genCorrelatedData3 {rockchalk}

Generate correlated data for simulations (third edition)

Description

This is a revision of genCorrelatedData2. The output is a data frame that has columns for the predictors along with an error term, the linear predictor, and the observed value of the outcome variable. The new features are in the user interface. It has a better way to specify beta coefficients. It is also more flexible in the specification of the names of the predictor columns.

Usage

genCorrelatedData3(
  formula,
  N = 100,
  means = c(x1 = 50, x2 = 50, x3 = 50),
  sds = 10,
  rho = 0,
  stde = 1,
  beta = c(0, 0.15, 0.1, -0.1),
  intercept = FALSE,
  col.names,
  verbose = FALSE,
  ...,
  distrib = rnorm
)

Arguments

formula

a text variable, e.g., "y ~ 1 + 2*x1". Use ":" to create squared and interaction terms, "y ~ 1 + 2*x1 + 1.1*x1:x1 + 0.2*x1:x2". Multi-way interactions are allowed, eg "y ~ 1 + 2*x1 + .4*x2 + .1*x3 + 1.1*x1:x1 + 0.2*x1:x2:x3". Note author can specify any order of interation.

N

sample size

means

averages of predictors, can include names c(x1 = 10, x2 = 20) that will be used in the data.frame result.

sds

standard deviations, 1 (common value for all variables) or as many elements as in means.

rho

correlations, can be 1 or a vech for a correlation matrix

stde

The scale of the error term. If distrib=rnorm, stde is the standard deviation of the error term. If the user changes the distribution, this is a scale parameter that may not be equal to the standard deviation. For example, distrib=rlogist has a scale parameter such that a value of stde implies the error's standard deviation will be stde * pi / sqrt(3).

beta

slope coefficients, use either this or formula, not both. It is easier (less error prone) to use named coefficients, but (for backwards compatability with genCorrelatedData2) names are not required. If named, use "Intercept" for the intercept coefficient, and use variable names that match the xmeans vector. Un-named coefficients follow same rules as in genCorrelatedData2. The first (1 + p) values are for the intercept and p main effects. With 3 predictors and no squares or interactions, specify four betas corresponding to c(Intercept, x1, x2, x3). The squared and interaction terms may follow. The largest possible model would correspond to c(Intercept, x1, x2, x3, x1:x1, x1:x2, x1:x3, x2:x2, x2:x3, x3:x3). Squares and interactions fill in a "lower triangle". The unnamed beta vector can be terminated with the last non-zero coefficient, the function will insert 0's for the coefficients at the end of the vector.

intercept

TRUE or FALSE. Should the output data set include a column of 1's. If beta is an unnamed vector, should the first element be treated as an intercept?

col.names

Can override names in means vector

verbose

TRUE for diagnostics

...

extra arguments, ignored for now. We use that to ignore unrecognized parameters.

distrib

An R random data generating function. Default is rnorm. Also rlogis or any other two-parameter location/scale distribution will work. Special configuration allows rt. See details.

Details

The enhanced methods for authors to specify a data-generating process are as follows. Either way will work and the choice between the methods is driven by the author's convenience.

• 1. Use the formula argument as a quoted string: "1 + 2.2 * x1 + 2.3 * x2 + 3.3 * x3 + 1.9 * x1:x2". The "*" represents multiplication of coefficient times variable, and the colon ":" has same meaning but it is used for products of variables.

• 2. Use the beta argument with parameter names, beta = c("Intercept" = 1, x1 = 2.2, x2 = 2.3, x3 = 3.3, "x1:x2" = 1.9) where the names are the same as the names of the variables in the formula. Names of the variables in the formula or the beta vector should be used also in either the means parameter or the col.names parameter.

The error distribution can be specified. Default is normal, with draws provided by R's rnorm. All error models assume E[e] = 0 and the scale coefficient is the parameter stde. Thus, the default setup's error will be drawn from rnorm(N, 0, stde). Any two parameter "location" and "scale" distribution should work as well, as long as the first coefficient is location (because we set that as 0 in all cases) and the second argument is scale. For example, distrib=rlogis, will lead to errors drawn from rlogis(N, 0, stde). Caution: in rlogis, the scale parameter is not the same as standard deviation.

The only one parameter distribution currently supported is the T distribution. If user specifies distrib=rt, then the stde is passed through to the parameter df. Note that if increasing the stde parameter will cause the standard deviation of rt to get smaller. df=1 implies sd = 794.6; df=2 implies sd = 3.27; df=3 implies 1.7773.

Methods to specify error distributions in a more flexible way need to be considered.

Value

a data frame

Author(s)

Paul Johnson [e-mail] and Gabor Grothendieck <[e-mail]>

Examples

set.seed(123123)
## note: x4 is an unused variable in formula
X1a <-
    genCorrelatedData3("y ~ 1.1 + 2.1 * x1 + 3 * x2 + 3.5 * x3 + 1.1 * x1:x3",
                       N = 1000, means = c(x1 = 1, x2 = -1, x3 = 3, x4 = 1),
                       sds = 1, rho = 0.4, stde = 5)
lm1a <- lm(y ~ x1 + x2 + x3 + x1:x3, data = X1a)
## note that normal errors have std.error. close to 5
summary(lm1a)
attr(X1a, "beta") 
attr(X1a, "formula")
## Demonstrate name beta vector method to provide named arguments
set.seed(123123)
X2 <- genCorrelatedData3(N = 1000, means = c(x1 = 1, x2 = -1, x3 = 3, x4 = 1),
          sds = 1, rho = 0.4, 
          beta = c("Intercept" = 1.1, x1 = 2.1, x2 = 3,
                    x3 = 3.5, "x1:x3" = 1.1),
                    intercept = TRUE, stde = 5)
attr(X2, c("beta"))
attr(X2, c("formula"))
head(X2)
lm2 <- lm(y ~ x1 + x2 + x3 + x1:x3, data = X2)
summary(lm2)

## Equivalent with unnamed beta vector. Must carefully count empty
## spots, fill in 0's when coefficient is not present. This
## method was in genCorrelated2. Order of coefficents is
## c(intercept, x1, ..., xp, x1:x1, x1:x2, x1:xp, x2:x2, x2:x3, ..., )
## filling in a lower triangle.
set.seed(123123)
X3 <- genCorrelatedData3(N = 1000, means = c(x1 = 1, x2 = -1, x3 = 3, x4 = 1),
          sds = 1, rho = 0.4, 
          beta = c(1.1, 2.1, 3, 3.5, 0, 0, 0, 1.1),
                    intercept = TRUE, stde = 5)
attr(X3, c("beta"))
attr(X3, c("formula"))
head(X3)
lm3 <- lm(y ~ x1 + x2 + x3 + x1:x3, data = X3)
summary(lm3)

## Same with more interesting variable names in the means vector
X3 <- genCorrelatedData3(N = 1000,
          means = c(friend = 1, enemy = -1, ally = 3, neutral = 1),
          sds = 1, rho = 0.4, 
          beta = c(1.1, 2.1, 3, 3.5, 0, 0, 0, 1.1),
                    intercept = TRUE, stde = 5)
head(X3)
attr(X3, c("beta"))


X3 <- genCorrelatedData3(N = 1000, means = c(x1 = 50, x2 = 50, x3 = 50),
          sds = 10, rho = 0.4,
          beta = c("Intercept" = .1, x1 = .01, x2 = .2, x3 = .5,
                   "x1:x3" = .1))
lm3 <- lm(y ~ x1 + x2 + x3 + x1:x3, data = X3)


## Names via col.names argument: must match formula
X2 <- genCorrelatedData3("y ~ 1.1 + 2.1 * educ + 3 * hlth + 3 * ses + 1.1 * educ:ses",
         N = 100, means = c(50, 50, 50, 20),
         sds = 10, rho = 0.4, col.names = c("educ", "hlth", "ses", "wght"))
str(X2) 

X3 <- genCorrelatedData3("y ~ 1.1 + 2.1 * educ + 3 * hlth + 3 * ses + 1.1 * educ:ses",
         N = 100, means = c(50, 50, 50, 20),
         sds = 10, rho = 0.4, col.names = c("educ", "hlth", "ses", "wght"),
         intercept = TRUE)
str(X3)

## note the logistic errors have residual std.error approximately 5 * pi/sqrt(3)
X1b <-
    genCorrelatedData3("y ~ 1.1 + 2.1 * x1 + 3 * x2 + 3.5 * x3 + 1.1 * x1:x3",
                       N = 1000, means = c(x1 = 1, x2 = -1, x3 = 3),
                       sds = 1, rho = 0.4, stde = 5, distrib = rlogis)
lm1b <- lm(y ~ x1 + x2 + x3 + x1:x3, data = X1b)
summary(lm1b)

## t distribution is very sensitive for fractional df between 1 and 2 (recall
## stde parameter is passed through to df in rt.
X1c <-
    genCorrelatedData3("y ~ 1.1 + 2.1 * x1 + 3 * x2 + 3.5 * x3 + 1.1 * x1:x3",
                       N = 1000, means = c(x1 = 1, x2 = -1, x3 = 3),
                       sds = 1, rho = 0.4, stde = 1.2, distrib = rt)
lm1c <- lm(y ~ x1 + x2 + x3 + x1:x3, data = X1c)
summary(lm1c)