NIPALS Algorithm for PCA

Principal component analysis using NIPALS algorithm.

General

Element Name Description
Detail of computed results reported Detail of computed results; if Minimal results is requested, then summary spreadsheet and graph will be displayed; if Comprehensive results is requested, the components spreadsheet, the line plots of the components, and the scatterplots of the components will be displayed.
Maximum number of iterations Specifies the maximum number of iterations to generate the components.
Convergence criterion Convergence criterion
Fitting method Specifies the method to fit the model.
Fixed number of components Fixed number of components.
Minimum eigenvalue limit Minimum eigenvalue limit.
Cross-validation specifications Specifies the cross-validation method.
V value V value for V-fold cross-validation.
Random seed Random seed for cross-validation.

Advanced

Element Name Description
Minimum percentage of valid cases per variable (%) Minimum percentage of valid cases per variable (%).
Minimum percentage of valid variables per case (%) Minimum percentage of valid variables per case (%).
Apply maximum data size Apply maximum data size.
Maximum data size, in MB Maximum data size, in MB.
Variable scale Variable scale.

Results

Element Name Description
Control limit (%) for T-Square chart and SPE(Q) chart Control limit (%) for T-Square chart and SPE(Q) chart.
Apply warning limit for T-Square chart and SPE (Q) chart Apply warning limit for T-Square chart and SPE (Q) chart.
Warning limit (%) for T-Square chart and SPE(Q) chart Warning limit (%) for T-Square chart and SPE(Q) chart.
Sort variables by importance Sort variables by importance.
Scree plot of eigenvalues Scree plot of eigenvalues
Number of eigenvalues to plot in scree plot Number of eigenvalues to plot in scree plot
Save components Save components for further analysis.

Quality

Element Name Description
Quality control chart Generate quality control chart for continuous variables and components.
Type of quality control chart Type of quality control chart.
Using user-defined target Using user-defined target.
User-defined target User-defined target.
Using user-defined sigma Using user-defined sigma
User-defined sigma User-defined sigma
Sample size Sample size
Minimum number of observations per sample Minimum number of observations per sample.

Plots

Element Name Description
Lineplots of scores and loadings Lineplots of scores and loadings.
Scatterplots of scores and loadings Scatterplots of scores and loadings.
Plot labels Plot labels.
Type of plot limit Type of plot limit.
Applying warning limit for plots Applying warning limit for plots
Multiply std. dev. to calculate control limit. Multiply std. dev. to calculate control limit
Multiply std. dev. to calculate warning limit Multiply std. dev. to calculate warning limit.
Compute control limit from probability value Compute control limit from probability value
Compute warning limit from probability value Compute warning limit from probability value

Deployment

Deployment is available if the Statistica installation is licensed for this feature.

Element Name Description
Generates C/C++ code Generates C/C++ code for deployment of predictive model.
Generates SVB code Generates Statistica Visual Basic code for deployment of predictive model.
Generates PMML code Generates PMML (Predictive Models Markup Language) code for deployment of predictive model. This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets.
Saves C/C++ code Save C/C++ code for deployment of predictive model.
File name for C/C code Specify the name and location of the file where to save the (C/C++) deployment code information.
Saves SVB code Save Statistica Visual Basic code for deployment of predictive model.
File name for SVB code Specify the name and location of the file where to save the (SVB/VB) deployment code information.
Saves PMML code Saves PMML (Predictive Models Markup Language) code for deployment of predictive model. This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets.
File name for PMML (XML) code Specify the name and location of the file where to save the (PMML/XML) deployment code information.