Author Archives: aptech
Discrete Choice Analysis Tools v2.0
NEW! Discrete Choice Analysis Tools 2.0
Easier to Use: From input to results, and everything in between!
 Fast and efficient handling of large data sets
 Large scale data classification
 Publication quality formatted results tables with optional exportation
 Updated implementation for simple data input, parameter control, and estimation
 New logistic regression modelling for large scale classification including L2/L1 regularized classifiers and L2/ L1loss linear SVM with crossvalidation and prediction

Econometricians and Microeconomists
Political choice researchers
Survey data analysts
Sociologist
Epidemiologists
Insurance, safety and accident analysts
...And more!
Supported Models: Encompasses a large variety of linear classification models:
 NEW! Large Scale Data Classification: Performs largescale binary linear classification using support vector machines [SVM] or logistic regression [LR] methodology. Available options include crossvalidation of model parameters and prediction plotting. Easy to access output includes estimated prediction weights, predicted classifications and crossvalidation accuracy.
 Adjacent Categories Multinomial Logit Model: The logodds of one category versus the next higher category is linear in the cutpoints and explanatory variables.
 Logit and Probit Regression Models: Estimates dichotomous dependent variable with either Normal or extreme value distributions.
 Conditional Logit Models: Includes both variables that are attributes of the responses as well as, optionally, exogenous variables that are properties of cases.
 Mutltinomial Logit Model: Qualitative responses are each modeled with a separate set of regression coefficients.
 Negative Binomial Regression Model (left or right truncated, left or right censored, or zeroinflated): Estimates model with negative binomial distributed dependent variable. This includes censored models  the dependent variable is not observed but independent variables are available  and truncated models where not even the independent variables are observed. Also, a zeroinflated negative binomial model can be estimated where the probability of the zero category is a mixture of a negative binomial consistent probability and an excess probability. The mixture coefficient can be a function of independent variables.
 Nested Logit Regression Model: Derived from the assumption that residuals have a generalized extreme value distribution and allows for a general pattern of dependence among the responses thus avoiding the IIA problem, i.e., the "independence of irrelevant alternatives."
 Ordered Logit and Probit Regression Models: Estimates model with an ordered qualitative dependent variable with Normal or extreme value distributions.
 Possion Regression Model (left or right truncated, left or right censored, or zeroinflated): Estimates model with Poisson distributed dependent variable. This includes censored models  the dependent variable is not observed but independent variables are available  and truncated models where not even the independent variables are observed. Also, a zeroinflated Poisson model can be estimated where the probability of the zero category is a mixture of a Poisson consistent probability and an excess probability. The mixture coefficient can be a function of independent variables.
 Stereotype Multinomial Logit Model: The coefficients of the regression in each category are linear functions of a reference regression.
Outputs: GAUSS Easy to access, store, and export:
 NEW! Predicted counts and residuals
 Parameter estimates
 Variancecovariance matrix for coefficient estimates
 Percentages of dependent variables by category (where applicable)
 Complete data description of all independent variables
 Marginal effects of independent variables (by category of dependent variable, when applicable)
 Variancecovariance matrices of marginal effects
Reporting: Performs and reports a number of goodness of fit tests including for model performance analysis:
 Full model and restricted model loglikelihoods
 Chisquare statistic
 Agresti's Gsquared statistic
 Likelihood ratio statistics and accompanying probability values
 McFadden's Psuedo Rsquared
 McKelvey and Zovcina's Psuedo RSquared
 Cragg and Uhler's normed likelihood ratios
 Count RSquared
 Adjusted count RSquared
 Akaike and Bayesian information criterions
Saving and loading GAUSS matrix files
Leave a reply
Saving and loading GAUSS matrix files
GAUSS allows you to read and write data from many different file types such as Excel, ASCII text and database files. All of these file formats have their place, but you should learn to read and write native GAUSS matrix files, because it is very simple and very fast.Basic saving and loading
Let's start by creating a small matrix and saving it with the GAUSS save command://create a 4x2 matrix x = { 3.2 5.2, 5.3 6.3, 2.5 1.5, 6.3 9.2 }; //save the data in 'x' //in a GAUSS matrix file save x;In its most basic usage, as seen above, the GAUSS save command saves the contents of a GAUSS matrix to a file. The name of the file will be the same as the name of the GAUSS variable, but with a .fmt file extension. In this case, the file will be named x.fmt. Since we did not specify a path location, GAUSS will create this file in the current working directory. We can load the file we created with the load command:
load x;As you may have guessed, this line of code above will look for a file named x.fmt in the current working directory and assign its contents to a GAUSS global variable called x.
Specifying a different file or variable name
Sometimes you will want the saved matrix file to have a different name than the GAUSS global variable that originally held the data. Continuing with the x from above, we can save it as the file new_var.fmt with the following statement:save new_var.fmt=x;Since the file is being assigned to, it is on the left side of the equals sign. The variable, x is the source and is therefore on the right side of the equals sign. When load'ing a matrix file into a GAUSS variable with a different name, the order from above is reversed. Since the GAUSS variable is being assigned to, it is now on the left side of the equals sign. For example:
load new_x=x.fmt;As in our first examples in this tutorial, we do not have to specify the file extension. We could enter this:
load new_x=x;However, the file extension is recommended in this case because it makes the code more explicit and thus easier to understand later.
Specifying a different path
To specify a full path in which you would like your data saved (or loaded), you can simply add the path to the file name in our examples from above. For example:save C:\gauss\myproject\x.fmt=x;
load new_x=C:\gauss\myproject\x.fmt;
Setting the save path and load path
If you have a separate file folder for the data in your project, it would be nice to tell GAUSS the path once rather than on each use of the save and load command. You can do this by setting the save path and the load path.
You set the save path by passing the word path as a flag to the save command. For example:save path=C:\gauss\myproject;After executing the command above, all subsequent variables save'd with the save command will be placed in the C:\gauss\myproject directory. So, this code snippet:
save path=C:\gauss\myproject; save x; save y; save z;would produce the same effect as:
save C:\gauss\myproject\x.fmt=x; save C:\gauss\myproject\y.fmt=y; save C:\gauss\myproject\z.fmt=z;The load path works in the same way as the save path, by passing the path flag to the load command:
load path=C:\gauss\myproject\mydata;This sets the location in which the load command will look for matrix files.