## ggplot2 RColorBrewer grid gridExtra plyr
## TRUE TRUE TRUE TRUE TRUE
## lme4 lsmeans knitr tidyr dplyr
## TRUE TRUE TRUE TRUE TRUE
## MASS magrittr reshape2 FactoMineR cowplot
## TRUE TRUE TRUE TRUE TRUE
Processing the data for graphical exploration and analysis.
#Reading the file
dry_weight <- read.csv(file = "../data/clean/dry_weight_allsets.csv")
dry_weight$Species <- trim(dry_weight$Species)
#Calculate root:shoot ratio
dry_weight$r_s <- with(dry_weight, Weight.per.root/Weight.per.shoot)
#Summarizing data using different parameters by plyr library
dryw_sum <- ddply(dry_weight, c("Species"), summarise,
N = length(Weight.per.root),
mean_wpr = mean(Weight.per.root),
sd_wpr = sd(Weight.per.root),
se_wpr = sd_wpr/sqrt(N),
mean_wps = mean(Weight.per.shoot),
sd_wps = sd(Weight.per.shoot),
se_wps = sd_wps/sqrt(N),
mean_rs = mean(r_s),
sd_rs = sd(r_s),
se_rs = sd_rs/sqrt(N)
)
#Setting limits for error bars
wpr_limits <- aes(ymax = mean_wpr + se_wpr, ymin=mean_wpr - se_wpr)
wps_limits <- aes(ymax = mean_wps + se_wps, ymin=mean_wps - se_wps)
rs_limits <- aes(ymax = mean_rs + se_rs, ymin=mean_rs - se_rs)
#melt data into long format
dryw_sum.t1 <- melt(dryw_sum, measure.vars = c("mean_wpr","mean_wps","mean_rs")) %>%
subset(select = c(Species, variable,value))
dryw_sum.t2 <- melt(dryw_sum, measure.vars = c("se_wpr","se_wps","se_rs")) %>%
subset(select = c(Species, variable,value)) %>%
dplyr::rename(Spp=Species, SE_var=variable, SE_val=value)
dryw_sum.t <- cbind(dryw_sum.t1, dryw_sum.t2)
Plots for dry weight per root, dry weight per shoot and root:shoot ratio.
plot_wpr <- ggplot(dryw_sum, aes(x = reorder(Species, mean_wpr, median), y = mean_wpr)) +
geom_point(stat = "summary", fun.y = "mean", size = 3, col="#d8b365") +
geom_errorbar(wpr_limits, width=0.2, col="#d8b365") + theme_gray() +
theme(axis.text.x=element_text(angle=90, hjust = 1, vjust = 0.5, face="italic"),
axis.text.y=element_text(angle=90, hjust = 0.5),
axis.title.x = element_text(angle=180),
plot.margin=unit(c(1,1,1,1), "mm")) +
labs(x="Species", y = "Weight per root (mg)")
plot_wps <- ggplot(dryw_sum, aes(x = reorder(Species, mean_wpr, median), y = mean_wps)) +
geom_point(stat = "summary", fun.y = "mean", size = 3, col="#7fbf7b") +
geom_errorbar(wps_limits, width=0.2, col="#7fbf7b") + theme_gray() +
#theme(axis.text.x=element_text(angle=90, hjust = 1, vjust = 0.5, face="italic"))) +
theme(axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y=element_text(angle=90, hjust = 0.5),
plot.margin=unit(c(1,1,-7,1), "mm")) +
labs(x="", y = "Weight per shoot (mg)")
plot_rs <- ggplot(dryw_sum, aes(x = reorder(Species, mean_wpr, median), y = mean_rs)) +
geom_point(stat = "summary", fun.y = "mean", size = 3, col="#4d4d4d") +
geom_errorbar(rs_limits, width=0.2, col="#4d4d4d") + theme_gray() +
#theme(axis.text.x=element_text(angle=90, hjust = 1, vjust = 0.5, face="italic"))) +
theme(axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y=element_text(angle=90, hjust = 0.5),
plot.margin=unit(c(1,1,-7,1), "mm")) +
labs(x="", y = "root:shoot ratio")
grid.draw(rbind(ggplotGrob(plot_rs), ggplotGrob(plot_wps), ggplotGrob(plot_wpr), size="last"))
#Reading the file
root_raw <- read.csv(file = "../data/clean/root_measurements_final.csv")
root_raw$Species <- trim(root_raw$Species)
#Data transformation using different parameters by plyr library
root_measures <- ddply(root_raw, c("Species","Isolate","Set","Rep"), summarise,
N = length(Area_cm),
area.r = mean(Area_cm),
length.r = mean(Length_cm)
)
#Summarizing data using different parameters by plyr library
root_sum <- ddply(root_measures, c("Species"), summarise,
N = length(area.r),
area.p = mean(area.r),
sd_ap = sd(area.r),
se_ap = sd_ap/sqrt(N),
ln.p = mean(length.r),
sd_lp = sd(length.r),
se_lp = sd_lp/sqrt(N)
)
#Setting limits for error bars
ap_limits <- aes(ymax = area.p + se_ap, ymin=area.p - se_ap)
lp_limits <- aes(ymax = ln.p + se_lp, ymin=ln.p - se_lp)
Plots for the root area and root length
plot_area <- ggplot(root_sum, aes(x=reorder(Species, area.p, median), y=area.p)) +
geom_point(stat = "summary", fun.y = "mean", size=3, col="#4d4d4d") +
geom_errorbar(ap_limits, width=0.2, col="#4d4d4d") + theme_gray() +
theme(axis.text.x=element_text(angle=90, hjust = 1, vjust = 0.5, face="italic"),
axis.text.y=element_text(angle=90, hjust = 0.5),
plot.margin=unit(c(1,1,1,1), "mm")) +
labs(x="Species", y = expression(paste("Root area (",cm^"2",")")))
plot_len <- ggplot(root_sum, aes(x=reorder(Species, area.p, median), y=ln.p)) +
geom_point(stat = "summary", fun.y = "mean", size=3, col="#7fbf7b") +
geom_errorbar(lp_limits, width=0.2, col="#7fbf7b") + theme_gray() +
#theme(axis.text.x=element_text(angle=90, hjust = 1, vjust = 0.5, face="italic"),
# axis.text.y=element_text(angle=90)) +
theme(axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y=element_text(angle=90, hjust = 0.5),
plot.margin=unit(c(1,1,-7,1), "mm")) +
labs(x="", y = "Root Length (cm)")
grid.draw(rbind(ggplotGrob(plot_len), ggplotGrob(plot_area), size="last"))
Merge data for root weight, shoot weight, and root measurements.
#Data for table publication
Root_data <- left_join(dryw_sum,root_sum, by="Species")
Root_data$N.x <- with(Root_data, N.x/9)
#Selecting the columns desired
Root_data <- Root_data %>% dplyr::select(Species,N.x,mean_wpr,
se_wpr,mean_wps,se_wps,
mean_rs,se_rs, area.p,se_ap,ln.p,se_lp)
#Renaming column data
Root_data <- dplyr::rename(Root_data, N=N.x, Weight.Root=mean_wpr, se.wr=se_wpr,
Weight.Shoot=mean_wps, se.ws=se_wps, Root.Shoot=mean_rs,
se.rs=se_rs, Root.Area=area.p, se.area=se_ap,
Root.length=ln.p, se.length=se_lp)
kable(Root_data, digits = 3, format = "markdown")
Species | N | Weight.Root | se.wr | Weight.Shoot | se.ws | Root.Shoot | se.rs | Root.Area | se.area | Root.length | se.length |
---|---|---|---|---|---|---|---|---|---|---|---|
Aphanomyces cladogamus | 1 | 28.667 | 4.853 | 140.889 | 3.450 | 0.207 | 0.038 | 2.330 | 0.503 | 31.646 | 7.178 |
Aphanomyces cochlioides | 1 | 38.000 | 6.000 | 150.444 | 4.444 | 0.248 | 0.037 | 4.379 | 0.613 | 53.977 | 9.329 |
CONTROL | 7 | 59.143 | 1.711 | 169.048 | 2.663 | 0.349 | 0.008 | 7.496 | 0.283 | 92.328 | 3.147 |
CONTROL_NR | 7 | 68.317 | 2.130 | 181.270 | 3.153 | 0.375 | 0.009 | 10.086 | 0.336 | 111.718 | 3.121 |
Phytophthora aff. rosacearum | 3 | 51.333 | 2.397 | 153.037 | 3.942 | 0.333 | 0.011 | 6.816 | 0.472 | 79.789 | 5.395 |
Phytophthora drechsleri | 1 | 16.444 | 2.863 | 126.889 | 5.619 | 0.130 | 0.022 | 2.257 | 0.582 | 21.703 | 5.614 |
Phytophthora inundata | 3 | 65.926 | 2.924 | 156.296 | 4.140 | 0.421 | 0.014 | 6.875 | 0.481 | 76.578 | 4.592 |
Phytophthora megasperma | 1 | 40.000 | 5.088 | 150.889 | 8.159 | 0.258 | 0.021 | 5.485 | 0.791 | 64.646 | 10.979 |
Phytophthora rosacearum | 3 | 42.000 | 2.377 | 142.370 | 4.631 | 0.298 | 0.015 | 4.973 | 0.631 | 58.232 | 8.622 |
Phytophthora sansomeana | 2 | 2.333 | 0.518 | 107.000 | 9.437 | 0.018 | 0.004 | 0.258 | 0.063 | 2.254 | 0.563 |
Phytophthora sojae | 3 | 20.926 | 3.647 | 138.148 | 5.372 | 0.137 | 0.022 | 2.147 | 0.389 | 28.181 | 5.324 |
Phytophthora sp. | 1 | 44.222 | 3.792 | 151.111 | 16.395 | 0.350 | 0.076 | 7.274 | 0.570 | 79.816 | 7.606 |
Phytopythium aff. vexans | 2 | 38.333 | 1.955 | 141.344 | 3.358 | 0.270 | 0.011 | 4.813 | 0.442 | 54.681 | 4.386 |
Phytopythium chamaehyphon | 3 | 42.074 | 2.074 | 146.963 | 3.543 | 0.285 | 0.012 | 4.637 | 0.346 | 54.824 | 3.552 |
Phytopythium helicoides | 2 | 36.111 | 1.699 | 141.444 | 4.906 | 0.257 | 0.010 | 3.794 | 0.318 | 41.125 | 2.731 |
Phytopythium litorale | 3 | 53.481 | 1.730 | 148.296 | 5.603 | 0.370 | 0.014 | 5.076 | 0.352 | 63.166 | 4.454 |
Phytopythium megacarpum | 1 | 36.222 | 2.778 | 145.333 | 5.706 | 0.249 | 0.015 | 5.326 | 0.575 | 59.494 | 6.113 |
Phytopythium mercuriale | 3 | 54.815 | 1.758 | 143.926 | 5.458 | 0.389 | 0.013 | 5.288 | 0.362 | 65.888 | 4.230 |
Pythiogeton sp. | 1 | 36.444 | 3.051 | 149.556 | 4.253 | 0.242 | 0.014 | 4.840 | 0.614 | 51.840 | 3.914 |
Pythium acanthicum | 3 | 68.963 | 3.586 | 174.000 | 4.458 | 0.391 | 0.013 | 10.091 | 0.658 | 121.304 | 7.037 |
Pythium acanthophoron | 1 | 55.111 | 5.964 | 159.333 | 8.145 | 0.338 | 0.021 | 8.961 | 0.875 | 93.521 | 9.012 |
Pythium acrogynum | 3 | 64.000 | 2.365 | 158.370 | 3.930 | 0.405 | 0.013 | 5.401 | 0.310 | 60.841 | 3.581 |
Pythium adhaerens | 1 | 39.778 | 8.127 | 156.667 | 10.693 | 0.241 | 0.036 | 7.093 | 1.519 | 70.184 | 13.626 |
Pythium aff. diclinum | 3 | 28.741 | 4.584 | 111.185 | 7.586 | 0.219 | 0.027 | 1.768 | 0.426 | 18.602 | 4.557 |
Pythium aff. dictyosporum | 2 | 18.778 | 4.110 | 120.222 | 6.216 | 0.139 | 0.028 | 2.186 | 0.498 | 22.220 | 5.379 |
Pythium aff. dissotocum | 3 | 27.926 | 2.545 | 148.741 | 3.330 | 0.188 | 0.016 | 2.715 | 0.301 | 38.133 | 4.282 |
Pythium aff. hypogynum | 1 | 44.889 | 5.488 | 140.444 | 13.979 | 0.328 | 0.032 | 5.630 | 0.572 | 59.058 | 5.346 |
Pythium aff. iwayamai | 1 | 38.444 | 2.977 | 138.667 | 5.270 | 0.279 | 0.022 | 5.568 | 0.509 | 63.914 | 3.509 |
Pythium aff. perplexum | 3 | 54.667 | 2.972 | 144.889 | 7.795 | 0.387 | 0.018 | 6.387 | 0.566 | 78.246 | 7.074 |
Pythium aff. torulosum | 3 | 28.222 | 2.536 | 140.074 | 5.445 | 0.205 | 0.018 | 2.889 | 0.352 | 38.977 | 5.004 |
Pythium amasculinum | 3 | 66.593 | 2.524 | 166.963 | 4.778 | 0.400 | 0.011 | 6.212 | 0.335 | 75.352 | 3.655 |
Pythium angustatum | 1 | 52.889 | 4.043 | 160.444 | 4.200 | 0.327 | 0.019 | 8.585 | 0.636 | 96.254 | 8.287 |
Pythium aphanidermatum | 3 | 56.741 | 2.906 | 150.000 | 4.679 | 0.379 | 0.016 | 4.073 | 0.350 | 51.256 | 4.599 |
Pythium aristosporum | 3 | 62.444 | 2.454 | 155.852 | 5.086 | 0.406 | 0.015 | 6.473 | 0.332 | 77.329 | 3.358 |
Pythium arrhenomanes | 3 | 55.630 | 2.517 | 151.185 | 5.964 | 0.373 | 0.014 | 6.148 | 0.471 | 73.888 | 5.519 |
Pythium attrantheridium | 3 | 23.259 | 2.368 | 142.222 | 4.228 | 0.166 | 0.018 | 2.051 | 0.247 | 27.772 | 3.462 |
Pythium camurandrum | 1 | 47.556 | 5.031 | 145.111 | 8.795 | 0.322 | 0.017 | 7.182 | 0.796 | 79.656 | 7.761 |
Pythium carolinianum | 3 | 54.222 | 2.722 | 155.926 | 3.256 | 0.348 | 0.016 | 6.849 | 0.590 | 74.149 | 5.954 |
Pythium catenulatum | 3 | 50.370 | 2.469 | 159.407 | 3.645 | 0.313 | 0.011 | 7.573 | 0.503 | 83.610 | 4.966 |
Pythium chondricola | 1 | 49.556 | 5.162 | 154.444 | 7.780 | 0.315 | 0.018 | 7.262 | 0.899 | 81.759 | 8.577 |
Pythium coloratum | 3 | 56.593 | 3.759 | 160.296 | 5.876 | 0.345 | 0.015 | 4.316 | 0.521 | 51.893 | 6.566 |
Pythium conidiophorum | 3 | 59.481 | 3.862 | 166.222 | 5.908 | 0.357 | 0.017 | 5.518 | 0.623 | 64.866 | 6.973 |
Pythium contiguanum | 3 | 62.074 | 2.859 | 159.111 | 4.349 | 0.387 | 0.012 | 5.440 | 0.472 | 66.416 | 4.915 |
Pythium cryptoirregulare | 1 | 19.556 | 2.375 | 132.000 | 5.657 | 0.149 | 0.018 | 1.740 | 0.215 | 17.280 | 2.335 |
Pythium glomeratum | 3 | 59.852 | 2.892 | 161.407 | 5.021 | 0.371 | 0.015 | 6.429 | 0.471 | 75.849 | 6.110 |
Pythium heterothallicum | 3 | 27.556 | 2.349 | 149.926 | 3.346 | 0.186 | 0.015 | 2.431 | 0.254 | 34.058 | 3.957 |
Pythium hydnosporum | 2 | 50.444 | 2.803 | 155.667 | 5.322 | 0.322 | 0.013 | 8.360 | 0.495 | 92.168 | 4.829 |
Pythium hypogynum | 3 | 53.185 | 2.230 | 144.593 | 6.265 | 0.380 | 0.019 | 5.164 | 0.379 | 62.808 | 4.852 |
Pythium inflatum | 3 | 64.370 | 3.040 | 176.815 | 3.633 | 0.362 | 0.014 | 7.879 | 0.513 | 100.851 | 6.087 |
Pythium intermedium | 3 | 41.852 | 5.690 | 132.815 | 9.483 | 0.272 | 0.031 | 3.410 | 0.632 | 42.129 | 7.779 |
Pythium irregulare | 3 | 20.963 | 2.325 | 137.852 | 3.497 | 0.151 | 0.016 | 1.618 | 0.291 | 19.662 | 3.980 |
Pythium kashmirense | 3 | 58.889 | 2.371 | 155.630 | 5.466 | 0.383 | 0.014 | 6.394 | 0.344 | 78.918 | 3.796 |
Pythium kunmingense | 2 | 13.667 | 1.510 | 123.778 | 4.774 | 0.106 | 0.010 | 1.301 | 0.108 | 10.641 | 1.174 |
Pythium litorale | 1 | 38.000 | 3.986 | 152.000 | 6.700 | 0.248 | 0.021 | 4.599 | 0.618 | 53.376 | 6.658 |
Pythium longandrum | 3 | 59.259 | 2.697 | 155.481 | 4.048 | 0.382 | 0.016 | 5.578 | 0.401 | 67.096 | 4.512 |
Pythium longisporangium | 3 | 44.667 | 3.257 | 150.815 | 3.371 | 0.296 | 0.020 | 5.844 | 0.578 | 61.178 | 5.407 |
Pythium lutarium | 3 | 26.148 | 2.542 | 143.926 | 4.074 | 0.178 | 0.015 | 2.535 | 0.333 | 33.427 | 4.884 |
Pythium middletonii | 3 | 57.333 | 1.725 | 150.000 | 4.668 | 0.389 | 0.014 | 5.898 | 0.367 | 70.706 | 4.533 |
Pythium minus | 3 | 48.444 | 2.731 | 152.148 | 2.826 | 0.318 | 0.017 | 5.075 | 0.365 | 58.632 | 3.784 |
Pythium monospermum | 2 | 64.556 | 3.391 | 164.667 | 5.044 | 0.393 | 0.018 | 7.119 | 0.575 | 80.031 | 7.670 |
Pythium nagaii | 3 | 52.889 | 2.112 | 150.296 | 7.172 | 0.362 | 0.013 | 5.602 | 0.321 | 71.571 | 3.604 |
Pythium nodosum | 3 | 72.000 | 3.614 | 176.889 | 5.308 | 0.404 | 0.012 | 10.035 | 0.683 | 119.017 | 6.931 |
Pythium nunn | 3 | 61.556 | 2.414 | 166.000 | 4.086 | 0.374 | 0.015 | 5.760 | 0.411 | 69.767 | 4.598 |
Pythium oligandrum | 3 | 63.111 | 2.833 | 150.074 | 3.024 | 0.420 | 0.016 | 6.786 | 0.455 | 78.245 | 3.923 |
Pythium oopapillum | 3 | 27.926 | 2.967 | 148.667 | 3.986 | 0.189 | 0.019 | 2.670 | 0.368 | 36.522 | 4.982 |
Pythium orthogonon | 3 | 64.815 | 2.801 | 170.667 | 5.014 | 0.380 | 0.014 | 6.637 | 0.273 | 79.670 | 3.170 |
Pythium pachycaule | 3 | 71.185 | 3.829 | 175.259 | 5.877 | 0.402 | 0.014 | 8.091 | 0.476 | 99.187 | 6.135 |
Pythium paroecandrum | 3 | 67.778 | 2.768 | 169.111 | 3.458 | 0.400 | 0.014 | 7.528 | 0.414 | 95.394 | 4.674 |
Pythium periilum | 3 | 49.556 | 4.088 | 152.815 | 3.189 | 0.318 | 0.023 | 5.543 | 0.561 | 59.440 | 5.378 |
Pythium periplocum | 3 | 38.489 | 4.391 | 141.185 | 4.697 | 0.261 | 0.027 | 4.998 | 0.716 | 52.741 | 7.378 |
Pythium perplexum | 3 | 76.444 | 3.522 | 185.704 | 5.867 | 0.409 | 0.009 | 10.063 | 0.662 | 120.464 | 6.615 |
Pythium pleroticum | 3 | 66.519 | 1.516 | 173.037 | 4.178 | 0.388 | 0.010 | 6.310 | 0.311 | 77.049 | 3.778 |
Pythium pyrilobum | 1 | 42.667 | 3.416 | 151.778 | 7.968 | 0.283 | 0.022 | 6.144 | 0.855 | 66.296 | 6.810 |
Pythium rhizosaccharum | 3 | 65.333 | 2.413 | 164.370 | 3.684 | 0.396 | 0.011 | 6.389 | 0.382 | 73.245 | 3.738 |
Pythium rostratifingens | 3 | 69.704 | 3.244 | 180.074 | 5.417 | 0.385 | 0.012 | 8.624 | 0.545 | 107.139 | 6.327 |
Pythium sp. balticum | 3 | 63.852 | 2.175 | 161.926 | 4.508 | 0.397 | 0.013 | 6.227 | 0.334 | 75.855 | 3.665 |
Pythium spinosum | 3 | 65.704 | 4.223 | 170.963 | 5.198 | 0.380 | 0.018 | 6.754 | 0.756 | 82.996 | 8.953 |
Pythium sterilum | 1 | 44.222 | 4.313 | 157.111 | 9.262 | 0.278 | 0.013 | 6.356 | 0.756 | 67.293 | 4.689 |
Pythium sylvaticum | 3 | 28.667 | 3.027 | 137.704 | 3.783 | 0.208 | 0.020 | 1.995 | 0.241 | 26.115 | 3.424 |
Pythium tardicrescens | 3 | 43.704 | 4.007 | 134.370 | 6.043 | 0.320 | 0.029 | 4.745 | 0.629 | 57.746 | 7.890 |
Pythium terrestris | 1 | 36.222 | 4.576 | 135.333 | 5.944 | 0.264 | 0.026 | 2.379 | 0.368 | 23.475 | 4.138 |
Pythium torulosum | 3 | 68.444 | 3.063 | 168.519 | 5.041 | 0.405 | 0.011 | 6.622 | 0.496 | 74.909 | 4.860 |
Pythium ultimum | 3 | 5.111 | 1.397 | 86.000 | 4.829 | 0.050 | 0.013 | 0.183 | 0.039 | 1.539 | 0.431 |
Pythium ultimum var. sporangiiferum | 3 | 19.200 | 2.497 | 135.852 | 3.871 | 0.137 | 0.018 | 1.337 | 0.240 | 15.196 | 3.169 |
Pythium ultimum var. ultimum | 3 | 8.256 | 1.252 | 117.037 | 4.114 | 0.068 | 0.010 | 0.481 | 0.070 | 4.228 | 0.738 |
Pythium vanterpoolii | 3 | 47.704 | 2.306 | 146.889 | 3.366 | 0.328 | 0.016 | 6.023 | 0.567 | 70.135 | 7.034 |
Evaluation of data and factor mining
## Combine dataset for multivariate analysis
root_final <- full_join(root_measures,dry_weight, by=c("Species","Isolate","Set","Rep"))
root_final <- root_final[c(-5,-8,-9,-10)]
## Log transforming data
root.log <- root_final
root.log[,5:9] <- log10(root.log[,5:9] + 1)
#### PCA exploration of the data
library(FactoMineR)
rd.pca <- PCA(root_final[5:9], scale.unit = TRUE, ncp=5, graph = T)
rd.pca$var$contrib
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## area.r 21.85143 0.07472866 30.960461 46.799414397 0.31396659
## length.r 22.45814 0.29210541 24.422796 52.802181492 0.02477689
## Weight.per.root 23.47647 0.46252424 16.603211 0.367029876 59.09076595
## Weight.per.shoot 13.72364 68.96951804 7.844296 0.029938280 9.43261043
## r_s 18.49032 30.20112366 20.169236 0.001435955 31.13788014
dimdesc(rd.pca, axes = c(1,2))
## $Dim.1
## $Dim.1$quanti
## correlation p.value
## Weight.per.root 0.9554455 0
## length.r 0.9344938 0
## area.r 0.9217846 0
## r_s 0.8479335 0
## Weight.per.shoot 0.7305066 0
##
##
## $Dim.2
## $Dim.2$quanti
## correlation p.value
## Weight.per.shoot 0.65660863 1.088758e-245
## Weight.per.root -0.05377064 1.647129e-02
## r_s -0.43449976 2.176565e-92
rd.hcpc <- HCPC(rd.pca, nb.clust = 3)
rd.hcpc$desc.var
## $quanti.var
## Eta2 P-value
## area.r 0.7151537 0.000000e+00
## length.r 0.7369130 0.000000e+00
## Weight.per.root 0.7466415 0.000000e+00
## r_s 0.6461200 0.000000e+00
## Weight.per.shoot 0.4185402 1.472615e-234
##
## $quanti
## $quanti$`1`
## v.test Mean in category Overall mean sd in category
## Weight.per.shoot -20.94764 126.0626398 152.5058824 28.68287792
## area.r -30.19480 1.2790328 5.4490842 1.01800530
## length.r -31.22751 14.7305969 64.3352076 12.82965337
## Weight.per.root -33.65093 16.8445190 48.9700855 10.78044091
## r_s -34.50523 0.1292444 0.3112623 0.08166911
## Overall sd p.value
## Weight.per.shoot 30.3039523 1.971432e-97
## area.r 3.3153470 2.771605e-200
## length.r 38.1333071 4.510673e-214
## Weight.per.root 22.9178307 3.021926e-248
## r_s 0.1266336 6.696198e-261
##
## $quanti$`2`
## v.test Mean in category Overall mean sd in category
## r_s 7.661747 0.3361948 0.3112623 0.08027482
## length.r -4.862159 59.5706430 64.3352076 17.25607021
## area.r -5.745685 4.9595756 5.4490842 1.57738798
## Weight.per.shoot -7.661306 146.5397674 152.5058824 20.93595661
## Overall sd p.value
## r_s 0.1266336 1.834204e-14
## length.r 38.1333071 1.161121e-06
## area.r 3.3153470 9.154959e-09
## Weight.per.shoot 30.3039523 1.840523e-14
##
## $quanti$`3`
## v.test Mean in category Overall mean sd in category
## area.r 32.54870 8.7995100 5.4490842 2.30525927
## length.r 32.53470 102.8554223 64.3352076 25.14670345
## Weight.per.root 30.37072 70.5806452 48.9700855 13.12750825
## Weight.per.shoot 26.41642 177.3607038 152.5058824 21.57199182
## r_s 22.34608 0.3991216 0.3112623 0.06366111
## Overall sd p.value
## area.r 3.3153470 2.184623e-232
## length.r 38.1333071 3.446421e-232
## Weight.per.root 22.9178307 1.338450e-202
## Weight.per.shoot 30.3039523 8.875378e-154
## r_s 0.1266336 1.318344e-110
##
##
## attr(,"class")
## [1] "catdes" "list "
root.clusters <- rd.hcpc$data.clust$clust
root.log.pca <- prcomp(root_final[5:9],
center = TRUE,
scale. = TRUE)
summary(root.log.pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 1.9719 0.7906 0.64773 0.20607 0.15620
## Proportion of Variance 0.7777 0.1250 0.08391 0.00849 0.00488
## Cumulative Proportion 0.7777 0.9027 0.98663 0.99512 1.00000
#Bi-plot for PCA analysis
library(ggbiplot)
## Loading required package: scales
g <- ggbiplot(root.log.pca, obs.scale = 1, var.scale = 1,
groups = root.clusters, ellipse = TRUE,
circle = FALSE, alpha=0.2)
g <- g + scale_color_discrete(name = '') + theme_gray()
(g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top'))
## MANOVA analysis
# fit <- manova(cbind(area.r, length.r, Weight.per.root)~Species, data=root_scale)
# summary(fit, test=c("Pillai"))
# summary(fit, test=c("Wilks"))
# summary(fit, test=c("Hotelling-Lawley"))
library(nlme)
##
## Attaching package: 'nlme'
## The following object is masked from 'package:dplyr':
##
## collapse
## The following object is masked from 'package:lme4':
##
## lmList
fit <- lme(cbind(area.r, length.r, Weight.per.root) ~ Species, random= ~1|Isolate, data=root.log)
fit1 <- lme(cbind(area.r, length.r, Weight.per.root) ~ Species, random= ~1|Set/Isolate, data=root.log)
fit2 <- lme(cbind(area.r, length.r, Weight.per.root) ~ Species, random = ~1|Set, data=root.log)
fit3 <- lme(cbind(area.r, length.r, Weight.per.root) ~ Species,
random = ~1|Set/Species/Isolate, data=root.log)
#model tests
anova(fit,fit1,fit2, fit3)
## Model df AIC BIC logLik Test L.Ratio p-value
## fit 1 88 -1070.347 -581.8425 623.1735
## fit1 2 89 -1475.426 -981.3700 826.7128 1 vs 2 407.0787 <.0001
## fit2 3 88 -1020.193 -531.6890 598.0967 2 vs 3 457.2322 <.0001
## fit3 4 90 -1473.426 -973.8188 826.7128 3 vs 4 457.2322 <.0001
#Checking correlation of dependent variables
library(car)
scatterplotMatrix(~ area.r + length.r + Weight.per.root, data = root.log)
cor.test(root.log$area.r, root.log$length.r, method="pearson")
##
## Pearson's product-moment correlation
##
## data: root.log$area.r and root.log$length.r
## t = 140.31, df = 1987, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9488559 0.9569286
## sample estimates:
## cor
## 0.9530613
cor.test(root.log$Weight.per.root, root.log$length.r, method="pearson")
##
## Pearson's product-moment correlation
##
## data: root.log$Weight.per.root and root.log$length.r
## t = 93.139, df = 1987, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.893488 0.909896
## sample estimates:
## cor
## 0.9020173
cor.test(root.log$area.r, root.log$Weight.per.root, method="pearson")
##
## Pearson's product-moment correlation
##
## data: root.log$area.r and root.log$Weight.per.root
## t = 67.17, df = 1987, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8192690 0.8461816
## sample estimates:
## cor
## 0.8332181
cor.test(root_final$area.r, root_final$length.r, method="pearson")
##
## Pearson's product-moment correlation
##
## data: root_final$area.r and root_final$length.r
## t = 146.27, df = 1987, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9526648 0.9601486
## sample estimates:
## cor
## 0.956564
cor.test(root_final$Weight.per.root, root_final$length.r, method="pearson")
##
## Pearson's product-moment correlation
##
## data: root_final$Weight.per.root and root_final$length.r
## t = 61.371, df = 1987, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7933566 0.8237539
## sample estimates:
## cor
## 0.8090957
cor.test(root_final$area.r, root_final$Weight.per.root, method="pearson")
##
## Pearson's product-moment correlation
##
## data: root_final$area.r and root_final$Weight.per.root
## t = 56.95, df = 1987, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7701663 0.8036013
## sample estimates:
## cor
## 0.7874624
library(lsmeans)
plot(fit1)
#Variance
VarCorr(fit1)
## Variance StdDev
## Set = pdLogChol(1)
## (Intercept) 0.006668143 0.0816587
## Isolate = pdLogChol(1)
## (Intercept) 0.014536808 0.1205687
## Residual 0.013565139 0.1164695
#Confidence intervals
fit1.ci <- intervals(fit1)
fit1.ci.2 <- as.data.frame(fit1.ci[[1]]) %>% add_rownames(var = "Species") %>% arrange(est.)
ggplot(fit1.ci.2, aes(x = reorder(Species, est., mean),
y = est., ymin = lower, ymax = upper)) +
geom_errorbar() + geom_point() + coord_flip() + theme_bw()
#Contrasts
fit1.ls <- lsmeans(fit1, "Species")
plot(fit1.ls)
fit1.d <- contrast(fit1.ls, "trt.vs.ctrl", ref=4, adjust="bon")
fit1.dc <- as.data.frame(summary(fit1.d))
kable(fit1.dc, format = "markdown")
contrast | estimate | SE | df | t.ratio | p.value |
---|---|---|---|---|---|
Aphanomyces cladogamus - CONTROL_NR | -0.4951197 | 0.0915492 | 557 | -5.4082386 | 0.0000080 |
Aphanomyces cochlioides - CONTROL_NR | -0.3405201 | 0.0915492 | 557 | -3.7195327 | 0.0186840 |
CONTROL - CONTROL_NR | -0.1231859 | 0.0426040 | 557 | -2.8914201 | 0.3387035 |
Phytophthora aff. rosacearum - CONTROL_NR | -0.1740862 | 0.0643896 | 557 | -2.7036390 | 0.6007798 |
Phytophthora drechsleri - CONTROL_NR | -0.6023674 | 0.0915492 | 557 | -6.5797163 | 0.0000000 |
Phytophthora inundata - CONTROL_NR | -0.1381653 | 0.0643896 | 557 | -2.1457710 | 1.0000000 |
Phytophthora megasperma - CONTROL_NR | -0.2767935 | 0.0915492 | 557 | -3.0234414 | 0.2222095 |
Phytophthora rosacearum - CONTROL_NR | -0.3243565 | 0.0643896 | 557 | -5.0374058 | 0.0000543 |
Phytophthora sansomeana - CONTROL_NR | -0.8784392 | 0.0721445 | 557 | -12.1761095 | 0.0000000 |
Phytophthora sojae - CONTROL_NR | -0.5540462 | 0.0643896 | 557 | -8.6045917 | 0.0000000 |
Phytophthora sp. - CONTROL_NR | -0.1521497 | 0.0915492 | 557 | -1.6619452 | 1.0000000 |
Phytopythium aff. vexans - CONTROL_NR | -0.3210865 | 0.0721445 | 557 | -4.4506028 | 0.0008794 |
Phytopythium chamaehyphon - CONTROL_NR | -0.3178642 | 0.0643896 | 557 | -4.9365769 | 0.0000894 |
Phytopythium helicoides - CONTROL_NR | -0.3961406 | 0.0721445 | 557 | -5.4909332 | 0.0000052 |
Phytopythium litorale - CONTROL_NR | -0.2774939 | 0.0643896 | 557 | -4.3096082 | 0.0016437 |
Phytopythium megacarpum - CONTROL_NR | -0.2763121 | 0.0915492 | 557 | -3.0181827 | 0.2260381 |
Phytopythium mercuriale - CONTROL_NR | -0.2593012 | 0.0643896 | 557 | -4.0270661 | 0.0054662 |
Pythiogeton sp. - CONTROL_NR | -0.3130774 | 0.0915492 | 557 | -3.4197742 | 0.0571731 |
Pythium acanthicum - CONTROL_NR | -0.0370781 | 0.0643896 | 557 | -0.5758404 | 1.0000000 |
Pythium acanthophoron - CONTROL_NR | -0.0805921 | 0.0915492 | 557 | -0.8803155 | 1.0000000 |
Pythium acrogynum - CONTROL_NR | -0.2225047 | 0.0643896 | 557 | -3.4556002 | 0.0502275 |
Pythium adhaerens - CONTROL_NR | -0.2452541 | 0.0915492 | 557 | -2.6789340 | 0.6463359 |
Pythium aff. diclinum - CONTROL_NR | -0.6901490 | 0.0643896 | 557 | -10.7183308 | 0.0000000 |
Pythium aff. dictyosporum - CONTROL_NR | -0.6621413 | 0.0721445 | 557 | -9.1779892 | 0.0000000 |
Pythium aff. dissotocum - CONTROL_NR | -0.4379118 | 0.0643896 | 557 | -6.8009711 | 0.0000000 |
Pythium aff. hypogynum - CONTROL_NR | -0.2548470 | 0.0915492 | 557 | -2.7837183 | 0.4723035 |
Pythium aff. iwayamai - CONTROL_NR | -0.2542446 | 0.0915492 | 557 | -2.7771374 | 0.4818378 |
Pythium aff. perplexum - CONTROL_NR | -0.2163223 | 0.0643896 | 557 | -3.3595852 | 0.0708911 |
Pythium aff. torulosum - CONTROL_NR | -0.4236240 | 0.0643896 | 557 | -6.5790755 | 0.0000000 |
Pythium amasculinum - CONTROL_NR | -0.1889503 | 0.0643896 | 557 | -2.9344842 | 0.2956927 |
Pythium angustatum - CONTROL_NR | -0.0894360 | 0.0915492 | 557 | -0.9769174 | 1.0000000 |
Pythium aphanidermatum - CONTROL_NR | -0.3610141 | 0.0643896 | 557 | -5.6067140 | 0.0000028 |
Pythium aristosporum - CONTROL_NR | -0.1768109 | 0.0643896 | 557 | -2.7459541 | 0.5294246 |
Pythium arrhenomanes - CONTROL_NR | -0.2124559 | 0.0643896 | 557 | -3.2995371 | 0.0875779 |
Pythium attrantheridium - CONTROL_NR | -0.5230560 | 0.0643896 | 557 | -8.1232993 | 0.0000000 |
Pythium camurandrum - CONTROL_NR | -0.1650529 | 0.0915492 | 557 | -1.8028882 | 1.0000000 |
Pythium carolinianum - CONTROL_NR | -0.1797910 | 0.0643896 | 557 | -2.7922368 | 0.4602156 |
Pythium catenulatum - CONTROL_NR | -0.1519902 | 0.0643896 | 557 | -2.3604782 | 1.0000000 |
Pythium chondricola - CONTROL_NR | -0.1687392 | 0.0915492 | 557 | -1.8431545 | 1.0000000 |
Pythium coloratum - CONTROL_NR | -0.3682536 | 0.0643896 | 557 | -5.7191480 | 0.0000015 |
Pythium conidiophorum - CONTROL_NR | -0.2734251 | 0.0643896 | 557 | -4.2464173 | 0.0021634 |
Pythium contiguanum - CONTROL_NR | -0.2388730 | 0.0643896 | 557 | -3.7098079 | 0.0193984 |
Pythium cryptoirregulare - CONTROL_NR | -0.6349253 | 0.0915492 | 557 | -6.9353489 | 0.0000000 |
Pythium glomeratum - CONTROL_NR | -0.1745490 | 0.0643896 | 557 | -2.7108268 | 0.5880795 |
Pythium heterothallicum - CONTROL_NR | -0.4642784 | 0.0643896 | 557 | -7.2104572 | 0.0000000 |
Pythium hydnosporum - CONTROL_NR | -0.1026331 | 0.0721445 | 557 | -1.4226051 | 1.0000000 |
Pythium hypogynum - CONTROL_NR | -0.2778289 | 0.0643896 | 557 | -4.3148110 | 0.0016067 |
Pythium inflatum - CONTROL_NR | -0.1321535 | 0.0643896 | 557 | -2.0524049 | 1.0000000 |
Pythium intermedium - CONTROL_NR | -0.5289618 | 0.0643896 | 557 | -8.2150198 | 0.0000000 |
Pythium irregulare - CONTROL_NR | -0.5981411 | 0.0643896 | 557 | -9.2894056 | 0.0000000 |
Pythium kashmirense - CONTROL_NR | -0.1833714 | 0.0643896 | 557 | -2.8478426 | 0.3879541 |
Pythium kunmingense - CONTROL_NR | -0.7097188 | 0.0721445 | 557 | -9.8374635 | 0.0000000 |
Pythium litorale - CONTROL_NR | -0.3352350 | 0.0915492 | 557 | -3.6618036 | 0.0233170 |
Pythium longandrum - CONTROL_NR | -0.2229081 | 0.0643896 | 557 | -3.4618645 | 0.0490971 |
Pythium longisporangium - CONTROL_NR | -0.2637165 | 0.0643896 | 557 | -4.0956382 | 0.0041095 |
Pythium lutarium - CONTROL_NR | -0.4644811 | 0.0643896 | 557 | -7.2136047 | 0.0000000 |
Pythium middletonii - CONTROL_NR | -0.2179257 | 0.0643896 | 557 | -3.3844860 | 0.0648812 |
Pythium minus - CONTROL_NR | -0.2820693 | 0.0643896 | 557 | -4.3806653 | 0.0012019 |
Pythium monospermum - CONTROL_NR | -0.1323096 | 0.0721445 | 557 | -1.8339526 | 1.0000000 |
Pythium nagaii - CONTROL_NR | -0.2336312 | 0.0643896 | 557 | -3.6283997 | 0.0264703 |
Pythium nodosum - CONTROL_NR | -0.0401710 | 0.0643896 | 557 | -0.6238735 | 1.0000000 |
Pythium nunn - CONTROL_NR | -0.2267637 | 0.0643896 | 557 | -3.5217450 | 0.0394277 |
Pythium oligandrum - CONTROL_NR | -0.1399959 | 0.0643896 | 557 | -2.1742010 | 1.0000000 |
Pythium oopapillum - CONTROL_NR | -0.4591797 | 0.0643896 | 557 | -7.1312712 | 0.0000000 |
Pythium orthogonon - CONTROL_NR | -0.1576552 | 0.0643896 | 557 | -2.4484569 | 1.0000000 |
Pythium pachycaule - CONTROL_NR | -0.0900390 | 0.0643896 | 557 | -1.3983462 | 1.0000000 |
Pythium paroecandrum - CONTROL_NR | -0.1444927 | 0.0643896 | 557 | -2.2440382 | 1.0000000 |
Pythium periilum - CONTROL_NR | -0.2723205 | 0.0643896 | 557 | -4.2292624 | 0.0023296 |
Pythium periplocum - CONTROL_NR | -0.3871455 | 0.0643896 | 557 | -6.0125476 | 0.0000003 |
Pythium perplexum - CONTROL_NR | -0.0369668 | 0.0643896 | 557 | -0.5741117 | 1.0000000 |
Pythium pleroticum - CONTROL_NR | -0.1801958 | 0.0643896 | 557 | -2.7985239 | 0.4514746 |
Pythium pyrilobum - CONTROL_NR | -0.2365405 | 0.0915492 | 557 | -2.5837545 | 0.8522678 |
Pythium rhizosaccharum - CONTROL_NR | -0.1615637 | 0.0643896 | 557 | -2.5091578 | 1.0000000 |
Pythium rostratifingens - CONTROL_NR | -0.0958170 | 0.0643896 | 557 | -1.4880814 | 1.0000000 |
Pythium sp. balticum - CONTROL_NR | -0.1885839 | 0.0643896 | 557 | -2.9287946 | 0.3010742 |
Pythium spinosum - CONTROL_NR | -0.2218928 | 0.0643896 | 557 | -3.4460977 | 0.0519887 |
Pythium sterilum - CONTROL_NR | -0.2123254 | 0.0915492 | 557 | -2.3192504 | 1.0000000 |
Pythium sylvaticum - CONTROL_NR | -0.5220404 | 0.0643896 | 557 | -8.1075271 | 0.0000000 |
Pythium tardicrescens - CONTROL_NR | -0.3674595 | 0.0643896 | 557 | -5.7068153 | 0.0000016 |
Pythium terrestris - CONTROL_NR | -0.5396362 | 0.0915492 | 557 | -5.8944965 | 0.0000006 |
Pythium torulosum - CONTROL_NR | -0.1554468 | 0.0643896 | 557 | -2.4141609 | 1.0000000 |
Pythium ultimum - CONTROL_NR | -0.9938347 | 0.0643896 | 557 | -15.4347095 | 0.0000000 |
Pythium ultimum var. sporangiiferum - CONTROL_NR | -0.6533043 | 0.0643896 | 557 | -10.1461153 | 0.0000000 |
Pythium ultimum var. ultimum - CONTROL_NR | -0.8115652 | 0.0643896 | 557 | -12.6039807 | 0.0000000 |
Pythium vanterpoolii - CONTROL_NR | -0.2369967 | 0.0643896 | 557 | -3.6806673 | 0.0216959 |
#### Univariate analysis
#### Area
fit.area <- lme(area.r ~ Species, random= ~1|Set/Isolate, data=root.log)
fit.area.ls <- lsmeans(fit.area, "Species")
fit.area.d <- contrast(fit.area.ls, "trt.vs.ctrl", ref=4, adjust="bon")
area.d.t <- as.data.frame(summary(fit.area.d))
#### Length
fit.length <- lme(length.r ~ Species, random= ~1|Set/Isolate, data=root.log)
fit.length.ls <- lsmeans(fit.length, "Species")
fit.length.d <- contrast(fit.length.ls, "trt.vs.ctrl", ref=4, adjust="bon")
length.d.t <- as.data.frame(summary(fit.length.d))
#### Weight per root
fit.wpr <- lme(Weight.per.root ~ Species, random= ~1|Set/Isolate, data=root.log)
fit.wpr.ls <- lsmeans(fit.wpr, "Species")
fit.wpr.d <- contrast(fit.wpr.ls, "trt.vs.ctrl", ref=4, adjust="bon")
wpr.d.t <- as.data.frame(summary(fit.wpr.d))
#### Merge results
univ.t1 <- full_join(area.d.t, length.d.t, by="contrast") %>%
dplyr::select(c(contrast, contains("p.value"))) %>%
dplyr::rename(ar.p = p.value.x, ln.p = p.value.y)
univ.t2 <- full_join(univ.t1, wpr.d.t, by="contrast") %>%
dplyr::select(c(contrast, ar.p, ln.p, p.value)) %>%
dplyr::rename(wpr.p = p.value)
univ.t <- full_join(univ.t2, fit1.dc, by="contrast") %>%
dplyr::select(c(contrast, p.value, ar.p, ln.p, wpr.p)) %>%
dplyr::rename(mnv.p = p.value)
univ.t <- cbind(colsplit(univ.t$contrast," - ", names=c('Species','contrast')),univ.t[2:5])
kable(univ.t, digits = 3, format = "markdown")
Species | contrast | mnv.p | ar.p | ln.p | wpr.p |
---|---|---|---|---|---|
Aphanomyces cladogamus | CONTROL_NR | 0.000 | 0.000 | 0.002 | 0.044 |
Aphanomyces cochlioides | CONTROL_NR | 0.019 | 0.019 | 1.000 | 1.000 |
CONTROL | CONTROL_NR | 0.339 | 0.339 | 1.000 | 1.000 |
Phytophthora aff. rosacearum | CONTROL_NR | 0.601 | 0.601 | 1.000 | 1.000 |
Phytophthora drechsleri | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Phytophthora inundata | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Phytophthora megasperma | CONTROL_NR | 0.222 | 0.222 | 1.000 | 1.000 |
Phytophthora rosacearum | CONTROL_NR | 0.000 | 0.000 | 0.031 | 1.000 |
Phytophthora sansomeana | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Phytophthora sojae | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Phytophthora sp. | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Phytopythium aff. vexans | CONTROL_NR | 0.001 | 0.001 | 0.388 | 0.587 |
Phytopythium chamaehyphon | CONTROL_NR | 0.000 | 0.000 | 0.163 | 1.000 |
Phytopythium helicoides | CONTROL_NR | 0.000 | 0.000 | 0.016 | 0.251 |
Phytopythium litorale | CONTROL_NR | 0.002 | 0.002 | 0.677 | 1.000 |
Phytopythium megacarpum | CONTROL_NR | 0.226 | 0.226 | 1.000 | 1.000 |
Phytopythium mercuriale | CONTROL_NR | 0.005 | 0.005 | 1.000 | 1.000 |
Pythiogeton sp. | CONTROL_NR | 0.057 | 0.057 | 1.000 | 1.000 |
Pythium acanthicum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium acanthophoron | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium acrogynum | CONTROL_NR | 0.050 | 0.050 | 1.000 | 1.000 |
Pythium adhaerens | CONTROL_NR | 0.646 | 0.646 | 1.000 | 0.518 |
Pythium aff. diclinum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium aff. dictyosporum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium aff. dissotocum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium aff. hypogynum | CONTROL_NR | 0.472 | 0.472 | 1.000 | 1.000 |
Pythium aff. iwayamai | CONTROL_NR | 0.482 | 0.482 | 1.000 | 1.000 |
Pythium aff. perplexum | CONTROL_NR | 0.071 | 0.071 | 1.000 | 1.000 |
Pythium aff. torulosum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium amasculinum | CONTROL_NR | 0.296 | 0.296 | 1.000 | 1.000 |
Pythium angustatum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium aphanidermatum | CONTROL_NR | 0.000 | 0.000 | 0.016 | 1.000 |
Pythium aristosporum | CONTROL_NR | 0.529 | 0.529 | 1.000 | 1.000 |
Pythium arrhenomanes | CONTROL_NR | 0.088 | 0.088 | 1.000 | 1.000 |
Pythium attrantheridium | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium camurandrum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium carolinianum | CONTROL_NR | 0.460 | 0.460 | 1.000 | 1.000 |
Pythium catenulatum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium chondricola | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium coloratum | CONTROL_NR | 0.000 | 0.000 | 0.003 | 1.000 |
Pythium conidiophorum | CONTROL_NR | 0.002 | 0.002 | 0.343 | 1.000 |
Pythium contiguanum | CONTROL_NR | 0.019 | 0.019 | 1.000 | 1.000 |
Pythium cryptoirregulare | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium glomeratum | CONTROL_NR | 0.588 | 0.588 | 1.000 | 1.000 |
Pythium heterothallicum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium hydnosporum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium hypogynum | CONTROL_NR | 0.002 | 0.002 | 0.339 | 1.000 |
Pythium inflatum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium intermedium | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium irregulare | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium kashmirense | CONTROL_NR | 0.388 | 0.388 | 1.000 | 1.000 |
Pythium kunmingense | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium litorale | CONTROL_NR | 0.023 | 0.023 | 1.000 | 1.000 |
Pythium longandrum | CONTROL_NR | 0.049 | 0.049 | 1.000 | 1.000 |
Pythium longisporangium | CONTROL_NR | 0.004 | 0.004 | 0.139 | 0.894 |
Pythium lutarium | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium middletonii | CONTROL_NR | 0.065 | 0.065 | 1.000 | 1.000 |
Pythium minus | CONTROL_NR | 0.001 | 0.001 | 0.451 | 1.000 |
Pythium monospermum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium nagaii | CONTROL_NR | 0.026 | 0.026 | 1.000 | 1.000 |
Pythium nodosum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium nunn | CONTROL_NR | 0.039 | 0.039 | 1.000 | 1.000 |
Pythium oligandrum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium oopapillum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium orthogonon | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium pachycaule | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium paroecandrum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium periilum | CONTROL_NR | 0.002 | 0.002 | 0.186 | 1.000 |
Pythium periplocum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.003 |
Pythium perplexum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium pleroticum | CONTROL_NR | 0.451 | 0.451 | 1.000 | 1.000 |
Pythium pyrilobum | CONTROL_NR | 0.852 | 0.852 | 1.000 | 1.000 |
Pythium rhizosaccharum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium rostratifingens | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium sp. balticum | CONTROL_NR | 0.301 | 0.301 | 1.000 | 1.000 |
Pythium spinosum | CONTROL_NR | 0.052 | 0.052 | 1.000 | 1.000 |
Pythium sterilum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium sylvaticum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium tardicrescens | CONTROL_NR | 0.000 | 0.000 | 0.001 | 0.104 |
Pythium terrestris | CONTROL_NR | 0.000 | 0.000 | 0.000 | 1.000 |
Pythium torulosum | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium ultimum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium ultimum var. sporangiiferum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium ultimum var. ultimum | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium vanterpoolii | CONTROL_NR | 0.022 | 0.022 | 1.000 | 1.000 |
### Creating two data sets for downstream analysis
#### Root original data set
root_final$Treatment[root_final$Species=="CONTROL"] <- "Control"
root_final$Treatment[root_final$Species=="CONTROL_NR"] <- "Control"
root_final$Treatment[is.na(root_final$Treatment)] <- "Inoculated"
rownames(root_final) <- make.names(root_final[,1], unique = TRUE)
t.test(root_final$Weight.per.shoot~root_final$Treatment)
##
## Welch Two Sample t-test
##
## data: root_final$Weight.per.shoot by root_final$Treatment
## t = 10.805, df = 153.19, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 19.76301 28.60684
## sample estimates:
## mean in group Control mean in group Inoculated
## 175.1587 150.9738
t.test(root_final$Weight.per.root~root_final$Treatment)
##
## Welch Two Sample t-test
##
## data: root_final$Weight.per.root by root_final$Treatment
## t = 10.383, df = 162.34, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 12.76145 18.75522
## sample estimates:
## mean in group Control mean in group Inoculated
## 63.73016 47.97182
t.test(root_final$length.r~root_final$Treatment)
##
## Welch Two Sample t-test
##
## data: root_final$length.r by root_final$Treatment
## t = 15.932, df = 160.52, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 35.24927 45.22433
## sample estimates:
## mean in group Control mean in group Inoculated
## 102.02307 61.78627
t.test(root_final$area.r~root_final$Treatment)
##
## Welch Two Sample t-test
##
## data: root_final$area.r by root_final$Treatment
## t = 13.786, df = 148.72, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 3.056356 4.079121
## sample estimates:
## mean in group Control mean in group Inoculated
## 8.790812 5.223074
## Analyzing by variable
##Function boxplot
draw_bp <- function(d, v, c, l){
plot1 <- ggplot(data=d, aes_string(y=v, x=c)) +
geom_boxplot(position="identity") + theme_bw() + labs(y=l)
plot1
}
ar.root <- draw_bp(root_final, "area.r", "Treatment", expression(paste("Root area (",cm^"2",")")))
ln.root <- draw_bp(root_final, "length.r", "Treatment", "Root length (cm)")
wpr <- draw_bp(root_final, "Weight.per.root", "Treatment", "Weight per root (mg)")
wpp <- draw_bp(root_final, "Weight.per.shoot", "Treatment", "Weight per shoot (mg)")
plot_grid(ar.root,ln.root,wpr,wpp, ncol=2, nrow=2, labels = c("A","B","C","D"))
## Summarizing data by species - full dataset
root.sp <- ddply(root_final, c("Species"), summarise,
N = length(area.r),
ar = mean(area.r),
ar.se = sd(area.r)/sqrt(N),
ln = mean(length.r),
ln.se = sd(length.r)/sqrt(N),
wpr = mean(Weight.per.root),
wpr.se = sd(Weight.per.root)/sqrt(N)
)
root.sp <- dplyr::full_join(root.sp, univ.t, by="Species")
root.sp[is.na(root.sp)] <- 1
kable(root.sp, digits = 3, format = "markdown")
Species | N | ar | ar.se | ln | ln.se | wpr | wpr.se | contrast | mnv.p | ar.p | ln.p | wpr.p |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Aphanomyces cladogamus | 9 | 2.330 | 0.503 | 31.646 | 7.178 | 28.667 | 4.853 | CONTROL_NR | 0.000 | 0.000 | 0.002 | 0.044 |
Aphanomyces cochlioides | 9 | 4.379 | 0.613 | 53.977 | 9.329 | 38.000 | 6.000 | CONTROL_NR | 0.019 | 0.019 | 1.000 | 1.000 |
CONTROL | 63 | 7.496 | 0.283 | 92.328 | 3.147 | 59.143 | 1.711 | CONTROL_NR | 0.339 | 0.339 | 1.000 | 1.000 |
CONTROL_NR | 63 | 10.086 | 0.336 | 111.718 | 3.121 | 68.317 | 2.130 | 1 | 1.000 | 1.000 | 1.000 | 1.000 |
Phytophthora aff. rosacearum | 27 | 6.816 | 0.472 | 79.789 | 5.395 | 51.333 | 2.397 | CONTROL_NR | 0.601 | 0.601 | 1.000 | 1.000 |
Phytophthora drechsleri | 9 | 2.257 | 0.582 | 21.703 | 5.614 | 16.444 | 2.863 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Phytophthora inundata | 27 | 6.875 | 0.481 | 76.578 | 4.592 | 65.926 | 2.924 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Phytophthora megasperma | 9 | 5.485 | 0.791 | 64.646 | 10.979 | 40.000 | 5.088 | CONTROL_NR | 0.222 | 0.222 | 1.000 | 1.000 |
Phytophthora rosacearum | 27 | 4.973 | 0.631 | 58.232 | 8.622 | 42.000 | 2.377 | CONTROL_NR | 0.000 | 0.000 | 0.031 | 1.000 |
Phytophthora sansomeana | 18 | 0.258 | 0.063 | 2.254 | 0.563 | 2.333 | 0.518 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Phytophthora sojae | 27 | 2.147 | 0.389 | 28.181 | 5.324 | 20.926 | 3.647 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Phytophthora sp. | 9 | 7.274 | 0.570 | 79.816 | 7.606 | 44.222 | 3.792 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Phytopythium aff. vexans | 18 | 4.813 | 0.442 | 54.681 | 4.386 | 38.333 | 1.955 | CONTROL_NR | 0.001 | 0.001 | 0.388 | 0.587 |
Phytopythium chamaehyphon | 27 | 4.637 | 0.346 | 54.824 | 3.552 | 42.074 | 2.074 | CONTROL_NR | 0.000 | 0.000 | 0.163 | 1.000 |
Phytopythium helicoides | 18 | 3.794 | 0.318 | 41.125 | 2.731 | 36.111 | 1.699 | CONTROL_NR | 0.000 | 0.000 | 0.016 | 0.251 |
Phytopythium litorale | 27 | 5.076 | 0.352 | 63.166 | 4.454 | 53.481 | 1.730 | CONTROL_NR | 0.002 | 0.002 | 0.677 | 1.000 |
Phytopythium megacarpum | 9 | 5.326 | 0.575 | 59.494 | 6.113 | 36.222 | 2.778 | CONTROL_NR | 0.226 | 0.226 | 1.000 | 1.000 |
Phytopythium mercuriale | 27 | 5.288 | 0.362 | 65.888 | 4.230 | 54.815 | 1.758 | CONTROL_NR | 0.005 | 0.005 | 1.000 | 1.000 |
Pythiogeton sp. | 9 | 4.840 | 0.614 | 51.840 | 3.914 | 36.444 | 3.051 | CONTROL_NR | 0.057 | 0.057 | 1.000 | 1.000 |
Pythium acanthicum | 27 | 10.091 | 0.658 | 121.304 | 7.037 | 68.963 | 3.586 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium acanthophoron | 9 | 8.961 | 0.875 | 93.521 | 9.012 | 55.111 | 5.964 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium acrogynum | 27 | 5.401 | 0.310 | 60.841 | 3.581 | 64.000 | 2.365 | CONTROL_NR | 0.050 | 0.050 | 1.000 | 1.000 |
Pythium adhaerens | 9 | 7.093 | 1.519 | 70.184 | 13.626 | 39.778 | 8.127 | CONTROL_NR | 0.646 | 0.646 | 1.000 | 0.518 |
Pythium aff. diclinum | 27 | 1.768 | 0.426 | 18.602 | 4.557 | 28.741 | 4.584 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium aff. dictyosporum | 18 | 2.186 | 0.498 | 22.220 | 5.379 | 18.778 | 4.110 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium aff. dissotocum | 27 | 2.715 | 0.301 | 38.133 | 4.282 | 27.926 | 2.545 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium aff. hypogynum | 9 | 5.630 | 0.572 | 59.058 | 5.346 | 44.889 | 5.488 | CONTROL_NR | 0.472 | 0.472 | 1.000 | 1.000 |
Pythium aff. iwayamai | 9 | 5.568 | 0.509 | 63.914 | 3.509 | 38.444 | 2.977 | CONTROL_NR | 0.482 | 0.482 | 1.000 | 1.000 |
Pythium aff. perplexum | 27 | 6.387 | 0.566 | 78.246 | 7.074 | 54.667 | 2.972 | CONTROL_NR | 0.071 | 0.071 | 1.000 | 1.000 |
Pythium aff. torulosum | 27 | 2.889 | 0.352 | 38.977 | 5.004 | 28.222 | 2.536 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium amasculinum | 27 | 6.212 | 0.335 | 75.352 | 3.655 | 66.593 | 2.524 | CONTROL_NR | 0.296 | 0.296 | 1.000 | 1.000 |
Pythium angustatum | 9 | 8.585 | 0.636 | 96.254 | 8.287 | 52.889 | 4.043 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium aphanidermatum | 27 | 4.073 | 0.350 | 51.256 | 4.599 | 56.741 | 2.906 | CONTROL_NR | 0.000 | 0.000 | 0.016 | 1.000 |
Pythium aristosporum | 27 | 6.473 | 0.332 | 77.329 | 3.358 | 62.444 | 2.454 | CONTROL_NR | 0.529 | 0.529 | 1.000 | 1.000 |
Pythium arrhenomanes | 27 | 6.148 | 0.471 | 73.888 | 5.519 | 55.630 | 2.517 | CONTROL_NR | 0.088 | 0.088 | 1.000 | 1.000 |
Pythium attrantheridium | 27 | 2.051 | 0.247 | 27.772 | 3.462 | 23.259 | 2.368 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium camurandrum | 9 | 7.182 | 0.796 | 79.656 | 7.761 | 47.556 | 5.031 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium carolinianum | 27 | 6.849 | 0.590 | 74.149 | 5.954 | 54.222 | 2.722 | CONTROL_NR | 0.460 | 0.460 | 1.000 | 1.000 |
Pythium catenulatum | 27 | 7.573 | 0.503 | 83.610 | 4.966 | 50.370 | 2.469 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium chondricola | 9 | 7.262 | 0.899 | 81.759 | 8.577 | 49.556 | 5.162 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium coloratum | 27 | 4.316 | 0.521 | 51.893 | 6.566 | 56.593 | 3.759 | CONTROL_NR | 0.000 | 0.000 | 0.003 | 1.000 |
Pythium conidiophorum | 27 | 5.518 | 0.623 | 64.866 | 6.973 | 59.481 | 3.862 | CONTROL_NR | 0.002 | 0.002 | 0.343 | 1.000 |
Pythium contiguanum | 27 | 5.440 | 0.472 | 66.416 | 4.915 | 62.074 | 2.859 | CONTROL_NR | 0.019 | 0.019 | 1.000 | 1.000 |
Pythium cryptoirregulare | 9 | 1.740 | 0.215 | 17.280 | 2.335 | 19.556 | 2.375 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium glomeratum | 27 | 6.429 | 0.471 | 75.849 | 6.110 | 59.852 | 2.892 | CONTROL_NR | 0.588 | 0.588 | 1.000 | 1.000 |
Pythium heterothallicum | 27 | 2.431 | 0.254 | 34.058 | 3.957 | 27.556 | 2.349 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium hydnosporum | 18 | 8.360 | 0.495 | 92.168 | 4.829 | 50.444 | 2.803 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium hypogynum | 27 | 5.164 | 0.379 | 62.808 | 4.852 | 53.185 | 2.230 | CONTROL_NR | 0.002 | 0.002 | 0.339 | 1.000 |
Pythium inflatum | 27 | 7.879 | 0.513 | 100.851 | 6.087 | 64.370 | 3.040 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium intermedium | 27 | 3.410 | 0.632 | 42.129 | 7.779 | 41.852 | 5.690 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium irregulare | 27 | 1.618 | 0.291 | 19.662 | 3.980 | 20.963 | 2.325 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium kashmirense | 27 | 6.394 | 0.344 | 78.918 | 3.796 | 58.889 | 2.371 | CONTROL_NR | 0.388 | 0.388 | 1.000 | 1.000 |
Pythium kunmingense | 18 | 1.301 | 0.108 | 10.641 | 1.174 | 13.667 | 1.510 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium litorale | 9 | 4.599 | 0.618 | 53.376 | 6.658 | 38.000 | 3.986 | CONTROL_NR | 0.023 | 0.023 | 1.000 | 1.000 |
Pythium longandrum | 27 | 5.578 | 0.401 | 67.096 | 4.512 | 59.259 | 2.697 | CONTROL_NR | 0.049 | 0.049 | 1.000 | 1.000 |
Pythium longisporangium | 27 | 5.844 | 0.578 | 61.178 | 5.407 | 44.667 | 3.257 | CONTROL_NR | 0.004 | 0.004 | 0.139 | 0.894 |
Pythium lutarium | 27 | 2.535 | 0.333 | 33.427 | 4.884 | 26.148 | 2.542 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium middletonii | 27 | 5.898 | 0.367 | 70.706 | 4.533 | 57.333 | 1.725 | CONTROL_NR | 0.065 | 0.065 | 1.000 | 1.000 |
Pythium minus | 27 | 5.075 | 0.365 | 58.632 | 3.784 | 48.444 | 2.731 | CONTROL_NR | 0.001 | 0.001 | 0.451 | 1.000 |
Pythium monospermum | 18 | 7.119 | 0.575 | 80.031 | 7.670 | 64.556 | 3.391 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium nagaii | 27 | 5.602 | 0.321 | 71.571 | 3.604 | 52.889 | 2.112 | CONTROL_NR | 0.026 | 0.026 | 1.000 | 1.000 |
Pythium nodosum | 27 | 10.035 | 0.683 | 119.017 | 6.931 | 72.000 | 3.614 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium nunn | 27 | 5.760 | 0.411 | 69.767 | 4.598 | 61.556 | 2.414 | CONTROL_NR | 0.039 | 0.039 | 1.000 | 1.000 |
Pythium oligandrum | 27 | 6.786 | 0.455 | 78.245 | 3.923 | 63.111 | 2.833 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium oopapillum | 27 | 2.670 | 0.368 | 36.522 | 4.982 | 27.926 | 2.967 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium orthogonon | 27 | 6.637 | 0.273 | 79.670 | 3.170 | 64.815 | 2.801 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium pachycaule | 27 | 8.091 | 0.476 | 99.187 | 6.135 | 71.185 | 3.829 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium paroecandrum | 27 | 7.528 | 0.414 | 95.394 | 4.674 | 67.778 | 2.768 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium periilum | 27 | 5.543 | 0.561 | 59.440 | 5.378 | 49.556 | 4.088 | CONTROL_NR | 0.002 | 0.002 | 0.186 | 1.000 |
Pythium periplocum | 27 | 4.998 | 0.716 | 52.741 | 7.378 | 38.489 | 4.391 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.003 |
Pythium perplexum | 27 | 10.063 | 0.662 | 120.464 | 6.615 | 76.444 | 3.522 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium pleroticum | 27 | 6.310 | 0.311 | 77.049 | 3.778 | 66.519 | 1.516 | CONTROL_NR | 0.451 | 0.451 | 1.000 | 1.000 |
Pythium pyrilobum | 9 | 6.144 | 0.855 | 66.296 | 6.810 | 42.667 | 3.416 | CONTROL_NR | 0.852 | 0.852 | 1.000 | 1.000 |
Pythium rhizosaccharum | 27 | 6.389 | 0.382 | 73.245 | 3.738 | 65.333 | 2.413 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium rostratifingens | 27 | 8.624 | 0.545 | 107.139 | 6.327 | 69.704 | 3.244 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium sp. balticum | 27 | 6.227 | 0.334 | 75.855 | 3.665 | 63.852 | 2.175 | CONTROL_NR | 0.301 | 0.301 | 1.000 | 1.000 |
Pythium spinosum | 27 | 6.754 | 0.756 | 82.996 | 8.953 | 65.704 | 4.223 | CONTROL_NR | 0.052 | 0.052 | 1.000 | 1.000 |
Pythium sterilum | 9 | 6.356 | 0.756 | 67.293 | 4.689 | 44.222 | 4.313 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium sylvaticum | 27 | 1.995 | 0.241 | 26.115 | 3.424 | 28.667 | 3.027 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium tardicrescens | 27 | 4.745 | 0.629 | 57.746 | 7.890 | 43.704 | 4.007 | CONTROL_NR | 0.000 | 0.000 | 0.001 | 0.104 |
Pythium terrestris | 9 | 2.379 | 0.368 | 23.475 | 4.138 | 36.222 | 4.576 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 1.000 |
Pythium torulosum | 27 | 6.622 | 0.496 | 74.909 | 4.860 | 68.444 | 3.063 | CONTROL_NR | 1.000 | 1.000 | 1.000 | 1.000 |
Pythium ultimum | 27 | 0.183 | 0.039 | 1.539 | 0.431 | 5.111 | 1.397 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium ultimum var. sporangiiferum | 27 | 1.337 | 0.240 | 15.196 | 3.169 | 19.200 | 2.497 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium ultimum var. ultimum | 27 | 0.481 | 0.070 | 4.228 | 0.738 | 8.256 | 1.252 | CONTROL_NR | 0.000 | 0.000 | 0.000 | 0.000 |
Pythium vanterpoolii | 27 | 6.023 | 0.567 | 70.135 | 7.034 | 47.704 | 2.306 | CONTROL_NR | 0.022 | 0.022 | 1.000 | 1.000 |
root.sp$ar.sg <- ifelse(root.sp$ar.p<0.05,"SG","NS")
root.sp$ln.sg <- ifelse(root.sp$ln.p<0.05,"SG","NS")
root.sp$wpr.sg <- ifelse(root.sp$wpr.p<0.05,"SG","NS")
#Setting limits for error bars
ar_lt <- aes(ymax = ar + ar.se, ymin=ar - ar.se)
ln_lt <- aes(ymax = ln + ln.se, ymin=ln - ln.se)
wpr_lt <- aes(ymax = wpr + wpr.se, ymin=wpr - wpr.se)
plot_ar <- ggplot(root.sp, aes(x=reorder(Species, ar, median), y=ar)) +
geom_point(aes(shape=ar.sg), stat = "summary",
fun.y = "mean", size=3, colour="#4d4d4d") +
geom_errorbar(ar_lt, width=0.2, col="#4d4d4d") + theme_gray() +
scale_shape_manual(values = c(21,19)) +
theme(axis.text.x=element_text(angle=90, hjust = 1, vjust = 0.5,
face="italic", size = 12),
axis.text.y=element_text(angle=90, hjust = 0.5),
plot.margin=unit(c(1,1,1,1), "mm"),
legend.position="none") +
labs(x="Species", y = expression(paste("Root area (",cm^"2",")")))
plot_ln <- ggplot(root.sp, aes(x=reorder(Species, ar, median), y=ln)) +
geom_point(aes(shape=ln.sg), stat = "summary", fun.y = "mean",
size=3, colour="#005824") +
geom_errorbar(ln_lt, width=0.2, col="#005824") + theme_gray() +
scale_shape_manual(values = c(21,19)) +
#theme(axis.text.x=element_text(angle=90, hjust = 1, vjust = 0.5, face="italic"),
# axis.text.y=element_text(angle=90)) +
theme(axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y=element_text(angle=90, hjust = 0.5),
plot.margin=unit(c(1,1,-7,1), "mm"),
legend.position="none") +
labs(x="", y = "Root Length (cm)")
plot_wpr <- ggplot(root.sp, aes(x=reorder(Species, ar, median), y=wpr)) +
geom_point(aes(shape=wpr.sg), stat = "summary", fun.y = "mean",
size=3, colour = "#bf812d") +
geom_errorbar(wpr_lt, width=0.2, col="#bf812d") + theme_gray() +
scale_shape_manual(values = c(21,19)) +
#theme(axis.text.x=element_text(angle=90, hjust = 1, vjust = 0.5, face="italic"),
# axis.text.y=element_text(angle=90)) +
theme(axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y=element_text(angle=90, hjust = 0.5),
plot.margin=unit(c(1,1,-7,1), "mm"),
legend.position="none") +
labs(x="", y = "Weight per root (mg)")
grid.draw(rbind(ggplotGrob(plot_wpr), ggplotGrob(plot_ln), ggplotGrob(plot_ar), size="last"))
root.sp1 <- root.sp
root.sp1$Group[root.sp1$mnv.p>0.05] <- "Group 1"
root.sp1$Group[(root.sp1$mnv.p<0.05) & (root.sp1$ln.p>0.05) | (root.sp1$mnv.p<0.05) &(root.sp1$wpr.p>0.05)] <- "Group 2"
root.sp1$Group[(root.sp1$mnv.p<0.05) & (root.sp1$ln.p<0.05) & (root.sp1$wpr.p<0.05)] <- "Group 3"
## Plots by clusters
a <- draw_bp(root.sp1, "ar", "Group", expression(paste("Root area (",cm^"2",")")))
b <- draw_bp(root.sp1, "ln", "Group", "Root length (cm)")
c <- draw_bp(root.sp1, "wpr", "Group", "Weight per root (mg)")
plot_grid(a,b,c, ncol=3, nrow=1, labels = c("A","B","C"))
#function for actual mode of data
#mode_f <- function(x){as.integer(which.max(table(x)))}
#### LDA analysis data set
root.lda.data <- cbind(root_final[1:4],log10(root_final[,5:9] + 1), root_final[10])
root.lda.data[5:8] <- scale(root.lda.data[5:8], center = TRUE, scale = TRUE)
root_lda <- lda(Species ~ area.r + length.r + Weight.per.root, data=root.lda.data)
root.lda.p <- predict(root_lda)
prop.lda <- root_lda$svd^2/sum(root_lda$svd^2)
dataset = data.frame(species = root.lda.data[,"Treatment"],
pca = root.log.pca$x, lda = root.lda.p$x)
(p1 <- ggplot(dataset) + geom_point(aes(lda.LD1, lda.LD2, colour = species, shape = species), size = 2.5, position = position_jitter(width=0.3, height=0.3)) + theme_gray() +
labs(x = paste("LD1 (", percent(prop.lda[1]), ")", sep=""),
y = paste("LD2 (", percent(prop.lda[2]), ")", sep="")))
#Vector for pathogenic species
c3 <- root.sp1[(root.sp1$mnv.p<0.05) & (root.sp1$ln.p<0.05),1]
#Read abundance data
Isolate_data <- read.csv("../data/clean/Isolates_11-12_final.csv")
#Summarise data by year
Data_11_12 <- ddply(Isolate_data, c("Species","State"), summarise,
N = as.numeric(length(qDef)),
freq = (N/length(Isolate_data$Year))*100
)
#Subsetting the data
Data_11_12.sub <- subset(Data_11_12, Data_11_12$Species %in% c3, c(Species, State, N))
D11_12 <- spread(Data_11_12.sub, State, N)
D11_12[is.na(D11_12)] <- 0
D11_12 <- gather(D11_12, State, N, Arkansas:Wisconsin)
D11_12$brk <- cut(D11_12$N, breaks = c(-0.5,0,10,50,100,150,200),
labels=c("0","1-10","10-50","50-100","100-150","150-200"))
#Tile plot
(prev.plot <- ggplot(D11_12, aes(x=State,y=reorder(Species, N, median))) +
geom_tile(aes(fill=brk)) +
scale_fill_brewer(palette = "GnBu", type = "seq", name="Isolates (n)") +
theme_bw() +
labs(y="Species") +
theme(axis.text.x=element_text(angle=-60, hjust=0, colour="black"),
axis.text.y=element_text(face="italic", colour="black")))
## Non pathogenic species prevalence
c4 <- root.sp1[(root.sp1$mnv.p>0.05) & (root.sp1$ln.p>0.05),1]
Data_11_12.sub2 <- subset(Data_11_12, Data_11_12$Species %in% c4, c(Species, State, N))
D11_12.2 <- spread(Data_11_12.sub2, State, N)
D11_12.2[is.na(D11_12.2)] <- 0
D11_12.2 <- gather(D11_12.2, State, N, Arkansas:Wisconsin)
D11_12.2$brk <- cut(D11_12.2$N, breaks = c(-0.5,0,10,50,100,150,200),
labels=c("0","1-10","10-50","50-100","100-150","150-200"))
(prev.plot2 <- ggplot(D11_12.2, aes(x=State,y=reorder(Species, N, median))) +
geom_tile(aes(fill=brk)) +
scale_fill_brewer(palette = "GnBu", type = "seq", name="Isolates (n)") +
theme_bw() +
labs(y="Species") +
theme(axis.text.x=element_text(angle=-60, hjust=0, colour="black"),
axis.text.y=element_text(face="italic", colour="black")))