R Programming Questions and Answers – Scoping Rules – 2

This set of R Programming Language Multiple Choice Questions & Answers (MCQs) focuses on “Scoping Rules – 2”.

1. ________ functions can be “built which contain all of the necessary data for evaluating the function.
a) Objective
b) reflective
c) Nested
d) Symmetry
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2. Point out the wrong statement?
a) Dynamic scoping turns out to be particularly useful for simplifying statistical computations
b) Lexical scoping turns out to be particularly useful for simplifying statistical computations
c) The scoping rules of a language determine how values are assigned to free variables
d) An environment is a collection of (symbol, value) pairs, i.e. x is a symbol and 3.14 might be its value
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3. Which of the following language supports lexical scoping?
a) Perl
b) Python
c) Common Lisp
d) All of the mentioned
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4. _________ require you to pass a function whose argument is a vector of parameters.
a) optimize()
b) optimise()
c) opt()
d) opm()
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5. Point out the correct statement?
a) An environment is a collection of (symbol, value) pairs, i.e. x is a symbol and 3.14 might be its value
b) If the value of a symbol is not found in the environment in which a function was defined, then the search is continued in the child environment
c) After the top-level environment, the search continues down the search list until we hit the parent environment
d) Dynamic scoping turns out to be particularly useful for simplifying statistical computations
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6. What will be the output of the following R code?

function(p) {
          params[!fixed] <- p
          mu <- params[1]
          sigma <- params[2]
## Calculate the Normal density
          a <- -0.5*length(data)*log(2*pi*sigma^2)
          b <- -0.5*sum((data-mu)^2) / (sigma^2)
          -(a + b)
}
> ls(environment(nLL))

a) “data” “fixed” “param”
b) “data” “variable” “params”
c) “data” “fixed” “params”
d) “data” “param”
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7. What will be the output of the following R code?

> nLL <- make.NegLogLik(normals, c(FALSE, 2))
> optimize(nLL, c(-1, 3))$minimum

a) 1.217775
b) 2.217775
c) 3
d) empty
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8. What will be the output of the following R code?

> nLL <- make.NegLogLik(normals, c(1, FALSE))
> optimize(nLL, c(1e-6, 10))$minimum

a) 1.217775
b) 1.800596
c) 3.73424
d) empty
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9. The _________ function is used to plot negative likelihood.
a) plot()
b) graph()
c) graph.plot()
d) plot.graph()
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10. What will be the output of the following R code?

> g <- function(x) {
+               a <- 3
+               x+a+y
+             ## 'y' is a free variable
+ }
> y <- 3
> g(2)

a) 9
b) 42
c) 8
d) Error
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