Description Usage Arguments Details Value See Also Examples
Performs a multiple hypotheses testing in linear mixed models
1 2 |
object |
Object of class "mhtp". |
Ynew |
Response variable of length n. |
z |
Random effects matrix. Of size n*q. |
grp |
Grouping variable of length n. |
D |
Logical value. If TRUE, the random effects are considered to be independent, i.e. |
fix |
Number of variables which are not submitted to selection. They have to be in the first columns of data. Default is 1, the selection is not performed on the intercept. |
rand |
A vector of length q: each entry k is the position of the random effects number k in the data matrix, 0 otherwise. If z contains variables that have both a fixed and a random effect, it is advised to not submit them to selection. |
alpha |
A user supplied type I error sequence. Default is (0.1,0.05). |
step |
The algorithm performs at most |
num |
Number of variables one wishes to order. Default is min(n-1,p-1,30). |
ordre |
Several possible algorithms to order the variables, ordre=c("bolasso","pval","pval_hd","FR"). "bolasso" uses the dyadic algorithm with the Bolasso techniaue, "pval" uses the p-values obtained with a regression on the full set of variables (only when p<n), "pval_hd" uses marginal regression, "FR" uses Forward Regression. Default is "bolasso". |
m |
Number of bootstrapped iteration of the Lasso. Only use if the algorithm is set to "bolasso". Default is m=10. |
show |
Vector of logical values, show=(showordre,showresult,showit). Default is (0,0,0). If showordre==TRUE, show the ordered variables at each step of the algorithm. if showresult==TRUE, show the value of the statistics and the estimated quantile at each step of the procedure; if ordre=bolasso. if showit==TRUE, show the iterations of the algorithm. |
IT |
Number of simulations in the calculation of the quantile. Default is 10000. |
maxq |
Number of maximum multiple hypotheses testing to do. Default is min(log(min(n,p)-1,2),5). |
speed |
Logical value. If TRUE, the algorithm is speeded up once the criterion convergence in |
... |
not used |
See mhtp
for more details.
A 'mhtp object' is returned.
data |
List of the user-data: the scaled matrix used in the algorithm, the first column being (1,...,1); Y; z and grp. |
beta |
Estimation of the selected fixed effects. |
fitted.values |
Fitted values calculated with the fixed effects and the random effects. |
u |
Matrix with #alpha columns. Each column is the concatenation of the estimated random effects (u_1',...,u_q')' for the user level alpha. |
Psi |
Variance of the random effects. Matrix of dimension q*q. |
sigma_e |
Variance of the noise. |
it |
Number of iterations of the algorithm. |
quantile |
Array of all the estimated quantiles calculated during the procedure. |
ordrebeta |
All different order that has been used during the procedure. |
converge |
Did the algorithm converge? |
call |
The call that produced this object. |
arg |
List of all the arguments of the function. |
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 | ## Not run:
N <- 20 # number of groups
p <- 20 # number of covariates (including intercept)
q <- 2 # number of random effect covariates
ni <- rep(6,N) # observations per group
n <- sum(ni) # total number of observations
grp <- factor(rep(1:N,ni)) # grouping variable
grp=rbind(grp,grp)
beta <- c(1,2,4,3,rep(0,p-3)) # fixed-effects coefficients
x <- cbind(1,matrix(rnorm(n*p),nrow=n)) # design matrix
u1=rnorm(N,0,sd=sqrt(2))
u2=rnorm(N,0,sd=sqrt(2))
bi1 <- rep(u1,ni)
bi2 <- rep(u2,ni)
bi <- rbind(bi1,bi2)
z=x[,1:2,drop=FALSE]
epsilon=rnorm(120)
y <- numeric(n)
for (k in 1:n) y[k] <- x[k,]%*%beta + t(z[k,])%*%bi[,k] + epsilon[k]
########
fit=mhtp(x,y,z,grp,D=0,fix=1,rand=c(1,2),alpha=0.1,num=15)
fit2=refit(fit,Ynew=y)
## End(Not run)
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