generic functions in r · outline introduction objects in r generic functions do it yourself...

Outline Introduction Objects in R Generic functions Do it yourself Conclusion

Generic Functions in R

Martin Gregory

Merck Serono

PhUSE 2013

Brussels, October 2013


1 Introduction

2 Objects in R

3 Generic functions

4 Do it yourself

5 Conclusion


Preliminary definition

A generic function is one which may be applied to different types

of inputs producing results depending on the type of input.

Examples are plot() and summary()

We demonstrate these differences using sample data from

Dalgaard’s Introductory Statistics with R


Sample data: melanom

> melanom

no status days ulc thick sex1 789 3 10 1 676 22 13 3 30 2 65 23 97 2 35 2 134 24 16 3 99 2 290 15 21 1 185 1 1208 2...

where

status 1=dead from melanoma, 2=alive, 3=dead from other

causes,

days is the observation time in days,

thick is the tumor thickness in 1/100mm and

sex 1=female, 2=male


Kaplan-Meier survival function

Estimate the Kaplan-Meier survival function using sex as an

explanatory variable:

> surv.bysex <- survfit(Surv(days,status==1)~sex)

Surv() converts status to a suitable censoring variable

survfit() calculates the K-M survival function and associated statistics


View the survival function

> summary(surv.bysex)Call: survfit(formula = Surv(days, status == 1) ~ sex)

sex=1lower upper

time n.risk n.event survival std.err 95% CI 95% CI279 124 1 0.992 0.00803 0.976 1.000295 123 1 0.984 0.01131 0.962 1.000386 121 1 0.976 0.01384 0.949 1.000...

sex=2lower upper

time n.risk n.event survival std.err 95% CI 95% CI185 76 1 0.987 0.0131 0.962 1.000204 75 1 0.974 0.0184 0.938 1.000210 74 1 0.961 0.0223 0.918 1.000...


summary(melanom)

> summary(melanom)status days thick sex

Min. :1.000 Min. : 10 Min. : 10 Min. :1.0001st Qu.:1.000 1st Qu.:1525 1st Qu.: 97 1st Qu.:1.000Median :2.000 Median :2005 Median : 194 Median :1.000Mean :1.790 Mean :2153 Mean : 292 Mean :1.3853rd Qu.:2.000 3rd Qu.:3042 3rd Qu.: 356 3rd Qu.:2.000Max. :3.000 Max. :5565 Max. :1742 Max. :2.000


plot(melanom)

> plot(melanom)


plot(surv.bysex)

> plot(surv.bysex)


Objects in R

The R Language Definition defines an object as a

specialized data structure which can be referred tothrough symbols or variables

The symbols are themselves objects and are accessible to programs.


Built-in R objects

The most basic types of objects are vectors:

> x <- c(1,2,3)> y <- x-1> z <- y-1

All elements have the same data type.

we can view the contents of vectors:

> x;y;zx y z

[1] 1 [1] 0 [1] -1[2] 2 [2] 1 [2] 0[3] 3 [3] 2 [3] 1


Built-in R objects

Matrices are two-dimensional vectors and can, for example, be

built by “stacking” vectors:

> mat <- cbind(x,y,z)

and we can examine these:

> matx y z

[1,] 1 0 -1[2,] 2 1 0[3,] 3 2 1

Arrays are n-dimensional vectors


Built-in R objects

Lists are collections of objects of any type:

> lst <- list(x,y,mat,z )

and we can examine these:

> lst

[[1]] [[2]][1] 1 2 3 [1] 0 1 2

[[3]] [[4]]x y z [1] -1 0 1

[1,] 1 0 -1[2,] 2 1 0[3,] 3 2 1


Structure of the survfit object

The basic type of an object is found by the typeof() function:

> typeof(surv.bysex)[1] "list"

The class of the object with class():

> class(surv.bysex)[1] "survfit"

The names of the elements of an object with names():

> names(surv.bysex)[1] "n" "time" "n.risk" "n.event" "n.censor"[6] "surv" "type" "strata" "std.err" "upper"

[11] "lower" "conf.type" "conf.int" "call"


Contents of the survfit object

> str(surv.bysex,give.attr=FALSE,vec.len=3,give.length=FALSE)List of 14$ n : int 126 79$ time : num 99 232 279 295 355 386 469 667 ...$ n.risk : num 126 125 124 123 122 121 120 119 ...$ n.event : num 0 0 1 1 0 1 1 1 ...$ n.censor : num 1 1 0 0 1 0 0 0 ...$ surv : num 1 1 0.992 0.984 ...$ type : chr "right"$ strata : Named int 124 79$ std.err : num 0 0 0.0081 0.0115 ...$ upper : num 1 1 1 1 ...$ lower : num 1 1 0.976 0.962 ...$ conf.type: chr "log"$ conf.int : num 0.95$ call : language

survfit(formula = Surv(days, status == 1) ~ sex)


Generic functions are simple

Generic functions are simple: the complete definition of plot() is a

single line:

plot <- function (x, y, ...) UseMethod("plot")

UseMethod

examines the class x of its first argument

searches for a function plot.x

executes plot.x with the arguments passed to plot

If plot.x is not found, UseMethod repeats the search using the next

class attribute, if any. If no class-specific function is found, it uses

plot.default


Convert polar to cartesian

Plotting in R requires cartesian coordinates but we might have

data in polar form.

We define:

a class polar

a class cartes

a function xypos which takes either polar or cartesian coords

as input and returns cartesian


Define classes

Create data frames for both cartesian and polar coordinates:

> pxy <- data.frame(x=c(1,2,3),y=c(1,2,3))> class(pxy) <- c(class(pxy),"cartes")

> qrt <- data.frame(r=c(sqrt(2),sqrt(8),sqrt(18)),theta=pi/4)> class(qrt) <- c(class(qrt),"polar")

These objects both have two classes:

> class(pxy);class(qrt)[1] "data.frame" "cartes"[1] "data.frame" "polar"


Define classes

We can call the summary function

> summary(pxy)x y

Min. :1.0 Min. :1.01st Qu.:1.5 1st Qu.:1.5Median :2.0 Median :2.0Mean :2.0 Mean :2.03rd Qu.:2.5 3rd Qu.:2.5Max. :3.0 Max. :3.0

and see that the method for data frames is called.


Define generic function

xypos <- function(z,...) UseMethod("xypos")

minimal number of parameters because all parameters

specified on the generic must appear on every method

use . . . for passing class-specific parameters


Cartesian method

xypos.cartes <- function(z,x="x",y="y",names,...) {xy <- data.frame(z[c(x,y)])if (!missing(names)) colnames(xy) <- namesclass(xy) <- c(class(xy),"cartes")return(xy)

}


Polar method

xypos.polar <- function(z,r="r",theta="theta",names,...) {xy <- data.frame(z[r]*cos(z[theta]),z[r]*sin(z[theta]))if (missing(names) || length(names)==0) names=c("x","y")if (length(names)==1) names[2]="y"colnames(xy) <- namesclass(xy) <- c(class(xy),"cartes")return(xy)

}


Example calls

Passing cartesian coordinates:

> xypos(pxy) > xypos(pxy,names=(c("U","V")))x y U V

1 1 1 1 1 12 2 2 2 2 23 3 3 3 3 3

If we pass in polar co-ordinates:

> qrt > xypos(qrt)r theta x y

1 1.414214 0.7853982 1 1 12 2.828427 0.7853982 2 2 23 4.242641 0.7853982 3 3 3


Conclusion

Generic functions

produce a sensible result based on the input object type

hide complexity from the user

reduce the amount of checking when programming

contribute to standardization

allow for the extension of existing functions to new objects

generic functions in r · outline introduction objects in r generic functions do it yourself...

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