emergence.indices: Nonparametric estimates of indices measuring the global slope...
In binnednp: Nonparametric Estimation for Interval-Grouped Data

Description Usage Arguments Value References Examples

Nonparametric estimates of indices measuring the global slope and curvature of the density function for binned data.

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emergence.indices(n, y, w, ni, hseq, hn = 200, nmix = 4, B = 500,
  method = "np", last.iter.np = F, confint = FALSE, B.conf = 1000,
  alpha = 0.05, print = TRUE, parallel = FALSE, pars = new.env())

`n`	Positive integer. Size of the complete sample.
`y`	Vector. Observed values. They define the extremes of the sequence of intervals in which data is binned.
`w`	Vector. Proportion of observations within each interval.
`ni`	Vector. Number of observations within each interval.
`hseq`	Vector. Grid of bandwidths for which MSE is approximated through bootstrap. If missing, a grid of size hn is considered.
`hn`	Positive integer. Size of the grid of bandwidths for which MSE will be approximated. Defaults to 200.
`nmix`	Positive integer. Maximum number of components for the normal mixture model. Defaults to 4.
`B`	Positive integer. Number of bootstrap resamples used to find the bandwidth that minimizes MSE. Defaults to 500.
`method`	Character. If method="np", resamples are taken from kernel density estimator with pilot bandwidth. If method="mix", a normal mixture pilot model is considered. If method="plugin", plug-in estimates are returned. Defaults to "np".
`last.iter.np`	Logical. If FALSE, normality is assumed at the last step when calculating the plug-in bandwidth. Otherwise, rule-of-thumb selector is used. Defaults to FALSE.
`confint`	Logical. If TRUE, bootstrap confidence intervals are constructed for the indices. Defaults to FALSE.
`B.conf`	Positive integer. Number of bootstrap resamples considered to construct the confidence intervals.
`alpha`	Real number between 0 and 1. Significance level considered for the confidence intervals.
`print`	Logical. If TRUE, status of the script and results are printed. Defaults to TRUE.
`parallel`	Logical. If TRUE, confidence bands are estimated using parallel computing with sockets.
`pars`	Environment. Needed for the well functioning of the script. DO NOT modify this argument.

Nonparametric estimates of the indices and (optional) confidence intervals.

\insertRef

Indices2011binnednp

\insertRef

Test2017binnednp

set.seed(1)
n <- 200 #complete sample size
k <- 30 #number of intervals
x <- rnorm(n,6,1) #complete sample
y <- seq(min(x)-0.2,max(x)+0.2,len=k+1) #intervals
w <- c(sapply(2:k,function(i)sum( x<y[i]&x>=y[i-1] )), sum(x<=y[k+1]&x>=y[k]) )/n #proportions
emergence.indices(n,y,w)