HEIclust <- hclust(res.dist, method = "ward.D") ## create clusters
plot(HEIclust) ## create dendrogram to visualize clusters
fviz_nbclust(HEI.clust.df, FUNcluster = hcut, method = "wss") ## identify number of clusters
HEIgroup <- cutree(HEIclust, k = 4) ## break clusters into desirable number of groups
km.res <- kmeans(HEI.clust.df[1:13], 4, nstart = 25)
fviz_cluster(km.res, data = HEI.clust.df[1:13], ggtheme = theme_light())
## calculate mean score for each cluster
tapply(HEI.clust.df$sugarHEI, HEIgroup, mean)## 1 2 3 4
## 6.1538462 4.5555556 3.8888889 0.2380952tapply(HEI.clust.df$fatHEI, HEIgroup, mean)## 1 2 3 4
## 4.844231 3.297333 4.144444 2.757143tapply(HEI.clust.df$SuFatHEI, HEIgroup, mean)## 1 2 3 4
## 10.998077 7.852889 8.033333 2.995238tapply(HEI.clust.df$sHEI, HEIgroup, mean)## 1 2 3 4
## 44.99000 54.02244 50.70519 33.01524## identify individuals within each cluster
HEIclust1 <- subset(HEI.clust.df, HEIgroup == 1)
HEIclust2 <- subset(HEI.clust.df, HEIgroup == 2)
HEIclust3 <- subset(HEI.clust.df, HEIgroup == 3)
HEIclust4 <- subset(HEI.clust.df, HEIgroup == 4)Behavior hierarchial clustering
AEBQ.df <- AEBQ.df %>% select(EF:SR) #select relevant columns
res.dist <- dist(x = AEBQ.df, method = "euclidean") ## calculate distance
AEBQclust <- hclust(res.dist, method = "ward.D") ## create clusters
plot(AEBQclust) ## create dendrogram to visualize clusters
fviz_nbclust(AEBQ.df, FUNcluster = hcut, method = "wss") ## identify number of clusters
AEBQgroup <- cutree(AEBQclust, k = 4) ## break clusters into desirable number of groups
km.res <- kmeans(AEBQ.df, 4, nstart = 25)
fviz_cluster(km.res, data = AEBQ.df, ggtheme = theme_light())
## calculate mean score for each cluster
tapply(liking.df$sweet.liking, Likinggroup, mean)## 1 2 3
## 43.91852 30.84661 15.17101tapply(liking.df$unhealthyfat.liking, Likinggroup, mean)## 1 2 3
## 37.69778 28.98214 -2.38750tapply(liking.df$alcohol.liking, Likinggroup, mean)## 1 2 3
## 22.736667 5.669048 -13.178125tapply(liking.df$saltyfat.liking, Likinggroup, mean)## 1 2 3
## 55.54984 43.93727 -0.78125## identify individuals within each cluster
Likeclust1 <- subset(liking.df, Likinggroup == 1)
Likeclust2 <- subset(liking.df, Likinggroup == 2)
Likeclust3 <- subset(liking.df, Likinggroup == 3)
Likeclust4 <- subset(liking.df, Likinggroup == 4)AChemS poster graphs
summary(aov(sugar.intake ~ sex, data = EB.df))## Df Sum Sq Mean Sq F value Pr(>F)
## sex 1 61026 61026 0.964 0.328
## Residuals 143 9050643 63291summary(aov(sweet.liking ~ sex, data = EB.df))## Df Sum Sq Mean Sq F value Pr(>F)
## sex 1 630 629.8 0.975 0.325
## Residuals 143 92409 646.2summary(aov(restraint ~ sex, data = EB.df))## Df Sum Sq Mean Sq F value Pr(>F)
## sex 1 7 7.014 0.496 0.482
## Residuals 143 2021 14.134summary(aov(emotional ~ sex, data = EB.df))## Df Sum Sq Mean Sq F value Pr(>F)
## sex 1 51.5 51.54 2.447 0.12
## Residuals 143 3012.3 21.06summary(aov(uncontrolled ~ sex, data = EB.df))## Df Sum Sq Mean Sq F value Pr(>F)
## sex 1 102.5 102.50 4.719 0.0315 *
## Residuals 143 3105.7 21.72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Age X sweet liking
EB.df %>%
ggplot(aes(age, sweet.liking)) +
geom_point()+
geom_smooth(method = lm)+
stat_cor(method = "pearson") +
theme_light() +
ylim(-100, 100)## `geom_smooth()` using formula = 'y ~ x'
## BMI X sweet liking
EB.df %>%
ggplot(aes(BMI, sweet.liking)) +
geom_point()+
geom_smooth(method = lm)+
stat_cor(method = "pearson") +
theme_light() +
ylim(-100, 100)## `geom_smooth()` using formula = 'y ~ x'
## Differents in eating behavior
EB.df %>%
select(sex, restraint, uncontrolled, emotional) %>%
pivot_longer(cols = c("restraint", "uncontrolled", "emotional"), names_to = "TFEQ", values_to = "score") %>%
group_by(sex, TFEQ) %>%
summarize(mean = mean (score), sd = sd(score)) %>%
ggplot(aes(x = TFEQ, y = mean, fill = as.factor(sex))) +
geom_bar(stat = "identity", color = "black", position = position_dodge()) +
geom_errorbar(aes(ymin = mean - sd/sqrt(5), ymax = mean + sd/sqrt(5), width = 0.3), position = position_dodge(1)) +
scale_fill_manual(values = c("#00897B", "#FBC02D","#00897B", "#FBC02D", "#00897B", "#FBC02D" ))+
theme_bw() +
ylim(0, 24)## `summarise()` has grouped output by 'sex'. You can override using the `.groups`
## argument.
## Sweet liking and sugar intake
EB.df %>%
ggplot(aes(sweet.liking, sugar.intake/4)) +
geom_point()+
geom_smooth(method = lm, fullrange = FALSE)+
stat_cor(method = "pearson") +
theme_light() +
ylim(-10, 330)## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating
EB.df %>%
ggplot(aes(uncontrolled, sweet.liking)) +
geom_point()+
geom_smooth(method = lm)+
stat_cor(method = "pearson") +
theme_light() +
ylim(-100, 100)## `geom_smooth()` using formula = 'y ~ x'
Social media poster graphs