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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