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lm(V1 ~ V2)

linear model where response V1 is modelled by V2

aov(V1 ~ V2)

fit an analysis of variance model on V1 by a call to lm for each group (stratum) of V2

glm(V1 ~ V2, family=poisson())

generalized linear model where a response (e.g. count poisson) V1 is modelled by V2

deviance(Model)

returns the deviance of fitted model object Model

df.residual(Model)

returns the residual degrees-of-freedom extracted from fitted model object Model

influence.measures(Model)

compute regression (leave-one-out deletion) diagnostics for linear or generalized linear Model

arima(Ts, order=c(1, 0, 0))

fit an ARIMA (e.g. with order AR 1) model to a univariate time series Ts

HoltWinters(Ts)

computes Holt-Winters Filtering of a given time series Ts

stl(Ts)

decompose a time series Ts into seasonal, trend and irregular components

confint(Model)

computes confidence intervals for one or more parameters in a fitted model Model

predict(Model)

makes predictions from the results of a Model

t.test(V1, V2)

performs two sample t-tests on vectors V1 and V2

TukeyHSD(Model, which="var1")

tests differences between the means of the levels of a factor (e.g. which="var1") in Model

prop.test(x=c(25, 27), n=c(100, 100))

proportions test used for testing that probabilities in several groups are the same (e.g. 25 over 100 and 27 over 100) or that they equal a value

chisq.test(M)

performs chi-squared contingency table tests over matrix M

bartlett.test(V1 ~ V2)

performs Bartlett's test on V1 of the hypotesis that variances in each of the groups of V2 are the same

dist(M)

computes and returns the distance matrix of matrix-like object M

kmeans(M, centers=2)

perform k-means clustering on a data matrix M finding n clusters (e.g. 2 clusters/centers)

hclust(X)

hierarchical cluster analysis on a distance matrix X

prcomp(M)

performs a principal components analysis on the given data matrix M

smooth.spline(V1, V2)

fits a cubic smoothing spline given the predictor variable V1 and the response variable V2

mad(V)

compute the median absolute deviation (a robust measure of the standard deviation) of V

rnorm(10)

random generation of 10 numbers from the normal distribution

runif(10)

random generation of 10 numbers from the uniform distribution

rbinom(10, size=10, prob=0.1)

random generation of 10 numbers from the binomial distribution with parameters size (e.g. 10 trials) and prob (e.g. 0.1)

rpois(10, lambda=10)

random generation of 10 numbers from the Poisson distribution with parameter lambda (e.g. mean of 10)

pnorm(V)

distribution function of quantiles V from the normal distribution

punif(V)

distribution function of quantiles V from the uniform distribution

pbinom(V, size=10, prob=0.1)

distribution function of quantiles V from the binomial distribution with parameters size (e.g. 10 trials) and prob (e.g. 0.1)

ppois(V, lambda=10)

distribution function of quantiles V from the Poisson distribution with parameter lambda (e.g. mean of 10)

dnorm(V)

density of quantiles V from the normal distribution

dunif(V)

density of quantiles V from the uniform distribution

dbinom(V, size=10, prob=0.1)

density of quantiles V from the binomial distribution with parameters size (e.g. 10 trials) and prob (e.g. 0.1)

dpois(V, lambda=10)

density of quantiles V from the Poisson distribution with parameter lambda (e.g. mean of 10)

qnorm(V)

quantile function of probabilities V from the normal distribution

qunif(V)

quantile function of probabilities V from the uniform distribution

qbinom(V, size=10, prob=0.1)

quantile function of probabilities V from the binomial distribution with parameters size (e.g. 10 trials) and prob (e.g. 0.1)

qpois(V, lambda=10)

quantile function of probabilities V from the Poisson distribution with parameter lambda (e.g. mean of 10)

ks.test(V, "pnorm")

perform a one (or two) sample Kolmogorov-Smirnov test over V for continuous distributions (e.g. Norma with "pnorm")

optim(V, Fun)

general purpose optimization of parameters V over function Fun

library(help="stats")

shows help on additional statistical methods from pakage "stats"