Revert "Add tidySEM to lesson 1"

This reverts commit b3f7a76.

01 path analysis/Exercise 1 - solution.R

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+# Exercise ----------------------------------------------------------------
+
+income_psych <- read.csv("income_psych.csv")
+library(lavaan)
+
+## 1. Look at the plot in "plot2.png". Fit this model with `lavaan`.
+
+model <- "
+# We need to build a regression equation for each endogenous variable:
+ anxiety ~ shyness
+mood_neg ~ anxiety
+  income ~ anxiety + mood_neg + shyness
+"
+
+fit <- sem(model, data = income_psych)
+summary(fit, standardize = TRUE)
+
+
+library(semPlot)
+semPaths(fit)
+# See `advanced plotting` for making "plot2.png"
+
+
+
+
+
+
+## 2. Compute all the paths in this model from *anxiety* to *income*, and 
+##    the total of these paths.
+model <- "
+ anxiety ~ a * shyness
+mood_neg ~ b * anxiety
+  income ~ c * anxiety + d * mood_neg + e * shyness
+  
+  # Define paths:
+       direct := c
+  ind_shyness := a * e
+     ind_mood := b * d
+        total := direct + ind_shyness + ind_mood
+"
+
+fit <- sem(model, data = income_psych)
+summary(fit, standardize = TRUE)
+# Seems like the total path is not significant.
+# Looks like a case of supression... Some paths are +, some are -...
+# They LOOK like they cancel each other out!
+
+# However...
+# The total is significant in the standerdized solution!! *
+standardizedsolution(fit, output = "text")
+
+## - Why is the std total not equal exactly to the real correlation?
+cor(income_psych$anxiety, income_psych$income)
+# Theres a diff of (-0.001). Because the model isn't *just-identified*.
+# We are missing the arrow from `shyness` to `mood_neg`.
+
+
+## - is it very different?
+# The diff is of (-0.001). Is this a lot? Is it significant?
+# We will see next time how to answer these questions!
+
+
+# * -----------------------------------------------------------------------
+
+# Why does this happen?
+# In SEM, standard errors (SE) are NOT invariant - that is, data
+# transformation do not affect SEs the same way they do estimates (the 
+# parameters).
+# However, Chis-square atatistics ARE invariant - so this is not an issue 
+# in model comparison (which we will learn next week).
+# 
+# You can read more (mathy), here:
+# https://doi.org/10.1007/BF01065882
+# (Thanks to Michael Neale for his explanation)

01 path analysis/advanced plotting.R

@@ -0,0 +1,184 @@
+library(lavaan)
+library(tidySEM)
+library(dplyr)
+
+income_psych <- read.csv("income_psych.csv")
+
+mediation_model <- '
+    income ~ anxiety + mood_neg
+  mood_neg ~ anxiety
+'
+fit <- sem(mediation_model, data = income_psych)
+
+## See below for how "plot1.png" and "plot2.png" were made.
+
+## Based on:
+## https://cjvanlissa.github.io/tidySEM/articles/Plotting_graphs.html
+
+
+# Changing the layout -----------------------------------------------------
+
+lay <- get_layout(NA, "mood_neg", NA,
+                  NA, NA, NA,
+                  "anxiety", NA, "income",
+                  rows = 3)
+
+
+
+
+graph_data <- prepare_graph(fit, layout = lay, label = "est_std")
+
+plot(graph_data)
+
+
+
+
+# Adding var labels -------------------------------------------------------
+
+# We can add labels to our variables (our "nodes") by chaging the "label" column
+# in the "nodes" part of the plot:
+
+(N <- nodes(graph_data))
+
+nodes(graph_data) <- N %>% 
+  mutate(label = c("Anxiety", "Income", "Negative Mood"))
+
+plot(graph_data)
+
+
+
+# Adding / changing arrow labels ------------------------------------------
+
+
+(tab <- table_results(fit, columns = c("label", "est_std", "confint_std", "pval"), digits = 2))
+
+(E <- edges(graph_data))
+
+edges(graph_data) <- E %>% 
+  mutate(est_std = tab$est_std,
+         label = paste0(round(est_std, 2), " ",tab$confint))
+
+plot(graph_data)
+
+
+
+
+
+# Even more ---------------------------------------------------------------
+
+E <- edges(graph_data)
+
+edges(graph_data) <- E %>% 
+  mutate(
+    # color lines by sign:
+    colour = case_when(
+      arrow == "both" ~ "black",
+      est_std < 0 ~ "red",
+      est_std > 0 ~ "green"
+    ),
+    # mark which is sig
+    linetype = c("dashed","dashed","solid", "dashed", "solid", "solid")
+  )
+
+
+N <- nodes(graph_data)
+
+nodes(graph_data) <- N %>% 
+  mutate(size = c(1,3,1))
+
+plot(graph_data)
+
+
+
+
+
+# Making "plot1" ----------------------------------------------------------
+
+mediation_model <- '
+    income ~ anxiety + mood_neg
+  mood_neg ~ anxiety
+'
+
+fit <- sem(mediation_model, data = income_psych)
+
+
+
+lay <- get_layout(NA, "mood_neg", NA,
+                  "anxiety", NA, "income",
+                  rows = 2)
+
+graph_data <- prepare_graph(fit, layout = lay)
+
+E <- edges(graph_data)
+E$label <- ""
+E$connect_from[3] <- "top"
+E$connect_to[3] <- "left"
+E$connect_from[2] <- "right"
+E <- E[1:3,]
+edges(graph_data) <- E
+
+
+
+(N <- nodes(graph_data))
+N$label <- c("Anxiety", "Income", "Negative Mood")
+nodes(graph_data) <- N
+
+
+
+
+
+(p1 <- plot(graph_data))
+
+# because the result it a ggplot, we will use ggsave to save it
+ggplot2::ggsave(p1, filename = "plot1.png", width = 6, height = 3)
+
+
+
+
+
+# Making "plot2" ----------------------------------------------------------
+
+
+mediation_model <- '
+    income ~ anxiety + mood_neg + shyness
+  mood_neg ~ anxiety
+   anxiety ~ shyness
+'
+
+fit <- sem(mediation_model, data = income_psych)
+
+
+
+
+
+
+lay <- get_layout(NA, NA, "mood_neg", NA,
+                  NA, "anxiety", NA, "income",
+                  "shyness", NA, NA, NA,
+                  rows = 3)
+
+graph_data <- prepare_graph(fit, layout = lay)
+
+E <- edges(graph_data)
+E$label <- ""
+E$connect_from[5] <- "top"
+E$connect_to[5] <- "left"
+E$connect_from[4] <- "top"
+E$connect_to[4] <- "left"
+E$connect_to[3] <- "bottom"
+E <- E[1:5,]
+edges(graph_data) <- E
+
+plot(graph_data)
+
+(N <- nodes(graph_data))
+N$label <- c("Anxiety", "Income", "Negative Mood", "Shyness")
+N$node_xmin[3] <- N$node_xmin[3]-0.5
+N$node_xmax[3] <- N$node_xmax[3]+0.5
+nodes(graph_data) <- N
+
+
+(p2 <- plot(graph_data))
+
+# because the result it a ggplot, we will use ggsave to save it
+ggplot2::ggsave(p2, filename = "plot2.png", width = 6, height = 3)