corr_case()

Conducts Pearson (default method), Spearman rank, or Kendall’s Tau-b correlation analysis using case wise deletion. Returns the relevant information and results in 3 DataFrames for easy exporting.

DataFrame 1 is the testing information, i.e. the type of correlation analysis conducted and the number of observations used.

DataFrame 2 contains the r value results in a matrix style look.

DataFrame 3 contains the p-values in a matrix style look.

Arguments

def corr_case(dataframe, method = “pearson”)

  • dataframe can either be a single Pandas Series or multiple Series/an entire DataFrame.

  • method takes the values of “pearson” [] (the default if nothing is passed), “spearman” [], or “kendall” [].

Examples

import researchpy, numpy, pandas


numpy.random.seed(12345)

df = pandas.DataFrame(numpy.random.randint(10, size= (100, 2)),
                  columns= ['beck', 'srq'])
# Since it returns 3 DataFrames for easy exporting, if the DataFrames
# aren't assigned to an object the outputting tuple is rather messy
# and ugly

researchpy.correlation.corr_case(df[['beck', 'srq']])
(  Pearson correlation test using list-wise deletion
 0                     Total observations used = 100,         beck     srq
 beck       1  0.0029
 srq   0.0029       1,         beck     srq
 beck  0.0000  0.9775
 srq   0.9775  0.0000)
# As noted above, the 3 return DataFrames are information, r values,
# and p-values
# The 3 DataFrame design was decided on so each DataFrame can easily
# be exported using already supported Pandas methods

i, r, p  = researchpy.correlation.corr_case(df[['beck', 'srq']])

i

Pearson correlation test using list-wise deletion

Total observations used = 100

r

beck

srq

beck

1

0.0029

srq

0.0029

1

p

beck

srq

beck

0.0000

0.9775

srq

0.9775

0.0000

References