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.

Abstract: A computing system determines a design space for designing a design system. The computing system receives a request to identify input(s) that will provide a response of the design system that advances a user-defined goal for the design system. The computing system, responsive to the request to identify input(s), generates a design for the design space that provides design cases for generating the response of the design system and obtains responses of the design system generated for multiple design cases of the design for the design system. The computing system selects at least one design case based on obtained responses of the design system for the multiple design cases, and based on the at least one design case, outputs an indication of suggested input(s) for the design system. The suggested input(s) advances the user-defined goal for the design system and is not disallowed according to disallowed combination(s).

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.

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).

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).

Abstract: The computing device receives a first user input request to modify a structural equation model (SEM) in a graphical user interface. The modification of the SEM includes modifying one or more SEM path diagram elements. The computing device detects whether a first SEM path diagram element is modified responsive to the received first user input request. Based on the detection, the computing device determines whether the modification violates a first set of SEM rules, a second set of SEM rules, or one or more launch conditions prior to initiating execution of the SEM. Based on determining a violation of the SEM rules or the launch conditions or that there was not a violation, the computing device displays a graphical indicator for indicating a fatal error for the SEM modification, a warning error for the SEM modification, or a valid SEM modification.

Abstract: A computing system determines a design space for designing a design system. The computing system receives a request to identify input(s) that will provide a response of the design system that advances a user-defined goal for the design system. The computing system, responsive to the request to identify input(s), generates a design for the design space that provides design cases for generating the response of the design system and obtains responses of the design system generated for multiple design cases of the design for the design system. The computing system selects at least one design case based on obtained responses of the design system for the multiple design cases, and based on the at least one design case, outputs an indication of suggested input(s) for the design system. The suggested input(s) advances the user-defined goal for the design system and is not disallowed according to disallowed combination(s).

Abstract: The computing device receives a first user input request to modify a structural equation model (SEM) in a graphical user interface. The modification of the SEM includes modifying one or more SEM path diagram elements. The computing device detects whether a first SEM path diagram element is modified responsive to the received first user input request. Based on the detection, the computing device determines whether the modification violates a first set of SEM rules, a second set of SEM rules, or one or more launch conditions prior to initiating execution of the SEM. Based on determining a violation of the SEM rules or the launch conditions or that there was not a violation, the computing device displays a graphical indicator for indicating a fatal error for the SEM modification, a warning error for the SEM modification, or a valid SEM modification.

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.

Abstract: Graphical interactive prediction evaluation is provided. An extrapolation threshold value is computed using an extrapolation threshold function with an explanatory variable value of each of a plurality of explanatory variables read for each observation vector of a plurality of observation vectors. A model is fit to the observation vectors. Model results are presented in a display that include a first value for each explanatory variable. An indicator of a second value of at least one of the explanatory variables that is different from the first value is received. An extrapolation value is computed using an extrapolation function with the second value and the first value of others of the explanatory variables. The extrapolation value is compared to the extrapolation threshold value. An extrapolation indicator is presented in the display when the comparison indicates that the second value is an extrapolation.

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.