Habitat Suitability for Breeding Bird Communities

Description

Environmental variables and bird counts for identifying suitable bird habitats

Usage

data("birds")

Format

A data frame with 258 observations on the following 10 variables.

GST

Growing stock per grid

DBH

Mean diameter of the largest three trees

AOT

Age of oldest tree

AFS

Age of forest stand

DWC

Amount of dead wood of conifers

LOG

Amount of logs per grid

x_gk

grid location, x coordinate

y_gk

grid location, y coordinate

SG4

observed number of birds from structural gild 4: Requirement of regeneration (Phylloscopus trochilus, Aegithalos caudatus)

SG5

observed number of birds from structural gild 5: Requirement of regeneration combined with planted conifers (Phylloscopus collybita, Turdus merula, Sylvia atricapilla).

Details

Counts of breeding bird communities collected at 258 observation plots in a northern Bavarian forest district are the response variable of interest. Along with the number of birds in two structural gilds, 6 covariates are given here and one is interested in quantifying their impact on habitat suitability.

Source

Joerg Mueller (2005). Forest structures as key factor for beetle and bird communities in beech forests. PhD thesis, Munich University of Technology.

References

Thomas Kneib and Joerg Mueller and Torsten Hothorn (2008), Spatial smoothing techniques for the assessment of habitat suitability, Environmental and Ecological Statistics, 15(3), 343–364.

Prediction of Body Fat by Skinfold Thickness, Circumferences, and Bone Breadths

Description

For 71 healthy female subjects, body fat measurements and several anthropometric measurements are available for predictive modelling of body fat.

Usage

data("bodyfat")

Format

A data frame with 71 observations on the following 10 variables.

age

age in years.

DEXfat

body fat measured by DXA, response variable.

waistcirc

waist circumference.

hipcirc

hip circumference.

elbowbreadth

breadth of the elbow.

kneebreadth

breadth of the knee.

anthro3a

sum of logarithm of three anthropometric measurements.

anthro3b

sum of logarithm of three anthropometric measurements.

anthro3c

sum of logarithm of three anthropometric measurements.

anthro4

sum of logarithm of three anthropometric measurements.

Details

Garcia et al. (2005) report on the development of predictive regression equations for body fat content by means of common anthropometric measurements which were obtained for 71 healthy German women. In addition, the women's body composition was measured by Dual Energy X-Ray Absorptiometry (DXA). This reference method is very accurate in measuring body fat but finds little applicability in practical environments, mainly because of high costs and the methodological efforts needed. Therefore, a simple regression equation for predicting DXA measurements of body fat is of special interest for the practitioner. Backward-elimination was applied to select important variables from the available anthropometrical measurements, and Garcia (2005) report a final linear model utilizing hip circumference, knee breadth and a compound covariate which is defined as the sum of log chin skinfold, log triceps skinfold and log subscapular skinfold.

Source

Ada L. Garcia, Karen Wagner, Torsten Hothorn, Corinna Koebnick, Hans-Joachim F. Zunft and Ulrike Trippo (2005), Improved prediction of body fat by measuring skinfold thickness, circumferences, and bone breadths. Obesity Research, 13(3), 626–634.

Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.

Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid (2012). Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost. Computational Statistics.
doi:10.1007/s00180-012-0382-5

Available as vignette via: vignette(package = "mboostDevel", "mboost_tutorial")

Examples

    data("bodyfat", package = "TH.data")

    ### final model proposed by Garcia et al. (2005)
    fmod <- lm(DEXfat ~ hipcirc + anthro3a + kneebreadth, data = bodyfat)
    coef(fmod)

German Breast Cancer Study Group 2

Description

A data frame containing the observations from the GBSG2 study.

Usage

data("GBSG2")

Format

This data frame contains the observations of 686 women:

horTh

hormonal therapy, a factor at two levels no and yes.

age

of the patients in years.

menostat

menopausal status, a factor at two levels pre (premenopausal) and post (postmenopausal).

tsize

tumor size (in mm).

tgrade

tumor grade, a ordered factor at levels I < II < III.

pnodes

number of positive nodes.

progrec

progesterone receptor (in fmol).

estrec

estrogen receptor (in fmol).

time

recurrence free survival time (in days).

cens

censoring indicator (0- censored, 1- event).

Source

W. Sauerbrei and P. Royston (1999). Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials. Journal of the Royal Statistics Society Series A, Volume 162(1), 71–94.

References

M. Schumacher, G. Basert, H. Bojar, K. Huebner, M. Olschewski, W. Sauerbrei, C. Schmoor, C. Beyerle, R.L.A. Neumann and H.F. Rauschecker for the German Breast Cancer Study Group (1994), Randomized 2\times2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. Journal of Clinical Oncology, 12, 2086–2093.

Examples

data(GBSG2)

thsum <- function(x) {
  ret <- c(median(x), quantile(x, 0.25), quantile(x,0.75))
  names(ret)[1] <- "Median"
  ret
}

t(apply(GBSG2[,c("age", "tsize", "pnodes", 
                 "progrec", "estrec")], 2, thsum))

table(GBSG2$menostat)
table(GBSG2$tgrade)
table(GBSG2$horTh)

Old Faithful Geyser Data

Description

A version of the eruptions data from the ‘Old Faithful’ geyser in Yellowstone National Park, Wyoming. This version comes from Azzalini and Bowman (1990) and is of continuous measurement from August 1 to August 15, 1985.

Some nocturnal duration measurements have originally been described as ‘short’, ‘medium’ or ‘long’ and are given as interval censored observations in this version of the dataset.

Usage

geyser

Format

A data frame with 299 observations on 2 variables.

Note

Variable duration was converted to a Surv object for representing interval censored nocturnal observations.

References

Azzalini, A. and Bowman, A. W. (1990) A look at some data on the Old Faithful geyser. Applied Statistics 39, 357–365.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

faithful, geyser.

Glaucoma Database

Description

The GlaucomaM data has 196 observations in two classes. 62 variables are derived from a confocal laser scanning image of the optic nerve head, describing its morphology. Observations are from normal and glaucomatous eyes, respectively.

Usage

data("GlaucomaM")

Format

This data frame contains the following predictors describing the morphology of the optic nerve head and a membership variable:

ag

area global.

at

area temporal.

as

area superior.

an

area nasal.

ai

area inferior.

eag

effective area global.

eat

effective area temporal.

eas

effective area superior.

ean

effective area nasal.

eai

effective area inferior.

abrg

area below reference global.

abrt

area below reference temporal.

abrs

area below reference superior.

abrn

area below reference nasal.

abri

area below reference inferior.

hic

height in contour.

mhcg

mean height contour global.

mhct

mean height contour temporal.

mhcs

mean height contour superior.

mhcn

mean height contour nasal.

mhci

mean height contour inferior.

phcg

peak height contour.

phct

peak height contour temporal.

phcs

peak height contour superior.

phcn

peak height contour nasal.

phci

peak height contour inferior.

hvc

height variation contour.

vbsg

volume below surface global.

vbst

volume below surface temporal.

vbss

volume below surface superior.

vbsn

volume below surface nasal.

vbsi

volume below surface inferior.

vasg

volume above surface global.

vast

volume above surface temporal.

vass

volume above surface superior.

vasn

volume above surface nasal.

vasi

volume above surface inferior.

vbrg

volume below reference global.

vbrt

volume below reference temporal.

vbrs

volume below reference superior.

vbrn

volume below reference nasal.

vbri

volume below reference inferior.

varg

volume above reference global.

vart

volume above reference temporal.

vars

volume above reference superior.

varn

volume above reference nasal.

vari

volume above reference inferior.

mdg

mean depth global.

mdt

mean depth temporal.

mds

mean depth superior.

mdn

mean depth nasal.

mdi

mean depth inferior.

tmg

third moment global.

tmt

third moment temporal.

tms

third moment superior.

tmn

third moment nasal.

tmi

third moment inferior.

mr

mean radius.

rnf

retinal nerve fiber thickness.

mdic

mean depth in contour.

emd

effective mean depth.

mv

mean variability.

Class

a factor with levels glaucoma and normal.

Details

All variables are derived from a laser scanning image of the eye background taken by the Heidelberg Retina Tomograph. Most of the variables describe either the area or volume in certain parts of the papilla and are measured in four sectors (temporal, superior, nasal and inferior) as well as for the whole papilla (global). The global measurement is, roughly, the sum of the measurements taken in the four sector.

The observations in both groups are matched by age and sex to prevent any bias.

Source

Torsten Hothorn and Berthold Lausen (2003), Double-Bagging: Combining classifiers by bootstrap aggregation. Pattern Recognition, 36(6), 1303–1309.

Mammography Experience Study

Description

Data from a questionaire on the benefits of mammography.

Usage

data(mammoexp)

Format

A data frame with 412 observations on the following 6 variables.

ME

Mammograph experience, an ordered factor with levels Never < Within a Year < Over a Year

SYMPT

Agreement with the statement: ‘You do not need a mamogram unless you develop symptoms.’ A factor with levels Strongly Agree, Agree, Disagree and Strongly Disagree

PB

Perceived benefit of mammography, the sum of five scaled responses, each on a four point scale. A low value is indicative of a woman with strong agreement with the benefits of mammography.

HIST

Mother or Sister with a history of breast cancer; a factor with levels No and Yes.

BSE

Answers to the question: ‘Has anyone taught you how to examine your own breasts?’ A factor with levels No and Yes.

DECT

Answers to the question: 'How likely is it that a mammogram could find a new case of breast cancer?' An ordered factor with levels Not likely < Somewhat likely < Very likely.

Source

Hosmer and Lemeshow (2000). Applied Logistic Regression, 2nd edition. John Wiley & Sons Inc., New York. Section 8.1.2, page 264.

I.Q. and attitude towards science

Description

Responses given by 2982 New Jersey high-school seniors on 4 questions concerning attitude towards science. Also recorded was whether students had a high or low I.Q.

Usage

data(mn6.9)

Format

A data frame with 2982 observations on the following 5 variables.

y1

Agree=1/disagree=0 to "The development of new ideas is the scientist's greatest source of satisfaction"

y2

Agree=1/disagree=0 to "Scientists and engineers should be eliminated form the military draft"

y3

Agree=1/disagree=0 to "The scientist will make his maximum contribution to society when he has freedom to work on problems that interest him"

y4

Agree=1/disagree=0 to "The monetary compensation of a Nobel Prize-winner in physics should be at least equal to that given to popular entertainers"

group

I.Q. levels: 1=low, 2=high

Source

McCullagh, P. and Nelder, J.A. (1989, p. 239). Generalized Linear Models. Second Edition. Chapman & Hall/CRC.

copied from multmod package 1.0 (CRAN archive)

S-phase Fraction of Tumor Cells

Description

S-phase fraction of tumor cells in breast cancer patients.

Usage

data("sphase")

Format

This data frame contains the following columns:

SPF

S-phase fraction

RFS

recurrence free survival

event

censoring indicator: FALSE means censored, TRUE is an event.

Details

The data have been used to address the question whether a simple cutpoint in S-phase fraction can be used to discriminate between patients with good and bad prognosis (for example in Hothorn & Lausen, 2003).

Source

J. Pfisterer, F. Kommoss, W. Sauerbrei, D. Menzel, M. Kiechle, E. Giese, M. Hilgarth & A. Pfleiderer (1995). DNA flow cytometry in node positive breast cancer: Prognostic value and correlation to morphological and clinical factors. Analytical and Quantitative Cytology and Histology 7(6), 406–412.

References

Torsten Hothorn & Berthold Lausen (2003). On the Exact Distribution of Maximally Selected Rank Statistics. Computational Statistics & Data Analysis 43(2), 121–137.

Breast Cancer Gene Expression

Description

Gene expressions for 7129 genes in 49 breast cancer samples and the status of lymph node involvement.

Usage

data("Westbc")

Format

An list with two elements to be converted to class ExpressionSet (see package Biobase).

Details

A full description of the data can be found in West et al. (2001) and an application of boosted linear models is given by Buehlmann (2006).

Source

Mike West, Carrie Blanchette, Holly Dressman, Erich Huang, Seiichi Ishida, Rainer Spang, Harry Zuzan, John A. Olson Jr., Jeffrey R. Marks and Joseph R. Nevins (2001), Predicting the clinical status of human breast cancer by using gene expression profiles, Proceedings of the National Academy of Sciences, 98, 11462-11467.

References

Peter Buehlmann (2006), Boosting for high-dimensional linear models. The Annals of Statistics, 34(2), 559–583.

Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.

Examples

  ## Not run: 
    library("Biobase")
    data("Westbc", package = "TH.data")
    westbc <- new("ExpressionSet",
          phenoData = new("AnnotatedDataFrame", data = Westbc$pheno),
          assayData = assayDataNew(exprs = Westbc$assay))
  
## End(Not run)

Wisconsin Prognostic Breast Cancer Data

Description

Each record represents follow-up data for one breast cancer case. These are consecutive patients seen by Dr. Wolberg since 1984, and include only those cases exhibiting invasive breast cancer and no evidence of distant metastases at the time of diagnosis.

Usage

data("wpbc")

Format

A data frame with 198 observations on the following 34 variables.

status

a factor with levels N (nonrecur) and R (recur)

time

recurrence time (for status == "R") or disease-free time (for status == "N").

mean_radius

radius (mean of distances from center to points on the perimeter) (mean).

mean_texture

texture (standard deviation of gray-scale values) (mean).

mean_perimeter

perimeter (mean).

mean_area

area (mean).

mean_smoothness

smoothness (local variation in radius lengths) (mean).

mean_compactness

compactness (mean).

mean_concavity

concavity (severity of concave portions of the contour) (mean).

mean_concavepoints

concave points (number of concave portions of the contour) (mean).

mean_symmetry

symmetry (mean).

mean_fractaldim

fractal dimension (mean).

SE_radius

radius (mean of distances from center to points on the perimeter) (SE).

SE_texture

texture (standard deviation of gray-scale values) (SE).

SE_perimeter

perimeter (SE).

SE_area

area (SE).

SE_smoothness

smoothness (local variation in radius lengths) (SE).

SE_compactness

compactness (SE).

SE_concavity

concavity (severity of concave portions of the contour) (SE).

SE_concavepoints

concave points (number of concave portions of the contour) (SE).

SE_symmetry

symmetry (SE).

SE_fractaldim

fractal dimension (SE).

worst_radius

radius (mean of distances from center to points on the perimeter) (worst).

worst_texture

texture (standard deviation of gray-scale values) (worst).

worst_perimeter

perimeter (worst).

worst_area

area (worst).

worst_smoothness

smoothness (local variation in radius lengths) (worst).

worst_compactness

compactness (worst).

worst_concavity

concavity (severity of concave portions of the contour) (worst).

worst_concavepoints

concave points (number of concave portions of the contour) (worst).

worst_symmetry

symmetry (worst).

worst_fractaldim

fractal dimension (worst).

tsize

diameter of the excised tumor in centimeters.

pnodes

number of positive axillary lymph nodes observed at time of surgery.

Details

The first 30 features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.

There are two possible learning problems: predicting status or predicting the time to recur.

1) Predicting field 2, outcome: R = recurrent, N = non-recurrent - Dataset should first be filtered to reflect a particular endpoint; e.g., recurrences before 24 months = positive, non-recurrence beyond 24 months = negative. - 86.3 previous version of this data.

2) Predicting Time To Recur (field 3 in recurrent records) - Estimated mean error 13.9 months using Recurrence Surface Approximation.

The data are originally available from the UCI machine learning repository.

Source

W. Nick Street, Olvi L. Mangasarian and William H. Wolberg (1995). An inductive learning approach to prognostic prediction. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 522–530, San Francisco, Morgan Kaufmann.

Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.

Examples

    data("wpbc", package = "TH.data")

    ### fit logistic regression model 
    coef(glm(status ~ ., data = wpbc[,colnames(wpbc) != "time"],
             family = binomial()))