Burned Lithics: An Experimental Approach to Characterize the Effects of Fire on the Lithic Archaeological Record of Northern Patagonia, Argentina. Lithic Technology.2024 To link to this article: https://doi.org/10.1080/01977261.2024.2425876
A field-based experiment was carried out to assess the impact of heat on lithic raw materials commonly used for knapping tools along the north Patagonian coast of Argentina. Descriptive data for the experimental items were recorded both before and after exposure to fire. Through network analysis, we explore the relationship between variables to establish the co-occurring of traits resulting from the material’s exposure to heat. Statistical analysis of network topology allows us to identify clusters of variables, which were categorized into morphological, mechanical, and pseudo-technological groups. These findings indicate that alterations within the same category covary more than those across different categories. The results obtained are relevant to addressing taphonomic issues in the northern coast of Patagonia, where frequent fire seasons affect surface sites. Ultimately, we aim to characterize these traits and build a reference framework to compare the transformations observed in the lithic archaeological record caused by natural fires.
#Packages
library(bootnet)## Cargando paquete requerido: ggplot2## This is bootnet 1.6## For questions and issues, please see github.com/SachaEpskamp/bootnet.library(qgraph)
library(igraph)## Warning: package 'igraph' was built under R version 4.4.2##
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## diversitylibrary(reshape2)
library(dplyr)##
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## intersect, setdiff, setequal, unionlibrary(vcdExtra)## Cargando paquete requerido: vcd## Cargando paquete requerido: grid## Cargando paquete requerido: gnm##
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## gnm##
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## summariselibrary(jakR)
library(rstatix)##
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## filterlibrary(changepoint)## Warning: package 'changepoint' was built under R version 4.4.2## Cargando paquete requerido: zoo##
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## WARNING: From v.2.3 the default method in cpt.* functions has changed from AMOC to PELT.
## See NEWS for details of all changes.library(MASS) ##
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## select## The following object is masked from 'package:dplyr':
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## selectlibrary(reshape2) #Datasets
Fire_Dat=read.table("FireDat2.txt", T) ##Experimental results traits, complete
Fire_Means=read.table("FireMeans.txt", T) ##Average measure of temperature for #each experiment
RawTraits2=read.table("RawMat_Traits.txt",T)#Traits for BA and VB only
Frag_Vol=read.table("Frag_Vol.txt",T)print(Fire_Dat)## Col Lus IOx Disc Crac Craz Exf Soot Adh FlScar Scar Rip Bulb PI
## 1 1 0 0 1 1 1 1 1 0 1 0 0 0 0
## 2 1 0 1 1 0 0 0 0 0 0 0 0 0 0
## 3 1 1 1 1 1 1 0 1 1 1 0 0 1 0
## 4 1 1 1 1 1 1 0 1 1 0 0 0 1 1
## 5 1 0 1 1 0 0 0 0 1 0 0 0 0 0
## 6 1 1 1 1 0 0 0 0 0 0 0 0 0 0
## 7 1 1 1 1 1 1 1 1 1 0 1 0 1 1
## 8 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 9 1 1 0 0 1 1 0 1 1 1 1 0 0 0
## 10 1 1 0 1 0 1 0 1 1 1 1 0 0 0
## 11 1 1 0 1 1 1 0 1 1 1 0 0 0 0
## 12 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 13 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 14 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 15 1 1 0 1 1 1 1 1 1 0 0 0 0 0
## 16 0 0 1 1 0 0 0 1 1 0 0 0 0 0
## 17 1 0 0 1 1 1 0 1 0 1 0 0 0 0
## 18 1 0 0 1 0 0 0 0 0 0 1 0 1 0
## 19 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 20 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 21 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 22 1 1 1 1 1 1 0 1 1 1 0 0 0 0
## 23 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 24 1 1 1 1 1 0 0 1 1 0 0 0 0 0
## 25 1 1 0 1 1 1 0 1 1 1 1 0 0 0
## 26 1 1 0 1 0 0 0 1 0 0 0 0 0 0
## 27 1 0 0 1 0 1 0 1 1 0 0 0 0 0
## 28 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 29 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 30 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 31 1 0 1 1 0 1 0 1 1 0 0 0 0 0
## 32 1 0 1 1 1 1 1 1 1 0 0 0 0 0
## 33 1 0 0 1 0 1 0 1 1 0 0 0 0 0
## 34 1 0 1 1 0 1 1 1 1 0 0 0 0 0
## 35 1 0 1 1 0 1 1 1 1 0 0 0 0 0
## 36 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 37 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 38 1 0 1 1 1 1 0 1 1 1 1 0 0 0
## 39 1 0 0 1 0 0 0 0 1 1 1 0 0 0
## 40 1 0 1 0 0 0 0 0 0 0 1 0 0 0
## 41 1 0 0 1 0 1 0 0 1 0 1 0 1 0
## 42 1 0 0 0 0 0 0 1 0 0 1 0 1 0
## 43 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 44 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 45 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 46 1 0 0 1 0 0 0 0 1 0 0 0 0 0
## 47 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 48 1 0 1 1 0 1 1 1 1 1 0 0 0 0
## 49 1 0 1 1 0 1 0 1 1 0 1 0 0 0
## 50 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 51 1 0 0 0 0 0 0 1 0 0 0 0 0 0
## 52 1 0 1 1 1 1 0 1 1 1 1 0 0 1
## 53 1 0 1 1 1 1 0 1 1 0 1 0 1 0
## 54 1 1 1 1 0 0 0 1 1 0 1 0 0 1
## 55 1 0 0 1 0 0 0 1 1 0 1 0 0 0
## 56 1 1 1 1 0 0 0 1 1 0 1 0 0 0
## 57 1 1 1 1 0 0 0 1 1 0 1 0 0 0
## 58 1 1 1 1 0 0 0 1 1 0 1 0 1 0
## 59 1 1 1 1 0 1 0 1 1 1 1 0 1 0
## 60 1 1 1 1 0 1 0 1 1 0 1 1 1 1
## 61 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 62 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 63 1 1 0 0 0 0 0 1 0 0 1 1 0 0
## 64 1 1 0 0 0 0 0 1 0 0 1 0 0 0
## 65 1 1 0 0 0 0 0 1 0 1 1 1 1 1
## 66 1 1 1 1 0 0 0 0 1 0 1 0 1 0
## 67 1 1 0 1 0 0 0 0 0 0 1 1 1 1
## 68 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 69 1 0 1 1 0 0 0 0 1 0 1 1 1 1
## 70 1 1 1 1 0 0 0 1 1 0 1 0 1 1
## 71 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 72 1 1 1 1 1 1 0 1 1 1 1 0 1 0
## 73 1 1 1 1 1 1 1 1 1 1 1 0 1 0
## 74 1 0 1 1 0 0 0 1 1 0 1 0 1 0
## 75 1 0 1 1 0 0 0 1 1 0 1 0 1 0
## 76 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 77 1 1 1 1 0 0 0 1 1 0 1 1 1 1
## 78 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 79 1 0 1 1 0 1 1 0 1 0 1 0 0 0
## 80 1 0 1 1 0 0 0 0 0 0 1 0 0 1
## 81 1 0 0 1 0 0 0 0 0 1 1 1 1 1
## 82 1 1 1 1 1 0 0 1 1 1 1 1 1 0
## 83 1 1 1 1 0 1 1 1 1 1 1 0 1 0
## 84 1 0 1 1 0 1 1 1 1 1 1 0 1 1
## 85 1 0 0 1 0 0 0 0 1 1 1 1 0 0
## 86 1 1 1 1 0 0 0 0 1 1 1 0 1 0
## 87 1 1 0 1 1 1 0 1 1 1 1 1 1 0
## 88 1 0 1 1 0 1 0 1 1 1 1 0 1 0
## 89 1 0 1 1 1 1 0 1 1 1 1 1 0 0
## 90 1 0 1 1 0 1 0 0 0 1 1 0 0 0
## 91 1 1 0 1 0 1 0 0 0 1 1 1 0 0
## 92 1 1 1 1 0 1 0 1 0 1 1 1 0 0
## 93 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 94 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 95 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 96 1 1 1 1 0 1 0 1 1 0 0 1 1 1
## 97 1 0 1 1 0 0 0 0 1 0 1 1 1 1
## 98 1 0 0 1 1 1 0 0 1 0 1 1 1 1
## 99 1 1 0 0 1 1 0 1 0 0 1 1 1 1
## 100 1 1 1 1 0 0 0 1 1 0 1 1 1 0
## 101 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 102 1 0 1 1 0 0 0 1 1 0 1 1 1 0
## 103 0 0 0 0 0 1 0 1 0 0 1 1 1 0
## 104 0 0 1 1 0 0 0 0 0 0 1 0 0 0
## 105 1 0 1 1 0 0 0 0 0 0 1 1 1 0
## 106 1 0 0 0 0 0 0 1 1 0 0 0 0 0
## 107 0 0 0 1 0 0 0 0 1 0 1 0 0 0
## 108 1 1 1 1 0 0 0 0 1 0 1 1 0 0
## 109 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 0 1 1 1 0 0 0 1 1 1 0
## 111 0 0 0 0 0 0 0 0 0 0 1 1 1 0
## 112 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 113 0 1 0 0 0 0 0 0 0 0 0 1 0 0
## 114 1 1 0 1 1 1 1 1 1 0 0 0 0 0
## 115 1 1 0 1 1 0 1 1 1 0 0 0 0 0
## 116 1 1 0 1 1 0 0 1 1 0 0 0 0 0
## 117 1 0 0 1 1 0 0 1 0 1 0 0 0 0
## 118 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 119 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 120 1 0 0 1 1 1 1 1 1 0 0 0 0 0
## 121 1 1 1 1 0 1 0 1 1 0 0 0 0 0
## 122 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 123 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 124 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 125 1 0 1 1 0 1 0 1 1 0 0 0 0 0
## 126 1 1 1 1 1 1 1 1 1 1 1 0 1 0
## 127 1 0 1 1 1 1 0 1 1 1 1 1 1 0
## 128 1 0 1 1 0 0 0 1 1 0 0 0 0 0
## 129 1 0 1 1 0 0 0 1 1 0 1 0 0 0
## 130 1 0 1 1 0 1 0 1 1 0 0 0 1 0
## 131 1 1 1 1 1 1 1 1 1 1 0 0 0 0
## 132 1 1 1 1 0 0 0 1 0 0 0 0 0 0
## 133 1 1 1 1 0 0 0 1 0 0 0 0 0 0
## 134 1 0 1 1 1 1 0 1 1 1 0 0 0 0
## 135 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 136 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 137 1 0 1 1 1 1 0 1 0 0 0 0 0 0
## 138 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 139 1 1 0 1 1 1 0 1 1 1 0 0 0 0
## 140 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 141 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 142 1 1 1 1 1 1 1 1 0 1 0 0 0 0
## 143 1 1 1 1 1 1 0 1 1 0 0 0 0 0print(Fire_Dat)## Col Lus IOx Disc Crac Craz Exf Soot Adh FlScar Scar Rip Bulb PI
## 1 1 0 0 1 1 1 1 1 0 1 0 0 0 0
## 2 1 0 1 1 0 0 0 0 0 0 0 0 0 0
## 3 1 1 1 1 1 1 0 1 1 1 0 0 1 0
## 4 1 1 1 1 1 1 0 1 1 0 0 0 1 1
## 5 1 0 1 1 0 0 0 0 1 0 0 0 0 0
## 6 1 1 1 1 0 0 0 0 0 0 0 0 0 0
## 7 1 1 1 1 1 1 1 1 1 0 1 0 1 1
## 8 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 9 1 1 0 0 1 1 0 1 1 1 1 0 0 0
## 10 1 1 0 1 0 1 0 1 1 1 1 0 0 0
## 11 1 1 0 1 1 1 0 1 1 1 0 0 0 0
## 12 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 13 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 14 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 15 1 1 0 1 1 1 1 1 1 0 0 0 0 0
## 16 0 0 1 1 0 0 0 1 1 0 0 0 0 0
## 17 1 0 0 1 1 1 0 1 0 1 0 0 0 0
## 18 1 0 0 1 0 0 0 0 0 0 1 0 1 0
## 19 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 20 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 21 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 22 1 1 1 1 1 1 0 1 1 1 0 0 0 0
## 23 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 24 1 1 1 1 1 0 0 1 1 0 0 0 0 0
## 25 1 1 0 1 1 1 0 1 1 1 1 0 0 0
## 26 1 1 0 1 0 0 0 1 0 0 0 0 0 0
## 27 1 0 0 1 0 1 0 1 1 0 0 0 0 0
## 28 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 29 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 30 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 31 1 0 1 1 0 1 0 1 1 0 0 0 0 0
## 32 1 0 1 1 1 1 1 1 1 0 0 0 0 0
## 33 1 0 0 1 0 1 0 1 1 0 0 0 0 0
## 34 1 0 1 1 0 1 1 1 1 0 0 0 0 0
## 35 1 0 1 1 0 1 1 1 1 0 0 0 0 0
## 36 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 37 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 38 1 0 1 1 1 1 0 1 1 1 1 0 0 0
## 39 1 0 0 1 0 0 0 0 1 1 1 0 0 0
## 40 1 0 1 0 0 0 0 0 0 0 1 0 0 0
## 41 1 0 0 1 0 1 0 0 1 0 1 0 1 0
## 42 1 0 0 0 0 0 0 1 0 0 1 0 1 0
## 43 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 44 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 45 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 46 1 0 0 1 0 0 0 0 1 0 0 0 0 0
## 47 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 48 1 0 1 1 0 1 1 1 1 1 0 0 0 0
## 49 1 0 1 1 0 1 0 1 1 0 1 0 0 0
## 50 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 51 1 0 0 0 0 0 0 1 0 0 0 0 0 0
## 52 1 0 1 1 1 1 0 1 1 1 1 0 0 1
## 53 1 0 1 1 1 1 0 1 1 0 1 0 1 0
## 54 1 1 1 1 0 0 0 1 1 0 1 0 0 1
## 55 1 0 0 1 0 0 0 1 1 0 1 0 0 0
## 56 1 1 1 1 0 0 0 1 1 0 1 0 0 0
## 57 1 1 1 1 0 0 0 1 1 0 1 0 0 0
## 58 1 1 1 1 0 0 0 1 1 0 1 0 1 0
## 59 1 1 1 1 0 1 0 1 1 1 1 0 1 0
## 60 1 1 1 1 0 1 0 1 1 0 1 1 1 1
## 61 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 62 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 63 1 1 0 0 0 0 0 1 0 0 1 1 0 0
## 64 1 1 0 0 0 0 0 1 0 0 1 0 0 0
## 65 1 1 0 0 0 0 0 1 0 1 1 1 1 1
## 66 1 1 1 1 0 0 0 0 1 0 1 0 1 0
## 67 1 1 0 1 0 0 0 0 0 0 1 1 1 1
## 68 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 69 1 0 1 1 0 0 0 0 1 0 1 1 1 1
## 70 1 1 1 1 0 0 0 1 1 0 1 0 1 1
## 71 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 72 1 1 1 1 1 1 0 1 1 1 1 0 1 0
## 73 1 1 1 1 1 1 1 1 1 1 1 0 1 0
## 74 1 0 1 1 0 0 0 1 1 0 1 0 1 0
## 75 1 0 1 1 0 0 0 1 1 0 1 0 1 0
## 76 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 77 1 1 1 1 0 0 0 1 1 0 1 1 1 1
## 78 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 79 1 0 1 1 0 1 1 0 1 0 1 0 0 0
## 80 1 0 1 1 0 0 0 0 0 0 1 0 0 1
## 81 1 0 0 1 0 0 0 0 0 1 1 1 1 1
## 82 1 1 1 1 1 0 0 1 1 1 1 1 1 0
## 83 1 1 1 1 0 1 1 1 1 1 1 0 1 0
## 84 1 0 1 1 0 1 1 1 1 1 1 0 1 1
## 85 1 0 0 1 0 0 0 0 1 1 1 1 0 0
## 86 1 1 1 1 0 0 0 0 1 1 1 0 1 0
## 87 1 1 0 1 1 1 0 1 1 1 1 1 1 0
## 88 1 0 1 1 0 1 0 1 1 1 1 0 1 0
## 89 1 0 1 1 1 1 0 1 1 1 1 1 0 0
## 90 1 0 1 1 0 1 0 0 0 1 1 0 0 0
## 91 1 1 0 1 0 1 0 0 0 1 1 1 0 0
## 92 1 1 1 1 0 1 0 1 0 1 1 1 0 0
## 93 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 94 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 95 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 96 1 1 1 1 0 1 0 1 1 0 0 1 1 1
## 97 1 0 1 1 0 0 0 0 1 0 1 1 1 1
## 98 1 0 0 1 1 1 0 0 1 0 1 1 1 1
## 99 1 1 0 0 1 1 0 1 0 0 1 1 1 1
## 100 1 1 1 1 0 0 0 1 1 0 1 1 1 0
## 101 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 102 1 0 1 1 0 0 0 1 1 0 1 1 1 0
## 103 0 0 0 0 0 1 0 1 0 0 1 1 1 0
## 104 0 0 1 1 0 0 0 0 0 0 1 0 0 0
## 105 1 0 1 1 0 0 0 0 0 0 1 1 1 0
## 106 1 0 0 0 0 0 0 1 1 0 0 0 0 0
## 107 0 0 0 1 0 0 0 0 1 0 1 0 0 0
## 108 1 1 1 1 0 0 0 0 1 0 1 1 0 0
## 109 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 0 1 1 1 0 0 0 1 1 1 0
## 111 0 0 0 0 0 0 0 0 0 0 1 1 1 0
## 112 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 113 0 1 0 0 0 0 0 0 0 0 0 1 0 0
## 114 1 1 0 1 1 1 1 1 1 0 0 0 0 0
## 115 1 1 0 1 1 0 1 1 1 0 0 0 0 0
## 116 1 1 0 1 1 0 0 1 1 0 0 0 0 0
## 117 1 0 0 1 1 0 0 1 0 1 0 0 0 0
## 118 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 119 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 120 1 0 0 1 1 1 1 1 1 0 0 0 0 0
## 121 1 1 1 1 0 1 0 1 1 0 0 0 0 0
## 122 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 123 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 124 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 125 1 0 1 1 0 1 0 1 1 0 0 0 0 0
## 126 1 1 1 1 1 1 1 1 1 1 1 0 1 0
## 127 1 0 1 1 1 1 0 1 1 1 1 1 1 0
## 128 1 0 1 1 0 0 0 1 1 0 0 0 0 0
## 129 1 0 1 1 0 0 0 1 1 0 1 0 0 0
## 130 1 0 1 1 0 1 0 1 1 0 0 0 1 0
## 131 1 1 1 1 1 1 1 1 1 1 0 0 0 0
## 132 1 1 1 1 0 0 0 1 0 0 0 0 0 0
## 133 1 1 1 1 0 0 0 1 0 0 0 0 0 0
## 134 1 0 1 1 1 1 0 1 1 1 0 0 0 0
## 135 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 136 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 137 1 0 1 1 1 1 0 1 0 0 0 0 0 0
## 138 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 139 1 1 0 1 1 1 0 1 1 1 0 0 0 0
## 140 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 141 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 142 1 1 1 1 1 1 1 1 0 1 0 0 0 0
## 143 1 1 1 1 1 1 0 1 1 0 0 0 0 0print(Fire_Means)## Measure Exp1 Exp2 Exp3
## 1 1 197.67 371.67 416.6667
## 2 2 286.00 643.33 770.6667
## 3 3 706.33 713.33 682.0000
## 4 4 562.33 643.33 670.6667
## 5 5 226.67 557.67 633.0000
## 6 6 336.67 534.33 625.3333
## 7 7 509.00 527.33 537.6667
## 8 8 553.00 485.67 424.6667
## 9 9 512.33 452.67 442.0000
## 10 10 393.33 436.00 353.0000
## 11 11 445.00 402.67 359.6667
## 12 12 489.67 456.33 302.0000
## 13 13 375.00 387.00 337.0000
## 14 14 445.00 404.00 311.3333
## 15 15 506.33 358.67 275.0000
## 16 16 385.67 379.67 271.6667
## 17 17 288.00 351.67 261.6667
## 18 18 301.67 336.33 299.6667
## 19 19 400.33 299.33 230.3333
## 20 20 329.00 300.33 230.6667
## 21 21 331.00 261.67 258.6667
## 22 22 304.33 236.33 260.0000
## 23 23 337.67 242.33 213.0000
## 24 24 284.33 239.67 223.3333
## 25 25 230.67 210.00 237.6667
## 26 26 226.67 227.67 227.0000
## 27 27 206.67 196.33 207.0000
## 28 28 211.00 176.67 227.3333
## 29 29 187.00 193.33 201.6667
## 30 30 208.67 193.00 218.6667
## 31 31 149.33 192.00 212.6667
## 32 32 180.67 201.67 175.6667
## 33 33 152.67 168.33 199.3333
## 34 34 161.33 184.33 196.3333
## 35 35 156.33 172.67 183.3333
## 36 36 157.33 154.00 182.6667
## 37 37 150.33 146.00 181.3333
## 38 38 138.00 152.67 155.0000
## 39 39 97.00 132.00 135.0000
## 40 40 54.33 117.33 143.0000
## 41 41 44.33 119.00 125.6667print(RawTraits2)## Rock_Type Col Lus IOx Disc Crac Craz Exf Soot Adh FlScar Scar Rip Bulb PI
## 1 VA 1 0 0 1 1 1 1 1 0 1 0 0 0 0
## 2 VA 1 0 1 1 0 0 0 0 0 0 0 0 0 0
## 3 VA 1 1 1 1 1 1 0 1 1 1 0 0 1 0
## 4 VA 1 1 1 1 1 1 0 1 1 0 0 0 1 1
## 5 VA 1 0 1 1 0 0 0 0 1 0 0 0 0 0
## 6 VA 1 1 1 1 0 0 0 0 0 0 0 0 0 0
## 7 VA 1 1 1 1 1 1 1 1 1 0 1 0 1 1
## 8 VA 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 9 VA 1 1 0 0 1 1 0 1 1 1 1 0 0 0
## 10 VA 1 1 0 1 0 1 0 1 1 1 1 0 0 0
## 11 VA 1 1 0 1 1 1 0 1 1 1 0 0 0 0
## 12 VB 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 13 VA 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 14 VA 1 1 0 1 1 1 0 1 1 0 0 0 0 0
## 15 VB 1 1 0 1 1 1 1 1 1 0 0 0 0 0
## 16 VA 0 0 1 1 0 0 0 1 1 0 0 0 0 0
## 17 VB 1 0 0 1 1 1 0 1 0 1 0 0 0 0
## 18 VB 1 0 0 1 0 0 0 0 0 0 1 0 1 0
## 19 VB 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 20 VB 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 21 VB 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 22 VA 1 1 1 1 1 1 0 1 1 1 0 0 0 0
## 23 VA 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 24 VA 1 1 1 1 1 0 0 1 1 0 0 0 0 0
## 25 VB 1 1 0 1 1 1 0 1 1 1 1 0 0 0
## 26 VB 1 1 0 1 0 0 0 1 0 0 0 0 0 0
## 27 VB 1 0 0 1 0 1 0 1 1 0 0 0 0 0
## 28 VB 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 29 VB 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 30 VA 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 31 VB 1 0 1 1 0 1 0 1 1 0 0 0 0 0
## 32 VB 1 0 1 1 1 1 1 1 1 0 0 0 0 0
## 33 VA 1 0 0 1 0 1 0 1 1 0 0 0 0 0
## 34 VA 1 0 1 1 0 1 1 1 1 0 0 0 0 0
## 35 VA 1 0 1 1 0 1 1 1 1 0 0 0 0 0
## 36 VA 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 37 VA 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 38 VA 1 0 1 1 1 1 0 1 1 1 1 0 0 0
## 39 VA 1 0 0 1 0 0 0 0 1 1 1 0 0 0
## 40 VA 1 0 1 0 0 0 0 0 0 0 1 0 0 0
## 41 VA 1 0 0 1 0 1 0 0 1 0 1 0 1 0
## 42 VA 1 0 0 0 0 0 0 1 0 0 1 0 1 0
## 43 VA 1 0 0 1 0 0 0 0 0 0 1 0 0 0
## 44 VA 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 45 VA 1 0 0 1 0 0 0 1 1 0 0 0 0 0
## 46 VA 1 0 0 1 0 0 0 0 1 0 0 0 0 0
## 47 VA 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 48 VA 1 0 1 1 0 1 1 1 1 1 0 0 0 0
## 49 VA 1 0 1 1 0 1 0 1 1 0 1 0 0 0
## 50 VA 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 51 VA 1 0 0 0 0 0 0 1 0 0 0 0 0 0
## 52 VB 1 0 1 1 1 1 0 1 1 1 1 0 0 1
## 53 VB 1 0 1 1 1 1 0 1 1 0 1 0 1 0
## 54 VB 1 1 1 1 0 0 0 1 1 0 1 0 0 1
## 55 VB 1 0 0 1 0 0 0 1 1 0 1 0 0 0
## 56 VB 1 1 1 1 0 0 0 1 1 0 1 0 0 0
## 57 VB 1 1 1 1 0 0 0 1 1 0 1 0 0 0
## 58 VB 1 1 1 1 0 0 0 1 1 0 1 0 1 0
## 59 VB 1 1 1 1 0 1 0 1 1 1 1 0 1 0
## 60 VB 1 1 1 1 0 1 0 1 1 0 1 1 1 1
## 61 VB 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 62 VB 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 63 VB 1 1 0 0 0 0 0 1 0 0 1 1 0 0
## 64 VB 1 1 0 0 0 0 0 1 0 0 1 0 0 0
## 65 VB 1 1 0 0 0 0 0 1 0 1 1 1 1 1
## 66 VB 1 1 1 1 0 0 0 0 1 0 1 0 1 0
## 67 VB 1 1 0 1 0 0 0 0 0 0 1 1 1 1
## 68 VB 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 69 VB 1 0 1 1 0 0 0 0 1 0 1 1 1 1
## 70 VB 1 1 1 1 0 0 0 1 1 0 1 0 1 1
## 71 VB 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 72 VB 1 1 1 1 1 1 0 1 1 1 1 0 1 0
## 73 VB 1 1 1 1 1 1 1 1 1 1 1 0 1 0
## 74 VB 1 0 1 1 0 0 0 1 1 0 1 0 1 0
## 75 VB 1 0 1 1 0 0 0 1 1 0 1 0 1 0
## 76 VB 1 0 1 1 1 1 1 1 1 1 0 0 0 0
## 77 VB 1 1 1 1 0 0 0 1 1 0 1 1 1 1
## 78 VB 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 79 VB 1 0 1 1 0 1 1 0 1 0 1 0 0 0
## 80 VB 1 0 1 1 0 0 0 0 0 0 1 0 0 1
## 81 VB 1 0 0 1 0 0 0 0 0 1 1 1 1 1
## 82 VB 1 1 1 1 1 0 0 1 1 1 1 1 1 0
## 83 VB 1 1 1 1 0 1 1 1 1 1 1 0 1 0
## 84 VB 1 0 1 1 0 1 1 1 1 1 1 0 1 1
## 85 VB 1 0 0 1 0 0 0 0 1 1 1 1 0 0
## 86 VB 1 1 1 1 0 0 0 0 1 1 1 0 1 0
## 87 VB 1 1 0 1 1 1 0 1 1 1 1 1 1 0
## 88 VB 1 0 1 1 0 1 0 1 1 1 1 0 1 0
## 89 VB 1 0 1 1 1 1 0 1 1 1 1 1 0 0
## 90 VB 1 0 1 1 0 1 0 0 0 1 1 0 0 0
## 91 VB 1 1 0 1 0 1 0 0 0 1 1 1 0 0
## 92 VB 1 1 1 1 0 1 0 1 0 1 1 1 0 0
## 93 VB 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 94 VB 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 95 VB 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 96 VB 1 1 1 1 0 1 0 1 1 0 0 1 1 1
## 97 VB 1 0 1 1 0 0 0 0 1 0 1 1 1 1
## 98 VB 1 0 0 1 1 1 0 0 1 0 1 1 1 1
## 99 VB 1 1 0 0 1 1 0 1 0 0 1 1 1 1
## 100 VB 1 1 1 1 0 0 0 1 1 0 1 1 1 0
## 101 VB 0 0 1 1 0 0 0 0 1 0 1 1 1 0
## 102 VB 1 0 1 1 0 0 0 1 1 0 1 1 1 0
## 103 VB 0 0 0 0 0 1 0 1 0 0 1 1 1 0
## 104 VB 0 0 1 1 0 0 0 0 0 0 1 0 0 0
## 105 VB 1 0 1 1 0 0 0 0 0 0 1 1 1 0
## 106 VB 1 0 0 0 0 0 0 1 1 0 0 0 0 0
## 107 VB 0 0 0 1 0 0 0 0 1 0 1 0 0 0
## 108 VB 1 1 1 1 0 0 0 0 1 0 1 1 0 0
## 109 VB 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 110 VB 0 0 0 0 1 1 1 0 0 0 1 1 1 0
## 111 VB 0 0 0 0 0 0 0 0 0 0 1 1 1 0
## 112 VB 1 0 0 1 0 0 0 0 1 0 1 0 0 0
## 113 VB 0 1 0 0 0 0 0 0 0 0 0 1 0 0
## 114 VB 1 1 0 1 1 1 1 1 1 0 0 0 0 0
## 115 VB 1 1 0 1 1 0 1 1 1 0 0 0 0 0
## 116 VB 1 1 0 1 1 0 0 1 1 0 0 0 0 0
## 117 VB 1 0 0 1 1 0 0 1 0 1 0 0 0 0
## 118 VB 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 119 VB 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 120 VB 1 0 0 1 1 1 1 1 1 0 0 0 0 0
## 121 VB 1 1 1 1 0 1 0 1 1 0 0 0 0 0
## 122 VB 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 123 VB 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 124 VB 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 125 VB 1 0 1 1 0 1 0 1 1 0 0 0 0 0
## 126 VA 1 1 1 1 1 1 1 1 1 1 1 0 1 0
## 127 VA 1 0 1 1 1 1 0 1 1 1 1 1 1 0
## 128 VA 1 0 1 1 0 0 0 1 1 0 0 0 0 0
## 129 VA 1 0 1 1 0 0 0 1 1 0 1 0 0 0
## 130 VA 1 0 1 1 0 1 0 1 1 0 0 0 1 0
## 131 VA 1 1 1 1 1 1 1 1 1 1 0 0 0 0
## 132 VA 1 1 1 1 0 0 0 1 0 0 0 0 0 0
## 133 VA 1 1 1 1 0 0 0 1 0 0 0 0 0 0
## 134 VB 1 0 1 1 1 1 0 1 1 1 0 0 0 0
## 135 VB 1 0 1 1 1 1 0 1 1 0 0 0 0 0
## 136 VA 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 137 VA 1 0 1 1 1 1 0 1 0 0 0 0 0 0
## 138 VB 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 139 VB 1 1 0 1 1 1 0 1 1 1 0 0 0 0
## 140 VA 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 141 VA 1 1 1 1 1 1 0 1 1 0 0 0 0 0
## 142 VB 1 1 1 1 1 1 1 1 0 1 0 0 0 0
## 143 VB 1 1 1 1 1 1 0 1 1 0 0 0 0 0print(Frag_Vol)## Volume Frag
## 1 128.772 0
## 2 326.536 0
## 3 262.080 0
## 4 260.442 0
## 5 35.280 0
## 6 179.400 0
## 7 290.360 2
## 8 230.050 0
## 9 390.720 0
## 10 241.020 2
## 11 455.328 0
## 12 611.010 0
## 13 247.104 0
## 14 217.620 5
## 15 297.369 6
## 16 290.880 0
## 17 357.840 4
## 18 136.500 0
## 19 170.100 3
## 20 278.850 0
## 21 214.500 0
## 22 572.355 0
## 23 284.130 0
## 24 115.200 3
## 25 215.730 0
## 26 241.056 0
## 27 154.734 3
## 28 598.575 0
## 29 161.280 0
## 30 319.200 2
## 31 127.280 0
## 32 71.680 10
## 33 113.220 0
## 34 205.200 2
## 35 73.749 0
## 36 180.000 0
## 37 336.600 0
## 38 60.320 0
## 39 180.320 5
## 40 404.712 0
## 41 402.215 0
## 42 226.352 0
## 43 394.630 0
## 44 591.075 0
## 45 163.530 2
## 46 136.000 0
## 47 97.539 0
## 48 183.150 31
## 49 304.803 0
## 50 132.000 0
## 51 232.400 0
## 52 58.996 0
## 53 430.100 0
## 54 128.928 0
## 55 328.860 5
## 56 143.820 0
## 57 145.728 2
## 58 26.609 6
## 59 270.300 0
## 60 469.000 0#Analysis of experimental data. Fire variation
FireLong<- melt(Fire_Means, ##reshape to extended format
variable.name = "Exp",
value.name = "Temp",
measure.var = c("Exp1", "Exp2", "Exp3"),
na.rm = TRUE)
colnames(FireLong)[colnames(FireLong) == "value"] <- "Temp"
colnames(FireLong)[colnames(FireLong) == "variable"] <- "Exp"
##Mean of all firings
Global_Mean <- aggregate(Temp ~ Measure, data = FireLong, mean)##mean of all
#experiments
##Descriptive statistics of the mean temperature of the three experiments
print(summary(Global_Mean$Temp))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 96.33 186.00 283.78 307.12 394.11 700.55##Plot##
ggplot(FireLong, aes(x = Measure, y = Temp, group = Exp)) +
geom_line(aes(color = Exp)) +
geom_point(aes(color = Exp)) +
geom_smooth(data = Global_Mean , aes(x = Measure, y = Temp),
method = "loess", se = FALSE, color = "black", group = 1)+
theme_classic()## `geom_smooth()` using formula = 'y ~ x'
###comparison between average mean of each experiment
Temp_Test <- kruskal.test(Temp ~ Exp, data =FireLong)
print(Temp_Test)##
## Kruskal-Wallis rank sum test
##
## data: Temp by Exp
## Kruskal-Wallis chi-squared = 0.11682, df = 2, p-value = 0.9433
Experimental setting
cpTemp =cpt.mean(Global_Mean$Temp)
# Results
print(cpTemp)## Class 'cpt' : Changepoint Object
## ~~ : S4 class containing 12 slots with names
## cpttype date version data.set method test.stat pen.type pen.value minseglen cpts ncpts.max param.est
##
## Created on : Mon Nov 25 09:42:11 2024
##
## summary(.) :
## ----------
## Created Using changepoint version 2.3
## Changepoint type : Change in mean
## Method of analysis : PELT
## Test Statistic : Normal
## Type of penalty : MBIC with value, 11.14072
## Minimum Segment Length : 1
## Maximum no. of cpts : Inf
## Number of changepoints: 34plot(cpTemp)
# Mean before change point
mean_before=mean(Global_Mean$Temp[1:16])
print(mean_before)## [1] 460.3334#Mean after change point
mean_after= mean(Global_Mean$Temp[16:length(Global_Mean$Temp)])
print(mean_after)## [1] 214.3204# Effect sife of KW test
Fire_EfSize=kruskal_effsize(FireLong,Temp ~ Exp,
ci = TRUE, conf.level = 0.95, ci.type = "perc",nboot = 1000)
print(Fire_EfSize)## # A tibble: 1 × 7
## .y. n effsize conf.low conf.high method magnitude
## * <chr> <int> <dbl> <dbl> <dbl> <chr> <ord>
## 1 Temp 123 -0.0157 -0.02 0.05 eta2[H] small##Chi2 test with Fragmented vs Entire acid and basic vulcanites
dat = data.frame(
Rock = c("VA", "VB"),
Entire = c(16, 23),
Fragmented = c(25, 61)
)
table_Frag = as.matrix(dat[, -1])
row.names(table_Frag) = dat$Rock
print(table_Frag)## Entire Fragmented
## VA 16 25
## VB 23 61##proportions by row
print(prop.table(table_Frag,1))## Entire Fragmented
## VA 0.3902439 0.6097561
## VB 0.2738095 0.7261905# Chi2 test
Chisq_Frag=chisq.test(table_Frag)
print(Chisq_Frag)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table_Frag
## X-squared = 1.2399, df = 1, p-value = 0.2655##Effect size (Square root of the chi-square value divided by the number of degrees of freedom)
EffectFrag=sqrt(Chisq_Frag$statistic /sum(table_Frag))
EffectFrag## X-squared
## 0.09959684##Chi2 test of fragmentation by shape of the nodules
dat2 = data.frame(
Shape = c("Tabular", "Elliptic"),
Entire = c(9, 34),
Fragmented = c(13, 80)
)
table_frag_shape = as.matrix(dat2[, -1])
row.names(table_frag_shape) = dat2$Shape
print(table_frag_shape)## Entire Fragmented
## Tabular 9 13
## Elliptic 34 80##proporion of fragmentation by shape
print(prop.table(table_frag_shape,1))## Entire Fragmented
## Tabular 0.4090909 0.5909091
## Elliptic 0.2982456 0.7017544# Chi2 test
chisq_Shape=chisq.test(table_frag_shape)
print(chisq_Shape)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table_frag_shape
## X-squared = 0.598, df = 1, p-value = 0.4393##Effect size (Square root of the chi-square value divided by the number of degrees of freedom)
EffectShape=sqrt(chisq_Shape$statistic /sum(table_frag_shape))
print(EffectShape)## X-squared
## 0.06631012traits_by_rocktype <- aggregate(. ~ Rock_Type, data = RawTraits2, sum)
print(traits_by_rocktype) ## Rock_Type Col Lus IOx Disc Crac Craz Exf Soot Adh FlScar Scar Rip Bulb PI
## 1 VA 46 19 31 43 22 29 9 39 37 14 15 1 8 2
## 2 VB 83 44 56 84 37 48 16 64 68 28 61 28 38 18tab<- as.table(as.matrix(traits_by_rocktype[, -1]))
rownames(tab) <- c("VA", "VB")
##proportion of each alteration by rock type
print(prop.table(tab,1))## Col Lus IOx Disc Crac Craz
## VA 0.146031746 0.060317460 0.098412698 0.136507937 0.069841270 0.092063492
## VB 0.123328380 0.065378900 0.083209510 0.124814264 0.054977712 0.071322437
## Exf Soot Adh FlScar Scar Rip
## VA 0.028571429 0.123809524 0.117460317 0.044444444 0.047619048 0.003174603
## VB 0.023774146 0.095096582 0.101040119 0.041604755 0.090638930 0.041604755
## Bulb PI
## VA 0.025396825 0.006349206
## VB 0.056463596 0.026745914##Chi2 test by rocktype
Xtest_by_rocktype<- chisq.test(tab)
print(Xtest_by_rocktype)##
## Pearson's Chi-squared test
##
## data: tab
## X-squared = 30.996, df = 13, p-value = 0.003376#plot
assoc(tab, shade=T)
# Exttact residuals
norm_res <- Xtest_by_rocktype$res
# Show normalized residuals
print(norm_res)## Col Lus IOx Disc Crac Craz Exf
## VA 0.7596040 -0.2423236 0.6193939 0.3943128 0.7353408 0.8982089 0.3646004
## VB -0.5196788 0.1657843 -0.4237548 -0.2697669 -0.5030792 -0.6145045 -0.2494393
## Soot Adh FlScar Scar Rip Bulb PI
## VA 1.0751051 0.6089416 0.1665091 -1.8752289 -2.7118457 -1.7406404 -1.7331531
## VB -0.7355271 -0.4166039 -0.1139163 1.2829273 1.8552939 1.1908493 1.1857269##Effect size (Square root of the chi-square value divided by the number of degrees of freedom)
EffectType=sqrt(Xtest_by_rocktype$statistic /sum(tab))
print(EffectType)## X-squared
## 0.1771238Frag_Vol$Frag=as.numeric(Frag_Vol$Frag)
cor_Vol_Frag=cor.test(Frag_Vol$Volume, Frag_Vol$Frag, method="spearman")## Warning in cor.test.default(Frag_Vol$Volume, Frag_Vol$Frag, method =
## "spearman"): Cannot compute exact p-value with tiesprint(cor_Vol_Frag)##
## Spearman's rank correlation rho
##
## data: Frag_Vol$Volume and Frag_Vol$Frag
## S = 41500, p-value = 0.2429
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1530926#Network analysis between fire related binary variables
Net_Fire <- estimateNetwork(Fire_Dat, default = "IsingFit")## Estimating Network. Using package::function:
## - IsingFit::IsingFit for network computation
## - Using glmnet::glmnet## | | | 0% | |===== | 7% | |========== | 14% | |=============== | 21% | |==================== | 29% | |========================= | 36% | |============================== | 43% | |=================================== | 50% | |======================================== | 57% | |============================================= | 64% | |================================================== | 71% | |======================================================= | 79% | |============================================================ | 86% | |================================================================= | 93% | |======================================================================| 100%#plot
Net_Graph=plot(Net_Fire)##network grafo
##Using igraph package to estimate clusters of variables
##transform to igraph object
Net_Fire_Ig= as.igraph(Net_Graph, attributes=TRUE)
##estimate communalities
Net_Clust<- cluster_spinglass(Net_Fire_Ig, implementation = c("neg"))
Net_Clust## IGRAPH clustering spinglass, groups: 3, mod: 0.48
## + groups:
## $`1`
## [1] 1 2 3 4 8 9
##
## $`2`
## [1] 5 6 7 10
##
## $`3`
## [1] 11 12 13 14
## ##Create a dataframe with clustermembership
Comm_Data=data.frame(Net_Clust$membership)
##transform numeric membership into factor
Comm_Data$Net_Clust.membership=as.factor(Comm_Data$Net_Clust.membership)###Network plot with group membership estimated by igrapgh
items=c("Color alteration.",
"Luster.",
"Iron Oxidation.",
"Discoloration.",
"Cracking.",
"Crazing.",
"Exfoliation.",
"Sooting.",
"Adherences.",
"Flake scars.",
"Scars.",
"Ripples.",
"Bulb.",
"Point of impact.")
plot(Net_Fire,
layout = "spring",
groups =Comm_Data$Net_Clust.membership,
label.cex = 0.7, # scalar on label size
label.color = 'black', # string on label colors
label.prop = 0.9, # proportion of the width of the node
# that the label scales
legend.cex = 0.4, # scalar of the legend
legend.mode = 'style2',
nodeNames = items,
font = 2)
### plot centrality coefficients to explore node attributes
centralityPlot(Net_Fire, include = c("Strength", "Betweenness","ExpectedInfluence") , scale="z-scores")## Note: z-scores are shown on x-axis rather than raw centrality indices.
- Strength
- Soot: Highest strength, indicating strong connections with other nodes.
- Adh: Lowest strength, indicating fewer or weaker connections.
- Betweenness
- Soot: Highest betweenness, indicating it often lies on the shortest paths between other nodes.
- Col, Bulb, Adh: Lowest betweenness, indicating they rarely lie on shortest paths.
- Expected Influence
- Craz: Highest expected influence, suggesting it has a strong overall impact on the network, followed by Soot.
- Col, Bulb, Adh: Lowest expected influence, indicating minimal overall impact.
Summary of Trends for Each Node:
- Soot: Dominates all centrality measures, indicating it is the most influential, well-connected, and central node.
- Scar, Rip, PI: Generally have high centrality across all measures, making them important secondary nodes.
- Lus to IOx: Moderate centrality values, indicating they are somewhat important.
- FIScar to Crac: Gradually decreasing centrality values across measures, suggesting lesser importance.
- Col, Bulb, Adh: Lowest in all centrality measures, indicating they are the least central and influential nodes in the network.
##Hypothesis testing for differences in occurrence in fire trait between clusters using nonmapametric distance
#based Adonis function of Vegan package
adonis2(t(Fire_Dat)~Comm_Data$Net_Clust.membership,
data=Fire_Dat,
permutations = 10000, method = "euclidian")Curated and published: Cardillo and Carranza