Improved conditional evaluation of vignette

vignettes/WeightIt.Rmd

@@ -15,7 +15,7 @@ link-citations: true
 ---
 
 ```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE, eval=T)
+knitr::opts_chunk$set(echo = TRUE, eval=TRUE)
 options(width = 200, digits= 4)
 
 me_ok <- requireNamespace("marginaleffects", quietly = TRUE)
@@ -86,12 +86,13 @@ Indeed, we have achieved perfect balance on the means of the covariates. However
 
 Now that we have our weights stored in `W.out`, let's extract them and estimate our treatment effect. The functions `lm_weightit()` and `glm_weightit()` make it easy to fit (generalized) linear models that account for estimation of of the weights in their standard errors. We can then use functions in `marginaleffects` to perform g-computation to extract a treatment effect estimation from the outcome model.
 
-```{r, message=FALSE, eval = me_ok}
+```{r, message=FALSE}
 # Fit outcome model
 fit <- lm_weightit(re78 ~ treat * (age + educ + race + married +
                                      nodegree + re74 + re75),
                    data = lalonde, weightit = W.out)
-
+```
+```{r, message=FALSE, eval = me_ok}
 # G-computation for the treatment effect
 library("marginaleffects")
 avg_comparisons(fit, variables = "treat",

vignettes/estimating-effects.Rmd

@@ -437,7 +437,7 @@ fit <- lm_weightit(Y_C ~ splines::ns(Ac, df = 4) *
 
 Next we use `avg_predictions()` first to compute the expected potential outcome under a representative set of treatment values. We'll examine 31 treatment values from the 10th to 90th percentiles of `Ac` because estimates outside those ranges tend to be imprecise.
 
-```{r}
+```{r, eval = me_ok}
 #Represenative values of Ac:
 values <- with(d, seq(quantile(Ac, .1),
                       quantile(Ac, .9),
@@ -452,7 +452,7 @@ Although one can examine the expected potential outcomes, it is often more usefu
 
 [^4]: You can also use `plot_predictions()`, though after requesting the predictions in the prior step it is quicker to use `ggplot()`.
 
-```{r, fig.height=3.5, fig.width=7}
+```{r, eval = me_ok, fig.height=3.5, fig.width=7}
 library("ggplot2")
 ggplot(p, aes(x = Ac)) +
   geom_line(aes(y = estimate)) +
@@ -468,7 +468,7 @@ Another way to characterize the effect of continuous treatments is to examine th
 
 [^5]: You can also use `plot_slopes()`
 
-```{r, fig.height=3.5, fig.width=7}
+```{r, eval = me_ok, fig.height=3.5, fig.width=7}
 # Estimate the pointwise derivatives at representative
 # values of Ac
 s <- avg_slopes(fit,
@@ -520,14 +520,14 @@ fit <- glm_weightit(Y_B ~ A_1 * A_2 * A_3 * (X1_0 + X2_0),
 
 Then, we compute the average expected potential outcomes under each treatment regime using `marginaleffects::avg_predictions()`:
 
-```{r}
+```{r, eval = me_ok}
 (p <- avg_predictions(fit,
                       variables = c("A_1", "A_2", "A_3")))
 ```
 
 We can compare individual predictions using `marginaleffects::hypotheses()`. For example, to compare all treatment histories to just the first treatment history (i.e., in which all units are untreated for all time periods), we can run the following:
 
-```{r}
+```{r, eval = me_ok}
 hypotheses(p, "reference")
 ```
 
@@ -563,14 +563,14 @@ fit <- lm_weightit(Y_C ~ A * X5 * (X1 + X2 + X3),
 
 To estimate the subgroup ATEs, we can use `avg_comparisons()`, this time specifying the `by` argument to signify that we want treatment effects stratified by the moderator.
 
-```{r}
+```{r, eval = me_ok}
 avg_comparisons(fit, variables = "A",
                 by = "X5")
 ```
 
 We can see that the subgroup mean differences differ from each other, and we can formally test for moderation using another call to `avg_comparisons()`, this time using the `hypothesis` argument to signify that we want to compare effects between subgroups:
 
-```{r}
+```{r, eval = me_ok}
 avg_comparisons(fit, variables = "A",
                 by = "X5",
                 hypothesis = "pairwise")