Title: Ports the Workflow of "Resampling Stats" Add-in to R
Version: 0.1.1
Description: Resampling Stats (http://www.resample.com) is an add-in for running randomization tests in Excel worksheets. The workflow is (1) to define a statistic of interest that can be calculated from a data table, (2) to randomize rows ad/or columns of a data table to simulate a null hypothesis and (3) and to score the value of the statistic from many randomizations. The relative frequency distribution of the statistic in the simulations is then used to infer the probability of the observed value be generated by the null process (probability of Type I error). This package intends to translate this logic for R for teaching purposes. Keeping the original workflow is favored over performance.
Depends: R (≥ 3.0.0)
Imports: graphics, stats, utils
License: GPL-2
LazyData: true
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 5.0.1
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2016-06-07 03:50:26 UTC; chalom
Author: Paulo Prado [aut, cre], Andr'e Chalom [aut], Alexandre Oliveira [aut]
Maintainer: Paulo Prado <prado@ib.usp.br>
Repository: CRAN
Date/Publication: 2016-06-07 14:46:12

Repeats randomizations and scores summary statistics

Description

Repeats resampling/shuffling of dataframes and scores the values returned by user-define function which is applied to each randomized dataframe.

Usage

Rsampling(type = c("normal_rand", "rows_as_units", "columns_as_units",
  "within_rows", "within_columns"), dataframe, statistics, ntrials = 10000,
  simplify = TRUE, progress = "text", fix.zeroes = FALSE, ...)

Arguments

type

character; the name of the randomization function to be applied to dataframe. See randomization functions.

dataframe

a dataframe with the data to be shuffled or resampled.

statistics

a function that calculates the statistics of interest from the dataframe. The first argument should be the dataframe with the data and preferably should return a (named) vector, data frame, matrix or array.

ntrials

integer; number of randomizations to perform.

simplify

logical; should the result be simplified to a vector, matrix or higher dimensional array if possible?

progress

which kind of progress bar should be used (currently unimplemented!)

fix.zeroes

logical; for normal_rand, within_rows or within_columns, should zeroes in the dataframe be kept in place? See the help on zfsample for more details.

...

further arguments to be passed to the randomization functions (e.g., cols, replace, stratum).

Value

a list of objects returned by the function defined by statistics or a vector, matrix or array when simplify=TRUE and simplification can be done (see simplify2array).

Details

This function corresponds to Repeat and score in Resampling Stats add-in for Excel (www.resample.com). The randomization function defined by type is applied ntrials times on the data provided by dataframe. At each trial the function defined by argument statistics is applied to the resulting dataframe and the resulting objects are returned.

References

Statistics.com LCC. 2009. Resampling Stats Add-in for Excel User's Guide. http://www.resample.com/content/software/excel/userguide/RSXLHelp.pdf

An experiment on ant recruitment

Description

Number of Azteca ants recruited by leaf extracts of their host plant , Cecropia trees.

Usage

azteca

Format

A data frame with 21 rows and 3 variables:

plant

plant id, integer

extract.new

number of recruited ants in the leaf that received drops of smashed new leaves extract

extract.old

number of recruited ants in the leaf that received drops of smashed old leaves extract

Details

The ant colonies live in the hollow trunk of Cecropia and can detect and expel leaf-chewing insects. To test if this response is more intense in young leaves, drops of extract of smashed young and old leaves were poured in two neighbor leaves of the same plant. After 7 minutes the number of recruited ants in each leaf was recorded.

Source

Kondrat, H. 2012. Estímulos químicos de folhas novas promovem recrutamento eficiente de formigas associadas à embaúba Cecropia glaziovi (Urticaceae). Curso de campo "Ecologia da Mata Atlântica" (G. Machado; P.I. Prado & A.M.Z. Martini, eds.). Universidade de São Paulo, São Paulo. http://ecologia.ib.usp.br/curso/2012/PDF/PI-Hebert.pdf

Shuffling and resampling functions

Description

Functions to run (un)restricted sampling with or without replacement in a dataframe.

Usage

within_rows(dataframe, cols = 1:ncol(dataframe), replace = FALSE,
  FUN = base::sample)

within_columns(dataframe, cols = 1:ncol(dataframe), stratum = rep(1,
  nrow(dataframe)), replace = FALSE, FUN = base::sample)

normal_rand(dataframe, cols = 1:ncol(dataframe), stratum = rep(1,
  nrow(dataframe)), replace = FALSE, FUN = base::sample)

rows_as_units(dataframe, stratum = rep(1, nrow(dataframe)), replace = FALSE,
  length.out = NULL)

columns_as_units(dataframe, cols = 1:ncol(dataframe), replace = FALSE,
  length.out = NULL)

Arguments

dataframe

a dataframe with the data to be shuffled or resampled.

cols

columns of dataframe that should be selected to be resampled/shuffled. Defaults for all columns.

replace

(logical) should the data be permuted (FALSE) or resampled with replacement (TRUE) ?

FUN

function used for the sampling procedure. The default is sample, and a new function zfsample is provided for sampling with fixed zeroes.

stratum

factor or integer vector that separates data in groups or strata. Randomizations will be performed within each level of the stratum. Needs at least two observations in each level. Default is a single-level stratum.

length.out

(integer) specifies the size of the resulting data set. For columns_as_units, a data.frame with length.out columns will be returned, and for rows_as_units, a data.frame with length.out rows will be returned. Note that if length.out is larger than the relevant dimension, replace must also be specified.

Value

a dataframe with the same structure of those input in dataframe with values randomized accordingly.

Details

Each function performs as close as possible the corresponding options in Resampling Stats add-in for Excel (www.resample.com) for permutation (shuffling) and sampling with replacement (resampling) values in tabular data:

All functions assemble the randomized values in a dataframe of the same configuration of the original. Columns that were not selected to be randomized with argument cols are then bound to the resulting dataframe. The order and names of the rows and columns are preserved, except if length.out is specified. In this case, the randomized rows/columns may be shifted to the end of the table.

When both stratum and length.out are used, the function will try to keep the proportion of each strata close to the original.

References

Statistics.com LCC. 2009. Resampling Stats Add-in for Excel User's Guide. http://www.resample.com/content/software/excel/userguide/RSXLHelp.pdf

Statistic distribution plot

Description

Plots the distribution of the statistic of interest. Has switches to plot the extreme values and null hypothesis rejection region (also known as critical region).

Usage

dplot(dist, svalue, pside = c("Two sided", "Greater", "Lesser"),
  extreme = TRUE, vline = TRUE, rejection = TRUE, ...)

Arguments

dist

the statistic distribution, as generated by Rsampling (numeric vector)

svalue

the result of applying the statistic over the original data

pside

the alternative hypothesis for the hypothesis testing

extreme

logical. should extreme points be highlighted in the plot?

vline

logical. should the svalue be displayed as a vertical line?

rejection

logical. should the critical region be highlighted?

...

further arguments to be passed to hist function.

See Also

See the package vignettes for more information about how to interpret this graph

Vine infestation on Cecropia trees

Description

Presence/absence data of vines on Cecropia trees of two morphotypes.

Usage

embauba

Format

A data frame with 152 rows (plants) and 2 variables:

morphotype

the tree morphotype, factor with two levels

with.vines

does the tree harbor vines? Logical.

Details

Two morphotypes of Cecropia trees differ in the occupancy by ant colonies. Ants attack and drive out other insects that get to the trees. To test if this protection also affects infestation by vines, trees of similar size of both morphs were sampled and inspected for the presence of vines.

Source

Mello, T.J. 2012. Infestação por lianas e comportamento de poda por formigas em Cecropia (Urticaceae). Curso de campo "Ecologia da Mata Atlântica" (G. Machado; P.I. Prado & A.M.Z. Martini, eds.). Universidade de São Paulo, São Paulo. http://ecologia.ib.usp.br/curso/2012/PDF/PI-Thayna.pdf

Preference of hunting spiders by hairy leaves

Description

Occupancy of Peucetia spiders on parts of an experimental arena covered by leaves with or without trichomes.

Usage

peucetia

Format

A data frame with 27 rows (trials) and 6 variables:

t1,t2,t3,t4,t5,t6

Is the spider on the part covered by hairy leaves? Logical, for each of 6 successive inspections (time 1, 2, ...

Details

Spiders of the genus Peucetia do not make webs and hunt actively on the vegetation. The data is from an experiment to test if spiders prefer to stay in hairy leaves, that can stick their prey. The spiders were kept in Petri dishes that had half of lower plate covered with hairy leaves. The other half was covered by leaves without trichomes. The placement of each spider was recorded 6 times at each 30 min.

Source

Werneck, R.T. 2010. Lar, viscoso lar. Experimento de seleção de habitat e forrageio de aranhas em plantas com tricomas glandulares. Curso de campo "Ecologia da Mata Atlântica" (G. Machado; P.I. Prado & A.A. Oliveira, eds.). Universidade de São Paulo, São Paulo. http://ecologia.ib.usp.br/curso/2010/pages/pdf/PI/relatorios/rachel.pdf

Aphids recorded on goldenrods

Description

Occurrences of aphids of the genus Dactynotus on plants of the genus Solidago in Canada

Usage

pielou

Format

A dataframe with 10 rows (aphid species of the genus Dactynotus) and 12 columns (plant species of the genus Solidago). Each entry is the number of records of a given aphid species on a plant species.

Details

Data from a field survey by E.C. Pielou in Ontario to exemplify a method to calculate niche overlap and niche width. The niche overlap gauges the overall similarity of the plant ranges used by the aphids. The niche width expresses how diverse is the average diversity of plants used by the aphids.

Source

Pielou, E.C. 1972. Niche width and niche overlap: a method for measuring them. Ecology, 53: 687–692.

Allometry in mangrove trees

Description

Canopy to height ratio and variables of root area in mangrove trees sampled in two soil types.

Usage

rhyzophora

Format

A data frame with 24 rows (trees) and 4 variables:

soil.instability

soil type according to instability; factor with two levels (high / medium)

canopy.trunk

ratio between canopy and trunk area, both in m2, numeric

root

area covered by aerial roots, numeric (m2)

n.roots

number of aerial roots, integer

Details

Data from a field practical exercise to test if mangrove trees in more unstable soil allocates more biomass in supporting roots.

Source

Prado, A. et al. 2013. Variações na morfologia de sustentação em Rhizophora mangle (Rizophoraceae) em diferentes condições de inundação do solo. Curso de campo "Ecologia da Mata Atlântica" (G. Machado, P.I. Prado & A.M.Z. Martini eds.). Universidade de São Paulo, São Paulo. http://ecologia.ib.usp.br/curso/2013/pdf/PO4-2.pdf

Spaghetti plot

Description

Quick plot of paired differences, for exploratory purposes.

Usage

splot(p1, p2, highlight = TRUE, col.dif = c("black", "grey"),
  groups.names = c(1, 2), ...)

Arguments

p1, p2

vectors of paired values (numerical vectors)

highlight

should positive and negative differences within pairs highlighted with different colors? Logical

col.dif

color vector if highlight = TRUE

groups.names

labels for the groups names; numeric or character vector of length two.

...

further arguments to be passed to plot function.

Zero-fixed (re)sampling

Description

This function builds on sample to provide sampling from a vector, but with all zero entries fixed. This way, zfsample(c(0,1,0,2)) may result in (0,1,0,2) or (0,2,0,1), but the positions that were initially zero will remain zeroed.

Usage

zfsample(x, replace = FALSE)

Arguments

x

Either a vector of one or more elements from which to choose, or a positive integer.

replace

Should sampling be with replacement?

Value

a vector of the same length of 'x' with elements drawn from 'x'.

Details

The actual sampling is done by sample, so its help page should be checked for details on the parameter handling. The parameter 'size' is always passed as length(x), and 'prob' is not supported.

Examples

# Sampling without replacement
zfsample(c(0,1,2,0,3,4,0))
# Sampling with replacement
zfsample(c(0,1,2,0,3,4,0), replace=TRUE)
# With no zeroes, zfsample just calls sample
set.seed(42); s1<-sample(c(1,2,3,4,5,6))
set.seed(42); s2<-zfsample(c(1,2,3,4,5,6))
all.equal(s1, s2)