Description Usage Arguments Value Note Author(s) References Examples
Generates a nonlinear regression based on partial moment quadrant means.
1 2 3 4 5 6 7  NNS.reg(x, y, factor.2.dummy = TRUE, order = NULL, stn = 0.98,
dim.red.method = NULL, tau = NULL, type = NULL, point.est = NULL,
location = "top", return.values = TRUE, plot = TRUE,
plot.regions = FALSE, residual.plot = TRUE, std.errors = FALSE,
confidence.interval = NULL, threshold = 0, n.best = NULL,
noise.reduction = "mean", norm = NULL, dist = "L2",
multivariate.call = FALSE)

x 
a vector, matrix or data frame of variables of numeric or factor data types. 
y 
a numeric or factor vector with compatible dimsensions to 
factor.2.dummy 
logical; 
order 
integer; Controls the number of partial moment quadrant means. Users are encouraged to try different 
stn 
numeric [0, 1]; Signal to noise parameter, sets the threshold of 
dim.red.method 
options: ("cor", "NNS.cor", "NNS.caus", "all", NULL) method for determining synthetic X* coefficients. Selection of a method automatically engages the dimension reduction regression. The default is 
tau 
options("ts", NULL);

type 

point.est 
a numeric or factor vector with compatible dimsensions to 
location 
Sets the legend location within the plot, per the 
return.values 
logical; 
plot 
logical; 
plot.regions 
logical; 
residual.plot 
logical; 
std.errors 
logical; 
confidence.interval 
numeric [0, 1]; 
threshold 
numeric [0, 1]; 
n.best 
integer; 
noise.reduction 
the method of determing regression points options: ("mean", "median", "mode", "off"); In low signal:noise situations, 
norm 

dist 
options:("L1", "L2") the method of distance calculation; Selects the distance calculation used. 
multivariate.call 
Internal parameter for multivariate regressions. 
UNIVARIATE REGRESSION RETURNS THE FOLLOWING VALUES:
"R2"
provides the goodness of fit;
"SE"
returns the overall standard error of the estimate between y
and y.hat
;
"Prediction.Accuracy"
returns the correct rounded "Point.est"
used in classifications versus the categorical y
;
"derivative"
for the coefficient of the x
and its applicable range;
"Point"
returns the x
point(s) being evaluated;
"Point.est"
for the predicted value generated;
"regression.points"
provides the points used in the regression equation for the given order of partitions;
"Fitted"
returns a vector containing only the fitted values, y.hat
;
"Fitted.xy"
returns a data.table of x
,y
, y.hat
, and NNS.ID
;
MULTIVARIATE REGRESSION RETURNS THE FOLLOWING VALUES:
"R2"
provides the goodness of fit;
"equation"
returns the numerator of the synthetic X* dimension reduction equation as a data.table consisting of regressor and its coefficient. Denominator is simply the length of all coefficients > 0, returned in last row of equation
data.table.
"x.star"
returns the synthetic X* as a vector;
"rhs.partitions"
returns the partition points for each regressor x
;
"RPM"
provides the Regression Point Matrix, the points for each x
used in the regression equation for the given order of partitions;
"Point.est"
returns the predicted value generated;
"Fitted"
returns a vector containing only the fitted values, y.hat
;
"Fitted.xy"
returns a data.table of x
,y
, y.hat
, gradient
, and NNS.ID
.
Please ensure point.est
is of compatible dimensions to x
, error message will ensue if not compatible. Also, upon visual inspection of the data, if a highly periodic variable is observed set (stn = 0)
or (order = "max")
to ensure a proper fit.
Identical regressors can be used as long as they do not share the same name. For instance,
NNS.reg(cbind(x, 1 * x), y)
will work as NNS.reg
is not affected by multicollinearity.
NNS (>= v.0.3.4)
has repurposed parameter (type = "CLASS")
. (type = "CLASS")
is now restricted to signifying a classification analysis for NNS.reg
while (dim.red.method)
enables dimension reduction regressions.
Fred Viole, OVVO Financial Systems
Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" http://amzn.com/1490523995
Vinod, H. and Viole, F. (2017) "Nonparametric Regression Using Clusters" https://link.springer.com/article/10.1007/s1061401797135
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62  set.seed(123)
x < rnorm(100) ; y < rnorm(100)
NNS.reg(x, y)
## Manual {order} selection
## Not run:
NNS.reg(x, y, order = 2)
## End(Not run)
## Maximum {order} selection
## Not run:
NNS.reg(x, y, order = "max")
## End(Not run)
## xonly paritioning (Univariate only)
## Not run:
NNS.reg(x, y, type = "XONLY")
## End(Not run)
## For Multiple Regression:
## Not run:
x < cbind(rnorm(100), rnorm(100), rnorm(100)) ; y < rnorm(100)
NNS.reg(x, y, point.est = c(.25, .5, .75))
## End(Not run)
## For Multiple Regression based on Synthetic X* (Dimension Reduction):
## Not run:
x < cbind(rnorm(100), rnorm(100), rnorm(100)) ; y<rnorm(100)
NNS.reg(x, y, point.est = c(.25, .5, .75), dim.red.method = "cor")
## End(Not run)
## IRIS dataset examples:
# Dimension Reduction:
## Not run:
NNS.reg(iris[,1:4], iris[,5], dim.red.method = "cor", order = 5)
## End(Not run)
# Dimension Reduction using causal weights:
## Not run:
NNS.reg(iris[,1:4], iris[,5], dim.red.method = "NNS.caus", order = 5)
## End(Not run)
# Multiple Regression:
## Not run:
NNS.reg(iris[,1:4], iris[,5], order = 2, noise.reduction = "off")
## End(Not run)
# Classification:
## Not run:
NNS.reg(iris[,1:4], iris[,5], point.est = iris[1:10, 1:4], type = "CLASS")$Point.est
## End(Not run)
## To call fitted values:
## Not run:
x < rnorm(100) ; y < rnorm(100)
NNS.reg(x, y)$Fitted
## End(Not run)
## To call partial derivative (univariate regression only):
## Not run:
NNS.reg(x, y)$derivative
## End(Not run)

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