Graphical Methods for Assessing Logistic Regression Models

Landwehr, James M.; Pregibon, Daryl; Shoemaker, Anne C.

doi:10.2307/2288334

Cited by 175 publications

(90 citation statements)

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“…The discreteness of categorical outcomes makes it difficult to interpret such displays. Several authors (Landwehr et al, 1984;Wang, 1985Wang, , 1987O'Hara Hines and Carter, 1993) had developed residuals under the generalized linear model framework, and successfully implemented these residuals in creating diagnostic plots. In marginalizing covariate effects of RLCA, we provided a formula for residuals of categorical responses, which were a "vector"-version extension of previous residuals, and could be applied broadly.…”

Section: Discussionmentioning

confidence: 99%

“…A logistic regression version of the partial residual plot based on the pseudo-residuals (19) was suggested by Landwehr, Pregibon, and Shoemaker (1984). They used both simulated data and real examples to show that the partial residual plot based on (19) can detect possible nonlinearity between outcomes and predictors.…”

Section: Marginalizing the Covariate Effects On Conditional Probabilimentioning

confidence: 99%

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Selecting the number of classes under latent class regression: a factor analytic analogue

Huang

2005

Psychometrika

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Section: Discussionmentioning

confidence: 99%

Section: Marginalizing the Covariate Effects On Conditional Probabilimentioning

confidence: 99%

Selecting the number of classes under latent class regression: a factor analytic analogue

Huang

2005

Psychometrika

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“…The pseudo-residual (13) is defined by analogizing the iteratively reweighted least-squares of generalized linear models with the least-square estimates of linear regressions (Landwehr, Pregibon, & Shoemaker, 1984;Huang, 2005). Then, classify objects based on new response variables R im (continuous indicators) or R im (categorical indicators), as in the previous subsection.…”

Section: Latent Class Membership Assignment When Incorporating Covarimentioning

confidence: 99%

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

2011

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Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the conditional independence assumption underlying the latent variable model. The second stage then uses mixture modeling treating the class membership as known. The proposed approach is theoretically justifiable, directly checks the conditional independence assumption, and converges much faster than the full likelihood approach when analyzing high-dimensional data. This paper also develops a new classification rule based on latent variable models. The proposed classification procedure reduces the dimensionality of measured data and explicitly recognizes the heterogeneous nature of the complex disease, which makes it perfect for analyzing high-throughput genomic data. Simulation studies and real data analysis demonstrate the advantages of the proposed method.

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“…Landwehr et al (1984) presented graphical methods for diagnostic checking of logistic regression models for binary responses. But as far as I know, there is no study on the graphical methods for assessing the proportional odds assumption.…”

Section: Introductionmentioning

confidence: 99%

Assessing practical significance of the proportional odds assumption

Kim

2003

Statistics & Probability Letters

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Graphical Methods for Assessing Logistic Regression Models

Cited by 175 publications

References 0 publications

Selecting the number of classes under latent class regression: a factor analytic analogue

Selecting the number of classes under latent class regression: a factor analytic analogue

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

Assessing practical significance of the proportional odds assumption

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