In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

knitr::opts_chunk$set(
  error = TRUE,
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)

# The Linear Regression Model {#linreg-estimation-linreg-example}

library(testthat)
library(jeksterslabRlinreg)

Data

In this example, we are interested in predictors of wages. The regressor variables are gender, race, union membership, education, and work experience. The regressand variable is hourly wage in US dollars.

See jeksterslabRdatarepo::wages.matrix() for the data set used in this example.

X <- jeksterslabRdatarepo::wages.matrix[["X"]]
# age is removed
X <- X[, -ncol(X)]
y <- jeksterslabRdatarepo::wages.matrix[["y"]]
head(X)
head(y)

`jeksterslabRlinreg::linreg()`

The jeksterslabRlinreg::linreg() function fits a linear regression model using X and y. In this example, X consists of a column of constants, gender, race, union membership, education, and work experience. and y consists of hourly wages in US dollars.

The output includes the following:

Model assessment
ANOVA table
Table of regression coefficients with the following columns
Regression coefficients
Standard errors
$t$ statistic
$p$ value
Standardized coefficients
Confidence intervals (0.05, 0.5, 2.5, 97.5, 99.5, 99.95)
Means and standard deviations
Scatterplot matrix
Residual plots

Using Unbiased Standard Errors

linreg(
  X = X,
  y = y
)

Using Biased Standard Errors

linreg(
  X = X,
  y = y,
  sehatbetahattype = "biased"
)

`lm()` function

The lm() function is the default option for fitting a linear model in R.

lmobj <- lm(
  wages ~ gender + race + union + education + experience,
  data = jeksterslabRdatarepo::wages
)
summary(lmobj)
#
## `lavaan::sem()` function
#
# Linear regression in SEM
#
#
# model <- c(
#  wages ~ gender + race + union + education + experience
# )
#
# Build errors with lavaan dependency
#
### Wishart Likelihood (Unbiased)
#
#
# lavobj <- lavaan::sem(
#  model = model,
#  data = jeksterslabRdatarepo::wages,
#  likelihood = "wishart"
# )
# summary(lavobj)
#
### Normal Likelihood (Biased)
#
#
# lavobj <- lavaan::sem(
#  model = model,
#  data = jeksterslabRdatarepo::wages,
#  likelihood = "normal"
# )
# summary(lavobj)