Interactions

Misc

Also see
- Post-Hoc Analysis, emmeans >> Interactions
- Introduction: Adding Partial Residuals to Marginal Effects Plots for detecting interactions using residuals of a regression model.
- Diagnostics, Model Agnostic >>
  - DALEX >> Instance Level >> Break-Down >> Example: Assume Interactions
  - DALEX >> Instance Level >> Ceteris Paribus (CP)
  - DALEX >> Dataset Level >> Partial Dependence Plots (PDPs)
  - SHAP >> Interactions and {treeshap}
  - IML >> Interaction PDP and H-Statistic
Resources
- {marginaleffects} book - Docs for the package but also tons of examples; python and r code
  - Experiments, Observational data, Causal inference with G-Computation, Machine learning models, Bayesian modeling, Multilevel regression with post-stratification (MRP), Missing data, Matching, Inverse probability weighting, Conformal prediction
Rule of Thumb: In a balanced experiment, “you need 16 times the sample size to estimate an interaction than to estimate a main effect” in order to measure the interaction effect with the same precision as the main effect. There are contexts where it’s lower depending on the effect size. Gelman
Selecting values of continuous predictors to use for calculating marginal effects
- Mean/Median and quantiles are popular choices
- Check if they’re multi-modal. If so, then the mean or median may not be a useful value to use for that variable when computing marginal effects.
  - Modes for the distribution would be a suitable alternative.
- May also be useful to try to find intervals of values where there is no significant marginal effect.
- Using all combinations of values in the observed data to compute individual marginal effects and then taking the average will result in the average interaction effect
  - Helpful in generating inferences about the whole population of interest instead of scenarios of interest or groups of interest
Gelman and Hill: “Interactions can be important. In practice, inputs that have large main effects also tend to have large interactions with other inputs. (However, small main effects do not preclude the possibility of large interactions.)”
We can probe or decompose interactions by asking the following research questions:
- What is the predicted Y given a particular X and W? (predicted value)
- What is relationship of X on Y at particular values of W? (simple slopes/effects)(marginal effects/predicted means)
- Is there a difference in the relationship of X on Y for different values of W? (comparing marginal effects)
Predictors that are uncorrelated with each other still might have interaction effects on the response variable.
- Example: plant growth requires sunlight and rain. Without rain, sunlight will not increase growth and vice versa, but rain with sunlight does cause growth. This interaction effect exists even though amounts of sunlight and rain aren’t (very) correlated with each other
Quadratic interaction example from {marginaleffects} vignette
Dropping main effects
- For the cont x cat interaction, as long as you don’t drop a categorical main effect, I don’t think it affects the model. It may or may not reparameterize the interaction coefficients so they’re the marginal effects instead contrasts. (see article)
  - e.g. for admit ~ dept + gender:dept , the interpretation of an interaction coefficient might be something like male-female at dept A.
- Whether it’s okay to drop the categorical variable, may depend on whether the main effects are significant or not
- Dropping a continuous main effects changes the model, so best not to ever do that in cat x cont or cont x cont interactions.
- Harrell said in a SO post that you should never do it.
  - He had a link to blog post he’d written but the link was dead and there wasn’t anything in his RMS book.
False linearity assumptions of main effects can inflate apparent interaction effects because interactions may be colinear with the omitted non-linear effect
Interactions may also be non-linear, but shouldn’t do this w/smaller sample sizes
- With smaller sample sizes, make the main effect non-linear and the interaction component linear if the non-linear relationship is present between the interaction and outcome.

Terms

Disordinal Interactions (aka crossover or antagonistic) - Interaction results whose lines do cross. (see fig in Ordinal Interactions)
- When an interaction is significant and “disordinal”, interpretation of main effects is likely to be misleading.
  - To determine exactly which parts of the interaction are significant, the omnibus F test must be followed by more focused tests or comparisons.
Marginal Effects - Partial derivatives of the regression equation with respect to each variable in the model for each unit (i.e. observation) in the data; average marginal effects are simply the mean of these unit-specific partial derivatives over some sample
- The Simple Slope or Simple Effect calculation is the same as the Marginal Effectcalculation from everything I’ve read. The only difference seems to be that the Marginal Effect can pertain a regression model without an interaction while Simple Slopes and Simple Effects are terms only used in describing effects in models with an interaction.
- i.e. the slope of the prediction function, measured at a specific value of the regressor
- In OLS regression with no interactions or higher-order term, the estimated slope coefficients are average marginal effects
- In other cases and for generalized linear models, the coefficients are NOT marginal effects at least not on the scale of the response variable
Moderator Variable (MV) - A predictor that changes the relationship of a IV on the DV, and it can be continuous or categorical. Used in an interaction to estimate its effect on that relationship. (also see Causal Inference >> Moderation Analysis)
Ordinal Interactions - Interaction results whose lines do not cross
- This looks like an EDA plot but also see OLS >> continuous:continuous >> Plot Simple Slopes
  - Similar plot but for post-hoc analysis of a regression with an interaction
- Exponential Interaction: If the lines are not parallel but one line is steeper than the other, the interaction effect will be significant (i.e. detected) only if there’s enough statistical power.
- If the lines are exactly parallel, then there is no interaction effect
Simple Effect - When a Independent Variable interacts with a moderator variable (MV), it’s the differences in predicted values of the outcome variable at a particular level of the MV
- Simple Effect, rather than Simple Slope, is the term used when the moderator is a categorical variable instead of continuous, otherwise they’re essentially the same thing.
  - It may also be the case that “Simple” is only used with the moderator is equal to 0.
- The Simple Slope or Simple Effect calculation is the same as the Marginal Effect calculation from everything I’ve read. The only difference seems to be that the Marginal Effect can pertain a regression model without an interaction while Simple Slopes and Simple Effects are terms only used in describing effects in models with an interaction.
- Each of these predicted values is a Marginal Mean (term used in {emmeans}). So, the Simple Effect would be the difference (aka contrast) of Marginal Means.
  - {marginaleffects} calls these “Adjusted Predictions” or “Marginal Means” but both seem to be the same thing.
- The interaction coefficient is the difference of Simple Effects (aka the “difference in differences”)
- Example:
```
m_int <- lm(Y ~ group * X, data = d)
summary(m_int)
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   833.130    173.749   4.795 6.99e-05 ***
#> groupGb     -1308.898    233.218  -5.612 8.90e-06 ***
#> groupGc      -752.718    307.747  -2.446   0.0222 *  
#> X              -4.041      1.942  -2.081   0.0482 *  
#> groupGb:X      13.041      2.419   5.390 1.55e-05 ***
#> groupGc:X       7.731      3.178   2.432   0.0228 *  
```
  - -4.041 is the Simple Effect of X when group = Ga (i.e. When both dummy variables are fixed at 0, as Ga is the reference level)
  - Also note that the Simple Effect depends on what the reference level of the moderator is, since the coefficient of X is the expected change in Y when the moderator, group = 0, which is the reference level.
Simple Slopes - When a Independent Variable interacts with a moderator variable (MV), it is the slope of that IV at a particular value of the MV.
- Simple Slope, rather than Simple Effect, is used when the moderator is a continuous variable instead of categorical, otherwise they’re essentially the same thing.
  - It may also be the case that “Simple” is only used with the moderator is equal to 0.
- The Simple Slope or Simple Effect calculation is the same as the Marginal Effect calculation from everything I’ve read. The only difference seems to be that the Marginal Effect term can pertain a regression model without an interaction while Simple Slopes and Simple Effects are terms only used in describing effects in models with an interaction.
- The Simple Slope represents a conditional effect
  
  \[ \begin{aligned} \hat Y &= \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_1X_2 \\ &= \beta_0 + (\beta_1 + \beta_3X_2)X_1 + \beta_2X_2 \\ &\begin{aligned}\\ \therefore \quad \text{Slope of}\; X_1 = \beta_1 + \beta_3X_2 \end{aligned} \end{aligned} \]
  - Interpretation: With the moderator, $X_2$, fixed at 0, the Simple Slope, $\beta_1$, is the expected change in $Y$ after 1 unit change in $X_1$ when $X_2 = 0$
    - i.e. The difference in predicted values when $X_1$ increases by one unit while keeping $X_2$ held constant at 0
      # Predict for X1 = [0, 1] and X2 = 0 pred_grid <- data.frame(X1 = c(0, 1), X2 = 0) (predicted_means <- predict(model, newdata = pred_grid)) #> 1 2 #> 3.133346 3.228691 # Conditional slope of X which is the value of the b1 coefficient predicted_means[2] - predicted_means[1] #> 2 #> 0.09534522
Spotlight Analysis: Comparison of marginal effects to see analyze the effect of the variable of interest at different levels of the moderator
- When an interaction is significant, choose representative values for the moderator variable and plot the marginal effects of explanatory variable to decompose the relationship between the variables involved in the interaction.
- Made popular by Leona Aiken and Stephen West’s book Multiple Regression: Testing and Interpreting Interactions (1991).

Preprocessing

Center continuous variables
- Centering fixes collinearity issues when creating powers and interaction terms (CV post)
  - Collinearity between the created terms and the main effects
  - Possibly may not always be the case. See paper, Centering in Multiple Regression Does Not Always Reduce Multicollinearity: How to Tell When Your Estimates Will Not Benefit From Centering
- Interpretation of effects with be at the mean of the explanatory variable
  - e.g. Since X = 0 implies , the intercept, marginal effects and simple effects are interpreted at X = mean(x).
  - Discourages unreasonable extrapolations

Linear

Misc

Notes from Article
If the interaction term is not significant but the main effect is significant then the intercepts (i.e. response means) of at least one pair of the levels DO differ.
- {emmeans::emmeans} can explore and test these types of scenarios
factor(var) might be necessary for character variables with more than one level (to get the contrasts) but isn’t necessary for 0/1 variables (same coefficient estimates with or without).
{emtrends} takes all differences (aka contrasts) from the reference group. So signs may be different from the summary stats values

Continuous:Continuous

Example: Does the effect of workout duration (IV) on weight loss (Outcome) change due to the intensity of your workout (Moderator)?

contcont <- lm(loss ~ hours + effort + hours:effort, data=dat)
#>               Estimate Std. Error t value Pr(>|t|) 
#> (Intercept)   7.79864  11.60362   0.672   0.5017 
#> hours        -9.37568   5.66392  -1.655   0.0982 .
#> effort       -0.08028   0.38465  -0.209   0.8347 
#> hours:effort  0.39335   0.18750   2.098   0.0362 *

(Intercept): The intercept, or the predicted outcome when Hours = 0 and Effort = 0.
hours (Indepenedent Variable (IV)): For a one unit change in hours, the predicted change in weight loss at effort = 0.
effort (Moderator): For a one unit change in effort the predicted change in weight loss at $\text{hours}=0$.
hours:effort: The change in the slope of hours for every one unit increase in effort (or vice versa).

Calculate representative values for the moderator variable

eff_high <- round(mean(dat$effort) + sd(dat$effort), 1) # 34.8
eff <- round(mean(dat$effort), 1) # 29.7
eff_low <- round(mean(dat$effort) - sd(dat$effort), 1) # 24.5

mean, +1 sd, -1 sd

Calculate marginal effects for the IV at 3 representative values for the moderator variable

# via {emmeans}
mylist <- list(effort = c(eff_low, eff, eff_high)) 
emtrends(contcont, ~effort, var = "hours", at = mylist)
#> effort    hours.trend    SE      df     lower.CL upper.CL
#> 24.5      0.261          1.352   896   -2.392    2.91
#> 29.7      2.307          0.915   896    0.511    4.10
#> 34.8      4.313          1.308   896    1.745    6.88
#> Confidence level used: 0.95

# via {marginaleffects}
loss_grid <- datagrid(effort = c(eff_high, eff, eff_low),
                      model = contcont)
slopes(contcont, newdata = loss_grid)
#>   rowid    type      term   dydx      std.error  hours     effort
#> 1 1        response  hours  4.3127872 1.30838659 2.002403  34.8
#> 2 2        response  hours  2.3067185 0.91488236 2.002403  29.7
#> 3 3        response  hours  0.2613152 1.35205286 2.002403  24.5
#> 4 1        response  effort 0.7073625 0.08795998 2.002403  34.8
#> 5 2        response  effort 0.7073625 0.08795998 2.002403  29.7
#> 6 3        response  effort 0.7073625 0.08795999 2.002403  24.5

{emmeans}
- contcont is the model (e.g. lm)
- ~effort tells emtrends to stratify by the moderator variable effort
- df is 896 because we have a sample size of 900, 3 predictors and 1 intercept (df = 900 - 3 -1 = 896)
{marginaleffects}
- Shows how hours is held constant at its mean when it’s marginal effect is calculated at the values specified for effort
- Additionally gives you the marginal effect for effort at the mean of hours
Interpretation:
- When $\text{effort} = 24.5$, a one unit increase in hours results in a 0.26 increase in weight loss on average
- As we increase levels of effort, the effect of hours on weight loss seems to increase
- The marginal effect of hours is significant only for mean effort levels and above (i.e. CIs for mean and high effort don’t include 0)

Plot marginal effects for the IV (hours)

# via {emmeans}
mylist <- list(hours = seq(0,4, by = 0.4), effort = c(eff_low, eff, eff_high))
emmip(contcont, effort ~ hours, at = mylist, CIs = TRUE)

# via {marginaleffects}
plot_predictions(contcont, condition = c("hours", "effort")) # creates it's own subset of values for effort

Predicted Outcome vs IV (newdata) by Moderator (new data)
- For each value of effort, the slope of the hours line at the mean of hours (for that effort level) is the value of the hours marginal effect
Interpretation:
- Suggests that hours spent exercising is only effective for weight loss if we put in more effort. (i.e. as effort increases so does the effect of hours)
- At the highest levels of effort, we achieve higher weight loss for a given time input.

ggplot

# via {emmeans}
contcontdat <- emmip(contcont,effort~hours,at=mylist, CIs=TRUE, plotit=FALSE)
contcontdat$feffort <- factor(contcontdat$effort)
levels(contcontdat$feffort) <- c("low","med","high")
ggplot(data=contcontdat, aes(x=hours,y=yvar, color=feffort)) +
    geom_line() +
    geom_ribbon(aes(ymax=UCL, ymin=LCL, fill=feffort), alpha=0.4) +
    labs(x="Hours", y="Weight Loss", color="Effort", fill="Effort")

# via {marginaleffects}
moose <- predictions(contcont, newdata = datagrid(hours = seq(0,4, by = 0.4), 
                                                  effort = c(eff_low, eff, eff_high),
                                                  grid.type = "counterfactual"))
ggplot(data=moose, aes(x=hours,y=predicted, color=factor(effort))) +
  geom_line() +
  geom_ribbon(aes(ymax=conf.high, ymin=conf.low, fill=factor(effort)), alpha=0.4) +
  labs(x="Hours", y="Weight Loss", color="Effort", fill="Effort")

Test pairwise differences (contrasts) of marginal effects
```
emtrends(contcont, pairwise ~effort, var="hours",at=mylist)
#> $contrasts
#>    contrast    estimate     SE   df t.ratio p.value
#> 24.5 - 29.7       -2.05  0.975  896  -2.098  0.0362 
#> 24.5 - 34.8       -4.05  1.931  896  -2.098  0.0362 
#> 29.7 - 34.8       -2.01  0.956  896  -2.098  0.0362 
#> Results are averaged over the levels of: hours
```
- {marginaleffects} doesn’t do this from what I can tell and IMO looking at contrasts of simple effects (next section) is a better method
- {emmeans}
  - pairwise ~effort tells the function that we want pairwise differences of the marginal effects of var=“hours” for each level of effort specified “at=mylist”
    - hours not specified in mylist just the 3 values for effort
  - The results are with the arg, adjust=“none”, which removes the Tukey multiple test adjustment. Removed it from the code, because it’s bad practice.
- Interpretation
  - A one unit change in hours when effort is high (34.8) results is 4 lbs more weight loss than a one unit change in hours when effort is low (24.5)
  - Based on the NHST, all the marginal effects are significantly different from each other
- For continuous:continuous, p-values will all be the same and identical the interaction p-value of the model (has to do with the slope formula)
  - Testing the simple effects contrasts (see below) may be the better option

Test pairwise differences (contrasts) of Simple Slopes

# via {emmeans}
mylist <- list(hours = 4, effort = c(eff_low, eff_high))
emmeans(contcont, pairwise ~ hours*effort, at=mylist)
#> $emmeans
#> hours  effort  emmean   SE   df lower.CL upper.CL
#>     4    24.5    6.88  2.79 896      1.4     12.4
#>     4    34.8   22.26  2.68 896     17.0     27.5
#> Confidence level used: 0.95 
#> $contrasts
#>    contrast        estimate    SE   df t.ratio p.value
#> 4,24.5 - 4,34.8       -15.4  3.97  896  -3.871  0.0001


# via {marginaleffects}
comparisons(contcont,
            variables = list(effort = c(eff_low, eff_high)),
            newdata = datagrid(hours = 4)) %>% 
  tidy()
#> rowid    type        term   contrast_effort comparison std.error statistic      p.value conf.low conf.high   effort hours
#> 1    1 response interaction     34.8 - 24.5   15.37904   3.97295  3.870938 0.0001084175 7.592203  23.16588 29.65922     4

# alternate (difference in results could be due to rounding of eff_low, eff_high)
comparisons(contcont,
            variables = list(effort = "2sd"),
            newdata = datagrid(hours = 4, 
                              grid.type = "counterfactual")) %>% 
  tidy()
#>       type        term     contrast_effort estimate std.error statistic      p.value conf.low conf.high
#> 1 response interaction (x + sd) - (x - sd) 15.35743  3.967367  3.870938 0.0001084175 7.581535  23.13333

One reason to look at the tests for the predicted means contrast instead of the slope contrasts is the constant p-value across slope contrasts (see above)
If hours isn’t specified emmeans holds hours at the sample mean.
- Using {marginaleffects} and holding hours at sample mean (and probably every other variable in the model), comparisons(contcont, variables = list(effort = "2sd")) %>% tidy()
Specification
- hours has a fixed value and the moderator is allowed to vary between high and low effort
- emmeans
  - pairwise ~ hours*effort to tell the function that we want pairwise contrasts of each distinct combination of [hours{.var-text} and effort specified at=mylist
- marginaleffects
  - effort = “2d” says compare (effort + 1 sd) to (effort - 1 sd)
  - grid.type = “counterfactual” because there is no observation in the data with hours = 4
Interpretation
- Based on the NHST, the difference between the predicted means of high and low effort are significantly different from each other when hours = 4
- After 4 hours of exercise, high effort (34.8) will result in about 15 pounds more weight loss than low effort (24.5) on average.

Continuous:Binary

The change in the slope of the continuous (IV) after a change from the reference level to the other level in the binary (Moderator)
- Note that this value is an additional change to the effect of the continuous (i.e when binary = 1, the effect = continuous effect + interaction effect)
Significant means that the factor variable does increase/decrease the effect of the continuous variable

Example: Does the effect of workout duration in hours (IV) on weight loss (Outcome) change because you are a male or female (Moderator)?

dat <- dat %>% 
  mutate(gender = factor(gender, labels = c("male", "female")) # male = 1, female = 2,
        gender = relevel(gender, ref = "female")
  )
contcat <- lm(loss ~ hours * gender, data=dat)
#>                  Estimate Std. Error t value Pr(>|t|) 
#> (Intercept)         3.335      2.731   1.221    0.222 
#> hours               3.315      1.332   2.489    0.013 *
#> gendermale          3.571      3.915   0.912    0.362 
#> hours:gendermale   -1.724      1.898  -0.908    0.364

Intercept ($\beta_0$): Predicted weight loss when Hours = 0 in the reference group of Gender, which is females.
hours ($\beta_1$, Independent Variable): The marginal effect of hours for the reference group (e.g. females).
- hours marginal effect for males is $\beta_1 + \beta_3$ or emtrends(contcat, ~ gender, var="hours") will give both marginal effects
gendermale ($\beta_2$, Moderator) (reference group: female): The difference in the effect of being male compared to female at hours = 0.
hours:gendermale ($\beta_3$): The difference in the marginal effects of hours for males to hours for females.
- emtrends can also give the summary stats for the interaction (sign different in this case, since difference is from the reference group, female)
```
hours_slope <- emtrends(contcat, pairwise ~ gender, var="hours")
hours_slope$contrasts
#> contrast      estimate   SE   df t.ratio p.value
#> female - male     1.72  1.9  896   0.908  0.3639
```

Marginal effects of continuous (IV) by each level of the categorical (moderator)

# via {emmeans}
emtrends(contcat, ~ gender, var="hours")
#> gender  hours.trend    SE    df  lower.CL  upper.CL
#> female         3.32  1.33  896      0.702      5.93
#> male           1.59  1.35  896     -1.063      4.25

# via {marginaleffects}
slopes(contcat) %>%
    tidy(by = "gender")
#>       type  term        contrast gender   estimate std.error statistic    p.value   conf.low conf.high
#> 1 response  hours          dY/dX   male 1.59113665 1.3523002 1.1766150 0.23934921 -1.0593230  4.241596
#> 2 response  hours          dY/dX female 3.31506746 1.3316491 2.4894453 0.01279426  0.7050833  5.925052
#> 3 response gender  male - female   male 0.09616813 0.9382760 0.1024945 0.91836418 -1.7428191  1.935155
#> 4 response gender  male - female female 0.14194405 0.9382968 0.1512784 0.87975610 -1.6970840  1.980972

{emmeans}
- ~gender says obtain separate estimates for females and males
- var=“hours” says obtain marginal effects (or trends) for hours
{marginaleffects}
- Can also use summary(by = "gender")
Interpretation
- hours by female is the same as the model coefficient above.
  - See model interpretation above

Simple effects at values of the moderator

# via {emmeans}
mylist <- list(hours=c(0,2,4),gender=c("male","female"))
emcontcat  <- emmeans(contcat, ~ hours*gender, at=mylist)
contrast(emcontcat, "pairwise",by="hours")
#> hours = 0:
#> contrast      estimate    SE  df t.ratio p.value
#> male - female    3.571 3.915 896   0.912  0.3619 
#> hours = 2:
#> contrast      estimate    SE  df t.ratio p.value
#> male - female    0.123 0.938 896   0.131  0.8955 
#> hours = 4:
#> contrast      estimate    SE  df t.ratio p.value
#> male - female  -3.325 3.905 896   -0.851  0.3948

# via {marginaleffects}
comparisons(contcat,
            variables = "gender",
            newdata = datagrid(hours = c(0, 2, 4)))
#>   rowid    type        term contrast_gender comparison std.error  statistic   p.value   conf.low conf.high gender hours
#> 1    1 response interaction   male - female  3.5710607  3.914762  0.9122038 0.3616614  -4.101731 11.243853   male     0
#> 2    2 response interaction   male - female  0.1231991  0.937961  0.1313478 0.8955002  -1.715171  1.961569   male     2
#> 3    3 response interaction   male - female -3.3246625  3.905153 -0.8513527 0.3945735 -10.978622  4.329297   male     4

The moderator is now hours
Note that output for hours = 0, the effect 3.571 matches the $\beta_2$ coefficient
Interpretation
- As hours increases, the male versus female difference becomes more negative (females are losing more weight than males)

Plotting the continuous by binary interaction
```
# via {emmeans}
mylist <- list(hours=seq(0,4,by=0.4),gender=c("female","male"))
emmip(contcat, gender ~hours, at=mylist,CIs=TRUE)
```
- For ggplot, see continuous:continuous >> Plot marginal effects for IV section (Should be similar code)
- Interpretation
  - Although it looks like there’s a cross-over interaction, the large overlap in confidence intervals suggests that the slope of hours is not different between males and females
    - Confirmed by looking at the standard error of the interaction coefficient (1.898) is large relative to the absolute value of the coefficient itself (1.724)

Categorical:Categorical

From {marginaleffects} Contrasts vignette, “Since derivatives are only properly defined for continuous variables, we cannot use them to interpret the effects of changes in categorical variables. For this, we turn to contrasts between adjusted predictions (aka Simple Effects).”
- i.e. Contrasts of marginal effects aren’t possible
2x3 cat:cat interaction

Example: Do males and females (IV) lose weight (Outcome) differently depending on the type of exercise (Moderator) they engage in?

dat <- dat %>% 
  mutate(gender = factor(gender, labels = c("male", "female")),
        gender = relevel(gender, ref = "female"),
        prog = factor(prog, labels = c("jog","swim","read")),
        prog = relevel(prog, ref="read")
  )
catcat <- lm(loss ~ gender * prog, data = dat)
#>                     Estimate Std. Error t value Pr(>|t|)   
#> (Intercept)          -3.6201     0.5322  -6.802 1.89e-11 ***
#> gendermale           -0.3355     0.7527  -0.446    0.656   
#> progjog               7.9088     0.7527  10.507  < 2e-16 ***
#> progswim             32.7378     0.7527  43.494  < 2e-16 ***
#> gendermale:progjog    7.8188     1.0645   7.345 4.63e-13 ***
#> gendermale:progswim  -6.2599     1.0645  -5.881 5.77e-09 ***

Description
- Intercept ($\beta_0$): The intercept, or the predicted weight loss when gendermale = 0, progjog = 0, and progswim = 0
  - i.e. The intercept for females in the reading program
- gendermale ($\beta_1$): The simple effect of males when progjog = 0 and progswim = 0
  - i.e. The male – female weight loss in the reading group
- progjog ($\beta_2$): the simple effect of jogging when gendermale = 0
  - i.e. The difference in weight loss between jogging versus reading for females
- progswim ($\beta_3$): The simple effect of swimming when gendermale = 0
  - i.e. The difference in weight loss between swimming versus reading for females
- gendermale:progjog ($\beta_4$): The male effect (male – female) in the jogging condition versus the male effect in the reading condition.
  - Also, the jogging effect (jogging – reading) for males versus the jogging effect for females.
- gendermale:progswim ($\beta_5$): the male effect (male – female) in the swimming condition versus the male effect in the reading condition.
  - Also, the swimming effect (swimming – reading) for males versus the swimming effect for females.
- gender is the independent variable
- Prog is the moderator
- Reference Groups
  - prog == read
  - gender == female
- $\beta_1 + \beta_4$: The marginal effect of gender == males in the prog == jogging group.
- $\beta_1 + \beta_5$: The marginal effect of gender == males in the prog == swimming group.

Marginal Means for all combinations of the interaction variable levels

# via {emmeans}
emcatcat <- emmeans(catcat, ~ gender*prog)
#> gender  prog  emmean     SE   df lower.CL upper.CL
#> female  read   -3.62  0.532  894    -4.66    -2.58
#>   male  read   -3.96  0.532  894    -5.00    -2.91
#> female   jog    4.29  0.532  894     3.24     5.33
#>   male   jog   11.77  0.532  894    10.73    12.82
#> female  swim   29.12  0.532  894    28.07    30.16
#>   male  swim   22.52  0.532  894    21.48    23.57

# via {marginaleffects}
predictions(catcat, variables = c("gender", "prog"))
#>   rowid    type predicted std.error  conf.low conf.high gender prog
#> 1    1 response 11.772028 0.5322428 10.727437 12.816619   male  jog
#> 2    2 response 22.522383 0.5322428 21.477792 23.566974   male swim
#> 3    3 response -3.955606 0.5322428 -5.000197 -2.911016   male read
#> 4    4 response  4.288681 0.5322428  3.244091  5.333272 female  jog
#> 5    5 response 29.117691 0.5322428 28.073100 30.162282 female swim
#> 6    6 response -3.620149 0.5322428 -4.664740 -2.575559 female read

Marginal means generated using the regression coefficients
- Kinda weird. Since these are predicted means (i.e. outcome values), I wouldn’t have thought they could be generated by the model coefficients. ¯\_(ツ)_/¯
Interpretation
- females in the reading program have an estimated weight gain of 3.62 pounds
- males in the swimming program have an average weight loss of 22.52 pounds

Simple Effects of the IV for all values of the moderator variable

# via {emmeans}
contrast(emcatcat, "revpairwise", by="prog") # see above code chunk for emcatcat
#> prog = read:
#> contrast      estimate    SE  df t.ratio  p.value
#> male - female   -0.335 0.753 894  -0.446   0.6559 
#> prog = jog:
#> contrast      estimate    SE  df t.ratio  p.value
#> male - female    7.483 0.753 894   9.942   <.0001 
#> prog = swim:
#> contrast      estimate    SE  df t.ratio  p.value
#> male - female   -6.595 0.753 894  -8.762   <.0001

# via {marginaleffects}
comparisons(catcat, 
            variables = "gender", 
            newdata = datagrid(prog = c("read", "jog", "swim")))
#>   rowid    type        term contrast_gender comparison std.error  statistic      p.value  conf.low conf.high gender prog
#> 1    1 response interaction   male - female -0.3354569 0.7527049 -0.4456686 6.558367e-01 -1.810731  1.139818   male read
#> 2    2 response interaction   male - female  7.4833463 0.7527049  9.9419388 2.734522e-23  6.008072  8.958621   male  jog
#> 3    3 response interaction   male - female -6.5953078 0.7527049 -8.7621425 1.915739e-18 -8.070582 -5.120033   male swim

{emmeans}
- revpairwise rather than pairwise because by default the reference group (female) would come first
- The results are with the arg, adjust=“none”, which removes the Tukey multiple test adjustment. Removed it from the code, because it’s bad practice.
Interpretation
- male effect for jogging and swimming are significant at the 0.05 level (with no adjustment of p-values), but not for the reading condition.
- males lose more weight in the jogging condition (positive) but females lose more weight in the swimming condition (negative)

Plotting the categorical by categorical interaction

{emmeans} emmip(catcat, prog ~ gender,CIs=TRUE)

{ggplot2}

catcatdat <- emmip(catcat, gender ~ prog, CIs=TRUE, plotit=FALSE)
#>   gender prog      yvar        SE  df       LCL       UCL   tvar xvar
#> 1 female read -3.620149 0.5322428 894 -4.664740 -2.575559 female read
#> 2   male read -3.955606 0.5322428 894 -5.000197 -2.911016   male read
#> 3 female  jog  4.288681 0.5322428 894  3.244091  5.333272 female  jog
#> 4   male  jog 11.772028 0.5322428 894 10.727437 12.816619   male  jog
#> 5 female swim 29.117691 0.5322428 894 28.073100 30.162282 female swim
#> 6   male swim 22.522383 0.5322428 894 21.477792 23.566974   male swim

# via {marginaleffects}
predictions(catcat, variables = c("gender", "prog"))

ggplot(data=catcatdat, aes(x=prog,y=yvar, fill=gender)) +
    geom_bar(stat="identity",position="dodge") +
    geom_errorbar(position=position_dodge(.9),width=.25, aes(ymax=UCL, ymin=LCL),alpha=0.3)
    labs(x="Program", y="Weight Loss", fill="Gender")

I think I like the emmeans plot better
Values come from the Marginal Means for all combinations of the interaction variable levels section above

Logistic Regression

Misc

* categorical:categorical and continuous:continuous should be should be similar to analysis for binary:continuous and the code/analysis in the OLS section *
- For more details on these other interaction types see the article linked below
Misc
- Notes from
  - How Can I Understand A Categorical By Continuous Interaction In Logistic Regression
  - Deciphering Interactions In Logistic Regression
    - See this article for continuous:continuous, cat:cat
- Also see
  - Regression, Logistic >> Marginal Effects
  - Post-Hoc Analysis, emmeans >> Logistic Regression
- Reminder: OR significance is being different from 1 and log odds ratios (logits) significance is being different from 0
- Although the interaction is quantified appropriately by the interaction coefficient when interpreting effects on the scale of log-odds or log-counts, the interaction effect will not reduce to the coefficient of the the interaction coefficient when describing effects on the natural response scale (i.e. probabilities, counts) because of the inverse-link transformation.
  - In order to get e.g. probabilities, the inverse-link function must be used, and this complicates the derivatives of the marginal effect calculation. The chain rule must be appied. This means the interaction effect is no longer equivalent to the interaction coefficient.
  - Therefore, effects or contrasts are differences in marginal effects rather than coefficients
    - Interaction effects represent change in a marginal effect of one predictor for a change in another predictoreraction effects represent change in a marg
- Standard errors of point estimate values of interaction effects (as well as their corresponding statistical significance) may also vary as a function of the predictors.
- Need to decide whether the interaction is to be conceptualized in terms of log odds (logits) or odds ratios or probability
  - An interaction that is significant in log odds may not be significant in terms of probability or vice versa
    - Even if they do agree, the p-values do not have the be the same. (See below, binary:continuous >> Simple Effects >> second example >> probabilities)

Binary:Continuous

Example:

Description

dat_clean <- dat %>% 
  mutate(f = factor(f, labels = c("female", "male")))
concat <- glm(y ~ f*s, family = binomial, data = dat_clean)
summary(concat)
#>             Estimate  Std. Error  z value  Pr(>|z|)   
#> (Intercept) -9.25380     1.94189   -4.765  1.89e-06 ***
#> fmale        5.78681     2.30252    2.513    0.0120 * 
#> s            0.17734     0.03644    4.867  1.13e-06 ***
#> fmale:s     -0.08955     0.04392   -2.039    0.0414 *

y: Binary Outcome
s (IV): Continuous Variable
f (moderator): Gender variable with female as the reference
cv1: Continuous Adjustment Variable

Marginal effects of continuous (IV) by each level of the categorical (moderator)

# via {marginaleffects}
slopes(concat, variables = "s") %>%
    tidy(by = "f")
#>       type term       f   estimate   std.error statistic     p.value   conf.low  conf.high
#> 1 response    s    male 0.01473352 0.003237072  4.551498 5.32654e-06 0.00838898 0.02107807
#> 2 response    s  female 0.02569069 0.001750066 14.679842 0.00000e+00 0.02226062 0.02912075

Interpetation
- Increasing s by 1 unit given that the person is male will result in around a 1% increase on average in the probability of the event, y.

Simple effects at values of the IV
```
# via {marginaleffects}
comp <- comparisons(concat2, 
                    variables = "f", 
                    newdata = datagrid(s = seq(20, 70, by = 2)))
head(comp)
#>   rowid    type        term    contrast_f comparison  std.error statistic    p.value     conf.low conf.high    f  s
#> 1    1 response interaction male - female  0.1496878 0.09871431  1.516374 0.12942480 -0.043788674 0.3431643 male 20
#> 2    2 response interaction male - female  0.1724472 0.10430544  1.653291 0.09827172 -0.031987695 0.3768821 male 22
#> 3    3 response interaction male - female  0.1975119 0.10886095  1.814351 0.06962377 -0.015851616 0.4108755 male 24
#> 4    4 response interaction male - female  0.2246979 0.11209440  2.004542 0.04501204  0.004996938 0.4443989 male 26
#> 5    5 response interaction male - female  0.2536377 0.11376972  2.229395 0.02578761  0.030653136 0.4766222 male 28
#> 6    6 response interaction male - female  0.2837288 0.11375073  2.494303 0.01262048  0.060781436 0.5066761 male 30
```
- Shows the predicted value contrasts for the moderator at different values of the independent variable.
  - concat2 model defined in “Simple Effects at values of the moderator and adjustment variable” (see below)
- For comparisons , the default is type = “response” so the output is probabilities. (type = “link” for log-ORs aka logits)
- Interpretation
  - For s = 26, 28, and 30, there is substantial evidence that there are differences in male and female probabilites of the event, y.
- Example: (article)
  - Description
    - lowbwt: Binary Outcome, low birthweight = 1
    - age (IV): Continuous, Age of the mother
    - ftv (moderator): Binary, whether or not the mother made frequent physician visits during the first trimester of pregnancy
  - Simple Effects: Odds Ratios
    - ORftv is the odds-ratio contrast of (ftv = 1) - (ftv = 0) at the specified age of the mother
    - Interpretation
      - A mother at the age of 30 who visits the physician frequently has 0.174 times the odds of having a low birth weight baby as compared to those of the same age who don’t visit the doctor frequently
      - For women whose ages are between 17 and 24, the 95% confidence intervals of the odds ratios include the null value of 1, so we do not have strong evidence of an association between frequent doctor visits and low birth weight for that age range.
      - For mothers aged 25 years and older, the odds of having a low birth weight baby significantly decrease if the mother frequently visits her physician.
  - Simple Effects: Probabilities
    - “Difference in probability” is the probability contrast of (ftv = 1) - (ftv = 0) at the specified age of the mother
    - Note that the results agree with the OR results, but the pvals are different
      - So it’s probably possible that in terms of “significance,” you could get different conclusions based on the metric used.
    - Interpretation
      - For young mothers (less than 24 years old), we do not have strong evidence of an association between low birth weight and frequent physician [visits]{.var-text.
      - For mothers aged 25 years and older, we reject the null hypothesis of no difference in probability between those with frequent physician visits and those without.
      - Overall, the difference between the probability of low birth weight comparing those with frequent visits to those without increases as the mother’s age increases.
Plot the simple effects
```
ggplot(comp, aes(x = s, y = comparison)) + 
  geom_line() +
  geom_ribbon(aes(ymax=conf.high, ymin=conf.low), alpha=0.4) + 
  geom_hline(yintercept=0, linetype="dashed") +
  labs(title = "Male-Female Difference", y="Probability Contrast")
```
- {marginaleffects}
  - You change the comparisons (see above) type arg to type = link to get log-ORs (aka logits) instead of probabilities and then plot
- Interpretation
  - The difference in probabilities for male and females is statistically significant between values of s approximately between 28 to 55.

Simple Effects at values of the moderator and adjustment variable

concat2 <- glm(y ~ f*s + cv1, family = binomial, data = dat_clean)
comp2 <- comparisons(concat2, 
                    variables = "f", 
                    newdata = datagrid(s = seq(20, 70, by = 2),
                                      cv1 = c(40, 50, 60)))
head(comp2)
#>   rowid    type        term    contrast_f comparison  std.error statistic      p.value    conf.low conf.high    f  s cv1
#> 1    1 response interaction male - female  0.2307214 0.15001700  1.537968 1.240564e-01 -0.06330656 0.5247493 male 20  40
#> 2    2 response interaction male - female  0.6603841 0.20879886  3.162777 1.562722e-03  0.25114590 1.0696224 male 20  50
#> 3    3 response interaction male - female  0.9135206 0.07865839 11.613773 3.507873e-31  0.75935304 1.0676882 male 20  60
#> 4    4 response interaction male - female  0.2361502 0.14294726  1.652009 9.853270e-02 -0.04402131 0.5163217 male 22  40
#> 5    5 response interaction male - female  0.6663811 0.19447902  3.426494 6.114278e-04  0.28520927 1.0475530 male 22  50
#> 6    6 response interaction male - female  0.9097497 0.07571970 12.014703 2.974362e-33  0.76134178 1.0581575 male 22  60

Plot simple effects and facet by values of the adjustment variable

ggplot(comp2, aes(x = s, y = comparison, group = factor(cv1))) + 
  geom_line() +
  geom_ribbon(aes(ymax=conf.high, ymin=conf.low), alpha=0.4) + 
  geom_hline(yintercept=0, linetype="dashed") +
  facet_wrap(~ cv1) +
  labs(title = "Male-Female Difference", y="Probability Contrast")

Interpretation
- It seems clear from looking at the three graphs that the male-female difference in probability increases as cv1 increases except for high values of s.

Poisson/Neg.Binomial

Misc
- Notes from Paper Interpreting interaction effects in generalized linear models of nonlinear probabilities and counts
- Although the interaction is quantified appropriately by the interaction coefficient when interpreting effects on the scale of log-odds or log-counts, the interaction effect will not reduce to the coefficient of the the interaction coefficient when describing effects on the natural response scale (i.e. probabilities, counts) because of the inverse-link transformation.
  - In order to get e.g. probabilities, the inverse-link function must be used, and this complicates the derivatives of the marginal effect calculation. The chain rule must be appied. This means the interaction effect is no longer equivalent to the interaction coefficient.
  - Therefore, effects or contrasts are differences in marginal effects rather than coefficients
    - Interaction effects represent change in a marginal effect of one predictor for a change in another predictoreraction effects represent change in a marg
- Standard errors of point estimate values of interaction effects (as well as their corresponding statistical significance) may also vary as a function of the predictors.
Types
- Continuous:Continuous: The rate-of-change in the marginal effect of one predictor given a change in another predictor
  - Also, the amount the effect of $x_1$ on $\mathbb{E}[Y\;|\;x]$ changes for every one-unit increase in $x_2$ (and vice versa), holding all else constant.
- Continuous:Discrete: The Difference in the marginal effect of the continuous predictor between two selected values of the discrete predictor
  - Example: Poisson outcome (population)
    - $x_1$ is continuous, $x_2$ is continuous, $x_3$ is binary (e.g. sex where female = 1).
    - The interaction effect, $\gamma^2_{13}$, of $x_1x_3$ is the difference between the marginal effect of $x_1$ for females versus males
    - Note that $x_2$ is included in the effect calculation for an interaction it isn’t part of
    - Specific values for $x_1$ and $x_2$ must be chosen to the calculate the value of the effect
      - For $x_2$, the 25^th, 50^th, 75^th quartiles are used and $x_1$ would be held at one value of substantive interest (e.g. mean)
      - Standard errors via delta method for all 3 values can be computed along with p-values
    - Visual
      - $x_2$ is held at its mean, and various values of $x_1$ are used
      - Interpretation: the expected population (count of $Y$) is larger among females than males across all values of $x_1$ with $x_2$ held at its mean.
- Discrete:Discrete: The difference between the model evaluated at two categories of one variable (a, b) minus the difference in this model evaluated at two categories of another variable (c, d) (aka discrete double difference)