Convert to meta report to Rmd format intented to use with knitr::knit…

…_expand.

inst/shiny/run-reports.R

@@ -3,33 +3,49 @@ library("rmarkdown")
 library("GMCM")
 
 # GENERAL GMCM ----
-report_path <- "inst/shiny/www/report_full.R"
+report_path <- "inst/shiny/www/report_full.Rmd"
 
 # SPECIAL GMCM ----
-report_path <- "inst/shiny/www/report_meta.R"
-
-# Run directly
-rmarkdown::render(report_path,
-                  envir = new.env(parent = globalenv()))
-
-
-# Step-by-step
-# Spin document into Rmd
-rmd <- spin(hair = report_path, knit = FALSE, format = "Rmd")
-
-# Read yaml
-yaml <- rmarkdown::yaml_front_matter(rmd)
-params <- yaml$params
-
-
+report_path <- "inst/shiny/www/report_meta.Rmd"
+
+# Parameters to expand
+params <- list(input_file = "../../../data/u133VsExon.csv",
+               header = TRUE,
+               sep = ";",
+               quote = '\"',
+               model_cols = c("u133", "exon"),
+               meta_large_vals = FALSE,
+               init_par = c(pie1 = 0.5, mu = 1, sigma = 1, rho = 0.5),
+               meta_method = "NM",
+               meta_max_ite = 50L,
+               meta_positive_rho = TRUE,
+               meta_IDR_thres_type = "IDR",
+               meta_IDR_thres = 1e-04)
+
+# dput object to character string
+cput <- function(x) {
+  f <- tempfile()
+  dput(x, file = f)
+  return(readLines(f))
+}
+
+
+expand_args <- c(list(file = report_path), params)
+report_expanded <- do.call(knitr::knit_expand, expand_args)
+report_expanded_path <- gsub("/report_", "/report_expanded_", report_path)
+
+cat(report_expanded, file = report_expanded_path)
+
+# Render the expanded document
 rmarkdown::render(
-  input = rmd,
+  input = report_expanded_path,
   output_options = list(self_contained = TRUE),
   params = params,
   envir = new.env(parent = globalenv())
 )
 
 # We can also purl
-knitr::purl(rmd, output = file.path(dirname(rmd), "report_full_purl.R"), documentation = 0)
+knitr::purl(report_expanded_path, output = gsub(".Rmd$", ".R", report_expanded_path),
+            documentation = 0)
 
 

inst/shiny/www/report_meta.Rmd

@@ -0,0 +1,153 @@
+---
+title: "Meta analysis and clustering with special GMCMs"
+output: html_document
+date: '`r Sys.time()`'
+author: "Generated by the GMCM shiny app"
+---
+<!-- Old YAML params -->
+<!-- params: -->
+<!--   file: !r file.path(getwd(), "../../../data/freshVsFrozen.csv") -->
+<!--   header: TRUE -->
+<!--   sep: ";" -->
+<!--   quote: '"' -->
+<!--   model_cols: !r c("PreVsPost.Fresh.pval", "PreVsPost.Frozen.pval") -->
+<!--   meta_large_vals: FALSE -->
+<!--   init_par: !r c(pie1 = 0.5, mu = 1, sigma = 1, rho = 0.5) -->
+<!--   meta_method: "NM" -->
+<!--   meta_max_ite: 50 -->
+<!--   meta_positive_rho: TRUE -->
+<!--   meta_IDR_thres_type: "IDR" -->
+<!--   meta_IDR_thres: 0.05 -->
+
+```{r knit-int, echo=FALSE, include=FALSE}
+set.seed(5828993)
+```
+
+
+## Initalisation
+The **GMCM**^[1][1]^ package is loaded.
+
+```{r load-packages, include=TRUE}
+
+#install.packages("GMCM")  # Uncomment to install the GMCM package
+library("GMCM")
+```
+
+
+If **GMCM** is *not* installed, please uncomment the above line and rerun the script.
+
+## Load data
+The data is loaded and the first rows are printed
+
+```{r load-data, include=TRUE, echo=TRUE}
+ds <- read.table(file   = "{{input_file}}",
+                 header = {{header}},
+                 sep    = "{{sep}}",
+                 quote  = "\{{quote}}")
+head(ds, n = 4)
+```
+
+
+Next, the data is subset to the columns of interest.
+
+```{r select-data, include=TRUE, echo=TRUE}
+x <- ds[, {{cput(model_cols)}}]
+head(x, n = 2)
+```
+
+The rescaling is modified if the 'large values indicate strong evidence' checkbox was checked or not.
+The checkbox was `{{ ifelse(meta_large_vals, "checked.", "unchecked, so we reverse ranking.") }}`
+
+```{r preprocess-data}
+{{ ifelse(meta_large_vals, "u <- Uhat(x)", "u <- Uhat(-x)") }}
+```
+
+
+## Initial parameters
+The initial parameters, as chosen in the application, are set to
+
+```{r show-initial-params, include=TRUE, echo=TRUE}
+init_par <- {{cput(init_par)}}
+```
+
+
+## Model fitting
+With the data loaded and defined initial parameters, the model is can be fitted.
+
+```{r fit_model, error=TRUE}
+par <- fit.meta.GMCM(u = u,
+                     init.par = init_par,
+                     method = "{{meta_method}}",
+                     max.ite = {{meta_max_ite}},
+                     positive.rho = {{meta_positive_rho}},
+                     verbose = FALSE)
+print(par)
+```
+
+
+I.e. we set the fitting method to `"{{meta_method}}"` with a maximum number of iterations of `{{meta_max_ite}}`.
+
+## Meta analysis with unsupervised clustering
+The estimated parameters are used to calculate the local and adjusted irreproducibility discovery rates:
+
+```{r compute_probs}
+meta_IDR_thres <- {{meta_IDR_thres}}
+out <- get.IDR(x, par = par,
+               threshold = meta_IDR_thres) # Compute IDR
+str(out)
+
+# Use idr instead of IDR to threshold if specified
+out <- get.IDR(u, par = par, threshold = meta_IDR_thres)
+below <- (out[["{{meta_IDR_thres_type}}"]] < meta_IDR_thres)
+out$l <- sum(below)
+out$Khat <- ifelse(below, 2, 1)
+```
+
+By default, `get.IDR` computes thresholds on the adjusted IDR.
+The local irreproducibility discovery rate correspond to the posterior probability of the point originating from the irreproducible component.
+
+## Results
+The classes are counted by
+
+```{r classes_table}
+table(out$Khat)
+```
+
+The results are also displayed by plotting
+
+```{r plot_results}
+plot(x, col = out$Khat, asp = 1) # Plot of raw values
+plot(u, col = out$Khat, asp = 1) # Plot of copula values
+z <- GMCM:::qgmm.marginal(u, theta = meta2full(par, d = ncol(u))) # Estimate latent process
+plot(z, col = out$Khat, asp = 1) # Plot of estimated latent process
+```
+
+The fitted `par` object can be converted to a `theta` object which can be plotted directly:
+
+```{r plot_theta}
+plot(meta2full(par, d = ncol(u)))
+```
+
+
+### Session information
+This report was generated using **rmarkdown**^[2][2]^ and **knitr**^[3][3]^ under the session
+given below. The report utilizes [parameterized reports][2] and [`knitr::spin`][3].
+
+
+```{r session-info}
+sessionInfo()
+```
+
+
+### References
+Please cite the **GMCM** paper^[1][1]^ if you use the package or shiny app.
+
+```{r citation, echo=FALSE, results='asis'}
+cites <- lapply(c("GMCM", "knitr", "rmarkdown"), citation)
+fmt_cites <- unlist(lapply(cites, format, style = "text"))
+cat(paste0("  ", seq_along(fmt_cites), ". ", fmt_cites, "\n"))
+```
+
+[1]: http://doi.org/10.18637/jss.v070.i02
+[2]: https://bookdown.org/yihui/rmarkdown/parameterized-reports.html
+[3]: https://yihui.name/knitr/demo/stitch/