NCI60 data set

Description

cDNA microarrays were used to examine the variation in gene expression among the 60 cell lines. The cell lines are derived from tumors with different sites of origin. This data set contain 61 observations and 30 feature variables from 8 different tissue types.

• Type has 8 different tissue types, 9 cases of breast, 5 cases of central nervous system (CNS), 7 cases pf colon, 8 cases of leukemia, 8 cases of melanoma, 9 cases of non-small-cell lung carcinoma (NSCLC), 6 cases of ovarian and 9 cases of renal.

• Gene1 to Gen 30 gene expression information

Usage

data(NCI60)

Format

A data frame with 61 rows and 31 variables

Source

Dudoit, S., Fridlyand, J. and Speed, T. P. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American statistical Association 97 77-87.

Predict class for the test set and calculate prediction error after finding the PPtree structure, .

Description

Predict class for the test set and calculate prediction error after finding the PPtree structure, .

Usage

PPclassify2( Tree.result, test.data = NULL, Rule = 1, true.class = NULL)

Arguments

Value

predict.class predicted class

predict.error prediction error

References

Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection pursuit classification tree, Electronic Journal of Statistics, 7:1369-1386.

Examples

#crab data set

Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5)
Tree.crab

PPclassify2(Tree.crab)

Projection Pursuit Random Forest

Description

PPforest implements a random forest using projection pursuit trees algorithm (based on PPtreeViz package).

Usage

PPforest(data, class, std = TRUE, size.tr, m, PPmethod, size.p,
 lambda = .1, parallel = FALSE, cores = 2, rule = 1)

Arguments

Value

An object of class PPforest with components.

References

Natalia da Silva, Dianne Cook & Eun-Kyung Lee (2021) A Projection Pursuit Forest Algorithm for Supervised Classification, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2020.1870480

Examples

#crab example with all the observations used as training

pprf.crab <- PPforest(data = crab, class = 'Type',
 std = FALSE, size.tr = 1, m = 200, size.p = .5, 
 PPmethod = 'LDA' , parallel = TRUE, cores = 2, rule=1)
pprf.crab

Projection pursuit classification tree with random variable selection in each split

Description

Find tree structure using various projection pursuit indices of classification in each split.

Usage

PPtree_split(form, data, PPmethod='LDA', 
size.p=1,  lambda = 0.1,...)

Arguments

Value

An object of class PPtreeclass with components

References

Lee, YD, Cook, D., Park JW, and Lee, EK (2013) PPtree: Projection pursuit classification tree, Electronic Journal of Statistics, 7:1369-1386.

Examples

#crab data set

Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5)
Tree.crab

For each bootstrap sample grow a projection pursuit tree (PPtree object).

Description

For each bootstrap sample grow a projection pursuit tree (PPtree object).

Usage

baggtree(
  data,
  class,
  m = 500,
  PPmethod = "LDA",
  lambda = 0.1,
  size.p = 1,
  parallel = FALSE,
  cores = 2
)

Arguments

Value

data frame with trees_pp output for all the bootstraps samples.

Examples

#crab data set
crab.trees <- baggtree(data = crab, class = 'Type',
m =  200, PPmethod = 'LDA', lambda = .1, size.p = 0.5 , parallel = TRUE, cores = 2)
str(crab.trees, max.level = 1)

Astralian crabs

Description

Measurements on rock crabs of the genus Leptograpsus. The data set contains 200 observations from two species of crab (blue and orange), there are 50 specimens of each sex of each species, collected on site at Fremantle, Western Australia.

• Type is the class variable and has 4 classes with the combinations of specie and sex (BlueMale, BlueFemale, OrangeMale and OrangeFemale).

• FLthe size of the frontal lobe length, in mm

• RWrear width, in mm

• CLlength of midline of the carapace, in mm

• CWmaximum width of carapace, in mm

• BDdepth of the body; for females, measured after displacement of the abdomen, in mm

Usage

data(crab)

Format

A data frame with 200 rows and 6 variables

Source

Campbell, N. A. & Mahon, R. J. (1974), A Multivariate Study of Variation in Two Species of Rock Crab of genus Leptograpsus, Australian Journal of Zoology 22(3), 417 - 425.

Fish catch data set

Description

There are 159 fishes of 7 species are caught and measured. Altogether there are 7 variables. All the fishes are caught from the same lake(Laengelmavesi) near Tampere in Finland.

• Type has 7 fish classes, with 35 cases of Bream, 11 cases of Parkki, 56 cases of Perch 17 cases of Pike, 20 cases of Roach, 14 cases of Smelt and 6 cases of Whitewish.

• weight Weight of the fish (in grams)

• length1 Length from the nose to the beginning of the tail (in cm)

• length2 Length from the nose to the notch of the tail (in cm)

• length3 Length from the nose to the end of the tail (in cm)

• height Maximal height as % of Length3

• width Maximal width as % of Length3

Usage

data(fishcatch)

Format

A data frame with 159 rows and 7 variables

Source

urlhttp://www.amstat.org/publications/jse/jse_data_archive.htm

Glass data set

Description

Contains measurements 214 observations of 6 types of glass; defined in terms of their oxide content.

• Type has 6 types of glasses

• X1 refractive index

• X2 Sodium (unit measurement: weight percent in corresponding oxide).

• X3 Magnesium

• X4 Aluminum

• X5 Silicon

• X6 Potassium

• X7 Calcium

• X8 Barium

• X9 Iron

Usage

data(glass)

Format

A data frame with 214 rows and 10 variables

The image data set

Description

contains 2310 observations of instances from 7 outdoor images

• Type has 7 types of outdoor images, brickface, cement, foliage, grass, path, sky, and window.

• X1 the column of the center pixel of the region

• X2 the row of the center pixel of the region.

• X3 the number of pixels in a region = 9.

• X4 the results of a line extraction algorithm that counts how many lines of length 5 (any orientation) with low contrast, less than or equal to 5, go through the region.

• X5 measure the contrast of horizontally adjacent pixels in the region. There are 6, the mean and standard deviation are given. This attribute is used as a vertical edge detector.

• X6 X5 sd

• X7 measures the contrast of vertically adjacent pixels. Used for horizontal line detection.

• X8 sd X7

• X9 the average over the region of (R + G + B)/3

• X10 the average over the region of the R value.

• X11 the average over the region of the B value.

• X12 the average over the region of the G value.

• X13 measure the excess red: (2R - (G + B))

• X14 measure the excess blue: (2B - (G + R))

• X15 measure the excess green: (2G - (R + B))

• X16 3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics)

• X17 mean of X16

• X18 hue mean

Usage

data(image)

Format

A data frame contains 2310 observations and 19 variables

Leukemia data set

Description

This dataset comes from a study of gene expression in two types of acute leukemias, acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Gene expression levels were measured using Affymetrix high density oligonucleotide arrays containing 6817 human genes. A data set containing 72 observations from 3 leukemia types classes.

• Type has 3 classes with 38 cases of B-cell ALL, 25 cases of AML and 9 cases of T-cell ALL.

• Gene1 to Gen 40 gene expression levels

Usage

data(leukemia)

Format

A data frame with 72 rows and 41 variables

Source

Dudoit, S., Fridlyand, J. and Speed, T. P. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American statistical Association 97 77-87.

Lymphoma data set

Description

Gene expression in the three most prevalent adult lymphoid malignancies: B-cell chronic lymphocytic leukemia (B-CLL), follicular lymphoma (FL), and diffuse large B-cell lym- phoma (DLBCL). Gene expression levels were measured using a specialized cDNA microarray, the Lymphochip, containing genes that are preferentially expressed in lymphoid cells or that are of known immunologic or oncologic importance. This data set contain 80 observations from 3 lymphoma types.

• Type Class variable has 3 classes with 29 cases of B-cell ALL (B-CLL), 42 cases of diffuse large B-cell lymphoma (DLBCL) and 9 cases of follicular lymphoma (FL).

• Gene1 to Gen 50gene expression

Usage

data(lymphoma)

Format

A data frame with 80 rows and 51 variables

Source

Dudoit, S., Fridlyand, J. and Speed, T. P. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Ex- pression Data. Journal of the American statistical Association 97 77-87.

Data structure with the projected and boundary by node and class.

Description

Data structure with the projected and boundary by node and class.

Usage

node_data(ppf, tr, Rule = 1)

Arguments

Value

Data frame with projected data for each class and node id and the boundaries

Examples

#crab data set with all the observations used as training

pprf.crab <- PPforest(data = crab, std =TRUE, class = 'Type',
 size.tr = 1, m = 200, size.p = .5, PPmethod = 'LDA')
node_data(ppf = pprf.crab, tr = 1) 

The olive data set

Description

contains 572 observations and 10 variables

• RegionThree super-classes of Italy: North, South and the island of Sardinia

• area Nine collection areas: three from North, four from South and 2 from Sardinia

• palmitic fatty acids percent x 100

• palmitoleic fatty acids percent x 100

• stearicfatty acids percent x 100

• oleicfatty acids percent x 100

• linoleicfatty acids percent x 100

• linolenicfatty acids percent x 100

• arachidic fatty acids percent x 100

• eicosenoic fatty acids percent x 100

Usage

data(olive)

Format

A data frame contains 573 observations and 10 variables

Parkinson data set

Description

A data set containing 195 observations from 2 parkinson types.

• Type Class variable has 2 classes, there are 48 cases of healthy people and 147 cases with Parkinson. The feature variables are biomedical voice measures.

• X1 Average vocal fundamental frequency

• X2 Maximum vocal fundamental frequency

• X3 Minimum vocal fundamental frequency

• X4 MDVP:Jitter(%) measures of variation in fundamental frequency

• X5 MDVP:Jitter(Abs) measures of variation in fundamental frequency

• X6 MDVP:RAP measures of variation in fundamental frequency

• X7 MDVP:PPQ measures of variation in fundamental frequency

• X8 Jitter:DDP measures of variation in fundamental frequency

• X9 MDVP:Shimmer measures of variation in amplitude

• X10 MDVP:Shimmer(dB) measures of variation in amplitude

• X11 Shimmer:APQ3 measures of variation in amplitude

• X12 Shimmer:APQ5 measures of variation in amplitude

• X13 MDVP:APQ measures of variation in amplitude

• X14 Shimmer:DDA measures of variation in amplitude

• X15 NHR measures of ratio of noise to tonal components in the voice

• X16 HNR measures of ratio of noise to tonal components in the voice

• X17 RPDE nonlinear dynamical complexity measures

• X18 D2 nonlinear dynamical complexity measures

• X19 DFA - Signal fractal scaling exponent

• X20 spread1 Nonlinear measures of fundamental frequency variation

• X21 spread2 Nonlinear measures of fundamental frequency variation

• X22 PPE Nonlinear measures of fundamental frequency variation

Usage

data(parkinson)

Format

A data frame with 195 rows and 23 variables

Source

urlhttps://archive.ics.uci.edu/ml/datasets/Parkinsons

Obtain the permuted importance variable measure

Description

Obtain the permuted importance variable measure

Usage

permute_importance(ppf)

Arguments

Value

A data frame with permuted importance measures, imp is the permuted importance measure defined in Brieman paper, imp2 is the permuted importance measure defined in randomForest package, the standard deviation (sd.im and sd.imp2) for each measure is computed and the also the standardized mesure.

Examples

pprf.crab <- PPforest(data = crab, class = 'Type',
std = TRUE, size.tr = 1, m = 100, size.p = .4, PPmethod = 'LDA', parallel = TRUE, core = 2)
permute_importance(ppf = pprf.crab) 

Global importance measure for a PPforest object as the average IMP PPtree measure over all the trees in the forest

Description

Global importance measure for a PPforest object as the average IMP PPtree measure over all the trees in the forest

Usage

ppf_avg_imp(ppf, class)

Arguments

Value

Data frame with the global importance measure

Examples

#crab data set with all the observations used as training

pprf.crab <- PPforest(data = crab, std =TRUE, class = 'Type',
 size.tr = 1, m = 100, size.p = .5, PPmethod = 'LDA')
 ppf_avg_imp(pprf.crab, 'Type') 
 

Global importance measure for a PPforest object

Description

Global importance measure for a PPforest object

Usage

ppf_global_imp(data, class, ppf)

Arguments

Value

Data frame with the global importance measure

Examples

#crab data set with all the observations used as training

pprf.crab <- PPforest(data = crab, std = TRUE, class = 'Type',
 size.tr = 1, m = 200, size.p = .5, PPmethod = 'LDA', parallel = TRUE, cores = 2)
 
ppf_global_imp(data = crab, class = 'Type', pprf.crab) 

Print PPforest object

Description

Print PPforest object

Usage

## S3 method for class 'PPforest'
print(x, ...)

Arguments

Value

printed results for PPforest object

Data structure with the projected and boundary by node and class.

Description

Data structure with the projected and boundary by node and class.

Usage

ternary_str(ppf, id, sp, dx, dy)

Arguments

Value

Data frame needed to visualize a ternary plot

Examples

#crab data set with all the observations used as training
pprf.crab <- PPforest(data = crab, std =TRUE, class = "Type",
 size.tr = 1, m = 100, size.p = .5, PPmethod = 'LDA')
 require(dplyr)
pl_ter <- function(dat, dx, dy ){
  p1  <- dat[[1]] %>% dplyr::filter(pair %in% paste(dx, dy, sep = "-") ) %>%
    dplyr::select(Class, x, y) %>%
    ggplot2::ggplot(ggplot2::aes(x, y, color = Class)) +
    ggplot2::geom_segment(data = dat[[2]], ggplot2::aes(x = x1, xend = x2,
                                               y = y1, yend = y2), color = "black" ) +
    ggplot2::geom_point(size = I(3), alpha = .5) +
    ggplot2::labs(y = " ",  x = " ") +
    ggplot2::theme(legend.position = "none", aspect.ratio = 1) +
    ggplot2::scale_colour_brewer(type = "qual", palette = "Dark2") +
    ggplot2::labs(x = paste0("T", dx, " "), y = paste0("T", dy, " ")) +
    ggplot2::theme(aspect.ratio = 1)
  p1
}
#ternary plot in tree different selected dierections
 pl_ter(ternary_str(pprf.crab, id = c(1, 2, 3), sp = 3, dx = 1, dy = 2), 1, 2 )

Obtain predicted class for new data from baggtree function or PPforest

Description

Obtain predicted class for new data from baggtree function or PPforest

Usage

trees_pred(object, xnew, parallel = FALSE, cores = 2, rule = 1)

Arguments

Value

predicted values from PPforest or baggtree

Examples

## Not run: 
crab.trees <- baggtree(data = crab, class = 'Type', 
m =  200, PPmethod = 'LDA', lambda = .1, size.p = 0.4 )
 
pr <- trees_pred(  crab.trees,xnew = crab[, -1], parallel= FALSE, cores = 2)

pprf.crab <- PPforest(data = crab, class = 'Type',
 std = FALSE, size.tr = 2/3, m = 100, size.p = .4, PPmethod = 'LDA', parallel = TRUE )
 
trees_pred(pprf.crab, xnew = pprf.crab$test ,parallel = TRUE)

## End(Not run)

Wine data set

Description

A data set containing 178 observations from 3 wine grown cultivares in Italy.

Usage

data(wine)

Format

A data frame with 178 rows and 14 variables

Details

• Type Class variable has 3 classes that are 3 different wine grown cultivares in Italy.

• X1 to X13Check vbles