Title:	Dominance Analysis
Date:	2024-02-04
Encoding:	UTF-8
Description:	Dominance analysis is a method that allows to compare the relative importance of predictors in multiple regression models: ordinary least squares, generalized linear models, hierarchical linear models, beta regression and dynamic linear models. The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129> for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>, Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>, respectively.
Version:	2.1.0
Depends:	R (≥ 4.0.0)
License:	GPL-2
LazyData:	true
Imports:	methods, stats, ggplot2
Suggests:	lme4, boot, testthat, car, covr, knitr,rmarkdown,pscl, dynlm, reshape2, betareg, performance
RoxygenNote:	7.2.3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-02-04 18:31:23 UTC; cdx
Author:	Claudio Bustos Navarrete [aut, cre, cph], Filipa Coutinho Soares [aut]
Maintainer:	Claudio Bustos Navarrete <clbustos@gmail.com>
Repository:	CRAN
Date/Publication:	2024-02-05 20:50:17 UTC

Dominance analysis for general, generalized and mixed linear models

Description

The dominanceanalysis package allows to perform the dominance analysis for multiple regression models, such as OLS (univariate and multivariate), GLM and HLM. The dominance analysis on this package is performed by dominanceAnalysis function. To perform bootstrap procedures you should use bootDominanceAnalysis function. For both, standard print and summary functions are provided.

Main Features

• Provides complete, conditional and general dominance analysis for lm (univariate and multivariate), lmer and glm (family=binomial) models.

• Covariance / correlation matrixes could be used as input for OLS dominance analysis, using lmWithCov and mlmWithCov methods, respectively.

• Multiple criteria can be used as fit indices, which is useful especially for HLM.

About Dominance Analysis

Dominance analysis is a method developed to evaluate the importance of each predictor in the selected regression model: "one predictor is 'more important than another' if it contributes more to the prediction of the criterion than does its competitor at a given level of analysis." (Azen & Budescu, 2003, p.133).

The original method was developed for OLS regression (Budescu, 1993). Later, several definitions of dominance and bootstrap procedures were provided by Azen & Budescu (2003), as well as adaptations to Generalized Linear Models (Azen & Traxel, 2009) and Hierarchical Linear Models (Luo & Azen, 2013).

Author(s)

Claudio Bustos clbustos@gmail.com, Filipa Coutinho Soares (documentation)

References

• Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542-551. doi:10.1037/0033-2909.114.3.542

• Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129-148. doi:10.1037/1082-989X.8.2.129

• Azen, R., & Budescu, D. V. (2006). Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis. Journal of Educational and Behavioral Statistics, 31(2), 157-180. doi:10.3102/10769986031002157

• Azen, R., & Traxel, N. (2009). Using Dominance Analysis to Determine Predictor Importance in Logistic Regression. Journal of Educational and Behavioral Statistics, 34(3), 319-347. doi:10.3102/1076998609332754

• Luo, W., & Azen, R. (2013). Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis. Journal of Educational and Behavioral Statistics, 38(1), 3-31. doi:10.3102/1076998612458319

See Also

dominanceAnalysis , bootDominanceAnalysis

Examples

# Basic dominance analysis

data(longley)
lm.1<-lm(Employed~.,longley)
da<-dominanceAnalysis(lm.1)
print(da)
summary(da)
plot(da,which.graph='complete')
plot(da,which.graph='conditional')
plot(da,which.graph='general')

# Dominance analysis for HLM

library(lme4)
x1<-rnorm(1000)
x2<-rnorm(1000)
g<-gl(10,100)
g.x<-rnorm(10)[g]
y<-2*x1+x2+g.x+rnorm(1000,sd=0.5)
lmm1<-lmer(y~x1+x2+(1|g))
lmm0<-lmer(y~(1|g))
da.lmm<-dominanceAnalysis(lmm1, null.model=lmm0)
print(da.lmm)
summary(da.lmm)


# GLM analysis

x1<-rnorm(1000)
x2<-rnorm(1000)
x3<-rnorm(1000)
y<-runif(1000)<(1/(1+exp(-(2*x1+x2+1.5*x3))))
glm.1<-glm(y~x1+x2+x3,family="binomial")
da.glm<-dominanceAnalysis(glm.1)
print(da.glm)
summary(da.glm)

# Bootstrap procedure


da.boot<-bootDominanceAnalysis(lm.1,R=1000)
summary(da.boot)

da.glm.boot<-bootDominanceAnalysis(glm.1,R=200)
summary(da.glm.boot)

Retrieve average contribution of each predictor in a dominance analysis.

Description

Retrieve the average contribution for each predictor. Is calculated averaging all contribution by level. The average contribution defines general dominance.

Usage

averageContribution(da.object, fit.functions = NULL)

Arguments

Value

a list. Key corresponds to fit-index and the value is vector, with average contribution for each variable

See Also

Other retrieval methods: contributionByLevel(), dominanceBriefing(), dominanceMatrix(), getFits()

Examples

data(longley)
da.longley<-dominanceAnalysis(lm(Employed~.,longley))
averageContribution(da.longley)

Bootstrap Average Values for Dominance Analysis

Description

Bootstrap average values and corresponding standard errors for each predictor in the dominance analysis. These values are used for assessing general dominance.

Usage

bootAverageDominanceAnalysis(
  x,
  R,
  constants = c(),
  terms = NULL,
  fit.functions = "default",
  null.model = NULL,
  ...
)

Arguments

Details

Use summary() to obtain a nicely formatted data.frame object.

Value

An object of class 'bootAverageDominanceAnalysis' containing: -

See Also

dominanceAnalysis, boot

Examples

lm.1 <- lm(Employed ~ ., longley)
da.ave.boot <- bootAverageDominanceAnalysis(lm.1, R = 1000)
summary(da.ave.boot)

Bootstrap Analysis for Dominance Analysis

Description

Implements a bootstrap procedure as presented by Azen and Budescu (2003). Provides the expected level of dominance of predictor X_i over X_j, as the degree to which the pattern found in the sample is reproduced in the bootstrap samples.

Usage

bootDominanceAnalysis(
  x,
  R,
  constants = c(),
  terms = NULL,
  fit.functions = "default",
  null.model = NULL,
  ...
)

Arguments

Details

Use summary() to obtain a nicely formatted data.frame.

Value

An object of class bootDominanceAnalysis containing:

Examples

lm.1 <- lm(Employed ~ ., longley)
da.boot <- bootDominanceAnalysis(lm.1, R = 1000)
summary(da.boot)

Center the variables on groups mean.

Description

Returns a dataframe with variables groups means as x.mean and centered variables a x.centered

Usage

centerOnGroup(x, g)

Arguments

Value

New dataframe

Check if the given object have the dominanceAnalysis class

Description

Stop execution if object isn't a dominanceAnalysis object

Usage

checkDominanceAnalysis(x)

Arguments

Value

boolean TRUE if x is a dominanceAnalysis object, raises an error otherwise

Retrieve average contribution by level for each predictor

Description

Retrieve the average contribution by level for each predictor in a dominance analysis. The average contribution defines conditional dominance.

Usage

contributionByLevel(da.object, fit.functions = NULL)

Arguments

Value

a list. Key corresponds to fit-index and the value is a matrix, with contribution of each variable by level

See Also

Other retrieval methods: averageContribution(), dominanceBriefing(), dominanceMatrix(), getFits()

Examples

data(longley)
da.longley<-dominanceAnalysis(lm(Employed~.,longley))
contributionByLevel(da.longley)

Provides fit indices for betareg models.

Description

Note that the Nagelkerke and Estrella coefficients are designed for discrete dependent variables and thus cannot be used in this context. Instead, the Cox and Snell coefficient is recommended, along with the pseudo-R^2. It is worth noting that McFadden's index may produce negative values and should be avoided.

Usage

da.betareg.fit(original.model, newdata = NULL, ...)

Arguments

Value

A function described by using-fit-indices. You could retrieve following indices:

r2.pseudo

Provided by betareg by default

r2.m

McFadden(1974)

r2.cs

Cox and Snell(1989).

References

• Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.). London, UK: Chapman and Hall.

• Estrella, A. (1998). A new measure of fit for equations with dichotomous dependent variables. Journal of Business & Economic Statistics, 16(2), 198-205. doi: 10.1080/07350015.1998.10524753.

• McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 104-142). New York, NY: Academic Press.

• Shou, Y., & Smithson, M. (2015). Evaluating Predictors of Dispersion:A Comparison of Dominance Analysis and Bayesian Model Averaging. Psychometrika, 80(1), 236-256.

See Also

Other fit indices: da.clm.fit(), da.dynlm.fit(), da.glm.fit(), da.lm.fit(), da.lmWithCov.fit(), da.lmerMod.fit(), da.mlmWithCov.fit()

Provides fit indices for ordinal regression models, based on the Nagelkerke (1991) method.

Description

Provides fit indices for ordinal regression models, based on the Nagelkerke (1991) method.

Usage

da.clm.fit(original.model, newdata = NULL, ...)

Arguments

Value

A function described by using-fit-indices description for interface. You could retrieve r2.n index, corresponding to Nagelkerke method.

References

• Nagelkerke, N. J. D. (1991). A Note on a General Definition of the Coefficient of Determination. Biometrika, 78(3), 691-692. doi:10.1093/biomet/78.3.691

See Also

Other fit indices: da.betareg.fit(), da.dynlm.fit(), da.glm.fit(), da.lm.fit(), da.lmWithCov.fit(), da.lmerMod.fit(), da.mlmWithCov.fit()

Provides coefficient of determination for dynlm models.

Description

Uses R^2 (coefficient of determination) as fit index

Usage

da.dynlm.fit(original.model, newdata = NULL, ...)

Arguments

Value

A function described by using-fit-indices description for interface

See Also

Other fit indices: da.betareg.fit(), da.clm.fit(), da.glm.fit(), da.lm.fit(), da.lmWithCov.fit(), da.lmerMod.fit(), da.mlmWithCov.fit()

Provides fit indices for GLM models.

Description

These functions are only available for logistic regression models and are based on the work of Azen and Traxel (2009).

Usage

da.glm.fit(original.model, newdata = NULL, ...)

Arguments

Details

Check daRawResults.

Value

A function described by using-fit-indices. You could retrieve the following indices:

r2.m

McFadden(1974)

r2.cs

Cox and Snell(1989). Use with caution, because don't have 1 as upper bound

r2.n

Nagelkerke(1991), that corrects the upper bound of Cox and Snell(1989) index

r2.e

Estrella(1998)

References

• Azen, R. and Traxel, N. (2009). Using Dominance Analysis to Determine Predictor Importance in Logistic Regression. Journal of Educational and Behavioral Statistics, 34 (3), 319-347. doi:10.3102/1076998609332754.

• Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691-692. doi:10.1093/biomet/78.3.691.

• Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.). London, UK: Chapman and Hall.

• Estrella, A. (1998). A new measure of fit for equations with dichotomous dependent variables. Journal of Business & Economic Statistics, 16(2), 198-205. doi: 10.1080/07350015.1998.10524753

• McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 104-142). New York, NY: Academic Press.

See Also

Other fit indices: da.betareg.fit(), da.clm.fit(), da.dynlm.fit(), da.lm.fit(), da.lmWithCov.fit(), da.lmerMod.fit(), da.mlmWithCov.fit()

Examples

x1<-rnorm(1000)
x2<-rnorm(1000)
x3<-rnorm(1000)
y<-factor(runif(1000) > exp(x1+x2+x3)/(1+exp(x1+x2+x3)))
df.1=data.frame(x1,x2,x3,y)
glm.1<-glm(y~x1+x2+x3,data=df.1,family=binomial)
da.glm.fit(original.model=glm.1)("names")
da.glm.fit(original.model=glm.1)(y~x1)

Provides coefficient of determination for lm models.

Description

Uses R^2 (coefficient of determination) as fit index

Usage

da.lm.fit(original.model, newdata = NULL, ...)

Arguments

Value

A function described by using-fit-indices description for interface. You could retrieve r2 index.

See Also

Other fit indices: da.betareg.fit(), da.clm.fit(), da.dynlm.fit(), da.glm.fit(), da.lmWithCov.fit(), da.lmerMod.fit(), da.mlmWithCov.fit()

Examples

x1<-rnorm(1000)
x2<-rnorm(1000)
y <-x1+x2+rnorm(1000)
df.1=data.frame(y=y,x1=x1,x2=x2)
lm.1<-lm(y~x1+x2)
da.lm.fit(lm.1)("names")
da.lm.fit(lm.1)(y~x1)

Provides coefficient of determination for linear models, using covariance/correlation matrix.

Description

Uses R^2 (coefficient of determination). See lmWithCov.

Usage

da.lmWithCov.fit(base.cov, ...)

Arguments

Value

A function described by using-fit-indices description for interface. You could retrieve r2 index.

See Also

Other fit indices: da.betareg.fit(), da.clm.fit(), da.dynlm.fit(), da.glm.fit(), da.lm.fit(), da.lmerMod.fit(), da.mlmWithCov.fit()

Provides fit indices for hierarchical linear models, based on Nakagawa et al.(2017) and Luo and Azen (2013).

Description

Provides fit indices for hierarchical linear models, based on Nakagawa et al.(2017) and Luo and Azen (2013).

Usage

da.lmerMod.fit(original.model, null.model, newdata = NULL, ...)

Arguments

Value

A function described by using-fit-indices description for interface. By default, four indices are provided:

If performance library is available, the two following indices are also available:

References

• Luo, W., & Azen, R. (2013). Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis. Journal of Educational and Behavioral Statistics, 38(1), 3-31. doi:10.3102/1076998612458319

• Nakagawa, S., Johnson, P. C. D., and Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of The Royal Society Interface, 14(134), 20170213.

See Also

Other fit indices: da.betareg.fit(), da.clm.fit(), da.dynlm.fit(), da.glm.fit(), da.lm.fit(), da.lmWithCov.fit(), da.mlmWithCov.fit()

Provides coefficient of determination for multivariate models.

Description

Provides coefficient of determination for multivariate models.

Usage

da.mlmWithCov.fit(base.cov, ...)

Arguments

Value

A list with several fit indices

r.squared.xy

Corresponds to R^2_{XY}

p.squared.yx

Corresponds to P^2_{YX}

See mlmWithCov

References

Azen, R., & Budescu, D. V. (2006). Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis. Journal of Educational and Behavioral Statistics, 31(2), 157-180. doi:10.3102/10769986031002157

See Also

Other fit indices: da.betareg.fit(), da.clm.fit(), da.dynlm.fit(), da.glm.fit(), da.lm.fit(), da.lmWithCov.fit(), da.lmerMod.fit()

Calculates the average contribution by level for a daRawResult object

Description

Calculates the average contribution by level for a daRawResult object

Usage

daAverageContributionByLevel(x)

Arguments

Value

list, with key named as a fit index and values are matrix, with the average contribution of each variable on every level

Matrix of Complete dominance of one variable over another Uses daRawResults as input

Description

Matrix of Complete dominance of one variable over another Uses daRawResults as input

Usage

daCompleteDominance(daRR)

See Also

Other dominance matrices: daConditionalDominance(), daGeneralDominance()

Matrix of Conditional dominance of one variable over another.

Description

Uses daRawResults as input

Usage

daConditionalDominance(daRR)

See Also

Other dominance matrices: daCompleteDominance(), daGeneralDominance()

Matrix of General dominance of one variable over another Uses daRawResults as input

Description

Matrix of General dominance of one variable over another Uses daRawResults as input

Usage

daGeneralDominance(daRR)

See Also

Other dominance matrices: daCompleteDominance(), daConditionalDominance()

Retrieve raw results for dominance analysis.

Description

Provides name functions, base fit values and matrix for models vs predictors importance

Usage

daRawResults(
  x,
  constants = c(),
  terms = NULL,
  fit.functions = "default",
  newdata = NULL,
  null.model = NULL,
  ...
)

Arguments

Value

a list with this elements

fit.functions

Name of fit indices

fits

Increment on fit indices, when specific variable is added

base.fits

Raw fit indices for each model

level

Vector of levels, compatible with fits and base.fits

Returns all the submodels derived from full models.

Description

You could set some variables as constants, limiting the number of models. Includes, by default, the null model

Usage

daSubmodels(x, constants = NULL, terms = NULL)

Arguments

Value

list with elements level, pred.matrix, predictors, response, constants

Dominance analysis for OLS (univariate and multivariate), GLM and LMM models

Description

Dominance analysis for OLS (univariate and multivariate), GLM and LMM models

Usage

dominanceAnalysis(
  x,
  constants = c(),
  terms = NULL,
  fit.functions = "default",
  newdata = NULL,
  null.model = NULL,
  ...
)

Arguments

Value

Definition of Dominance Analysis

Budescu (1993) developed a clear and intuitive definition of importance in regression models, that states that a predictor's importance reflects its contribution in the prediction of the criterion and that one predictor is 'more important than another' if it contributes more to the prediction of the criterion than does its competitor at a given level of analysis.

Types of dominance

The original paper (Bodescu, 1993) defines that variable X_1 dominates X_2 when X_1 is chosen over X_2 in all possible subset of models where only one of these two predictors is to be entered. Later, Azen & Bodescu (2003), name the previously definition as 'complete dominance' and two other types of dominance: conditional and general dominance. Conditional dominance is calculated as the average of the additional contributions to all subset of models of a given model size. General dominance is calculated as the mean of average contribution on each level.

Fit indices availables

To obtain the fit-indices for each model, a function called da.<model>.fit is executed. For example, for a lm model, function da.lm.fit provides R^2 values. Currently, seven models are implemented:

lm

Provides R^2 or coefficient of determination. See da.lm.fit

glm

Provides four fit indices recommended by Azen & Traxel (2009): Cox and Snell(1989), McFadden (1974), Nagelkerke (1991), and Estrella (1998). See da.glm.fit

lmerMod

Provides four fit indices recommended by Lou & Azen (2012). See da.lmerMod.fit

lmWithCov

Provides R^2 for a correlation/covariance matrix. See lmWithCov to create the model and da.lmWithCov.fit for the fit index function.

mlmWithCov

Provides both R^2_{XY} and P^2_{XY} for multivariate regression models using a correlation/covariance matrix. See mlmWithCov to create the model and da.mlmWithCov.fit for the fit index function

dynlm

Provides R^2 for dynamic linear models. There is no literature reference about using dominance analysis on dynamic linear models, so you're warned!. See da.dynlm.fit

.

betareg

Provides pseudo-R^2, Cox and Snell(1989), McFadden (1974), and Estrella (1998). You could set the link function using link.betareg if automatic detection of link function doesn't work.

See da.betareg.fit

References

• Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129-148. doi:10.1037/1082-989X.8.2.129

• Azen, R., & Budescu, D. V. (2006). Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis. Journal of Educational and Behavioral Statistics, 31(2), 157-180. doi:10.3102/10769986031002157

• Azen, R., & Traxel, N. (2009). Using Dominance Analysis to Determine Predictor Importance in Logistic Regression. Journal of Educational and Behavioral Statistics, 34(3), 319-347. doi:10.3102/1076998609332754

• Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542-551. doi:10.1037/0033-2909.114.3.542

• Luo, W., & Azen, R. (2012). Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis. Journal of Educational and Behavioral Statistics, 38(1), 3-31. doi:10.3102/1076998612458319

Examples

data(longley)
lm.1<-lm(Employed~.,longley)
da<-dominanceAnalysis(lm.1)
print(da)
summary(da)
plot(da,which.graph='complete')
plot(da,which.graph='conditional')
plot(da,which.graph='general')

# Maintaining year as a constant on all submodels
da.no.year<-dominanceAnalysis(lm.1,constants='Year')
print(da.no.year)
summary(da.no.year)
plot(da.no.year,which.graph='complete')

# Parameter terms could be used to group variables
da.terms=c(GNP.rel='GNP.deflator+GNP',
           pop.rel='Unemployed+Armed.Forces+Population+Unemployed',
           year='Year')
da.grouped<-dominanceAnalysis(lm.1,terms=da.terms)
print(da.grouped)
summary(da.grouped)
plot(da.grouped, which.graph='complete')

Retrieve a briefing for complete, conditional and general dominance

Description

Retrieve a briefing for complete, conditional and general dominance

Usage

dominanceBriefing(da.object, fit.functions = NULL, abbrev = FALSE)

Arguments

Value

a list. Each element is a data.frame, that comprises the dominance analysis for a specific fit index. Each data.frame have the predictors as row and each column reports the predictors that are dominated for each predictor

See Also

Other retrieval methods: averageContribution(), contributionByLevel(), dominanceMatrix(), getFits()

Examples

# For matrix or data.frame
data(longley)
da.longley<-dominanceAnalysis(lm(Employed~.,longley))
dominanceBriefing(da.longley, abbrev=FALSE)
dominanceBriefing(da.longley, abbrev=TRUE)

Retrieve or calculates a dominance matrix for a given object

Description

This methods calculates or retrieve dominance matrix

This methods allows a common interface to retrieve all dominance matrices from dominanceAnalysis objects

Usage

dominanceMatrix(x, ...)

## S3 method for class 'data.frame'
dominanceMatrix(x, undefined.value = 0.5, ordered = FALSE, ...)

## S3 method for class 'matrix'
dominanceMatrix(x, undefined.value = 0.5, ordered = FALSE, ...)

## S3 method for class 'dominanceAnalysis'
dominanceMatrix(
  x,
  type,
  fit.functions = NULL,
  drop = TRUE,
  ordered = FALSE,
  ...
)

Arguments

Details

To calculate a dominance matrix from a matrix or dataframe, use

dominanceMatrix(x,undefined.value).

To retrieve the dominance matrices from a dominanceAnalysis object, use

dominanceMatrix(x,type,fit.function,drop)

Value

for matrix and data-frame, returns a matrix representing dominance. 1 represents domination of the row variable over the column variable, 0 dominance of the column over the row variable. Undefined dominance is represented by undefined.value parameter. For dominanceAnalysis object, returns a matrix, if drop parameter if TRUE and just one index is available. Else, a list is returned, with keys as name of fit-indices and values as matrices, as described previously.

See Also

Other retrieval methods: averageContribution(), contributionByLevel(), dominanceBriefing(), getFits()

Examples

# For matrix or data.frame
mm<-data.frame(a=c(5,3,2),b=c(4,2,1),c=c(5,4,3))
dominanceMatrix(mm)
# For dominanceAnalysis
data(longley)
da.longley<-dominanceAnalysis(lm(Employed~.,longley))
dominanceMatrix(da.longley,type="complete")

Return a list with formulas for a given daSubmodels object

Description

Return a list with formulas for a given daSubmodels object

Usage

formulas.daSubmodels(x, env = parent.frame())

Arguments

Value

list

Returns data from different models

Description

Returns data from different models

Usage

getData(x)

Return the index of row equals to r on m

Description

Return the index of row equals to r on m

Usage

getEqualRowId(m, r)

Arguments

Retrieve fit matrix or matrices

Description

Retrieve fit matrix or matrices for a given dominanceAnalysis object

Usage

getFits(da.object, fit.functions = NULL)

Arguments

Value

a list. Key corresponds to fit-index and the value is a matrix, with fits values

See Also

Other retrieval methods: averageContribution(), contributionByLevel(), dominanceBriefing(), dominanceMatrix()

Examples

data(longley)
da.longley<-dominanceAnalysis(lm(Employed~.,longley))
getFits(da.longley)

Uses covariance/correlation matrix for calculate OLS

Description

Calculate regression coefficients and R^2 for an OLS regression. Could be used with dominanceAnalysis to perform a dominance analysis without the original data.

Usage

lmWithCov(f, x)

Arguments

Value

Examples

cov.m<-matrix(c(1,0.2,0.3, 0.2,1,0.5,0.3,0.5,1),3,3,
dimnames=list(c("x1","x2","y"),c("x1","x2","y")))
lm.cov<-lmWithCov(y~x1+x2,cov.m)
da<-dominanceAnalysis(lm.cov)

Calculates several measures of fit for Linear Mixed Models based on Lou and Azen (2013) text. Models could be lmer or lme models.

Description

Calculates several measures of fit for Linear Mixed Models based on Lou and Azen (2013) text. Models could be lmer or lme models.

Usage

lmmR2(m.null, m.full)

Arguments

Value

lmmR2 class

Uses covariance/correlation matrix to calculate multivariate index of fit

Description

Calculate R^2_{XY} and P^2_{YX} for multivariate regression Could be used with dominanceAnalysis to perform a multivariate dominance analysis without original data.

Usage

mlmWithCov(f, x)

Arguments

Value

Examples

library(car)
cor.m<-matrix(c(
 1.0000000, 0.7951377, 0.2617168, 0.6720053, 0.3390278,
 0.7951377, 1.0000000, 0.3341037, 0.5876337, 0.3404206,
 0.2617168, 0.3341037, 1.0000000, 0.3703162, 0.2114153,
 0.6720053, 0.5876337, 0.3703162, 1.0000000, 0.3548077,
 0.3390278, 0.3404206, 0.2114153, 0.3548077, 1.0000000),
 5,5,
 byrow = TRUE,
 dimnames = list(
   c("na","ss","SAT","PPVT","Raven"),
   c("na","ss","SAT","PPVT","Raven")))
lwith<-mlmWithCov(cbind(na,ss)~SAT+PPVT+Raven,cor.m)
da<-dominanceAnalysis(lwith)
print(da)
summary(da)

Plot for a dominanceAnalysis object

Description

Plot for a dominanceAnalysis object

Usage

## S3 method for class 'dominanceAnalysis'
plot(
  x,
  which.graph = c("general", "complete", "complete_no_facet", "conditional"),
  fit.function = NULL,
  complete_flipped_axis = TRUE,
  ...
)

Arguments

Value

a ggplot object

Examples

data(longley)
lm.1<-lm(Employed~.,longley)
da<-dominanceAnalysis(lm.1)
# By default, plot() shows the general dominance plot
plot(da)
# Parameter which.graph defines which type of dominance to plot
plot(da,which.graph='conditional')
plot(da,which.graph='complete')
# Parameter complete_flipped_axis allows to flip axis on complete plot, to better visualization
plot(da,which.graph='complete', complete_flipped_axis=TRUE)
plot(da,which.graph='complete', complete_flipped_axis=FALSE)

Print the results of a dominanceAnalysis object

Description

Print the results of a dominanceAnalysis object

Usage

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

Value

an object of type dominanceAnalysis

Print method for lmmR2 object, that retrieves the summary.

Description

Print method for lmmR2 object, that retrieves the summary.

Usage

## S3 method for class 'lmmR2'
print(x, ...)

Arguments

Value

an lmmR2 object

Print a summary.bootDominanceAnalysis object

Description

Print a summary.bootDominanceAnalysis object

Usage

## S3 method for class 'summary.bootDominanceAnalysis'
print(x, round.digits = 3, ...)

Arguments

Value

an object summary.bootDominanceAnalysis

Print a summary.dominanceAnalysis object

Description

Print a summary.dominanceAnalysis object

Usage

## S3 method for class 'summary.dominanceAnalysis'
print(x, round.digits = 3, ...)

Arguments

Value

a summary.dominanceAnalysis object

Print method for lmmR2 models summary

Description

Print method for lmmR2 models summary

Usage

## S3 method for class 'summary.lmmR2'
print(x, ...)

Arguments

Value

a summary.lmmR2 object

Dominance of each factor over others. Dominance requires that a variable have higher values on all submodels. This method allows to visualize those relation.

Description

Dominance of each factor over others. Dominance requires that a variable have higher values on all submodels. This method allows to visualize those relation.

Usage

rankUsingMatrix(x)

Arguments

Value

a vector of string, each one showing what factors dominates others

Replace terms by name using the terms definition

Description

Replace terms by name using the terms definition

Usage

replaceTermsInString(string, replacement)

Arguments

Summary for bootAverageDominanceAnalysis.

Description

Summary for bootAverageDominanceAnalysis.

Usage

## S3 method for class 'bootAverageDominanceAnalysis'
summary(object, fit.functions = NULL, ...)

Arguments

Value

An object summary.bootAverageDominanceAnalysis, that containts a list of data frames containing summary statistics for each fit index.

Summary for bootDominanceAnalysis.

Description

Summary for bootDominanceAnalysis.

Usage

## S3 method for class 'bootDominanceAnalysis'
summary(object, fit.functions = NULL, ...)

Arguments

Value

An object summary.bootDominanceAnalysis, that contains data frames with bootstrap summary statistics for each fit index

Summary for a dominanceAnalysis object

Description

Summary for a dominanceAnalysis object

Usage

## S3 method for class 'dominanceAnalysis'
summary(object, ...)

Arguments

Value

A list with values:

• average.contribution: vector of average contributions of each variable

• summary.matrix: matrix with all calculations for dominance analysis

Summary for lmmR2 models

Description

Summary for lmmR2 models

Usage

## S3 method for class 'lmmR2'
summary(object, ...)

Arguments

Value

An object of class "summary.lmmR2" containing:

Distribution of a tropical native bird species inhabiting a small oceanic island.

Description

The dataset contains information about points distributed across a small oceanic island (Soares, 2017). In each of these points, a 10-minute count was carried out to record the species presence (assuming 1 if the species was present, or 0 if it was absent). The species' presence/absence is the binary response variable (i.e., dependent variable). Additionally, all sampled points were characterized by multiple environmental variables.

Usage

tropicbird

Format

A data frame with 2398 rows and 8 variables:

ID

Point identification

rem

remoteness is an index that represents the difficulty of movement through the landscape, with the highest values corresponding to the most remote areas

land

land use is an index that represents the land-use intensification, with the highest values corresponding to the more humanized areas (e.g., cities, agricultural areas, horticultures, oil-palm monocultures)

alt

altitude is a continuous variable, with the highest values corresponding to the higher altitude areas

slo

slope is a continuous variable, with the highest values corresponding to the steepest areas

rain

rainfall is a continuous variable, with the highest values corresponding to the rainy wet areas

coast

distance to the coast is the minimum linear distance between each point and the coast line, with the highest values corresponding to the points further away from the coastline

pres

Species presence

Source

Soares, F.C., 2017. Modelling the distribution of Sao Tome bird species: Ecological determinants and conservation prioritization. Faculdade de Ciencias da Universidade de Lisboa.

Provides fit indices for different regression models.

Description

dominanceAnalysis tries to infer, based on the class of the model provided, the appropriate fit indices, using the scheme da.CLASS.fit for name. This method has two interfaces, one for retrieving the names of the fit indices, and another to retrieve the indices based on the data.

Arguments

Details

Interfaces are:

• da.CLASS.fit("names") returns a vector with names for fit indices

• da.CLASS.fit(original.model, data, null.model, base.cov=NULL) returns a function with one parameter, the formula to calculate the submodel.