(You can learn more about these by typing help(function name)
or ex(function name))
anova(out) gives ANOVA table of output from lm
arithmetic
a+b add a and b
a-b subtract b from a
a*b multiply a times b
a**b or a^b raise a to bth power
bplot(x) boxplot of x
c(4,2,9)->z creates vector z with values z[1]=4,z[2]=2,
z[3]=9
c(x,y)->z combines x and y into long vector z
cbind(x,y) combines n by 1 vectors x and y into n by 2
matrix z
count(x>0) counts number of elements of x which are > 0
count(x==0) counts number of elements of x which are = 0
data.frame(x,y) creates data frame from x and y
dbinom(x,N=20,p=.7)->y evaluates binomial prob. function at x
dexp(x,beta=3)->y evaluates exponential density with mean=3 at x
dgam(x,alpha=2,beta=3)->y evaluates gamma density at x
dnorm(x,mean=2,sd=3)->y evaluates normal density at x
dunif(x,min=5,max=20)-> evaluates uniform density on (5,20) at x
dweib(x,alpha=4,beta=2)->y evaluates Weibull density at x
ex(plot) gives examples for many functions
help(plot) gives information about any function
hplot(x) histogram of x
lines(x,y) add lines to existing plot
lm(y~x,data=z)->out linear model of z$y=intercept+(beta)(x)
log(x)->y puts natural logarithm of x into y
log10(x)->y puts base 10 logarithm of x into y
lplot(x,y,z) plot allowing character variables and optional
labeling of point by z
means(y,x1,x2,..) analysis of means of y for factors x1, x2, ...
mean(x) returns mean of x
mplot(y,x1,x2,..) means plot of y by factors x1 and x2
names(x) gives names of vectors in x
c("first","sec")->names(x) gives names "first" and "sec" to x with 2
columns. Use after read.table.
plot(x,y) scatterplot of x and y
pnorm(x,mean=2,sd=3)->y evaluates normal distribution function at x
see also, dnorm, rnorm, qnorm, and dbinom,
dexp, dgam, dunif, dweib, etc.
points(x,y) adds points to existing plot
qnorm(p,mean=2,sd=3)->y gets pth quantile of normal distribution
see also dnorm, pnorm, rnorm, etc.
read.data()->y type in data set to y
read.table("your.data")->y read ascii file your.data into y, variable
names are y$V1, y$V2, ... by default
rnorm(50,mean=2,sd=3)->y puts 50 random normal variables in y
sd(x) gives standard deviation of x
seq(2,5,,20)->x vector x of equally spaced points in (2,5)
set.panel(3,2) Sets plot window to make panel of 3
rows of 2 graphs
simulate.samples(50,5, simulate 50 samples of size 5 from
DIST=rnorm,mean=2,sd=3)->y a normal dist., sample means and std's in y
source("sn") executes S commands in Unix file sn
sqrt(x)->y puts square root of x into y
stats(x) summary statistics for x
stem(x) stem and leaf plot for x
sum(x) sums up values of x
if lm(y~x1+x2,data=z)->out
summary(out) regression summary of lm output
title("Y versus X") add title to existing plot
xline(0) add vertical line at x=0
yline(0) add horizontal line at y=0