Crie gráficos precisos com rapidez
Produtor: Systat Systems
Última versão: SigmaPlot 15 (Setembro 2022)
Sistema operativo: Windows
Versão teste: Sim (https://systatsoftware.com/downloads/download-sigmaplot)
Áreas: Gráficos, Estatística, Análise de Dados
Webinar Gratuito: Data to Presentation with SigmaPlot v15
Data: 22/08/2023 | 11h às 12h – Inscrições Aqui
O SigmaPlot permite a criação de gráficos precisos com rapidez
Com a nova interface Graph Properties, pode selecionar a categoria da propriedade na árvore à esquerda e depois alterar as propriedades à direita. A mudança é imediatamente representada graficamente e, se tirar o cursor do painel, ficará transparente e poderá ver o efeito das suas alterações sem sair do painel.
O procedimento “selecionar à esquerda e alterar à direita” facilita a edição dos seus gráficos de maneira rápida e fácil. O SigmaPlot permite ao utilizador ir além de simples folhas de cálculo e ajudá-lo a mostrar o seu trabalho com clareza e precisão. Com o SigmaPlot, pode produzir gráficos de alta qualidade sem gastar horas em frente de um computador. O SigmaPlot oferece uma integração perfeita do Microsoft Office®, para que possa aceder facilmente aos dados das folhas de cálculo do Microsoft Excel® e apresentar os seus resultados em apresentações do Microsoft PowerPoint®
O que o SigmaPlot poderá fazer para si?
• O SigmaPlot software ajuda a criar gráficos precisos e de uma forma rápida e simples
• Software gráfico que facilita a visualização de dados
• Mais de 100 models de gráficos técnicos em 2D e 3D
• Personaliza cada detalhes dos seus gráficos e tabelas
• Cria graficamente os seus dados a partir dos templates de gráficos existentes em gráficos numa galeria de estilos próprios
• Publica as suas tabelas e gráficos em qualquer lado
• Partilha com alta qualidade os seus dados e gráficos na Web
Mais informação: https://systatsoftware.com/downloads/download-sigmaplot/
What’s New in Version 15
New Graph Type (via Macro)
- Heat Map
- Clustered Heat Maps Macro
- Description of Heat Maps
A clustered heat map is a visualization of numeric data assigned to the levels of two categorical variables. This type of data can be displayed in a table where the rows refer to the levels of one variable and the columns refer to the levels of the other variable. The data table is typed into a SigmaPlot worksheet.
A heat map for this two-way data is constructed as a rectangular array of solid colors. The dimensions of the array and the positions of its individual color cells match the arrangement of the heat map data in the worksheet. The methods of assigning colors to data values are discussed below.
Heat maps assist in visualizing variations in the density of values in the data table. Put another way, heat maps are used to identify clusters of data.
The primary benefit of heat maps is that they make complicated data simpler to understand than the output of many other graphical or numerical techniques. One of the original applications of heat maps that is frequently used is to examine population density in a city or region. Heat maps are used by professionals in a variety of different fields:
– Business analytics – exploring data for a range of improvements and degrees of performance.
– Websites – visualizing the behavior of visitors.
– Exploratory Data Analysis – obtaining a relatively quick examination of data before deciding on how to model the data for more in-depth analysis.
– Molecular Biology – study patterns of difference and similarity in DNA and RNA.
– Marketing and Sales – showing how marketing and sales can be targeted by assessing sale trends and customer response in various geographic or demographic areas.
- Input Data
Worksheet data for a heat map is arranged in a number of columns. One column is for column labels and another column is for row labels. These labels appear on the axes for the heat map. You are allowed to create a heat map without providing labels.
The data to be selected for the heat map must be entered in adjoining columns. The number of rows of data can vary among the columns. When the heat map is created, the number of rows in the heat map equals the maximum number of rows in the selected data. Non-numeric data is allowed in the data table, but will be treated as missing values. The color assigned to a missing value is transparent.
One more column is needed for a color palette that will be used to generate the heat map colors. The palette is created by the user by using the Insert Graphic Cells dialog, the transform language, or manually by typing in a color code.
The colors assigned to the worksheet data depend on whether a discrete or continuous color scale is selected. The scales are based on the palette in the color column. For discrete color scales, the color column often uses an existing color scheme of 7 to 10 colors. For continuous color scales, the color column often contains only two or three colors, but can contain more.
Examples of heat map data are accessed from the Macro Data button in the Samples Files group on the Help tab of the SigmaPlot ribbon. When the button is pressed, the Macro Data Sets notebook opens in the Notebook Manager and heat map examples are provided in three sections.
An example of input data for heat maps is shown below.
The Create Heat Map macro is accessed by pressing the Heat Map button in the Graphing Tools group on the Tools tab of the SigmaPlot ribbon. When running the macro, a dialog box appears for setting options to create a heat map. The settings are:
- Select the worksheet column in the Column labels drop-down list for the heat map’s column labels. Leave the display value blank if no column labels will be used.
- Select the worksheet column in the Row labels drop-down list for the heat map’s row labels. Leave the display value blank if no row labels will be used.
- Select the worksheet column in the First data column drop-down list for the first or leftmost column of the heat map’s data table. This selection is required.
- Select the worksheet column in the Last data column drop-down list for the first or leftmost column of the heat map’s data table. This selection is required.
- Select the type of color scale used in the heat map, either Discrete color scale or Continuous color scale.
If using a discrete color scale, the first color of the color column is assigned to the minimum of the data and the last color of the color column is assigned to the maximum of the data. The range of the data is then divided up uniformly into groups and the data within each group is assigned to the corresponding color in the palette.
If using a continuous color scale, the first color of the color column or default color palette is assigned to the minimum of the data and the last color of the color column or default color palette is assigned to the maximum of the data. Linear interpolation on the components of the palette colors is used to assign a color to a particular data value based on the proportion of the data range for locating that data value.
- Color bars show the data to color conversion scale. Select the Show Color Bars to indicate whether color bars are or are not displayed, and if displayed, decide whether the color bar orientation is vertical (the default) or horizontal.
- When the Display numeric values in heat map checkbox is checked, a symbol plot showing the heat map data table is created and placed on top of the heat map so that numeric values, including missing values, are displayed over the corresponding heat map cells.
- The Select colors group box has two options for selecting the color palette. A Color column drop-down list is provided to select the palette from the worksheet. The alternative is to select the Use default colors shown below If this checkbox is checked, the heat map colors are created using a continuous color scale.
The Create Heat Map macro dialog box is shown below with settings made for the worksheet data shown above.
The settings in the dialog are saved to a text file in the user’s profile folder after pressing the OK button. When relaunching the macro, the dialog controls will show the same settings as before.
After pressing the OK button, the macro code computes the graph data for the heat map, color bar, and data table plot, depending on the selected settings. After the graph data is placed in the worksheet, the graphing code in the macro creates a graph page showing the selected plots.
The heat map graph data consists of a rectangular array of all 1s that has the same dimensions as the heat map’s data table. The heat map is actually created as a horizontal stacked bar chart and this array is used to set the number and relative size of the bars for the heat map. In addition, there is a column of color data for the heat map where the number of colors in the column equals the number of cells in the data table. This gives a total of N + 1 columns for the graph data of a heat map where N is number of columns in the input data table.
The data table symbol plot contains three columns of graph data. The first two columns give the coordinate positions of the data values in the heat map. The last column is a list of all data values in the data table, column by column, but converted to text as is needed for a symbol plot.
The color bar also contains three columns of graph data. A vertical (horizontal) color bar is created as a horizontal (vertical) bar chart. If using a continuous color scale, the first column consists of 101 numeric values that uniformly span the range of the heat map data. The second column consists of 101 values, all equal to 1, for setting the size of the bars. The last column applies the color scale to the first column of data values to generate 101 colors for the color bar. If using a discrete color scale, then the same types of columns are created, but each column has the same number of rows as the color column selected in the dialog.
For the above worksheet data and setting shown above in the dialog, the heat map appears as below. After the graph is created, the Graph Properties dialog can be used for any desired edits.
UI Modifications for Improved User Experience
New and refreshed Ribbon Manager:
- The new and refreshed ribbon manager enhances the already commendable user experience in SigmaPlot.
- Ribbons provide context sensitive feature groupings for enhanced functionality and ease of use.
- Icons are now desaturated, have high resolution and have expanded tooltips containing hot keys.
- Keyboard shortcuts are provided by the Alt Key-Tips.
This refreshed ribbons manager enhances the user experience providing the user with easy navigation across the various functions of SigmaPlot v15.
- New Home Button
The eye catching SigmaPlot Home button provides the user with easy navigation to Quick Start, Create New Notebooks, Open any saved SigmaPlot file, export to previous versions of SigmaPlot (up to SigmaPlot 11) and workbook protection with password and audit trails.
- Separate Macros Tab
– SigmaPlot’s easy-to-use macro language helps users create macros in no time. Not a programmer? No problem. With SigmaPlot, you can record macros by point-and-click with the macro recorder. Use macros to acquire your data, execute powerful analytical methods, and create industry-specific or field-specific graphs.
– Use one of the thirty built-in macros as provided or use these macros as a base to quickly create your own macros.
– The refreshed SigmaPlot v15, has a separate tab for Macros, making it easier for users to navigate to this great functionality of SigmaPlot.
– There are over 30 macros in SigmaPlot, giving users the capability of programmatic control over SigmaPlot features.
- Expanded Help
– Help topics are immediately visible and separated into groups of similar types.
– Faster access to topics than the prior Help menu where topics were displayed using drop-down lists.
– Improved search in Help, giving you instant access to various Help Topics like Tutorials, User Guides, Technical Support and information on your current licensed version of SigmaPlot.
- Ribbons Configuration functionality available via a Quick Access Tab
– The Ribbons configuration is now available on a Quick Access Tab from where instantly you can change the configurations between Default, Compact, Graphing & Analysis
- Histograms group on Analysis Tab
– The analysis tab is the primary interface for data analysis features in SigmaPlot.
– The tab has been rearranged for easier use and includes a new Histograms group for better access to creating graphs with more advanced properties.
- New Tools Tab
– Provides access to macros and apps that interface with SigmaPlot features to extend the program’s capabilities.
– The Graphing Tools group provide macros (tools) to create special graph types.
– The Office Tools group provides macros to send SigmaPlot results to Microsoft Office products.
– The Pharmacology group provides useful tools for Pharmacology studies.
- Dot Density Macro
– The Dot Density Overlay Plots macro is now exposed on the new Tools tab for greater accessibility
- Result Graphs
– Save result graph data with statistical reports.
– View and Save statistics results’ graph data with the option to revert to not saving result graph data with reports in case storage size becomes a problem. (Save result graph data with reports for anytime graph creation, turned on by default).
- User-Defined Transform Dialog
– A new check box allows the dialog to keep running after the Run button is pressed. Helpful for testing the results of a transform after making a series of changes to the transform text.
- Changed clipboard format for Excel to CF_SYLK
– So numbers pasted from Excel have full precision with the option to change Excel pasting in SigmaPlot to use the clipboard format CF_UNICODETEXT. (Paste numeric data from Excel into SigmaPlot using full precision without ability to paste non-ASCII characters.)
- New Licensing System
– New Cloud based License Manager Server with 24/7 availability
– SigmaPlot v15 has the latest Sentinel License Manager which is compatible with the latest Microsoft Server 2022
Removed older Microsoft VS 2005 Redistributable
The new SigmaPlot v15 Removes all dependencies on old Visual Studio 2005 redistributable by replacing older software for graph export
New Graph Features Include:
• Forest Plots
• Kernel Density Plots
• 10 New Color Schemes
• Dot Density Graph with mean and standard error bars
• Legend Improvements
• Horizontal, Vertical and Rectangular Legend Shapes
• Cursor over side or upper or lower handle
• allows for multi-column legends
• User interface to set number of legend item columns in the Properties dialog. The permissible column numbers are displayed in the combo list
• Change the number of legend item columns by selecting and dragging the middle handle in the bounding box
• Reorder legend items
• Through properties dialog – move one or multiple legend items up or down using the up/down control on top of the list box
• Through cursor movement – move one or multiple legend items up or down. Select the legend item(s) and use keyboard up and down arrow key for movement within the bounding box
• Through mouse select and cursor movement for items in the bounding box
• Individual legend items property settings – select individual legend items and use the mini tool bar to change the properties
• Legend box blank region control through cursor
• Cursor over corner handle
• allows proportional resizing
• Add simple direct labeling
• Support “Direct Labeling” in properties dialog using the checkbox control “Direct Labeling”
• Ungroup legend items – the individual legend items can be moved to preferred locations and move in conjunction with the graph
• Legend Title support has been added (no title by default). The user can add a title to the legend box using the legend properties panel
• Reverse the legend items using the right click context menu
• Open Legend Properties by double clicking either Legend Solid or Legend Text
• Reset has been added to legends to reset legend options to default
New Analysis Features Include:
• Principal Component Analysis (PCA)
• Analysis of Covariance (ANCOVA)
• Added P values to multiple comparisons for non-parametric ANOVAs
• Removed the combo box choices for multiple comparison significant levels and tied the significance level of multiple comparisons to the main (omnibus) test
• Added the Akaike Information Criterion to Regression Wizard and Dynamic Fit Wizard reports and the Report Options dialog
• Added back the Rerun button in the SigmaStat group
• Updated the fit library standard.jfl
o Added probability functions, to now include 24, for curve fitting or function visualization
o The tolerance value for all equations has been modified to use “e-notation” instead of fixed decimal. This allows the user to read the value without scrolling.
o Add seven weighting functions to all curve fit equations in standard.jfl. There is a slight variant added for 3D equations.
New User Interface Features
• Rearrange Notebook items in a section by dragging
• New SigmaPlot tutorial PDF file
• Line widths from a worksheet column
New Import/Export Features
• Added the SVG and SWF file formats for scalable vector graphics export
• Added Vector PDF export to improve on the existing raster PDF
• File import and export support is added for Versions 13 and 14 of Minitab, Version 9 of SAS, Version 19 of SPSS and Version 13 of Symphony
SigmaPlot Product Features
A forest plot is one form of “meta-analysis” which is used to combine multiple analyses addressing the same question. Meta-analysis statistically combines the samples of each contributing study to create an overall summary statistic that is more precise than the effect size in the individual studies. Individual study values and their 95% confidence intervals are shown as square symbols with horizontal error bars and the overall summary statistic as a diamond with width equal to its 95% confidence interval.
The kernel density feature will generate an estimate of the underlying data distribution. This should be compared to the step-like histogram. It has advantages (no bars) and disadvantages (loss of count information) over a histogram and should be used in conjunction with the histogram. They can be created simultaneously.
Dot Density with Mean & Standard Error Bars
The mean plus standard error bar computation, symbol plus error bars, has been added to the Dot Density graph. This enhances the other possible dot density display statistics – mean, median, percentiles and boxplot.
New Color Schemes
Ten new color schemes have been implemented. Three examples are shown below:
Legend Improvements – Shapes
Vertical, horizontal and rectangular legend shapes are now available.
Reverse Legend Order
You can now select to reverse the legend item order. This provides a more logical order for some graph types.
Reorder Legend Items
There are three ways to reorder the legend items. As shown here, you canmove one or multiple legend items up or down using the up/down arrow controls in the Legends panel of Graph Properties. Even easier, just select the item in the legend and use the keyboard up and down arrow keys. Or select the legend item and drag it to the new position with the mouse cursor.
Mini-Toolbar Editing of Legend Items
Legend items may now be edited by clicking on the item and using the mini-toolbar.
The legend can now be ungrouped and individual legend items placed adjacent to the appropriate plots. The labels will move with the graph to maintain position with respect to the graph. Since the label is adjacent to the plot, visual identification of each plot is now much easier.
Principal Component Analysis (PCA)
Principal component analysis (PCA) is a technique for reducing the complexity of high-dimensional data by approximating the data with fewer dimensions. Each new dimension is called a principal component and represents a linear combination of the original variables. The first principal component accounts for as much variation in the data as possible. Each subsequent principal component accounts for as much of the remaining variation as possible and is orthogonal to all of the previous principal components.
You can examine principal components to understand the sources of variation in your data. You can also use them in forming predictive models. If most of the variation in your data exists in a low-dimensional subset, you might be able to model your response variable in terms of the principal components. You can use principal components to reduce the number of variables in regression, clustering, and other statistical techniques.
The primary goal of Principal Components Analysis is to explain the sources of variability in the data and to represent the data with fewer variables while preserving most of the total variance.
Graphical output consists of Scree, Component Loadings and Component Scores plots.
Analysis of Covariance (ANCOVA)
A single-factor ANOVA model is based on a completely randomized design in which the subjects of a study are randomly sampled from a population and then each subject is randomly assigned to one of several factor levels or treatments so that each subject has an equal probability of receiving a treatment. A common assumption of this design is that the subjects are homogeneous. This means that any other variable, where differences between the subjects exist,does not significantly alter the treatment effect and need not be included in the model. However, there are often variables, outside the investigator’s control, that affect the observations within one or more factor groups, leading to necessary adjustments in the group means, their errors, the sources of variability,and the P-values of the group effect, including multiple comparisons.
These variables are called covariates. They are typically continuous variables, but can also be categorical. Since they are usually of secondary importance to the study and, as mentioned above, not controllable by the investigator, they do not represent additional main-effects factors, but can still be included into the model to improve the precision of the results. Covariates are also known as nuisancevariables or concomitant variables.
ANCOVA (Analysis of Covariance) is an extension of ANOVA obtained by specifying one or more covariates as additional variables in the model. If you arrange ANCOVA data in a SigmaPlot worksheet using the indexed data format, one column will represent the factor and one column will represent the dependent variable (the observations) as in an ANOVA design. In addition, you will have one column for each covariate. When using a model that includes the effects of covariates, there is more explained variability in the value of the dependent variable.
This generally reduces the unexplained variance that is attributed to random sampling variability, which increases the sensitivity of the ANCOVA as compared to the same model without covariates (the ANOVA model). Higher test sensitivity means that smaller mean differences between treatments will become significant as compared to a standard ANOVA model, thereby increasing statistical power.
As a simple example of using ANCOVA, consider an experiment where students are randomly assigned to one of three types of teaching methods and their achievement scores are measured. The goal is to measure the effect of the different methods and determine if one method achieves a significantly higher average score than the others. The methods are Lecture, Self-paced, and Cooperative Learning.
Performing a One Way ANOVA on this hypothetical data gives the results in the table below, under the ANOVA column heading. We conclude there is no significant difference among the teaching methods. Also note that the variance unexplained by the ANOVA model which is due to the random sampling variability in the observations is estimated as 35.17.
It is possible that students in our study may benefit more from one method than the others, based on their previous academic performance. Suppose we refine the study to include a covariate that measures some prior ability, such as a state-sanctioned Standards Based Assessment (SBA). Performing a One Way ANCOVA on this data gives the results in the table below, under the ANCOVA column heading.
|Method||Mean||Std. Error||Adjusted Mean||Std. Error|
|P = 0.124||P = 0.039|
|MSres = 35.17||MSres = 3.355|
The adjusted mean that is given in the table for each method is a correction to the group mean to control for the effects of the covariate. The results show the adjusted means are significantly different with the Lecture method as the more successful. Notice how the standard errors of the means have decreased by almost a factor of three while the variance due to random sample variability has decreased by a factor of ten. A reduction in error is the usual consequence of introducing covariates and performing an ANCOVA analysis.
There are four ANCOVA result graphs – Regression Lines in Groups, Scatter Plot of Residuals, Adjusted Means with Confidence Intervals, and Normality Probability Plot:
P Values for Nonparametric ANOVAs
The non-parametric ANOVA tests in SigmaPlot are the Kruskal-Wallis test (One-Way ANOVA on Ranks) and the Friedman test (One-Way Repeated Measures ANOVA on Ranks). Both of these provide four post-hoc testing procedures to determine the source of significant effects in the treatment factor. The four procedures are Tukey, SNK, Dunn’s, and Dunnett’s.
The first three procedures can be used to test the significance of each pairwise comparison of the treatment groups, while the last two can be used to test the significance of comparisons against a control group. Dunn’s method is the only procedure available if the treatment groups have unequal sample sizes.
When a post-hoc testing procedure is used, a table is given in the report listing the results for the pairwise comparisons of the treatment levels. The last column of the table shows whether the difference in ranks is significant or not. In previous versions of SigmaPlot there is no adjusted p-value given that can be compared to the significance level of the ANOVA (usually .05) to determine significance.
This is because SigmaPlot had been determining significance by comparing the observed test statistic, computed for each comparison, to a critical value of the distribution of the statistic that is obtained from a lookup table. SigmaPlot had two sets of lookup tables for the probability distributions corresponding to the four post-hoc methods, where one set was for a significance level of .05 and another set was for a significance level of .01.
This was recently changed to use analytical procedures to compute the p-values of these distributions, making the lookup tables obsolete. Because of this change, we are now able to report the adjusted p-values for each pairwise comparison. This change also makes it possible to remove the restriction of using .05 and .01 as the only significance levels for multiple comparisons. Thus the user can enter any valid P value significance level from 0 to 1.
[/toggle] [toggle border=’2′ title=’Akaike Information Criterion (AICc)’]
Akaike Information Criterion (AICc)
The Akaike Information Criterion (AIC) provides a method for measuring the relative performance in fitting a regression model to a given set of data. Founded on the concept of information entropy, the criterion offers a relative measure of the information lost in using a model to describe the data. More specifically, it gives a tradeoff between maximizing the likelihood for the estimated model (the same as minimizing the residual sum of squares if the data is normally distributed) and keeping the number of free parameters in the model to a minimum, reducing its complexity. Although goodness-of-fit is almost always improved by adding more parameters, overfitting will increase the sensitivity of the model to changes in the input data and can ruin its predictive capability.
The basic reason for using AIC is as a guide to model selection. In practice, it is computed for a set of candidate models and a given data set. The model with the smallest AIC value is selected as the model in the set which best represents the “true” model, or the model that minimizes the information loss, which is what AIC is designed to estimate. After the model with the minimum AIC has been determined, a relative likelihood can also be computed for each of the other candidate models to measure the probability of reducing the information loss relative to the model with the minimum AIC. The relative likelihood can assist the investigator in deciding whether more than one model in the set should be kept for further consideration.
The computation of AIC is based on the following general formula obtained by Akaike
Nonlinear Regression Probability Functions
24 new probability fit functions have been added to the fit library standard.jfl. These functions and some equations and graph shapes are shown below.
Nonlinear Regression Weighting Functions
There are now seven different weighting functions built into each nonlinear regression equation (3D are slightly different). These functions are reciprocal y, reciprocal y squared, reciprocal x, reciprocal x squared, reciprocal predicteds, reciprocal predicteds squared and Cauchy. The iteratively reweighted least squares algorithm is used to allow the weights to change during each nonlinear regression iteration.In this way “weighting by predicteds”, a commonly used method, can be obtained by selecting the reciprocal_pred weighting option.
Also, Cauchy weighting (select weight_Cauchy) can be used to fit an equation to data that contains outliers and the effect of the outliers will be minimized. Users can create their own weighting methods in terms of residuals and/or parameters to implement other robust fitting methods. The equation section of a fit file is shown with the seven built-in weighting functions.
User Interface Features – Rearrange items in your notebook by dragging
Objects in a notebook section are not necessarily created in a logical order. You can now drag items within a section to new positions to place them more logically.
An Updated SigmaPlot Tutorial
The new tutorial makes creating graphs for the first time easy. It starts with simple examples and gradually becomes more complex.
Specify Plot Line Widths from a Worksheet Column
Line width values can now be entered in a worksheet column. These values may be used within a graph or across multiple graphs on the page.
New Vector Export File Formats
SVG (Scalable Vector Graphics), SWF (Adobe Flash Player) and Vector PDF file formats have been added. These are scalable formats where no resolution is lost when zooming to different levels. SVG is the standard graphics format for the web and SWF can be used with Adobe Flash Player. Because pdf is used so frequently, the vector PDF format is now attached to the Create PDF button on the Home ribbon.
Updated Application File Formats
File import and export support has been updated to Versions 13 and 14 of Minitab, Version 9 of SAS and Version 19 of SPSS.
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