moore

A dataset containing the number of transistors and the corresponding #' years. The Moore's Law #'states that the number of transistors in a dense integrated circuit doubles approximately every two #'years.
moore.

datasets

We propose a pair of summary measures for the predictive power of a prediction
function based on a regression model. The regression model can be linear
or nonlinear, parametric, semi-parametric, or nonparametric, and correctly
specified or mis-specified. The first measure, R-squared, is an extension of
the classical R-squared statistic for a linear model, quantifying the prediction
function's ability to capture the variability of the response. The second
measure, L-squared, quantifies the prediction function's bias for predicting the
mean regression function. When used together, they give a complete summary of
the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <arXiv:1611.03063> for more details.

Xiaoyan Wang

PAmeasures

Prediction and Accuracy Measures for Nonlinear Models and for
Right-Censored Time-to-Event Data

moore function

A data frame with 48 rows and 3 variables:<dl class="dl-horizontal">
 <dt>year</dt><dd>year, from 1973 to 2011</dd>
 <dt>time</dt><dd>time starting from 1973</dd>
 <dt>count</dt><dd>number of transistors</dd>
</dl>

Format

Moore's Law data — moore

A data frame with 48 rows and 3 variables:<dl class='dl-horizontal'>
 <dt>year</dt><dd>year, from 1973 to 2011</dd>
 <dt>time</dt><dd>time starting from 1973</dd>
 <dt>count</dt><dd>number of transistors</dd>
</dl>

moore: Moore's Law data

Description

Usage

Arguments

Format