| Type: | Package |
| Title: | Core Functionality for Simulating Quantities of Interest from Generalised Linear Models |
| Description: | Core functions for simulating quantities of interest from generalised linear models (GLM). This package will form the backbone of a series of other packages that improve the interpretation of GLM estimates. |
| Version: | 0.2.4 |
| Date: | 2017-05-15 |
| BugReports: | https://github.com/christophergandrud/coreSim/issues |
| License: | GPL (≥ 3) |
| LazyData: | TRUE |
| RoxygenNote: | 6.0.1 |
| Depends: | R (≥ 2.10) |
| Imports: | dplyr (≥ 0.5.0), MASS |
| Suggests: | car, splines, survival, testthat |
| NeedsCompilation: | no |
| Packaged: | 2017-05-15 20:01:23 UTC; cgandrud |
| Author: | Christopher Gandrud [aut, cre] |
| Maintainer: | Christopher Gandrud <christopher.gandrud@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2017-05-15 22:18:31 UTC |
Graduate school admissions data
Description
A data set containing 400 graduate school admissions decisions.
Usage
Admission
Format
A data set with 400 rows and 4 variables.
Source
UCLA IDRE http://stats.idre.ucla.edu/r/dae/logit-regression/
Simulate coefficients from a GLM by making draws from the multivariate normal distribution
Description
Simulate coefficients from a GLM by making draws from the multivariate normal distribution
Usage
b_sim(obj, mu, Sigma, nsim = 1000)
Arguments
obj |
a fitted model object. |
mu |
an optional vector giving the means of the variables. If |
Sigma |
an optional positive-definite symmetric matrix specifying the
covariance matrix of the variables. If |
nsim |
number of simulations to draw. |
Value
A data frame of simulated coefficients from obj.
Examples
library(car)
# Estimate model
m1 <- lm(prestige ~ education + type, data = Prestige)
# Create fitted values
prestige_sims <- b_sim(m1)
# Manually supply coefficient means and covariance matrix
coefs <- coef(m1)
vcov_matrix <- vcov(m1)
prestige_sims_manual <- b_sim(mu = coefs, Sigma = vcov_matrix)
Find the systematic component in the linear form for fitted values in across
each simulation (note: largely for internal use by qi_builder)
Description
Find the systematic component in the linear form for fitted values in across
each simulation (note: largely for internal use by qi_builder)
Usage
linear_systematic(b_sims, newdata, inc_intercept = TRUE)
Arguments
b_sims |
a data frame created by |
newdata |
a data frame of fitted values with column names corresponding
to variable names in |
inc_intercept |
logical whether to include the intercept in the lineary systematic component. |
Value
A data frame fitted values supplied in newdata and associated
linear systematic component estimates for all simulationed coefficient
estimates. The linear systematic components are included in a column
named ls_.
Source
King, Gary, Michael Tomz, and Jason Wittenberg. 2000. "Making the Most of Statistical Analyses: Improving Interpretation and Presentation." American Journal of Political Science 44(2): 341-55.
Examples
library(car)
# Estimate model
m1 <- lm(prestige ~ education + type, data = Prestige)
# Create fitted values
fitted_df <- expand.grid(education = 6:16, typewc = 1)
# Simulate coefficients
m1_sims <- b_sim(m1, nsim = 1000)
# Find linear systematic component for fitted values
ls <- linear_systematic(b_sims = m1_sims, newdata = fitted_df)
Find quantities of interest from generalized linear models
Description
Find quantities of interest from generalized linear models
Usage
qi_builder(obj, newdata, FUN, ci = 0.95, nsim = 1000, slim = FALSE,
large_computation = FALSE, original_order = FALSE, b_sims, mu, Sigma,
verbose = TRUE, ...)
Arguments
obj |
a fitted model object from which to base coefficient simulations on. |
newdata |
an optional data frame of fitted values with column names
corresponding to coefficient names in |
FUN |
a function for calculating how to find the quantity of interest
from a vector of the fitted linear systematic component. It must return
a numeric vector. If |
ci |
the proportion of the central interval of the simulations to
return. Must be in (0, 1] or equivalently (0, 100]. Note: if |
nsim |
number of simulations to draw. |
slim |
logical indicating whether to (if |
large_computation |
logical. If |
original_order |
logical whether or not to keep the original scenario
order when |
b_sims |
an optional data frame created by |
mu |
an optional vector giving the means of the variables. If |
Sigma |
an optional positive-definite symmetric matrix specifying the
covariance matrix of the variables. If |
verbose |
logical. Whether to include full set of messages or not. |
... |
arguments to passed to |
Value
If slimmer = FALSE a data frame of fitted values supplied in
newdata and associated simulated quantities of interest for all
simulations in the central interval specified by ci. The quantities
of interest are in a column named qi_.
If slimmer = TRUE a data frame of fitted values supplied in
newdata and the minimum, median, and maximum values of the central
interval specified by ci for each scenario are returned in three
columns named qi_min, qi_median, and qi_max,
respectively.
Examples
library(car)
## Normal linear model
m1 <- lm(prestige ~ education + type, data = Prestige)
# Using observed data as scenarios
linear_qi_obs <- qi_builder(m1)
# Create fitted values
fitted_df_1 <- expand.grid(education = 6:16, typewc = 1)
linear_qi <- qi_builder(m1, newdata = fitted_df_1)
# Manually supply coefficient means and covariance matrix
coefs <- coef(m1)
vcov_matrix <- vcov(m1)
linear_qi_custom_mu_Sigma <- qi_builder(mu = coefs, Sigma = vcov_matrix,
newdata = fitted_df_1)
## Logistic regression
# Load data
data(Admission)
Admission$rank <- as.factor(Admission$rank)
# Estimate model
m2 <- glm(admit ~ gre + gpa + rank, data = Admission, family = 'binomial')
# Specify fitted values
m2_fitted <- expand.grid(gre = seq(220, 800, by = 10), gpa = c(2, 4),
rank = '4')
# Function to find predicted probabilities from logistic regression models
pr_function <- function(x) 1 / (1 + exp(-x))
# Find quantity of interest
logistic_qi_1 <- qi_builder(m2, m2_fitted, FUN = pr_function)
logistic_qi_2 <- qi_builder(m2, m2_fitted, FUN = pr_function,
slim = TRUE)
Find maximum, minimum, and median values for each scenario found using
qi_builder
Description
Find maximum, minimum, and median values for each scenario found using
qi_builder
Usage
qi_slimmer(df, scenario_var = "scenario_", qi_var = "qi_")
Arguments
df |
a data frame of simulated quantities of interest created by
|
scenario_var |
character string of the variable name marking the scenarios. |
qi_var |
character string of the name of the variable with the simulated quantity of interest values. |
Details
This funciton slims down a simulation data set to some of its key features (minimun, median, and maximum value for each fitted scenario) so that it takes up less memory and can be easily plotted.
The function is incorporated into qi_builder and can be run
using slim = TRUE.
Value
A data frame with the fitted values and the minimum (qi_min),
median (qi_median), and maximum (qi_max) values from the
central interval specified with ci in qi_builder.
Examples
library(car)
# Normal linear model
m1 <- lm(prestige ~ education + type, data = Prestige)
# Simulate coefficients
m1_sims <- b_sim(m1)
# Create fitted values
fitted_df <- expand.grid(education = 6:16, typewc = 1)
# Find predicted outcomes (95% central interval, by default)
linear_qi <- qi_builder(b_sims = m1_sims, newdata = fitted_df, slim = FALSE)
# Slim data set
linear_slim <- qi_slimmer(linear_qi)