| Title: | Estimation, Comparison and Selection of Transformations |
|---|---|
| Description: | Estimation, selection and comparison of several families of transformations. The families of transformations included in the package are the following: Bickel-Doksum (Bickel and Doksum 1981 <doi:10.2307/2287831>), Box-Cox, Dual (Yang 2006 <doi:10.1016/j.econlet.2006.01.011>), Glog (Durbin et al. 2002 <doi:10.1093/bioinformatics/18.suppl_1.S105>), gpower (Kelmansky et al. 2013 <doi:10.1515/sagmb-2012-0030>), Log, Log-shift opt (Feng et al. 2016 <doi:10.1002/sta4.104>), Manly, modulus (John and Draper 1980 <doi:10.2307/2986305>), Neglog (Whittaker et al. 2005 <doi:10.1111/j.1467-9876.2005.00520.x>), Reciprocal and Yeo-Johnson. The package simplifies to compare linear models with untransformed and transformed dependent variable as well as linear models where the dependent variable is transformed with different transformations. Furthermore, the package employs maximum likelihood approaches, moments optimization and divergence minimization to estimate the optimal transformation parameter. |
| Authors: | Lily Medina [aut, cre], Piedad Castro [aut], Ann-Kristin Kreutzmann [aut], Natalia Rojas-Perilla [aut] |
| Maintainer: | Lily Medina <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.3 |
| Built: | 2024-11-24 03:55:00 UTC |
| Source: | https://github.com/akreutzmann/trafo |
The data frame that is returned contains the variables that are used in the model and additionally a variable with the transformed dependent variable. To the variable name of the dependent variable a t is added for transformed.
## S3 method for class 'trafo' as.data.frame(x, row.names = NULL, optional = FALSE, std = FALSE, ...)## S3 method for class 'trafo' as.data.frame(x, row.names = NULL, optional = FALSE, std = FALSE, ...)
x |
an object of type |
row.names |
NULL or a character vector giving the row names for the data frame. Missing values are not allowed. |
optional |
logical. If TRUE, setting row names and converting column names (to syntactic names: see make.names) is optional. Note that all of R's base package as.data.frame() methods use optional only for column names treatment, basically with the meaning of data.frame(*, check.names = !optional) |
std |
logical. If |
... |
other parameters that can be passed to the function. |
A data frame with the original variables and the transformed variable.
bickeldoksum, boxcox, dual,
glog, gpower, log,
logshiftopt, manly, modulus,
neglog, sqrtshift, yeojohnson
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using divergence minimization following # Kolmogorov-Smirnov logshiftopt_trafo <- logshiftopt(object = lm_cars, method = "div.ks", plotit = FALSE) # Get a data frame with the added transformed variable as.data.frame(logshiftopt_trafo)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using divergence minimization following # Kolmogorov-Smirnov logshiftopt_trafo <- logshiftopt(object = lm_cars, method = "div.ks", plotit = FALSE) # Get a data frame with the added transformed variable as.data.frame(logshiftopt_trafo)
Gives a first overview if a transformation is useful and which transformation is promising to fulfill the model assumptions normality, homoscedasticity and linearity.
assumptions(object, method = "ml", std = FALSE, ...)assumptions(object, method = "ml", std = FALSE, ...)
object |
an object of type |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
std |
logical. If |
... |
other parameters that can be passed to the function, e.g. other
lambdaranges. Self-defined lambdaranges are given to the function as an
argument that is the combination of the name of the transformation and lr and
the range needs to be a numeric vector of length 2. For instance, changing the
lambdarange for the Manly transformation would mean to add an argument
|
A table with tests for normality and homoscedasticity. Furthermore, scatterplots are returned to check the linearity assumption.
bickeldoksum, boxcox, dual,
glog, gpower, log,
logshiftopt, manly, modulus,
neglog, sqrtshift, yeojohnson
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) assumptions(lm_cars) assumptions(lm_cars, method = "skew", manly_lr = c(0.000005,0.00005))# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) assumptions(lm_cars) assumptions(lm_cars, method = "skew", manly_lr = c(0.000005,0.00005))
The function transforms the dependent variable of a linear model using the Bickel-Doksum transformation. The transformation parameter can either be estimated using different estimation methods or given.
bickeldoksum(object, lambda = "estim", method = "ml", lambdarange = c(1e-11, 2), plotit = TRUE)bickeldoksum(object, lambda = "estim", method = "ml", lambdarange = c(1e-11, 2), plotit = TRUE)
object |
an object of type lm. |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
The Bickel-Doksum transformation is only defined for positive values of
lambda. Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Bickel PJ, Doksum KA (1981). An analysis of transformations revisited. Journal of the American Statistical Association, 76, 296-311.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using a maximum likelihood approach bickeldoksum(object = lm_cars, plotit = FALSE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using a maximum likelihood approach bickeldoksum(object = lm_cars, plotit = FALSE)
The function transforms the dependent variable of a linear model using the Box-Cox transformation. The transformation parameter can either be estimated using different estimation methods or given. The Box-Cox transformation is only defined for positive response values. In case the response contains zero or negative values a shift is automatically added such that y + shift > 0.
boxcox(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)boxcox(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)
object |
an object of type |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Box GEP, Cox DR (1964). An Analysis of Transformations. Journal of the Royal Statistical Society B, 26(2), 211-252.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using skewness minimization boxcox(object = lm_cars, method = "skew", plotit = FALSE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using skewness minimization boxcox(object = lm_cars, method = "skew", plotit = FALSE)
Returns information about the transformation and selected diagnostics to check model assumptions.
diagnostics(object, ...)diagnostics(object, ...)
object |
an object that contains two models that should be compared. |
... |
other parameters that can be passed to the function. |
The return depends on the class of its argument. The documentation of particular methods gives detailed information about the return of that method.
diagnostics.trafo_lm,
diagnostics.trafo_compare
Returns information about the applied transformations and selected diagnostics to check model assumptions. Two models are compared where the dependent variable is transformed by different transformations.
## S3 method for class 'trafo_compare' diagnostics(object, ...)## S3 method for class 'trafo_compare' diagnostics(object, ...)
object |
an object of type |
... |
additional arguments that are not used in this method |
An object of class diagnostics.trafo_compare. The method
print.diagnostics.trafo_compare can be used for this class.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform with Bickel-Doksum transformation bd_trafo <- bickeldoksum(object = lm_cars, plotit = FALSE) # Transform with Box-Cox transformation bc_trafo <- boxcox(object = lm_cars, method = "skew", plotit = FALSE) # Compare transformed models compare <- trafo_compare(object = lm_cars, trafos = list(bd_trafo, bc_trafo)) # Get diagnostics diagnostics(compare)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform with Bickel-Doksum transformation bd_trafo <- bickeldoksum(object = lm_cars, plotit = FALSE) # Transform with Box-Cox transformation bc_trafo <- boxcox(object = lm_cars, method = "skew", plotit = FALSE) # Compare transformed models compare <- trafo_compare(object = lm_cars, trafos = list(bd_trafo, bc_trafo)) # Get diagnostics diagnostics(compare)
Returns information about the applied transformation and selected diagnostics to check model assumptions. The return helps to compare the untransformed and the transformed model with regard to model assumptions.
## S3 method for class 'trafo_lm' diagnostics(object, ...)## S3 method for class 'trafo_lm' diagnostics(object, ...)
object |
an object of type |
... |
additional arguments that are not used in this method |
An object of class diagnostics.trafo_lm. The method
print.diagnostics.trafo_lm can be used for this class.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Compare transformed models BD_lm <- trafo_lm(object = lm_cars, trafo = "bickeldoksum", method = "skew", lambdarange = c(1e-11, 2)) # Get diagnostics diagnostics(BD_lm)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Compare transformed models BD_lm <- trafo_lm(object = lm_cars, trafo = "bickeldoksum", method = "skew", lambdarange = c(1e-11, 2)) # Get diagnostics diagnostics(BD_lm)
The function transforms the dependent variable of a linear model using the Dual transformation. The transformation parameter can either be estimated using different estimation methods or given.
dual(object, lambda = "estim", method = "ml", lambdarange = c(0, 2), plotit = TRUE)dual(object, lambda = "estim", method = "ml", lambdarange = c(0, 2), plotit = TRUE)
object |
an object of type lm. |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
The Dual transformation is not defined for negative values of
lambda. Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Yang Z (2006). A modified family of power transformations. Economics Letters, 92(1), 14-19.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using divergence minimization following # Cramer-von-Mises dual(object = lm_cars, method = "div.cvm", plotit = TRUE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using divergence minimization following # Cramer-von-Mises dual(object = lm_cars, method = "div.cvm", plotit = TRUE)
The function transforms the dependent variable of a linear model using the Glog transformation.
glog(object)glog(object)
object |
an object of type lm. |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Durbin BP, Hardin JS, Hawkins DM, Rocke DM (2002). A Variance-stabilizing Transformation for Gene-expression Microarray Data. Bioinformatics, 18, 105-110.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable glog(object = lm_cars)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable glog(object = lm_cars)
The function transforms the dependent variable of a linear model using the Gpower transformation. The transformation parameter can either be estimated using different estimation methods or given.
gpower(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)gpower(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)
object |
an object of type lm. |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Kelmansky DM, Martinez EJ, Leiva V (2013). A New Variance Stabilizing Transformation for Gene Expression Data Analysis. Statistical applications in genetics and molecular biology, 12(6), 653-666.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using divergence minimization following # Kullback-Leibler gpower(object = lm_cars, method = "div.kl", plotit = FALSE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using divergence minimization following # Kullback-Leibler gpower(object = lm_cars, method = "div.kl", plotit = FALSE)
The function transforms the dependent variable of a linear model using the Log shift opt transformation. The transformation parameter can either be estimated using different estimation methods or given.
logshiftopt(object, lambda = "estim", method = "ml", lambdarange = NULL, plotit = TRUE)logshiftopt(object, lambda = "estim", method = "ml", lambdarange = NULL, plotit = TRUE)
object |
an object of type lm. |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using divergence minimization following # Kolmogorov-Smirnof logshiftopt(object = lm_cars, method = "div.ks", plotit = FALSE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using divergence minimization following # Kolmogorov-Smirnof logshiftopt(object = lm_cars, method = "div.ks", plotit = FALSE)
The function transforms the dependent variable of a linear model using the Log transformation. The Log transformation is only defined for positive response values. In case the response contains zero or negative values a shift is automatically added such that y + shift > 0.
logtrafo(object)logtrafo(object)
object |
an object of type lm. |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Box GEP, Cox DR (1964). An Analysis of Transformations. Journal of the Royal Statistical Society B, 26(2), 211-252.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable logtrafo(object = lm_cars)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable logtrafo(object = lm_cars)
The function transforms the dependent variable of a linear model using the Manly transformation. The transformation parameter can either be estimated using different estimation methods or given.
manly(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)manly(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)
object |
an object of type lm. |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Manly BFJ (1976). Exponential data transformations. Journal of the Royal Statistical Society: Series D, 25, 37-42.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using a maximum likelihood approach manly(object = lm_cars, plotit = FALSE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using a maximum likelihood approach manly(object = lm_cars, plotit = FALSE)
The function transforms the dependent variable of a linear model using the Modulus transformation. The transformation parameter can either be estimated using different estimation methods or given.
modulus(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)modulus(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)
object |
an object of type lm. |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
John JA, Draper NR (1980). An alternative family of transformations. Journal of the Royal Statistical Society: Series C, 29, 190-197.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable with fixed lambda modulus(object = lm_cars, lambda = 0.8, plotit = FALSE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable with fixed lambda modulus(object = lm_cars, lambda = 0.8, plotit = FALSE)
The function transforms the dependent variable of a linear model using the Neg log transformation.
neglog(object)neglog(object)
object |
an object of type lm. |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Whittaker J, Whitehead C, Somers M (2005). The neglog transformation and quantile regression for the analysis of a large credit scoring database. Journal of the Royal Statistical Society. Series C (Applied Statistics), 54(4), 863-878.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable neglog(object = lm_cars)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable neglog(object = lm_cars)
For the two transformed models a range of plots is returned in order to check model assumptions graphically.
## S3 method for class 'trafo_compare' plot(x, ...)## S3 method for class 'trafo_compare' plot(x, ...)
x |
an object of type |
... |
additional arguments that are not used in this method |
For the untransformed and transformed model a range of plots is returned in order to check model assumptions graphically.
## S3 method for class 'trafo_lm' plot(x, which = "all", ...)## S3 method for class 'trafo_lm' plot(x, which = "all", ...)
x |
an object of type |
which |
one element character that determines the return of plots. For single plot returns the possible values are: "qqplot", "hist", "residVsFitted", "residVsObs", "scatter", "cooks", "scaleLoc", "residLev". The default is set to "all". |
... |
additional arguments that are not used in this method |
for panel.cor function used in scatter plot: Smith, C.A., Want, E.J, O'Maille, G.,Abagyan,R. and Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching and identification. Analytical Chemistry, 78, 779-787. also: https://github.com/sneumann/xcms/blame/master/R/functions-xcmsSet.R#L654
Prints diagnostics of two trafo objects.
## S3 method for class 'diagnostics.trafo_compare' print(x, ...)## S3 method for class 'diagnostics.trafo_compare' print(x, ...)
x |
an object of type |
... |
additional arguments that are not used in this method |
Prints diagnostics of an untransformed and a transformed model.
## S3 method for class 'diagnostics.trafo_lm' print(x, ...)## S3 method for class 'diagnostics.trafo_lm' print(x, ...)
x |
an object of type |
... |
additional arguments that are not used in this method |
Prints objects to be shown in the summary function for objects of
type trafo_compare.
## S3 method for class 'summary.trafo_compare' print(x, ...)## S3 method for class 'summary.trafo_compare' print(x, ...)
x |
an object of type |
... |
additional arguments that are not used in this method |
prints objects to be shown in the summary function for objects of
type trafo_lm
## S3 method for class 'summary.trafo_lm' print(x, ...)## S3 method for class 'summary.trafo_lm' print(x, ...)
x |
an object of type |
... |
additional arguments that are not used in this method |
Prints object of type trafo
## S3 method for class 'trafo' print(x, ...)## S3 method for class 'trafo' print(x, ...)
x |
an object of type |
... |
other parameters that can be passed to the function. |
Prints object of type trafo_compare
## S3 method for class 'trafo_compare' print(x, ...)## S3 method for class 'trafo_compare' print(x, ...)
x |
an object of type trafo_compare. |
... |
other parameters that can be passed to the function. |
Prints object of type trafo_lm
## S3 method for class 'trafo_lm' print(x, ...)## S3 method for class 'trafo_lm' print(x, ...)
x |
an object of type |
... |
other parameters that can be passed to the function. |
The function transforms the dependent variable of a linear model using the Reciprocal transformation.
reciprocal(object)reciprocal(object)
object |
an object of type lm. |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable reciprocal(object = lm_cars)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable reciprocal(object = lm_cars)
The function transforms the dependent variable of a linear model using the Square-root shift transformation. The transformation parameter can either be estimated using different estimation methods or given.
sqrtshift(object, lambda = "estim", method = "ml", lambdarange = NULL, plotit = TRUE)sqrtshift(object, lambda = "estim", method = "ml", lambdarange = NULL, plotit = TRUE)
object |
an object of type lm. |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using a maximum likelihood approach sqrtshift(object = lm_cars, plotit = TRUE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using a maximum likelihood approach sqrtshift(object = lm_cars, plotit = TRUE)
The summary contains the summary for two transformed models. The summary is
based on the summary for objects of type lm.
## S3 method for class 'trafo_compare' summary(object, ...)## S3 method for class 'trafo_compare' summary(object, ...)
object |
an object of type |
... |
additional arguments that are not used in this method |
An object of class summary.trafo_compare. The method
print.summary.trafo_compare can be used for this class.
The summary method for class trafo_lm contains a summary for an
untransformed and a transformed model. The resulting summary is based on the
summary for objects of type lm.
## S3 method for class 'trafo_lm' summary(object, ...)## S3 method for class 'trafo_lm' summary(object, ...)
object |
an object of type |
... |
additional arguments that are not used in this method |
An object of class summary.trafo_lm. The method
print.summary.trafo_lm can be used for this class.
Estimation, selection and comparison of several families of transformations. The families of transformations included in the package are the following: Bickel-Doksum, Box-Cox, Dual, Glog, Gpower, Log, Log-shift opt, Manly, Modulus, Neglog, Reciprocal and Yeo-Johnson. The package simplifies to compare linear models with untransformed and transformed dependent variable as well as linear models where the dependent variable is transformed with different transformations. Furthermore, the package employs maximum likelihood approaches, skewness and divergence minimization to estimate the optimal transformation parameter.
An overview of all currently provided functions can be requested by
library(help=trafo).
The function transforms vectors as the prediction or confidence intervals naively back to the original scale of the used transformation in trafo_lm.
trafo_back(object, prediction)trafo_back(object, prediction)
object |
an object of type |
prediction |
the return of the predict.lm method. |
The backtransformed prediction.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Compare untransformed and transformed model trafo_cars <- trafo_lm(object = lm_cars, trafo = "bickeldoksum", method = "skew", lambdarange = c(1e-11, 2)) # Back-transform prediction and confidence interval trafo_back(trafo_cars, predict(trafo_cars$trafo_mod, interval = "confidence"))# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Compare untransformed and transformed model trafo_cars <- trafo_lm(object = lm_cars, trafo = "bickeldoksum", method = "skew", lambdarange = c(1e-11, 2)) # Back-transform prediction and confidence interval trafo_back(trafo_cars, predict(trafo_cars$trafo_mod, interval = "confidence"))
Function trafo_compare compares linear models where the dependent
variable is transformed by different transformations.
trafo_compare(object, trafos, std = FALSE)trafo_compare(object, trafos, std = FALSE)
object |
an object of type lm |
trafos |
a list of two |
std |
logical. If |
An object of class trafo_compare. Methods such as
diagnostics.trafo_compare, print.trafo_compare,
plot.trafo_compare and summary.trafo_compare can
be used for this class.
bickeldoksum, boxcox, dual,
glog, gpower, log,
logshiftopt, manly, modulus,
neglog, sqrtshift, yeojohnson
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform with Bickel-Doksum transformation bd_trafo <- bickeldoksum(object = lm_cars, plotit = FALSE) # Transform with Box-Cox transformation bc_trafo <- boxcox(object = lm_cars, method = "skew", plotit = FALSE) # Compare transformed models trafo_compare(object = lm_cars, trafos = list(bd_trafo, bc_trafo))# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform with Bickel-Doksum transformation bd_trafo <- bickeldoksum(object = lm_cars, plotit = FALSE) # Transform with Box-Cox transformation bc_trafo <- boxcox(object = lm_cars, method = "skew", plotit = FALSE) # Compare transformed models trafo_compare(object = lm_cars, trafos = list(bd_trafo, bc_trafo))
Function trafo_lm fits linear models with transformed dependent
variable. The main return are two lm objects where one is the
untransformed linear model and the other one the transformed linear model.
trafo_lm(object, trafo = "boxcox", lambda = "estim", method = "ml", lambdarange = NULL, std = FALSE, custom_trafo = NULL)trafo_lm(object, trafo = "boxcox", lambda = "estim", method = "ml", lambdarange = NULL, std = FALSE, custom_trafo = NULL)
object |
an object of type |
trafo |
a character string. Different transformations can be used for transforming the dependent variable in a linear model: (i) "bickeldoksum", (ii) "boxcox", (iii) "dual", (iv) "glog", (v) "gpower", (vi) "log", (vii) "logshiftopt", (viii) "manly", (ix) "modulus", (x) "neglog", (xi) "reciprocal", (xii) "yeojohnson". Defaults to "boxcox". |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
Defaults to |
std |
logical. If |
custom_trafo |
a list. The list has two elements where the first element
is a function specifying the desired transformation and the second element is
a function specifying the corresponding standardized transformation.
Defaults to |
An object of class trafo_lm. Methods such as
diagnostics.trafo_lm, print.trafo_lm,
plot.trafo_lm and summary.trafo_lm can
be used for this class.
bickeldoksum, boxcox, dual,
glog, gpower, log,
logshiftopt, manly, modulus,
neglog, sqrtshift, yeojohnson
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Compare untransformed and transformed model trafo_lm(object = lm_cars, trafo = "bickeldoksum", method = "skew", lambdarange = c(1e-11, 2))# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Compare untransformed and transformed model trafo_lm(object = lm_cars, trafo = "bickeldoksum", method = "skew", lambdarange = c(1e-11, 2))
The function transforms the dependent variable of a linear model using the Yeo-Johnson transformation. The transformation parameter can either be estimated using different estimation methods or given.
yeojohnson(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)yeojohnson(object, lambda = "estim", method = "ml", lambdarange = c(-2, 2), plotit = TRUE)
object |
an object of type lm. |
lambda |
either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim". |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
lambdarange |
a numeric vector with two elements defining an interval
that is used for the estimation of the optimal transformation parameter.
Defaults to |
plotit |
logical. If |
An object of class trafo. Methods such as
as.data.frame.trafo and print.trafo can
be used for this class.
Yeo IK, Johnson RA (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954-959.
# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using a maximum likelihood approach yeojohnson(object = lm_cars, plotit = FALSE)# Load data data("cars", package = "datasets") # Fit linear model lm_cars <- lm(dist ~ speed, data = cars) # Transform dependent variable using a maximum likelihood approach yeojohnson(object = lm_cars, plotit = FALSE)