README.Rmd

---
title: "bw: Dynamic Body Weight Model"
output: github_document
---

```{r setup, include=FALSE, echo=FALSE, warning=FALSE,message=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(fig.height = 12)
library(ggplot2)
library(bw)
```

The ``bw`` package implements the [Dynamic Weight Change model from _Hall et al._ for adults](https://www.niddk.nih.gov/research-funding/at-niddk/labs-branches/LBM/integrative-physiology-section/research-behind-body-weight-planner/Documents/Hall_Lancet_Web_Appendix.pdf) and the [Children dynamic weight model from Hall **et al**](http://www.thelancet.com/journals/lancet/article/PIIS2213-8587(13)70051-2/abstract) for children.


## Installation

To install the latest version please run the following code:

```{r, eval = FALSE}
if (!require(devtools)){install.packages("devtools")}
devtools::install_github("INSP-RH/bw", build_vignettes = TRUE)
```

## Adult Model

The main function to estimate weight change is ``adult_weight``. It takes as input body weight (kg), height (m),
age (yrs), sex (either `"male"` or `"female"`) either as column vectors from a database or as individual level. In addition it takes a day-by-day `matrix` for change in energy intake from baseline (kcal/day) and another `matrix` for change in sodium (mg/day).

```{r}
#Individual's parameters
bw  <- 80
ht  <- 1.8
age <- 32
sex <- "female"

#Matrix of energy intake change for 100 days
deltaEI     <- rep(-100, 365)

#Function of sodium intake
deltaNA     <- rep(-10, 365)

#Estimate weight change. 
wtrajectory  <- adult_weight(bw, ht, age, sex, deltaEI, deltaNA)
```

The variable `wtrajectory` is a list containing matrices of all the modelled variables: 

```{r}
names(wtrajectory)
```

These can be plotted with `model_plot`:

```{r, fig.height=12}
model_plot(wtrajectory)
```

The model can also be used for computing weight change for several individuals at a time:

```{r}
#Antropometric data
weights <- c(45, 67, 58, 92, 81)
heights <- c(1.30, 1.73, 1.77, 1.92, 1.73)
ages    <- c(45, 23, 66, 44, 23)
sexes   <- c("male", "female", "female", "male", "male") 

#Matrix of energy consumption reduction: 
EIchange <- rbind(rep(-100, 365), rep(-200, 365), rep(-200, 365), 
                  rep(-123, 365), rep(-50, 365))

#Returns a weight change matrix and other matrices
model_weight <- adult_weight(weights, heights, ages, sexes, 
                             EIchange)

model_plot(model_weight)
```

Average for included variables can be done:
```{r, eval = FALSE}
model_mean(model_weight)
```

```{r, echo = FALSE}
head(model_mean(model_weight))
```

BMI prevalence can be computed: 

```{r, eval = FALSE}
adult_bmi(model_weight)
```

```{r, echo = FALSE}
head(adult_bmi(model_weight))
```


## Children Model

The function `child_weight` estimates weight change the same way as `adult_weight`. It takes as input age (yrs), sex ("male" or "female"), fat free mass (kg), and fat mass (kg). The functions `model_mean` and `model_plot` can be also com
A function of energy intake (kcals/yr) for all the years can be inputed. 

```{r, fig.height=8}
#Antropometric data
FatFree <- c(32, 17.2, 18.8, 20, 24.1)
Fat     <- c(4.30, 2.02, 3.07, 1.12, 2.93)
ages    <- c(10, 6.2, 5.4, 4, 4.1)
sexes   <- c("male", "female", "female", "male", "male") 

#Returns a weight change matrix and other matrices
model_weight <- child_weight(ages, sexes, Fat, FatFree)

#Plot
model_plot(model_weight)
```

Mean by sex:
```{r, eval = FALSE}
model_mean(model_weight, group = sexes)
```

```{r, echo = FALSE}
head(model_mean(model_weight, group = sexes))
```

Additional information on usage can be found on the package's vignette:

```{r, eval = FALSE}
browseVignettes("bw")
```