## Replicate examples from Greenacre 2007, p. 59-68
data("countries")
## Compute principal components analysis
## All rows and all columns obtain the same weight
row_w <- rep(1 / nrow(countries), nrow(countries)) # 1/13
col_w <- rep(1 / ncol(countries), ncol(countries)) # 1/6
Y <- pca(countries, scale = FALSE, weight_row = row_w, weight_col = col_w)
## Row-metric-preserving biplot (form biplot)
biplot(Y, type = "form")
## Column-metric-preserving biplot (covariance biplot)
biplot(Y, type = "covariance", legend = list(x = "bottomright"))
## Replicate examples from Greenacre 2007, p. 79-88
data("benthos")
## Compute correspondence analysis
X <- ca(benthos)
## Symetric CA biplot
biplot(X, labels = "columns", legend = list(x = "bottomright"))
## Row principal CA biplot
biplot(X, type = "row", labels = "columns", legend = list(x = "bottomright"))
## Column principal CA biplot
biplot(X, type = "column", labels = "columns", legend = list(x = "bottomright"))
## Contribution CA biplot
biplot(X, type = "contrib", labels = NULL, legend = list(x = "bottomright"))
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