---
title: "Continuous Predictors"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Continuous Predictors}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)
ggplot2::theme_set(ggplot2::theme_bw())
set.seed(8675309)
```

```{r, warning=FALSE, message=FALSE}
library(faux)
library(dplyr)
library(tidyr)
library(ggplot2)
library(cowplot) # for multi-panel plots
```

## One continuous predictor

```{r}
dat <- sim_design(within = list(vars = c("dv", "predictor")),
                mu = list(dv = 100, predictor = 0),
                sd = list(dv = 10, predictor = 1),
                r = 0.5, plot = FALSE)
```


```{r echo = FALSE}
ggplot(dat, aes(predictor, dv)) + 
  geom_point() + geom_smooth(formula = 'y~x', method = lm)
```

## Between continuous predictors

Here, pred1 is correlated r = 0.5 to the DV, and pred2 is correlated 0.0 to the DV, and pred1 and pred2 are correlated r = -0.2 to each other.

```{r}
dat <- sim_design(within = list(vars = c("dv", "pred1", "pred2")),
                mu = list(dv = 100, pred1 = 0, pred2 = 0),
                sd = list(dv = 10, pred1 = 1, pred2 = 1),
                r = c(0.5, 0, -0.2), plot = FALSE)
```


```{r echo = FALSE}
d1 <- ggplot(dat, aes(pred1, dv)) + 
  geom_point() + geom_smooth(formula = 'y~x', method = lm)

d2 <- ggplot(dat, aes(pred2, dv)) + 
  geom_point() + geom_smooth(formula = 'y~x', method = lm)

pred <- ggplot(dat, aes(pred1, pred2)) + 
  geom_point() + geom_smooth(formula = 'y~x', method = lm)

cowplot::plot_grid(d1, d2, pred, ncol = 3)
```

## Within continuous predictors

If the continuous predictors are within-subjects (e.g., dv and predictor are measured at pre- an post-test), you can set it up like below. 

The correlation matrix can start getting tricky, so I usually map out the upper right triangle of the correlation matrix separately. Here, the dv and predictor are correlated 0.0 in the pre-test and 0.5 in the post-test. The dv is correlated 0.8 between pre- and post-test and the predictor is correlated 0.3 between pre- and post-test. There is no correlation between the pre-test predictor and the post-test dv, but I'm not sure what values are possible then for the correlation between the post-test predictor and pre-test dv, so I can set that to NA and use the `pos_def_limits` function to determine the range of possible correlations (gven the existing correlation structure). Those range from -0.08 to 0.88, so I'll set the value to the mean.

```{r}
#      pre_pred, post_dv, post_pred
r <- c(     0.0,     0.8,        NA, # pre_dv
                     0.0,       0.3, # pre_pred
                                0.5) # post_dv
                                        
lim <- faux::pos_def_limits(r)
r[[3]] <- mean(c(lim$min, lim$max))

dat <- sim_design(within = list(time = c("pre", "post"),
                                vars = c("dv", "pred")),
                mu = list(pre_dv = 100, pre_pred = 0, 
                          post_dv = 110, post_pred = 0.1),
                sd = list(pre_dv = 10, pre_pred = 1,
                          post_dv = 10, post_pred = 1),
                r = r, plot = FALSE)
```

You have to make this sort of dataset in wide format and then manually convert it to long. I prefer `gather` and `spread`, but I'm trying to learn the new pivot functions, so I'll use them here.

```{r}
long_dat <- dat %>%
  pivot_longer(-id, "var", "value") %>%
  separate(var, c("time", "var")) %>%
  pivot_wider(names_from = var, values_from = value)
```


```{r within-cont, echo = FALSE}
ggplot(long_dat, aes(pred, dv, color = time)) +
  geom_point() + geom_smooth(formula = 'y~x', method = lm)
```


## One continuous, one categorical predictor

In this design, the DV is 10 higher for group B than group A and the correlation between the predictor and DV is 0.5 for group A and 0.0 for group B.

```{r}
dat <- sim_design(between = list(group = c("A", "B")),
                  within = list(vars = c("dv", "predictor")),
                  mu = list(A = c(dv = 100, predictor = 0),
                            B = c(dv = 110, predictor = 0)),
                  sd = list(A = c(dv =  10, predictor = 1),
                            B = c(dv =  10, predictor = 1)),
                  r  = list(A = 0.5, B = 0), plot = FALSE)
```


```{r cont-cat, echo = FALSE}
ggplot(dat, aes(predictor, dv, color = group)) + 
  geom_point() + geom_smooth(formula = 'y~x', method = lm)
```


## Add a continuous predictor 

If you already have a dataset and want to add a continuous predictor, you can make a new column with a specified mean, SD and correlation to one other column.

First, let's make a simple dataset with one between-subject factor.

```{r}
dat <- sim_design(between = list(group = c("A", "B")), 
                  mu = list(A = 100, B = 120), sd = 10, plot = FALSE)
```

Now we can add a continuous predictor with `rnorm_pre` by specifying the vector it should be correlated with, the mean, and the SD. By default, this produces values sampled from a population with that mean, SD and r. If you set `empirical` to TRUE, the resulting vector will have that sample mean, SD and r.

```{r}
dat$pred <- rnorm_pre(dat$y, 0, 1, 0.5)
```


```{r add-pred, echo = FALSE}
ggplot(dat, aes(pred, y, color = group)) +
  geom_point() + geom_smooth(formula = 'y~x', method = lm)
```

If you want to set a different mean, SD or r for the between-subject groups, you can split and re-merge the dataset (or use your data wrangling skills to devise a more elegant way using purrr).

```{r}
A <- filter(dat, group == "A") %>%
  mutate(pred = rnorm_pre(y, 0, 1, -0.5))
B <- filter(dat, group == "B") %>%
  mutate(pred = rnorm_pre(y, 0, 1, 0.5))

dat <- bind_rows(A, B)
```


```{r echo = FALSE}
ggplot(dat, aes(pred, y, color = group)) +
  geom_point() + geom_smooth(formula = 'y~x', method = lm)
```


You can also specify correlations to more than one vector by setting the first argument to a data frame containing only the continuous columns and r to the correlation with each column.

```{r}
dat <- sim_design(2, r = 0.5, plot = FALSE)
dat$B <- rnorm_pre(dat[, 2:3], r = c(A1 = 0.5, A2 = 0))
cor(dat[, 2:4])
```
Not all correlation patterns are possible, so you'll get an error message if the correlations you ask for are impossible.

```{r, error = TRUE, perl = FALSE}
dat$C <- rnorm_pre(dat[, 2:4], r = c(A1 = 0.9, A2 = 0.9, B = -0.9))
```
