nca_random {NCA}

generating random data that meets necessity

Description

Generate N datapoints, with 'normal' or 'uniform' distributions for X and Y

Usage

nca_random(n, intercepts, slopes, corner=1,
                  distribution.x = "uniform", distribution.y = "uniform",
                  mean.x = 0.5, mean.y = 0.5, sd.x = 0.2, sd.y = 0.2)

Arguments

n

Number of observations to generate, should be an integer > 1.

intercepts

The intercept or a vector of intercepts of the line.

slopes

The slope or a vector if slopes of the line.

corner

Define which corner should be empty, default is 1 (upper left).

distribution.x

Type of the distribution for X, "uniform" (default) or "normal".
The latter is a truncated normal distribution.

distribution.y

Type of the distribution for Y, "uniform" (default) or "normal".
The latter is a truncated normal distribution.

mean.x

Distribution Mean of X (default 0.5), ignored distribution.x == "uniform".

mean.y

Distribution Mean of Y (default 0.5), ignored distribution.y == "uniform".

sd.x

Distribution SD of X (default 0.2), ignored distribution.x == "uniform".

sd.y

Distribution SD of Y (default 0.2), ignored distribution.y == "uniform".

Examples

# Generate a uniform dataset, default for X and Y
data <- nca_random(100, 0, 1)

# It is also possible to generate a dataset with multiple independent variables,
# by supplying vectors for the intercepts and slopes
data <- nca_random(100, c(0, 0.25), c(1, 0.75))
# Single values will be repeated to complement a vector  
data <- nca_random(100, c(0, 0.25), 1)

# The default is an empty space in the upper left corner.
# A different corner can be selected with the corner argument
data <- nca_random(100, 0, 1, corner=4)

# Generate a dataset with a normal distribution for X and a uniform distribution for Y
data <- nca_random(100, 0, 1, distribution.x = "normal", distribution.y = "uniform")

# Generate a dataset with a normal distribution for X and Y, with adjusted MEAN
data <- nca_random(100, 0, 1, distribution.x = "normal", distribution.y = "normal",
                   mean.x = 0.75, mean.y = 0.75)

# Generate a dataset with a normal distribution for X and Y, with adjusted SD
data <- nca_random(100, 0, 1, distribution.x = "normal",
                   distribution.y = "normal", sd.x = 0.1, sd.y = 0.1)