| Title: | Yet Another General Regression Neural Network |
| Version: | 0.1.1 |
| Author: | WenSui Liu |
| Maintainer: | WenSui Liu <liuwensui@gmail.com> |
| Description: | Another implementation of general regression neural network in R based on Specht (1991) <doi:10.1109/72.97934>. It is applicable to the functional approximation or the classification. |
| URL: | https://github.com/statcompute/yager |
| Depends: | R (≥ 3.6.0) |
| Imports: | datasets, stats, randtoolbox, lhs, MLmetrics, graphics, parallel |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2020-10-25 18:07:29 UTC; liuwensui |
| Repository: | CRAN |
| Date/Publication: | 2020-10-25 18:30:02 UTC |
Generate a list of index for the n-fold cross-validation
Description
The function folds generates a list of index for the n-fold cross-validation
Usage
folds(idx, n, seed = 1)
Arguments
idx |
A vector of index list |
n |
The number of n folds |
seed |
The seed value to generate random n-fold index |
Value
A list of n-fold index
Examples
folds(seq(10), n = 3, seed = 2020)
Generate random numbers of latin hypercube sampling
Description
The function gen_latin generates a vector of random numbers by latin hypercube sampling
Usage
gen_latin(min = 0, max = 1, n, seed = 1)
Arguments
min |
The minimum value of random numbers |
max |
The maxinum value of random numbers |
n |
The number of random numbers to gernate |
seed |
The seed value of random number generation |
Value
A vector of random numbers bounded by the min and max
Examples
gen_latin(0, 1, 10, 2020)
Generate sobol sequence
Description
The function gen_sobol generates a vector of scrambled sobol sequence
Usage
gen_sobol(min = 0, max = 1, n, seed = 1)
Arguments
min |
The minimum value of random numbers |
max |
The maxinum value of random numbers |
n |
The number of random numbers to gernate |
seed |
The seed value of random number generation |
Value
A vector of sobol sequence bounded by the min and max
Examples
gen_sobol(0, 1, 10, 2020)
Generate Uniform random numbers
Description
The function gen_unifm generates a vector of uniform random numbers
Usage
gen_unifm(min = 0, max = 1, n, seed = 1)
Arguments
min |
The minimum value of random numbers |
max |
The maxinum value of random numbers |
n |
The number of random numbers to gernate |
seed |
The seed value of random number generation |
Value
A vector of uniform random numbers bounded by the min and max
Examples
gen_unifm(0, 1, 10, 2020)
Create a general regression neural network
Description
The function grnn.fit creates a general regression neural network (GRNN)
Usage
grnn.fit(x, y, sigma = 1, w = rep(1, length(y)))
Arguments
x |
The matrix of predictors |
y |
The vector of response variable |
sigma |
The scalar of smoothing parameter |
w |
The vector of weights with default = 1 for each record |
Value
A general regression neural network object
References
Donald Specht. (1991). A General Regression Neural Network.
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
Derive the importance rank of all predictors used in the GRNN
Description
The function grnn.imp derives the importance rank of all predictors used in the GRNN
It essentially is a wrapper around the function grnn.x_imp.
Usage
grnn.imp(net, class = FALSE)
Arguments
net |
The GRNN object generated by grnn.fit() |
class |
TRUE or FALSE, whether it is for the classification or not |
Value
A dataframe with important values of all predictors in the GRNN
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:3])
gnet <- grnn.fit(x = X, y = Y)
## Not run:
grnn.imp(net = gnet, class = TRUE)
## End(Not run)
Derive the marginal effect of a predictor used in a GRNN
Description
The function grnn.margin derives the marginal effect of a predictor used in a GRNN
by assuming mean values for the rest predictors
Usage
grnn.margin(net, i, plot = TRUE)
Arguments
net |
The GRNN object generated by grnn.fit() |
i |
The ith predictor in the GRNN |
plot |
TRUE or FALSE to plot the marginal effect |
Value
A plot of the marginal effect or a dataframe of the marginal effect
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.margin(gnet, 1, plot = FALSE)
Optimize the optimal value of GRNN smoothing parameter based on AUC
Description
The function grnn.optmiz_auc optimize the optimal value of GRNN smoothing parameter by cross-validation.
It is applicable to the classification.
Usage
grnn.optmiz_auc(net, lower = 0, upper, nfolds = 4, seed = 1, method = 1)
Arguments
net |
A GRNN object generated by grnn.fit() |
lower |
A scalar for the lower bound of the smoothing parameter |
upper |
A scalar for the upper bound of the smoothing parameter |
nfolds |
A scalar for the number of n-fold, 4 by default |
seed |
The seed value for the n-fold cross-validation, 1 by default |
method |
A scalar referring to the optimization method, 1 for Golden section searc and 2 for Brent’s method |
Value
The best outcome
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
## Not run:
grnn.optmiz_auc(net = gnet, lower = 3, upper = 7, nfolds = 2)
## End(Not run)
Calculate predicted values of GRNN by using parallelism
Description
The function grnn.parpred calculates a vector of GRNN predicted values based on an input matrix
Usage
grnn.parpred(net, x)
Arguments
net |
The GRNN object generated by grnn.fit() |
x |
The matrix of input predictors |
Value
A vector of predicted values
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.parpred(gnet, X[seq(5), ])
Derive the partial effect of a predictor used in a GRNN
Description
The function grnn.partial derives the partial effect of a predictor used in a GRNN
by average-out values of the rest predictors.
Usage
grnn.partial(net, i, plot = TRUE)
Arguments
net |
The GRNN object generated by grnn.fit() |
i |
The ith predictor in the GRNN |
plot |
TRUE or FALSE to plot the partial effect |
Value
A plot of the partial effect or a dataframe of the partial effect
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
## Not run:
grnn.partial(gnet, 1, plot = FALSE)
## End(Not run)
Derive the PFI rank of all predictors used in the GRNN
Description
The function grnn.pfi derives the PFI rank of all predictors used in the GRNN
It essentially is a wrapper around the function grnn.x_pfi.
Usage
grnn.pfi(net, class = FALSE, ntry = 1000, seed = 1)
Arguments
net |
The GRNN object generated by grnn.fit() |
class |
TRUE or FALSE, whether it is for the classification or not |
ntry |
The number of random permutations to try, 1e3 times by default |
seed |
The seed value for the random permutation |
Value
A dataframe with PFI values of all predictors in the GRNN
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:3])
gnet <- grnn.fit(x = X, y = Y)
## Not run:
grnn.pfi(net = gnet, class = TRUE)
## End(Not run)
Calculate predicted values of GRNN
Description
The function grnn.predict calculates a vector of GRNN predicted values based on an input matrix
Usage
grnn.predict(net, x)
Arguments
net |
The GRNN object generated by grnn.fit() |
x |
The matrix of input predictors |
Value
A vector of predicted values
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.predict(gnet, X[seq(5), ])
Calculate a predicted value of GRNN
Description
The function grnn.predone calculates a predicted value of GRNN based on an input vector
The function grnn.predone calculates a predicted value of GRNN based on an input vector
Usage
grnn.predone(net, x, type = 1)
grnn.predone(net, x, type = 1)
Arguments
net |
The GRNN object generated by grnn.fit() |
x |
The vector of input predictors |
type |
A scalar, 1 for euclidean distance and 2 for manhattan distance |
Value
A scalar of the predicted value
A scalar of the predicted value
References
Donald Specht. (1991). A General Regression Neural Network.
Donald Specht. (1991). A General Regression Neural Network.
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
for (i in seq(5)) print(grnn.predone(gnet, X[i, ]))
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
for (i in seq(5)) print(grnn.predone(gnet, X[i, ]))
Search for the optimal value of GRNN smoothing parameter based on AUC
Description
The function grnn.search_auc searches for the optimal value of GRNN smoothing parameter by cross-validation.
It is applicable to the classification.
Usage
grnn.search_auc(net, sigmas, nfolds = 4, seed = 1)
Arguments
net |
A GRNN object generated by grnn.fit() |
sigmas |
A numeric vector to search for the best smoothing parameter |
nfolds |
A scalar for the number of n-fold, 4 by default |
seed |
The seed value for the n-fold cross-validation, 1 by default |
Value
The list of all searching outcomes and the best outcome
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.search_auc(net = gnet, sigmas = c(3, 5, 7), nfolds = 2)
Search for the optimal value of GRNN smoothing parameter based on r-square
Description
The function grnn.search_rsq searches for the optimal value of GRNN smoothing parameter by cross-validation.
It is applicable to the functional approximation
Usage
grnn.search_rsq(net, sigmas, nfolds = 4, seed = 1)
Arguments
net |
A GRNN object generated by grnn.fit() |
sigmas |
A numeric vector to search for the best smoothing parameter |
nfolds |
A scalar for the number of n-fold, 4 by default |
seed |
The seed value for the n-fold cross-validation, 1 by default |
Value
The list of all searching outcomes and the best outcome
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.search_rsq(net = gnet, sigmas = seq(3), nfolds = 2)
Derive the importance of a predictor used in the GRNN
Description
The function grnn.x_imp derives the importance of a predictor used in the GRNN
by using the loss of predictability after eliminating the impact of the predictor in interest.
Usage
grnn.x_imp(net, i, class = FALSE)
Arguments
net |
The GRNN object generated by grnn.fit() |
i |
The ith predictor in the GRNN |
class |
TRUE or FALSE, whether it is for the classification or not |
Value
A vector with the variable name and two values of importance measurements, namely "imp1" and "imp2". The "imp1" measures the loss of predictability after replacing all values of the predictor with its mean. The "imp2" measures the loss of predictability after dropping the predictor from the GRNN.
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.x_imp(net = gnet, 1)
Derive the permutation feature importance of a predictor used in the GRNN
Description
The function grnn.x_pfi derives the permutation feature importance (PFI) of a predictor used in the GRNN
Usage
grnn.x_pfi(net, i, class = FALSE, ntry = 1000, seed = 1)
Arguments
net |
The GRNN object generated by grnn.fit() |
i |
The ith predictor in the GRNN |
class |
TRUE or FALSE, whether it is for the classification or not |
ntry |
The number of random permutations to try, 1e3 times by default |
seed |
The seed value for the random permutation |
Value
A vector with the variable name and the PFI value.
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.x_pfi(net = gnet, 1)