Marginal or Conditional Effects Plots by using Stata

Plotting the adjusted marginal or conditional effects helps to understand and communicate what a logistic model implies about probabilities. For example, we could find the predicted probability of any thermal distress incidents as a function of temp, holding date constant at relatively low (early) and high (late) values. Elapsed dates in these data range

Diagnostic Statistics and Plots by using Stata

As mentioned earlier, the logistic regression influence and diagnostic statistics obtained by predict refer not to individual observations, as do the OLS regression diagnostics of Chapter 7. Rather, logistic diagnostics refer to x patterns. In the space shuttle data, however, each x pattern is unique — no two flights share the same combination of

Logistic Regression with Ordered-Category y by using Stata

logit and logistic fit models for variables with two outcomes, coded 0 and 1. We need other methods for models in whichy takes on more than two values. Two important possibilities are ordered and multinomial logistic regression. ologit   Ordered logistic regression, where y is an ordinal (ordered-category) variable. The numeric values representing the categories

Multinomial Logistic Regression by using Stata

When the dependent variable’s categories are not ordinal, multinomial logit regression (also called polytomous logit regression) provides appropriate tools. If y has only two categories, mlogit (and ologit) both fit the same model as logistic. Otherwise, though, an mlogit model is more complex. Multiple-category dependent variables occur often in survey data. The Granite State

Multiple Imputation of Missing Values – Logit Regression by using Stata

Chapter 8 introduced Stata’s methods for multiple imputation of missing values, illustrated by a regression example. Multiple-imputation methods work with other types of analysis as well, including the logit-type models discussed in this chapter. For an illustration, we return to the Granite State Poll data and the climate-change belief indicator warmop2. The previous section

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Survival-Time Data in Stata

Survival-time data contain, at a minimum, one variable measuring how much time elapsed before a certain event occurred for each observation. The literature often terms this event of interest a “failure,” regardless of its real-world meaning. When failure has not occurred to an observation by the time data collection ends, that observation is said

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Count-Time Data with Stata

Survival-time (st) datasets like aids.dta contain information on individual people or things, with variables indicating the time at which failure or censoring occurred for each individual. A different type of dataset called count-time (ct) contains aggregate data, with variables counting the number of individuals that failed or were censored at time t. For example,

Kaplan–Meier Survivor Functions by using Stata

Let n t represent the number of observations that have not failed, and are not censored, at the beginning of time period t. dt represents the number of failures that occur to these observations during time period t. The Kaplan-Meier estimator of surviving beyond time t is the product of survival probabilities in t

Cox Proportional Hazard Models by using Stata

Regression methods allow us to take survival analysis further and examine the effects of multiple continuous or categorical predictors. One widely-used method known as Cox regression employs a proportional hazard model. The hazard rate for failure at time t is defined as the rate of failures at time t among those who have survived

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Exponential and Weibull Regression by using Stata

Cox regression estimates the baseline survivor function empirically without reference to any theoretical distribution. Several alternative parametric approaches begin instead from assumptions that survival times do follow a known theoretical distribution. Possible distribution families include the exponential, Weibull, lognormal, log-logistic, Gompertz or generalized gamma. Models based on any of these can be fit through

Poisson Regression by using Stata

If events occur independently and with constant rate, then counts of events over a given period of time follow a Poisson distribution. Let r j represent the incidence rate: The denominator in [10.4] is termed the “exposure” and is often measured in units such as person-years. We model the logarithm of incidence rate as

Generalized Linear Models by using Stata

Generalized linear models (GLM) have the form where    g[ ] is the link function and F the distribution        family. This general formulation encompasses many specific models. For example, if g[ ] is the identity function and y follows a normal (Gaussian) distribution, we have a linear regression model: If g[ ]     is the logit

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Principal Component Analysis and Principal Component Factoring by using Stata

To illustrate principal component and factor analysis, we start with the small dataset, planets.dta, describing the nine classical planets of this solar system (from Beatty et al. 1981). The data include several variables in both raw and natural logarithm form. Logarithms are employed here to reduce skew and linearize relationships among the variables. Principal

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Rotation by using Stata

Rotation further simplifies factor structure. After factoring, type rotate followed by an option to specify the type of rotation. Two common types are: varimax   Varimax orthogonal rotation, producing uncorrelated factors or components (default). promax( )   Promax oblique rotation, allowing correlated factors or components. Choose a number (promax power) < 4; the higher the number,

Factor Scores by using Stata

Factor scores are linear composites, formed by standardizing each variable to zero mean and unit variance, and then weighting with factor score coefficients and summing for each factor. predict performs these calculations automatically, using the most recent rotate or factor results. In the predict command we supply names for the new variables, such as

Different regression models with Panel data (fixed-effects, random-effects, and pooled OLS)

Panel data, also known as longitudinal or cross-sectional time-series data, is a dataset in which the behaviors of entities are observed across time. These entities could be states, companies, individuals, countries etc. Panel data allows us to control for variables we cannot observe or measure across entities; or variables that change over time but

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Principal Factoring by using Stata

The examples above involve principal component factoring, specified by the command factor with option pcf. Other factor options perform different kinds of factor analysis. pcf       Principal component factoring pf          Principal factoring (default) ipf         Principal factoring with iterated communalities ml         Maximum-likelihood factoring Principal factoring extracts principal components from a modified correlation matrix, in

Maximum-Likelihood Factoring by using Stata

Maximum-likelihood factoring, unlike Stata’s other factor options, provides formal hypothesis tests that help in determining the appropriate number of factors. To obtain a single maximum- likelihood factor for the planetary data, type The ml output includes two likelihood-ratio % 2 tests: LR test: independent vs. saturated This tests whether a no-factor (independent) model fits

Cluster Analysis — 1

Cluster analysis encompasses a variety of methods that divide observations into groups or clusters, based on their dissimilarities across a number of variables. It is most often used as an exploratory approach, for developing empirical typologies, rather than as a means of testing pre­specified hypotheses. Indeed, there exists little formal theory to guide hypothesis

Cluster Analysis — 2

Discovering a simple, robust typology to describe nine planets was straightforward. For a more challenging example, consider the cross-national data in Nations2.dta. These United Nations human-development variables could be applied to develop an empirical typology of nations. . use C:\data\Natlons2.dta, clear Working with the same data in Chapter7, we saw that nonlinear transformations such