From e1ccaf073737b383319bba17e3fde625ef419e46 Mon Sep 17 00:00:00 2001 From: Clemens Schmid Date: Mon, 23 Oct 2023 15:14:39 +0200 Subject: [PATCH] removed backicks around mobest --- docs/advanced.md | 2 +- docs/estimation.md | 2 +- docs/types.md | 8 ++++---- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/advanced.md b/docs/advanced.md index 28f0c45..5844dba 100644 --- a/docs/advanced.md +++ b/docs/advanced.md @@ -509,7 +509,7 @@ Merged similarity search result based on two temporal resampling runs. ## Summarising origin vectors -In a very final step of the pipeline supported by `mobest`, we can combine origin vectors to meaningful summaries. Which summary statistics turn out to be useful strongly depends on the research questions guiding a particular project, so it is well likely that the following functions are not appropriate for a given use case. +In a very final step of the pipeline supported by mobest, we can combine origin vectors to meaningful summaries. Which summary statistics turn out to be useful strongly depends on the research questions guiding a particular project, so it is well likely that the following functions are not appropriate for a given use case. `mobest::pack_origin_vectors` takes an object of class `mobest_originvectors` and merges iterations of origin vectors that might have emerged from permutations in `mobest::locate_multi` into a single, mean (!) origin vector for each search individual. diff --git a/docs/estimation.md b/docs/estimation.md index bf295ec..9c86aad 100644 --- a/docs/estimation.md +++ b/docs/estimation.md @@ -1,6 +1,6 @@ # Parameter estimation for optimal ancestry interpolation -One important question for the Gaussian process regression performed within multiple of the core functions of `mobest` is how to find correct and useful settings for the kernel hyperparameters (see {ref}`Kernel parameter settings ` in the basic workflow description). Supplementary Text 2 of {cite:p}`Schmid2023` discusses this in detail. Based on this `mobest` provides different helper functions to either estimate the parameters or prepare data products that can be used to estimate them. Here we explain a practical way to estimate the nugget and lengthscale values. +One important question for the Gaussian process regression performed within multiple of the core functions of mobest is how to find correct and useful settings for the kernel hyperparameters (see {ref}`Kernel parameter settings ` in the basic workflow description). Supplementary Text 2 of {cite:p}`Schmid2023` discusses this in detail. Based on this mobest provides different helper functions to either estimate the parameters or prepare data products that can be used to estimate them. Here we explain a practical way to estimate the nugget and lengthscale values. For this tutorial we will use the data introduced and prepared in {doc}`A basic similarity search workflow `, specifically a `samples_projected.csv` table prepared in {ref}`Reading the input samples `. diff --git a/docs/types.md b/docs/types.md index 440d1d2..1a95602 100644 --- a/docs/types.md +++ b/docs/types.md @@ -1,14 +1,14 @@ # Input data types in the mobest R package -The following section briefly lists the main `mobest` input data types with their constructors. +The following section briefly lists the main mobest input data types with their constructors. ## Basic data types -`mobest` employs a number of basic [S3 data types](http://adv-r.had.co.nz/S3.html) to formalize the input to almost all of its functions. Most of then are tabular and inherit from `tibble::tibble()`. The constructors check certain properties to insure input correctness. +mobest employs a number of basic [S3 data types](http://adv-r.had.co.nz/S3.html) to formalize the input to almost all of its functions. Most of them are tabular and inherit from `tibble::tibble`. The constructors check certain properties to insure input correctness. ### Spatial coordinates -`mobest::create_geopos` creates an object of class `mobest_spatialpositions` which is a `tibble` that represents spatial positions. Spatial positions in `mobest` are always 2-dimensional coordinates in a Cartesian space. For real world coordinates that means, that they have to be transformed to a projected coordinate system (e.g. with `sf::st_transform`): `mobest` can not be used with longitude and latitude coordinates (also see See {ref}`Projecting the spatial data `). +`mobest::create_geopos()` creates an object of class `mobest_spatialpositions` which is a `tibble` that represents spatial positions. Spatial positions in mobest are always 2-dimensional coordinates in a Cartesian space. For real world coordinates that means, that they have to be transformed to a projected coordinate system (e.g. with `sf::st_transform`): mobest can not be used with longitude and latitude coordinates (also see See {ref}`Projecting the spatial data `). ```r mobest::create_geopos( @@ -90,7 +90,7 @@ If a function requires both input of type `mobest_observations` and `mobest_kern ## Permutation data types -When working with real data we often need to explore permutations of data or account for uncertainty by sampling from distributions (e.g. uncertain dating). To represent that, `mobest` provides wrapper classes and constructors with a `*_multi` suffix, to bundle multiple individual elements in a list class. Some of the core functions provide interfaces that automatically consider all permutations of these input lists. +When working with real data we often need to explore permutations of data or account for uncertainty by sampling from distributions (e.g. uncertain dating). To represent that, mobest provides wrapper classes and constructors with a `*_multi` suffix, to bundle multiple individual elements in a list class. Some of the core functions provide interfaces that automatically consider all permutations of these input lists. Available are: