pls: Interfaces for pls 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 pls functions that can be used
in a pipeline implemented by magrittr.
Usage
1
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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(pls)
## cppls
## Original function to interface
yarn.cppls <- cppls(density ~ NIR, ncomp = 6, data = yarn, validation = "CV")
summary(yarn.cppls)
## The interface reverses the order of data and formula
yarn.cppls <- ntbt_cppls(yarn, density ~ NIR, ncomp = 6, validation = "CV")
summary(yarn.cppls)
## so it can be used easily in a pipeline.
yarn %>%
ntbt_cppls(density ~ NIR, ncomp = 6, validation = "CV") %>%
summary()
## mvr
## Original function to interface
yarn.mvr <- mvr(density ~ NIR, ncomp = 6, data = yarn, validation = "CV",
method = "oscorespls")
summary(yarn.mvr)
## The interface reverses the order of data and formula
yarn.mvr <- ntbt_mvr(yarn, density ~ NIR, ncomp = 6, validation = "CV",
method = "oscorespls")
summary(yarn.mvr)
## so it can be used easily in a pipeline.
yarn %>%
ntbt_mvr(density ~ NIR, ncomp = 6, validation = "CV",
method = "oscorespls") %>%
summary()
## pcr
## Original function to interface
yarn.pcr <- pcr(density ~ NIR, ncomp = 6, data = yarn, validation = "CV")
summary(yarn.pcr)
## The interface reverses the order of data and formula
yarn.pcr <- ntbt_pcr(yarn, density ~ NIR, ncomp = 6, validation = "CV")
summary(yarn.pcr)
## so it can be used easily in a pipeline.
yarn %>%
ntbt_pcr(density ~ NIR, ncomp = 6, validation = "CV") %>%
summary()
## plsr
## Original function to interface
yarn.plsr <- plsr(density ~ NIR, ncomp = 6, data = yarn, validation = "CV")
summary(yarn.plsr)
## The interface reverses the order of data and formula
yarn.plsr <- ntbt_plsr(yarn, density ~ NIR, ncomp = 6, validation = "CV")
summary(yarn.plsr)
## so it can be used easily in a pipeline.
yarn %>%
ntbt_plsr(density ~ NIR, ncomp = 6, validation = "CV") %>%
summary()
## End(Not run)
Example output
Attaching package: 'pls'
The following object is masked from 'package:stats':
loadings
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: cppls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 5.624 4.167 2.038 0.6565 0.4674 0.4446
adjCV 27.46 4.986 4.145 2.010 0.6319 0.4574 0.4375
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: cppls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.994 4.036 1.924 0.5724 0.4432 0.3890
adjCV 27.46 4.555 4.114 1.902 0.5614 0.4357 0.3854
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: cppls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.416 4.212 1.983 0.6813 0.4926 0.4447
adjCV 27.46 4.180 4.203 1.971 0.6657 0.4826 0.4358
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: oscorespls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.701 3.738 2.070 0.8420 0.5040 0.4431
adjCV 27.46 4.279 3.843 2.062 0.8064 0.4947 0.4409
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: oscorespls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.446 4.320 2.185 0.7941 0.5196 0.4483
adjCV 27.46 4.065 4.273 2.166 0.7540 0.5071 0.4426
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: oscorespls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.420 3.900 2.067 0.7476 0.4871 0.4503
adjCV 27.46 4.149 3.881 2.060 0.7124 0.4770 0.4417
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: svdpc
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 28.50 4.285 2.84 2.690 1.647 0.4834
adjCV 27.46 30.37 4.249 2.79 2.691 1.570 0.4725
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 52.17 98.60 99.47 99.70 99.88 99.97
density 5.50 98.15 99.40 99.58 99.95 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: svdpc
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 29.67 4.177 2.470 2.397 1.407 0.4945
adjCV 27.46 31.29 4.147 2.439 2.428 1.322 0.4825
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 52.17 98.60 99.47 99.70 99.88 99.97
density 5.50 98.15 99.40 99.58 99.95 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: svdpc
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 28.47 4.052 2.524 2.541 1.701 0.4794
adjCV 27.46 30.02 4.028 2.493 2.559 1.594 0.4680
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 52.17 98.60 99.47 99.70 99.88 99.97
density 5.50 98.15 99.40 99.58 99.95 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: kernelpls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 5.007 3.881 2.288 1.080 0.5980 0.4211
adjCV 27.46 4.529 3.863 2.267 1.036 0.5782 0.4189
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: kernelpls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.958 3.942 2.113 0.8831 0.4897 0.4255
adjCV 27.46 4.445 3.937 2.102 0.8397 0.4803 0.4267
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: kernelpls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.817 4.077 2.090 0.7077 0.5047 0.4609
adjCV 27.46 4.430 4.076 2.062 0.6893 0.4951 0.4563
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
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 pls functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 3 4 |
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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | ## Not run:
library(intubate)
library(magrittr)
library(pls)
## cppls
## Original function to interface
yarn.cppls <- cppls(density ~ NIR, ncomp = 6, data = yarn, validation = "CV")
summary(yarn.cppls)
## The interface reverses the order of data and formula
yarn.cppls <- ntbt_cppls(yarn, density ~ NIR, ncomp = 6, validation = "CV")
summary(yarn.cppls)
## so it can be used easily in a pipeline.
yarn %>%
ntbt_cppls(density ~ NIR, ncomp = 6, validation = "CV") %>%
summary()
## mvr
## Original function to interface
yarn.mvr <- mvr(density ~ NIR, ncomp = 6, data = yarn, validation = "CV",
method = "oscorespls")
summary(yarn.mvr)
## The interface reverses the order of data and formula
yarn.mvr <- ntbt_mvr(yarn, density ~ NIR, ncomp = 6, validation = "CV",
method = "oscorespls")
summary(yarn.mvr)
## so it can be used easily in a pipeline.
yarn %>%
ntbt_mvr(density ~ NIR, ncomp = 6, validation = "CV",
method = "oscorespls") %>%
summary()
## pcr
## Original function to interface
yarn.pcr <- pcr(density ~ NIR, ncomp = 6, data = yarn, validation = "CV")
summary(yarn.pcr)
## The interface reverses the order of data and formula
yarn.pcr <- ntbt_pcr(yarn, density ~ NIR, ncomp = 6, validation = "CV")
summary(yarn.pcr)
## so it can be used easily in a pipeline.
yarn %>%
ntbt_pcr(density ~ NIR, ncomp = 6, validation = "CV") %>%
summary()
## plsr
## Original function to interface
yarn.plsr <- plsr(density ~ NIR, ncomp = 6, data = yarn, validation = "CV")
summary(yarn.plsr)
## The interface reverses the order of data and formula
yarn.plsr <- ntbt_plsr(yarn, density ~ NIR, ncomp = 6, validation = "CV")
summary(yarn.plsr)
## so it can be used easily in a pipeline.
yarn %>%
ntbt_plsr(density ~ NIR, ncomp = 6, validation = "CV") %>%
summary()
## End(Not run)
|
Example output
Attaching package: 'pls'
The following object is masked from 'package:stats':
loadings
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: cppls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 5.624 4.167 2.038 0.6565 0.4674 0.4446
adjCV 27.46 4.986 4.145 2.010 0.6319 0.4574 0.4375
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: cppls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.994 4.036 1.924 0.5724 0.4432 0.3890
adjCV 27.46 4.555 4.114 1.902 0.5614 0.4357 0.3854
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: cppls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.416 4.212 1.983 0.6813 0.4926 0.4447
adjCV 27.46 4.180 4.203 1.971 0.6657 0.4826 0.4358
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: oscorespls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.701 3.738 2.070 0.8420 0.5040 0.4431
adjCV 27.46 4.279 3.843 2.062 0.8064 0.4947 0.4409
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: oscorespls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.446 4.320 2.185 0.7941 0.5196 0.4483
adjCV 27.46 4.065 4.273 2.166 0.7540 0.5071 0.4426
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: oscorespls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.420 3.900 2.067 0.7476 0.4871 0.4503
adjCV 27.46 4.149 3.881 2.060 0.7124 0.4770 0.4417
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: svdpc
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 28.50 4.285 2.84 2.690 1.647 0.4834
adjCV 27.46 30.37 4.249 2.79 2.691 1.570 0.4725
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 52.17 98.60 99.47 99.70 99.88 99.97
density 5.50 98.15 99.40 99.58 99.95 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: svdpc
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 29.67 4.177 2.470 2.397 1.407 0.4945
adjCV 27.46 31.29 4.147 2.439 2.428 1.322 0.4825
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 52.17 98.60 99.47 99.70 99.88 99.97
density 5.50 98.15 99.40 99.58 99.95 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: svdpc
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 28.47 4.052 2.524 2.541 1.701 0.4794
adjCV 27.46 30.02 4.028 2.493 2.559 1.594 0.4680
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 52.17 98.60 99.47 99.70 99.88 99.97
density 5.50 98.15 99.40 99.58 99.95 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: kernelpls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 5.007 3.881 2.288 1.080 0.5980 0.4211
adjCV 27.46 4.529 3.863 2.267 1.036 0.5782 0.4189
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: kernelpls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.958 3.942 2.113 0.8831 0.4897 0.4255
adjCV 27.46 4.445 3.937 2.102 0.8397 0.4803 0.4267
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
Data: X dimension: 28 268
Y dimension: 28 1
Fit method: kernelpls
Number of components considered: 6
VALIDATION: RMSEP
Cross-validated using 10 random segments.
(Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
CV 27.46 4.817 4.077 2.090 0.7077 0.5047 0.4609
adjCV 27.46 4.430 4.076 2.062 0.6893 0.4951 0.4563
TRAINING: % variance explained
1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
X 46.83 98.38 99.46 99.67 99.85 99.97
density 98.12 98.25 99.64 99.97 99.99 99.99
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