R – Funktionen

_Statistik

Author

Affiliation

Prof. Dr. Armin Eichinger

TH Deggendorf

Im Folgenden werden die (meisten) Funktionen aufgelistet, die wir in den Übungen verwendet haben. Die Reihenfolge ist einigermaßen chronologisch.

read.csv2()

• Reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file.
• read.csv2(file, header = TRUE, sep = ";", quote = "\"",
dec = ",", fill = TRUE, comment.char = "", …)

table()

• uses the cross-classifying factors to build a contingency table of the counts at each combination of factor levels.
• table(…, exclude = if (useNA == "no") c(NA, NaN),
useNA = c("no", "ifany", "always"),
dnn = list.names(…), deparse.level = 1)

replace()

• replaces the values in x with indices given in list by those given in values. If necessary, the values in values are recycled.
• replace(x, list, values)

hist()

• The generic function hist computes a histogram of the given data values. If plot = TRUE, the resulting object of class “histogram” is plotted by plot.histogram, before it is returned.
• hist(x, breaks = "Sturges", freq = NULL, probability = !freq,
include.lowest = TRUE, right = TRUE,
density = NULL, angle = 45, col = NULL, border = NULL,
main = paste("Histogram of" , xname),
xlim = range(breaks), ylim = NULL,
xlab = xname, ylab,
axes = TRUE, plot = TRUE, labels = FALSE,
nclass = NULL, warn.unused = TRUE, …)

mean()

• Generic function for the (trimmed) arithmetic mean.
• mean(x, trim = 0, na.rm = FALSE, …)

sd()

• This function computes the standard deviation of the values in x. If na.rm is TRUE then missing values are removed before computation proceeds.
• sd(x, na.rm = FALSE)

c()

• This is a generic function which combines its arguments. The default method combines its arguments to form a vector. All arguments are coerced to a common type which is the type of the returned value, and all attributes except names are removed.
• c(…, recursive = FALSE, use.names = TRUE)

sum()

• sum returns the sum of all the values present in its arguments.
• sum(…, na.rm = FALSE)

scale()

• scale(x, center = TRUE, scale = TRUE)
• scale is generic function whose default method centers and/or scales the columns of a numeric matrix.

dnorm(), pnorm(), qnorm(), rnorm()

• Density, distribution function, quantile function and random generation for the normal distribution with mean equal to mean and standard deviation equal to sd.
• dnorm(x, mean = 0, sd = 1, log = FALSE)
pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
qnorm(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
rnorm(n, mean = 0, sd = 1)

dt(), pt(), qt(), rt()

• Density, distribution function, quantile function and random generation for the t distribution with df degrees of freedom (and optional non-centrality parameter ncp).
• dt(x, df, ncp, log = FALSE)
pt(q, df, ncp, lower.tail = TRUE, log.p = FALSE)
qt(p, df, ncp, lower.tail = TRUE, log.p = FALSE)
rt(n, df, ncp)

set.seed()

• Seeding Random Variate Generators
• set.seed(seed, kind = NULL, normal.kind = NULL)

round()

• rounds the values in its first argument to the specified number of decimal places (default 0).
• round(x, digits = 0)

length()

• Get or set the length of vectors (including lists) and factors, and of any other R object for which a method has been defined.
• length(x)

sqrt()

• Computes the square root of the specified float value.
• sqrt(x)

t.test()

• Performs one and two sample t-tests on vectors of data.
• t.test(x, y = NULL,
alternative = c("two.sided", "less", "greater"),
mu = 0, paired = FALSE, var.equal = FALSE,
conf.level = 0.95, …)
• t.test(formula, data, subset, na.action, …)

chisq.test

• chisq.test performs chi-squared contingency table tests and goodness-of-fit tests.
• chisq.test(x, y = NULL, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = FALSE, B = 2000)

cramerV

• Calculates Cramer’s V for a table of nominal variables
• cramerV(
x,
...)

cor

• cor computes the correlation of x and y if these are vectors. If x and y are matrices then the correlations between the columns of x and the columns of y are computed.
• cor(x, y = NULL, use = "everything",
method = c("pearson", "kendall", "spearman"))

cor.test

• Test for association between paired samples, using one of Pearson’s product moment correlation coefficient, Kendall’s \(\tau\) or Spearman’s \(\rho\).
• cor.test(x, y,
alternative = c("two.sided", "less", "greater"),
method = c("pearson", "kendall", "spearman"),
exact = NULL, conf.level = 0.95, continuity = FALSE, …)

lm

• lm is used to fit linear models. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance (although aov may provide a more convenient interface for these).
• lm(formula, data, subset, weights, na.action,
method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE,
singular.ok = TRUE, contrasts = NULL, offset, …)

Hilfsfunktionen: plot(), abline(), summary()

anova

• Compute analysis of variance (or deviance) tables for one or more fitted model objects.
• anova(object, …)

aov

• Fit an Analysis of Variance Model -aov(formula, data = NULL, projections = FALSE, qr = TRUE,
contrasts = NULL, …)