library(tidyverse)
library(tidytext)
library(textdata)
library(pdftools)
library(ggwordcloud)
library(here)
library(pander)
library(ggthemes)
here::i_am("dinnerparty.txt")
An overview of using the MplusAutomation package in R.
A series of R functions that could be useful when preparing your dataset for analysis.
This markdown follows along the logistic regression lecture presented in a linear models class and provides two examples to demonstrate the models and their interpretation.
The final Machine Learning project that covers visualization, exploratory analysis, and classification techniques using R.
library(pander) #pander()
library(psych) # describe()
library(gtsummary) #tbl_summary()
library(equatiomatic) # extract_eq()
library(sjPlot) # tab_xtab(), tab_model()
library(tidyverse)
This lab follows along the logistic regression lecture presented in class and provides two examples to demonstrate the models and their interpretation.
It is a regression with an outcome variable (or dependent variable) that is dichotomous/binary (i.e., only two categories, such as Yes or No, 0 or 1, Disorder or No Disorder, Win or Lose).
library(haven) #read_sav()
library(mediation) # mediate() (Tingley, Yamamoto, Hirose, Keele, & Imai, 2014)
library(gvlma) # gvlma()
library(kableExtra) #kable()
library(corrr) #correlate()
library(psych) #mediate()
library(tidyverse)
This examples comes from a tutorial paper on mediation found here. This is an open source paper that shared the data set used in this example. The following is code and interpretation are based on this paper. Note that they use a different package to estimate the model and you will see that our estimates are very close, though not exact.
library(knitr) #include_graphics()
library(equatiomatic) # extract_eq()
library(psych) #describe()
library(gtsummary) #tbl_summary()
library(summarytools) #descr()
library(stargazer) #stargazer()
library(sjPlot) #tab_model()
library(interactions) #interact_plot(), sim_slopes()
library(jtools) #summ()
library(tidyverse)
When the research hypotheses state that different categories, or levels of another variable, may have differing responses to other independent variables, we need to use interaction terms
- Also called moderation
Example: The relationship between discrimination and grades depends on prog.
Graph drawn using draw.io.
Suppose you are doing a simple study on weight loss and notice that people who spend more time exercising lose more weight. Upon further analysis you notice that those who spend the same amount of time exercising lose more weight if they are more effortful. The more effort people put into their workouts, the less time they need to spend exercising. This is popular in workouts like high intensity interval training (HIIT).
library(psych) #describe()
library(PerformanceAnalytics) #chart.correlation()
library(lm.beta) #lm.beta()
library(sjPlot) #tab_model()
library(gridExtra) #grid.arrange()
library(tidyverse)
This lab will be an overview of simple linear regression and was created along side Karen’s lectures and her code.
The equation of simple linear regression is this:
$$Y_i = \alpha + \beta{x}_i + \epsilon_i $$ Expressed as Betas:
#Load packages here
library(haven) #for read_sav() function
library(psych) # describe()
library(ggpubr) # ggdensity() and ggqqplot()
library(apaTables) # apa.cor.table()
library(tidyverse)
The data sets used in this lab are used in Chapter 4 of Tabachnick & Fidell (2012; I believe there is a new version from 2019 now). The chapter is provided in GauchoSpace and is a useful resource if you need more information on how to clean and screen your data and write up the results.