14-one-distal.Rmd

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, 
                      warning = FALSE,
                      message = FALSE) #Here, I have made it so that when you knit your .rmd, warnings and messages will not show up in the html markdown. 
library(extrafont)
loadfonts()
```

# Manual 3-Step Distal Only

------------------------------------------------------------------------

**Data source:**

1.  The first examples utilizes the public-use dataset, *The Longitudinal Survey of American Youth* (**LSAY**): [See documentation here](https://www.lsay.org/)

2.  The second example utilizes a dataset on undergraduate *Cheating* available from the `poLCA` package (Dayton, 1998): [See documentation here](https://cran.r-project.org/web/packages/poLCA/poLCA.pdf)

------------------------------------------------------------------------

## Load packages

```{r}
library(MplusAutomation)
library(tidyverse) #collection of R packages designed for data science
library(here) #helps with filepaths
library(janitor) #clean_names
library(gt) # create tables
library(cowplot) # a ggplot theme
library(DiagrammeR) # create path diagrams
library(glue) # allows us to paste expressions into R code
library(data.table) # used for `melt()` function  
library(poLCA)
library(reshape2)
```

------------------------------------------------------------------------

Our example is Math IRT Score as a distal outcome of Math Attitude classes

```{r, echo=FALSE, eval=TRUE, fig.align='center'}
grViz(" digraph lca_model {

# The `graph` statement - No editing needed

    graph [layout = dot, overlap = true]
 
# Two `node` statements
 
# One for measured variables (box) 

    node [shape=box]
    Enjoy Useful Logical Job Adult MathScore;
 
# One for latent variables (circle) 
 
    node [shape=circle]
    math [label=<Math <br/>Attitudes <br/>C<sub>k=4</sub>>];
    
# `edge` statements
 
    edge [minlen = 2]
    math -> {Enjoy Useful Logical Job Adult}
    math -> MathScore [minlen = 4];
    
 {rank = same; math; MathScore}
 
 }") 
```


------------------------------------------------------------------------

Application: Longitudinal Study of American Youth, **Math Attitudes**

```{r, eval=TRUE, echo=FALSE}
tribble(
~"Name", ~" Variable Description", 
#----------|-------------|,
"enjoy"    , "I enjoy math." ,
"useful"   , "Math is useful in everyday problems." ,
"logical"  , "Math helps a person think logically." ,
"job"      , "It is important to know math to get a good job." ,
"adult"    , "I will use math in many ways as an adult.",
"math_irt" , "Standardized IRT math test score - 12th grade." ) %>% 
gt() %>% 
tab_header(title = md("**LCA Indicators & Auxiliary Variables: Math Attitudes Example**"), subtitle = md("&nbsp;")) %>%
tab_row_group(group = "", rows = 1:5) %>% 
tab_row_group(group = "Auxiliary Variables", rows = 6) %>%
row_group_order(groups = c("","Auxiliary Variables")) %>% 
tab_options(column_labels.font.weight = "bold", row_group.font.weight = "bold") 

```

------------------------------------------------------------------------

The data can be found in the `data` folder and is called `lsay_subset.csv`.

```{r}
lsay_data <- read_csv(here("three_step","data","lsay_subset.csv")) %>% 
  clean_names() %>%   # make variable names lowercase
  mutate(female = recode(gender, `1` = 0, `2` = 1)) # relabel values from 1,2 to 0,1

```

------------------------------------------------------------------------

## Descriptive Statistics

```{r}
dframe <- lsay_data %>%
  pivot_longer(
    c(enjoy, useful, logical, job, adult),
    names_to = "Variable"
  ) %>%
  group_by(Variable) %>%
  summarise(
    Count = sum(value == 1, na.rm = TRUE),
    Total = n(),
    .groups = "drop"
  ) %>%
  mutate(`Proportion Endorsed` = round(Count / Total, 3)) %>%
  dplyr::select(Variable, `Proportion Endorsed`, Count)

gt(dframe) %>%
  tab_header(
    title = md("**LCA Indicator Endorsement**"),
    subtitle = md("&nbsp;")
  ) %>%
  tab_options(
    column_labels.font.weight = "bold",
    row_group.font.weight = "bold"
  )
```

Math IRT Score

```{r}
summary(lsay_data$math_irt)
```

------------------------------------------------------------------------

## Manual ML Three-step

------------------------------------------------------------------------

### Step 1 - Class Enumeration w/ Auxiliary Specification

------------------------------------------------------------------------

This step is done after class enumeration (or after you have selected the best latent class model). In this example, the four class model was the best. Now, we re-estimate the four-class model using `optseed` for efficiency. The difference here is the `SAVEDATA` command, where I can save the posterior probabilities and the modal class assignment that will be used in steps two and three. 


```{r, eval = FALSE}

step1  <- mplusObject(
  TITLE = "Step 1 - Three-Step using LSAL", 
  VARIABLE = 
  "categorical = enjoy useful logical job adult; 
   usevar = enjoy useful logical job adult;
    
   classes = c(4); 
    
   auxiliary =  math_irt  ! distal outcome ",
  
  ANALYSIS = 
   "estimator = mlr; 
    type = mixture;
    starts = 0;
    optseed = 568405;",
  
  SAVEDATA = 
   "File=savedata_dis.dat;
    Save=cprob;",
  
  OUTPUT = "residual tech11 tech14",
  
  PLOT = 
    "type = plot3; 
    series = enjoy-adult(*);",
  
  usevariables = colnames(lsay_data),
  rdata = lsay_data)

step1_fit <- mplusModeler(step1,
                            dataout=here("three_step", "manual_3step", "Step1.dat"),
                            modelout=here("three_step", "manual_3step", "one_dis.inp") ,
                            check=TRUE, run = TRUE, hashfilename = FALSE)
```

------------------------------------------------------------------------


```{r, out.width="90%"}
source(here("functions", "plot_lca.R"))
output_lsay <- readModels(here("three_step", "manual_3step","one_dis.out"))

plot_lca(model_name = output_lsay)
```

------------------------------------------------------------------------

### Step 2 - Determine Measurement Error

------------------------------------------------------------------------

Extract logits for the classification probabilities for the most likely latent class

```{r}
logit_cprobs <- as.data.frame(output_lsay[["class_counts"]]
                                       [["logitProbs.mostLikely"]])
```

Extract saved dataset which is part of the mplusObject "step1_fit"

```{r}
savedata <- as.data.frame(output_lsay[["savedata"]])
```

Rename the column in savedata named "C" and change to "N"

```{r}
colnames(savedata)[colnames(savedata)=="C"] <- "N"
```

------------------------------------------------------------------------

### Step 3 - LCA Auxiliary Variable Model with 1 Distal Outcome

------------------------------------------------------------------------

```{r, cache = TRUE}
step3  <- mplusObject(
  TITLE = "Step3 - 3step LSAY", 
  
  VARIABLE = 
 "nominal=N;
  usevar = n;
  
  classes = c(4);
  
  usevar = math_irt;" ,
  
  ANALYSIS = 
 "estimator = mlr; 
  type = mixture; 
  starts = 0;",
  
  MODEL =
  glue(
 " %OVERALL%
 

     %C#1%
  [n#1@{logit_cprobs[1,1]}]; ! MUST EDIT if you do not have a 4-class model. 
  [n#2@{logit_cprobs[1,2]}];
  [n#3@{logit_cprobs[1,3]}];
  
  [math_irt](m1);    ! conditional distal mean 
  math_irt;          ! conditional distal variance (freely estimated)

  %C#2%
  [n#1@{logit_cprobs[2,1]}];
  [n#2@{logit_cprobs[2,2]}];
  [n#3@{logit_cprobs[2,3]}];
  
  [math_irt](m2);
  math_irt;
  
  %C#3%
  [n#1@{logit_cprobs[3,1]}];
  [n#2@{logit_cprobs[3,2]}];
  [n#3@{logit_cprobs[3,3]}];
  
  [math_irt](m3);
  math_irt;

  %C#4%
  [n#1@{logit_cprobs[4,1]}];
  [n#2@{logit_cprobs[4,2]}];
  [n#3@{logit_cprobs[4,3]}];
  
  [math_irt](m4);
  math_irt; "),
  
  MODELCONSTRAINT = 
   "New (diff12 diff13 diff23 
    diff14 diff24 diff34);
  
    diff12 = m1-m2;  ! test pairwise distal mean differences
    diff13 = m1-m3;
    diff23 = m2-m3;
    diff14 = m1-m4;
    diff24 = m2-m4;
    diff34 = m3-m4;",
  
  MODELTEST = "     ! omnibus test of distal means 
    m1=m2;
    m2=m3;
    m3=m4;",
  
 
  usevariables = colnames(savedata), 
  rdata = savedata)

step3_fit <- mplusModeler(step3,
               dataout=here("three_step", "manual_3step", "Step3.dat"), 
               modelout=here("three_step", "manual_3step", "three_dis.inp"), 
               check=TRUE, run = TRUE, hashfilename = FALSE)
```


------------------------------------------------------------------------

#### Wald Test Table

This is testing if there is a relation between the latent class variable and the distal outcome (mathirt)

```{r}
modelParams <- readModels(here("three_step", "manual_3step", "three_dis.out"))

# Extract information as data frame
wald <- as.data.frame(modelParams[["summaries"]]) %>%
  dplyr::select(WaldChiSq_Value:WaldChiSq_PValue) %>% 
  mutate(WaldChiSq_DF = paste0("(", WaldChiSq_DF, ")")) %>% 
  unite(wald_test, WaldChiSq_Value, WaldChiSq_DF, sep = " ") %>% 
  rename(pval = WaldChiSq_PValue) %>% 
  mutate(pval = ifelse(pval<0.001, paste0("<.001*"),
                       ifelse(pval<0.05, paste0(scales::number(pval, accuracy = .001), "*"),
                              scales::number(pval, accuracy = .001))))

# Create table

wald_table <- wald %>% 
  gt() %>%
    tab_header(
    title = "Wald Test Distal Means (Math IRT Scores)") %>%
    cols_label(
      wald_test = md("Wald Test (*df*)"), 
      pval = md("*p*-value")) %>% 
  cols_align(align = "center") %>% 
  opt_align_table_header(align = "left") %>% 
  gt::tab_options(table.font.names = "serif")

wald_table
```

Save figure

```{r}
gtsave(wald_table, here("figures","wald_table.docx"))
```


------------------------------------------------------------------------

#### Table of Pairwise Distal Outcome Differences

```{r}
modelParams <- readModels(here("three_step", "manual_3step", "three_dis.out"))

# Extract information as data frame
diff <- as.data.frame(modelParams[["parameters"]][["unstandardized"]]) %>%
  filter(grepl("DIFF", param)) %>% 
  dplyr::select(param:pval) %>% 
  mutate(se = paste0("(", format(round(se,2), nsmall =2), ")")) %>% 
  unite(estimate, est, se, sep = " ") %>% 
  mutate(param = str_remove(param, "DIFF"),
         param = as.numeric(param)) %>% 
  separate(param, into = paste0("Group", 1:2), sep = 1) %>% 
  mutate(class = paste0("Class ", Group1, " vs ", Group2)) %>% 
  dplyr::select(class, estimate, pval) %>% 
  mutate(pval = ifelse(pval<0.001, paste0("<.001*"),
                       ifelse(pval<0.05, paste0(scales::number(pval, accuracy = .001), "*"),
                              scales::number(pval, accuracy = .001))))

# Create table

diff %>% 
  gt() %>%
    tab_header(
    title = "Distal Outcome Differences") %>%
    cols_label(
      class = "Class",
      estimate = md("Mean (*se*)"),
      pval = md("*p*-value")) %>% 
    sub_missing(1:3,
              missing_text = "") %>%
  cols_align(align = "center") %>% 
  opt_align_table_header(align = "left") %>% 
  gt::tab_options(table.font.names = "serif")
```

------------------------------------------------------------------------

#### Plot Distal Outcome Means

```{r}
modelParams <- readModels(here("three_step", "manual_3step", "three_dis.out"))

# Extract class size 
c_size <- as.data.frame(modelParams[["class_counts"]][["modelEstimated"]][["proportion"]]) %>% 
  rename("cs" = 1) %>% 
  mutate(cs = round(cs*100, 2))

c_size_val <- paste0("C", 1:nrow(c_size), glue(" ({c_size[1:nrow(c_size),]}%)"))


# Extract information as data frame
estimates <- as.data.frame(modelParams[["parameters"]][["unstandardized"]]) %>%
  filter(paramHeader == "Means") %>%
  dplyr::select(param, est, se) %>% 
  filter(param == "MATH_IRT") %>% 
  mutate(across(c(est, se), as.numeric)) %>% 
  mutate(LatentClass = c_size_val)

# Add labels (NOTE: You must change the labels to match the significance testing!!) 
#value_labels <- paste0(estimates$est, c("a"," bc"," abd"," cd"))

estimates$LatentClass <- fct_inorder(estimates$LatentClass)

# Plot bar graphs
estimates %>%
  ggplot(aes(x=LatentClass, y = est, fill = LatentClass)) +
  geom_col(position = "dodge", color = "black") +
  geom_errorbar(aes(ymin=est-se, ymax=est+se),
                linewidth=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9)) +
  geom_text(aes(label = est), 
            family = "serif", size = 4,
            position=position_dodge(.9),
            vjust = 8) +  
 # scale_fill_grey(start = .4, end = .7) + # Remove for colorful bars
  labs(y="Math Scores", x="") +
  theme_cowplot() +
  theme(text = element_text(family = "serif", size = 15),
        axis.text.x = element_text(size=15),
        legend.position="none")

# Save plot
ggsave(here("figures","ManualDistal_Plot.jpeg"),              
       dpi=300, width=10, height = 7, units="in") 
```

------------------------------------------------------------------------

## Automated Three-Step

------------------------------------------------------------------------

*Application: Undergraduate Cheating behavior*


"Dichotomous self-report responses by 319 undergraduates to four questions about cheating behavior" (poLCA, 2016).

------------------------------------------------------------------------

Prepare data

```{r}

data(cheating)

cheating <- cheating %>% clean_names() 

df_cheat <-  cheating %>%                                  
  dplyr::select(1:4) %>%                                  
  mutate_all(funs(.-1)) %>%                                
  mutate(gpa = cheating$gpa)

# Detaching packages that mask the dpylr functions 
detach(package:poLCA, unload = TRUE)
detach(package:MASS, unload = TRUE)
```

------------------------------------------------------------------------

### DU3STEP 

**DU3STEP** incorporates distal outcome variables (assumed to have unequal means and variances) with mixture models.

------------------------------------------------------------------------


```{r, echo=FALSE, eval=TRUE, fig.align='center'}

grViz(" digraph cfa_model {

# The `graph` statement - No editing needed

    graph [layout = dot, overlap = true]
 
# Two `node` statements
 
# One for measured variables (box) 

    node [shape=box]
    GPA LieExam LiePaper Fraud CopyExam;
 
# One for latent variables (circle) 
 
    node [shape=circle]
    bully [label=<Cheating <br/>Behavior <br/>C<sub>k=2</sub>>];
    
# `edge` statements
 
    edge [minlen = 2]
    bully -> {LieExam LiePaper Fraud CopyExam}
    bully -> GPA [minlen = 4];
    
 {rank = same; bully; GPA}
 
 }") 
```

------------------------------------------------------------------------

#### Run the **DU3step** model with `gpa` as distal outcome

```{r, cache = TRUE}

m_stepdu  <- mplusObject(
  TITLE = "DU3STEP - GPA as Distal", 
  VARIABLE = 
   "categorical = lieexam-copyexam; 
    usevar = lieexam-copyexam;
    auxiliary = gpa (du3step);
    classes = c(2);",
  
  ANALYSIS = 
   "estimator = mlr; 
    type = mixture;
    starts = 500 100; 
    processors = 10;",
  
  OUTPUT = "sampstat patterns tech11 tech14;",
  
  PLOT = 
    "type = plot3; 
     series = lieexam-copyexam(*);",
  
  usevariables = colnames(df_cheat),
  rdata = df_cheat)

m_stepdu_fit <- mplusModeler(m_stepdu, 
                            dataout=here("three_step", "auto_3step", "du3step.dat"),
                            modelout=here("three_step", "auto_3step", "c2_du3step.inp") ,
                            check=TRUE, run = TRUE, hashfilename = FALSE)

```

------------------------------------------------------------------------

#### Plot Distal Outcome mean differences

```{r}
modelParams <- readModels(here("three_step", "auto_3step", "c2_du3step.out"))

# Extract class size 
c_size <- as.data.frame(modelParams[["class_counts"]][["modelEstimated"]][["proportion"]]) %>% 
  rename("cs" = 1) %>% 
  mutate(cs = round(cs*100, 2))

c_size_val <- paste0("C", 1:nrow(c_size), glue(" ({c_size[1:nrow(c_size),]}%)"))


# Extract information as data frame
estimates <- as.data.frame(modelParams[["lcCondMeans"]][["overall"]]) %>%
  reshape2::melt(id.vars = "var") %>%
  mutate(variable = as.character(variable),
         LatentClass = case_when(
           endsWith(variable, "1") ~ c_size_val[1],
           endsWith(variable, "2") ~ c_size_val[2])) %>% #Add to this based on the number of classes you have
  head(-3) %>% 
  pivot_wider(names_from = variable, values_from = value) %>% 
  unite("mean", contains("m"), na.rm = TRUE) %>% 
  unite("se", contains("se"), na.rm = TRUE) %>% 
  mutate(across(c(mean, se), as.numeric))

# Add labels (NOTE: You must change the labels to match the significance testing!!) 
value_labels <- paste0(estimates$mean, c(" a"," b"))

# Plot bar graphs
estimates %>%
  ggplot(aes(fill = LatentClass, y = mean, x = LatentClass)) +
  geom_bar(position = "dodge", stat = "identity", color = "black") +
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se),
                size=.3,    
                width=.2,
                position=position_dodge(.9)) +
  geom_text(aes(y = mean, label = value_labels), 
            family = "serif", size = 4,
            position=position_dodge(.9),
            vjust = 8) +
  #scale_fill_grey(start = .5, end = .7) +
  labs(y="GPA", x="") +
  theme_cowplot() +
  theme(text = element_text(family = "serif", size = 12),
        axis.text.x = element_text(size=12),
        legend.position="none") +
  coord_cartesian(expand = FALSE, 
                  ylim=c(0,max(estimates$mean*1.5))) # Change ylim based on distal outcome rang


# Save plot
ggsave(here("figures","Du3STEP_plot.jpeg"),                
       dpi=300, width=10, height = 7, units="in")  
```


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