Bivariate Chi squared and Fisher Test analysis for 2 categories.

Generates a HTML table of bivariate Chi squared and Fisher Test analysis for 2 categories. Display a table arranged dataframe with Chi squared statistic, minimum expected frecuencies, Chi squared p value, Fisher Test p value, and Odds ratio with 95 confidence levels. Note that you must recode factors and level the database factors in order to compute exact p values. Variable names can be assigned using table1::label() function.

Usage

dichotomous_2k_2sid(data, referencevar, flextableformat = TRUE)

Arguments

data

Data frame from which variables will be extractred

referencevar

Reference variable. Must have exactly 2 levels

flextableformat

Logical operator to indicate the output desired. Default is TRUE. When FALSE, function will return a dataframe format.

Value

Returns a dataframe or flextable containing statistical values for Chi squared tests or Fisher's test.

Author

JAFG

Examples

  # Not run

 # Create a sample dataframe
 df <- data.frame(
   has = c("Yes", "No", "Yes", "Yes", "No", "No", "Yes"),
   smoke = c("Yes", "No", "No", "Yes", "No", "Yes", "No"),
   gender = c("Male", "Female", "Male", "Female", "Female", "Male", "Male"))

 df$has <- as.factor(df$has)
 df$smoke <- as.factor(df$smoke)
 df$gender <- as.factor(df$gender)

# Set a value as reference level
 df$has <- relevel(df$has, ref= "Yes")
 df$smoke <- relevel(df$smoke, ref= "Yes")
 df$gender <- relevel(df$gender, ref= "Female")

 # Apply function
dichotomous_2k_2sid(df, referencevar="has")
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
Variable
Chi_Squared
Min_Expected
P_Chi
P_Fisher
Odds_Ratio
CI_Lower
CI_Upper
smoke
0.00
1.29
1.00
1.00
2.00
0.09
44.35
gender
0.11
1.29
0.74
0.49
0.17
0.01
4.51
dichotomous_2k_2sid(df, referencevar="has", flextableformat = FALSE)
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#>   Variable Chi_Squared Min_Expected   P_Chi P_Fisher Odds_Ratio CI_Lower
#> 1    smoke     0.00000      1.28571 1.00000  1.00000    2.00000  0.09019
#> 2   gender     0.10937      1.28571 0.74086  0.48571    0.16667  0.00615
#>   CI_Upper
#> 1 44.35043
#> 2  4.51476

# Set names to variables
if(requireNamespace("table1")){
table1::label(df$has) <- "Hypertension"
table1::label(df$smoke) <- "Smoking Habits"
table1::label(df$gender) <- "Gender"

dichotomous_2k_2sid(df, referencevar="has", flextableformat = FALSE)
}
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#>         Variable Chi_Squared Min_Expected   P_Chi P_Fisher Odds_Ratio CI_Lower
#> 1 Smoking Habits     0.00000      1.28571 1.00000  1.00000    2.00000  0.09019
#> 2         Gender     0.10937      1.28571 0.74086  0.48571    0.16667  0.00615
#>   CI_Upper
#> 1 44.35043
#> 2  4.51476