vignettes/NNSvignette_Clustering_and_Regression.R

In OVVO-Financial/NNS: Nonlinear Nonparametric Statistics

## ----setup, include=FALSE, message=FALSE--------------------------------------
knitr::opts_chunk$set(echo = TRUE)
library(NNS)
library(data.table)
data.table::setDTthreads(2L)
options(mc.cores = 1)
Sys.setenv("OMP_THREAD_LIMIT" = 2)

## ----setup2, message=FALSE, warning=FALSE-------------------------------------
library(NNS)
library(data.table)
require(knitr)
require(rgl)

## ----linear-------------------------------------------------------------------
x = seq(-5, 5, .05); y = x ^ 3

for(i in 1 : 4){NNS.part(x, y, order = i, Voronoi = TRUE, obs.req = 0)}

## ----x part,results='hide'----------------------------------------------------
for(i in 1 : 4){NNS.part(x, y, order = i, type = "XONLY", Voronoi = TRUE)}

## ----res2, echo=FALSE---------------------------------------------------------
NNS.part(x,y,order = 4, type = "XONLY")

## ----depreg},results='hide'---------------------------------------------------
for(i in 1 : 3){NNS.part(x, y, order = i, obs.req = 0, Voronoi = TRUE, type = "XONLY") ; NNS.reg(x, y, order = i, ncores = 1)}

## ----nonlinear,fig.width=5,fig.height=3,fig.align = "center"------------------
NNS.reg(x, y, ncores = 1)

## ----nonlinear multi,fig.width=5,fig.height=3,fig.align = "center"------------
f = function(x, y) x ^ 3 + 3 * y - y ^ 3 - 3 * x
y = x ; z <- expand.grid(x, y)
g = f(z[ , 1], z[ , 2])
NNS.reg(z, g, order = "max", plot = FALSE, ncores = 1)

## ----nonlinear_class,fig.width=5,fig.height=3,fig.align = "center", message = FALSE----
NNS.reg(iris[ , 1 : 4], iris[ , 5], dim.red.method = "cor", location = "topleft", ncores = 1)$equation

## ----nonlinear_class2,fig.width=5,fig.height=3,fig.align = "center", message = FALSE, echo=FALSE----
a = NNS.reg(iris[ , 1 : 4], iris[ , 5], dim.red.method = "cor", location = "topleft", ncores = 1, plot = FALSE)$equation

## ----nonlinear class threshold,fig.width=5,fig.height=3,fig.align = "center"----
NNS.reg(iris[ , 1 : 4], iris[ , 5], dim.red.method = "cor", threshold = .75, location = "topleft", ncores = 1)$equation

## ----nonlinear class threshold 2,fig.width=5,fig.height=3,fig.align = "center", echo=FALSE----
a = NNS.reg(iris[ , 1 : 4], iris[ , 5], dim.red.method = "cor", threshold = .75, location = "topleft", ncores = 1, plot = FALSE)$equation

## ----final,fig.width=5,fig.height=3,fig.align = "center"----------------------
NNS.reg(iris[ , 1 : 4], iris[ , 5], dim.red.method = "cor", threshold = .75, point.est = iris[1 : 10, 1 : 4], location = "topleft", ncores = 1)$Point.est

## ----class,fig.width=5,fig.height=3,fig.align = "center", message=FALSE-------
NNS.reg(iris[ , 1 : 4], iris[ , 5], type = "CLASS", point.est = iris[1 : 10, 1 : 4], location = "topleft", ncores = 1)$Point.est

## ----stack,fig.width=5,fig.height=3,fig.align = "center", message=FALSE, eval=FALSE----
#  NNS.stack(IVs.train = iris[ , 1 : 4],
#            DV.train = iris[ , 5],
#            IVs.test = iris[1 : 10, 1 : 4],
#            dim.red.method = "cor",
#            obj.fn = expression( mean(round(predicted) == actual) ),
#            objective = "max", type = "CLASS",
#            folds = 1, ncores = 1)

## ----stackevalres, eval = FALSE-----------------------------------------------
#  Folds Remaining = 0
#  Current NNS.reg(... , threshold = 0.935 ) MAX Iterations Remaining = 2
#  Current NNS.reg(... , threshold = 0.795 ) MAX Iterations Remaining = 1
#  Current NNS.reg(... , threshold = 0.44 ) MAX Iterations Remaining = 0
#  Current NNS.reg(... , n.best = 1 ) MAX Iterations Remaining = 12
#  Current NNS.reg(... , n.best = 2 ) MAX Iterations Remaining = 11
#  Current NNS.reg(... , n.best = 3 ) MAX Iterations Remaining = 10
#  Current NNS.reg(... , n.best = 4 ) MAX Iterations Remaining = 9
#  $OBJfn.reg
#  [1] 1
#  
#  $NNS.reg.n.best
#  [1] 4
#  
#  $probability.threshold
#  [1] 0.43875
#  
#  $OBJfn.dim.red
#  [1] 0.9666667
#  
#  $NNS.dim.red.threshold
#  [1] 0.935
#  
#  $reg
#   [1] 1 1 1 1 1 1 1 1 1 1
#  
#  $reg.pred.int
#  NULL
#  
#  $dim.red
#   [1] 1 1 1 1 1 1 1 1 1 1
#  
#  $dim.red.pred.int
#  NULL
#  
#  $stack
#   [1] 1 1 1 1 1 1 1 1 1 1
#  
#  $pred.int
#  NULL

## ----stack2, message = FALSE,fig.width=5,fig.height=3,fig.align = "center",results='hide', eval = FALSE----
#  set.seed(123)
#  x = rnorm(100); y = rnorm(100)
#  
#  nns.params = NNS.stack(IVs.train = cbind(x, x),
#                          DV.train = y,
#                          method = 1, ncores = 1)

## ----stack2optim, echo = FALSE------------------------------------------------
set.seed(123)
x = rnorm(100); y = rnorm(100)

nns.params = list()
nns.params$NNS.reg.n.best = 100

## ----stack2res, fig.width=5,fig.height=3,fig.align = "center",results='hide'----
NNS.reg(cbind(x, x), y, 
        n.best = nns.params$NNS.reg.n.best,
        point.est = cbind(x, x), 
        residual.plot = TRUE,  
        ncores = 1, confidence.interval = .95)

## ----threads, echo = FALSE----------------------------------------------------
Sys.setenv("OMP_THREAD_LIMIT" = "")