Bottles data on filling amount of six bottles over five time points.

Description

Ott and Snee (1973) analyzed the filled weight (weight) of bottles filled by six machine heads (heads) over five time points (time).

Usage

data(bottles)

Format

A data frame with 30 rows and 3 variables:

weight

the response, the weight of material filled into each bottle.

time

the time point of each filling.

heads

the head of the machine that fills the bottle.

References

Ott ER, Snee RD (1973). “Identifying useful differences in a multiple-head machine.” Journal of Qualitym Technology, 5(2), 2–13.

Lymphoma data on genomic hybridization signal from six dogs with normal and tumor tissue samples taken.

Description

Franck et. al. (2013) analyzes the genomic hybridization signal measured from normal and tumor tissue samples taken from six dogs.

Usage

data(lymphoma)

Format

A data frame with 6 rows and 2 variables:

V1

the signals from the normal tissue samples.

V2

the signals from the tumor tissue samples.

References

Franck CT, Nielsen DM, Osborne JA (2013). “A method for detecting hidden additivity in two-factor unreplicated experiments.” Computational Statistics & Data Analysis, 67(Supplement C), 95 - 104. ISSN 0167-9473, doi:10.1016/j.csda.2013.05.002.

Converts a two-way layout into tall format with row and column index labels.

Description

maketall Converts a two-way layout into tall format with row and column index labels.

Usage

maketall(data_matrix)

Arguments

Value

maketall returns a data frame containing the original observations, row labels, and column labels.

Examples

library(slgf)
data(lymphoma)
maketall(lymphoma)

Bayesian Model Selection with Latent Group-Based Regression Effects and Heteroscedasticity

Description

ms_slgf Implements the model selection method proposed by (Metzger and Franck 2019).

Usage

ms_slgf(
  dataf,
  response,
  lgf_beta,
  min_levels_beta = 1,
  lgf_Sigma,
  min_levels_Sigma = 1,
  same_scheme = TRUE,
  usermodels,
  het = rep(0, length(usermodels)),
  prior = "flat",
  m0 = NULL
)

Arguments

Value

ms_slgf returns a list of five elements if the flat prior is used, and six elements if the Zellner-Siow mixture g-prior is used:
1) models, an M by 7 matrix where columns contain the model selection results and information for each model, including:
- Model, the formula associated with each model;
- Scheme.beta, the grouping scheme associated with the fixed effects;
- Scheme.Sigma, the grouping scheme associated with the variances;
- Log-Marginal, the fractional log-marginal likelihood associated with each model;
- FmodProb, the fractional posterior probability associated with each model;
- ModPrior, the prior assigned to each model;
- Cumulative, the cumulative fractional posterior probability associated with a given model and the previous models;
2) class_probabilities, a vector containing cumulative posterior probabilities associated with each model class;
3) coefficients, MLEs for each model's regression effects;
4) variances, MLEs based on concentrated likelihood for each model's variance(s);
5) gs, MLEs based on concentrated likelihood for each model's g; only included if prior="zs".

Author(s)

Thomas A. Metzger and Christopher T. Franck

References

Metzger TA, Franck CT (2019). “Detection of latent heteroscedasticity and group-based regression effects in linear models via Bayesian model selection.” arXiv e-prints.

Examples

# Analyze the smell and textile data sets.

library(numDeriv)


data(smell)
out_smell <- ms_slgf(dataf = smell, response = "olf", het=c(1,1),
                     lgf_beta = "agecat", lgf_Sigma = "agecat",
                     same_scheme=TRUE, min_levels_beta=1, min_levels_Sigma=1,
                     usermodels = list("olf~agecat", "olf~group"), m0=4)
out_smell$models[1:5,]
out_smell$coefficients[[46]]
out_smell$variances[[46]]

# textile data set
data(textile)
out_textile <- ms_slgf(dataf = textile, response = "strength",
                     lgf_beta = "starch", lgf_Sigma = "starch",
                     same_scheme=FALSE, min_levels_beta=1, min_levels_Sigma=1,
                     usermodels = list("strength~film+starch", "strength~film*starch",
                                       "strength~film+group", "strength~film*group"),
                     het=c(1,1,1,1), prior="flat", m0=8)
out_textile$models[1:5,c(1,2,3,5)]
out_textile$class_probabilities
out_textile$coefficients[31]
out_textile$variances[31]

Roadwear data on four tires, each comprising three compounds, from a balanced incompleted block design.

Description

Davies (1954) analyzes the wear on four tires, where each tire comprises three distinct compounds.

Usage

data(roadwear)

Format

A data frame with 12 rows and 3 variables:

abrasion

the measurement of abrasion.

compound

the compound from which each measurement was taken, either A, B, C, or D.

tire

the tire from which each measurement was taken, either 1, 2, 3, or 4.

References

Davies OL (1954). The Design and Analysis of Industrial Experiments. Oliver and Boyd, London.

Smell data on olfactory function by age group

Description

O'Brien and Heft (1995) studied the University of Pennsylvania Smell Identification Test (UPSIT). 180 subjects of different age groups were asked to describe 40 different odors. Olfactory index was quantified by the Freeman-Tukey modified arcsine transformation on the proportion of correctly identified odors. Subjects were divided into five age groups: group 1 if age 2 or younger; group 2 if between ages 26 and 40; group 3 if between ages 41 and 55; group 4 if between ages 56 and 70; and group 5 if older than 75.

Usage

data(smell)

Format

A data frame with 180 rows and 2 variables:

agecat

age category, from 1 to 5.

olf

olfactory function, measured as the Freeman-Tukey modified arcsine transformation on the proportion of correctly identified odors.

Source

SAS/STAT 15.2 User's Guide

References

OBrien RG, Heft MW (1995). “New Discrimination Indexes and Models for Studying Sensory Functioning in Aging.” Journal of Applied Statistics, 22, 9-27.

Textile data on breaking strength by starch type and chip thickness

Description

Furry (1939) analyzes the breaking strength of a starch chip as a function of the chip’s thickness (measured in 10^-4 inches) and the type of plant from which the starch was derived (corn, canna, or potato).

Usage

data(textile)

Format

A data frame with 49 rows and 3 variables:

strength

the response, the breaking strength.

film

the chip's film thickness, measured in 10^-4 inches.

starch

the chip's starch component: canna, corn, or potato

References

Furry MS (1939). “Breaking strength, elongation and folding endurance of films of starches and gelatin used in sizing.” Technical Bulletin (United States Department of Agriculture), 674, 1-36.

Locknut data on torque required to tighten a fixture by plating method

Description

Meek and Ozgur (1991) analyzes the torque required to strengthen a fixture (bolt or mandrel) as a function of the fixture's plating method (cadmium and wax, heat treating, and phosphate and oil, denoted CW, HT, and PO, respectively).

Usage

data(torque)

Format

A data frame with 60 rows and 3 variables:

Torque

the response, the torque required to tighten the fixture.

Fixture

the type of fixture, bolt or mandrel.

Plating

the plating treatment, CW, HT, or PO.

References

Meek GE, Ozgur CO (1991). “Torque Variation Analysis.” Journal of the Industrial Mathematics Society, 41, 1-16.