Last data update: 2014.03.03

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colorFacet (Package: Hmisc) : Miscellaneous ggplot2 and grid Helper Functions

These functions are used on ggplot2 objects or as layers when building a ggplot2 object, and to facilitate use of gridExtra. colorFacet colors the thin rectangles used to separate panels created by facet_grid (and probably by facet_wrap). A better approach may be found at arrGrob is a front-end to gridExtra::arrangeGrob that allows for proper printing. See The arrGrob print method calls grid::grid.draw.
● Data Source: CranContrib
● Keywords: hplot
● Alias: arrGrob, colorFacet, print.arrGrob
● 0 images

list.tree (Package: Hmisc) :

This is a function to pretty-print the structure of any data object (usually a list). It is similar to the R function str.
● Data Source: CranContrib
● Keywords: documentation
● Alias: list.tree
● 0 images

units (Package: Hmisc) :

Sets or retrieves the "units" attribute of an object. For units.default replaces the builtin version, which only works for time series objects. If the variable is also given a label, subsetting (using [.labelled) will retain the "units" attribute. For a Surv object, units first looks for an overall "units" attribute, then it looks for units for the time2 variable then for time1.
● Data Source: CranContrib
● Keywords: interface, utilities
● Alias: units, units.Surv, units.default, units<-.default
● 0 images

cut2 (Package: Hmisc) :

Function like cut but left endpoints are inclusive and labels are of the form [lower, upper), except that last interval is [lower,upper]. If cuts are given, will by default make sure that cuts include entire range of x. Also, if cuts are not given, will cut x into quantile groups (g given) or groups with a given minimum number of observations (m). Whereas cut creates a category object, cut2 creates a factor object.
● Data Source: CranContrib
● Keywords: category, nonparametric
● Alias: cut2
● 0 images

valueTags (Package: Hmisc) : Store Discriptive Information About an Object

Functions get or set useful information about the contents of the object for later use.
● Data Source: CranContrib
● Keywords: attribute, misc, utilities
● Alias: valueLabel, valueLabel<-, valueName, valueName<-, valueTags, valueTags<-, valueUnit, valueUnit<-
1 images

describe (Package: Hmisc) :

describe is a generic method that invokes, describe.matrix, describe.vector, or describe.formula. describe.vector is the basic function for handling a single variable. This function determines whether the variable is character, factor, category, binary, discrete numeric, and continuous numeric, and prints a concise statistical summary according to each. A numeric variable is deemed discrete if it has <= 10 unique values. In this case, quantiles are not printed. A frequency table is printed for any non-binary variable if it has no more than 20 unique values. For any variable with at least 20 unique values, the 5 lowest and highest values are printed. This behavior can be overriden for long character variables with many levels using the listunique parameter, to get a complete tabulation.
● Data Source: CranContrib
● Keywords: category, distribution, interface, models, nonparametric, robust
● Alias: [.describe, describe,, describe.default, describe.formula, describe.matrix, describe.vector, latex.describe, latex.describe.single, print.describe, print.describe.single
● 0 images

summarize (Package: Hmisc) : Summarize Scalars or Matrices by Cross-Classification

summarize is a fast version of summary.formula(formula, method="cross",overall=FALSE) for producing stratified summary statistics and storing them in a data frame for plotting (especially with trellis xyplot and dotplot and Hmisc xYplot). Unlike aggregate, summarize accepts a matrix as its first argument and a multi-valued FUN argument and summarize also labels the variables in the new data frame using their original names. Unlike methods based on tapply, summarize stores the values of the stratification variables using their original types, e.g., a numeric by variable will remain a numeric variable in the collapsed data frame. summarize also retains "label" attributes for variables. summarize works especially well with the Hmisc xYplot function for displaying multiple summaries of a single variable on each panel, such as means and upper and lower confidence limits.
● Data Source: CranContrib
● Keywords: category, manip, multivariate
● Alias: asNumericMatrix, matrix2dataFrame, summarize
5 images

tex (Package: Hmisc) :

tex is a little function to save typing when including TeX commands in graphs that are used with the psfrag package in LaTeX to typeset any LaTeX text inside a postscript graphic. tex surrounds the input character string with \tex[options]{}. This is especially useful for getting Greek letters and math symbols in postscript graphs. By default tex returns a string with psfrag commands specifying that the string be centered, not rotated, and not specially enlarged or shrunk.
● Data Source: CranContrib
● Keywords: device, hplot
● Alias: tex
● 0 images

transcan (Package: Hmisc) :

transcan is a nonlinear additive transformation and imputation function, and there are several functions for using and operating on its results. transcan automatically transforms continuous and categorical variables to have maximum correlation with the best linear combination of the other variables. There is also an option to use a substitute criterion - maximum correlation with the first principal component of the other variables. Continuous variables are expanded as restricted cubic splines and categorical variables are expanded as contrasts (e.g., dummy variables). By default, the first canonical variate is used to find optimum linear combinations of component columns. This function is similar to ace except that transformations for continuous variables are fitted using restricted cubic splines, monotonicity restrictions are not allowed, and NAs are allowed. When a variable has any NAs, transformed scores for that variable are imputed using least squares multiple regression incorporating optimum transformations, or NAs are optionally set to constants. Shrinkage can be used to safeguard against overfitting when imputing. Optionally, imputed values on the original scale are also computed and returned. For this purpose, recursive partitioning or multinomial logistic models can optionally be used to impute categorical variables, using what is predicted to be the most probable category.
● Data Source: CranContrib
● Keywords: methods, models, multivariate, regression, smooth
● Alias: Function, Function.transcan, [.transcan, fit.mult.impute, ggplot.transcan, impute.transcan, invertTabulated, plot.transcan, predict.transcan, print.transcan, summary.transcan, transcan, vcov.default,
16 images

plotCorrPrecision (Package: Hmisc) : Plot Precision of Estimate of Pearson Correlation Coefficient

This function plots the precision (margin of error) of the product-moment linear correlation coefficient r vs. sample size, for a given vector of correlation coefficients rho. Precision is defined as the larger of the upper confidence limit minus rho and rho minus the lower confidence limit. labcurve is used to automatically label the curves.
● Data Source: CranContrib
● Keywords: htest
● Alias: plotCorrPrecision
2 images