Package psda. September 5, 2017

Transcription

1 Title Polygonal Symbolic Data Analysis Version Date Package psda September 5, 2017 An implementation of symbolic al data analysis. The package presents the estimation of main descriptive statistical measures, e.g, mean, covariance, variance, correlation and coefficient of variation. In addition, transformation of the data in s. Empirical probability distribution function based on al histogram and regression models are presented. Depends R (>= 3.1) License GPL-2 Imports ggplot2, rgeos, plyr, sp, raster, stats LazyData true RoxygenNote Suggests testthat NeedsCompilation no Author Wagner Silva [aut, cre, ths], Renata Souza [aut], Francisco Cysneiros [aut] Maintainer Wagner Silva <wjfs@cin.ufpe.br> Repository CRAN Date/Publication :31:01 UTC R topics documented: parea pconvex pcorr pcov pfreq plr

2 2 pconvex pmean pmean_id pplot psim psmi ptransform pvar pvari Index 11 parea Polygonal Area Compute the area of. parea() a matrix representing the. a integer the area of. x <- psim(1, 3) x <- x[[1]] parea(x) pconvex Convex verification Verify convexity of the s. pconvex()

3 pcorr 3 A matrix of dimension l x 2 where l represent number of sides. A boolean. x <- psim(1,4) x <- x[[1]] pconvex(x) x <- matrix(unlist(psim(1,4)), ncol= 2) pconvex(x) #run pcorr Polygonal symbolic correlation Compute the empirical correlation between two al variables. pcorr(s) s A list of matrices of dimension l x 2 where l represent number of sides. The method returns a integer. x = psim(10, 3) #simulate 10 s of 3 sides pcorr(x)

4 4 pfreq pcov Polygonal symbolic covariance Compute the empirical covariance of two al variables. pcov(s) s A list of al datas. The method returns a integer. x <- psim(10, 3) #simulate 10 s of 3 sides pcov(x) pfreq Polygonal Symbolic Relative Frequency Compute the bivariate relative frequency. pfreq(pol) pol A list of matrices of dimension l x 2, where l represent number of sides. This matrices represent the s. x <- psim(10, 3) #simulate 10 s of 3 sides frequency <- pfreq(x)

5 plr 5 plr Polygonal linear regression plr is used to fit al linear models. plr(formula, env, residuals_type = "mse", intercept = TRUE) formula env an object of class "formula": a symbolic description of the model to be fitted. environment that contain the variables of study. residuals_type type of residual, e.g., rmsd and mse residuals. intercept logical, if TRUE the model present intercept, otherwise it do not present intercept. beta_hat a vector of estimators for beta proposed in model. SSRES sum square residuals. SSREG sum square of regressions. SST sum square toral. residuals_type a type of residuals mse or rmsd. prediction_s a list of prediction s by plr model. require(psda) yp <- psim(10, 4) xp1 <- psim(10, 4) xp2 <- psim(10, 4) e <- new.env() e$yp <- yp e$xp1 <- xp1 e$xp2 <- xp2 fit <- plr(yp~xp1+xp2, env = e)

6 6 pmean_id pmean Polygonal empiric mean Compute the al empirical mean for al variable. pmean(s) s A list of matrices of dimension l x 2 where l represent number of sides. The method returns a vector containing the al empiric mean of variable one and variable two, respectively. x <- psim(10, 3) #simulate 10 s of 3 sides pmean(x) pmean_id Polygonal symbolic internal mean Compute the al empirical mean for only one al variable. pmean_id() a matrix representing the. a al empiric mean of a. x <- psim(1, 3) x <- x[[1]] pmean_id(x)

7 pplot 7 pplot Plot al symbolic variable Prints all overlaid graphics in the display. pplot() A list of matrices with dimension l x 2 where l represents vertices number of. x <- psim(10, 3) #simulate 10 s of 3 sides pplot(x) psim Polygonal symbolic data simulation Function to create a al data list. psim(objects, vertices) objects vertices number of objects simulated. number of vertex of the A list of s. psim(2, 3)

8 8 ptransform psmi Polygonal internam second moment Caltulate al second moment for al data. psmi() a matrix that represents a al variable. The internal variance. x <- psim(1, 3) #simulate a of 3 sides psmi(x[[1]]) ptransform Polygon data transformation It transform labeled data in al datas. ptransform(data, vertices) data vertices A data frame. integer that represent number of vertices of. A list of matrices of dimension l x 2 where l represent number of vertices transformed.

9 pvar 9 cat <- as.factor(sample(1:20, 1000, replace = TRUE)) cv <- runif(1000) #classical variable cvc <- data.frame(category = cat, cv) p <- ptransform(cvc, 4) pvar Polygonal symbolic variance Estime the al empirical variance between al variables. pvar(s) s A list of matrices of dimension l x 2 where l represent number of sides. The method returns a vector. x <- psim(10, 3) #simulate 10 s of 3 sides pvar(x) pvari Polygonal internal variance Caltulate al internal variance for al data. pvari() a matrix that represents a al variable.

10 10 pvari The internal variance. x <- psim(1, 3) #simulate a s of 3 sides pvari(x[[1]])

11 Index parea, 2 pconvex, 2 pcorr, 3 pcov, 4 pfreq, 4 plr, 5 pmean, 6 pmean_id, 6 pplot, 7 psim, 7 psmi, 8 ptransform, 8 pvar, 9 pvari, 9 11