Phenoarch platform - Cleaning procedure - Curve level - CARBayesST library
I.Sanchez
septembre 04, 2020
CARBayesSTReport.RmdObjective
Detection of outliers in time courses of an experiment in PhenoArch greenhouse. In this vignette, we use a toy data set of the openSilexStatR library (anonymized real data set).
Please, have a look on the names of the columns of the input data set. The function is not generic and needs specific columns names in the input data set:
- Line: the x-coordinate in the lattice (numeric)
- Position: the y-coordinate in the lattice (numeric)
- Ref: a unique identifiant by pot, repetition and alias
- scenario: the scenario applied to the experiment
- genotypeAlias: the used genotypes in the experiment
The example is conducted on plantHeight parameter, we modelise the plantHeight taking into account the temporality (thermal time) and the spatiality (lattice structure) with a bayesian spatio-temporal ANOVA model [3].
To detect outlier points, we retrieve the standardised residuals computed by the model and a point is an outlier if abs(res) > threshold (here threshold==4). the user will be able to change this threshold.
We produced the output of the model as well as some graphics.
Import of data
The input dataset must contain some predefined columns, have a look to the structure of mydata:
library(lubridate) library(dplyr) library(openSilexStatR) myReport<-substr(now(),1,10) mydata<-plant1 str(mydata)
## 'data.frame': 47022 obs. of 14 variables:
## $ Ref : Factor w/ 1680 levels "manip1_10_10_WW",..: 131 131 131 131 131 131 131 131 131 131 ...
## $ experimentAlias: Factor w/ 1 level "manip1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Day : Factor w/ 42 levels "2013-02-01","2013-02-02",..: 3 4 5 6 7 9 9 10 11 12 ...
## $ potAlias : int 1 1 1 1 1 1 1 1 1 1 ...
## $ scenario : Factor w/ 2 levels "WD","WW": 2 2 2 2 2 2 2 2 2 2 ...
## $ genotypeAlias : Factor w/ 274 levels "11430_H","A310_H",..: 165 165 165 165 165 165 165 165 165 165 ...
## $ repetition : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Line : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Position : int 1 1 1 1 1 1 1 1 1 1 ...
## $ thermalTime : num 1.29 2.65 3.98 5.32 6.66 ...
## $ plantHeight : num 140 151 213 239 271 ...
## $ leafArea : num 0.018 0.019 0.0208 0.0222 0.0235 ...
## $ biovolume : num 0.253 0.62 1.201 1.68 3.396 ...
## $ Repsce : Factor w/ 15 levels "1_WD","1_WW",..: 2 2 2 2 2 2 2 2 2 2 ...CARBayesST library
A bayesian approach to model these data where the spatio-temporal structure is modelled via sets of autocorrelated random effets. Conditional autoregressive (CAR) priors and spatio-temporal extensions thereof are typically assigned to these random effects to capture the autocorrelation, which are special cases of a Gaussian Markov random Filed (GMRF) [3].
ST.CARanova() decomposes the spatio-temporal variation into 3 components:
- an overall spatial effect common to all time periods
- an overall temporal trend common to all spatial units
- a set of independent space-time interactions
we can add others factors (here the scenario (quali) …)
The spatio-temporal auto-correlation is modelled by a common set of spatial random effect and a common set of temporal random effects and both are modelled by the CAR prior (Conditional AutoRegressive).
Description of the results’s table:
- Median: posterior median estimation
- 2.5 and 97.5%: 95% credible interval
- Geweke.diag: the convergence diagnostic implemented in the coda package. takes form of a Z-score, so the convergence is suggested by the statistic being within the range (-1.96; 1.96)
Model fit criteria
- DIC: deviance information criterion
- p.d: its corresponding estimated effective number of parameters
- WAIC: the Watanabe-Akaike information criterion
- p.w: its corresponding estimated number of effective parameters
- LMPL: the log marginal predictive likelihood
In model’s comparison, the best fitting model is the one that minimises the DIC and WAIC but maximises the LMPL.
As the bayesian model can take a while, in this example, we initialise burnin=10 and n.sample=110 - not enough in real analysis!
# We suppress observations with missing data in time variable (here thermalTime) mydata<-filter(mydata,!is.na(mydata$thermalTime)) model<-fitCARBayesST(datain=mydata,xvar="thermalTime",trait="plantHeight",k=2, graphDist=TRUE,burnin=10,n.sample=110, formulaModel=as.formula(plantHeight~scenario+genotypeAlias),typeModel="anova",verbose=TRUE)
## [1] 22.35558
## Setting up the model.
## Generating 100 post burnin and thinned (if requested) samples.
##
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## Summarising results.
## Finished in 138.8 seconds.
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3642614 194.6 5520292 294.9 3642614 194.6
## Vcells 67027796 511.4 212701377 1622.8 67027796 511.4# print the result of the bayesian modelling printCARBayesST(modelin=model[[1]])
## Median 2.5% 97.5% Geweke.diag
## (Intercept) 789.1258 769.8793 809.8913 -2.3
## scenarioWW 5.0597 0.8237 8.4879 -1.8
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## genotypeAliasP465P_H -24.3036 -77.1944 16.6073 -1.2
## genotypeAliasPa35_H -55.1760 -97.7421 -30.5173 -0.9
## genotypeAliasPa36_H -15.7666 -41.7581 8.4925 1.1
## genotypeAliasPa374_H -13.4556 -48.2506 39.6466 -0.9
## genotypeAliasPa405_H -10.9355 -39.2599 21.7862 -1.0
## genotypeAliasPa91_H 79.0610 34.4282 124.5759 -4.7
## genotypeAliasPB116_H 31.9436 -10.5747 54.4968 -4.9
## genotypeAliasPB98TR_H -29.5586 -60.5039 -8.0206 -5.0
## genotypeAliasPH207_H -93.3195 -127.1792 -71.9081 -1.6
## genotypeAliasPHB09_H -46.7440 -79.0350 -20.1476 -6.8
## genotypeAliasPHG35_H 60.6127 6.1538 82.8289 -3.5
## genotypeAliasPHG39_H 28.2011 -7.1656 59.7728 -1.7
## genotypeAliasPHG47_H 82.1234 10.0487 116.0044 -4.3
## genotypeAliasPHG50_H 59.7727 31.6362 100.0496 0.2
## genotypeAliasPHG71_H 21.9966 -25.1502 52.7295 -2.2
## genotypeAliasPHG80_H -53.9288 -95.1778 -20.7154 0.4
## genotypeAliasPHG83_H -73.0015 -122.4170 -51.9958 0.2
## genotypeAliasPHG84_H 90.0107 68.0902 118.5824 -1.4
## genotypeAliasPHG86_H -20.3597 -73.7470 7.3174 -2.8
## genotypeAliasPHH93_H 43.9510 -18.2044 69.9202 -5.9
## genotypeAliasPHJ40_H 94.4187 70.6233 115.8367 2.6
## genotypeAliasPHK29_H 40.6430 12.1787 86.6364 -0.8
## genotypeAliasPHK76_H 5.6263 -18.3114 39.2293 -0.8
## genotypeAliasPHR36_H 96.0153 37.4911 138.8841 -4.5
## genotypeAliasPHT77_H 59.3523 42.3303 86.8147 1.0
## genotypeAliasPHV63_H 56.4534 28.0692 81.7999 -2.8
## genotypeAliasPHW65_H -0.7724 -44.9244 24.3476 -5.9
## genotypeAliasPHZ51_H 117.2205 87.8109 149.9307 -1.1
## genotypeAliasPP147_H 21.3939 -32.8841 54.2785 1.8
## genotypeAliasSC-Malawi_H -10.1327 -38.6521 17.7138 0.6
## genotypeAliasUH_2500_H -6.3234 -34.6501 37.2207 -0.5
## genotypeAliasUH250_H 70.8540 40.8624 96.9486 -0.7
## genotypeAliasUH304_H -46.4057 -105.7144 -16.6218 -2.6
## genotypeAliasUH_6102_H 24.2133 2.7987 56.3390 -0.3
## genotypeAliasUH_6179_H -51.0781 -87.7474 -24.2470 -1.6
## genotypeAliasUH_P024_H -132.9016 -157.4648 -93.0016 4.2
## genotypeAliasUH_P060_H 37.8416 -0.1161 77.6244 -4.1
## genotypeAliasUH_P064_H 12.8759 -23.6575 34.2524 2.4
## genotypeAliasUH_P074_H -65.5361 -98.2608 -43.0155 2.8
## genotypeAliasUH_P087_H -36.2252 -56.0842 -6.4146 1.0
## genotypeAliasUH_P089_H 17.0583 -3.6671 46.8271 -1.9
## genotypeAliasUH_P104_H 103.0462 65.4691 132.6336 -4.5
## genotypeAliasUH_P115_H 85.5897 61.1030 111.4937 0.4
## genotypeAliasUH_P128_H 78.8203 50.5082 113.9136 -1.9
## genotypeAliasUH_P148_H -30.2066 -64.6634 -3.2706 -2.2
## genotypeAliasUH_S018_H -8.3702 -28.9768 12.2129 -1.5
## genotypeAliasUH_S020_H -45.7264 -97.2854 -13.8611 -2.7
## genotypeAliasUH_S025_H 82.7534 49.9840 108.8224 -2.6
## genotypeAliasVa26_H 43.8857 19.3223 75.1117 2.9
## genotypeAliasW117 -144.4556 -194.3770 -86.2115 3.7
## genotypeAliasW117_H 128.1677 86.0450 173.1891 2.1
## genotypeAliasW153R_H -45.7736 -86.4146 -20.0249 -5.3
## genotypeAliasW182E_H -14.7338 -31.4863 6.9002 0.5
## genotypeAliasW23_H 17.5847 -23.1603 49.1260 -2.7
## genotypeAliasW602S_H 26.3704 -3.3642 49.9855 -1.6
## genotypeAliasW604S_H -51.7390 -88.5164 -24.1534 0.5
## genotypeAliasW64A_H -60.4985 -86.3283 -36.6257 -0.6
## genotypeAliasW9 -474.3791 -533.2429 -433.1502 0.0
## genotypeAliasW95115_H 31.1635 -15.3837 49.6413 -1.1
## genotypeAliasW9_H -39.7016 -103.1672 4.9525 1.8
## genotypeAliasWf9_H 139.4442 124.3776 170.3892 0.8
## genotypeAliasX061_H 60.9865 36.8142 88.0306 -1.4
## genotypeAliasX697_H 33.9312 5.0678 61.6646 -1.9
## tau2.S 4625.3717 2713.7318 5144.0853 -5.6
## tau2.T 11305.8163 2148.5654 45679.4499 20.5
## nu2 6303.3624 6165.8336 9365.4338 2.5
## rho.S 0.4136 0.2237 0.4799 -7.8
## rho.T 0.9099 0.6090 0.9999 -18.1
## DIC p.d WAIC p.w LMPL
## 496887.000 2147.925 504149.917 6642.962 -249609.718
## loglikelihood
## -246295.575outtmp<-outlierCARBayesST(modelin=model[[1]],datain=model[[2]],threshold=4,trait="plantHeight")
Outlier detection
The output report can be over-sized (more than 1Mb), for size of sub-directories in packages purposes, I choose to represent only the first genotypes…
mygeno<-as.character(unique(model[[2]][,"genotypeAlias"])) mygeno<-mygeno[1:6] for (i in seq(1,length(mygeno),by=15)){ myvec<-seq(i,i+14,1) plotCARBayesST(datain=model[[2]],outlierin=outtmp,myselect=myvec,trait="plantHeight",xvar="thermalTime") }
Session info
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=French_France.1252 LC_CTYPE=French_France.1252
## [3] LC_MONETARY=French_France.1252 LC_NUMERIC=C
## [5] LC_TIME=French_France.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] openSilexStatR_1.1.0 dplyr_1.0.2 lubridate_1.7.9
##
## loaded via a namespace (and not attached):
## [1] tidyr_1.1.2 splines_4.0.2 dotCall64_1.0-0 gtools_3.8.2
## [5] assertthat_0.2.1 expm_0.999-5 CARBayesdata_2.2 sp_1.4-2
## [9] stats4_4.0.2 yaml_2.2.1 LearnBayes_2.15.1 truncdist_1.0-2
## [13] pillar_1.4.6 backports_1.1.9 lattice_0.20-41 glue_1.4.2
## [17] digest_0.6.25 RColorBrewer_1.1-2 colorspace_1.4-1 plyr_1.8.6
## [21] htmltools_0.5.0 Matrix_1.2-18 pkgconfig_2.0.3 raster_3.3-13
## [25] CARBayesST_3.1 gmodels_2.18.1 purrr_0.3.4 scales_1.1.1
## [29] gdata_2.18.0 tibble_3.0.3 farver_2.0.3 generics_0.0.2
## [33] ggplot2_3.3.2 ellipsis_0.3.1 magrittr_1.5 crayon_1.3.4
## [37] deldir_0.1-28 memoise_1.1.0 evaluate_0.14 GGally_2.0.0
## [41] fs_1.4.2 nlme_3.1-148 MASS_7.3-51.6 foreign_0.8-80
## [45] truncnorm_1.0-8 class_7.3-17 data.table_1.13.0 tools_4.0.2
## [49] shapefiles_0.7 lifecycle_0.2.0 matrixStats_0.56.0 stringr_1.4.0
## [53] munsell_0.5.0 compiler_4.0.2 pkgdown_1.5.1 e1071_1.7-3
## [57] evd_2.3-3 rlang_0.4.7 classInt_0.4-3 units_0.6-7
## [61] grid_4.0.2 rstudioapi_0.11 htmlwidgets_1.5.1 spam_2.5-1
## [65] crosstalk_1.1.0.1 labeling_0.3 rmarkdown_2.3 SpATS_1.0-11
## [69] boot_1.3-25 testthat_2.3.2 gtable_0.3.0 codetools_0.2-16
## [73] reshape_0.8.8 DBI_1.1.0 R6_2.4.1 gridExtra_2.3
## [77] knitr_1.29 rgdal_1.5-16 rprojroot_1.3-2 spdep_1.1-5
## [81] KernSmooth_2.23-17 desc_1.2.0 matrixcalc_1.0-3 stringi_1.4.6
## [85] Rcpp_1.0.5 vctrs_0.3.4 sf_0.9-5 leaflet_2.0.3
## [89] spData_0.3.8 tidyselect_1.1.0 xfun_0.16 coda_0.19-3References
- R Development Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
- Roger S. Bivand, Edzer Pebesma, Virgilio Gomez-Rubio, 2013. Applied spatial data analysis with R, Second edition. Springer, NY. http://www.asdar-book.org/
- Duncan Lee, Alastair Rushworth and Gary Napier (2017). CARBayesST: Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data. R package version 2.5. https://CRAN.R-project.org/package=CARBayesST