[下载]SPSS.SYSTAT.PeakFit 4.12

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关键词：PeakFit SYSTAT Peak SPSS Sys

QUOTES About PeakFit

• "We know of no other comparable program that offers such sophisticated data analysis..." -- Journal of the American Chemical Society,Vol. 114, No. 20

• "PeakFit has been a big time saver. I wish I had found this product years ago." -- John Spradling Communications Engineer

• "PeakFit is extremely powerful and has all the features I need. It is especially adept at finding hidden peaks...with almost no effort on my part!" -- Margaret B. Aydelotte, Ph.D. Rush Medical College

PEAK FITTING Built-In Nonlinear Functions (83 total)

• Spectroscopy (18): Gauss Amp, Gauss Area, Lorentz Amp, Lorentz Area, Voigt Amp, Voigt Area, Voigt Amp Approx, Voigt Amp G/L, Voigt Area G/l, Gauss Cnstr Amp, Gauss Cnstr Area, Pearson VII Amp, Pearson VII Area, Gauss+Lor Area, Gauss*Lor, Gamma Ray, Compton Edge.

• Chromatography (8): HVL, NLC, Giddings, EMG, GMG, EMG+GMG, GEMG, GEMG 5-parm.

• Statistical (31): Log Normal Amp, Log Normal Area, Logistic Amp, Logistic Area, Laplace Amp, Laplace Area, Extr Value Amp, Extr Value Area, Log Normal-4 Amp, Log Normal-4 Area, Eval4 Amp Tailed, Eval4 Area Tailed, Eval4 Amp Frtd, Eval4 Area Frtd, Gamma Amp, Gamma Area, Inv Gamma Amp, Inv Gamma Area, Weibull Amp, Weibull Area, Error Amp, Error Area, Chi-Sq Amp, Chi-Sq Area, Student t Amp, Student t Area, Beta Amp, Beta Area, F Variance Amp, F Variance Area, Pearson IV.

• General Peak (12): Erfc Pk, Pulse Pk, LDR Pk, Asym Lgstc Pk, Lgstc pow Pk, Pulse pow Pk, Pulse Wid2 Pk, Intermediate Pk, Sym Dbl Sigmoid, Sym Dbl GaussCum, Asym Dbl Sigmoid, Asym Dbl GaussCum.

• Transition (14): Sigmoid Asc, Sigmoid Desc, GaussCum Asc, GaussCum Desc, LorentzCum Asc, LorentzCum Desc, LgstcDose Rsp Asc, LgstcDoseRsp Desc, LogNormCum Asc, LogNormCum Desc, ExtrValCum Asc, ExtrValCum Desc, PulseCum Asc, PulseCum Desc.

User Defined Functions

• Up to 10 Parameters per function.

• Up to 15 UDF's active during fit.
• Estimates can contain formulas and constraints.
• Extensive mathematical, statistical, Bessel, and logic functions.

Baseline Fit and Subtract

• Automatic detection of baseline points by constant second derivatives.
• Real-time Fitting in conjunction with data point selection, deselection.
• Background Functions (10): Constant, Linear, Progressive Linear, Quadratic, Cubic, Logarithmic, Exponential, Power, Hyperbolic and Non-Parametric.

Graphical Review

• Component and Sum Curve graphs.
• Residuals Graphs, including Distribution and Stabilized Normal Probability Plots.
• Confidence and Prediction Intervals.
• Peak Labels (Amplitude, Center, or Area).

Numerical Review

• Peak Characterization data: Center, Amplitude, Integrated Area, Analytical Area, FWHM, FW10, FWBASE, Asymmetry at HM, Asymmetry at 10 percent, First and Second Moments, Column Efficiency and Resolution, Percentage Areas, Overlap Areas.

• Parameter Statistics: Parameter Values, Confidence Limits (90, 95, 99 percent), t-values, Standard Errors.

• Fit Statistics: Analysis of Variance, F-statistic, Overall Standard Error, r2 value.

• Data Statistics: Residual Values, Predicted Y-values, Confidence/Prediction Intervals.

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GENERAL FEATURES

Data Input

• ASCII
• Excel^®
• Lotus 123^®
• Quattro Pro^® Windows^®
• SigmaPlot^®
• AIA Chromatography
• dBase^® III+, IV
• DIF
• ASCII and Spreadsheet - like Editors
• Averaging Digital Filter

Data Preparation

• Gaussian Deconvolution to remove Spectrophotometer Instrument Response smearing
• Exponential Deconvolution to remove Chromatographic Detector Response smearing
• Smoothing (Savitsky Golay, FFT Filter, Loess, Gaussian Convolution)
• Real - time FFT/Time Domain Graphical Editor
• Dual Graph Data Sectioning with Graphical data point exclusion
• Non - Parametric Digital Filter to Filter or augment data
• Compare with Reference
• Subtract Baseline Imported from File
• Data Transforms
• Area Normalization
• Inspect Second and Fourth Derivatives
• Data Weighting

Peak Autoplacement

• Automatic by Local Maxima and Residuals
• Automatic by Second Derivatives
• Automatic by Deconvolved Local Maxima
• Graphical Placement and Adjustments
• Manual Parameter Adjustments
• Share and Lock Parameters
• Constant or Variable Widths and/or Shape in a single step

Non - Linear Curve Fitting

• Marquardt - Levenburg Algorithm
• Least - Squares and 3 Robust (Maximum Likelihood) methods
• Up to 100 Peaks and 1000 Parameters
• Intelligent Constraints to insure Fit Integrity
• Sparse Curvature Matrix for Faster Fitting
• Both Numeric and Graphical Fitting Options
• Zoom - in or Toggle Points during Fitting

Output and Export Options

• File Export with full Generated Data: Lotus 123, Excel, Quattro Pro Windows, SigmaPlot, and ASCII.
• Graphs to clipboard or file in BMP or WMF formats
• All Numeric data in Graph to Clipboard in Spreadsheet Format

PEAK FITTING Built-In Nonlinear Functions (83 total)

• Spectroscopy (18): Gauss Amp, Gauss Area, Lorentz Amp, Lorentz Area, Voigt Amp, Voigt Area, Voigt Amp Approx, Voigt Amp G/L, Voigt Area G/l, Gauss Cnstr Amp, Gauss Cnstr Area, Pearson VII Amp, Pearson VII Area, Gauss+Lor Area, Gauss*Lor, Gamma Ray, Compton Edge.

• Chromatography (8): HVL, NLC, Giddings, EMG, GMG, EMG+GMG, GEMG, GEMG 5-parm.

• Statistical (31): Log Normal Amp, Log Normal Area, Logistic Amp, Logistic Area, Laplace Amp, Laplace Area, Extr Value Amp, Extr Value Area, Log Normal-4 Amp, Log Normal-4 Area, Eval4 Amp Tailed, Eval4 Area Tailed, Eval4 Amp Frtd, Eval4 Area Frtd, Gamma Amp, Gamma Area, Inv Gamma Amp, Inv Gamma Area, Weibull Amp, Weibull Area, Error Amp, Error Area, Chi-Sq Amp, Chi-Sq Area, Student t Amp, Student t Area, Beta Amp, Beta Area, F Variance Amp, F Variance Area, Pearson IV.

• General Peak (12): Erfc Pk, Pulse Pk, LDR Pk, Asym Lgstc Pk, Lgstc pow Pk, Pulse pow Pk, Pulse Wid2 Pk, Intermediate Pk, Sym Dbl Sigmoid, Sym Dbl GaussCum, Asym Dbl Sigmoid, Asym Dbl GaussCum.

• Transition (14): Sigmoid Asc, Sigmoid Desc, GaussCum Asc, GaussCum Desc, LorentzCum Asc, LorentzCum Desc, LgstcDose Rsp Asc, LgstcDoseRsp Desc, LogNormCum Asc, LogNormCum Desc, ExtrValCum Asc, ExtrValCum Desc, PulseCum Asc, PulseCum Desc.

User Defined Functions

• Up to 10 Parameters per function.

• Up to 15 UDF's active during fit.
• Estimates can contain formulas and constraints.
• Extensive mathematical, statistical, Bessel, and logic functions.

Baseline Fit and Subtract

• Automatic detection of baseline points by constant second derivatives.
• Real-time Fitting in conjunction with data point selection, deselection.
• Background Functions (10): Constant, Linear, Progressive Linear, Quadratic, Cubic, Logarithmic, Exponential, Power, Hyperbolic and Non-Parametric.

Graphical Review

• Component and Sum Curve graphs.
• Residuals Graphs, including Distribution and Stabilized Normal Probability Plots.
• Confidence and Prediction Intervals.
• Peak Labels (Amplitude, Center, or Area).

Numerical Review

• Peak Characterization data: Center, Amplitude, Integrated Area, Analytical Area, FWHM, FW10, FWBASE, Asymmetry at HM, Asymmetry at 10 percent, First and Second Moments, Column Efficiency and Resolution, Percentage Areas, Overlap Areas.

• Parameter Statistics: Parameter Values, Confidence Limits (90, 95, 99 percent), t-values, Standard Errors.

• Fit Statistics: Analysis of Variance, F-statistic, Overall Standard Error, r2 value.

• Data Statistics: Residual Values, Predicted Y-values, Confidence/Prediction Intervals.

A LIST OF ALL WHITE PAPERS ON SYSTAT SOFTWARE INC. PRODUCTS :

Classifying New Cases Using Discriminant Analysis How SYSTAT classifies cases in Discriminant Analysis, how to use group classification function coefficients to classify new cases and how to use the Discriminant Analysis procedure to automatically classify new cases.
Confidence Interval and Confidence Bands Calculating confidence intervals for the mean of Y, confidence intervals for the linear regression of Y on X and the prediction interval using SYSTAT.
Machismo Values Display a derived value for machismo for US states, using SYSTAT maps and a 3-D plot with smoother.
Relationships Between Component Loadings and Factor Scores in SYSTAT Use SYSTAT's MATRIX module to explore the relationship between eigenvalues, component loadings and factor scores.
Automated Curve Fitting in TableCurve 2D TableCurve 2D Version 5 can fit multiple data sets automatically in a batch-processing mode.
Explicit Function Approximation A closed form expression for F cannot be obtained for the Baker and Lonsdale equation. However, since the expression for F does not contain the parameter k, it can be numerically 'inverted'. The numerical inversion was done with TableCurve 2D.
Measurement of Oil Droplet Size and Distribution TableCurve 2D is used to characterize the size distribution of oil droplets.
USA's Young America Optimized With TableCurve 2D PACT95, the coalition that designed the fastest American yacht in the 1995 America’s Cup race used TableCurve 2D to help optimize their design.
Precisely Characterizing Changes in Surface Condition of Material Combusted in Microgravity Environment Using Ultrasonic Imaging and TableCurve 3D Three dimensional TableCurve 3D graphs before-and-after combustion in Space Shuttle microgravity of a plastic specimen surface height profile demonstrate the flame spread mechanism.
Single Channel Analysis - Fitting Multiple Simultaneous Gaussian Functions PeakFit is used to deconvolve Ca2+-activated potassium channel amplitude histograms.

SINGLE CHANNEL ANALYSIS - FITTING MULTIPLE SIMULTANEOUS GAUSSIAN FUNCTIONS :
We are interested in the regulation of large conductance Ca2+-activated potassium channels of CNS origin. We are using single channel recording techniques to study the effects of a variety of signal transduction elements on the activity of these important channels. Regulation of activity can be compared using the open probability - the amount of time the channel(s) are open. Experiments are performed under control conditions and in the presence of the desired bioactive substance in the same patch. When the patch contains only 1 or 2 channels, the determination of open probability is relatively simple. However, there are often 4-8 channels in a given patch. Determination of the open probability requires the deconvolution of an amplitude histogram comprised of multiple Gaussian distributions. There is a unique Gaussian distribution associated with each channel state. In the present example there are 5 channels. Therefore there are 6 Gaussian distributions in the amplitude histogram; all channels closed (level 0 on the activity records), 1 channel open (level 1), 2 channels open (level 2), 3 channels open (level 3), 4 channels open (level 4) and 5 channels open (level 5). Often there is significant overlap between distributions and if the time spent with all channels open is small (in this example all channels are open approximately 1% of the time) it is difficult to solve for this distribution. The excised patch configuration of the patch-clamp technique was used for data acquisition. Electrodes were fabricated in two steps and were fire polished to a final tip resistance between 3.0 and 5M . Recordings were performed at room temperature with a patch clamp amplifier. The recordings in this example were conducted with the patch depolarized to +20 mV. Single channel data were stored initially on digital audiotape. Single channel data were digitized at a sampling rate of 2 kHz and filtered at 1 kHz using a 4-pole low pass Bessel filter. Each data file contains approximately 100,000 events. Amplitude histograms are constructed from the digitized single channel data using commercially available software. Single channel records and the amplitude histogram data were exported to SigmaPlot and PeakFit as ASCII files. Line plots were created in SigmaPlot for the desired number of activity records (3 in the present example) and annotated as desired.
A vbar graph was created for the amplitude histogram data. The activity records and amplitude histogram were visually inspected to determine the most likely number of distributions contained in the data.
Click to View Larger Image Click to View Larger Image
The amplitude histogram was imported into PeakFit and fitted using a sum of Gaussian distribution functions. PeakFit Method 1. was selected for analysis and several hidden peaks were found. These arise from the overlap in distributions and were removed to give a function with 6 Gaussian peaks.		Click to View Larger Image
The data were then fit automatically. Estimates for individual peak areas, means and standard deviations were obtained from the peak fit results.		Click to View Larger Image
Equation 1. was used to calculate the open probability times the number of channels (NP0). i is the number of open channels, N is the total number of channels in the patch and Ai is the area associated with each channel state as determined from the curve-fit individual peak areas. The open probability (P0) was then calculated as NP0/N.
In the present example, NP0= 1.11 and P0=0.22. Author : Donald D. Denson, Ph.D., Department of Anesthesiology, Emory University School of Medicine

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