Using Simple Correspondence Analysis to Present Tabular Data that Shows Change Over Time
**Results of the Correspondence Analysis (CA)**
The row variable has 4 categories; the column variable has 3 categories
The chi square of independence between the two variables is equal to 15.66818 (p-value = 0.01565024 ).
*The results are available in the following objects:
name description
1 "$eig" "eigenvalues"
2 "$col" "results for the columns"
3 "$col$coord" "coord. for the columns"
4 "$col$cos2" "cos2 for the columns"
5 "$col$contrib" "contributions of the columns"
6 "$row" "results for the rows"
7 "$row$coord" "coord. for the rows"
8 "$row$cos2" "cos2 for the rows"
9 "$row$contrib" "contributions of the rows"
10 "$call" "summary called parameters"
11 "$call$marge.col" "weights of the columns"
12 "$call$marge.row" "weights of the rows"
**Results of the Correspondence Analysis (CA)**
The row variable has 8 categories; the column variable has 4 categories
The chi square of independence between the two variables is equal to 436.1788 (p-value = 2.930305e-79 ).
*The results are available in the following objects:
name description
1 "$eig" "eigenvalues"
2 "$col" "results for the columns"
3 "$col$coord" "coord. for the columns"
4 "$col$cos2" "cos2 for the columns"
5 "$col$contrib" "contributions of the columns"
6 "$row" "results for the rows"
7 "$row$coord" "coord. for the rows"
8 "$row$cos2" "cos2 for the rows"
9 "$row$contrib" "contributions of the rows"
10 "$call" "summary called parameters"
11 "$call$marge.col" "weights of the columns"
12 "$call$marge.row" "weights of the rows"
Figure 1: Correspondence analysis biplot of a stacked table
