interca: interca

In interca: Multiple Correspondence Analysis Based on Interpretive Coordinates

interca

Description

The function performs multiple correspondence analysis (MCA) on a given data set and a given number of categorical variables. The function returns for each point for each axis the coordinates, the interpretive coordinates, the contributions, and the quality of display.

Usage

interca(data = data, numaxes = 10)

Arguments

data	A data frame containing the data to be analyzed.
numaxes	The number of categorical variables that will be retained for the calculation of the elements referred to in the function description.

Value

The function returns a list with the principal MCA coordinates coordinates (coords), the interpretive MCA coordinates (ecoords), the values of the CTR (ctr) and COR (cor), the labels of the variable categories (lbl) and the scree plot (plot).

References

Moschidis, S., Markos, A., & Thanopoulos, A. C. (2022). "Automatic" interpretation of multiple correspondence analysis (MCA) results for nonexpert users, using R programming. Applied Computing and Informatics, (ahead-of-print).

Examples

# Set the seed to ensure reproducibility
set.seed(123)
# Create three categorical variables
X1 <- sample(c("X1_1", "X1_2"), size = 200, replace = TRUE)
X2 <- sample(c("X2_1", "X2_2", "X2_3"), size = 200, replace = TRUE)
X3 <- sample(c("X3_1", "X3_2", "X3_3", "X3_4"), size = 200, replace = TRUE)
# the resulting data frame
df <- data.frame(cbind(X1,X2,X3))
# convert to factors
df$X1 <- factor(df$X1)
df$X2 <- factor(df$X2)
df$X3 <- factor(df$X3)

res <- interca(df, 5)

interca documentation built on Jan. 23, 2023, 5:43 p.m.