Obsidian | Level 7

## How do I interpret this probability graph of logistic regression with the stated variables?

How do I interpret the graph below, given the variables with their corresponding values below the title?

The variables are in the logistic model. What are those values? How did the procedure arrived at those values?

Help appreciated, thanks,

Saiful.

1 ACCEPTED SOLUTION

Accepted Solutions
Super User

## Re: How do I interpret this probability graph of logistic regression with the stated variables?

They're usually the average value for continuous variables and reference value for categorical variables.

See the documentation:

PLOTS=EFFECT Plots Only one PLOTS=EFFECT plot is produced by default; you must specify other effect-options to produce multiple plots. For binary response models, the following plots are produced when an EFFECT option is specified with no effect-options:
If you only have continuous covariates in the model, then a plot of the predicted probability versus the first continuous covariate fixing all other continuous covariates at their means is displayed. See Output 72.7.4 for an example with one continuous covariate.
If you only have classification covariates in the model, then a plot of the predicted probability versus the first CLASS covariate at each level of the second CLASS covariate, if any, holding all other CLASS covariates at their reference levels is displayed.
If you have CLASS and continuous covariates, then a plot of the predicted probability versus the first continuous covariate at up to 10 cross-classifications of the CLASS covariate levels, while fixing all other continuous covariates at their means and all other CLASS covariates at their reference levels, is displayed.
For example, if your model has four binary covariates, there are 16 cross-classifications of the CLASS covariate levels. The plot displays the 8 cross-classifications of the levels of the first three covariates while the fourth covariate is fixed at its reference level.
3 REPLIES 3
Super User

## Re: How do I interpret this probability graph of logistic regression with the stated variables?

Predicted value of bleed = 0/1 at fixed parameters above but for varying values of age.

The formula is from parameter estimates and plugging in values at top of graph, varying age.

Obsidian | Level 7

## Re: How do I interpret this probability graph of logistic regression with the stated variables?

Are those values picked at random or there's a reason for it?

Super User

## Re: How do I interpret this probability graph of logistic regression with the stated variables?

They're usually the average value for continuous variables and reference value for categorical variables.

See the documentation:

PLOTS=EFFECT Plots Only one PLOTS=EFFECT plot is produced by default; you must specify other effect-options to produce multiple plots. For binary response models, the following plots are produced when an EFFECT option is specified with no effect-options:
If you only have continuous covariates in the model, then a plot of the predicted probability versus the first continuous covariate fixing all other continuous covariates at their means is displayed. See Output 72.7.4 for an example with one continuous covariate.
If you only have classification covariates in the model, then a plot of the predicted probability versus the first CLASS covariate at each level of the second CLASS covariate, if any, holding all other CLASS covariates at their reference levels is displayed.
If you have CLASS and continuous covariates, then a plot of the predicted probability versus the first continuous covariate at up to 10 cross-classifications of the CLASS covariate levels, while fixing all other continuous covariates at their means and all other CLASS covariates at their reference levels, is displayed.
For example, if your model has four binary covariates, there are 16 cross-classifications of the CLASS covariate levels. The plot displays the 8 cross-classifications of the levels of the first three covariates while the fourth covariate is fixed at its reference level.
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