sample_exe.md
In y-yasutomo/malia:
Marine Litter Analysis Manual
1. Introduction
We’d like to demonstrate how marine litter analyses are conducted. In
the analysis, —expl
2. Prerequisite
2.1 Installation of packages
If the follwing pacakges have not been installed, please execute next
commented lines first.
#install.packages("devtools")
#install.packages("dplyr")
library(devtools)
library(dplyr)
#install_github("y-yasutomo/malia")
#calling package in order to analyze marine debris data
library(malia)
3. See Data and set up of estimation
3.1 Data handling
Create data for malia package Reading ‘raw’ data (at first time)
Sighting data
#Designate data name
#Data.name<-"おしょろ丸目視まとめC056"
#Rev.name<-"O18y1"
#Sight.hand(Data.name,Rev.name)
Effort data
#Designate data name
#Data.name<-"レグ番号順 おしょろC056"
#Rev.name<-"O18y1"
#Effort.hand(Data.name,Rev.name)
3.2 Data visualization
Survey track line
#Designate voyage name
Vname<-"O18y1"
survey.plot(read.csv(paste(Vname,".effort.csv",sep="")))
3.3 Set up of estimation
Simple estimation using MALIA function Reading data
Sight.Data<-read.csv(paste(Vname,".debris.csv",sep=""))
Effort.Data<-read.csv(paste(Vname,".effort.csv",sep=""))
3.4 Estimation
We shall estimate density of ‘EPS’ by ‘MALIA’ function
tmp.Data<-Sight.Data %>%filter(type == "EPS")
res<-MALIA(tmp.Data,Effort.Data,key="hn",td=200)
Start from 789.711634597682
ite 1 value = 638.886473193106
ite 2 value = 638.886473193106
generated initial value!
initial value 638.886473
final value 637.884609
converged
Densities in each
leg
leg.D.plot(res$leg.D.obs$leg.result,xl=c(120,160),yl=c(20,50),save=F,Type="EPS")
3.5 Using full data
SDAM function can conduct whole analysis i.e) estimate detection
function, calculate densities in each debris type, covariate and
detection function As it takes some time to finish all trials, we use
already created results for mapping, model selection and making
table.
#res<-SDAM(Voyage.name=Vname,COVARIATE = c("conv", "occo","weather", "size"), key.list = c("hn","hr", "hhn", "hhr"),td=200,cp=10)
#Reading result object
res<-readRDS(paste(Vname,".result.obj",sep=""))
4. Result handling
4.1 Drawing maps
Maps with trackline and estimated density
#let's see type 'EPS'
leg.D.res<-res$hhr$weather$EPS$leg.D.obs$leg.result
leg.D.plot(leg.D.res,save=F,Type='EPS')
Density of “EPS” by grid
grid.D.res<-res$hhr$weather$EPS$grid.D.res
grid.D.plot(grid.D.res,save=F,Type='EPS')
4.2 Model selection
model summary
aic.mat<-aic.summary(res)
aic.mat$EPS
Key Covariate AIC
1 hn conv 1277.769
2 hr conv 3394.093
3 hhn conv 1291.816
4 hhr conv 2444.953
5 hn occo 1665.684
6 hr occo 1211.122
7 hhn occo 1254.887
8 hhr occo 1148.979
9 hn weather 1266.095
10 hr weather 1216.888
11 hhn weather 1252.324
12 hhr weather 1156.173
13 <NA> <NA> NA
14 <NA> <NA> NA
15 <NA> <NA> NA
16 <NA> <NA> NA
model.extract function can extract best models in each debris type
easily from SDAM result
best.model<-model.extract(aic.mat,res)
best.model$best.mat
Key Covariate AIC
DW hhr weather 651.510514
EPS hhr occo 1148.979327
FGF hhr conv 75.470455
FGN hhn size 47.266631
FGO hr size 36.766292
FP hhr conv 171.723667
G hn conv 6.861077
M hhn weather 76.033831
NO hhr size 848.659985
PBA hhr size 513.600085
PBO hhn weather 257.996027
PC hhr size 1280.944963
SW hhr size 2068.776258
UK hhr conv 27.835253
UO hhr size 315.889185
W hr conv 47.545739
4.3 Creating table
Summary of densities by leg
leg.table<-leg.D.table(best.model$best.list)
head(leg.table)
Voyage_name Leg.No. Leg.Length DW_Density DW_Abundance
1 O18y1 1 18.90206 4.111245 77.71101
DW_Detected.Number EPS_Density EPS_Abundance EPS_Detected.Number
1 1 16.81634 317.8635 17
FGF_Density FGF_Abundance FGF_Detected.Number FGN_Density FGN_Abundance
1 0.5702303 10.77853 1 0 0
FGN_Detected.Number FGO_Density FGO_Abundance FGO_Detected.Number
1 0 0 0 0
FP_Density FP_Abundance FP_Detected.Number G_Density G_Abundance
1 0 0 0 0 0
G_Detected.Number M_Density M_Abundance M_Detected.Number NO_Density
1 0 6.320801 119.4762 1 1.237268
NO_Abundance NO_Detected.Number PBA_Density PBA_Abundance
1 23.38691 1 1.491695 28.19612
PBA_Detected.Number PBO_Density PBO_Abundance PBO_Detected.Number
1 1 11.96439 226.1517 10
PC_Density PC_Abundance PC_Detected.Number SW_Density SW_Abundance
1 7.330453 138.5607 4 5.335478 100.8515
SW_Detected.Number UK_Density UK_Abundance UK_Detected.Number UO_Density
1 2 0 0 0 12.93287
UO_Abundance UO_Detected.Number W_Density W_Abundance W_Detected.Number
1 244.4579 7 3.475549 65.69505 1
Lat.Start Lon.Start Lat.End Lon.End
1 35.68841 141.6653 35.80892 141.813
[ reached 'max' / getOption("max.print") -- omitted 5 rows ]
#write.xlsx(leg.table,file=paste(table.pass,"leg.D.table.xlsx",sep=""),row.names=F)
Summary of densities by grid
grid.table<-grid.D.table(best.model$best.list)
head(grid.table)
Voyage_name lat.min lat.max lon.min lon.max Total.length DW_Density
14 O18y1 35.5 36.0 141.5 142.0 42.658119 5.8793415
26 O18y1 35.5 36.0 142.0 142.5 6.396301 9.0399166
27 O18y1 36.0 36.5 142.0 142.5 57.057558 0.8387192
39 O18y1 36.0 36.5 142.5 143.0 59.763436 2.4715164
EPS_Density FGF_Density FGN_Density FGO_Density FP_Density G_Density
14 10.332905 0.5053448 2.7670900 0.000000 0.7987498 0
26 2.513522 0.0000000 0.9640199 0.000000 1.7795218 0
27 2.277147 0.0000000 0.0000000 0.000000 0.7966613 0
39 2.367067 0.1727018 0.7806773 1.676246 2.5902512 0
M_Density NO_Density PBA_Density PBO_Density PC_Density SW_Density
14 2.8007842 0.5482406 3.634846 6.3617901 13.515534 13.98354
26 0.0000000 0.0000000 2.207678 0.0000000 17.753716 29.07629
27 0.0000000 2.5177683 0.000000 1.4814525 3.533654 43.25810
39 0.3789837 1.3516362 5.568032 0.3694174 10.353659 25.55756
UK_Density UO_Density W_Density
14 0.000000 5.730631 1.540036
26 0.000000 0.000000 0.000000
27 0.000000 1.398079 0.000000
39 2.325975 2.212597 1.030055
[ reached 'max' / getOption("max.print") -- omitted 2 rows ]
#write.xlsx(grid.table,file=paste(table.pass,"grid.D.table.xlsx",sep=""),row.names=F)
Summary of densities by area
area.table<-area.D.table(best.model$best.list)
head(area.table)
Voyage_name Area Total.Length DW_Density EPS_Density FGF_Density
1 O18y1 1 0 NaN NaN NaN
2 O18y1 2 0 NaN NaN NaN
3 O18y1 3 0 NaN NaN NaN
4 O18y1 4 0 NaN NaN NaN
FGN_Density FGO_Density FP_Density G_Density M_Density NO_Density
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
PBA_Density PBO_Density PC_Density SW_Density UK_Density UO_Density
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
W_Density
1 NaN
2 NaN
3 NaN
4 NaN
[ reached 'max' / getOption("max.print") -- omitted 2 rows ]
#write.xlsx(area.table,file=paste(table.pass,"area.D.table.xlsx",sep=""),row.names=F)
5. Ar and Nt products
5.1 Classify each debris into categories
ls. function can sum up artificial and natural products
Ar<-c("FGN","FGF","FGO","EPS","PBA","PBO","FP","PC","G","M","W","UO")
Nt<-c("SW","DW","NO")
An.table<-ls.(leg.table,Voyage.name,Ar,Nt)
head(An.table)
Leg.No. Leg.Length Lat.Start Lon.Start Lat.End Lon.End Voyage_name
1 1 18.902062 35.68841 141.6653 35.80892 141.8130 O18y1
2 2 9.823665 35.80892 141.8130 35.87148 141.8899 O18y1
3 3 18.586601 35.87148 141.8899 35.98891 142.0368 O18y1
4 4 9.038264 35.98891 142.0368 36.04769 142.1062 O18y1
5 5 18.423266 36.04769 142.1062 36.16471 142.2514 O18y1
6 6 9.174922 36.16471 142.2514 36.22143 142.3256 O18y1
Artificial_Number Artificial_Density Natural_Number Natural_Density
1 42 60.90233 4 10.68399
2 15 48.48070 5 19.85985
3 17 30.11885 13 33.99661
4 3 12.12651 9 49.12217
5 7 7.37107 17 47.62894
6 3 10.69208 17 90.80294
Plot of Artificial
product
Artificial.leg<-An.table %>% select(Leg.No.,Leg.Length,Lat.Start,Lon.Start,Lat.End,Lon.End,Artificial_Density) %>% rename(Density=Artificial_Density)
leg.D.plot(Artificial.leg,save=F)
Artificial.grid<-grid.D(Artificial.leg)
grid.D.plot(Artificial.grid,save = F)
Plot of Natural
product
Natural.leg<-An.table %>% select(Leg.No.,Leg.Length,Lat.Start,Lon.Start,Lat.End,Lon.End,Natural_Density) %>% rename(Density=Natural_Density)
leg.D.plot(Natural.leg,save=F)
Natural.grid<-grid.D(Natural.leg)
grid.D.plot(Natural.grid,save = F)
5.2 Combining results
Combining results across multiple surveys. In this procedure, it is
necessary result objects has to already be created. Survey legs
vname<-c("S18y4","O18y1","K18y1")
tmp<-data.frame()
for(i in 1:length(vname)){
Effort.Data<-read.csv(paste(vname[i],".effort.csv",sep=""))
Effort.Data$voyage_name<-vname[i]
tmp<-rbind(tmp,Effort.Data)
}
survey.plot(tmp,multi=T)
Combine leg D results In the leg.D.comb function, model selection and
extraction of the best model are conducted.
tmp<-leg.D.comb(vname)
#let's see type "DW"
tmp2<-grid.D(tmp$DW)
grid.D.plot(tmp2,save = F)
#area D
tmp3<-area.D(tmp$DW)
tmp3$area.density
Area Density Total.Length
1 1 NaN 0.0000
2 2 NaN 0.0000
3 3 NaN 0.0000
4 4 NaN 0.0000
5 5 NaN 0.0000
6 6 NaN 0.0000
7 7 18.7962061 1034.6821
8 8 NaN 0.0000
9 9 NaN 0.0000
10 10 0.0000000 100.6888
11 11 0.2542736 217.3841
12 12 0.1216781 454.2729
13 13 NaN 0.0000
14 14 NaN 0.0000
15 15 0.1274597 564.9288
16 16 NaN 0.0000
17 17 NaN 0.0000
Artificial and Natural products Artificial product
tmp<-vanc(vname)
leg.D.plot(tmp$Ar.leg.D,save=F)
Artificial.grid<-grid.D(tmp$Ar.leg.D)
grid.D.plot(Artificial.grid,save = F)
Natural product
leg.D.plot(tmp$Nt.leg.D,save=F)
Natural.grid<-grid.D(tmp$Nt.leg.D)
grid.D.plot(Natural.grid,save = F)
y-yasutomo/malia documentation built on Sept. 21, 2020, 3:38 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Marine Litter Analysis Manual
1. Introduction
We’d like to demonstrate how marine litter analyses are conducted. In the analysis, —expl
2. Prerequisite
2.1 Installation of packages
If the follwing pacakges have not been installed, please execute next commented lines first.
#install.packages("devtools")
#install.packages("dplyr")
library(devtools)
library(dplyr)
#install_github("y-yasutomo/malia")
#calling package in order to analyze marine debris data
library(malia)
3. See Data and set up of estimation
3.1 Data handling
Create data for malia package Reading ‘raw’ data (at first time) Sighting data
#Designate data name
#Data.name<-"おしょろ丸目視まとめC056"
#Rev.name<-"O18y1"
#Sight.hand(Data.name,Rev.name)
Effort data
#Designate data name
#Data.name<-"レグ番号順 おしょろC056"
#Rev.name<-"O18y1"
#Effort.hand(Data.name,Rev.name)
3.2 Data visualization
Survey track line
#Designate voyage name
Vname<-"O18y1"
survey.plot(read.csv(paste(Vname,".effort.csv",sep="")))
3.3 Set up of estimation
Simple estimation using MALIA function Reading data
Sight.Data<-read.csv(paste(Vname,".debris.csv",sep=""))
Effort.Data<-read.csv(paste(Vname,".effort.csv",sep=""))
3.4 Estimation
We shall estimate density of ‘EPS’ by ‘MALIA’ function
tmp.Data<-Sight.Data %>%filter(type == "EPS")
res<-MALIA(tmp.Data,Effort.Data,key="hn",td=200)
Start from 789.711634597682
ite 1 value = 638.886473193106
ite 2 value = 638.886473193106
generated initial value!
initial value 638.886473
final value 637.884609
converged
Densities in each leg
leg.D.plot(res$leg.D.obs$leg.result,xl=c(120,160),yl=c(20,50),save=F,Type="EPS")
3.5 Using full data
SDAM function can conduct whole analysis i.e) estimate detection function, calculate densities in each debris type, covariate and detection function As it takes some time to finish all trials, we use already created results for mapping, model selection and making table.
#res<-SDAM(Voyage.name=Vname,COVARIATE = c("conv", "occo","weather", "size"), key.list = c("hn","hr", "hhn", "hhr"),td=200,cp=10)
#Reading result object
res<-readRDS(paste(Vname,".result.obj",sep=""))
4. Result handling
4.1 Drawing maps
Maps with trackline and estimated density
#let's see type 'EPS'
leg.D.res<-res$hhr$weather$EPS$leg.D.obs$leg.result
leg.D.plot(leg.D.res,save=F,Type='EPS')
Density of “EPS” by grid
grid.D.res<-res$hhr$weather$EPS$grid.D.res
grid.D.plot(grid.D.res,save=F,Type='EPS')
4.2 Model selection
model summary
aic.mat<-aic.summary(res)
aic.mat$EPS
Key Covariate AIC
1 hn conv 1277.769
2 hr conv 3394.093
3 hhn conv 1291.816
4 hhr conv 2444.953
5 hn occo 1665.684
6 hr occo 1211.122
7 hhn occo 1254.887
8 hhr occo 1148.979
9 hn weather 1266.095
10 hr weather 1216.888
11 hhn weather 1252.324
12 hhr weather 1156.173
13 <NA> <NA> NA
14 <NA> <NA> NA
15 <NA> <NA> NA
16 <NA> <NA> NA
model.extract function can extract best models in each debris type easily from SDAM result
best.model<-model.extract(aic.mat,res)
best.model$best.mat
Key Covariate AIC
DW hhr weather 651.510514
EPS hhr occo 1148.979327
FGF hhr conv 75.470455
FGN hhn size 47.266631
FGO hr size 36.766292
FP hhr conv 171.723667
G hn conv 6.861077
M hhn weather 76.033831
NO hhr size 848.659985
PBA hhr size 513.600085
PBO hhn weather 257.996027
PC hhr size 1280.944963
SW hhr size 2068.776258
UK hhr conv 27.835253
UO hhr size 315.889185
W hr conv 47.545739
4.3 Creating table
Summary of densities by leg
leg.table<-leg.D.table(best.model$best.list)
head(leg.table)
Voyage_name Leg.No. Leg.Length DW_Density DW_Abundance
1 O18y1 1 18.90206 4.111245 77.71101
DW_Detected.Number EPS_Density EPS_Abundance EPS_Detected.Number
1 1 16.81634 317.8635 17
FGF_Density FGF_Abundance FGF_Detected.Number FGN_Density FGN_Abundance
1 0.5702303 10.77853 1 0 0
FGN_Detected.Number FGO_Density FGO_Abundance FGO_Detected.Number
1 0 0 0 0
FP_Density FP_Abundance FP_Detected.Number G_Density G_Abundance
1 0 0 0 0 0
G_Detected.Number M_Density M_Abundance M_Detected.Number NO_Density
1 0 6.320801 119.4762 1 1.237268
NO_Abundance NO_Detected.Number PBA_Density PBA_Abundance
1 23.38691 1 1.491695 28.19612
PBA_Detected.Number PBO_Density PBO_Abundance PBO_Detected.Number
1 1 11.96439 226.1517 10
PC_Density PC_Abundance PC_Detected.Number SW_Density SW_Abundance
1 7.330453 138.5607 4 5.335478 100.8515
SW_Detected.Number UK_Density UK_Abundance UK_Detected.Number UO_Density
1 2 0 0 0 12.93287
UO_Abundance UO_Detected.Number W_Density W_Abundance W_Detected.Number
1 244.4579 7 3.475549 65.69505 1
Lat.Start Lon.Start Lat.End Lon.End
1 35.68841 141.6653 35.80892 141.813
[ reached 'max' / getOption("max.print") -- omitted 5 rows ]
#write.xlsx(leg.table,file=paste(table.pass,"leg.D.table.xlsx",sep=""),row.names=F)
Summary of densities by grid
grid.table<-grid.D.table(best.model$best.list)
head(grid.table)
Voyage_name lat.min lat.max lon.min lon.max Total.length DW_Density
14 O18y1 35.5 36.0 141.5 142.0 42.658119 5.8793415
26 O18y1 35.5 36.0 142.0 142.5 6.396301 9.0399166
27 O18y1 36.0 36.5 142.0 142.5 57.057558 0.8387192
39 O18y1 36.0 36.5 142.5 143.0 59.763436 2.4715164
EPS_Density FGF_Density FGN_Density FGO_Density FP_Density G_Density
14 10.332905 0.5053448 2.7670900 0.000000 0.7987498 0
26 2.513522 0.0000000 0.9640199 0.000000 1.7795218 0
27 2.277147 0.0000000 0.0000000 0.000000 0.7966613 0
39 2.367067 0.1727018 0.7806773 1.676246 2.5902512 0
M_Density NO_Density PBA_Density PBO_Density PC_Density SW_Density
14 2.8007842 0.5482406 3.634846 6.3617901 13.515534 13.98354
26 0.0000000 0.0000000 2.207678 0.0000000 17.753716 29.07629
27 0.0000000 2.5177683 0.000000 1.4814525 3.533654 43.25810
39 0.3789837 1.3516362 5.568032 0.3694174 10.353659 25.55756
UK_Density UO_Density W_Density
14 0.000000 5.730631 1.540036
26 0.000000 0.000000 0.000000
27 0.000000 1.398079 0.000000
39 2.325975 2.212597 1.030055
[ reached 'max' / getOption("max.print") -- omitted 2 rows ]
#write.xlsx(grid.table,file=paste(table.pass,"grid.D.table.xlsx",sep=""),row.names=F)
Summary of densities by area
area.table<-area.D.table(best.model$best.list)
head(area.table)
Voyage_name Area Total.Length DW_Density EPS_Density FGF_Density
1 O18y1 1 0 NaN NaN NaN
2 O18y1 2 0 NaN NaN NaN
3 O18y1 3 0 NaN NaN NaN
4 O18y1 4 0 NaN NaN NaN
FGN_Density FGO_Density FP_Density G_Density M_Density NO_Density
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
PBA_Density PBO_Density PC_Density SW_Density UK_Density UO_Density
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
W_Density
1 NaN
2 NaN
3 NaN
4 NaN
[ reached 'max' / getOption("max.print") -- omitted 2 rows ]
#write.xlsx(area.table,file=paste(table.pass,"area.D.table.xlsx",sep=""),row.names=F)
5. Ar and Nt products
5.1 Classify each debris into categories
ls. function can sum up artificial and natural products
Ar<-c("FGN","FGF","FGO","EPS","PBA","PBO","FP","PC","G","M","W","UO")
Nt<-c("SW","DW","NO")
An.table<-ls.(leg.table,Voyage.name,Ar,Nt)
head(An.table)
Leg.No. Leg.Length Lat.Start Lon.Start Lat.End Lon.End Voyage_name
1 1 18.902062 35.68841 141.6653 35.80892 141.8130 O18y1
2 2 9.823665 35.80892 141.8130 35.87148 141.8899 O18y1
3 3 18.586601 35.87148 141.8899 35.98891 142.0368 O18y1
4 4 9.038264 35.98891 142.0368 36.04769 142.1062 O18y1
5 5 18.423266 36.04769 142.1062 36.16471 142.2514 O18y1
6 6 9.174922 36.16471 142.2514 36.22143 142.3256 O18y1
Artificial_Number Artificial_Density Natural_Number Natural_Density
1 42 60.90233 4 10.68399
2 15 48.48070 5 19.85985
3 17 30.11885 13 33.99661
4 3 12.12651 9 49.12217
5 7 7.37107 17 47.62894
6 3 10.69208 17 90.80294
Plot of Artificial product
Artificial.leg<-An.table %>% select(Leg.No.,Leg.Length,Lat.Start,Lon.Start,Lat.End,Lon.End,Artificial_Density) %>% rename(Density=Artificial_Density)
leg.D.plot(Artificial.leg,save=F)
Artificial.grid<-grid.D(Artificial.leg)
grid.D.plot(Artificial.grid,save = F)
Plot of Natural product
Natural.leg<-An.table %>% select(Leg.No.,Leg.Length,Lat.Start,Lon.Start,Lat.End,Lon.End,Natural_Density) %>% rename(Density=Natural_Density)
leg.D.plot(Natural.leg,save=F)
Natural.grid<-grid.D(Natural.leg)
grid.D.plot(Natural.grid,save = F)
5.2 Combining results
Combining results across multiple surveys. In this procedure, it is necessary result objects has to already be created. Survey legs
vname<-c("S18y4","O18y1","K18y1")
tmp<-data.frame()
for(i in 1:length(vname)){
Effort.Data<-read.csv(paste(vname[i],".effort.csv",sep=""))
Effort.Data$voyage_name<-vname[i]
tmp<-rbind(tmp,Effort.Data)
}
survey.plot(tmp,multi=T)
Combine leg D results In the leg.D.comb function, model selection and extraction of the best model are conducted.
tmp<-leg.D.comb(vname)
#let's see type "DW"
tmp2<-grid.D(tmp$DW)
grid.D.plot(tmp2,save = F)
#area D
tmp3<-area.D(tmp$DW)
tmp3$area.density
Area Density Total.Length
1 1 NaN 0.0000
2 2 NaN 0.0000
3 3 NaN 0.0000
4 4 NaN 0.0000
5 5 NaN 0.0000
6 6 NaN 0.0000
7 7 18.7962061 1034.6821
8 8 NaN 0.0000
9 9 NaN 0.0000
10 10 0.0000000 100.6888
11 11 0.2542736 217.3841
12 12 0.1216781 454.2729
13 13 NaN 0.0000
14 14 NaN 0.0000
15 15 0.1274597 564.9288
16 16 NaN 0.0000
17 17 NaN 0.0000
Artificial and Natural products Artificial product
tmp<-vanc(vname)
leg.D.plot(tmp$Ar.leg.D,save=F)
Artificial.grid<-grid.D(tmp$Ar.leg.D)
grid.D.plot(Artificial.grid,save = F)
Natural product
leg.D.plot(tmp$Nt.leg.D,save=F)
Natural.grid<-grid.D(tmp$Nt.leg.D)
grid.D.plot(Natural.grid,save = F)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.