Login or Register Log in with. Forums FAQ. Search in titles only. Posts Latest Activity. Page of 1. Filtered by:. Monzur Alam. Outputting logistic regression results using outreg2 command? Dear all, I am trying to output the raw coefficients and odds ratio of a logit model using outreg2. I am using the logit command to display the raw coefficients and the logistic command to display the odds ratios. However when I try to display the odds ratio using outreg2, I end up getting the raw coefficients instead of odds ratios, as shown in the last table at the bottom.

I would like some advice on where I might be going wrong with the commands. Tags: None. Richard Williams. See Code:. Comment Post Cancel. Oded Mcdossi. As Richard note, please see the help outreg2. Sartaj Alam. Hi Manzoor, With the given paradigm as you mentioned, you will not be able to get your desired results.

So a single model is sufficient for getting both type of the results I hope this will suffice. Paul Martinez.To facilitate and automate the task of processing result from SPost for inclusion in reports and publications, estadd provides tools to integrate SPost results with estout or esttab.

SPost for Stata 8 spostado is not supported.

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See the SPost section in estadd 's documentation for further details. Below is a range of examples covering various models and applications. The general procedure to tabulate results from an SPost command in esttab or estout is to.

For example, to tabulate a number of fitstat information measures for a linear regression model, type:. Here is an example of the latter, using eststo to store the models:.

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The default for estadd prchange is to return a matrix called e dc containing the 0 to 1 change effects for binary variables and the standard deviation change effects for continuous variables in the first row, followed by additional rows containing separate results for the different effect types computed by prchange. To tabulate the contents of the first row simply refer to dc in esttab or estout. For example, to tabulate the marginal effects for continuous variables and the 0 to 1 change effects for binary variables see the helpfile for the list of available effects typestype:.

Use the label option to label the single predictions. If you want to tabulate differences in predictions, first apply prvalue or asprvalue with the save option and then estadd prvalue or estadd asprvalue with the diff option. The following example illustrates how to tabulate these results:. Interval Rate: 1. Interval Predicted y: 2.We will begin our discussion of binomial logistic regression by comparing it to regular ordinary least squares OLS regression.

Perhaps the most obvious difference between the two is that in OLS regression the dependent variable is continuous and in binomial logistic regression, it is binary and coded as 0 and 1. Because the dependent variable is binary, different assumptions are made in logistic regression than are made in OLS regression, and we will discuss these assumptions later.

Logistic regression is similar to OLS regression in that it is used to determine which predictor variables are statistically significant, diagnostics are used to check that the assumptions are valid, a test-statistic is calculated that indicates if the overall model is statistically significant, and a coefficient and standard error for each of the predictor variables is calculated.

For the examples in this chapter, we will use a set of data collected by the state of California from high schools measuring academic achievement.

Our dependent variable is called hiqual. This variable was created from a continuous variable api00 using a cut-off point of After running the regression, we will obtain the fitted values and then graph them against observed variables. Cases with missing values on any variable used in the analysis have been dropped listwise deletion. We will discuss this issue further later on in the chapter.

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In the graph above, we have plotted the predicted values called "fitted values" in the legend, the blue line along with the observed data values the red dots. Upon inspecting the graph, you will notice that some things that do not make sense. Such values are not possible with our outcome variable.

Also, the line does a poor job of "fitting" or "describing" the data points. As before, we have calculated the predicted probabilities and have graphed them against the observed values.

OUTREG2: Stata module to arrange regression outputs into an illustrative table

Also, the logistic regression curve does a much better job of "fitting" or "describing" the data points. Probability is defined as the quantitative expression of the chance that an event will occur.

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More formally, it is the number of times the event "occurs" divided by the number of times the event "could occur". On average, you get heads once out of every two tosses. In common parlance, probability and odds are used interchangeably.

However, in statistics, probability and odds are not the same.

OUTREG2: Stata module to arrange regression outputs into an illustrative table

The odds of an event happening is defined as the probability that the event occurs divided by the probability that the event does not occur.This page shows an example of a multinomial logistic regression analysis with footnotes explaining the output. The data were collected on high school students and are scores on various tests, including a video game and a puzzle.

The outcome measure in this analysis is the preferred flavor of ice cream — vanilla, chocolate or strawberry- from which we are going to see what relationships exists with video game scores videopuzzle scores puzzle and gender female. In out example, this will be vanilla.

By default, Stata chooses the most frequently occurring group to be the referent group. The first half of this page interprets the coefficients in terms of multinomial log-odds logits. These will be close to but not equal to the log-odds achieved in a logistic regression with two levels of the outcome variable. The second half interprets the coefficients in terms of relative risk ratios.

Before running the regression, obtaining a frequency of the ice cream flavors in the data can inform the selection of a reference group. Vanilla is the most frequently occurring ice cream flavor and will be the reference group in this example. Iteration Log — This is a listing of the log likelihoods at each iteration. Remember that multinomial logistic regression, like binary and ordered logistic regression, uses maximum likelihood estimation, which is an iterative procedure.

The first iteration called iteration 0 is the log likelihood of the "null" or "empty" model; that is, a model with no predictors. At the next iteration, the predictor s are included in the model.

outreg2 logistic

At each iteration, the log likelihood increases because the goal is to maximize the log likelihood. When the difference between successive iterations is very small, the model is said to have "converged", the iterating stops, and the results are displayed.

Scott Long page Log Likelihood — This is the log likelihood of the fitted model. Number of obs — This is the number of observations used in the multinomial logistic regression. It may be less than the number of cases in the dataset if there are missing values for some variables in the equation.

By default, Stata does a listwise deletion of incomplete cases. The number in the parentheses indicates the degrees of freedom of the Chi-Square distribution used to test the LR Chi-Square statistic and is defined by the number of models estimated 2 times the number of predictors in the model 3. In other words, this is the probability of obtaining this chi-square statistic Logistic regression, also called a logit model, is used to model dichotomous outcome variables.

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In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. Please note: The purpose of this page is to show how to use various data analysis commands. It does not cover all aspects of the research process which researchers are expected to do. In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics and potential follow-up analyses.

Stata Introduction, How to use Stata for a beginner 1/2

For our data analysis below, we are going to expand on Example 2 about getting into graduate school. We have generated hypothetical data, which can be obtained from our website.

We will treat the variables gre and gpa as continuous. The variable rank takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest.

Below is a list of some analysis methods you may have encountered. Some of the methods listed are quite reasonable while others have either fallen out of favor or have limitations. OLS regression. When used with a binary response variable, this model is knownas a linear probability model and can be used as a way to describe conditional probabilities.

However, the errors i. Two-group discriminant function analysis. A multivariate method for dichotomous outcome variables. This will produce an overall test of significance but will not. The i. Note that this syntax was introduced in Stata The likelihood ratio chi-square of Both gre and gpa are statistically significant, as are the three indicator variables for rank. The logistic regression coefficients give the change in the log odds of the outcome for a one unit increase in the predictor variable.

We can test for an overall effect of rank using the test command. Below we see that the overall effect of rank is statistically significant. We can also test additional hypotheses about the differences in the coefficients for different levels of rank. Note that if we wanted to estimate this difference, we could do so using the lincom command.

You could also use the logistic command.This page shows an example of logistic regression regression analysis with footnotes explaining the output. These data were collected on high schools students and are scores on various tests, including science, math, reading and social studies socst. The variable female is a dichotomous variable coded 1 if the student was female and 0 if male. Because we do not have a suitable dichotomous variable to use as our dependent variable, we will create one which we will call honcompfor honors composition based on the continuous variable write.

We do not advocate making dichotomous variables out of continuous variables; rather, we do this here only for purposes of this illustration.

This is a listing of the log likelihoods at each iteration. Remember that logistic regression uses maximum likelihood, which is an iterative procedure. The first iteration called iteration 0 is the log likelihood of the "null" or "empty" model; that is, a model with no predictors. At the next iteration, the predictor s are included in the model.

At each iteration, the log likelihood increases because the goal is to maximize the log likelihood. Scott Long. Log likelihood — This is the log likelihood of the final model. The value Number of obs — This is the number of observations that were used in the analysis. This number may be smaller than the total number of observations in your data set if you have missing values for any of the variables used in the logistic regression.

Stata uses a listwise deletion by default, which means that if there is a missing value for any variable in the logistic regression, the entire case will be excluded from the analysis.

LR chi2 3 — This is the likelihood ratio LR chi-square test. This is minus two i. The number in the parenthesis indicates the number of degrees of freedom. In this model, there are three predictors, so there are three degrees of freedom. In other words, this is the probability of obtaining this chi-square statistic This is, of course, the p-value, which is compared to a critical value, perhaps.

In this case, the model is statistically significant because the p-value is less than. Pseudo R2 — This is the pseudo R-squared. Logistic regression does not have an equivalent to the R-squared that is found in OLS regression; however, many people have tried to come up with one.

There are a wide variety of pseudo-R-square statistics. Because this statistic does not mean what R-square means in OLS regression the proportion of variance explained by the predictorswe suggest interpreting this statistic with great caution.

The variables listed below it are the independent variables. They are in log-odds units. Similar to OLS regression, the prediction equation is.

STATA输出Logistic回归的odds ratio

Expressed in terms of the variables used in this example, the logistic regression equation is. These estimates tell you about the relationship between the independent variables and the dependent variable, where the dependent variable is on the logit scale. Note: For the independent variables which are not significant, the coefficients are not significantly different from 0, which should be taken into account when interpreting the coefficients.

outreg2 logistic

See the columns with the z-values and p-values regarding testing whether the coefficients are statistically significant. Because these coefficients are in log-odds units, they are often difficult to interpret, so they are often converted into odds ratios. You can do this by hand by exponentiating the coefficient, or by using the or option with logit command, or by using the logistic command. This means that for a one-unit increase in female in other words, going from male to femalewe expect a 1.

In most cases, this is not interesting. Also, oftentimes zero is not a realistic value for a variable to take.More about this item Keywords regression ; output ; tables ; tab-delimited output ; LaTeX ; Word ; Excel ; Statistics Access and download statistics Corrections All material on this site has been provided by the respective publishers and authors.

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Registered: Roy Wada. The regression outputs are produced piecemeal and are difficult to compare without some type of rearrangement.

outreg2 logistic

The functionality of outreg2 is based on the earlier package outreg, by John Luke Gallup. Roy Wada, Handle: RePEc:boc:bocode:s Note: This module should be installed from within Stata by typing "ssc install outreg2". Windows users should not attempt to download these files with a web browser.


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