Hands-on Exercise 4b: Visual Statistical Analysis

Author

Alexei Jason

Published

January 31, 2024

Modified

February 2, 2024

Learning Outcome

In this hands-on exercise, you will gain hands-on experience on using:

  • ggstatsplot package to create visual graphics with rich statistical information,

  • performance package to visualise model diagnostics, and

  • parameters package to visualise model parameters

Visual Statistical Analysis with ggstatsplot

  • ggstatsplot is an extension of ggplot2 package for creating graphics with details from statistical tests included in the information-rich plots themselves.
    • To provide alternative statistical inference methods by default.
    • To follow best practices for statistical reporting. For all statistical tests reported in the plots, the default template abides by the APA gold standard for statistical reporting. For example, here are results from a robust t-test:

Getting Started

Installing and launching R packages

In this exercise, ggstatsplot and tidyverse will be used.

pacman::p_load(ggstatsplot, tidyverse)

Importing data

Do-It-Yourself

Importing Exam.csv data by using appropriate tidyverse package.

# A tibble: 322 × 7
   ID         CLASS GENDER RACE    ENGLISH MATHS SCIENCE
   <chr>      <chr> <chr>  <chr>     <dbl> <dbl>   <dbl>
 1 Student321 3I    Male   Malay        21     9      15
 2 Student305 3I    Female Malay        24    22      16
 3 Student289 3H    Male   Chinese      26    16      16
 4 Student227 3F    Male   Chinese      27    77      31
 5 Student318 3I    Male   Malay        27    11      25
 6 Student306 3I    Female Malay        31    16      16
 7 Student313 3I    Male   Chinese      31    21      25
 8 Student316 3I    Male   Malay        31    18      27
 9 Student312 3I    Male   Malay        33    19      15
10 Student297 3H    Male   Indian       34    49      37
# ℹ 312 more rows

One-sample test: gghistostats() method

In the code chunk below, gghistostats() is used to to build an visual of one-sample test on English scores.

set.seed(1234)

gghistostats(
  data = exam,
  x = ENGLISH,
  type = "bayes",
  test.value = 60,
  xlab = "English scores"
)

Default information: - statistical details - Bayes Factor - sample sizes - distribution summary

Unpacking the Bayes Factor

  • A Bayes factor is the ratio of the likelihood of one particular hypothesis to the likelihood of another. It can be interpreted as a measure of the strength of evidence in favor of one theory among two competing theories.

  • That’s because the Bayes factor gives us a way to evaluate the data in favor of a null hypothesis, and to use external information to do so. It tells us what the weight of the evidence is in favor of a given hypothesis.

  • When we are comparing two hypotheses, H1 (the alternate hypothesis) and H0 (the null hypothesis), the Bayes Factor is often written as B10. It can be defined mathematically as

  • The Schwarz criterion is one of the easiest ways to calculate rough approximation of the Bayes Factor.

How to interpret Bayes Factor

A Bayes Factor can be any positive number. One of the most common interpretations is this one—first proposed by Harold Jeffereys (1961) and slightly modified by Lee and Wagenmakers in 2013:

Two-sample mean test: ggbetweenstats()

In the code chunk below, ggbetweenstats() is used to build a visual for two-sample mean test of Maths scores by gender.

ggbetweenstats(
  data = exam,
  x = GENDER, 
  y = MATHS,
  type = "np",
  messages = FALSE
)

Default information: - statistical details - Bayes Factor - sample sizes - distribution summary

Oneway ANOVA Test: ggbetweenstats() method

In the code chunk below, ggbetweenstats() is used to build a visual for One-way ANOVA test on English score by race.

ggbetweenstats(
  data = exam,
  x = RACE, 
  y = ENGLISH,
  type = "p",
  mean.ci = TRUE, 
  pairwise.comparisons = TRUE, 
  pairwise.display = "s",
  p.adjust.method = "fdr",
  messages = FALSE
)

  • “ns” → only non-significant
  • “s” → only significant
  • “all” → everything

orrelation: ggscatterstats()

In the code chunk below, ggscatterstats() is used to build a visual for Significant Test of Correlation between Maths scores and English scores.

ggscatterstats(
  data = exam,
  x = MATHS,
  y = ENGLISH,
  marginal = FALSE,
  )

Significant Test of Association (Depedence) : ggbarstats() methods

In the code chunk below, the Maths scores is binned into a 4-class variable by using cut().

exam1 <- exam %>% 
  mutate(MATHS_bins = 
           cut(MATHS, 
               breaks = c(0,60,75,85,100))
)

In this code chunk below ggbarstats() is used to build a visual for Significant Test of Association

ggbarstats(exam1, 
           x = MATHS_bins, 
           y = GENDER)

Visualising Models

In this section, you will learn how to visualise model diagnostic and model parameters by using parameters package.

  • Toyota Corolla case study will be used. The purpose of study is to build a model to discover factors affecting prices of used-cars by taking into consideration a set of explanatory variables.

Getting Started

Installing and loading the required libraries

Do-It-Yourself
pacman::p_load(readxl, performance, parameters, see)

Importing Excel file: readxl methods

In the code chunk below, read_xls() of readxl package is used to import the data worksheet of ToyotaCorolla.xls workbook into R.

car_resale <- read_xls("data/ToyotaCorolla.xls", 
                       "data")
car_resale
# A tibble: 1,436 × 38
      Id Model    Price Age_08_04 Mfg_Month Mfg_Year     KM Quarterly_Tax Weight
   <dbl> <chr>    <dbl>     <dbl>     <dbl>    <dbl>  <dbl>         <dbl>  <dbl>
 1    81 TOYOTA … 18950        25         8     2002  20019           100   1180
 2     1 TOYOTA … 13500        23        10     2002  46986           210   1165
 3     2 TOYOTA … 13750        23        10     2002  72937           210   1165
 4     3  TOYOTA… 13950        24         9     2002  41711           210   1165
 5     4 TOYOTA … 14950        26         7     2002  48000           210   1165
 6     5 TOYOTA … 13750        30         3     2002  38500           210   1170
 7     6 TOYOTA … 12950        32         1     2002  61000           210   1170
 8     7  TOYOTA… 16900        27         6     2002  94612           210   1245
 9     8 TOYOTA … 18600        30         3     2002  75889           210   1245
10    44 TOYOTA … 16950        27         6     2002 110404           234   1255
# ℹ 1,426 more rows
# ℹ 29 more variables: Guarantee_Period <dbl>, HP_Bin <chr>, CC_bin <chr>,
#   Doors <dbl>, Gears <dbl>, Cylinders <dbl>, Fuel_Type <chr>, Color <chr>,
#   Met_Color <dbl>, Automatic <dbl>, Mfr_Guarantee <dbl>,
#   BOVAG_Guarantee <dbl>, ABS <dbl>, Airbag_1 <dbl>, Airbag_2 <dbl>,
#   Airco <dbl>, Automatic_airco <dbl>, Boardcomputer <dbl>, CD_Player <dbl>,
#   Central_Lock <dbl>, Powered_Windows <dbl>, Power_Steering <dbl>, …

Notice that the output object car_resale is a tibble data frame.

Multiple Regression Model using lm()

The code chunk below is used to calibrate a multiple linear regression model by using lm() of Base Stats of R.

model <- lm(Price ~ Age_08_04 + Mfg_Year + KM + 
              Weight + Guarantee_Period, data = car_resale)
model

Call:
lm(formula = Price ~ Age_08_04 + Mfg_Year + KM + Weight + Guarantee_Period, 
    data = car_resale)

Coefficients:
     (Intercept)         Age_08_04          Mfg_Year                KM  
      -2.637e+06        -1.409e+01         1.315e+03        -2.323e-02  
          Weight  Guarantee_Period  
       1.903e+01         2.770e+01  

Model Diagnostic: checking for multicolinearity:

In the code chunk, check_collinearity() of performance package.

check_collinearity(model)
# Check for Multicollinearity

Low Correlation

             Term  VIF     VIF 95% CI Increased SE Tolerance Tolerance 95% CI
               KM 1.46 [ 1.37,  1.57]         1.21      0.68     [0.64, 0.73]
           Weight 1.41 [ 1.32,  1.51]         1.19      0.71     [0.66, 0.76]
 Guarantee_Period 1.04 [ 1.01,  1.17]         1.02      0.97     [0.86, 0.99]

High Correlation

      Term   VIF     VIF 95% CI Increased SE Tolerance Tolerance 95% CI
 Age_08_04 31.07 [28.08, 34.38]         5.57      0.03     [0.03, 0.04]
  Mfg_Year 31.16 [28.16, 34.48]         5.58      0.03     [0.03, 0.04]
check_c <- check_collinearity(model)
plot(check_c)

Model Diagnostic: checking normality assumption

In the code chunk, check_normality() of performance package.

model1 <- lm(Price ~ Age_08_04 + KM + 
              Weight + Guarantee_Period, data = car_resale)
check_n <- check_normality(model1)
plot(check_n)

Model Diagnostic: Check model for homogeneity of variances

In the code chunk, check_heteroscedasticity() of performance package.

check_h <- check_heteroscedasticity(model1)
plot(check_h)

Model Diagnostic: Complete check

We can also perform the complete by using check_model().

check_model(model1)

Visualising Regression Parameters: see methods

In the code below, plot() of see package and parameters() of parameters package is used to visualise the parameters of a regression model.

plot(parameters(model1))

Visualising Regression Parameters: ggcoefstats() methods

In the code below, ggcoefstats() of ggstatsplot package to visualise the parameters of a regression model.

ggcoefstats(model1, 
            output = "plot")