minor edits

Author: dinanajiarch

03-enumeration.Rmd

@@ -458,7 +458,7 @@ gtsave(fit_table1, here("figures", "fit_table.png"))
 
 ## Information Criteria Plot
 
-```{r height=5, width=7}
+```{r height=3, width=5}
 allFit %>%
   dplyr::select(2:7) %>%
   rowid_to_column() %>%

05-three-step.Rmd

@@ -338,7 +338,7 @@ Parameterization using Reference Class 1
 
 ## Manual ML Three-step
 
-### Unlike the automatic three-step, the manual ML three-step can relate the latent class variable to both distal outcomes and covarites. 
+Unlike the automatic three-step, the manual ML three-step can relate the latent class variable to both distal outcomes and covarites. 
 
 ```{r, echo=FALSE, eval=TRUE, fig.align='center'}
 
@@ -468,7 +468,7 @@ step1_fit <- mplusModeler(step1,
 ------------------------------------------------------------------------
 
 
-```{r, fig.height=6, fig.width=10}
+```{r, out.width="90%"}
 source(here("functions", "plot_lca.R"))
 output_lsay <- readModels(here("three_step", "manual_3step","one.out"))
 
@@ -506,6 +506,7 @@ colnames(savedata)[colnames(savedata)=="C"] <- "N"
 #### Step 3 - LCA Auxiliary Variable Model with 2 covariates and 1 distal outcome
 
 ------------------------------------------------------------------------
+
 ##### Estimate LCA Model
 
 Model with 2 covariates (gender and mother's education) and 1 distal outcome (math IRT scores)
@@ -818,7 +819,6 @@ combined %>%
 
 Is there a relation between the distal outcome (Math IRT Scores) and the covariate (Gender)?
 
-
 ```{r}
 modelParams <- readModels(here("three_step", "manual_3step", "three.out"))
 
@@ -859,12 +859,12 @@ donx %>%
 ```
 
 
-
 ------------------------------------------------------------------------
 
 #### Step 3 - LCA Auxiliary Variable Model with 1 covariate
 
 ------------------------------------------------------------------------
+
 ##### Estimate LCA Model
 
 ```{r, cache = TRUE}

06-LPA.Rmd

@@ -256,7 +256,7 @@ ds %>%
 
 Histograms
 
-```{r}
+```{r out.width="90%"}
 data_long <- pisa %>%
   pivot_longer(broad_interest:self_efficacy, names_to = "variable")
 

07-lca-moderation.Rmd

@@ -36,7 +36,7 @@ library(janitor)
 
 ## Moderation Path Diagram
 
-```{r, echo=FALSE, eval=TRUE}
+```{r, echo=FALSE, eval=TRUE, out.width="90%"}
 knitr::include_graphics(here("figures", "moderation_diagram.png"))
 ```
 
@@ -283,7 +283,7 @@ gtsave(fit_table, here("figures", "fit_table_lca.png"))
 
 ### Information Criteria Plot
 
-```{r height=5, width=7}
+```{r out.width="90%"}
 source(here("functions", "ic_plot_lca.R"))
 ic_plot(output_enum)
 ```

10-gmm.Rmd

@@ -6,7 +6,7 @@ knitr::opts_chunk$set(echo = TRUE,
 ```
 
 
-# Growth mixture Models
+# Growth Mixture Models
 
 ------------------------------------------------------------------------