Title: | 'SciViews' - Data Processing and Visualization with the 'SciViews::R' Dialect |
---|---|
Description: | The 'SciViews::R' dialect provides a set of functions that streamlines data input, process, analysis and visualization especially, but not exclusively, for beginners or occasional users. It mixes base R and tidyverse, plus another set of CRAN packages for an easy and coherent use of R. |
Authors: | Philippe Grosjean [aut, cre] |
Maintainer: | Philippe Grosjean <[email protected]> |
License: | GPL-2 |
Version: | 1.8.0 |
Built: | 2024-09-01 11:19:47 UTC |
Source: | https://github.com/SciViews/SciViews |
The SciViews::R
dialect is base R + tidyverse + a series of additional
SciViews packages like data.io, svBase, svFlow, tabularise or chart.
R()
for loading the 'SciViews::R“ packages,
pcomp()
for a PCA analysis (unifying various methods),
correlation()
to calculate and plot a correlation matrix,
panel_reg()
and others to plot panels in pairs
or coplot
graphs,
panel_boxplot()
and others for univariate panels in pairs
plots.
rwb_colors()
and others to generate color palettes.
enum()
to enumerate items in a vector,
timing()
to determine the time required to run an R expression,
nr()
and co as convenient shorthand to columns and rows,
ln()
and others for natural logarithm.
Maintainer: Philippe Grosjean [email protected] (ORCID)
Useful links:
Report bugs at https://github.com/SciViews/SciViews/issues
The question is sent to the chatbot server, and the answer is returned. The default chatbot server is ollama running locally, and the default model is codestral:latest. It was also tested with mistral:7b-instruct-v0.2-q6_K for an even smaller model.
ai_ask( question, context = NULL, max_tokens = getOption("SciViews.chatbot.max_tokens", Sys.getenv("SCIVIEWS_CHATBOT_MAX_TOKENS", 1000L)), lang = getOption("data.io_lang", "en"), url = getOption("SciViews.chatbot.url", Sys.getenv("SCIVIEWS_CHATBOT_URL", "http://localhost:11434/api/chat")), model = getOption("SciViews.chatbot.model", Sys.getenv("SCIVIEWS_CHATBOT_MODEL", "codestral:latest")), api_key = Sys.getenv("CONNECT_API_KEY", ""), verbose = FALSE ) ai_explain_term(term, lang = getOption("data.io_lang", "en"), ...) ai_explain_function( fun, package = NULL, lang = getOption("data.io_lang", "en"), ... ) ai_explain_code(code, lang = getOption("data.io_lang", "en"), ...) ai_explain_error( code = NULL, error = NULL, lang = getOption("data.io_lang", "en"), ... )
ai_ask( question, context = NULL, max_tokens = getOption("SciViews.chatbot.max_tokens", Sys.getenv("SCIVIEWS_CHATBOT_MAX_TOKENS", 1000L)), lang = getOption("data.io_lang", "en"), url = getOption("SciViews.chatbot.url", Sys.getenv("SCIVIEWS_CHATBOT_URL", "http://localhost:11434/api/chat")), model = getOption("SciViews.chatbot.model", Sys.getenv("SCIVIEWS_CHATBOT_MODEL", "codestral:latest")), api_key = Sys.getenv("CONNECT_API_KEY", ""), verbose = FALSE ) ai_explain_term(term, lang = getOption("data.io_lang", "en"), ...) ai_explain_function( fun, package = NULL, lang = getOption("data.io_lang", "en"), ... ) ai_explain_code(code, lang = getOption("data.io_lang", "en"), ...) ai_explain_error( code = NULL, error = NULL, lang = getOption("data.io_lang", "en"), ... )
question |
A character string with the question to ask. |
context |
An R object used as context (usually a data frame). This is not used yet, but it should be implemented in the future. |
max_tokens |
The maximum number of tokens to return in the answer. By default, it is 1000. |
lang |
The language to use for the answer. Default is "en". You can also use "fr" for instance. |
url |
The URL of the chatbot server. Default is http://localhost:11434/api/chat |
model |
The LLM (large language model) to use. Default is codestral. Make sure you complies to its license (see https://mistral.ai/news/mistral-ai-non-production-license-mnpl/), or switch to another model that better suits your requirements. |
api_key |
The API key to use for connecting to the chatbot server (optional, see your server administrator). |
verbose |
Should more information be printed? |
term |
The term to describe. |
... |
Further arguments passed to |
fun |
The R function to explain. |
package |
The R package that provides the function. |
code |
A small chunk of R code to explain. |
error |
The error message that R returns. |
The answer is returned invisibly. The function is used for its side-effect of displaying the chatbot help page with the question, answer and examples
## Not run: # Basic questions ai_ask("Who are you?") ai_ask("What is a chatbot?") ai_ask("Qui es-tu ?") ai_ask("Qu'est-ce que R ?") ai_ask("Qu'est-ce que RStudio ?") ai_ask("What is GitHub?") ai_ask("Qu'est-ce que le R Markdown ?") ai_ask("What is data science?") # Inappropriate questions ai_ask("Qu'est ce qu'un Acanthurus sp ?") ai_ask("Raconte-moi une bonne blague.") ai_ask("Va te faire voir !") # Now, more complex questions ai_ask("Comment filtrer un data frame en R?") ai_ask("Write R code to filter a data frame.") ai_ask("Que fait AIC()? Donne un exemple.") ai_ask("Qu'est ce que l'hétéroscédasticite et comment la détecter dans une ANOVA à un facteur ?") ai_ask("How to determine which model is better using an ANOVA for nested linear models?") # Explain terms ai_explain_term("True positive") ai_explain_term("percentile", lang = "fr") ai_explain_term("git push") ai_explain_term("Quarto", lang = "fr") ai_explain_term("boite à moustaches") # Language mismatch ai_explain_term("boites à moustaches parallèles", lang = "fr") # Explain R functions ai_explain_function("mean") ai_explain_function("fmean", lang = "fr") ai_explain_function("collapse::fmean", lang = "fr") ai_explain_function("glm", package = "stats", lang = "fr") ai_explain_function("replace_na", "tidyr", lang = "fr") # collapse::replace_na() used instead! try(ai_explain_function("nonexistingfunction")) # Error try(ai_explain_function("apropos", package = "stats")) # Wrong package try(ai_explain_function("apropos", package = "unknownpkg")) # Unknown package # Explain R code ai_explain_code("y <- c(1, 5, 7, NA, -Inf, 8)") ai_explain_code(r"-[ mtcars |> filter(cyl == 4) |> summarise(mean_hp = mean(hp), median_disp = median(disp))]-") ai_explain_code(r"-[ mtcars %>.% sfilter(., cyl == 4) %>.% ssummarise(., mean_hp = fmean(hp), median_disp = fmedian(disp)) ]-") ai_explain_code(r"-[ chart(data = trees, Volume ~ Girth) + geom_point() + geom_smooth() ]-") ai_explain_error(error = "longer object length is not a multiple of shorter object length") ai_explain_error(error = "Error: object 'mydata' not found") ai_explain_error(error = "Error in lenght(1:10) : could not find function \"lenght\"", lang = "fr") ai_explain_error(code = r"-[y <- c(1, 5, 7, NA, -Inf, 8, )]-", lang = "fr") ai_explain_error(code = r"-[trees %>.% filter(Girth > 10))]-", lang = "fr") ai_explain_error(code = r"-[ urchin <- read("urchin", package = "data.io") ]-", error = r"-[ Error in read("urchin", package = "data.io") : dataset 'urchin' not found in package 'data.io' ]-", lang = "fr") ## End(Not run)
## Not run: # Basic questions ai_ask("Who are you?") ai_ask("What is a chatbot?") ai_ask("Qui es-tu ?") ai_ask("Qu'est-ce que R ?") ai_ask("Qu'est-ce que RStudio ?") ai_ask("What is GitHub?") ai_ask("Qu'est-ce que le R Markdown ?") ai_ask("What is data science?") # Inappropriate questions ai_ask("Qu'est ce qu'un Acanthurus sp ?") ai_ask("Raconte-moi une bonne blague.") ai_ask("Va te faire voir !") # Now, more complex questions ai_ask("Comment filtrer un data frame en R?") ai_ask("Write R code to filter a data frame.") ai_ask("Que fait AIC()? Donne un exemple.") ai_ask("Qu'est ce que l'hétéroscédasticite et comment la détecter dans une ANOVA à un facteur ?") ai_ask("How to determine which model is better using an ANOVA for nested linear models?") # Explain terms ai_explain_term("True positive") ai_explain_term("percentile", lang = "fr") ai_explain_term("git push") ai_explain_term("Quarto", lang = "fr") ai_explain_term("boite à moustaches") # Language mismatch ai_explain_term("boites à moustaches parallèles", lang = "fr") # Explain R functions ai_explain_function("mean") ai_explain_function("fmean", lang = "fr") ai_explain_function("collapse::fmean", lang = "fr") ai_explain_function("glm", package = "stats", lang = "fr") ai_explain_function("replace_na", "tidyr", lang = "fr") # collapse::replace_na() used instead! try(ai_explain_function("nonexistingfunction")) # Error try(ai_explain_function("apropos", package = "stats")) # Wrong package try(ai_explain_function("apropos", package = "unknownpkg")) # Unknown package # Explain R code ai_explain_code("y <- c(1, 5, 7, NA, -Inf, 8)") ai_explain_code(r"-[ mtcars |> filter(cyl == 4) |> summarise(mean_hp = mean(hp), median_disp = median(disp))]-") ai_explain_code(r"-[ mtcars %>.% sfilter(., cyl == 4) %>.% ssummarise(., mean_hp = fmean(hp), median_disp = fmedian(disp)) ]-") ai_explain_code(r"-[ chart(data = trees, Volume ~ Girth) + geom_point() + geom_smooth() ]-") ai_explain_error(error = "longer object length is not a multiple of shorter object length") ai_explain_error(error = "Error: object 'mydata' not found") ai_explain_error(error = "Error in lenght(1:10) : could not find function \"lenght\"", lang = "fr") ai_explain_error(code = r"-[y <- c(1, 5, 7, NA, -Inf, 8, )]-", lang = "fr") ai_explain_error(code = r"-[trees %>.% filter(Girth > 10))]-", lang = "fr") ai_explain_error(code = r"-[ urchin <- read("urchin", package = "data.io") ]-", error = r"-[ Error in read("urchin", package = "data.io") : dataset 'urchin' not found in package 'data.io' ]-", lang = "fr") ## End(Not run)
A chatbot does not always provide reliable results. Take this with a grain of salt!
Question: No question
No answer
# No example
# No example
A chatbot does not always provide reliable results. Take this with a grain of salt!
Question: No question
No answer
# No example
# No example
A chatbot does not always provide reliable results. Take this with a grain of salt!
Question: No question
No answer
# No example
# No example
Create vectors of n
colors.
rwb_colors(n, alpha = 1, s = 0.9, v = 0.9) rwb.colors(n, alpha = 1, s = 0.9, v = 0.9) rwg_colors(n, alpha = 1, s = 0.9, v = 0.9) rwg.colors(n, alpha = 1, s = 0.9, v = 0.9) ryg_colors(n, alpha = 1, s = 0.9, v = 0.9) ryg.colors(n, alpha = 1, s = 0.9, v = 0.9) cwm_colors(n, alpha = 1, s = 0.9, v = 0.9) cwm.colors(n, alpha = 1, s = 0.9, v = 0.9)
rwb_colors(n, alpha = 1, s = 0.9, v = 0.9) rwb.colors(n, alpha = 1, s = 0.9, v = 0.9) rwg_colors(n, alpha = 1, s = 0.9, v = 0.9) rwg.colors(n, alpha = 1, s = 0.9, v = 0.9) ryg_colors(n, alpha = 1, s = 0.9, v = 0.9) ryg.colors(n, alpha = 1, s = 0.9, v = 0.9) cwm_colors(n, alpha = 1, s = 0.9, v = 0.9) cwm.colors(n, alpha = 1, s = 0.9, v = 0.9)
n |
The number of colors (>= 1) to be in the palette. |
alpha |
The alpha transparency, a number in |
s |
The 'saturation' to be used to complete the HSV color descriptions. |
v |
The 'value' to use for the HSV color descriptions. |
cwm_colors(s = 0.5, v = 1)
gives very similar colors to
cm.colors()
.
ryg_colors()
is similar to rainbow(start = 0, end = 2/6)
.
The xxx_colors()
(tidyverse name-compatible) and xxx.colors()
(grDevices name-compatible) functions are synonyms.
cm.colors()
, colorRampPalette()
# Draw color wheels with various palettes opar <- par(mfrow = c(2, 2)) pie(rep(1, 11), col = cwm.colors(11), main = "Cyan - white - magenta") pie(rep(1, 11), col = rwb.colors(11), main = "Red - white - blue") pie(rep(1, 11), col = rwg.colors(11), main = "Red - white - green") pie(rep(1, 11), col = ryg.colors(11), main = "Red - yellow - green") par(opar)
# Draw color wheels with various palettes opar <- par(mfrow = c(2, 2)) pie(rep(1, 11), col = cwm.colors(11), main = "Cyan - white - magenta") pie(rep(1, 11), col = rwb.colors(11), main = "Red - white - blue") pie(rep(1, 11), col = rwg.colors(11), main = "Red - white - green") pie(rep(1, 11), col = ryg.colors(11), main = "Red - yellow - green") par(opar)
Compute the correlation matrix between all columns of a matrix or data frame.
correlation(x, ...) Correlation(x, ...) ## S3 method for class 'formula' correlation(formula, data = NULL, subset, na.action, ...) ## Default S3 method: correlation( x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman"), ... ) is.Correlation(x) is.correlation(x) as.Correlation(x) as.correlation(x) ## S3 method for class 'Correlation' print(x, digits = 3, cutoff = 0, ...) ## S3 method for class 'Correlation' summary( object, cutpoints = c(0.3, 0.6, 0.8, 0.9, 0.95), symbols = c(" ", ".", ",", "+", "*", "B"), ... ) ## S3 method for class 'summary.Correlation' print(x, ...) ## S3 method for class 'Correlation' plot( x, y = NULL, outline = TRUE, cutpoints = c(0.3, 0.6, 0.8, 0.9, 0.95), palette = rwb.colors, col = NULL, numbers = TRUE, digits = 2, type = c("full", "lower", "upper"), diag = (type == "full"), cex.lab = par("cex.lab"), cex = 0.75 * par("cex"), ... ) ## S3 method for class 'Correlation' lines( x, choices = 1L:2L, col = par("col"), lty = 2, ar.length = 0.1, pos = NULL, cex = par("cex"), labels = rownames(x), ... )
correlation(x, ...) Correlation(x, ...) ## S3 method for class 'formula' correlation(formula, data = NULL, subset, na.action, ...) ## Default S3 method: correlation( x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman"), ... ) is.Correlation(x) is.correlation(x) as.Correlation(x) as.correlation(x) ## S3 method for class 'Correlation' print(x, digits = 3, cutoff = 0, ...) ## S3 method for class 'Correlation' summary( object, cutpoints = c(0.3, 0.6, 0.8, 0.9, 0.95), symbols = c(" ", ".", ",", "+", "*", "B"), ... ) ## S3 method for class 'summary.Correlation' print(x, ...) ## S3 method for class 'Correlation' plot( x, y = NULL, outline = TRUE, cutpoints = c(0.3, 0.6, 0.8, 0.9, 0.95), palette = rwb.colors, col = NULL, numbers = TRUE, digits = 2, type = c("full", "lower", "upper"), diag = (type == "full"), cex.lab = par("cex.lab"), cex = 0.75 * par("cex"), ... ) ## S3 method for class 'Correlation' lines( x, choices = 1L:2L, col = par("col"), lty = 2, ar.length = 0.1, pos = NULL, cex = par("cex"), labels = rownames(x), ... )
x |
A numeric vector, matrix or data frame (or any object for
|
... |
Further arguments passed to functions. |
formula |
A formula with no response variable, referring only to numeric variables. |
data |
An optional data frame (or similar, see |
subset |
An optional vector used to select rows (observations) of the
data matrix |
na.action |
A function which indicates what should happen when the data
contain |
y |
|
use |
An optional character string giving a method for computing
correlations in the presence of missing values. This must be (an abbreviation
of) one of the strings |
method |
A character string indicating which correlation coefficient is
to be computed. One of |
digits |
Digits to print after the decimal separator. |
cutoff |
Correlation coefficients lower than this (in absolute value) are suppressed. |
object |
A 'Correlation' object. |
cutpoints |
The cut points to use for categories. Specify only positive values (absolute value of correlation coefficients are summarized, or negative equivalents are automatically computed for the graph. Do not include 0 or 1 in the cutpoints). |
symbols |
The symbols to use to summarize the correlation matrix. |
outline |
Do we draw the outline of the ellipse? |
palette |
A function that can produce a palette of colors. |
col |
Color of the ellipse. If |
numbers |
Do we print correlation values in the center of the ellipses? |
type |
Do we plot a complete matrix, or only lower or upper triangle? |
diag |
Do we plot items on the diagonal? They have always a correlation of one. |
cex.lab |
The expansion factor for labels. |
cex |
The expansion factor for text. |
choices |
The items to select. |
lty |
The line type to draw. |
ar.length |
The length of the arrow head. |
pos |
The position relative to arrows. |
labels |
The label to draw near the arrows. |
Correlation()
and as.Correlation()
create a 'Correlation'
object, while is.Correlation()
tests for it.
There are print()
and summary()
methods for the 'Correlation' object
that differ in the symbolic encoding of the correlations,
(using symnum()
for summary()
), which makes large correlation matrices
more readable.
The plot()
method draws ellipses on a graph to represent the correlation
matrix visually. This is essentially the plotcorr()
function from package
ellipse, with slightly different default arguments and with default
cutpoints
equivalent to those used in the summary()
method.
Philippe Grosjean [email protected], wrapping code in package
ellipse, function plotcorr()
for the plot.Correlation()
method.
cov()
, cov2cor()
, cov.wt()
, symnum()
, plotcorr()
and look
also at panel_cor()
# This is a simple correlation coefficient cor(rnorm(10), runif(10)) Correlation(rnorm(10), runif(10)) # 'Correlation' objects allow better inspection of the correlation matrices # than the output of default R cor() function (longley.cor <- Correlation(longley)) summary(longley.cor) # Synthetic view of the correlation matrix plot(longley.cor) # Graphical representation # Use of the formula interface (mtcars.cor <- Correlation(~ mpg + cyl + disp + hp, data = mtcars, method = "spearman", na.action = "na.omit")) mtcars.cor2 <- Correlation(mtcars, method = "spearman") print(mtcars.cor2, cutoff = 0.6) summary(mtcars.cor2) plot(mtcars.cor2, type = "lower") mtcars.cor2["mpg", "cyl"] # Extract a correlation from the correlation matrix
# This is a simple correlation coefficient cor(rnorm(10), runif(10)) Correlation(rnorm(10), runif(10)) # 'Correlation' objects allow better inspection of the correlation matrices # than the output of default R cor() function (longley.cor <- Correlation(longley)) summary(longley.cor) # Synthetic view of the correlation matrix plot(longley.cor) # Graphical representation # Use of the formula interface (mtcars.cor <- Correlation(~ mpg + cyl + disp + hp, data = mtcars, method = "spearman", na.action = "na.omit")) mtcars.cor2 <- Correlation(mtcars, method = "spearman") print(mtcars.cor2, cutoff = 0.6) summary(mtcars.cor2) plot(mtcars.cor2, type = "lower") mtcars.cor2["mpg", "cyl"] # Extract a correlation from the correlation matrix
enum()
creates a vector of integers from 1 to length of the object (it
enumerates items in the object), except if the object is empty. It is
particularly useful in the for(i in enum(object))
construct.
enum(x)
enum(x)
x |
Any object. |
The pattern for(i in 1:length(object))
is often found, but it fails
in case length(object) == 0
! enum()
is indeed a synonym of seq_along()
,
but the later one is less expressive in the context.
enum(letters) enum(numeric(0)) # Compare with: 1:length(numeric(0)) enum(NULL) letters5 <- letters[1:5] for (i in enum(letters5)) cat("letter", i, "=", letters5[i], "\n")
enum(letters) enum(numeric(0)) # Compare with: 1:length(numeric(0)) enum(NULL) letters5 <- letters[1:5] for (i in enum(letters5)) cat("letter", i, "=", letters5[i], "\n")
ln()
computes natural logarithm, lg()
computes base 10 logarithm, and
lb()
computes binary (base 2) logarithm.
ln1p()
and lg1p()
computes ln(x + 1)
and lg(x + 1)
accurately also
for |x| << 1
.
E
is the Euler constant and is equal to exp(1)
.
ln(x) lg(x) lb(x) ln1p(x) lg1p(x) E
ln(x) lg(x) lb(x) ln1p(x) lg1p(x) E
x |
A numeric or complex vector. |
An object of class numeric
of length 1.
Those functions are synonyms of log()
, log10()
, log2()
,
log1p()
for those who prefer the shorter notation. Beginners sometimes
make confusion between log()
and log10()
. Using ln()
for natural
logarithms instead of log()
eliminates this confusion. E
is provided for
convenience as exp(1)
, although the use of exp()
is usually familiar
enough to everyone.
ln(exp(3)) # Same as log(exp(3)) lg(10^3) # Same as log10(10^3) lb(1:3) # Wrapper for log2() ln1p(c(0, 1, 10, 100)) # Wrapper for log1p() lg1p(c(0, 1, 10, 100)) # log10(x + 1), but optimized for x << 1 E^4 # Similar to exp(4), but different calculation!
ln(exp(3)) # Same as log(exp(3)) lg(10^3) # Same as log10(10^3) lb(1:3) # Wrapper for log2() ln1p(c(0, 1, 10, 100)) # Wrapper for log1p() lg1p(c(0, 1, 10, 100)) # log10(x + 1), but optimized for x << 1 E^4 # Similar to exp(4), but different calculation!
nr()
and nc()
are synonyms of the ugly NROW()
or NCOL()
that get the number of row and columns in a matrix or data frame, but also in
a vector (they return a value even if the dim
attribute of the object is
not set, on the contrary to nrow()
or ncol()
).
ROWS
and COLS
are constants that makes call to apply()
more expressive.
ROWS = 1L
and COLS = 2L
.
nr(x) nc(x) ROWS COLS
nr(x) nc(x) ROWS COLS
x |
Any object. |
An object of class integer
of length 1.
An object of class integer
of length 1.
mm <- matrix(1:6, nrow = 3) nr(mm) nc(mm) vv <- 1:6 nr(vv) nc(vv) # ROWS and COLS constants used with apply() apply(mm, ROWS, mean) # Idem apply(mm, 1, mean) apply(mm, COLS, mean) # Idem apply(mm, 2, mean)
mm <- matrix(1:6, nrow = 3) nr(mm) nc(mm) vv <- 1:6 nr(vv) nc(vv) # ROWS and COLS constants used with apply() apply(mm, ROWS, mean) # Idem apply(mm, 1, mean) apply(mm, COLS, mean) # Idem apply(mm, 2, mean)
Several panel plots that can be used with coplot()
and pairs()
.
panel_reg( x, y, col = par("col"), bg = par("bg"), pch = par("pch"), cex = par("cex"), lwd = par("lwd"), line.reg = lm, line.col = "red", line.lwd = lwd, untf = TRUE, ... ) panel.reg( x, y, col = par("col"), bg = par("bg"), pch = par("pch"), cex = par("cex"), lwd = par("lwd"), line.reg = lm, line.col = "red", line.lwd = lwd, untf = TRUE, ... ) panel_ellipse( x, y, col = par("col"), bg = par("bg"), pch = par("pch"), cex = par("cex"), el.level = 0.7, el.col = "cornsilk", el.border = "red", major = TRUE, ... ) panel.ellipse( x, y, col = par("col"), bg = par("bg"), pch = par("pch"), cex = par("cex"), el.level = 0.7, el.col = "cornsilk", el.border = "red", major = TRUE, ... ) panel_cor( x, y, use = "everything", method = c("pearson", "kendall", "spearman"), alternative = c("two.sided", "less", "greater"), digits = 2, prefix = "", cex = par("cex"), cor.cex = cex, stars.col = "red", ... ) panel.cor( x, y, use = "everything", method = c("pearson", "kendall", "spearman"), alternative = c("two.sided", "less", "greater"), digits = 2, prefix = "", cex = par("cex"), cor.cex = cex, stars.col = "red", ... ) panel_smooth( x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, col.smooth = 2, span = 2/3, iter = 3, ... )
panel_reg( x, y, col = par("col"), bg = par("bg"), pch = par("pch"), cex = par("cex"), lwd = par("lwd"), line.reg = lm, line.col = "red", line.lwd = lwd, untf = TRUE, ... ) panel.reg( x, y, col = par("col"), bg = par("bg"), pch = par("pch"), cex = par("cex"), lwd = par("lwd"), line.reg = lm, line.col = "red", line.lwd = lwd, untf = TRUE, ... ) panel_ellipse( x, y, col = par("col"), bg = par("bg"), pch = par("pch"), cex = par("cex"), el.level = 0.7, el.col = "cornsilk", el.border = "red", major = TRUE, ... ) panel.ellipse( x, y, col = par("col"), bg = par("bg"), pch = par("pch"), cex = par("cex"), el.level = 0.7, el.col = "cornsilk", el.border = "red", major = TRUE, ... ) panel_cor( x, y, use = "everything", method = c("pearson", "kendall", "spearman"), alternative = c("two.sided", "less", "greater"), digits = 2, prefix = "", cex = par("cex"), cor.cex = cex, stars.col = "red", ... ) panel.cor( x, y, use = "everything", method = c("pearson", "kendall", "spearman"), alternative = c("two.sided", "less", "greater"), digits = 2, prefix = "", cex = par("cex"), cor.cex = cex, stars.col = "red", ... ) panel_smooth( x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, col.smooth = 2, span = 2/3, iter = 3, ... )
x |
A numeric vector. |
y |
A numeric vector of same length as |
col |
The color of the points. |
bg |
The background color for symbol used for the points. |
pch |
The symbol used for the points. |
cex |
The expansion factor used for the points. |
lwd |
The line width. |
line.reg |
A function that calculates coefficients of a straight line,
for instance, |
line.col |
The color of the line. |
line.lwd |
The width of the line. |
untf |
Logical asking whether to untransform the straight line in case one or both axis are in log scale. |
... |
Further arguments to plot functions. |
el.level |
The confidence level for the bivariate normal ellipse around data; the default value of 0.7 draws an ellipse of roughly +/-1 sd. |
el.col |
The color used to fill the ellipse. |
el.border |
The color used to draw the border of the ellipse and the standardized major axis. |
major |
If |
use |
One of |
method |
One of the three correlation coefficients |
alternative |
The alternative hypothesis in correlation test, see
|
digits |
The number of decimal digits to print when the correlation coefficient is printed in the graph. |
prefix |
A prefix (character string) to use before the correlation coefficient printed in the graph. |
cor.cex |
Expansion coefficient for text in printing correlation coefficients. |
stars.col |
The color used for significance stars (with: *** p < 0.001, ** p < 0.1, * p < 0.05, . p < 0.1. |
col.smooth |
Color to be used by lines for drawing the smooths. |
span |
Smoothing parameter |
iter |
Number of robustness iterations for |
Theses functions should be used outside of the diagonal in
pairs()
, or with coplot()
, as they are bivariate plots.
These functions return nothing and are used for their side effect of plotting in panels of composite plots.
Philippe Grosjean [email protected], but code inspired from
panel.smooth()
in graphics and panel.car()
in package car.
coplot()
, pairs()
, panel.smooth()
, lm()
, ellipse()
,
cor()
and cor.test()
# Smooth lines in lower graphs and straight lines in upper graphs pairs(trees, lower.panel = panel_smooth, upper.panel = panel_reg) # Robust regression lines library(MASS) # For rlm() pairs(trees, panel = panel_reg, diag.panel = panel_boxplot, reg.line = rlm, line.col = "blue", line.lwd = 2) # A Double log graph pairs(trees, lower.panel = panel_smooth, upper.panel = panel_reg, log = "xy") # Graph suitables to explore correlations (take care there are potentially # many simultaneous tests done here... So, you loose much power in the whole # analysis... use it just as an indication!) # Pearson's r pairs(trees, lower.panel = panel_ellipse, upper.panel = panel_cor) # Spearman's rho (ellipse and straight lines not suitable here!) pairs(trees, lower.panel = panel_smooth, upper.panel = panel_cor, method = "spearman", span = 1) # Several groups (visualize how bad it is to consider the whole set at once!) pairs(iris[, -5], lower.panel = panel_smooth, upper.panel = panel_cor, method = "kendall", span = 1, col = c("red3", "blue3", "green3")[iris$Species]) # Now analyze correlation for one species only pairs(iris[iris$Species == "virginica", -5], lower.panel = panel_ellipse, upper.panel = panel_cor) # A coplot with custom panes coplot(Petal.Length ~ Sepal.Length | Species, data = iris, panel = panel_ellipse)
# Smooth lines in lower graphs and straight lines in upper graphs pairs(trees, lower.panel = panel_smooth, upper.panel = panel_reg) # Robust regression lines library(MASS) # For rlm() pairs(trees, panel = panel_reg, diag.panel = panel_boxplot, reg.line = rlm, line.col = "blue", line.lwd = 2) # A Double log graph pairs(trees, lower.panel = panel_smooth, upper.panel = panel_reg, log = "xy") # Graph suitables to explore correlations (take care there are potentially # many simultaneous tests done here... So, you loose much power in the whole # analysis... use it just as an indication!) # Pearson's r pairs(trees, lower.panel = panel_ellipse, upper.panel = panel_cor) # Spearman's rho (ellipse and straight lines not suitable here!) pairs(trees, lower.panel = panel_smooth, upper.panel = panel_cor, method = "spearman", span = 1) # Several groups (visualize how bad it is to consider the whole set at once!) pairs(iris[, -5], lower.panel = panel_smooth, upper.panel = panel_cor, method = "kendall", span = 1, col = c("red3", "blue3", "green3")[iris$Species]) # Now analyze correlation for one species only pairs(iris[iris$Species == "virginica", -5], lower.panel = panel_ellipse, upper.panel = panel_cor) # A coplot with custom panes coplot(Petal.Length ~ Sepal.Length | Species, data = iris, panel = panel_ellipse)
Several panel plots that can be used with pairs()
.
panel_boxplot(x, col = par("col"), box.col = "cornsilk", ...) panel.boxplot(x, col = par("col"), box.col = "cornsilk", ...) panel_density( x, adjust = 1, rug = TRUE, col = par("col"), lwd = par("lwd"), line.col = col, line.lwd = lwd, ... ) panel.density( x, adjust = 1, rug = TRUE, col = par("col"), lwd = par("lwd"), line.col = col, line.lwd = lwd, ... ) panel_hist( x, breaks = "Sturges", hist.col = "cornsilk", hist.border = NULL, hist.density = NULL, hist.angle = 45, ... ) panel.hist( x, breaks = "Sturges", hist.col = "cornsilk", hist.border = NULL, hist.density = NULL, hist.angle = 45, ... ) panel_qqnorm( x, pch = par("pch"), col = par("col"), bg = par("bg"), cex = par("cex"), lwd = par("lwd"), qq.pch = pch, qq.col = col, qq.bg = bg, qq.cex = cex, qqline.col = qq.col, qqline.lwd = lwd, ... ) panel.qqnorm( x, pch = par("pch"), col = par("col"), bg = par("bg"), cex = par("cex"), lwd = par("lwd"), qq.pch = pch, qq.col = col, qq.bg = bg, qq.cex = cex, qqline.col = qq.col, qqline.lwd = lwd, ... )
panel_boxplot(x, col = par("col"), box.col = "cornsilk", ...) panel.boxplot(x, col = par("col"), box.col = "cornsilk", ...) panel_density( x, adjust = 1, rug = TRUE, col = par("col"), lwd = par("lwd"), line.col = col, line.lwd = lwd, ... ) panel.density( x, adjust = 1, rug = TRUE, col = par("col"), lwd = par("lwd"), line.col = col, line.lwd = lwd, ... ) panel_hist( x, breaks = "Sturges", hist.col = "cornsilk", hist.border = NULL, hist.density = NULL, hist.angle = 45, ... ) panel.hist( x, breaks = "Sturges", hist.col = "cornsilk", hist.border = NULL, hist.density = NULL, hist.angle = 45, ... ) panel_qqnorm( x, pch = par("pch"), col = par("col"), bg = par("bg"), cex = par("cex"), lwd = par("lwd"), qq.pch = pch, qq.col = col, qq.bg = bg, qq.cex = cex, qqline.col = qq.col, qqline.lwd = lwd, ... ) panel.qqnorm( x, pch = par("pch"), col = par("col"), bg = par("bg"), cex = par("cex"), lwd = par("lwd"), qq.pch = pch, qq.col = col, qq.bg = bg, qq.cex = cex, qqline.col = qq.col, qqline.lwd = lwd, ... )
x |
A numeric vector. |
col |
The color of the points. |
box.col |
The filling color of the boxplots. |
... |
Further arguments to plot functions, or functions that construct
items, like |
adjust |
The bandwidth adjustment factor, see |
rug |
Do we add a rug representation (1-d plot) of the points too? |
lwd |
The line width. |
line.col |
The color of the line. |
line.lwd |
The width of the line. |
breaks |
The number of breaks, the name of a break algorithm, a vector
of breakpoints, or any other acceptable value for |
hist.col |
The filling color for the histograms. |
hist.border |
The border color for the histograms. |
hist.density |
The density for filling lines in the histograms. |
hist.angle |
The angle for filling lines in the histograms. |
pch |
The symbol used for the points. |
bg |
The background color for symbol used for the points. |
cex |
The expansion factor used for the points. |
qq.pch |
The symbol used to plot points in the QQ-plots. |
qq.col |
The color of the symbol used to plot points in the QQ-plots. |
qq.bg |
The background color of the symbol used to plot points in the QQ-plots. |
qq.cex |
The expansion factor for points in the QQ-plots. |
qqline.col |
The color for the QQ-plot lines. |
qqline.lwd |
The width for the QQ-plot lines. |
Panel functions panel_boxplot()
, panel_density()
, panel_hist()
and panel_qqnorm()
should be used only to plot univariate data on the
diagonals of pairs()
plots (or scatterplot matrix).
These functions return nothing and are used for their side effect of plotting in panels of composite plots.
Philippe Grosjean [email protected], but code inspired from
spm()
in package car.
pairs()
, boxplot()
, hist()
, density()
, qqnorm()
# Example of scatterplot matrices with custom plots on the diagonal # Boxplots pairs(trees, panel = panel_smooth, diag.panel = panel_boxplot) pairs(trees, diag.panel = panel_boxplot, box.col = "gray") # Densities pairs(trees, panel = panel_smooth, diag.panel = panel_density) pairs(trees, diag.panel = panel_density, line.col = "red", adjust = 0.5) # Histograms pairs(trees, panel = panel_smooth, diag.panel = panel_hist) pairs(trees, diag.panel = panel_hist, hist.col = "gray", breaks = "Scott") # QQ-plots against Normal theoretical distribution pairs(trees, panel = panel_smooth, diag.panel = panel_qqnorm) pairs(trees, diag.panel = panel_qqnorm, qqline.col = 2, qq.cex = .5, qq.pch = 3)
# Example of scatterplot matrices with custom plots on the diagonal # Boxplots pairs(trees, panel = panel_smooth, diag.panel = panel_boxplot) pairs(trees, diag.panel = panel_boxplot, box.col = "gray") # Densities pairs(trees, panel = panel_smooth, diag.panel = panel_density) pairs(trees, diag.panel = panel_density, line.col = "red", adjust = 0.5) # Histograms pairs(trees, panel = panel_smooth, diag.panel = panel_hist) pairs(trees, diag.panel = panel_hist, hist.col = "gray", breaks = "Scott") # QQ-plots against Normal theoretical distribution pairs(trees, panel = panel_smooth, diag.panel = panel_qqnorm) pairs(trees, diag.panel = panel_qqnorm, qqline.col = 2, qq.cex = .5, qq.pch = 3)
Perform a principal components analysis (PCA) on a matrix or data frame and
return a pcomp
object.
pcomp(x, ...) ## S3 method for class 'formula' pcomp(formula, data = NULL, subset, na.action, method = c("svd", "eigen"), ...) ## Default S3 method: pcomp( x, method = c("svd", "eigen"), scores = TRUE, center = TRUE, scale = TRUE, tol = NULL, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x))), ... ) ## S3 method for class 'pcomp' print(x, ...) ## S3 method for class 'pcomp' summary(object, loadings = TRUE, cutoff = 0.1, ...) ## S3 method for class 'summary.pcomp' print(x, digits = 3, loadings = x$print.loadings, cutoff = x$cutoff, ...) ## S3 method for class 'pcomp' plot( x, which = c("screeplot", "loadings", "correlations", "scores"), choices = 1L:2L, col = par("col"), bar.col = "gray", circle.col = "gray", ar.length = 0.1, pos = NULL, labels = NULL, cex = par("cex"), main = paste(deparse(substitute(x)), which, sep = " - "), xlab, ylab, ... ) ## S3 method for class 'pcomp' screeplot( x, npcs = min(10, length(x$sdev)), type = c("barplot", "lines"), col = "cornsilk", main = deparse(substitute(x)), ... ) ## S3 method for class 'pcomp' points( x, choices = 1L:2L, type = "p", pch = par("pch"), col = par("col"), bg = par("bg"), cex = par("cex"), ... ) ## S3 method for class 'pcomp' lines( x, choices = 1L:2L, groups, type = c("p", "e"), col = par("col"), border = par("fg"), level = 0.9, ... ) ## S3 method for class 'pcomp' text( x, choices = 1L:2L, labels = NULL, col = par("col"), cex = par("cex"), pos = NULL, ... ) ## S3 method for class 'pcomp' biplot(x, choices = 1L:2L, scale = 1, pc.biplot = FALSE, ...) ## S3 method for class 'pcomp' pairs( x, choices = 1L:3L, type = c("loadings", "correlations"), col = par("col"), circle.col = "gray", ar.col = par("col"), ar.length = 0.05, pos = NULL, ar.cex = par("cex"), cex = par("cex"), ... ) ## S3 method for class 'pcomp' predict(object, newdata, dim = length(object$sdev), ...) ## S3 method for class 'pcomp' correlation(x, newvars, dim = length(x$sdev), ...) scores(x, ...) ## S3 method for class 'pcomp' scores(x, labels = NULL, dim = length(x$sdev), ...)
pcomp(x, ...) ## S3 method for class 'formula' pcomp(formula, data = NULL, subset, na.action, method = c("svd", "eigen"), ...) ## Default S3 method: pcomp( x, method = c("svd", "eigen"), scores = TRUE, center = TRUE, scale = TRUE, tol = NULL, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x))), ... ) ## S3 method for class 'pcomp' print(x, ...) ## S3 method for class 'pcomp' summary(object, loadings = TRUE, cutoff = 0.1, ...) ## S3 method for class 'summary.pcomp' print(x, digits = 3, loadings = x$print.loadings, cutoff = x$cutoff, ...) ## S3 method for class 'pcomp' plot( x, which = c("screeplot", "loadings", "correlations", "scores"), choices = 1L:2L, col = par("col"), bar.col = "gray", circle.col = "gray", ar.length = 0.1, pos = NULL, labels = NULL, cex = par("cex"), main = paste(deparse(substitute(x)), which, sep = " - "), xlab, ylab, ... ) ## S3 method for class 'pcomp' screeplot( x, npcs = min(10, length(x$sdev)), type = c("barplot", "lines"), col = "cornsilk", main = deparse(substitute(x)), ... ) ## S3 method for class 'pcomp' points( x, choices = 1L:2L, type = "p", pch = par("pch"), col = par("col"), bg = par("bg"), cex = par("cex"), ... ) ## S3 method for class 'pcomp' lines( x, choices = 1L:2L, groups, type = c("p", "e"), col = par("col"), border = par("fg"), level = 0.9, ... ) ## S3 method for class 'pcomp' text( x, choices = 1L:2L, labels = NULL, col = par("col"), cex = par("cex"), pos = NULL, ... ) ## S3 method for class 'pcomp' biplot(x, choices = 1L:2L, scale = 1, pc.biplot = FALSE, ...) ## S3 method for class 'pcomp' pairs( x, choices = 1L:3L, type = c("loadings", "correlations"), col = par("col"), circle.col = "gray", ar.col = par("col"), ar.length = 0.05, pos = NULL, ar.cex = par("cex"), cex = par("cex"), ... ) ## S3 method for class 'pcomp' predict(object, newdata, dim = length(object$sdev), ...) ## S3 method for class 'pcomp' correlation(x, newvars, dim = length(x$sdev), ...) scores(x, ...) ## S3 method for class 'pcomp' scores(x, labels = NULL, dim = length(x$sdev), ...)
x |
A matrix or data frame with numeric data. |
... |
Arguments passed to or from other methods. If |
formula |
A formula with no response variable, referring only to numeric variables. |
data |
An optional data frame (or similar, see |
subset |
An optional vector used to select rows (observations) of the
data matrix |
na.action |
A function which indicates what should happen when the data
contain |
method |
Either |
scores |
A logical value indicating whether the score on each principal component should be calculated. |
center |
A logical value indicating whether the variables should
centered. Alternately, a vector of length equal the number of columns of |
scale |
A logical value indicating whether the variables should be
scaled to have unit variance before the analysis takes place. The default is
|
tol |
Only when |
covmat |
A covariance matrix, or a covariance list as returned by
|
object |
A 'pcomp' object. |
loadings |
Do we also summarize the loadings? |
cutoff |
The cutoff value below which loadings are replaced by white spaces in the table. That way, larger values are easier to spot and to read in large tables. |
digits |
The number of digits to print. |
which |
The graph to plot. |
choices |
Which principal axes to plot. For 2D graphs, specify two integers. |
col |
The color to use in graphs. |
bar.col |
The color of bars in the screeplot. |
circle.col |
The color for the circle in the loadings or correlations plots. |
ar.length |
The length of the arrows in the loadings and correlations plots. |
pos |
The position of text relative to arrows in loadings and correlation plots. |
labels |
The labels to write. If |
cex |
The factor of expansion for text (labels) in the graphs. |
main |
The title of the graph. |
xlab |
The label of the x-axis. |
ylab |
The label of the y-axis. |
npcs |
The number of principal components to represent in the screeplot. |
type |
The type of screeplot ( |
pch |
The type of symbol to use. |
bg |
The background color for symbols. |
groups |
A grouping factor. |
border |
The color of the border. |
level |
The probability level to use to draw the ellipse. |
pc.biplot |
Do we create a Gabriel's biplot (see |
ar.col |
Color of arrows. |
ar.cex |
Expansion factor for text on arrows. |
newdata |
New individuals with observations for the same variables as those used for calculating the PCA. You can then plot these additional individuals in the scores plot. |
dim |
The number of principal components to keep. |
newvars |
New variables with observations for same individuals as those used for calculating the PCA. Correlation with PCs is calculated. You can then plot these additional variables in the correlation plot. |
pcomp()
is a generic function with "formula"
and "default"
methods. It is essentially a wrapper around prcomp()
and princomp()
to
provide a coherent interface and object for both methods.
A 'pcomp' object is created. It inherits from 'pca' (as in labdsv package, but not compatible with the version of 'pca' in ade4) and of 'princomp'.
For more information on algorithms, refer to prcomp()
for
method = "svd"
or princomp()
for method = "eigen"
.
A c("pcomp", "pca", "princomp")
object.
The signs of the columns for the loadings and scores are arbitrary. So, they could differ between functions for PCA, and even between different builds of R.
Philippe Grosjean [email protected], but the core code is indeed in package stats.
prcomp()
, princomp()
, loadings()
, vectorplot()
,
Correlation()
# Let's analyze mtcars without the Mercedes data (rows 8:14) data(mtcars) cars.pca <- pcomp(~ mpg + cyl + disp + hp + drat + wt + qsec, data = mtcars, subset = -(8:14)) cars.pca summary(cars.pca) screeplot(cars.pca) # Loadings are extracted and plotted this way: (cars.ldg <- loadings(cars.pca)) plot(cars.pca, which = "loadings") # Equivalent to vectorplot(cars.ldg) # Similarly, correlations of variables with PCs are extracted and plotted: (cars.cor <- Correlation(cars.pca)) plot(cars.pca, which = "correlations") # Equivalent to vectorplot(cars.cor) # One can add supplementary variables on this graph lines(Correlation(cars.pca, newvars = mtcars[-(8:14), c("vs", "am", "gear", "carb")])) # Plot the scores: plot(cars.pca, which = "scores", cex = 0.8) # Similar to plot(scores(x)[, 1:2]) # Add supplementary individuals to this plot (labels), also points() or lines() text(predict(cars.pca, newdata = mtcars[8:14, ]), labels = rownames(mtcars[8:14, ]), col = "gray", cex = 0.8) # Pairs plot for 3 PCs iris.pca <- pcomp(iris[, -5]) pairs(iris.pca, col = (2:4)[iris$Species])
# Let's analyze mtcars without the Mercedes data (rows 8:14) data(mtcars) cars.pca <- pcomp(~ mpg + cyl + disp + hp + drat + wt + qsec, data = mtcars, subset = -(8:14)) cars.pca summary(cars.pca) screeplot(cars.pca) # Loadings are extracted and plotted this way: (cars.ldg <- loadings(cars.pca)) plot(cars.pca, which = "loadings") # Equivalent to vectorplot(cars.ldg) # Similarly, correlations of variables with PCs are extracted and plotted: (cars.cor <- Correlation(cars.pca)) plot(cars.pca, which = "correlations") # Equivalent to vectorplot(cars.cor) # One can add supplementary variables on this graph lines(Correlation(cars.pca, newvars = mtcars[-(8:14), c("vs", "am", "gear", "carb")])) # Plot the scores: plot(cars.pca, which = "scores", cex = 0.8) # Similar to plot(scores(x)[, 1:2]) # Add supplementary individuals to this plot (labels), also points() or lines() text(predict(cars.pca, newdata = mtcars[8:14, ]), labels = rownames(mtcars[8:14, ]), col = "gray", cex = 0.8) # Pairs plot for 3 PCs iris.pca <- pcomp(iris[, -5]) pairs(iris.pca, col = (2:4)[iris$Species])
List required packages or conflicting functions. These functions
are inspired by tidyverse::tidyverse_packages()
and
tidyverse::tidyverse_conflicts()
, but adapted to the SciViews::R context.
SciViews_packages(..., all = FALSE) SciViews_packages_topics(all = FALSE) SciViews_conflicts(all = TRUE) ## S3 method for class 'SciViews_conflicts' print(x, ..., startup = FALSE)
SciViews_packages(..., all = FALSE) SciViews_packages_topics(all = FALSE) SciViews_conflicts(all = TRUE) ## S3 method for class 'SciViews_conflicts' print(x, ..., startup = FALSE)
... |
Further topics to consider in |
all |
Should all packages be listed ( |
x |
A SciViews_conflicts object |
startup |
Should the message be printed at startup? |
A list of packages for SciViews_packages()
, or a
SciViews_conflicted object with a print()
method for
SciViews_conflicts()
.
# List of packages attached to the search path with SciViews::R SciViews_packages() # More complete list of packages used by SciViews::R SciViews_packages(all = TRUE) # Even more packages, by adding also 'model' and 'ml' topics SciViews_packages("model", "ml", all = TRUE) # Conflicts SciViews_conflicts()
# List of packages attached to the search path with SciViews::R SciViews_packages() # More complete list of packages used by SciViews::R SciViews_packages(all = TRUE) # Even more packages, by adding also 'model' and 'ml' topics SciViews_packages("model", "ml", all = TRUE) # Conflicts SciViews_conflicts()
Load required packages like data.table, collapse, ggplot2, dplyr, svMisc, ...
to get a fully functional SciViews::R
dialect environment.
R(..., lang = NULL, dtx = NULL, threads.percent = 75, silent = TRUE) ## S3 method for class 'SciViews_R' print(x, ...)
R(..., lang = NULL, dtx = NULL, threads.percent = 75, silent = TRUE) ## S3 method for class 'SciViews_R' print(x, ...)
... |
Further topics to include to configure R (load more packages).
Currently, |
lang |
What is the default natural language to use, e.g., |
dtx |
Which dtx object is to be used be default? |
threads.percent |
The percentage of threads to use for {data.table} and {collapse} parallel code (number of threads depend on how many are available, and the value is rounded towards zero). |
silent |
If |
x |
An object to print. |
Use SciViews::R
instruction in the beginning of an R script, or in
the setup or first chunk of an R Markdown/Notebook to ensure the SciViews::R
dialect is correctly installed. The report indicating attached packages and
conflicts is largely inspired by the corresponding tidyverse
code,
written by Hadley Wickham.
library()
, utils::install.packages()
## Not run: SciViews::R ## End(Not run)
## Not run: SciViews::R ## End(Not run)
Similar to system.time()
but returns a more convenient 'difftime' object
with the overall timing (details are stored in the details
attribute).
timing(expr, gc.first = TRUE)
timing(expr, gc.first = TRUE)
expr |
Valid R expression to be timed. If missing, |
gc.first |
Logical - should a garbage collection be performed immediately
before the timing? Default is |
test <- timing(Sys.sleep(0.5)) test attr(test, "details")
test <- timing(Sys.sleep(0.5)) test attr(test, "details")
Plots vectors with 0 < norms < 1 inside a circle. These plots are mainly designed to represent variables in principal components space for PCAs.
vectorplot(x, ...) ## Default S3 method: vectorplot( x, y, col = par("col"), circle.col = "gray", ar.length = 0.1, pos = NULL, cex = par("cex"), labels = NULL, ... ) ## S3 method for class 'loadings' vectorplot( x, choices = 1L:2L, col = par("col"), circle.col = "gray", ar.length = 0.1, pos = NULL, cex = par("cex"), labels = rownames(x), main = deparse(substitute(x)), ... ) ## S3 method for class 'Correlation' vectorplot( x, choices = 1L:2L, col = par("col"), circle.col = "gray", ar.length = 0.1, pos = NULL, cex = par("cex"), labels = rownames(x), main = deparse(substitute(x)), ... )
vectorplot(x, ...) ## Default S3 method: vectorplot( x, y, col = par("col"), circle.col = "gray", ar.length = 0.1, pos = NULL, cex = par("cex"), labels = NULL, ... ) ## S3 method for class 'loadings' vectorplot( x, choices = 1L:2L, col = par("col"), circle.col = "gray", ar.length = 0.1, pos = NULL, cex = par("cex"), labels = rownames(x), main = deparse(substitute(x)), ... ) ## S3 method for class 'Correlation' vectorplot( x, choices = 1L:2L, col = par("col"), circle.col = "gray", ar.length = 0.1, pos = NULL, cex = par("cex"), labels = rownames(x), main = deparse(substitute(x)), ... )
x |
An object that has a |
... |
Further arguments passed to plot functions. |
y |
A numeric vector with 0 < values < 1 of same length as 'x. |
col |
Color of the arrows and labels. |
circle.col |
The color for the circle around the vector plot. |
ar.length |
The length of the arrows. |
pos |
The position of text relative to arrows. If |
cex |
The factor of expansion for labels in the graph. |
labels |
The labels to draw near the arrows. |
choices |
A vector of two integers indicating the axes to plot. |
main |
The title of the plot. |
The object 'x' is returned invisibly. These functions are called for their side-effect of drawing a vector plot.
pcomp()
, loadings()
, Correlation()
# Create a PCA and plot loadings and correlations iris.pca <- pcomp(iris[, -5]) vectorplot(loadings(iris.pca)) vectorplot(Correlation(iris.pca)) # Note: on screen devices, change aspect ratio of the graph by resizing # the window to reveal cropped labels...
# Create a PCA and plot loadings and correlations iris.pca <- pcomp(iris[, -5]) vectorplot(loadings(iris.pca)) vectorplot(Correlation(iris.pca)) # Note: on screen devices, change aspect ratio of the graph by resizing # the window to reveal cropped labels...