Logistic

Misc

  • Also see
  • Packages
    • {glmnet}
    • {brglm2}
      • Estimation and inference from generalized linear models using various methods for bias reduction
      • Can be used in models with Separation (See Diagnostics, GLM >> Separation)
      • Able to fit multinomial and logistic regression models
      • Reduction of estimation bias is achieved by solving either:
    • {LogisticCopula} (Paper) - A Copula Based Extension of Logistic Regression that aims to account for non-linear main effects and complex interactions, while keeping the model inherently explainable.
      • Uses a generative specification of the model, consisting of a combination of certain margins on natural exponential form, combined with vine copulas.
        • The model is constructed by starting from a Naive Bayes model resulting in linear log odds, and then adding vine copula terms which introduce additional non-linear effects and interaction terms in the form of between - covariate dependence, conditional on the response.
        • The resulting model for the log odds ratio becomes a sum of the logistic regression model with linear main effects and some non-linear terms that involve two covariates or more.
      • Performs best when non-linearities and complex interactions are present, even when n is not large compared to p.
  • glm needs the outcome to be numeric 0/1 (not factor)
  • Regularized Logistic Regression is most necessary when the number of candidate predictors is large in relationship to the effective sample size 3np(1−p) where p is the proportion of Y=1 Harrell
  • Log-Binomial Regression
    • Assumes a binomial distribution for a binary outcome but, unlike logistic regression, uses a log link function
    • Notes from
      • Introduction to Regression Methods for Public Health Using R, Ch. 6.21
        • Brief introduction with a basic {logbin} example.
    • Packages
      • glm(..., family = binomial(link = "log"))
      • {logbin} (Vignette) - Methods for fitting log-link GLMs and GAMs to binomial data, including EM-type algorithms with more stable convergence properties than standard methods.
    • Papers
    • With logistic regression, the left-hand side is the log of the odds, whereas in log-binomial regression it is the log of the probability (\(p\)).
    • Exponentiating a regression coefficient in logistic regression results in an odds ratio. Similarly, exponentiating a regression coefficient in log-binomial regression results in a Risk Ratio (RR) or Prevalence Ratio (PR).
    • A disadvantage of log-binomial regression is that the left-hand side \(\ln(p)\) is constrained to be positive while the right-hand side can be anything from \(-\infty\) to \(\infty\), and this leads to convergence issues at times.
  • Sample size requirements
    • These are conservative estimates. Sample size estimates assume an event probability of 0.50.
    • Logistic Regression: (Harrell, link)
      • For just estimating the intercept and a margin of error for predicted probabilities of 0.1
        • With no covariates (i.e. population is homogeneous), n = 96
        • With 1 categorical covariate, n = 96 for each level of the covariate
          • e.g. For gender, you need 96 males and 96 females
      • For just estimating the intercept and a margin of error for predicted probabilities of 0.05
        • With no covariates (i.e. population is homogeneous), n = 384
        • If true probabilities of event (and non-event) are known to be extreme, i.e. \(p \notin [0.2, 0.8]\), n = 246
      • For estimating predicted probabilities with 1 continuous predictor
        • For a margin of error of 0.1, n = 150
        • For a margin of error of 0.07, n = 300
    • RF: 200 events per candidate feature (Harrell, link)
  • “Stable” AUC requirements for 0/1 outcome:
  • Non-Collapsibility: The conditional odds ratio (OR) or hazard ratio (HR) is different from the marginal (unadjusted) ratio even in the complete absence of confounding.
  • Don’t use percents to report probabilities (Harrell)
    • Examples (Good)
      • The chance of stroke went from 0.02 to 0.03
      • The chance of stroke increased by 0.01 (or the absolute chance of stroke increased by 0.01)
      • The chance of stroke increased by a factor of 1.5
      • If 0.02 corresponded to treatment A and 0.03 corresponded to treatment B: treatment A multiplied the risk of stroke by 2/3 in comparison to treatment B.
      • Treatment A modified the risk of stroke by a factor of 2/3
      • The treatment A: treatment B risk ratio is 2/3 or 0.667.
  • RCTs (notes from Harrell)
    • outcome ~ treatment
      • These simple models are for homogeneous patient populations, i.e.,  patients with no known risk factors
      • When heterogeneity (patients have strong risk factors) is present, patients come from a mixture of distributions and this causes the treatment effect to shrink towards 0 in logistic and cox-ph models. (see Ch. 13 for in Harrell’s biostats book details
        • In a linear model, this heterogeneity (i.e. risk factors) that’s unaccounted for gets absorbed into the error term (residual variance ↑, power ↓), but logistic/cox-ph models don’t have residuals so the treatment effect shrinks as a result.
    • outcome ~ treatment + risk_factor_vars
      • Adjusting for risk factors stops a loss of power but never increases power like it does for linear models.
    • In Cox and logistic models there are no residual terms, and unaccounted outcome heterogeneity has nowhere to go. So it goes into the regression coefficients that are in the model, attenuating them towards zero. Failure to adjust for easily accountable outcome heterogeneity in nonlinear models causes a loss of power as a result.
    • Modeling is a question of approximating the effects of baseline variables that explain outcome heterogeneity. The better the model the more complete the conditioning and the more accurate the patient-specific effects that are estimated from the model. Omitted covariates or under-fitting strong nonlinear relationships results in effectively conditioning on only part of what one would like to know. This partial conditioning still results in useful estimates, and the estimated treatment effect will be somewhere between a fully correctly adjusted effect and a non-covariate-adjusted effect.

Interpretation

  • Misc

    • Also see Visualization >> Log Odds Ratios vs Odds Ratios
    • A logit link, which means that the effects (parameter magnitudes or CIs) can’t easily be translated to the probability scale without picking a baseline value (unlike identity-link models or log-link models such as Poisson regression). Consider also reporting the marginal effects as they are more interpretable. See Marginal Effects
    • “Divide by 4” shortcut (Gelman)
      • Valid for coefficients and standard errors
      • “1 unit increase in X results in a β/4 increase in probability that Y = 1”
      • Always round down
      • Better appproximations when the probability is close to 0.50

  • Definitions

    • Example: Event probability is 0.20 (source)

      # odds
o <- 0.2/(1 - 0.2)

# log odds
lo <- log(o)
lo <- qlogis(0.2)

# reconstruct probability
p <- plogis(lo)
p <- o / (1 + o)

  • Effect is non-linear in p

  • Probability prediction:

  • Odds Ratio - Describes the percent change in odds of the outcome based on a one unit increase in the input variable.

    • OR significance is being different from 1 and log odds ratios (logits) significance is being different from 0

    • logit(p) = 0, p = 0.5, OR = 1

    • logit(p) = 6, p = always (close to 1)

    • logit(p) = -6, p = never (close to 0)

    • Guidelines

      • OR > 1 means increased occurrence of an event
      • OR < 1 means decreased occurrence of an event
      • OR = 1 ↔︎ log OR = 0 ⇒ No difference in odds
      • OR < 0.5 or OR > 2 ⇒ Moderate effect
      • OR > 4 is pretty strong and unlikely to be explained by unmeasured variables

    • Code

      prostate_model %>% 
      broom::tidy(exp = TRUE)
##  term        estimate std.error statistic  p.value
##  <chr>          <dbl>    <dbl>    <dbl>    <dbl>
## 1 (Intercept)  0.0318    0.435    -7.93 2.15e-15
## 2 fam_hx        0.407      0.478    -1.88 6.05e- 2
## 3 b_gs          3.27      0.219      5.40 6.70e- 8
      • Exponentiates coefficients to get ORs
      • Interpretation: the odds ratio estimate of 0.407 means that for someone with a positive family history (fam_hx) of prostate cancer, the odds of their having a recurrence are 59.3% ((1-0.407) x 100) lower than someone without a family history. Similarly, for each unit increase in the baseline Gleason score (b_gs), the odds of recurrence increase by 227% ((3.27-1) x 100).

  • Summary

    ## glm(formula = recurrence ~ fam_hx + b_gs, family = binomial, 
##    data = .)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q      Max 
## -1.2216  -0.5089  -0.4446  -0.2879  2.5315 
## 
## Coefficients:
##            Estimate Std. Error z value            Pr(>|z|)
## (Intercept)  -3.4485    0.4347  -7.932 0.00000000000000215
## fam_hx      -0.8983    0.4785  -1.877              0.0605
## b_gs          1.1839    0.2193  5.399 0.00000006698872947
##               
## (Intercept) ***
## fam_hx      . 
## b_gs        ***
## ---
## Signif. codes: 
## 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##    Null deviance: 281.88  on 313  degrees of freedom
## Residual deviance: 246.81  on 311  degrees of freedom
##  (2 observations deleted due to missingness)
## AIC: 252.81
## 
## Number of Fisher Scoring iterations: 5
    • The Deviance Residuals should have a Median near zero, and be roughly symmetric around zero. If the median is close to zero, the model is not biased in one direction (the outcome is not over- nor under-estimated).
    • The Coefficients estimate how much a change of one unit in each predictor will affect the outcome (in logit units).
      • The family history predictor (fam_hx) is not significant, but trends toward an association with a decreased odds of recurrence, while the baseline Gleason score (b_gs) is significant and is associated with an 18% increased log-odds of recurrence for each extra point in the Gleason score.
    • Null Deviance and Residual Deviance. The null deviance is measured for the null model, with only an intercept. The residual deviance is measured for your model with predictors. Your residual deviance should be lower than the null deviance.
      • You can even measure whether your model is significantly better than the null model by calculating the difference between the Null Deviance and the Residual Deviance. This difference [281.9 - 246.8 = 35.1] has a chi-square distribution. You can look up the value for chi-square with 2 degrees (because you had 2 predictors) of freedom.
      • Or you can calculate this in R with pchisq(q = 35.1, df=2, lower.tail = TRUE) which gives you a p value of 1.
    • The degrees of freedom are related to the number of observations, and how many predictors you have used. If you look at the mean value in the prostate dataset for recurrence, it is 0.1708861, which means that 17% of the participants experienced a recurrence of prostate cancer. If you are calculating the mean of 315 of the 316 observations, and you know the overall mean of all 315, you (mathematically) know the value of the last observation - recurrence or not - it has no degrees of freedom. So for 316 observations, you have n-1 or 315, degrees of freedom. For each predictor in your model you ‘use up’ one degree of freedom. The degrees of freedom affect the significance of the test statistic (T, or chi-squared, or F statistic).
    • Observations deleted due to missingness - the logistic model will only work on complete cases, so if one of your predictors or the outcome is frequently missing, your effective dataset size will shrink rapidly. You want to know if this is an issue, as this might change which predictors you use (avoid frequently missing ones), or lead you to consider imputation of missing values.

  • Predicted Risk - predicted probabilities from a logistic regression model that’s used to predict the risk of an event given a set of variables.

    str(salespeople)
## 'data.frame':    350 obs. of  4 variables:
##  $ promoted    : int  0 0 1 0 1 1 0 0 0 0 ...
##  $ sales        : int  594 446 674 525 657 918 318 364 342 387 ...
##  $ customer_rate: num  3.94 4.06 3.83 3.62 4.4 4.54 3.09 4.89 3.74 3 ...
##  $ performance  : int  2 3 4 2 3 2 3 1 3 3 ...

model <- glm(formula = promoted ~ ., data = salespeople, family = "binomial")
exp(model$coefficients) %>% 
  round(2)
##  (Intercept)        sales customer_rate  performance2  performance3 
##          0.00          1.04          0.33          1.30          1.98 
##  performance4 
##          2.08
    • Interpretation:
      1. For two salespeople with the same customer rating and the same performance, each additional thousand dollars in sales increases the odds of promotion by 4%.
        • Sales in thousands of dollars
      2. For two salespeople with the same sales and performance, each additional point in customer rating decreases the odds of promotion by 67%
      3. Increasing performance4 by 1 unit and holding the rest of the variables constant increases the odds of getting a promotion by 108%.
        • I think this is being treated as a factor variable, and therefore the estimate is relative to the reference level (performance1).
        • Should be: “Performance4 increases the odds of getting a promotion by 108% relative to having a performance1”
      4. For two salespeople of the same sales and customer rating, performance rating has no significant effect on the odds of promotion.
        • None of the levels of performance were statistically significant

Assumptions

  • performance::model_check(mod) - provides a facet panel of diagnostic charts with subtitles to help interprete each chart.
    • Looks more geared towards a regression model than a logistic model.
  • Linear relationship between the logit of the binary outcome and each continuous explanatory variable
    • car::boxTidwell
      • May not work with factor response so might need to as.numeric(response) and use 1,2 values instead of 0,1
      • p < 0.05 indicates non-linear relationship which is what you want
    • Can also look a scatter plot of logit(response) vs numeric predictor
  • No outliers
    • Cook’s Distance
      • Different opinions regarding what cut-off values to use. One standard threshold is 4/N (where N = number of observations), meaning that observations with Cook’s Distance > 4/N are deemed as influential
    • Standardized Residuals
      • Absolute standardized residual values greater than 3 represent possible extreme outliers
  • Absence of Multicollinearity
    • VIF
    • Correlation Heatmap
  • iid
    • Deviance residuals (y-axis) vs row index (x-axis) should show points randomly around zero (y-axis)
      • Jesper’s response shows calculation and R code
      • I think the dHARMA pkg also handles this
  • Sufficiently large sample size
    • Rules of thumb
      • 10 observations with the least frequent outcome for each independent variable
      • > 500 observations total
    • I’m sure Harrell has thoughts on this somewhere

Diagnostics

  • Misc
    • ** The formulas for the deviances for a logistic regression model are slightly different from other GLMs since the deviance for the saturated logistic regression model is 0 **
    • Also see
  • Residual Deviance (G2)
    • -2 * LogLikelihood(proposed_mod)))
  • Null Deviance
    • -2 * LogLikelihood(null_mod)))
    • i.e. deviance for the intercept-only model
  • McFadden’s Pseudo R2 = (LL(null_mod) - LL(proposed_mod)) / LL(null_mod))

    • See What are Pseudo-R Squareds? for formulas to various alternative R2s for logistic regression

    • The p-value for this R2 is the same as the p-value for:

      • 2 * (LL(proposed_mod) - LL(null_mod))
      • Null Deviance - Residual Deviance
        • For the dof, use proposed_dof - null_dof
          • dof for the null model is 1

    • Example: Getting the p-value

      m1 <- glm(outcome ~ treat)
m2 <- glm(outcome ~ 1)
(ll_diff <- logLik(m1) - logLik(m2))
## 'log Lik.' 3.724533 (df=3)
1 - pchisq(2*ll_diff, 3)
  • Compare nested models

    • Models

      model1 <- glm(TenYearCHD ~ ageCent + currentSmoker + totChol, 
              data = heart_data, family = binomial)
model2 <- glm(TenYearCHD ~ ageCent + currentSmoker + totChol + 
                as.factor(education), 
              data = heart_data, family = binomial)
      • Add Education or not?

    • Extract Deviances

      # Deviances
(dev_model1 <- glance(model1)$deviance)
## [1] 2894.989
(dev_model2 <- glance(model2)$deviance)
## [1] 2887.206

    • Calculate difference and test significance

      # Drop-in-deviance test statistic
(test_stat <- dev_model1 - dev_model2)
## [1] 7.783615

# p-value
1 - pchisq(test_stat, 3)  # 3 = number of new model terms in model2 (i.e. 3(?) levels of education)
## [1] 0.05070196

# Using anova
anova(model1, model2, test = "Chisq")
## Analysis of Deviance Table
## 
## Model 1: TenYearCHD ~ ageCent + currentSmoker + totChol
## Model 2: TenYearCHD ~ ageCent + currentSmoker + totChol + as.factor(education)
##  Resid. Df Resid. Dev Df Deviance Pr(>Chi) 
## 1      3654    2895.0                       
## 2      3651    2887.2  3  7.7836  0.0507 .
  • |β| > 10
    • Extreme
    • Implies probabilities close to 0 or 1 which is suspect
    • Consider removal of the variable or outlier(s) influencing the model
  • Intercepts ≈ -17
    • Indicate a need for a simpler model (see bkmk - Troubleshooting glmmTMB)
  • If residuals are heteroskedastic, see {glmx}
    • neg.bin, hurdle, logistic - Extended techniques for generalized linear models (GLMs), especially for binary responses, including parametric links and heteroskedastic latent variables
  • Binned Residuals

    • It is not useful to plot the raw residuals, so examine binned residual plots

    • Misc

      • {arm} will mask some {tidyverse} functions, so don’t load whole package

    • Look for :

      • Patterns
      • Nonlinear trend may be indication that squared term or log transformation of predictor variable required
      • If bins have average residuals with large magnitude
      • Look at averages of other predictor variables across bins
      • Interaction may be required if large magnitude residuals correspond to certain combinations of predictor variables

    • Process

      • Extract raw residuals
        • Include type.residuals = "response" in the broom::augment function to get the raw residuals
      • Order observations either by the values of the predicted probabilities (or by numeric predictor variable)
      • Use the ordered data to create g bins of approximately equal size.
        • Default value: g = sqrt(n)
      • Calculate average residual value in each bin
      • Plot average residuals vs. average predicted probability (or average predictor value)

    • Example: vs Predicted Values

      arm::binnedplot(x = risk_m_aug$.fitted, y = risk_m_aug$.resid,
                xlab = "Predicted Probabilities",
                main = "Binned Residual vs. Predicted Values",
                col.int = FALSE)

      • Example: vs Predictor

        arm::binnedplot(x = risk_m_aug$ageCent,
                y = risk_m_aug$.resid,
                col.int = FALSE,
                xlab = "Age (Mean-Centered)",
                main = "Binned Residual vs. Age")

    • Check that residuals have mean zero: mean(resid(mod))

    • Check that residuals for each level of categorical have mean zero

      risk_m_aug %>%
  group_by(currentSmoker) %>%
  summarize(mean_resid = mean(.resid))

Marginal Effects

  • Misc
  • In general
    • A “marginal effect” is a measure of the association between an infinitely small change in a regressor and a change in the response variable
      • “infinitely small” because we’re using partial derivatives
      • Example: If I change the cost (regressor) of taking the bus, how does that change the probability (not odds or log odds) of taking a bus to work instead of a car (response)
        • Allows you to ask counterfactuals.
    • In OLS regression with no interactions or higher-order term, the estimated slope coefficients are marginal effects, but for glms, the coefficients are not marginal effects at least not on the scale of the response variable
    • Marginal effects are partial derivatives of the regression equation with respect to each variable in the model for each unit in the data. (also see notebook, Regression >> logistic section)
  • Logistic Regression
    • The partial derivative gives the slope of a tangent line at point on a nonlinear curve (e.g. logit) which is the linear change in probability at a single point on the nonlinear curve
      \[ \frac {\partial P}{\partial X_{k}} = \beta_{k} \times P \times (1 - P) \]
      • Where P is the predicted probability and β is the model coefficient for the kth predictor
  • 3 types of marginal effects
    • Marginal effect at the Means
      • The marginal effect that is “typical” of the sample
      • Each model coefficient is multiplied times their respective independent variables mean and summed along with the intercept.
        • This sum is transformed from a logit to a probability, P, is used in the partial derivative equation to calculate the marginal effect
      • Interpretation
        • continuous: an infinitely small change in while all other predictors are held at their means results in a change in
          • e.g. an infinitely small change in this predictor for a hypothetical average person results in this amount of change in the dependent variable
      • “at the medians” can be easily calculated using marginaleffects::typical  (this is outdated) without any columns specified and then used to calculate marginal effects
        • to get “at the means” you’d have to supply each column with its mean value
    • Marginal effect at Representative (“Typical”) Values
      • The marginal effect that is “typical” of a group represented in the sample
      • A real “average” person doesn’t usually have the mean/median values for predictor values (e.g  mean age of 54.68 years), so you might want to find the marginal effect for a “typical” person of a certain demographic or group you’re interested in by specifying values for predictors
      • Interpretation
        • continuous: an infinitely small change in for a person with results in a change in
      • Calculate using marginaleffects::typical  (this is outdated) to specify column values (this is outdated)
    • Average Marginal Effect (AME)
      • The marginal effect of a case chosen at random from the sample
        • Considered the best summary of an independent variable
      • Calculate marginal effect for each observation (distribution of marginal effects) and then take the average
        • Multiply the model coefficient times each value of an independent variable, repeat for each predictor, sum with intercept, use predicted probabilities to calculate marginal effect, and average all marginal effects across all obseravation for a predictor to get the AME
      • Interpretation
        • continuous: Holding all covariates constant, an infinitely small change in results in a change in on average.
      • Use marginaleffects::marginaleffects +summary or tidy
  • {marginaleffects}
    • Example: Palmer penguins data and logistic regression model

      • Create marginal effects object

        mfx <- marginaleffects(mod)
head(mfx)
#>  rowid              term        dydx  std.error large_penguin bill_length_mm
#> 1    1    bill_length_mm 0.017622745 0.007837288            0          39.1
#> 2    1 flipper_length_mm 0.006763748 0.001561740            0          39.1
#> 3    2    bill_length_mm 0.035846649 0.011917159            0          39.5
#> 4    2 flipper_length_mm 0.013758244 0.002880123            0          39.5
#> 5    3    bill_length_mm 0.084433436 0.021119186            0          40.3
#> 6    3 flipper_length_mm 0.032406447 0.008159318            0          40.3
#>  flipper_length_mm species
#> 1              181  Adelie
#> 2              181  Adelie
#> 3              186  Adelie
#> 4              186  Adelie
#> 5              195  Adelie
#> 6              195  Adelie

      • Average Marginal Effect (AME)

        summary(mfx)
#> Average marginal effects 
#>      type              Term          Contrast    Effect Std. Error  z value
#> 1 response    bill_length_mm              <NA>  0.02757    0.00849  3.24819
#> 2 response flipper_length_mm              <NA>  0.01058    0.00332  3.18766
#> 3 response          species Chinstrap / Adelie  0.00547    0.00574 -4.96164
#> 4 response          species    Gentoo / Adelie  2.19156    2.75319  0.62456
#> 5 response          species Gentoo / Chinstrap 400.60647  522.34202  4.59627
#>    Pr(>|z|)      2.5 %    97.5 %
#> 1  0.0011614    0.01093    0.04421
#> 2  0.0014343    0.00408    0.01709
#> 3 2.0906e-06  -0.00578    0.01673
#> 4  0.8066373  -3.20459    7.58770
#> 5 1.2828e-05 -623.16509 1424.37802
        • Interpretation:
          • Holding all covariates constant, for an infinitely small increase in bill length, the probability of being a large penguin increases on average by 2.757%
          • Species contrasts are from {emmeans}(also see Post-Hoc Analysis, emmeans)
            • Contrasts from get_contrasts.R on package github using emmeans::contrast(emmeans_obj, method = "revpairwise")
            • odds ratios
            • You can get response means per category on the probability scale using emmeans::emmeans(mod, "species", type = "response")
    • Other features available for marginaleffects object
      • tidy
        • coef stats with AME, similar to summary, just a different a object class I think
      • glance
        • model GOF stats
      • typical
        • generate artificial predictor values and get marginal effects for them
        • median (or mode depending on variable type) values used for columns that aren’t provided
      • counterfactual
        • use observed data for all but a one or a few columns. Provide values for those column(s) (e.g. flipper_length_mm = c(160, 180))
        • genereates a new larger dataset where each observation has each of the provided values
      • Viz
        • {modelsummary} tables
        • plot (error bar plot for AME) and plot_cme (line plot for interaction term)
          • outputs ggplot objects
  • Categorical Variables
    • The 3 types of marginal effects can be modified for categoricals
    • Steps (“at the means”, binary)
      1. Calculate the predicted probability when the variable = 1 and the other predictors are equal to their means.
      2. Calculate the predicted probability when the variable = 0 and the other predictors are equal to their means.
      3. The difference in predicted probabilities is the marginal effect for a change from the “base level” (aka reference category)
    • This can extended to categorical variables with multiple categories by calculating the pairwise differences between each category and the reference category (contrasts)

Probit

  • “For some reason, econometricians have never really taken on the benefits of the generalized linear modelling framework. So you are more likely to see an econometrician use a probit model than a logistic regression, for example. Probit models tended to go out of fashion in statistics after the GLM revolution prompted by Nelder and Wedderburn (1972).” (Hyndman)
  • Very similar to Logistic
  • Where probit and logistic curves differ
  • link function: Φ-1(p) where Φ is the standard normal CDF:
  • The probability prediction, p:

Visualization

  • Misc

    • Notes from
      • Visualizing logistic regression results for non-technical audiences (github, vignette, video)
    • Tables
      • Appropriate for technical audiences who are familiar with logistic regression
      • Can be taxing on the eyes making it difficult to absorb insights from your model

  • Log Odds Ratio vs Odds Ratio

    • Using log odds ratio is recommended for visualizations for puposes of comparing effect magnitudes
      • Each coefficient represents an additive change on the log odds scale; when we exponentiate to get odds, each coefficient represents a multiplicative change
      • Odds ratios makes the chart asymmetric and squishes some bars (e.g. Pet: Fish)
      • Odds ratios can be misleading in comparing negative vs postive variable effects
        • e.g. Prior GPA looks like it has much bigger effect than Pet: Fish when using odds ratio
      • There’s a danger that the percent change in odds might be misinterpreted as the absolute probability of the outcome (or the change in its probability)
        • e.g. A 300% change in the odds ratio is a tripling of the odds ratio, not an 300% increase in probability. (see example below)
    • Although numerically, changes in odds ratios may be a bit easier to describe for your audience than changes in log odds.
      • Example: tripling the odds ratio is like going from 3-to-1 odds to 9-to-1 odds, or from 1-to-3 odds to an even chance.)

  • Error bar chart (caterpillar chart)

    • Change in log odds
      • These are untransformed parameter estimates (See Misc >> Tables for estimate values)
    • Change in probability from a baseline
      • Since the effect is non-linear (See Interpetation >> Effect is non-linear in p), a baseline is needed in order to properly interpret the changes in probability due to the increases of 1 unit of a variable
      • Process:
        • Choose an appropriate baseline
        • Compute the marginal effect of a predictor given that baseline
      • Intercept as a baseline
        • The x-axis is the problem. Values shown depict changes from the baseline
        • Both charts have the same values, but the chart on the left more clearly indicates the meaning while the chart on the right includes the CIs
        • Estimates transformations
          • Intercept: invlogit(intercept)
          • Other parameters: invlogit(<param> + intercept)
            • Including CI values
        • Vertical line is the output of the inverse logit of the intercept
          • Representing the probability of passing for a student with average prior GPA, average height, and the baseline value of each categorical variable – not a Mac user, doesn’t wear glasses, has no pet, favorite color is blue, and didn’t go to tutoring.

  • Outcome vs Predictor with Logistic Curve

    # Plot of data with a logistic curve fit
  ggplot(data, aes(x = z_homr, y = as.numeric(alive) - 1)) +
    geom_jitter(height = 0.1, size =1, alpha = 0.5) +
    geom_smooth(method = "glm",
                method.args = list(family = "binomial")) +
    theme_minimal() +
    scale_y_continuous(breaks = c(0, 1), labels = c("Alive", "Dead")) +
    ylab("") +
    xlab("HOMR Linear Predictor")
    • Dead/Alive is the outcome and HOMR is the predictor

  • Predictor by outcome (count)

    g1 <- ggplot(data, aes(x = z_homr, fill = alive)) +
    geom_histogram() +
    theme_minimal() +
    xlab("HOMR Linear Predictor") +
    ylab("Number of Participants") +
    scale_fill_brewer("Alive", palette = "Paired")

  • Predictor by outcome (proportion)

    g2 <- ggplot(data, aes(x = z_homr, fill = alive)) +
    geom_histogram(position = "fill") +
    theme_minimal() +
    xlab("HOMR Linear Predictor") +
    ylab("Proportion") +
    scale_fill_brewer("Alive", palette = "Paired")

  • Predictor vs Outcome (beeswarm)

    ggplot(data, aes(y = z_homr, x = alive, fill = alive, color = alive)) +
    geom_beeswarm() +
    geom_boxplot(alpha = 0, color = "black") +
    theme_minimal() +
    ylab("HOMR Linear Predictor") +
    xlab("Alive at 1 year") +
    scale_fill_brewer(guide = FALSE, palette = "Paired") +
    scale_color_brewer(guide = FALSE, palette = "Paired")