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公开(公告)号：US20210365842A1
公开(公告)日：2021-11-25
申请号：US17387135
申请日：2021-07-28
Applicant: SAS Institute Inc.
Abstract: Graphical interactive model selection is provided. A response variable vector for each value of a group variable and an explanatory variable vector are defined. A wavelet function is fit to the explanatory variable vector paired with the response variable vector defined for each value of the group variable. Each fit wavelet function defines coefficients for each value of the group variable. A curve is presented for each value of the group variable and is defined by the plurality of coefficients of an associated fit wavelet function. An indicator is received of a request to perform functional analysis using the coefficients for each value of the of the group variable based on a predefined factor variable. A model is trained using the coefficients for each value of the group variable and a factor variable value associated with each observation vector of each plurality of observation vectors as a model effect.
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公开(公告)号：US20210174207A1
公开(公告)日：2021-06-10
申请号：US17158062
申请日：2021-01-26
Applicant: SAS Institute Inc.
Inventor： Ryan Jeremy Parker , Clayton Adam Barker , Christopher Michael Gotwalt
Abstract: An analytic system provides direct functional principal component analysis. (A) A next group variable value is selected from values of a group variable. (B) Explanatory variable values of observations having the selected next group variable value are sorted in ascending order. (C) The response variable value associated with each sorted explanatory variable value is stored in a next row of a data matrix. (D) (A) through (C) are repeated. (E) An eigenfunction index is incremented. (F) An FPCA is performed using the data matrix to define an eigenfunction for the eigenfunction index. (G) (E) and (F) are repeated. (H) FPCA results from the performed FPCA are presented within a window of a display. The FPCA results include an eigenvalue and an eigenfunction associated with the eigenvalue for each functional principal component identified from the performed FPCA in (F).
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公开(公告)号：US10970651B1
公开(公告)日：2021-04-06
申请号：US16904698
申请日：2020-06-18
Applicant: SAS Institute Inc.
Inventor： Clayton Adam Barker , Ryan Jeremy Parker , Christopher Michael Gotwalt
IPC: G06N20/00
Abstract: Graphical interactive model selection is provided. A dataset includes observation vectors defined for each value of a plurality of values of a group variable. A nonlinear model is trained with each plurality of observation vectors to describe the response variable based on the explanatory variable for each value of the plurality of values of the group variable. Nonlinear model results are presented within a first sub-window of a first window. An indicator of a request to perform parameter analysis of the nonlinear model results is received. A linear model is trained. Trained linear model results from the trained linear model are presented within a second sub-window of the first window for each parameter variable of the nonlinear model. Predicted response variable values are presented as a function of the explanatory variable and the factor variable value using the trained nonlinear model within a third sub-window of the first window.
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Patent Agency Ranking
公开(公告)号：US11100395B2
公开(公告)日：2021-08-24
申请号：US17158062
申请日：2021-01-26
Applicant: SAS Institute Inc.
Inventor： Ryan Jeremy Parker , Clayton Adam Barker , Christopher Michael Gotwalt
Abstract: An analytic system provides direct functional principal component analysis. (A) A next group variable value is selected from values of a group variable. (B) Explanatory variable values of observations having the selected next group variable value are sorted in ascending order. (C) The response variable value associated with each sorted explanatory variable value is stored in a next row of a data matrix. (D) (A) through (C) are repeated. (E) An eigenfunction index is incremented. (F) An FPCA is performed using the data matrix to define an eigenfunction for the eigenfunction index. (G) (E) and (F) are repeated. (H) FPCA results from the performed FPCA are presented within a window of a display. The FPCA results include an eigenvalue and an eigenfunction associated with the eigenvalue for each functional principal component identified from the performed FPCA in (F).
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公开(公告)号：US09773211B2
公开(公告)日：2017-09-26
申请号：US14597988
申请日：2015-01-15
Applicant: SAS Institute Inc.
Inventor： Clayton Adam Barker
IPC: G06F17/30 , G06N99/00 , G06F3/0481
CPC classification number: G06N99/005 , G06F3/04812 , G06F17/30554 , G06F17/30958
Abstract: A graphical display of values generated according to a penalized regression model for multiple parameters of a data set shows the values as a graph having a first axis that represents magnitude of multiple parameter estimates of the penalized regression model and having a second axis that represents parameter estimate values of the multiple parameters of the penalized regression model. A user input is received that comprises a change to a parameter handle of the graphical display and changes at least one data parameter of the penalized regression model. The graphical display is changed such that the graphical display shows a representation of the values for the penalized regression model in accordance with the changes.
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公开(公告)号：US11361255B2
公开(公告)日：2022-06-14
申请号：US17387135
申请日：2021-07-28
Applicant: SAS Institute Inc.
Abstract: Graphical interactive model selection is provided. A response variable vector for each value of a group variable and an explanatory variable vector are defined. A wavelet function is fit to the explanatory variable vector paired with the response variable vector defined for each value of the group variable. Each fit wavelet function defines coefficients for each value of the group variable. A curve is presented for each value of the group variable and is defined by the plurality of coefficients of an associated fit wavelet function. An indicator is received of a request to perform functional analysis using the coefficients for each value of the of the group variable based on a predefined factor variable. A model is trained using the coefficients for each value of the group variable and a factor variable value associated with each observation vector of each plurality of observation vectors as a model effect.
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公开(公告)号：US10963788B1
公开(公告)日：2021-03-30
申请号：US16919309
申请日：2020-07-02
Applicant: SAS Institute Inc.
Inventor： Ryan Jeremy Parker , Clayton Adam Barker , Christopher Michael Gotwalt
Abstract: Graphical interactive model selection is provided. A basis function is fit to each plurality of observation vectors defined for each value of a group variable. Basis results are presented within a first sub-window of a first window of a display. Functional principal component analysis (FPCA) is automatically performed on each basis function. FPCA results are presented within a second sub-window of the first window. An indicator of a request to perform functional analysis using the FPCA results based on a predefined factor variable is received in association with the first window. A model is trained using an eigenvalue and an eigenfunction computed as a result of the FPCA for each plurality of observation vectors using the factor variable value as a model effect. (G) Trained model results are presented within a third sub-window of the first window of the display.
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8.发明申请SYSTEMS AND METHODS FOR INTERACTIVE GRAPHS FOR BUILDING PENALIZED REGRESSION MODELS 有权用于建立惩罚性回归模型的交互式图形的系统和方法公开(公告)号：US20150331963A1
公开(公告)日：2015-11-19
申请号：US14597988
申请日：2015-01-15
Applicant: SAS Institute Inc.
Inventor： Clayton Adam Barker
IPC: G06F17/30 , G06N99/00 , G06F3/0481
CPC classification number: G06N99/005 , G06F3/04812 , G06F17/30554 , G06F17/30958
Abstract: A graphical display of values generated according to a penalized regression model for multiple parameters of a data set shows the values as a graph having a first axis that represents magnitude of multiple parameter estimates of the penalized regression model and having a second axis that represents parameter estimate values of the multiple parameters of the penalized regression model. A user input is received that comprises a change to a parameter handle of the graphical display and changes at least one data parameter of the penalized regression model. The graphical display is changed such that the graphical display shows a representation of the values for the penalized regression model in accordance with the changes.
Abstract translation: 根据用于数据集的多个参数的惩罚回归模型生成的值的图形显示将值显示为具有第一轴的图，该第一轴表示惩罚回归模型的多个参数估计的大小，并具有表示参数估计的第二轴 惩罚回归模型的多个参数的值。 接收到包括对图形显示的参数句柄的改变并改变惩罚回归模型的至少一个数据参数的用户输入。 图形显示被改变，使得图形显示器根据变化显示惩罚回归模型的值的表示。
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