Minor README update

README.Rmd

@@ -9,7 +9,8 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
   fig.path = "man/figures/README-",
-  out.width = "100%"
+  fig.align='center',
+  dpi = 300
 )
 ```
 
@@ -26,41 +27,40 @@ The goal of **segregatr** is to provide segregation analysis for clinical varian
 ## Installation
 
 You can install **segregatr** from CRAN as follows:
-
 ```{r, eval = FALSE}
 install.packages("segregatr")
 ```
 
-Alternatively, you can obtain the latest development version from GitHub:
+Alternatively, obtain the latest development version from GitHub:
 ```{r, eval = FALSE}
 devtools::install_github("magnusdv/segregatr")
 ```
 
 ## Example
 
-We start by loading **segregatr**:
-```{r}
+```{r, message=F}
 library(segregatr)
 ```
 
-The family below shows four brothers, all affected with a rare dominant disease with 90% penetrance and phenocopy rate 1%. The parents have unknown affection status. All four brothers are shown to carry a candidate variant, warranting a segregation analysis. pathogenic variant.
+The family below shows four brothers, all affected with a rare dominant disease with 90% penetrance and phenocopy rate 1%. The parents have unknown affection status. All four brothers are shown to carry a candidate variant.
 
-```{r sibex, echo = F, results = F, fig.height = 2.5, fig.width = 3, fig.align='center', out.width = "35%"}
+```{r sibex, echo = F, results = F, fig.height = 2.4, fig.width = 3, out.width = "40%"}
 x = nuclearPed(4)
-plotSegregation(x, affected = 3:6, unknown = 1:2L, proband = 3,
-                carriers = 3:6, margins = c(1,3,1,1))
+plotSegregation(x, affected = 3:6, unknown = 1:2, proband = 3, carriers = 3:6, margins = 1.5)
 ```
 
-In order to compute the full-likelihood Bayes factor, we first create the pedigree.
+We will use **segregatr** to analyse the co-segregation of the variant and the disease in this pedigree. Specifically we want to compute the *full-likelihood Bayes factor* (FLB), quantifying the evidence that the variant is pathogenic.
+
+To create the pedigree we use the `nuclearPed()` function from the **pedtools** package, which is automatically loaded together with **segregatr**.
 
 ```{r}
 x = nuclearPed(4)
 ```
 
 Then we run the `FLB()` function, filling in the necessary data:
 ```{r}
-FLB(x, carriers = 3:6, aff = 3:6, unknown = 1:2,
+FLB(x, carriers = 3:6, affected = 3:6, unknown = 1:2,
     freq = 0.0001, penetrances = c(0.01, 0.9, 0.9), proband = 3)
 ```
 
-The answer indicates only suggestive evidence for pathogenicity.
+The resulting FLB score is less than 8, which unfortunately only indicates suggestive evidence for pathogenicity.

README.md

@@ -22,32 +22,33 @@ You can install **segregatr** from CRAN as follows:
 install.packages("segregatr")
 ```
 
-Alternatively, you can obtain the latest development version from
-GitHub:
+Alternatively, obtain the latest development version from GitHub:
 
 ``` r
 devtools::install_github("magnusdv/segregatr")
 ```
 
 ## Example
 
-We start by loading **segregatr**:
-
 ``` r
 library(segregatr)
-#> Loading required package: pedtools
 ```
 
 The family below shows four brothers, all affected with a rare dominant
 disease with 90% penetrance and phenocopy rate 1%. The parents have
 unknown affection status. All four brothers are shown to carry a
-candidate variant, warranting a segregation analysis. pathogenic
-variant.
+candidate variant.
+
+<img src="man/figures/README-sibex-1.png" width="40%" style="display: block; margin: auto;" />
 
-<img src="man/figures/README-sibex-1.png" width="35%" style="display: block; margin: auto;" />
+We will use **segregatr** to analyse the co-segregation of the variant
+and the disease in this pedigree. Specifically we want to compute the
+*full-likelihood Bayes factor* (FLB), quantifying the evidence that the
+variant is pathogenic.
 
-In order to compute the full-likelihood Bayes factor, we first create
-the pedigree.
+To create the pedigree we use the `nuclearPed()` function from the
+**pedtools** package, which is automatically loaded together with
+**segregatr**.
 
 ``` r
 x = nuclearPed(4)
@@ -56,9 +57,10 @@ x = nuclearPed(4)
 Then we run the `FLB()` function, filling in the necessary data:
 
 ``` r
-FLB(x, carriers = 3:6, aff = 3:6, unknown = 1:2,
+FLB(x, carriers = 3:6, affected = 3:6, unknown = 1:2,
     freq = 0.0001, penetrances = c(0.01, 0.9, 0.9), proband = 3)
 #> [1] 7.732161
 ```
 
-The answer indicates only suggestive evidence for pathogenicity.
+The resulting FLB score is less than 8, which unfortunately only
+indicates suggestive evidence for pathogenicity.