University of California, Santa Barbara
Figure. Picture has been adapted from study by Múthen, 2006.
Lab preparation
Creating a version-controlled R-Project with Github
Download repository here: https://github.com/garberadamc/SEM-Lab9
On the Github repository webpage:
forkyour ownbranchof the lab repository- copy the repository web URL address from the
clone or downloadmenu
Within R-Studio:
- click “NEW PROJECT”
- choose option
Version Control - choose option
Git - paste the repository web URL path copied from the
clone or downloadmenu on Github page - choose location of the R-Project
Data source:
The first example utilizes a dataset on undergraduate Cheating available from the
poLCApackage (Dayton, 1998): \(\color{blue}{\text{See documentation here}}\)The second examples utilizes the public-use dataset, The Longitudinal Survey of American Youth (LSAY): \(\color{blue}{\text{See documentation here}}\)
Load packages
library(naniar)
library(tidyverse)
library(haven)
library(glue)
library(MplusAutomation)
library(rhdf5)
library(here)
library(janitor)
library(gt)
library(poLCA)Incorparating distal outcome variables with mixture models
Note: Prior to adding covariates or distals enumeration must be conducted.
\(\color{blue}{\text{See Lab 7 for examples of enumeration with MplusAutomation}}\)
DU3step
Application: Undergraduate Cheating behavior
“Dichotomous self-report responses by 319 undergraduates to four questions about cheating behavior” (poLCA, 2016).
Prepare data
data(cheating)
cheating <- cheating %>% clean_names()
df_cheat <- cheating %>% #
dplyr::select(1:4) %>% #
mutate_all(funs(.-1)) %>% #
mutate(gpa = cheating$gpa)Run the DU3step model with gpa as distal outcome
m_stepdu <- mplusObject(
TITLE = "DU3STEP add distal GPA - Lab8",
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("du3step_mplus", "c_lca_du3step_Lab8.dat"),
modelout=here("du3step_mplus", "c4_lca_du3step_Lab8.inp") ,
check=TRUE, run = TRUE, hashfilename = FALSE)Application: Longitudinal Study of American Youth, Science Attitudes
Load data
lsay_data <- read_csv(here("data", "lca_lsay_sci.csv"), na = c("9999", "9999.00")) %>% #
clean_names() %>% #
dplyr::select(1:5, female, mathg12, #
Enjoy = ab39m, Useful = ab39t, #
Logical = ab39u, Job = ab39w, Adult = ab39x) #Use {naniar} to look at missing on covariates and distals
Manual 3-step
Integrate covariates and distals with a mixture model ____________________________________
Step 1
step1 <- mplusObject(
TITLE = "Step1 - 3step LSAY - Lab9",
VARIABLE =
"categorical = Enjoy-Adult;
usevar = Enjoy-Adult;
classes = c(4);
auxiliary = ! list all potential covariates and distals here
female ! covariate
mathg12; ! distal math test score in 12th grade ",
ANALYSIS =
"estimator = mlr;
type = mixture;
starts = 500 100;",
SAVEDATA =
"File=3step_savedata.dat;
Save=cprob;
Missflag= 999;",
OUTPUT = "sampstat residual tech11 tech14",
PLOT =
"type = plot3;
series = Enjoy-Adult(*);",
usevariables = colnames(lsay_data),
rdata = lsay_data)
step1_fit <- mplusModeler(step1,
dataout=here("3step_mplus", "Step1_3step_LSAY.dat"),
modelout=here("3step_mplus", "Step1_3step_LSAY.inp") ,
check=TRUE, run = TRUE, hashfilename = FALSE)Step 2
Extract logits for the classification probabilities for the most likely latent class
logit_cprobs <- as.data.frame(step1_fit[["results"]]
[["class_counts"]]
[["logitProbs.mostLikely"]])Extract saved dataset which is part of the mplusObject “step1_10_fit”
Rename the column in savedata named “C” and change to “N”
Run step 2
step2 <- mplusObject(
TITLE = "Step2 - 3step LSAY - Lab9",
VARIABLE =
"nominal=N;
USEVAR = n;
missing are all (999);
classes = c(4); ",
ANALYSIS =
"estimator = mlr;
type = mixture;
starts = 0;",
MODEL =
glue(
"%C#1%
[n#1@{logit_cprobs[1,1]}];
[n#2@{logit_cprobs[1,2]}];
[n#3@{logit_cprobs[1,3]}];
%C#2%
[n#1@{logit_cprobs[2,1]}];
[n#2@{logit_cprobs[2,2]}];
[n#3@{logit_cprobs[2,3]}];
%C#3%
[n#1@{logit_cprobs[3,1]}];
[n#2@{logit_cprobs[3,2]}];
[n#3@{logit_cprobs[3,3]}];
%C#4%
[n#1@{logit_cprobs[4,1]}];
[n#2@{logit_cprobs[4,2]}];
[n#3@{logit_cprobs[4,3]}];"),
usevariables = colnames(savedata),
rdata = savedata)
step2_fit <- mplusModeler(step2,
dataout=here("3step_mplus", "Step2_3step_LSAY.dat"),
modelout=here("3step_mplus", "Step2_3step_LSAY.inp"),
check=TRUE, run = TRUE, hashfilename = FALSE)Step 3
Model with 1 covariate and 1 distal outcome
step3 <- mplusObject(
TITLE = "Step3 - 3step LSAY - Lab9",
VARIABLE =
"nominal=N;
usevar = n;
missing are all (999);
classes = c(4);
usevar = female mathg12;" ,
ANALYSIS =
"estimator = mlr;
type = mixture;
starts = 0;",
MODEL =
glue(
" %OVERALL%
C on female; ! covariate as predictor of C
%C#1%
[n#1@{logit_cprobs[1,1]}];
[n#2@{logit_cprobs[1,2]}];
[n#3@{logit_cprobs[1,3]}];
[mathg12](m1); ! conditional distal mean
mathg12; ! 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]}];
[mathg12](m2);
mathg12;
%C#3%
[n#1@{logit_cprobs[3,1]}];
[n#2@{logit_cprobs[3,2]}];
[n#3@{logit_cprobs[3,3]}];
[mathg12](m3);
mathg12;
%C#4%
[n#1@{logit_cprobs[4,1]}];
[n#2@{logit_cprobs[4,2]}];
[n#3@{logit_cprobs[4,3]}];
[mathg12](m4);
mathg12; "),
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("3step_mplus", "Step3_3step_LSAY.dat"),
modelout=here("3step_mplus", "Step3_3step_LSAY.inp"),
check=TRUE, run = TRUE, hashfilename = FALSE)End of manual 3-step
\(C_k\) as moderator
step3mod <- mplusObject(
TITLE = "Step3 - 3step LSAY - Lab9",
VARIABLE =
"nominal=N;
usevar = n;
missing are all (999);
classes = c(4);
usevar = female mathg12;" ,
ANALYSIS =
"estimator = mlr;
type = mixture;
starts = 0;",
MODEL =
glue(
"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!DISTAL = mathg12, COVARIATE = female, MODERATOR = C!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
%OVERALL%
mathg12 on female;
mathg12;
%C#1%
[n#1@{logit_cprobs[1,1]}];
[n#2@{logit_cprobs[1,2]}];
[n#3@{logit_cprobs[1,3]}];
mathg12 on female(s1); ! conditional slope (class 1)
[mathg12](m1); ! conditional distal mean
mathg12; ! 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]}];
mathg12 on female(s2);
[mathg12](m2);
mathg12;
%C#3%
[n#1@{logit_cprobs[3,1]}];
[n#2@{logit_cprobs[3,2]}];
[n#3@{logit_cprobs[3,3]}];
mathg12 on female(s3);
[mathg12](m3);
mathg12;
%C#4%
[n#1@{logit_cprobs[4,1]}];
[n#2@{logit_cprobs[4,2]}];
[n#3@{logit_cprobs[4,3]}];
mathg12 on female(s4);
[mathg12](m4);
mathg12; "),
MODELCONSTRAINT =
"New (slope12 slope13 slope23
slope14 slope24 slope34);
slope12 = s1-s2; ! test pairwise slope differences
slope13 = s1-s3;
slope23 = s2-s3;
slope14 = s1-s4;
slope24 = s2-s4;
slope34 = s3-s4;",
MODELTEST = " ! can run only a single Omnibus test per model
s1=s2;
s2=s3;
s3=s4;",
usevariables = colnames(savedata),
rdata = savedata)
step3mod_fit <- mplusModeler(step3mod,
dataout=here("3step_mplus", "Step3_moderation_LSAY.dat"),
modelout=here("3step_mplus", "Step3_moderation_LSAY.inp"),
check=TRUE, run = TRUE, hashfilename = FALSE)References
Drew A. Linzer, Jeffrey B. Lewis (2011). poLCA: An R Package for Polytomous Variable Latent Class Analysis. Journal of Statistical Software, 42(10), 1-29. URL http://www.jstatsoft.org/v42/i10/.
Hallquist, M. N., & Wiley, J. F. (2018). MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus. Structural equation modeling: a multidisciplinary journal, 25(4), 621-638.
Miller, J. D., Hoffer, T., Suchner, R., Brown, K., & Nelson, C. (1992). LSAY codebook. Northern Illinois University.
Muthén, B. O., Muthén, L. K., & Asparouhov, T. (2017). Regression and mediation analysis using Mplus. Los Angeles, CA: Muthén & Muthén.
Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus User’s Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/
Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
