Col Shuffle(<By var,...>)
Description
Creates a random ordering of the row numbers of the current data table when used in a column formula.
Note: This function is generally used in a column formula.
Returns
A random integer between 1 and the number of rows in the current data table.
Argument
By var
(Optional) A By variable enables you to randomly order the rows within the groups of the By variable values.
Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "Shuffle", Numeric, Continuous, Set Formula( Col Shuffle() ) );
This example creates a column formula that shuffles the order of the row numbers (1 to 40) each time the formula is evaluated. Each number appears only once.
Make Validation Formula(rates, <<Stratification Columns(cols), <<Grouped Columns(cols), <<Cutpoint Column(col))
Description
Generates a validation column. This function is primarily used by the Make Validation Column utility.
Arguments
rates
Vector of three rates that specify the training, validation, and test rates, respectively.
<<Stratification Columns
Assigns one or more stratification columns.
<<Grouped Columns
Assigns one or more grouping columns.
<<Cutpoint Column
Assigns a numeric cutpoint column.
See Also
Make Validation Column in Predictive and Specialized Modeling
Random Beta(alpha, beta, <theta=0>, <sigma=1>)
Description
Returns a random number from a beta distribution with two shape parameters, alpha and beta, and optional parameters theta and sigma.
Arguments
alpha, beta
Shape parameters α and β, which must both be greater than 0.
theta
Optional threshold parameter θ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
Random Beta Binomial(n, p, <delta=0>)
Description
Returns a random number from a beta binomial distribution for n trials with probability p and overdispersion parameter delta.
Arguments
n
The number of trials, which must be greater than or equal to 2. If the specified n is not an integer, the non-integer part is truncated.
p
The probability of success for each trial, which must be between 0 and 1.
delta
The overdispersion parameter δ, which must be between Maximum[-p/(n-p-1), -(1-p)/(n-2+p)] and 1. The default is 0.
Random Binomial(n, p)
Description
Returns a random number from a binomial distribution with n trials and probability p of the event of interest occurring.
Arguments
p
The probability of success for each trial, which must be between 0 and 1.
n
The number of trials.
Random Category(probA, resultA, probB, resultB, <..., ...,> resultElse)
Description
Returns one of the specified result expressions at random, chosen from pairs of probability and result expressions. A random uniform number is generated and compared to the prob arguments to determine which result argument is returned.
Arguments
probA
Numeric value between 0 and 1 that represents the probability of the corresponding result expression being returned.
resultA
Expression that corresponds to probA.
resultElse
Expression that is returned if no previous result expression has been returned.
Random Cauchy()
Description
Returns a random number from a Cauchy distribution with a median of zero.
Random ChiSquare(df, <nc=0>)
Description
Returns a random number from a chi-square distribution with given df (degrees of freedom) and optional noncentrality parameter.
Arguments
df
The degrees of freedom n, which must be greater than 0.
nc
Optional noncentrality parameter λ, which must be nonnegative. The default is 0.
Random Exp()
Description
Returns a random number from an exponential distribution with scale parameter equal to 1. Equivalent to the negative log of Random Uniform.
Random F(dfnum, dfden, <noncentral=0>)
Description
Returns a random number from an F distribution with a given dfnum, dfden, and optional noncentrality parameter.
Arguments
dfnum
The degrees of freedom, v1, of the chi-square distribution in the numerator of the F-distribution. dfnum must be greater than 0.
dfden
The degrees of freedom, v2, of the chi-square distribution in the denominator of the F-distribution. dfden must be greater than 0.
noncentral
Optional noncentrality parameter λ, which must be nonnegative. The default is 0.
Random Frechet(<mu=0>, <sigma=1>)
Description
Returns a random number from a Fréchet distribution with the location mu and scale sigma.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
Random Gamma(alpha, <scale=1>)
Description
Returns a random numbers from a gamma distribution for given alpha and optional scale.
Arguments
alpha
The shape parameter α, which must be greater than 0.
scale
Optional scale parameter β, which must be greater than 0. The default is 1.
Random Gamma Poisson(lambda, <sigma=1>)
Description
Returns a random number from a gamma Poisson distribution with parameters lambda and sigma.
Arguments
lambda
The shape parameter λ, which much be greater than 0.
sigma
Optional overdispersion parameter σ, which must be greater than or equal to 1. The default is 1. When the overdispersion parameter is 1, the distribution reduces to a Poisson(λ) distribution.
Random GenGamma(<mu=0>, <sigma=1>, <lambda=0>)
Description
Returns a random number from an extended generalized gamma distribution with parameters mu, sigma, and lambda.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
lambda
Optional shape parameter λ. The default is 0.
Random Geometric(p)
Description
Returns a random number from the geometric distribution with probability p that a specific event occurs at any one trial.
Random GLog(mu, sigma, lambda)
Description
Returns a random number from a generalized logarithmic distribution with parameters mu, sigma, and lambda.
Arguments
mu
The location parameter μ.
sigma
The scale parameter σ, which must be greater than 0.
lambda
A shape parameter λ, which must be greater than 0.
Random Index(n, k)
Description
Returns a k by 1 matrix of random integers between 1 and n with no duplicates.
Random Integer(n)
Random Integer(k, n)
Description
Returns a random integer from 1 to n or from k to n.
Random Johnson Sb(gamma, delta, theta, sigma)
Description
Returns a random number from a Johnson Sb distribution with parameters gamma, delta, theta, and sigma.
Arguments
gamma
Shape parameter γ.
delta
Shape parameter δ, which must be greater than 0.
theta
Location parameter θ.
sigma
Scale parameter σ, which must be greater than 0.
Random Johnson Sl(gamma, delta, theta, <sigma=1>)
Description
Returns a random number from a Johnson Sl distribution with parameters gamma, delta, theta, and optional sigma.
Arguments
gamma
Shape parameter γ.
delta
Shape parameter δ, which must be greater than 0.
theta
Location parameter θ.
sigma
Optional parameter σ that indicates if the distribution is skewed positively or negatively. sigma must be equal to either +1 (skewed positively) or -1 (skewed negatively). The default is +1.
Random Johnson Su(gamma, delta, theta, sigma)
Description
Returns a random number from a Johnson Su distribution with parameters gamma, delta, theta, and sigma.
Arguments
gamma
Shape parameter γ.
delta
Shape parameter δ, which must be greater than 0.
theta
Location parameter θ.
sigma
Scale parameter σ, which must be greater than 0.
Random LEV(<mu=0>, <sigma=1>)
Description
Returns a random number from an LEV distribution with the location mu and scale sigma.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
Random LogGenGamma(<mu=0>, <sigma=1>, <lambda=0>)
Description
Returns a random number from a log generalized gamma distribution with parameters mu, sigma, and lambda.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
lambda
Optional shape parameter λ. The default is 0.
Random Logistic(<mu=0>, <sigma=1>)
Description
Returns a random number from a logistic distribution with location mu and scale sigma.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
Random Loglogistic(<mu=0>, <sigma=1>)
Description
Returns a random number from a loglogistic distribution with location mu and scale sigma.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
Random Lognormal(<mu=0>, <sigma=1>)
Description
Returns a random number from a lognormal distribution with location mu and scale sigma.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
Random Multivariate Normal(mean, covar, <nrows=1>)
Description
Returns a random vector from a multivariate normal distribution with mean vector mean and covariance matrix covar. To generate multiple vectors, specify an integer greater than 1 for the nrows argument. When nrows is greater than 1, the return value is a matrix. The number of columns in the random vector or matrix is equal to the number of rows in the covar argument.
Arguments
mean
Mean vector for the multivariate normal distribution.
covar
Covariance matrix for the multivariate normal distribution. This matrix must be a symmetric square matrix that contains the same number of columns as the mean vector.
nrows
Optional argument that specifies the number of random vectors returned. The default number of rows is 1.
Random Negative Binomial(n, p)
Description
Returns a random number from a negative binomial distribution for n successes with probability of success p.
Random Normal(<mu=0>, <sigma=1>)
Description
Returns a random number from a normal distribution with mean mu and standard deviation sigma.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
Random Normal Mixture(meanvec, sdvec, probabvec)
Description
Returns a random number from a normal mixture distribution with the specified arguments.
Arguments
meanvec
A vector that contains group means.
sdvec
A vector that contains the group standard deviations.
probvec
A vector that contains the group probabilities.
Random Poisson(lambda)
Description
Returns a random number from a Poisson distribution with shape parameter lambda.
Arguments
lambda
The shape parameter λ, which much be greater than 0.
Random Reset(seed)
Description
Restarts the random number sequences with seed. To ensure reproducible results, specify a positive integer less than or equal to 4,294,967,295 for the random seed.
Notes
You can use the Random Reset function to set a random seed that enables you to reproduce a set of random numbers. The random number algorithms in JMP are sometimes refined, and therefore the results might not be consistent across different versions of JMP.
Random Seed State(<seed state>)
Description
Retrieves or restores the random seed state to or from a BLOB object.
Random SEV(<mu=0>, <sigma=1>)
Description
Returns a random number from an SEV distribution with the specified location mu and scale sigma.
Arguments
mu
Optional location parameter μ. The default is 0.
sigma
Optional scale parameter σ, which must be greater than 0. The default is 1.
Random SHASH(gamma, delta, theta, sigma)
Description
Returns a random number from a sinh-arcsinh (SHASH) distribution with parameters gamma, delta, theta, and sigma.
Arguments
gamma
The shape parameter γ.
delta
The shape parameter δ, which must be greater than 0.
theta
The location parameter θ.
sigma
The scale parameter σ, which must be greater than 0.
Random Shuffle(matrix)
Description
Returns the matrix with the elements shuffled into a random order.
Random t(df, <noncentral=0>)
Description
Returns a random number from a t distribution with the specified df (degrees of freedom). The noncentrality argument may be negative or positive. The default value of noncentral is 0.
Random Triangular(min, mode, max)
Random Triangular(mode, max)
Random Triangular(mode)
Description
Generates a random number from a triangular distribution between 0 and 1 with the mode that you specify. The triangular distribution is typically used for populations that have a small number of data.
Arguments
min
Specifies the lower limit of the triangular distribution. The default value is 0.
mode
Specifies the mode of the triangular distribution.
max
Species the upper limit of the triangular distribution. The default value is 1.
Notes
If you specify only the mode, the minimum value is 0, and the maximum value is 1. If you specify the mode and maximum value, the minimum value is 0 by default.
Random Uniform()
Random Uniform(x)
Random Uniform(min, max)
Description
Generates a random number from a uniform distribution between 0 and 1. Random Uniform(x) generates a number between 0 and x. Random Uniform (min, max) generates a number between min and max. The result is an approximately even distribution.
Random Weibull(shape, <scale=1>)
Description
Returns a random number from a Weibull distribution with parameters shape and optional scale.
Arguments
shape
Shape parameter β, which must be greater than 0.
scale
Optional scale parameter α, which must be greater than 0. The default is 1.
Resample Freq(<rate=1, <column>>)
Description
Generates a frequency count for sampling with replacement. If no arguments are specified, the function generates a 100% resample.
Note: This function is generally used in a column formula.
Arguments
rate
(Optional) Specifies the rate of resampling. The default value is 1. A negative value specifies that fractional frequencies are allowed.
column
(Optional) If you specify column, you must also specify rate. The sample size is calculated by the rate multiplied by the sum of the specified column. If rate is negative, then the sample size is the negative of the rate multiplied by the sum of the specified column. If you do not specify a column, the generated frequencies sum to the number of rows.
Example
To ensure that the numbers in the frequency column match each time you run the script, use As Constant(). As Constant() evaluates an expression to create a constant value that does not change after it has been computed.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dc = dt << New Column( "column",
Formula(
As Constant(
Random Reset( 123 );
0;
) + Resample Freq()
)
);
dc << Eval Formula;
Notes
– A typical use of this function generates a column with many 1s, some 0s, some 2s, and so forth, corresponding to which rows were randomly assigned any of n randomly selected rows.
– A typical use of this with an existing frequency column produces a new frequency column whose values are similar to the old frequency column (have the same expected value); however, the values vary somewhat due to random selection at the rates corresponding to the old frequency column.