R/summary.IWTlm.R

In alessiapini/fdatest: Interval Wise Testing for Functional Data

#' @title Summarizing Functional Linear Model Fits
#' 
#' @description \code{summary} method for class "\code{IWTlm}".
#' Function returning a summary of the results of IWT for the test on a functional linear model: 
#' minimum IWT-adjusted p-values of the F-tests on the whole model and of t-tests on all covariates' effects are reported.
#' 
#' @param object  An object of class "\code{IWTlm}", usually, a result of a call 
#' to \code{\link{IWTlm}}.
#' 
#' 
#' @param ... Further arguments passed to or from other methods..
#' 
#' @return No value returned. 
#' The function \code{summary.IWTlm} computes and returns a list of 
#' summary statistics of the fitted functional analysis of variance 
#' given in \code{object}, using the component "\code{call}" from its 
#' arguments, plus:
#' \item{ttest}{A \code{L+1 x 1} matrix with columns for the functional regression coefficients, and corresponding (two-sided) IWT-adjusted minimum p-values of t-tests (i.e., the minimum p-value over all \code{p} basis components used to describe functional data). }
#' \item{R2}{Range of the functional R-squared.}
#' \item{ftest}{IWT-adjusted minimum p-value of functional F-test.}

#' @seealso \code{\link{IWTimage}} for the plot of p-values heatmaps. 
#' \code{\link{plot.IWTlm}} for the plot of regression results. 
#' See also \code{\link{IWT1}}, \code{\link{IWT2}} to perform the ITP to test on the mean of one population and test of differences between two populations. 
#' See \code{\link{ITPlmbspline}} for functional linear model based on B-spline basis representation
#' 
#' @examples 
#' # Importing the NASA temperatures data set
#' data(NASAtemp)
#' 
#' temperature <- rbind(NASAtemp$milan,NASAtemp$paris)
#' groups <- c(rep(0,22),rep(1,22))
#' 
#' # Performing the IWT
#' IWT.result <- IWTlm(temperature ~ groups,B=1000)
#' 
#' # Summary of the IWT results
#' summary(IWT.result)
#' 
#' # Plot of the IWT results
#' layout(1)
#' plot(IWT.result)
#' 
#' # All graphics on the same device
#' layout(matrix(1:4,nrow=2,byrow=FALSE))
#' plot(IWT.result,main='NASA data', plot_adjpval = TRUE,xlab='Day',xrange=c(1,365))
#' 
#' @references
#' Pini, A., & Vantini, S. (2017). Interval-wise testing for functional data. \emph{Journal of Nonparametric Statistics}, 29(2), 407-424
#'
#' Pini, A., Vantini, S., Colosimo, B. M., & Grasso, M. (2018). Domain‐selective functional analysis of variance for supervised statistical profile monitoring of signal data. \emph{Journal of the Royal Statistical Society: Series C (Applied Statistics)} 67(1), 55-81.
#'
#' Abramowicz, K., Hager, C. K., Pini, A., Schelin, L., Sjostedt de Luna, S., & Vantini, S. (2018).
#' Nonparametric inference for functional‐on‐scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament. \emph{Scandinavian Journal of Statistics} 45(4), 1036-1061.
#'
#' @export
summary.IWTlm <-
function(object, ...){
  printresult = vector('list')
  printresult$call = object$call
  #class(printresult) <- "lm"
  printresult$ttest = matrix(data=apply(object$adjusted_pval_part,1,min),ncol=1)
  var.names = rownames(object$adjusted_pval_part)
  rownames(printresult$ttest) = var.names
  printresult$ttest = as.data.frame(printresult$ttest)
  signif = rep('',length(var.names))
  signif[which(printresult$ttest[,1] <0.001)] = '***'
  signif[which(printresult$ttest[,1] <0.01 & printresult$ttest[,1] >= 0.001)] = '**'
  signif[which(printresult$ttest[,1] <0.05 & printresult$ttest[,1] >= 0.01)] = '*'
  signif[which(printresult$ttest[,1] <0.1 & printresult$ttest[,1] >= 0.05)] = '.'
  printresult$ttest[,2] = signif
  colnames(printresult$ttest) = c('Minimum p-value','')
  
  printresult$R2 = as.matrix(range(object$R2.eval))
  colnames(printresult$R2) = 'Range of functional R-squared'
  rownames(printresult$R2) = c('Min R-squared', 'Max R-squared')
  printresult$ftest = as.matrix(min(object$adjusted_pval_F))
  printresult$ftest = as.data.frame(printresult$ftest)
  signif.f = ''
  signif.f[which(printresult$ftest[,1] <0.001)] = '***'
  signif.f[which(printresult$ftest[,1] <0.01 & printresult$ftest[,1] >= 0.001)] = '**'
  signif.f[which(printresult$ftest[,1] <0.05 & printresult$ftest[,1] >= 0.01)] = '*'
  signif.f[which(printresult$ftest[,1] <0.1 & printresult$ftest[,1] >= 0.05)] = '.'
  printresult$ftest[,2] = signif.f
  colnames(printresult$ftest) = c('Minimum p-value','')
  printresult
  
}