---
title: "Which covariates to adjust for? (DAGs)"
author: "AGDamsbo"
date: "`r Sys.Date()`"
output:
  pdf_document: default
  html_document: default
---

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

# Labels

```{r}

```

```{r}
library(ggdag)
library(ggplot2)
theme_set(theme_dag())
```


```{r}
ex_ca_dag <- dagify(ever_smoker ~ ami + socio_eco,
  pad ~ ever_smoker,
  edu ~ socio_eco,
  ever_smoker ~ socio_eco,
  pase_0 ~ ever_smoker + edu + pad,
  labels = c(
    "pase_0" = "Physical\n activity",
    "ami" = "AMI",
    "ever_smoker" = "Smoking",
    "pad" = "PAD",
    "socio_eco" = "Socio\n status",
    "edu" = "Education"
  ),
  # latent = "unhealthy",
  exposure = "edu",
  outcome = "pase_0"
)

ggdag(ex_ca_dag, text = FALSE, use_labels = "label")
```

```{r}
ggdag_paths(ex_ca_dag, text = FALSE, use_labels = "label", shadow = TRUE)
```

```{r}
ggdag_adjustment_set(ex_ca_dag, text = FALSE, use_labels = "label", shadow = TRUE)
```

# Excercise

```{r}
dag <- dagify(death ~ chd + HF + age + sex + wght,
                    HF~chd,
                    age~chd,
                    chd~sex,
                    HF~sex,
                    wght~sex,
                    
  # latent = "unhealthy",
  exposure = "chd",
  outcome = "death"
)

dag |> ggdag(text = TRUE)

dag |> ggdag_adjustment_set(text = TRUE, shadow = TRUE)
```


```{r}
dag <- dagify(ami~fat+age+sex+obm1+obp1+SESm1,
              fat~sCm1+sex+sCp1+obm1+SESm1,
              sCp1~sCm1+obm1,
              sCm1~age,
              obp1~obm1+SESm1,
              obm1~SESm1,
              SESm1~age,
  # latent = "unhealthy",
  exposure = "fat",
  outcome = "ami"
)

dag |> ggdag(text = TRUE)

dag |> ggdag_adjustment_set(text = TRUE, shadow = TRUE)
```


```{r}
dag <- dagify(wgt~DM1+GA+smoke+par+BS,
              GA~DM1+smoke+par+BS+SES,
              smoke~DM1+par+SES,
              par~DM1+SES,
              BS~DM1+SES,
              DM1~SES,
  # latent = "unhealthy",
  exposure = "DM1",
  outcome = "wgt"
)

dag |> ggdag(text = TRUE,stylized = TRUE)

dag |> ggdag_adjustment_set(text = TRUE, shadow = TRUE,stylized = TRUE)
```

```{r}
testImplications <- function( covariance.matrix, sample.size ){
	library(ggm)
	tst <- function(i){ pcor.test( pcor(i,covariance.matrix), length(i)-2, sample.size )$pvalue }
tos <- function(i){ paste(i,collapse=" ") }
implications <- list(c("A","B"),
		c("A","D","E"),
		c("B","E"),
		c("D","Z","A","B"),
		c("D","Z","B","E"),
		c("E","Z","A"))
	data.frame( implication=unlist(lapply(implications,tos)),
		pvalue=unlist( lapply( implications, tst ) ) )

}

library(dagitty)

testImplications()
```