Package 'swfscMisc'

Title: Miscellaneous Functions for Southwest Fisheries Science Center
Description: Collection of conversion, analytical, geodesic, mapping, and plotting functions. Used to support packages and code written by researchers at the Southwest Fisheries Science Center of the National Oceanic and Atmospheric Administration.
Authors: Eric Archer [aut, cre]
Maintainer: Eric Archer <[email protected]>
License: GPL (>= 2)
Version: 1.6.5
Built: 2024-11-07 05:43:19 UTC
Source: https://github.com/ericarcher/swfscmisc

Help Index

Affinity Propagation

Description

Runs the Affinity Propagation clustering algorithm of Frey and Dueck, 2007.

Usage

affin.prop(
  sim.mat,
  num.iter = 100,
  stable.iter = 10,
  shared.pref = "min",
  lambda = 0.5
)

Arguments

sim.mat

a similarity matrix between individuals to be clustered.
num.iter

maximum number of iterations to attempt.
stable.iter

number of sequential iterations for which consistent clustering is considered acceptable.
shared.pref

type of shared preference to use. Can be one of "min", "median", or a numeric value.
lambda

damping factor.

Value

A matrix with one row per sample in 'sim.mat' and one column for each iteration. Values in columns indicate cluster assignment (arbitrary numbers) for each sample.

Author(s)

Eric Archer [email protected]

References

Frey, B.J., and D. Dueck. 2007. Clustering by passing messages between data points. Science 315:972-976

Examples

data(iris)

# Take 75 random iris rows for example
iris <- iris[sample(1:nrow(iris), 75), ]
iris <- droplevels(iris)

iris.sim <- -dist(iris[, -5])

iris.affin <- affin.prop(iris.sim, stable.iter = 5)
table(iris$Species, iris.affin[, ncol(iris.affin)])

Auto Time Interval Units

Description

Convert time interval units to natural values based on magnitude of difference.

Usage

autoUnits(x)

Arguments

x

an object inheriting from class difftime

Author(s)

Eric Archer [email protected]

Examples

autoUnits(as.difftime("0:3:35"))
autoUnits(as.difftime("15:3:35"))
autoUnits(ISOdate(2000, 5, 1) - ISOdate(2000, 4, 20))

Calculate Bearing Between Two Positions

Description

Calculates the bearing between two points, given each point's latitude and longitude coordinates

Usage

bearing(lat1, lon1, lat2, lon2)

Arguments

lat1, lon1

numeric. The latitude and longitude of the starting coordinate in decimal degrees.
lat2, lon2

numeric. The latitude and longitude of the ending coordinate in decimal degrees.

Value

vector with initial and final bearings.

Author(s)

Eric Archer [email protected]

Examples

# What is the bearing from San Diego, CA to Honolulu, HI?
bearing(32.87, -117.25, 21.35, -157.98)

Area of a Box

Description

Calculate the area of a square on the earth.

Usage

box.area(lat, lon, edge, units = "nm")

Arguments

lat, lon

The latitude and longitude of the lower right corner of the box in decimal degrees.
edge

The length of one side of the square in decimal degrees.
units

units of distance. Can be "km" (kilometers), "nm" (nautical miles), or "mi" (statute miles).

Author(s)

Eric Archer [email protected]

Examples

#What is the area of a 5 degree grid off of San Diego, CA?
box.area(32.87, -117.25, edge = 1, units = "nm")
box.area(32.87, -117.25, edge = 1, units = "km")
box.area(32.87, -117.25, edge = 1, units = "mi")

Braces

Description

Adds curly braces to a plot.

Usage

braces(
  xfrom,
  xto,
  yfrom,
  yto,
  radius = 1,
  col = par("fg"),
  lty = par("lty"),
  lwd = par("lwd")
)

Arguments

xfrom, xto, yfrom, yto

start and end points of braces. Direction of brace determined by from and to arguments.
radius

radius of curve in brace.
col, lty, lwd

color, line type, and line width of braces. See par for more details.

Note

Orientation of brace is either horizontal or vertical, with axis along largest range of x or y in plotting units.

Author(s)

Tim Gerrodette [email protected]

Examples

plot(x = c(0, 1), y = c(0, 1000), type = "n", xlab= "", ylab = "")
braces(xfrom = 0.2, xto = 0.8, yfrom = c(400, 600), yto = c(300, 700))
plot(x = c(0, 100), y = c(0, 17), type = "n", xlab = "x", ylab = "y")
text(10, 16, "radius =")
for (i in 1:8) {
  braces(xfrom = 10 * i + 10, xto = 10 * i + 18, yfrom = 1, 
         yto = 15, radius = i / 4, lwd = 2)
  text(10 * i + 12, 16, round(i / 4, 2))
}
plot(c(0, 100), c(0, 17), type = "n", xlab = "x", ylab = "y")
braces(30, 80, 13, 11, 1)

plot(c(0, 100), c(0, 17), type = "n", xlab = "x", ylab = "y")
braces(c(20, 80, 30), c(10,75,40), 1, 15, radius = c(0.2, 0.5, 0.1), 
       lwd = c(1, 2, 3), col = 1:2, lty = 1)

plot(c(0, 100), c(0, 17), type = "n")
braces(20, 80, 7, 5, 1)
braces(20, 80, 13, 15, 1)

Categorical Spatial Interpolation

Description

Create a raster of probability of categorical values interpolated across a 2-dimensional space given a set of points where each is assigned to one of several classes.

Usage

catSpatInterp(
  data,
  x.col = "x",
  y.col = "y",
  group.col = "group",
  num.grid = 100,
  knn = 10,
  hull.buffer = 0.1,
  num.cores = 1,
  num.batches = NULL
)

Arguments

data

matrix or data.frame containing points and grouping designation.
x.col, y.col, group.col

numbers or characters identifying which columns in data are the x and y values and grouping designation.
num.grid

number of grid cells for k-nearest neighbor interpolation.
knn

number of nearest neighbors to consider for interpolation.
hull.buffer

percent increase of convex hull to use as spatial area to interpolate over.
num.cores

number of cores to distribute interpolations over.
num.batches

number of batches to divide grid cell interpolations into.

Value

A list containing a raster and points of buffered convex hull.

Author(s)

Eric Archer [email protected]

References

Adapted from code originally presented in a blog post on Categorical Spatial Interpolation by Timo Grossenbacher https://timogrossenbacher.ch/2018/03/categorical-spatial-interpolation-with-r/

Examples

## Not run: 
iris.mds <- stats::cmdscale(dist(iris[, 1:4]), k = 2)
mds.df <- setNames(
  cbind(iris.mds, data.frame(iris$Species)),
  c("dim1", "dim2", "Species")
)

result <- catSpatInterp(
  mds.df, x.col = "dim1", y.col = "dim2", group.col = "Species", 
  num.grid = 300, knn = 20, hull.buffer = 0.05,
  num.cores = 5, num.batches = NULL
)

library(ggplot2)
ggplot(mapping = aes(dim1, dim2)) +
  geom_raster(
    aes(fill = Species, alpha = prob), 
    data = result$raster
  ) +
  geom_polygon(data = result$hull.poly, fill = NA, col = "black") +
  geom_hline(yintercept = 0, col = "white") +
  geom_vline(xintercept = 0, col = "white") +
  geom_point(
    aes(fill = Species), 
    data = mds.df, 
    col = "black", 
    shape = 21, 
    size = 4
  ) + 
  theme(
    axis.ticks = element_blank(),
    axis.text = element_blank(),
    axis.title = element_blank(),
    legend.position = "top",
    panel.grid = element_blank(),
    panel.background = element_blank()
  )

## End(Not run)

Central Quantile

Description

Upper and lower values of central quantile

Usage

central.quantile(x, pct = 0.95)

Arguments

x

numeric vector.
pct

central percentile desired.

Value

a two element vector giving the lower and upper quantiles.

Author(s)

Eric Archer [email protected]

Examples

x <- runif(1000)
central.quantile(x)
central.quantile(x, pct = 0.75)

Circle Polygon (on Earth)

Description

Creates a circular polygon (optionally on the earth) centered at a given point with a constant radius.

Usage

circle.polygon(
  x,
  y,
  radius,
  brng.limits = 0,
  sides = 1,
  by.length = TRUE,
  units = "nm",
  ellipsoid = datum(),
  dist.method = "lawofcosines",
  destination.type = "ellipsoid",
  poly.type = "cart.earth"
)

Arguments

x, y

number specifying the coordinates of the center of the circle in decimal degrees. If poly.type is "simple.earth" or "complex.earth", this will be longitude and latitude respectively.
radius

radius of sphere.
brng.limits

number, or vector of two numbers. If one value is given, it is used as the starting bearing in degrees for the first point of the circle. If a vector of two values is given, then they are used as the start and end bearings of arc.
sides

number that represents either length of sides or number of sides, as specified by the 'by.length' argument.
by.length

logical. If TRUE, then sides is the length of sides, if FALSE, then sides is number of sides.
units

character for units of distance: Can be "km" (kilometers), "nm" (nautical miles), "mi" (statute miles).
ellipsoid

ellipsoid model parameters as returned from a call to datum.
dist.method

character specifying method for calculating distance for type = "cart.earth". See method argument of distance for more information.
destination.type

character specifying type of surface for type = "gc.earth". See type argument of destination for more information.
poly.type

character specifying the type of polygon calculation to use. Can be one of "cartesian" using basic cartesian coordinates, "cart.earth" for a simple polygon on the earth's surface treating latitude and longitude as cartesian coordinates, or "gc.earth" for a more precise calculation keeping a constant great-circle radius.

Value

A matrix representing the desired circle polygon centered at lat, lon of radius.

Author(s)

Eric Archer [email protected]

Examples

cart.earth <- circle.polygon(-117.24, 32.86, 40, poly.type = "cart.earth")

lat.range <- c(32, 34)
lon.range <- c(-118.5, -116)

op <- par(mar = c(3, 5, 5, 5) + 0.1, oma = c(1, 1, 1, 1))

plot.new()
plot.window(xlim = lon.range, ylim = lat.range)
points(-117.24, 32.86, pch = 19, col = "red")
polygon(cart.earth, border = "red", lwd = 3)
lat.lon.axes(n = 3)
box(lwd = 2)
mtext("poly.type = 'cart.earth'", line = 3)

par(op)

Color Name

Description

Return the name of a color listed given the number.

Usage

color.name(i)

Arguments

i

integer specifying color .

Value

character value of 'i' color.

Author(s)

Eric Archer [email protected]

Angle Conversion

Description

Converts angles between radians and degrees.

Usage

convert.angle(x, from = c("degrees", "radians"), to = c("radians", "degrees"))

Arguments

x

numeric. The angle to be converted.
from, to

character. Units to convert from and to. Can be "radians" or "degrees" or any partial match (case-sensitive).

Author(s)

Eric Archer [email protected]

Examples

convert.angle(45, "deg", "rad")
convert.angle(4.5, "r", "d")

Distance Conversion

Description

Convert distances between kilometers, nautical miles, and statute miles.

Usage

convert.distance(x, from = c("nm", "km", "mi"), to = c("km", "nm", "mi"))

Arguments

x

numeric. The distance to be converted.
from, to

character. Units to convert from and to. Can be "km" (kilometers), "nm" (nautical miles), or "mi" (statute miles), or any partial match thereof (case sensitive).

Author(s)

Eric Archer [email protected]

Copy Matrix Triangles

Description

Copy between lower left and upper right triangles of a matrix.

Usage

copy.tri(x, from = "lower")

Arguments

x

a matrix.
from

triangle to copy from. Can be "lower" or "upper".

Value

a matrix.

Author(s)

Eric Archer [email protected]

Examples

x <- matrix(1:9, nrow = 3)
print(x)
copy.tri(x)

Crossing Point

Description

Return point where two lines cross

Usage

crossing.point(l1, l2)

Arguments

l1, l2

matrices representing two lines, where first two columns are x and y values respectively

Value

a data.frame of x and y values of points where lines cross

Author(s)

Eric Archer [email protected]

Examples

x <- 1:100
line1 <- cbind(x, 3 + 3 * x)
line2 <- cbind(x, 10 - 3 * x)
plot(line1[, 1], line1[, 2], type = "l", col = "red")
lines(line2[, 1], line2[, 2], col = "blue")
cr.pt <- crossing.point(line1, line2)
print(cr.pt)

Datum

Description

Return parameters specifying ellipsoid datum model.

Usage

datum(model = c("wgs84", "grs80", "airy", "international", "clarke", "grs67"))

Arguments

model

character, specifying which model to use for ellipsoid model. Options are: "wgs84", "grs80", "airy", "international", "clarke", "grs67", or partial matches thereof (case-sensitive).

Value

vector of a, b, and f parameters.

Note

Model parameters are based on distances in km.

Author(s)

Eric Archer [email protected]

Destination on Sphere or Ellipsoid

Description

Calculates latitude and longitude of the destination along a sphere or ellipsoid.

Usage

destination(
  lat,
  lon,
  brng,
  distance,
  units = c("nm", "km", "mi"),
  ellipsoid = datum(),
  radius = convert.distance(6371, "km", "nm"),
  type = c("ellipsoid", "sphere", "vincenty")
)

Arguments

lat, lon

numeric. The latitude and longitude of the coordinate in decimal degrees.
brng

numeric. The bearing, ranging from 0 to 360 degrees.
distance

numeric. The distance travelled, in units specified by units.
units

units of distance. Can be "km" (kilometers), "nm" (nautical miles), or "mi" (statute miles), or any partial match thereof (case sensitive).
ellipsoid

ellipsoid model parameters as returned from a call to datum.
radius

numeric. Define the radius for type = "sphere". In units of units.
type

Character defining type of surface. Can be "sphere", "ellipsoid", "vincenty", or partial match thereof (case-sensitive).

Value

latitude and longitude of destination.

Author(s)

Eric Archer [email protected]

References

Ellipsoid code adapted from JavaScript by Larry Bogan.
Vincenty code adapted from JavaScript by Chris Veness.
Vincenty, T. 1975. Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Survey Review 22(176):88-93.

Examples

destination(32.87, -117.25, 262, 4174, units = "km", type = "sphere")
destination(32.87, -117.25, 262, 4174, units = "km", type = "ellipsoid")
destination(32.87, -117.25, 262, 4174, units = "km", type = "vincenty")

Distance Between Coordinates

Description

Calculates the distance between two coordinates using the Law of Cosines, Haversine, or Vincenty methods.

Usage

distance(
  lat1,
  lon1,
  lat2,
  lon2,
  radius = convert.distance(6371, "km", "nm"),
  units = c("nm", "km", "mi"),
  ellipsoid = datum(),
  iter.limit = 20,
  method = c("lawofcosines", "haversine", "vincenty")
)

Arguments

lat1, lon1, lat2, lon2

The latitude and longitude of the first and second points in decimal degrees.
radius

radius of sphere.
units

units of distance. Can be "km" (kilometers), "nm" (nautical miles), or "mi" (statute miles), or any partial match thereof (case sensitive).
ellipsoid

ellipsoid model parameters as returned from a call to datum.
iter.limit

An integer value defining the limit of iterations for Vincenty method.
method

Character defining the distance method to use. Can be "lawofcosines", "haversine", "vincenty", or any partial match thereof (case sensitive).

Author(s)

Eric Archer [email protected]

References

Code adapted from JavaScript by Chris Veness
Vincenty, T. 1975. Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Survey Review 22(176):88-93.

Examples

# What is the distance from San Diego, CA to Honolulu, HI?
distance(32.87, -117.25, 21.35, -157.98, method = "lawofcosines")
distance(32.87, -117.25, 21.35, -157.98, method = "haversine")
distance(32.87, -117.25, 21.35, -157.98, method = "vincenty")

Distribution summary

Description

Summarize a numerical distribution.

Usage

distSmry(x, p = 0.95, ...)

Arguments

x

vector of numerical values.
p

percent of distribution to summarized by quantile interval (ci) and highest posterior density interval (hdi).
...

arguments passed to mlv to estimate the mode if use.mlv is TRUE.

Author(s)

Eric Archer [email protected]

Unbiased Estimate of Diversity

Description

Calculate unbiased estimate of diversity for a vector of items

Usage

diversity(x)

Arguments

x

character or numeric vector or factor

Author(s)

Eric Archer [email protected]

Examples

x <- sample(1:5, 100, replace = TRUE)
diversity(x)

Fisher's Method p-value

Description

Calculate Fisher's method p-value to summarize a vector of p-values based on a chi-squared distribution.

Usage

fisher.p(p)

Arguments

p

vector of p-values.

Author(s)

Eric Archer [email protected]

Geometric Mean

Description

Calculates the geometric mean of a vector.

Usage

geometric.mean(x, w = NULL, na.rm = FALSE)

Arguments

x

a numeric vector.
w

an optional numerical vector of weights the same length as x.
na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

Author(s)

Eric Archer [email protected]

Examples

x <- rlnorm(100)
mean(x)
median(x)
geometric.mean(x)

ggBiplot

Description

Plot a biplot of a Principal Components Analysis using ggplot2.

Usage

ggBiplot(pca, x = 1, y = 2, mult.fac = 0.8, arrow.size = 1.5, label.size = 6)

Arguments

pca

result from a call to princomp.
x, y

the number or column names of the components to plot.
mult.fac

multiplier factor for lengths of arrows from 0:1.
arrow.size

thickness of arrow lines.
label.size

size of labels.

Value

the ggplot2 object is invisibly returned.

Author(s)

Eric Archer [email protected]

Examples

pc.cr <- princomp(USArrests, cor = TRUE)
ggBiplot(pc.cr)

Harmonic Mean

Description

Calculate the harmonic mean of a set of numbers.

Usage

harmonic.mean(x, w = NULL, na.rm = FALSE)

Arguments

x

a numeric vector.
w

an optional numerical vector of weights the same length as x.
na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

Note

If zeroes are present in x, function will return approximation with a warning. In this case, weights will not be used.

Author(s)

Eric Archer [email protected]

Examples

x <- rlnorm(100)
mean(x)
median(x)
harmonic.mean(x)

Iterative Missing Data Optimization (IMDO)

Description

Identify optimal combination of variables to minimize number of samples with missing data.

Usage

imdo(x, groups = NULL, plot = TRUE)

imdoPlot(opt.smry, equal.axes = FALSE)

Arguments

x

data.frame or matrix to optimize.
groups

vector of groups as long as number of rows in x.
plot

generate a plot of the optimization results.
opt.smry

data.frame of optimization summary results from run of imdo in ($opt.smry element).
equal.axes

show imdo plot with both axes on same scale?

Author(s)

Eric Archer [email protected]

Intersecting Point

Description

Calculates the perpendicular point and distance to a line for a series of points.

Usage

intersectingPoint(pts, p1 = NULL, p2 = NULL, intercept = NULL, slope = NULL)

Arguments

pts

two element vector or two column matrix of x and y values of points.
p1, p2

two element vectors of two points laying on line.
intercept, slope

the intercept and slope of the line.

Value

A matrix containing columns giving the x and y values of the intersecting point on the line, and the distance to each point.

Note

The line can be specified by providing either p1 and p2 or intercept and slope. If intercept and slope are specified, then p1 and p2 will be ignored.

Author(s)

Eric Archer [email protected]

Examples

pts <- cbind(x = runif(5, 0, 10), y = runif(5, 0, 10))

intersectingPoint(pts, p1 = c(-1, -1), p2 = c(60, 60))

intersectingPoint(pts, intercept = 0, slope = 1)

Between

Description

Is a numeric value between two other values?

Usage

isBetween(x, a, b = NULL, include.ends = FALSE, na.convert = TRUE)

Arguments

x

vector of numeric values to check.
a, b

numeric values describing range.
include.ends

logical. Should test include a and b? Is test > and < or >= and <= ?
na.convert

logical. If TRUE and result of test is NA because either x, a, or b is NA, return FALSE, otherwise return NA.

Details

Order of a and b does not matter. If b is NULL the range will be taken from values in a.

Author(s)

Eric Archer [email protected]

Label Width

Description

Calculate width of labels for plots.

Usage

lab.wid(labels)

Arguments

labels

vector of labels to be used on plots

Latitude and Longitude axes

Description

Add latitude and longitude axes to a map.

Usage

lat.lon.axes(n = 5, lon.n = n, lat.n = n)

Arguments

n, lon.n, lat.n

the number of tick marks desired. Can be specified separately for longitude (lon.n) or latitude (lat.n). See pretty for more details.

Author(s)

Eric Archer [email protected]

Convert Months to Seasons

Description

Convert numeric month to season: Winter = Dec-Feb, Spring = Mar-May, Summer = Jun-Aug, Fall = Sep-Nov

Usage

month2Season(x)

Arguments

x

a vector of months from 1:12

Author(s)

Eric Archer [email protected]

Examples

months <- sample(1:12, 10, rep = TRUE)
months
month2Season(months)

Count NAs

Description

Counts NAs in an object.

Usage

na.count(x)

Arguments

x

a vector, data.frame, or matrix.

Author(s)

Eric Archer [email protected]

Examples

x <- sample(c(1:10, NA), 30, replace = TRUE)
na.count(x)
x.df <- do.call(data.frame, lapply(1:4, function(i) sample(c(1:10, NA), 30, replace = TRUE)))
colnames(x.df) <- paste("X", 1:4, sep = "")
na.count(x.df)

Odds Conversion

Description

odds converts probability to odds
logOdds converts odds to log-odds
invOdds converts odds to probability
invLogOdds converts log-odds to odds

Usage

odds(x)

logOdds(x)

invOdds(x)

invLogOdds(x)

Arguments

x

a numeric vector of probabilities (0 to 1), odds (0 to Inf), or log.odds (-Inf to Inf).

Author(s)

Eric Archer [email protected]

Examples

x <- sort(runif(10))
odds.df <- data.frame(x = x, odds = odds(x), logOdds = logOdds(x))
odds.df
invOdds(odds.df$odds)
invLogOdds(odds.df$logOdds)

One Argument

Description

Does the function have just one argument?

Usage

one.arg(f)

Arguments

f

a function.

Author(s)

Eric Archer [email protected]

Examples

one.arg(mean)
one.arg(one.arg)

Perpendicular Distance

Description

Calculate the perpendicular distance of a matrix of points to a line.

Usage

perpDist(pts, line)

Arguments

pts

two column matrix of points.
line

either a 2x2 matrix of points defining line or two element vector giving intercept and slope of line.

Author(s)

Eric Archer [email protected]

Examples

ran.pts <- matrix(runif(10), ncol = 2)
x <- perpDist(ran.pts, c(0, 1))
x

plot.new()
plot.window(xlim = c(0, 1), ylim = c(0, 1), asp = 1)
abline(a = 0, b = 1)
points(ran.pts[, 1], ran.pts[, 2])
segments(ran.pts[, 1], ran.pts[, 2], x[, 1], x[, 2], lty = "dashed")
points(x[, 1], x[, 2], col = "red")
axis(1, pos = 0)
axis(2, pos = 0)

Perpendicular Point

Description

Compute the perpendicular point between points and a line specified by an intercept and slope.

Usage

perpPoint(pts, line)

Arguments

pts

two column matrix of points.
line

two element vector giving intercept and slope of a line.

Author(s)

Eric Archer [email protected]

Plot assignment distributions

Description

Plot individual assignment probability distributions.

Usage

plotAssignments(
  probs,
  orig,
  type = NULL,
  ylab = NULL,
  freq.sep.line = TRUE,
  plot = TRUE
)

Arguments

probs

matrix or data.frame of individual assignment probabilities. Each column represents probability of assignment to that group and rows sum to one.
orig

vector of original group assignments
type

either area for stacked continuous area plot or bar for discrete stacked bar chart. The latter is prefered for small numbers of cases. If not specified, a bar chart will be used if all classes have <= 30 cases.
ylab

label for y-axis
freq.sep.line

put frequency of original group on second line in facet label? If FALSE, labels are single line. If NULL frequencies will not be included in labels.
plot

display the plot?

Value

the ggplot object is invisibly returned.

Author(s)

Eric Archer [email protected]

Examples

n <- 40
probs <- abs(c(rnorm(n, 80, 10), rnorm(n, 20, 10)))
probs <- (probs - min(probs)) / max(probs)
probs <- cbind(probs, 1 - probs)
colnames(probs) <- NULL
orig <- rep(c("Group.1", "Group.2"), each = n)

plotAssignments(probs, orig)

n <- 15
probs <- abs(c(rnorm(n, 80, 10), rnorm(n, 20, 10)))
probs <- (probs - min(probs)) / max(probs)
probs <- cbind(probs, 1 - probs)
colnames(probs) <- NULL
orig <- rep(c("Group.1", "Group.2"), each = n)

plotAssignments(probs, orig)

Permutation Test P-value

Description

Calculate the p-value for a permutation test.

Usage

pVal(obs, null.dist)

Arguments

obs

observed value.
null.dist

vector of values from permutation null distribution.

Author(s)

Eric Archer [email protected]

Examples

null.dist <- rnorm(1000)
obs <- rnorm(1, mean = 1)

plot(density(null.dist), xlim = range(c(obs, null.dist)), main = "")
abline(v = obs)
print(obs)
pVal(obs, null.dist)

Rounding Numbers for Data Frames

Description

Rounds numeric columns in data.frames

Usage

## S3 method for class 'data.frame'
ceiling(x)

## S3 method for class 'data.frame'
floor(x)

## S3 method for class 'data.frame'
trunc(x, ...)

## S3 method for class 'data.frame'
round(x, digits = 0)

## S3 method for class 'data.frame'
signif(x, digits = 6)

Arguments

x

a data.frame with numeric columns.
...

arguments to be passed to methods.
digits

integer indicating the number of decimal places (round) or significant digits (signif) to be used. See round for more details.

Details

Takes a data.frame and returns a data.frame with the specified function applied to each numeric column.

Author(s)

Eric Archer [email protected]

See Also

Round

Examples

data(mtcars)

round(mtcars, 0)

signif(mtcars, 2)

Number of Rows and Columns on Page

Description

Return the number of rows and columns for n that best fits on a page of size width x height.

Usage

row.col.page.fit(n, width = 8.5, height = 11)

Arguments

n

number of items (e.g., plots) to fit on page.
width, height

dimensions of page.

Value

A vector listing the number of rows and columns to use.

Author(s)

Eric Archer [email protected]

Examples

# 9 frames on US letter paper
row.col.page.fit(9)

# 9 frames on a square
row.col.page.fit(9, width = 10, height = 10)

Convert runjags posterior to list

Description

Convert runjags posterior to named list of vectors or arrays.

Usage

runjags2list(post, collapse.chains = TRUE)

Arguments

post

list of class 'runjags'. The output from a call to run.jags.
collapse.chains

return array with dimension for each chain?

Note

If collapse.chains = TRUE, the last dimension of arrays will always be samples from the posterior. If collapse.chains = FALSE, the last dimension of arrays will be individual chains, and the one prior to that will be samples from the posterior for each chain.

See Also

aperm to transpose the array if necessary. as.data.frame.table to convert arrays to data.frames.

Scatter Plot with Density Margins

Description

Produce a scatter plot with a histogram or density plot in the margins

Usage

scatterdens(x, y, dens.frac = 1/5, ...)

scatterhist(x, y, xlab = "", ylab = "", dens.frac = 1/5, ...)

Arguments

x, y

vectors of points to plot.
dens.frac

fraction of screen to be taken up by density plots on margins.
...

Arguments to be passed to plot.
xlab, ylab

labels for x and y axes.

Author(s)

Eric Archer [email protected]

References

Original code by Ken Kleiman

Examples

x <- rnorm(100)
y <- rlnorm(100)
op <- par(ask = TRUE)
scatterdens(x, y, xlab = "x", ylab = "y")
par(op)

Setup Clusters

Description

Setup parallel clusters for different operating systems.

Usage

setupClusters(num.cores = 1, max.cores = NULL)

Arguments

num.cores

number of cores for multithreading. If NULL, the number used is set to the value of parallel::detectCores() - 1.
max.cores

maximum number of cores to use.

Value

an object of class c("SOCKcluster", "cluster").

Author(s)

Eric Archer [email protected]

Sex Symbols

Description

Plots male and female symbols on current plot.

Usage

sex.symbols(x, y, sex = 1, col = par("fg"), lwd = par("lwd"), cex = 1)

Arguments

x, y

the x and y coordinates on the current plot.
sex

a numeric vector containing the values 1 (male) or 2 (female). If of length one, then value is recycled for all symbols.
col, lwd, cex

color, line width, and character expansion for each point. lwd and col are recycled as necessary to cover all points. See par for more details.

Author(s)

Tim Gerrodette [email protected]

Examples

x <- runif(20, 0, 10)
y <- runif(20, 0, 200)
plot(x, y, type = "n")
sex.symbols(x, y, sex = 1:2, cex = 1.5, lwd = c(1.5, 4), col = c("blue", "red"))

Skew-Normal parameter computation

Description

Compute parameters and moments of skew normal distribution.

Usage

sn.location(mode, scale, shape)

sn.mean(dp)

sn.mode(dp)

sn.variance(scale, shape)

sn.skewness(shape)

sn.delta(shape)

sn.m0(shape)

Arguments

mode

mode of skew normal distribution.
scale

skew normal scale parameter.
shape

skew normal shape parameter.
dp

3 element vector of (in order) location, scale, and shape parameters.

Value

sn.location location parameter computed from mode, scale, and shape.
sn.mean mean of the skew normal distribution.
sn.mode mode of the skew normal distribution.
sn.variance variance of the skew normal distribution.
sn.skewness skewness of the skew normal distribution.
sn.delta value used in other moment computations.
sn.m0 value used in mode computation.

Author(s)

Eric Archer [email protected]

References

https://en.wikipedia.org/wiki/Skew_normal_distribution

See Also

sn package by Adelchi Azzalini for skew normal PDF and CDF functions.
Azzalini, A. with the collaboration of Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.

Stouffer's Method p-value

Description

Calculate Fisher's method p-value to summarize a vector of p-values based on a chi-squared distribution.

Usage

stouffer.p(p, w = NULL)

Arguments

p

vector of p-values.
w

vector weights.

Author(s)

Eric Archer [email protected]

swfscMisc package

Description

SWFSC Miscellaneous Functions

Author(s)

Maintainer: Eric Archer [email protected]

See Also

Useful links:

Transparent Colors

Description

Return transparent form of a named color.

Usage

transparent(col, percent = 50)

Arguments

col

vector of colors as name, hexadecimal, or positive integer (see col2rgb).
percent

percent of transparency (0 = solid, 100 = transparent).

Author(s)

Eric Archer [email protected]

Examples

pct <- seq(0, 100, by = 10)
plot(pct, pct, bg = transparent("red", pct), pch = 21, cex = 4, xlab = "X", ylab = "Y")

Uniform Distribution Test

Description

Tests whether a histogram is significantly different from a uniform distribution.

Usage

uniform.test(hist.output, B = NULL)

Arguments

hist.output

output from a call to hist.
B

number of replicates for chi-squared permutation.

Value

result of chi-squared test.

Author(s)

Eric Archer [email protected]

Examples

x.unif <- runif(100)
uniform.test(hist(x.unif), B = 1000)
x.lnorm <- rlnorm(100)
uniform.test(hist(x.lnorm), B = 1000)

Weighted Fisher's Method p-value

Description

Calculate weighted Fisher's method p-value to summarize a vector of p-values based on a chi-squared distribution.

Usage

weighted.fisher.p(p, w = NULL)

Arguments

p

vector of p-values.
w

vector weights.

Author(s)

Eric Archer [email protected]

Which Nearest

Description

Find values of one vector that are nearest to values in another vector.

Usage

which.nearest(x, y)

Arguments

x

vector of values to be compared against.
y

vector of values to examine relative to x. May be of length 1.

@return For each value in y, returns index of value of x which is nearest to y in absolute value. In the case of ties, the function returns the first index of x. If nearest value is min(x) or max(x), a warning is issued. NAs and NaNs in x are ignored; NAs and NaNs in y are returned.

Author(s)

Tim Gerrodette [email protected]

Examples

x <- sort(sample(1:100, 20))
y <- sort(sample(min(x):max(x), 5))
i <- which.nearest(x, y)
x
y
x[i]

Zero Pad Integers

Description

Return character representation of integers that are zero-padded to the left so all are the same length.

Usage

zero.pad(x)

Arguments

x

a vector of integers.

Author(s)

Eric Archer [email protected]

Examples

x <- c(0, 1, 3, 4, 10) 
zero.pad(x)
x <- c(x, 11, 12, 100, 1000)
zero.pad(x)