Title: General Regression Neural Networks Package
Version: 0.1.0
Description: This General Regression Neural Networks Package uses various distance functions. It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous published paper Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>. This package includes various functions, although "euclidean" distance is used traditionally.
License: GPL (≥ 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: cvTools, rdist, scales, stats, vegan
Depends: R (≥ 3.5.0)
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-09-06 09:43:08 UTC; paleowind
Author: Shufeng LI ORCID iD [aut, cre]
Maintainer: Shufeng LI <lisf@xtbg.org.cn>
Repository: CRAN
Date/Publication: 2021-09-08 09:30:04 UTC

Find best spread

Description

Find best spread

Usage

findSpread(p_train, v_train, k, fun, scale = TRUE)

Arguments

p_train

The dataframe of training predictor dataset

v_train

The dataframe of training response variables

k

The numeric number of k folds

fun

The distance function

scale

The logic statements (TRUE/FALSE)

Value

Best spread

Examples

data("met")
data("physg")
## Not run: best.spread<-findSpread(physg,met,10,"bray",scale=TRUE)

find best spreads using Rdist

Description

find best spreads using Rdist

Usage

findSpreadRdist(x, y, k, fun, scale = TRUE)

Arguments

x

The dataframe of training predictor dataset

y

The dataframe of training response variables

k

The numeric number of k folds

fun

The distance function

scale

The logic statements (TRUE/FALSE)

Value

The vector of best spreads

Find best spread using vegan function

Description

Find best spread using vegan function

Usage

findSpreadVegan(x, y, k, fun, scale = TRUE)

Arguments

x

The dataframe of training predictor dataset

y

The dataframe of training response variables

k

The numeric number of k folds

fun

The distance function

scale

The logic statements (TRUE/FALSE)

Value

The vector of best spreads

General Regression Neural Networks (GRNNs)

Description

This GRNNs uses various distance functions including: "euclidean", "minkowski", "manhattan", "maximum", "canberra", "angular", "correlation", "absolute_correlation", "hamming", "jaccard","bray", "kulczynski", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao","mahalanobis".

Usage

grnn(p_input, p_train, v_train, fun = "euclidean", best.spread, scale = TRUE)

Arguments

p_input

The dataframe of input predictors

p_train

The dataframe of training predictor dataset

v_train

The dataframe of training response variables

fun

The distance function

best.spread

The vector of best spreads

scale

The logic statements (TRUE/FALSE)

Value

The predictions

Examples

data("met")
data("physg")
best.spread<-c(0.33,0.33,0.31,0.34,0.35,0.35,0.32,0.31,0.29,0.35,0.35)
predict<-physg[1,]
physg.train<-physg[-1,]
met.train<-met[-1,]
prediction<-grnn(predict,physg.train,met.train,fun="euclidean",best.spread,scale=TRUE)

grnn distance

Description

grnn distance

Usage

grnn.distance(x, y, fun)

Arguments

x

The dataframe of training predictor dataset

y

The dataframe of training response variables

fun

The distance function

Value

The matrix of distance between a and b

Examples

data("physg")
physg.train<-physg[1:10,]
physg.test<-physg[11:30,]
distance<-grnn.distance(physg.test,physg.train,"bray")

General Regression Neural Networks (GRNNs)

Description

General Regression Neural Networks (GRNNs)

Usage

grnn.kfold(x, y, k, fun, scale = TRUE)

Arguments

x

The dataframe of training predictor dataset

y

The dataframe of training response variables

k

The numeric number of k folds

fun

The distance function

scale

The logic statements (TRUE/FALSE)

Value

rmse,stdae,stdev,mae,r,pvalue,best spread

Examples

data("met")
data("physg")
results_kfold<-grnn.kfold(physg,met,10,"euclidean",scale=TRUE)

meteorological dataset

Description

Data from a global collection by Robert A. Spicer. It include 11 climate variables from 378 sites.

Usage

met

Format

A data frame with 378 rows and 11 variables:

MAT

double COLUMN_DESCRIPTION

WMMT

double COLUMN_DESCRIPTION

CMMT

double COLUMN_DESCRIPTION

GROWSEAS

double COLUMN_DESCRIPTION

GSP

double COLUMN_DESCRIPTION

MMGSP

double COLUMN_DESCRIPTION

Three_WET

double COLUMN_DESCRIPTION

Three_DRY

double COLUMN_DESCRIPTION

RH

double COLUMN_DESCRIPTION

SH

double COLUMN_DESCRIPTION

ENTHAL

double COLUMN_DESCRIPTION

Details

DETAILS

physiognomy dataset

Description

Data from a global collection by Robert A. Spicer. It include 31 leaf physiognomies variables from 378 sites.

Usage

physg

Format

A data frame with 378 rows and 31 variables:

Lobed

double COLUMN_DESCRIPTION

No.Teeth

double COLUMN_DESCRIPTION

Regular.teeth

double COLUMN_DESCRIPTION

Close.teeth

double COLUMN_DESCRIPTION

Round.teeth

double COLUMN_DESCRIPTION

Acute.teeth

double COLUMN_DESCRIPTION

Compound.teeth

double COLUMN_DESCRIPTION

Nanophyll

double COLUMN_DESCRIPTION

Leptophyll.1

double COLUMN_DESCRIPTION

Leptophyll.2

double COLUMN_DESCRIPTION

Microphyll.1

double COLUMN_DESCRIPTION

Microphyll.2

double COLUMN_DESCRIPTION

Microphyll.3

double COLUMN_DESCRIPTION

Mesophyll.1

double COLUMN_DESCRIPTION

Mesophyll.2

double COLUMN_DESCRIPTION

Mesophyll.3

double COLUMN_DESCRIPTION

Emarginate.apex

double COLUMN_DESCRIPTION

Round.apex

double COLUMN_DESCRIPTION

Acute.apex

double COLUMN_DESCRIPTION

Attenuate.apex

double COLUMN_DESCRIPTION

Cordate.base

double COLUMN_DESCRIPTION

Round.base

double COLUMN_DESCRIPTION

Acute.base

double COLUMN_DESCRIPTION

L.W..1.1

double COLUMN_DESCRIPTION

L.W.1.2.1

double COLUMN_DESCRIPTION

L.W.2.3.1

double COLUMN_DESCRIPTION

L.W.3.4.1

double COLUMN_DESCRIPTION

L.W..4.1

double COLUMN_DESCRIPTION

Obovate

double COLUMN_DESCRIPTION

Elliptic

double COLUMN_DESCRIPTION

Ovate

double COLUMN_DESCRIPTION

Details

DETAILS

distance using vegdist

Description

distance using vegdist

Usage

veg.distance(a, b, fun = "bray")

Arguments

a

The dataframe of training predictor dataset

b

The dataframe of validation predictor dataset

fun

The distance function

Value

The matrix of distance between a and b

Examples

data("physg")
physg.train<-physg[1:10,]
physg.test<-physg[11:30,]
distance<-veg.distance(physg.test,physg.train,"bray")