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Update tests
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@paulgovan
paulgovan committed Dec 3, 2017
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1 change: 0 additions & 1 deletion inst/bn/server.R
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#' @import bnlearn
#' @import deal
#' @import heatmaply
#' @import plotly
#' @import rintrojs
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322 changes: 20 additions & 302 deletions inst/bn/tests/mytest-expected/003.json
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"rmd": "## Sample Code\nHere is some sample markdown to help illustrate the editor.\n\n### Get some sample data and show the first few values\n### Use `dat()` to get the active data set\n```{r}\ndat <- learning.test\nhead(dat)\n```\n\n### Learn the structure of the network\n```{r}\ndag <- bnlearn::cextend(bnlearn::gs(dat))\n```\n\n### Plot the force directed network\n```{r}\nnetworkData <- data.frame(bnlearn::arcs(dag))\n\nnetworkD3::simpleNetwork(\n networkData,\n Source = \"from\",\n Target = \"to\"\n)\n```\n\n### Print the network score\n```{r}\nbnlearn::score(dag, dat)\n```\n\n### Fit the model parameters and show the CPT for node A\n```{r}\nfit <- bnlearn::bn.fit(dag, dat)\nfit$A\n```\n\n### Plot the model parameters for node A\n```{r}\nbnlearn::bn.fit.barchart(fit[[\"A\"]])\n```\n\n### Get the Markov blanket for node A\n```{r}\nbnlearn::mb(dag, \"A\")\n```\n\n### Plot a d3 heatmap of the adjacency matrix\n```{r}\nheatmaply::heatmaply(\n bnlearn::amat(dag),\n grid_gap = 1,\n colors = blues9,\n dendrogram = \"both\",\n symm = TRUE,\n margins = c(100, 100, NA, 0),\n hide_colorbar = TRUE\n)\n````\n\n### Generate some random data from the network and show the first few values\n```{r}\nset.seed(1)\nsimData <- bnlearn::rbn(fit, n = 100, dat)\nhead(simData)\n```\n\n```{r}\n# Put your own code here...\n",
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"export": {

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