homework05.Rmd

---
title: "Homework 05"
---

## Homework 05 - due 11/14/2018

### Answer Key

* R Output (Rmarkdown to HTML) [https://melindahiggins2000.github.io/N736Fall2017_Homework5Key/Homework5Key_Rmarkdown_2018.html](https://melindahiggins2000.github.io/N736Fall2017_Homework5Key/Homework5Key_Rmarkdown_2018.html)
* R Code [https://github.com/melindahiggins2000/N736Fall2017_Homework5Key/raw/master/Homework5_RCodeKey_2018.R](https://github.com/melindahiggins2000/N736Fall2017_Homework5Key/raw/master/Homework5_RCodeKey_2018.R)
* SPSS Output [https://melindahiggins2000.github.io/N736Fall2017_Homework5Key/Homework5Key_SPSS_Output.htm](https://melindahiggins2000.github.io/N736Fall2017_Homework5Key/Homework5Key_SPSS_Output.htm)
* SPSS Code [https://github.com/melindahiggins2000/N736Fall2017_Homework5Key/raw/master/Homework5Key_SPSS_Syntax.sps](https://github.com/melindahiggins2000/N736Fall2017_Homework5Key/raw/master/Homework5Key_SPSS_Syntax.sps)
* SAS Output [https://melindahiggins2000.github.io/N736Fall2017_Homework5Key/Homework5Key_SAS_output.htm](https://melindahiggins2000.github.io/N736Fall2017_Homework5Key/Homework5Key_SAS_output.htm)
* SAS Code [https://github.com/melindahiggins2000/N736Fall2017_Homework5Key/raw/master/Homework5Key_SAS_Code.sas](https://github.com/melindahiggins2000/N736Fall2017_Homework5Key/raw/master/Homework5Key_SAS_Code.sas)

### Analysis of Covariance and Moderation Exercise

For Homework 05, you will be using the HELP dataset, learn more at:

* [https://melindahiggins2000.github.io/N736Fall2017_HELPdataset/](https://melindahiggins2000.github.io/N736Fall2017_HELPdataset/) &
* [https://github.com/melindahiggins2000/N736Fall2017_HELPdataset](https://github.com/melindahiggins2000/N736Fall2017_HELPdataset)

Complete the following for these variables:

* OUTCOME VARIABLE (Y): `mcs`
* INDEPENDENT VARIABLE (X): `age`
* COVARIATES (other X's): `homeless`

1. [MODEL 1] Run a model testing to see if there is a difference in mental health `mcs` scores by homelessness (`homeless`) _(run as a regression model with `mcs` as the outcome)_ 
    * Discuss the interpretation of the intercept _(i.e. when `homeless` = 0)_ and 
    * discuss the interpretation of the slope _(i.e. what happens to `mcs` scores when going from not homeless (`homeless`=0) to homeless (`homeless`=1))_.
    
2. [MODEL 2] Run a model testing for an association between `mcs` as the outcome with `age` as the predictor adjusting for homelessness (`homeless`) as a "covariate". 
    * Use a regression approach and show the stepwise approach comparing 
        - model 1: mcs = homeless
        - model 2: mcs = homeless + age
    * present the change in R2 between these 2 models and the test for significant change in R2
    * discuss these results - is there a significant association between `mcs` and `age` after adjusting for `homeless`?
    
3. [MODEL 3] Run a full model with both main effects and an interaction (moderation) effect _(using a regression, ANOVA, or GLM approach - your choice)_ for the association between the SF36 Mental Component Score (`mcs`) and Age (`age`) adjusting for homelessness (`homeless`). Remember to:
    * check for the assumption of homogenity of slopes _(i.e. is the interaction term significant?)_
    * make an "effects plot" plot of the interaction between `age` and `homeless` 
    * and additionally report the change in R2 for
        - model 3: mcs = homless + age + homeless_x_age
    * discuss the results - does homelessness moderate the association between `mcs` and `age`?
    
## Variables in HELP dataset to be used for Homework 05

```{r, echo=FALSE, message=FALSE, warning=FALSE}
helpdata <- readRDS("helpmkh.rds")
library(tidyverse)
sub1 <- helpdata %>%
  select(mcs,age,homeless)

# create a function to get the label
# label output from the attributes() function
getlabel <- function(x) attributes(x)$label
# getlabel(sub1$age)

library(purrr)
ldf <- purrr::map_df(sub1, getlabel) # this is a 1x15 tibble data.frame
# t(ldf) # transpose for easier reading to a 15x1 single column list

# using knitr to get a table of these
# variable names for Rmarkdown
library(knitr)
knitr::kable(t(ldf),
             col.names = c("Variable Label"),
             caption="Use these variables from HELP dataset for Homework 05")

```