BMI Predictors
Allison Londerée
February 2, 2018
RS Measures
fpnetwork - mean network connectivity for fp network defined by Power (2011) rnetwork - mean network connectivity for “limping” reward network partially defined by Power’s coordiates rslOFC_lIFG - ROI to ROI connectivtiy between left OFC and IFG
fMRI Measures
l_OFC_fvn- z scored Activation for food vs neutal ads in the left OFC l_IFG_fvn- z scored Activation for for food vs neutal ads in the left IFG balanceScore_fvn- lopez calucuation on food vs. neutral ROIs wagnerbalance_fvn- wagner calucuation on food vs. neutral ROIs l_IFG_fvn - l_OFC_fvn
DTI Measures
FA_thr50 - FA connectivity values between lIFG and lOFC thresholded at 50% FA_thr75 - FA connectivity values between lIFG and lOFC thresholded at 75%
Other Measures
Age_Months - age in months BMI - BMI calucluated from self reported hight and weight
Load in Data
Note- this is exculudeing 3 people, 2 for BMI outliers and 1 for DTI outlier.
library(ggplot2)
library("PerformanceAnalytics")
indiffraw = read.csv("E:/Box Sync/wlab/lab-members/aml/certs1-pilot/3-experiment-fmri/eye-tracking-MRI/individualdiff_food.csv", stringsAsFactors = FALSE)
indiffrestraw = read.csv("E:/Box Sync/wlab/lab-members/aml/certs1-pilot/3-experiment-fmri/eye-tracking-MRI/individualdiffRESTINCLUDEDONLY_food.csv", stringsAsFactors = FALSE)
indiffrest <- indiffrestraw[-c(24,20,9),]
indiff <-indiffraw[-c(30,24,13),]my_data <- indiffrest[,c('BMI','Age_Months', 'l_OFC','l_IFG', 'wagnerBalance', 'zDTDiff', 'FA_thr50', 'fpnetwork', 'rslIFG_lOFC','balanceScore', 'MMTTotal', 'l_OFC_fvn','l_IFG_fvn','wagnerBalance_fvn', 'balanceScore_fvn', 'l_OFC_fvo','l_IFG_fvo','wagnerBalance_fvo', 'balanceScore_fvo')]
chart.Correlation(my_data, histogram=TRUE, pch=19)
my_data <- indiff[,c('BMI','Age_Months', 'l_OFC','l_IFG', 'wagnerBalance', 'zDTDiff', 'FA_thr50', 'balanceScore', 'MMTTotal', 'l_OFC_fvn','l_IFG_fvn','wagnerBalance_fvn', 'balanceScore_fvn', 'l_OFC_fvo','l_IFG_fvo','wagnerBalance_fvo', 'balanceScore_fvo')]
chart.Correlation(my_data, histogram=TRUE, pch=19)
BMI and fMRI
cor.test(indiff$BMI, indiff$l_OFC_fvn)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$l_OFC_fvn
## t = 2.0716, df = 40, p-value = 0.04479
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.008115718 0.562037637
## sample estimates:
## cor
## 0.3112793r <- round(cor(indiff$BMI, indiff$l_OFC_fvn), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$l_OFC_fvn)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$l_OFC_fvn, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,1.9,label= r)+ annotate('text', 30,1.7,label= p)
ggsave(file = 'BMI and l_OFC_fvn.pdf')## Saving 7 x 5 in imagecor.test(indiff$BMI, indiff$l_IFG_fvn)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$l_IFG_fvn
## t = 1.2114, df = 40, p-value = 0.2328
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1228311 0.4654423
## sample estimates:
## cor
## 0.1881234r <- round(cor(indiff$BMI, indiff$l_IFG_fvn), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$l_IFG_fvn)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$l_IFG_fvn, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,1.9,label= r)+ annotate('text', 30,1.7,label= p)
ggsave(file = 'BMI and l_IFG_fvn.pdf')## Saving 7 x 5 in imageBMI and Balance Scores
cor.test(indiff$BMI, indiff$wagnerBalance_fvn)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$wagnerBalance_fvn
## t = -0.73306, df = 40, p-value = 0.4678
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.404899 0.195641
## sample estimates:
## cor
## -0.1151365r <- round(cor(indiff$BMI, indiff$wagnerBalance_fvn), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$wagnerBalance_fvn)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$wagnerBalance_fvn, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,1.9,label= r)+ annotate('text', 30,1.7,label= p)
ggsave(file = 'BMI and wagnerBalance_fvn.pdf')## Saving 7 x 5 in imagecor.test(indiff$BMI, indiff$balanceScore_fvn)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$balanceScore_fvn
## t = 2.3774, df = 40, p-value = 0.02231
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05367055 0.59243723
## sample estimates:
## cor
## 0.3518623r <- round(cor(indiff$BMI, indiff$balanceScore_fvn), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$balanceScore_fvn)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$balanceScore_fvn, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,1.9,label= r)+ annotate('text', 30,1.7,label= p)
ggsave(file = 'BMI and balanceScore_fvn.pdf')## Saving 7 x 5 in imageBMI and DTI
cor.test(indiff$BMI, indiff$FA_thr50)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$FA_thr50
## t = -1.1458, df = 40, p-value = 0.2587
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4574084 0.1328714
## sample estimates:
## cor
## -0.1782587r <- round(cor(indiff$BMI, indiff$FA_thr50), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$FA_thr50)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$FA_thr50, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,.3,label= r)+ annotate('text', 30,.35,label= p)
ggsave(file = 'BMI and FA_thr50.pdf')## Saving 7 x 5 in imagecor.test(indiff$BMI, indiff$FA_thr75)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$FA_thr75
## t = -1.4618, df = 40, p-value = 0.1516
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.49523137 0.08452067
## sample estimates:
## cor
## -0.2251956r <- round(cor(indiff$BMI, indiff$FA_thr75), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$FA_thr75)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$FA_thr75, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,.3,label= r)+ annotate('text', 30,.35,label= p)
ggsave(file = 'BMI and FA_thr75.pdf')## Saving 7 x 5 in imageBMI and Rest
cor.test(indiffrest$BMI, indiffrest$rslIFG_lOFC)##
## Pearson's product-moment correlation
##
## data: indiffrest$BMI and indiffrest$rslIFG_lOFC
## t = 1.4719, df = 32, p-value = 0.1508
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09439377 0.54367940
## sample estimates:
## cor
## 0.2518099r <- round(cor(indiffrest$BMI, indiffrest$rslIFG_lOFC), 4)
r<- paste('r =',r)
p <- round(cor.test(indiffrest$BMI, indiffrest$rslIFG_lOFC)$p.value, 4)
p <- paste('p =',p)
qplot(indiffrest$BMI, indiffrest$rslIFG_lOFC, data = indiffrest) + geom_smooth(method = 'lm') + annotate('text', 30,.3,label= r)+ annotate('text', 30,.35,label= p)
ggsave(file = 'BMI and rslIFG_lOFC.pdf')## Saving 7 x 5 in imagecor.test(indiffrest$BMI, indiffrest$fpnetwork)##
## Pearson's product-moment correlation
##
## data: indiffrest$BMI and indiffrest$fpnetwork
## t = -0.18375, df = 32, p-value = 0.8554
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3666059 0.3090941
## sample estimates:
## cor
## -0.03246496r <- round(cor(indiffrest$BMI, indiffrest$fpnetwork), 4)
r<- paste('r =',r)
p <- round(cor.test(indiffrest$BMI, indiffrest$fpnetwork)$p.value, 4)
p <- paste('p =',p)
qplot(indiffrest$BMI, indiffrest$fpnetwork, data = indiffrest) + geom_smooth(method = 'lm') + annotate('text', 30,.3,label= r)+ annotate('text', 30,.35,label= p)
ggsave(file = 'BMI and fpnetwork.pdf')## Saving 7 x 5 in imageA few Model Tests
m1<-lm(BMI ~ l_OFC_fvn, data = indiff)
summary(m1)##
## Call:
## lm(formula = BMI ~ l_OFC_fvn, data = indiff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4929 -2.1742 -0.5599 1.8392 8.1744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.2577 0.5039 44.167 <2e-16 ***
## l_OFC_fvn 1.4984 0.7233 2.072 0.0448 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.954 on 40 degrees of freedom
## Multiple R-squared: 0.09689, Adjusted R-squared: 0.07432
## F-statistic: 4.292 on 1 and 40 DF, p-value: 0.04479m1<-lm(BMI ~ wagnerBalance_fvn, data = indiff)
summary(m1)##
## Call:
## lm(formula = BMI ~ wagnerBalance_fvn, data = indiff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3732 -2.3429 -0.6865 1.8845 8.2085
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.7339 0.4782 47.539 <2e-16 ***
## wagnerBalance_fvn -0.3538 0.4827 -0.733 0.468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.087 on 40 degrees of freedom
## Multiple R-squared: 0.01326, Adjusted R-squared: -0.01141
## F-statistic: 0.5374 on 1 and 40 DF, p-value: 0.4678m1<-lm(BMI ~ balanceScore_fvn + rslIFG_lOFC, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ balanceScore_fvn + rslIFG_lOFC, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5374 -1.5344 -0.4688 1.8138 3.3232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.8892 0.3536 61.903 < 2e-16 ***
## balanceScore_fvn 1.4654 0.5100 2.873 0.00727 **
## rslIFG_lOFC 3.1105 1.9147 1.625 0.11439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.014 on 31 degrees of freedom
## Multiple R-squared: 0.2604, Adjusted R-squared: 0.2127
## F-statistic: 5.457 on 2 and 31 DF, p-value: 0.009321m1<-lm(BMI ~ wagnerBalance_fvn + rslIFG_lOFC, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ wagnerBalance_fvn + rslIFG_lOFC, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0558 -1.7080 -0.5694 1.7426 4.1066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.8247 0.3974 54.913 <2e-16 ***
## wagnerBalance_fvn 0.4044 0.4539 0.891 0.380
## rslIFG_lOFC 3.3482 2.1427 1.563 0.128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.238 on 31 degrees of freedom
## Multiple R-squared: 0.08679, Adjusted R-squared: 0.02788
## F-statistic: 1.473 on 2 and 31 DF, p-value: 0.2448m1<-lm(BMI ~ balanceScore_fvn + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ balanceScore_fvn + FA_thr50, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3508 -1.4403 -0.3666 1.7748 3.2262
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.889 10.162 1.662 0.1066
## balanceScore_fvn 1.444 0.531 2.719 0.0106 *
## FA_thr50 12.032 23.853 0.504 0.6175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.09 on 31 degrees of freedom
## Multiple R-squared: 0.204, Adjusted R-squared: 0.1526
## F-statistic: 3.972 on 2 and 31 DF, p-value: 0.02914m1<-lm(BMI ~ wagnerBalance_fvn + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ wagnerBalance_fvn + FA_thr50, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2520 -1.5838 -0.0256 1.9758 4.5215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.4238 11.8762 1.383 0.177
## wagnerBalance_fvn 0.2437 0.4944 0.493 0.626
## FA_thr50 13.0367 27.9218 0.467 0.644
##
## Residual standard error: 2.317 on 31 degrees of freedom
## Multiple R-squared: 0.02175, Adjusted R-squared: -0.04137
## F-statistic: 0.3446 on 2 and 31 DF, p-value: 0.7112m1<-lm(BMI ~ balanceScore_fvn + rslIFG_lOFC + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ balanceScore_fvn + rslIFG_lOFC + FA_thr50,
## data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5270 -1.3940 -0.5031 1.5846 3.0866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.4072 9.9717 1.445 0.15888
## balanceScore_fvn 1.4318 0.5156 2.777 0.00936 **
## rslIFG_lOFC 3.3134 1.9471 1.702 0.09916 .
## FA_thr50 17.5549 23.3814 0.751 0.45862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.029 on 30 degrees of freedom
## Multiple R-squared: 0.274, Adjusted R-squared: 0.2014
## F-statistic: 3.775 on 3 and 30 DF, p-value: 0.02071m1<-lm(BMI ~ wagnerBalance_fvn + rslIFG_lOFC + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ wagnerBalance_fvn + rslIFG_lOFC + FA_thr50,
## data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0033 -1.6618 -0.4754 1.6466 4.3167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.3041 11.6589 1.227 0.229
## wagnerBalance_fvn 0.3044 0.4837 0.629 0.534
## rslIFG_lOFC 3.4962 2.1753 1.607 0.118
## FA_thr50 17.6770 27.3876 0.645 0.524
##
## Residual standard error: 2.26 on 30 degrees of freedom
## Multiple R-squared: 0.0993, Adjusted R-squared: 0.009232
## F-statistic: 1.103 on 3 and 30 DF, p-value: 0.3634m1<-lm(BMI ~ rslIFG_lOFC + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ rslIFG_lOFC + FA_thr50, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1018 -1.8403 -0.6811 1.6666 4.1453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.992 10.957 1.094 0.282
## rslIFG_lOFC 3.389 2.147 1.578 0.125
## FA_thr50 23.197 25.691 0.903 0.374
##
## Residual standard error: 2.238 on 31 degrees of freedom
## Multiple R-squared: 0.08741, Adjusted R-squared: 0.02853
## F-statistic: 1.485 on 2 and 31 DF, p-value: 0.2423FOOD VS OUTDOOR CONTRAST
BMI and fMRI
cor.test(indiff$BMI, indiff$l_OFC_fvo)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$l_OFC_fvo
## t = 0.023908, df = 40, p-value = 0.981
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3004964 0.3073584
## sample estimates:
## cor
## 0.003780199r <- round(cor(indiff$BMI, indiff$l_OFC_fvo), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$l_OFC_fvo)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$l_OFC_fvo, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,1.9,label= r)+ annotate('text', 30,1.7,label= p)
ggsave(file = 'BMI and l_OFC_fvo.pdf')## Saving 7 x 5 in imagecor.test(indiff$BMI, indiff$l_IFG_fvo)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$l_IFG_fvo
## t = -0.023808, df = 40, p-value = 0.9811
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3073440 0.3005109
## sample estimates:
## cor
## -0.003764303r <- round(cor(indiff$BMI, indiff$l_IFG_fvo), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$l_IFG_fvo)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$l_IFG_fvo, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,1.9,label= r)+ annotate('text', 30,1.7,label= p)
ggsave(file = 'BMI and l_IFG_fvo.pdf')## Saving 7 x 5 in imageBMI and Balance Scores
cor.test(indiff$BMI, indiff$wagnerBalance_fvo)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$wagnerBalance_fvo
## t = -0.039801, df = 40, p-value = 0.9684
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3096321 0.2982088
## sample estimates:
## cor
## -0.006292944r <- round(cor(indiff$BMI, indiff$wagnerBalance_fvo), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$wagnerBalance_fvo)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$wagnerBalance_fvo, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,1.9,label= r)+ annotate('text', 30,1.7,label= p)
ggsave(file = 'BMI and wagnerBalance_fvo.pdf')## Saving 7 x 5 in imagecor.test(indiff$BMI, indiff$balanceScore_fvo)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$balanceScore_fvo
## t = 0.82465, df = 40, p-value = 0.4145
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1817801 0.4168454
## sample estimates:
## cor
## 0.1292947r <- round(cor(indiff$BMI, indiff$balanceScore_fvo), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$balanceScore_fvo)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$balanceScore_fvo, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,1.9,label= r)+ annotate('text', 30,1.7,label= p)
ggsave(file = 'BMI and balanceScore_fvo.pdf')## Saving 7 x 5 in imageBMI and DTI
cor.test(indiff$BMI, indiff$FA_thr50)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$FA_thr50
## t = -1.1458, df = 40, p-value = 0.2587
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4574084 0.1328714
## sample estimates:
## cor
## -0.1782587r <- round(cor(indiff$BMI, indiff$FA_thr50), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$FA_thr50)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$FA_thr50, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,.3,label= r)+ annotate('text', 30,.35,label= p)
ggsave(file = 'BMI and FA_thr50.pdf')## Saving 7 x 5 in imagecor.test(indiff$BMI, indiff$FA_thr75)##
## Pearson's product-moment correlation
##
## data: indiff$BMI and indiff$FA_thr75
## t = -1.4618, df = 40, p-value = 0.1516
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.49523137 0.08452067
## sample estimates:
## cor
## -0.2251956r <- round(cor(indiff$BMI, indiff$FA_thr75), 4)
r<- paste('r =',r)
p <- round(cor.test(indiff$BMI, indiff$FA_thr75)$p.value, 4)
p <- paste('p =',p)
qplot(indiff$BMI, indiff$FA_thr75, data = indiff) + geom_smooth(method = 'lm') + annotate('text', 30,.3,label= r)+ annotate('text', 30,.35,label= p)
ggsave(file = 'BMI and FA_thr75.pdf')## Saving 7 x 5 in imageBMI and Rest
cor.test(indiffrest$BMI, indiffrest$rslIFG_lOFC)##
## Pearson's product-moment correlation
##
## data: indiffrest$BMI and indiffrest$rslIFG_lOFC
## t = 1.4719, df = 32, p-value = 0.1508
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09439377 0.54367940
## sample estimates:
## cor
## 0.2518099r <- round(cor(indiffrest$BMI, indiffrest$rslIFG_lOFC), 4)
r<- paste('r =',r)
p <- round(cor.test(indiffrest$BMI, indiffrest$rslIFG_lOFC)$p.value, 4)
p <- paste('p =',p)
qplot(indiffrest$BMI, indiffrest$rslIFG_lOFC, data = indiffrest) + geom_smooth(method = 'lm') + annotate('text', 30,.3,label= r)+ annotate('text', 30,.35,label= p)
ggsave(file = 'BMI and rslIFG_lOFC.pdf')## Saving 7 x 5 in imagecor.test(indiffrest$BMI, indiffrest$fpnetwork)##
## Pearson's product-moment correlation
##
## data: indiffrest$BMI and indiffrest$fpnetwork
## t = -0.18375, df = 32, p-value = 0.8554
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3666059 0.3090941
## sample estimates:
## cor
## -0.03246496r <- round(cor(indiffrest$BMI, indiffrest$fpnetwork), 4)
r<- paste('r =',r)
p <- round(cor.test(indiffrest$BMI, indiffrest$fpnetwork)$p.value, 4)
p <- paste('p =',p)
qplot(indiffrest$BMI, indiffrest$fpnetwork, data = indiffrest) + geom_smooth(method = 'lm') + annotate('text', 30,.3,label= r)+ annotate('text', 30,.35,label= p)
ggsave(file = 'BMI and fpnetwork.pdf')## Saving 7 x 5 in imageA few Model Tests
m1<-lm(BMI ~ l_OFC_fvo, data = indiff)
summary(m1)##
## Call:
## lm(formula = BMI ~ l_OFC_fvo, data = indiff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1157 -2.3418 -0.5933 1.7163 8.2036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.69723 0.54101 41.954 <2e-16 ***
## l_OFC_fvo 0.01565 0.65450 0.024 0.981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.108 on 40 degrees of freedom
## Multiple R-squared: 1.429e-05, Adjusted R-squared: -0.02499
## F-statistic: 0.0005716 on 1 and 40 DF, p-value: 0.981m1<-lm(BMI ~ wagnerBalance_fvo, data = indiff)
summary(m1)##
## Call:
## lm(formula = BMI ~ wagnerBalance_fvo, data = indiff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1285 -2.3274 -0.6148 1.7344 8.2128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.70354 0.47963 47.34 <2e-16 ***
## wagnerBalance_fvo -0.01622 0.40757 -0.04 0.968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.108 on 40 degrees of freedom
## Multiple R-squared: 3.96e-05, Adjusted R-squared: -0.02496
## F-statistic: 0.001584 on 1 and 40 DF, p-value: 0.9684m1<-lm(BMI ~ balanceScore_fvo + rslIFG_lOFC, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ balanceScore_fvo + rslIFG_lOFC, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6261 -1.6191 -0.3282 1.7995 3.7438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.8668 0.3879 56.373 <2e-16 ***
## balanceScore_fvo 0.6335 0.4953 1.279 0.210
## rslIFG_lOFC 3.2134 2.1012 1.529 0.136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.209 on 31 degrees of freedom
## Multiple R-squared: 0.1104, Adjusted R-squared: 0.05296
## F-statistic: 1.923 on 2 and 31 DF, p-value: 0.1632m1<-lm(BMI ~ wagnerBalance_fvo + rslIFG_lOFC, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ wagnerBalance_fvo + rslIFG_lOFC, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4111 -2.1224 -0.3066 1.9163 3.7632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.7900 0.3933 55.403 <2e-16 ***
## wagnerBalance_fvo 0.4993 0.3834 1.302 0.2024
## rslIFG_lOFC 4.3526 2.3012 1.891 0.0679 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.207 on 31 degrees of freedom
## Multiple R-squared: 0.112, Adjusted R-squared: 0.0547
## F-statistic: 1.955 on 2 and 31 DF, p-value: 0.1587m1<-lm(BMI ~ balanceScore_fvo + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ balanceScore_fvo + FA_thr50, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5138 -1.8208 0.0724 1.7296 4.3661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.2697 11.0530 1.381 0.177
## balanceScore_fvo 0.5888 0.5112 1.152 0.258
## FA_thr50 15.7937 25.9466 0.609 0.547
##
## Residual standard error: 2.278 on 31 degrees of freedom
## Multiple R-squared: 0.05454, Adjusted R-squared: -0.00646
## F-statistic: 0.8941 on 2 and 31 DF, p-value: 0.4193m1<-lm(BMI ~ wagnerBalance_fvo + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ wagnerBalance_fvo + FA_thr50, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3316 -1.7560 0.0358 1.9520 4.4673
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.0755 11.2871 1.336 0.191
## wagnerBalance_fvo 0.1771 0.3691 0.480 0.635
## FA_thr50 16.2336 26.5026 0.613 0.545
##
## Residual standard error: 2.317 on 31 degrees of freedom
## Multiple R-squared: 0.02135, Adjusted R-squared: -0.04179
## F-statistic: 0.3381 on 2 and 31 DF, p-value: 0.7157m1<-lm(BMI ~ balanceScore_fvo + rslIFG_lOFC + FA_thr50 , data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ balanceScore_fvo + rslIFG_lOFC + FA_thr50,
## data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6035 -1.6765 -0.5412 1.6176 3.7505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.7287 10.8863 1.169 0.252
## balanceScore_fvo 0.6100 0.4984 1.224 0.231
## rslIFG_lOFC 3.4575 2.1311 1.622 0.115
## FA_thr50 21.4422 25.5279 0.840 0.408
##
## Residual standard error: 2.22 on 30 degrees of freedom
## Multiple R-squared: 0.1308, Adjusted R-squared: 0.04388
## F-statistic: 1.505 on 3 and 30 DF, p-value: 0.2334m1<-lm(BMI ~ wagnerBalance_fvo + rslIFG_lOFC + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ wagnerBalance_fvo + rslIFG_lOFC + FA_thr50,
## data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3970 -1.8531 -0.4393 1.8393 4.2784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6638 10.8731 1.165 0.2533
## wagnerBalance_fvo 0.4812 0.3859 1.247 0.2220
## rslIFG_lOFC 4.5552 2.3247 1.959 0.0594 .
## FA_thr50 21.4208 25.5041 0.840 0.4076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.218 on 30 degrees of freedom
## Multiple R-squared: 0.1324, Adjusted R-squared: 0.04563
## F-statistic: 1.526 on 3 and 30 DF, p-value: 0.228m1<-lm(BMI ~ rslIFG_lOFC + FA_thr50, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = BMI ~ rslIFG_lOFC + FA_thr50, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1018 -1.8403 -0.6811 1.6666 4.1453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.992 10.957 1.094 0.282
## rslIFG_lOFC 3.389 2.147 1.578 0.125
## FA_thr50 23.197 25.691 0.903 0.374
##
## Residual standard error: 2.238 on 31 degrees of freedom
## Multiple R-squared: 0.08741, Adjusted R-squared: 0.02853
## F-statistic: 1.485 on 2 and 31 DF, p-value: 0.2423Dwell Time Scrap
m1<-lm(zDTDiff ~ wagnerBalance , data = indiffrest)
summary(m1)##
## Call:
## lm(formula = zDTDiff ~ wagnerBalance, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0429 -0.6621 0.1011 0.7253 2.1072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1582 0.1984 0.797 0.431
## wagnerBalance -0.3416 0.2128 -1.606 0.118
##
## Residual standard error: 1.076 on 32 degrees of freedom
## Multiple R-squared: 0.07455, Adjusted R-squared: 0.04563
## F-statistic: 2.578 on 1 and 32 DF, p-value: 0.1182m1<-lm(zDTDiff ~ l_OFC + rslIFG_lOFC, data = indiffrest)
summary(m1)##
## Call:
## lm(formula = zDTDiff ~ l_OFC + rslIFG_lOFC, data = indiffrest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.87858 -0.67461 -0.08071 0.78097 2.03642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2749 0.1897 -1.449 0.15750
## l_OFC 0.8927 0.2810 3.177 0.00336 **
## rslIFG_lOFC 1.5080 0.9245 1.631 0.11299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9674 on 31 degrees of freedom
## Multiple R-squared: 0.2757, Adjusted R-squared: 0.229
## F-statistic: 5.901 on 2 and 31 DF, p-value: 0.006736