read.csv("D:\\Dropbox\\100_PROJECTS\\2019.1_PROJECT_SamFireFDI\\20190227_Flammability.csv")%>%
tbl_df()%>%
mutate(FWI_index=case_when(FWI>30~"Very Extreme",
FWI>25&FWI<=30~"Extreme",
FWI>13&FWI<=25~"High",
FWI>6&FWI<=13~"Moderate",
FWI>2&FWI<=6~"Low",
FWI<=2 ~ "Very Low"),
FWI_index=factor(FWI_index,levels=rev(c("Very Low",
"Low",
"Moderate",
"High",
"Extreme",
"Very Extreme"))))->
dat
Data check
dat%>%
ggplot(aes(FMC))+
geom_histogram()+
facet_wrap(~SPP)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
1 Correlations
dat%>%
ggplot(aes((FWI),(FMC)))+
geom_point()+
facet_wrap(~SPP)+
geom_smooth(method="lm")
dat%>%
group_by(SPP)%>%
summarise(p=cor.test(x=FWI,y=FMC)$p.value,
p=round(p,3),
sig=ifelse(p<0.05,"*",""),
r=cor(x=FWI,y=FMC),
r=round(r,2),
r2=cor(x=FWI,y=FMC)^2,
r2=round(r2,2))%>%
knitr::kable()
| ACACYC |
0.176 |
|
-0.31 |
0.09 |
| ACAMEA |
0.925 |
|
-0.02 |
0.00 |
| ACAMEL |
0.529 |
|
-0.15 |
0.02 |
| ACASAL |
0.469 |
|
-0.17 |
0.03 |
| AGAOVA |
0.207 |
|
-0.29 |
0.08 |
| ASPSPI |
0.405 |
|
-0.19 |
0.04 |
| CALVIM |
0.077 |
|
-0.39 |
0.16 |
| CASPER |
0.366 |
|
0.21 |
0.04 |
| CESLAE |
0.676 |
|
0.10 |
0.01 |
| CLIERI |
0.518 |
|
-0.15 |
0.02 |
| CLIILI |
0.754 |
|
-0.07 |
0.01 |
| DIODIC |
0.584 |
|
0.13 |
0.02 |
| ERICAN |
0.812 |
|
-0.06 |
0.00 |
| ERIDIS |
0.526 |
|
-0.15 |
0.02 |
| EUCCAM |
0.034 |
* |
-0.46 |
0.22 |
| GYMBUX |
0.044 |
* |
0.44 |
0.20 |
| LEPLAE |
0.235 |
|
0.27 |
0.07 |
| LEUEUC |
0.224 |
|
0.28 |
0.08 |
| METMUR |
0.000 |
* |
0.73 |
0.53 |
| OSTMON |
0.125 |
|
0.35 |
0.12 |
| OSYCOM |
0.101 |
|
0.37 |
0.14 |
| PASRIG |
0.248 |
|
-0.26 |
0.07 |
| PHYAXI |
0.348 |
|
0.22 |
0.05 |
| PINPIN |
0.256 |
|
-0.26 |
0.07 |
| PINRAD |
0.043 |
* |
-0.45 |
0.20 |
| PTETRI |
0.915 |
|
0.02 |
0.00 |
| SCOZEY |
0.362 |
|
0.21 |
0.04 |
| SEALUC |
0.137 |
|
-0.34 |
0.11 |
| SIDINE |
0.377 |
|
0.20 |
0.04 |
| TARLIT |
0.220 |
|
-0.28 |
0.08 |
Other correlations with FMC
dat%>%
#arrange(desc(FWI_index))%>%
ggplot(aes(FMC,BURNINTENSITY))+
geom_point(aes(colour=FWI_index))+
scale_colour_manual(values = cols)+
facet_wrap(~SPP)+
geom_smooth(method="lm")
dat%>%
ggplot(aes(FMC,BIOMASSBURNT_PROP))+
geom_point(aes(colour=FWI_index))+
facet_wrap(~SPP)+
scale_colour_manual(values = cols)+
geom_smooth(method="lm")
dat%>%
ggplot(aes(FMC,TIMETOIGNITION))+
geom_point(aes(colour=FWI_index))+
facet_wrap(~SPP)+
scale_colour_manual(values = cols)+
geom_smooth(method="lm")
2 GAMs: Flam vs FWI + FMC
gam(BURNINTENSITY~s(FWI)+s(FMC),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BURNINTENSITY ~ s(FWI) + s(FMC)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 585.420 4.701 124.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 7.027 7.887 9.629 1.62e-12 ***
## s(FMC) 2.367 3.019 24.714 3.07e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.183 Deviance explained = 19.5%
## GCV = 14158 Scale est. = 13924 n = 630
gam(BURNINTENSITY~s(FWI)+s(FMC)+(SPP),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BURNINTENSITY ~ s(FWI) + s(FMC) + (SPP)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 626.767 21.566 29.063 < 2e-16 ***
## SPPACAMEA 6.174 30.391 0.203 0.839098
## SPPACAMEL 66.537 30.628 2.172 0.030225 *
## SPPACASAL -88.395 32.605 -2.711 0.006902 **
## SPPAGAOVA -73.792 30.916 -2.387 0.017309 *
## SPPASPSPI -113.180 30.906 -3.662 0.000273 ***
## SPPCALVIM -22.667 31.714 -0.715 0.475052
## SPPCASPER 72.889 30.785 2.368 0.018223 *
## SPPCESLAE -39.056 37.005 -1.055 0.291658
## SPPCLIERI -164.431 30.220 -5.441 7.79e-08 ***
## SPPCLIILI -108.737 30.392 -3.578 0.000375 ***
## SPPDIODIC -98.290 30.767 -3.195 0.001475 **
## SPPERICAN -16.922 30.589 -0.553 0.580327
## SPPERIDIS -9.811 32.624 -0.301 0.763716
## SPPEUCCAM 97.863 30.533 3.205 0.001423 **
## SPPGYMBUX -161.848 30.511 -5.305 1.60e-07 ***
## SPPLEPLAE -51.845 30.947 -1.675 0.094407 .
## SPPLEUEUC -72.997 30.715 -2.377 0.017792 *
## SPPMETMUR -45.393 30.434 -1.492 0.136355
## SPPOSTMON -198.572 39.201 -5.066 5.46e-07 ***
## SPPOSYCOM -86.121 30.363 -2.836 0.004720 **
## SPPPASRIG -21.907 30.543 -0.717 0.473500
## SPPPHYAXI 36.984 34.227 1.081 0.280334
## SPPPINPIN 30.989 31.113 0.996 0.319648
## SPPPINRAD 38.732 30.273 1.279 0.201251
## SPPPTETRI -16.936 30.487 -0.556 0.578761
## SPPSCOZEY -152.295 30.384 -5.012 7.13e-07 ***
## SPPSEALUC 27.514 30.374 0.906 0.365402
## SPPSIDINE -69.563 31.478 -2.210 0.027497 *
## SPPTARLIT -5.337 30.453 -0.175 0.860937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 7.515 8.309 12.110 <2e-16 ***
## s(FMC) 6.122 7.370 1.667 0.0965 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.438 Deviance explained = 47.6%
## GCV = 10289 Scale est. = 9576.6 n = 630
gam(BURNINTENSITY~s(FWI)+s(FMC)+(VEGTYPE),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BURNINTENSITY ~ s(FWI) + s(FMC) + (VEGTYPE)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 555.631 8.057 68.965 < 2e-16 ***
## VEGTYPEIAP 79.474 11.369 6.990 7.16e-12 ***
## VEGTYPEThi 9.895 11.598 0.853 0.394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 7.118 7.969 10.21 1.74e-13 ***
## s(FMC) 5.491 6.692 15.09 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.257 Deviance explained = 27.4%
## GCV = 12990 Scale est. = 12668 n = 630
gam(BURNINTENSITY~s(FWI)+s(FMC)+(VEGTYPE)+(SPP),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BURNINTENSITY ~ s(FWI) + s(FMC) + (VEGTYPE) + (SPP)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 527.647 5.871 89.874 < 2e-16 ***
## VEGTYPEIAP 99.120 20.735 4.780 2.22e-06 ***
## VEGTYPEThi 27.513 8.369 3.287 0.001072 **
## SPPACAMEA 6.174 30.391 0.203 0.839098
## SPPACAMEL 66.537 30.628 2.172 0.030225 *
## SPPACASAL -88.395 32.605 -2.711 0.006902 **
## SPPAGAOVA 25.328 20.853 1.215 0.225016
## SPPASPSPI -14.060 20.719 -0.679 0.497651
## SPPCALVIM -22.667 31.714 -0.715 0.475052
## SPPCASPER 144.497 21.194 6.818 2.30e-11 ***
## SPPCESLAE -39.056 37.005 -1.055 0.291658
## SPPCLIERI -65.311 20.786 -3.142 0.001762 **
## SPPCLIILI -9.616 20.966 -0.459 0.646635
## SPPDIODIC -26.683 20.987 -1.271 0.204093
## SPPERICAN 82.198 20.612 3.988 7.51e-05 ***
## SPPERIDIS 89.309 22.228 4.018 6.64e-05 ***
## SPPEUCCAM 97.863 30.533 3.205 0.001423 **
## SPPGYMBUX -90.241 20.908 -4.316 1.86e-05 ***
## SPPLEPLAE -51.845 30.947 -1.675 0.094407 .
## SPPLEUEUC 26.123 21.772 1.200 0.230669
## SPPMETMUR 53.727 20.605 2.608 0.009353 **
## SPPOSTMON -126.965 30.420 -4.174 3.45e-05 ***
## SPPOSYCOM -14.513 20.876 -0.695 0.487198
## SPPPASRIG 77.213 20.712 3.728 0.000212 ***
## SPPPHYAXI 136.104 23.990 5.673 2.20e-08 ***
## SPPPINPIN 30.989 31.113 0.996 0.319648
## SPPPINRAD 38.732 30.273 1.279 0.201251
## SPPPTETRI 54.672 20.997 2.604 0.009452 **
## SPPSCOZEY -80.688 20.753 -3.888 0.000113 ***
## SPPSEALUC 99.121 20.822 4.760 2.44e-06 ***
## SPPSIDINE 2.044 21.730 0.094 0.925087
## SPPTARLIT 66.270 20.799 3.186 0.001518 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 7.515 8.309 12.110 <2e-16 ***
## s(FMC) 6.122 7.370 1.667 0.0965 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 48/50
## R-sq.(adj) = 0.438 Deviance explained = 47.6%
## GCV = 10289 Scale est. = 9576.6 n = 630
gam(TIMETOIGNITION~s(FWI)+s(FMC),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## TIMETOIGNITION ~ s(FWI) + s(FMC)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 67.575 1.472 45.92 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 8.742 8.974 13.72 < 2e-16 ***
## s(FMC) 2.295 2.929 12.64 7.3e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.198 Deviance explained = 21.2%
## GCV = 1390.8 Scale est. = 1364.2 n = 630
gam(TIMETOIGNITION~s(FWI)+s(FMC)+(SPP),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## TIMETOIGNITION ~ s(FWI) + s(FMC) + (SPP)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.8417 6.1132 12.406 < 2e-16 ***
## SPPACAMEA -30.8132 8.6665 -3.555 0.000408 ***
## SPPACAMEL -14.9459 8.6782 -1.722 0.085552 .
## SPPACASAL 26.4725 8.8981 2.975 0.003049 **
## SPPAGAOVA -6.3183 8.7438 -0.723 0.470210
## SPPASPSPI -40.8412 8.6529 -4.720 2.95e-06 ***
## SPPCALVIM -2.1808 8.7752 -0.249 0.803817
## SPPCASPER 27.9884 8.7110 3.213 0.001385 **
## SPPCESLAE -18.0441 9.5513 -1.889 0.059358 .
## SPPCLIERI -17.8065 8.6456 -2.060 0.039874 *
## SPPCLIILI -11.7100 8.6517 -1.353 0.176417
## SPPDIODIC 14.5711 8.7049 1.674 0.094681 .
## SPPERICAN -50.4147 8.6994 -5.795 1.11e-08 ***
## SPPERIDIS -24.8900 8.9524 -2.780 0.005605 **
## SPPEUCCAM -13.9268 8.6939 -1.602 0.109710
## SPPGYMBUX 0.7012 8.6635 0.081 0.935517
## SPPLEPLAE -5.3297 8.7317 -0.610 0.541844
## SPPLEUEUC -3.7462 8.6879 -0.431 0.666481
## SPPMETMUR -45.4672 8.6729 -5.242 2.21e-07 ***
## SPPOSTMON 23.8216 9.9183 2.402 0.016624 *
## SPPOSYCOM 11.0075 8.6524 1.272 0.203807
## SPPPASRIG -51.5508 8.6689 -5.947 4.69e-09 ***
## SPPPHYAXI -28.4868 9.0761 -3.139 0.001782 **
## SPPPINPIN -3.9583 8.7059 -0.455 0.649514
## SPPPINRAD -55.1797 8.6514 -6.378 3.62e-10 ***
## SPPPTETRI 33.5521 8.6531 3.877 0.000117 ***
## SPPSCOZEY 39.3604 8.6696 4.540 6.82e-06 ***
## SPPSEALUC -8.8318 8.6646 -1.019 0.308481
## SPPSIDINE 28.9724 8.7465 3.312 0.000981 ***
## SPPTARLIT -20.0167 8.6877 -2.304 0.021568 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 8.837 8.99 24.051 <2e-16 ***
## s(FMC) 1.000 1.00 0.494 0.482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.539 Deviance explained = 56.7%
## GCV = 837.76 Scale est. = 784.79 n = 630
gam(TIMETOIGNITION~s(FWI)+s(FMC)+(VEGTYPE),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## TIMETOIGNITION ~ s(FWI) + s(FMC) + (VEGTYPE)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.741 2.358 20.673 < 2e-16 ***
## VEGTYPEIAP 14.852 3.324 4.468 9.4e-06 ***
## VEGTYPEThi 41.649 3.392 12.278 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 8.806 8.985 17.59 < 2e-16 ***
## s(FMC) 4.414 5.496 2.95 0.00793 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.361 Deviance explained = 37.6%
## GCV = 1116.3 Scale est. = 1087.6 n = 630
gam(TIMETOIGNITION~s(FWI)+s(FMC)+(VEGTYPE)+(SPP),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## TIMETOIGNITION ~ s(FWI) + s(FMC) + (VEGTYPE) + (SPP)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.2453 1.6701 29.486 < 2e-16 ***
## VEGTYPEIAP 26.5963 5.8647 4.535 6.98e-06 ***
## VEGTYPEThi 37.9172 2.3654 16.030 < 2e-16 ***
## SPPACAMEA -30.8132 8.6665 -3.555 0.000408 ***
## SPPACAMEL -14.9459 8.6782 -1.722 0.085552 .
## SPPACASAL 26.4725 8.8981 2.975 0.003049 **
## SPPAGAOVA 20.2780 5.8828 3.447 0.000607 ***
## SPPASPSPI -14.2449 5.8405 -2.439 0.015022 *
## SPPCALVIM -2.1808 8.7752 -0.249 0.803817
## SPPCASPER 16.6675 5.9786 2.788 0.005477 **
## SPPCESLAE -18.0441 9.5513 -1.889 0.059358 .
## SPPCLIERI 8.7899 5.8713 1.497 0.134902
## SPPCLIILI 14.8863 5.9066 2.520 0.011988 *
## SPPDIODIC 3.2502 5.8396 0.557 0.578020
## SPPERICAN -23.8184 5.8500 -4.072 5.31e-05 ***
## SPPERIDIS 1.7064 6.0937 0.280 0.779559
## SPPEUCCAM -13.9268 8.6939 -1.602 0.109710
## SPPGYMBUX -10.6197 5.8067 -1.829 0.067924 .
## SPPLEPLAE -5.3297 8.7317 -0.610 0.541844
## SPPLEUEUC 22.8501 6.0097 3.802 0.000158 ***
## SPPMETMUR -18.8708 5.8378 -3.233 0.001295 **
## SPPOSTMON 12.5007 7.3390 1.703 0.089036 .
## SPPOSYCOM -0.3134 5.8478 -0.054 0.957278
## SPPPASRIG -24.9545 5.8370 -4.275 2.23e-05 ***
## SPPPHYAXI -1.8905 6.2338 -0.303 0.761794
## SPPPINPIN -3.9583 8.7059 -0.455 0.649514
## SPPPINRAD -55.1797 8.6514 -6.378 3.62e-10 ***
## SPPPTETRI 22.2312 5.8035 3.831 0.000141 ***
## SPPSCOZEY 28.0395 5.8920 4.759 2.45e-06 ***
## SPPSEALUC -20.1527 5.8803 -3.427 0.000652 ***
## SPPSIDINE 17.6515 5.8820 3.001 0.002805 **
## SPPTARLIT -31.3376 5.9316 -5.283 1.79e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 8.837 8.99 24.051 <2e-16 ***
## s(FMC) 1.000 1.00 0.494 0.482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 48/50
## R-sq.(adj) = 0.539 Deviance explained = 56.7%
## GCV = 837.76 Scale est. = 784.79 n = 630
gam(BIOMASSBURNT_PROP~s(FWI)+s(FMC),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BIOMASSBURNT_PROP ~ s(FWI) + s(FMC)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.53200 0.00691 76.99 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 8.916 8.997 8.619 2.44e-12 ***
## s(FMC) 1.000 1.000 33.607 1.06e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.14 Deviance explained = 15.4%
## GCV = 0.030613 Scale est. = 0.030083 n = 630
gam(BIOMASSBURNT_PROP~s(FWI)+s(FMC)+(SPP),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BIOMASSBURNT_PROP ~ s(FWI) + s(FMC) + (SPP)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.567534 0.033984 16.700 < 2e-16 ***
## SPPACAMEA 0.003330 0.047895 0.070 0.94459
## SPPACAMEL 0.023608 0.048280 0.489 0.62503
## SPPACASAL -0.033049 0.051643 -0.640 0.52246
## SPPAGAOVA -0.038591 0.048747 -0.792 0.42888
## SPPASPSPI -0.078710 0.048755 -1.614 0.10698
## SPPCALVIM -0.081733 0.050014 -1.634 0.10275
## SPPCASPER 0.041072 0.048591 0.845 0.39831
## SPPCESLAE 0.048847 0.058885 0.830 0.40714
## SPPCLIERI -0.123800 0.047614 -2.600 0.00956 **
## SPPCLIILI -0.003068 0.047918 -0.064 0.94898
## SPPDIODIC -0.084957 0.048568 -1.749 0.08078 .
## SPPERICAN 0.090315 0.048219 1.873 0.06156 .
## SPPERIDIS -0.042934 0.051500 -0.834 0.40480
## SPPEUCCAM 0.089599 0.048124 1.862 0.06313 .
## SPPGYMBUX -0.139554 0.048146 -2.899 0.00389 **
## SPPLEPLAE -0.124204 0.048855 -2.542 0.01127 *
## SPPLEUEUC -0.152926 0.048493 -3.154 0.00170 **
## SPPMETMUR 0.037429 0.047981 0.780 0.43566
## SPPOSTMON -0.128645 0.061933 -2.077 0.03822 *
## SPPOSYCOM -0.126473 0.047865 -2.642 0.00846 **
## SPPPASRIG 0.066262 0.048157 1.376 0.16936
## SPPPHYAXI 0.104180 0.054295 1.919 0.05550 .
## SPPPINPIN 0.027645 0.049272 0.561 0.57497
## SPPPINRAD 0.059222 0.047702 1.241 0.21492
## SPPPTETRI -0.143688 0.048124 -2.986 0.00295 **
## SPPSCOZEY -0.225443 0.047882 -4.708 3.12e-06 ***
## SPPSEALUC -0.005972 0.047866 -0.125 0.90075
## SPPSIDINE -0.147931 0.049837 -2.968 0.00312 **
## SPPTARLIT 0.024167 0.047992 0.504 0.61477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 8.912 8.997 9.907 2.97e-14 ***
## s(FMC) 7.475 8.463 2.121 0.0371 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.321 Deviance explained = 37%
## GCV = 0.025653 Scale est. = 0.023764 n = 630
gam(BIOMASSBURNT_PROP~s(FWI)+s(FMC)+(VEGTYPE),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BIOMASSBURNT_PROP ~ s(FWI) + s(FMC) + (VEGTYPE)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.54763 0.01211 45.233 < 2e-16 ***
## VEGTYPEIAP 0.02247 0.01710 1.314 0.189
## VEGTYPEThi -0.06935 0.01744 -3.977 7.82e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 8.902 8.996 8.488 4.42e-12 ***
## s(FMC) 7.401 8.396 4.357 5.03e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.186 Deviance explained = 20.9%
## GCV = 0.029399 Scale est. = 0.028498 n = 630
gam(BIOMASSBURNT_PROP~s(FWI)+s(FMC)+(VEGTYPE)+(SPP),data=dat)%>%summary()
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BIOMASSBURNT_PROP ~ s(FWI) + s(FMC) + (VEGTYPE) + (SPP)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.505981 0.009269 54.588 < 2e-16 ***
## VEGTYPEIAP 0.061553 0.032678 1.884 0.060113 .
## VEGTYPEThi -0.029263 0.013219 -2.214 0.027238 *
## SPPACAMEA 0.003330 0.047895 0.070 0.944587
## SPPACAMEL 0.023608 0.048280 0.489 0.625033
## SPPACASAL -0.033049 0.051643 -0.640 0.522458
## SPPAGAOVA 0.022963 0.032908 0.698 0.485598
## SPPASPSPI -0.017157 0.032710 -0.525 0.600119
## SPPCALVIM -0.081733 0.050014 -1.634 0.102752
## SPPCASPER 0.131888 0.033492 3.938 9.21e-05 ***
## SPPCESLAE 0.048847 0.058885 0.830 0.407142
## SPPCLIERI -0.062247 0.032768 -1.900 0.057974 .
## SPPCLIILI 0.058486 0.033076 1.768 0.077545 .
## SPPDIODIC 0.005859 0.033163 0.177 0.859823
## SPPERICAN 0.151868 0.032517 4.670 3.73e-06 ***
## SPPERIDIS 0.018619 0.035134 0.530 0.596351
## SPPEUCCAM 0.089599 0.048124 1.862 0.063129 .
## SPPGYMBUX -0.048738 0.033052 -1.475 0.140870
## SPPLEPLAE -0.124204 0.048855 -2.542 0.011270 *
## SPPLEUEUC -0.091372 0.034446 -2.653 0.008204 **
## SPPMETMUR 0.098982 0.032518 3.044 0.002441 **
## SPPOSTMON -0.037828 0.048129 -0.786 0.432200
## SPPOSYCOM -0.035656 0.032972 -1.081 0.279966
## SPPPASRIG 0.127815 0.032702 3.909 0.000104 ***
## SPPPHYAXI 0.165733 0.038219 4.336 1.71e-05 ***
## SPPPINPIN 0.027645 0.049272 0.561 0.574971
## SPPPINRAD 0.059222 0.047702 1.241 0.214921
## SPPPTETRI -0.052872 0.033250 -1.590 0.112344
## SPPSCOZEY -0.134627 0.032720 -4.115 4.44e-05 ***
## SPPSEALUC 0.084844 0.032850 2.583 0.010044 *
## SPPSIDINE -0.057115 0.034510 -1.655 0.098454 .
## SPPTARLIT 0.114983 0.032805 3.505 0.000492 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(FWI) 8.912 8.997 9.907 2.97e-14 ***
## s(FMC) 7.475 8.463 2.121 0.0371 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 48/50
## R-sq.(adj) = 0.321 Deviance explained = 37%
## GCV = 0.025653 Scale est. = 0.023764 n = 630
VegType Kruskal.Wallace
res <- matrix(NA,nrow=21,ncol=3)
colnames(res)<-c("BIOMASSBURNT_PROP","TIMETOIGNITION","BURNINTENSITY")
rownames(res) <- as.character(sort(unique(dat$FWI)))
for (i.FWI in sort(dat$FWI)){
ftutu <- subset(dat,FWI==i.FWI)
kruskal.test(BURNINTENSITY~VEGTYPE,data=ftutu)->kt.bi
kt.bi$p.value%>%round(3)->res[as.character(i.FWI),"BURNINTENSITY"]
kruskal.test(TIMETOIGNITION~VEGTYPE,data=ftutu)->kt.tti
kt.tti$p.value%>%round(3)->res[as.character(i.FWI),"TIMETOIGNITION"]
kruskal.test(BIOMASSBURNT_PROP~VEGTYPE,data=ftutu)->kt.bbp
kt.bbp$p.value%>%round(3)->res[as.character(i.FWI),"BIOMASSBURNT_PROP"]
}
res2 <-as.data.frame(res)
colnames(res2) <- paste(colnames(res2),"_p",sep="")
res2$FWI<-rownames(res)%>%as.numeric()
res2$BBP_sig<- ifelse(res2$BIOMASSBURNT_PROP_p<0.05|res2$BIOMASSBURNT_PROP_p>0.95,"*","")
res2$TTI_sig<- ifelse(res2$TIMETOIGNITION_p<0.05|res2$TIMETOIGNITION_p>0.95,"*","")
res2$BI_sig<- ifelse(res2$BURNINTENSITY_p<0.05|res2$BURNINTENSITY_p>0.95,"*","")
res2%>%select(FWI,BURNINTENSITY_p,BI_sig,BIOMASSBURNT_PROP_p,BBP_sig,TIMETOIGNITION_p,TTI_sig)->res2
knitr::kable(res2)
| 5.974422971 |
5.974423 |
0.152 |
|
0.130 |
|
0.841 |
|
| 11.98535478 |
11.985355 |
0.264 |
|
0.801 |
|
0.008 |
* |
| 13.53526668 |
13.535267 |
0.917 |
|
0.588 |
|
0.184 |
|
| 14.22309147 |
14.223092 |
0.287 |
|
0.002 |
* |
0.015 |
* |
| 14.37820053 |
14.378201 |
0.774 |
|
0.165 |
|
0.043 |
* |
| 15.52382668 |
15.523827 |
0.062 |
|
0.588 |
|
0.065 |
|
| 16.40149557 |
16.401496 |
0.185 |
|
0.590 |
|
0.032 |
* |
| 16.82607625 |
16.826076 |
0.064 |
|
0.072 |
|
0.115 |
|
| 17.74736909 |
17.747369 |
0.118 |
|
0.745 |
|
0.053 |
|
| 17.87391472 |
17.873915 |
0.064 |
|
0.035 |
* |
0.009 |
* |
| 17.89486646 |
17.894866 |
0.619 |
|
0.279 |
|
0.005 |
* |
| 17.91383597 |
17.913836 |
0.412 |
|
0.056 |
|
0.011 |
* |
| 19.33126698 |
19.331267 |
0.028 |
* |
0.001 |
* |
0.005 |
* |
| 20.53705301 |
20.537053 |
0.921 |
|
0.517 |
|
0.001 |
* |
| 20.64195653 |
20.641956 |
0.416 |
|
0.044 |
* |
0.001 |
* |
| 21.53585892 |
21.535859 |
0.265 |
|
0.026 |
* |
0.000 |
* |
| 22.20818532 |
22.208185 |
0.070 |
|
0.968 |
* |
0.000 |
* |
| 24.78807203 |
24.788072 |
0.020 |
* |
0.042 |
* |
0.071 |
|
| 25.23025712 |
25.230257 |
0.687 |
|
0.246 |
|
0.003 |
* |
| 31.25728048 |
31.257281 |
0.265 |
|
0.581 |
|
0.001 |
* |
| 59.81397766 |
59.813978 |
0.637 |
|
0.119 |
|
0.000 |
* |
dat%>%
left_join(y=res2,by="FWI")%>%
group_by(FWI)%>%
mutate(pval = ifelse(BURNINTENSITY_p<0.05|BURNINTENSITY_p>0.95,"*SIG*",""),
FWI_lab=paste(FWI,pval),
FWI_lab=ifelse(FWI<10,paste("0",FWI_lab,sep=""),
FWI_lab))%>%
ggplot(aes(VEGTYPE,BURNINTENSITY))+
geom_boxplot()+
facet_wrap(~FWI_lab)
dat%>%
left_join(y=res2,by="FWI")%>%
group_by(FWI)%>%
mutate(pval = ifelse(TIMETOIGNITION_p<0.05|TIMETOIGNITION_p>0.95,"*SIG*",""),
FWI_lab=paste(FWI,pval),
FWI_lab=ifelse(FWI<10,paste("0",FWI_lab,sep=""),
FWI_lab))%>%
ggplot(aes(VEGTYPE,TIMETOIGNITION))+
geom_boxplot()+
facet_wrap(~FWI_lab)
dat%>%
left_join(y=res2,by="FWI")%>%
group_by(FWI)%>%
mutate(pval = ifelse(BIOMASSBURNT_PROP_p<0.05|BIOMASSBURNT_PROP_p>0.95,"*SIG*",""),
FWI_lab=paste(FWI,pval),
FWI_lab=ifelse(FWI<10,paste("0",FWI_lab,sep=""),
FWI_lab))%>%
ggplot(aes(VEGTYPE,BIOMASSBURNT_PROP))+
geom_boxplot()+
facet_wrap(~FWI_lab)
dat%>%
mutate(FWI_lab2=as.character(FWI),
FWI_lab2=ifelse(FWI<10,paste("0",FWI_lab2,sep=""),
FWI_lab2))%>%
ggplot(aes(FWI_lab2,BURNINTENSITY,fill=FWI_index))+
geom_boxplot()+
scale_fill_manual(values = cols)+
facet_wrap(~VEGTYPE)+
coord_flip()
dat%>%
mutate(FWI_lab2=as.character(FWI),
FWI_lab2=ifelse(FWI<10,paste("0",FWI_lab2,sep=""),
FWI_lab2))%>%
ggplot(aes(FWI_lab2,TIMETOIGNITION,fill=FWI_index))+
geom_boxplot()+
scale_fill_manual(values = cols)+
facet_wrap(~VEGTYPE)+
coord_flip()
dat%>%
mutate(FWI_lab2=as.character(FWI),
FWI_lab2=ifelse(FWI<10,paste("0",FWI_lab2,sep=""),
FWI_lab2))%>%
ggplot(aes(FWI_lab2,BIOMASSBURNT_PROP,fill=FWI_index))+
geom_boxplot()+
scale_fill_manual(values = cols)+
facet_wrap(~VEGTYPE)+
coord_flip()
