XLSTAT - LATENTCLASS kaufen
291.55

 

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inkl. 19 % USt

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291,55

XLSTAT - LatentClass wird gemeinsam von Addinsoft (Schnittstelle) und Statistical Innovations (mathematische Kernel) entwickelt. Das XLSTAT - LatentClass-Clustermodul gewährleistet eine zeitgemäße Clusteranalyse anhand von latenten Klassenmodellen. Neben fortlaufenden Variablen lassen sich auch ordinale, nominale und Zähler bzw. eine beliebige Kombination dieser Variablen verwenden. Die Clusterbeschreibung wird durch die Möglichkeit, auch Kovarianzen direkt in das Modell aufzunehmen, weiter verbessert. Außerdem lässt sich das latente Klassenclustermodell auch als herkömmliches latentes Klassenmodell zur Behandlung von Mess- und Klassifikationsfehlern in Kategorievariablen einsetzen.

XLSTAT - Base wird zur Verwendung von LatentClass benötigt!

XLSTAT-LatentClass - Clusterbildung und prädiktive Modellierung mithilfe von Mischmodellen

XLSTATs Produktlinie wurde komplett überarbeitet, erfahren Sie hier mehr!

Latente Klassenanalyse umfasst die Erzeugung von Latent Classes, die unbemerkt (latente) Untergruppen oder Segmente innerhalb der Stichprobe sind. Die latenten Klassen werden auf der Grundlage der beobachteten (manifest) Eigenschaften der Beobachtungen mit Hilfe einer Reihe von Indikatorvariablen konstruiert. Fällen in der gleichen Latenten Klasse sind in sich homogen, während sich die unterschiedlichen Latenten Klassen stark voneinader unterscheiden.

Dieses Modul ist nicht ohne eine XLSTAT-Base Lizenz nutzbar!


Benutzeroberfläche von XLSTAT-LG

Benutzeroberfläche von XLSTAT-LatentClass

Ein LatentClass Cluster Modell:

  • enthält ein nominal latente Variable x mit k Kategorien, die einzelnen Kategorien repräsentieren ein Cluster.
  • ein Cluster umfasst eine homogene Gruppe von Personen (Fällen), welche gleiche Interessen, Werte, Charakterristiken und/oder Verhaltensweisen gemeinsam haben.
  • diese Interessen, Werte, Charakterristiken und/oder Verhaltensweisen bilden die beobachteten Variablen (Indikatoren) auf die Y­Variable ab aus denen die latente Clustern abgeleitet werden.

Das optionale Modul XLSTAT-LatentClass fügt sich nahtlos in Ihre XLSTAT Umgebung ein und ist von dort aus verfügbar. XLSTAT-LatentClass ermöglicht automatisiert Berechnungen verschiedener Modelle mit unterschiedlichen Anzahlen von Klassen und Eigenschaften wie Sets von zufälligen Startwerten und Iterationsparametern. Dabei ist es möglich Bayes Konstanten und Ewartungswert Maximierungen mit Hilfe des Newton­Raphson Algorithmus für die Modellbildung zu schätzen.

Weitere Informationen

Systemvoraussetzungen für die Software XLSTAT

  Windows® Mac
Andere Voraussetzungen Microsoft Excel 2003, 2007, 2010, 2013, 2016
benötigt eine vorinstallierte XLSTAT-Lösung!
Microsoft Excel 2011
benötigt eine vorinstallierte XLSTAT-Lösung!
Betriebssystem Windows XP, Vista, 7, 8, 10 (32-/64-Bit) OS X
Min. CPU 800 MHz 800 MHz
Min. RAM 128 MB 128 MB
Festplattenplatz 150 MB 150 MB

Functions in XLSTAT - LG

XLSTAT - LG provides one section per model (each model being represented by a specific number of classes):

Model Summary Statistics: Number of cases used in model estimation, number of distinct parameters estimated, seed and best seed that can reproduce the current model more quickly using the number of starting sets = 0.

Estimation Summary: for each of the Expectation-Maximization and Newton-Raphson algorithms, XLSTAT reports the number of iterations used, the log-posterior value, the likelihood-ratio goodness-of-fit value, as well as the final convergence value.

Chi-Square Statistics:

  • Likelihood-ratio goodness-of-fit value (L2) for the current model and the associated bootstrap p-value
  • X2 and Cressie-Read. These are alternatives to L2 that should yield a similar p-value according to large sample theory if the model specified is valid and the data is not sparse
  • BIC, AIC, AIC3 and CAIC and SABIC (based on L2). These statistics (information criteria) weight fit and parsimony by adjusting the LL to account for the number of parameters in the model. The lower the value, the better the model
  • Dissimilarity index: A descriptive measure indicating how much the observed and estimated cell frequencies differ from one another. It indicates the proportion of the sample that needs to be movedto another cell to get a perfect fit

Log-likelihood Statistics:

  • log-likelihood (LL), log-prior (associated to Bayes constants) as well as the log-posterior
  • BIC, AIC, AIC3, CAIC and SABIC (based on LL). these statistics (information criteria) weight fit and parsimony by adjusting the LL to account for the number of parameters in the model. The lower the value, the better the model

Classification Statistics:

  • Classification errors (based on modal assignment)
  • Reduction of errors (Lambda), entropy R2, standard R2. These pseudo R-squared statistics indicate how well one can predict class memberships based on the observed variables (indicators and covariates). The closer these values are to 1 the better the predictions
  • Classification log-likelihood under the assumption that the true class membership is known
  • AWE (similar to BIC, but also takes into account classification performance)
  • Entropy
  • CLC

Classification Table:

  • Modal table: Cross-tabulates modal class assignments
  • Proportional table: Cross-tabulates probabilistic class assignments

Prediction statistics table:

The columns in this table correspond to:

  • prediction error of the baseline model (also referred to as null-model)
  • Model: the prediction error of the estimated model
  • R2: the proportional reduction of errors in the estimated model compared to the baseline model

The rows in this table correspond to:

  • Squared Error: Average prediction error based on squared error
  • Minus Log-likelihood: Average prediction error based on minus the log-likelihood
  • Absolute Error: Average prediction error based on absolute error
  • Prediction error: Average prediction error based on proportion of prediction errors (for categorical variables only)

Prediction Table: For nominal and ordinal dependent variables, a prediction table that cross-classifies observed and against estimated values is provided.

Parameters table:

  • R2: class-specific and overall R2 values. The overall R2 indicates how well the dependent variable is overall predicted by the model (same measure as appearing in Prediction Statistics). For ordinal, continuous, and (binomial) counts, these are standard R2 measures. For nominal dependent variables, these can be seen as weighted averages of separate R2 measures for each category treated as a separate dichotomous response variable.
  • Intercept: intercept of the linear regression equation
  • s.e.: standard errors of the parameters
  • z-value: z-test statistics corresponding to the parameter tests
  • Wald: Wald statistics are provided in the output to assess the statistical significance of the set of parameter estimates associated with a given variable. Specifically, for each variable, the Wald statistic tests the restriction that each of the parameter estimates in that set equals zero (for variables specified as Nominal, the set includes parameters for each category of the variable). For Regression models, by default, two Wald statistics (Wald, Wald(=)) are provided in the table when more than 1 class has been estimated. For each set of parameter estimates, the Wald(=) statistic considers the subset associated with each class and tests the restriction that each parameter in that subset equals the corresponding parameter in the subsets associated with each of the other classes. That is, the Wald(=) statistic tests the equality of each set of regression effects across classes.
  • p-value: measures of significance for the estimates
  • Mean: means for the regression coefficients
  • Std.Dev: standard deviations for the regression coefficients

Classification: Outputs for each observation the posterior class memberships and the modal assignment based on the current model.

Classification Table:

  • Modal table: Cross-tabulates modal class assignments
  • Proportional table: Cross-tabulates probabilistic class assignments

Profile table, which includes:

  • Number of clusters
  • Indiatorsc: The body of the table contains (marginal) conditional probabilities that show how the clusters are related to the Nominal or Ordinal indicator variables. These probabilities sum to 1. For indicators specified as Continuous, the body of the table contains means instead of probabilities. For indicators specified as Ordinal, means are displayed in addition to the conditional probabilities within each cluster (column).
  • Standard errors for the (marginal) conditional probabilities

Probabilities and means that appear in the Profile Output, are displayed graphically in a Profile Plot.

Frequencies / Residuals:

Table of observed vs. estimated expected frequencies (and residuals). Note: Residuals having magnitude greater than 2 are statistically significant. This output is not reported in the case of 1 or more continuous indicators.

Bivariate Residuals: a table containing the bivariate residuals (BVRs) for a model. Large BVRs suggest violation of the local independence assumption.

Scoring equation: regression coefficients associated with the multinomial logit model.

Classification: Outputs for each observation the posterior class memberships and the modal assignment based on the current model.