Burn chamber - Stats and figures for AFE 2022
Georgia Harrison
9/2/2022
Background work - get everything set up for figures and stats!
library tidyverse
library(tidyverse)Import the data
import the individual and multiple species lab info
one<- read.csv("C:/Users/harr4718/OneDrive - University of Idaho/Research/Grass Combustion/copy of burn data/one_sp_to_graph.csv")
two<- read.csv("C:/Users/harr4718/OneDrive - University of Idaho/Research/Grass Combustion/copy of burn data/two_sp_to_Graph.csv")First we need to turn the two conditions we will use as factors for anovas and graphing into factors!
two$AG.GOAL.DRY.MASS <- as.factor(two$AG.GOAL.DRY.MASS)
two$AG.GOAL.MOISTURE. <- as.factor(two$AG.GOAL.MOISTURE.)
one$goal_moisture_percent <-as.factor(one$goal_moisture_percent)
one$goal_weight <-as.factor(one$weight_goal)
#order species for figures
one$SPECIES <- factor(one$SPECIES,levels = c("BRTE", "ACTH7", "PSSP6"))Remove 5 an 10 % BRTE FM for the poster
#one <- filter(one, !goal_moisture_percent==5)
#one <- filter(one, !goal_moisture_percent==10)can we group all three thermocouples together?
Create a gathered dataset where outputs (i.e. max temp) from all three thermocouples are combined into one column, and then test is the output differs by channel type.
Max temp
Two_gathered_1 <- gather(two, channel, max, max_ch1:max_ch3)
aov1 <- aov(max ~ channel, data = Two_gathered_1)
summary(aov1)## Df Sum Sq Mean Sq F value Pr(>F)
## channel 2 8085577 4042789 41.55 <2e-16 ***
## Residuals 417 40571291 97293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = max ~ channel, data = Two_gathered_1)
##
## $channel
## diff lwr upr p adj
## max_ch2-max_ch1 -54.2880 -141.9781 33.40206 0.3132604
## max_ch3-max_ch1 263.4086 175.7185 351.09863 0.0000000
## max_ch3-max_ch2 317.6966 230.0065 405.38663 0.0000000Channel 3 is significantly different from both 1 and 2
Heat Load
Two_gathered_2 <- gather(two, channel, load, load_ch1:load_ch3)
aov1 <- aov(load ~ channel, data = Two_gathered_2)
summary(aov1)## Df Sum Sq Mean Sq F value Pr(>F)
## channel 2 1.681e+11 8.407e+10 80.45 <2e-16 ***
## Residuals 417 4.358e+11 1.045e+09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = load ~ channel, data = Two_gathered_2)
##
## $channel
## diff lwr upr p adj
## load_ch2-load_ch1 6906.695 -2181.644 15995.03 0.1750145
## load_ch3-load_ch1 45475.130 36386.791 54563.47 0.0000000
## load_ch3-load_ch2 38568.435 29480.096 47656.77 0.0000000Channel 3 is significantly different from both 1 and 2
Burn Duration
Two_gathered_3 <- gather(two, channel, dur, dur_ch1:dur_ch3)
aov1 <- aov(dur ~ channel, data = Two_gathered_3)
summary(aov1)## Df Sum Sq Mean Sq F value Pr(>F)
## channel 2 48929 24464 16.18 1.7e-07 ***
## Residuals 417 630389 1512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dur ~ channel, data = Two_gathered_3)
##
## $channel
## diff lwr upr p adj
## dur_ch2-dur_ch1 10.34286 -0.587776 21.27349 0.0680840
## dur_ch3-dur_ch1 26.24286 15.312224 37.17349 0.0000001
## dur_ch3-dur_ch2 15.90000 4.969367 26.83063 0.0019746Channel 3 is significantly different from both 1 and 2
No we cannot group all three thermocouples together
we will just use combination of TC 1 and 2
Set theme for figures
theme_set(theme_bw(base_size = 15)+
theme(
panel.background = element_rect(fill='transparent'), #transparent panel bg
plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg
panel.grid.major = element_blank(), #remove major gridlines
panel.grid.minor = element_blank(), #remove minor gridlines
legend.background = element_rect(fill='transparent'), #transparent legend bg
legend.box.background = element_rect(fill='transparent'), #transparent legend panel
axis.text = element_text(color="black")
))Title: Flammability of annual and perennial grasses of the Western US from green-up through senescence
Question 1: Did combustion traits differ among different grass species?
1. Maximum temperature
one_max_condensed <- gather(one, channel, max, max_ch1:max_ch2)
one_max_condensed$max <- as.numeric(one_max_condensed$max)
one_max_condensed$SPECIES <- factor(one_max_condensed$SPECIES,levels = c("BRTE", "ACTH7", "PSSP6"))
g1 <- ggplot(data = one_max_condensed, aes(x = SPECIES,
y= max, fill =goal_moisture_percent))
g1 <- g1 + geom_boxplot()+
scale_fill_brewer(palette = "Oranges")+
scale_y_continuous(limits = c(0, 1500), expand = c(0, 0))
g1 <- g1 + labs(x = "Species", y = "Maximum temperature (C)")+
guides(fill=guide_legend(title="FM (%)"))
g1 <- g1 + labs(title = "Combustibility: Max temperature")
g1<- g1 + scale_x_discrete(labels=c("BRTE" = "Cheatgrass", "ACTH7" = "Needlegrass",
"PSSP6" = "Bluebunch"))+theme(legend.position = "none")
g1
#save plot
ggsave <- g1
ggsave('figures/indiv_sp/indiv_species_maxTemp.png', ggsave, width = 5, height = 4, bg='transparent')save one with legend
g1 <- ggplot(data = one_max_condensed, aes(x = SPECIES,
y= max, fill =goal_moisture_percent))
g1 <- g1 + geom_boxplot()+
scale_fill_brewer(palette = "Oranges")+
scale_y_continuous(limits = c(0, 1500), expand = c(0, 0))
g1 <- g1 + labs(x = "Species", y = "Maximum temperature (C)")+
guides(fill=guide_legend(title="FM (%)"))
g1 <- g1 + labs(title = "Combustibility: Max temperature")
g1<- g1 + scale_x_discrete(labels=c("BRTE" = "Cheatgrass", "ACTH7" = "Needlegrass",
"PSSP6" = "Bluebunch"))
g1
#save plot
ggsave <- g1
ggsave('figures/indiv_sp/legend_side.png', ggsave, width = 5, height = 4, bg='transparent')
g1 <- ggplot(data = one_max_condensed, aes(x = SPECIES,
y= max, fill =goal_moisture_percent))
g1 <- g1 + geom_boxplot()+
scale_fill_brewer(palette = "Oranges")+
scale_y_continuous(limits = c(0, 1500), expand = c(0, 0))
g1 <- g1 + labs(x = "Species", y = "Maximum temperature (C)")+
guides(fill=guide_legend(title="FM (%)"))
g1 <- g1 + labs(title = "Combustibility: Max temperature")
g1<- g1 + scale_x_discrete(labels=c("BRTE" = "Cheatgrass", "ACTH7" = "Needlegrass",
"PSSP6" = "Bluebunch"))+
theme(legend.position = "top")
g1
#save plot
ggsave <- g1
ggsave('figures/indiv_sp/legend_top.png', ggsave, width = 5, height = 4, bg='transparent')stats
aov1 <- aov(max ~ SPECIES + goal_moisture_percent + SPECIES*goal_moisture_percent, data = one_max_condensed)
summary(aov1)#interaction was non sign, remove for post hoc## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 668106 334053 3.460 0.0337 *
## goal_moisture_percent 6 5687439 947907 9.818 2.9e-09 ***
## SPECIES:goal_moisture_percent 8 1220117 152515 1.580 0.1343
## Residuals 167 16123578 96548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aov2 <- aov(max ~ SPECIES + goal_moisture_percent, data = one_max_condensed)
summary(aov2)## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 668106 334053 3.371 0.0366 *
## goal_moisture_percent 6 5687439 947907 9.564 4.32e-09 ***
## Residuals 175 17343695 99107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = max ~ SPECIES + goal_moisture_percent, data = one_max_condensed)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE 93.02638 -41.049313 227.1021 0.2316211
## PSSP6-BRTE 138.33081 5.987281 270.6743 0.0381982
## PSSP6-ACTH7 45.30443 -105.070310 195.6792 0.7566319
##
## $goal_moisture_percent
## diff lwr upr p adj
## 10-5 250.61075 -194.8290 696.050537 0.6314381
## 15-5 23.08342 -343.9688 390.135629 0.9999962
## 25-5 -22.40417 -398.8695 354.061158 0.9999973
## 35-5 -184.81993 -545.7542 176.114315 0.7279678
## 45-5 -316.88515 -693.3505 59.580185 0.1617851
## 55-5 -358.32698 -731.9933 15.339315 0.0695401
## 15-10 -227.52733 -563.2073 108.152629 0.4046907
## 25-10 -273.01492 -618.9627 72.932842 0.2247922
## 35-10 -435.43068 -764.4098 -106.451593 0.0021580
## 45-10 -567.49590 -913.4437 -221.548131 0.0000456
## 55-10 -608.93773 -951.8374 -266.038021 0.0000072
## 25-15 -45.48759 -282.1109 191.135685 0.9974707
## 35-15 -207.90335 -418.9435 3.136832 0.0564542
## 45-15 -339.96857 -576.5918 -103.345288 0.0005888
## 55-15 -381.41040 -613.5546 -149.266200 0.0000440
## 35-25 -162.41576 -389.4329 64.601381 0.3374805
## 45-25 -294.48097 -545.4579 -43.504086 0.0103933
## 55-25 -335.92281 -582.6813 -89.164317 0.0014137
## 45-35 -132.06522 -359.0824 94.951918 0.5933666
## 55-35 -173.50705 -395.8517 48.837578 0.2368101
## 55-45 -41.44183 -288.2003 205.316656 0.9988171#first test for differences by species using an anova
aov1 <- aov(max ~ SPECIES, data = one_max_condensed)
summary(aov1)#nope## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 668106 334053 2.625 0.0752 .
## Residuals 181 23031134 127244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#tranform the fm into a continious variable for linear regression
one_max_condensed$goal_moisture_percent_cont <- as.numeric(one_max_condensed$goal_moisture_percent)
#then test for differences by fuel moisture
lm <-lm(max~ goal_moisture_percent_cont,
data = one_max_condensed)
summary(lm)##
## Call:
## lm(formula = max ~ goal_moisture_percent_cont, data = one_max_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -682.76 -292.01 -12.74 279.38 747.61
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 998.47 71.50 13.965 < 2e-16 ***
## goal_moisture_percent_cont -87.77 14.62 -6.001 1.04e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 329.7 on 182 degrees of freedom
## Multiple R-squared: 0.1652, Adjusted R-squared: 0.1606
## F-statistic: 36.01 on 1 and 182 DF, p-value: 1.036e-08The maximum temperature of BRTE was less than both perennial grass species. For all species, maximum temperature decreased with increasing FM.
2. Flame length
g1 <- ggplot(data = one, aes(x = SPECIES, y= FLAME.HEIGHT.CM, fill = goal_moisture_percent))
g1 <- g1 +
geom_boxplot()+
scale_fill_brewer(palette = "Oranges")+
scale_y_continuous(limits = c(15, 150), expand = c(0, 0))
g1 <- g1 + labs(x = "Species", y = "Flame length (cm)")+
guides(fill=guide_legend(title="FM (%)"))
g1 <- g1 + labs(title = "Combustibility: Flame length") + scale_x_discrete(labels=c("BRTE" = "Cheatgrass", "ACTH7" = "Needlegrass",
"PSSP6" = "Bluebunch"))+theme(legend.position = "none")
g1## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
ggsave <- g1
ggsave('figures/indiv_sp/indiv_sp_fl.png', ggsave, height = 4, width = 5, bg='transparent')## Warning: Removed 15 rows containing non-finite values (stat_boxplot).aov1 <- aov(FLAME.HEIGHT.CM ~ SPECIES + goal_moisture_percent + SPECIES* goal_moisture_percent , data = one)
summary(aov1) #interaction non sig, remove ## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 1544 771.9 1.591 0.212
## goal_moisture_percent 6 18819 3136.5 6.466 2.69e-05 ***
## SPECIES:goal_moisture_percent 8 2608 326.0 0.672 0.714
## Residuals 60 29104 485.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingnessaov2 <- aov(FLAME.HEIGHT.CM ~ SPECIES + goal_moisture_percent, data = one)
summary(aov2) #interaction non sig, remove ## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 1544 771.9 1.655 0.199
## goal_moisture_percent 6 18819 3136.5 6.726 1.27e-05 ***
## Residuals 68 31712 466.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingnessTukeyHSD(aov2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FLAME.HEIGHT.CM ~ SPECIES + goal_moisture_percent, data = one)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE -10.000000 -24.05504 4.055040 0.2108064
## PSSP6-BRTE -7.619048 -22.06311 6.825011 0.4203539
## PSSP6-ACTH7 2.380952 -13.23656 17.998461 0.9291659
##
## $goal_moisture_percent
## diff lwr upr p adj
## 10-5 2.7500000 -41.26479 46.764791 0.9999958
## 15-5 11.6997354 -24.56942 47.968894 0.9566441
## 25-5 5.0396825 -32.84222 42.921583 0.9996378
## 35-5 5.1934524 -31.48554 41.872445 0.9994814
## 45-5 -5.0811688 -43.39113 33.228789 0.9996441
## 55-5 -37.1158009 -75.42576 1.194157 0.0636566
## 15-10 8.9497354 -24.21947 42.118939 0.9820987
## 25-10 2.2896825 -32.63570 37.215069 0.9999943
## 35-10 2.4434524 -31.17340 36.060304 0.9999896
## 45-10 -7.8311688 -43.22039 27.558054 0.9936964
## 55-10 -39.8658009 -75.25502 -4.476578 0.0173537
## 25-15 -6.6600529 -31.11271 17.792609 0.9812193
## 35-15 -6.5062831 -29.05052 16.037957 0.9748393
## 45-15 -16.7809043 -41.89160 8.329794 0.4056227
## 55-15 -48.8155363 -73.92623 -23.704838 0.0000025
## 35-25 0.1537698 -24.90275 25.210292 1.0000000
## 45-25 -10.1208514 -37.50942 17.267717 0.9189122
## 55-25 -42.1554834 -69.54405 -14.766915 0.0002786
## 45-35 -10.2746212 -35.97372 15.424480 0.8860633
## 55-35 -42.3092532 -68.00835 -16.610152 0.0000841
## 55-45 -32.0346320 -60.01227 -4.056994 0.0147199#first test for differences by species using an anova
aov1 <- aov(FLAME.HEIGHT.CM ~ SPECIES, data = one)
summary(aov1)#nope## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 1544 771.9 1.13 0.328
## Residuals 74 50531 682.9
## 15 observations deleted due to missingness#tranform the fm into a continious variable for linear regression
one$goal_moisture_percent_cont <- as.numeric(one$goal_moisture_percent)
#then test for differences by fuel moisture
lm <-lm(FLAME.HEIGHT.CM~ goal_moisture_percent_cont,
data = one)
summary(lm)##
## Call:
## lm(formula = FLAME.HEIGHT.CM ~ goal_moisture_percent_cont, data = one)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.701 -10.322 -0.511 14.678 41.894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 142.537 7.334 19.436 < 2e-16 ***
## goal_moisture_percent_cont -7.405 1.555 -4.763 9.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.09 on 75 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.2322, Adjusted R-squared: 0.222
## F-statistic: 22.68 on 1 and 75 DF, p-value: 9.109e-06Results: Across all species, flame lengths decrease with increasing fuel moisture.
3. Mass Consumption
g1 <- ggplot(data = one, aes(x = SPECIES, y= MASS.CONSUMPTION, , fill = goal_moisture_percent))
g1 <- g1 +
geom_boxplot()+
scale_fill_brewer(palette = "Oranges")+
scale_y_continuous(limits = c(25, 100), expand = c(0, 0))
g1 <- g1 + labs(x = "Species", y = "Mass Consumption (%)")+
guides(fill=guide_legend(title="FM (%)"))
g1 <- g1 + labs(title = "Consumption") + scale_x_discrete(labels=c("BRTE" = "Cheatgrass", "ACTH7" = "Needlegrass",
"PSSP6" = "Bluebunch"))+theme(legend.position = "none")
g1
ggsave <- g1
ggsave('figures/indiv_sp/indiv_species_MC.png', plot = ggsave, width = 5, height =4, bg='transparent')aov1 <- aov(MASS.CONSUMPTION ~ SPECIES + goal_moisture_percent + SPECIES * goal_moisture_percent, data = one)
summary(aov1) #no sig interation, remove## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 1806 903.1 6.055 0.00365 **
## goal_moisture_percent 6 4406 734.4 4.924 0.00027 ***
## SPECIES:goal_moisture_percent 8 2073 259.1 1.737 0.10365
## Residuals 75 11186 149.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aov2 <- aov(MASS.CONSUMPTION ~ SPECIES + goal_moisture_percent, data = one)
summary(aov2) ## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 1806 903.1 5.653 0.004991 **
## goal_moisture_percent 6 4406 734.4 4.597 0.000446 ***
## Residuals 83 13258 159.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MASS.CONSUMPTION ~ SPECIES + goal_moisture_percent, data = one)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE -10.828198 -18.513445 -3.142951 0.0033227
## PSSP6-BRTE -3.803373 -11.389332 3.782586 0.4586011
## PSSP6-ACTH7 7.024825 -1.594687 15.644337 0.1326376
##
## $goal_moisture_percent
## diff lwr upr p adj
## 10-5 -3.4554643 -29.07567 22.16473691 0.9996253
## 15-5 1.6328321 -19.47878 22.74444293 0.9999856
## 25-5 2.1399722 -19.51305 23.79299430 0.9999389
## 35-5 -5.3463521 -26.10608 15.41337390 0.9864472
## 45-5 -12.2552367 -33.90826 9.39778535 0.6112017
## 55-5 -16.6119496 -38.10398 4.88008121 0.2398533
## 15-10 5.0882964 -14.21889 24.39548075 0.9847231
## 25-10 5.5954365 -14.30232 25.49319068 0.9787025
## 35-10 -1.8908878 -20.81266 17.03088482 0.9999348
## 45-10 -8.7997724 -28.69753 11.09798174 0.8327866
## 55-10 -13.1564853 -32.87893 6.56595482 0.4127653
## 25-15 0.5071401 -13.10264 14.11691680 0.9999998
## 35-15 -6.9791842 -19.11751 5.15913859 0.5932757
## 45-15 -13.8880688 -27.49785 -0.27829214 0.0423944
## 55-15 -18.2447817 -31.59694 -4.89262663 0.0016193
## 35-25 -7.4863243 -20.54359 5.57093927 0.5965226
## 45-25 -14.3952089 -28.83056 0.04013911 0.0511204
## 55-25 -18.7519218 -32.94464 -4.55920195 0.0025935
## 45-35 -6.9088846 -19.96615 6.14837900 0.6833498
## 55-35 -11.2655975 -24.05411 1.52291911 0.1212764
## 55-45 -4.3567129 -18.54943 9.83600700 0.9670304#first test for differences by species using an anova
aov1 <- aov(MASS.CONSUMPTION ~ SPECIES, data = one)
summary(aov1)#yes## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 1806 903.1 4.55 0.0131 *
## Residuals 89 17664 198.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov1) #ACTH7-BRTE only sig## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MASS.CONSUMPTION ~ SPECIES, data = one)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE -10.828198 -19.384326 -2.272070 0.0092565
## PSSP6-BRTE -3.803373 -12.248962 4.642216 0.5330003
## PSSP6-ACTH7 7.024825 -2.571438 16.621088 0.1944119#then test for differences by fuel moisture
lm <-lm(MASS.CONSUMPTION~ goal_moisture_percent_cont,
data = one)
summary(lm)##
## Call:
## lm(formula = MASS.CONSUMPTION ~ goal_moisture_percent_cont, data = one)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.084 -2.680 2.821 7.416 18.841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 106.502 3.989 26.697 < 2e-16 ***
## goal_moisture_percent_cont -4.086 0.816 -5.008 2.72e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.01 on 90 degrees of freedom
## Multiple R-squared: 0.2179, Adjusted R-squared: 0.2092
## F-statistic: 25.08 on 1 and 90 DF, p-value: 2.725e-06At the lowest FM, all grasses had nearly complete consumption. Cheatgrass maintained high mass consumption even at high fuel moistures.
4. Flaming duration
one_dur_condensed <- gather(one, channel, dur, dur_ch1:dur_ch2)
one_dur_condensed$dur <- as.numeric(one_dur_condensed$dur)
one_max_condensed$SPECIES <- factor(one_max_condensed$SPECIES,levels = c("BRTE", "ACTH7", "PSSP6"))
g1 <- ggplot(data = one_dur_condensed,
aes(x = SPECIES, y= dur, fill =goal_moisture_percent))
g1 <- g1 + geom_boxplot()+
scale_fill_brewer(palette = "Oranges")+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0))
g1 <- g1 + labs(x = "Species", y = "Flaming duration >100 C (s)")+
guides(fill=guide_legend(title="FM (%)"))
g1 <- g1 + labs(title = "Sustainability") + scale_x_discrete(labels=c("BRTE" = "Cheatgrass", "ACTH7" = "Needlegrass",
"PSSP6" = "Bluebunch"))+theme(legend.position = "none")
g1## Warning: Removed 16 rows containing non-finite values (stat_boxplot).
ggsave <- g1
ggsave('figures/indiv_sp/indiv_species_dur.png', plot = ggsave, width =5, height = 4, bg='transparent')## Warning: Removed 16 rows containing non-finite values (stat_boxplot).aov1 <- aov(dur ~ SPECIES + goal_moisture_percent + SPECIES*goal_moisture_percent, data = one_dur_condensed)
summary(aov1) #no sig interaction, remove ## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 7797 3898 6.044 0.002986 **
## goal_moisture_percent 6 18847 3141 4.870 0.000141 ***
## SPECIES:goal_moisture_percent 8 4789 599 0.928 0.495183
## Residuals 151 97404 645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 16 observations deleted due to missingnessaov2 <- aov(dur ~ SPECIES + goal_moisture_percent, data = one_dur_condensed)
summary(aov2)## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 7797 3898 6.066 0.00289 **
## goal_moisture_percent 6 18847 3141 4.887 0.00013 ***
## Residuals 159 102193 643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 16 observations deleted due to missingnessTukeyHSD(aov2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dur ~ SPECIES + goal_moisture_percent, data = one_dur_condensed)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE 15.232961 3.9713909 26.494532 0.0046992
## PSSP6-BRTE 11.791385 0.6754362 22.907334 0.0347662
## PSSP6-ACTH7 -3.441576 -15.8172113 8.934059 0.7881400
##
## $goal_moisture_percent
## diff lwr upr p adj
## 10-5 8.950000 -26.96372 44.8637164 0.9895139
## 15-5 6.062682 -23.53101 35.6563759 0.9963677
## 25-5 10.529098 -20.08186 41.1400527 0.9470248
## 35-5 2.965459 -26.62823 32.5591537 0.9999401
## 45-5 -16.606829 -47.21778 14.0041266 0.6695343
## 55-5 -15.584414 -46.19537 15.0265408 0.7321960
## 15-10 -2.887318 -29.95162 24.1769788 0.9999134
## 25-10 1.579098 -26.59394 29.7521346 0.9999981
## 35-10 -5.984541 -33.04884 21.0797566 0.9944809
## 45-10 -25.556829 -53.72987 2.6162085 0.1026852
## 55-10 -24.534414 -52.70745 3.6386227 0.1327351
## 25-15 4.466416 -15.01978 23.9526094 0.9932954
## 35-15 -3.097222 -20.94289 14.7484469 0.9985616
## 45-15 -22.669510 -42.15570 -3.1833166 0.0115263
## 55-15 -21.647096 -41.13329 -2.1609024 0.0189590
## 35-25 -7.563638 -27.04983 11.9225552 0.9081561
## 45-25 -27.135926 -48.13487 -6.1369841 0.0030797
## 55-25 -26.113512 -47.11245 -5.1145699 0.0051503
## 45-35 -19.572288 -39.05848 -0.0860944 0.0482125
## 55-35 -18.549874 -38.03607 0.9363198 0.0733622
## 55-45 1.022414 -19.97653 22.0213562 0.9999992#first test for differences by species using an anova
aov1 <- aov(dur ~ SPECIES, data = one_dur_condensed)
summary(aov1)#yes## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 7797 3898 5.314 0.0058 **
## Residuals 165 121040 734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 16 observations deleted due to missingnessTukeyHSD(aov1) #ACTH7-BRTE only sig## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dur ~ SPECIES, data = one_dur_condensed)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE 15.232961 3.20589109 27.260031 0.0088336
## PSSP6-BRTE 11.791385 -0.08016505 23.662935 0.0520005
## PSSP6-ACTH7 -3.441576 -16.65843909 9.775287 0.8116660#tranform the fm into a continious variable for linear regression
one_dur_condensed$goal_moisture_percent_cont <- as.numeric(one_dur_condensed$goal_moisture_percent)
#then test for differences by fuel moisture
lm <-lm(dur~ goal_moisture_percent_cont,
data = one_dur_condensed)
summary(lm)##
## Call:
## lm(formula = dur ~ goal_moisture_percent_cont, data = one_dur_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.213 -16.414 -2.642 19.909 74.697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 91.776 5.972 15.368 < 2e-16 ***
## goal_moisture_percent_cont -3.795 1.240 -3.061 0.00257 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27.1 on 166 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.05342, Adjusted R-squared: 0.04772
## F-statistic: 9.369 on 1 and 166 DF, p-value: 0.002575Cheatgrass has shorter flaming duration than both perennial grasses. In general, flaming duration decreased with increasing fuel moistures.
5. Heat load
one_load_condensed <- gather(one, channel, load, load_ch1:load_ch2)
one_load_condensed$dur <- as.numeric(one_load_condensed$load)
one_load_condensed$SPECIES <- factor(one_load_condensed$SPECIES,levels = c("BRTE", "ACTH7", "PSSP6"))
g1 <- ggplot(data = one_load_condensed,
aes(x = SPECIES, y= load/1000, fill =goal_moisture_percent))
g1 <- g1 + geom_boxplot()+
scale_fill_brewer(palette = "Oranges")+
scale_y_continuous(limits = c(0, 105), expand = c(0, 0))
g1 <- g1 + labs(x = "Species", y = "Thermal dose (C) / 1,000")+
guides(fill=guide_legend(title="FM (%)"))
g1 <- g1 + labs(title = "Combustibility: Thermal dose") + scale_x_discrete(labels=c("BRTE" = "Cheatgrass", "ACTH7" = "Needlegrass",
"PSSP6" = "Bluebunch"))+theme(legend.position = "none")
g1## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
ggsave <- g1
ggsave('figures/indiv_sp/indiv_species_load.png', ggsave, height = 4, width = 5, bg='transparent')## Warning: Removed 15 rows containing non-finite values (stat_boxplot).aov1 <- aov(load ~ SPECIES + goal_moisture_percent +SPECIES*goal_moisture_percent , data = one_load_condensed)
summary(aov1) # no sig interaction## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 6.209e+09 3.105e+09 8.781 0.000246 ***
## goal_moisture_percent 6 1.888e+10 3.147e+09 8.902 2.51e-08 ***
## SPECIES:goal_moisture_percent 8 2.196e+09 2.745e+08 0.776 0.624125
## Residuals 152 5.374e+10 3.536e+08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingnessaov2 <- aov(load ~ SPECIES + goal_moisture_percent , data = one_load_condensed)
summary(aov2)## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 6.209e+09 3.105e+09 8.880 0.00022 ***
## goal_moisture_percent 6 1.888e+10 3.147e+09 9.002 1.78e-08 ***
## Residuals 160 5.594e+10 3.496e+08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingnessTukeyHSD(aov2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = load ~ SPECIES + goal_moisture_percent, data = one_load_condensed)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE 14382.250 6076.9528 22687.548 0.0001954
## PSSP6-BRTE 8645.996 498.9908 16793.002 0.0346612
## PSSP6-ACTH7 -5736.254 -14817.4740 3344.966 0.2963474
##
## $goal_moisture_percent
## diff lwr upr p adj
## 10-5 17127.729 -9357.719 43613.1764 0.4629414
## 15-5 5777.869 -16046.724 27602.4624 0.9856042
## 25-5 10448.189 -12126.607 33022.9861 0.8106356
## 35-5 -2071.313 -23895.905 19753.2805 0.9999565
## 45-5 -13622.392 -36197.189 8952.4046 0.5487053
## 55-5 -15841.019 -38317.235 6635.1978 0.3549981
## 15-10 -11349.859 -31309.087 8609.3680 0.6183189
## 25-10 -6679.539 -27456.434 14097.3551 0.9616747
## 35-10 -19199.041 -39158.269 760.1860 0.0678894
## 45-10 -30750.121 -51527.015 -9973.2264 0.0003593
## 55-10 -32968.747 -53638.489 -12299.0062 0.0000858
## 25-15 4670.320 -9700.250 19040.8894 0.9596081
## 35-15 -7849.182 -21009.907 5311.5428 0.5627979
## 45-15 -19400.262 -33770.831 -5029.6922 0.0016329
## 55-15 -21618.888 -35834.095 -7403.6806 0.0002182
## 35-25 -12519.502 -26890.071 1851.0677 0.1324420
## 45-25 -24070.582 -39556.764 -8584.3987 0.0001438
## 55-25 -26289.208 -41631.330 -10947.0857 0.0000180
## 45-35 -11551.080 -25921.649 2819.4898 0.2052851
## 55-35 -13769.706 -27984.913 445.5013 0.0643770
## 55-45 -2218.626 -17560.749 13123.4958 0.9994924#first test for differences by species using an anova
aov1 <- aov(load ~ SPECIES, data = one_load_condensed)
summary(aov1)#yes## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 6.209e+09 3.105e+09 6.888 0.00134 **
## Residuals 166 7.482e+10 4.507e+08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingnessTukeyHSD(aov1) #ACTH7-BRTE only sig, PSSP to BRTE is close## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = load ~ SPECIES, data = one_load_condensed)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE 14382.250 4955.1995 23809.301 0.0011819
## PSSP6-BRTE 8645.996 -601.3828 17893.375 0.0722345
## PSSP6-ACTH7 -5736.254 -16044.0272 4571.519 0.3882522#tranform the fm into a continious variable for linear regression
one_load_condensed$goal_moisture_percent_cont <- as.numeric(one_load_condensed$goal_moisture_percent)
#then test for differences by fuel moisture
lm <-lm(load~ goal_moisture_percent_cont,
data = one_load_condensed)
summary(lm)##
## Call:
## lm(formula = load ~ goal_moisture_percent_cont, data = one_load_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38140 -16134 -273 12955 67996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60332.9 4558.6 13.235 < 2e-16 ***
## goal_moisture_percent_cont -4334.0 943.3 -4.595 8.51e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20750 on 167 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1122, Adjusted R-squared: 0.1069
## F-statistic: 21.11 on 1 and 167 DF, p-value: 8.506e-06Cheatgrass had lower thermal dose than both perennial grasses. Thermal dose decreases with increasing fuel moistures.
Ignition frequency
summarize the likelyhood that a sample would ignite
one$COMBUSTION. <- as.factor(one$COMBUSTION.)
one_sp_ignition <- one %>%
group_by(SPECIES,goal_moisture_percent) %>%
summarise(count_no = sum(COMBUSTION. == "N"),
count_yes = sum(COMBUSTION. == "Y"),
ignition_freq = (count_yes/(count_yes+count_no)))## `summarise()` has grouped output by 'SPECIES'. You can override using the
## `.groups` argument.one_sp_ignition## # A tibble: 17 x 5
## # Groups: SPECIES [3]
## SPECIES goal_moisture_percent count_no count_yes ignition_freq
## <fct> <fct> <int> <int> <dbl>
## 1 BRTE 5 0 3 1
## 2 BRTE 10 0 5 1
## 3 BRTE 15 0 8 1
## 4 BRTE 25 1 3 0.75
## 5 BRTE 35 6 6 0.5
## 6 BRTE 45 1 4 0.8
## 7 BRTE 55 2 3 0.6
## 8 ACTH7 15 0 5 1
## 9 ACTH7 25 0 5 1
## 10 ACTH7 35 0 5 1
## 11 ACTH7 45 0 4 1
## 12 ACTH7 55 1 4 0.8
## 13 PSSP6 15 0 5 1
## 14 PSSP6 25 1 4 0.8
## 15 PSSP6 35 1 4 0.8
## 16 PSSP6 45 2 3 0.6
## 17 PSSP6 55 2 3 0.6#write.csv(one_sp_ignition, "one_sp_ignition_freq.csv")graph the ignition frequency
one_sp_ignition$goal_moisture_percent<- as.factor(one_sp_ignition$goal_moisture_percent)
g1 <- ggplot(data = one_sp_ignition, aes(x = SPECIES, y= ignition_freq))
g1 <- g1 +
geom_bar(aes(fill = goal_moisture_percent),
stat = "identity", color="black", position = "dodge") +
scale_fill_brewer(palette = "Oranges")+
scale_y_continuous(limits = c(0, 1), expand = c(0, 0))
g1 <- g1 + labs(title="Ignitability", x = "Species", y = "Ignition probability")+
guides(fill=guide_legend(title="FM (%)")) +
scale_x_discrete(labels=c("BRTE" = "Cheatgrass", "ACTH7" = "Needlegrass",
"PSSP6" = "Bluebunch"))+theme(legend.position = "none")
g1
ggsave('figures/indiv_sp/indiv_species_Ignition.png', g1, height = 4, width = 5, bg='transparent')can we combine all of these stats to one output table
#1. max temp
#first test for differences by species using an anova
max_aov <- aov(max ~ SPECIES, data = one_max_condensed)
summary(max_aov)#nope## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 668106 334053 2.625 0.0752 .
## Residuals 181 23031134 127244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#tranform the fm into a continious variable for linear regression
one_max_condensed$goal_moisture_percent_cont <- as.numeric(one_max_condensed$goal_moisture_percent)
#then test for differences by fuel moisture
max_lm <-lm(max~ goal_moisture_percent_cont,
data = one_max_condensed)
summary(max_lm)##
## Call:
## lm(formula = max ~ goal_moisture_percent_cont, data = one_max_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -682.76 -292.01 -12.74 279.38 747.61
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 998.47 71.50 13.965 < 2e-16 ***
## goal_moisture_percent_cont -87.77 14.62 -6.001 1.04e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 329.7 on 182 degrees of freedom
## Multiple R-squared: 0.1652, Adjusted R-squared: 0.1606
## F-statistic: 36.01 on 1 and 182 DF, p-value: 1.036e-08#2 flame height
#first test for differences by species using an anova
fl_aov <- aov(FLAME.HEIGHT.CM ~ SPECIES, data = one)
summary(fl_aov)#nope## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 1544 771.9 1.13 0.328
## Residuals 74 50531 682.9
## 15 observations deleted due to missingnesssummary(fl_aov)$"Pr(>F)"[1]## NULL#tranform the fm into a continious variable for linear regression
one$goal_moisture_percent_cont <- as.numeric(one$goal_moisture_percent)
#then test for differences by fuel moisture
fl_aov <-lm(FLAME.HEIGHT.CM~ goal_moisture_percent_cont,
data = one)
summary(fl_aov)##
## Call:
## lm(formula = FLAME.HEIGHT.CM ~ goal_moisture_percent_cont, data = one)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.701 -10.322 -0.511 14.678 41.894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 142.537 7.334 19.436 < 2e-16 ***
## goal_moisture_percent_cont -7.405 1.555 -4.763 9.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.09 on 75 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.2322, Adjusted R-squared: 0.222
## F-statistic: 22.68 on 1 and 75 DF, p-value: 9.109e-06#3 mass consumption
#first test for differences by species using an anova
mc_aov <- aov(MASS.CONSUMPTION ~ SPECIES, data = one)
summary(mc_aov)#yes## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 1806 903.1 4.55 0.0131 *
## Residuals 89 17664 198.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(mc_aov) #ACTH7-BRTE only sig## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MASS.CONSUMPTION ~ SPECIES, data = one)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE -10.828198 -19.384326 -2.272070 0.0092565
## PSSP6-BRTE -3.803373 -12.248962 4.642216 0.5330003
## PSSP6-ACTH7 7.024825 -2.571438 16.621088 0.1944119#then test for differences by fuel moisture
mc_lm <-lm(MASS.CONSUMPTION~ goal_moisture_percent_cont,
data = one)
summary(mc_lm)##
## Call:
## lm(formula = MASS.CONSUMPTION ~ goal_moisture_percent_cont, data = one)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.084 -2.680 2.821 7.416 18.841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 106.502 3.989 26.697 < 2e-16 ***
## goal_moisture_percent_cont -4.086 0.816 -5.008 2.72e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.01 on 90 degrees of freedom
## Multiple R-squared: 0.2179, Adjusted R-squared: 0.2092
## F-statistic: 25.08 on 1 and 90 DF, p-value: 2.725e-06#4 flaming duration
#first test for differences by species using an anova
dur_aov <- aov(dur ~ SPECIES, data = one_dur_condensed)
summary(dur_aov)#yes## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 7797 3898 5.314 0.0058 **
## Residuals 165 121040 734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 16 observations deleted due to missingnessTukeyHSD(dur_aov) #ACTH7-BRTE only sig## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dur ~ SPECIES, data = one_dur_condensed)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE 15.232961 3.20589109 27.260031 0.0088336
## PSSP6-BRTE 11.791385 -0.08016505 23.662935 0.0520005
## PSSP6-ACTH7 -3.441576 -16.65843909 9.775287 0.8116660#tranform the fm into a continious variable for linear regression
one_dur_condensed$goal_moisture_percent_cont <- as.numeric(one_dur_condensed$goal_moisture_percent)
#then test for differences by fuel moisture
dur_lm <-lm(dur~ goal_moisture_percent_cont,
data = one_dur_condensed)
summary(dur_lm)##
## Call:
## lm(formula = dur ~ goal_moisture_percent_cont, data = one_dur_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.213 -16.414 -2.642 19.909 74.697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 91.776 5.972 15.368 < 2e-16 ***
## goal_moisture_percent_cont -3.795 1.240 -3.061 0.00257 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27.1 on 166 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.05342, Adjusted R-squared: 0.04772
## F-statistic: 9.369 on 1 and 166 DF, p-value: 0.002575#5 load
#first test for differences by species using an anova
load_aov <- aov(load ~ SPECIES, data = one_load_condensed)
summary(load_aov)#yes## Df Sum Sq Mean Sq F value Pr(>F)
## SPECIES 2 6.209e+09 3.105e+09 6.888 0.00134 **
## Residuals 166 7.482e+10 4.507e+08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingnessTukeyHSD(load_aov) #ACTH7-BRTE only sig, PSSP to BRTE is close## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = load ~ SPECIES, data = one_load_condensed)
##
## $SPECIES
## diff lwr upr p adj
## ACTH7-BRTE 14382.250 4955.1995 23809.301 0.0011819
## PSSP6-BRTE 8645.996 -601.3828 17893.375 0.0722345
## PSSP6-ACTH7 -5736.254 -16044.0272 4571.519 0.3882522#tranform the fm into a continious variable for linear regression
one_load_condensed$goal_moisture_percent_cont <- as.numeric(one_load_condensed$goal_moisture_percent)
#then test for differences by fuel moisture
load_lm <-lm(load~ goal_moisture_percent_cont,
data = one_load_condensed)
summary(load_lm)##
## Call:
## lm(formula = load ~ goal_moisture_percent_cont, data = one_load_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38140 -16134 -273 12955 67996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60332.9 4558.6 13.235 < 2e-16 ***
## goal_moisture_percent_cont -4334.0 943.3 -4.595 8.51e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20750 on 167 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1122, Adjusted R-squared: 0.1069
## F-statistic: 21.11 on 1 and 167 DF, p-value: 8.506e-06####################
# Merge ANOVA results
aov_results <- data.frame(
Test = c("max", "flame height", "mass consumption",
"flaming duration", "load"),
Species_P_Value = c(
ifelse(is.na(summary(max_aov)$coefficients[2,4]), NA, summary(max_aov)$coefficients[2,4]), ifelse(is.na(summary(fl_aov)$coefficients[2,4]), NA, summary(fl_aov)$coefficients[2,4]), ifelse(is.na(summary(mc_aov)$coefficients[2,4]), NA, summary(mc_aov)$coefficients[2,4]), ifelse(is.na(summary(dur_aov)$coefficients[2,4]), NA, summary(dur_aov)$coefficients[2,4]), ifelse(is.na(summary(load_aov)$coefficients[2,4]), NA, summary(load_aov)$coefficients[2,4])),
Fuel_Moisture_P_Value = c(NA, NA, ifelse(is.na(summary(mc_lm)$coefficients[2,4]), NA, summary(mc_lm)$coefficients[2,4]), ifelse(is.na(summary(dur_lm)$coefficients[2,4]), NA, summary(dur_lm)$coefficients[2,4]), ifelse(is.na(summary(load_lm)$coefficients[2,4]), NA, summary(load_lm)$coefficients[2,4])))
aov_results## Test Species_P_Value Fuel_Moisture_P_Value
## 1 max 9.108966e-06 NA
## 2 flame height 9.108966e-06 NA
## 3 mass consumption 9.108966e-06 2.724898e-06
## 4 flaming duration 9.108966e-06 2.574630e-03
## 5 load 9.108966e-06 8.506381e-06# Print tables
aov_results## Test Species_P_Value Fuel_Moisture_P_Value
## 1 max 9.108966e-06 NA
## 2 flame height 9.108966e-06 NA
## 3 mass consumption 9.108966e-06 2.724898e-06
## 4 flaming duration 9.108966e-06 2.574630e-03
## 5 load 9.108966e-06 8.506381e-06# Merge linear regression results
lm_results <- data.frame(Test = c("max", "flame height", "mass consumption", "flaming duration", "load"),
Coefficient = c(summary(max_lm)$coefficients[2,1], summary(fl_aov)$coefficients[2,1], summary(mc_lm)$coefficients[2,1], summary(dur_lm)$coefficients[2,1], summary(load_lm)$coefficients[2,1]),
SE = c(summary(max_lm)$coefficients[2,2], summary(fl_aov)$coefficients[2,2], summary(mc_lm)$coefficients[2,2], summary(dur_lm)$coefficients[2,2], summary(load_lm)$coefficients[2,2]),
T_Value = c(summary(max_lm)$coefficients[2,3], summary(fl_aov)$coefficients[2,3], summary(mc_lm)$coefficients[2,3], summary(dur_lm)$coefficients[2,3], summary(load_lm)$coefficients[2,3]),
P_Value = c(summary(max_lm)$coefficients[2,4], summary(fl_aov)$coefficients[2,4], summary(mc_lm)$coefficients[2,4], summary(dur_lm)$coefficients[2,4], summary(load_lm)$coefficients[2,4]))
# Print tables
lm_results## Test Coefficient SE T_Value P_Value
## 1 max -87.766555 14.6248190 -6.001206 1.036175e-08
## 2 flame height -7.405183 1.5548150 -4.762742 9.108966e-06
## 3 mass consumption -4.086139 0.8159741 -5.007682 2.724898e-06
## 4 flaming duration -3.794770 1.2397692 -3.060868 2.574630e-03
## 5 load -4334.043956 943.2771985 -4.594666 8.506381e-06Question 2: How does the additional of annual grasses impact perennial grass flammability?
import two species data with perennial grass from one species added in ACTH and PSSP 35 and 55% FM for 0 Cheatgrass added to mix dad
two_with_one<- read.csv("C:/Users/harr4718/OneDrive - University of Idaho/Research/Grass Combustion/copy of burn data/two_sp_to_Graph_plus0gBRTE.csv")
two_with_one$AG.GOAL.DRY.MASS <- as.factor(two_with_one$AG.GOAL.DRY.MASS)
two_with_one$AG.GOAL.MOISTURE. <- as.factor(two_with_one$AG.GOAL.MOISTURE.)Ignition frequency
summarize the likelyhood that a sample would ignite
head(two)## Full_ID REP.. PG.SPECIES PG_Goal_moisture AG.dry.weight..g.
## 1 ACTH7_35_BRTE_5g_5 1 ACTH7 35 4.95
## 2 ACTH7_35_BRTE_5g_5 2 ACTH7 35 5.05
## 3 ACTH7_35_BRTE_5g_5 3 ACTH7 35 5.07
## 4 ACTH7_35_BRTE_5g_5 4 ACTH7 35 5.30
## 5 ACTH7_35_BRTE_5g_25 1 ACTH7 35 4.88
## 6 ACTH7_35_BRTE_5g_25 2 ACTH7 35 5.09
## AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. PG.WET.ACTUAL AG.WET.ACTUAL PG.MOISTURE
## 1 5 5 26.07 5.39 33.28
## 2 5 5 27.44 5.46 34.84
## 3 5 5 27.18 5.46 34.35
## 4 5 5 26.64 5.74 35.16
## 5 5 25 27.40 6.29 35.18
## 6 5 25 25.97 6.30 29.53
## AG.MOISTURE PRE.BURN.COMBINED.WET.MASS POST.BURN.MASS
## 1 8.89 31.46 6.66
## 2 8.12 32.90 3.77
## 3 7.69 32.64 6.06
## 4 8.30 32.38 6.38
## 5 28.89 33.69 8.72
## 6 23.77 32.27 7.36
## WET.MASS.CONSUMPTION.... DRY.CONSUMPTION.... FLAME.HEIGHT..CM. combust
## 1 78.83 27.17 120 Y
## 2 88.54 14.84 140 Y
## 3 81.43 23.95 140 Y
## 4 80.30 25.51 145 Y
## 5 74.12 34.67 110 Y
## 6 77.19 29.28 140 Y
## dur_ch1 dur_ch2 dur_ch3 max_ch1 max_ch2 max_ch3 load_ch1 load_ch2 load_ch3
## 1 67.0 0.0 188.5 423.37 29.52 443.20 24236.58 0.00 109082.80
## 2 90.0 128.0 98.5 480.10 514.48 1103.34 38487.70 59435.43 108535.03
## 3 90.5 102.5 68.5 1144.22 869.35 901.54 75637.74 68582.54 65611.46
## 4 72.0 74.5 60.0 724.41 834.94 1035.42 44439.55 51573.08 53971.34
## 5 107.5 75.0 81.5 1092.74 868.40 1123.60 79589.02 42732.32 67388.23
## 6 81.0 99.0 83.5 780.12 850.71 1177.45 49924.46 55076.96 80711.22two$combust <- as.factor(two$combust)
two_sp_ignition <- two %>%
group_by(PG.SPECIES, PG_Goal_moisture, AG.GOAL.DRY.MASS, AG.GOAL.MOISTURE.) %>%
summarise(count_no = sum(combust == "N"),
count_yes = sum(combust == "Y"),
ignition_freq = (count_yes/(count_yes+count_no)))## `summarise()` has grouped output by 'PG.SPECIES', 'PG_Goal_moisture',
## 'AG.GOAL.DRY.MASS'. You can override using the `.groups` argument.two_sp_ignition## # A tibble: 35 x 7
## # Groups: PG.SPECIES, PG_Goal_moisture, AG.GOAL.DRY.MASS [11]
## PG.SPECIES PG_Goal_moisture AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. count_no
## <chr> <int> <fct> <fct> <int>
## 1 ACTH7 35 2.5 5 0
## 2 ACTH7 35 2.5 25 1
## 3 ACTH7 35 5 5 0
## 4 ACTH7 35 5 25 0
## 5 ACTH7 55 2.5 5 1
## 6 ACTH7 55 2.5 15 0
## 7 ACTH7 55 2.5 25 0
## 8 ACTH7 55 2.5 35 0
## 9 ACTH7 55 5 5 0
## 10 ACTH7 55 5 15 1
## # ... with 25 more rows, and 2 more variables: count_yes <int>,
## # ignition_freq <dbl>#write.csv(two_sp_ignition, "two_sp_ignition_freq.csv")graph the ignition frequency
two_sp_ignition$AG.GOAL.DRY.MASS<- as.factor(two_sp_ignition$AG.GOAL.DRY.MASS)
two_sp_ignition$AG.GOAL.MOISTURE.<- as.factor(two_sp_ignition$AG.GOAL.MOISTURE.)
custom_colors = c("#EFF3FF", "#BDD7E7", "#6BAED6", "#2171B5", "#E6550D")
g1 <- ggplot(data= filter(two_sp_ignition, PG.SPECIES == "PSSP6", PG_Goal_moisture == "55"), aes(x = AG.GOAL.DRY.MASS, y= ignition_freq,fill = AG.GOAL.MOISTURE.))
g1 <- g1 +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = custom_colors) +
scale_y_continuous(limits = c(0, 1), expand = c(0, 0))
g1 <- g1 + labs(title = "Ignitability", subtitle = "Bluebunch 55% FM", x = "Cheatgrass (g)", y = "Ignition frequency")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+
theme(legend.position = "none")
g1
ggsave('figures/PSSP55_Ignition.png', g1, height = 4, width = 5, bg='transparent')
g2 <- ggplot(data= filter(two_sp_ignition, PG.SPECIES == "PSSP6", PG_Goal_moisture == "35"), aes(x = AG.GOAL.DRY.MASS, y= ignition_freq,fill = AG.GOAL.MOISTURE.))
g2 <- g2 +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = custom_colors) +
scale_y_continuous(limits = c(0, 1), expand = c(0, 0))+
labs(title = "Ignitability", subtitle = "Bluebunch 35% FM",
x = "Cheatgrass (g)", y = "Ignition frequency")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+
theme(legend.position = "none")
g2
ggsave('figures/PSSP55_Ignition.png', g1, height = 4, width = 5, bg='transparent')
g1 <- ggplot(data= filter(two_sp_ignition, PG.SPECIES == "ACTH7", PG_Goal_moisture == "55"), aes(x = AG.GOAL.DRY.MASS, y= ignition_freq,fill = AG.GOAL.MOISTURE.))
g1 <- g1 +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = custom_colors) +
scale_y_continuous(limits = c(0, 1), expand = c(0, 0))
g1 <- g1 + labs(title = "Ignitability", subtitle="Needlegrass 55% FM", x = "Cheatgrass (g)", y = "Ignition frequency")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) +
theme(legend.position = "none")
g1
ggsave('figures/ACTH55_Ignition.png', g1, height = 4, width = 5, bg='transparent')
g1 <- ggplot(data= filter(two_sp_ignition, PG.SPECIES == "ACTH7", PG_Goal_moisture == "35"), aes(x = AG.GOAL.DRY.MASS, y= ignition_freq,fill = AG.GOAL.MOISTURE.))
g1 <- g1 +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = custom_colors) +
scale_y_continuous(limits = c(0, 1), expand = c(0, 0))
g1 <- g1 + labs(title = "Ignitability", subtitle="Needlegrass 55% FM", x = "Cheatgrass (g)", y = "Ignition frequency")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) +
theme(legend.position = "none")
g1
ggsave('figures/ACTH35_Ignition.png', g1, height = 4, width = 5, bg='transparent')build two sets of custom colors
print(custom_colors)## [1] "#EFF3FF" "#BDD7E7" "#6BAED6" "#2171B5" "#E6550D"pssp_custom_colors = c('#636363', "#ece7f2", "#a6bddb", "#2b8cbe")
#ece7f2
#a6bddb
#2b8cbe
acth_custom_colors= c('#636363', "#ffffcc", "#c2e699", "#78c679", "#238443")
#ffffcc
#c2e699
#78c679
#238443#confirm data format
summary(two_with_one)## Full_ID REP.. PG.SPECIES PG_Goal_moisture
## Length:160 Min. :1.00 Length:160 Min. :35.00
## Class :character 1st Qu.:1.75 Class :character 1st Qu.:55.00
## Mode :character Median :2.50 Mode :character Median :55.00
## Mean :2.50 Mean :50.25
## 3rd Qu.:3.25 3rd Qu.:55.00
## Max. :4.00 Max. :55.00
## NA's :20
## AG.dry.weight..g. AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. PG.WET.ACTUAL
## Min. : 2.370 0 :20 5 :44 Min. :25.97
## 1st Qu.: 2.595 2.5:48 15 :28 1st Qu.:30.07
## Median : 5.045 5 :44 25 :40 Median :30.87
## Mean : 6.530 10 :32 35 :28 Mean :30.03
## 3rd Qu.:10.010 15 :16 NA's:20 3rd Qu.:30.96
## Max. :15.450 Max. :32.21
## NA's :20 NA's :20
## AG.WET.ACTUAL PG.MOISTURE AG.MOISTURE PRE.BURN.COMBINED.WET.MASS
## Min. : 2.610 Min. :29.53 Min. :-16.250 Min. :29.40
## 1st Qu.: 3.275 1st Qu.:50.12 1st Qu.: 9.175 1st Qu.:33.69
## Median : 5.940 Median :54.21 Median : 16.650 Median :36.50
## Mean : 7.798 Mean :49.99 Mean : 19.297 Mean :37.83
## 3rd Qu.:11.525 3rd Qu.:54.67 3rd Qu.: 27.742 3rd Qu.:42.50
## Max. :20.800 Max. :61.00 Max. : 53.910 Max. :51.67
## NA's :20 NA's :20 NA's :20 NA's :20
## POST.BURN.MASS WET.MASS.CONSUMPTION.... DRY.CONSUMPTION....
## Min. : 3.770 Min. :13.87 Min. : 14.84
## 1st Qu.: 6.912 1st Qu.:69.64 1st Qu.: 25.50
## Median : 8.695 Median :81.14 Median : 32.41
## Mean :11.033 Mean :75.79 Mean : 41.97
## 3rd Qu.:11.865 3rd Qu.:90.02 3rd Qu.: 46.36
## Max. :40.290 Max. :96.34 Max. :122.82
## NA's :20
## FLAME.HEIGHT..CM. combust dur_ch1 dur_ch2
## Min. : 10.0 Length:160 Min. : 0.00 Min. : 0.00
## 1st Qu.:105.0 Class :character 1st Qu.: 56.75 1st Qu.: 60.50
## Median :120.0 Mode :character Median : 72.75 Median : 83.75
## Mean :112.9 Mean : 71.16 Mean : 82.41
## 3rd Qu.:140.0 3rd Qu.: 91.25 3rd Qu.:108.00
## Max. :145.0 Max. :184.00 Max. :189.50
## NA's :25 NA's :2 NA's :2
## dur_ch3 max_ch1 max_ch2 max_ch3
## Min. : 0.00 Min. : 39.9 Min. : 29.52 Min. : 32.23
## 1st Qu.: 75.75 1st Qu.: 241.6 1st Qu.: 290.55 1st Qu.: 672.86
## Median : 94.75 Median : 529.9 Median : 553.35 Median : 904.34
## Mean : 98.11 Mean : 559.5 Mean : 528.63 Mean : 819.09
## 3rd Qu.:118.88 3rd Qu.: 821.8 3rd Qu.: 733.59 3rd Qu.:1031.13
## Max. :298.00 Max. :1257.2 Max. :1363.22 Max. :1277.58
## NA's :2
## load_ch1 load_ch2 load_ch3
## Min. : 0 Min. : 0 Min. : 0
## 1st Qu.: 21107 1st Qu.: 28172 1st Qu.: 58957
## Median : 35696 Median : 43133 Median : 75272
## Mean : 35055 Mean : 43240 Mean : 79958
## 3rd Qu.: 48349 3rd Qu.: 58553 3rd Qu.:101053
## Max. :112188 Max. :165681 Max. :317965
## NA's :1 NA's :2 NA's :2#transform AG goal moisture to continuous
two_with_one$AG.GOAL.MOISTURE._cont <- as.numeric(two_with_one$AG.GOAL.MOISTURE.)
#transform AG mass to continuous
two_with_one$AG.GOAL.DRY.MASS_cont <- as.numeric(two_with_one$AG.GOAL.DRY.MASS)
#transformPG FM to continuous
two_with_one$PG_Goal_moisture_cont <- as.numeric(two_with_one$PG_Goal_moisture)1. Maximum temperature
two_max_condensed <- gather(two_with_one, channel, max, max_ch1:max_ch2)
two_max_condensed$max <- as.numeric(two_max_condensed$max)# try linear models
max_combined_lm <- lm(max ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES +PG_Goal_moisture_cont,
data = two_max_condensed)
summary(max_combined_lm)##
## Call:
## lm(formula = max ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES + PG_Goal_moisture_cont, data = two_max_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -712.30 -251.44 3.99 225.94 684.89
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1192.514 118.998 10.021 < 2e-16 ***
## AG.GOAL.DRY.MASS_cont 17.715 19.359 0.915 0.361
## AG.GOAL.MOISTURE._cont 6.120 16.347 0.374 0.708
## PG.SPECIESPSSP6 51.964 37.082 1.401 0.162
## PG_Goal_moisture_cont -14.570 2.444 -5.963 7.6e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 298.9 on 275 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.1259, Adjusted R-squared: 0.1132
## F-statistic: 9.903 on 4 and 275 DF, p-value: 1.687e-07#only pssp
max_lm_pssp <- lm(max ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_max_condensed,
PG.SPECIES == "PSSP6"))
summary(max_lm_pssp)##
## Call:
## lm(formula = max ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG_Goal_moisture, data = filter(two_max_condensed, PG.SPECIES ==
## "PSSP6"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -527.94 -277.01 -34.85 275.66 719.12
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1284.763 190.914 6.730 6.81e-10 ***
## AG.GOAL.DRY.MASS_cont 61.833 37.888 1.632 0.105
## AG.GOAL.MOISTURE._cont 34.640 26.537 1.305 0.194
## PG_Goal_moisture -19.145 3.986 -4.803 4.72e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 315 on 116 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1659, Adjusted R-squared: 0.1444
## F-statistic: 7.693 on 3 and 116 DF, p-value: 9.853e-05#only acth
max_lm_acth <- lm(max ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_max_condensed,
PG.SPECIES == "ACTH7"))
summary(max_lm_acth)##
## Call:
## lm(formula = max ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG_Goal_moisture, data = filter(two_max_condensed, PG.SPECIES ==
## "ACTH7"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -698.43 -215.25 15.63 175.99 679.55
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1154.114 148.022 7.797 8.44e-13 ***
## AG.GOAL.DRY.MASS_cont -1.047 21.970 -0.048 0.962047
## AG.GOAL.MOISTURE._cont -12.372 20.551 -0.602 0.548043
## PG_Goal_moisture -11.733 3.064 -3.829 0.000186 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 284.8 on 156 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1077, Adjusted R-squared: 0.09058
## F-statistic: 6.279 on 3 and 156 DF, p-value: 0.0004747#only 35 fm
max_lm_35 <- lm(max ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_max_condensed,
PG_Goal_moisture == "35"))
summary(max_lm_35)##
## Call:
## lm(formula = max ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES, data = filter(two_max_condensed, PG_Goal_moisture ==
## "35"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -724.89 -242.05 31.51 243.35 554.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 441.11 262.82 1.678 0.0993 .
## AG.GOAL.DRY.MASS_cont 100.46 89.36 1.124 0.2661
## AG.GOAL.MOISTURE._cont 11.92 44.68 0.267 0.7907
## PG.SPECIESPSSP6 121.33 89.36 1.358 0.1804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 319.7 on 52 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.04803, Adjusted R-squared: -0.006888
## F-statistic: 0.8746 on 3 and 52 DF, p-value: 0.4603#only 55 fm
max_lm_55 <- lm(max ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_max_condensed,
PG_Goal_moisture == "55"))
summary(max_lm_55)##
## Call:
## lm(formula = max ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES, data = filter(two_max_condensed, PG_Goal_moisture ==
## "55"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -466.07 -257.92 11.28 218.98 689.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 414.124 85.841 4.824 2.62e-06 ***
## AG.GOAL.DRY.MASS_cont 11.833 19.693 0.601 0.549
## AG.GOAL.MOISTURE._cont 7.467 17.614 0.424 0.672
## PG.SPECIESPSSP6 35.614 40.995 0.869 0.386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 294.7 on 220 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.004964, Adjusted R-squared: -0.008604
## F-statistic: 0.3659 on 3 and 220 DF, p-value: 0.7777individual figures for each PG species and FM
pssp55 <- ggplot(filter(two_max_condensed, PG.SPECIES == "PSSP6", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= max, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 1250), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Maximum temperature (C)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp55
pssp35 <- ggplot(filter(two_max_condensed, PG.SPECIES == "PSSP6", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= max, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 1250), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Maximum temperature (C)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp35## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
acth55 <- ggplot(filter(two_max_condensed, PG.SPECIES == "ACTH7", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= max, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 1250), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Maximum temperature (C)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth55
acth35 <- ggplot(filter(two_max_condensed, PG.SPECIES == "ACTH7", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= max, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 1250), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Maximum temperature (C)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth35
combine
library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.0.5max_55 <-
ggarrange(acth55,
pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
max_55
ggsave('figures/combine_twosp/maxtemp55.png', max_55, height = 3, width = 5,bg='transparent')
max_35 <-
ggarrange(acth35,
pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)## Warning: Removed 2 rows containing non-finite values (stat_boxplot).max_35
ggsave('figures/combine_twosp/maxtemp35.png', max_35, height = 3, width = 5,bg='transparent')2. Flame Length
# try linear models
combined_lm <- lm(FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES +PG_Goal_moisture,
data = two_with_one)
summary(combined_lm)##
## Call:
## lm(formula = FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES + PG_Goal_moisture, data = two_with_one)
##
## Residuals:
## Min 1Q Median 3Q Max
## -106.08 -12.62 10.03 18.47 40.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 126.1510 17.1563 7.353 3.41e-11 ***
## AG.GOAL.DRY.MASS_cont 9.4471 2.9838 3.166 0.00199 **
## AG.GOAL.MOISTURE._cont 1.0447 2.4555 0.425 0.67133
## PG.SPECIESPSSP6 3.7102 5.5440 0.669 0.50473
## PG_Goal_moisture -0.8575 0.3539 -2.423 0.01699 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.38 on 112 degrees of freedom
## (43 observations deleted due to missingness)
## Multiple R-squared: 0.09818, Adjusted R-squared: 0.06597
## F-statistic: 3.048 on 4 and 112 DF, p-value: 0.01993#only pssp
lm_pssp <- lm(FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_with_one,
PG.SPECIES == "PSSP6"))
summary(lm_pssp)##
## Call:
## lm(formula = FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG_Goal_moisture, data = filter(two_with_one, PG.SPECIES ==
## "PSSP6"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.514 -16.551 5.104 18.458 42.531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 131.9996 21.6797 6.089 1.84e-07 ***
## AG.GOAL.DRY.MASS_cont 17.3349 4.3917 3.947 0.000258 ***
## AG.GOAL.MOISTURE._cont 5.4778 3.1365 1.746 0.087128 .
## PG_Goal_moisture -1.5570 0.4654 -3.345 0.001603 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.06 on 48 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.3034, Adjusted R-squared: 0.2599
## F-statistic: 6.969 on 3 and 48 DF, p-value: 0.00055#only acth
max_lm_acth <- lm(FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_with_one,
PG.SPECIES == "ACTH7"))
summary(max_lm_acth)##
## Call:
## lm(formula = FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG_Goal_moisture, data = filter(two_with_one, PG.SPECIES ==
## "ACTH7"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -108.859 -9.137 11.141 19.387 36.880
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 125.6915 24.6007 5.109 3.43e-06 ***
## AG.GOAL.DRY.MASS_cont 5.1645 3.9512 1.307 0.196
## AG.GOAL.MOISTURE._cont -2.4431 3.5802 -0.682 0.498
## PG_Goal_moisture -0.4205 0.5032 -0.836 0.407
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.66 on 61 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 0.04284, Adjusted R-squared: -0.004237
## F-statistic: 0.91 on 3 and 61 DF, p-value: 0.4415#only 35 fm
max_lm_35 <- lm(FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_with_one,
PG_Goal_moisture == "35"))
summary(max_lm_35)##
## Call:
## lm(formula = FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES, data = filter(two_with_one, PG_Goal_moisture ==
## "35"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -101.652 -11.763 5.402 15.937 28.348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 85.692 32.497 2.637 0.0147 *
## AG.GOAL.DRY.MASS_cont 12.411 11.092 1.119 0.2747
## AG.GOAL.MOISTURE._cont 1.138 5.546 0.205 0.8392
## PG.SPECIESPSSP6 10.536 11.092 0.950 0.3521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27.67 on 23 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.07166, Adjusted R-squared: -0.04943
## F-statistic: 0.5918 on 3 and 23 DF, p-value: 0.6267#only 55 fm
max_lm_55 <- lm(FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont
+ PG.SPECIES,
data = filter(two_with_one,
PG_Goal_moisture == "55"))
summary(max_lm_55)##
## Call:
## lm(formula = FLAME.HEIGHT..CM. ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES, data = filter(two_with_one, PG_Goal_moisture ==
## "55"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -104.453 -13.085 8.918 19.742 38.935
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.223 13.711 5.851 8.64e-08 ***
## AG.GOAL.DRY.MASS_cont 9.194 3.176 2.895 0.00481 **
## AG.GOAL.MOISTURE._cont 1.227 2.798 0.438 0.66221
## PG.SPECIESPSSP6 1.725 6.523 0.264 0.79214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.24 on 86 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.08952, Adjusted R-squared: 0.05776
## F-statistic: 2.818 on 3 and 86 DF, p-value: 0.04376individual figures for each PG species and FM
pssp55 <- ggplot(filter(two_with_one, PG.SPECIES == "PSSP6", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= FLAME.HEIGHT..CM., fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Flame length (cm)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp55## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
pssp35 <- ggplot(filter(two_with_one, PG.SPECIES == "PSSP6", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= FLAME.HEIGHT..CM., fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Flame length (cm)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp35
acth55 <- ggplot(filter(two_with_one, PG.SPECIES == "ACTH7", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= FLAME.HEIGHT..CM., fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Flame length (cm)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth55## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
acth35 <- ggplot(filter(two_with_one, PG.SPECIES == "ACTH7", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= FLAME.HEIGHT..CM., fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Flame length (cm)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth35## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
combine
library(ggpubr)
fl_55 <-
ggarrange(acth55,
pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)## Warning: Removed 15 rows containing non-finite values (stat_boxplot).## Warning: Removed 9 rows containing non-finite values (stat_boxplot).fl_55
ggsave('figures/combine_twosp/fl_55.png', fl_55, height = 3, width = 5,bg='transparent')
fl_35 <-
ggarrange(acth35,
pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).fl_35
ggsave('figures/combine_twosp/FL_35.png', fl_35, height = 3, width = 5,bg='transparent')3. Mass Consupmtion
# try linear models
combined_lm <- lm(WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES +PG_Goal_moisture,
data = two_with_one)
summary(combined_lm)##
## Call:
## lm(formula = WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont +
## AG.GOAL.MOISTURE._cont + PG.SPECIES + PG_Goal_moisture, data = two_with_one)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57.616 -2.977 3.132 10.292 20.434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.53309 9.37419 6.564 1.03e-09 ***
## AG.GOAL.DRY.MASS_cont 8.10928 1.52504 5.317 4.25e-07 ***
## AG.GOAL.MOISTURE._cont 0.05292 1.28778 0.041 0.9673
## PG.SPECIESPSSP6 -5.18533 2.92120 -1.775 0.0781 .
## PG_Goal_moisture -0.18054 0.19250 -0.938 0.3500
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.65 on 135 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.2222, Adjusted R-squared: 0.1992
## F-statistic: 9.642 on 4 and 135 DF, p-value: 6.879e-07#only pssp
lm_pssp <- lm(WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont+
AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_with_one,
PG.SPECIES == "PSSP6"))
summary(lm_pssp)##
## Call:
## lm(formula = WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont +
## AG.GOAL.MOISTURE._cont + PG_Goal_moisture, data = filter(two_with_one,
## PG.SPECIES == "PSSP6"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.359 -4.168 3.092 7.778 24.249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 96.1583 13.1659 7.304 1.09e-09 ***
## AG.GOAL.DRY.MASS_cont 8.3776 2.6129 3.206 0.002223 **
## AG.GOAL.MOISTURE._cont 2.6799 1.8301 1.464 0.148686
## PG_Goal_moisture -1.0964 0.2749 -3.988 0.000195 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.36 on 56 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.2637, Adjusted R-squared: 0.2242
## F-statistic: 6.684 on 3 and 56 DF, p-value: 0.0006166#only acth
lm_acth <- lm(WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont+
AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_with_one,
PG.SPECIES == "ACTH7"))
summary(lm_acth)##
## Call:
## lm(formula = WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont +
## AG.GOAL.MOISTURE._cont + PG_Goal_moisture, data = filter(two_with_one,
## PG.SPECIES == "ACTH7"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.758 -2.846 5.337 9.907 21.065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.0751 11.6945 2.828 0.00598 **
## AG.GOAL.DRY.MASS_cont 7.4743 1.7358 4.306 4.9e-05 ***
## AG.GOAL.MOISTURE._cont -1.3417 1.6236 -0.826 0.41119
## PG_Goal_moisture 0.4842 0.2421 2.000 0.04908 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.91 on 76 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.3026, Adjusted R-squared: 0.2751
## F-statistic: 10.99 on 3 and 76 DF, p-value: 4.488e-06#only 35 fm
lm_35 <- lm(WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont+
AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_with_one,
PG_Goal_moisture == "35"))
summary(lm_35)##
## Call:
## lm(formula = WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont +
## AG.GOAL.MOISTURE._cont + PG.SPECIES, data = filter(two_with_one,
## PG_Goal_moisture == "35"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -47.593 -5.077 3.873 9.302 26.052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.964 20.188 1.732 0.0961 .
## AG.GOAL.DRY.MASS_cont 14.626 6.864 2.131 0.0436 *
## AG.GOAL.MOISTURE._cont -2.753 3.432 -0.802 0.4304
## PG.SPECIESPSSP6 17.295 6.864 2.520 0.0188 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.36 on 24 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.3291, Adjusted R-squared: 0.2452
## F-statistic: 3.923 on 3 and 24 DF, p-value: 0.02065#only 55 fm
lm_55 <- lm(WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont+
AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_with_one,
PG_Goal_moisture == "55"))
summary(lm_55)##
## Call:
## lm(formula = WET.MASS.CONSUMPTION.... ~ AG.GOAL.DRY.MASS_cont +
## AG.GOAL.MOISTURE._cont + PG.SPECIES, data = filter(two_with_one,
## PG_Goal_moisture == "55"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.161 -4.304 4.117 8.978 22.268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.625 6.278 8.701 4.10e-14 ***
## AG.GOAL.DRY.MASS_cont 7.199 1.440 4.999 2.24e-06 ***
## AG.GOAL.MOISTURE._cont 1.076 1.288 0.835 0.405392
## PG.SPECIESPSSP6 -11.223 2.998 -3.743 0.000293 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.24 on 108 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.3234, Adjusted R-squared: 0.3046
## F-statistic: 17.21 on 3 and 108 DF, p-value: 3.329e-09individual figures for each PG species and FM
pssp55 <- ggplot(filter(two_with_one, PG.SPECIES == "PSSP6", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= WET.MASS.CONSUMPTION...., fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 100), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Mass consumption (%)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp55
pssp35 <- ggplot(filter(two_with_one, PG.SPECIES == "PSSP6", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= WET.MASS.CONSUMPTION...., fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 100), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Mass consumption (%)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp35
acth55 <- ggplot(filter(two_with_one, PG.SPECIES == "ACTH7", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= WET.MASS.CONSUMPTION...., fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 100), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Mass consumption (%)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth55
acth35 <- ggplot(filter(two_with_one, PG.SPECIES == "ACTH7", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= WET.MASS.CONSUMPTION...., fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 100), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Mass consumption (%)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth35
combine
library(ggpubr)
mc_55 <-
ggarrange(acth55,
pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
mc_55
ggsave('figures/combine_twosp/mc_55.png', mc_55, height = 3, width = 5,bg='transparent')
mc_35 <-
ggarrange(acth35,
pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
mc_35
ggsave('figures/combine_twosp/mc_35.png', mc_35, height = 3, width = 5,bg='transparent')4. Flaming duration
two_dur_condensed <- gather(two_with_one, channel, dur, dur_ch1:dur_ch2)
two_dur_condensed$dur <- as.numeric(two_dur_condensed$dur)# try linear models
combined_lm <- lm(dur ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES +PG_Goal_moisture,
data = two_dur_condensed)
summary(combined_lm)##
## Call:
## lm(formula = dur ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES + PG_Goal_moisture, data = two_dur_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -107.772 -13.824 2.588 18.628 90.067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.0683 13.0913 5.505 8.46e-08 ***
## AG.GOAL.DRY.MASS_cont 18.7974 2.1298 8.826 < 2e-16 ***
## AG.GOAL.MOISTURE._cont -2.0412 1.7984 -1.135 0.257372
## PG.SPECIESPSSP6 -2.0707 4.0795 -0.508 0.612147
## PG_Goal_moisture -0.9484 0.2688 -3.528 0.000491 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.89 on 275 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.24, Adjusted R-squared: 0.229
## F-statistic: 21.71 on 4 and 275 DF, p-value: 1.383e-15#only pssp
lm_pssp <- lm(dur ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_dur_condensed,
PG.SPECIES == "PSSP6"))
summary(lm_pssp)##
## Call:
## lm(formula = dur ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG_Goal_moisture, data = filter(two_dur_condensed, PG.SPECIES ==
## "PSSP6"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -69.560 -14.290 4.371 14.980 54.976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 87.8683 16.2722 5.400 3.59e-07 ***
## AG.GOAL.DRY.MASS_cont 22.3609 3.2293 6.924 2.59e-10 ***
## AG.GOAL.MOISTURE._cont 4.5360 2.2618 2.005 0.0472 *
## PG_Goal_moisture -1.8000 0.3398 -5.298 5.65e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.85 on 116 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.3356, Adjusted R-squared: 0.3184
## F-statistic: 19.53 on 3 and 116 DF, p-value: 2.552e-10#only acth
max_lm_acth <- lm(dur ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_dur_condensed,
PG.SPECIES == "ACTH7"))
summary(max_lm_acth)##
## Call:
## lm(formula = dur ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG_Goal_moisture, data = filter(two_dur_condensed, PG.SPECIES ==
## "ACTH7"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -104.513 -14.372 2.049 19.352 96.151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60.7194 18.6111 3.263 0.00136 **
## AG.GOAL.DRY.MASS_cont 17.1860 2.7623 6.222 4.33e-09 ***
## AG.GOAL.MOISTURE._cont -6.4779 2.5839 -2.507 0.01320 *
## PG_Goal_moisture -0.4128 0.3853 -1.071 0.28565
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35.8 on 156 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.2263, Adjusted R-squared: 0.2115
## F-statistic: 15.21 on 3 and 156 DF, p-value: 9.862e-09#only 35 fm
max_lm_35 <- lm(dur ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_dur_condensed,
PG_Goal_moisture == "35"))
summary(max_lm_35)##
## Call:
## lm(formula = dur ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES, data = filter(two_dur_condensed, PG_Goal_moisture ==
## "35"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -79.734 -15.346 -0.461 16.633 48.266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.480 20.144 0.868 0.38950
## AG.GOAL.DRY.MASS_cont 18.773 6.849 2.741 0.00838 **
## AG.GOAL.MOISTURE._cont 5.934 3.425 1.733 0.08908 .
## PG.SPECIESPSSP6 13.430 6.849 1.961 0.05527 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.5 on 52 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1688, Adjusted R-squared: 0.1208
## F-statistic: 3.519 on 3 and 52 DF, p-value: 0.02128#only 55 fm
max_lm_55 <- lm(dur ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_dur_condensed,
PG_Goal_moisture == "55"))
summary(max_lm_55)##
## Call:
## lm(formula = dur ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES, data = filter(two_dur_condensed, PG_Goal_moisture ==
## "55"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -108.387 -12.165 3.965 19.668 90.690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.807 10.002 2.480 0.0139 *
## AG.GOAL.DRY.MASS_cont 18.727 2.295 8.161 2.55e-14 ***
## AG.GOAL.MOISTURE._cont -3.351 2.052 -1.633 0.1040
## PG.SPECIESPSSP6 -5.318 4.777 -1.113 0.2668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34.34 on 220 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.2649, Adjusted R-squared: 0.2549
## F-statistic: 26.43 on 3 and 220 DF, p-value: 1.223e-14individual figures for each PG species and FM
pssp55 <- ggplot(filter(two_dur_condensed, PG.SPECIES == "PSSP6", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= dur, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Flaming duration >100 C (s)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp55## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
pssp35 <- ggplot(filter(two_dur_condensed, PG.SPECIES == "PSSP6", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= dur, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Flaming duration >100 C (s)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp35
acth55 <- ggplot(filter(two_dur_condensed, PG.SPECIES == "ACTH7", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= dur, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Flaming duration >100 C (s)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth55## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
acth35 <- ggplot(filter(two_dur_condensed, PG.SPECIES == "ACTH7", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= dur, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 150), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Flaming duration >100 C (s)")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth35
combine
library(ggpubr)
dur_55 <-
ggarrange(acth55,
pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)## Warning: Removed 9 rows containing non-finite values (stat_boxplot).## Warning: Removed 2 rows containing non-finite values (stat_boxplot).dur_55
ggsave('figures/combine_twosp/dur_55.png', dur_55, height = 3, width = 5,bg='transparent')
dur_35 <-
ggarrange(acth35,
pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
dur_35
ggsave('figures/combine_twosp/dur_35.png', dur_35, height = 3, width = 5,bg='transparent')5. Heat Load
two_load_condensed <- gather(two_with_one, channel, load, load_ch1:load_ch2)
two_load_condensed$load <- as.numeric(two_load_condensed$load)# try linear models
combined_lm <- lm(load ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES +PG_Goal_moisture,
data = two_load_condensed)
summary(combined_lm)##
## Call:
## lm(formula = load ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES + PG_Goal_moisture, data = two_load_condensed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54485 -14314 -72 12027 111564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60715.9 8802.1 6.898 3.63e-11 ***
## AG.GOAL.DRY.MASS_cont 8598.3 1432.0 6.005 6.05e-09 ***
## AG.GOAL.MOISTURE._cont -1287.3 1209.2 -1.065 0.288
## PG.SPECIESPSSP6 -1495.4 2742.9 -0.545 0.586
## PG_Goal_moisture -878.2 180.7 -4.859 1.99e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22110 on 275 degrees of freedom
## (40 observations deleted due to missingness)
## Multiple R-squared: 0.1599, Adjusted R-squared: 0.1477
## F-statistic: 13.08 on 4 and 275 DF, p-value: 9.079e-10#only pssp
lm_pssp <- lm(load ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_load_condensed,
PG.SPECIES == "PSSP6"))
summary(lm_pssp)##
## Call:
## lm(formula = load ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG_Goal_moisture, data = filter(two_load_condensed, PG.SPECIES ==
## "PSSP6"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -34288 -10381 -224 11118 44222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64587.3 10261.8 6.294 5.69e-09 ***
## AG.GOAL.DRY.MASS_cont 10479.4 2036.5 5.146 1.10e-06 ***
## AG.GOAL.MOISTURE._cont 2910.8 1426.4 2.041 0.0436 *
## PG_Goal_moisture -1281.3 214.3 -5.980 2.53e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16930 on 116 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.2892, Adjusted R-squared: 0.2708
## F-statistic: 15.73 on 3 and 116 DF, p-value: 1.194e-08#only acth
max_lm_acth <- lm(load ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +PG_Goal_moisture,
data = filter(two_load_condensed,
PG.SPECIES == "ACTH7"))
summary(max_lm_acth)##
## Call:
## lm(formula = load ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG_Goal_moisture, data = filter(two_load_condensed, PG.SPECIES ==
## "ACTH7"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -55043 -15169 -2235 14457 107830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58259.4 12923.1 4.508 1.28e-05 ***
## AG.GOAL.DRY.MASS_cont 7829.2 1918.1 4.082 7.12e-05 ***
## AG.GOAL.MOISTURE._cont -4215.0 1794.2 -2.349 0.0201 *
## PG_Goal_moisture -642.5 267.5 -2.402 0.0175 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24860 on 156 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1347, Adjusted R-squared: 0.1181
## F-statistic: 8.095 on 3 and 156 DF, p-value: 4.797e-05#only 35 fm
max_lm_35 <- lm(load ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_load_condensed,
PG_Goal_moisture == "35"))
summary(max_lm_35)##
## Call:
## lm(formula = load ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES, data = filter(two_load_condensed, PG_Goal_moisture ==
## "35"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -49662 -11654 -2490 10860 41475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16847 16845 1.000 0.3219
## AG.GOAL.DRY.MASS_cont 10161 5728 1.774 0.0819 .
## AG.GOAL.MOISTURE._cont 2332 2864 0.814 0.4191
## PG.SPECIESPSSP6 4603 5728 0.804 0.4252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20490 on 52 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.06333, Adjusted R-squared: 0.00929
## F-statistic: 1.172 on 3 and 52 DF, p-value: 0.3294#only 55 fm
max_lm_55 <- lm(load ~ AG.GOAL.DRY.MASS_cont+ AG.GOAL.MOISTURE._cont +
PG.SPECIES,
data = filter(two_load_condensed,
PG_Goal_moisture == "55"))
summary(max_lm_55)##
## Call:
## lm(formula = load ~ AG.GOAL.DRY.MASS_cont + AG.GOAL.MOISTURE._cont +
## PG.SPECIES, data = filter(two_load_condensed, PG_Goal_moisture ==
## "55"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -51595 -14438 -646 11921 110343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14724 6556 2.246 0.0257 *
## AG.GOAL.DRY.MASS_cont 8497 1504 5.649 4.95e-08 ***
## AG.GOAL.MOISTURE._cont -1871 1345 -1.391 0.1656
## PG.SPECIESPSSP6 -2704 3131 -0.864 0.3887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22510 on 220 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1509, Adjusted R-squared: 0.1393
## F-statistic: 13.03 on 3 and 220 DF, p-value: 7.286e-08individual figures for each PG species and FM
pssp55 <- ggplot(filter(two_load_condensed, PG.SPECIES == "PSSP6", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= load/1000, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 100), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Thermal dose (C) x 1,000")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp55## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
pssp35 <- ggplot(filter(two_load_condensed, PG.SPECIES == "PSSP6", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= load/1000, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp_custom_colors)+
scale_y_continuous(limits = c(0, 100), expand = c(0, 0))+
labs(x = "Cheatgrass (g)", y = "Thermal dose (C) x 1,000")+
guides(fill=guide_legend(title="Cheatgrass FM (%)"))+ theme(legend.position = "none")
pssp35
acth55 <- ggplot(filter(two_load_condensed, PG.SPECIES == "ACTH7", PG_Goal_moisture == "55"),
aes(x = AG.GOAL.DRY.MASS, y= load/1000, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 100), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Thermal dose (C) x 1,000")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth55## Warning: Removed 5 rows containing non-finite values (stat_boxplot).
acth35 <- ggplot(filter(two_load_condensed, PG.SPECIES == "ACTH7", PG_Goal_moisture == "35"),
aes(x = AG.GOAL.DRY.MASS, y= load/1000, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth_custom_colors)+
scale_y_continuous(limits = c(0, 100), expand = c(0, 0)) +
labs(x = "Cheatgrass (g)", y = "Thermal dose (C) x 1,000")+
guides(fill=guide_legend(title="Cheatgrass FM (%)")) + theme(legend.position = "none")
acth35
combine
library(ggpubr)
load_55 <-
ggarrange(acth55,
pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)## Warning: Removed 5 rows containing non-finite values (stat_boxplot).## Warning: Removed 1 rows containing non-finite values (stat_boxplot).load_55
ggsave('figures/combine_twosp/load_55.png', load_55, height = 3, width = 5,bg='transparent')
load_35 <-
ggarrange(acth35,
pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
load_35
ggsave('figures/combine_twosp/load_35.png', load_35, height = 3, width = 5,bg='transparent')Combine all the 55 % fm into one
combine_55 <-
ggarrange(mc_55 + theme(axis.title.x = element_blank()),
dur_55 + theme(axis.title.x = element_blank()),
fl_55 + theme(axis.title.x = element_blank()),
max_55 + theme(axis.title.x = element_blank()),
load_55,
ncol = 1, nrow =5)
combine_55
ggsave('figures/combine_twosp/all_55.png', combine_55, height = 15, width = 5,bg='transparent')
combine_noFL_55 <-
ggarrange(mc_55 + theme(axis.title.x = element_blank()),
dur_55 + theme(axis.title.x = element_blank()),
max_55 + theme(axis.title.x = element_blank()),
load_55,
ncol = 1, nrow =4)
ggsave('figures/combine_twosp/all_noFL_55.png', combine_noFL_55, height = 15, width = 5,bg='transparent')
combine_35 <-
ggarrange(mc_35 + theme(axis.title.x = element_blank()),
dur_35 + theme(axis.title.x = element_blank()),
fl_35 + theme(axis.title.x = element_blank()),
max_35 + theme(axis.title.x = element_blank()),
load_35,
ncol = 1, nrow =5)
combine_35
ggsave('figures/combine_twosp/all_35.png', combine_35, height = 15, width = 5,bg='transparent')
combine_noFL_35 <-
ggarrange(mc_35 + theme(axis.title.x = element_blank()),
dur_35 + theme(axis.title.x = element_blank()),
max_35 + theme(axis.title.x = element_blank()),
load_35,
ncol = 1, nrow =4)
ggsave('figures/combine_twosp/all_noFL_35.png', combine_noFL_35, height = 15, width = 5,bg='transparent')summarize one species info for additive vs non additive stuff
write.csv(one, "additive_data/one_sp_raw.csv", row.names = F)
write.csv(two, "additive_data/two_sp_raw.csv", row.names = F)
#for the data using multiple TC, we want the combined TC 1 and 2 dataset
one_load_condensed## SPECIES REP DRY.WEIGHT weight_goal goal_moisture_percent WET....bag.G.
## 1 PSSP6 5 19.29 20 25 36.32
## 2 PSSP6 4 19.38 20 15 34.59
## 3 BRTE 1 19.66 20 15 34.48
## 4 ACTH7 4 19.69 20 45 40.13
## 5 ACTH7 4 19.70 20 15 34.79
## 6 PSSP6 4 19.79 20 45 40.45
## 7 BRTE 1 19.80 20 35 38.50
## 8 BRTE 3 19.81 20 15 34.71
## 9 PSSP6 3 (REDO) 19.87 20 25 36.61
## 10 BRTE 5 19.91 20 35 38.65
## 11 ACTH7 5 (REDO) 19.94 20 15 34.66
## 12 BRTE 2 (REDO) 19.94 20 10 34.07
## 13 BRTE 5 19.96 20 10 33.91
## 14 BRTE 10 19.97 20 35 38.68
## 15 BRTE 8 19.97 20 35 39.34
## 16 BRTE 4 19.98 20 5 32.88
## 17 BRTE 6 19.98 20 15 34.86
## 18 PSSP6 1 (REDO) 19.98 20 15 34.83
## 19 BRTE 2 19.99 20 45 40.76
## 20 BRTE 11 19.99 20 15 35.06
## 21 BRTE 4 19.99 20 15 35.03
## 22 BRTE 3 (REDO) 19.99 20 45 39.80
## 23 BRTE 5 20.01 20 15 34.88
## 24 BRTE 12 20.01 20 15 35.03
## 25 BRTE 4 20.01 20 25 37.17
## 26 BRTE 12 20.01 20 35 39.03
## 27 BRTE 7 20.01 20 35 38.92
## 28 BRTE 1 (REDO) 20.01 20 45 41.04
## 29 BRTE 4 20.02 20 10 33.96
## 30 BRTE 9 20.02 20 35 39.14
## 31 BRTE 13 20.02 20 35 39.17
## 32 BRTE 11 20.04 20 35 39.01
## 33 ACTH7 5 (REDO) 20.05 20 45 40.60
## 34 PSSP6 5 20.06 20 15 35.27
## 35 BRTE 9 20.07 20 35 39.23
## 36 ACTH7 5 (REDO) 20.08 20 35 38.92
## 37 BRTE 4 20.09 20 35 39.12
## 38 ACTH7 4 20.11 20 55 43.30
## 39 ACTH7 2 (REDO) 20.11 20 55 42.60
## 40 ACTH7 1 (REDO) 20.12 20 55 43.45
## 41 BRTE 2 20.20 20 15 35.18
## 42 BRTE 5 20.21 20 45 41.32
## 43 PSSP6 5 20.22 20 55 43.63
## 44 BRTE 1 20.24 20 25 37.13
## 45 BRTE 4 20.24 20 45 41.26
## 46 ACTH7 3 (REDO) 20.36 20 55 43.22
## 47 ACTH7 4 20.39 20 35 39.60
## 48 ACTH7 5 20.41 20 25 38.36
## 49 BRTE 2 20.43 20 25 37.66
## 50 BRTE 1 20.45 20 10 34.54
## 51 BRTE 5 20.45 20 25 37.43
## 52 BRTE 2 20.46 20 55 43.79
## 53 BRTE 2 20.50 20 5 34.05
## 54 PSSP6 5 20.52 20 45 41.55
## 55 BRTE 3 20.56 20 5 33.59
## 56 BRTE 4 20.56 20 55 43.88
## 57 PSSP6 4 20.60 20 55 44.08
## 58 BRTE 2 20.61 20 35 39.81
## 59 ACTH7 5 20.62 20 55 43.80
## 60 PSSP6 5 20.62 20 35 40.50
## 61 ACTH7 4 20.64 20 25 38.16
## 62 BRTE 1 20.74 20 5 33.99
## 63 BRTE 3 20.76 20 10 34.88
## 64 BRTE 1 20.78 20 55 44.02
## 65 BRTE 5 20.79 20 55 44.24
## 66 BRTE 3 20.80 20 55 44.47
## 67 PSSP6 4 20.82 20 35 40.25
## 68 PSSP6 4 20.94 20 25 38.38
## 69 PSSP6 1 29.11 30 35 50.25
## 70 PSSP6 3 29.23 30 55 58.06
## 71 ACTH7 2 29.32 30 25 47.61
## 72 ACTH7 2 29.48 30 45 54.78
## 73 PSSP6 1 29.59 30 55 58.00
## 74 ACTH7 1 29.60 30 45 54.98
## 75 PSSP6 3 29.63 30 15 48.16
## 76 PSSP6 1 29.63 30 25 48.32
## 77 PSSP6 2 30.12 30 55 58.64
## 78 PSSP6 3 30.13 30 45 55.91
## 79 PSSP6 2 30.19 30 45 55.27
## 80 PSSP6 2 30.23 30 35 53.45
## 81 ACTH7 1 30.36 30 15 47.02
## 82 ACTH7 3 30.41 30 15 46.76
## 83 ACTH7 3 30.41 30 15 46.76
## 84 ACTH7 1 30.48 30 25 50.46
## 85 PSSP6 3 30.50 30 35 53.22
## 86 ACTH7 2 30.54 30 35 53.13
## 87 ACTH7 3 30.60 30 35 53.27
## 88 ACTH7 1 30.62 30 35 53.45
## 89 PSSP6 2 30.68 30 25 49.60
## 90 PSSP6 2 30.90 30 15 47.61
## 91 PSSP6 1 30.91 30 45 57.11
## 92 ACTH7 3 31.01 30 25 50.86
## 93 PSSP6 5 19.29 20 25 36.32
## 94 PSSP6 4 19.38 20 15 34.59
## 95 BRTE 1 19.66 20 15 34.48
## 96 ACTH7 4 19.69 20 45 40.13
## 97 ACTH7 4 19.70 20 15 34.79
## 98 PSSP6 4 19.79 20 45 40.45
## 99 BRTE 1 19.80 20 35 38.50
## 100 BRTE 3 19.81 20 15 34.71
## 101 PSSP6 3 (REDO) 19.87 20 25 36.61
## 102 BRTE 5 19.91 20 35 38.65
## 103 ACTH7 5 (REDO) 19.94 20 15 34.66
## 104 BRTE 2 (REDO) 19.94 20 10 34.07
## 105 BRTE 5 19.96 20 10 33.91
## 106 BRTE 10 19.97 20 35 38.68
## 107 BRTE 8 19.97 20 35 39.34
## 108 BRTE 4 19.98 20 5 32.88
## 109 BRTE 6 19.98 20 15 34.86
## 110 PSSP6 1 (REDO) 19.98 20 15 34.83
## 111 BRTE 2 19.99 20 45 40.76
## 112 BRTE 11 19.99 20 15 35.06
## 113 BRTE 4 19.99 20 15 35.03
## 114 BRTE 3 (REDO) 19.99 20 45 39.80
## 115 BRTE 5 20.01 20 15 34.88
## 116 BRTE 12 20.01 20 15 35.03
## 117 BRTE 4 20.01 20 25 37.17
## 118 BRTE 12 20.01 20 35 39.03
## 119 BRTE 7 20.01 20 35 38.92
## 120 BRTE 1 (REDO) 20.01 20 45 41.04
## 121 BRTE 4 20.02 20 10 33.96
## 122 BRTE 9 20.02 20 35 39.14
## 123 BRTE 13 20.02 20 35 39.17
## 124 BRTE 11 20.04 20 35 39.01
## 125 ACTH7 5 (REDO) 20.05 20 45 40.60
## 126 PSSP6 5 20.06 20 15 35.27
## 127 BRTE 9 20.07 20 35 39.23
## 128 ACTH7 5 (REDO) 20.08 20 35 38.92
## 129 BRTE 4 20.09 20 35 39.12
## 130 ACTH7 4 20.11 20 55 43.30
## 131 ACTH7 2 (REDO) 20.11 20 55 42.60
## 132 ACTH7 1 (REDO) 20.12 20 55 43.45
## 133 BRTE 2 20.20 20 15 35.18
## 134 BRTE 5 20.21 20 45 41.32
## 135 PSSP6 5 20.22 20 55 43.63
## 136 BRTE 1 20.24 20 25 37.13
## 137 BRTE 4 20.24 20 45 41.26
## 138 ACTH7 3 (REDO) 20.36 20 55 43.22
## 139 ACTH7 4 20.39 20 35 39.60
## 140 ACTH7 5 20.41 20 25 38.36
## 141 BRTE 2 20.43 20 25 37.66
## 142 BRTE 1 20.45 20 10 34.54
## 143 BRTE 5 20.45 20 25 37.43
## 144 BRTE 2 20.46 20 55 43.79
## 145 BRTE 2 20.50 20 5 34.05
## 146 PSSP6 5 20.52 20 45 41.55
## 147 BRTE 3 20.56 20 5 33.59
## 148 BRTE 4 20.56 20 55 43.88
## 149 PSSP6 4 20.60 20 55 44.08
## 150 BRTE 2 20.61 20 35 39.81
## 151 ACTH7 5 20.62 20 55 43.80
## 152 PSSP6 5 20.62 20 35 40.50
## 153 ACTH7 4 20.64 20 25 38.16
## 154 BRTE 1 20.74 20 5 33.99
## 155 BRTE 3 20.76 20 10 34.88
## 156 BRTE 1 20.78 20 55 44.02
## 157 BRTE 5 20.79 20 55 44.24
## 158 BRTE 3 20.80 20 55 44.47
## 159 PSSP6 4 20.82 20 35 40.25
## 160 PSSP6 4 20.94 20 25 38.38
## 161 PSSP6 1 29.11 30 35 50.25
## 162 PSSP6 3 29.23 30 55 58.06
## 163 ACTH7 2 29.32 30 25 47.61
## 164 ACTH7 2 29.48 30 45 54.78
## 165 PSSP6 1 29.59 30 55 58.00
## 166 ACTH7 1 29.60 30 45 54.98
## 167 PSSP6 3 29.63 30 15 48.16
## 168 PSSP6 1 29.63 30 25 48.32
## 169 PSSP6 2 30.12 30 55 58.64
## 170 PSSP6 3 30.13 30 45 55.91
## 171 PSSP6 2 30.19 30 45 55.27
## 172 PSSP6 2 30.23 30 35 53.45
## 173 ACTH7 1 30.36 30 15 47.02
## 174 ACTH7 3 30.41 30 15 46.76
## 175 ACTH7 3 30.41 30 15 46.76
## 176 ACTH7 1 30.48 30 25 50.46
## 177 PSSP6 3 30.50 30 35 53.22
## 178 ACTH7 2 30.54 30 35 53.13
## 179 ACTH7 3 30.60 30 35 53.27
## 180 ACTH7 1 30.62 30 35 53.45
## 181 PSSP6 2 30.68 30 25 49.60
## 182 PSSP6 2 30.90 30 15 47.61
## 183 PSSP6 1 30.91 30 45 57.11
## 184 ACTH7 3 31.01 30 25 50.86
## ACTUAL.MOISTURE.percent POST.BURN.MASS.ACTUAL MASS.CONSUMPTION
## 1 25.04 5.64 94.36000
## 2 15.89 3.14 96.86000
## 3 14.65 2.50 97.50000
## 4 43.12 20.12 79.88000
## 5 15.08 5.58 94.42000
## 6 44.42 13.52 86.48000
## 7 34.14 3.09 96.91000
## 8 15.04 3.65 96.35000
## 9 24.11 2.96 87.99676
## 10 34.10 3.17 96.83000
## 11 13.74 4.19 81.52557
## 12 9.68 3.93 82.03018
## 13 9.87 5.83 94.17000
## 14 34.25 7.98 92.02000
## 15 35.70 8.27 91.73000
## 16 4.80 3.50 96.50000
## 17 14.66 7.95 92.05000
## 18 14.66 3.72 83.76255
## 19 44.17 5.65 94.35000
## 20 14.46 4.22 95.78000
## 21 14.66 2.90 97.10000
## 22 38.42 5.53 80.01446
## 23 14.74 2.86 97.14000
## 24 15.54 4.01 95.99000
## 25 24.64 22.22 77.78000
## 26 35.08 4.10 95.90000
## 27 34.68 21.17 78.83000
## 28 44.18 4.35 84.92201
## 29 10.14 3.59 96.41000
## 30 34.72 4.21 95.79000
## 31 35.21 23.61 76.39000
## 32 34.93 18.20 81.80000
## 33 43.29 15.30 46.74556
## 34 15.05 4.23 95.77000
## 35 34.68 24.29 75.71000
## 36 34.36 18.08 32.98740
## 37 34.40 6.00 94.00000
## 38 54.30 11.15 88.85000
## 39 52.71 20.05 34.71182
## 40 55.57 16.93 45.91054
## 41 15.15 2.97 97.03000
## 42 43.99 10.56 89.44000
## 43 55.69 5.96 94.04000
## 44 24.56 3.44 96.56000
## 45 45.21 18.63 81.37000
## 46 52.01 18.73 39.48304
## 47 34.58 10.98 89.02000
## 48 28.42 4.93 95.07000
## 49 26.14 3.20 96.80000
## 50 10.46 2.99 97.01000
## 51 24.65 4.20 95.80000
## 52 55.52 26.24 73.76000
## 53 5.95 4.07 95.93000
## 54 44.54 5.72 94.28000
## 55 5.11 2.06 97.94000
## 56 54.13 3.96 96.04000
## 57 55.10 9.99 90.01000
## 58 35.23 4.04 95.96000
## 59 53.10 21.62 78.38000
## 60 38.51 14.65 85.35000
## 61 25.82 4.54 95.46000
## 62 5.11 2.50 97.50000
## 63 10.65 2.06 97.94000
## 64 54.48 4.74 95.26000
## 65 54.02 17.44 82.56000
## 66 55.34 3.26 96.74000
## 67 34.97 5.01 94.99000
## 68 24.40 7.14 92.86000
## 69 34.59 3.66 96.34000
## 70 57.17 37.41 62.59000
## 71 20.94 3.87 96.13000
## 72 44.30 13.29 86.71000
## 73 54.78 39.99 60.01000
## 74 44.46 15.27 84.73000
## 75 21.70 4.09 95.91000
## 76 23.76 5.52 94.48000
## 77 53.95 6.17 93.83000
## 78 45.10 3.95 96.05000
## 79 42.56 39.45 60.55000
## 80 36.59 3.91 96.09000
## 81 15.51 2.40 97.60000
## 82 14.40 4.55 95.45000
## 83 14.40 4.55 95.45000
## 84 25.59 2.86 97.14000
## 85 34.59 8.80 91.20000
## 86 34.18 6.43 93.57000
## 87 33.89 3.78 96.22000
## 88 34.75 5.13 94.87000
## 89 25.62 3.65 96.35000
## 90 14.89 4.04 95.96000
## 91 45.16 41.88 58.12000
## 92 24.77 2.44 97.56000
## 93 25.04 5.64 94.36000
## 94 15.89 3.14 96.86000
## 95 14.65 2.50 97.50000
## 96 43.12 20.12 79.88000
## 97 15.08 5.58 94.42000
## 98 44.42 13.52 86.48000
## 99 34.14 3.09 96.91000
## 100 15.04 3.65 96.35000
## 101 24.11 2.96 87.99676
## 102 34.10 3.17 96.83000
## 103 13.74 4.19 81.52557
## 104 9.68 3.93 82.03018
## 105 9.87 5.83 94.17000
## 106 34.25 7.98 92.02000
## 107 35.70 8.27 91.73000
## 108 4.80 3.50 96.50000
## 109 14.66 7.95 92.05000
## 110 14.66 3.72 83.76255
## 111 44.17 5.65 94.35000
## 112 14.46 4.22 95.78000
## 113 14.66 2.90 97.10000
## 114 38.42 5.53 80.01446
## 115 14.74 2.86 97.14000
## 116 15.54 4.01 95.99000
## 117 24.64 22.22 77.78000
## 118 35.08 4.10 95.90000
## 119 34.68 21.17 78.83000
## 120 44.18 4.35 84.92201
## 121 10.14 3.59 96.41000
## 122 34.72 4.21 95.79000
## 123 35.21 23.61 76.39000
## 124 34.93 18.20 81.80000
## 125 43.29 15.30 46.74556
## 126 15.05 4.23 95.77000
## 127 34.68 24.29 75.71000
## 128 34.36 18.08 32.98740
## 129 34.40 6.00 94.00000
## 130 54.30 11.15 88.85000
## 131 52.71 20.05 34.71182
## 132 55.57 16.93 45.91054
## 133 15.15 2.97 97.03000
## 134 43.99 10.56 89.44000
## 135 55.69 5.96 94.04000
## 136 24.56 3.44 96.56000
## 137 45.21 18.63 81.37000
## 138 52.01 18.73 39.48304
## 139 34.58 10.98 89.02000
## 140 28.42 4.93 95.07000
## 141 26.14 3.20 96.80000
## 142 10.46 2.99 97.01000
## 143 24.65 4.20 95.80000
## 144 55.52 26.24 73.76000
## 145 5.95 4.07 95.93000
## 146 44.54 5.72 94.28000
## 147 5.11 2.06 97.94000
## 148 54.13 3.96 96.04000
## 149 55.10 9.99 90.01000
## 150 35.23 4.04 95.96000
## 151 53.10 21.62 78.38000
## 152 38.51 14.65 85.35000
## 153 25.82 4.54 95.46000
## 154 5.11 2.50 97.50000
## 155 10.65 2.06 97.94000
## 156 54.48 4.74 95.26000
## 157 54.02 17.44 82.56000
## 158 55.34 3.26 96.74000
## 159 34.97 5.01 94.99000
## 160 24.40 7.14 92.86000
## 161 34.59 3.66 96.34000
## 162 57.17 37.41 62.59000
## 163 20.94 3.87 96.13000
## 164 44.30 13.29 86.71000
## 165 54.78 39.99 60.01000
## 166 44.46 15.27 84.73000
## 167 21.70 4.09 95.91000
## 168 23.76 5.52 94.48000
## 169 53.95 6.17 93.83000
## 170 45.10 3.95 96.05000
## 171 42.56 39.45 60.55000
## 172 36.59 3.91 96.09000
## 173 15.51 2.40 97.60000
## 174 14.40 4.55 95.45000
## 175 14.40 4.55 95.45000
## 176 25.59 2.86 97.14000
## 177 34.59 8.80 91.20000
## 178 34.18 6.43 93.57000
## 179 33.89 3.78 96.22000
## 180 34.75 5.13 94.87000
## 181 25.62 3.65 96.35000
## 182 14.89 4.04 95.96000
## 183 45.16 41.88 58.12000
## 184 24.77 2.44 97.56000
## DRY.MASS.CONSUMED COMBUSTION. FLAME.HEIGHT.CM dur_ch1 dur_ch2 dur_ch3
## 1 18.58000 Y 115 87.5 86.5 112.0
## 2 18.54000 Y 140 73.0 65.5 64.5
## 3 18.79000 Y 110 70.5 72.0 112.0
## 4 19.71000 Y 40 36.0 28.0 23.5
## 5 18.98000 Y 140 67.0 88.5 63.5
## 6 19.47000 Y 100 71.0 65.5 77.5
## 7 18.96000 Y 105 67.0 107.5 107.5
## 8 18.99000 Y 110 69.0 72.0 108.5
## 9 14.89683 Y 140 73.5 69.0 91.5
## 10 19.07000 Y 140 68.0 83.0 129.5
## 11 21.01304 Y 135 83.0 105.0 118.0
## 12 19.70913 Y 140 69.5 73.0 142.0
## 13 19.25000 Y 120 78.0 87.0 102.5
## 14 19.37000 N NA 46.5 49.0 110.5
## 15 19.38000 Y 140 55.0 54.0 79.0
## 16 19.16000 Y 140 60.5 70.0 94.0
## 17 19.38000 Y 135 65.5 76.5 82.0
## 18 18.61862 Y 140 91.0 79.5 102.5
## 19 19.27000 Y 110 34.5 47.0 79.5
## 20 19.20000 Y 110 64.0 73.5 93.0
## 21 19.14000 Y 140 70.5 82.5 125.0
## 22 27.66383 Y 115 65.0 77.0 122.5
## 23 19.15000 Y 140 60.5 70.0 82.0
## 24 19.21000 Y 140 74.0 77.0 114.5
## 25 20.12000 N NA NA NA NA
## 26 19.21000 N NA 62.0 76.0 142.0
## 27 20.07000 N NA NA NA NA
## 28 21.73913 Y 140 68.0 75.0 118.5
## 29 19.20000 Y 115 106.5 104.0 110.5
## 30 19.23000 Y 135 60.0 74.0 113.5
## 31 20.20000 N NA NA NA NA
## 32 19.95000 N NA NA NA 69.5
## 33 76.30923 Y 110 40.0 43.0 61.0
## 34 19.27000 Y 115 78.0 77.5 77.5
## 35 20.28000 N NA NA NA NA
## 36 90.03984 Y 130 24.5 48.0 46.0
## 37 19.39000 Y 110 86.5 89.5 135.5
## 38 19.66000 Y 120 51.5 80.0 63.0
## 39 99.70164 N NA NA NA NA
## 40 84.14513 Y 90 46.0 108.0 117.0
## 41 19.35000 Y 110 66.0 66.0 94.0
## 42 19.73000 Y 90 54.5 59.5 107.0
## 43 19.51000 N 35 70.0 97.0 116.5
## 44 19.41000 Y 110 64.5 78.0 117.0
## 45 20.16000 N NA NA NA NA
## 46 91.99411 Y 75 47.5 59.0 64.5
## 47 19.93000 Y 120 72.0 98.5 55.0
## 48 19.65000 Y 125 89.5 89.5 75.5
## 49 19.59000 Y 110 89.5 111.0 114.5
## 50 19.60000 Y 110 78.5 77.5 101.0
## 51 19.66000 Y 125 53.0 83.0 92.0
## 52 20.74000 Y 20 10.0 0.0 36.0
## 53 19.70000 Y 110 55.0 76.5 105.5
## 54 19.80000 Y 140 84.5 62.0 55.0
## 55 19.66000 Y 110 71.5 73.5 101.0
## 56 19.75000 N NA 69.5 74.0 92.5
## 57 20.08000 Y 115 76.0 88.5 85.5
## 58 19.81000 Y 110 59.5 55.0 82.5
## 59 20.67000 Y 35 39.0 46.5 48.0
## 60 20.33000 N 80 21.5 8.0 295.0
## 61 19.86000 Y 100 80.5 76.0 63.5
## 62 19.86000 Y 105 65.0 72.0 90.0
## 63 19.86000 Y 110 48.0 47.5 86.5
## 64 20.01000 Y 90 52.0 43.5 68.5
## 65 20.63000 N NA 42.0 25.0 62.5
## 66 19.96000 Y 90 72.0 83.5 125.5
## 67 20.06000 Y 135 87.0 99.0 102.5
## 68 20.28000 N NA 55.0 52.5 117.5
## 69 28.24000 Y 105 99.0 110.0 99.0
## 70 29.51000 N NA 32.5 40.0 41.0
## 71 28.45000 Y 110 106.5 107.5 96.0
## 72 28.93000 Y 95 67.5 121.5 63.5
## 73 29.94000 Y 20 NA NA NA
## 74 29.12000 Y 110 113.0 118.5 147.0
## 75 28.77000 Y 110 105.0 112.5 94.5
## 76 28.82000 Y 110 110.0 101.0 118.5
## 77 29.32000 Y 110 111.0 115.0 121.0
## 78 29.26000 Y 110 98.5 120.5 112.0
## 79 30.50000 N NA 7.5 6.0 0.0
## 80 29.36000 Y 110 114.0 105.0 125.0
## 81 29.44000 Y 110 95.0 94.5 98.5
## 82 29.56000 Y 110 87.5 79.5 97.0
## 83 29.56000 Y 110 112.5 116.0 79.5
## 84 29.57000 Y 110 98.5 101.5 90.0
## 85 29.79000 Y 105 102.5 118.0 117.0
## 86 29.75000 Y 110 93.0 109.0 81.0
## 87 29.72000 Y 110 128.0 147.5 143.0
## 88 29.79000 Y 110 40.0 108.0 119.5
## 89 29.80000 Y 110 123.5 128.5 128.5
## 90 30.03000 Y 110 102.0 94.5 77.5
## 91 31.26000 N NA 6.0 6.5 0.0
## 92 30.09000 Y 110 98.5 98.0 84.0
## 93 18.58000 Y 115 87.5 86.5 112.0
## 94 18.54000 Y 140 73.0 65.5 64.5
## 95 18.79000 Y 110 70.5 72.0 112.0
## 96 19.71000 Y 40 36.0 28.0 23.5
## 97 18.98000 Y 140 67.0 88.5 63.5
## 98 19.47000 Y 100 71.0 65.5 77.5
## 99 18.96000 Y 105 67.0 107.5 107.5
## 100 18.99000 Y 110 69.0 72.0 108.5
## 101 14.89683 Y 140 73.5 69.0 91.5
## 102 19.07000 Y 140 68.0 83.0 129.5
## 103 21.01304 Y 135 83.0 105.0 118.0
## 104 19.70913 Y 140 69.5 73.0 142.0
## 105 19.25000 Y 120 78.0 87.0 102.5
## 106 19.37000 N NA 46.5 49.0 110.5
## 107 19.38000 Y 140 55.0 54.0 79.0
## 108 19.16000 Y 140 60.5 70.0 94.0
## 109 19.38000 Y 135 65.5 76.5 82.0
## 110 18.61862 Y 140 91.0 79.5 102.5
## 111 19.27000 Y 110 34.5 47.0 79.5
## 112 19.20000 Y 110 64.0 73.5 93.0
## 113 19.14000 Y 140 70.5 82.5 125.0
## 114 27.66383 Y 115 65.0 77.0 122.5
## 115 19.15000 Y 140 60.5 70.0 82.0
## 116 19.21000 Y 140 74.0 77.0 114.5
## 117 20.12000 N NA NA NA NA
## 118 19.21000 N NA 62.0 76.0 142.0
## 119 20.07000 N NA NA NA NA
## 120 21.73913 Y 140 68.0 75.0 118.5
## 121 19.20000 Y 115 106.5 104.0 110.5
## 122 19.23000 Y 135 60.0 74.0 113.5
## 123 20.20000 N NA NA NA NA
## 124 19.95000 N NA NA NA 69.5
## 125 76.30923 Y 110 40.0 43.0 61.0
## 126 19.27000 Y 115 78.0 77.5 77.5
## 127 20.28000 N NA NA NA NA
## 128 90.03984 Y 130 24.5 48.0 46.0
## 129 19.39000 Y 110 86.5 89.5 135.5
## 130 19.66000 Y 120 51.5 80.0 63.0
## 131 99.70164 N NA NA NA NA
## 132 84.14513 Y 90 46.0 108.0 117.0
## 133 19.35000 Y 110 66.0 66.0 94.0
## 134 19.73000 Y 90 54.5 59.5 107.0
## 135 19.51000 N 35 70.0 97.0 116.5
## 136 19.41000 Y 110 64.5 78.0 117.0
## 137 20.16000 N NA NA NA NA
## 138 91.99411 Y 75 47.5 59.0 64.5
## 139 19.93000 Y 120 72.0 98.5 55.0
## 140 19.65000 Y 125 89.5 89.5 75.5
## 141 19.59000 Y 110 89.5 111.0 114.5
## 142 19.60000 Y 110 78.5 77.5 101.0
## 143 19.66000 Y 125 53.0 83.0 92.0
## 144 20.74000 Y 20 10.0 0.0 36.0
## 145 19.70000 Y 110 55.0 76.5 105.5
## 146 19.80000 Y 140 84.5 62.0 55.0
## 147 19.66000 Y 110 71.5 73.5 101.0
## 148 19.75000 N NA 69.5 74.0 92.5
## 149 20.08000 Y 115 76.0 88.5 85.5
## 150 19.81000 Y 110 59.5 55.0 82.5
## 151 20.67000 Y 35 39.0 46.5 48.0
## 152 20.33000 N 80 21.5 8.0 295.0
## 153 19.86000 Y 100 80.5 76.0 63.5
## 154 19.86000 Y 105 65.0 72.0 90.0
## 155 19.86000 Y 110 48.0 47.5 86.5
## 156 20.01000 Y 90 52.0 43.5 68.5
## 157 20.63000 N NA 42.0 25.0 62.5
## 158 19.96000 Y 90 72.0 83.5 125.5
## 159 20.06000 Y 135 87.0 99.0 102.5
## 160 20.28000 N NA 55.0 52.5 117.5
## 161 28.24000 Y 105 99.0 110.0 99.0
## 162 29.51000 N NA 32.5 40.0 41.0
## 163 28.45000 Y 110 106.5 107.5 96.0
## 164 28.93000 Y 95 67.5 121.5 63.5
## 165 29.94000 Y 20 NA NA NA
## 166 29.12000 Y 110 113.0 118.5 147.0
## 167 28.77000 Y 110 105.0 112.5 94.5
## 168 28.82000 Y 110 110.0 101.0 118.5
## 169 29.32000 Y 110 111.0 115.0 121.0
## 170 29.26000 Y 110 98.5 120.5 112.0
## 171 30.50000 N NA 7.5 6.0 0.0
## 172 29.36000 Y 110 114.0 105.0 125.0
## 173 29.44000 Y 110 95.0 94.5 98.5
## 174 29.56000 Y 110 87.5 79.5 97.0
## 175 29.56000 Y 110 112.5 116.0 79.5
## 176 29.57000 Y 110 98.5 101.5 90.0
## 177 29.79000 Y 105 102.5 118.0 117.0
## 178 29.75000 Y 110 93.0 109.0 81.0
## 179 29.72000 Y 110 128.0 147.5 143.0
## 180 29.79000 Y 110 40.0 108.0 119.5
## 181 29.80000 Y 110 123.5 128.5 128.5
## 182 30.03000 Y 110 102.0 94.5 77.5
## 183 31.26000 N NA 6.0 6.5 0.0
## 184 30.09000 Y 110 98.5 98.0 84.0
## max_ch1 max_ch2 max_ch3 load_ch3 goal_weight goal_moisture_percent_cont
## 1 1191.82 1200.90 750.11 91275.52 20 4
## 2 1127.21 644.91 1124.74 60260.71 20 3
## 3 1129.76 510.94 944.73 93122.91 20 3
## 4 531.77 155.97 125.17 5479.93 20 6
## 5 750.82 821.46 1010.50 53782.18 20 3
## 6 1043.17 873.63 929.19 67764.44 20 6
## 7 796.92 624.04 430.26 45930.35 20 5
## 8 575.37 328.21 721.88 73293.10 20 3
## 9 1086.83 639.24 759.97 67671.65 20 4
## 10 1104.43 1065.73 881.77 116235.28 20 5
## 11 646.92 343.24 873.19 98959.15 20 3
## 12 969.71 846.11 948.46 128904.45 20 2
## 13 713.14 666.75 782.23 87583.23 20 2
## 14 132.37 182.85 973.69 101774.82 20 5
## 15 621.67 721.95 559.33 47952.07 20 5
## 16 808.98 635.76 892.46 68891.96 20 1
## 17 969.85 676.07 1061.20 69648.74 20 3
## 18 1482.78 760.04 678.30 78461.91 20 3
## 19 428.47 597.44 682.58 45685.23 20 6
## 20 325.22 361.67 718.95 56370.83 20 3
## 21 1114.94 703.95 898.29 112993.41 20 3
## 22 401.81 337.40 1364.77 114346.43 20 6
## 23 891.82 813.94 792.50 55338.45 20 3
## 24 1013.31 982.98 693.30 66903.87 20 3
## 25 37.02 36.76 37.26 NA 20 4
## 26 162.78 237.60 722.61 110943.65 20 5
## 27 35.67 33.93 29.69 NA 20 5
## 28 267.98 344.36 1399.75 117521.66 20 6
## 29 1283.66 1116.18 1026.00 81247.29 20 2
## 30 918.33 732.24 838.85 72468.70 20 5
## 31 43.25 38.50 28.80 NA 20 5
## 32 46.31 37.72 210.51 23125.98 20 5
## 33 406.99 216.81 265.97 23209.97 20 6
## 34 1190.79 1075.88 894.44 77282.65 20 3
## 35 34.23 33.54 32.60 NA 20 5
## 36 165.26 263.36 977.80 42320.75 20 5
## 37 845.24 829.63 875.51 103533.47 20 5
## 38 121.57 947.21 1180.34 60735.76 20 7
## 39 55.44 66.37 65.12 NA 20 7
## 40 565.99 667.22 889.77 99165.55 20 7
## 41 513.54 436.58 779.91 61021.02 20 3
## 42 630.03 137.55 333.60 47165.76 20 6
## 43 271.34 311.81 533.67 70390.19 20 7
## 44 1015.29 826.56 1006.52 105821.33 20 4
## 45 54.33 51.21 73.16 NA 20 6
## 46 376.60 583.45 667.19 48651.29 20 7
## 47 921.59 1114.65 874.30 39377.79 20 5
## 48 986.83 879.02 906.05 58364.92 20 4
## 49 410.66 541.64 1254.28 139031.69 20 4
## 50 1238.78 1126.08 572.03 56389.12 20 2
## 51 118.15 947.63 971.13 63434.19 20 4
## 52 101.82 93.52 394.18 14519.48 20 7
## 53 227.94 370.78 598.12 54974.49 20 1
## 54 777.72 790.02 1015.68 45070.62 20 6
## 55 1213.26 865.57 675.41 75742.97 20 1
## 56 399.51 502.27 900.66 64572.33 20 7
## 57 961.17 874.73 901.18 74467.78 20 7
## 58 520.64 218.60 201.83 24758.37 20 5
## 59 350.08 759.02 1204.28 47044.34 20 7
## 60 179.73 116.32 293.86 101834.72 20 5
## 61 1207.44 1037.25 918.72 53429.13 20 4
## 62 511.88 719.36 371.24 39792.57 20 1
## 63 757.89 479.72 534.62 37691.26 20 2
## 64 238.42 267.11 453.48 19730.11 20 7
## 65 186.15 108.60 359.53 29195.46 20 7
## 66 422.97 422.16 581.77 80466.03 20 7
## 67 1067.28 1109.41 893.80 94851.95 20 5
## 68 213.10 251.51 805.96 65241.06 20 4
## 69 1056.48 825.28 718.91 91291.85 30 5
## 70 172.65 570.34 549.23 27303.30 30 7
## 71 1072.49 608.97 947.84 96815.15 30 4
## 72 156.09 815.32 1202.49 70701.49 30 6
## 73 95.16 57.73 55.66 NA 30 7
## 74 343.40 446.77 724.15 89440.46 30 6
## 75 826.13 729.02 1162.92 92456.09 30 3
## 76 1213.85 506.84 884.76 84506.48 30 4
## 77 602.51 486.58 492.12 59598.31 30 7
## 78 908.59 775.70 1013.80 111516.65 30 6
## 79 116.87 111.81 73.15 0.00 30 6
## 80 497.75 555.26 329.62 53147.01 30 5
## 81 588.30 601.66 697.02 68236.77 30 3
## 82 652.67 717.86 968.63 69013.38 30 3
## 83 431.68 745.96 1025.93 87354.42 30 3
## 84 1159.68 844.68 813.05 72874.62 30 4
## 85 625.77 890.62 854.11 83032.57 30 5
## 86 1021.57 834.93 1140.78 92148.49 30 5
## 87 690.68 689.15 1000.97 101891.05 30 5
## 88 119.14 863.51 959.62 101707.53 30 5
## 89 278.85 404.44 891.28 73417.31 30 4
## 90 814.42 935.56 1323.93 68387.66 30 3
## 91 132.58 138.30 83.57 0.00 30 6
## 92 909.92 796.24 1115.20 88252.82 30 4
## 93 1191.82 1200.90 750.11 91275.52 20 4
## 94 1127.21 644.91 1124.74 60260.71 20 3
## 95 1129.76 510.94 944.73 93122.91 20 3
## 96 531.77 155.97 125.17 5479.93 20 6
## 97 750.82 821.46 1010.50 53782.18 20 3
## 98 1043.17 873.63 929.19 67764.44 20 6
## 99 796.92 624.04 430.26 45930.35 20 5
## 100 575.37 328.21 721.88 73293.10 20 3
## 101 1086.83 639.24 759.97 67671.65 20 4
## 102 1104.43 1065.73 881.77 116235.28 20 5
## 103 646.92 343.24 873.19 98959.15 20 3
## 104 969.71 846.11 948.46 128904.45 20 2
## 105 713.14 666.75 782.23 87583.23 20 2
## 106 132.37 182.85 973.69 101774.82 20 5
## 107 621.67 721.95 559.33 47952.07 20 5
## 108 808.98 635.76 892.46 68891.96 20 1
## 109 969.85 676.07 1061.20 69648.74 20 3
## 110 1482.78 760.04 678.30 78461.91 20 3
## 111 428.47 597.44 682.58 45685.23 20 6
## 112 325.22 361.67 718.95 56370.83 20 3
## 113 1114.94 703.95 898.29 112993.41 20 3
## 114 401.81 337.40 1364.77 114346.43 20 6
## 115 891.82 813.94 792.50 55338.45 20 3
## 116 1013.31 982.98 693.30 66903.87 20 3
## 117 37.02 36.76 37.26 NA 20 4
## 118 162.78 237.60 722.61 110943.65 20 5
## 119 35.67 33.93 29.69 NA 20 5
## 120 267.98 344.36 1399.75 117521.66 20 6
## 121 1283.66 1116.18 1026.00 81247.29 20 2
## 122 918.33 732.24 838.85 72468.70 20 5
## 123 43.25 38.50 28.80 NA 20 5
## 124 46.31 37.72 210.51 23125.98 20 5
## 125 406.99 216.81 265.97 23209.97 20 6
## 126 1190.79 1075.88 894.44 77282.65 20 3
## 127 34.23 33.54 32.60 NA 20 5
## 128 165.26 263.36 977.80 42320.75 20 5
## 129 845.24 829.63 875.51 103533.47 20 5
## 130 121.57 947.21 1180.34 60735.76 20 7
## 131 55.44 66.37 65.12 NA 20 7
## 132 565.99 667.22 889.77 99165.55 20 7
## 133 513.54 436.58 779.91 61021.02 20 3
## 134 630.03 137.55 333.60 47165.76 20 6
## 135 271.34 311.81 533.67 70390.19 20 7
## 136 1015.29 826.56 1006.52 105821.33 20 4
## 137 54.33 51.21 73.16 NA 20 6
## 138 376.60 583.45 667.19 48651.29 20 7
## 139 921.59 1114.65 874.30 39377.79 20 5
## 140 986.83 879.02 906.05 58364.92 20 4
## 141 410.66 541.64 1254.28 139031.69 20 4
## 142 1238.78 1126.08 572.03 56389.12 20 2
## 143 118.15 947.63 971.13 63434.19 20 4
## 144 101.82 93.52 394.18 14519.48 20 7
## 145 227.94 370.78 598.12 54974.49 20 1
## 146 777.72 790.02 1015.68 45070.62 20 6
## 147 1213.26 865.57 675.41 75742.97 20 1
## 148 399.51 502.27 900.66 64572.33 20 7
## 149 961.17 874.73 901.18 74467.78 20 7
## 150 520.64 218.60 201.83 24758.37 20 5
## 151 350.08 759.02 1204.28 47044.34 20 7
## 152 179.73 116.32 293.86 101834.72 20 5
## 153 1207.44 1037.25 918.72 53429.13 20 4
## 154 511.88 719.36 371.24 39792.57 20 1
## 155 757.89 479.72 534.62 37691.26 20 2
## 156 238.42 267.11 453.48 19730.11 20 7
## 157 186.15 108.60 359.53 29195.46 20 7
## 158 422.97 422.16 581.77 80466.03 20 7
## 159 1067.28 1109.41 893.80 94851.95 20 5
## 160 213.10 251.51 805.96 65241.06 20 4
## 161 1056.48 825.28 718.91 91291.85 30 5
## 162 172.65 570.34 549.23 27303.30 30 7
## 163 1072.49 608.97 947.84 96815.15 30 4
## 164 156.09 815.32 1202.49 70701.49 30 6
## 165 95.16 57.73 55.66 NA 30 7
## 166 343.40 446.77 724.15 89440.46 30 6
## 167 826.13 729.02 1162.92 92456.09 30 3
## 168 1213.85 506.84 884.76 84506.48 30 4
## 169 602.51 486.58 492.12 59598.31 30 7
## 170 908.59 775.70 1013.80 111516.65 30 6
## 171 116.87 111.81 73.15 0.00 30 6
## 172 497.75 555.26 329.62 53147.01 30 5
## 173 588.30 601.66 697.02 68236.77 30 3
## 174 652.67 717.86 968.63 69013.38 30 3
## 175 431.68 745.96 1025.93 87354.42 30 3
## 176 1159.68 844.68 813.05 72874.62 30 4
## 177 625.77 890.62 854.11 83032.57 30 5
## 178 1021.57 834.93 1140.78 92148.49 30 5
## 179 690.68 689.15 1000.97 101891.05 30 5
## 180 119.14 863.51 959.62 101707.53 30 5
## 181 278.85 404.44 891.28 73417.31 30 4
## 182 814.42 935.56 1323.93 68387.66 30 3
## 183 132.58 138.30 83.57 0.00 30 6
## 184 909.92 796.24 1115.20 88252.82 30 4
## channel load dur
## 1 load_ch1 63500.05 63500.05
## 2 load_ch1 54010.14 54010.14
## 3 load_ch1 47058.15 47058.15
## 4 load_ch1 17135.67 17135.67
## 5 load_ch1 44065.50 44065.50
## 6 load_ch1 63542.88 63542.88
## 7 load_ch1 34568.82 34568.82
## 8 load_ch1 32824.50 32824.50
## 9 load_ch1 50137.03 50137.03
## 10 load_ch1 45710.07 45710.07
## 11 load_ch1 43406.99 43406.99
## 12 load_ch1 42528.20 42528.20
## 13 load_ch1 38652.30 38652.30
## 14 load_ch1 10795.13 10795.13
## 15 load_ch1 25745.45 25745.45
## 16 load_ch1 32733.36 32733.36
## 17 load_ch1 42461.87 42461.87
## 18 load_ch1 65292.57 65292.57
## 19 load_ch1 13029.17 13029.17
## 20 load_ch1 21202.22 21202.22
## 21 load_ch1 53793.88 53793.88
## 22 load_ch1 25219.60 25219.60
## 23 load_ch1 36113.73 36113.73
## 24 load_ch1 44788.19 44788.19
## 25 load_ch1 NA NA
## 26 load_ch1 16824.58 16824.58
## 27 load_ch1 NA NA
## 28 load_ch1 25878.56 25878.56
## 29 load_ch1 100261.23 100261.23
## 30 load_ch1 38046.09 38046.09
## 31 load_ch1 NA NA
## 32 load_ch1 NA NA
## 33 load_ch1 16475.96 16475.96
## 34 load_ch1 68893.65 68893.65
## 35 load_ch1 NA NA
## 36 load_ch1 6635.73 6635.73
## 37 load_ch1 41750.58 41750.58
## 38 load_ch1 11606.96 11606.96
## 39 load_ch1 NA NA
## 40 load_ch1 25814.38 25814.38
## 41 load_ch1 31468.23 31468.23
## 42 load_ch1 23229.58 23229.58
## 43 load_ch1 23412.62 23412.62
## 44 load_ch1 45301.23 45301.23
## 45 load_ch1 NA NA
## 46 load_ch1 21225.06 21225.06
## 47 load_ch1 49545.35 49545.35
## 48 load_ch1 55460.64 55460.64
## 49 load_ch1 39851.35 39851.35
## 50 load_ch1 59971.40 59971.40
## 51 load_ch1 11614.71 11614.71
## 52 load_ch1 2026.31 2026.31
## 53 load_ch1 17859.34 17859.34
## 54 load_ch1 49777.26 49777.26
## 55 load_ch1 56208.44 56208.44
## 56 load_ch1 26233.83 26233.83
## 57 load_ch1 47223.31 47223.31
## 58 load_ch1 22528.57 22528.57
## 59 load_ch1 15418.84 15418.84
## 60 load_ch1 5517.72 5517.72
## 61 load_ch1 75929.14 75929.14
## 62 load_ch1 26340.12 26340.12
## 63 load_ch1 25404.77 25404.77
## 64 load_ch1 14830.33 14830.33
## 65 load_ch1 12073.72 12073.72
## 66 load_ch1 26703.04 26703.04
## 67 load_ch1 53474.81 53474.81
## 68 load_ch1 14733.57 14733.57
## 69 load_ch1 56704.48 56704.48
## 70 load_ch1 8539.53 8539.53
## 71 load_ch1 70190.42 70190.42
## 72 load_ch1 17560.30 17560.30
## 73 load_ch1 0.00 0.00
## 74 load_ch1 43422.90 43422.90
## 75 load_ch1 58794.90 58794.90
## 76 load_ch1 86440.66 86440.66
## 77 load_ch1 62327.91 62327.91
## 78 load_ch1 46197.34 46197.34
## 79 load_ch1 1645.45 1645.45
## 80 load_ch1 45318.24 45318.24
## 81 load_ch1 48637.77 48637.77
## 82 load_ch1 45532.64 45532.64
## 83 load_ch1 61409.91 61409.91
## 84 load_ch1 75863.08 75863.08
## 85 load_ch1 40415.76 40415.76
## 86 load_ch1 62837.33 62837.33
## 87 load_ch1 50866.20 50866.20
## 88 load_ch1 9028.10 9028.10
## 89 load_ch1 40841.40 40841.40
## 90 load_ch1 52927.75 52927.75
## 91 load_ch1 1398.74 1398.74
## 92 load_ch1 75621.94 75621.94
## 93 load_ch2 62385.88 62385.88
## 94 load_ch2 42059.95 42059.95
## 95 load_ch2 38326.84 38326.84
## 96 load_ch2 7286.98 7286.98
## 97 load_ch2 55604.26 55604.26
## 98 load_ch2 52775.81 52775.81
## 99 load_ch2 43225.46 43225.46
## 100 load_ch2 32774.70 32774.70
## 101 load_ch2 37795.01 37795.01
## 102 load_ch2 56898.25 56898.25
## 103 load_ch2 42802.81 42802.81
## 104 load_ch2 45252.22 45252.22
## 105 load_ch2 40155.59 40155.59
## 106 load_ch2 14407.13 14407.13
## 107 load_ch2 26857.91 26857.91
## 108 load_ch2 36264.98 36264.98
## 109 load_ch2 40200.60 40200.60
## 110 load_ch2 48155.11 48155.11
## 111 load_ch2 19925.76 19925.76
## 112 load_ch2 26000.14 26000.14
## 113 load_ch2 50813.98 50813.98
## 114 load_ch2 28610.22 28610.22
## 115 load_ch2 39674.85 39674.85
## 116 load_ch2 47880.71 47880.71
## 117 load_ch2 NA NA
## 118 load_ch2 26821.75 26821.75
## 119 load_ch2 NA NA
## 120 load_ch2 33114.51 33114.51
## 121 load_ch2 97300.02 97300.02
## 122 load_ch2 39167.01 39167.01
## 123 load_ch2 NA NA
## 124 load_ch2 NA NA
## 125 load_ch2 12526.59 12526.59
## 126 load_ch2 55106.91 55106.91
## 127 load_ch2 NA NA
## 128 load_ch2 17371.53 17371.53
## 129 load_ch2 48323.63 48323.63
## 130 load_ch2 63351.34 63351.34
## 131 load_ch2 NA NA
## 132 load_ch2 49631.02 49631.02
## 133 load_ch2 29560.02 29560.02
## 134 load_ch2 14159.36 14159.36
## 135 load_ch2 30990.75 30990.75
## 136 load_ch2 49407.69 49407.69
## 137 load_ch2 NA NA
## 138 load_ch2 35739.26 35739.26
## 139 load_ch2 87760.78 87760.78
## 140 load_ch2 57427.75 57427.75
## 141 load_ch2 55951.83 55951.83
## 142 load_ch2 57652.57 57652.57
## 143 load_ch2 52061.46 52061.46
## 144 load_ch2 0.00 0.00
## 145 load_ch2 31574.19 31574.19
## 146 load_ch2 42706.88 42706.88
## 147 load_ch2 45629.64 45629.64
## 148 load_ch2 32460.61 32460.61
## 149 load_ch2 47910.58 47910.58
## 150 load_ch2 17648.39 17648.39
## 151 load_ch2 32440.59 32440.59
## 152 load_ch2 1697.93 1697.93
## 153 load_ch2 70532.80 70532.80
## 154 load_ch2 37461.14 37461.14
## 155 load_ch2 19188.00 19188.00
## 156 load_ch2 13058.86 13058.86
## 157 load_ch2 5264.34 5264.34
## 158 load_ch2 32961.17 32961.17
## 159 load_ch2 61581.90 61581.90
## 160 load_ch2 14847.84 14847.84
## 161 load_ch2 61044.32 61044.32
## 162 load_ch2 24827.91 24827.91
## 163 load_ch2 57336.75 57336.75
## 164 load_ch2 102324.49 102324.49
## 165 load_ch2 NA NA
## 166 load_ch2 43893.20 43893.20
## 167 load_ch2 63718.45 63718.45
## 168 load_ch2 52999.08 52999.08
## 169 load_ch2 57832.53 57832.53
## 170 load_ch2 65899.98 65899.98
## 171 load_ch2 1284.30 1284.30
## 172 load_ch2 41331.31 41331.31
## 173 load_ch2 57845.41 57845.41
## 174 load_ch2 48113.95 48113.95
## 175 load_ch2 85351.05 85351.05
## 176 load_ch2 83842.88 83842.88
## 177 load_ch2 58422.77 58422.77
## 178 load_ch2 78429.00 78429.00
## 179 load_ch2 70360.52 70360.52
## 180 load_ch2 66373.06 66373.06
## 181 load_ch2 56077.78 56077.78
## 182 load_ch2 59933.88 59933.88
## 183 load_ch2 1545.71 1545.71
## 184 load_ch2 69014.85 69014.85load_cols <- select(one_load_condensed,SPECIES:DRY.MASS.CONSUMED, load)
head(load_cols)## SPECIES REP DRY.WEIGHT weight_goal goal_moisture_percent WET....bag.G.
## 1 PSSP6 5 19.29 20 25 36.32
## 2 PSSP6 4 19.38 20 15 34.59
## 3 BRTE 1 19.66 20 15 34.48
## 4 ACTH7 4 19.69 20 45 40.13
## 5 ACTH7 4 19.70 20 15 34.79
## 6 PSSP6 4 19.79 20 45 40.45
## ACTUAL.MOISTURE.percent POST.BURN.MASS.ACTUAL MASS.CONSUMPTION
## 1 25.04 5.64 94.36
## 2 15.89 3.14 96.86
## 3 14.65 2.50 97.50
## 4 43.12 20.12 79.88
## 5 15.08 5.58 94.42
## 6 44.42 13.52 86.48
## DRY.MASS.CONSUMED load
## 1 18.58 63500.05
## 2 18.54 54010.14
## 3 18.79 47058.15
## 4 19.71 17135.67
## 5 18.98 44065.50
## 6 19.47 63542.88one_dur_condensed## SPECIES REP DRY.WEIGHT weight_goal goal_moisture_percent WET....bag.G.
## 1 PSSP6 5 19.29 20 25 36.32
## 2 PSSP6 4 19.38 20 15 34.59
## 3 BRTE 1 19.66 20 15 34.48
## 4 ACTH7 4 19.69 20 45 40.13
## 5 ACTH7 4 19.70 20 15 34.79
## 6 PSSP6 4 19.79 20 45 40.45
## 7 BRTE 1 19.80 20 35 38.50
## 8 BRTE 3 19.81 20 15 34.71
## 9 PSSP6 3 (REDO) 19.87 20 25 36.61
## 10 BRTE 5 19.91 20 35 38.65
## 11 ACTH7 5 (REDO) 19.94 20 15 34.66
## 12 BRTE 2 (REDO) 19.94 20 10 34.07
## 13 BRTE 5 19.96 20 10 33.91
## 14 BRTE 10 19.97 20 35 38.68
## 15 BRTE 8 19.97 20 35 39.34
## 16 BRTE 4 19.98 20 5 32.88
## 17 BRTE 6 19.98 20 15 34.86
## 18 PSSP6 1 (REDO) 19.98 20 15 34.83
## 19 BRTE 2 19.99 20 45 40.76
## 20 BRTE 11 19.99 20 15 35.06
## 21 BRTE 4 19.99 20 15 35.03
## 22 BRTE 3 (REDO) 19.99 20 45 39.80
## 23 BRTE 5 20.01 20 15 34.88
## 24 BRTE 12 20.01 20 15 35.03
## 25 BRTE 4 20.01 20 25 37.17
## 26 BRTE 12 20.01 20 35 39.03
## 27 BRTE 7 20.01 20 35 38.92
## 28 BRTE 1 (REDO) 20.01 20 45 41.04
## 29 BRTE 4 20.02 20 10 33.96
## 30 BRTE 9 20.02 20 35 39.14
## 31 BRTE 13 20.02 20 35 39.17
## 32 BRTE 11 20.04 20 35 39.01
## 33 ACTH7 5 (REDO) 20.05 20 45 40.60
## 34 PSSP6 5 20.06 20 15 35.27
## 35 BRTE 9 20.07 20 35 39.23
## 36 ACTH7 5 (REDO) 20.08 20 35 38.92
## 37 BRTE 4 20.09 20 35 39.12
## 38 ACTH7 4 20.11 20 55 43.30
## 39 ACTH7 2 (REDO) 20.11 20 55 42.60
## 40 ACTH7 1 (REDO) 20.12 20 55 43.45
## 41 BRTE 2 20.20 20 15 35.18
## 42 BRTE 5 20.21 20 45 41.32
## 43 PSSP6 5 20.22 20 55 43.63
## 44 BRTE 1 20.24 20 25 37.13
## 45 BRTE 4 20.24 20 45 41.26
## 46 ACTH7 3 (REDO) 20.36 20 55 43.22
## 47 ACTH7 4 20.39 20 35 39.60
## 48 ACTH7 5 20.41 20 25 38.36
## 49 BRTE 2 20.43 20 25 37.66
## 50 BRTE 1 20.45 20 10 34.54
## 51 BRTE 5 20.45 20 25 37.43
## 52 BRTE 2 20.46 20 55 43.79
## 53 BRTE 2 20.50 20 5 34.05
## 54 PSSP6 5 20.52 20 45 41.55
## 55 BRTE 3 20.56 20 5 33.59
## 56 BRTE 4 20.56 20 55 43.88
## 57 PSSP6 4 20.60 20 55 44.08
## 58 BRTE 2 20.61 20 35 39.81
## 59 ACTH7 5 20.62 20 55 43.80
## 60 PSSP6 5 20.62 20 35 40.50
## 61 ACTH7 4 20.64 20 25 38.16
## 62 BRTE 1 20.74 20 5 33.99
## 63 BRTE 3 20.76 20 10 34.88
## 64 BRTE 1 20.78 20 55 44.02
## 65 BRTE 5 20.79 20 55 44.24
## 66 BRTE 3 20.80 20 55 44.47
## 67 PSSP6 4 20.82 20 35 40.25
## 68 PSSP6 4 20.94 20 25 38.38
## 69 PSSP6 1 29.11 30 35 50.25
## 70 PSSP6 3 29.23 30 55 58.06
## 71 ACTH7 2 29.32 30 25 47.61
## 72 ACTH7 2 29.48 30 45 54.78
## 73 PSSP6 1 29.59 30 55 58.00
## 74 ACTH7 1 29.60 30 45 54.98
## 75 PSSP6 3 29.63 30 15 48.16
## 76 PSSP6 1 29.63 30 25 48.32
## 77 PSSP6 2 30.12 30 55 58.64
## 78 PSSP6 3 30.13 30 45 55.91
## 79 PSSP6 2 30.19 30 45 55.27
## 80 PSSP6 2 30.23 30 35 53.45
## 81 ACTH7 1 30.36 30 15 47.02
## 82 ACTH7 3 30.41 30 15 46.76
## 83 ACTH7 3 30.41 30 15 46.76
## 84 ACTH7 1 30.48 30 25 50.46
## 85 PSSP6 3 30.50 30 35 53.22
## 86 ACTH7 2 30.54 30 35 53.13
## 87 ACTH7 3 30.60 30 35 53.27
## 88 ACTH7 1 30.62 30 35 53.45
## 89 PSSP6 2 30.68 30 25 49.60
## 90 PSSP6 2 30.90 30 15 47.61
## 91 PSSP6 1 30.91 30 45 57.11
## 92 ACTH7 3 31.01 30 25 50.86
## 93 PSSP6 5 19.29 20 25 36.32
## 94 PSSP6 4 19.38 20 15 34.59
## 95 BRTE 1 19.66 20 15 34.48
## 96 ACTH7 4 19.69 20 45 40.13
## 97 ACTH7 4 19.70 20 15 34.79
## 98 PSSP6 4 19.79 20 45 40.45
## 99 BRTE 1 19.80 20 35 38.50
## 100 BRTE 3 19.81 20 15 34.71
## 101 PSSP6 3 (REDO) 19.87 20 25 36.61
## 102 BRTE 5 19.91 20 35 38.65
## 103 ACTH7 5 (REDO) 19.94 20 15 34.66
## 104 BRTE 2 (REDO) 19.94 20 10 34.07
## 105 BRTE 5 19.96 20 10 33.91
## 106 BRTE 10 19.97 20 35 38.68
## 107 BRTE 8 19.97 20 35 39.34
## 108 BRTE 4 19.98 20 5 32.88
## 109 BRTE 6 19.98 20 15 34.86
## 110 PSSP6 1 (REDO) 19.98 20 15 34.83
## 111 BRTE 2 19.99 20 45 40.76
## 112 BRTE 11 19.99 20 15 35.06
## 113 BRTE 4 19.99 20 15 35.03
## 114 BRTE 3 (REDO) 19.99 20 45 39.80
## 115 BRTE 5 20.01 20 15 34.88
## 116 BRTE 12 20.01 20 15 35.03
## 117 BRTE 4 20.01 20 25 37.17
## 118 BRTE 12 20.01 20 35 39.03
## 119 BRTE 7 20.01 20 35 38.92
## 120 BRTE 1 (REDO) 20.01 20 45 41.04
## 121 BRTE 4 20.02 20 10 33.96
## 122 BRTE 9 20.02 20 35 39.14
## 123 BRTE 13 20.02 20 35 39.17
## 124 BRTE 11 20.04 20 35 39.01
## 125 ACTH7 5 (REDO) 20.05 20 45 40.60
## 126 PSSP6 5 20.06 20 15 35.27
## 127 BRTE 9 20.07 20 35 39.23
## 128 ACTH7 5 (REDO) 20.08 20 35 38.92
## 129 BRTE 4 20.09 20 35 39.12
## 130 ACTH7 4 20.11 20 55 43.30
## 131 ACTH7 2 (REDO) 20.11 20 55 42.60
## 132 ACTH7 1 (REDO) 20.12 20 55 43.45
## 133 BRTE 2 20.20 20 15 35.18
## 134 BRTE 5 20.21 20 45 41.32
## 135 PSSP6 5 20.22 20 55 43.63
## 136 BRTE 1 20.24 20 25 37.13
## 137 BRTE 4 20.24 20 45 41.26
## 138 ACTH7 3 (REDO) 20.36 20 55 43.22
## 139 ACTH7 4 20.39 20 35 39.60
## 140 ACTH7 5 20.41 20 25 38.36
## 141 BRTE 2 20.43 20 25 37.66
## 142 BRTE 1 20.45 20 10 34.54
## 143 BRTE 5 20.45 20 25 37.43
## 144 BRTE 2 20.46 20 55 43.79
## 145 BRTE 2 20.50 20 5 34.05
## 146 PSSP6 5 20.52 20 45 41.55
## 147 BRTE 3 20.56 20 5 33.59
## 148 BRTE 4 20.56 20 55 43.88
## 149 PSSP6 4 20.60 20 55 44.08
## 150 BRTE 2 20.61 20 35 39.81
## 151 ACTH7 5 20.62 20 55 43.80
## 152 PSSP6 5 20.62 20 35 40.50
## 153 ACTH7 4 20.64 20 25 38.16
## 154 BRTE 1 20.74 20 5 33.99
## 155 BRTE 3 20.76 20 10 34.88
## 156 BRTE 1 20.78 20 55 44.02
## 157 BRTE 5 20.79 20 55 44.24
## 158 BRTE 3 20.80 20 55 44.47
## 159 PSSP6 4 20.82 20 35 40.25
## 160 PSSP6 4 20.94 20 25 38.38
## 161 PSSP6 1 29.11 30 35 50.25
## 162 PSSP6 3 29.23 30 55 58.06
## 163 ACTH7 2 29.32 30 25 47.61
## 164 ACTH7 2 29.48 30 45 54.78
## 165 PSSP6 1 29.59 30 55 58.00
## 166 ACTH7 1 29.60 30 45 54.98
## 167 PSSP6 3 29.63 30 15 48.16
## 168 PSSP6 1 29.63 30 25 48.32
## 169 PSSP6 2 30.12 30 55 58.64
## 170 PSSP6 3 30.13 30 45 55.91
## 171 PSSP6 2 30.19 30 45 55.27
## 172 PSSP6 2 30.23 30 35 53.45
## 173 ACTH7 1 30.36 30 15 47.02
## 174 ACTH7 3 30.41 30 15 46.76
## 175 ACTH7 3 30.41 30 15 46.76
## 176 ACTH7 1 30.48 30 25 50.46
## 177 PSSP6 3 30.50 30 35 53.22
## 178 ACTH7 2 30.54 30 35 53.13
## 179 ACTH7 3 30.60 30 35 53.27
## 180 ACTH7 1 30.62 30 35 53.45
## 181 PSSP6 2 30.68 30 25 49.60
## 182 PSSP6 2 30.90 30 15 47.61
## 183 PSSP6 1 30.91 30 45 57.11
## 184 ACTH7 3 31.01 30 25 50.86
## ACTUAL.MOISTURE.percent POST.BURN.MASS.ACTUAL MASS.CONSUMPTION
## 1 25.04 5.64 94.36000
## 2 15.89 3.14 96.86000
## 3 14.65 2.50 97.50000
## 4 43.12 20.12 79.88000
## 5 15.08 5.58 94.42000
## 6 44.42 13.52 86.48000
## 7 34.14 3.09 96.91000
## 8 15.04 3.65 96.35000
## 9 24.11 2.96 87.99676
## 10 34.10 3.17 96.83000
## 11 13.74 4.19 81.52557
## 12 9.68 3.93 82.03018
## 13 9.87 5.83 94.17000
## 14 34.25 7.98 92.02000
## 15 35.70 8.27 91.73000
## 16 4.80 3.50 96.50000
## 17 14.66 7.95 92.05000
## 18 14.66 3.72 83.76255
## 19 44.17 5.65 94.35000
## 20 14.46 4.22 95.78000
## 21 14.66 2.90 97.10000
## 22 38.42 5.53 80.01446
## 23 14.74 2.86 97.14000
## 24 15.54 4.01 95.99000
## 25 24.64 22.22 77.78000
## 26 35.08 4.10 95.90000
## 27 34.68 21.17 78.83000
## 28 44.18 4.35 84.92201
## 29 10.14 3.59 96.41000
## 30 34.72 4.21 95.79000
## 31 35.21 23.61 76.39000
## 32 34.93 18.20 81.80000
## 33 43.29 15.30 46.74556
## 34 15.05 4.23 95.77000
## 35 34.68 24.29 75.71000
## 36 34.36 18.08 32.98740
## 37 34.40 6.00 94.00000
## 38 54.30 11.15 88.85000
## 39 52.71 20.05 34.71182
## 40 55.57 16.93 45.91054
## 41 15.15 2.97 97.03000
## 42 43.99 10.56 89.44000
## 43 55.69 5.96 94.04000
## 44 24.56 3.44 96.56000
## 45 45.21 18.63 81.37000
## 46 52.01 18.73 39.48304
## 47 34.58 10.98 89.02000
## 48 28.42 4.93 95.07000
## 49 26.14 3.20 96.80000
## 50 10.46 2.99 97.01000
## 51 24.65 4.20 95.80000
## 52 55.52 26.24 73.76000
## 53 5.95 4.07 95.93000
## 54 44.54 5.72 94.28000
## 55 5.11 2.06 97.94000
## 56 54.13 3.96 96.04000
## 57 55.10 9.99 90.01000
## 58 35.23 4.04 95.96000
## 59 53.10 21.62 78.38000
## 60 38.51 14.65 85.35000
## 61 25.82 4.54 95.46000
## 62 5.11 2.50 97.50000
## 63 10.65 2.06 97.94000
## 64 54.48 4.74 95.26000
## 65 54.02 17.44 82.56000
## 66 55.34 3.26 96.74000
## 67 34.97 5.01 94.99000
## 68 24.40 7.14 92.86000
## 69 34.59 3.66 96.34000
## 70 57.17 37.41 62.59000
## 71 20.94 3.87 96.13000
## 72 44.30 13.29 86.71000
## 73 54.78 39.99 60.01000
## 74 44.46 15.27 84.73000
## 75 21.70 4.09 95.91000
## 76 23.76 5.52 94.48000
## 77 53.95 6.17 93.83000
## 78 45.10 3.95 96.05000
## 79 42.56 39.45 60.55000
## 80 36.59 3.91 96.09000
## 81 15.51 2.40 97.60000
## 82 14.40 4.55 95.45000
## 83 14.40 4.55 95.45000
## 84 25.59 2.86 97.14000
## 85 34.59 8.80 91.20000
## 86 34.18 6.43 93.57000
## 87 33.89 3.78 96.22000
## 88 34.75 5.13 94.87000
## 89 25.62 3.65 96.35000
## 90 14.89 4.04 95.96000
## 91 45.16 41.88 58.12000
## 92 24.77 2.44 97.56000
## 93 25.04 5.64 94.36000
## 94 15.89 3.14 96.86000
## 95 14.65 2.50 97.50000
## 96 43.12 20.12 79.88000
## 97 15.08 5.58 94.42000
## 98 44.42 13.52 86.48000
## 99 34.14 3.09 96.91000
## 100 15.04 3.65 96.35000
## 101 24.11 2.96 87.99676
## 102 34.10 3.17 96.83000
## 103 13.74 4.19 81.52557
## 104 9.68 3.93 82.03018
## 105 9.87 5.83 94.17000
## 106 34.25 7.98 92.02000
## 107 35.70 8.27 91.73000
## 108 4.80 3.50 96.50000
## 109 14.66 7.95 92.05000
## 110 14.66 3.72 83.76255
## 111 44.17 5.65 94.35000
## 112 14.46 4.22 95.78000
## 113 14.66 2.90 97.10000
## 114 38.42 5.53 80.01446
## 115 14.74 2.86 97.14000
## 116 15.54 4.01 95.99000
## 117 24.64 22.22 77.78000
## 118 35.08 4.10 95.90000
## 119 34.68 21.17 78.83000
## 120 44.18 4.35 84.92201
## 121 10.14 3.59 96.41000
## 122 34.72 4.21 95.79000
## 123 35.21 23.61 76.39000
## 124 34.93 18.20 81.80000
## 125 43.29 15.30 46.74556
## 126 15.05 4.23 95.77000
## 127 34.68 24.29 75.71000
## 128 34.36 18.08 32.98740
## 129 34.40 6.00 94.00000
## 130 54.30 11.15 88.85000
## 131 52.71 20.05 34.71182
## 132 55.57 16.93 45.91054
## 133 15.15 2.97 97.03000
## 134 43.99 10.56 89.44000
## 135 55.69 5.96 94.04000
## 136 24.56 3.44 96.56000
## 137 45.21 18.63 81.37000
## 138 52.01 18.73 39.48304
## 139 34.58 10.98 89.02000
## 140 28.42 4.93 95.07000
## 141 26.14 3.20 96.80000
## 142 10.46 2.99 97.01000
## 143 24.65 4.20 95.80000
## 144 55.52 26.24 73.76000
## 145 5.95 4.07 95.93000
## 146 44.54 5.72 94.28000
## 147 5.11 2.06 97.94000
## 148 54.13 3.96 96.04000
## 149 55.10 9.99 90.01000
## 150 35.23 4.04 95.96000
## 151 53.10 21.62 78.38000
## 152 38.51 14.65 85.35000
## 153 25.82 4.54 95.46000
## 154 5.11 2.50 97.50000
## 155 10.65 2.06 97.94000
## 156 54.48 4.74 95.26000
## 157 54.02 17.44 82.56000
## 158 55.34 3.26 96.74000
## 159 34.97 5.01 94.99000
## 160 24.40 7.14 92.86000
## 161 34.59 3.66 96.34000
## 162 57.17 37.41 62.59000
## 163 20.94 3.87 96.13000
## 164 44.30 13.29 86.71000
## 165 54.78 39.99 60.01000
## 166 44.46 15.27 84.73000
## 167 21.70 4.09 95.91000
## 168 23.76 5.52 94.48000
## 169 53.95 6.17 93.83000
## 170 45.10 3.95 96.05000
## 171 42.56 39.45 60.55000
## 172 36.59 3.91 96.09000
## 173 15.51 2.40 97.60000
## 174 14.40 4.55 95.45000
## 175 14.40 4.55 95.45000
## 176 25.59 2.86 97.14000
## 177 34.59 8.80 91.20000
## 178 34.18 6.43 93.57000
## 179 33.89 3.78 96.22000
## 180 34.75 5.13 94.87000
## 181 25.62 3.65 96.35000
## 182 14.89 4.04 95.96000
## 183 45.16 41.88 58.12000
## 184 24.77 2.44 97.56000
## DRY.MASS.CONSUMED COMBUSTION. FLAME.HEIGHT.CM dur_ch3 max_ch1 max_ch2
## 1 18.58000 Y 115 112.0 1191.82 1200.90
## 2 18.54000 Y 140 64.5 1127.21 644.91
## 3 18.79000 Y 110 112.0 1129.76 510.94
## 4 19.71000 Y 40 23.5 531.77 155.97
## 5 18.98000 Y 140 63.5 750.82 821.46
## 6 19.47000 Y 100 77.5 1043.17 873.63
## 7 18.96000 Y 105 107.5 796.92 624.04
## 8 18.99000 Y 110 108.5 575.37 328.21
## 9 14.89683 Y 140 91.5 1086.83 639.24
## 10 19.07000 Y 140 129.5 1104.43 1065.73
## 11 21.01304 Y 135 118.0 646.92 343.24
## 12 19.70913 Y 140 142.0 969.71 846.11
## 13 19.25000 Y 120 102.5 713.14 666.75
## 14 19.37000 N NA 110.5 132.37 182.85
## 15 19.38000 Y 140 79.0 621.67 721.95
## 16 19.16000 Y 140 94.0 808.98 635.76
## 17 19.38000 Y 135 82.0 969.85 676.07
## 18 18.61862 Y 140 102.5 1482.78 760.04
## 19 19.27000 Y 110 79.5 428.47 597.44
## 20 19.20000 Y 110 93.0 325.22 361.67
## 21 19.14000 Y 140 125.0 1114.94 703.95
## 22 27.66383 Y 115 122.5 401.81 337.40
## 23 19.15000 Y 140 82.0 891.82 813.94
## 24 19.21000 Y 140 114.5 1013.31 982.98
## 25 20.12000 N NA NA 37.02 36.76
## 26 19.21000 N NA 142.0 162.78 237.60
## 27 20.07000 N NA NA 35.67 33.93
## 28 21.73913 Y 140 118.5 267.98 344.36
## 29 19.20000 Y 115 110.5 1283.66 1116.18
## 30 19.23000 Y 135 113.5 918.33 732.24
## 31 20.20000 N NA NA 43.25 38.50
## 32 19.95000 N NA 69.5 46.31 37.72
## 33 76.30923 Y 110 61.0 406.99 216.81
## 34 19.27000 Y 115 77.5 1190.79 1075.88
## 35 20.28000 N NA NA 34.23 33.54
## 36 90.03984 Y 130 46.0 165.26 263.36
## 37 19.39000 Y 110 135.5 845.24 829.63
## 38 19.66000 Y 120 63.0 121.57 947.21
## 39 99.70164 N NA NA 55.44 66.37
## 40 84.14513 Y 90 117.0 565.99 667.22
## 41 19.35000 Y 110 94.0 513.54 436.58
## 42 19.73000 Y 90 107.0 630.03 137.55
## 43 19.51000 N 35 116.5 271.34 311.81
## 44 19.41000 Y 110 117.0 1015.29 826.56
## 45 20.16000 N NA NA 54.33 51.21
## 46 91.99411 Y 75 64.5 376.60 583.45
## 47 19.93000 Y 120 55.0 921.59 1114.65
## 48 19.65000 Y 125 75.5 986.83 879.02
## 49 19.59000 Y 110 114.5 410.66 541.64
## 50 19.60000 Y 110 101.0 1238.78 1126.08
## 51 19.66000 Y 125 92.0 118.15 947.63
## 52 20.74000 Y 20 36.0 101.82 93.52
## 53 19.70000 Y 110 105.5 227.94 370.78
## 54 19.80000 Y 140 55.0 777.72 790.02
## 55 19.66000 Y 110 101.0 1213.26 865.57
## 56 19.75000 N NA 92.5 399.51 502.27
## 57 20.08000 Y 115 85.5 961.17 874.73
## 58 19.81000 Y 110 82.5 520.64 218.60
## 59 20.67000 Y 35 48.0 350.08 759.02
## 60 20.33000 N 80 295.0 179.73 116.32
## 61 19.86000 Y 100 63.5 1207.44 1037.25
## 62 19.86000 Y 105 90.0 511.88 719.36
## 63 19.86000 Y 110 86.5 757.89 479.72
## 64 20.01000 Y 90 68.5 238.42 267.11
## 65 20.63000 N NA 62.5 186.15 108.60
## 66 19.96000 Y 90 125.5 422.97 422.16
## 67 20.06000 Y 135 102.5 1067.28 1109.41
## 68 20.28000 N NA 117.5 213.10 251.51
## 69 28.24000 Y 105 99.0 1056.48 825.28
## 70 29.51000 N NA 41.0 172.65 570.34
## 71 28.45000 Y 110 96.0 1072.49 608.97
## 72 28.93000 Y 95 63.5 156.09 815.32
## 73 29.94000 Y 20 NA 95.16 57.73
## 74 29.12000 Y 110 147.0 343.40 446.77
## 75 28.77000 Y 110 94.5 826.13 729.02
## 76 28.82000 Y 110 118.5 1213.85 506.84
## 77 29.32000 Y 110 121.0 602.51 486.58
## 78 29.26000 Y 110 112.0 908.59 775.70
## 79 30.50000 N NA 0.0 116.87 111.81
## 80 29.36000 Y 110 125.0 497.75 555.26
## 81 29.44000 Y 110 98.5 588.30 601.66
## 82 29.56000 Y 110 97.0 652.67 717.86
## 83 29.56000 Y 110 79.5 431.68 745.96
## 84 29.57000 Y 110 90.0 1159.68 844.68
## 85 29.79000 Y 105 117.0 625.77 890.62
## 86 29.75000 Y 110 81.0 1021.57 834.93
## 87 29.72000 Y 110 143.0 690.68 689.15
## 88 29.79000 Y 110 119.5 119.14 863.51
## 89 29.80000 Y 110 128.5 278.85 404.44
## 90 30.03000 Y 110 77.5 814.42 935.56
## 91 31.26000 N NA 0.0 132.58 138.30
## 92 30.09000 Y 110 84.0 909.92 796.24
## 93 18.58000 Y 115 112.0 1191.82 1200.90
## 94 18.54000 Y 140 64.5 1127.21 644.91
## 95 18.79000 Y 110 112.0 1129.76 510.94
## 96 19.71000 Y 40 23.5 531.77 155.97
## 97 18.98000 Y 140 63.5 750.82 821.46
## 98 19.47000 Y 100 77.5 1043.17 873.63
## 99 18.96000 Y 105 107.5 796.92 624.04
## 100 18.99000 Y 110 108.5 575.37 328.21
## 101 14.89683 Y 140 91.5 1086.83 639.24
## 102 19.07000 Y 140 129.5 1104.43 1065.73
## 103 21.01304 Y 135 118.0 646.92 343.24
## 104 19.70913 Y 140 142.0 969.71 846.11
## 105 19.25000 Y 120 102.5 713.14 666.75
## 106 19.37000 N NA 110.5 132.37 182.85
## 107 19.38000 Y 140 79.0 621.67 721.95
## 108 19.16000 Y 140 94.0 808.98 635.76
## 109 19.38000 Y 135 82.0 969.85 676.07
## 110 18.61862 Y 140 102.5 1482.78 760.04
## 111 19.27000 Y 110 79.5 428.47 597.44
## 112 19.20000 Y 110 93.0 325.22 361.67
## 113 19.14000 Y 140 125.0 1114.94 703.95
## 114 27.66383 Y 115 122.5 401.81 337.40
## 115 19.15000 Y 140 82.0 891.82 813.94
## 116 19.21000 Y 140 114.5 1013.31 982.98
## 117 20.12000 N NA NA 37.02 36.76
## 118 19.21000 N NA 142.0 162.78 237.60
## 119 20.07000 N NA NA 35.67 33.93
## 120 21.73913 Y 140 118.5 267.98 344.36
## 121 19.20000 Y 115 110.5 1283.66 1116.18
## 122 19.23000 Y 135 113.5 918.33 732.24
## 123 20.20000 N NA NA 43.25 38.50
## 124 19.95000 N NA 69.5 46.31 37.72
## 125 76.30923 Y 110 61.0 406.99 216.81
## 126 19.27000 Y 115 77.5 1190.79 1075.88
## 127 20.28000 N NA NA 34.23 33.54
## 128 90.03984 Y 130 46.0 165.26 263.36
## 129 19.39000 Y 110 135.5 845.24 829.63
## 130 19.66000 Y 120 63.0 121.57 947.21
## 131 99.70164 N NA NA 55.44 66.37
## 132 84.14513 Y 90 117.0 565.99 667.22
## 133 19.35000 Y 110 94.0 513.54 436.58
## 134 19.73000 Y 90 107.0 630.03 137.55
## 135 19.51000 N 35 116.5 271.34 311.81
## 136 19.41000 Y 110 117.0 1015.29 826.56
## 137 20.16000 N NA NA 54.33 51.21
## 138 91.99411 Y 75 64.5 376.60 583.45
## 139 19.93000 Y 120 55.0 921.59 1114.65
## 140 19.65000 Y 125 75.5 986.83 879.02
## 141 19.59000 Y 110 114.5 410.66 541.64
## 142 19.60000 Y 110 101.0 1238.78 1126.08
## 143 19.66000 Y 125 92.0 118.15 947.63
## 144 20.74000 Y 20 36.0 101.82 93.52
## 145 19.70000 Y 110 105.5 227.94 370.78
## 146 19.80000 Y 140 55.0 777.72 790.02
## 147 19.66000 Y 110 101.0 1213.26 865.57
## 148 19.75000 N NA 92.5 399.51 502.27
## 149 20.08000 Y 115 85.5 961.17 874.73
## 150 19.81000 Y 110 82.5 520.64 218.60
## 151 20.67000 Y 35 48.0 350.08 759.02
## 152 20.33000 N 80 295.0 179.73 116.32
## 153 19.86000 Y 100 63.5 1207.44 1037.25
## 154 19.86000 Y 105 90.0 511.88 719.36
## 155 19.86000 Y 110 86.5 757.89 479.72
## 156 20.01000 Y 90 68.5 238.42 267.11
## 157 20.63000 N NA 62.5 186.15 108.60
## 158 19.96000 Y 90 125.5 422.97 422.16
## 159 20.06000 Y 135 102.5 1067.28 1109.41
## 160 20.28000 N NA 117.5 213.10 251.51
## 161 28.24000 Y 105 99.0 1056.48 825.28
## 162 29.51000 N NA 41.0 172.65 570.34
## 163 28.45000 Y 110 96.0 1072.49 608.97
## 164 28.93000 Y 95 63.5 156.09 815.32
## 165 29.94000 Y 20 NA 95.16 57.73
## 166 29.12000 Y 110 147.0 343.40 446.77
## 167 28.77000 Y 110 94.5 826.13 729.02
## 168 28.82000 Y 110 118.5 1213.85 506.84
## 169 29.32000 Y 110 121.0 602.51 486.58
## 170 29.26000 Y 110 112.0 908.59 775.70
## 171 30.50000 N NA 0.0 116.87 111.81
## 172 29.36000 Y 110 125.0 497.75 555.26
## 173 29.44000 Y 110 98.5 588.30 601.66
## 174 29.56000 Y 110 97.0 652.67 717.86
## 175 29.56000 Y 110 79.5 431.68 745.96
## 176 29.57000 Y 110 90.0 1159.68 844.68
## 177 29.79000 Y 105 117.0 625.77 890.62
## 178 29.75000 Y 110 81.0 1021.57 834.93
## 179 29.72000 Y 110 143.0 690.68 689.15
## 180 29.79000 Y 110 119.5 119.14 863.51
## 181 29.80000 Y 110 128.5 278.85 404.44
## 182 30.03000 Y 110 77.5 814.42 935.56
## 183 31.26000 N NA 0.0 132.58 138.30
## 184 30.09000 Y 110 84.0 909.92 796.24
## max_ch3 load_ch1 load_ch2 load_ch3 goal_weight
## 1 750.11 63500.05 62385.88 91275.52 20
## 2 1124.74 54010.14 42059.95 60260.71 20
## 3 944.73 47058.15 38326.84 93122.91 20
## 4 125.17 17135.67 7286.98 5479.93 20
## 5 1010.50 44065.50 55604.26 53782.18 20
## 6 929.19 63542.88 52775.81 67764.44 20
## 7 430.26 34568.82 43225.46 45930.35 20
## 8 721.88 32824.50 32774.70 73293.10 20
## 9 759.97 50137.03 37795.01 67671.65 20
## 10 881.77 45710.07 56898.25 116235.28 20
## 11 873.19 43406.99 42802.81 98959.15 20
## 12 948.46 42528.20 45252.22 128904.45 20
## 13 782.23 38652.30 40155.59 87583.23 20
## 14 973.69 10795.13 14407.13 101774.82 20
## 15 559.33 25745.45 26857.91 47952.07 20
## 16 892.46 32733.36 36264.98 68891.96 20
## 17 1061.20 42461.87 40200.60 69648.74 20
## 18 678.30 65292.57 48155.11 78461.91 20
## 19 682.58 13029.17 19925.76 45685.23 20
## 20 718.95 21202.22 26000.14 56370.83 20
## 21 898.29 53793.88 50813.98 112993.41 20
## 22 1364.77 25219.60 28610.22 114346.43 20
## 23 792.50 36113.73 39674.85 55338.45 20
## 24 693.30 44788.19 47880.71 66903.87 20
## 25 37.26 NA NA NA 20
## 26 722.61 16824.58 26821.75 110943.65 20
## 27 29.69 NA NA NA 20
## 28 1399.75 25878.56 33114.51 117521.66 20
## 29 1026.00 100261.23 97300.02 81247.29 20
## 30 838.85 38046.09 39167.01 72468.70 20
## 31 28.80 NA NA NA 20
## 32 210.51 NA NA 23125.98 20
## 33 265.97 16475.96 12526.59 23209.97 20
## 34 894.44 68893.65 55106.91 77282.65 20
## 35 32.60 NA NA NA 20
## 36 977.80 6635.73 17371.53 42320.75 20
## 37 875.51 41750.58 48323.63 103533.47 20
## 38 1180.34 11606.96 63351.34 60735.76 20
## 39 65.12 NA NA NA 20
## 40 889.77 25814.38 49631.02 99165.55 20
## 41 779.91 31468.23 29560.02 61021.02 20
## 42 333.60 23229.58 14159.36 47165.76 20
## 43 533.67 23412.62 30990.75 70390.19 20
## 44 1006.52 45301.23 49407.69 105821.33 20
## 45 73.16 NA NA NA 20
## 46 667.19 21225.06 35739.26 48651.29 20
## 47 874.30 49545.35 87760.78 39377.79 20
## 48 906.05 55460.64 57427.75 58364.92 20
## 49 1254.28 39851.35 55951.83 139031.69 20
## 50 572.03 59971.40 57652.57 56389.12 20
## 51 971.13 11614.71 52061.46 63434.19 20
## 52 394.18 2026.31 0.00 14519.48 20
## 53 598.12 17859.34 31574.19 54974.49 20
## 54 1015.68 49777.26 42706.88 45070.62 20
## 55 675.41 56208.44 45629.64 75742.97 20
## 56 900.66 26233.83 32460.61 64572.33 20
## 57 901.18 47223.31 47910.58 74467.78 20
## 58 201.83 22528.57 17648.39 24758.37 20
## 59 1204.28 15418.84 32440.59 47044.34 20
## 60 293.86 5517.72 1697.93 101834.72 20
## 61 918.72 75929.14 70532.80 53429.13 20
## 62 371.24 26340.12 37461.14 39792.57 20
## 63 534.62 25404.77 19188.00 37691.26 20
## 64 453.48 14830.33 13058.86 19730.11 20
## 65 359.53 12073.72 5264.34 29195.46 20
## 66 581.77 26703.04 32961.17 80466.03 20
## 67 893.80 53474.81 61581.90 94851.95 20
## 68 805.96 14733.57 14847.84 65241.06 20
## 69 718.91 56704.48 61044.32 91291.85 30
## 70 549.23 8539.53 24827.91 27303.30 30
## 71 947.84 70190.42 57336.75 96815.15 30
## 72 1202.49 17560.30 102324.49 70701.49 30
## 73 55.66 0.00 NA NA 30
## 74 724.15 43422.90 43893.20 89440.46 30
## 75 1162.92 58794.90 63718.45 92456.09 30
## 76 884.76 86440.66 52999.08 84506.48 30
## 77 492.12 62327.91 57832.53 59598.31 30
## 78 1013.80 46197.34 65899.98 111516.65 30
## 79 73.15 1645.45 1284.30 0.00 30
## 80 329.62 45318.24 41331.31 53147.01 30
## 81 697.02 48637.77 57845.41 68236.77 30
## 82 968.63 45532.64 48113.95 69013.38 30
## 83 1025.93 61409.91 85351.05 87354.42 30
## 84 813.05 75863.08 83842.88 72874.62 30
## 85 854.11 40415.76 58422.77 83032.57 30
## 86 1140.78 62837.33 78429.00 92148.49 30
## 87 1000.97 50866.20 70360.52 101891.05 30
## 88 959.62 9028.10 66373.06 101707.53 30
## 89 891.28 40841.40 56077.78 73417.31 30
## 90 1323.93 52927.75 59933.88 68387.66 30
## 91 83.57 1398.74 1545.71 0.00 30
## 92 1115.20 75621.94 69014.85 88252.82 30
## 93 750.11 63500.05 62385.88 91275.52 20
## 94 1124.74 54010.14 42059.95 60260.71 20
## 95 944.73 47058.15 38326.84 93122.91 20
## 96 125.17 17135.67 7286.98 5479.93 20
## 97 1010.50 44065.50 55604.26 53782.18 20
## 98 929.19 63542.88 52775.81 67764.44 20
## 99 430.26 34568.82 43225.46 45930.35 20
## 100 721.88 32824.50 32774.70 73293.10 20
## 101 759.97 50137.03 37795.01 67671.65 20
## 102 881.77 45710.07 56898.25 116235.28 20
## 103 873.19 43406.99 42802.81 98959.15 20
## 104 948.46 42528.20 45252.22 128904.45 20
## 105 782.23 38652.30 40155.59 87583.23 20
## 106 973.69 10795.13 14407.13 101774.82 20
## 107 559.33 25745.45 26857.91 47952.07 20
## 108 892.46 32733.36 36264.98 68891.96 20
## 109 1061.20 42461.87 40200.60 69648.74 20
## 110 678.30 65292.57 48155.11 78461.91 20
## 111 682.58 13029.17 19925.76 45685.23 20
## 112 718.95 21202.22 26000.14 56370.83 20
## 113 898.29 53793.88 50813.98 112993.41 20
## 114 1364.77 25219.60 28610.22 114346.43 20
## 115 792.50 36113.73 39674.85 55338.45 20
## 116 693.30 44788.19 47880.71 66903.87 20
## 117 37.26 NA NA NA 20
## 118 722.61 16824.58 26821.75 110943.65 20
## 119 29.69 NA NA NA 20
## 120 1399.75 25878.56 33114.51 117521.66 20
## 121 1026.00 100261.23 97300.02 81247.29 20
## 122 838.85 38046.09 39167.01 72468.70 20
## 123 28.80 NA NA NA 20
## 124 210.51 NA NA 23125.98 20
## 125 265.97 16475.96 12526.59 23209.97 20
## 126 894.44 68893.65 55106.91 77282.65 20
## 127 32.60 NA NA NA 20
## 128 977.80 6635.73 17371.53 42320.75 20
## 129 875.51 41750.58 48323.63 103533.47 20
## 130 1180.34 11606.96 63351.34 60735.76 20
## 131 65.12 NA NA NA 20
## 132 889.77 25814.38 49631.02 99165.55 20
## 133 779.91 31468.23 29560.02 61021.02 20
## 134 333.60 23229.58 14159.36 47165.76 20
## 135 533.67 23412.62 30990.75 70390.19 20
## 136 1006.52 45301.23 49407.69 105821.33 20
## 137 73.16 NA NA NA 20
## 138 667.19 21225.06 35739.26 48651.29 20
## 139 874.30 49545.35 87760.78 39377.79 20
## 140 906.05 55460.64 57427.75 58364.92 20
## 141 1254.28 39851.35 55951.83 139031.69 20
## 142 572.03 59971.40 57652.57 56389.12 20
## 143 971.13 11614.71 52061.46 63434.19 20
## 144 394.18 2026.31 0.00 14519.48 20
## 145 598.12 17859.34 31574.19 54974.49 20
## 146 1015.68 49777.26 42706.88 45070.62 20
## 147 675.41 56208.44 45629.64 75742.97 20
## 148 900.66 26233.83 32460.61 64572.33 20
## 149 901.18 47223.31 47910.58 74467.78 20
## 150 201.83 22528.57 17648.39 24758.37 20
## 151 1204.28 15418.84 32440.59 47044.34 20
## 152 293.86 5517.72 1697.93 101834.72 20
## 153 918.72 75929.14 70532.80 53429.13 20
## 154 371.24 26340.12 37461.14 39792.57 20
## 155 534.62 25404.77 19188.00 37691.26 20
## 156 453.48 14830.33 13058.86 19730.11 20
## 157 359.53 12073.72 5264.34 29195.46 20
## 158 581.77 26703.04 32961.17 80466.03 20
## 159 893.80 53474.81 61581.90 94851.95 20
## 160 805.96 14733.57 14847.84 65241.06 20
## 161 718.91 56704.48 61044.32 91291.85 30
## 162 549.23 8539.53 24827.91 27303.30 30
## 163 947.84 70190.42 57336.75 96815.15 30
## 164 1202.49 17560.30 102324.49 70701.49 30
## 165 55.66 0.00 NA NA 30
## 166 724.15 43422.90 43893.20 89440.46 30
## 167 1162.92 58794.90 63718.45 92456.09 30
## 168 884.76 86440.66 52999.08 84506.48 30
## 169 492.12 62327.91 57832.53 59598.31 30
## 170 1013.80 46197.34 65899.98 111516.65 30
## 171 73.15 1645.45 1284.30 0.00 30
## 172 329.62 45318.24 41331.31 53147.01 30
## 173 697.02 48637.77 57845.41 68236.77 30
## 174 968.63 45532.64 48113.95 69013.38 30
## 175 1025.93 61409.91 85351.05 87354.42 30
## 176 813.05 75863.08 83842.88 72874.62 30
## 177 854.11 40415.76 58422.77 83032.57 30
## 178 1140.78 62837.33 78429.00 92148.49 30
## 179 1000.97 50866.20 70360.52 101891.05 30
## 180 959.62 9028.10 66373.06 101707.53 30
## 181 891.28 40841.40 56077.78 73417.31 30
## 182 1323.93 52927.75 59933.88 68387.66 30
## 183 83.57 1398.74 1545.71 0.00 30
## 184 1115.20 75621.94 69014.85 88252.82 30
## goal_moisture_percent_cont channel dur
## 1 4 dur_ch1 87.5
## 2 3 dur_ch1 73.0
## 3 3 dur_ch1 70.5
## 4 6 dur_ch1 36.0
## 5 3 dur_ch1 67.0
## 6 6 dur_ch1 71.0
## 7 5 dur_ch1 67.0
## 8 3 dur_ch1 69.0
## 9 4 dur_ch1 73.5
## 10 5 dur_ch1 68.0
## 11 3 dur_ch1 83.0
## 12 2 dur_ch1 69.5
## 13 2 dur_ch1 78.0
## 14 5 dur_ch1 46.5
## 15 5 dur_ch1 55.0
## 16 1 dur_ch1 60.5
## 17 3 dur_ch1 65.5
## 18 3 dur_ch1 91.0
## 19 6 dur_ch1 34.5
## 20 3 dur_ch1 64.0
## 21 3 dur_ch1 70.5
## 22 6 dur_ch1 65.0
## 23 3 dur_ch1 60.5
## 24 3 dur_ch1 74.0
## 25 4 dur_ch1 NA
## 26 5 dur_ch1 62.0
## 27 5 dur_ch1 NA
## 28 6 dur_ch1 68.0
## 29 2 dur_ch1 106.5
## 30 5 dur_ch1 60.0
## 31 5 dur_ch1 NA
## 32 5 dur_ch1 NA
## 33 6 dur_ch1 40.0
## 34 3 dur_ch1 78.0
## 35 5 dur_ch1 NA
## 36 5 dur_ch1 24.5
## 37 5 dur_ch1 86.5
## 38 7 dur_ch1 51.5
## 39 7 dur_ch1 NA
## 40 7 dur_ch1 46.0
## 41 3 dur_ch1 66.0
## 42 6 dur_ch1 54.5
## 43 7 dur_ch1 70.0
## 44 4 dur_ch1 64.5
## 45 6 dur_ch1 NA
## 46 7 dur_ch1 47.5
## 47 5 dur_ch1 72.0
## 48 4 dur_ch1 89.5
## 49 4 dur_ch1 89.5
## 50 2 dur_ch1 78.5
## 51 4 dur_ch1 53.0
## 52 7 dur_ch1 10.0
## 53 1 dur_ch1 55.0
## 54 6 dur_ch1 84.5
## 55 1 dur_ch1 71.5
## 56 7 dur_ch1 69.5
## 57 7 dur_ch1 76.0
## 58 5 dur_ch1 59.5
## 59 7 dur_ch1 39.0
## 60 5 dur_ch1 21.5
## 61 4 dur_ch1 80.5
## 62 1 dur_ch1 65.0
## 63 2 dur_ch1 48.0
## 64 7 dur_ch1 52.0
## 65 7 dur_ch1 42.0
## 66 7 dur_ch1 72.0
## 67 5 dur_ch1 87.0
## 68 4 dur_ch1 55.0
## 69 5 dur_ch1 99.0
## 70 7 dur_ch1 32.5
## 71 4 dur_ch1 106.5
## 72 6 dur_ch1 67.5
## 73 7 dur_ch1 NA
## 74 6 dur_ch1 113.0
## 75 3 dur_ch1 105.0
## 76 4 dur_ch1 110.0
## 77 7 dur_ch1 111.0
## 78 6 dur_ch1 98.5
## 79 6 dur_ch1 7.5
## 80 5 dur_ch1 114.0
## 81 3 dur_ch1 95.0
## 82 3 dur_ch1 87.5
## 83 3 dur_ch1 112.5
## 84 4 dur_ch1 98.5
## 85 5 dur_ch1 102.5
## 86 5 dur_ch1 93.0
## 87 5 dur_ch1 128.0
## 88 5 dur_ch1 40.0
## 89 4 dur_ch1 123.5
## 90 3 dur_ch1 102.0
## 91 6 dur_ch1 6.0
## 92 4 dur_ch1 98.5
## 93 4 dur_ch2 86.5
## 94 3 dur_ch2 65.5
## 95 3 dur_ch2 72.0
## 96 6 dur_ch2 28.0
## 97 3 dur_ch2 88.5
## 98 6 dur_ch2 65.5
## 99 5 dur_ch2 107.5
## 100 3 dur_ch2 72.0
## 101 4 dur_ch2 69.0
## 102 5 dur_ch2 83.0
## 103 3 dur_ch2 105.0
## 104 2 dur_ch2 73.0
## 105 2 dur_ch2 87.0
## 106 5 dur_ch2 49.0
## 107 5 dur_ch2 54.0
## 108 1 dur_ch2 70.0
## 109 3 dur_ch2 76.5
## 110 3 dur_ch2 79.5
## 111 6 dur_ch2 47.0
## 112 3 dur_ch2 73.5
## 113 3 dur_ch2 82.5
## 114 6 dur_ch2 77.0
## 115 3 dur_ch2 70.0
## 116 3 dur_ch2 77.0
## 117 4 dur_ch2 NA
## 118 5 dur_ch2 76.0
## 119 5 dur_ch2 NA
## 120 6 dur_ch2 75.0
## 121 2 dur_ch2 104.0
## 122 5 dur_ch2 74.0
## 123 5 dur_ch2 NA
## 124 5 dur_ch2 NA
## 125 6 dur_ch2 43.0
## 126 3 dur_ch2 77.5
## 127 5 dur_ch2 NA
## 128 5 dur_ch2 48.0
## 129 5 dur_ch2 89.5
## 130 7 dur_ch2 80.0
## 131 7 dur_ch2 NA
## 132 7 dur_ch2 108.0
## 133 3 dur_ch2 66.0
## 134 6 dur_ch2 59.5
## 135 7 dur_ch2 97.0
## 136 4 dur_ch2 78.0
## 137 6 dur_ch2 NA
## 138 7 dur_ch2 59.0
## 139 5 dur_ch2 98.5
## 140 4 dur_ch2 89.5
## 141 4 dur_ch2 111.0
## 142 2 dur_ch2 77.5
## 143 4 dur_ch2 83.0
## 144 7 dur_ch2 0.0
## 145 1 dur_ch2 76.5
## 146 6 dur_ch2 62.0
## 147 1 dur_ch2 73.5
## 148 7 dur_ch2 74.0
## 149 7 dur_ch2 88.5
## 150 5 dur_ch2 55.0
## 151 7 dur_ch2 46.5
## 152 5 dur_ch2 8.0
## 153 4 dur_ch2 76.0
## 154 1 dur_ch2 72.0
## 155 2 dur_ch2 47.5
## 156 7 dur_ch2 43.5
## 157 7 dur_ch2 25.0
## 158 7 dur_ch2 83.5
## 159 5 dur_ch2 99.0
## 160 4 dur_ch2 52.5
## 161 5 dur_ch2 110.0
## 162 7 dur_ch2 40.0
## 163 4 dur_ch2 107.5
## 164 6 dur_ch2 121.5
## 165 7 dur_ch2 NA
## 166 6 dur_ch2 118.5
## 167 3 dur_ch2 112.5
## 168 4 dur_ch2 101.0
## 169 7 dur_ch2 115.0
## 170 6 dur_ch2 120.5
## 171 6 dur_ch2 6.0
## 172 5 dur_ch2 105.0
## 173 3 dur_ch2 94.5
## 174 3 dur_ch2 79.5
## 175 3 dur_ch2 116.0
## 176 4 dur_ch2 101.5
## 177 5 dur_ch2 118.0
## 178 5 dur_ch2 109.0
## 179 5 dur_ch2 147.5
## 180 5 dur_ch2 108.0
## 181 4 dur_ch2 128.5
## 182 3 dur_ch2 94.5
## 183 6 dur_ch2 6.5
## 184 4 dur_ch2 98.0dur_cols <- select(one_dur_condensed,SPECIES:DRY.MASS.CONSUMED, dur)
head(one_max_condensed)## SPECIES REP DRY.WEIGHT weight_goal goal_moisture_percent WET....bag.G.
## 1 PSSP6 5 19.29 20 25 36.32
## 2 PSSP6 4 19.38 20 15 34.59
## 3 BRTE 1 19.66 20 15 34.48
## 4 ACTH7 4 19.69 20 45 40.13
## 5 ACTH7 4 19.70 20 15 34.79
## 6 PSSP6 4 19.79 20 45 40.45
## ACTUAL.MOISTURE.percent POST.BURN.MASS.ACTUAL MASS.CONSUMPTION
## 1 25.04 5.64 94.36
## 2 15.89 3.14 96.86
## 3 14.65 2.50 97.50
## 4 43.12 20.12 79.88
## 5 15.08 5.58 94.42
## 6 44.42 13.52 86.48
## DRY.MASS.CONSUMED COMBUSTION. FLAME.HEIGHT.CM dur_ch1 dur_ch2 dur_ch3 max_ch3
## 1 18.58 Y 115 87.5 86.5 112.0 750.11
## 2 18.54 Y 140 73.0 65.5 64.5 1124.74
## 3 18.79 Y 110 70.5 72.0 112.0 944.73
## 4 19.71 Y 40 36.0 28.0 23.5 125.17
## 5 18.98 Y 140 67.0 88.5 63.5 1010.50
## 6 19.47 Y 100 71.0 65.5 77.5 929.19
## load_ch1 load_ch2 load_ch3 goal_weight channel max
## 1 63500.05 62385.88 91275.52 20 max_ch1 1191.82
## 2 54010.14 42059.95 60260.71 20 max_ch1 1127.21
## 3 47058.15 38326.84 93122.91 20 max_ch1 1129.76
## 4 17135.67 7286.98 5479.93 20 max_ch1 531.77
## 5 44065.50 55604.26 53782.18 20 max_ch1 750.82
## 6 63542.88 52775.81 67764.44 20 max_ch1 1043.17
## goal_moisture_percent_cont
## 1 4
## 2 3
## 3 3
## 4 6
## 5 3
## 6 6max_cols <- select(one_max_condensed,SPECIES:DRY.MASS.CONSUMED, max)
head(max_cols)## SPECIES REP DRY.WEIGHT weight_goal goal_moisture_percent WET....bag.G.
## 1 PSSP6 5 19.29 20 25 36.32
## 2 PSSP6 4 19.38 20 15 34.59
## 3 BRTE 1 19.66 20 15 34.48
## 4 ACTH7 4 19.69 20 45 40.13
## 5 ACTH7 4 19.70 20 15 34.79
## 6 PSSP6 4 19.79 20 45 40.45
## ACTUAL.MOISTURE.percent POST.BURN.MASS.ACTUAL MASS.CONSUMPTION
## 1 25.04 5.64 94.36
## 2 15.89 3.14 96.86
## 3 14.65 2.50 97.50
## 4 43.12 20.12 79.88
## 5 15.08 5.58 94.42
## 6 44.42 13.52 86.48
## DRY.MASS.CONSUMED max
## 1 18.58 1191.82
## 2 18.54 1127.21
## 3 18.79 1129.76
## 4 19.71 531.77
## 5 18.98 750.82
## 6 19.47 1043.17merging_cols = c("SPECIES", "REP", "DRY.WEIGHT", "weight_goal", "goal_moisture_percent",
"WET....bag.G.", "ACTUAL.MOISTURE.percent", "POST.BURN.MASS.ACTUAL",
"MASS.CONSUMPTION", "DRY.MASS.CONSUMED")
one_cols = inner_join(load_cols, dur_cols, by = merging_cols) %>%
inner_join(., max_cols, by = merging_cols)
#remove NAs
one_cols<- one_cols %>% na.omit()
summary(one_cols$load)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 25863 43658 42233 56753 102324head(one_cols)## SPECIES REP DRY.WEIGHT weight_goal goal_moisture_percent WET....bag.G.
## 1 PSSP6 5 19.29 20 25 36.32
## 2 PSSP6 5 19.29 20 25 36.32
## 3 PSSP6 5 19.29 20 25 36.32
## 4 PSSP6 5 19.29 20 25 36.32
## 5 PSSP6 4 19.38 20 15 34.59
## 6 PSSP6 4 19.38 20 15 34.59
## ACTUAL.MOISTURE.percent POST.BURN.MASS.ACTUAL MASS.CONSUMPTION
## 1 25.04 5.64 94.36
## 2 25.04 5.64 94.36
## 3 25.04 5.64 94.36
## 4 25.04 5.64 94.36
## 5 15.89 3.14 96.86
## 6 15.89 3.14 96.86
## DRY.MASS.CONSUMED load dur max
## 1 18.58 63500.05 87.5 1191.82
## 2 18.58 63500.05 87.5 1200.90
## 3 18.58 63500.05 86.5 1191.82
## 4 18.58 63500.05 86.5 1200.90
## 5 18.54 54010.14 73.0 1127.21
## 6 18.54 54010.14 73.0 644.91one_output_additive = one_cols %>%
group_by(SPECIES, weight_goal, goal_moisture_percent) %>%
summarize(n=n(),
load = mean(load),
max= mean(max),
dur= mean(dur),
cons_percent = mean(MASS.CONSUMPTION))## `summarise()` has grouped output by 'SPECIES', 'weight_goal'. You can override
## using the `.groups` argument.one_output_additive## # A tibble: 26 x 8
## # Groups: SPECIES, weight_goal [5]
## SPECIES weight_goal goal_moisture_perc~ n load max dur cons_percent
## <fct> <int> <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 BRTE 20 5 32 35509. 669. 68 97.0
## 2 BRTE 20 10 40 52637. 920. 77.0 93.5
## 3 BRTE 20 15 64 38434. 709. 70.6 96.1
## 4 BRTE 20 25 24 42365. 643. 79.8 96.4
## 5 BRTE 20 35 64 31832. 607. 68.3 94.9
## 6 BRTE 20 45 32 22896. 393. 60.1 87.2
## 7 BRTE 20 55 40 16561. 274. 47.2 88.9
## 8 ACTH7 20 15 16 46470. 641. 85.9 88.0
## 9 ACTH7 20 25 16 64838. 1028. 83.9 95.3
## 10 ACTH7 20 35 16 40328. 616. 60.8 61.0
## # ... with 16 more rowswrite.csv(one_output_additive, "additive_data/one_sp_add.csv", row.names = F)summarize one species info for additive vs non additive stuff
#for the data using multiple TC, we want the combined TC 1 and 2 dataset
load_cols <- select(two_load_condensed,Full_ID:WET.MASS.CONSUMPTION...., load)
dur_cols <- select(two_dur_condensed,Full_ID:WET.MASS.CONSUMPTION...., dur)
max_cols <- select(two_max_condensed,Full_ID:WET.MASS.CONSUMPTION...., max)
two_cols = left_join(load_cols, dur_cols)## Joining, by = c("Full_ID", "REP..", "PG.SPECIES", "PG_Goal_moisture",
## "AG.dry.weight..g.", "AG.GOAL.DRY.MASS", "AG.GOAL.MOISTURE.", "PG.WET.ACTUAL",
## "AG.WET.ACTUAL", "PG.MOISTURE", "AG.MOISTURE", "PRE.BURN.COMBINED.WET.MASS",
## "POST.BURN.MASS", "WET.MASS.CONSUMPTION....")two_cols = left_join(two_cols, max_cols)## Joining, by = c("Full_ID", "REP..", "PG.SPECIES", "PG_Goal_moisture",
## "AG.dry.weight..g.", "AG.GOAL.DRY.MASS", "AG.GOAL.MOISTURE.", "PG.WET.ACTUAL",
## "AG.WET.ACTUAL", "PG.MOISTURE", "AG.MOISTURE", "PRE.BURN.COMBINED.WET.MASS",
## "POST.BURN.MASS", "WET.MASS.CONSUMPTION....")#remove NAs
two_cols<- two_cols %>% na.omit()
summary(two_cols$load)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 24210 38530 39010 52045 165681head(two_cols)## Full_ID REP.. PG.SPECIES PG_Goal_moisture AG.dry.weight..g.
## 1 ACTH7_35_BRTE_5g_5 1 ACTH7 35 4.95
## 2 ACTH7_35_BRTE_5g_5 1 ACTH7 35 4.95
## 3 ACTH7_35_BRTE_5g_5 1 ACTH7 35 4.95
## 4 ACTH7_35_BRTE_5g_5 1 ACTH7 35 4.95
## 5 ACTH7_35_BRTE_5g_5 2 ACTH7 35 5.05
## 6 ACTH7_35_BRTE_5g_5 2 ACTH7 35 5.05
## AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. PG.WET.ACTUAL AG.WET.ACTUAL PG.MOISTURE
## 1 5 5 26.07 5.39 33.28
## 2 5 5 26.07 5.39 33.28
## 3 5 5 26.07 5.39 33.28
## 4 5 5 26.07 5.39 33.28
## 5 5 5 27.44 5.46 34.84
## 6 5 5 27.44 5.46 34.84
## AG.MOISTURE PRE.BURN.COMBINED.WET.MASS POST.BURN.MASS
## 1 8.89 31.46 6.66
## 2 8.89 31.46 6.66
## 3 8.89 31.46 6.66
## 4 8.89 31.46 6.66
## 5 8.12 32.90 3.77
## 6 8.12 32.90 3.77
## WET.MASS.CONSUMPTION.... load dur max
## 1 78.83 24236.58 67 423.37
## 2 78.83 24236.58 67 29.52
## 3 78.83 24236.58 0 423.37
## 4 78.83 24236.58 0 29.52
## 5 88.54 38487.70 90 480.10
## 6 88.54 38487.70 90 514.48two_output_additive = two_cols %>%
group_by(PG.SPECIES, PG_Goal_moisture, AG.GOAL.DRY.MASS, AG.GOAL.MOISTURE.) %>%
summarize(n=n(),
load = mean(load),
max= mean(max),
dur= mean(dur),
cons_percent = mean(WET.MASS.CONSUMPTION....))## `summarise()` has grouped output by 'PG.SPECIES', 'PG_Goal_moisture',
## 'AG.GOAL.DRY.MASS'. You can override using the `.groups` argument.two_output_additive## # A tibble: 35 x 9
## # Groups: PG.SPECIES, PG_Goal_moisture, AG.GOAL.DRY.MASS [11]
## PG.SPECIES PG_Goal_moisture AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. n load
## <chr> <int> <fct> <fct> <int> <dbl>
## 1 ACTH7 35 2.5 5 32 45812.
## 2 ACTH7 35 2.5 25 32 38440.
## 3 ACTH7 35 5 5 32 45299.
## 4 ACTH7 35 5 25 32 58104.
## 5 ACTH7 55 2.5 5 32 21773.
## 6 ACTH7 55 2.5 15 32 27181.
## 7 ACTH7 55 2.5 25 32 30917.
## 8 ACTH7 55 2.5 35 32 30126.
## 9 ACTH7 55 5 5 32 48529.
## 10 ACTH7 55 5 15 32 41295.
## # ... with 25 more rows, and 3 more variables: max <dbl>, dur <dbl>,
## # cons_percent <dbl>write.csv(two_output_additive, "additive_data/two_sp_add.csv", row.names = F)graphing effect size
effect <- read.csv("additive_data/effect_size.csv", header=T)
head(effect)## Full_ID REP.. PG.SPECIES PG_Goal_moisture AG.dry.weight..g.
## 1 ACTH7_35_BRTE_2.5g_25 2 ACTH7 35 2.50
## 2 ACTH7_35_BRTE_2.5g_25 3 ACTH7 35 2.51
## 3 ACTH7_35_BRTE_2.5g_25 1 ACTH7 35 2.62
## 4 ACTH7_35_BRTE_2.5g_25 4 ACTH7 35 2.52
## 5 ACTH7_35_BRTE_2.5g_5 2 ACTH7 35 2.63
## 6 ACTH7_35_BRTE_2.5g_5 3 ACTH7 35 2.66
## AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. PG.WET.ACTUAL AG.WET.ACTUAL PG.MOISTURE
## 1 2.5 25 26.58 3.05 34.24
## 2 2.5 25 26.67 3.32 34.97
## 3 2.5 25 26.93 3.28 34.72
## 4 2.5 25 26.70 3.37 34.37
## 5 2.5 5 27.47 2.82 35.19
## 6 2.5 5 26.85 2.87 35.61
## AG.MOISTURE dur_obs max_obs load_obs consumption_obs dur_exp max_exp
## 1 22.00 33.0 461.460 15190.560 27.57 50.55556 540.2333
## 2 32.27 39.5 220.640 13005.610 15.67 50.55556 540.2333
## 3 25.19 103.5 1059.485 72057.110 70.74 50.55556 540.2333
## 4 33.73 104.5 657.335 53506.100 82.01 50.55556 540.2333
## 5 7.22 26.0 157.375 6801.395 13.87 49.88889 559.7683
## 6 7.89 64.5 789.495 42510.425 78.77 49.88889 559.7683
## load_exp consumption_exp dur_effectSize max_effectSize load_effectSize
## 1 28553.34 64.93514 -0.3472527 -0.1458135 -0.4679936
## 2 28553.34 64.93514 -0.2186813 -0.5915839 -0.5445153
## 3 28553.34 64.93514 1.0472527 0.9611618 1.5235963
## 4 28553.34 64.93514 1.0670330 0.2167613 0.8738997
## 5 28667.74 64.99968 -0.4788419 -0.7188569 -0.7627509
## 6 28667.74 64.99968 0.2928731 0.4103960 0.4828664
## Consumption_effectSize
## 1 -0.57542249
## 2 -0.75868228
## 3 0.08939474
## 4 0.26295254
## 5 -0.78661432
## 6 0.21185218effect$PG.SPECIES <-factor(effect$PG.SPECIES)
effect$PG_Goal_moisture <-factor(effect$PG_Goal_moisture)
effect$AG.GOAL.DRY.MASS <-factor(effect$AG.GOAL.DRY.MASS)
effect$AG.GOAL.MOISTURE. <-factor(effect$AG.GOAL.MOISTURE.)
effect$Full_ID <-factor(effect$Full_ID)set custom colors
acth55_effect_colors = c("#ffffcc", "#c2e699", "#78c679", "#238443")
pssp55_effect_colors = c("#ece7f2","#a6bddb", "#2b8cbe")
acth35_effect_colors = c("#ffffcc", "#c2e699")
print(pssp_custom_colors)## [1] "#636363" "#ece7f2" "#a6bddb" "#2b8cbe"stats
library(car)## Warning: package 'car' was built under R version 4.0.5## Loading required package: carData## Warning: package 'carData' was built under R version 4.0.5##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## someLoop through t test with all flammability attributes and for each trail This will tell us if effect size is different from 0 If different = some non-additive effects
# create an empty output table
output_table <- data.frame(Full_ID = character(), variable = character(), p_value = numeric(), sample_size = integer(), significant = character(), stringsAsFactors = FALSE)
# define the columns to loop through
cols_effectSize <- c("load_effectSize", "max_effectSize", "dur_effectSize", "Consumption_effectSize")
summary(effect)## Full_ID REP.. PG.SPECIES PG_Goal_moisture
## ACTH7_35_BRTE_2.5g_25: 4 Min. :1.000 ACTH7:72 35:27
## ACTH7_35_BRTE_2.5g_5 : 4 1st Qu.:2.000 PSSP6:54 55:99
## ACTH7_35_BRTE_5g_25 : 4 Median :2.000
## ACTH7_55_BRTE_10g_15 : 4 Mean :2.492
## ACTH7_55_BRTE_10g_25 : 4 3rd Qu.:3.000
## ACTH7_55_BRTE_10g_35 : 4 Max. :4.000
## (Other) :102
## AG.dry.weight..g. AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. PG.WET.ACTUAL
## Min. : 2.370 2.5:42 5 :40 Min. :25.97
## 1st Qu.: 2.610 5 :38 15:24 1st Qu.:29.69
## Median : 5.135 10 :32 25:38 Median :30.86
## Mean : 6.651 15 :14 35:24 Mean :29.96
## 3rd Qu.:10.010 3rd Qu.:30.95
## Max. :15.450 Max. :32.20
##
## AG.WET.ACTUAL PG.MOISTURE AG.MOISTURE dur_obs
## Min. : 2.610 Min. :29.53 Min. :-16.250 Min. : 25.75
## 1st Qu.: 3.280 1st Qu.:48.34 1st Qu.: 8.925 1st Qu.: 66.00
## Median : 6.180 Median :54.26 Median : 17.745 Median : 82.25
## Mean : 7.923 Mean :49.68 Mean : 19.337 Mean : 83.74
## 3rd Qu.:11.585 3rd Qu.:54.67 3rd Qu.: 27.483 3rd Qu.:101.38
## Max. :20.800 Max. :61.00 Max. : 53.910 Max. :168.25
##
## max_obs load_obs consumption_obs dur_exp
## Min. : 118.3 Min. : 6722 Min. :13.87 Min. :47.89
## 1st Qu.: 377.1 1st Qu.: 32228 1st Qu.:72.50 1st Qu.:50.60
## Median : 605.5 Median : 42539 Median :81.41 Median :53.67
## Mean : 589.0 Mean : 42884 Mean :78.72 Mean :58.44
## 3rd Qu.: 789.3 3rd Qu.: 53985 3rd Qu.:89.94 3rd Qu.:71.00
## Max. :1302.7 Max. :138934 Max. :94.10 Max. :72.56
##
## max_exp load_exp consumption_exp dur_effectSize
## Min. :371.5 Min. :19736 Min. :64.94 Min. :-0.6418
## 1st Qu.:446.2 1st Qu.:23096 1st Qu.:69.75 1st Qu.: 0.1083
## Median :552.1 Median :28668 Median :77.28 Median : 0.4656
## Mean :532.5 Mean :28160 Mean :80.23 Mean : 0.4770
## 3rd Qu.:620.6 3rd Qu.:34297 3rd Qu.:92.56 3rd Qu.: 0.8432
## Max. :683.1 Max. :36450 Max. :93.67 Max. : 2.1575
##
## max_effectSize load_effectSize Consumption_effectSize
## Min. :-0.8015 Min. :-0.80612 Min. :-0.78661
## 1st Qu.:-0.2376 1st Qu.: 0.02816 1st Qu.:-0.14979
## Median : 0.1883 Median : 0.50662 Median :-0.02615
## Mean : 0.1260 Mean : 0.59157 Mean : 0.00135
## 3rd Qu.: 0.4096 3rd Qu.: 1.03111 3rd Qu.: 0.21105
## Max. : 1.2734 Max. : 4.59185 Max. : 0.33652
## # loop through all unique values of Full_ID and variable
for (id in unique(effect$Full_ID)) {
for (col in cols_effectSize) {
# filter the data for the current Full_ID and variable
trial_dataset <- effect %>% filter(Full_ID == id) %>% select(Full_ID, .data[[col]])
# check the sample size of the current dataset
sample_size <- nrow(trial_dataset)
# check if there are at least two observations
if (sample_size >= 2) {
# perform the t-test and extract the p-value
ttest_result <- t.test(trial_dataset[[2]], mu = 0, alternative = "two.sided")
p_value <- ttest_result$p.value
# determine if the p-value is significant at the 0.05 level
if (p_value < 0.05) {
significant <- "Yes"
} else {
significant <- "No"
}
# add the Full_ID, variable, p-value, sample size, and significance to the output table
output_table <- rbind(output_table, data.frame(Full_ID = id, variable = col, p_value = p_value, sample_size = sample_size, significant = significant))
} else {
# add a row with NA if there are less than two observations
output_table <- rbind(output_table, data.frame(Full_ID = id, variable = col, p_value = NA, sample_size = sample_size, significant = "NA"))
}
}
}
# print the output table
print(output_table)## Full_ID variable p_value sample_size
## 1 ACTH7_35_BRTE_2.5g_25 load_effectSize 5.458653e-01 4
## 2 ACTH7_35_BRTE_2.5g_25 max_effectSize 7.593821e-01 4
## 3 ACTH7_35_BRTE_2.5g_25 dur_effectSize 3.917266e-01 4
## 4 ACTH7_35_BRTE_2.5g_25 Consumption_effectSize 3.966437e-01 4
## 5 ACTH7_35_BRTE_2.5g_5 load_effectSize 3.373221e-01 4
## 6 ACTH7_35_BRTE_2.5g_5 max_effectSize 4.109086e-01 4
## 7 ACTH7_35_BRTE_2.5g_5 dur_effectSize 3.612269e-01 4
## 8 ACTH7_35_BRTE_2.5g_5 Consumption_effectSize 7.969136e-01 4
## 9 ACTH7_35_BRTE_5g_25 load_effectSize 7.302964e-04 4
## 10 ACTH7_35_BRTE_5g_25 max_effectSize 1.870819e-02 4
## 11 ACTH7_35_BRTE_5g_25 dur_effectSize 9.910582e-05 4
## 12 ACTH7_35_BRTE_5g_25 Consumption_effectSize 1.893914e-01 4
## 13 ACTH7_35_BRTE_5g_5 load_effectSize 7.451071e-02 3
## 14 ACTH7_35_BRTE_5g_5 max_effectSize 3.293856e-01 3
## 15 ACTH7_35_BRTE_5g_5 dur_effectSize 5.764703e-02 3
## 16 ACTH7_35_BRTE_5g_5 Consumption_effectSize 2.750948e-02 3
## 17 ACTH7_55_BRTE_10g_15 load_effectSize 1.468915e-01 4
## 18 ACTH7_55_BRTE_10g_15 max_effectSize 4.779817e-01 4
## 19 ACTH7_55_BRTE_10g_15 dur_effectSize 2.375957e-02 4
## 20 ACTH7_55_BRTE_10g_15 Consumption_effectSize 1.067426e-01 4
## 21 ACTH7_55_BRTE_10g_25 load_effectSize 6.476324e-02 4
## 22 ACTH7_55_BRTE_10g_25 max_effectSize 4.127060e-01 4
## 23 ACTH7_55_BRTE_10g_25 dur_effectSize 1.735073e-02 4
## 24 ACTH7_55_BRTE_10g_25 Consumption_effectSize 2.609981e-03 4
## 25 ACTH7_55_BRTE_10g_35 load_effectSize 4.715725e-02 4
## 26 ACTH7_55_BRTE_10g_35 max_effectSize 5.727492e-01 4
## 27 ACTH7_55_BRTE_10g_35 dur_effectSize 2.829428e-02 4
## 28 ACTH7_55_BRTE_10g_35 Consumption_effectSize 7.240491e-02 4
## 29 ACTH7_55_BRTE_10g_5 load_effectSize 1.598429e-02 4
## 30 ACTH7_55_BRTE_10g_5 max_effectSize 7.322858e-01 4
## 31 ACTH7_55_BRTE_10g_5 dur_effectSize 9.239756e-06 4
## 32 ACTH7_55_BRTE_10g_5 Consumption_effectSize 5.124320e-04 4
## 33 ACTH7_55_BRTE_15g_15 load_effectSize 6.253714e-03 4
## 34 ACTH7_55_BRTE_15g_15 max_effectSize 5.428178e-02 4
## 35 ACTH7_55_BRTE_15g_15 dur_effectSize 5.746847e-05 4
## 36 ACTH7_55_BRTE_15g_15 Consumption_effectSize 1.365024e-04 4
## 37 ACTH7_55_BRTE_15g_25 load_effectSize 2.855472e-01 4
## 38 ACTH7_55_BRTE_15g_25 max_effectSize 2.364880e-02 4
## 39 ACTH7_55_BRTE_15g_25 dur_effectSize 1.298246e-01 4
## 40 ACTH7_55_BRTE_15g_25 Consumption_effectSize 8.051905e-02 4
## 41 ACTH7_55_BRTE_15g_35 load_effectSize 1.349081e-02 2
## 42 ACTH7_55_BRTE_15g_35 max_effectSize 1.407539e-02 2
## 43 ACTH7_55_BRTE_15g_35 dur_effectSize 1.022887e-01 2
## 44 ACTH7_55_BRTE_15g_35 Consumption_effectSize 6.243707e-02 2
## 45 ACTH7_55_BRTE_15g_5 load_effectSize 7.607961e-02 4
## 46 ACTH7_55_BRTE_15g_5 max_effectSize 2.386745e-01 4
## 47 ACTH7_55_BRTE_15g_5 dur_effectSize 1.023898e-02 4
## 48 ACTH7_55_BRTE_15g_5 Consumption_effectSize 6.468080e-04 4
## 49 ACTH7_55_BRTE_2.5g_15 load_effectSize 3.924190e-01 4
## 50 ACTH7_55_BRTE_2.5g_15 max_effectSize 6.580319e-01 4
## 51 ACTH7_55_BRTE_2.5g_15 dur_effectSize 4.095383e-01 4
## 52 ACTH7_55_BRTE_2.5g_15 Consumption_effectSize 3.227389e-01 4
## 53 ACTH7_55_BRTE_2.5g_25 load_effectSize 3.360005e-01 3
## 54 ACTH7_55_BRTE_2.5g_25 max_effectSize 3.873424e-01 3
## 55 ACTH7_55_BRTE_2.5g_25 dur_effectSize 2.771563e-01 3
## 56 ACTH7_55_BRTE_2.5g_25 Consumption_effectSize 8.428912e-01 3
## 57 ACTH7_55_BRTE_2.5g_35 load_effectSize 1.846489e-01 3
## 58 ACTH7_55_BRTE_2.5g_35 max_effectSize 1.663685e-01 3
## 59 ACTH7_55_BRTE_2.5g_35 dur_effectSize 2.197175e-01 3
## 60 ACTH7_55_BRTE_2.5g_35 Consumption_effectSize 3.004099e-02 3
## 61 ACTH7_55_BRTE_2.5g_5 load_effectSize 3.701585e-02 2
## 62 ACTH7_55_BRTE_2.5g_5 max_effectSize 7.117074e-02 2
## 63 ACTH7_55_BRTE_2.5g_5 dur_effectSize 1.370839e-01 2
## 64 ACTH7_55_BRTE_2.5g_5 Consumption_effectSize 8.594297e-01 2
## 65 ACTH7_55_BRTE_5g_15 load_effectSize 8.354410e-02 4
## 66 ACTH7_55_BRTE_5g_15 max_effectSize 4.073210e-01 4
## 67 ACTH7_55_BRTE_5g_15 dur_effectSize 1.407103e-02 4
## 68 ACTH7_55_BRTE_5g_15 Consumption_effectSize 3.892326e-05 4
## 69 ACTH7_55_BRTE_5g_25 load_effectSize 3.869476e-01 4
## 70 ACTH7_55_BRTE_5g_25 max_effectSize 7.082865e-01 4
## 71 ACTH7_55_BRTE_5g_25 dur_effectSize 2.798697e-01 4
## 72 ACTH7_55_BRTE_5g_25 Consumption_effectSize 1.614950e-04 4
## 73 ACTH7_55_BRTE_5g_35 load_effectSize 2.243718e-01 3
## 74 ACTH7_55_BRTE_5g_35 max_effectSize 4.042255e-01 3
## 75 ACTH7_55_BRTE_5g_35 dur_effectSize 9.969321e-02 3
## 76 ACTH7_55_BRTE_5g_35 Consumption_effectSize 1.908080e-03 3
## 77 ACTH7_55_BRTE_5g_5 load_effectSize 1.229905e-02 4
## 78 ACTH7_55_BRTE_5g_5 max_effectSize 1.456504e-02 4
## 79 ACTH7_55_BRTE_5g_5 dur_effectSize 8.124120e-03 4
## 80 ACTH7_55_BRTE_5g_5 Consumption_effectSize 1.375000e-05 4
## 81 PSSP6_35_BRTE_2.5g_25 load_effectSize 1.786715e-02 4
## 82 PSSP6_35_BRTE_2.5g_25 max_effectSize 5.766435e-02 4
## 83 PSSP6_35_BRTE_2.5g_25 dur_effectSize 1.066888e-02 4
## 84 PSSP6_35_BRTE_2.5g_25 Consumption_effectSize 5.191669e-02 4
## 85 PSSP6_35_BRTE_2.5g_5 load_effectSize 2.234015e-01 4
## 86 PSSP6_35_BRTE_2.5g_5 max_effectSize 1.329867e-01 4
## 87 PSSP6_35_BRTE_2.5g_5 dur_effectSize 2.053925e-01 4
## 88 PSSP6_35_BRTE_2.5g_5 Consumption_effectSize 1.972517e-02 4
## 89 PSSP6_35_BRTE_5g_5 load_effectSize 5.965648e-02 4
## 90 PSSP6_35_BRTE_5g_5 max_effectSize 2.032887e-01 4
## 91 PSSP6_35_BRTE_5g_5 dur_effectSize 3.334566e-02 4
## 92 PSSP6_35_BRTE_5g_5 Consumption_effectSize 4.152246e-02 4
## 93 PSSP6_55_BRTE_10g_15 load_effectSize 1.511231e-01 4
## 94 PSSP6_55_BRTE_10g_15 max_effectSize 3.630695e-01 4
## 95 PSSP6_55_BRTE_10g_15 dur_effectSize 5.969838e-02 4
## 96 PSSP6_55_BRTE_10g_15 Consumption_effectSize 5.487829e-02 4
## 97 PSSP6_55_BRTE_10g_25 load_effectSize 4.918807e-02 4
## 98 PSSP6_55_BRTE_10g_25 max_effectSize 1.665639e-01 4
## 99 PSSP6_55_BRTE_10g_25 dur_effectSize 1.011345e-01 4
## 100 PSSP6_55_BRTE_10g_25 Consumption_effectSize 3.371099e-03 4
## 101 PSSP6_55_BRTE_10g_35 load_effectSize 1.462149e-01 4
## 102 PSSP6_55_BRTE_10g_35 max_effectSize 3.896217e-01 4
## 103 PSSP6_55_BRTE_10g_35 dur_effectSize 2.420803e-02 4
## 104 PSSP6_55_BRTE_10g_35 Consumption_effectSize 5.069548e-03 4
## 105 PSSP6_55_BRTE_10g_5 load_effectSize 2.373840e-02 4
## 106 PSSP6_55_BRTE_10g_5 max_effectSize 7.767653e-02 4
## 107 PSSP6_55_BRTE_10g_5 dur_effectSize 3.023908e-02 4
## 108 PSSP6_55_BRTE_10g_5 Consumption_effectSize 2.237805e-03 4
## 109 PSSP6_55_BRTE_2.5g_15 load_effectSize 5.208268e-01 3
## 110 PSSP6_55_BRTE_2.5g_15 max_effectSize 1.660691e-01 3
## 111 PSSP6_55_BRTE_2.5g_15 dur_effectSize 6.247317e-02 3
## 112 PSSP6_55_BRTE_2.5g_15 Consumption_effectSize 1.329954e-02 3
## 113 PSSP6_55_BRTE_2.5g_25 load_effectSize 3.216293e-01 3
## 114 PSSP6_55_BRTE_2.5g_25 max_effectSize 6.271538e-01 3
## 115 PSSP6_55_BRTE_2.5g_25 dur_effectSize 2.734124e-01 3
## 116 PSSP6_55_BRTE_2.5g_25 Consumption_effectSize 7.566748e-02 3
## 117 PSSP6_55_BRTE_2.5g_35 load_effectSize 7.358142e-01 4
## 118 PSSP6_55_BRTE_2.5g_35 max_effectSize 8.105976e-01 4
## 119 PSSP6_55_BRTE_2.5g_35 dur_effectSize 1.165255e-01 4
## 120 PSSP6_55_BRTE_2.5g_35 Consumption_effectSize 1.894199e-03 4
## 121 PSSP6_55_BRTE_2.5g_5 load_effectSize 5.150497e-01 4
## 122 PSSP6_55_BRTE_2.5g_5 max_effectSize 1.537413e-01 4
## 123 PSSP6_55_BRTE_2.5g_5 dur_effectSize 2.391278e-01 4
## 124 PSSP6_55_BRTE_2.5g_5 Consumption_effectSize 1.723614e-02 4
## 125 PSSP6_55_BRTE_5g_15 load_effectSize NA 1
## 126 PSSP6_55_BRTE_5g_15 max_effectSize NA 1
## 127 PSSP6_55_BRTE_5g_15 dur_effectSize NA 1
## 128 PSSP6_55_BRTE_5g_15 Consumption_effectSize NA 1
## 129 PSSP6_55_BRTE_5g_25 load_effectSize 9.778662e-02 4
## 130 PSSP6_55_BRTE_5g_25 max_effectSize 4.440999e-02 4
## 131 PSSP6_55_BRTE_5g_25 dur_effectSize 2.516522e-01 4
## 132 PSSP6_55_BRTE_5g_25 Consumption_effectSize 9.208205e-03 4
## 133 PSSP6_55_BRTE_5g_35 load_effectSize 4.130465e-01 4
## 134 PSSP6_55_BRTE_5g_35 max_effectSize 5.130328e-01 4
## 135 PSSP6_55_BRTE_5g_35 dur_effectSize 7.149295e-01 4
## 136 PSSP6_55_BRTE_5g_35 Consumption_effectSize 3.328304e-02 4
## 137 PSSP6_55_BRTE_5g_5 load_effectSize 8.015165e-01 3
## 138 PSSP6_55_BRTE_5g_5 max_effectSize 3.746766e-01 3
## 139 PSSP6_55_BRTE_5g_5 dur_effectSize 8.559401e-01 3
## 140 PSSP6_55_BRTE_5g_5 Consumption_effectSize 6.628102e-02 3
## significant
## 1 No
## 2 No
## 3 No
## 4 No
## 5 No
## 6 No
## 7 No
## 8 No
## 9 Yes
## 10 Yes
## 11 Yes
## 12 No
## 13 No
## 14 No
## 15 No
## 16 Yes
## 17 No
## 18 No
## 19 Yes
## 20 No
## 21 No
## 22 No
## 23 Yes
## 24 Yes
## 25 Yes
## 26 No
## 27 Yes
## 28 No
## 29 Yes
## 30 No
## 31 Yes
## 32 Yes
## 33 Yes
## 34 No
## 35 Yes
## 36 Yes
## 37 No
## 38 Yes
## 39 No
## 40 No
## 41 Yes
## 42 Yes
## 43 No
## 44 No
## 45 No
## 46 No
## 47 Yes
## 48 Yes
## 49 No
## 50 No
## 51 No
## 52 No
## 53 No
## 54 No
## 55 No
## 56 No
## 57 No
## 58 No
## 59 No
## 60 Yes
## 61 Yes
## 62 No
## 63 No
## 64 No
## 65 No
## 66 No
## 67 Yes
## 68 Yes
## 69 No
## 70 No
## 71 No
## 72 Yes
## 73 No
## 74 No
## 75 No
## 76 Yes
## 77 Yes
## 78 Yes
## 79 Yes
## 80 Yes
## 81 Yes
## 82 No
## 83 Yes
## 84 No
## 85 No
## 86 No
## 87 No
## 88 Yes
## 89 No
## 90 No
## 91 Yes
## 92 Yes
## 93 No
## 94 No
## 95 No
## 96 No
## 97 Yes
## 98 No
## 99 No
## 100 Yes
## 101 No
## 102 No
## 103 Yes
## 104 Yes
## 105 Yes
## 106 No
## 107 Yes
## 108 Yes
## 109 No
## 110 No
## 111 No
## 112 Yes
## 113 No
## 114 No
## 115 No
## 116 No
## 117 No
## 118 No
## 119 No
## 120 Yes
## 121 No
## 122 No
## 123 No
## 124 Yes
## 125 NA
## 126 NA
## 127 NA
## 128 NA
## 129 No
## 130 Yes
## 131 No
## 132 Yes
## 133 No
## 134 No
## 135 No
## 136 Yes
## 137 No
## 138 No
## 139 No
## 140 Nowrite.csv(output_table, "tables/effectSize_diffFrom0_output.csv", row.names = F)try that without AG FM since that is non sig
#create new ID column without AG FM
effect$ID_noAGFM <- paste(effect$PG.SPECIES, effect$PG_Goal_moisture, effect$AG.GOAL.DRY.MASS, sep = "_")
unique(effect$ID_noAGFM)## [1] "ACTH7_35_2.5" "ACTH7_35_5" "ACTH7_55_10" "ACTH7_55_15" "ACTH7_55_2.5"
## [6] "ACTH7_55_5" "PSSP6_35_2.5" "PSSP6_35_5" "PSSP6_55_10" "PSSP6_55_2.5"
## [11] "PSSP6_55_5"# create an empty output table
output_table_noAGFM <- data.frame(Full_ID = character(), variable = character(), p_value = numeric(), sample_size = integer(), significant = character(), stringsAsFactors = FALSE)
# define the columns to loop through
cols_effectSize <- c("load_effectSize", "max_effectSize", "dur_effectSize", "Consumption_effectSize")
# loop through all unique values of Full_ID and variable
for (id in unique(effect$ID_noAGFM)) {
for (col in cols_effectSize) {
# filter the data for the current Full_ID and variable
trial_dataset <- effect %>% filter(ID_noAGFM == id) %>% select(ID_noAGFM, .data[[col]])
# check the sample size of the current dataset
sample_size <- nrow(trial_dataset)
# check if there are at least two observations
if (sample_size >= 2) {
# perform the t-test and extract the p-value
ttest_result <- t.test(trial_dataset[[2]], mu = 0, alternative = "two.sided")
p_value <- ttest_result$p.value
# determine if the p-value is significant at the 0.05 level
if (p_value < 0.05) {
significant <- "Yes"
} else {
significant <- "No"
}
# add the Full_ID, variable, p-value, sample size, and significance to the output table
output_table_noAGFM <- rbind(output_table_noAGFM, data.frame(Full_ID = id, variable = col, p_value = p_value, sample_size = sample_size, significant = significant))
} else {
# add a row with NA if there are less than two observations
output_table_noAGFM <- rbind(output_table_noAGFM, data.frame(Full_ID = id, variable = col, p_value = NA, sample_size = sample_size, significant = "NA"))
}
}
}
# print the output table
print(output_table_noAGFM)## Full_ID variable p_value sample_size significant
## 1 ACTH7_35_2.5 load_effectSize 2.100308e-01 8 No
## 2 ACTH7_35_2.5 max_effectSize 3.504323e-01 8 No
## 3 ACTH7_35_2.5 dur_effectSize 1.661714e-01 8 No
## 4 ACTH7_35_2.5 Consumption_effectSize 3.712065e-01 8 No
## 5 ACTH7_35_5 load_effectSize 1.129824e-04 7 Yes
## 6 ACTH7_35_5 max_effectSize 1.018466e-02 7 Yes
## 7 ACTH7_35_5 dur_effectSize 6.162431e-05 7 Yes
## 8 ACTH7_35_5 Consumption_effectSize 1.828926e-02 7 Yes
## 9 ACTH7_55_10 load_effectSize 2.601908e-05 16 Yes
## 10 ACTH7_55_10 max_effectSize 4.603927e-01 16 No
## 11 ACTH7_55_10 dur_effectSize 3.776972e-08 16 Yes
## 12 ACTH7_55_10 Consumption_effectSize 5.114068e-07 16 Yes
## 13 ACTH7_55_15 load_effectSize 1.301135e-03 14 Yes
## 14 ACTH7_55_15 max_effectSize 9.384052e-01 14 No
## 15 ACTH7_55_15 dur_effectSize 2.158398e-06 14 Yes
## 16 ACTH7_55_15 Consumption_effectSize 6.042400e-08 14 Yes
## 17 ACTH7_55_2.5 load_effectSize 5.831942e-03 12 Yes
## 18 ACTH7_55_2.5 max_effectSize 1.587186e-02 12 Yes
## 19 ACTH7_55_2.5 dur_effectSize 5.597125e-03 12 Yes
## 20 ACTH7_55_2.5 Consumption_effectSize 1.202494e-01 12 No
## 21 ACTH7_55_5 load_effectSize 3.817549e-04 15 Yes
## 22 ACTH7_55_5 max_effectSize 4.484418e-02 15 Yes
## 23 ACTH7_55_5 dur_effectSize 3.299839e-05 15 Yes
## 24 ACTH7_55_5 Consumption_effectSize 3.314841e-18 15 Yes
## 25 PSSP6_35_2.5 load_effectSize 7.935356e-03 8 Yes
## 26 PSSP6_35_2.5 max_effectSize 7.693314e-03 8 Yes
## 27 PSSP6_35_2.5 dur_effectSize 5.560543e-03 8 Yes
## 28 PSSP6_35_2.5 Consumption_effectSize 1.303155e-03 8 Yes
## 29 PSSP6_35_5 load_effectSize 5.965648e-02 4 No
## 30 PSSP6_35_5 max_effectSize 2.032887e-01 4 No
## 31 PSSP6_35_5 dur_effectSize 3.334566e-02 4 Yes
## 32 PSSP6_35_5 Consumption_effectSize 4.152246e-02 4 Yes
## 33 PSSP6_55_10 load_effectSize 7.779420e-04 16 Yes
## 34 PSSP6_55_10 max_effectSize 9.372142e-01 16 No
## 35 PSSP6_55_10 dur_effectSize 3.743279e-05 16 Yes
## 36 PSSP6_55_10 Consumption_effectSize 3.097020e-08 16 Yes
## 37 PSSP6_55_2.5 load_effectSize 2.900605e-01 14 No
## 38 PSSP6_55_2.5 max_effectSize 7.148576e-01 14 No
## 39 PSSP6_55_2.5 dur_effectSize 1.904810e-03 14 Yes
## 40 PSSP6_55_2.5 Consumption_effectSize 2.710397e-05 14 Yes
## 41 PSSP6_55_5 load_effectSize 3.332921e-01 12 No
## 42 PSSP6_55_5 max_effectSize 4.623547e-02 12 Yes
## 43 PSSP6_55_5 dur_effectSize 2.876348e-01 12 No
## 44 PSSP6_55_5 Consumption_effectSize 4.561912e-06 12 Yes#write.csv(output_table_noAGFM, "tables/effectSize_diffFrom0_noAGFM_output.csv", row.names = F)additional stats - linear models set up the data
#confirm data format
summary(effect)## Full_ID REP.. PG.SPECIES PG_Goal_moisture
## ACTH7_35_BRTE_2.5g_25: 4 Min. :1.000 ACTH7:72 35:27
## ACTH7_35_BRTE_2.5g_5 : 4 1st Qu.:2.000 PSSP6:54 55:99
## ACTH7_35_BRTE_5g_25 : 4 Median :2.000
## ACTH7_55_BRTE_10g_15 : 4 Mean :2.492
## ACTH7_55_BRTE_10g_25 : 4 3rd Qu.:3.000
## ACTH7_55_BRTE_10g_35 : 4 Max. :4.000
## (Other) :102
## AG.dry.weight..g. AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. PG.WET.ACTUAL
## Min. : 2.370 2.5:42 5 :40 Min. :25.97
## 1st Qu.: 2.610 5 :38 15:24 1st Qu.:29.69
## Median : 5.135 10 :32 25:38 Median :30.86
## Mean : 6.651 15 :14 35:24 Mean :29.96
## 3rd Qu.:10.010 3rd Qu.:30.95
## Max. :15.450 Max. :32.20
##
## AG.WET.ACTUAL PG.MOISTURE AG.MOISTURE dur_obs
## Min. : 2.610 Min. :29.53 Min. :-16.250 Min. : 25.75
## 1st Qu.: 3.280 1st Qu.:48.34 1st Qu.: 8.925 1st Qu.: 66.00
## Median : 6.180 Median :54.26 Median : 17.745 Median : 82.25
## Mean : 7.923 Mean :49.68 Mean : 19.337 Mean : 83.74
## 3rd Qu.:11.585 3rd Qu.:54.67 3rd Qu.: 27.483 3rd Qu.:101.38
## Max. :20.800 Max. :61.00 Max. : 53.910 Max. :168.25
##
## max_obs load_obs consumption_obs dur_exp
## Min. : 118.3 Min. : 6722 Min. :13.87 Min. :47.89
## 1st Qu.: 377.1 1st Qu.: 32228 1st Qu.:72.50 1st Qu.:50.60
## Median : 605.5 Median : 42539 Median :81.41 Median :53.67
## Mean : 589.0 Mean : 42884 Mean :78.72 Mean :58.44
## 3rd Qu.: 789.3 3rd Qu.: 53985 3rd Qu.:89.94 3rd Qu.:71.00
## Max. :1302.7 Max. :138934 Max. :94.10 Max. :72.56
##
## max_exp load_exp consumption_exp dur_effectSize
## Min. :371.5 Min. :19736 Min. :64.94 Min. :-0.6418
## 1st Qu.:446.2 1st Qu.:23096 1st Qu.:69.75 1st Qu.: 0.1083
## Median :552.1 Median :28668 Median :77.28 Median : 0.4656
## Mean :532.5 Mean :28160 Mean :80.23 Mean : 0.4770
## 3rd Qu.:620.6 3rd Qu.:34297 3rd Qu.:92.56 3rd Qu.: 0.8432
## Max. :683.1 Max. :36450 Max. :93.67 Max. : 2.1575
##
## max_effectSize load_effectSize Consumption_effectSize ID_noAGFM
## Min. :-0.8015 Min. :-0.80612 Min. :-0.78661 Length:126
## 1st Qu.:-0.2376 1st Qu.: 0.02816 1st Qu.:-0.14979 Class :character
## Median : 0.1883 Median : 0.50662 Median :-0.02615 Mode :character
## Mean : 0.1260 Mean : 0.59157 Mean : 0.00135
## 3rd Qu.: 0.4096 3rd Qu.: 1.03111 3rd Qu.: 0.21105
## Max. : 1.2734 Max. : 4.59185 Max. : 0.33652
## #transform AG goal moisture to continuous
effect$AG.GOAL.MOISTURE._cont <- as.numeric(effect$AG.GOAL.MOISTURE.)
#transform AG mass to continuous
effect$AG.GOAL.DRY.MASS_cont <- as.numeric(effect$AG.GOAL.DRY.MASS)
# define the columns to loop through
cols_effectSize <- c("load_effectSize", "max_effectSize", "dur_effectSize", "Consumption_effectSize")heat load
lm_effect_load <- lm(load_effectSize ~ PG.SPECIES + PG_Goal_moisture + AG.GOAL.MOISTURE._cont + AG.GOAL.DRY.MASS_cont, data = effect)
summary(lm_effect_load)##
## Call:
## lm(formula = load_effectSize ~ PG.SPECIES + PG_Goal_moisture +
## AG.GOAL.MOISTURE._cont + AG.GOAL.DRY.MASS_cont, data = effect)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6445 -0.4285 0.0034 0.3968 3.3117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.68868 0.20859 3.302 0.001264 **
## PG.SPECIESPSSP6 -0.59351 0.12491 -4.751 5.61e-06 ***
## PG_Goal_moisture55 -0.31398 0.16347 -1.921 0.057128 .
## AG.GOAL.MOISTURE._cont -0.04432 0.05537 -0.801 0.424954
## AG.GOAL.DRY.MASS_cont 0.23743 0.06665 3.562 0.000527 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6752 on 121 degrees of freedom
## Multiple R-squared: 0.2804, Adjusted R-squared: 0.2566
## F-statistic: 11.79 on 4 and 121 DF, p-value: 4.048e-08# AG moisture is not sig, so lets drop it
lm_effect_load <- lm(load_effectSize ~ PG.SPECIES*PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
data = effect)
summary(lm_effect_load)##
## Call:
## lm(formula = load_effectSize ~ PG.SPECIES * PG_Goal_moisture +
## AG.GOAL.DRY.MASS_cont, data = effect)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5027 -0.4040 -0.0210 0.3951 3.3275
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37435 0.19570 1.913 0.058123 .
## PG.SPECIESPSSP6 -0.04274 0.25610 -0.167 0.867747
## PG_Goal_moisture55 -0.01942 0.20468 -0.095 0.924556
## AG.GOAL.DRY.MASS_cont 0.22735 0.06533 3.480 0.000697 ***
## PG.SPECIESPSSP6:PG_Goal_moisture55 -0.70916 0.29016 -2.444 0.015966 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6609 on 121 degrees of freedom
## Multiple R-squared: 0.3107, Adjusted R-squared: 0.2879
## F-statistic: 13.63 on 4 and 121 DF, p-value: 3.327e-09#only needlegrass
lm_effect_acth_load <- lm(load_effectSize ~ PG_Goal_moisture +AG.GOAL.DRY.MASS_cont, data = (effect %>% filter(PG.SPECIES == "ACTH7")))
summary(lm_effect_acth_load)##
## Call:
## lm(formula = load_effectSize ~ PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
## data = (effect %>% filter(PG.SPECIES == "ACTH7")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4815 -0.5416 -0.0059 0.5300 3.3488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.396010 0.248557 1.593 0.116
## PG_Goal_moisture55 -0.003256 0.253466 -0.013 0.990
## AG.GOAL.DRY.MASS_cont 0.212586 0.095301 2.231 0.029 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.796 on 69 degrees of freedom
## Multiple R-squared: 0.07954, Adjusted R-squared: 0.05286
## F-statistic: 2.981 on 2 and 69 DF, p-value: 0.0573#PG FM is non sig
#AG mass is sig
#only bluebunch
lm_effect_pssp_load <- lm(load_effectSize ~
PG_Goal_moisture +AG.GOAL.DRY.MASS_cont,
data = (effect %>% filter(PG.SPECIES == "PSSP6")))
summary(lm_effect_pssp_load)##
## Call:
## lm(formula = load_effectSize ~ PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
## data = (effect %>% filter(PG.SPECIES == "PSSP6")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.83231 -0.26534 -0.04958 0.22276 1.15435
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.28944 0.15668 1.847 0.070498 .
## PG_Goal_moisture55 -0.75118 0.14785 -5.081 5.4e-06 ***
## AG.GOAL.DRY.MASS_cont 0.25899 0.07392 3.503 0.000965 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4219 on 51 degrees of freedom
## Multiple R-squared: 0.3632, Adjusted R-squared: 0.3382
## F-statistic: 14.54 on 2 and 51 DF, p-value: 1.006e-05#PG FM is sig
#AG mass is sigmax temp
lm_effect <- lm(max_effectSize ~ PG.SPECIES + PG_Goal_moisture + AG.GOAL.MOISTURE._cont + AG.GOAL.DRY.MASS_cont,
data = effect)
summary(lm_effect)##
## Call:
## lm(formula = max_effectSize ~ PG.SPECIES + PG_Goal_moisture +
## AG.GOAL.MOISTURE._cont + AG.GOAL.DRY.MASS_cont, data = effect)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.16953 -0.37191 0.03289 0.31313 1.11827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.48265 0.14728 3.277 0.00137 **
## PG.SPECIESPSSP6 -0.26121 0.08820 -2.962 0.00368 **
## PG_Goal_moisture55 -0.23162 0.11542 -2.007 0.04701 *
## AG.GOAL.MOISTURE._cont 0.02590 0.03909 0.663 0.50888
## AG.GOAL.DRY.MASS_cont -0.05787 0.04706 -1.230 0.22120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4767 on 121 degrees of freedom
## Multiple R-squared: 0.1178, Adjusted R-squared: 0.08866
## F-statistic: 4.04 on 4 and 121 DF, p-value: 0.004131# AG moisture is not sig, so lets drop it
# ALSO! AG mass is non sig
#only needlegrass
lm_effect_acth <- lm(max_effectSize ~
PG_Goal_moisture +AG.GOAL.DRY.MASS_cont,
data = (effect %>% filter(PG.SPECIES == "ACTH7")))
summary(lm_effect_acth)##
## Call:
## lm(formula = max_effectSize ~ PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
## data = (effect %>% filter(PG.SPECIES == "ACTH7")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.13224 -0.38782 -0.00744 0.35533 1.13446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.53058 0.15921 3.333 0.00139 **
## PG_Goal_moisture55 -0.04007 0.16235 -0.247 0.80580
## AG.GOAL.DRY.MASS_cont -0.11719 0.06104 -1.920 0.05901 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5099 on 69 degrees of freedom
## Multiple R-squared: 0.06732, Adjusted R-squared: 0.04029
## F-statistic: 2.49 on 2 and 69 DF, p-value: 0.09031#PG FM is non sig
#AG mass is non sig
#only bluebunch
lm_effect_pssp<- lm(max_effectSize ~
PG_Goal_moisture +AG.GOAL.DRY.MASS_cont,
data = (effect %>% filter(PG.SPECIES == "PSSP6")))
summary(lm_effect_pssp)##
## Call:
## lm(formula = max_effectSize ~ PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
## data = (effect %>% filter(PG.SPECIES == "PSSP6")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.70405 -0.32639 0.05982 0.30141 0.85625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.24031 0.15362 1.564 0.1239
## PG_Goal_moisture55 -0.43204 0.14496 -2.980 0.0044 **
## AG.GOAL.DRY.MASS_cont 0.04716 0.07248 0.651 0.5181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4137 on 51 degrees of freedom
## Multiple R-squared: 0.1511, Adjusted R-squared: 0.1178
## F-statistic: 4.54 on 2 and 51 DF, p-value: 0.01533#PG FM is sig
#AG mass is non sigduration
lm_effect <- lm(dur_effectSize ~ PG.SPECIES + PG_Goal_moisture + AG.GOAL.MOISTURE._cont + AG.GOAL.DRY.MASS_cont,
data = effect)
summary(lm_effect)##
## Call:
## lm(formula = dur_effectSize ~ PG.SPECIES + PG_Goal_moisture +
## AG.GOAL.MOISTURE._cont + AG.GOAL.DRY.MASS_cont, data = effect)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.12612 -0.16802 -0.01798 0.24357 0.98655
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.40465 0.11572 3.497 0.000659 ***
## PG.SPECIESPSSP6 -0.43330 0.06930 -6.253 6.29e-09 ***
## PG_Goal_moisture55 -0.32216 0.09069 -3.552 0.000545 ***
## AG.GOAL.MOISTURE._cont -0.03932 0.03072 -1.280 0.202947
## AG.GOAL.DRY.MASS_cont 0.28195 0.03698 7.625 6.12e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3746 on 121 degrees of freedom
## Multiple R-squared: 0.5181, Adjusted R-squared: 0.5021
## F-statistic: 32.52 on 4 and 121 DF, p-value: < 2.2e-16# AG moisture is not sig, so lets drop it
lm_effect <- lm(dur_effectSize ~ PG.SPECIES + PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
data = effect)
summary(lm_effect)##
## Call:
## lm(formula = dur_effectSize ~ PG.SPECIES + PG_Goal_moisture +
## AG.GOAL.DRY.MASS_cont, data = effect)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.11909 -0.17555 -0.00974 0.23955 1.04033
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.32559 0.09811 3.319 0.001193 **
## PG.SPECIESPSSP6 -0.43261 0.06948 -6.227 7.0e-09 ***
## PG_Goal_moisture55 -0.34897 0.08847 -3.944 0.000134 ***
## AG.GOAL.DRY.MASS_cont 0.28514 0.03699 7.709 3.8e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3756 on 122 degrees of freedom
## Multiple R-squared: 0.5116, Adjusted R-squared: 0.4995
## F-statistic: 42.59 on 3 and 122 DF, p-value: < 2.2e-16#only needlegrass
lm_effect_acth <- lm(dur_effectSize ~
PG_Goal_moisture +AG.GOAL.DRY.MASS_cont,
data = (effect %>% filter(PG.SPECIES == "ACTH7")))
summary(lm_effect_acth)##
## Call:
## lm(formula = dur_effectSize ~ PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
## data = (effect %>% filter(PG.SPECIES == "ACTH7")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.14936 -0.19016 0.03555 0.27885 1.01007
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15150 0.13102 1.156 0.252
## PG_Goal_moisture55 -0.08555 0.13361 -0.640 0.524
## AG.GOAL.DRY.MASS_cont 0.27037 0.05023 5.382 9.55e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4196 on 69 degrees of freedom
## Multiple R-squared: 0.3165, Adjusted R-squared: 0.2967
## F-statistic: 15.98 on 2 and 69 DF, p-value: 1.986e-06#PG FM is non sig
#AG mass is non sig
#only bluebunch
lm_effect_pssp<- lm(dur_effectSize ~
PG_Goal_moisture +AG.GOAL.DRY.MASS_cont,
data = (effect %>% filter(PG.SPECIES == "PSSP6")))
summary(lm_effect_pssp)##
## Call:
## lm(formula = dur_effectSize ~ PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
## data = (effect %>% filter(PG.SPECIES == "PSSP6")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.59436 -0.18673 0.01677 0.13611 0.65329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1412 0.0958 1.474 0.147
## PG_Goal_moisture55 -0.6616 0.0904 -7.319 1.71e-09 ***
## AG.GOAL.DRY.MASS_cont 0.2825 0.0452 6.249 8.33e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.258 on 51 degrees of freedom
## Multiple R-squared: 0.574, Adjusted R-squared: 0.5573
## F-statistic: 34.36 on 2 and 51 DF, p-value: 3.552e-10#PG FM is sig
#AG mass is non sigconsumption
lm_effect <- lm(Consumption_effectSize ~ PG.SPECIES + PG_Goal_moisture + AG.GOAL.MOISTURE._cont + AG.GOAL.DRY.MASS_cont,
data = effect)
summary(lm_effect)##
## Call:
## lm(formula = Consumption_effectSize ~ PG.SPECIES + PG_Goal_moisture +
## AG.GOAL.MOISTURE._cont + AG.GOAL.DRY.MASS_cont, data = effect)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85869 -0.04302 0.02488 0.11278 0.20696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.034716 0.053909 0.644 0.5208
## PG.SPECIESPSSP6 -0.318231 0.032283 -9.858 <2e-16 ***
## PG_Goal_moisture55 0.031496 0.042249 0.745 0.4574
## AG.GOAL.MOISTURE._cont -0.008039 0.014309 -0.562 0.5753
## AG.GOAL.DRY.MASS_cont 0.045400 0.017225 2.636 0.0095 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1745 on 121 degrees of freedom
## Multiple R-squared: 0.5125, Adjusted R-squared: 0.4963
## F-statistic: 31.8 on 4 and 121 DF, p-value: < 2.2e-16# AG moisture is not sig, so lets drop it
lm_effect <- lm(Consumption_effectSize ~ PG.SPECIES + PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
data = effect)
summary(lm_effect)##
## Call:
## lm(formula = Consumption_effectSize ~ PG.SPECIES + PG_Goal_moisture +
## AG.GOAL.DRY.MASS_cont, data = effect)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85122 -0.04352 0.02228 0.11766 0.19985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01855 0.04546 0.408 0.68396
## PG.SPECIESPSSP6 -0.31809 0.03219 -9.881 < 2e-16 ***
## PG_Goal_moisture55 0.02602 0.04099 0.635 0.52684
## AG.GOAL.DRY.MASS_cont 0.04605 0.01714 2.687 0.00821 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.174 on 122 degrees of freedom
## Multiple R-squared: 0.5112, Adjusted R-squared: 0.4992
## F-statistic: 42.53 on 3 and 122 DF, p-value: < 2.2e-16#only needlegrass
lm_effect_acth <- lm(Consumption_effectSize ~
PG_Goal_moisture +AG.GOAL.DRY.MASS_cont,
data = (effect %>% filter(PG.SPECIES == "ACTH7")))
summary(lm_effect_acth)##
## Call:
## lm(formula = Consumption_effectSize ~ PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
## data = (effect %>% filter(PG.SPECIES == "ACTH7")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.74997 -0.04351 0.02548 0.12535 0.30490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06670 0.06051 -1.102 0.27418
## PG_Goal_moisture55 0.18086 0.06170 2.931 0.00458 **
## AG.GOAL.DRY.MASS_cont 0.03005 0.02320 1.295 0.19954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1938 on 69 degrees of freedom
## Multiple R-squared: 0.1894, Adjusted R-squared: 0.166
## F-statistic: 8.064 on 2 and 69 DF, p-value: 0.0007127#PG FM is sig
#AG mass is non sig
#only bluebunch
lm_effect_pssp<- lm(Consumption_effectSize ~
PG_Goal_moisture +AG.GOAL.DRY.MASS_cont,
data = (effect %>% filter(PG.SPECIES == "PSSP6")))
summary(lm_effect_pssp)##
## Call:
## lm(formula = Consumption_effectSize ~ PG_Goal_moisture + AG.GOAL.DRY.MASS_cont,
## data = (effect %>% filter(PG.SPECIES == "PSSP6")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39033 -0.04047 0.02444 0.05759 0.18195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.18396 0.03922 -4.690 2.08e-05 ***
## PG_Goal_moisture55 -0.15958 0.03701 -4.312 7.42e-05 ***
## AG.GOAL.DRY.MASS_cont 0.06128 0.01851 3.311 0.00171 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1056 on 51 degrees of freedom
## Multiple R-squared: 0.3032, Adjusted R-squared: 0.2758
## F-statistic: 11.09 on 2 and 51 DF, p-value: 9.997e-05#PG FM is sig
#AG mass is sigduration
#graph it
summary(effect$dur_effectSize)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.6418 0.1083 0.4656 0.4770 0.8432 2.1575acth55 <- ggplot(effect %>% filter(PG.SPECIES == "ACTH7" & PG_Goal_moisture == 55),
aes(x = AG.GOAL.DRY.MASS, y= dur_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth55_effect_colors) +
ylim(-1,2.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(fill = "", x="AG mass (g)",
y="Effect size (duration)",)+
theme(legend.position = "none")
acth55
pssp55 <- ggplot(effect %>% filter(PG.SPECIES == "PSSP6" & PG_Goal_moisture == 55),
aes(x = AG.GOAL.DRY.MASS, y= dur_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp55_effect_colors) +
ylim(-1,2.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(y="Effect size", x="AG mass (g)")+
theme(legend.position = "none")
pssp55
effect_dur_55 <- ggarrange(acth55,
pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
effect_dur_55
ggsave('figures/additive/pg_55/dur55.png', effect_dur_55, height = 3, width = 5,bg='transparent')
acth35 <- ggplot(effect %>% filter(PG.SPECIES == "ACTH7" & PG_Goal_moisture == 35),
aes(x = AG.GOAL.DRY.MASS, y= dur_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth55_effect_colors) +
ylim(-1,2)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(fill = "",y="Effect size (duration)", x="AG mass (g)")+
theme(legend.position = "none")
acth35
pssp35 <- ggplot(effect %>% filter(PG.SPECIES == "PSSP6" & PG_Goal_moisture == 35),
aes(x = AG.GOAL.DRY.MASS, y= dur_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp55_effect_colors) +
ylim(-1,2)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(y="Effect size", x="AG mass (g)")+
theme(legend.position = "none")
pssp35
effect_dur_35 <-
ggarrange(acth35,
pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
effect_dur_35
ggsave('figures/additive/pg_35/dur35.png', effect_dur_35, height = 3, width = 5,bg='transparent')head load Effect size
#first up, we should see if either AG.goal moisutre or dry mass are significant
#test for normality of the dur_effectSize
result = bartlett.test(load_effectSize~AG.GOAL.MOISTURE., effect)
print(result)##
## Bartlett test of homogeneity of variances
##
## data: load_effectSize by AG.GOAL.MOISTURE.
## Bartlett's K-squared = 10.449, df = 3, p-value = 0.01511result = bartlett.test(load_effectSize~AG.GOAL.DRY.MASS, effect)
print(result)##
## Bartlett test of homogeneity of variances
##
## data: load_effectSize by AG.GOAL.DRY.MASS
## Bartlett's K-squared = 11.653, df = 3, p-value = 0.00867# NOT normally distributed
#now test for differences in Effect size by ag mass and moisture
kruskal.test(load_effectSize ~ AG.GOAL.DRY.MASS, data = effect)##
## Kruskal-Wallis rank sum test
##
## data: load_effectSize by AG.GOAL.DRY.MASS
## Kruskal-Wallis chi-squared = 10.739, df = 3, p-value = 0.01322# p 0.01322
# yes there are differences by ag mass
kruskal.test(load_effectSize ~ AG.GOAL.MOISTURE., data = effect)##
## Kruskal-Wallis rank sum test
##
## data: load_effectSize by AG.GOAL.MOISTURE.
## Kruskal-Wallis chi-squared = 4.546, df = 3, p-value = 0.2082# p 0.2082
# no diff by ag moisture#graph it
summary(effect$load_effectSize)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.80612 0.02816 0.50662 0.59157 1.03111 4.59185acth55 <- ggplot(effect %>% filter(PG.SPECIES == "ACTH7" & PG_Goal_moisture == 55),
aes(x = AG.GOAL.DRY.MASS, y= load_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth55_effect_colors) +
ylim(-1,2.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(fill = "",y="Effect size (thermal dose)", x="AG mass (g)")+
theme(legend.position = "none")
acth55## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
#outlier ACTH7 55 at 4.59 for AG mass 15 g
pssp55 <- ggplot(effect %>% filter(PG.SPECIES == "PSSP6" & PG_Goal_moisture == 55),
aes(x = AG.GOAL.DRY.MASS, y= load_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp55_effect_colors) +
ylim(-1,2.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(y="Effect size", x="AG mass (g)")+
theme(legend.position = "none")
pssp55
effect_load_55 <-
ggarrange(acth55, pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).effect_load_55
ggsave('figures/additive/pg_55/load55.png', effect_load_55, height = 3, width = 5,bg='transparent')
acth35 <- ggplot(effect %>% filter(PG.SPECIES == "ACTH7" & PG_Goal_moisture == 35),
aes(x = AG.GOAL.DRY.MASS, y= load_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth55_effect_colors) +
ylim(-1,2)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(fill = "",y="Effect size (thermal dose)", x="AG mass (g)")+
theme(legend.position = "none")
acth35
pssp35 <- ggplot(effect %>% filter(PG.SPECIES == "PSSP6" & PG_Goal_moisture == 35),
aes(x = AG.GOAL.DRY.MASS, y= load_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp55_effect_colors) +
ylim(-1,2)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(y="Effect size", x="AG mass (g)")+
theme(legend.position = "none")
pssp35
effect_load_35 <-
ggarrange(acth35, pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
effect_load_35
ggsave('figures/additive/pg_35/load35.png', effect_load_35, height = 3, width = 5,bg='transparent')outlier ACTH7 55 at 4.59 for AG mass 15 g
max temp Effect size
#first up, we should see if either AG.goal moisutre or dry mass are significant
#test for normality of the dur_effectSize
result = bartlett.test(max_effectSize~AG.GOAL.MOISTURE., effect)
print(result)##
## Bartlett test of homogeneity of variances
##
## data: max_effectSize by AG.GOAL.MOISTURE.
## Bartlett's K-squared = 0.95333, df = 3, p-value = 0.8125result = bartlett.test(max_effectSize~AG.GOAL.DRY.MASS, effect)
print(result)##
## Bartlett test of homogeneity of variances
##
## data: max_effectSize by AG.GOAL.DRY.MASS
## Bartlett's K-squared = 1.5068, df = 3, p-value = 0.6807# YES normally distributed
#now test for differences in Effect size by ag mass and moisture
aov1 <- aov(max_effectSize ~ AG.GOAL.DRY.MASS+ AG.GOAL.MOISTURE., data = effect)
summary(aov1) #no interaction## Df Sum Sq Mean Sq F value Pr(>F)
## AG.GOAL.DRY.MASS 3 0.568 0.1892 0.754 0.522
## AG.GOAL.MOISTURE. 3 0.750 0.2500 0.997 0.397
## Residuals 119 29.857 0.2509#neither ag mass nor moisture is sig#graph it
acth55 <- ggplot(effect %>% filter(PG.SPECIES == "ACTH7" & PG_Goal_moisture == 55),
aes(x = AG.GOAL.DRY.MASS, y= max_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth55_effect_colors) +
ylim(-1,2.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(fill = "",y="Effect size (max temp.)", x="AG mass (g)")+
theme(legend.position = "none")
acth55
pssp55 <- ggplot(effect %>% filter(PG.SPECIES == "PSSP6" & PG_Goal_moisture == 55),
aes(x = AG.GOAL.DRY.MASS, y= max_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp55_effect_colors) +
ylim(-1,2.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(y="Effect size", x="AG mass (g)")+
theme(legend.position = "none")
pssp55
effect_max_55 <-
ggarrange(acth55, pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
effect_max_55
ggsave('figures/additive/pg_55/max55.png', effect_max_55, height = 3, width = 5,bg='transparent')
acth35 <- ggplot(effect %>% filter(PG.SPECIES == "ACTH7" & PG_Goal_moisture == 35),
aes(x = AG.GOAL.DRY.MASS, y= max_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth55_effect_colors) +
ylim(-1,2)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(fill = "",y="Effect size (max temp.)", x="AG mass (g)")+
theme(legend.position = "none")
acth35
pssp35 <- ggplot(effect %>% filter(PG.SPECIES == "PSSP6" & PG_Goal_moisture == 35),
aes(x = AG.GOAL.DRY.MASS, y= max_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp55_effect_colors) +
ylim(-1,2)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(y="Effect size", x="AG mass (g)")+
theme(legend.position = "none")
pssp35
effect_max_35 <-
ggarrange(acth35, pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
effect_max_35
ggsave('figures/additive/pg_35/max35.png', effect_max_35, height = 3, width = 5,bg='transparent')consumption
#first up, we should see if either AG.goal moisutre or dry mass are significant
#test for normality of the dur_effectSize
result = bartlett.test(Consumption_effectSize~AG.GOAL.MOISTURE., effect)
print(result)##
## Bartlett test of homogeneity of variances
##
## data: Consumption_effectSize by AG.GOAL.MOISTURE.
## Bartlett's K-squared = 2.0112, df = 3, p-value = 0.5701result = bartlett.test(Consumption_effectSize~AG.GOAL.DRY.MASS, effect)
print(result)##
## Bartlett test of homogeneity of variances
##
## data: Consumption_effectSize by AG.GOAL.DRY.MASS
## Bartlett's K-squared = 27.719, df = 3, p-value = 4.16e-06# YES normally distributed
#now test for differences in Effect size by ag mass and moisture
aov1 <- aov(Consumption_effectSize ~ AG.GOAL.DRY.MASS+ AG.GOAL.MOISTURE., data = effect)
summary(aov1) #no interaction## Df Sum Sq Mean Sq F value Pr(>F)
## AG.GOAL.DRY.MASS 3 1.160 0.3866 7.314 0.000153 ***
## AG.GOAL.MOISTURE. 3 0.108 0.0359 0.679 0.566819
## Residuals 119 6.290 0.0529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#ag mass is important#graph it
acth55 <- ggplot(effect %>% filter(PG.SPECIES == "ACTH7" & PG_Goal_moisture == 55),
aes(x = AG.GOAL.DRY.MASS, y= Consumption_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth55_effect_colors) +
ylim(-1,2.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(fill = "",y="Effect size (consumption)", x="AG mass (g)")+
theme(legend.position = "none")
acth55
pssp55 <- ggplot(effect %>% filter(PG.SPECIES == "PSSP6" & PG_Goal_moisture == 55),
aes(x = AG.GOAL.DRY.MASS, y= Consumption_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp55_effect_colors) +
ylim(-1,2.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(y="Effect size", x="AG mass (g)")+
theme(legend.position = "none")
pssp55
effect_mc_55 <-
ggarrange(acth55, pssp55+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
effect_mc_55
ggsave('figures/additive/pg_55/cons55.png', effect_mc_55, height = 3, width = 5,bg='transparent')
acth35 <- ggplot(effect %>% filter(PG.SPECIES == "ACTH7" & PG_Goal_moisture == 35),
aes(x = AG.GOAL.DRY.MASS, y= Consumption_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = acth55_effect_colors) +
ylim(-1,2)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(fill = "",y="Effect size (consumption)", x="AG mass (g)")+
theme(legend.position = "none")
acth35
pssp35 <- ggplot(effect %>% filter(PG.SPECIES == "PSSP6" & PG_Goal_moisture == 35),
aes(x = AG.GOAL.DRY.MASS, y= Consumption_effectSize, fill = AG.GOAL.DRY.MASS))+
geom_boxplot()+
scale_fill_manual(values = pssp55_effect_colors) +
ylim(-1,2)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(y="Effect size", x="AG mass (g)")+
theme(legend.position = "none")
pssp35
effect_mc_35 <-
ggarrange(acth35, pssp35+theme(axis.title.y = element_blank()),
ncol = 2, nrow =1)
effect_mc_35
ggsave('figures/additive/pg_35/cons35.png', effect_mc_35, height = 3, width = 5,bg='transparent')Combine all the 55 % fm into one
effect_55 <-
ggarrange(effect_mc_55, effect_dur_55,
effect_max_55,
effect_load_55,
ncol = 1, nrow =4)
effect_55
ggsave('figures/additive/effect_55.png', effect_55, height = 15, width = 5,bg='transparent')
combine_35 <-
ggarrange(effect_mc_35, effect_dur_35,
effect_max_35,
effect_load_35,
ncol = 1, nrow =4)
combine_35
ggsave('figures/additive/effect_35.png', combine_35, height = 15, width = 5,bg='transparent')aov1 <- aov(max ~ SPECIES + goal_moisture_percent + SPECIES*goal_moisture_percent, data = one_max_condensed) summary(aov1)#interaction was non sign, remove for post hoc aov2 <- aov(max ~ SPECIES + goal_moisture_percent, data = one_max_condensed) summary(aov2) TukeyHSD(aov2)
#save plot ggsave <- g1 ggsave(‘figures/indiv_species_maxTemp.png’, ggsave, width = 5, height = 4, bg=‘transparent’)
head(effect)## Full_ID REP.. PG.SPECIES PG_Goal_moisture AG.dry.weight..g.
## 1 ACTH7_35_BRTE_2.5g_25 2 ACTH7 35 2.50
## 2 ACTH7_35_BRTE_2.5g_25 3 ACTH7 35 2.51
## 3 ACTH7_35_BRTE_2.5g_25 1 ACTH7 35 2.62
## 4 ACTH7_35_BRTE_2.5g_25 4 ACTH7 35 2.52
## 5 ACTH7_35_BRTE_2.5g_5 2 ACTH7 35 2.63
## 6 ACTH7_35_BRTE_2.5g_5 3 ACTH7 35 2.66
## AG.GOAL.DRY.MASS AG.GOAL.MOISTURE. PG.WET.ACTUAL AG.WET.ACTUAL PG.MOISTURE
## 1 2.5 25 26.58 3.05 34.24
## 2 2.5 25 26.67 3.32 34.97
## 3 2.5 25 26.93 3.28 34.72
## 4 2.5 25 26.70 3.37 34.37
## 5 2.5 5 27.47 2.82 35.19
## 6 2.5 5 26.85 2.87 35.61
## AG.MOISTURE dur_obs max_obs load_obs consumption_obs dur_exp max_exp
## 1 22.00 33.0 461.460 15190.560 27.57 50.55556 540.2333
## 2 32.27 39.5 220.640 13005.610 15.67 50.55556 540.2333
## 3 25.19 103.5 1059.485 72057.110 70.74 50.55556 540.2333
## 4 33.73 104.5 657.335 53506.100 82.01 50.55556 540.2333
## 5 7.22 26.0 157.375 6801.395 13.87 49.88889 559.7683
## 6 7.89 64.5 789.495 42510.425 78.77 49.88889 559.7683
## load_exp consumption_exp dur_effectSize max_effectSize load_effectSize
## 1 28553.34 64.93514 -0.3472527 -0.1458135 -0.4679936
## 2 28553.34 64.93514 -0.2186813 -0.5915839 -0.5445153
## 3 28553.34 64.93514 1.0472527 0.9611618 1.5235963
## 4 28553.34 64.93514 1.0670330 0.2167613 0.8738997
## 5 28667.74 64.99968 -0.4788419 -0.7188569 -0.7627509
## 6 28667.74 64.99968 0.2928731 0.4103960 0.4828664
## Consumption_effectSize ID_noAGFM AG.GOAL.MOISTURE._cont
## 1 -0.57542249 ACTH7_35_2.5 3
## 2 -0.75868228 ACTH7_35_2.5 3
## 3 0.08939474 ACTH7_35_2.5 3
## 4 0.26295254 ACTH7_35_2.5 3
## 5 -0.78661432 ACTH7_35_2.5 1
## 6 0.21185218 ACTH7_35_2.5 1
## AG.GOAL.DRY.MASS_cont
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1summary(effect$load_effectSize)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.80612 0.02816 0.50662 0.59157 1.03111 4.59185g1 <- ggplot(effect %>% filter(PG_Goal_moisture == 55),
aes(x = AG.GOAL.MOISTURE., y= load_effectSize))+
geom_boxplot()+
facet_grid(~PG.SPECIES)+
geom_jitter(width=.15)+
ylim(-1,5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(title = "Thermal dose", subtitle = "only PG 55% FM", y="Effect size", x="AG moisture")
g1
summary(effect$max_effectSize)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.8015 -0.2376 0.1883 0.1260 0.4096 1.2734g1 <- ggplot(effect %>% filter(PG_Goal_moisture == 55),
aes(x = AG.GOAL.MOISTURE., y= max_effectSize))+
geom_boxplot()+
facet_grid(~PG.SPECIES)+
geom_jitter(width=.15)+
ylim(-1,1.5)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(title = "Maximum temperature", subtitle = "only PG 55% FM", y="Effect size", x="AG moisture")
g1
summary(effect$dur_effectSize)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.6418 0.1083 0.4656 0.4770 0.8432 2.1575g1 <- ggplot(effect %>% filter(PG_Goal_moisture == 55),
aes(x = AG.GOAL.MOISTURE., y= dur_effectSize))+
geom_boxplot()+
facet_grid(~PG.SPECIES)+
geom_jitter(width=.15)+
ylim(-1,3)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(title = "Flaming duration", subtitle = "only PG 55% FM", y="Effect size", x="AG moisture")
g1
summary(effect$Consumption_effectSize)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.78661 -0.14979 -0.02615 0.00135 0.21105 0.33652g1 <- ggplot(effect %>% filter(PG_Goal_moisture == 55),
aes(x = AG.GOAL.MOISTURE., y= Consumption_effectSize))+
geom_boxplot()+
facet_grid(~PG.SPECIES)+ylim(-1,1)+
geom_jitter(width=.15)+geom_hline(yintercept=0, linetype="dashed", color="black")+
labs(title = "Consumption", subtitle = "only PG 55% FM", y="Effect size", x="AG moisture")
g1