GlmSimulatoR (version 1.0.0)

simulate_gaussian: Create ideal data for a generalized linear model.

Description

Create ideal data for a generalized linear model.

Usage

simulate_gaussian(
  N = 10000,
  link = "identity",
  weights = 1:3,
  x_range = 1,
  unrelated = 0,
  ancillary = 1
)
simulate_binomial(
  N = 10000,
  link = "logit",
  weights = c(0.1, 0.2),
  x_range = 1,
  unrelated = 0
)
simulate_gamma(
  N = 10000,
  link = "inverse",
  weights = 1:3,
  x_range = 1,
  unrelated = 0,
  ancillary = 0.05
)
simulate_poisson(
  N = 10000,
  link = "log",
  weights = c(0.5, 1),
  x_range = 1,
  unrelated = 0
)
simulate_inverse_gaussian(
  N = 10000,
  link = "1/mu^2",
  weights = 1:3,
  x_range = 1,
  unrelated = 0,
  ancillary = 0.3333
)
simulate_negative_binomial(
  N = 10000,
  link = "log",
  weights = c(0.5, 1),
  x_range = 1,
  unrelated = 0,
  ancillary = 1
)
simulate_tweedie(
  N = 10000,
  link = "log",
  weights = 0.02,
  x_range = 1,
  unrelated = 0,
  ancillary = 1.15
)

Value

A tibble with a response variable and predictors.

Arguments

N

Sample size. (Default: 10000)

link

Link function. See family for details.

weights

Betas in glm model.

x_range

range of x variables.

unrelated

Number of unrelated features to return. (Default: 0)

ancillary

Ancillary parameter for continuous families and negative binomial. See details.

Details

For many families, it is possible to pick weights that cause inverse link(X * weights) to be mathematically invalid. For example, the log link for binomial regression defines P(Y=1) as exp(X * weights) which can be above one. If this happens, the function will error with a helpful message.

The intercept in the underlying link(Y) = X * weights + intercept is always max(weights). In simulate_gaussian(link = "inverse", weights = 1:3), the model is (1/Y) = 1*X1 + 2*X2 + 3*X3 + 3.

links

• gaussian: identity, log, inverse

• binomial: logit, probit, cauchit, loglog, cloglog, log, logc, identity

• gamma: inverse, identity, log

• poisson: log, identity, sqrt

• inverse gaussian: 1/mu^2, inverse, identity, log

• negative binomial: log, identity, sqrt

• tweedie: log, identity, sqrt, inverse

The default link is the first link listed for each family.

ancillary parameter

• gaussian: standard deviation

• binomial: N/A

• gamma: scale parameter

• poisson: N/A

• inverse gaussian: dispersion parameter

• negative binomial: theta.

• tweedie: rho

Examples

library(GlmSimulatoR)
library(ggplot2)
library(MASS)

# Do glm and lm estimate the same weights? Yes
set.seed(1)
simdata <- simulate_gaussian()
linear_model <- lm(Y ~ X1 + X2 + X3, data = simdata)
glm_model <- glm(Y ~ X1 + X2 + X3,
  data = simdata,
  family = gaussian(link = "identity")
)
summary(linear_model)
summary(glm_model)
rm(linear_model, glm_model, simdata)

# If the link is not identity, will the response
# variable still be normal? Yes
set.seed(1)
simdata <- simulate_gaussian(N = 1000, link = "log", weights = c(.1, .2))

ggplot(simdata, aes(x = Y)) +
  geom_histogram(bins = 30)
rm(simdata)

# Is AIC lower for the correct link? For ten thousand data points, depends
# on seed!
set.seed(1)
simdata <- simulate_gaussian(N = 10000, link = "inverse", weights = 1)
glm_correct_link <- glm(Y ~ X1,
  data = simdata,
  family = gaussian(link = "inverse")
)
glm_wrong_link <- glm(Y ~ X1,
  data = simdata,
  family = gaussian(link = "identity")
)
summary(glm_correct_link)$aic
summary(glm_wrong_link)$aic
rm(simdata, glm_correct_link, glm_wrong_link)


# Does a stepwise search find the correct model for logistic regression? Yes
# 3 related variables. 3 unrelated variables.
set.seed(1)
simdata <- simulate_binomial(
  N = 10000, link = "logit",
  weights = c(.3, .4, .5), unrelated = 3
)

scope_arg <- list(
  lower = Y ~ 1,
  upper = Y ~ X1 + X2 + X3 + Unrelated1 + Unrelated2 + Unrelated3
)

starting_model <- glm(Y ~ 1,
  data = simdata,
  family = binomial(link = "logit")
)
glm_model <- stepAIC(starting_model, scope_arg)
summary(glm_model)
rm(simdata, scope_arg, starting_model, glm_model)

# When the resposne is a gamma distribution, what does a scatter plot between
# X and Y look like?
set.seed(1)
simdata <- simulate_gamma(weights = 1)
ggplot(simdata, aes(x = X1, y = Y)) +
  geom_point()
rm(simdata)