= pd.DataFrame([[2 ,3 ,4 ],[5 ,6 ,7 ],[8 ,8 ,6 ],[9 ,10 ,3 ],[11 ,4 ,6 ]]); X
0
1
2
0
2
3
4
1
5
6
7
2
8
8
6
3
9
10
3
4
11
4
6
Variable
0
1
2
0
1
2
Time
t
t
t
t-1
t-1
t-1
0
5
6
7
2
3
4
1
8
8
6
5
6
7
2
9
10
3
8
8
6
3
11
4
6
9
10
3
Here we define the so-called imposter matrix, which will be used for comparing correlation matrices against randomly shuffled ones to determine when a correlation is better than a random one.
source
imposter_matrix
imposter_matrix (X:pandas.core.frame.DataFrame, random_state=42)
X
DataFrame
Matrix to be randomized
random_state
int
42
Random state to be used
Returns DataFrame The input matrix with shuffled values for each column
0
1
2
0
5
6
7
1
11
4
6
2
8
8
6
3
2
3
4
4
9
10
3
source
correlation_matrix
correlation_matrix (X:pandas.core.frame.DataFrame,
y:pandas.core.frame.DataFrame)
X
DataFrame
Matrix 1 for calculating the correlation matrix
y
DataFrame
Matrix 2 for calculating the correlation matrix
Returns ndarray Correlation matrix as a Numpy array
array([[ 1. , 0.41978508, 0.0860663 ],
[ 0.41978508, 1. , -0.27628324],
[ 0.0860663 , -0.27628324, 1. ]])
source
ACM
ACM (X:pandas.core.frame.DataFrame, lag:int)
X
DataFrame
Dataframe of raw data for which to calculate the ACM
lag
int
Lag to calculate correlation against
Returns DataFrame Autocorrelation Matrix (ACM)
0
1
2
t
t
t
0
t-2
0.981981
0.953821
0.500000
1
t-2
-0.654654
-0.563621
0.142857
2
t-2
0.000000
-0.114708
-0.755929
source
partial_correlation_matrix
partial_correlation_matrix (X:pandas.core.frame.DataFrame)
X
DataFrame
Matrix for calculating partial correlation
Returns ndarray Partial correlation matrix as a Numpy array
array([[ 1. , 0.46324708, 0.23162552],
[ 0.46324708, 1. , -0.34549142],
[ 0.23162552, -0.34549142, 1. ]])
source
PACM
PACM (X:pandas.core.frame.DataFrame, lag:int)
X
DataFrame
Matrix for calculating partial autocorrelation
lag
int
Lag to calculate partial correlation against
Variable
0
1
2
Time
t
t
t
Variable
Time
0
t-2
-0.807661
-0.109185
0.375985
1
t-2
-0.606224
-0.387828
0.624375
2
t-2
0.018772
-0.876408
0.974483