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1 Version Date Package BiDimRegression May 16, 2018 Title Calculates the Bidimensional Regression Between Two 2D Configurations Imports Formula, methods Depends R (>= 1.8.0) Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965). License GPL-3 URL NeedsCompilation no Repository CRAN RoxygenNote LazyData true Suggests testthat, knitr, rmarkdown, dplyr, ggplot2 VignetteBuilder knitr Author Claus-Christian Carbon [aut], Alexander (Sasha) Pastukhov [cre, ctb] Maintainer Alexander (Sasha) Pastukhov <pastukhov.alexander@gmail.com> Date/Publication :32:59 UTC R topics documented: anova.lm BiDimRegression CarbonExample1Data CarbonExample2Data CarbonExample3Data EyegazeData FriedmanKohlerData
2 2 anova.lm2 FriedmanKohlerData lm NakayaData predict.lm Index 12 anova.lm2 Anova for lm2 objects Anova for lm2 objects, returns a table with pairwise comparisons between models or, if only one model was supplied, with the null model. ## S3 method for class 'lm2' anova(object,...) Arguments object an object of class "lm2"... further objects of class "lm2" Value an anova data frame See Also lm2 lm2euc <- lm2(depv1+depv2~indepv1+indepv2, NakayaData, transformation = 'Euclidean') lm2aff <- lm2(depv1+depv2~indepv1+indepv2, NakayaData, transformation = 'Affine') anova(lm2euc, lm2aff)
3 BiDimRegression 3 BiDimRegression Calculates the bidimensional regression between two 2D configurations Calculates the bidimensional regression between two 2D configurations using both Euclidean and Affine transformations following the approach by Tobler (1965). This function assumes strict data format and returns all coefficients and statistics in a single structure. Same functionality is now re-implemented in a R-friendly style, see lm2 function. BiDimRegression(coord) Arguments coord table that must contain two columns for dependent variables (named depv1 and depv2) and two columns for independent variables (named indepv1 and indepv2). Value an S3 class BiDimRegression containing all essential measures of the bidimensional regression euclidean.r, affine.r - the regression coefficient, defined analogously to Pearson s r. euclidean.rsqr, affine.rsqr - the squared regression coefficient. euclidean.diabsqr, affine.diabsqr - the squared distortion index for dependent variables; following Waterman and Gordon s (1984) extension of the bidimensional regression, it provides a measure of comparison of distortions, but the range of values is 0 to 1 following Friedman and Kohler (2003). euclidean.dmaxabsqr, affine.dmaxabsqr - the maximal squared distortion index for dependent variables. euclidean.dixysqr, affine.dixysqr - the distortion index for independent variables. euclidean.dmaxxysqr, affine.dmaxxysqr - the maximal squared distortion index for independent variables. euclidean.scalefactorx, affine.scalefactorx - the scaling factor of the first dimension (1.0 means no scaling; values below 1.0 indicate a contraction, values above 1.0 indicate an expansion). euclidean.scalefactory, affine.scalefactory - the scaling factor of the second dimension. euclidean.angledeg, affine.angledeg - the rotation angle in degrees. euclidean.shear, affine.shear - shearing of the transformed configuration, always zero for the Euclidean transformation.
4 4 CarbonExample1Data See Also lm2 euclidean.ttestdf, affine.ttestdf - degrees of freedom (DF) for the t-tests regarding the model parameters (alphas and betas). euclidean.alpha1.*, euclidean.alpha2.*, affine.alpha1.*, affine.alpha2.* - intercept vectors, information includes.coeff for coefficient,.se for standard error, tvalue for t-statistics, and pvalue for significance. euclidean.beta1.*, euclidean.beta2.*, affine.beta1.*, affine.beta2.*, affine.beta3.*, affine.bet - slope vectors, information includes.coeff for coefficient,.se for standard error, tvalue for t-statistics, and pvalue for significance. euclidean.fvalue, affine.fvalue - F-statistics, following the advice of Nakaya (1997). euclidean.df1, affine.df1 - degrees of freedom of the nominator used for the F-statistics propagated by Nakaya (1997); df1 = p-2, with p is the number of elements needed to calculate the referring model: p=4 for the Euclidean and p=6 for the affine geometry Nakaya, 1997, Table 1. euclidean.df2, affine.df2 - degrees of freedom of the denominator used for the F- statistics propagated by Nakaya (1997); df2 = 2n-p, with p is the number of elements needed to calculate the referring model (see df1) and n is the number of coordinate pairs. euclidean.pvalue, affine.pvalue - the significance level based on the preceding F- statistics. euclidean.daicso, affine.daicso - the AIC difference between the regarding bidimensional regression model and the bidimensional null model (S0) according to Nakaya (1997), formula 56. eucvsaff.* - statistical comparison between Euclidean and Affine models, include.fvalue for F-statistics,.df1 and.df2 for the degrees of freedom,.pvalue for the significance level, and.daic for AIC difference between two models. resultingmeasures <- BiDimRegression(NakayaData) print(resultingmeasures) CarbonExample1Data Data from Carbon, C. C. (2013). BiDimRegression: Bidimensional Regression Modeling Using R. \ Journal of Statistical Software, Code Snippets, 52(1), 1-11 (URL Example 1 from the domain of aesthetics to show how the method can be utilized for assessing the similarity of two portrayed persons, actually the Mona Lisa in the world famous Louvre version and the only recently re-discovered Prado version
5 CarbonExample2Data 5 data(carbonexample1data) A data frame with 36 observations on the following 4 variables. depv1 a numeric vector depv2 a numeric vector indepv1 a numeric vector indepv2 a numeric vector data(carbonexample1data) ## maybe str(carbonexample1data) ; plot(carbonexample1data)... CarbonExample2Data Data from Carbon, C. C. (2013). BiDimRegression: Bidimensional Regression Modeling Using R. \ Journal of Statistical Software, Code Snippets, 52(1), 1-11 (URL Example 2 originates from the area of geography and inspects the accuracy of different maps of the city of Paris which were created over the last 350 years as compared to a recent map data(carbonexample2data) A data frame with 13 observations on the following 4 variables. depv1 a numeric vector depv2 a numeric vector indepv1 a numeric vector indepv2 a numeric vector data(carbonexample2data) ## maybe str(carbonexample2data) ; plot(carbonexample2data)...
6 6 EyegazeData CarbonExample3Data Data from Carbon, C. C. (2013). BiDimRegression: Bidimensional Regression Modeling Using R. \ Journal of Statistical Software, Code Snippets, 52(1), 1-11 (URL Example 3 focuses on demonstrating how good a cognitive map recalculated from averaged cognitive distance data fits with a related real map data(carbonexample3data) A data frame with 10 observations on the following 4 variables. depv1 a numeric vector depv2 a numeric vector indepv1 a numeric vector indepv2 a numeric vector data(carbonexample3data) ## maybe str(carbonexample3data) ; plot(carbonexample3data)... EyegazeData Eye gaze calibration data A dataset containing a monocular eye gaze recording with calibration sequence. Courtesy of Bamberger Baby Institut: BamBI. EyegazeData
7 FriedmanKohlerData1 7 A data frame with 365 rows and 6 variables: time sample timestamp, in milliseconds x, y recorded gaze, in internal eye tracker units target_x, target_y location of the calibration target on the screen, in pixels target index of the target within the sequence... FriedmanKohlerData1 Data from Friedman, A., & Kohler, B. (2003). Bidimensional regression: Assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets. Psychological Methods, 8(4), Data from Friedman, A., & Kohler, B. (2003). Bidimensional regression: Assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets. Psychological Methods, 8(4), data(friedmankohlerdata1) A data frame with 4 observations on the following 4 variables. depv1 a numeric vector depv2 a numeric vector indepv1 a numeric vector indepv2 a numeric vector data(friedmankohlerdata1) ## maybe str(friedmankohlerdata1) ; plot(friedmankohlerdata1)...
8 8 lm2 FriedmanKohlerData2 Data from Friedman, A., & Kohler, B. (2003). Bidimensional regression: Assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets. Psychological Methods, 8(4), Data from Friedman, A., & Kohler, B. (2003). Bidimensional regression: Assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets. Psychological Methods, 8(4), data(friedmankohlerdata2) A data frame with 4 observations on the following 4 variables. depv1 a numeric vector depv2 a numeric vector indepv1 a numeric vector indepv2 a numeric vector data(friedmankohlerdata2) ## maybe str(friedmankohlerdata2) ; plot(friedmankohlerdata2)... lm2 Fitting Bidimensional Regression Models lm2 is used to fit bidimensional linear regression models using Euclidean and Affine transformations following the approach by Tobler (1965). lm2(formula, data, transformation)
9 lm2 9 Arguments formula a symbolic description of the model to be fitted in the format A + B ~ C + D, where A and B are dependent and C and D are independent variables data a data frame containing variables for the model. transformation the transformation to be used, either 'euclidean', 'affine', or 'projective'. Value lm2 returns an object of class "lm2". An object of class "lm" is a list containing at least the following components: transformation string with the transformation type (euclidean, affine, or projective) npredictors number of predictors used in the model: 4 for euclidean, 6 for affine, 8 for projective. df_model, df_residual degrees of freedom for the model and for the residuals transformation_matrix 3x3 transformation matrix coeff transformation coefficients, with a denoting the intercept terms. transformed_coeff scale, angle, and sheer coefficients, depends on transformation. fitted_values residuals data frame containing fitted values for the original data set data frame containing residuals for the original fit r.squared, adj.r.squared R-squared and adjusted R-squared. F, p.value F-statistics and the corresponding p-value, given the df_model and df_residual degrees of freedom. daic Akaike Information Criterion (AIC) difference between the regression model and the null model. A negative values indicates that the regression model is better. See Nakaya (1997). distortion_index Distortion index following Waterman and Gordon (1984), as adjusted by Friedman and Kohler (2003) lm formula data Call an underlying linear model for Euclidean and affine transformations. formula, describing input and output columns data used to fit the model function call information, incorporates the formula, transformation, and data. See Also anova.lm2 BiDimRegression
10 10 NakayaData lm2euc <- lm2(depv1 + depv2 ~ indepv1 + indepv2, NakayaData, 'euclidean') lm2aff <- lm2(depv1 + depv2 ~ indepv1 + indepv2, NakayaData, 'affine') lm2prj <- lm2(depv1 + depv2 ~ indepv1 + indepv2, NakayaData, 'projective') anova(lm2euc, lm2aff, lm2prj) predict(lm2euc) summary(lm2euc) NakayaData Data from Nakaya, T. (1997). Statistical inferences in bidimensional regression models. Geographical Analysis, 29(2), Data from Nakaya, T. (1997). Statistical inferences in bidimensional regression models. Geographical Analysis, 29(2), data(nakayadata) A data frame with 19 observations on the following 4 variables. depv1 a numeric vector depv2 a numeric vector indepv1 a numeric vector indepv2 a numeric vector data(nakayadata) ## maybe str(nakayadata) ; plot(nakayadata)...
11 predict.lm2 11 predict.lm2 Predict method for Bidimensional Regression Model Fits Predicted values based on the bidimensional regressional model object. ## S3 method for class 'lm2' predict(object, newdata,...) Arguments Value object newdata See Also an object of class "lm2" An optional two column data frame with independent variables. If omitted, the fitted values are used.... optional arguments a two column data frame with predicted values for dependent variables. lm2 lm2euc <- lm2(depv1+depv2~indepv1+indepv2, NakayaData, transformation = 'Euclidean') predict(lm2euc, NakayaData[, 3:4])
12 Index Topic datasets CarbonExample1Data, 4 CarbonExample2Data, 5 CarbonExample3Data, 6 EyegazeData, 6 FriedmanKohlerData1, 7 FriedmanKohlerData2, 8 NakayaData, 10 anova.lm2, 2, 9 BiDimRegression, 3, 9 CarbonExample1Data, 4 CarbonExample2Data, 5 CarbonExample3Data, 6 EyegazeData, 6 FriedmanKohlerData1, 7 FriedmanKohlerData2, 8 linear model, 9 lm2, 2 4, 8, 11 NakayaData, 10 predict.lm2, 11 12
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Version 2.0.3 Date 2018-07-26 Package basictrendline July 26, 2018 Title Add Trendline and Confidence Interval of Basic Regression Models to Plot Maintainer Weiping Mei Plot, draw
Package fusionclust. September 19, 2017
Package fusionclust September 19, 2017 Title Clustering and Feature Screening using L1 Fusion Penalty Version 1.0.0 Provides the Big Merge Tracker and COSCI algorithms for convex clustering and feature
Package labelvector. July 28, 2018
Title Label Attributes for Atomic Vectors Version 0.1.0 Package labelvector July 28, 2018 Labels are a common construct in statistical software providing a human readable description of a variable. While
Package rnn. R topics documented: June 21, Title Recurrent Neural Network Version 0.8.1
Title Recurrent Neural Network Version 0.8.1 Package rnn June 21, 2018 Implementation of a Recurrent Neural Network in R. Depends R (>= 3.2.2) License GPL-3 LazyData true RoxygenNote 6.0.1 URL http://qua.st/rnn,
Package linear.tools
Type Package Package linear.tools July 6, 2016 Title Manipulate Formulas and Evaluate Marginal Effects Version 1.3.0 Author Fan Yang Maintainer Fan Yang Provides tools to manipulate formulas,
Package CausalImpact
Package CausalImpact September 15, 2017 Title Inferring Causal Effects using Bayesian Structural Time-Series Models Date 2017-08-16 Author Kay H. Brodersen , Alain Hauser
Package dkanr. July 12, 2018
Title Client for the 'DKAN' API Version 0.1.2 Package dkanr July 12, 2018 Provides functions to facilitate access to the 'DKAN' API (), including
Package msgps. February 20, 2015
Type Package Package msgps February 20, 2015 Title Degrees of freedom of elastic net, adaptive lasso and generalized elastic net Version 1.3 Date 2012-5-17 Author Kei Hirose Maintainer Kei Hirose
Package semver. January 6, 2017
Type Package Title 'Semantic Versioning V2.0.0' Parser Version 0.2.0 Package semver January 6, 2017 Tools and functions for parsing, rendering and operating on semantic version strings. Semantic versioning
Package omu. August 2, 2018
Package omu August 2, 2018 Title A Metabolomics Analysis Tool for Intuitive Figures and Convenient Metadata Collection Version 1.0.2 Facilitates the creation of intuitive figures to describe metabolomics
Package sure. September 19, 2017
Type Package Package sure September 19, 2017 Title Surrogate Residuals for Ordinal and General Regression Models An implementation of the surrogate approach to residuals and diagnostics for ordinal and
Package enpls. May 14, 2018
Package enpls May 14, 2018 Type Package Title Ensemble Partial Least Squares Regression Version 6.0 Maintainer Nan Xiao An algorithmic framework for measuring feature importance, outlier detection,
Package modmarg. R topics documented:
Package modmarg February 1, 2018 Title Calculating Marginal Effects and Levels with Errors Version 0.9.2 Calculate predicted levels and marginal effects, using the delta method to calculate standard errors.
Package reval. May 26, 2015
Package reval May 26, 2015 Title Repeated Function Evaluation for Sensitivity Analysis Version 2.0.0 Date 2015-05-25 Author Michael C Koohafkan [aut, cre] Maintainer Michael C Koohafkan
Package lcc. November 16, 2018
Type Package Title Longitudinal Concordance Correlation Version 1.0 Author Thiago de Paula Oliveira [aut, cre], Rafael de Andrade Moral [aut], John Hinde [aut], Silvio Sandoval Zocchi [aut, ctb], Clarice
Package sfc. August 29, 2016
Type Package Title Substance Flow Computation Version 0.1.0 Package sfc August 29, 2016 Description Provides a function sfc() to compute the substance flow with the input files --- ``data'' and ``model''.
Package MatchIt. April 18, 2017
Version 3.0.1 Date 2017-04-18 Package MatchIt April 18, 2017 Title Nonparametric Preprocessing for Parametric Casual Inference Selects matched samples of the original treated and control groups with similar
Package rollply. R topics documented: August 29, Title Moving-Window Add-on for 'plyr' Version 0.5.0
Title Moving-Window Add-on for 'plyr' Version 0.5.0 Package rollply August 29, 2016 Author ``Alexandre Genin [aut, cre]'' Maintainer Alexandre Genin Apply a function
Package nullabor. February 20, 2015
Version 0.3.1 Package nullabor February 20, 2015 Tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine
Package PDN. November 4, 2017
Type Package Title Personalized Disease Network Version 0.1.0 Package PDN November 4, 2017 Author Javier Cabrera , Fei Wang Maintainer Zhenbang Wang
Package clusternomics
Type Package Package clusternomics March 14, 2017 Title Integrative Clustering for Heterogeneous Biomedical Datasets Version 0.1.1 Author Evelina Gabasova Maintainer Evelina Gabasova