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pacman::p_load(ggstatsplot, tidyverse, rstantools)Hands-on Exercise 04a - Visual Statistical Analysis
1. Getting Started
1.1. Installing and Loading the required R Packages
In this exercise using Exam_data, we will be using ggstatsplot and tidyverse. rstantools and PMCMRplus are also be required for plotting the ONEWAY ANOVA graph.
1.2. Importing Data (Exam_data)
For this exercise, Exam_data.csv provided will be imported into R by using read_csv() of readr package.
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exam_data <- read_csv("data/Exam_data.csv")1.3. 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, with default information: - statistical details - Bayes Factor - sample sizes - distribution summary.
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set.seed(1234)
gghistostats(
data = exam_data,
x = ENGLISH,
type = "bayes",
test.value = 60,
xlab = "English scores"
)
1.3.1. Additional Information on 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.
1.3.1.1. Interpreting the 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:
1.4. Two-sample mean test: ggbetweenstats() method
The code chunk below shows ggbetweenstats() being used to build a visual for two-sample mean test of Maths scores by gender.Default information: - statistical details - Bayes Factor - sample sizes - distribution summary
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ggbetweenstats(
data = exam_data,
x = GENDER,
y = MATHS,
type = "np",
messages = FALSE
)
1.5. Oneway ANOVA Test: ggbetweenstats() method
The code chunk below shows ggbetweenstats() being used to build a visual for One-way ANOVA test on English score by race.
- “ns” → only non-significant
- “s” → only significant
- “all” → everything
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ggbetweenstats(
data = exam_data,
x = RACE,
y = ENGLISH,
type = "p",
mean.ci = TRUE,
pairwise.comparisons = TRUE,
pairwise.display = "s",
p.adjust.method = "fdr",
messages = FALSE
)
1.5.1. ggbetweenstats - Summary of tests
1.6. Significant Test of Correlation: ggscatterstats() method
The code chunk below shows ggscatterstats() being used to build a visual for Significant Test of Correlation between Maths scores and English scores.
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ggscatterstats(
data = exam_data,
x = MATHS,
y = ENGLISH,
marginal = FALSE,
)
1.7. 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().
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exam1 <- exam_data %>%
mutate(MATHS_bins =
cut(MATHS,
breaks = c(0,60,75,85,100))
)We then use ggbarstats() to build a visual for Significant Test of Association, as shown in the code chunk below.
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ggbarstats(exam1,
x = MATHS_bins,
y = GENDER)
2. Visualising Models
In this section, we will 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.
2.1. Getting Started - Installing and Loading the required R Packages
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pacman::p_load(readxl, performance, parameters, see)2.2. Importing Data (ToyotaCorolla.xls)
For this exercise, ToyotaCorolla.xls provided will be imported into R by using read_xls() of readxl package.
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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>, …2.3. 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.
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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 2.4. Model Diagnostic: checking for multicolinearity
Using check_collinearity() method from the performance package
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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]Code
check_c <- check_collinearity(model)
plot(check_c)
2.5. Model Diagnostic: checking normality assumption
Using check_normality() method from the performance package
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model1 <- lm(Price ~ Age_08_04 + KM +
Weight + Guarantee_Period, data = car_resale)
check_n <- check_normality(model1)
plot(check_n)
2.6. Model Diagnostic: Check model for homogeneity of variances
Using check_heteroscedasticity() method from the performance package
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check_h <- check_heteroscedasticity(model1)
plot(check_h)
2.7. Model Diagnostic: Complete check
We can also perform the complete by using check_model().
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check_model(model1)
2.8. Visualising Regression Parameters: see methods
In the code below, plot() of see package and parameters() of parameters package are used to visualise the parameters of a regression model.
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plot(parameters(model1))
2.9. Visualising Regression Parameters: ggcoefstats() method
This example uses ggcoefstats() of ggstatsplot package to visualise the parameters of a regression model.
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ggcoefstats(model1,
output = "plot")