| Title: | Projection Pursuit Classification Forest |
|---|---|
| Description: | Implements projection pursuit forest algorithm for supervised classification. |
| Authors: | Natalia da Silva, Eun-Kyung Lee, Di Cook |
| Maintainer: | Natalia da Silva <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.3 |
| Built: | 2025-03-04 04:34:09 UTC |
| Source: | https://github.com/natydasilva/ppforest |
For each bootstrap sample grow a projection pursuit tree (PPtree object).
baggtree( data, class, m = 500, PPmethod = "LDA", lambda = 0.1, size.p = 1, parallel = FALSE, cores = 2 )baggtree( data, class, m = 500, PPmethod = "LDA", lambda = 0.1, size.p = 1, parallel = FALSE, cores = 2 )
data |
Data frame with the complete data set. |
class |
A character with the name of the class variable. |
m |
is the number of bootstrap replicates, this corresponds with the number of trees to grow. To ensure that each observation is predicted a few times we have to select this number no too small. |
PPmethod |
is the projection pursuit index to be optimized, options LDA or PDA, by default it is LDA. |
lambda |
a parameter for PDA index |
size.p |
proportion of random sample variables in each split. |
parallel |
logical condition, if it is TRUE then parallelize the function |
cores |
number of cores used in the parallelization |
data frame with trees_pp output for all the bootstraps samples.
#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)#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)
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.
is the class variable and has 4 classes with the combinations of specie and sex (BlueMale, BlueFemale, OrangeMale and OrangeFemale)
.
the size of the frontal lobe length, in mm
rear width, in mm
length of midline of the carapace, in mm
maximum width of carapace, in mm
depth of the body; for females, measured after displacement of the abdomen, in mm
data(crab)data(crab)
A data frame with 200 rows and 6 variables
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.
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.
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 of the fish (in grams)
Length from the nose to the beginning of the tail (in cm)
Length from the nose to the notch of the tail (in cm)
Length from the nose to the end of the tail (in cm)
Maximal height as % of Length3
Maximal width as % of Length3
data(fishcatch)data(fishcatch)
A data frame with 159 rows and 7 variables
[http://www.amstat.org/publications/jse/jse_data_archive.htm](fishcatch)
Contains measurements 214 observations of 6 types of glass; defined in terms of their oxide content.
has 6 types of glasses
refractive index
Sodium (unit measurement: weight percent in corresponding oxide).
Magnesium
Aluminum
Silicon
Potassium
Calcium
Barium
Iron
data(glass)data(glass)
A data frame with 214 rows and 10 variables
contains 2310 observations of instances from 7 outdoor images
has 7 types of outdoor images, brickface, cement, foliage, grass, path, sky, and window.
the column of the center pixel of the region
the row of the center pixel of the region.
the number of pixels in a region = 9.
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.
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.
X5 sd
measures the contrast of vertically adjacent pixels. Used for horizontal line detection.
sd X7
the average over the region of (R + G + B)/3
the average over the region of the R value.
the average over the region of the B value.
the average over the region of the G value.
measure the excess red: (2R - (G + B))
measure the excess blue: (2B - (G + R))
measure the excess green: (2G - (R + B))
3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics)
mean of X16
hue mean
data(image)data(image)
A data frame contains 2310 observations and 19 variables
has 3 classes with 38 cases of B-cell ALL, 25 cases of AML and 9 cases of T-cell ALL
.
gene expression levels
Leukemia data set 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.
has 3 classes with 38 cases of B-cell ALL, 25 cases of AML and 9 cases of T-cell ALL
.
gene expression levels
data(leukemia)data(leukemia)
A data frame with 72 rows and 41 variables
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.
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.
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)
.
gene expression
data(lymphoma)data(lymphoma)
A data frame with 80 rows and 51 variables
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.
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.
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.
gene expression information
data(NCI60)data(NCI60)
A data frame with 61 rows and 31 variables
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.
Data structure with the projected and boundary by node and class.
node_data(ppf, tr, Rule = 1)node_data(ppf, tr, Rule = 1)
ppf |
is a PPforest object |
tr |
numerical value to identify a tree |
Rule |
split rule 1:mean of two group means, 2:weighted mean, 3: mean of max(left group) and min(right group), 4: weighted mean of max(left group) and min(right group) |
Data frame with projected data for each class and node id and the boundaries
#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)#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)
contains 572 observations and 10 variables
Three super-classes of Italy: North, South and the island of Sardinia
Nine collection areas: three from North, four from South and 2 from Sardinia
fatty acids percent x 100
fatty acids percent x 100
fatty acids percent x 100
fatty acids percent x 100
fatty acids percent x 100
fatty acids percent x 100
fatty acids percent x 100
fatty acids percent x 100
data(olive)data(olive)
A data frame contains 573 observations and 10 variables
A data set containing 195 observations from 2 parkinson types.
Class variable has 2 classes, there are 48 cases of healthy people and 147 cases with Parkinson. The feature variables are biomedical voice measures
.
Average vocal fundamental frequency
Maximum vocal fundamental frequency
Minimum vocal fundamental frequency
MDVP:Jitter(%) measures of variation in fundamental frequency
MDVP:Jitter(Abs) measures of variation in fundamental frequency
MDVP:RAP measures of variation in fundamental frequency
MDVP:PPQ measures of variation in fundamental frequency
Jitter:DDP measures of variation in fundamental frequency
MDVP:Shimmer measures of variation in amplitude
MDVP:Shimmer(dB) measures of variation in amplitude
Shimmer:APQ3 measures of variation in amplitude
Shimmer:APQ5 measures of variation in amplitude
MDVP:APQ measures of variation in amplitude
Shimmer:DDA measures of variation in amplitude
NHR measures of ratio of noise to tonal components in the voice
HNR measures of ratio of noise to tonal components in the voice
RPDE nonlinear dynamical complexity measures
D2 nonlinear dynamical complexity measures
DFA - Signal fractal scaling exponent
spread1 Nonlinear measures of fundamental frequency variation
spread2 Nonlinear measures of fundamental frequency variation
PPE Nonlinear measures of fundamental frequency variation
data(parkinson)data(parkinson)
A data frame with 195 rows and 23 variables
[https://archive.ics.uci.edu/ml/datasets/Parkinsons](Parkinson)
Obtain the permuted importance variable measure
permute_importance(ppf)permute_importance(ppf)
ppf |
is a PPforest object |
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.
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)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)
Predict class for the test set and calculate prediction error after finding the PPtree structure, .
PPclassify2( Tree.result, test.data = NULL, Rule = 1, true.class = NULL)PPclassify2( Tree.result, test.data = NULL, Rule = 1, true.class = NULL)
Tree.result |
the result of PP.Tree |
test.data |
the test dataset |
Rule |
split rule 1:mean of two group means, 2:weighted mean, 3: mean of max(left group) and min(right group), 4: weighted mean of max(left group) and min(right group) |
true.class |
true class of test dataset if available |
predict.class predicted class
predict.error prediction error
Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection pursuit classification tree, Electronic Journal of Statistics, 7:1369-1386.
#crab data set Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5) Tree.crab PPclassify2(Tree.crab)#crab data set Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5) Tree.crab PPclassify2(Tree.crab)
Global importance measure for a PPforest object as the average IMP PPtree measure over all the trees in the forest
ppf_avg_imp(ppf, class)ppf_avg_imp(ppf, class)
ppf |
is a PPforest object |
class |
A character with the name of the class variable. |
Data frame with the global importance measure
#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')#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
ppf_global_imp(data, class, ppf)ppf_global_imp(data, class, ppf)
data |
Data frame with the complete data set. |
class |
A character with the name of the class variable. |
ppf |
is a PPforest object |
Data frame with the global importance measure
#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)#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)
PPforest implements a random forest using projection pursuit trees algorithm (based on PPtreeViz package).
PPforest(data, class, std = TRUE, size.tr, m, PPmethod, size.p, lambda = .1, parallel = FALSE, cores = 2, rule = 1)PPforest(data, class, std = TRUE, size.tr, m, PPmethod, size.p, lambda = .1, parallel = FALSE, cores = 2, rule = 1)
data |
Data frame with the complete data set. |
class |
A character with the name of the class variable. |
std |
if TRUE standardize the data set, needed to compute global importance measure. |
size.tr |
is the size proportion of the training if we want to split the data in training and test. |
m |
is the number of bootstrap replicates, this corresponds with the number of trees to grow. To ensure that each observation is predicted a few times we have to select this number no too small. |
PPmethod |
is the projection pursuit index to optimize in each classification tree. The options are |
size.p |
proportion of variables randomly sampled in each split. |
lambda |
penalty parameter in PDA index and is between 0 to 1 . If |
parallel |
logical condition, if it is TRUE then parallelize the function |
cores |
number of cores used in the parallelization |
rule |
split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size |
An object of class PPforest with components.
prediction.training |
predicted values for training data set. |
training.error |
error of the training data set. |
prediction.test |
predicted values for the test data set if |
error.test |
error of the test data set if |
oob.error.forest |
out of bag error in the forest. |
oob.error.tree |
out of bag error for each tree in the forest. |
boot.samp |
information of bootrap samples. |
output.trees |
output from a |
proximity |
Proximity matrix, if two cases are classified in the same terminal node then the proximity matrix is increased by one in |
votes |
a matrix with one row for each input data point and one column for each class, giving the fraction of (OOB) votes from the |
n.tree |
number of trees grown in |
n.var |
number of predictor variables selected to use for spliting at each node. |
type |
classification. |
confusion |
confusion matrix of the prediction (based on OOB data). |
call |
the original call to |
train |
is the training data based on |
test |
is the test data based on |
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
#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#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
Find tree structure using various projection pursuit indices of classification in each split.
PPtree_split(form, data, PPmethod='LDA', size.p=1, lambda = 0.1,...)PPtree_split(form, data, PPmethod='LDA', size.p=1, lambda = 0.1,...)
form |
A character with the name of the class variable. |
data |
Data frame with the complete data set. |
PPmethod |
index to use for projection pursuit: 'LDA', 'PDA' |
size.p |
proportion of variables randomly sampled in each split, default is 1, returns a PPtree. |
lambda |
penalty parameter in PDA index and is between 0 to 1 . If |
... |
arguments to be passed to methods |
An object of class PPtreeclass with components
Tree.Struct |
Tree structure of projection pursuit classification tree |
projbest.node |
1-dim optimal projections of each split node |
splitCutoff.node |
cutoff values of each split node |
origclass |
original class |
origdata |
original data |
Lee, YD, Cook, D., Park JW, and Lee, EK (2013) PPtree: Projection pursuit classification tree, Electronic Journal of Statistics, 7:1369-1386.
#crab data set Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5) Tree.crab#crab data set Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5) Tree.crab
Print PPforest object
## S3 method for class 'PPforest' print(x, ...)## S3 method for class 'PPforest' print(x, ...)
x |
is a PPforest class object |
... |
additional parameter |
printed results for PPforest object
Data structure with the projected and boundary by node and class.
ternary_str(ppf, id, sp, dx, dy)ternary_str(ppf, id, sp, dx, dy)
ppf |
is a PPforest object |
id |
is a vector with the selected projection directions |
sp |
is the simplex dimensions, if k is the number of classes sp = k - 1 |
dx |
first direction included in id |
dy |
second direction included in id |
Data frame needed to visualize a ternary plot
#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 )#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
trees_pred(object, xnew, parallel = FALSE, cores = 2, rule = 1)trees_pred(object, xnew, parallel = FALSE, cores = 2, rule = 1)
object |
Projection pursuit classification forest structure from PPforest or baggtree |
xnew |
data frame with explicative variables used to get new predicted values. |
parallel |
logical condition, if it is TRUE then parallelize the function |
cores |
number of cores used in the parallelization |
rule |
split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size |
predicted values from PPforest or baggtree
## 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$test ,parallel = TRUE) ## End(Not run)## Not run: crab.trees <- baggtree(data = crab, class = 'Type', m = 200, PPmethod = 'LDA', lambda = .1, size.p = 0.4 ) pr <- trees_pred( crab.trees,xnew = crab[, -1], parallel= FALSE, cores = 2) pprf.crab <- PPforest(data = crab, class = 'Type', std = FALSE, size.tr = 2/3, m = 100, size.p = .4, PPmethod = 'LDA', parallel = TRUE ) trees_pred(pprf.crab, xnew = pprf.crab$test ,parallel = TRUE) ## End(Not run)
A data set containing 178 observations from 3 wine grown cultivares in Italy.
Class variable has 3 classes that are 3 different wine grown cultivares in Italy.
Check vbles
data(wine)data(wine)
A data frame with 178 rows and 14 variables