test(x, y, method = "lm", family = "gaussian", nboot = 50, speedup=TRUE,
unique=FALSE, num.h0=1)lm"), generalized additive models ("glm") or generalized additive models ("gam").gaussian", "binomial" or "poisson".TRUE (default), the testing procedure is accelerated by a minor change in the statistic.TRUE, the test is performed only for one null hypothesis, given by the argument num.h0.unique is TRUE, num.h0 is the integer number $q$ of $H_0(q)$ to be tested.$$Y=m(\textbf{X})+\varepsilon \quad {\rm{where}} \quad m(\textbf{X})= m_{1}(X_{1})+m_{2}(X_{2})+\ldots+m_{p}(X_{p})$$
the following strategy is considered: for a subset of size $q$, considerations will be given to a test for the null hypothesis
$$H_{0} (q): \sum_{j=1}^p I_{{m_j \ne 0}} \le q$$
vs. the general hypothesis
$$H_{1} : \sum_{j=1}^p I_{{m_j \ne 0}} > q$$
selectionlibrary(FWDselect)
data(pollution)
x=pollution[,-19]
y=pollution[,19]
test(x,y,method="lm",nboot=5)Run the code above in your browser using DataLab