| Title: | Inference Tests for ExPosition |
|---|---|
| Description: | Non-parametric resampling-based inference tests for ExPosition. |
| Authors: | Derek Beaton, Joseph Dunlop, Herve Abdi |
| Maintainer: | Derek Beaton <[email protected]> |
| License: | GPL-2 |
| Version: | 0.12.7.1 |
| Built: | 2024-10-31 21:23:10 UTC |
| Source: | CRAN |
ExPosition).
InPosition provides multiple forms of inference tests for the ExPosition package.
| Package: | InPosition |
| Type: | Package |
| Version: | 0.12.7 |
| Date: | 2013-12-09 |
| Depends: | R (>=2.15.0), prettyGraphs (>= 2.1.4), ExPosition (>= 2.0.0) |
| License: | GPL-2 |
| URL: | http://www.utdallas.edu/~derekbeaton/software/ExPosition |
Questions, comments, compliments, and complaints go to Derek Beaton [email protected]. Also see the bug-tracking and live update website for ExPosition: http://code.google.com/p/exposition-family/
Primary authors and contributors are: Derek Beaton, Joseph Dunlop, and Hervé Abdi
Permutation:
Berry, K. J., Johnston, J. E., & Mielke, P. W. (2011). Permutation methods. Wiley Interdisciplinary Reviews: Computational Statistics,3, 527–542.
Peres-Neto, P. R., Jackson, D. A., & Somers, K. M. (2005). How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Computational Statistics & Data Analysis, 49(4), 974-997.
Bootstrap:
Chernick, M. R. (2008). Bootstrap methods: A guide for practitioners and researchers (Vol. 619). Wiley-Interscience.
Hesterberg, T. (2011). Bootstrap. Wiley Interdisciplinary Reviews: Computational Statistics, 3, 497–526.
epPCA.inference.battery, epGPCA.inference.battery, epCA.inference.battery, epMCA.inference.battery. There are no inference tests for MDS at this time. We recommend PCA for inference instead of MDS (some MDS inference tests require the rectangluar table, not the distances, so it is easier to just use PCA).
See also inGraphs for graphing and caChiTest for an alternate to resampling methods for Correspondence Analysis.
#For more examples, see each individual function (as noted above).#For more examples, see each individual function (as noted above).
fj as supplemental elements.
This function computes a bootstrap resampled set of data and projects fj as supplemental elements.
boot.compute.fj(DATA, res, DESIGN = NULL, constrained = FALSE)boot.compute.fj(DATA, res, DESIGN = NULL, constrained = FALSE)
DATA |
The original data matrix to be bootstrapped. Rows will be bootstrapped and are assumed to be observations. |
res |
of class |
DESIGN |
A design matrix (in disjunctive coding). Only used if |
constrained |
a boolean. If TRUE, bootstrap resampling will occur within groups as designated by the |
fjj |
a set of factor scores of the measures (columns, |
Derek Beaton
Chernick, M. R. (2008). Bootstrap methods: A guide for practitioners and researchers (Vol. 619). Wiley-Interscience.
Hesterberg, T. (2011). Bootstrap. Wiley Interdisciplinary Reviews: Computational Statistics, 3, 497–526.
See the functions supplementaryCols and link{boot.samples}
##the following code generates 100 bootstrap resampled ##projections of the measures from the Iris data set. data(ep.iris) data <- ep.iris$data design <- ep.iris$design iris.pca <- epGPCA(data,scale="SS1",DESIGN=design,make_design_nominal=FALSE) boot.fjs.unconstrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100)) boot.fjs.constrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100)) for(i in 1:100){ #unconstrained means we resample any of the 150 flowers boot.fjs.unconstrained[,,i] <- boot.compute.fj(ep.iris$data,iris.pca) #constrained resamples within each of the 3 groups boot.fjs.constrained[,,i] <- boot.compute.fj(data,iris.pca,design,TRUE) }##the following code generates 100 bootstrap resampled ##projections of the measures from the Iris data set. data(ep.iris) data <- ep.iris$data design <- ep.iris$design iris.pca <- epGPCA(data,scale="SS1",DESIGN=design,make_design_nominal=FALSE) boot.fjs.unconstrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100)) boot.fjs.constrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100)) for(i in 1:100){ #unconstrained means we resample any of the 150 flowers boot.fjs.unconstrained[,,i] <- boot.compute.fj(ep.iris$data,iris.pca) #constrained resamples within each of the 3 groups boot.fjs.constrained[,,i] <- boot.compute.fj(data,iris.pca,design,TRUE) }
Performs bootstrap ratio test which is analogous to a t- or z-score.
boot.ratio.test(boot.cube, critical.value = 2)boot.ratio.test(boot.cube, critical.value = 2)
boot.cube |
an |
critical.value |
numeric. This is the value that would be used as a cutoff in a t- or z-test. Default is 2 (i.e., 1.96 rounded up). The higher the number, the more difficult to reject the null. |
A list with the following items:
return(list(sig.boot.ratios=significant.boot.ratios,boot.ratios=boot.ratios,critical.value=critical.value))
sig.boot.ratios |
This is a matrix with the same number of rows and columns as |
boot.ratios |
This is a matrix with bootstrap ratio values that has the same number of rows and columns as |
critical.value |
the critical value input is also returned. |
Derek Beaton and Hervé Abdi
The name bootstrap ratio comes from the Partial Least Squares in Neuroimaging literature. See:
McIntosh, A. R., & Lobaugh, N. J. (2004). Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage, 23, S250–S263.
The bootstrap ratio is related to other tests of values with respect to the bootstrap distribution, such as the Interval-t. See:
Chernick, M. R. (2008). Bootstrap methods: A guide for practitioners and researchers (Vol. 619). Wiley-Interscience.
Hesterberg, T. (2011). Bootstrap. Wiley Interdisciplinary Reviews: Computational Statistics, 3, 497–526.
##the following code generates 100 bootstrap resampled ##projections of the measures from the Iris data set. data(ep.iris) data <- ep.iris$data design <- ep.iris$design iris.pca <- epGPCA(data,scale="SS1",DESIGN=design,make_design_nominal=FALSE) boot.fjs.unconstrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100)) boot.fjs.constrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100)) for(i in 1:100){ #unconstrained means we resample any of the 150 flowers boot.fjs.unconstrained[,,i] <- boot.compute.fj(ep.iris$data,iris.pca) #constrained resamples within each of the 3 groups boot.fjs.constrained[,,i] <- boot.compute.fj(data,iris.pca,design,TRUE) } #now compute the bootstrap ratios: ratios.unconstrained <- boot.ratio.test(boot.fjs.unconstrained) ratios.constrained <- boot.ratio.test(boot.fjs.constrained)##the following code generates 100 bootstrap resampled ##projections of the measures from the Iris data set. data(ep.iris) data <- ep.iris$data design <- ep.iris$design iris.pca <- epGPCA(data,scale="SS1",DESIGN=design,make_design_nominal=FALSE) boot.fjs.unconstrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100)) boot.fjs.constrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100)) for(i in 1:100){ #unconstrained means we resample any of the 150 flowers boot.fjs.unconstrained[,,i] <- boot.compute.fj(ep.iris$data,iris.pca) #constrained resamples within each of the 3 groups boot.fjs.constrained[,,i] <- boot.compute.fj(data,iris.pca,design,TRUE) } #now compute the bootstrap ratios: ratios.unconstrained <- boot.ratio.test(boot.fjs.unconstrained) ratios.constrained <- boot.ratio.test(boot.fjs.constrained)
This function computes a set of indicies for bootstrap resampling. It can be unconstrained or bootstrap within a group design.
boot.samples(DATA, DESIGN = NULL, constrained = FALSE)boot.samples(DATA, DESIGN = NULL, constrained = FALSE)
DATA |
The original data matrix to be bootstrapped. Rows will be bootstrapped and are assumed to be observations. |
DESIGN |
A design matrix (in disjunctive coding). Only used if |
constrained |
a boolean. If TRUE, bootstrap resampling will occur within groups as designated by the |
a set of indicies to be used to be used as the bootstrap resampled indices.
Derek Beaton
boot.compute.fj and boot.ratio.test
data(ep.iris) unconstrained.indices <- boot.samples(ep.iris$data) #ep.iris$data[unconstrained.indices,] constrained.indices <- boot.samples(ep.iris$data,DESIGN=ep.iris$design,constrained=TRUE) #ep.iris$data[constrained.indices,]data(ep.iris) unconstrained.indices <- boot.samples(ep.iris$data) #ep.iris$data[unconstrained.indices,] constrained.indices <- boot.samples(ep.iris$data,DESIGN=ep.iris$design,constrained=TRUE) #ep.iris$data[constrained.indices,]
caChiTest performs 3 sets of chi-square tests along the lines of Lebart's v-tests. These tests are designed to be conservative estimates of chi-square tests on contingency data. The tests treat this data in a standard chi-square framework, but are helpful to understand correspondence analysis data when permutation and bootstrap become unfeasible.
caChiTest(DATA, res, critical.value = 2)caChiTest(DATA, res, critical.value = 2)
DATA |
Data as would be entered for Correspondence Analysis (see |
res |
Results from correspondence analysis (e.g., output from |
critical.value |
numeric. A value, analogous to a z- or t-score to be used to determine significance (via bootstrap ratio). |
a list with the following values:
j.sig.vals |
boolean matrix. Identifies which column items are significant (based on |
j.signed.vals |
chi-square values associated to column items, multiplied by the sign of their component scores ($fj). |
j.p.vals |
p values associated to column items in a chi-square test. |
i.sig.vals |
boolean matrix. Identifies which row items are significant (based on |
i.signed.vals |
chi-square values associated to row items, multiplied by the sign of their component scores ($fi). |
i.p.vals |
p values associated to row items in a chi-square test. |
omni.val |
chi-square value associated to the table. |
omni.p |
p value associated to a chi-square tests of the table. |
Derek Beaton
Bootstrap or permutation resampling for contingency tables. More specifically, for correspondence analysis (epCA).
contingency.data.break(DATA, boot = FALSE)contingency.data.break(DATA, boot = FALSE)
DATA |
A contingency table to resample. |
boot |
a boolean. If TRUE, use bootstrap (resample with replacement) resampling. If FALSE, use permutation (resample with no replacement). |
A resampled contingency table.
Joseph Dunlop and Derek Beaton
data(authors) boot.authors <- contingency.data.break(authors$ca$data,boot=TRUE) perm.authors <- contingency.data.break(authors$ca$data)data(authors) boot.authors <- contingency.data.break(authors$ca$data,boot=TRUE) perm.authors <- contingency.data.break(authors$ca$data)
This function asks the user if they want to continue with resampling if the total time for resampling takes more than 1 minute. It also provides an estimate of how long resampling takes. This function is required for InPosition and TInPosition and we do not recommend others use it.
continueResampling(cycle.time)continueResampling(cycle.time)
cycle.time |
Is the subtraction of two calls to |
If computation time is expected to take more than 1 minute and interactive() is TRUE, this asks the user if they would like to continue. If 'Y', looping continues. If 'N', it stops.
If computation time is expected to takre more than 1 minute and interactive() is FALSE, the function will proceed as is and perform inference tests.
A progress bar is provided so the user can see how long the tests will take.
See inference battery functions for details.
Derek Beaton
Correspondence Analysis (CA) and a battery of inference tests via InPosition. The battery includes permutation and bootstrap tests.
epCA.inference.battery(DATA, DESIGN = NULL, make_design_nominal = TRUE, masses = NULL, weights = NULL, hellinger = FALSE, symmetric = TRUE, graphs = TRUE, k = 0, test.iters = 100, critical.value = 2)epCA.inference.battery(DATA, DESIGN = NULL, make_design_nominal = TRUE, masses = NULL, weights = NULL, hellinger = FALSE, symmetric = TRUE, graphs = TRUE, k = 0, test.iters = 100, critical.value = 2)
DATA |
original data to perform a CA on. |
DESIGN |
a design matrix to indicate if rows belong to groups. |
make_design_nominal |
a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix. |
masses |
a diagonal matrix or column-vector of masses for the row items. |
weights |
a diagonal matrix or column-vector of weights for the column it |
hellinger |
a boolean. If FALSE (default), Chi-square distance will be used. If TRUE, Hellinger distance will be used. |
symmetric |
a boolean. If TRUE (default) symmetric factor scores for rows and columns are computed. If FALSE, the simplex (column-based) will be returned. |
graphs |
a boolean. If TRUE (default), graphs and plots are provided (via |
k |
number of components to return. |
test.iters |
number of iterations |
critical.value |
numeric. A value, analogous to a z- or t-score to be used to determine significance (via bootstrap ratio). |
epCA.inference.battery performs correspondence analysis and inference tests on a data matrix.
If the expected time to compute the results (based on test.iters) exceeds 1 minute, you will be asked (via command line) if you want to continue.
Returns two lists ($Fixed.Data and $Inference.Data). For $Fixed.Data, see epCA, coreCA for details on the descriptive (fixed-effects) results.
$Inference.Data returns:
components |
Permutation tests of components. p-values ($p.vals) and distributions of eigenvalues ($eigs.perm) for each component |
fj.boots |
Bootstrap tests of measures (columns). See |
omni |
Permutation tests of components. p-values ($p.val) and distributions of total inertia ($inertia.perm) |
Derek Beaton, Joseph Dunlop, and Hervé Abdi.
epCA, epMCA, epMCA.inference.battery, caChiTest
##warning: this example takes a while to compute. This is why it is reduced. data(authors) ca.authors.res <- epCA.inference.battery(authors$ca$data/100)##warning: this example takes a while to compute. This is why it is reduced. data(authors) ca.authors.res <- epCA.inference.battery(authors$ca$data/100)
Generalized Principal Component Analysis (PCA) and a battery of inference tests via InPosition. The battery includes permutation and bootstrap tests.
epGPCA.inference.battery(DATA, scale = TRUE, center = TRUE, DESIGN = NULL, make_design_nominal = TRUE, masses = NULL, weights = NULL, graphs = TRUE, k = 0, test.iters = 100, constrained = FALSE, critical.value = 2)epGPCA.inference.battery(DATA, scale = TRUE, center = TRUE, DESIGN = NULL, make_design_nominal = TRUE, masses = NULL, weights = NULL, graphs = TRUE, k = 0, test.iters = 100, constrained = FALSE, critical.value = 2)
DATA |
original data to perform a PCA on. |
scale |
a boolean, vector, or string. See |
center |
a boolean, vector, or string. See |
DESIGN |
a design matrix to indicate if rows belong to groups. |
make_design_nominal |
a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix. |
masses |
a diagonal matrix or column-vector of masses for the row items. |
weights |
a diagonal matrix or column-vector of weights for the column items. |
graphs |
a boolean. If TRUE (default), graphs and plots are provided (via |
k |
number of components to return. |
test.iters |
number of iterations |
constrained |
a boolean. If a DESIGN matrix is used, this will constrain bootstrap resampling to be within groups. |
critical.value |
numeric. A value, analogous to a z- or t-score to be used to determine significance (via bootstrap ratio). |
epGPCA.inference.battery performs generalized principal components analysis and inference tests on a data matrix.
If the expected time to compute the results (based on test.iters) exceeds 1 minute, you will be asked (via command line) if you want to continue.
Returns two lists ($Fixed.Data and $Inference.Data). For $Fixed.Data, see epGPCA, corePCA for details on the descriptive (fixed-effects) results.
$Inference.Data returns:
components |
Permutation tests of components. p-values ($p.vals) and distributions of eigenvalues ($eigs.perm) for each component |
fj.boots |
Bootstrap tests of measures (columns). See |
Derek Beaton and Hervé Abdi.
epGPCA, epPCA, epPCA.inference.battery
#this is for ExPosition's iris data data(ep.iris) data<-ep.iris$data design<-ep.iris$design gpca.iris.res <- epGPCA.inference.battery(data,DESIGN=design,make_design_nominal=FALSE)#this is for ExPosition's iris data data(ep.iris) data<-ep.iris$data design<-ep.iris$design gpca.iris.res <- epGPCA.inference.battery(data,DESIGN=design,make_design_nominal=FALSE)
Multiple Correspondence Analysis (CA) and a battery of inference tests via InPosition. The battery includes permutation and bootstrap tests.
epMCA.inference.battery(DATA, make_data_nominal = TRUE, DESIGN = NULL, make_design_nominal = TRUE, masses = NULL, weights = NULL, hellinger = FALSE, symmetric = TRUE, correction = c("b"), graphs = TRUE, k = 0, test.iters = 100, constrained = FALSE, critical.value = 2)epMCA.inference.battery(DATA, make_data_nominal = TRUE, DESIGN = NULL, make_design_nominal = TRUE, masses = NULL, weights = NULL, hellinger = FALSE, symmetric = TRUE, correction = c("b"), graphs = TRUE, k = 0, test.iters = 100, constrained = FALSE, critical.value = 2)
DATA |
original data to perform a MCA on. This data can be in original formatting (qualitative levels) or in dummy-coded variables. |
make_data_nominal |
a boolean. If TRUE (default), DATA is recoded as a dummy-coded matrix. If FALSE, DATA is a dummy-coded matrix. |
DESIGN |
a design matrix to indicate if rows belong to groups. |
make_design_nominal |
a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix. |
masses |
a diagonal matrix or column-vector of masses for the row items. |
weights |
a diagonal matrix or column-vector of weights for the column it |
hellinger |
a boolean. If FALSE (default), Chi-square distance will be used. If TRUE, Hellinger distance will be used. |
symmetric |
a boolean. If TRUE symmetric factor scores for rows. |
correction |
which corrections should be applied? "b" = Benzécri correction, "bg" = Greenacre adjustment to Benzécri correction. |
graphs |
a boolean. If TRUE (default), graphs and plots are provided (via |
k |
number of components to return. |
test.iters |
number of iterations |
constrained |
a boolean. If a DESIGN matrix is used, this will constrain bootstrap resampling to be within groups. |
critical.value |
numeric. A value, analogous to a z- or t-score to be used to determine significance (via bootstrap ratio). |
epMCA.inference.battery performs multiple correspondence analysis and inference tests on a data matrix.
If the expected time to compute the results (based on test.iters) exceeds 1 minute, you will be asked (via command line) if you want to continue.
Returns two lists ($Fixed.Data and $Inference.Data). For $Fixed.Data, see epMCA, coreCA for details on the descriptive (fixed-effects) results.
$Inference.Data returns:
components |
Permutation tests of components. p-values ($p.vals) and distributions of eigenvalues ($eigs.perm) for each component |
fj.boots |
Bootstrap tests of measures (columns). See |
omni |
Permutation tests of components. p-values ($p.val) and distributions of total inertia ($inertia.perm). This is only useful if |
Derek Beaton, Joseph Dunlop, and Hervé Abdi.
epMCA, epCA, epCA.inference.battery
data(mca.wine) mca.wine.res <- epMCA.inference.battery(mca.wine$data)data(mca.wine) mca.wine.res <- epMCA.inference.battery(mca.wine$data)
Principal Component Analysis (PCA) and a battery of inference tests via InPosition. The battery includes permutation and bootstrap tests.
epPCA.inference.battery(DATA, scale = TRUE, center = TRUE, DESIGN = NULL, make_design_nominal = TRUE, graphs = TRUE, k = 0, test.iters = 100, constrained = FALSE, critical.value = 2)epPCA.inference.battery(DATA, scale = TRUE, center = TRUE, DESIGN = NULL, make_design_nominal = TRUE, graphs = TRUE, k = 0, test.iters = 100, constrained = FALSE, critical.value = 2)
DATA |
original data to perform a PCA on. |
scale |
a boolean, vector, or string. See |
center |
a boolean, vector, or string. See |
DESIGN |
a design matrix to indicate if rows belong to groups. |
make_design_nominal |
a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix. |
graphs |
a boolean. If TRUE (default), graphs and plots are provided (via |
k |
number of components to return. |
test.iters |
number of iterations |
constrained |
a boolean. If a DESIGN matrix is used, this will constrain bootstrap resampling to be within groups. |
critical.value |
numeric. A value, analogous to a z- or t-score to be used to determine significance (via bootstrap ratio). |
epPCA.inference.battery performs principal components analysis and inference tests on a data matrix.
If the expected time to compute the results (based on test.iters) exceeds 1 minute, you will be asked (via command line) if you want to continue.
Returns two lists ($Fixed.Data and $Inference.Data). For $Fixed.Data, see epPCA, corePCA for details on the descriptive (fixed-effects) results.
$Inference.Data returns:
components |
Permutation tests of components. p-values ($p.vals) and distributions of eigenvalues ($eigs.perm) for each component |
fj.boots |
Bootstrap tests of measures (columns). See |
Derek Beaton and Hervé Abdi.
epPCA, epGPCA, epGPCA.inference.battery
data(words) pca.words.res <- epPCA.inference.battery(words$data)data(words) pca.words.res <- epPCA.inference.battery(words$data)
InPosition plotting function which is an interface to prettyGraphs.
inGraphs(res, DESIGN = NULL, x_axis = NULL, y_axis = NULL, inference.info = NULL, color.by.boots = TRUE, boot.cols = c("plum4", "darkseagreen", "firebrick3"), fi.col = NULL, fi.pch = NULL, fj.col = NULL, fj.pch = NULL, col.offset = NULL, constraints = NULL, xlab = NULL, ylab = NULL, main = NULL, bootstrapBars = TRUE, correlationPlotter = TRUE, biplots = FALSE)inGraphs(res, DESIGN = NULL, x_axis = NULL, y_axis = NULL, inference.info = NULL, color.by.boots = TRUE, boot.cols = c("plum4", "darkseagreen", "firebrick3"), fi.col = NULL, fi.pch = NULL, fj.col = NULL, fj.pch = NULL, col.offset = NULL, constraints = NULL, xlab = NULL, ylab = NULL, main = NULL, bootstrapBars = TRUE, correlationPlotter = TRUE, biplots = FALSE)
res |
results from InPosition or ExPosition. If results are from ExPosition, |
DESIGN |
A design matrix to apply colors (by pallete selection) to row items |
x_axis |
which component should be on the x axis? |
y_axis |
which component should be on the y axis? |
inference.info |
Inference data as output by InPosition (of class inpoOutput). |
color.by.boots |
a boolean. If TRUE, items are colored by bootstrap ratio test. Items larger than |
boot.cols |
vector of colors: |
fi.col |
A matrix of colors for the row items. If NULL, colors will be selected. |
fi.pch |
A matrix of pch values for the row items. If NULL, pch values are all 21. |
fj.col |
A matrix of colors for the column items. If NULL, colors will be selected. |
fj.pch |
A matrix of pch values for the column items. If NULL, pch values are all 21. |
col.offset |
A numeric offset value. Is passed to |
constraints |
Plot constraints as returned from |
xlab |
x axis label |
ylab |
y axis label |
main |
main label for the graph window |
bootstrapBars |
a boolean. If TRUE (default), bootstrap ratio bar plots will be created. |
correlationPlotter |
a boolean. If TRUE (default), a correlation circle plot will be created. Applies to PCA family of methods (CA is excluded for now). |
biplots |
a boolean. If FALSE (default), separate plots are made for row items ($fi) and column items ($fj). If TRUE, row ($fi) and column ($fj) items will be on the same plot. |
Currently, nothing is returned. This function, for now, works as a visualizer for inference tests. Colors and constraints come from the descriptive (fixed effects) analysis.
Derek Beaton
data(ep.iris) data<-ep.iris$data design<-ep.iris$design gpca.iris.res <- epGPCA.inference.battery(data,DESIGN=design,make_design_nominal=FALSE) inGraphs(gpca.iris.res,y_axis=3)data(ep.iris) data<-ep.iris$data design<-ep.iris$design gpca.iris.res <- epGPCA.inference.battery(data,DESIGN=design,make_design_nominal=FALSE) inGraphs(gpca.iris.res,y_axis=3)
Print Correspondence Analysis (CA) Inference results.
## S3 method for class 'epCA.inference.battery' print(x,...)## S3 method for class 'epCA.inference.battery' print(x,...)
x |
an list that contains items to make into the epCA.inference.battery class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
Print Generalized Principal Components Analysis (GPCA) Inference results.
## S3 method for class 'epGPCA.inference.battery' print(x,...)## S3 method for class 'epGPCA.inference.battery' print(x,...)
x |
an list that contains items to make into the epGPCA.inference.battery class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
Print Multiple Correspondence Analysis (MCA) Inference results.
## S3 method for class 'epMCA.inference.battery' print(x,...)## S3 method for class 'epMCA.inference.battery' print(x,...)
x |
an list that contains items to make into the epMCA.inference.battery class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
Print Principal Components Analysis (PCA) Inference results.
## S3 method for class 'epPCA.inference.battery' print(x,...)## S3 method for class 'epPCA.inference.battery' print(x,...)
x |
an list that contains items to make into the epPCA.inference.battery class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
Print bootstrap results from the InPosition.
## S3 method for class 'inpoBoot' print(x,...)## S3 method for class 'inpoBoot' print(x,...)
x |
an list that contains items to make into the inpoBoot class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
Print bootstrap ratio tests results from the InPosition.
## S3 method for class 'inpoBootTests' print(x,...)## S3 method for class 'inpoBootTests' print(x,...)
x |
an list that contains items to make into the inpoBootTests class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
Print Components permutation test results from the inposition.
## S3 method for class 'inpoComponents' print(x,...)## S3 method for class 'inpoComponents' print(x,...)
x |
an list that contains items to make into the inpoComponents class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
Print Omnibus permutation test results from the inposition.
## S3 method for class 'inpoOmni' print(x,...)## S3 method for class 'inpoOmni' print(x,...)
x |
an list that contains items to make into the inpoOmni class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
Print results from the InPosition.
## S3 method for class 'inpoOutput' print(x,...)## S3 method for class 'inpoOutput' print(x,...)
x |
an list that contains items to make into the inpoOutput class. |
... |
inherited/passed arguments for S3 print method(s). |
Derek Beaton and Cherise Chin-Fatt
epPCA.inference.battery, inGraphs
rebuildMCAtable takes the disjunctive table used in MCA and rebuilds a categorical form of it. This function is used for permutation tests when only a disjunctive table is available.
rebuildMCAtable(DATA)rebuildMCAtable(DATA)
DATA |
Disjunctive coded data table |
A categorical data table is returned. It has the same structure as the disjunctive table in a format that can be permuted.
Derek Beaton