Description Usage Arguments Value Author(s) References See Also Examples
conTest_summary computes all available hypothesis tests and returns
and object of class conTest
for which a print function is available. The
conTest_summary
can be used directly and is called by the conTest
function if type = "summary"
.
1 2  ## S3 method for class 'restriktor'
conTest_summary(object, test = "F", ...)

object 
an object of class 
test 
test statistic; for information about the nulldistribution see details.

... 
the same arguments as passed to the 
An object of class conTest, for which a print is available. More specifically, it is a list with the following items:
CON 
a list with useful information about the constraints. 
Amat 
constraints matrix. 
bvec 
vector of righthand side elements. 
meq 
number of equality constraints. 
meq.alt 
same as input neq.alt. 
iact 
number of active constraints. 
type 
same as input. 
test 
same as input. 
Ts 
teststatistic value. 
df.residual 
the residual degrees of freedom. 
pvalue 
tail probability for 
b.eqrestr 
equality restricted regression coefficients.
Only available for 
b.unrestr 
unrestricted regression coefficients. 
b.restr 
restricted regression coefficients. 
b.restr.alt 
restricted regression coefficients under HA if some equality constraints are maintained. 
Sigma 
variancecovariance matrix of unrestricted model. 
R2.org 
unrestricted Rsquared. 
R2.reduced 
restricted Rsquared. 
boot 
same as input. 
model.org 
original model. 
Leonard Vanbrabant and Yves Rosseel
Shapiro, A. (1988). Towards a unified theory of inequalityconstrained testing in multivariate analysis. International Statistical Review 56, 49–62.
Silvapulle, M. (1992a). Robust tests of inequality constraints and onesided hypotheses in the linear model. Biometrika, 79, 621–630.
Silvapulle, M. (1992b). Robust WaldType Tests of OneSided Hypotheses in the Linear Model. Journal of the American Statistical Association, 87, 156–161.
Silvapulle, M. and Silvapulle, P. (1995). A score test against onesided alternatives. American statistical association, 90, 342–349.
Silvapulle, M. (1996) On an Ftype statistic for testing onesided hypotheses and computation of chibarsquared weights. Statistics & probability letters, 28, 137–141.
Silvapulle, M. (1996) Robust bounded influence tests against onesided hypotheses in general parametric models. Statistics & probability letters, 31, 45–50.
Silvapulle, M.J. and Sen, P.K. (2005). Constrained Statistical Inference. Wiley, New York
Wolak, F. (1987). An exact test for multiple inequality and equality constraints in the linear regression model. Journal of the American statistical association, 82, 782–793.
quadprog,
conTest
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40  ## example 1:
# the data consist of ages (in months) at which an
# infant starts to walk alone.
# prepare data
DATA1 < subset(ZelazoKolb1972, Group != "Control")
# fit unrestricted linear model
fit1.lm < lm(Age ~ 1 + Group, data = DATA1)
# the variable names can be used to impose constraints on
# the corresponding regression parameters.
coef(fit1.lm)
# constraint syntax: assuming that the walking
# exercises would not have a negative effect of increasing the
# mean age at which a child starts to walk.
myConstraints1 < ' GroupActive < GroupPassive;
GroupPassive < GroupNo '
conTest(fit1.lm, myConstraints1)
# another way is to first fit the restricted model
fit.restr1 < restriktor(fit1.lm, constraints = myConstraints1)
conTest(fit.restr1)
## Not run:
# Or in matrix notation.
Amat1 < rbind(c(1, 0, 1),
c( 0, 1, 1))
myRhs1 < rep(0L, nrow(Amat1))
myNeq1 < 0
fit1.con < restriktor(fit1.lm, constraints = Amat1,
rhs = myRhs1, neq = myNeq1)
conTest(fit1.con)
## End(Not run)

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