Package ForImp. R topics documented: February 19, Type Package. Title Imputation of Missing Values Through a Forward Imputation.

Size: px
Start display at page:

Download "Package ForImp. R topics documented: February 19, Type Package. Title Imputation of Missing Values Through a Forward Imputation."

Transcription

1 Type Package Package ForImp February 19, 2015 Title Imputation of Missing s Through a Forward Imputation Algorithm Version Date Author Alessandro Barbiero, Pier Alda Ferrari, Giancarlo Manzi Maintainer Alessandro Barbiero <alessandro.barbiero@unimi.it> Imputation of missing values in datasets of ordinal variables through a forward imputation algorithm License GPL LazyLoad yes Depends homals, sampling, mvtnorm Repository CRAN Date/Publication :47:37 NeedsCompilation no R topics documented: ForImp-package ForImp ld meanimp medianimp missing missing missingness modeimp rancat transfcat vcosw Index 15 1

2 2 ForImp-package ForImp-package Forward Imputation The package contains a function for the imputation of missing values in rices of ordinal data, called Forward Imputation, and other functions for generating ordinal data or imputing missing values. Package: ForImp Type: Package Version: 1.0 Date: License: GPL LazyLoad: yes Alessandro Barbiero<alessandro.barbiero@unimi.it>, Giancarlo Manzi<giancarlo.manzi@unimi.it>, Pier Alda Ferrari<pieralda.ferrari@unimi.it> Maintainer: Alessandro Barbiero<alessandro.barbiero@unimi.it> References Ferrari P.A., Annoni P., Barbiero A., Manzi G. (2011) An imputation method for categorical variables with application to nonlinear principal component analysis, Computational Statistics & Data Analysis, vol. 55, issue 7, pages Ferrari P.A., Barbiero A., Manzi G.: Handling missing data in presence of ordinal variables: a new imputation procedure. In "New Perspectives in Statistical Modeling and Data Analysis", S. Ingrassia, R. Rocci, M. Vichi, Eds., Springer, 2011 Little, R.J.A., Rubin, D.B., Statistical Analysis with Missing Data, 2nd ed. John Wiley & Sons, Inc.

3 ForImp 3 ForImp Forward Imputation procedure Forward Imputation of missing data ForImp(, p=2) p a rix/dataframe the parameter for computing the Minkowski distance used in the nearest neighbor procedure for missing value imputation. p can be any positive number (p=2 gives the euclidean distance); if a negative number or Inf is entered, the procedure will use the maximum distance (or supremum norm) The function implements the Forward Imputation algorithm (see reference) on a rix of ordinal data with missing values. The algorithm alternates NonLinear Principal Component Analysis (NLPCA) on a subset of the data with no missing data and sequential imputations of missing values by the nearest neighbor method. This sequential process starts from the units with the lowest number of missing values and ends with the units with the highest number of missing values. the imputed rix References Ferrari P.A., Annoni P., Barbiero A., Manzi G. (2011) An imputation method for categorical variables with application to nonlinear principal component analysis, Computational Statistics & Data Analysis, vol. 55, issue 7, pages Ferrari P.A., Barbiero A., Manzi G.: Handling missing data in presence of ordinal variables: a new imputation procedure. In "New Perspectives in Statistical Modeling and Data Analysis", S. Ingrassia, R. Rocci, M. Vichi, Eds., Springer, 2011

4 4 ld modeimp, medianimp, meanimp set.seed(1) # correlation rix sigma<-rix(c(1,0.5,0.5,0.5,0.5,1,0.5,0.5,0.5,0.5,1,0.5,0.5,0.5,0.5,1),4,4) # generate a 500*4 rix from a multivariate normal c<-rmvnorm(n=500, mean=rep(0,4), sigma=sigma) # transform the numerical values into ordinal categories (Likert scale) # obtaining rix o o<-transfcat(c,4) # set the number of desired missing values nummissing<-100 # create the random missing values, obtaining rix <-missing(o, nummissing, pattern="r") # use function \code{forimp} to impute missing values, obtaining rix i i<-forimp() # number of correct imputations nummissing-sum(i!=o) ld Listwise deletion Listwise deletion ld() a rix or a dataframe This function implements the listwise deletion on a given dataset, removing all the rows or units containing at least one missing value The rix/dataframe in input with the rows/units with missing values removed

5 meanimp 5 meanimp, modeimp, medianimp n<-10 m<-4 <-rix(rnorm(n*m),n,m) [c(3,6),1]<-na [10,2]<-NA ld() meanimp Mean imputation Mean imputation meanimp() A numerical rix The function implements the unconditional mean imputation on a numerical rix with missing values, substituting to each missing value the arithmetic mean of the corresponding variable The imputed rix modeimp, medianimp

6 6 medianimp set.seed(1) n<-10 m<-3 <-rix(rnorm(n*m),n,m) m<- m[1,1]<-na m[2,2:3]<-na # rix with missing values m # imputed rix meanimp() # original rix with no missing values medianimp Median imputation Median imputation medianimp() A rix of ordinal values, ordered according to the Likert scale (1, 2, 3,...) The function implements the median imputation on a rix of ordinal data with missing values. The function substitutes to each missing value the median of the corresponding variable. The imputed rix modeimp, meanimp

7 missing 7 set.seed(1) n<-10 m<-3 <-rix(ceiling(runif(n*m)*4),n,m) m<- m[1,3]<-na m[9:10,1]<-na # rix with missing values m # imputed rix medianimp(m) # original rix with no missing values missing Random generation of missing values Random generation of missing values in rices of numerical data or preferably categorical data coded as integers missing(, nummissing, pattern = "r", nk = 1, p = 0.1, w = 3) nummissing pattern nk p w A rix of numerical values number of missing values pattern of missing values ("r" random, "l" lowest value, "b" block, "n" not at random) category percentage of missing values weight for the lowest category in pps sampling (pattern "n") The function generates random missing values on a rix of categorical data according to a specific pattern. "r" is the random pattern, "l" generates a percentage p of missing values on the lowest values of variable nk, "b" generates random blocks of missing values on the group of variables indexed by nk, "n" generates a kind of not at random missing values: specifically, lowest values are more likely to be missing, since they are assigned a weight w (greater than 1, the default is 3) and the values are sampled according to an unequal probability sampling design (pivotal, see the reference for more details)

8 8 missing2 The original rix with the desired number of values randomly substituted by missing values References Ferrari P.A., Annoni P., Barbiero A., Manzi G. (2011) An imputation method for categorical variables with application to nonlinear principal component analysis, Computational Statistics & Data Analysis, vol. 55, issue 7, pages , pii/s set.seed(1) # correlation rix sigma<-rix(c(1,0.5,0.5,0.5,0.5,1,0.5,0.5,0.5,0.5,1,0.5,0.5,0.5,0.5,1),4,4) # generate a n*m rix from a multivariate normal n<-500 m<-4 c<-rmvnorm(n, mean=rep(0,m), sigma=sigma) # transform the numerical values into ordinal categories (Likert scale) # obtaining rix o o<-transfcat(c,c(2,3,4,5)) # set the number of desired missing values nummissing<-150 # create the random missing values # random missing values c<-missing(o, nummissing, pattern= "r") c # random blocks of missing values on variables 1,2 and 3 c<-missing(o, nummissing, pattern= "b", nk=c(2,3)) c # missing values on lowest category of variable 4 l<-missing(o, nummissing, pattern= "l", nk=4, p=0.1) l # not at random missing values on variable 4 n<-missing(o, nummissing, pattern= "n", nk=4, w=4) n missing2 Random generation of missing values Random generation of missing values in rices

9 missingness 9 missing2(, missing) missing a rix (n rows, m columns) a vector: element i contains the desired number of rows with i missing values (1<=i<=m) a rix with the specified pattern of missing values missing,missingness <-rix(rnorm(500),100,5) # if you want 20 rows with 1 missing, 10 rows with 2 missing, # 4 rows with 3 missing, 1 row with 4 missing missing<-c(20,10,4,1) m<-missing2(, missing) m # check that the function works missingness(m) missingness Missing values Summary for the missing values in a rix missingness() a rix/dataframe with missing values

10 10 modeimp The function provides a summary for the missing values in a rix (units for variables) number_of_missing_values Total number of missing values in the rix missing_values_per_unit Number of units with a certain number of missing values missing_values_per_variable Number of missing values for each variable n<-100 m<-3 <-rix(rnorm(n*m),n,m) nummissing<-50 index<-sample(n*m,nummissing,replace=false) [index]<-na missingness() modeimp Mode imputation Mode imputation modeimp() A rix of categorical or ordinal values, coded as integer values (1, 2, 3,...) The function implements the mode imputation on a rix of categorical or ordinal data with missing values. The function substitutes to each missing value the mode of the corresponding variable. The imputed rix

11 rancat 11 Alessandro barbiero, Giancarlo Manzi, Pier Alda Ferrari medianimp, modeimp set.seed(1) n<-10 m<-3 <-rix(ceiling(runif(n*m)*4),n,m) m<- m[1,3]<-na m[9:10,1]<-na # rix with missing values m # imputed rix modeimp() # original rix with no missing values rancat Generating a random rix of ordinal variables The function generates a random rix of integer (ordinal) variables, with independent and uniform marginal distributions rancat(n, m, cat = 3) n m cat number of rows/units number of columns, variables number of categories for each variable The function generates a random rix of integer (ordinal) variables (coded with 1, 2, 3...), with independent and uniform marginal distributions a rix of ordinal values

12 12 transfcat Alessandro Barbiero, Giancarlo Manzi, Pieralda Ferrari transfcat n<-500 m<-3 <-rancat(n,m,c(3,4,5)) # let s check the marginal distributions... apply(,2,tabulate) #... should be "quite" uniform transfcat Transforming a rix of continuous values into a rix of ordinal values The function transforms a rix of continuous numerical values into a rix of integer (ordinal) values, with uniform marginal distributions and the desired number of categories transfcat(, cat = 3) cat a rix or a dataframe the number of categories, one for each column/variable of the rix/dataframe The function converts the rix in input, containing continuous numerical values, into a rix of ordinal values (1,2,3,... i.e.: Likert scale) according to the cat-1 normal quantiles corresponding to each variable (column) of. the rix of ordinal values

13 vcosw 13 References Ferrari P.A., Barbiero A., Manzi G.: Handling missing data in presence of ordinal variables: a new imputation procedure. In "New Perspectives in Statistical Modeling and Data Analysis", S. Ingrassia, R. Rocci, M. Vichi, Eds., Springer, 2011 Ferrari P.A., Annoni P., Barbiero A., Manzi G. (2011) An imputation method for categorical variables with application to nonlinear principal component analysis, Computational Statistics & Data Analysis, vol. 55, issue 7, pages , pii/s rancat # generate a 40*3 rix from a multivariate normal r.v. # whose independent components have mean 10 and standard deviation 4 <-rix(rnorm(40,3),10,4) # transform the rix of normal data into a rix of ordinal data transfcat(, cat=c(2,3,4,3)) vcosw Cosine of the angle between two vectors The function calculates the cosine of the angle between two vectors, defined as the inner product of the vectors divided by the product of their euclidean norms vcosw(v, w) v w a vector a vector, of the same length of v The cosine of the angle between the two vectors

14 14 vcosw Ferrari P.A., Annoni P., Barbiero A., Manzi G. (2011) An imputation method for categorical variables with application to nonlinear principal component analysis, Computational Statistics & Data Analysis, vol. 55, issue 7, pages , pii/s a<-1:10 b<-2:11 vcosw(a,b) # e<-c(1,2,3) f<-c(3,-3,1) vcosw(e,f) # e and f are orthogonal vectors!

15 Index Topic datagen ForImp, 3 ld, 4 meanimp, 5 medianimp, 6 missing, 7 missing2, 8 missingness, 9 modeimp, 10 rancat, 11 transfcat, 12 vcosw, 13 Topic multivariate ForImp, 3 ld, 4 meanimp, 5 medianimp, 6 missing, 7 missing2, 8 missingness, 9 modeimp, 10 rancat, 11 transfcat, 12 vcosw, 13 Topic package ForImp-package, 2 transfcat, 12, 12 vcosw, 13 ForImp, 3 ForImp-package, 2 ld, 4 meanimp, 4, 5, 5, 6 medianimp, 4, 5, 6, 11 missing, 7, 9 missing2, 8 missingness, 9, 9 modeimp, 4 6, 10, 11 rancat, 11, 13 15

Package schoenberg. June 26, 2018

Package schoenberg. June 26, 2018 Type Package Title Tools for 12-Tone Musical Composition Version 2.0.2 Date 2018-06-26 Author Jeffrey A. Dahlke Package schoenberg June 26, 2018 Maintainer Jeffrey A. Dahlke

More information

Package RSentiment. October 15, 2017

Package RSentiment. October 15, 2017 Type Package Title Analyse Sentiment of English Sentences Version 2.2.1 Imports plyr,stringr,opennlp,nlp Date 2017-10-15 Package RSentiment October 15, 2017 Author Subhasree Bose

More information

Package spotsegmentation

Package spotsegmentation Version 1.53.0 Package spotsegmentation February 1, 2018 Author Qunhua Li, Chris Fraley, Adrian Raftery Department of Statistics, University of Washington Title Microarray Spot Segmentation and Gridding

More information

Package hcandersenr. January 20, 2019

Package hcandersenr. January 20, 2019 Type Package Title H.C. Andersens Fairy Tales Version 0.2.0 Package hcandersenr January 20, 2019 Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish

More information

NETFLIX MOVIE RATING ANALYSIS

NETFLIX MOVIE RATING ANALYSIS NETFLIX MOVIE RATING ANALYSIS Danny Dean EXECUTIVE SUMMARY Perhaps only a few us have wondered whether or not the number words in a movie s title could be linked to its success. You may question the relevance

More information

What is Statistics? 13.1 What is Statistics? Statistics

What is Statistics? 13.1 What is Statistics? Statistics 13.1 What is Statistics? What is Statistics? The collection of all outcomes, responses, measurements, or counts that are of interest. A portion or subset of the population. Statistics Is the science of

More information

Package painter. August 13, 2018

Package painter. August 13, 2018 Package painter August 13, 2018 Type Package Title Creation and Manipulation of Color Palettes Version 0.1.0 Functions for creating color palettes, visualizing palettes, modifying colors, and assigning

More information

Resampling Statistics. Conventional Statistics. Resampling Statistics

Resampling Statistics. Conventional Statistics. Resampling Statistics Resampling Statistics Introduction to Resampling Probability Modeling Resample add-in Bootstrapping values, vectors, matrices R boot package Conclusions Conventional Statistics Assumptions of conventional

More information

Package Polychrome. R topics documented: November 20, 2017

Package Polychrome. R topics documented: November 20, 2017 Title Qualitative Palettes with Many Colors Version 1.0.0 Date 2017-11-18 Author Kevin R. Coombes, Guy Brock Package Polychrome November 20, 2017 Tools for creating, viewing, and assessing qualitative

More information

Homework Packet Week #5 All problems with answers or work are examples.

Homework Packet Week #5 All problems with answers or work are examples. Lesson 8.1 Construct the graphical display for each given data set. Describe the distribution of the data. 1. Construct a box-and-whisker plot to display the number of miles from school that a number of

More information

Introduction to IBM SPSS Statistics (v24)

Introduction to IBM SPSS Statistics (v24) to IBM SPSS Statistics (v24) to IBM SPSS Statistics is a two day instructor-led classroom course that guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis process.

More information

Package colorpatch. June 10, 2017

Package colorpatch. June 10, 2017 Type Package Package colorpatch June 10, 2017 Title Optimized Rendering of Fold Changes and Confidence s Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values

More information

A combination of approaches to solve Task How Many Ratings? of the KDD CUP 2007

A combination of approaches to solve Task How Many Ratings? of the KDD CUP 2007 A combination of approaches to solve Tas How Many Ratings? of the KDD CUP 2007 Jorge Sueiras C/ Arequipa +34 9 382 45 54 orge.sueiras@neo-metrics.com Daniel Vélez C/ Arequipa +34 9 382 45 54 José Luis

More information

Package icaocularcorrection

Package icaocularcorrection Type Package Package icaocularcorrection February 20, 2015 Title Independent Components Analysis (ICA) based artifact correction. Version 3.0.0 Date 2013-07-12 Depends fastica, mgcv Author Antoine Tremblay,

More information

Multiple-point simulation of multiple categories Part 1. Testing against multiple truncation of a Gaussian field

Multiple-point simulation of multiple categories Part 1. Testing against multiple truncation of a Gaussian field Multiple-point simulation of multiple categories Part 1. Testing against multiple truncation of a Gaussian field Tuanfeng Zhang November, 2001 Abstract Multiple-point simulation of multiple categories

More information

COMP Test on Psychology 320 Check on Mastery of Prerequisites

COMP Test on Psychology 320 Check on Mastery of Prerequisites COMP Test on Psychology 320 Check on Mastery of Prerequisites This test is designed to provide you and your instructor with information on your mastery of the basic content of Psychology 320. The results

More information

Chapter 1 Midterm Review

Chapter 1 Midterm Review Name: Class: Date: Chapter 1 Midterm Review Multiple Choice Identify the choice that best completes the statement or answers the question. 1. A survey typically records many variables of interest to the

More information

COMP 249 Advanced Distributed Systems Multimedia Networking. Video Compression Standards

COMP 249 Advanced Distributed Systems Multimedia Networking. Video Compression Standards COMP 9 Advanced Distributed Systems Multimedia Networking Video Compression Standards Kevin Jeffay Department of Computer Science University of North Carolina at Chapel Hill jeffay@cs.unc.edu September,

More information

MATH& 146 Lesson 11. Section 1.6 Categorical Data

MATH& 146 Lesson 11. Section 1.6 Categorical Data MATH& 146 Lesson 11 Section 1.6 Categorical Data 1 Frequency The first step to organizing categorical data is to count the number of data values there are in each category of interest. We can organize

More information

Release Year Prediction for Songs

Release Year Prediction for Songs Release Year Prediction for Songs [CSE 258 Assignment 2] Ruyu Tan University of California San Diego PID: A53099216 rut003@ucsd.edu Jiaying Liu University of California San Diego PID: A53107720 jil672@ucsd.edu

More information

Ferenc, Szani, László Pitlik, Anikó Balogh, Apertus Nonprofit Ltd.

Ferenc, Szani, László Pitlik, Anikó Balogh, Apertus Nonprofit Ltd. Pairwise object comparison based on Likert-scales and time series - or about the term of human-oriented science from the point of view of artificial intelligence and value surveys Ferenc, Szani, László

More information

Machine Vision System for Color Sorting Wood Edge-Glued Panel Parts

Machine Vision System for Color Sorting Wood Edge-Glued Panel Parts Machine Vision System for Color Sorting Wood Edge-Glued Panel Parts Q. Lu, S. Srikanteswara, W. King, T. Drayer, R. Conners, E. Kline* The Bradley Department of Electrical and Computer Eng. *Department

More information

Package rasterimage. September 10, Index 5. Defines a color palette

Package rasterimage. September 10, Index 5. Defines a color palette Type Package Title An Improved Wrapper of Image() Version 0.3.0 Author Martin Seilmayer Package rasterimage September 10, 2016 Maintainer Martin Seilmayer Description This is a wrapper

More information

STAT 503 Case Study: Supervised classification of music clips

STAT 503 Case Study: Supervised classification of music clips STAT 503 Case Study: Supervised classification of music clips 1 Data Description This data was collected by Dr Cook from her own CDs. Using a Mac she read the track into the music editing software Amadeus

More information

Phenopix - Exposure extraction

Phenopix - Exposure extraction Phenopix - Exposure extraction G. Filippa December 2, 2015 Based on images retrieved from stardot cameras, we defined a suite of functions that perform a simplified OCR procedure to extract Exposure values

More information

Visual Encoding Design

Visual Encoding Design CSE 442 - Data Visualization Visual Encoding Design Jeffrey Heer University of Washington A Design Space of Visual Encodings Mapping Data to Visual Variables Assign data fields (e.g., with N, O, Q types)

More information

Various Artificial Intelligence Techniques For Automated Melody Generation

Various Artificial Intelligence Techniques For Automated Melody Generation Various Artificial Intelligence Techniques For Automated Melody Generation Nikahat Kazi Computer Engineering Department, Thadomal Shahani Engineering College, Mumbai, India Shalini Bhatia Assistant Professor,

More information

Normalization Methods for Two-Color Microarray Data

Normalization Methods for Two-Color Microarray Data Normalization Methods for Two-Color Microarray Data 1/13/2009 Copyright 2009 Dan Nettleton What is Normalization? Normalization describes the process of removing (or minimizing) non-biological variation

More information

Graphical User Interface for Modifying Structables and their Mosaic Plots

Graphical User Interface for Modifying Structables and their Mosaic Plots Graphical User Interface for Modifying Structables and their Mosaic Plots UseR 2011 Heiberger and Neuwirth 1 Graphical User Interface for Modifying Structables and their Mosaic Plots Richard M. Heiberger

More information

Algebra I Module 2 Lessons 1 19

Algebra I Module 2 Lessons 1 19 Eureka Math 2015 2016 Algebra I Module 2 Lessons 1 19 Eureka Math, Published by the non-profit Great Minds. Copyright 2015 Great Minds. No part of this work may be reproduced, distributed, modified, sold,

More information

ISOMET. Compensation look-up-table (LUT) and How to Generate. Isomet: Contents:

ISOMET. Compensation look-up-table (LUT) and How to Generate. Isomet: Contents: Compensation look-up-table (LUT) and How to Generate Contents: Description Background theory Basic LUT pg 2 Creating a LUT pg 3 Using the LUT pg 7 Comment pg 9 The compensation look-up-table (LUT) contains

More information

I. Model. Q29a. I love the options at my fingertips today, watching videos on my phone, texting, and streaming films. Main Effect X1: Gender

I. Model. Q29a. I love the options at my fingertips today, watching videos on my phone, texting, and streaming films. Main Effect X1: Gender 1 Hopewell, Sonoyta & Walker, Krista COM 631/731 Multivariate Statistical Methods Dr. Kim Neuendorf Film & TV National Survey dataset (2014) by Jeffres & Neuendorf MANOVA Class Presentation I. Model INDEPENDENT

More information

Data Mining. Dr. Raed Ibraheem Hamed. University of Human Development, College of Science and Technology Department of CS

Data Mining. Dr. Raed Ibraheem Hamed. University of Human Development, College of Science and Technology Department of CS Data Mining Dr. Raed Ibraheem Hamed University of Human Development, College of Science and Technology Department of CS 2016 2017 Road map Common Distance measures The Euclidean Distance between 2 variables

More information

SECTION I. THE MODEL. Discriminant Analysis Presentation~ REVISION Marcy Saxton and Jenn Stoneking DF1 DF2 DF3

SECTION I. THE MODEL. Discriminant Analysis Presentation~ REVISION Marcy Saxton and Jenn Stoneking DF1 DF2 DF3 Discriminant Analysis Presentation~ REVISION Marcy Saxton and Jenn Stoneking COM 631/731--Multivariate Statistical Methods Instructor: Prof. Kim Neuendorf (k.neuendorf@csuohio.edu) Cleveland State University,

More information

A STATISTICAL VIEW ON THE EXPRESSIVE TIMING OF PIANO ROLLED CHORDS

A STATISTICAL VIEW ON THE EXPRESSIVE TIMING OF PIANO ROLLED CHORDS A STATISTICAL VIEW ON THE EXPRESSIVE TIMING OF PIANO ROLLED CHORDS Mutian Fu 1 Guangyu Xia 2 Roger Dannenberg 2 Larry Wasserman 2 1 School of Music, Carnegie Mellon University, USA 2 School of Computer

More information

Table of Contents. 2 Select camera-lens configuration Select camera and lens type Listbox: Select source image... 8

Table of Contents. 2 Select camera-lens configuration Select camera and lens type Listbox: Select source image... 8 Table of Contents 1 Starting the program 3 1.1 Installation of the program.......................... 3 1.2 Starting the program.............................. 3 1.3 Control button: Load source image......................

More information

Measuring Variability for Skewed Distributions

Measuring Variability for Skewed Distributions Measuring Variability for Skewed Distributions Skewed Data and its Measure of Center Consider the following scenario. A television game show, Fact or Fiction, was canceled after nine shows. Many people

More information

Restoration of Hyperspectral Push-Broom Scanner Data

Restoration of Hyperspectral Push-Broom Scanner Data Restoration of Hyperspectral Push-Broom Scanner Data Rasmus Larsen, Allan Aasbjerg Nielsen & Knut Conradsen Department of Mathematical Modelling, Technical University of Denmark ABSTRACT: Several effects

More information

Package machina. October 7, 2016

Package machina. October 7, 2016 Type Package Package machina October 7, 2016 Title Machina Time Series Generation and Backtesting Version 0.1.6 Connects to and allows the creation of time series, and running backtests

More information

Moving on from MSTAT. March The University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID

Moving on from MSTAT. March The University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID Moving on from MSTAT March 2000 The University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID Contents 1. Introduction 3 2. Moving from MSTAT to Genstat 4 2.1 Analysis

More information

AP Statistics Sampling. Sampling Exercise (adapted from a document from the NCSSM Leadership Institute, July 2000).

AP Statistics Sampling. Sampling Exercise (adapted from a document from the NCSSM Leadership Institute, July 2000). AP Statistics Sampling Name Sampling Exercise (adapted from a document from the NCSSM Leadership Institute, July 2000). Problem: A farmer has just cleared a field for corn that can be divided into 100

More information

MVP: Capture-Power Reduction with Minimum-Violations Partitioning for Delay Testing

MVP: Capture-Power Reduction with Minimum-Violations Partitioning for Delay Testing MVP: Capture-Power Reduction with Minimum-Violations Partitioning for Delay Testing Zhen Chen 1, Krishnendu Chakrabarty 2, Dong Xiang 3 1 Department of Computer Science and Technology, 3 School of Software

More information

Music Genre Classification and Variance Comparison on Number of Genres

Music Genre Classification and Variance Comparison on Number of Genres Music Genre Classification and Variance Comparison on Number of Genres Miguel Francisco, miguelf@stanford.edu Dong Myung Kim, dmk8265@stanford.edu 1 Abstract In this project we apply machine learning techniques

More information

Using DICTION. Some Basics. Importing Files. Analyzing Texts

Using DICTION. Some Basics. Importing Files. Analyzing Texts Some Basics 1. DICTION organizes its work units by Projects. Each Project contains three folders: Project Dictionaries, Input, and Output. 2. DICTION has three distinct windows: the Project Explorer window

More information

Cryptanalysis of LILI-128

Cryptanalysis of LILI-128 Cryptanalysis of LILI-128 Steve Babbage Vodafone Ltd, Newbury, UK 22 nd January 2001 Abstract: LILI-128 is a stream cipher that was submitted to NESSIE. Strangely, the designers do not really seem to have

More information

WHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG?

WHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG? WHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG? NICHOLAS BORG AND GEORGE HOKKANEN Abstract. The possibility of a hit song prediction algorithm is both academically interesting and industry motivated.

More information

Lesson 7: Measuring Variability for Skewed Distributions (Interquartile Range)

Lesson 7: Measuring Variability for Skewed Distributions (Interquartile Range) : Measuring Variability for Skewed Distributions (Interquartile Range) Exploratory Challenge 1: Skewed Data and its Measure of Center Consider the following scenario. A television game show, Fact or Fiction,

More information

Detecting Musical Key with Supervised Learning

Detecting Musical Key with Supervised Learning Detecting Musical Key with Supervised Learning Robert Mahieu Department of Electrical Engineering Stanford University rmahieu@stanford.edu Abstract This paper proposes and tests performance of two different

More information

Skip Length and Inter-Starvation Distance as a Combined Metric to Assess the Quality of Transmitted Video

Skip Length and Inter-Starvation Distance as a Combined Metric to Assess the Quality of Transmitted Video Skip Length and Inter-Starvation Distance as a Combined Metric to Assess the Quality of Transmitted Video Mohamed Hassan, Taha Landolsi, Husameldin Mukhtar, and Tamer Shanableh College of Engineering American

More information

Module 8 VIDEO CODING STANDARDS. Version 2 ECE IIT, Kharagpur

Module 8 VIDEO CODING STANDARDS. Version 2 ECE IIT, Kharagpur Module 8 VIDEO CODING STANDARDS Lesson 27 H.264 standard Lesson Objectives At the end of this lesson, the students should be able to: 1. State the broad objectives of the H.264 standard. 2. List the improved

More information

Frequencies. Chapter 2. Descriptive statistics and charts

Frequencies. Chapter 2. Descriptive statistics and charts An analyst usually does not concentrate on each individual data values but would like to have a whole picture of how the variables distributed. In this chapter, we will introduce some tools to tabulate

More information

STAT 113: Statistics and Society Ellen Gundlach, Purdue University. (Chapters refer to Moore and Notz, Statistics: Concepts and Controversies, 8e)

STAT 113: Statistics and Society Ellen Gundlach, Purdue University. (Chapters refer to Moore and Notz, Statistics: Concepts and Controversies, 8e) STAT 113: Statistics and Society Ellen Gundlach, Purdue University (Chapters refer to Moore and Notz, Statistics: Concepts and Controversies, 8e) Learning Objectives for Exam 1: Unit 1, Part 1: Population

More information

Package clustrd. May 3, 2018

Package clustrd. May 3, 2018 Type Package Package clustrd May 3, 2018 Title Methods for Joint Dimension Reduction and Clustering Description A class of methods that combine dimension reduction and clustering of continuous or categorical

More information

Klee or Kid? The subjective experience of drawings from children and Paul Klee Pronk, T.

Klee or Kid? The subjective experience of drawings from children and Paul Klee Pronk, T. UvA-DARE (Digital Academic Repository) Klee or Kid? The subjective experience of drawings from children and Paul Klee Pronk, T. Link to publication Citation for published version (APA): Pronk, T. (Author).

More information

Solution of Linear Systems

Solution of Linear Systems Solution of Linear Systems Parallel and Distributed Computing Department of Computer Science and Engineering (DEI) Instituto Superior Técnico November 30, 2011 CPD (DEI / IST) Parallel and Distributed

More information

STAT 250: Introduction to Biostatistics LAB 6

STAT 250: Introduction to Biostatistics LAB 6 STAT 250: Introduction to Biostatistics LAB 6 Dr. Kari Lock Morgan Sampling Distributions In this lab, we ll explore sampling distributions using StatKey: www.lock5stat.com/statkey. We ll be using StatKey,

More information

COSC282 BIG DATA ANALYTICS FALL 2015 LECTURE 11 - OCT 21

COSC282 BIG DATA ANALYTICS FALL 2015 LECTURE 11 - OCT 21 COSC282 BIG DATA ANALYTICS FALL 2015 LECTURE 11 - OCT 21 1 Topics for Today Assignment 6 Vector Space Model Term Weighting Term Frequency Inverse Document Frequency Something about Assignment 6 Search

More information

Intra-frame JPEG-2000 vs. Inter-frame Compression Comparison: The benefits and trade-offs for very high quality, high resolution sequences

Intra-frame JPEG-2000 vs. Inter-frame Compression Comparison: The benefits and trade-offs for very high quality, high resolution sequences Intra-frame JPEG-2000 vs. Inter-frame Compression Comparison: The benefits and trade-offs for very high quality, high resolution sequences Michael Smith and John Villasenor For the past several decades,

More information

Clarification for 3G Coverage Obligation Verification Data

Clarification for 3G Coverage Obligation Verification Data Clarification for 3G Coverage Obligation Verification Data Publication date: 7 June 2013 Contents Section Page 1 Introduction 1 2 Data Processing 3 3 Data Formatting 7 4 Data Validation 9 Annex Page 1

More information

Paired plot designs experience and recommendations for in field product evaluation at Syngenta

Paired plot designs experience and recommendations for in field product evaluation at Syngenta Paired plot designs experience and recommendations for in field product evaluation at Syngenta 1. What are paired plot designs? 2. Analysis and reporting of paired plot designs 3. Case study 1 : analysis

More information

Fundamentals and applications of resampling methods for the analysis of speech production and perception data.

Fundamentals and applications of resampling methods for the analysis of speech production and perception data. Fundamentals and applications of resampling methods for the analysis of speech production and perception data. Olivier Crouzet 1 Laboratoire de Linguistique de Nantes (LLING UMR 6310, Université de Nantes

More information

SIDRA INTERSECTION 8.0 UPDATE HISTORY

SIDRA INTERSECTION 8.0 UPDATE HISTORY Akcelik & Associates Pty Ltd PO Box 1075G, Greythorn, Vic 3104 AUSTRALIA ABN 79 088 889 687 For all technical support, sales support and general enquiries: support.sidrasolutions.com SIDRA INTERSECTION

More information

Linköping University Post Print. Packet Video Error Concealment With Gaussian Mixture Models

Linköping University Post Print. Packet Video Error Concealment With Gaussian Mixture Models Linköping University Post Print Packet Video Error Concealment With Gaussian Mixture Models Daniel Persson, Thomas Eriksson and Per Hedelin N.B.: When citing this work, cite the original article. 2009

More information

Detecting Medicaid Data Anomalies Using Data Mining Techniques Shenjun Zhu, Qiling Shi, Aran Canes, AdvanceMed Corporation, Nashville, TN

Detecting Medicaid Data Anomalies Using Data Mining Techniques Shenjun Zhu, Qiling Shi, Aran Canes, AdvanceMed Corporation, Nashville, TN Paper SDA-04 Detecting Medicaid Data Anomalies Using Data Mining Techniques Shenjun Zhu, Qiling Shi, Aran Canes, AdvanceMed Corporation, Nashville, TN ABSTRACT The purpose of this study is to use statistical

More information

AP Statistics Sec 5.1: An Exercise in Sampling: The Corn Field

AP Statistics Sec 5.1: An Exercise in Sampling: The Corn Field AP Statistics Sec.: An Exercise in Sampling: The Corn Field Name: A farmer has planted a new field for corn. It is a rectangular plot of land with a river that runs along the right side of the field. The

More information

Modeling memory for melodies

Modeling memory for melodies Modeling memory for melodies Daniel Müllensiefen 1 and Christian Hennig 2 1 Musikwissenschaftliches Institut, Universität Hamburg, 20354 Hamburg, Germany 2 Department of Statistical Science, University

More information

Skycoor Manual PEKASAT SE 2016

Skycoor Manual PEKASAT SE 2016 Skycoor Manual PEKASAT SE 2016 1 Contents: 1 Introduction... 3 2 Online activation... 4 2.1 Demo... 4 2.2 Full versions... 4 3 General description of common actions... 5 3.1 Start screen... 5 3.2 Database

More information

Audio Compression Technology for Voice Transmission

Audio Compression Technology for Voice Transmission Audio Compression Technology for Voice Transmission 1 SUBRATA SAHA, 2 VIKRAM REDDY 1 Department of Electrical and Computer Engineering 2 Department of Computer Science University of Manitoba Winnipeg,

More information

Analysis of data from the pilot exercise to develop bibliometric indicators for the REF

Analysis of data from the pilot exercise to develop bibliometric indicators for the REF February 2011/03 Issues paper This report is for information This analysis aimed to evaluate what the effect would be of using citation scores in the Research Excellence Framework (REF) for staff with

More information

Chord Classification of an Audio Signal using Artificial Neural Network

Chord Classification of an Audio Signal using Artificial Neural Network Chord Classification of an Audio Signal using Artificial Neural Network Ronesh Shrestha Student, Department of Electrical and Electronic Engineering, Kathmandu University, Dhulikhel, Nepal ---------------------------------------------------------------------***---------------------------------------------------------------------

More information

Feature-Based Analysis of Haydn String Quartets

Feature-Based Analysis of Haydn String Quartets Feature-Based Analysis of Haydn String Quartets Lawson Wong 5/5/2 Introduction When listening to multi-movement works, amateur listeners have almost certainly asked the following situation : Am I still

More information

2D Interleaver Design for Image Transmission over Severe Burst-Error Environment

2D Interleaver Design for Image Transmission over Severe Burst-Error Environment 2D Interleaver Design for Image Transmission over Severe Burst- Environment P. Hanpinitsak and C. Charoenlarpnopparut Abstract The aim of this paper is to design sub-optimal 2D interleavers and compare

More information

MusCat: A Music Browser Featuring Abstract Pictures and Zooming User Interface

MusCat: A Music Browser Featuring Abstract Pictures and Zooming User Interface MusCat: A Music Browser Featuring Abstract Pictures and Zooming User Interface 1st Author 1st author's affiliation 1st line of address 2nd line of address Telephone number, incl. country code 1st author's

More information

Instrument Recognition in Polyphonic Mixtures Using Spectral Envelopes

Instrument Recognition in Polyphonic Mixtures Using Spectral Envelopes Instrument Recognition in Polyphonic Mixtures Using Spectral Envelopes hello Jay Biernat Third author University of Rochester University of Rochester Affiliation3 words jbiernat@ur.rochester.edu author3@ismir.edu

More information

Optical Signals Application Plug-in Programmer Manual

Optical Signals Application Plug-in Programmer Manual xx ZZZ Optical Signals Application Plug-in Programmer Manual *P077125000* 077-1250-00 xx ZZZ Optical Signals Application Plug-in Programmer Manual www.tek.com 077-1250-00 Copyright Tektronix. All rights

More information

ENGINEERING COMMITTEE

ENGINEERING COMMITTEE ENGINEERING COMMITTEE Interface Practices Subcommittee SCTE STANDARD SCTE 45 2017 Test Method for Group Delay NOTICE The Society of Cable Telecommunications Engineers (SCTE) Standards and Operational Practices

More information

What's New in Journal Citation Reports?

What's New in Journal Citation Reports? What's New in Journal Citation Reports? 2018 JCR RELEASE This release of Journal Citation Reports provides 2017 data. The 2018 data will be made available in the 2019 Journal Citation Reports release.

More information

CHAPTER1: Digital Logic Circuits

CHAPTER1: Digital Logic Circuits CS224: Computer Organization S.KHABET CHAPTER1: Digital Logic Circuits 1 Sequential Circuits Introduction Composed of a combinational circuit to which the memory elements are connected to form a feedback

More information

VLSI Design: 3) Explain the various MOSFET Capacitances & their significance. 4) Draw a CMOS Inverter. Explain its transfer characteristics

VLSI Design: 3) Explain the various MOSFET Capacitances & their significance. 4) Draw a CMOS Inverter. Explain its transfer characteristics 1) Explain why & how a MOSFET works VLSI Design: 2) Draw Vds-Ids curve for a MOSFET. Now, show how this curve changes (a) with increasing Vgs (b) with increasing transistor width (c) considering Channel

More information

OPERATIONS SEQUENCING IN A CABLE ASSEMBLY SHOP

OPERATIONS SEQUENCING IN A CABLE ASSEMBLY SHOP OPERATIONS SEQUENCING IN A CABLE ASSEMBLY SHOP Ahmet N. Ceranoglu* 1, Ekrem Duman*, M. Hamdi Ozcelik**, * Dogus University, Dept. of Ind. Eng., Acibadem, Istanbul, Turkey ** Yapi Kredi Bankasi, Dept. of

More information

TWO-FACTOR ANOVA Kim Neuendorf 4/9/18 COM 631/731 I. MODEL

TWO-FACTOR ANOVA Kim Neuendorf 4/9/18 COM 631/731 I. MODEL 1 TWO-FACTOR ANOVA Kim Neuendorf 4/9/18 COM 631/731 I. MODEL Using the Humor and Public Opinion Data, a two-factor ANOVA was run, using the full factorial model: MAIN EFFECT: Political Philosophy (3 groups)

More information

CS2401-COMPUTER GRAPHICS QUESTION BANK

CS2401-COMPUTER GRAPHICS QUESTION BANK SRI VENKATESWARA COLLEGE OF ENGINEERING AND TECHNOLOGY THIRUPACHUR. CS2401-COMPUTER GRAPHICS QUESTION BANK UNIT-1-2D PRIMITIVES PART-A 1. Define Persistence Persistence is defined as the time it takes

More information

Bootstrap Methods in Regression Questions Have you had a chance to try any of this? Any of the review questions?

Bootstrap Methods in Regression Questions Have you had a chance to try any of this? Any of the review questions? ICPSR Blalock Lectures, 2003 Bootstrap Resampling Robert Stine Lecture 3 Bootstrap Methods in Regression Questions Have you had a chance to try any of this? Any of the review questions? Getting class notes

More information

PLANE TESSELATION WITH MUSICAL-SCALE TILES AND BIDIMENSIONAL AUTOMATIC COMPOSITION

PLANE TESSELATION WITH MUSICAL-SCALE TILES AND BIDIMENSIONAL AUTOMATIC COMPOSITION PLANE TESSELATION WITH MUSICAL-SCALE TILES AND BIDIMENSIONAL AUTOMATIC COMPOSITION ABSTRACT We present a method for arranging the notes of certain musical scales (pentatonic, heptatonic, Blues Minor and

More information

Pattern Creator/Converter Software User Manual

Pattern Creator/Converter Software User Manual Application Note: HFAN-9.5.0 Rev.1; 04/08 Pattern Creator/Converter Software User Manual Pattern Creator/Converter Software User Manual 1 Introduction The Pattern Creator/Converter software (available

More information

NAA ENHANCING THE QUALITY OF MARKING PROJECT: THE EFFECT OF SAMPLE SIZE ON INCREASED PRECISION IN DETECTING ERRANT MARKING

NAA ENHANCING THE QUALITY OF MARKING PROJECT: THE EFFECT OF SAMPLE SIZE ON INCREASED PRECISION IN DETECTING ERRANT MARKING NAA ENHANCING THE QUALITY OF MARKING PROJECT: THE EFFECT OF SAMPLE SIZE ON INCREASED PRECISION IN DETECTING ERRANT MARKING Mudhaffar Al-Bayatti and Ben Jones February 00 This report was commissioned by

More information

ISOMET. Compensation look-up-table (LUT) and Scan Uniformity

ISOMET. Compensation look-up-table (LUT) and Scan Uniformity Compensation look-up-table (LUT) and Scan Uniformity The compensation look-up-table (LUT) contains both phase and amplitude data. This is automatically applied to the Image data to maximize diffraction

More information

An Approach to Classifying Four-Part Music

An Approach to Classifying Four-Part Music An Approach to Classifying Four-Part Music Gregory Doerfler, Robert Beck Department of Computing Sciences Villanova University, Villanova PA 19085 gdoerf01@villanova.edu Abstract - Four-Part Classifier

More information

Estimating. Proportions with Confidence. Chapter 10. Copyright 2006 Brooks/Cole, a division of Thomson Learning, Inc.

Estimating. Proportions with Confidence. Chapter 10. Copyright 2006 Brooks/Cole, a division of Thomson Learning, Inc. Estimating Chapter 10 Proportions with Confidence Copyright 2006 Brooks/Cole, a division of Thomson Learning, Inc. Principal Idea: Survey 150 randomly selected students and 41% think marijuana should be

More information

Recognising Cello Performers using Timbre Models

Recognising Cello Performers using Timbre Models Recognising Cello Performers using Timbre Models Chudy, Magdalena; Dixon, Simon For additional information about this publication click this link. http://qmro.qmul.ac.uk/jspui/handle/123456789/5013 Information

More information

Section 6.8 Synthesis of Sequential Logic Page 1 of 8

Section 6.8 Synthesis of Sequential Logic Page 1 of 8 Section 6.8 Synthesis of Sequential Logic Page of 8 6.8 Synthesis of Sequential Logic Steps:. Given a description (usually in words), develop the state diagram. 2. Convert the state diagram to a next-state

More information

Orthogonal rotation in PCAMIX

Orthogonal rotation in PCAMIX Orthogonal rotation in PCAMIX Marie Chavent 1,2, Vanessa Kuentz 3 and Jérôme Saracco 2,4 1 Université de Bordeaux, IMB, CNRS, UMR 5251, France 2 INRIA Bordeaux Sud-Ouest, CQFD team, France 3 CEMAGREF,

More information

Outline. Why do we classify? Audio Classification

Outline. Why do we classify? Audio Classification Outline Introduction Music Information Retrieval Classification Process Steps Pitch Histograms Multiple Pitch Detection Algorithm Musical Genre Classification Implementation Future Work Why do we classify

More information

TechNote: MuraTool CA: 1 2/9/00. Figure 1: High contrast fringe ring mura on a microdisplay

TechNote: MuraTool CA: 1 2/9/00. Figure 1: High contrast fringe ring mura on a microdisplay Mura: The Japanese word for blemish has been widely adopted by the display industry to describe almost all irregular luminosity variation defects in liquid crystal displays. Mura defects are caused by

More information

Network Operations Subcommittee SCTE STANDARD SCTE SCTE-HMS-QAM-MIB

Network Operations Subcommittee SCTE STANDARD SCTE SCTE-HMS-QAM-MIB Network Operations Subcommittee SCTE STANDARD SCTE 154-2 2018 SCTE-HMS-QAM-MIB NOTICE The Society of Cable Telecommunications Engineers (SCTE) / International Society of Broadband Experts (ISBE) Standards

More information

Recognising Cello Performers Using Timbre Models

Recognising Cello Performers Using Timbre Models Recognising Cello Performers Using Timbre Models Magdalena Chudy and Simon Dixon Abstract In this paper, we compare timbre features of various cello performers playing the same instrument in solo cello

More information

Chapter 6. Normal Distributions

Chapter 6. Normal Distributions Chapter 6 Normal Distributions Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania Edited by José Neville Díaz Caraballo University of

More information

Package knitcitations

Package knitcitations Package knitcitations March 18, 2013 Type Package Title Citations for knitr markdown files Version 0.4-4 knitcitations provides the ability to create dynamic citations in which the bibliographic information

More information

WHAT'S HOT: LINEAR POPULARITY PREDICTION FROM TV AND SOCIAL USAGE DATA Jan Neumann, Xiaodong Yu, and Mohamad Ali Torkamani Comcast Labs

WHAT'S HOT: LINEAR POPULARITY PREDICTION FROM TV AND SOCIAL USAGE DATA Jan Neumann, Xiaodong Yu, and Mohamad Ali Torkamani Comcast Labs WHAT'S HOT: LINEAR POPULARITY PREDICTION FROM TV AND SOCIAL USAGE DATA Jan Neumann, Xiaodong Yu, and Mohamad Ali Torkamani Comcast Labs Abstract Large numbers of TV channels are available to TV consumers

More information

Chord Representations for Probabilistic Models

Chord Representations for Probabilistic Models R E S E A R C H R E P O R T I D I A P Chord Representations for Probabilistic Models Jean-François Paiement a Douglas Eck b Samy Bengio a IDIAP RR 05-58 September 2005 soumis à publication a b IDIAP Research

More information