Package rrr. R topics documented: December 9, Title Reduced-Rank Regression Version URL
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1 Title Reduced-Ran Regression Version URL Pacage rrr December 9, 2016 Reduced-ran regression, diagnostics and graphics. Depends R (>= 3.2.0) Imports Rcpp, MASS, magrittr, dplr, ggplot2, plotl, GGall Suggests testthat, nitr, rmardown, readr Collate utilit_functions.r reduce_ran_regression.r pca.r cva.r lda.r rrr.r data.r License GPL-3 Encoding UTF-8 LazData true RogenNote VignetteBuilder nitr NeedsCompilation no Author Chris Add [aut, cre] Maintainer Chris Add <chris.william.add@gmail.com> Repositor CRAN Date/Publication :15:55 R topics documented: COMBO pairwise_plot pendigits ran_trace residuals rrr scores threewise_plot tobacco
2 2 pairwise_plot Inde 11 COMBO17 MMST COMBO17 DATA Format COMBO-17 gala photometric catalogue, 216, 219, 235 COMBO17 A data frame with 3462 observations on 65 numeric variables. A. Izenman (2008). Modern Multivariate Statistical Techniques. Springer. Wolf, C. Meisenheimer, M., Kleinheinrich, M., Borch, A., De, S., Gra, M., Wisotsi, L., Bell, E.F., Ri, H., W. Cimatti, A., Hasinger, G., and Szool, G. (2004). A catalogue of the Chandra Deep Field South with multi-colour classification and photometric redshifts from COMBO-17, Astronom & Astrophsics. pairwise_plot Pairwise Plots Pairwise Plots pairwise_plot(,, tpe = "pca", pair_ = 1, pair_ = 2, ran = "full", = 0, interactive = FALSE, point_size = 2.5) data frame or matri of predictor variables tpe = "cva" fits a canonical variate analsis model as a special case of reduced-ran regression. tpe = "lda" fits a linear discriminant analsis model as a special case of reduced-ran regression.
3 pendigits 3 pair_ pair_ ran interactive point_size variable to be plotted on the X-ais variable to be plotted on the Y -ais ran of coefficient matri. logical. If interactive = FALSE, the default, plots a static pairwise plot. If interactive = FALSE plots an interactive pairwise plot. size of points in scatter plot. Value ggplot2 object if interactive = FALSE; plotl object if interactive = TRUE. Izenman, A.J. (2008) Modern Multivariate Statistical Techniques. Springer. Eamples data(pendigits) digits_features <- pendigits[,1:34] digits_class <- pendigits[,35] pairwise_plot(digits_features, digits_class, tpe = "pca", pair_ = 1, pair_ = 3) librar(dplr) gala <- as_data_frame(combo17) gala <- select(gala, -starts_with("e."), -Nr, -UFS:-IFD) gala <- na.omit(gala) gala_ <- select(gala, -Rmag:-chi2red) gala_ <- select(gala, Rmag:chi2red) pairwise_plot(gala_, gala_, tpe = "cva") data(iris) iris_ <- iris[,1:4] iris_ <- iris[5] pairwise_plot(iris_, iris_, tpe = "lda") pendigits MMST PENDIGITS DATA pen-based handwritten digit recognition, 211, 234, 274, 348, 391, 631
4 4 ran_trace pendigits Format a data frame with observations on 36 unnamed variables Source A. Izenman (2008) Modern Multivariate Statistical Techniques. Springer. ran_trace Ran Trace Plot ran_trace is a plot used to determine the effective dimensionalit, i.e., t = ran (C), of the reduced-ran regression equation. ran_trace(,, tpe = "identit", = 0, plot = TRUE, interactive = FALSE) data frame or matri of predictor variables tpe = "cva" fits a canonical variate analsis model as a special case of reduced-ran regression. tpe = "lda" fits a linear discriminant analsis model as a special case of reduced-ran regression. Value plot interactive if FALSE, returns data frame of ran trace coordinates. if TRUE, creates an interactive plotl graphic. plot of ran trace coordinates if plot = TRUE, the default, or data frame of ran trace coordinates if plot = FALSE.
5 residuals 5 Izenman, A.J. (2008) Modern Multivariate Statistical Techniques. Springer. Eamples data(tobacco) tobacco_ <- tobacco[,4:9] tobacco_ <- tobacco[,1:3] gamma <- diag(1, dim(tobacco_)[2]) ran_trace(tobacco_, tobacco_) ran_trace(tobacco_, tobacco_, plot = FALSE) ran_trace(tobacco_, tobacco_, tpe = "cva") data(pendigits) digits_features <- pendigits[, -35:-36] ran_trace(digits_features, digits_features, tpe = "pca") librar(dplr) gala <- as_data_frame(combo17) gala <- select(gala, -starts_with("e."), -Nr, -UFS:-IFD) gala <- na.omit(gala) gala_ <- select(gala, -Rmag:-chi2red) gala_ <- select(gala, Rmag:chi2red) ran_trace(gala_, gala_, tpe = "cva") residuals Reduced-Ran Regression Residuals residuals calculates the regression residuals for reduced-ran regression and canonical variate analsis. residuals(,, tpe = "identit", ran = "full", = 0, plot = TRUE) data frame or matri of predictor variables tpe = "cva" fits a canonical variate analsis model as a special case of reduced-ran regression. tpe = "lda" fits a linear discriminant analsis model as a special case of reduced-ran regression.
6 6 rrr ran plot ran of coefficient matri. if FALSE, returns data frame of ran trace coordinates. Value scatterplot matri of residuals if plot = TRUE, the default, or a data frame of residuals if plot = FALSE. Izenman, A.J. (2008) Modern Multivariate Statistical Techniques. Springer. Eamples data(tobacco) tobacco_ <- tobacco[,4:9] tobacco_ <- tobacco[,1:3] tobacco_rrr <- rrr(tobacco_, tobacco_, ran = 1) residuals(tobacco_, tobacco_, ran = 1, plot = FALSE) residuals(tobacco_, tobacco_, ran = 1) librar(dplr) gala <- as_data_frame(combo17) gala <- select(gala, -starts_with("e."), -Nr, -UFS:-IFD) gala <- na.omit(gala) gala_ <- select(gala, -Rmag:-chi2red) gala_ <- select(gala, Rmag:chi2red) residuals(gala_, gala_, tpe = "cva", ran = 2, = 0.001) rrr Fit Reduced-Ran Regression Model rrr fits a reduced-ran regression model. rrr(,, tpe = "identit", ran = "full", = 0)
7 rrr 7 data frame or matri of predictor variables tpe = "cva" fits a canonical variate analsis model as a special case of reduced-ran regression. tpe = "lda" fits a linear discriminant analsis model as a special case of reduced-ran regression. ran ran of coefficient matri. Value list containing estimates of coefficients and means, and eigenvalue-based diagnostics. Izenman, A.J. (2008) Modern Multivariate Statistical Techniques. Springer. Eamples data(tobacco) tobacco_ <- tobacco[,4:9] tobacco_ <- tobacco[,1:3] rrr(tobacco_, tobacco_, ran = 1) data(pendigits) digits_features <- pendigits[, -35:-36] rrr(digits_features, digits_features, tpe = "pca", ran = 3) librar(dplr) gala <- as_data_frame(combo17) gala <- select(gala, -starts_with("e."), -Nr, -UFS:-IFD) gala <- na.omit(gala) gala_ <- select(gala, -Rmag:-chi2red) gala_ <- select(gala, Rmag:chi2red) rrr(gala_, gala_, tpe = "cva", ran = 2) data(iris) iris_ <- iris[,1:4] iris_ <- iris[5] rrr(iris_, iris_, tpe = "lda")
8 8 scores scores Compute Latent Variable Scores Compute Latent Variable Scores scores(,, tpe = "pca", ran = "full", = 0) data frame or matri of predictor variables tpe = "cva" fits a canonical variate analsis model as a special case of reduced-ran regression. tpe = "lda" fits a linear discriminant analsis model as a special case of reduced-ran regression. ran ran of coefficient matri. Izenman, A.J. (2008) Modern Multivariate Statistical Techniques. Springer. Eamples data(pendigits) digits_features <- pendigits[, -35:-36] scores(digits_features, digits_features, tpe = "pca", ran = 3) librar(dplr) gala <- as_data_frame(combo17) gala <- select(gala, -starts_with("e."), -Nr, -UFS:-IFD) gala <- na.omit(gala) gala_ <- select(gala, -Rmag:-chi2red) gala_ <- select(gala, Rmag:chi2red) scores(gala_, gala_, tpe = "cva", ran = 4) data(iris) iris_ <- iris[,1:4] iris_ <- iris[5] scores(iris_, iris_, tpe = "lda")
9 threewise_plot 9 threewise_plot 3-D Reduced Ran Regression Plots Create three-dimensional, interactive plotl graphics for eploration and diagnostics. threewise_plot(,, tpe = "pca", pair_ = 1, pair_ = 2, pair_z = 3, ran = "full", = 0, point_size = 2.5) data frame or matri of predictor variables tpe = "cva" fits a canonical variate analsis model as a special case of reduced-ran regression. tpe = "lda" fits a linear discriminant analsis model as a special case of reduced-ran regression. pair_ pair_ pair_z ran Value point_size variable to be plotted on the X-ais variable to be plotted on the Y -ais variable to be plotted on the Z-ais ran of coefficient matri. size of points in scatter plot. three-dimensional plot. If tpe = "pca" returns three principal components scores - defaulted to the first three - against each other. If tpe = "cva" returns three-dimensional plot of residuals. If tpe = "lda" returns three-dimensional plot of three linear discriminant scores plotted against each other. Eamples ## Not run: data(pendigits) digits_features <- pendigits[, -35:-36] threewise_plot(digits_features, digits_class, tpe = "pca", = ) librar(dplr)
10 10 tobacco gala <- as_data_frame(combo17) gala <- select(gala, -starts_with("e."), -Nr, -UFS:-IFD) gala <- na.omit(gala) gala_ <- select(gala, -Rmag:-chi2red) gala_ <- select(gala, Rmag:chi2red) threewise_plot(gala_, gala_, tpe = "cva") data(iris) iris_ <- iris[,1:4] iris_ <- iris[5] threewise_plot(iris_, iris_, tpe = "lda") ## End(Not run) tobacco MMST TOBACCO DATA chemical composition of tobacco, 183, 187 tobacco Format a data frame with 25 observations on the following 9 variables. Y1.BurnRate a numeric vector Y2.PercentSugar a numeric vector Y3.PercentNicotine a numeric vector X1.PercentNitrogen a numeric vector X2.PercentChlorine a numeric vector X3.PercentPotassium a numeric vector X4.PercentPhosphorus a numeric vector X5.PercentCalcium a numeric vector X6.PercentMagnesium a numeric vector A. Izenman (2008). Modern Multivariate Statistical Techniques. Springer. Anderson, R.L. and Bancroft, T.A. (1952). Statistical Theor in Research. New Yor: Mcgraw-Hill.
11 Inde Topic datasets COMBO17, 2 pendigits, 3 tobacco, 10 COMBO17, 2 pairwise_plot, 2 pendigits, 3 ran_trace, 4 residuals, 5 rrr, 6 scores, 8 threewise_plot, 9 tobacco, 10 11
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Package cellwise. June 26, 2018
Type Package Version 2.0.10 Date 2018-06-25 Title Analyzing Data with Cellwise Outliers Depends R (>= 3.2.0) Package cellwise June 26, 2018 Suggests knitr, robusthd, robustbase, rrcov, MASS, svd, ellipse
Package RootsExtremaInflections
Type Package Package RootsExtremaInflections May 10, 2017 Title Finds Roots, Extrema and Inflection Points of a Curve Version 1.1 Date 2017-05-10 Author Demetris T. Christopoulos Maintainer Demetris T.
Kernel Sliced Inverse Regression With Applications to Classification
May 21-24, 2008 in Durham, NC Kernel Sliced Inverse Regression With Applications to Classification Han-Ming Wu (Hank) Department of Mathematics, Tamkang University Taipei, Taiwan 2008/05/22 http://www.hmwu.idv.tw
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Type Package Package Rarity December 23, 2016 Title Calculation of Rarity Indices for Species and Assemblages of Species Version 1.3-6 Date 2016-12-23 Author Boris Leroy Maintainer Boris Leroy
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Type Package Version 0.8.1 Title Hazard Discrimination Summary Package hds December 31, 2016 Functions for calculating the hazard discrimination summary and its standard errors, as described in Liang and
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Package BGGE August 10, 2018 Title Bayesian Genomic Linear Models Applied to GE Genome Selection Version 0.6.5 Date 2018-08-10 Description Application of genome prediction for a continuous variable, focused
Package LPTime. March 3, 2015
Type Package Package LPTime March 3, 2015 Title LP Nonparametric Approach to Non-Gaussian Non-Linear Time Series Modelling Version 1.0-2 Date 2015-03-03 URL http://sites.temple.edu/deepstat/d-products/
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Package December 21, 2015 Type Package Title Boosting Conditional Logit Model Version 1.1 Date 2015-12-09 Author Haolun Shi and Guosheng Yin Maintainer A set of functions to fit a boosting conditional
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Type Package Title g-and-k and g-and-h Distribution Functions Version 0.5.1 Date 2018-02-04 Package gk February 4, 2018 Functions for the g-and-k and generalised g-and-h distributions. License GPL-2 Imports
Package misctools. November 25, 2016
Version 0.6-22 Date 2016-11-25 Title Miscellaneous Tools and Utilities Author, Ott Toomet Package misctools November 25, 2016 Maintainer Depends R (>= 2.14.0) Suggests Ecdat
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Package thief. R topics documented: January 24, Version 0.3 Title Temporal Hierarchical Forecasting
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Type Package Title Bayesian Estimation of DINA Model Version 1.0.2 Date 2017-04-26 Package dina April 26, 2017 Estimate the Deterministic Input, Noisy ``And'' Gate (DINA) cognitive diagnostic model parameters
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Title Multivariate AutoRegressive analysis Version 1.1-2 Author S. M. Barbosa Package mar February 20, 2015 R functions for multivariate autoregressive analysis Depends MASS Maintainer S. M. Barbosa
Package chemcal. July 17, 2018
Version 0.2.1 Date 2018-07-17 Package chemcal July 17, 2018 Title Calibration Functions for Analytical Chemistry Suggests MASS, knitr, testthat, investr Simple functions for plotting linear calibration