nnet: Interfaces for nnet package for data science pipelines.
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Interfaces to nnet functions that can be used
in a pipeline implemented by magrittr.
Usage
1
2
Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
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## Not run:
library(intubate)
library(magrittr)
library(nnet)
## multinom
options(contrasts = c("contr.treatment", "contr.poly"))
library(MASS)
example(birthwt)
## Original function to interface
multinom(low ~ ., bwt)
## The interface reverses the order of data and formula
ntbt_multinom(bwt, low ~ .)
## so it can be used easily in a pipeline.
bwt %>%
ntbt_multinom(low ~ .)
## nnet
ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3])
targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)))
set.seed(6789) ## for reproducible results
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
species = factor(c(rep("s",50), rep("c", 50), rep("v", 50))))
## Original function to interface
set.seed(12345) ## for reproducible results
nnet(species ~ ., data = ird, subset = samp,
size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
## The interface reverses the order of data and formula
set.seed(12345) ## for reproducible results
ntbt_nnet(data = ird, species ~ ., subset = samp,
size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
## so it can be used easily in a pipeline.
set.seed(12345) ## for reproducible results
ird %>%
ntbt_nnet(species ~ ., subset = samp,
size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
## End(Not run)
Example output
brthwt> bwt <- with(birthwt, {
brthwt+ race <- factor(race, labels = c("white", "black", "other"))
brthwt+ ptd <- factor(ptl > 0)
brthwt+ ftv <- factor(ftv)
brthwt+ levels(ftv)[-(1:2)] <- "2+"
brthwt+ data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
brthwt+ ptd, ht = (ht > 0), ui = (ui > 0), ftv)
brthwt+ })
brthwt> options(contrasts = c("contr.treatment", "contr.poly"))
brthwt> glm(low ~ ., binomial, bwt)
Call: glm(formula = low ~ ., family = binomial, data = bwt)
Coefficients:
(Intercept) age lwt raceblack raceother smokeTRUE
0.82302 -0.03723 -0.01565 1.19241 0.74068 0.75553
ptdTRUE htTRUE uiTRUE ftv1 ftv2+
1.34376 1.91317 0.68020 -0.43638 0.17901
Degrees of Freedom: 188 Total (i.e. Null); 178 Residual
Null Deviance: 234.7
Residual Deviance: 195.5 AIC: 217.5
# weights: 12 (11 variable)
initial value 131.004817
iter 10 value 98.029803
final value 97.737759
converged
Call:
multinom(formula = low ~ ., data = bwt)
Coefficients:
(Intercept) age lwt raceblack raceother smokeTRUE
0.82320102 -0.03723828 -0.01565359 1.19240391 0.74065606 0.75550487
ptdTRUE htTRUE uiTRUE ftv1 ftv2+
1.34375901 1.91320116 0.68020207 -0.43638470 0.17900392
Residual Deviance: 195.4755
AIC: 217.4755
# weights: 12 (11 variable)
initial value 131.004817
iter 10 value 98.029803
final value 97.737759
converged
Call:
multinom(formula = low ~ ., data = bwt)
Coefficients:
(Intercept) age lwt raceblack raceother smokeTRUE
0.82320102 -0.03723828 -0.01565359 1.19240391 0.74065606 0.75550487
ptdTRUE htTRUE uiTRUE ftv1 ftv2+
1.34375901 1.91320116 0.68020207 -0.43638470 0.17900392
Residual Deviance: 195.4755
AIC: 217.4755
# weights: 12 (11 variable)
initial value 131.004817
iter 10 value 98.029803
final value 97.737759
converged
Call:
multinom(formula = low ~ ., data = .)
Coefficients:
(Intercept) age lwt raceblack raceother smokeTRUE
0.82320102 -0.03723828 -0.01565359 1.19240391 0.74065606 0.75550487
ptdTRUE htTRUE uiTRUE ftv1 ftv2+
1.34375901 1.91320116 0.68020207 -0.43638470 0.17900392
Residual Deviance: 195.4755
AIC: 217.4755
# weights: 19
initial value 82.461465
iter 10 value 22.247391
iter 20 value 7.566993
iter 30 value 5.849848
iter 40 value 5.214272
iter 50 value 4.436323
iter 60 value 1.807985
iter 70 value 1.194316
iter 80 value 1.152826
iter 90 value 1.140506
iter 100 value 1.137393
iter 110 value 1.135124
iter 120 value 1.134293
iter 130 value 1.134165
iter 140 value 1.134094
iter 150 value 1.134039
iter 160 value 1.134019
iter 170 value 1.134017
iter 180 value 1.134015
iter 190 value 1.134014
final value 1.134013
converged
a 4-2-3 network with 19 weights
inputs: Sepal.L. Sepal.W. Petal.L. Petal.W.
output(s): species
options were - softmax modelling decay=5e-04
# weights: 19
initial value 82.461465
iter 10 value 22.247391
iter 20 value 7.566993
iter 30 value 5.849848
iter 40 value 5.214272
iter 50 value 4.436323
iter 60 value 1.807985
iter 70 value 1.194316
iter 80 value 1.152826
iter 90 value 1.140506
iter 100 value 1.137393
iter 110 value 1.135124
iter 120 value 1.134293
iter 130 value 1.134165
iter 140 value 1.134094
iter 150 value 1.134039
iter 160 value 1.134019
iter 170 value 1.134017
iter 180 value 1.134015
iter 190 value 1.134014
final value 1.134013
converged
a 4-2-3 network with 19 weights
inputs: Sepal.L. Sepal.W. Petal.L. Petal.W.
output(s): species
options were - softmax modelling decay=5e-04
# weights: 19
initial value 82.461465
iter 10 value 22.247391
iter 20 value 7.566993
iter 30 value 5.849848
iter 40 value 5.214272
iter 50 value 4.436323
iter 60 value 1.807985
iter 70 value 1.194316
iter 80 value 1.152826
iter 90 value 1.140506
iter 100 value 1.137393
iter 110 value 1.135124
iter 120 value 1.134293
iter 130 value 1.134165
iter 140 value 1.134094
iter 150 value 1.134039
iter 160 value 1.134019
iter 170 value 1.134017
iter 180 value 1.134015
iter 190 value 1.134014
final value 1.134013
converged
a 4-2-3 network with 19 weights
inputs: Sepal.L. Sepal.W. Petal.L. Petal.W.
output(s): species
options were - softmax modelling decay=5e-04
intubate documentation built on May 2, 2019, 2:46 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Interfaces to nnet functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 |
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
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 | ## Not run:
library(intubate)
library(magrittr)
library(nnet)
## multinom
options(contrasts = c("contr.treatment", "contr.poly"))
library(MASS)
example(birthwt)
## Original function to interface
multinom(low ~ ., bwt)
## The interface reverses the order of data and formula
ntbt_multinom(bwt, low ~ .)
## so it can be used easily in a pipeline.
bwt %>%
ntbt_multinom(low ~ .)
## nnet
ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3])
targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)))
set.seed(6789) ## for reproducible results
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
species = factor(c(rep("s",50), rep("c", 50), rep("v", 50))))
## Original function to interface
set.seed(12345) ## for reproducible results
nnet(species ~ ., data = ird, subset = samp,
size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
## The interface reverses the order of data and formula
set.seed(12345) ## for reproducible results
ntbt_nnet(data = ird, species ~ ., subset = samp,
size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
## so it can be used easily in a pipeline.
set.seed(12345) ## for reproducible results
ird %>%
ntbt_nnet(species ~ ., subset = samp,
size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
## End(Not run)
|
Example output
brthwt> bwt <- with(birthwt, {
brthwt+ race <- factor(race, labels = c("white", "black", "other"))
brthwt+ ptd <- factor(ptl > 0)
brthwt+ ftv <- factor(ftv)
brthwt+ levels(ftv)[-(1:2)] <- "2+"
brthwt+ data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
brthwt+ ptd, ht = (ht > 0), ui = (ui > 0), ftv)
brthwt+ })
brthwt> options(contrasts = c("contr.treatment", "contr.poly"))
brthwt> glm(low ~ ., binomial, bwt)
Call: glm(formula = low ~ ., family = binomial, data = bwt)
Coefficients:
(Intercept) age lwt raceblack raceother smokeTRUE
0.82302 -0.03723 -0.01565 1.19241 0.74068 0.75553
ptdTRUE htTRUE uiTRUE ftv1 ftv2+
1.34376 1.91317 0.68020 -0.43638 0.17901
Degrees of Freedom: 188 Total (i.e. Null); 178 Residual
Null Deviance: 234.7
Residual Deviance: 195.5 AIC: 217.5
# weights: 12 (11 variable)
initial value 131.004817
iter 10 value 98.029803
final value 97.737759
converged
Call:
multinom(formula = low ~ ., data = bwt)
Coefficients:
(Intercept) age lwt raceblack raceother smokeTRUE
0.82320102 -0.03723828 -0.01565359 1.19240391 0.74065606 0.75550487
ptdTRUE htTRUE uiTRUE ftv1 ftv2+
1.34375901 1.91320116 0.68020207 -0.43638470 0.17900392
Residual Deviance: 195.4755
AIC: 217.4755
# weights: 12 (11 variable)
initial value 131.004817
iter 10 value 98.029803
final value 97.737759
converged
Call:
multinom(formula = low ~ ., data = bwt)
Coefficients:
(Intercept) age lwt raceblack raceother smokeTRUE
0.82320102 -0.03723828 -0.01565359 1.19240391 0.74065606 0.75550487
ptdTRUE htTRUE uiTRUE ftv1 ftv2+
1.34375901 1.91320116 0.68020207 -0.43638470 0.17900392
Residual Deviance: 195.4755
AIC: 217.4755
# weights: 12 (11 variable)
initial value 131.004817
iter 10 value 98.029803
final value 97.737759
converged
Call:
multinom(formula = low ~ ., data = .)
Coefficients:
(Intercept) age lwt raceblack raceother smokeTRUE
0.82320102 -0.03723828 -0.01565359 1.19240391 0.74065606 0.75550487
ptdTRUE htTRUE uiTRUE ftv1 ftv2+
1.34375901 1.91320116 0.68020207 -0.43638470 0.17900392
Residual Deviance: 195.4755
AIC: 217.4755
# weights: 19
initial value 82.461465
iter 10 value 22.247391
iter 20 value 7.566993
iter 30 value 5.849848
iter 40 value 5.214272
iter 50 value 4.436323
iter 60 value 1.807985
iter 70 value 1.194316
iter 80 value 1.152826
iter 90 value 1.140506
iter 100 value 1.137393
iter 110 value 1.135124
iter 120 value 1.134293
iter 130 value 1.134165
iter 140 value 1.134094
iter 150 value 1.134039
iter 160 value 1.134019
iter 170 value 1.134017
iter 180 value 1.134015
iter 190 value 1.134014
final value 1.134013
converged
a 4-2-3 network with 19 weights
inputs: Sepal.L. Sepal.W. Petal.L. Petal.W.
output(s): species
options were - softmax modelling decay=5e-04
# weights: 19
initial value 82.461465
iter 10 value 22.247391
iter 20 value 7.566993
iter 30 value 5.849848
iter 40 value 5.214272
iter 50 value 4.436323
iter 60 value 1.807985
iter 70 value 1.194316
iter 80 value 1.152826
iter 90 value 1.140506
iter 100 value 1.137393
iter 110 value 1.135124
iter 120 value 1.134293
iter 130 value 1.134165
iter 140 value 1.134094
iter 150 value 1.134039
iter 160 value 1.134019
iter 170 value 1.134017
iter 180 value 1.134015
iter 190 value 1.134014
final value 1.134013
converged
a 4-2-3 network with 19 weights
inputs: Sepal.L. Sepal.W. Petal.L. Petal.W.
output(s): species
options were - softmax modelling decay=5e-04
# weights: 19
initial value 82.461465
iter 10 value 22.247391
iter 20 value 7.566993
iter 30 value 5.849848
iter 40 value 5.214272
iter 50 value 4.436323
iter 60 value 1.807985
iter 70 value 1.194316
iter 80 value 1.152826
iter 90 value 1.140506
iter 100 value 1.137393
iter 110 value 1.135124
iter 120 value 1.134293
iter 130 value 1.134165
iter 140 value 1.134094
iter 150 value 1.134039
iter 160 value 1.134019
iter 170 value 1.134017
iter 180 value 1.134015
iter 190 value 1.134014
final value 1.134013
converged
a 4-2-3 network with 19 weights
inputs: Sepal.L. Sepal.W. Petal.L. Petal.W.
output(s): species
options were - softmax modelling decay=5e-04
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