Tests of Linear Combinations of Regression Coefficients

Produces point estimates, interval estimates, and p-values for linear combinations of regression coefficients using a uRegress object.

Usage

lincom(
  reg,
  comb,
  null.hypoth = 0,
  conf.level = 0.95,
  robustSE = TRUE,
  joint.test = FALSE,
  useFdstn = FALSE,
  eform = reg$fnctl != "mean"
)

Arguments

reg: an object of class uRegress.
comb: a vector or matrix containing the values of the constants which create the linear combination of the form $$c_0 + c_1\beta_1 + \dots$$ Zeroes must be given if coefficients aren't going to be included. For testing multiple combinations, this must be a matrix with number of columns equal to the number of coefficients in the model.
null.hypoth: the null hypothesis to compare the linear combination of coefficients against. This is a scalar if one combination is given, and a vector or matrix otherwise. The default value is 0.
conf.level: a number between 0 and 1, indicating the desired confidence level for intervals.
robustSE: a logical value indicating whether or not to use robust standard errors in calculation. Defaults to TRUE. If TRUE, then robustSE must have been TRUE when reg was created.
joint.test: a logical value indicating whether or not to use a joint Chi-square test for all the null hypotheses. If joint.test is TRUE, then no confidence interval is calculated. Defaults to FALSE.
useFdstn: a logical indicator that the F distribution should be used for test statistics instead of the chi squared distribution. Defaults to TRUE. This option is not supported when input reg is a hazard regression (i.e., fnctl="hazard").
eform: a logical value indicating whether or not to exponentiate the estimated coefficient. By default this is performed based on the type of regression used.

Value

A list of class lincom (joint.test is False) or lincom.joint (joint.test is True). For the lincom class, comb entries in the list are labeled comb1, comb2, etc. for as many linear combinations were used. Each is a list with the following components:

printMat: A formatted table with inferential results for the linear combination of coefficients. These include the point estimate, standard error, confidence interval, and t-test for the linear combination.
nms: The name of the linear combination, for printing.
null.hypoth: The null hypothesis for the linear combination.

Examples

# Loading required libraries
library(sandwich)

# Reading in a dataset
data(mri)

# Linear regression of LDL on age (with robust SE by default)
testReg <- regress ("mean", ldl~age+stroke, data = mri)

# Testing coefficient created by .5*age - stroke (the first 0 comes from excluding the intercept)
testC <- c(0, 0.5, -1)
lincom(testReg, testC)
#> 
#> H0: 0.5*age-1*stroke   =  0 
#> Ha: 0.5*age-1*stroke  !=  0 
#>      Estimate Std. Err.   95%L   95%H      T Pr(T > |t|)
#> [1,]   -1.367     2.152 -5.593  2.859 -0.635       0.526

# Test multiple combinations: 
# whether separately whether .5*age - stroke = 0 or Intercept + 60*age = 125 
testC <- matrix(c(0, 0.5, -1, 1, 60, 0), byrow = TRUE, nrow = 2)
lincom(testReg, testC, null.hypoth = c(0, 125))
#> 
#> H0: 0.5*age-1*stroke   =  0 
#> Ha: 0.5*age-1*stroke  !=  0 
#>      Estimate Std. Err.   95%L   95%H      T Pr(T > |t|)
#> [1,]   -1.367     2.152 -5.593  2.859 -0.635       0.526
#> 
#> H0: 1*(Intercept)+60*age   =  125 
#> Ha: 1*(Intercept)+60*age  !=  125 
#>      Estimate Std. Err.    95%L    95%H     T Pr(T > |t|)
#> [1,]  126.989     3.568 119.984 133.994 0.557       0.577

# Test joint null hypothesis:
# H0: .5*age - stroke = 0 AND Intercept + 60*age = 125 
lincom(testReg, testC, null.hypoth = c(0, 125), joint.test = TRUE)
#>      Chi2 stat df p value
#> [1,]    0.6911  2   0.708