r4ds/model.Rmd

---
layout: default
title: Model
---

# Model

Models are one of the most important tools for data scientists, because models describe relationships. Would you list out every value of a variable, or would you state the mean? Would you list out every pair of values, or would you state the function between variables?

### Outline

*Section 1* will explain what models are and what they can do for you.

*Section 2* will show you how to use R to build linear models, the most commonly used modeling tool. The section introduces R's model syntax, a general syntax that you can reuse with any of R's modelling functions.

*Section 3* will teach you to build and interpret multivariate linear models, models that use more than one variable to make a prediction.

*Section 4* will explain how to use categorical variables in your models and how to interpret the results.

*Section 5* will present a logical way to extend models to non-linear settings.

### Prerequisites

To access the functions and data sets that we will use in the chapter, load the `ggplot2`, `dplyr`, `mgcv`, `splines`, and `broom` packages:

```{r}
# install.packages("")
library(ggplot2)
library(dplyr)
library(mgcv)
library(splines)
library(broom)
```

**Note: the current examples use a data set that will be replaced in later drafts.**
  
## What is a model?

1. A model is just a summary, like a mean, median, or variance.
    + Example problem/data set
    
```{r echo = FALSE}
heights <- read.csv("data/heights.csv")
```

```{r}
head(heights)
```
    
2. As normally taught, modeling is a conflation of three subjects
    + Models as summaries
    + Hypothesis testing
    + Predictive modeling
3. C. This chapter shows how to build a model and use it as a summary. The methods for building a model apply to all three subjects.
  
## How to build a model

1. Best fit
    + Best fit of what? A certain class of function.
    + But how do you know which class to use? In some cases, the data can provide suggestions. In other cases existing theory can provide suggestions. But ultimately, you'll never know for sure. But that's okay, good enough is good enough.
2. What does best fit mean?
    + It may or may not accurately describe the true relationship. Heck, there might not even be a true relationship. But it is the best guess given the data.
    + Example problem/data set
    + It does not mean causation exists. Causation is just one type of relations, which is difficult enough to define, let alone prove.
3. How do you find the best fit?
    + With an algorithm. There is an algorithm to fit each specific class of function. We will cover some of the most useful here.
4. How do you know how good the fit is? 
    + Adjusted $R^{2}$
5. Are we making assumptions when we fit a model?
    + No. Not unless you assume that you've selected the correct type of function (and I see no reason why you should assume that).
    + Assumptions come when you start hypothesis testing.

## Linear models

1. Linear models fit linear functions
2. How to fit in R
    + model syntax, which is reusable with all model functions

```{r}
earn ~ height 
lm(earn ~ height, data = heights)
```

    + save model output
```{r}
hmod <- lm(earn ~ height, data = heights)
coef(hmod)
summary(hmod)
```

    + visualize
```{r}
ggplot(data = heights, mapping = aes(x = height, y = earn)) +
  geom_point() +
  geom_smooth(method = lm)
```
    
    + intercept or no intercept
```{r}
0 + earn ~ height 
lm(earn ~ 0 + height, data = heights)
lm(earn ~ 0 + height, data = heights)
```

3. How to interpret
    + extract information. Resid. Predict.
```{r eval = FALSE}
resid(hmod)
predict(hmod)
```
    + Interpret coefficient
4. How to use the results (with `broom`)
    + tidy. augment. glance.
```{r eval = FALSE}
tidy(hmod)
augment(hmod)
glance(hmod)
```

```{r}
heights %>% 
  group_by(sex)  %>% 
  do(glance(lm(earn ~ height, data = .)))
```

## Categorical data

```{r}
smod <- lm(earn ~ sex, data = heights)
smod
```

1. Factors

```{r}
heights$sex <- factor(heights$sex, levels = c("male", "female"))

smod2 <- lm(earn ~ sex, data = heights)

smod
smod2
```

2. How to interpret

```{r}
coef(smod)
```


## Multiple Variables

1. How to fit multivariate models in R

```{r}
mmod <- lm(earn ~ height + sex, data = heights)
mmod
```

2. How to interpret

```{r}
coef(mmod)
```

3. Interaction effects

```{r}
lm(earn ~ height + sex, data = heights)
lm(earn ~ height + sex + height:sex, data = heights)
lm(earn ~ height * sex, data = heights)
```

```{r}
lm(earn ~ height + ed, data = heights)
lm(earn ~ height * ed, data = heights)
```

4. Partition variance
    + Checking residuals
```{r}
m1 <- lm(earn ~ height, data = heights)
# plot histogram of residuals
# plot residulas vs. sex
m2 <- lm(earn ~ height + sex, data = heights)
# plot histogram of residuals
# plot residuals vs. education
m3 <- lm(earn ~ height + sex + ed, data = heights)
# plot histogram of residuals
m4 <- lm(earn ~ height + sex + race + ed + age, 
  data = heights)
# plot histogram of residuals
m5 <- lm(earn ~ ., data = heights)
```


## Non-linear functions (recipes?)

0. Transformations
    + Log
```{r}
ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point()
ggplot(diamonds, aes(x = log(carat), y = log(price))) +
  geom_point()
```

```{r}
lm(log(price) ~ log(carat), data = diamonds)
# visualize model line
```

    + Logit with `glm()`
    
    
What if no handy transformation exists?

```{r}
ggplot(data = heights, mapping = aes(x= age, y = earn)) + 
  geom_point() +
  geom_smooth() + 
  coord_cartesian(ylim = c(0, 50000))
```

1. Polynomials
    + How to fit
    
```{r}
lm(earn ~ poly(age, 3), data = heights)

ggplot(data = heights, mapping = aes(x= age, y = earn)) +
  geom_point() +
  geom_smooth(method = lm, formula = y ~ poly(x, 3))
```

    + How to interpret
    + Strengths and Weaknesses
2. Splines
    + How to fit. Knots. Different types of splines.
```{r eval = FALSE}
bs(degree = 1) # linear splines
bs()           # cubic splines
ns()           # natural splines
```

```{r}
lm(earn ~ ns(age, knots = c(40, 60)), data = heights)
lm(earn ~ ns(age, df = 4), data = heights)
```    

```{r}
lm(earn ~ ns(age, df = 6), data = heights)

ggplot(data = heights, mapping = aes(x= age, y = earn)) +
  geom_point() +
  geom_smooth(method = lm, formula = y ~ ns(x, df = 6)) +  
  coord_cartesian(ylim = c(0, 50000))
```

    + How to interpret
    + Strengths and weaknesses
    
    
3. General Additive Models
    + How to fit
    
```{r}
gmod <- gam(earn ~ s(height), data = heights)

ggplot(data = heights, mapping = aes(x= age, y = earn)) +
  geom_point() +
  geom_smooth(method = gam, formula = y ~ s(x))
```
    + How to interpret
    + Strengths and weaknesses
    
```{r eval = FALSE}
# Linear z
gam(y ~ s(x) + z, data = df)

# Smooth x and smooth z
gam(y ~ s(x) + s(z), data = df)

# Smooth surface of x and z 
# (a smooth function that takes both x and z)
gam(y ~ s(x, z), data = df)
```