examples.Rmd

---
title: "rTisane examples"
output: html_document
date: "2024-04-23"
---

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

# Variables
Variables that are used in below examples: 
```{r}
## Variables
person <- Unit(name = "person")
# Age: Continuous measure
age <- continuous(unit = person, "Age")
# Race: There are 5 categories: White, Black/African American, American Indian or Alaska Native, Asian or Pacific Islander, Mixed Race
race <- categories(unit = person, "Race", cardinality = 9)
# Highest Education Completed: 5 categories: Grade 12, 1 year of college, 2 years of college, 4 years of college, 5+ years of colleges
edu <- categories(unit = person, "Education", order=list("Grade 12", "1 year of college", "2 years of college", "4 years of college", "5+ years of college"))
# Current Employment Status: 3 categories: N/A, Works for wage, Self-employed
employ <- categories(unit=person, "Employment", cardinality=2)
# Sex: 2 categories: Male, Female
sex <- categories(unit = person, "Sex",cardinality = 2)
# Income: Continuous measure
income <- continuous(unit = person, "Income")
```

# Simplest conceptual model: Only causes
```{r}
## Conceptual model
cm <- ConceptualModel()%>%
assume(causes(edu, income))%>%
assume(causes(sex, income))%>%
hypothesize(causes(employ, income))

## Query
script <- query(conceptualModel=cm, dv=income, iv=employ)
```

# Simple conceptual model: Multiple relates, no direct relationships between "IVs", no circular relationships
This example is adapted from a participant's program from the evaluation study.
```{r}
## Conceptual model
cm <- ConceptualModel()%>%
assume(relates(edu, income))%>%
assume(relates(sex, income))%>%
hypothesize(relates(employ, income))

## Query for statistical model
script <- query(conceptualModel=cm, dv=income, iv=employ)
```
Conceptual disambiguation: Need to specify all relates relationships

# Simple conceptual model: Multiple relates and cause, no direct relationships between "IVs", no circular relationships
```{r}
## Variables: same as above

## Conceptual model
cm <- ConceptualModel()%>%
assume(relates(edu, income))%>%
assume(relates(sex, income))%>%
hypothesize(causes(employ, income))

## Query for statistical model
script <- query(conceptualModel=cm, dv=income, iv=employ)
```
# Circular relationships
```{r}
## Variables: same as above

## Conceptual model
cm <- ConceptualModel()%>%
assume(relates(edu, income))%>%
assume(relates(sex, income))%>%
assume(relates(edu, sex))%>%
hypothesize(relates(employ, income))

## Query for statistical model
script <- query(conceptualModel=cm, dv=income, iv=employ)
```