Compares Variability of Variables
Investigation of the variables' variances/concentrations to support specification of lambda for k-prototypes clustering.
lambdaest(x, num.method = 1, fac.method = 1, outtype = "numeric")
x |
Original data. |
num.method |
Integer 1 or 2. Specifies the heuristic used for numeric variables. |
fac.method |
Integer 1 or 2. Specifies the heuristic used for factor variables. |
outtype |
Specifies the desired output: either 'numeric', 'vector' or 'variation'. |
Variance (num.method = 1) or standard deviation (num.method = 2) of numeric variables
and 1-∑_i p_i^2 (fac.method = 1) or 1-\max_i p_i (fac.method = 2) for factors is computed.
lambda |
Ratio of averages over all numeric/factor variables is returned.
In case of |
# generate toy data with factors and numerics
n <- 100
prb <- 0.9
muk <- 1.5
clusid <- rep(1:4, each = n)
x1 <- sample(c("A","B"), 2*n, replace = TRUE, prob = c(prb, 1-prb))
x1 <- c(x1, sample(c("A","B"), 2*n, replace = TRUE, prob = c(1-prb, prb)))
x1 <- as.factor(x1)
x2 <- sample(c("A","B"), 2*n, replace = TRUE, prob = c(prb, 1-prb))
x2 <- c(x2, sample(c("A","B"), 2*n, replace = TRUE, prob = c(1-prb, prb)))
x2 <- as.factor(x2)
x3 <- c(rnorm(n, mean = -muk), rnorm(n, mean = muk), rnorm(n, mean = -muk), rnorm(n, mean = muk))
x4 <- c(rnorm(n, mean = -muk), rnorm(n, mean = muk), rnorm(n, mean = -muk), rnorm(n, mean = muk))
x <- data.frame(x1,x2,x3,x4)
lambdaest(x)
res <- kproto(x, 4, lambda = lambdaest(x))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.