flatr

Overview

flatr is a package designed to make the analysis of contingency tables easier.

Contingency tables are a popular means of presenting categorical data in textbooks, as they take up very little space, while still allowing to present all the data. However, this means makes it tough to run analysis on them. flatr helps ease this pain by turning i × j × k contingency tables into "tidy" data.

Functions

• flatten_ct() takes an i × j × k contingency table, and turns it into a tibble.

• goodness_of_fit() takes a logistic or probit regression model, and does a χ² Goodness of Fit Test. The test statistic is one of:

• 

• 

Tidy Data

flatr is designed to work with the tidyverse series of packages. Tidy data is data in a "long" format, where each variable has its own column.

Usage

Beijing	Lung
Smoking	Yes	No
Yes	126	100
No	35	61

Shanghai	Lung
Smoking	Yes	No
Yes	908	688
No	497	807

Shenyang	Lung
Smoking	Yes	No
Yes	913	747
No	336	598

Nanjing	Lung
Smoking	Yes	No
Yes	235	172
No	58	121

Harbin	Lung
Smoking	Yes	No
Yes	402	308
No	121	215

Zhengzhou	Lung
Smoking	Yes	No
Yes	182	156
No	72	98

Taiyuan	Lung
Smoking	Yes	No
Yes	60	99
No	11	43

Nanchang	Lung
Smoking	Yes	No
Yes	104	89
No	21	36

lung_tidy <- flatten_ct(lung_cancer)
lung_tidy
#> # A tibble: 8,419 x 3
#>    Smoking   Lung    City
#>     <fctr> <fctr>  <fctr>
#>  1       Y      Y Beijing
#>  2       Y      Y Beijing
#>  3       Y      Y Beijing
#>  4       Y      Y Beijing
#>  5       Y      Y Beijing
#>  6       Y      Y Beijing
#>  7       Y      Y Beijing
#>  8       Y      Y Beijing
#>  9       Y      Y Beijing
#> 10       Y      Y Beijing
#> # ... with 8,409 more rows

lung_logit <- glm(Lung ~ Smoking + City, family = binomial, data = lung_tidy)
goodness_of_fit(model = lung_logit, response = "Lung", type = "Chisq")
#> 
#> Chi-squared Goodness of Fit Test 
#> 
#> model: lung_logit 
#> Chi-squared = 5.19987, df = 7, p-value = 0.63559

lung_tidy %>% 
  glm(
    Lung ~ Smoking + City
    ,family = binomial(link = "probit")
    ,data = .
  ) %>% 
  goodness_of_fit(response = "Lung", type = "Gsq")
#> 
#> G-squared Goodness of Fit Test 
#> 
#> model: . 
#> G-squared = 5.15871, df = 7, p-value = 0.6406