Package 'segmetric'

segmetric

Description

Metrics for assessing segmentation accuracy for geospatial data.

Purpose

The segmetric package provides a set of metrics for the segmentation accuracy assessment (or evaluation) of geospatial data. It includes more than 20 metrics used in the literature for spatial segmentation assessment (Van Rijsbergen, 1979; Levine and Nazif, 1982; Janssen and Molenaar, 1995; Lucieer and Stein, 2002; Carleer et al., 2005; Moller et al., 2007; van Coillie et al., 2008; Costa et al., 2008; Weidner, 2008; Feitosa et al., 2010; Clinton et al. 2010; Persello and Bruzzone, 2010; Yang et al., 2014; and Zhang et al., 2015).

Extensions

The segmetric package is extensible and provides a set of functions to ease the implementation of new metrics. See ?sm_reg_metric() to find how new metrics are implemented.

Contributions

Contribution to this package could be done at segmetric's page on GitHub: https://github.com/michellepicoli/segmetric.

Author(s)

Maintainer: Michelle Picoli [email protected] (ORCID)

Authors:

• Rolf Simoes [email protected] (ORCID)

• Alber Sanchez [email protected] (ORCID)

References

• Carleer, A.P., Debeir, O., Wolff, E., 2005. Assessment of very high spatial resolution satellite image segmentations. Photogramm. Eng. Remote. Sens. 71, 1285-1294. doi:10.14358/PERS.71.11.1285.

• Clinton, N., Holt, A., Scarborough, J., Yan, L., Gong, P., 2010. Accuracy assessment measures for object-based image segmentation goodness. Photogramm. Eng. Remote. Sens. 76, pp. 289-299.

• Costa, G.A.O.P., Feitosa, R.Q., Cazes, T.B., Feijo, B., 2008. Genetic adaptation of segmentation parameters. In: Blaschke, T., Lang, S., Hay, G.J. (Eds.), Object-based Image Analysis. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 679-695. doi:10.1007/978-3-540-77058-9_37.

• Dice, L.R., 1945. Measures of the amount of ecologic association between species. Ecology, 26(3), pp.297-302.

• Feitosa, R.Q., Ferreira, R.S., Almeida, C.M., Camargo, F.F., Costa, G.A.O.P., 2010. Similarity metrics for genetic adaptation of segmentation parameters. In: 3rd International Conference on Geographic Object-Based Image Analysis (GEOBIA 2010). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Ghent.

• Jaccard, P., 1912. The distribution of the flora in the alpine zone.

1. New phytologist, 11(2), pp.37-50. doi:10.1111/j.1469-8137.1912.tb05611.x

• Janssen, L.L.F., Molenaar, M., 1995. Terrain objects, their dynamics and their monitoring by the integration of GIS and remote sensing. IEEE Trans. Geosci. Remote Sens. 33, pp. 749-758. doi:10.1109/36.387590.

• Levine, M.D., Nazif, A.M., 1982. An experimental rule based system for testing low level segmentation strategies. In: Preston, K., Uhr, L. (Eds.), Multicomputers and Image Processing: Algorithms and Programs. Academic Press, New York, pp. 149-160.

• Lucieer, A., Stein, A., 2002. Existential uncertainty of spatial objects segmented from satellite sensor imagery. Geosci. Remote. Sens. IEEE Trans. 40, pp. 2518-2521. doi:10.1109/TGRS.2002.805072.

• Möller, M., Lymburner, L., Volk, M., 2007. The comparison index: a tool for assessing the accuracy of image segmentation. Int. J. Appl. Earth Obs. Geoinf. 9, pp. 311-321. doi:10.1016/j.jag.2006.10.002.

• Persello, C., Bruzzone, L., 2010. A novel protocol for accuracy assessment in classification of very high resolution images. IEEE Trans. Geosci. Remote Sens. 48, pp. 1232-1244. doi:10.1109/TGRS.2009.2029570.

• Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.,

1. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 658-666.

• Van Coillie, F.M.B., Verbeke, L.P.C., De Wulf, R.R., 2008. Semi-automated forest stand delineation using wavelet based segmentation of very high resolution optical imagery. In: Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, pp. 237-256. doi:10.1007/978-3-540-77058-9_13.

• Van Rijsbergen, C.J., 1979. Information Retrieval. Butterworth-Heinemann, London.

• Weidner, U., 2008. Contribution to the assessment of segmentation quality for remote sensing applications. In: Proceedings of the 21st Congress for the International Society for Photogrammetry and Remote Sensing, 03–11 July, Beijing, China. Vol. XXXVII. Part B7, pp. 479-484.

• Yang, J., Li, P., He, Y., 2014. A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation. ISPRS J. Photogramm. Remote Sens. 94, pp. 13-24. doi:10.1016/j.isprsjprs.2014.04.008.

• Yang, J., He, Y., Caspersen, J. P., Jones, T. A., 2017. Delineating Individual Tree Crowns in an Uneven-Aged, Mixed Broadleaf Forest Using Multispectral Watershed Segmentation and Multiscale Fitting. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10(4), pp. 1390-1401. doi:10.1109/JSTARS.2016.2638822.

• Zhan, Q., Molenaar, M., Tempfli, K., Shi, W., 2005. Quality assessment for geo‐spatial objects derived from remotely sensed data. International Journal of Remote Sensing, 26(14), pp.2953-2974. doi:10.1080/01431160500057764

• Zhang, X., Feng, X., Xiao, P., He, G., Zhu, L., 2015a. Segmentation quality evaluation using region-based precision and recall measures for remote sensing images. ISPRS J. Photogramm. Remote Sens. 102, pp. 73-84. doi:10.1016/j.isprsjprs.2015.01.009.

See Also

Useful links:

• https://michellepicoli.github.io/segmetric/

• Report bugs at https://github.com/michellepicoli/segmetric/issues

---

General functions

Description

These functions manipulate segmetric objects.

• sm_area(): Return a vector of areas, one for each polygon.

• sm_centroid(): Return the centroids of the given polygons.

• sm_intersection(): Return the intersection of the given simple features.

• sm_subset_union(): Return the union of the given simple features.

• sm_rbind(): Return the merge of unique simple features.

• sm_summarize_groups(): Summarizes metric values by a group (either reference or segment).

• sm_options(): Get/Set segmetric options.

Usage

sm_area(s, order = NULL)

sm_centroid(s, order = NULL)

sm_distance(s1, s2)

sm_intersection(s1, s2, touches = TRUE)

sm_subset_union(s)

sm_rbind(...)

sm_apply_group(x, groups, fn, ...)

sm_summarize_group(x, groups, fn, ...)

sm_norm_left(x, y)

sm_norm_right(x, y)

sm_options(..., digits = NULL)

Arguments

s, s1, s2	Either a ref_sf, a seg_sf, or a subset_sf object (inherited from sf).
order	A subset_sf. This argument arranges the returned values according to the object passed here.
touches	A logical. Is the border part of the intersection?
...	For sm_rbind(), a set of subset_sf objects to be merged.
x, y	A numeric values (e.g. areas, lengths) to compute metrics.
groups	A group identifier vector used to aggregate a metric for each group.
fn	A function to aggregate a metric for a group.
digits	An integer indicating how many digits used to round metric values.

Value

• sm_area(): Return a numeric vector with polygons' area.

• sm_centroid(): Return a subset_sf object with polygons' centroid.

• sm_intersection(): Return a subset_sf object with intersection between polygons.

• sm_subset_union(): Return a subset_sf object with union between intersecting polygons.

• sm_rbind(): Return a subset_sf object with unique features.

---

Metric functions

Description

The sm_compute() computes a given metric (metric_id parameter) from segmentation objects. It compares the reference to the segmentation polygons using a metric.

A list with all supported metrics can be obtained by sm_list_metrics() (see Details for more information).

The sm_metric_subset() returns the subset used to compute the metrics in segmetric object.

Usage

sm_compute(m, metric_id, ...)

sm_metric_subset(m, metric_id = NULL)

Arguments

m	A segmetric object.
metric_id	A character vector with metrics id to be computed.
...	Any additional argument to compute a metric (see Details).

Details

• "OS1" refers to Oversegmentation. Its values range from 0 (optimal) to 1 (Clinton et al., 2010).

• "US1" refers to Undersegmentation. Its values range from 0 (optimal) to 1 (Clinton et al., 2010).

• "OS2" refers to Oversegmentation. Its values range from 0 (optimal) to 1 (Persello and Bruzzone, 2010).

• "US2" refers to Undersegmentation. Its values range from 0 (optimal) to 1 (Persello and Bruzzone, 2010).

• "OS3" refers to Oversegmentation. Its values range from 0 (optimal) to 1 (Yang et al., 2014).

• "US3" refers to Undersegmentation. Its values range from 0 (optimal) to 1 (Yang et al., 2014).

• "AFI" refers to Area Fit Index. Its optimal value is 0 (Lucieer and Stein, 2002; Clinton et al., 2010).

• "QR" refers to Quality Rate. Its values range from 0 (optimal) to 1 (Weidner, 2008; Clinton et al., 2010).

• "D_index" refers to Index D. Its values range from 0 (optimal) to 1 (Levine and Nazif, 1982; Clinton et al., 2010).

• "precision" refers to Precision. Its values range from 0 to 1 (optimal) (Van Rijsbergen, 1979; Zhang et al., 2015).

• "recall" refers to Recall. Its values range from 0 to 1 (optimal) (Van Rijsbergen, 1979; Zhang et al., 2015).

• "UMerging" refers to Undermerging. Its values range from 0 (optimal) to 1 (Levine and Nazif, 1982; Clinton et al., 2010).

• "OMerging" refers to Overmerging. Its optimal value is 0 (Levine and Nazif, 1982; Clinton et al., 2010).

• "M" refers to Match. Its values range from 0 to 1 (optimal) (Janssen and Molenaar, 1995; Feitosa et al., 2010).

• "E" refers to Evaluation Measure. Its values range from 0 (optimal) to 100 (Carleer et al., 2005).

• "RAsub" refers to Relative Area. Its values range from 0 to 1 (optimal) (Müller et al., 2007; Clinton et al., 2010).

• "RAsuper" refers to Relative area. Its values range from 0 to 1 (optimal) (Müller et al., 2007; Clinton et al., 2010).

• "PI" refers to Purity Index. Its values range from 0 to 1 (optimal) (van Coillie et al., 2008).

• "Fitness" refers to Fitness Function. Its optimal value is 0 (Costa et al., 2008).

• "ED3" refers to Euclidean Distance. Its values range from 0 (optimal) to 1 (Yang et al., 2014).

• "F_measure" refers to F-measure metric. Its values range from 0 to 1 (optimal) (Van Rijsbergen, 1979; Zhang et al., 2015). It takes the optional weight argument alpha, ranging from 0.0 to 1.0 (the default is 0.5).

• "IoU" refers to Intersection over Union metric. Its values range from 0 to 1 (optimal) (Jaccard, 1912; Rezatofighi et al., 2019).

• "SimSize" refers to the similarity size metric. Its values range from 0 to 1 (optimal) (Zhan et al., 2005).

• "qLoc"refers to quality of object’s location metric. Its optimal value is 0 (Zhan et al., 2005).

• "RPsub" refers to Relative Position (sub) metric. Optimal value is 0 (Möller et al., 2007, Clinton et al., 2010).

• "RPsuper" refers to Relative Position (super) metric. Its values range from 0 (optimal) to 1 (Möller et al., 2007, Clinton et al., 2010).

• "OI2 refers to Overlap Index metric. Its values range from 0 to 1 (optimal) (Yang et al., 2017).

Value

Return a numeric vector with computed metric.

References

A complete list of cited references is available in ?segmetric.

See Also

sm_list_metrics()

Examples

# load sample datasets
data("sample_ref_sf", package = "segmetric")
data("sample_seg_sf", package = "segmetric")

# create segmetric object
m <- sm_read(ref_sf = sample_ref_sf, seg_sf = sample_seg_sf)

# compute AFI metric and summarize it
sm_compute(m, "AFI") %>% summary()

# compute three metrics and summarize them
sm_compute(m, c("AFI", "OS1", "US2")) %>% summary()

# compute OS1, F_measure, and US2 metrics using pipe
m <- sm_compute(m, "OS1") %>%
  sm_compute("F_measure") %>%
  sm_compute("US2")

# summarize them
summary(m)

---

Plot function

Description

Plot a segmetric map according to the parameter type:

• "base": simple plot of the reference or segmentation polygons;

• "subset": plot polygons from a subset over the base plot;

• "choropleth": plot a choropleth map from polygons of a subset using metric values.

Usage

## S3 method for class 'segmetric'
plot(
  x,
  type = "base",
  ...,
  title = NULL,
  layers = c("ref_sf", "seg_sf"),
  background = "#FFFFFF",
  ref_color = "#FF00009F",
  ref_fill = "#FFFFFF00",
  ref_label = "reference",
  ref_size = 2,
  ref_symbol = 2,
  seg_color = "#0000009F",
  seg_fill = "#FFFFFF00",
  seg_label = "segment",
  seg_size = 1,
  seg_symbol = 3,
  selected_fill = "#9A9AFF50",
  plot_centroids = TRUE,
  centroids_color = "#000000FF",
  centroids_label = "centroid",
  subset_id = NULL,
  subset_color = "#FFFFFF00",
  subset_fill = "#F0E4167F",
  metric_id = NULL,
  break_style = "jenks",
  choropleth_palette = "YlGnBu",
  choropleth_palette_reverse = FALSE,
  choropleth_size = 0.1,
  plot_extent = NULL,
  plot_legend = TRUE,
  plot_axes = TRUE
)

Arguments

x	A segmetric object.
type	A character. Either "base", "subset", or "choropleth".
...	Ignored.
title	A character with plot title
layers	A character. One or both of "ref_sf" and "seg_sf" (works only for type = "base" and type = "subset").
background	A character with valid color used in graph's background.
ref_color, seg_color, ref_fill, seg_fill	A character with a valid hexadecimal color in rgb or rgba format. Set the border and fill colors for reference and segmentation polygons.
ref_label, seg_label, centroids_label	A character with legend labels for reference polygons, segmentation polygons, and centroids.
ref_size, seg_size	A numeric. Set symbol's size for centroids.
ref_symbol, seg_symbol	An integer. Symbol to represent polygons' centroids (see pch param in points).
selected_fill	A character with a valid hexadecimal color in rgb or rgba format. Set the fill color of selected reference or segmentation polygons depending on subset_id.
plot_centroids	A logical. Plot centroids or not.
centroids_color	A character with a valid hexadecimal color in rgb or rgba format. Set the border colors for centroids.
subset_id	A character with subset name (required for type = "subset")
subset_color, subset_fill	A character with a valid hexadecimal color in rgb or rgba format. Set the border and fill colors for subset polygons (works only with type = "subset").
metric_id	A character with metric to be plotted in choropleth maps (required for type = "choropleth")
break_style	A character with the name of a method to compute the intervals for choropleth maps. Can be one of "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", "fisher", "jenks", "dpih", and "headtails" (see style parameter in classIntervals).
choropleth_palette	A character with a valid palette to be used in choropleth plots.
choropleth_palette_reverse	A logical indicating if palette should be generated in reversely.
choropleth_size	A numeric with border size used to plot polygons.
plot_extent	A sf object. Set the map extent for a plot.
plot_legend	A logical. Plot legend or not.
plot_axes	A logical. Plot coordinates axis or not.

---

LEM+ dataset

Description

ref_sf: a dataset containing field boundaries from Luiz Eduardo Magalhaes municipality, Brazil.

The data covers the following extent: xmin: -46.37683 ymin: -12.34579 xmax: -46.15776 ymax: -12.13663 CRS: EPSG:4326

sample_ref_sf: a subset of ref_sf dataset.

Usage

ref_sf

sample_ref_sf

Format

These datasets are objects of class sf (inherited from tbl_df, tbl, data.frame) with 2 variables:

• id: identification

• geometry: polygons

ref_sf: a dataset with 195 features.

sample_ref_sf: a dataset with 5 features.

Source

Oldoni et al. (2020) doi:10.1016/j.dib.2020.106553.

References

• Oldoni, L.V., Sanches, I.D.A., Picoli, M.C.A., Covre, R.M. and Fronza, J.G., 2020. LEM+ dataset: For agricultural remote sensing applications. Data in Brief, 33, p.106553.

Examples

data("ref_sf", package = "segmetric")
data("sample_ref_sf", package = "segmetric")

---

Segmentation dataset

Description

seg200_sf,seg500_sf,seg800_sf,seg1000_sf: a dataset containing segments generated from PlanetScope image, level 3B, acquired on Feb 18, 2020, with 3.7-meter resolution (Planet Team, 2017), using the multiresolution segmentation method (Baatz and Schape, 2000).

The data covers the approximately the same area of LEM+ dataset (see ref_sf).

The data was post-processed using the spectral difference algorithm on band 3.

The polygons were simplified using the Douglas-Peucker algorithm in QGIS.

Self-intersections were removed using SAGA's Polygon Self-Intersection.

Segmentation parameters:

• ⁠scale parameter⁠: 200 (seg200_sf), 500 (seg500_sf), 800 (seg800_sf), and 1000 (seg1000_sf)

• shape: 0.9

• compactness: 0.1

Spectral difference parameters:

• ⁠spectral difference⁠: 20

Simplification parameter:

• distance: 10-meters

Only those polygons intersecting reference data with an area-perimeter ratio above 25 were selected.

sample_seg_sf: a subset of seg_sf dataset.

Usage

seg200_sf

seg500_sf

seg800_sf

seg1000_sf

sample_seg_sf

Format

These datasets are objects of class sf (inherited from tbl_df, tbl, data.frame) with 2 variables:

• id: identification

• geometry: polygons

seg200_sf: a dataset with 547 features. seg500_sf: a dataset with 215 features. seg800_sf: a dataset with 169 features. seg1000_sf: a dataset with 158 features.

An object of class sf (inherits from tbl_df, tbl, data.frame) with 215 rows and 2 columns.

An object of class sf (inherits from tbl_df, tbl, data.frame) with 169 rows and 2 columns.

An object of class sf (inherits from tbl_df, tbl, data.frame) with 158 rows and 2 columns.

sample_seg_sf: a dataset with 6 features extracted from seg500_sf dataset.

References

• Planet Team, 2017. Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. https://www.planet.com

• Baatz, M., Schape, A., 2000. Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesebner, G. (Eds.), Angewandte Geographische Informations-Verarbeitung XII. Wichmann Verlag, Karlsruhe, Germany, pp. 12-23. <>

Examples

data("seg200_sf", package = "segmetric")
data("seg500_sf", package = "segmetric")
data("seg800_sf", package = "segmetric")
data("seg1000_sf", package = "segmetric")
data("sample_seg_sf", package = "segmetric")

---

Set functions

Description

These functions compute subsets required to calculate segmentation metrics as described in Clinton et al. (2010) and Costa et al. (2017).

• sm_ref() returns the set of $n$ polygons of reference, represented by $X = \{x_{i}: i = 1, ....., n\}$

• sm_seg() returns the set of $m$ segmentation polygons, represented by $Y = \{y_{j}: j = 1, ....., m\}$

• sm_ytilde() returns $\tilde{Y}_{i}$, a subset of $Y$, where $\tilde{Y}_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) \neq 0\}$

• sm_xtilde() returns $\tilde{X}_{j}$, a subset of $X$, where $\tilde{X}_{j} = \{x_{i}: \rm{area}(y_{j} \cap x_{i}) \neq 0\}$

• sm_yprime() returns $Y'_{i}$, a subset of $Y$, where $Y'_{i} = \{y_{j}: max(\rm{area}(x_{i} \cap y_{j}))\}$

• sm_xprime() returns $X'_{j}$, a subset of $X$, where $X'_{j} = \{x_{i}: max(\rm{area}(y_{j} \cap x_{i}))\}$

• sm_ya() returns $Y\!a_{i}$, a subset of $\tilde{Y}_{i}$, where $Y\!a_{i} = \{y_{j}: \rm{centroid}(x_{i}) \:\rm{in}\: y_{j}\}$

• sm_yb() returns $Y\!b_{i}$, a subset of $\tilde{Y}_{i}$, where $Y\!b_{i} = \{y_{j}: \rm{centroid}(y_{j}) \:\rm{in}\: x_{i}\}$

• sm_yc() returns $Y\!c_{i}$, a subset of $\tilde{Y}_{i}$, where $Y\!c_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(y_{j}) > 0.5\}$

• sm_yd() returns $Y\!d_{i}$, a subset of $\tilde{Y}_{i}$, where $Y\!d_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(x_{i}) > 0.5\}$

• sm_ystar() returns ${Y}^{*}_{i}$, where ${Y}^{*}_{i} = Y\!a_{i} \cup Y\!b_{i} \cup Y\!c_{i} \cup Y\!c_{i}$

• sm_ycd() returns $Y\!cd_{i}$, where $Y\!cd_{i} = Y\!c_{i} \cup Y\!d_{i}$

• sm_ye() returns $Y\!e_{i}$, a subset of $\tilde{Y}_{i}$, where $Y\!e_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(y_{j}) = 1\}$

• sm_yf() returns $Y\!f_{i}$, a subset of $\tilde{Y}_{i}$, where $Y\!f_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(y_{j}) > 0.55\}$

• sm_yg() returns $Y\!g_{i}$, a subset of $\tilde{Y}_{i}$, where $Y\!g_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(y_{j}) > 0.75\}$

Usage

sm_ytilde(m)

sm_xtilde(m)

sm_yprime(m)

sm_xprime(m)

sm_ya(m)

sm_yb(m)

sm_yc(m)

sm_yd(m)

sm_ystar(m)

sm_ycd(m)

sm_ye(m)

sm_yf(m)

sm_yg(m)

Arguments

m	A segmetric object.

Value

• sm_ref(): Return an object of class ref_sf (inherited from sf) containing identification (ref_id) and geometry (geometry) columns.

• sm_seg(): Return an object of class seg_sf (inherited from sf) containing identification (seg_id) and geometry (geometry) columns.

• sm_ytilde(), sm_xtilde(), sm_yprime(), sm_xprime(), sm_ya(), sm_yb(), sm_yc(), sm_yd(), sm_ystar(), sm_ycd(), sm_ye(), sm_yf(), and sm_yg(): Return an object of class subset_sf (inherited from sf) containing identification (ref_id and seg_id), and geometry (geometry) columns.

References

Clinton, N., Holt, A., Scarborough, J., Yan, L., & Gong, P. (2010). Accuracy Assessment Measures for Object-based Image Segmentation Goodness. Photogrammetric Engineering & Remote Sensing, 76(3), 289–299. doi:10.14358/PERS.76.3.289.

Costa, H., Foody, G. M., & Boyd, D. S. (2018). Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205(December 2017), 338–351. doi:10.1016/j.rse.2017.11.024.

---

Subset handling functions

Description

These functions are intended to be used in new metric extensions. They handle subset_sf data (inherited from sf class) stored in segmetric objects.

• sm_list() lists subsets already computed and stored in a segmetric object.

• sm_exists() verifies if a subset_id exists in a segmetric object.

• sm_subset() evaluates and stores a subset_sf object.

• sm_indirect() finds the subset_id of a given subset_sf object stored in a segmetric object.

• sm_segmetric() returns the segmetric object that stores a given subset object (either a ref_sf, a seg_sf, or a subset_sf).

• sm_get() retrieves a subset_sf object stored in a segmetric object.

• sm_inset() operator equivalent to inner join but returns only objects from s1, or its corresponding row in s2 if parameter return_index is TRUE.

• sm_group_by(): Apply a function to groups of subset_sf.

Usage

sm_list(m)

sm_exists(m, subset_id)

sm_subset(m, subset_id, expr = NULL)

sm_indirect(s)

sm_segmetric(s)

sm_get(m, subset_id)

sm_ref(m)

sm_seg(m)

sm_inset(s1, s2, return_index = FALSE)

## S3 method for class 'ref_sf'
sm_inset(s1, s2, return_index = FALSE)

## S3 method for class 'seg_sf'
sm_inset(s1, s2, return_index = FALSE)

## S3 method for class 'subset_sf'
sm_inset(s1, s2, return_index = FALSE)

sm_group_by(s, by, fn, ...)

Arguments

m	A segmetric object.
subset_id	A character value informing a subset name.
expr	A valid piece of code in R inside curly braces. This code is evaluated to generate a subset.
s, s1, s2	Either a ref_sf, a seg_sf, or a subset_sf object.
return_index	A logical value indicating if the corresponding rows in s1 should be returned instead of the actual corresponding values of s2.
by	A character value with the column to group.
fn	A function to apply on each group.
...	For sm_group_by(), extra parameter to pass to fn function.

Value

• sm_list(): Return a character vector with all names of subsets stored in the segmetric object.

• sm_exists(): Return a logical value indicating if a given subset name is stored in the segmetric object.

• sm_subset(): Return a subset_sf object.

• sm_indirect(): Return the subset name of a given subset_sf object stored in a segmetric object.

• sm_segmetric(): Return a segmetric object that stores a given subset_sf object.

• sm_get(): Return a subset_sf object stored in a segmetric object.

• sm_inset(): Return either a subset_sf object or an integer vector with the index of corresponding rows of s2 object.

• sm_group_by(): Return a subset_sf object.

Examples

# load sample datasets
data("sample_ref_sf", package = "segmetric")
data("sample_seg_sf", package = "segmetric")

# create segmetric object
m <- sm_read(ref_sf = sample_ref_sf, seg_sf = sample_seg_sf)

# lists only 'ref_sf' and 'seg_sf'
sm_list(m)

# computes 'Y_tilde' subset and stores it as 'test_subset' subset id
# sm_ytilde(m) also stores a subset under 'Y_tilde' id
s <- sm_subset(m, "test_subset", sm_ytilde(m))

# lists 'ref_sf', 'seg_sf', 'test_subset', and 'Y_tilde'
sm_list(m)

# which segmetric object stores `s` subset?
m2 <- sm_segmetric(s)

# m is identical to m2
identical(m, m2)

# which name `s` subset is stored in `m` segmetric object?
sm_indirect(s)

# retrieve 'test_subset' data from `m` object
s2 <- sm_get(m, 'test_subset')

# s is identical to s2
identical(s, s2)

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