PoweR (version 1.0.7)

powcomp.easy: Computation of power and level tables for hypothesis tests.

Description

Functions for the computation of power and level tables for hypothesis tests, in LaTeX format.

Usage

powcomp.easy(params,M=10^5,model=NULL,Rlaws=NULL,Rstats=NULL,center=FALSE, scale=FALSE)

Arguments

M

number of Monte Carlo repetitions to use.

params

matrix with (at least) 11 named-columns with names (n, law, stat, level, cL, cR, alter, par1, par2, par3, par4). Each row of params gives the necessary parameters for a simulation of powers.

n : sample size;

law : integer giving the index of the law considered;

stat : integer giving the index of the test statistic considered (can be 0 if you want to use your own function for some test statistic; see 'Rstats' argument);

level : double, this is the significance level desired;

cL : left critical value (can be NA);

cR : right critical value (can be NA);

alter : type of test (integer value in 0,1,2,3,4);

parj: values of the parameters of the distribution specified by law (can be NA).

See 'Details section'.

model

NOT YET IMPLEMENTED. If NULL, no model is used. If an integer \(i>0\), the model coded in the C function modele\(i\) is used. Else this should be an R function that takes three arguments: eps (vector of \(\epsilon\) values), thetavec (vector of \(\theta\) values) and xvec (vector or matrix of \(x\) values). This function should take a vector of errors, generate observations from a model (with parameters thetavec and values xvec) based on these errors, then compute and return the residuals from the model. See function modele1.R in directory inst/doc/ for an example in multiple linear regression

Rlaws

When some law indices in second column of 'params' are equal to 0, this means that you will be using some R random generators not hardcoded in C in the package. In that case, you should provide the names of the random generation functions in the corresponding components of a list; the other components should be set to NULL.

Rstats

A list. If in a given row of the 'params' matrix, the value of 'stat' is set to 0, the corresponding component of the list 'Rstats' should be an R function that outputs a list with components 'statistic' (value of the test statistic), 'pvalue' (pvalue of the test; if not computable should be set to 0), 'decision' (1 if we reject the null, 0 otherwise), 'alter' (see above), 'stat.pars' (see above), 'pvalcomp' (1L if the pvalue can be computed, 0L otherwise), 'nbparstat' (length of stat.pars). If the value of 'stat' is not 0, then the corresponding component of 'Rstats' should be set to 'NULL'.

center

Logical. Should we center the data generated

scale

Logical. Should we center the data generated

Value

The powers for the different statistics and laws specified in the rows of params, NOT YET provided in the form of a LaTeX table. This version is easier to use (but slower) than the powcomp.fast version. It should be used in the process of investigating the power of test statistics under different alternatives. But when you are ready to produce results for publication in a paper, please use the powcomp.fast version and its print method..

Details

If both cL and cR are NA, no critical values are used and the decision to reject (or not) the hypothesis is taken using the \(p\)-value.

If a test statistic depends upon some parameters, these can be added (in a correct order) in the last columns of params. If other test statistics are considered simultaneously (in the same params matrix) and if not all the test statistics have the same number of parameters, NA values should be used to complete empty cells of the matrix.

References

Pierre Lafaye de Micheaux, Viet Anh Tran (2016). PoweR: A Reproducible Research Tool to Ease Monte Carlo Power Simulation Studies for Studies for Goodness-of-fit Tests in R. Journal of Statistical Software, 69(3), 1--42. doi:10.18637/jss.v069.i03

Examples

# NOT RUN {
# Warning: the order of the parameters of the law (4 maximum) is important!
sim1 <- c(n=100,law=2,stat=10,level=0.05,cL=NA,cR=0.35,alter=3,
          par1= 2.0,par2=NA,par3=NA,par4=NA,parstat1=NA,parstat2=NA)
sim2 <- c(n=100,law=2,stat=17,level=0.10,cL=-0.30,cR=NA,alter=1,
          par1=-1.0,par2=3.0,par3=NA,par4=NA,parstat1=NA,parstat2=NA)
sim3 <- c(n=100,law=2,stat=31,level=0.10,cL=NA,cR=0.50,alter=3,
          par1=-1.0,par2=3.0,par3=NA,par4=NA,parstat1=0.7,parstat2=NA)
sim4 <- c(n=100,law=7,stat=80,level=0.10,cL=NA,cR=9.319,alter=3,
          par1=NA,par2=NA,par3=NA,par4=NA,parstat1=1,parstat2=5)
params <- rbind(sim1,sim2,sim3,sim4)
powcomp.easy(params,M=10^2)
sim5 <- c(n=100,law=0,stat=80,level=0.10,cL=NA,cR=9.319,alter=3,
          par1=NA,par2=NA,par3=NA,par4=NA,parstat1=1,parstat2=5)
params <- rbind(params,sim5)
powcomp.easy(params,M=10^2,Rlaws=list(NULL,NULL,NULL,NULL,rnorm))
# }