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In the normal Lattice formula, y x|g1*g2 has combinations of optional con-

rpois(n, lambda) Poisson

ditioning variables g1 and g2 plotted on separate panels. Lattice functions

rweibull(n, shape, scale=1) Weibull

take many of the same arguments as base graphics plus also data= the data

rcauchy(n, location=0, scale=1) Cauchy

frame for the formula variables and subset= for subsetting. Use panel=

rbeta(n, shape1, shape2) beta

to deﬁne a custom panel function (see apropos("panel") and ?llines).

rt(n, df) ‘Student’ (t)

Lattice functions return an object of class trellis and have to be print-ed to

rf(n, df1, df2) Fisher–Snedecor (F) (χ

)

produce the graph. Use print(xyplot(...)) inside functions where auto-

rchisq(n, df) Pearson

matic printing doesn’t work. Use lattice.theme and lset to change Lattice

rbinom(n, size, prob) binomial

defaults.

rgeom(n, prob) geometric

rhyper(nn, m, n, k) hypergeometric

Optimization and model ﬁtting

rlogis(n, location=0, scale=1) logistic

optim(par, fn, method = c("Nelder-Mead", "BFGS",

rlnorm(n, meanlog=0, sdlog=1) lognormal

"CG", "L-BFGS-B", "SANN") general-purpose optimization;

rnbinom(n, size, prob) negative binomial

par is initial values, fn is function to optimize (normally minimize)

runif(n, min=0, max=1) uniform

nlm(f,p) minimize function f using a Newton-type algorithm with starting

rwilcox(nn, m, n), rsignrank(nn, n) Wilcoxon’s statistics

values p

All these functions can be used by replacing the letter r with d, p or q to

lm(formula) ﬁt linear models; formula is typically of the form response

get, respectively, the probability density (dfunc(x, ...)), the cumulative

termA + termB + ...; use I(x*y) + I(xˆ2) for terms made of

probability density (pfunc(x, ...)), and the value of quantile (qfunc(p,

nonlinear components

...), with 0

1).

glm(formula,family=) ﬁt generalized linear models, speciﬁed by giv-

ing a symbolic description of the linear predictor and a description of

the error distribution; family is a description of the error distribution

and link function to be used in the model; see ?family

Programming

nls(formula) nonlinear least-squares estimates of the nonlinear model

function( arglist ) expr function deﬁnition

parameters

return(value)

approx(x,y=) linearly interpolate given data points; x can be an xy plot-

if(cond) expr

ting structure

if(cond) cons.expr else alt.expr

spline(x,y=) cubic spline interpolation

for(var in seq) expr

loess(formula) ﬁt a polynomial surface using local ﬁtting

while(cond) expr

Many of the formula-based modeling functions have several common argu-

repeat expr

ments: data= the data frame for the formula variables, subset= a subset of

break

variables used in the ﬁt, na.action= action for missing values: "na.fail",

"na.omit", or a function. The following generics often apply to model ﬁtting

Use braces

around statements

functions:

ifelse(test, yes, no) a value with the same shape as test ﬁlled

predict(fit,...) predictions from fit based on input data

with elements from either yes or no

df.residual(fit) returns the number of residual degrees of freedom

do.call(funname, args) executes a function call from the name of

coef(fit) returns the estimated coefﬁcients (sometimes with their

the function and a list of arguments to be passed to it

standard-errors)

residuals(fit) returns the residuals

deviance(fit) returns the deviance

fitted(fit) returns the ﬁtted values

logLik(fit) computes the logarithm of the likelihood and the number of

parameters

AIC(fit) computes the Akaike information criterion or AIC

Statistics

aov(formula) analysis of variance model

anova(fit,...) analysis of variance (or deviance) tables for one or more

ﬁtted model objects

density(x) kernel density estimates of x

binom.test(),

pairwise.t.test(),

power.t.test(),

prop.test(), t.test(), ... use help.search("test")

Distributions

rnorm(n, mean=0, sd=1) Gaussian (normal)

rexp(n, rate=1) exponential

rgamma(n, shape, scale=1) gamma

R Reference Card Page 4

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