Create forest plot of model coefficients with confidence intervals

Description

Create a ggplot forest plot of model coefficients with confidence intervals.

Usage

coefficient_forestplot(
  model_list,
  model_names = NULL,
  conflevel = 0.95,
  horiz = TRUE
)

Arguments

Details

The forest plot groups variables along the axis determined by the horiz parameter and colors the data by model. If model_names = NULL, the default, models are numbered sequentially in the order they appear in model_list (Model 1, Model 2, Model 3, etc.).

Value

A ggplot object to compare model coefficient estimates with their corresponding confidence interval(s), grouped by coefficient.

Examples

 states = as.data.frame(state.x77)

 m1 = lm(`Life Exp` ~ Income + Illiteracy, data=states,
         subset=state.region=='Northeast')
 m2 = lm(`Life Exp` ~ Income + Illiteracy, data=states,
         subset=state.region=='South')
 m3 = lm(`Life Exp` ~ Income + Illiteracy, data=states,
         subset=state.region=='North Central')
 m4 = lm(`Life Exp` ~ Income + Illiteracy, data=states,
         subset=state.region=='West')

 mList = list(m1, m2, m3, m4)

 coefficient_forestplot(model_list = mList,
                        model_names =c('Northeast', 'South',
                                       'North Central', 'West'),
                        horiz = FALSE)

Compare coefficient vectors across models

Description

Compare coefficient vectors, after removing intercept, across multiple models.

Usage

compare_coef_vectors(model_list)

Arguments

Details

This function currently supports comparing coefficient vectors from two models. The intercepts of the models are removed, if they exist, and the coefficient vectors are compared by Hotelling's T^2 test. This can be considered as an initial omnibus test for differences among the coefficients before searching through all coefficients for individual differences using, for example, compare_coefs.

Value

List of test results. This includes the chi-squared statistic, degrees of freedom, and p-value.

Examples

 ##Simulate data

 N = 500

 m = rep(1:2, each=N)

 x1 = rnorm(n=N*2)
 x2 = rnorm(n=N*2)
 x3 = rnorm(n=N*2)

 y = x1 + x2 + x3 + rnorm(n=N*2)

 dat = data.frame(m, x1, x2, x3, y)

 m1 = lm(y ~ x1 + x2 + x3, data=dat, subset=m==1)
 m2 = lm(y ~ x1 + x2 + x3, data=dat, subset=m==2)

 mList = list(m1, m2)

 compare_coef_vectors(model_list = mList)

Compare shared coefficients across models

Description

Compares predictor coefficients across models.

Usage

compare_coefs(model_list, padj = "none")

Arguments

Details

This function currently supports comparing coefficients from two models. For each model predictor, coefficients are compared across models. P-values come from a two-sided alternative hypothesis. They can, and should, be adjusted for multiple testing to reduce the probability of chance significant findings.

Value

Data frame of shared coefficients, the difference between them, the standard error of the difference, the test statistic comparing them, and the p-value adjusted using the method provided in padj.

Examples

 ##Simulate data

 N = 500

 m = rep(1:2, each=N)

 x1 = rnorm(n=N*2)
 x2 = rnorm(n=N*2)
 x3 = rnorm(n=N*2)

 y = x1 + x2 + x3 + rnorm(n=N*2)

 dat = data.frame(m, x1, x2, x3, y)

 m1 = lm(y ~ x1 + x2 + x3, data=dat, subset=m==1)
 m2 = lm(y ~ x1 + x2 + x3, data=dat, subset=m==2)

 mList = list(m1, m2)

 compare_coefs(model_list = mList, padj='fdr')

Compare regression residual standard deviation across models

Description

Compare residual standard deviation across models. Works for linear regression (lm) only.

Usage

compare_resids(model_list)

Arguments

Details

This function currently supports comparing residual standard deviation from two models. Residuals are assumed to be normally distributed (as also assumed in the linear model itself) and are compared by an F test.

Value

Vector of results. This includes the residual standard deviation from each model, the F statistic comparing the standard deviations, the numerator and denominator degrees of freedom, and the p-value.

Examples

 ##Simulate data

 N = 500

 m = rep(1:2, each=N)

 x1 = rnorm(n=N*2)
 x2 = rnorm(n=N*2)
 x3 = rnorm(n=N*2)

 y = x1 + x2 + x3 + rnorm(n=N*2)

 dat = data.frame(m, x1, x2, x3, y)

 m1 = lm(y ~ x1 + x2 + x3, data=dat, subset=m==1)
 m2 = lm(y ~ x1 + x2 + x3, data=dat, subset=m==2)

 mList = list(m1, m2)

 compare_resids(model_list = mList)