Survival of passengers on the Titanic

Description

Matrix of Cramer's V computed on the variables economic status (class), sex, age and survival of the fate of passengers on the fatal maiden voyage of the ocean liner 'Titanic'.

Usage

data(Titanic.cramer)

Format

A 4x4 matrix with Cramer's V computed on

Class

1st, 2nd, 3rd, Crew

Sex

Male, Female

Age

Child, Adult

Survived

No, Yes

Source

The data are derived from the Survival of passengers on the Titanic data set.

Examples

data(Titanic.cramer)
plot(as.assoc(Titanic.cramer))

New York Air Quality Measurements

Description

p-values of pairwise correlation test of the complete-cases of daily air quality measurements in New York, May to September 1973.

Usage

data(air.pvalue)

Format

A 4x4 matrix with p values of pairwise correlation tests (cor.test).

Ozone

Ozone (ppb)

Solar.R

Solar R (lang)

Wind

Wind (mph)

Temp

Temperature (degrees F)

Source

The data are derived from the New York Air Quality Measurements data set.

References

Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983) Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

Examples

data(air.pvalue)
plot(as.pvalue(air.pvalue))

as.XXX conversion functions

Description

as.XXX conversion functions

Usage

as.cor(x)

as.assoc(x)

as.pvalue(x)

Arguments

Value

a matrix with an appropriate class

Examples

# as.cor
c <- cor(airquality, use="complete.obs")
# as.assoc
# as.pvalue
data(air.pvalue)
plot(as.pvalue(air.pvalue))

assignColors

Description

Assign to each value in x a color according to the choices of breaks and col.

Usage

assignColors(x, breaks = NULL, col = heat.colors, na.col = "white")

Arguments

Details

Depending if x is a numeric or non-numeric vector colors are assigned to each value.

In case of a numeric vector breaks can be

• a number, giving the number of intervals covering the range of x,

• a vector of two numbers, given the range to cover with 10 intervals, or

• a vector with more than two numbers, specify the interval borders

In case of a non-numeric vector breaks must contain all values which are will get a color. If breaks is not given then a sensible default is choosen: in case of a numeric vector derived from pretty and otherwise all unique values/levels are used.

col can be either be a vector of colors or a function which generates via col(n) a set of n colors. The default is to use heat.colors.

Possible color functions in R packages can be found by vignette('plot.matrix').

Value

vector of color with the same length as x with the attributes breaks the breaks used, col the color coding and na.col the color for NA and out-of-range entries

Examples

## numeric vector
x <- runif(10)
assignColors(x)
# set breaks
assignColors(x, breaks=15)
assignColors(x, breaks=c(0,1))
# set colors
assignColors(x, col=c("red", "green", "blue"))
assignColors(x, col=topo.colors)
# NA and out-of-range
x[5] <- NA
assignColors(x, breaks=seq(0.5, 1, by=0.1), na.col="red")
## logical vector
l <- sample(c(NA, TRUE, FALSE), size=10, replace=TRUE)
assignColors(l)
assignColors(l, breaks=c("FALSE", "TRUE"), col=c("red", "blue"))
## character vector
t <- sample(letters, size=10, replace=TRUE)
assignColors(t)
assignColors(t, col=rainbow(5))

25 Personality items representing 5 factors

Description

25 personality self report items taken from the International Personality Item Pool (ipip.ori.org) were included as part of the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. In contrast to the original data bfi from the library psych (version 1.8.12) it contains only the 25 personality self report items and the 2436 complete observations.

Usage

data(bfi.2)

Format

A data frame with 2436 observations on the following 25 variables (the q numbers are the SAPA item numbers).

A1

Am indifferent to the feelings of others. (q_146)

A2

Inquire about others' well-being. (q_1162)

A3

Know how to comfort others. (q_1206)

A4

Love children. (q_1364)

A5

Make people feel at ease. (q_1419)

C1

Am exacting in my work. (q_124)

C2

Continue until everything is perfect. (q_530)

C3

Do things according to a plan. (q_619)

C4

Do things in a half-way manner. (q_626)

C5

Waste my time. (q_1949)

E1

Don't talk a lot. (q_712)

E2

Find it difficult to approach others. (q_901)

E3

Know how to captivate people. (q_1205)

E4

Make friends easily. (q_1410)

E5

Take charge. (q_1768)

N1

Get angry easily. (q_952)

N2

Get irritated easily. (q_974)

N3

Have frequent mood swings. (q_1099

N4

Often feel blue. (q_1479)

N5

Panic easily. (q_1505)

O1

Am full of ideas. (q_128)

O2

Avoid difficult reading material.(q_316)

O3

Carry the conversation to a higher level. (q_492)

O4

Spend time reflecting on things. (q_1738)

O5

Will not probe deeply into a subject. (q_1964)

The 25 items are organized by five putative factors: Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Opennness.

The item data were collected using a 6 point response scale:

1

Very Inaccurate

2

Moderately Inaccurate

3

Slightly Inaccurate

4

Slightly Accurate

5

Moderately Accurate

6

Very Accurate

as part of the Synthetic Apeture Personality Assessment (SAPA https://www.sapa-project.org/) project. To see an example of the data collection technique, visit https://www.sapa-project.org/ or the International Cognitive Ability Resource at https://icar-project.com/. The items given were sampled from the International Personality Item Pool of Lewis Goldberg using the sampling technique of SAPA. This is a sample data set taken from the much larger SAPA data bank.

Note

The bfi.2 data set and items should not be confused with the BFI (Big Five Inventory) of Oliver John and colleagues (John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory–Versions 4a and 54. Berkeley, CA: University of California,Berkeley, Institute of Personality and Social Research.)

Source

The items are from the ipip (Goldberg, 1999). The data are from the SAPA project (Revelle, Wilt and Rosenthal, 2010), collected Spring, 2010 ( https://www.sapa-project.org/).

References

Goldberg, L.R. (1999) A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. In Mervielde, I. and Deary, I. and De Fruyt, F. and Ostendorf, F. (eds) Personality psychology in Europe. 7. Tilburg University Press. Tilburg, The Netherlands.

Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.

Revelle, W, Condon, D.M., Wilt, J., French, J.A., Brown, A., and Elleman, L.G. (2016) Web and phone based data collection using planned missing designs. In Fielding, N.G., Lee, R.M. and Blank, G. (Eds). SAGE Handbook of Online Research Methods (2nd Ed), Sage Publcations.

Examples

data(bfi.2)
library("psych")
fa <- fa(bfi.2, 5, rotate="varimax")
par(mar=c(5.1, 4.1, 4.1, 4.1)) # adapt margins
plot(loadings(fa), cex=0.5)

fmt

Description

Rounds and adds trailing zeros (by default if digits is lower than 4).

Usage

fmt(x, digits = 2L, zeros = digits < 4L, ...)

Arguments

Value

formatted x as text.

Source

Similar to function fmt from the package exams. In the function round2 has been replaced by round.

See Also

exams::fmt

Examples

## this is also employed internally in the fmt() formatting function
fmt(c(0.005, 0.015))

## the main purpose of fmt() is that some numeric result can be displayed
## both at high accuracy and then at the rounding that students should do
## (e.g., with 2 or 3 digits)
sol <- runif(1)
fmt(sol, 6)
fmt(sol, 2)

## but fmt() also assures showing a very high number of significant digits
## (up to 12)
sol <- 123456 + sol
sol
fmt(sol, 6)
fmt(sol, 2)

## and fmt() also takes care of adding trailing zeros (if digits < 4)
fmt(1)
fmt(1, digits = 3)
fmt(1, digits = 6)

plot.assoc

Description

Visualizes a association matrix with a colored or gray heatmap. As a rule of thumb the breaks are determined by the effect sizes given by Cohen (c(-1, -0.4, -0.2, -0.05, 0, +0.05, +0.2, +0.4, +1). You may need to modify mar with the par command from its default c(5.1,4.1,4.1,2.1). See

• vignette('plot.matrix') for detailed examples, and

• plot.matrix for further parameters.

Usage

## S3 method for class 'assoc'
plot(x, reorder = TRUE, gray = FALSE, grey = FALSE, ...)

Arguments

Details

If either the parameter grey or gray is TRUE then a gray color palette is used.

Value

a plot

Examples

par(mar=c(5.1, 4.1, 4.1, 4.1))
# association matrix
data(Titanic.cramer)
plot(as.assoc(Titanic.cramer))
plot(as.assoc(Titanic.cramer), gray=TRUE)
plot(as.assoc(Titanic.cramer[,1:3]), reorder=FALSE)

plot.cor

Description

Visualizes a correlation matrix with a colored or gray heatmap. As a rule of thumb the breaks are determined by the effect sizes given by Cohen (c(-1, -0.4, -0.2, -0.05, 0, +0.05, +0.2, +0.4, +1). You may need to modify mar with the par command from its default c(5.1,4.1,4.1,2.1). See

• vignette('plot.matrix') for detailed examples, and

• plot.matrix for further parameters.

Usage

## S3 method for class 'cor'
plot(x, reorder = TRUE, gray = FALSE, grey = FALSE, ...)

Arguments

Details

If either the parameter grey or gray is TRUE then a gray color palette is used.

Value

a plot

Examples

par(mar=c(5.1, 4.1, 4.1, 4.1))
# correlation matrix
c <- cor(airquality[,1:4], use="pairwise")
plot(as.cor(c))
plot(as.cor(c), gray=TRUE)
plot(as.cor(c[,1:3]), reorder=FALSE)

plot.loadings

Description

Visualizes the loadings matrix from a Factor Analysis or a Principal Component Analysis matrix with a gray or colored heatmap. As a rule of thumb the breaks are determined by c(-1, -0.866, -0.707, -0.5, -0.4, 0, +0.4, +0.5, +0.707, +0.866, +1) is used. You may need to modify mar with the par command from its default c(5.1,4.1,4.1,2.1). See

• vignette('plot.matrix') for detailed examples, and

• plot.matrix for further parameters.

Usage

## S3 method for class 'loadings'
plot(x, reorder = TRUE, gray = FALSE, grey = FALSE, ...)

Arguments

Details

If either the parameter grey or gray is TRUE then a gray color palette is used.

Value

a plot

Examples

data(bfi.2)
library("psych")
par(mar=c(5.1, 4.1, 4.1, 4.1))
# Factor analysis
fa <- factanal(bfi.2, 5)
plot(loadings(fa))
plot(loadings(fa), grey=TRUE)
# Principal Component Analysis I
pa <- princomp(bfi.2)
plot(loadings(pa), digits=NA)
# Principal Component Analysis II
pa <- prcomp(bfi.2)
ld <- structure(pa$rotation, class="loadings") 
plot(ld, digits=NA)

plot.matrix

Description

Visualizes a matrix with a colored heatmap and optionally a color key. It distinguishes between numeric and non-numeric matrices. You may need to modify mar with the graphics::par() command from its default c(5.1,4.1,4.1,2.1). For further see the vignette vignette('plot.matrix')

Usage

## S3 method for class 'matrix'
plot(
  x,
  y = NULL,
  breaks = NULL,
  col = heat.colors,
  na.col = "white",
  na.cell = TRUE,
  na.print = TRUE,
  digits = NA,
  fmt.cell = NULL,
  fmt.key = NULL,
  spacing.key = c(1, 0.5, 0),
  polygon.cell = NULL,
  polygon.key = NULL,
  text.cell = NULL,
  key = list(side = 4, las = 1),
  axis.col = list(side = 1),
  axis.row = list(side = 2),
  axis.key = NULL,
  max.col = 70,
  ...
)

Arguments

Details

A color key is drawn if either key (defaults to list(cex=1)) or fmt.key (defaults to NULL) is not NULL.

If you want to plot the matrix entries you must set either digits or fmt. For a non-numeric matrix digits gives the length of the string printed, a negative value results in right-justified string. For a numeric matrix digits determines the number of decimal places, a negative value uses a "exponential" decimal notation. You may set format strings fmt and fmt.key directly. Settings digits leads to the following format strings (n the absolute value of digits):

If no colors are given then the grDevices::heat.colors() will be used. Alternatively you may specify your own color function that delivers a vector with n colors if called by col(n). The final colors and breaks used depend if plot.matrix gets a numeric or non-numeric matrix.

Numeric matrix: In general it must hold length(col)+1==length(breaks).

1. breaks==NULL and col==NULL

The colors are taken from heat.colors(10) and the eleven breaks are calculated as an equidistant grid between min(x) and max(x).

2. breaks==NULL and col is a color function

Ten colors are taken from the color function and eleven breaks are calculated as an equidistant grid between min(x) and max(x).

3. breaks==NULL and col is a vector of colors

The length(col)+1 breaks are calculated as an equidistant grid between min(x) and max(x).

4. breaks are given and col==NULL

The colors are taken from heat.colors(length(breaks)-1).

5. breaks are given and col is a color function

The length(breaks)-1 colors are taken from the color function.

6. breaks are given and col is a vector of colors

If not length(col)+1==length(breaks) holds then the length(col)+1 breaks are calculated as an equidistant grid between min(breaks) and max(breaks).

Non-numeric matrix: In general it must hold length(col)==length(breaks). At first the number of unique elements in x is determined: nu.

1. breaks==NULL and col==NULL

The colors are taken from heat.colors(nu) and the breaks are set to the unique elements of x.

2. breaks==NULL and col is a color function

The nu colors are taken from color function and the breaks are set to the unique elements of x.

3. breaks==NULL and col is a vector of colors

The length(col) breaks are calculated as an equidistant grid between min(x) and max(x).

4. breaks are given and color==NULL

The colors are taken from heat.colors(length(breaks)).

5. breaks are given and color is a color function

The length(breaks) colors are taken from color function.

6. breaks are given and color is a vector of colors

If not length(colors)==length(breaks) holds then either breaks or color is shorten to the shorter of both.

If the difference between polygon color and the text color is smaller max.col then as text color is either white or black (depending which one is further away from the polygon color). The distance is computed as \Delta C/3 as in https://en.wikipedia.org/wiki/Color_difference#Euclidean given.

Value

invisibly a list with elements

cell.polygon[[i,j]]

the polygon parameters used to draw the elements of the matrix

cell.text[[i,j]]

the text parameters used to draw the elements of the matrix

plot

the plot parameters used to draw the basic plot

axis.col

the axis parameters used to draw column axis

axis.row

the axis parameters used to draw row axis

key.polygon[[i]]

the polygon parameters used to draw the elements of the key

key.axis

the axis parameters used to draw key axis

A NULL means the elements has not been drawn.

Note

The use of fmt or fmt.key have the same restrictions as the use of fmt in base::sprintf():

The format string is passed down the OS's sprintf function, and incorrect formats can cause the latter to crash the R process. R does perform sanity checks on the format, but not all possible user errors on all platforms have been tested, and some might be terminal.

Examples

par(mar=c(5.1, 4.1, 4.1, 4.1))
# numeric matrix
x <- matrix(runif(50), nrow=10)
plot(x)
plot(x, key=NULL)
plot(x, key=list(cex.axis=0.5, tick=FALSE))
plot(x, digits=3)
plot(x, breaks=range(x), digits=3, cex=0.6)
# logical matrix
m <- matrix(runif(50)<0.5, nrow=10)
plot(m)
plot(m, col=c("red", "blue"))
plot(m, key=NULL, digits=1)
# character matrix
s <- matrix(sample(letters[1:10], 50, replace=TRUE), nrow=10)
plot(s)
plot(s, col=topo.colors)
plot(s, digits=10)
plot(s, digits=1, col=heat.colors(5), breaks=letters[1:5])
plot(s, digits=1, col=heat.colors(5), breaks=c('a', 'c', 'e', 'g', 'i'))
# contingency table
tab <- table(round(rnorm(100)), round(rnorm(100)))
plot(unclass(tab))
# chisquare test residuals
cst <- chisq.test(apply(HairEyeColor, 1:2, sum))
col <- colorRampPalette(c("blue", "white", "red"))
plot(cst$residuals, col=col, breaks=c(-7.5,7.5))
# triangular matrix
x[upper.tri(x)] <- NA
plot(x, digit=2)
plot(x, na.print=FALSE)
plot(x, na.cell=FALSE)
# use the standard plot instead of plot.matrix
x <- matrix(runif(50), nrow=2)
plot(as.data.frame(x))
plot.default(x)
## Not run: 
# unload the package permanently with devtools
library("devtools")
unload('plot.matrix')

## End(Not run)

plot.pvalue

Description

Visualizes a matrix of p-values with a colored or gray heatmap. As a rule of thumb the breaks are determined by c(0, 0.001, 0.01, 0.05, 0.1, 1) You may need to modify mar with the par command from its default c(5.1,4.1,4.1,2.1). See

• vignette('plot.matrix') for detailed examples, and

• plot.matrix for further parameters.

Usage

## S3 method for class 'pvalue'
plot(x, reorder = TRUE, gray = FALSE, grey = FALSE, ...)

Arguments

Details

If either the parameter grey or gray is TRUE then a gray color palette is used.

Value

a plot

Examples

par(mar=c(5.1, 4.1, 4.1, 4.1))
# correlation matrix
data(air.pvalue)
plot(as.pvalue(air.pvalue))
plot(as.pvalue(air.pvalue), gray=TRUE)
plot(as.pvalue(air.pvalue[,1:3]), reorder=FALSE)