sirt (version 4.1-15)

sirt-utilities: Utility Functions in sirt

Description

Utility functions in sirt.

Usage

# bounds entries in a vector
bounds_parameters( pars, lower=NULL, upper=NULL)
# improper density function which always returns a value of 1
dimproper(x)
# generalized inverse of a symmetric function
ginverse_sym(A, eps=1E-8)
# hard thresholding function
hard_thresholding(x, lambda)
# soft thresholding function
soft_thresholding(x, lambda)
# power function x^a, like in Cpp
pow(x, a)
# trace of a matrix
tracemat(A)
#** matrix functions
sirt_matrix2(x, nrow)   # matrix() function with byrow=TRUE
sirt_colMeans(x, na.rm=TRUE)
sirt_colSDs(x, na.rm=TRUE)
sirt_colMins(x, na.rm=TRUE)
sirt_colMaxs(x, na.rm=TRUE)
sirt_colMedians(x, na.rm=TRUE)
#* normalize vector to have sum of one
sirt_sum_norm(x, na.rm=TRUE)
#* discrete normal distribution
sirt_dnorm_discrete(x, mean=0, sd=1, ...)
# plyr::rbind.fill implementation in sirt
sirt_rbind_fill(x, y)
# Fisher-z transformation, see psych::fisherz
sirt_fisherz(rho)
# inverse Fisher-z transformation, see psych::fisherz2r
sirt_antifisherz(z)
# smooth approximation of the absolute value function
sirt_abs_smooth(x, deriv=0, eps=1e-4)
# permutations with replacement
sirt_permutations(r,v)
  #-> is equivalent to gtools::permutations(n=length(v), r=D, v=v, repeats.allowed=TRUE)
# attach all elements in a list in a specified environment
sirt_attach_list_elements(x, envir)
# switch between stats::optim and stats::nlminb
sirt_optimizer(optimizer, par, fn, grad=NULL, method="L-BFGS-B", hessian=TRUE,
                   control=list(), ...)
# print objects in a summary
sirt_summary_print_objects(obji, from=NULL, to=NULL, digits=3, rownames_null=TRUE,
      grep_string=NULL)
# print package version and R session
sirt_summary_print_package_rsession(pack)
# print package version
sirt_summary_print_package(pack)
# print R session
sirt_summary_print_rsession()
# print call
sirt_summary_print_call(CALL)
# print a data frame x with fixed numbers of digits after the decimal
print_digits(x, digits=NULL)
# discrete inverse function
sirt_rcpp_discrete_inverse(x0, y0, y)
# move variables in a data frame
move_variables_df(x, after_var, move_vars)

Arguments

pars

Numeric vector

lower

Numeric vector

upper

Numeric vector

x

Numeric vector or a matrix or a list

eps

Numerical. Shrinkage parameter of eigenvalue in ginverse_sym

a

Numeric vector

lambda

Numeric value

A

Matrix

nrow

Integer

na.rm

Logical

mean

Numeric

sd

Numeric

y

Matrix

rho

Numeric

deriv

Integer indicating the order of derivative

z

Numeric

r

Integer

v

Vector

envir

Environment

optimizer

Can be one of the following optimizers: optim, nlminb, bobyqa (from the minqa packlage), Rvmmin (from the optimx package) or nloptr (from the nloptr package using the argument opts$algorithm="NLOPT_LD_MMA").

par

Initial parameter

fn

Function

grad

Gradient function

method

Optimization method

hessian

Logical

control

Control list for R optimizers

...

Further arguments to be passed

obji

Data frame

from

Integer

to

Integer

digits

Integer

rownames_null

Logical

grep_string

String

pack

Package name

CALL

Call statement

x0

Vector

y0

Vector

after_var

String indicating variable name after which variable specified variables in move_vars should be moved

move_vars

Variables which should be moved after after_var

Examples

#############################################################################
## EXAMPLE 1: Trace of a matrix
#############################################################################

set.seed(86)
A <- matrix( stats::runif(4), 2,2 )
tracemat(A)
sum(diag(A))    #=sirt::tracemat(A)

#############################################################################
## EXAMPLE 2: Power function
#############################################################################

x <- 2.3
a <- 1.7
pow(x=x,a=a)
x^a            #=sirt::pow(x,a)

#############################################################################
## EXAMPLE 3: Soft and hard thresholding function (e.g. in LASSO estimation)
#############################################################################

x <- seq(-2, 2, length=100)
y <- sirt::soft_thresholding( x, lambda=.5)
graphics::plot( x, y, type="l")

z <- sirt::hard_thresholding( x, lambda=.5)
graphics::lines( x, z, lty=2, col=2)

#############################################################################
## EXAMPLE 4: Bounds on parameters
#############################################################################

pars <- c(.721, .346)
bounds_parameters( pars=pars, lower=c(-Inf, .5), upper=c(Inf,1) )

#############################################################################
## EXAMPLE 5: Smooth approximation of absolute value function
#############################################################################

x <- seq(-1,1,len=100)
graphics::plot(x, abs(x), lwd=2, col=1, lty=1, type="l", ylim=c(-1,1) )
# smooth approximation
tt <- 2
graphics::lines(x, sirt::sirt_abs_smooth(x), lty=tt, col=tt, lwd=2)
# first derivative
tt <- 3
graphics::lines(x, sirt::sirt_abs_smooth(x, deriv=1), lty=tt, col=tt, lwd=2)
# second derivative
tt <- 4
graphics::lines(x, sirt::sirt_abs_smooth(x, deriv=2), lty=tt, col=tt, lwd=2)

# analytic computation of first and second derivative
stats::deriv( ~ sqrt(x^2 + eps), namevec="x", hessian=TRUE )

if (FALSE) {
#############################################################################
## EXAMPLE 6: Permutations with replacement
#############################################################################

D <- 4
v <- 0:1
sirt::sirt_permutations(r=D, v=v)
gtools::permutations(n=length(v), r=D, v=v, repeats.allowed=TRUE)
}