R/data.R

In BauerMichael/max-min-hill-climbing-algorithm: The max-min-hill-climbing algorithm

student <- function(rows) { # returns an example data frame with rows numbers of observations
    cols <- 5
    binary <- c(1, 2)
    trinary <- c(1, 2, 3)
    dimnames <- list(c(), c("difficulty", "intelligence", "SAT", "grade", "letter"))#, "prop"))
    studentMatrix <- matrix(, rows, cols, dimnames = dimnames)
    student <- data.frame(studentMatrix, check.names = FALSE)
    student$difficulty <- sample(binary, rows, replace = TRUE, prob = c(0.6, 0.4))
    student$intelligence <- sample(binary, rows, replace = TRUE, prob = c(0.7, 0.3))
    n <- 1
    for (i in student$intelligence) {
        if (i == binary[1])
            student$SAT[n] <- sample(binary, 1, replace = TRUE, prob = c(0.95, 0.05))
        if (i == binary[2])
            student$SAT[n] <- sample(binary, 1, replace = TRUE,prob = c(0.2, 0.8))
        if (i == binary[2] && student$difficulty[n] == binary[2])
            student$grade[n] <- sample(trinary, 1, replace = TRUE,prob = c(0.5, 0.3, 0.2))
        if (i == binary[2] && student$difficulty[n] == binary[1])
            student$grade[n] <- sample(trinary, 1, replace = TRUE,prob = c(0.9, 0.08, 0.02))
        if (i == binary[1] && student$difficulty[n] == binary[2])
            student$grade[n] <- sample(trinary, 1, replace = TRUE,prob = c(0.05, 0.25, 0.7))
        if (i == binary[1] && student$difficulty[n] == binary[1])
            student$grade[n] <- sample(trinary, 1, replace = TRUE,prob = c(0.3, 0.4, 0.3))
        if (student$grade[n] == trinary[1])
            student$letter[n] <- sample(binary, 1,replace = TRUE,prob = c(0.1, 0.9))
        if (student$grade[n] == trinary[2])
            student$letter[n] <- sample(binary, 1,replace = TRUE,prob = c(0.4, 0.6))
        if (student$grade[n] == trinary[3])
            student$letter[n] <- sample(binary, 1,replace = TRUE,prob = c(0.99, 0.01))
        n <- n+1
    }
    return (student)
}

rainy <- function(dim) { # returns an example data frame with rows numbers of observations
    cols <- 3
    binary <- c(1, 2)
    dimnames <- list(c(), c("sprinkler", "rain", "grassWet"))
    testMatrix <- matrix(, dim, cols, dimnames = dimnames)
    df <- data.frame(testMatrix, check.names = FALSE)
    df$rain <- sample(binary, dim, replace = TRUE, prob = c(0.2, 0.8))
    n <- 1
    for (i in df$rain) {
        if (i == binary[1])
            df$sprinkler[n] <- sample(binary, 1, replace = TRUE, prob = c(0.01, 0.99))
        if (i == binary[2])
            df$sprinkler[n] <- sample(binary, 1, replace = TRUE, prob = c(0.40, 0.60))
        if (i == binary[1] && df$sprinkler[n] == 1)
            df$grassWet[n] <- sample(binary, 1, replace = TRUE, prob = c(0.99, 0.01))
        if (i == binary[1] && df$sprinkler[n] == 2)
            df$grassWet[n] <- sample(binary, 1, replace = TRUE, prob = c(0.80, 0.20))
        if (i == binary[2] && df$sprinkler[n] == 1)
            df$grassWet[n] <- sample(binary, 1, replace = TRUE, prob = c(0.90, 0.10))
        if (i == binary[2] && df$sprinkler[n] == 2)
            df$grassWet[n] <- sample(binary, 1, replace = TRUE, prob = c(0.00, 1.00))
        n <- n+1
    }
    return (df)
}