From 117334a9eedded2a5786d0b0228fc0d784245874 Mon Sep 17 00:00:00 2001
From: John Kirkpatrick <133956382+Puzzled-Face@users.noreply.github.com>
Date: Mon, 7 Oct 2024 09:18:28 +0100
Subject: [PATCH] 852 update to pkgdown in the ci workflow generates new
 warnings and errors (#853)

* alt text for figures in the trial_definition vignette

* alt text for trial_analysis.Rmd

* alt text for rolling-crm

* Add remaining fig.alt texts

* Update pkgdown.yml to fix new CI warnings.  Add CRAN link.

* [skip style] [skip vbump] Restyle files

---------

Co-authored-by: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
---
 _pkgdown.yaml                   |   5 ++
 vignettes/crmPack-jss-paper.Rmd |  19 +++---
 vignettes/example.Rmd           | 109 ++++++++++++++++----------------
 vignettes/ordinal-crm.Rmd       |  10 ++-
 vignettes/rolling-crm.Rmd       |  17 +++--
 vignettes/trial_analysis.Rmd    |  20 +++---
 vignettes/trial_definition.Rmd  |   2 +-
 7 files changed, 103 insertions(+), 79 deletions(-)

diff --git a/_pkgdown.yaml b/_pkgdown.yaml
index 59cc7f067..a516f7e12 100644
--- a/_pkgdown.yaml
+++ b/_pkgdown.yaml
@@ -20,7 +20,12 @@ navbar:
           href: unit-test-report/
     github:
       icon: fa-github
+      aria-label: View on GitHub
       href: https://github.com/openpharma/crmPack
+    cran:
+      icon: fab fa-r-project
+      href: https://cloud.r-project.org/package=crmPack
+      aria-label: View on CRAN
 
 articles:
   - title: Articles
diff --git a/vignettes/crmPack-jss-paper.Rmd b/vignettes/crmPack-jss-paper.Rmd
index 464e68c64..44c4f9532 100644
--- a/vignettes/crmPack-jss-paper.Rmd
+++ b/vignettes/crmPack-jss-paper.Rmd
@@ -100,7 +100,7 @@ For describing the framework of the package we will adapt the general notation
 for early phase trials from [@Thall2010]. Figure \@ref(fig:schematic) summarizes
 the framework in a schematic.
 
-```{r schematic, fig.align='center', out.height = "540px", out.width="700px", fig.cap='Schematic of the framework'}
+```{r schematic, fig.align='center', out.height = "540px", out.width="700px", fig.cap='Schematic of the framework', fig.alt = "Schematic showing the general framework of an early phase dose escalation design.", echo = FALSE}
 knitr::include_graphics("./schematic.png")
 ```
 
@@ -280,8 +280,11 @@ optionally in the `ID` argument. The data can then be visualized by simply apply
 the `plot` function to the object, which also allows to produce a blinded plot (hiding patient
 IDs and placebo/treatment assignment) with the option `blind`, see Figure \@ref(fig:plot-data):
 
-```{r plot-data,fig.show='hold',fig.width=5,fig.height=5,out.width='.35\\linewidth',fig.align='center', fig.env='figure', fig.cap='Open and blinded data plots'}
+```{r plot-data,fig.width=5,fig.height=5,out.width='.35\\linewidth',fig.align='center', fig.env='figure', fig.cap='Open and blinded data plots', fig.alt = "An unblinded graph with patient id on the x axis and dose administered on the y axis.  Red triangles indicate patients who reported DLTs, black circles those who did not.  Symbols are annotated with patient IDs.  Vertical dashed green lines delineate cohorts."}
 plot(data)
+```
+
+```{r plot-data-blind,fig.width=5,fig.height=5,out.width='.35\\linewidth',fig.align='center', fig.env='figure', fig.cap='Open and blinded data plots', fig.env='figure', fig.cap='Open and blinded data plots', fig.alt = "A blinded graph with patient id on the x axis and dose administered on the y axis.  Red triangles indicate reports of DLTs, black circles indicate an absense of DLTs.  However, within each cohort, patients with DLTs appear before those without.  Symbols are not annotated with patient IDs.  Vertical dashed green lines delineate cohorts."}
 plot(data, blind = TRUE)
 ```
 
@@ -303,7 +306,7 @@ DLT rates can be obtained by supplying
 the samples, model and data to the generic `plot` function. Similarly we can
 also produce a similar plot without any data, which is then giving the prior, see Figure \@ref(fig:plot-model-fit):
 
-```{r plot-model-fit,fig.show='hold',fig.width=5,fig.height=5,out.width='.35\\linewidth',fig.align='center', fig.env='figure', fig.cap='Posterior and prior regression model fits'}
+```{r plot-model-fit,fig.show='hold',fig.width=5,fig.height=5,out.width='.35\\linewidth',fig.align='center', fig.env='figure', fig.cap='Posterior and prior regression model fits', fig.alt = "Prior and posterior dose-toxicity curves."}
 plot(samples, model, data) + ggtitle("Posterior")
 
 emptydata <- Data(doseGrid = data@doseGrid, placebo = TRUE)
@@ -346,7 +349,7 @@ nextDoseRes <- nextBest(myNextBest, nextMaxDose, samples, model, data)
 
 The returned list also contains an accompanying plot (`nextDoseRes$plot`), see Figure \@ref(fig:nextBest-ncrm).
 
-```{r nextBest-ncrm,echo=FALSE,fig.width=4,fig.height=4,out.width='.5\\linewidth',fig.align='center',fig.env='figure', fig.cap='Dose recommendation plot from NCRM design'}
+```{r nextBest-ncrm,echo=FALSE,fig.width=4,fig.height=4,out.width='.5\\linewidth',fig.align='center',fig.env='figure', fig.cap='Dose recommendation plot from NCRM design', fig.alt = "A graphical description of the logic between the recommendation of the nextBestRes object."}
 nextDoseRes$plot
 ```
 
@@ -417,7 +420,7 @@ mySims@data[[3]]@nObs
 mySims@doses[3]
 ```
 Furthermore, we can plot the `Simulations` object by calling the `plot` method on it, see Figure \@ref(fig:sim-plot). You can select the plots by changing the `type` argument of `plot`, which by default is `type = c("trajectory", "dosesTried")`.
-```{r sim-plot, echo=FALSE, fig.width=4,fig.height=4,out.width='.5\\linewidth',fig.align='center',fig.env='figure', fig.cap='Simulation plot'}
+```{r sim-plot, echo=FALSE, fig.width=4,fig.height=4,out.width='.5\\linewidth',fig.align='center',fig.env='figure', fig.cap='Simulation plot', fig.alt = "A graphical representation of the simulation results."}
 plot(mySims)
 ```
 
@@ -428,7 +431,7 @@ simSum <- summary(mySims, truth = myTruth)
 simSum
 ```
 A plot of the summary results can also be produced, see Figure \@ref(fig:sim-summary-plot).
-```{r sim-summary-plot, echo=FALSE, fig.width=6,fig.height=6,out.width='.6\\linewidth',fig.align='center', warning=FALSE, fig.env='figure', fig.cap='Simulation summary plot'}
+```{r sim-summary-plot, echo=FALSE, fig.width=6,fig.height=6,out.width='.6\\linewidth',fig.align='center', warning=FALSE, fig.env='figure', fig.cap='Simulation summary plot', fig.alt = "A graphical representation of the information in the simulation summary."}
 plot(simSum)
 ```
 
@@ -515,7 +518,7 @@ doseRecGain <- nextBest(GainNextBest,
 ```
 The plot for the next dose allocation is contained in `doseRecGain$plot` and shown in Figure \@ref(fig:doseRecommendation).
 \begin{figure}
-```{r doseRecommendation, echo=FALSE, fig.width=7,fig.height=5,out.width='.6\\linewidth',fig.align='center'}
+```{r doseRecommendation, echo=FALSE, fig.width=7,fig.height=5,out.width='.6\\linewidth',fig.align='center', fig.alt = "A graphical representation of the logic behind the recommendation of the doseResgen object."}
 print(doseRecGain$plot)
 ```
 
@@ -682,7 +685,7 @@ Now we can already use the model, for example in the following we specify the
 skeleton probabilities via the dose grid and use a standard exponential prior
 for $\theta$. The resulting posterior fit can be plotted as usual,
 see Figure \@ref(fig:OneParExp-model-example).
-```{r OneParExp-model-example, fig.width=5,fig.height=4,out.width='.5\\linewidth',fig.align='center', fig.env='figure', fig.cap='Model fit of the one parameter power model'}
+```{r OneParExp-model-example, fig.width=5,fig.height=4,out.width='.5\\linewidth',fig.align='center', fig.env='figure', fig.cap='Model fit of the one parameter power model', fig.alt = "The posterior dose-toxicity curve from the one parameter power model."}
 (skeleton_probs <- round(data@doseGrid / max(data@doseGrid) / 2, 2))
 newModel <- OneParExp(
   skeleton_probs = skeleton_probs,
diff --git a/vignettes/example.Rmd b/vignettes/example.Rmd
index fad61b827..869abc3ad 100644
--- a/vignettes/example.Rmd
+++ b/vignettes/example.Rmd
@@ -63,7 +63,7 @@ The whole R-package is built in a modular form, by using S4 classes and methods.
 
 # Data
 
-```{r, fig.cap="Data classes structure"}
+```{r, fig.cap="Data classes structure", fig.alt = "GeneralData is the grand parent, Data the child and DataDual the grand child."}
 df <- data.frame(
   pathString = c(
     "GeneralData/Data/DataDual"
@@ -128,7 +128,7 @@ data@ID
 You can get a visual summary of the data by applying `plot` to the
 object:
 
-```{r plotdata, fig=TRUE}
+```{r plotdata, fig=TRUE, fig.alt = "A graph with patient ID on the x axis and dose level on the y axis.  A red triangle indicates that patient 7 reported a DLT.  Black squares indicate that no other patient reported a toxicity.  Patients 1 to 5 are dosed in an ascending fashion from 0.1 to 6.  Patients 6 to 9 are dosed at 10."}
 print(plot(data))
 ```
 
@@ -139,7 +139,7 @@ print(plot(data))
 Figure \@ref(fig:model-classes) shows the structure of the model class defined in this package. The `AllModels` class is the parent class from which all model classes inherit. There are two sub-classes: First, the `GeneralModel` class from which all models inherit that are using `JAGS` to specify the model and the prior distribution and will then be estimated by MCMC later on. Then, the second subclass is the `ModelPseudo` class for which the prior of the models are specified in terms of pseudo data and standard maximum likelihood routines from R will be used for
 computational purposes. All models included in this package will have a parent class of either the `GeneralModel` or the `ModelPseudo` classes. There are two further classes under `ModelPseudo` which are the `ModelTox` class include all DLT (the occurrence of a dose-limiting toxicity) class models, and class `ModelEff` which includes all efficacy class models.
 
-```{r model-classes, echo = FALSE, fig.cap = "Model classes structure", fig.width = 6}
+```{r model-classes, echo = FALSE, fig.cap = "Model classes structure", fig.width = 6, fig.alt = "A dedrogram showing the inheritance structure for descendants of AllModels."}
 library(data.tree)
 df <- data.frame(
   pathString = c(
@@ -321,7 +321,7 @@ maximum dose, respectively.
 The result `minInfModel` is a list, and we can use its contents to
 illustrate the creation of the prior:
 
-```{r min-inf-res, fig=TRUE, fig.width=6}
+```{r min-inf-res, fig=TRUE, fig.width=6, fig.alt = "A graph showing the relationship between the requested quantiles of the prior and those obtained from MinimalInformative.  The approximation is less than ideal."}
 matplot(
   x = coarseGrid,
   y = minInfModel$required,
@@ -426,12 +426,12 @@ alpha0samples <- get(samples, "alpha0")
 understood by `ggmcmc` and we can produce plots with it, e.g. a trace plot
 and an autocorrelation plot:
 
-```{r ggmcmc, fig=TRUE}
+```{r ggmcmc, fig=TRUE, fig.alt = "A trace plot for alpha0"}
 library(ggmcmc)
 print(ggs_traceplot(alpha0samples))
 ```
 
-```{r ggmcmc2, fig=TRUE}
+```{r ggmcmc2, fig=TRUE, fig.alt = "An autocorrelation plot for alpha0"}
 print(ggs_autocorrelation(alpha0samples))
 ```
 
@@ -490,7 +490,7 @@ In the example, the `update` function is used to obtain the posterior modal esti
 After having obtained the parameter samples, we can plot the model fit, by
 supplying the samples, model and data to the generic plot function:
 
-```{r plot-model-fit, fig=TRUE}
+```{r plot-model-fit, fig=TRUE, fig.alt = "A graph with dose level on the x axis and the probability of DLT on the y axis.  The posterior mean probability of toxicity increases smoothly with dose.  The 95% credible interval is roughly symmetric about the mean."}
 print(plot(samples, model, data))
 ```
 
@@ -501,7 +501,7 @@ Note that you can also produce a plot of the prior mean curve and credible
 intervals, i.e. from the model without any data. This works in principle the
 same way as with data, just that we use an empty data object:
 
-```{r empty-data, fig=TRUE}
+```{r empty-data, fig=TRUE, fig.alt = "A graph with dose level on the x axis and the probability of DLT on the y axis.  The prior mean probability of toxicity increases with dose, but is much less smooth than the posterior.  Similarly, The 95% credible interval is not symmetric about the mean."}
 ## provide only the dose grid:
 emptydata <- Data(doseGrid = data@doseGrid)
 ## obtain prior samples with this Data object
@@ -512,14 +512,14 @@ print(plot(priorsamples, model, emptydata))
 
 
 This `plot` function can also apply to the `DLTmodel` when samples of the parameters have been generated:
-```{r plot-samplesdata1 , fig=TRUE}
+```{r plot-samplesdata1 , fig=TRUE, fig.alt = "A graph similar to those above, but based on the DLTmodel object."}
 print(plot(DLTsamples, DLTmodel, data1))
 ```
 
 
 In addition, we can also plot the fitted dose-response curve using the prior or the posterior modal estimates of the model parameters when no MCMC sampling is used. For example, we have the `DLTmodel` specified earlier under the `ModelTox` class with the data set `data1` we specified earlier:
 
-```{r emptydatanoDLTsamples, fig=TRUE}
+```{r emptydatanoDLTsamples, fig=TRUE, fig.alt = "A graph with dose level on the x axis and the probability of DLT on the y axis.  The modal probability of toxicity increases with dose.  No credible interval is plotted."}
 print(plot(data1, DLTmodel))
 ```
 
@@ -543,7 +543,7 @@ and `Stopping-class`.
 
 Figure \@ref(fig:increments) shows the structure of the `Increments` classes:
 
-```{r increments, fig.cap = "Increments classes structure", echo = FALSE}
+```{r increments, fig.cap = "Increments classes structure", echo = FALSE, fig.alt = "A dendrogram showing the inheritance structure of the Increments classes."}
 df <- data.frame(
   pathString = c(
     "Increments/IncrementsRelative/IncrementsRelativeParts",
@@ -603,7 +603,7 @@ The `IncrementsRelativeDLT` class works similarly, taking the number of DLTs in
 
 Figure \@ref(fig:rules) show the structure of the next best dose recommendation rules currently implemented in `crmPack`.
 
-```{r rules, fig.width=6, fig.cap="Escalation classes structure", echo = FALSE}
+```{r rules, fig.width=6, fig.cap="Escalation classes structure", echo = FALSE, fig.alt = "A dendrogram showing the inheritance structure of the NextBest classes.  Next Best is the parent class.  All other NextBest classes are children of NextBest."}
 df <- data.frame(
   pathString = c(
     "NextBest/NextBestMTD",
@@ -701,7 +701,7 @@ rule, therefore the plot gives the target-dosing and overdosing probabilities
 together with the safety bar of 25%, the maximum dose and the final
 recommendation (the red triangle):
 
-```{r next-best-results, fig=TRUE, fig.width=6}
+```{r next-best-results, fig=TRUE, fig.width=6, fig.alt = "Two graphs, one above the other.  Both have dose on the x axis.  In the upper plot, green vertical bars indicate the probability that each dose is in the target toxicity range.  In the lower, vertical red bars indicate the probability that each dose is in the overdose range.  On the lower graph, a horizontal black dashed line at 25% indicates that all doses above 20 have an unacceptable risk of toxicity.  On the upper graph, a red arrow pointing at 20 indicates that it is the dose with the highest probability of being in the target toxicity range whilst at the same time not being unacceptably toxic."}
 doseRecommendation$value
 print(doseRecommendation$plot)
 ```
@@ -714,7 +714,7 @@ doseRecDLT <- nextBest(TDNextBest, doselimit = 300, model = newDLTmodel, data =
 
 A list of numerical values and a plot showing how the next best dose was computed will be given. This list of results will provide the numerical values for the next dose level, `next_dose_drt`; the target probability of DLT used during the trial, `prob_target_drt`; the estimated dose level for which its probability of DLT equals the target probability used during the trial, `dose_target_drt`; the target probability of DLT used at the end of a trial, `prob_target_eot`; the estimated dose level for which its probability of DLT equals the target probability of DLT used at the end of a trial `dose_target_eot`; and the dose level at dose grid closest and less than the `dose_target_eot`, `next_dose_eot`. We can use the `$` operator to obtain these values and the plot from the list. For example,
 
-```{r next-bestTD-results, fig=TRUE}
+```{r next-bestTD-results, fig=TRUE, fig.alt = "A graph describing the results of applying TDNextBest rule to the data3 dataset and newDLTmodel model.  A red line indicates the posterior modal estimate of toxicity.  Vertical porle and dark red lines indicate the recommended dose for the next cohort (labelled Next) and the maximum dose permitted by the increments rule.  The maximum dose is 300, the recommended dose, 50.  A puple square and an orange triangle indicate the TD30 and TD35 estimates, both of which are close to 50."}
 doseRecDLT$next_dose_drt
 doseRecDLT$prob_target_drt
 doseRecDLT$dose_target_drt
@@ -735,9 +735,10 @@ doseRecDLTSamples <- nextBest(TDsamplesNextBest,
 
 The same list of results will be produced as in the example before: The values of the `next_dose_drt`, `prob_target_drt`, `dose_target_drt`, `prob_target_eot`, `dose_target_eot` and `next_dose_eot` can be obtained using the `$` operator. The only difference is that the plot in this example will look slightly different than in the previous example:
 
-```{r next-bestTDsamples-results, fig=TRUE}
+```{r next-bestTDsamples-results, fig=TRUE, fig.alt = "A denisty plot showing the PDFs for the TD35 and TD30 against dose.  Both are highly skewed to the left. Vertical blue and red lines indicate the recommended dose for the next cohort and the maximum dose permitted by the increments rule. The recommended dose is 50 and the maximum dose 300."}
 print(doseRecDLTSamples$plot)
 ```
+
 > This graph is incorrect
 
 In the plot, vertical lines are given to show the value for the next dose, the TD30 estimate, the TD35 estimate and the maximum allowable dose level. Since samples of model parameters were utilized, the density curves of the TD30 (pink) and the TD35 (grey) are plotted.
@@ -757,7 +758,7 @@ df <- data.frame(
 )
 
 tree <- as.Node(df)
-SetNodeStyle(tree, shape = "box")
+SetNodeStyle(tree, shape = "box", fig.alt = "A dendrogram showing the inheritance stricture of the CohortSize classes.  CohortSie is the parent class.  All other classes are children of CohortSize.")
 plot(tree)
 ```
 % All classes related to cohort size in this package are contains within `CohortSize` class.
@@ -897,7 +898,7 @@ In order to run simulations, we first have to build a specific design, that
 comprises a model, the escalation rules, starting data, a cohort size and a starting dose.
 
 The structure of the design classes in this package is shown in figure \@ref(fig:Design).
-```{r Design, fig.width=6, fig.cap="Design classes structure", echo = FALSE}
+```{r Design, fig.width=6, fig.cap="Design classes structure", echo = FALSE, fig.alt = "A dendrogram showing the inheritance structure of the Design classes.  RuleDesign is the grandparent.  Design, TDDesign and TDSampesDesign are its children and DualDeaign, DualResponsesDesign and DualResponsesTDSamplesDesign its grandchildren."}
 df <- data.frame(
   pathString = c(
     "RuleDesign/Design/DualDesign",
@@ -990,7 +991,7 @@ scenario, from which the data should arise. In this case, this only requires a
 function that computes the probability of DLT given a dose. Here we use a
 specific case of the function contained in the model space:
 
-```{r true-def, fig=TRUE}
+```{r true-def, fig=TRUE, fig.alt = "The CDF defined by the myTruth function"}
 ## define the true function
 myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)
 
@@ -1001,7 +1002,7 @@ curve(myTruth(x), from = 0, to = 80, ylim = c(0, 1))
 In a similar way, we can also simulate trials based on a true DLT scenario using the `TDDesign` and the `TDsamplesDesign`.
 
 First, we will specified the true DLT scenario such that
-```{r trueDLT, fig=TRUE}
+```{r trueDLT, fig=TRUE, fig.alt = "The CDF defined by the TrueDLT function"}
 ## define the true function
 TrueDLT <- probFunction(DLTmodel, phi1 = -53.66584, phi2 = 10.50499)
 
@@ -1059,7 +1060,7 @@ looking at the help pages for `Simulations` and the parent class
 produced `Data` objects of the simulated trials. Therefore, we can plot
 the course of e.g. the third simulated trial as follows:
 
-```{r third-trial, fig=TRUE}
+```{r third-trial, fig=TRUE, fig.alt = "A graph summarising dose allocations for the third trial in the simulation.  Patient number runs along the x axis, dose administered along the y axis.  Red triangle indicate patients who reported a DLT, black circles those who did not.  DLTs were reported by patients 10, 11, 12, 16 and 18, all at a dose of 24.  No other patients reported DLTs."}
 print(plot(mySims@data[[3]]))
 ```
 
@@ -1078,7 +1079,7 @@ mySims@stop_reasons[[3]]
 Furthermore, with this object, we can apply two methods. First, we can plot it,
 i.e. we can apply the plot method:
 
-```{r sim-plot, fig=TRUE}
+```{r sim-plot, fig=TRUE, fig.alt = "two plots in a single column.  The first shows patient number on the x axis and dose administered on the y axis.  Lines in various styles indicate the minimum, maximum, median, lower quartile and upper quartile of the dose administered tat each patient number, calculated over all simulations.  The lower graph has dose on the x axis and proportion of patients treated at that dose on the y axis.  The proportuon of patients treated is avreaged over all simulations."}
 print(plot(mySims))
 ```
 
@@ -1107,7 +1108,7 @@ designs.
 Now we can also produce a plot of the summary results, which gives a bit more
 detail than the textual summary we have just seen:
 
-```{r sim-sum-plot, fig=TRUE, fig.width=6}
+```{r sim-sum-plot, fig=TRUE, fig.width=6, fig.alt = "A 2x2 panel of four plots.  A description of each panel element is provided in the paragraph below."}
 simSum <- summary(mySims,
   truth = myTruth
 )
@@ -1133,7 +1134,7 @@ selected doses is too small and shows not the right x-axis window, we can only
 plot this one and add x-axis customization on top: (see the `ggplot2`
 documentation for more information on customizing the plots)
 
-```{r sim-sum-plot2, fig=TRUE}
+```{r sim-sum-plot2, fig=TRUE, fig.alt = "A fistogram showing the proportion of simulatd trials that identified each dose as the MTD.  All but one of the simulated trials identified a dose between 16 and 26 as the MTD."}
 dosePlot <- plot(simSum, type = "doseSelected") +
   scale_x_continuous(breaks = 10:30, limits = c(10, 30))
 print(dosePlot)
@@ -1180,11 +1181,11 @@ DLTsampSim@doses[3]
 
 The overall results of the 100 trials for these two simulations can also be plotted as
 
-```{r plotDLTSim, fig=TRUE}
+```{r plotDLTSim, fig=TRUE, fig.alt = "Two graphs in a single column describing the dose allocations in the DLTSim object."}
 print(plot(DLTSim))
 ```
 
-```{r plotDLTsampSim, fig=TRUE}
+```{r plotDLTsampSim, fig=TRUE, fig.alt = "Two graphs in a single column describing the dose allocations in the DLTsampSim object."}
 print(plot(DLTsampSim))
 ```
 
@@ -1206,7 +1207,7 @@ summary(DLTsampSim,
 
 Then we can also plot the summary of these two simulations using the `plot` function:
 
-```{r DLTSim-plotsummary,fig=TRUE, fig.width=6}
+```{r DLTSim-plotsummary,fig=TRUE, fig.width=6, fig.alt = "A 2x2 panel of graphs summarising the results of the DLTSim object.  Refer to simSum above for a description of the individual panel elements."}
 DLTsimSum <- summary(DLTSim,
   truth = TrueDLT
 )
@@ -1215,7 +1216,7 @@ print(plot(DLTsimSum))
 
 and
 
-```{r DLTsampSim-plotsummary,fig=TRUE, fig.width=6}
+```{r DLTsampSim-plotsummary,fig=TRUE, fig.width=6, fig.alt = "A 2x2 panel of graphs summarising the results of the DLTsampSim object.  Refer to simSum above for a description of the individual panel elements."}
 DLTsimsampSum <- summary(DLTsampSim,
   truth = TrueDLT
 )
@@ -1296,7 +1297,7 @@ And now, exactly in the same way as above for the operating characteristics
 simulations, we can summarize the resulting predictive simulations, for example
 show the predicted trajectories of doses:
 
-```{r sim-future-plot, fig=TRUE}
+```{r sim-future-plot, fig=TRUE, fig.alt = "A histrogram showing the proportions of simulations in simSum that identified each dose as the MTD."}
 print(plot(futureSims))
 ```
 
@@ -1369,7 +1370,7 @@ average true toxicity of
 Graphical summaries are again obtained by calling `plot` on the summary
 object:
 
-```{r three-sims-plot, fig=TRUE, fig.width=6}
+```{r three-sims-plot, fig=TRUE, fig.width=6, fig.alt = "A 2x2 panel of graphs summarising the results of the simulations in the threeSimsSum object."}
 print(plot(threeSimsSum))
 ```
 
@@ -1421,7 +1422,7 @@ data <- DataDual(
 
 The corresponding plot can again be obtained with:
 
-```{r dual-data-plot, fig=TRUE, fig.width=6}
+```{r dual-data-plot, fig=TRUE, fig.width=6, fig.alt = "Two graphs, side-by-side.  The first shows patient number on the x axis and dose administered on the y axis.  Different symbols indicate the toxicity status of each patient.  The second graph has dose on the x axis and biomarker value on the y axis.  Again, different symbols indicate toxicity status and annotations show the patient number with which each point is asscoated."}
 print(plot(data))
 ```
 
@@ -1469,7 +1470,7 @@ samples <- mcmc(data, model, options)
 And we check the convergence by picking a few of the fitted biomarker means and
 plotting their traceplots:
 
-```{r dual-conv, fig=TRUE}
+```{r dual-conv, fig=TRUE, fig.alt = "Traceplots for selected elements of the betaW array."}
 data@nGrid
 betaWpicks <- get(samples, "betaW", c(1L, 5L, 10L, 25L))
 ggs_traceplot(betaWpicks)
@@ -1481,7 +1482,7 @@ converged, as the traceplots show. (Remember that `data@nGrid` gives the
 number of grid points.) So we can plot the model fit:
 
 \setkeys{Gin}{width=\textwidth}
-```{r dual-modelfit, fig=TRUE}
+```{r dual-modelfit, fig=TRUE, fig.alt = "Two plots, side-by-side.  The first shows the posterior estimate of the dose-toxicity curve, with a credible interval.  The second shows the posterior estimate of biomarker level against dose, again with a credible interval."}
 print(plot(samples, model, data, extrapolate = FALSE))
 ```
 \setkeys{Gin}{width=0.65\textwidth}
@@ -1492,7 +1493,7 @@ biomarker fit to higher dose levels. We can also look at the estimated biomarker
 precision $1 / \sigma^{2}_{W}$. For that we extract the precision `precW` and
 then use another `ggmcmc` function to create the histogram:
 
-```{r dual-variance, fig=TRUE}
+```{r dual-variance, fig=TRUE, fig.alt = "A histogram of the biomarker precision in the samples object."}
 ggs_histogram(get(samples, "precW"))
 ```
 
@@ -1526,7 +1527,7 @@ nextDose$value
 A corresponding plot can be produced by printing the `plot` element of the
 returned list:
 
-```{r dual-nextdose-plot}
+```{r dual-nextdose-plot, fig.alt = "Two graphs in a single column that summarise the results of applying the NextBest rule.  For descriptions of the plots, refer to earlier examples in this vignette."}
 print(nextDose$plot)
 ```
 
@@ -1610,7 +1611,7 @@ trueTox <- function(dose) {
 
 We can draw the corresponding curves:
 
-```{r dual-sc-plot, fig=TRUE}
+```{r dual-sc-plot, fig=TRUE, fig.alt = "Two graphs representing the true relationships between dose and toxicity and dose and biomarker level."}
 par(mfrow = c(1, 2))
 curve(trueTox(x), from = 0, to = 80)
 curve(trueBiomarker(x), from = 0, to = 80)
@@ -1647,7 +1648,7 @@ Plotting the result gives not only an overview of the final dose recommendations
 and trial trajectories, but also a summary of the biomarker variance and
 correlation estimates in the simulations:
 
-```{r dual-sims-plot, fig=TRUE}
+```{r dual-sims-plot, fig=TRUE, fig.alt = "A panel of four plots summarisong the results of the simulation."}
 print(plot(mySims))
 ```
 
@@ -1668,7 +1669,7 @@ biomarker target instead, and the true biomarker response peaked at 50 mg.
 
 The corresponding plot looks as follows:
 
-```{r dual-sim-sum-plot, fig=TRUE, fig.width = 6}
+```{r dual-sim-sum-plot, fig=TRUE, fig.width = 6, fig.alt = "A panel of plots that dscribe the results of the simulation summary."}
 print(plot(sumOut))
 ```
 
@@ -1793,17 +1794,17 @@ newEffmodel2@RW
 
 The `plot` function can also be applied to the `Effloglog` model class or the `EffFlexi` model class objects, when samples of the parameters are generated under all these models:
 
-```{r plot-samplesdata2loglog, fig=TRUE, fig.width=6}
+```{r plot-samplesdata2loglog, fig=TRUE, fig.width=6, fig.alt = "Expected efficacy against dose for the data4 object, assuming the model described by the newEffmodel object."}
 print(plot(Effpostsamples, newEffmodel, data4))
 ```
 
-```{r plot-samplesdata2Flexi, fig=TRUE, fig.width=6}
+```{r plot-samplesdata2Flexi, fig=TRUE, fig.width=6, fig.alt = "Expected efficacy against dose for the data4 object, assuming the model described by the newEffmodel2 object."}
 print(plot(Effpostsamples2, newEffmodel2, data4))
 ```
 
 In addition, we can also plot the fitted dose-efficacy curve using the prior or the posterior modal estimates of the model parameters when no MCMC sampling is used. For example, using `Effmodel` and data set `data2` specified earlier:
 
-```{r plot-nosamplesEffmodel, fig=TRUE, fig.width=6}
+```{r plot-nosamplesEffmodel, fig=TRUE, fig.width=6, fig.alt = "Expected efficacy against dose for the data2 object, assuming the model described by the Effmodel object."}
 print(plot(data2, Effmodel))
 ```
 
@@ -1812,7 +1813,7 @@ Since no samples are involved, only the curves using the prior or posterior moda
 Furthermore, we can also plot the estimated DLT probability and efficacy curve side by side using the `plotDualResponses` function.
 For example, using the `DLTmodel`, `Effmodel` and `data2` specified in earlier examples:
 
-```{r plotDualResponseNoSamples, fig=TRUE, fig.width=6}
+```{r plotDualResponseNoSamples, fig=TRUE, fig.width=6, fig.alt = "Model estimates of toxicity and biomarker level by dose."}
 plotDualResponses(
   DLEmodel = DLTmodel,
   Effmodel = Effmodel, data = data2
@@ -1820,7 +1821,7 @@ plotDualResponses(
 ```
 
 When the MCMC samples are used, we have:
-```{r plotDualResponseSamples, fig=TRUE, fig.width=6}
+```{r plotDualResponseSamples, fig=TRUE, fig.width=6, fig.alt = "Model estimates of toxicity and biomarker level by dose, both with credible intervals."}
 plotDualResponses(
   DLEmodel = DLTmodel, DLEsamples = DLTsamples,
   Effmodel = Effmodel, Effsamples = Effsamples, data = data2
@@ -1840,7 +1841,7 @@ For example, the most ideal case is that both the probability of no DLT and the
 
 We can plot the gain function given a DLT model specified under the `ModelTox` class and an efficacy model specified under the `ModelEff` class using the `plotGain` function. For example, using the variables `newDLTmodel`, `newEffmodel` and the data set with observations, `data4`, specified in earlier examples, we have:
 
-```{r plotGain, fig=TRUE}
+```{r plotGain, fig=TRUE, fig.alt = "A summary of the gain function  for newDLEmodel and newEffmodel applied to data4."}
 plotGain(DLEmodel = newDLTmodel, Effmodel = newEffmodel, data = data4)
 ```
 
@@ -1866,7 +1867,7 @@ doseRecGain <- nextBest(GainNextBest,
 ```
 The results will be a list of numerical values with a plot illustrating how the next best dose was computed. The list of numerical values include the next best dose suggested, the values of the target probabilities of DLT used during and at the end of a trial. Furthermore, the estimated doses for these two targets, as well as the "Gstar" estimated dose (the dose with gives the maximum gain value) are provided along with the corresponding dose level in the dose grid for the above three estimates. We can also get to see the plot about the next best dose recommendation using the `$` operator.
 
-```{r nextbestplot-maxgain,fig=TRUE}
+```{r nextbestplot-maxgain,fig=TRUE, fig.alt = "A summary the results of applying the GainNextBest object to the gain function for newDLEmodel and newEffmodel applied to data4."}
 doseRecGain$plot
 ```
 
@@ -1902,7 +1903,7 @@ doseRecGainSamples <- nextBest(GainsamplesNextBest,
 )
 ```
 The list of numerical results given in the output will be the same as those given using `NextBestMaxGain` class object which includes the next dose suggested, the current estimates of TD30, TD35 and Gstar and their corresponding dose levels at dose Grid. We can also see the plot:
-```{r nextbest-NextBestMaxGainSamplesplot,fig=TRUE}
+```{r nextbest-NextBestMaxGainSamplesplot,fig=TRUE, fig.alt ="A description of this graph is given in the text below."}
 doseRecGainSamples$plot
 ```
 
@@ -2003,7 +2004,7 @@ myTruthGain <- function(dose) {
 ```
 
 The true DLT, efficacy and gain curves can be obtained. We can see the corresponding curves as
-```{r Truecurves, fig=TRUE}
+```{r Truecurves, fig=TRUE, fig.alt ="A description of this graph is given in the text below."}
 TruthTD <- doseFunction(DLTmodel, phi1 = -53.66584, phi2 = 10.50499)
 
 GAIN <- function(xi) {
@@ -2051,7 +2052,7 @@ myTruthGain1 <- myTruthEff1 * (1 - myTruthDLT(d1))
 
 The corresponding curves can also be plotted as:
 
-```{r TruecurvesFlexi,fig=TRUE}
+```{r TruecurvesFlexi,fig=TRUE, fig.alt = "A description of this graph is given in the text below."}
 maxg1 <- max(myTruthGain1)
 gstar1 <- data2@doseGrid[which.max(myTruthGain1)]
 DoseLevels1 <- seq(1, 300, 1)
@@ -2138,13 +2139,15 @@ For the specification of the arguments `object`, `args`, `trueDLE`, `trueEff`, `
 
 Furthermore, we can also plot, summarize and plot the summary of the simulated results using `plot` and `summary` function:
 
-```{r plotsimulationresults1,fig=TRUE}
+```{r plotsimulationresults1,fig=TRUE, fig.alt = "A description of the Sim1 object."}
 plot(Sim1)
 ```
-```{r plotsimulationresults2,fig=TRUE}
+
+```{r plotsimulationresults2,fig=TRUE, fig.alt = "A description of the Sim2 object."}
 plot(Sim2)
 ```
-```{r plotsimulationresults3,fig=TRUE}
+
+```{r plotsimulationresults3,fig=TRUE, fig.alt = "A description of the Sim3 object."}
 plot(Sim3)
 ```
 
@@ -2152,7 +2155,7 @@ The plots give an overview of the final dose recommendations and trial trajector
 
 Then, the summary and the plot of the summary of the simulations can be obtained by:
 
-```{r summarysimulationresults1, fig=TRUE, fig.width=6}
+```{r summarysimulationresults1, fig=TRUE, fig.width=6, fig.alt = "A description of the summary of the Sim1 object."}
 Sum1 <- summary(Sim1,
   trueDLE = myTruthDLT,
   trueEff = myTruthEff
@@ -2161,7 +2164,7 @@ Sum1
 print(plot(Sum1))
 ```
 
-```{r summarysimulationresults2,fig=TRUE, fig.width=6}
+```{r summarysimulationresults2,fig=TRUE, fig.width=6, fig.alt = "A description of the summary of the Sim2 object."}
 Sum2 <- summary(Sim2,
   trueDLE = myTruthDLT,
   trueEff = myTruthEff
@@ -2170,7 +2173,7 @@ Sum2
 print(plot(Sum2))
 ```
 
-```{r summarysimulationresults3,fig=TRUE, fig.width=6}
+```{r summarysimulationresults3,fig=TRUE, fig.width=6, fig.alt = "A description of the summary of the Sim3 object."}
 Sum3 <- summary(Sim3,
   trueDLE = myTruthDLT,
   trueEff = myTruthEff1
diff --git a/vignettes/ordinal-crm.Rmd b/vignettes/ordinal-crm.Rmd
index 42ad18959..b55a7c091 100644
--- a/vignettes/ordinal-crm.Rmd
+++ b/vignettes/ordinal-crm.Rmd
@@ -44,7 +44,7 @@ defines a `DataOrdinal` object with three toxicity grades, labelled "No tox`", "
 
 The `update`, `plot` and `dose_grid_range` methods work exactly as they do for `Data` objects:
 
-```{r, data-ordinal-2}
+```{r, data-ordinal-2, fig.alt = "A graph showing Patient ID on the x axis and dose administered on the y axis.  The shape and colour of the symbols indicate the toxicity status of the patient: red triangles for DLTs, orange circles for sub-toxic AEs and black triangles for no reported toxicities.  Patients 1 to 4 are dosed at 10, 20, 30 and 40, with no toxicitis reported.  patients 5 to 7 are dosed at 50, with patient 6 reporting a sub-toxic AE.  Patients 8 to 10 are treated at 60.  Patient 9 reports a sub-toxic AE and patient 10 a DLT."}
 dose_grid_range(empty_ordinal_data)
 
 ordinal_data <- update(empty_ordinal_data, x = 10, y = 0)
@@ -127,9 +127,15 @@ fit(samples, ordinal_model, ordinal_data, grade = 2L, cumulative = FALSE)
 
 The `plot` method also takes `grade` and `cumulative` parameters.
 
-```{r, plot}
+```{r, plot1, fig.alt = "A graph of the posterior probability of toxicity (DLT only) against dose.  The mean probability of toxicity is barely above 0% at a dose of zero and rises in a sigmoidal curve to around 65% at a dose of 100.  The confidence interval is relatively narrow for low doses but widens considerably for doses over 60, extending from around 15% to 100% for a dose of 100."}
 plot(samples, ordinal_model, ordinal_data, grade = 2L)
+```
+
+```{r, plot2, fig.alt = "A graph of the posterior cumulative probability of toxicity (sub-toxic AE or DLT) against dose.  The mean probability of toxicity is barely above 0% at a dose of zero and rises in a sigmoidal curve to around 75% at a dose of 100.  The confidence interval is relatively narrow for low doses but widens considerably for doses over 60, extending from around 30% to 100% for a dose of 100."}
 plot(samples, ordinal_model, ordinal_data, grade = 1L)
+```
+
+```{r, plot3, fig.alt = "A graph of the posterior probability of sub toxic AE against dose.  The mean probability of toxicity is barely above 0% at a dose of zero, rises to a peak of about 18% at a dose of 60 before falling to around 12% at a dose of 100.  The confidence interval is relatively narrow for low doses but widens considerably for doses over 60, extending from around 30% to 100% for a dose of 100."}
 plot(samples, ordinal_model, ordinal_data, grade = 1L, cumulative = FALSE)
 ```
 
diff --git a/vignettes/rolling-crm.Rmd b/vignettes/rolling-crm.Rmd
index 7fa56ab73..58c60f817 100644
--- a/vignettes/rolling-crm.Rmd
+++ b/vignettes/rolling-crm.Rmd
@@ -81,27 +81,31 @@ samples <- mcmc(data, model, options)
 
 ### Use ggmcmc to diagnose
 
-```{r Diagnose}
+```{r Diagnose-1, fig.alt = "A trace plot for alpha0.  It looks like skyscrapers ina big city, but there are only just over 200 samples in the chain."}
 library(ggmcmc)
 alpha0samples <- get(samples, "alpha0")
 
 print(ggs_traceplot(alpha0samples))
+```
 
+```{r Diagnose-2, fig.alt = "An auto correlation plot for aplha0.  There is significant auto-correlation of 0.25 or more even at lags of 50.  There is seasonality too, with three groups of negative auto-correlation and four of positive."}
 print(ggs_autocorrelation(alpha0samples))
 ```
 
 ### Plot the model fit
 
-```{r Fit}
+```{r Fit-1, fig.width = 7, fig.alt = "Two plots in a single row.  The first shows the posterior mean and ci for the probability of toxicity by dose.  The second shows 100 times the posterior hazard by time."}
 plot(samples, model, data, hazard = TRUE)
+```
 
+```{r Fit-2, fig.width = 7, fig.alt = "Two plots in a single row.  Both show the posterior mean and ci for the probability of toxicity by dose on the y axis.  In the first plot, the x axis is dose.  In the second, it is time."}
 plot(samples, model, data, hazard = FALSE)
 ```
 
 
 ### prior mean curve
 
-```{r Prior}
+```{r Prior, fig.width = 7, fig.alt = "Two plots in a single row.  Both show the prior mean and ci for the probability of toxicity by dose on the y axis.  In the first plot, the x axis is dose.  In the second, it is time."}
 emptydata <- DataDA(doseGrid = c(
   0.1, 0.5, 1.5, 3, 6,
   seq(from = 10, to = 80, by = 2)
@@ -142,7 +146,7 @@ myStopping <- (myStopping1 | myStopping2)
 
 ### Recommended dose for the next cohort
 
-```{r Recommend}
+```{r Recommend, fig.width = 7, fig.alt = "Two graphs arranged in a single column.  The upper graph shoes green lines of various heights that show the probability each dose is in the target toxicity range.  There is a big arrow pointing to the bar at a dose of 0.5, that this is the recommended dose for the next cohort.  The bars for other doses are higher, but they are not eligible for dosing because of the overdose rule illustrated in the second graph below.  The lower graph as a similar series of red lines, indicating the probability that each dose is in the overdose range.  There is a horizontal black dashed line at 25%, indicating that this is the highest acceptable probability of being in the overdose range.  The red bars for doses above 0.5 all extend above 25%, indicating that their toxicity is unacceptable.  The toxicity for doses of 0.1 and 0.5 lie below 25%."}
 doseRecommendation <- nextBest(myNextBest,
   doselimit = nextMaxDose,
   samples = samples,
@@ -150,7 +154,6 @@ doseRecommendation <- nextBest(myNextBest,
   data = data
 )
 
-
 doseRecommendation$plot
 doseRecommendation$value
 ```
@@ -177,7 +180,7 @@ design <- DADesign(
 
 ### Set up true curves
 
-```{r Truth}
+```{r Truth, fig.alt = "A logistic dose response curverising from 0 at dose 0 to almost 100% for a dose of 100."}
 myTruth <- probFunction(model, alpha0 = 2, alpha1 = 3)
 curve(myTruth(x), from = 0, to = 100, ylim = c(0, 1))
 
@@ -210,7 +213,7 @@ mySims <- simulate(design,
 
 Use a similar way as section 9.2 in the "using the package crmPack: introductory examples" document 
 
-```{r Interpret}
+```{r Interpret, fig.width = 7, fig.height = 7, fig.alt = "Two graphs in a single column, summarising the results of a single simulated trial.  The upper one plots patient number on the x axis and dose andministered on the y axis.  Different symbols indicate whether or not each participant reported a toxicity.  Sixteen patients were enrolled, four of which reported toxicities.  The points rise and fall like waves in response to changes in the model's recommended dose.  The lower one plots time on the x axis and patient number on the y axis.  For each patient, a horizontal line runs from their enrolment time to the time at which they reported a toxicity, completed their safety evaluatiuon window or (at the end of the trial) were censored.  Different coloured and shaped symbols at the right hand end of each line indicate whether or not the participant reported a toxicity."}
 a <- summary(mySims, truth = myTruth)
 b <- mySims@data[[1]]
 
diff --git a/vignettes/trial_analysis.Rmd b/vignettes/trial_analysis.Rmd
index 4727ab196..a29855b30 100644
--- a/vignettes/trial_analysis.Rmd
+++ b/vignettes/trial_analysis.Rmd
@@ -162,13 +162,13 @@ firstFour <- Data(
 Within a `Data` object, the doses at which each patient is treated are given by the `x` slot and their toxicity status (a Boolean where a toxicity is represented by a truthy value) by the `y` slot.
 
 The observed data is easily visualised
-```{r}
+```{r, fig.alt = "A visual representation of the data from the first four participants.  The first three, treated at doses 1, 3 and 9, do not report any toxicities.  The fourth, treated at 20, does."}
 plot(firstFour)
 ```
 
 and, since the `plot` method returns a `ggplot` object, it is easily customised.
 
-```{r}
+```{r, fig.alt = "The same graph as above, but with a white background to the plot area rather than a grey one."}
 plot(firstFour) + theme_light()
 ```
 
@@ -185,12 +185,12 @@ postSamples <- mcmc(
 
 The posterior estimate of the dose toxicity curve is easily visualised:
 
-```{r}
+```{r, fig.alt = "A plot of the posterior after the first four participants.  The mean probability of toxicity increases smoothly, with a slight convex curve, from about zero percent at a dose of zero to about 65% at a dose of 100.  The confidence interval extends from 0% to about 25% at a dose of zero and from about 30% to  about 90% at a dose of 100."}
 plot(postSamples, model, firstFour)
 ```
 
 A visual representation of the model's state is obtained with:
-```{r}
+```{r, fig.alt = "Two graphs arranged in a single column.  The upper graph shoes green lines of various heights that show the probability each dose is in the target toxicity range.  There is a big arrow pointing to the bar at a dose of 20, indicating tat this dose has the highest probability of being in the target toxicity range.  The lower graph as a similar series of red lines, indicating the probability that each dose is in the overdose range.  There is a horizontal black dashed line at 25%, indicating that this is the highest acceptable probability of being in the overdose range.  The red bars for doses of 30 and above all extend above 25%, indicating that their toxicity is unacceptable.  The toxicity for doses of 20 and below lie below 25%."}
 nextBest(
   my_next_best,
   doselimit = 100,
@@ -557,16 +557,20 @@ x
 
 crmPack provides a wealth of information about the trial's results.  The following code snippets illustrate some of the many possibilities for how the trial might be summarised.
 
-```{r}
+```{r, fig.alt = "A visual representation of the data after nineteen participants have been treated.  One each at doses 1, 3 and 9; four at a dose of 20; 6 at a dose of 30 and 6 at a dose of 45.  Toxicitiues were reported by participants 4 (at a dose of 20) and 18 and 19 (both at a dose of 45)."}
 plot(fifthFullCohort)
+```
+
+```{rfig.alt = "A plot of the posterior after nineteen participants have been treated.  The mean probability of toxicity increases smoothly from about zero percent at a dose of zero to about 55% at a dose of 100.  The confidence interval extends from 0% to about 6% at a dose of zero and from about 22% to  about 90% at a dose of 100."}
 plot(postSamples5, model, fifthFullCohort)
 ```
-```{r}
+
+```{rfig.alt = "Two graphs arranged in a single column.  The upper graph shoes green lines of various heights that show the probability each dose is in the target toxicity range.  There is a big arrow pointing to the bar at a dose of 45, indicating that this dose has the highest probability of being in the target toxicity range.  The lower graph as a similar series of red lines, indicating the probability that each dose is in the overdose range.  There is a horizontal black dashed line at 25%, indicating that this is the highest acceptable probability of being in the overdose range.  The red bars for doses of 60 and above all extend above 25%, indicating that their toxicity is unacceptable.  The toxicity for doses of 45 and below lie below 25%."}
 doseRecommendation$plot
 ```
 
 With a little bit of work, we can obtain a more detailed summary and plot of the posterior probabilities of toxicity at each dose:
-```{r, error=TRUE}
+```{r, fig.alt = "A graph showing the posterior density of of the probability of toxicity for all doses greater than nine.  The mode of each density moves to the right as dose increases.  The densities for low doses are heaviliy skewed to the left.  Densities for higher doses are more symmetric and flatter."}
 slotNames(model)
 
 fullSamples <- tibble(
@@ -620,7 +624,7 @@ fullSamples %>%
   )
 ```
 
-```{r, error=TRUE}
+```{r, fig.alt = "A visual representation of the posterior dose - toxicity curve.  Very closely spaced solid lines in black and blue, representing the mean and median estimate of toxicity for each dose rise almost linearly from zero percent for a dose of zero to about 55% for a dose of 100.  Shading extends to each side of the two solid lines.  The transparency of the shading increases with distance from the solid lines.  The shading is funnel shaped, with a narrow mneck at a dose of 100 and a wider mouth at a dose of 100.  The shading represents the central 90%, 80% and 50% confidence intervals for the posterior mean estimate of toxicity at each dose."}
 fullSummary %>%
   ggplot(aes(x = Dose)) +
   geom_ribbon(aes(ymin = Q05, ymax = Q95), fill = "steelblue", alpha = 0.25) +
diff --git a/vignettes/trial_definition.Rmd b/vignettes/trial_definition.Rmd
index 28cf3ae12..044db590d 100644
--- a/vignettes/trial_definition.Rmd
+++ b/vignettes/trial_definition.Rmd
@@ -95,7 +95,7 @@ $$
 
 It is easy to obtain a visual representation of the prior:
 
-```{r, fig.width=5}
+```{r, fig.width=5, fig.alt = "A visual representation of the prior.  The prior mean estimate of toxicity rises form almost zero for a dose of 0 to just under 0.75 for a dose of 100.  The confidence intervals are wide."}
 vignetteMcmcOptions <- McmcOptions(burnin = 100, step = 2, samples = 1000)
 prior_samples <- mcmc(
   data = empty_data,