Nutrition
Nutrition Quality is represented by a proxy of the number of portions of fruit and vegetables eaten per week. The following variables from understanding society that measure fruit and vegetable consumption were used:
The equations to generate the nutrition composite are as follows:
Both fruit and vegetable consumption frequency was measured in days per week, and reported as:
Never
1-3 days
4-6 days
Every day
The amount of fruit and veg per day was a continuous variable, indicating the number of portions of fruit or veg eaten on an average day when the respondent eats fruit or veg.
This gives us a continuous nutrition score, composed of the sum of two
proxy values for the amount of fruit and veg eaten per week.
Unfortunately because the days_eating_<>_per_week variables are
ordinal (levels = [Never, 1-3 days, 4-6 days, Everyday]) and not just
the number of days, we can’t calculate an actual value for
amount_per_week.
continuous_density(obs)
Fig. 29 plot of chunk nutrition_quality_data
Transition Model
We use a Linear Mixed Model (LMM) from the lme4 package in R to predict the next state of nutrition quality.
Formula:
print(summary(model))
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## nutrition_quality_new ~ scale(age) + factor(sex) + relevel(factor(ethnicity),
## ref = "WBI") + factor(region) + relevel(factor(education_state),
## ref = "1") + scale(hh_income) + factor(behind_on_bills) +
## factor(financial_situation) + (1 | pidp) + (1 | hidp)
## Data: data
## Weights: weight
##
## REML criterion at convergence: Inf
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1107 -0.5698 -0.1088 0.4053 10.4141
##
## Random effects:
## Groups Name Variance Std.Dev.
## hidp (Intercept) 2.542 1.594
## pidp (Intercept) 2.542 1.594
## Residual 2.542 1.594
## Number of obs: 70487, groups: hidp, 49936; pidp, 29227
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 16.78024 0.27070 61.987
## scale(age) 0.60449 0.03610 16.745
## factor(sex)Male -1.39758 0.06502 -21.494
## relevel(factor(ethnicity), ref = "WBI")BAN -1.73668 0.44716 -3.884
## relevel(factor(ethnicity), ref = "WBI")BLA -0.67357 0.29203 -2.306
## relevel(factor(ethnicity), ref = "WBI")BLC -0.93967 0.36796 -2.554
## relevel(factor(ethnicity), ref = "WBI")CHI 0.27003 0.47765 0.565
## relevel(factor(ethnicity), ref = "WBI")IND -0.91078 0.21522 -4.232
## relevel(factor(ethnicity), ref = "WBI")MIX -0.18856 0.27254 -0.692
## relevel(factor(ethnicity), ref = "WBI")OAS 0.68035 0.31114 2.187
## relevel(factor(ethnicity), ref = "WBI")OBL 0.82279 0.96326 0.854
## relevel(factor(ethnicity), ref = "WBI")OTH 0.25520 0.50079 0.510
## relevel(factor(ethnicity), ref = "WBI")PAK -2.17454 0.26700 -8.144
## relevel(factor(ethnicity), ref = "WBI")WHO 1.57690 0.16128 9.777
## factor(region)East of England 0.01103 0.15545 0.071
## factor(region)London 0.05260 0.15829 0.332
## factor(region)North East -0.21626 0.19899 -1.087
## factor(region)North West -0.48531 0.15445 -3.142
## factor(region)Northern Ireland -1.82362 0.23166 -7.872
## factor(region)Scotland -0.73583 0.17327 -4.247
## factor(region)South East 0.25464 0.14566 1.748
## factor(region)South West 0.44307 0.15918 2.783
## factor(region)Wales 0.72114 0.20801 3.467
## factor(region)West Midlands -0.05058 0.16090 -0.314
## factor(region)Yorkshire and The Humber -0.55816 0.16043 -3.479
## relevel(factor(education_state), ref = "1")0 -0.15438 0.24831 -0.622
## relevel(factor(education_state), ref = "1")2 0.39452 0.24710 1.597
## relevel(factor(education_state), ref = "1")3 0.98963 0.25818 3.833
## relevel(factor(education_state), ref = "1")5 1.21924 0.26058 4.679
## relevel(factor(education_state), ref = "1")6 2.33461 0.24965 9.352
## relevel(factor(education_state), ref = "1")7 2.83053 0.25511 11.095
## scale(hh_income) 0.36048 0.03334 10.814
## factor(behind_on_bills)2 -1.26620 0.17343 -7.301
## factor(behind_on_bills)3 -2.02134 0.61519 -3.286
## factor(financial_situation)2 -0.47243 0.07576 -6.236
## factor(financial_situation)3 -0.97586 0.09615 -10.150
## factor(financial_situation)4 -0.99608 0.16669 -5.976
## factor(financial_situation)5 -1.53106 0.26962 -5.679
##
## Correlation matrix not shown by default, as p = 38 > 12.
## Use print(summary(model), correlation=TRUE) or
## vcov(summary(model)) if you need it
## optimizer (nloptwrap) convergence code: 0 (OK)
## Gradient contains NAs
Validation
handover_boxplots(raw.dat, base.dat, v)
Fig. 30 plot of chunk nutrition_validation
handover_lineplots(raw.dat, base.dat, v)
Fig. 31 plot of chunk nutrition_validation
Results
