DASH Score
Zhaohu(Jonathan) Fan and Yichen Qin
June 13, 2019
- 1 Goal
- 2 Exploratory Data Analysis
- 3 The Gunther’s scoring system
- 3.1 step 1: initialization
- 3.2 step 2: the Gunther’s scores for each subject
- 3.3 Marginal Data Analysis
- 3.4 Step 1: we produce scatterplots of Gunther DASH Score vs SBP/DBP, respectively.
- 3.5 Step 2: we calculate correlation (pearson, spearman and kendal) below between Gunther DASH Scores and SBP/DBP.
- 3.5.1 correlation (pearson) between Gunther DASH Score and SBP
- 3.5.2 correlation (spearman) between Gunther DASH Score and SBP
- 3.5.3 correlation (kendall) between Gunther DASH Score and SBP
- 3.5.4 correlation (pearson) between Gunther DASH Score and SBP
- 3.5.5 correlation (spearman) between Gunther DASH Score and SBP
- 3.5.6 correlation (kendall) between Gunther DASH Score and SBP
- 4 The SuperWIN scoring system
- 4.1 step 1: initialization
- 4.2 step 2: SuperWIN Scores for each subject
- 4.3 Marginal Data Analysis
- 4.4 Step 1: we produce scatterplots of Gunther DASH Scores vs SBP/DBP, respectively.
- 4.5 Step 2: we calculate correlation (pearson, spearman and kendal) between SuperWIN DASH Scores and SBP/DBP below.
- 4.5.1 correlation (pearson) between SuperWIN DASH Score and SBP
- 4.5.2 correlation (spearman) between SuperWIN DASH Score and SBP
- 4.5.3 correlation (kendall) between SuperWIN DASH Score and SBP
- 4.5.4 correlation (pearson) between SuperWIN DASH Score and DBP
- 4.5.5 correlation (spearman) between SuperWIN DASH Score and DBP
- 4.5.6 correlation (kendall) between SuperWIN DASH Score and DBP
- 5 Further study
- 6 References
1 Goal
2 Exploratory Data Analysis
2.1 Import the data into R with the read.table function
DASH_Score <- read.table("C:/Users/fanzh/Dropbox/Project with Zhaohu at UC (Model confidence bounds for variable selection)/National Health and Nutrition Examination Survey/Copy of Copy of Couch DASH Baseline Data6-13-2019.csv", sep=",", header = TRUE) 2.2 1A Variable description
We have 69 demographical variables excluding the subject id variable and 152 treatments or variables of interest, which is considered as NDSR Food subcodes used in DASH score and to calculate total servings of different food groups.
DASH_Score_70=DASH_Score[,1:70]
description= cbind(names(DASH_Score_70),c("subject id", "Month of visit", "Day of visit","Year of visit","age at visit in years", "race 1=white; 2=black 3=hispanic; 4=native american; 5=asian pacific islander; 6=multiracial", "gender 1=males; 2=females",
"household income 1=<$20,000; 2=20-50; 3=50-80; 4=>$80,000",
"Systolic blood pressure",
"diastolic blood pressure",
"hypertension status 1=pre; 2=stage 1; 3=normal; 4=stage 2",
"weight in kg",
"height in meters",
"height percentile",
"EPIC calculated BMI",
"BMI percentile from clinic",
"Daily Metabolic equivalents",
"hours of sleep per week",
"minutes of light activity per week",
"minutes of moderate activity per week",
"minutes of hard activity per week",
"minutes of very hard activity per week",
"minutes of mod, hard and very hard activity per week",
"TwoD_TwoD_BDimension",
"TwoD_TwoD_BDimension_2",
"TwoD_TwoD_BDimension_3",
"TwoD_TwoD_60Dimension",
"TwoD_TwoD_60Dimension_2",
"TwoD_TwoD_60Dimension_3",
"TwoD_TwoD_90Dimension",
"TwoD_TwoD_90Dimension_2",
"TwoD_TwoD_90Dimension_3",
"TwoD_TwoD_120Dimension",
"TwoD_TwoD_120Dimension_2",
"TwoD_TwoD_120Dimension_3",
"DOPPLER_PSV_BVelocity",
"DOPPLER_PSV_BVelocity_2",
"DOPPLER_PSV_BVelocity_3",
"DOPPLER_PSV_I_4Velocity",
"DOPPLER_PSV_I_4Velocity_2",
"DOPPLER_PSV_I_4Velocity_3",
"DOPPLER_PSV_I_5Velocity",
"DOPPLER_PSV_I_5Velocity_2",
"DOPPLER_PSV_I_5Velocity_3",
"DOPPLER_PSV_I_6Velocity",
"DOPPLER_PSV_I_6Velocity_2",
"DOPPLER_PSV_I_6Velocity_3",
"DOPPLER_PSV_60Velocity",
"DOPPLER_PSV_60Velocity_2",
"DOPPLER_PSV_60Velocity_3",
"DOPPLER_PSV_90Velocity",
"DOPPLER_PSV_90Velocity_2",
"DOPPLER_PSV_90Velocity_3",
"DOPPLER_PSV_120Velocity",
"DOPPLER_PSV_120Velocity_2",
"DOPPLER_PSV_120Velocity_3",
"energy (kcal/day)",
"cholesterol (mg/day)",
"sucrose (grams/day)",
"total dietay fiber (grams/day)",
"calcium (mg/day)",
"magnesium (mg/day)",
"sodium (mg/day)",
"potassium (mg/day)",
"caffeine (mg/day)",
"percent kcal from fat",
"percent kcal from carbohydrate",
"percent kcal from protein",
"percent kcal from saturated fat",
"trans fatty acids (g/day)"))
colnames(description)=c("Variable", "Definition")
kable(description)| Variable | Definition |
|---|---|
| Subject | subject id |
| Month | Month of visit |
| Day | Day of visit |
| Year | Year of visit |
| Age | age at visit in years |
| Race | race 1=white; 2=black 3=hispanic; 4=native american; 5=asian pacific islander; 6=multiracial |
| Sex | gender 1=males; 2=females |
| Income | household income 1=<$20,000; 2=20-50; 3=50-80; 4=>$80,000 |
| SBP | Systolic blood pressure |
| DBP | diastolic blood pressure |
| HTNST | hypertension status 1=pre; 2=stage 1; 3=normal; 4=stage 2 |
| WTKG | weight in kg |
| HTM | height in meters |
| HTPCT | height percentile |
| BMIcal | EPIC calculated BMI |
| BMIPCT | BMI percentile from clinic |
| DMETS | Daily Metabolic equivalents |
| Sleep | hours of sleep per week |
| lightmin | minutes of light activity per week |
| modmin | minutes of moderate activity per week |
| hardmin | minutes of hard activity per week |
| vhardmin | minutes of very hard activity per week |
| actweek | minutes of mod, hard and very hard activity per week |
| X_D_B1 | TwoD_TwoD_BDimension |
| X_D_B2 | TwoD_TwoD_BDimension_2 |
| X_D_B3 | TwoD_TwoD_BDimension_3 |
| X_D_601 | TwoD_TwoD_60Dimension |
| X_D_602 | TwoD_TwoD_60Dimension_2 |
| X_D_603 | TwoD_TwoD_60Dimension_3 |
| X_D_901 | TwoD_TwoD_90Dimension |
| X_D_902 | TwoD_TwoD_90Dimension_2 |
| X_D_903 | TwoD_TwoD_90Dimension_3 |
| X_D_1201 | TwoD_TwoD_120Dimension |
| X_D_1202 | TwoD_TwoD_120Dimension_2 |
| X_D_1203 | TwoD_TwoD_120Dimension_3 |
| PSV_B_1 | DOPPLER_PSV_BVelocity |
| PSV_B_2 | DOPPLER_PSV_BVelocity_2 |
| PSV_B_3 | DOPPLER_PSV_BVelocity_3 |
| PSV_I_41 | DOPPLER_PSV_I_4Velocity |
| PSV_I_42 | DOPPLER_PSV_I_4Velocity_2 |
| PSV_I_43 | DOPPLER_PSV_I_4Velocity_3 |
| PSV_I_51 | DOPPLER_PSV_I_5Velocity |
| PSV_I_52 | DOPPLER_PSV_I_5Velocity_2 |
| PSV_I_53 | DOPPLER_PSV_I_5Velocity_3 |
| PSV_I_61 | DOPPLER_PSV_I_6Velocity |
| PSV_I_62 | DOPPLER_PSV_I_6Velocity_2 |
| PSV_I_63 | DOPPLER_PSV_I_6Velocity_3 |
| PSV_601 | DOPPLER_PSV_60Velocity |
| PSV_602 | DOPPLER_PSV_60Velocity_2 |
| PSV_603 | DOPPLER_PSV_60Velocity_3 |
| PSV_901 | DOPPLER_PSV_90Velocity |
| PSV_902 | DOPPLER_PSV_90Velocity_2 |
| PSV_903 | DOPPLER_PSV_90Velocity_3 |
| PSV_1201 | DOPPLER_PSV_120Velocity |
| PSV_1202 | DOPPLER_PSV_120Velocity_2 |
| PSV_1203 | DOPPLER_PSV_120Velocity_3 |
| energy | energy (kcal/day) |
| Chol | cholesterol (mg/day) |
| Sucrose | sucrose (grams/day) |
| Fiber | total dietay fiber (grams/day) |
| CA | calcium (mg/day) |
| Mg | magnesium (mg/day) |
| Na | sodium (mg/day) |
| K | potassium (mg/day) |
| Caffeine | caffeine (mg/day) |
| pfat | percent kcal from fat |
| pcarb | percent kcal from carbohydrate |
| ppro | percent kcal from protein |
| psfat | percent kcal from saturated fat |
| trans | trans fatty acids (g/day) |
2.3 Basic Inspections
We now know, that this dataset contains 188 subjects and 222 variables. There are several demographical variables, such as age, race, sex and income, etc.
dim(DASH_Score)## [1] 188 222names(DASH_Score)## [1] "Subject" "Month" "Day" "Year" "Age" "Race"
## [7] "Sex" "Income" "SBP" "DBP" "HTNST" "WTKG"
## [13] "HTM" "HTPCT" "BMIcal" "BMIPCT" "DMETS" "Sleep"
## [19] "lightmin" "modmin" "hardmin" "vhardmin" "actweek" "X_D_B1"
## [25] "X_D_B2" "X_D_B3" "X_D_601" "X_D_602" "X_D_603" "X_D_901"
## [31] "X_D_902" "X_D_903" "X_D_1201" "X_D_1202" "X_D_1203" "PSV_B_1"
## [37] "PSV_B_2" "PSV_B_3" "PSV_I_41" "PSV_I_42" "PSV_I_43" "PSV_I_51"
## [43] "PSV_I_52" "PSV_I_53" "PSV_I_61" "PSV_I_62" "PSV_I_63" "PSV_601"
## [49] "PSV_602" "PSV_603" "PSV_901" "PSV_902" "PSV_903" "PSV_1201"
## [55] "PSV_1202" "PSV_1203" "energy" "Chol" "Sucrose" "Fiber"
## [61] "CA" "Mg" "Na" "K" "Caffeine" "pfat"
## [67] "pcarb" "ppro" "psfat" "trans" "FRU0100" "FRU0200"
## [73] "FRU0300" "FRU0400" "FRU0500" "FRU0700" "VEG0100" "VEG0200"
## [79] "VEG0300" "VEG0400" "VEG0450" "VEG0700" "VEG0600" "VEG0500"
## [85] "FMC0100" "GRW0100" "GRS0100" "GRR0100" "GRW0200" "GRS0200"
## [91] "GRR0200" "GRW0300" "GRS0300" "GRR0300" "GRW0400" "GRS0400"
## [97] "GRR0400" "GRW0500" "GRS0500" "GRR0500" "GRW0600" "GRS0600"
## [103] "GRR0600" "GRW0700" "GRS0700" "GRR0700" "GRW0800" "GRS0800"
## [109] "GRR0800" "GRW1000" "GRS1000" "GRR1000" "GRW0900" "GRS0900"
## [115] "GRR0900" "GRW1100" "GRW1200" "MRF0100" "MRL0100" "MRF0200"
## [121] "MRL0200" "MRF0300" "MRL0300" "MRF0400" "MRL0400" "MCF0200"
## [127] "MCL0200" "MRF0500" "MPF0100" "MPL0100" "MPF0200" "MFF0100"
## [133] "MFL0100" "MFF0200" "MSL0100" "MSF0100" "MCF0100" "MCL0100"
## [139] "MOF0100" "FMC0200" "MOF0300" "MOF0400" "MOF0500" "MOF0600"
## [145] "MOF0700" "DMF0100" "DMR0100" "DML0100" "DMF0200" "DMR0200"
## [151] "DML0200" "DML0300" "DML0400" "DCF0100" "DCR0100" "DCL0100"
## [157] "DYF0100" "DYR0100" "DYL0100" "DYF0200" "DYR0200" "DYL0200"
## [163] "DOT0100" "DOT0300" "DOT0400" "FCF0100" "FCR0100" "FCL0100"
## [169] "DOT0500" "DOT0600" "DOT0700" "DOT0800" "FMF0100" "FMR0100"
## [175] "FOF0100" "FSF0100" "FAF0100" "FAR0100" "FDF0100" "FDR0100"
## [181] "SWT0400" "SWT0500" "SWT0700" "SWT0800" "SWT0100" "SWT0200"
## [187] "SWT0300" "BVS0400" "BVA0400" "BVU0300" "BVS0300" "BVA0300"
## [193] "BVS0500" "BVA0500" "BVU0400" "BVS0100" "BVA0100" "BVU0100"
## [199] "BVS0200" "BVA0200" "BVU0200" "BVS0600" "BVA0600" "BVU0500"
## [205] "BVS0700" "BVA0700" "BVU0600" "BVO0100" "BVO0200" "BVE0100"
## [211] "BVE0400" "BVE0300" "BVE0200" "MSC0100" "MSC0200" "MSC0300"
## [217] "MSC0400" "MSC0500" "MSC0600" "MSC0700" "MSC0800" "MSC0900"2.4 Check missing values
vis_dat(DASH_Score)
vis_miss(DASH_Score)
gg_miss_upset(DASH_Score)
2.5 Remove missing values
DASH_Score=DASH_Score[complete.cases(DASH_Score), ]vis_dat(DASH_Score)
vis_miss(DASH_Score)
2.6 Summary statistics of all variables
summary(DASH_Score) ## Subject Month Day Year
## Min. :4100 Min. : 1.000 Min. : 1.00 Min. : 12
## 1st Qu.:4147 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.:2009
## Median :4196 Median : 7.000 Median :14.00 Median :2010
## Mean :4200 Mean : 6.931 Mean :15.28 Mean :1987
## 3rd Qu.:4254 3rd Qu.:10.000 3rd Qu.:22.00 3rd Qu.:2011
## Max. :4306 Max. :12.000 Max. :31.00 Max. :2013
##
## Age Race Sex Income
## Min. :11.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:13.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :15.00 Median :1.000 Median :1.000 Median :2.000
## Mean :14.72 Mean :1.497 Mean :1.353 Mean :2.439
## 3rd Qu.:16.00 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :18.00 Max. :6.000 Max. :2.000 Max. :4.000
##
## SBP DBP HTNST WTKG
## Min. :107.0 Min. :50.0 Min. :1.000 Min. : 36.40
## 1st Qu.:123.0 1st Qu.:69.0 1st Qu.:1.000 1st Qu.: 71.40
## Median :128.0 Median :75.0 Median :1.000 Median : 86.90
## Mean :127.8 Mean :74.4 Mean :1.474 Mean : 90.43
## 3rd Qu.:133.0 3rd Qu.:81.0 3rd Qu.:2.000 3rd Qu.:109.40
## Max. :150.0 Max. :95.0 Max. :4.000 Max. :185.90
##
## HTM HTPCT BMIcal BMIPCT
## Min. :1.413 Min. : 3.00 Min. :16.58 Min. : 12.41
## 1st Qu.:1.634 1st Qu.:50.00 1st Qu.:25.40 1st Qu.: 90.00
## Median :1.703 Median :75.00 Median :29.70 Median : 97.00
## Mean :1.695 Mean :66.71 Mean :31.30 Mean : 90.57
## 3rd Qu.:1.759 3rd Qu.:90.00 3rd Qu.:36.90 3rd Qu.: 99.00
## Max. :1.957 Max. :99.90 Max. :83.60 Max. :100.00
##
## DMETS Sleep lightmin modmin hardmin
## 36.85714286: 3 67.5 : 6 6240 : 4 0 : 21 0 :60
## 32.5 : 2 55.5 : 5 6110 : 3 270 : 7 60 : 9
## 33.7 : 2 58 : 5 4925 : 2 30 : 7 20 : 6
## 33.9 : 2 66.5 : 5 4965 : 2 50 : 6 15 : 5
## 34 : 2 56 : 4 5460 : 2 60 : 6 40 : 5
## 38.1 : 2 58.25 : 4 5595 : 2 20 : 5 90 : 5
## (Other) :160 (Other):144 (Other):158 (Other):121 (Other):83
## vhardmin actweek X_D_B1 X_D_B2
## 0 :111 0 : 11 Min. :0.2111 Min. :0.2114
## 60 : 7 20 : 5 1st Qu.:0.2866 1st Qu.:0.2881
## 120 : 5 10 : 4 Median :0.3200 Median :0.3198
## 15 : 4 100 : 4 Mean :0.3219 Mean :0.3222
## 40 : 3 360 : 4 3rd Qu.:0.3582 3rd Qu.:0.3581
## 75 : 3 470 : 4 Max. :0.4297 Max. :0.4244
## (Other): 40 (Other):141
## X_D_B3 X_D_601 X_D_602 X_D_603
## Min. :0.2114 Min. :0.2178 Min. :0.2189 Min. :0.2238
## 1st Qu.:0.2877 1st Qu.:0.3008 1st Qu.:0.3001 1st Qu.:0.3016
## Median :0.3206 Median :0.3366 Median :0.3335 Median :0.3335
## Mean :0.3223 Mean :0.3345 Mean :0.3343 Mean :0.3344
## 3rd Qu.:0.3584 3rd Qu.:0.3671 3rd Qu.:0.3684 3rd Qu.:0.3686
## Max. :0.4299 Max. :0.4748 Max. :0.4745 Max. :0.4745
##
## X_D_901 X_D_902 X_D_903 X_D_1201
## Min. :0.2175 Min. :0.2174 Min. :0.2114 Min. :0.2110
## 1st Qu.:0.2988 1st Qu.:0.2976 1st Qu.:0.2959 1st Qu.:0.2938
## Median :0.3325 Median :0.3331 Median :0.3307 Median :0.3274
## Mean :0.3305 Mean :0.3311 Mean :0.3305 Mean :0.3260
## 3rd Qu.:0.3614 3rd Qu.:0.3633 3rd Qu.:0.3619 3rd Qu.:0.3609
## Max. :0.4477 Max. :0.4387 Max. :0.4298 Max. :0.4220
##
## X_D_1202 X_D_1203 PSV_B_1 PSV_B_2
## Min. :0.2154 Min. :0.2111 Min. : 54.82 Min. : 54.22
## 1st Qu.:0.2950 1st Qu.:0.2925 1st Qu.: 88.73 1st Qu.: 88.64
## Median :0.3274 Median :0.3275 Median :103.25 Median :102.70
## Mean :0.3263 Mean :0.3263 Mean :104.43 Mean :104.66
## 3rd Qu.:0.3598 3rd Qu.:0.3606 3rd Qu.:117.47 3rd Qu.:118.33
## Max. :0.4298 Max. :0.4298 Max. :173.91 Max. :173.91
##
## PSV_B_3 PSV_I_41 PSV_I_42 PSV_I_43
## Min. : 54.82 Min. : 94.29 Min. : 92.64 Min. : 90.98
## 1st Qu.: 88.01 1st Qu.:134.16 1st Qu.:134.30 1st Qu.:134.69
## Median :101.84 Median :156.91 Median :155.96 Median :155.34
## Mean :104.39 Mean :159.78 Mean :159.62 Mean :159.25
## 3rd Qu.:118.29 3rd Qu.:187.39 3rd Qu.:186.27 3rd Qu.:186.27
## Max. :169.02 Max. :271.97 Max. :269.43 Max. :269.43
##
## PSV_I_51 PSV_I_52 PSV_I_53 PSV_I_61
## Min. : 95.05 Min. : 95.05 Min. : 96.55 Min. : 94.3
## 1st Qu.:134.36 1st Qu.:135.19 1st Qu.:133.81 1st Qu.:136.9
## Median :160.46 Median :158.62 Median :159.64 Median :162.1
## Mean :160.96 Mean :160.60 Mean :160.59 Mean :162.4
## 3rd Qu.:187.73 3rd Qu.:187.91 3rd Qu.:187.91 3rd Qu.:187.7
## Max. :266.88 Max. :264.34 Max. :266.88 Max. :269.4
##
## PSV_I_62 PSV_I_63 PSV_601 PSV_602
## Min. : 93.56 Min. : 93.56 Min. : 68.86 Min. : 67.36
## 1st Qu.:134.85 1st Qu.:136.43 1st Qu.:111.59 1st Qu.:110.83
## Median :161.30 Median :161.99 Median :124.72 Median :124.47
## Mean :161.87 Mean :162.14 Mean :127.29 Mean :126.67
## 3rd Qu.:187.70 3rd Qu.:187.70 3rd Qu.:143.68 3rd Qu.:141.92
## Max. :269.43 Max. :269.43 Max. :198.28 Max. :200.80
##
## PSV_603 PSV_901 PSV_902 PSV_903
## Min. : 68.86 Min. : 75.5 Min. : 74.47 Min. : 74.47
## 1st Qu.:111.41 1st Qu.:106.5 1st Qu.:106.14 1st Qu.:106.60
## Median :125.55 Median :119.7 Median :119.73 Median :119.22
## Mean :127.05 Mean :122.4 Mean :122.49 Mean :122.37
## 3rd Qu.:143.67 3rd Qu.:136.9 3rd Qu.:137.66 3rd Qu.:137.30
## Max. :200.80 Max. :193.0 Max. :192.96 Max. :195.35
##
## PSV_1201 PSV_1202 PSV_1203 energy
## Min. : 66.19 Min. : 67.23 Min. : 67.23 Min. : 594.1
## 1st Qu.:101.75 1st Qu.:102.56 1st Qu.:102.50 1st Qu.:1250.4
## Median :114.83 Median :114.65 Median :115.78 Median :1667.4
## Mean :117.60 Mean :117.57 Mean :117.85 Mean :1691.9
## 3rd Qu.:130.68 3rd Qu.:127.85 3rd Qu.:129.70 3rd Qu.:2028.1
## Max. :187.40 Max. :187.40 Max. :208.23 Max. :3424.3
##
## Chol Sucrose Fiber CA
## Min. : 49.41 Min. : 3.402 Min. : 2.765 Min. : 228.1
## 1st Qu.:116.95 1st Qu.: 20.562 1st Qu.: 8.154 1st Qu.: 505.6
## Median :172.32 Median : 32.290 Median :11.240 Median : 807.8
## Mean :197.61 Mean : 35.434 Mean :11.912 Mean : 855.3
## 3rd Qu.:243.54 3rd Qu.: 46.435 3rd Qu.:14.969 3rd Qu.:1055.0
## Max. :873.22 Max. :131.194 Max. :30.307 Max. :2885.6
##
## Mg Na K Caffeine
## Min. : 42.28 Min. : 900.4 Min. : 606.8 Min. : 0.000
## 1st Qu.:140.12 1st Qu.:2165.9 1st Qu.:1269.8 1st Qu.: 2.463
## Median :186.20 Median :2798.6 Median :1754.2 Median : 17.067
## Mean :201.57 Mean :2876.9 Mean :1863.2 Mean : 32.635
## 3rd Qu.:247.71 3rd Qu.:3512.3 3rd Qu.:2257.0 3rd Qu.: 44.372
## Max. :470.60 Max. :5328.0 Max. :4891.1 Max. :422.941
##
## pfat pcarb ppro psfat
## Min. :17.12 Min. :32.44 Min. : 7.535 Min. : 4.608
## 1st Qu.:29.22 1st Qu.:46.47 1st Qu.:13.916 1st Qu.: 9.369
## Median :32.82 Median :51.01 Median :16.122 Median :10.976
## Mean :32.72 Mean :50.89 Mean :16.375 Mean :11.014
## 3rd Qu.:36.33 3rd Qu.:55.00 3rd Qu.:18.662 3rd Qu.:12.587
## Max. :51.37 Max. :74.48 Max. :29.204 Max. :16.541
##
## trans FRU0100 FRU0200 FRU0300
## Min. :0.349 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:1.762 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :3.096 Median :0.0000 Median :0.00000 Median :0.00000
## Mean :3.275 Mean :0.3413 Mean :0.24120 Mean :0.04062
## 3rd Qu.:4.630 3rd Qu.:0.3333 3rd Qu.:0.09267 3rd Qu.:0.00000
## Max. :9.031 Max. :6.6687 Max. :4.66667 Max. :1.33333
##
## FRU0400 FRU0500 FRU0700 VEG0100
## Min. :0.0000 Min. :0.000000 Min. :0.000000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000
## Median :0.2890 Median :0.000000 Median :0.000000 Median :0.0000
## Mean :0.4994 Mean :0.003717 Mean :0.001927 Mean :0.1729
## 3rd Qu.:0.6667 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.2170
## Max. :3.6093 Max. :0.377667 Max. :0.333333 Max. :1.5187
##
## VEG0200 VEG0300 VEG0400 VEG0450
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.:0.1160 1st Qu.:0.0000 1st Qu.:0.000000
## Median :0.0000 Median :0.2713 Median :0.0000 Median :0.004333
## Mean :0.0965 Mean :0.3533 Mean :0.2024 Mean :0.128351
## 3rd Qu.:0.1003 3rd Qu.:0.4993 3rd Qu.:0.2990 3rd Qu.:0.172000
## Max. :1.6037 Max. :1.6160 Max. :2.0347 Max. :2.003000
##
## VEG0700 VEG0600 VEG0500 FMC0100
## Min. :0.00000 Min. :0.0000 Min. :0.000000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:0.1213 1st Qu.:0.000000 1st Qu.:0.000
## Median :0.00000 Median :0.3223 Median :0.000000 Median :0.000
## Mean :0.08603 Mean :0.4576 Mean :0.003397 Mean :0.108
## 3rd Qu.:0.00000 3rd Qu.:0.7043 3rd Qu.:0.000000 3rd Qu.:0.000
## Max. :1.33333 Max. :2.7107 Max. :0.562667 Max. :2.430
##
## GRW0100 GRS0100 GRR0100 GRW0200
## Min. :0.00000 Min. :0.000000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.1390 1st Qu.:0.0000
## Median :0.00000 Median :0.000000 Median :0.6833 Median :0.0000
## Mean :0.08407 Mean :0.000973 Mean :1.0630 Mean :0.2878
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:1.5360 3rd Qu.:0.3333
## Max. :2.29867 Max. :0.168333 Max. :6.8607 Max. :3.3333
##
## GRS0200 GRR0200 GRW0300 GRS0300
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.:0.8837 1st Qu.:0.00000 1st Qu.:0.000000
## Median :0.0000 Median :1.5503 Median :0.00000 Median :0.000000
## Mean :0.1856 Mean :1.7969 Mean :0.03575 Mean :0.007505
## 3rd Qu.:0.0000 3rd Qu.:2.2533 3rd Qu.:0.00000 3rd Qu.:0.000000
## Max. :2.6667 Max. :7.3883 Max. :1.31433 Max. :0.684333
##
## GRR0300 GRW0400 GRS0400 GRR0400
## Min. :0.00000 Min. :0 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.08333 Median :0 Median :0.000000 Median :0.00000
## Mean :0.62286 Mean :0 Mean :0.007216 Mean :0.09872
## 3rd Qu.:1.03500 3rd Qu.:0 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :5.72333 Max. :0 Max. :0.352667 Max. :2.94900
##
## GRW0500 GRS0500 GRR0500 GRW0600
## Min. :0.00000 Min. :0 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0 Median :0.3177 Median :0.0000
## Mean :0.04025 Mean :0 Mean :0.5477 Mean :0.0714
## 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.:0.9693 3rd Qu.:0.0000
## Max. :1.33333 Max. :0 Max. :2.7650 Max. :2.3280
##
## GRS0600 GRR0600 GRW0700 GRS0700
## Min. :0.000000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.000000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.007249 Mean :0.03324 Mean :0.2064 Mean :0.04342
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.254000 Max. :2.04600 Max. :3.0373 Max. :1.76367
##
## GRR0700 GRW0800 GRS0800 GRR0800
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.00000 Median :0.00000 Median :0.1637
## Mean :0.1113 Mean :0.01366 Mean :0.01339 Mean :0.3620
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.5337
## Max. :1.9987 Max. :1.21200 Max. :0.52900 Max. :3.0597
##
## GRW1000 GRS1000 GRR1000 GRW0900
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.000
## Mean :0.03381 Mean :0.03365 Mean :0.0146 Mean :0.152
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.188
## Max. :1.00000 Max. :0.92500 Max. :0.8750 Max. :1.833
##
## GRS0900 GRR0900 GRW1100 GRW1200
## Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0
## Median :0.00000 Median :0.0000 Median :0.00000 Median :0
## Mean :0.01577 Mean :0.1106 Mean :0.04427 Mean :0
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0
## Max. :1.65800 Max. :1.9400 Max. :2.27367 Max. :0
##
## MRF0100 MRL0100 MRF0200 MRL0200 MRF0300
## Min. :0.0000 Min. :0.0000 Min. :0 Min. :0 Min. :0
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0.6690 Median :0.0000 Median :0 Median :0 Median :0
## Mean :0.8509 Mean :0.1995 Mean :0 Mean :0 Mean :0
## 3rd Qu.:1.3450 3rd Qu.:0.0000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :6.3950 Max. :3.7733 Max. :0 Max. :0 Max. :0
##
## MRL0300 MRF0400 MRL0400 MCF0200
## Min. :0 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0 Mean :0.09352 Mean :0.05998 Mean :0.04757
## 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :0 Max. :1.97333 Max. :1.85767 Max. :0.58800
##
## MCL0200 MRF0500 MPF0100 MPL0100
## Min. :0.00000 Min. :0 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0 Median :0.0000 Median :0.6113
## Mean :0.03487 Mean :0 Mean :0.2252 Mean :0.8871
## 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.:0.0000 3rd Qu.:1.3333
## Max. :1.57567 Max. :0 Max. :3.3100 Max. :5.4557
##
## MPF0200 MFF0100 MFL0100 MFF0200
## Min. :0.0000 Min. :0.000000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.000000 Median :0.0000 Median :0.00000
## Mean :0.6115 Mean :0.003399 Mean :0.1164 Mean :0.03639
## 3rd Qu.:0.9407 3rd Qu.:0.000000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :5.2440 Max. :0.588000 Max. :5.4320 Max. :2.23400
##
## MSL0100 MSF0100 MCF0100 MCL0100
## Min. :0.00000 Min. :0 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0 Median :0.2470 Median :0.0000
## Mean :0.01519 Mean :0 Mean :0.4894 Mean :0.2608
## 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.:0.7523 3rd Qu.:0.3333
## Max. :0.66633 Max. :0 Max. :5.0980 Max. :2.4773
##
## MOF0100 FMC0200 MOF0300 MOF0400 MOF0500
## Min. :0 Min. :0 Min. :0.0000 Min. :0 Min. :0.00000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.0000 1st Qu.:0 1st Qu.:0.00000
## Median :0 Median :0 Median :0.0000 Median :0 Median :0.00000
## Mean :0 Mean :0 Mean :0.2546 Mean :0 Mean :0.04068
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.3333 3rd Qu.:0 3rd Qu.:0.00000
## Max. :0 Max. :0 Max. :3.3590 Max. :0 Max. :1.35200
##
## MOF0600 MOF0700 DMF0100 DMR0100
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0210
## Mean :0.2514 Mean :0.0161 Mean :0.08948 Mean :0.2807
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.3333
## Max. :3.3333 Max. :2.1603 Max. :4.45833 Max. :6.0000
##
## DML0100 DMF0200 DMR0200 DML0200
## Min. :0.0000 Min. :0.000000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.000000 Median :0.00000 Median :0.0000
## Mean :0.3592 Mean :0.008499 Mean :0.04697 Mean :0.1567
## 3rd Qu.:0.3750 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.3230
## Max. :4.8367 Max. :0.916667 Max. :1.33333 Max. :2.0000
##
## DML0300 DML0400 DCF0100 DCR0100
## Min. :0.000000 Min. :0 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000000 1st Qu.:0 1st Qu.:0.1460 1st Qu.:0.0000
## Median :0.000000 Median :0 Median :0.3147 Median :0.1660
## Mean :0.001647 Mean :0 Mean :0.4035 Mean :0.2609
## 3rd Qu.:0.000000 3rd Qu.:0 3rd Qu.:0.5487 3rd Qu.:0.3920
## Max. :0.187667 Max. :0 Max. :1.7970 Max. :2.1250
##
## DCL0100 DYF0100 DYR0100
## Min. :0.0000000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.0000000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.0000000 Median :0.000000 Median :0.00000
## Mean :0.0005048 Mean :0.003832 Mean :0.03534
## 3rd Qu.:0.0000000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :0.0556667 Max. :0.189667 Max. :0.69433
##
## DYL0100 DYF0200 DYR0200 DYL0200
## Min. :0.000000 Min. :0 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.000000 Median :0 Median :0.000000 Median :0.00000
## Mean :0.001927 Mean :0 Mean :0.001927 Mean :0.02154
## 3rd Qu.:0.000000 3rd Qu.:0 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :0.333333 Max. :0 Max. :0.333333 Max. :0.79600
##
## DOT0100 DOT0300 DOT0400 FCF0100
## Min. :0.0000 Min. :0.000000 Min. :0.000000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000
## Median :0.0000 Median :0.000000 Median :0.000000 Median :0.0000
## Mean :0.1879 Mean :0.006329 Mean :0.003143 Mean :0.0938
## 3rd Qu.:0.2223 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.0000
## Max. :2.0000 Max. :0.288667 Max. :0.407667 Max. :2.5000
##
## FCR0100 FCL0100 DOT0500 DOT0600
## Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0
## Median :0.00000 Median :0.000000 Median :0.000000 Median :0
## Mean :0.02394 Mean :0.004447 Mean :0.001927 Mean :0
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0
## Max. :1.84467 Max. :0.310000 Max. :0.333333 Max. :0
##
## DOT0700 DOT0800 FMF0100 FMR0100
## Min. :0 Min. :0 Min. :0.0000 Min. :0.0000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0 Median :0 Median :0.0000 Median :0.0000
## Mean :0 Mean :0 Mean :0.3329 Mean :0.3631
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.5453 3rd Qu.:0.6667
## Max. :0 Max. :0 Max. :2.7000 Max. :4.0000
##
## FOF0100 FSF0100 FAF0100 FAR0100
## Min. :0.00000 Min. :0.000 Min. :0.0000 Min. :0
## 1st Qu.:0.05867 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0
## Median :0.57100 Median :0.000 Median :0.0000 Median :0
## Mean :0.77093 Mean :0.182 Mean :0.1976 Mean :0
## 3rd Qu.:1.15800 3rd Qu.:0.000 3rd Qu.:0.1667 3rd Qu.:0
## Max. :6.59933 Max. :4.082 Max. :4.0370 Max. :0
##
## FDF0100 FDR0100 SWT0400 SWT0500
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.05433 Median :0.04467
## Mean :0.2188 Mean :0.08257 Mean :0.32932 Mean :0.23431
## 3rd Qu.:0.3263 3rd Qu.:0.00000 3rd Qu.:0.23367 3rd Qu.:0.29533
## Max. :1.5297 Max. :1.72200 Max. :4.25000 Max. :3.36900
##
## SWT0700 SWT0800 SWT0100 SWT0200
## Min. :0.000000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.000000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.002177 Mean :0.02246 Mean :0.06351 Mean :0.05827
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :0.351000 Max. :0.83333 Max. :1.06833 Max. :1.33533
##
## SWT0300 BVS0400 BVA0400 BVU0300
## Min. :0.000000 Min. :0.0000 Min. :0.0000 Min. :0
## 1st Qu.:0.000000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0
## Median :0.000000 Median :0.3333 Median :0.0000 Median :0
## Mean :0.004994 Mean :0.6937 Mean :0.2469 Mean :0
## 3rd Qu.:0.000000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0
## Max. :0.379667 Max. :6.3180 Max. :3.6917 Max. :0
##
## BVS0300 BVA0300 BVS0500 BVA0500
## Min. :0.0000 Min. :0.00000 Min. :0.000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.000000
## Median :0.0000 Median :0.00000 Median :0.000 Median :0.000000
## Mean :0.3154 Mean :0.09824 Mean :0.122 Mean :0.004877
## 3rd Qu.:0.5000 3rd Qu.:0.00000 3rd Qu.:0.000 3rd Qu.:0.000000
## Max. :3.4167 Max. :3.33333 Max. :2.000 Max. :0.500000
##
## BVU0400 BVS0100 BVA0100 BVU0100
## Min. :0.00000 Min. :0.00000 Min. :0 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0 Median :0.00000
## Mean :0.04413 Mean :0.01566 Mean :0 Mean :0.03546
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.:0.00000
## Max. :1.52067 Max. :1.00000 Max. :0 Max. :2.00000
##
## BVS0200 BVA0200 BVU0200 BVS0600 BVA0600
## Min. :0 Min. :0 Min. :0 Min. :0.000000 Min. :0.000000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0.000000 1st Qu.:0.000000
## Median :0 Median :0 Median :0 Median :0.000000 Median :0.000000
## Mean :0 Mean :0 Mean :0 Mean :0.001445 Mean :0.004817
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.000000 3rd Qu.:0.000000
## Max. :0 Max. :0 Max. :0 Max. :0.250000 Max. :0.833333
##
## BVU0500 BVS0700 BVA0700 BVU0600
## Min. : 0.000 Min. :0.0000 Min. :0.00000 Min. :0
## 1st Qu.: 1.210 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0
## Median : 2.204 Median :0.0000 Median :0.00000 Median :0
## Mean : 2.910 Mean :0.1937 Mean :0.07189 Mean :0
## 3rd Qu.: 3.708 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0
## Max. :16.646 Max. :6.6667 Max. :2.66667 Max. :0
##
## BVO0100 BVO0200 BVE0100 BVE0400 BVE0300
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0.0000000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0.0000000
## Median :0 Median :0 Median :0 Median :0 Median :0.0000000
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0.0001927
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.0000000
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0.0333333
##
## BVE0200 MSC0100 MSC0200 MSC0300
## Min. :0.000000 Min. :0.0000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.000000 Median :0.0000 Median :0.000000 Median :0.00000
## Mean :0.001495 Mean :0.0518 Mean :0.002129 Mean :0.05542
## 3rd Qu.:0.000000 3rd Qu.:0.0000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :0.080667 Max. :1.3333 Max. :0.312667 Max. :0.50000
##
## MSC0400 MSC0500 MSC0600 MSC0700
## Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0
## 1st Qu.:0.05567 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0
## Median :0.47967 Median :0.00000 Median :0.000000 Median :0
## Mean :0.79988 Mean :0.07065 Mean :0.003601 Mean :0
## 3rd Qu.:1.06333 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0
## Max. :6.36633 Max. :1.50000 Max. :0.333333 Max. :0
##
## MSC0800 MSC0900
## Min. :0.00000 Min. :0
## 1st Qu.:0.00000 1st Qu.:0
## Median :0.00000 Median :0
## Mean :0.01811 Mean :0
## 3rd Qu.:0.00000 3rd Qu.:0
## Max. :0.41967 Max. :0
## 3 The Gunther’s scoring system
3.1 step 1: initialization
n=dim(DASH_Score)[1]
score_tg=rep(0,n)
score_wg=rep(0,n)
score_v=rep(0,n)
score_f=rep(0,n)
score_td=rep(0,n)
score_lfd=rep(0,n)
score_m=rep(0,n)
score_n=rep(0,n)
score_o=rep(0,n)
score_s=rep(0,n)
score_na=rep(0,n)attach(DASH_Score)
Tfruit = (FRU0100 + FRU0200+ FRU0300+FRU0400+FRU0500+FRU0700);# missing +FRU0600
Tveg = (VEG0100+VEG0200+VEG0300+VEG0400+VEG0450+VEG0600+VEG0500+ FMC0100);# missing 'VEG0800' +VEG0900
Tgrains = (GRW0100+GRS0100+GRR0100+GRW0200+GRS0200+GRR0200+GRW0300+GRS0300+GRR0300+GRW0400+GRS0400+GRR0400+GRW0500+GRS0500+GRR0500+GRW0600+ GRS0600+GRR0600+GRW0700+GRS0700+GRR0700+GRW0800+GRS0800+GRR0800+GRW1000+GRS1000+GRR1000+GRW0900+GRS0900+GRR0900+GRW1100+GRW1200);
Wgrain= (GRW0100 +GRW0200+ GRW0300 +GRW0400 +GRW0500+ GRW0600+ GRW0700 +GRW0800+ GRW1000 +GRW0900 +GRW1100+GRW1200)+(GRS0100 +GRS0200 +GRS0300 +GRS0400 +GRS0500+ GRS0600+ GRS0700+ GRS0800+ GRS1000 +GRS0900)/2;
Nuts = (VEG0700+MOF0500);
Meat = (MRF0100+MRL0100+MRF0200+MRL0200+MRF0300+MRL0300+MRF0400+MRL0400+MCF0200+MCL0200+MRF0500+ MPL0100+MPF0100+MPF0200+MFF0100+MFL0100+MFF0200+MSL0100+MSF0100+MCF0100+MCL0100+MOF0100+FMC0200+MOF0300+MOF0400+MOF0600+MOF0700);
Dairy = (DMF0100+DMR0100+DML0100+DMF0200+DMR0200+DML0200+DML0300+DML0400+DCF0100+DCR0100+DCL0100+DYF0100+DYR0100+DYL0100+DYF0200+DYR0200+DYL0200+DOT0100+DOT0300+DOT0400+FCF0100+FCR0100+FCL0100+DOT0500+DOT0600+DOT0700+DOT0800);# missing SWT0600+MSC1100
Lfdairy = (DML0100 +DML0200+DML0300+DML0400 + DCL0100+ DYL0100+ DYL0200+FCL0100); #missing SWT0600 +MSC1100
Fats = (FMF0100+FMR0100+FOF0100+FSF0100+FAF0100+FAR0100+FDF0100+FDR0100+MSC0100+MSC0200+MSC0300+MSC0400);
Sweets =(SWT0100+SWT0400+SWT0500+SWT0700+SWT0800+SWT0200+SWT0300+BVS0400+BVS0300+BVS0500+BVS0100+BVS0200+BVS0600+BVS0700+GRS0800+GRW0800+GRR0800+GRW1000+GRS1000+GRR1000+MSC0600);3.2 step 2: the Gunther’s scores for each subject
for (i in 1:n){
if(Sex[i]==1)
{
goal_tg = 9;
goal_wg=4.5;
goal_v=5;
goal_f=5;
goal_td=3;
goal_lfd=2.25;
goal_m=6;
goal_n=1.0;
goal_o=3;
goal_s=1.0;
}else{
goal_tg = 6;
goal_wg=3;
goal_v=4;
goal_f=4;
goal_td=3;
goal_lfd=2.25;
goal_m=6;
goal_n=0.6;
goal_o = 2;
goal_s=0.7;
}
if (Tgrains[i]>=goal_tg){
score_tg[i]=5;
} else {
score_tg[i] = 5/goal_tg * Tgrains[i]
}
if (Wgrain[i]>=goal_wg) {
score_wg[i]=5;
} else {
score_wg[i] = 5/goal_wg * Wgrain[i];
}
if (Tveg[i]>=goal_v) {
score_v[i]=10;
} else {
score_v[i] = 10/goal_v * Tveg[i];
}
if (Tfruit[i]>=goal_f) {
score_f[i]=10;
} else{
score_f[i] = 10/goal_f * Tfruit[i];
} ######## difference begins
if (Dairy[i]>=goal_td) {
score_td[i]=5;
} else {
score_td[i] = 5/goal_td * Dairy[i];
}
if (Lfdairy[i]>=goal_lfd) {
score_lfd[i]=5;
} else {
score_lfd[i] = 5/goal_lfd * Lfdairy[i];
} ######## difference ends
if (Meat[i]<=goal_m){
score_m[i]=10;
}else if (Meat[i]>=2*goal_m) {
score_m[i]=0;
} else {
score_m[i] = 10/goal_m * (2*goal_m - Meat[i]);
}
if (Nuts[i]>=goal_n){
score_n[i]=10;
} else {
score_n[i]= 10/goal_n * Nuts[i];
}
if (Fats[i]<=goal_o){
score_o[i]=10
} else if (Fats[i]>=2*goal_o){
score_o[i]=0;
} else {
score_o[i] = 10/goal_o * (2*goal_o - Fats[i]);
}
if (Sweets[i]<=goal_s){
score_s[i]=10;
} else if (Sweets[i]>=2*goal_s){
score_s[i]=0;
} else {
score_s[i] = 10/goal_s * (2*goal_s-Sweets[i]);
}
}
DASHSC_Gunther= (score_tg + score_wg + score_v + score_f + score_lfd + score_m + score_n+score_o + score_s);3.3 Marginal Data Analysis
3.4 Step 1: we produce scatterplots of Gunther DASH Score vs SBP/DBP, respectively.
par(mfrow=c(1,2))
plot(DASHSC_Gunther,SBP)
plot(DASHSC_Gunther,DBP)
3.5 Step 2: we calculate correlation (pearson, spearman and kendal) below between Gunther DASH Scores and SBP/DBP.
3.5.1 correlation (pearson) between Gunther DASH Score and SBP
data11=data.frame(DASHSC_Gunther,SBP)
cor(data11, use="complete.obs", method="pearson") ## DASHSC_Gunther SBP
## DASHSC_Gunther 1.0000000 -0.0149707
## SBP -0.0149707 1.00000003.5.2 correlation (spearman) between Gunther DASH Score and SBP
cor(data11, use="complete.obs", method="spearman") ## DASHSC_Gunther SBP
## DASHSC_Gunther 1.00000000 0.00608429
## SBP 0.00608429 1.000000003.5.3 correlation (kendall) between Gunther DASH Score and SBP
cor(data11, use="complete.obs", method="kendall") ## DASHSC_Gunther SBP
## DASHSC_Gunther 1.000000000 0.005965193
## SBP 0.005965193 1.0000000003.5.4 correlation (pearson) between Gunther DASH Score and SBP
data12=data.frame(DASHSC_Gunther,DBP)
cor(data12, use="complete.obs", method="pearson") ## DASHSC_Gunther DBP
## DASHSC_Gunther 1.0000000 -0.1724092
## DBP -0.1724092 1.00000003.5.5 correlation (spearman) between Gunther DASH Score and SBP
cor(data12, use="complete.obs", method="spearman") ## DASHSC_Gunther DBP
## DASHSC_Gunther 1.0000000 -0.1563493
## DBP -0.1563493 1.00000003.5.6 correlation (kendall) between Gunther DASH Score and SBP
cor(data12, use="complete.obs", method="kendall") ## DASHSC_Gunther DBP
## DASHSC_Gunther 1.000000 -0.107761
## DBP -0.107761 1.0000004 The SuperWIN scoring system
4.1 step 1: initialization
n=dim(DASH_Score)[1]
score_tg=rep(0,n)
score_wg=rep(0,n)
score_v=rep(0,n)
score_f=rep(0,n)
score_lfd=rep(0,n)
score_m=rep(0,n)
score_n=rep(0,n)
score_o=rep(0,n)
score_s=rep(0,n)
score_na=rep(0,n)
attach(DASH_Score)## The following objects are masked from DASH_Score (pos = 3):
##
## actweek, Age, BMIcal, BMIPCT, BVA0100, BVA0200, BVA0300,
## BVA0400, BVA0500, BVA0600, BVA0700, BVE0100, BVE0200, BVE0300,
## BVE0400, BVO0100, BVO0200, BVS0100, BVS0200, BVS0300, BVS0400,
## BVS0500, BVS0600, BVS0700, BVU0100, BVU0200, BVU0300, BVU0400,
## BVU0500, BVU0600, CA, Caffeine, Chol, Day, DBP, DCF0100,
## DCL0100, DCR0100, DMETS, DMF0100, DMF0200, DML0100, DML0200,
## DML0300, DML0400, DMR0100, DMR0200, DOT0100, DOT0300, DOT0400,
## DOT0500, DOT0600, DOT0700, DOT0800, DYF0100, DYF0200, DYL0100,
## DYL0200, DYR0100, DYR0200, energy, FAF0100, FAR0100, FCF0100,
## FCL0100, FCR0100, FDF0100, FDR0100, Fiber, FMC0100, FMC0200,
## FMF0100, FMR0100, FOF0100, FRU0100, FRU0200, FRU0300, FRU0400,
## FRU0500, FRU0700, FSF0100, GRR0100, GRR0200, GRR0300, GRR0400,
## GRR0500, GRR0600, GRR0700, GRR0800, GRR0900, GRR1000, GRS0100,
## GRS0200, GRS0300, GRS0400, GRS0500, GRS0600, GRS0700, GRS0800,
## GRS0900, GRS1000, GRW0100, GRW0200, GRW0300, GRW0400, GRW0500,
## GRW0600, GRW0700, GRW0800, GRW0900, GRW1000, GRW1100, GRW1200,
## hardmin, HTM, HTNST, HTPCT, Income, K, lightmin, MCF0100,
## MCF0200, MCL0100, MCL0200, MFF0100, MFF0200, MFL0100, Mg,
## modmin, MOF0100, MOF0300, MOF0400, MOF0500, MOF0600, MOF0700,
## Month, MPF0100, MPF0200, MPL0100, MRF0100, MRF0200, MRF0300,
## MRF0400, MRF0500, MRL0100, MRL0200, MRL0300, MRL0400, MSC0100,
## MSC0200, MSC0300, MSC0400, MSC0500, MSC0600, MSC0700, MSC0800,
## MSC0900, MSF0100, MSL0100, Na, pcarb, pfat, ppro, psfat,
## PSV_1201, PSV_1202, PSV_1203, PSV_601, PSV_602, PSV_603,
## PSV_901, PSV_902, PSV_903, PSV_B_1, PSV_B_2, PSV_B_3,
## PSV_I_41, PSV_I_42, PSV_I_43, PSV_I_51, PSV_I_52, PSV_I_53,
## PSV_I_61, PSV_I_62, PSV_I_63, Race, SBP, Sex, Sleep, Subject,
## Sucrose, SWT0100, SWT0200, SWT0300, SWT0400, SWT0500, SWT0700,
## SWT0800, trans, VEG0100, VEG0200, VEG0300, VEG0400, VEG0450,
## VEG0500, VEG0600, VEG0700, vhardmin, WTKG, X_D_1201, X_D_1202,
## X_D_1203, X_D_601, X_D_602, X_D_603, X_D_901, X_D_902,
## X_D_903, X_D_B1, X_D_B2, X_D_B3, YearTfruit = (FRU0100 + FRU0200+ FRU0300+FRU0400+FRU0500+FRU0700)
Tveg = (VEG0100+VEG0200+VEG0300+VEG0400+VEG0450+VEG0600+VEG0500+FMC0100)
Tgrains = (GRW0100+GRS0100+GRR0100+GRW0200+GRS0200+GRR0200+GRW0300+GRS0300+GRR0300+GRW0400+GRS0400+GRR0400+GRW0500+GRS0500+GRR0500+GRW0600+ GRS0600+GRR0600+GRW0700+GRS0700+GRR0700+GRW1100);
Wgrain= (GRW0100 +GRW0200+ GRW0300 +GRW0400 +GRW0500+ GRW0600+ GRW0700 +GRW0800+ GRW1000 +GRW0900 +GRW1100+GRW1200)+(GRS0100 +GRS0200 +GRS0300 +GRS0400 +GRS0500+ GRS0600+ GRS0700+ GRS0800+ GRS1000 +GRS0900)/2;
Nuts= (VEG0700+MOF0500+ MOF0600)
Meat = (MRL0100+ MRL0200+ MRL0300+ MRL0400+ MCL0200+MRF0500+ MPL0100+ MFF0100+MFL0100+MSL0100+ MCL0100+ MOF0300+MOF0400+MOF0700);
Lfdairy = (DML0100 +DML0200+DML0300+DML0400+DCL0100+DYL0100+ DYL0200+FCL0100); #missing +SWT0600 +MSC1100
Fats = (FMF0100+FMR0100+FOF0100+FSF0100+FAF0100+FAR0100+FDF0100+FDR0100+MSC0100+MSC0200+MSC0300+MSC0400);
Sweets =(SWT0100+SWT0400+SWT0500+SWT0700+SWT0800+SWT0200+SWT0300+BVS0400+BVS0300+BVS0500+BVS0100+BVS0200+BVS0600+BVS0700+MSC0600);
Gna=Na/1000;4.2 step 2: SuperWIN Scores for each subject
for (i in 1:n){
if(Sex[i]==1)
{
goal_tg = 9;
goal_wg=4.5;
goal_v=5;
goal_f=5;
goal_lfd=2.25;
goal_m=6;
goal_n=1.0;
goal_o = 3;
goal_s=1.0;
}else{
goal_tg = 6;
goal_wg=3;
goal_v=4;
goal_f=4;
goal_lfd=2;
goal_m=6;
goal_n=0.6;
goal_o = 2;
goal_s=0.7;
}
if (Tgrains[i]>=goal_tg){
score_tg[i]=5;
} else {
score_tg[i] = 5/goal_tg * Tgrains[i]
}
if (Wgrain[i]>=goal_wg) {
score_wg[i]=5;
} else {
score_wg[i] = 5/goal_wg * Wgrain[i];
}
if (Tveg[i]>=goal_v) {
score_v[i]=10;
} else {
score_v[i] = 10/goal_v * Tveg[i];
}
if (Tfruit[i]>=goal_f) {
score_f[i]=10;
} else{
score_f[i] = 10/goal_f * Tfruit[i];
}
if (Lfdairy[i]>=goal_lfd) {
score_lfd[i]=10;
} else {
score_lfd[i] = 10/goal_lfd * Lfdairy[i];
}
if (Meat[i]<=goal_m){
score_m[i]=10;
}else if (Meat[i]>=2*goal_m) {
score_m[i]=0;
} else {
score_m[i] = 10/goal_m * (2*goal_m - Meat[i]);
}
if (Nuts[i]>=goal_n){
score_n[i]=10;
} else {
score_n[i]= 10/goal_n * Nuts[i];
}
if (Fats[i]<=goal_o){
score_o[i]=10
} else if (Fats[i]>=2*goal_o){
score_o[i]=0;
} else {
score_o[i] = 10/goal_o * (2*goal_o - Fats[i]);
}
if (Sweets[i]<=goal_s){
score_s[i]=10;
} else if (Sweets[i]>=2*goal_s){
score_s[i]=0;
} else {
score_s[i] = 10/goal_s * (2*goal_s-Sweets[i]);
}
if (Gna[i]<=2.3){
score_na[i]=10;
} else if (Gna[i]>3.3){
score_na[i]=0;
}else {
score_na[i]=(3.3-Gna[i])*10;
}
}
DASHSC_SuperWIN= (score_tg + score_wg + score_v + score_f + score_lfd + score_m + score_n
+ score_o + score_s + score_na);4.3 Marginal Data Analysis
4.4 Step 1: we produce scatterplots of Gunther DASH Scores vs SBP/DBP, respectively.
par(mfrow=c(1,2))
plot(DASHSC_SuperWIN,SBP)
plot(DASHSC_SuperWIN,DBP)
4.5 Step 2: we calculate correlation (pearson, spearman and kendal) between SuperWIN DASH Scores and SBP/DBP below.
4.5.1 correlation (pearson) between SuperWIN DASH Score and SBP
data21=data.frame(DASHSC_SuperWIN,SBP)
cor(data21, use="complete.obs", method="pearson") ## DASHSC_SuperWIN SBP
## DASHSC_SuperWIN 1.00000000 -0.06400777
## SBP -0.06400777 1.000000004.5.2 correlation (spearman) between SuperWIN DASH Score and SBP
cor(data21, use="complete.obs", method="spearman") ## DASHSC_SuperWIN SBP
## DASHSC_SuperWIN 1.00000000 -0.07498156
## SBP -0.07498156 1.000000004.5.3 correlation (kendall) between SuperWIN DASH Score and SBP
cor(data21, use="complete.obs", method="kendall") ## DASHSC_SuperWIN SBP
## DASHSC_SuperWIN 1.00000000 -0.05162978
## SBP -0.05162978 1.00000000data22=data.frame(DASHSC_SuperWIN,DBP)4.5.4 correlation (pearson) between SuperWIN DASH Score and DBP
cor(data22, use="complete.obs", method="pearson") ## DASHSC_SuperWIN DBP
## DASHSC_SuperWIN 1.00000 -0.14891
## DBP -0.14891 1.000004.5.5 correlation (spearman) between SuperWIN DASH Score and DBP
cor(data22, use="complete.obs", method="spearman") ## DASHSC_SuperWIN DBP
## DASHSC_SuperWIN 1.0000000 -0.1714604
## DBP -0.1714604 1.00000004.5.6 correlation (kendall) between SuperWIN DASH Score and DBP
cor(data22, use="complete.obs", method="kendall") ## DASHSC_SuperWIN DBP
## DASHSC_SuperWIN 1.0000000 -0.1133572
## DBP -0.1133572 1.0000000par(mfrow=c(2,2))
plot(DASHSC_SuperWIN,SBP)
plot(DASHSC_SuperWIN,DBP)
plot(DASHSC_Gunther,SBP)
plot(DASHSC_Gunther,DBP)
plot(DASHSC_Gunther,DASHSC_SuperWIN)