Using R, I'd like to test whether multiple parameters in a regression model are
equal to specific values (by default, are multiple parameters equal to 0).
For example, in this regression model:
score ~ beta0 + beta1*i1 + beta2*i2 + beta3*age + beta4*i1*age + beta5*i2*age
I want to test H0: (beta2 = 0) and (beta5 = 0)
SAS can do this in PROC REG using the TEST statement.
SAS Program editor contents:
proc reg data=tolerate;
model score=i1 i2 age i1age i2age;
test i2=0, i2age=0; * do assc prof have same reg line as asst? ;
run;
SAS Output window contents:
Test 1 Results for Dependent Variable score
Mean
Source DF Square F Value Pr > F
Numerator 2 0.15581 0.38 0.6859
Denominator 24 0.40678
Here's code in R that starts the analysis:
# data description: http://statacumen.com/teach/ADA2/ADA2_HW_07_S13.pdf
tolerate <- read.csv("http://statacumen.com/teach/ADA2/ADA2_HW_07_tolerate.csv")
tolerate$rank <- factor(tolerate$rank)
tolerate$rank <- relevel(tolerate$rank, "3")
str(tolerate)
tolerate.manual <- data.frame(score = tolerate$score
, i1 = (tolerate$rank==1)
, i2 = (tolerate$rank==2)
, age = tolerate$age
, i1age = tolerate$age * (tolerate$rank==1)
, i2age = tolerate$age * (tolerate$rank==2)
)
lm.man <- lm(score ~ i1 + i2 + age + i1age + i2age, data = tolerate.manual)
summary(lm.man)
I have been unable to find a solution using library multcomp, contrast, or C().
Ideally, I could do this without creating separate terms in the model, but directly from this lm() statement:
lm.s.a.r.ar <- lm(score ~ age*rank, data = tolerate)
This gets close using a Wald test, but I'm looking for the same F-test SAS uses.
summary(lm.s.a.r.ar)
library(aod) # for wald.test()
coef.test.values <- rep(0, length(coef(lm.s.a.r.ar))) # typically, this will be all 0s
wald.test(b = coef(lm.s.a.r.ar) - coef.test.values
, Sigma = vcov(lm.s.a.r.ar)
, Terms = c(4,6))
Wald test:
----------
Chi-squared test:
X2 = 0.77, df = 2, P(> X2) = 0.68
Thanks for considering this question.
Best Answer
Do a full and reduced test by fitting a model with all the parameters, then another model with the reduced model (leave out the terms that you want to test with 0). Then do
anova(fit1,fit2)
to compute the F test.If you want to test for values other than 0 then use the
offset
function in the reduced formula.