Write a function base.function.most.methods() that returns a list with components base.function,

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1 Least absolute deviations regression:  a lad() function

 

Your package will have a function lad(x, y) which takes as arguments vectors x and y and returns an object having the (new) class "lad" and consisting of a list with these components:

 

• coefficients,  a  named  numeric  vector  of  length  2  containing  the  coefficients  (βˆ0, βˆ1)  of  a linear model and having the names "(Intercept)" and "x"

• fitted.values, a vector of the {yˆi}

• residuals, a vector of the {ei = yi − yˆi}

(The name “lad” stands for “least absolute deviations.” lad() will be a function somewhat like

lm(), which uses the usual “least squared deviations” regression.)

To  find  (βˆ0, βˆ1),  use  optim()  with  its  (default)  Nelder-Mead  algorithm  to  minimize  the  sum  of absolute deviations (SAD) of data yi from predictions yˆi.  That is, minimize

 

n

SAD(β0, β1) = SAD(β0, β1; →x, →y) = |yi − β0 − β1xi|

i=1

 

over (β0, β1). Use the least squares estimates from lm() as initial values.

Test your function by running

 

area = read.csv("http://www.stat.wisc.edu/~jgillett/305/2/farmLandArea.csv") lad(x=area$land, y=area$farm)

 

Make your function code general–we may test it on several data sets, so don’t write any dependence on "farmLandArea.csv" in lad().

 

2 Write an R package

 

Create a package robust containing:

 

• the function lad(), described above

• the data frame area (which should have no persisting dependence on "farmLandArea.csv")

• the method print.lad(x, ...), which writes the named coefficients vector to the console

• the method coef.lad(object, ...), which takes a lad object object and returns its vector of coefficients. (This will allow abline(reg) to work, where reg is a regression model object returned by lad(), making it easy to add a lad regression line to a scatterplot. See ?abline if you want more understanding of its reg parameter.)

• the method predict.lad(object, new.x, ...), which takes a lad object object and a vector new.x and returns a vector containing lad’s predictions at the x values in new.x.

 

Each of these items should be documented so that R’s help via ? works for them. Document the

... parameter in each of the methods via

@param ... further arguments passed to or from other methods

(This “...” parameter is necessary to pass check() without warnings, and to make our new methods behave correctly in object oriented programming situations we didn’t discuss.)

After installing your package, when we run example(lad), it must

 

• call your lad() function for the farmland dataset

• use print() to print βˆ0  and βˆ1

• make a scatterplot with the lm() line, the lad() line in a different color, and a legend

• use predict() to find yˆ for x values corresponding to the 0, 1/4, 1/2, 3/4, and 1 quantiles of the data; add these five predictions as solid green points to the graph

 

Cautions: lad() should not produce a graph. Do not call print.lad(), coef.lad(), or predict.lad() directly: use print(), abline(), and predict() on your lad object.

 

 

3 Optional (for extra credit in the form of candy)

 

• Include test code in your package. I recommend http://r-pkgs.had.co.nz/tests.html.

• Write a function base.function.most.methods() that returns  a  list  with  components  base.function, the base R generic function having the most methods, and n, the number of its methods. Hint:

See ?base for how to list base functions, ?methods for how to list the methods of a generic function, and ?try for how to prevent errors produced by calling methods() on non-generic functions from terminating your function.

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