ReadMe.rmd

---
title: "ARMIS"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Overview
The R package `ARMIS` analyze the repeated measured data from the same subject in the small sample size of study. This package uses three mixed models to capture the time effect, while accounting for the correlations of measurements over 
time from the same subject. First, we allow the flexible variance-covariance structures on the model. Second, we use baseline measurements as a covariate in the model. Third, we use percent-change from baseline as a data normalization method. These methods are described in the following manuscript: 
  
  **Lee, U., Garcia, T.P., Carroll, R.J., Gilbreth, K.R., Wu, G. (2019). "Analysis of repeated measures data in nutrition research", Frontiers In Bioscience, Landmark, 24, 1378-1390**
  

## Installation 

To install `ARMIS` from GitHub,
```{r, eval=FALSE}
devtools::install_github("unkyunglee/ARMIS")
```

## Example

```{r pseudo_data, eval=F, results='hide'}
We provide a pseudo dataset to run an example to show reproducibility of our methods in the manuscript. 
We consider the dataset `pseudo_steer_data` available from R package `ARMIS`. 

data(pseudo_steer)
head(pseudo_steer)
```


The data consist of 6 subjects' information with 4 variables. The amino acids are repeatedly measured at 6 different time points for each subject.

```{r data_info, eval=FALSE, message=FALSE}
head(pseudo_steer)
?pseudo_steer # this gives you more information on the dataset
```

We fit our methods to the `pseudo_steer_data` data. First, we specify the parameters and run the function `anova.test()`.
  
```{r pseudo_analysis, message=FALSE, eval=FALSE, results='hide'}

# Specify the parameters
data=pseudo_steer;
num.aa=1; 
n=6; 
time.points=6;
subid="Steer";
group="Group"; 
time="Time"; 
resp.var="citrulline"; 
amino.names="citrulline";
name.tt=c("time0", "time1", "time2", "time3", "time4", "time5");
name.steer=c("steer1","steer2","steer3","steer4","steer5","steer6");
interv.length=7; 
num.method=4; 
corStruct="gen.ar1"; 
hetero=TRUE;
file="cit_data.csv"

# produce one table and three figures of our manuscript
result<-anova.test(data=speudo_steer, num.aa=1, n=6, time.points=6, subid="Steer",
                   group="Group", time="Time", resp.var="citrulline", amino.names="citrulline",
                   interv.length=7, num.method=4, corStruct="gen.ar1", hetero=TRUE,
                  file="rpaa_cit_data.csv")

```