ADHD.Rmd

---
title: "ADHD data"
author: "Keon-Woong Moon"
date: '2016-08-07'
output:
  html_document: default
  pdf_document: default
  word_document: default
---

This example is about the teacher's intervention for students with ADHD. The ADHD data contained in semMediation package is a dataset contains measures about the teacher's knowlege, empathy and intervention about attention-deficit hyperactivity disorder(ADHD).(Source:Effects of teacher's knowledge and empathy on educational intervention for ADHD: Focused on the mediating effet of empathy. J Korean Acad Psychiatr Ment Health Nurs 2013:22;45-55.)

### Step 1. Define latent variables 

First of all, let's start from defining the latent variables. You can define latent variables with `=~` operator.

```{r,message=FALSE}
require(lavaan)
require(semPlot)
require(ReporteRs)
require(semMediation)
require(extrafont)

model='
knowledge =~ general+symptoms+treatmt
intervention =~ classrm+instruct
'
```
### Step 2. Total Effect Model

```{r,fig.width=9,fig.height=7,warning=FALSE}
model=paste0(model,"intervention~knowledge")
fit=sem(model,data=ADHD)
mediationPlot(fit,width=8,height=4,whatLabels="est",base_family="Arial",base_size=4)
```

### Step 3. Define the mediation effect

You can define mediation effects using the makeEquation() function. Here the `knowledge` is an independent variable(X) and `intervention` is a response variable(Y) and `empathy` is a mediator(M).

```{r,comment=NA}
model='
knowledge =~ general+symptoms+treatmt
empathy =~ cognitiv+emotion+disposit+attitude
intervention =~ classrm+instruct
'
mediationModel=makeEquation(X="knowledge",M="empathy",Y="intervention")
cat(mediationModel)
```

You can add this mediation effects to the model. The final model is as follows.
```{r,comment=NA}
model=paste0(model,mediationModel)
cat(model)
```

### Step 4. Fit the model

You can fit this model by using the lavaan::sem() function.
```{r,comment=NA}
fit=sem(model,data=ADHD)
mediationPlot(fit,width=8,mediationOnly = TRUE,base_family="Arial",whatLabels = "name")
summary(fit,standardized= TRUE ,fit.measures= FALSE ,rsquare= FALSE ,modindices= FALSE )
```

### Step 5. Draw a plot

You can draw figure using semPlot::semPaths() function. Alternatively, you can draw a plot with semMediation::mediationPlot().  

```{r,fig.width=10,fig.height=7}
mediationPlot(fit,width=8,height=4,base_family="Arial",whatLabels = "est",residuals = FALSE,base_size=4)
```

### Step 6. Make a Cronbach alpha table

You can make a table shows a Cronbacha alpha and Guttmans lambda 6.

```{r,warning=FALSE}
result=fit2alpha(fit)
df2Flextable(result)
```
```{r,out.width="360px",echo=FALSE}
#knitr::include_graphics("1.png")
```




### Step 7. Draw a correlation plot

You can draw correlation plot by CorPlot function

```{r,fig.width=9,fig.height=7,message=FALSE}
require(mycor)
require(ggplot2)
```
```{r}
corTable(fit)
```

```{r,out.width="360px",echo=FALSE}
#knitr::include_graphics("2.png")
```

```{r}
corPlot(fit)
```

### Step 8. Make a model fit measures table


You can make a table summarizing model fit measures.

```{r}
result=modelFitTable(fit)
df2Flextable(result,vanilla=TRUE,widths=c(rep(0.5,10),1.5,1,1))
```
```{r,out.width="360px",echo=FALSE}
#::include_graphics("3.png")
```

### Step 9. Make an estimates table 

```{r}
result=estimatesTable(fit,ci=TRUE)
df2Flextable(result,vanilla=TRUE)
```

```{r,out.width="360px",echo=FALSE}
#knitr::include_graphics("4.png")
```

### Step 10. Make a mediation effects  table

```{r}
result=estimatesTable(fit,mediation=TRUE,ci=TRUE)
MyTable=df2Flextable(result,vanilla=TRUE)
MyTable[result$Variables=='indirect effect',,side='top']=chprop(borderProperties(style='solid'))
MyTable
```

```{r,out.width="360px",echo=FALSE}
#knitr::include_graphics("5.png")
```

You can draw direct and indirect effects.
```{r,fig.width=9,fig.height=7}
mediationPlot(fit,whatLabels = "est",base_family="Arial",residuals = FALSE,indirect=TRUE,regression=TRUE,mediationOnly = TRUE)
```