In gmonette/spida2: Collection of tools developed for the Summer Programme in Data Analysis 2000-2012

knitr::opts_chunk$set(
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library(spida2)

spida2: Functions used in the Statistical Consulting Service at York University

The spida2 package is a collection of functions and datasets intended primarily for statistical consulting, with a particular emphasis on longitudinal and hierarchical data analysis. Some documents related to this package can be found at \url{http://blackwell.math.yorku.ca/R/spida2/doc}

General Bugs

• latticeExtra::+.trellis() in xyplot + xyplot requires the same data set to avoid registration of panels if some levels of panel factors are different in data sets for different panes.
• Use Rbind() to create a common data frame and different names for plotted tables, e.g. to plot both lines and points. }

New functions

• Rbind() works on data.frame, missing variables get NA: use to avoid fted panels in latticeExtra
• Apply() returns list with same structure, e.g. list array
• tabf() returns result of applying function as a list array
• waldx() temporary name for rank-deficient aware version of wald
• waldf() rank-deficient aware version of wald that returns a data frame L matrix
• subrow() subtracts selected rows of L matrix from ranges of rows, e.g. compare with comparators
• lchol() returns lower-triangular L so that G = L'L.
• getR(), getG() and getV() urn R, G and V matrices for nlme::lme() objects.
• pdInd() constructs a pdClass for a G matrix with patterns of zero ariances. See a vignette at pdInd: G matrix with a pattern of zeros. }

Wald tests and linear hypothesis matrices

• wald() Wald tests with L matrices optionally created with regular ressions. Uses SVD to handle linear dependencies in rows of L
• walddf() version of wald that returns a data frame
• as.data.frame.wald() return a data frame from a wald object
• coef.wald() method to extract estimated coefficients
• print.wald() printing method
• Lfx() creates hypothesis matrices for derivatives and differences. example for factor differences
• M() constructor for M objects to generate portions of design and othesis matrices. Used with Lfx()
• rpfmt() format estimated values and p-values from a wald test
• Lall() for lmer objects
• Lc() for lmer objects
• Lmu() for lmer objects }

Utilities for fitted objects

• getD() get data frame from a fitted object
• getData() older version using methods. Might work if previous fails
• getFix() get fixed effects from a fitted object
• getX() get X matrix from fitted object
• getV() get V matrix from a mixed model
• getG() get G matrix from a mixed model
• getR() get R matrix from a mixed model
• Vcov() get estimated variance covariance of fixed effects from a ted object }

Multilevel data frames

• capply(): capply(x,id,FUN) applies the function FUN to chunks of 'x' formed by levels of 'id'. The result has the same form as 'x' with replication within chunks, if needed.
• up(), agg() and up_apply() create summary data sets consisting, by default, of within-id-invariant variables. Summaries of id-varying variables can also be included. agg() can create mean incidence matrices for lower-level factors.
• cvar() and dvar() are designed to be used in linear model formulas to generate 'centered-within-group' and 'within-group deviation' variables. WIth factors, they generate mean incidence matrices.
• varLevel() and gicc(): the level of a variable h respect to a clustering formula and the 'generalized' intra-class correlation coefficient.
• tolong() and towide() are interfaces to stats(reshape) to facilitate the typical uses of reshape for longitudinal data. }

Splines -- parametric and non-parametric

• gsp() creates a function for a generalized spline that can be included in a linear model formula.
• sc() creates a spline contrast matrix for general spline hypotheses. The matrix can be included in hypothesis matrices for the wald() function.
• smsp() creates a matrix for a smoothing spline. }

Datasets

• hsfull() Classical data set on high school math achievement and ses. Bryk and Raudenbush and many other sources.
• iq() Recovery after traumatic brain injury
• Drugs() Longitudinal data on drugs and schizophrenia symptoms. ustrates role of control variables with non-random assignment.
• Indonesia() Xerophthalmia.
• migraines() Longitudinal data on migraine treatment and weather
• coffee() Artificial data on coffee, heart damage and stress. ustrates Simpson's Paradox with continuous predictors.
• hw() Artificial data on height, weight and health. Illustrates pression.
• Unemp() U.S. monthly unemployment from January 1995 to February 2019. }

Graphics

• gd() and td() are easy interface to set graphical parameters for lattice and graphics. gd() sets parameters to make graphs look like ggplot2 graphics.
• panel.fit() add fitted values and error bands with latticeExtra::layer() or latticeExtra::glayer()
• panel.dell() adds data ellipse with latticeExtra::layer() or latticeExtra::glayer() }

Miscellaneous utility functions

• sortdf() sort rows of a data frame -- useful in a magrittr pipeline
• assn() assign -- useful in a magrittr pipeline
• disp() utility to display value of a variable -- useful for debugging
• getFactorNames() get names of variables that are factors in a data me
• %less%() synonym for setdiff() as well as %and%() and %or%()
• labs() assign, extract and print labels for various objects
• pch() generate plotting character mnemonically
• pfmt() format p-values
• print.cat()
• rnd() round a vector to keep significant digits in variation in values
• run() evaluate a string as a command with try
• grepv() grep(..., value = TRUE) }

String manipulation functions

Function designed to work smoothly with pipes in \pkg{magrittr}: - sub_() and gsub_() handle substitution inline and return a factor if the input is a factor. - name() changes the names of an object and returns the renamed object

gmonette/spida2 documentation built on July 14, 2024, 12:45 p.m.