Paired Samples Contingency Tables

McNemar test

Usage

contTablesPaired(
  data,
  rows,
  cols,
  counts = NULL,
  chiSq = TRUE,
  chiSqCorr = FALSE,
  exact = FALSE,
  pcRow = FALSE,
  pcCol = FALSE,
  formula
)

Arguments

data

the data as a data frame

rows

the variable to use as the rows in the contingency table (not necessary when providing a formula, see the examples)

cols

the variable to use as the columns in the contingency table (not necessary when providing a formula, see the examples)

counts

the variable to use as the counts in the contingency table (not necessary when providing a formula, see the examples)

chiSq

TRUE (default) or FALSE, provide X²

chiSqCorr

TRUE or FALSE (default), provide X² with continuity correction

exact

TRUE or FALSE (default), provide an exact log odds ratio (requires exact2x2 to be installed)

pcRow

TRUE or FALSE (default), provide row percentages

pcCol

TRUE or FALSE (default), provide column percentages

formula

(optional) the formula to use, see the examples

Value

A results object containing:

results$freqs					a proportions table
results$test					a table of test results

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$freqs$asDF

as.data.frame(results$freqs)

Examples

dat <- data.frame(
    `1st survey` = c('Approve', 'Approve', 'Disapprove', 'Disapprove'),
    `2nd survey` = c('Approve', 'Disapprove', 'Approve', 'Disapprove'),
    `Counts` = c(794, 150, 86, 570),
    check.names=FALSE)

contTablesPaired(formula = Counts ~ `1st survey`:`2nd survey`, data = dat)
#> 
#>  PAIRED SAMPLES CONTINGENCY TABLES
#> 
#>  Contingency Tables                               
#>  ──────────────────────────────────────────────── 
#>    1st survey    Approve    Disapprove    Total   
#>  ──────────────────────────────────────────────── 
#>    Approve           794           150      944   
#>    Disapprove         86           570      656   
#>    Total             880           720     1600   
#>  ──────────────────────────────────────────────── 
#> 
#> 
#>  McNemar Test                          
#>  ───────────────────────────────────── 
#>          Value       df    p           
#>  ───────────────────────────────────── 
#>    χ²    17.35593     1    0.0000310   
#>    N     1600.000                      
#>  ───────────────────────────────────── 
#> 

#
#  PAIRED SAMPLES CONTINGENCY TABLES
#
#  Contingency Tables
#  ------------------------------------------------
#    1st survey    Approve    Disapprove    Total
#  ------------------------------------------------
#    Approve           794           150      944
#    Disapprove         86           570      656
#    Total             880           720     1600
#  ------------------------------------------------
#
#
#  McNemar Test
#  -----------------------------------------------------
#                                Value    df    p
#  -----------------------------------------------------
#    X²                           17.4     1    < .001
#    X² continuity correction     16.8     1    < .001
#  -----------------------------------------------------
#


# Alternatively, omit the left of the formula (`Counts`) from the
# formula if each row represents a single observation:

contTablesPaired(formula = ~ `1st survey`:`2nd survey`, data = dat)
#> 
#>  PAIRED SAMPLES CONTINGENCY TABLES
#> 
#>  Contingency Tables                               
#>  ──────────────────────────────────────────────── 
#>    1st survey    Approve    Disapprove    Total   
#>  ──────────────────────────────────────────────── 
#>    Approve             1             1        2   
#>    Disapprove          1             1        2   
#>    Total               2             2        4   
#>  ──────────────────────────────────────────────── 
#> 
#> 
#>  McNemar Test                          
#>  ───────────────────────────────────── 
#>          Value       df    p           
#>  ───────────────────────────────────── 
#>    χ²    0.000000     1    1.0000000   
#>    N            4                      
#>  ───────────────────────────────────── 
#>