inst/doc/exploring_links_for_the_gaussian_distribution.R

In GlmSimulatoR: Creates Ideal Data for Generalized Linear Models

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## -----------------------------------------------------------------------------
library(GlmSimulatoR)
library(ggplot2)
library(stats)

set.seed(1)
simdata <- simulate_gaussian(N = 1000, weights = c(1, 3), link = "inverse", 
                             unrelated = 1, ancillary = .005)

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

## -----------------------------------------------------------------------------
ggplot(simdata, aes(x=X1, y=Y)) + 
  geom_point()

## -----------------------------------------------------------------------------
ggplot(simdata, aes(x=X2, y=Y)) + 
  geom_point()

## -----------------------------------------------------------------------------
ggplot(simdata, aes(x=Unrelated1, y=Y)) + 
  geom_point()


## -----------------------------------------------------------------------------
cor(x=simdata$X1, y = simdata$Y)
cor(x=simdata$X2, y = simdata$Y)
cor(x=simdata$Unrelated1, y = simdata$Y)


## -----------------------------------------------------------------------------
glmInverseX2 <- glm(Y ~ X2, data = simdata, family = gaussian(link = "inverse"))
glmInverseX1X2<- glm(Y ~ X1 + X2, data = simdata, family = gaussian(link = "inverse"))
glmInverseX1X2U1<- glm(Y ~ X1 + X2 + Unrelated1, data = simdata, family = gaussian(link = "inverse"))

summary(glmInverseX2)$aic
summary(glmInverseX1X2)$aic
summary(glmInverseX1X2U1)$aic


## -----------------------------------------------------------------------------
library(GlmSimulatoR)
library(ggplot2)
library(stats)

set.seed(1)
simdata <- simulate_gaussian(N = 1000, weights = c(.3, .8), link = "log", 
                             unrelated = 1, ancillary = 1)

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

## -----------------------------------------------------------------------------
ggplot(simdata, aes(x=X1, y=Y)) + 
  geom_point()

## -----------------------------------------------------------------------------
ggplot(simdata, aes(x=X2, y=Y)) + 
  geom_point()

## -----------------------------------------------------------------------------
ggplot(simdata, aes(x=Unrelated1, y=Y)) + 
  geom_point()


## -----------------------------------------------------------------------------
cor(x=simdata$X1, y = simdata$Y)
cor(x=simdata$X2, y = simdata$Y)
cor(x=simdata$Unrelated1, y = simdata$Y)


## -----------------------------------------------------------------------------
glmIdentity <- glm(Y ~ X1 + X2, data = simdata, family = gaussian(link = "identity"))
glmInverse<- glm(Y ~ X1 + X2, data = simdata, family = gaussian(link = "inverse"))
glmLog<- glm(Y ~ X1 + X2, data = simdata, family = gaussian(link = "log"))

summary(glmIdentity)$aic
summary(glmInverse)$aic
summary(glmLog)$aic