library(pastecs)
options("scipen"=100, "digits"=3)
LMB_Sim<-subset(SimData, Species=="LMB")
Pred2LMB<-subset(LMB_Sim, Predators=="two")
Pred3LMB<-subset(LMB_Sim, Predators=="three")
stat.desc(Pred2LMB)
## Refuge_Time_Spent Food_Density Predators Species
## nbr.val 128.000000 128.0000 NA NA
## nbr.null 0.000000 0.0000 NA NA
## nbr.na 0.000000 0.0000 NA NA
## min 0.000768 0.2000 NA NA
## max 1.000000 1.0000 NA NA
## range 0.999232 0.8000 NA NA
## sum 60.391241 64.0000 NA NA
## median 0.457578 0.4000 NA NA
## mean 0.471807 0.5000 NA NA
## SE.mean 0.025412 0.0274 NA NA
## CI.mean.0.95 0.050285 0.0541 NA NA
## var 0.082657 0.0957 NA NA
## std.dev 0.287502 0.3094 NA NA
## coef.var 0.609364 0.6189 NA NA
stat.desc(Pred3LMB)
## Refuge_Time_Spent Food_Density Predators Species
## nbr.val 64.0000 64.0000 NA NA
## nbr.null 0.0000 0.0000 NA NA
## nbr.na 0.0000 0.0000 NA NA
## min 0.0279 0.6000 NA NA
## max 0.9662 1.0000 NA NA
## range 0.9383 0.4000 NA NA
## sum 32.7242 51.2000 NA NA
## median 0.5131 0.8000 NA NA
## mean 0.5113 0.8000 NA NA
## SE.mean 0.0266 0.0252 NA NA
## CI.mean.0.95 0.0532 0.0504 NA NA
## var 0.0454 0.0406 NA NA
## std.dev 0.2131 0.2016 NA NA
## coef.var 0.4168 0.2520 NA NA
stat.desc(LMB_Sim)
## Refuge_Time_Spent Food_Density Predators Species
## nbr.val 192.000000 192.0000 NA NA
## nbr.null 0.000000 0.0000 NA NA
## nbr.na 0.000000 0.0000 NA NA
## min 0.000768 0.2000 NA NA
## max 1.000000 1.0000 NA NA
## range 0.999232 0.8000 NA NA
## sum 93.115392 115.2000 NA NA
## median 0.483177 0.5500 NA NA
## mean 0.484976 0.6000 NA NA
## SE.mean 0.019134 0.0225 NA NA
## CI.mean.0.95 0.037740 0.0444 NA NA
## var 0.070290 0.0972 NA NA
## std.dev 0.265123 0.3117 NA NA
## coef.var 0.546671 0.5195 NA NA
library(pastecs)
options("scipen"=100, "digits"=3)
SMB_Sim<-subset(SimData, Species=="SMB")
Pred2SMB<-subset(SMB_Sim, Predators=="two")
Pred3SMB<-subset(SMB_Sim, Predators=="three")
stat.desc(Pred3SMB)
## Refuge_Time_Spent Food_Density Predators Species
## nbr.val 64.0000 64.0000 NA NA
## nbr.null 0.0000 0.0000 NA NA
## nbr.na 0.0000 0.0000 NA NA
## min 0.0244 0.6000 NA NA
## max 0.9679 1.0000 NA NA
## range 0.9435 0.4000 NA NA
## sum 35.2577 51.2000 NA NA
## median 0.5758 0.8000 NA NA
## mean 0.5509 0.8000 NA NA
## SE.mean 0.0245 0.0252 NA NA
## CI.mean.0.95 0.0489 0.0504 NA NA
## var 0.0384 0.0406 NA NA
## std.dev 0.1959 0.2016 NA NA
## coef.var 0.3555 0.2520 NA NA
stat.desc(Pred2SMB)
## Refuge_Time_Spent Food_Density Predators Species
## nbr.val 128.00000 128.0000 NA NA
## nbr.null 0.00000 0.0000 NA NA
## nbr.na 0.00000 0.0000 NA NA
## min 0.00874 0.2000 NA NA
## max 1.00000 1.0000 NA NA
## range 0.99126 0.8000 NA NA
## sum 53.53382 64.0000 NA NA
## median 0.35993 0.4000 NA NA
## mean 0.41823 0.5000 NA NA
## SE.mean 0.02507 0.0274 NA NA
## CI.mean.0.95 0.04960 0.0541 NA NA
## var 0.08043 0.0957 NA NA
## std.dev 0.28360 0.3094 NA NA
## coef.var 0.67810 0.6189 NA NA
stat.desc(SMB_Sim)
## Refuge_Time_Spent Food_Density Predators Species
## nbr.val 192.00000 192.0000 NA NA
## nbr.null 0.00000 0.0000 NA NA
## nbr.na 0.00000 0.0000 NA NA
## min 0.00874 0.2000 NA NA
## max 1.00000 1.0000 NA NA
## range 0.99126 0.8000 NA NA
## sum 88.79154 115.2000 NA NA
## median 0.44524 0.5500 NA NA
## mean 0.46246 0.6000 NA NA
## SE.mean 0.01910 0.0225 NA NA
## CI.mean.0.95 0.03768 0.0444 NA NA
## var 0.07006 0.0972 NA NA
## std.dev 0.26470 0.3117 NA NA
## coef.var 0.57237 0.5195 NA NA
SimData$Food_Density<-as.factor(SimData$Food_Density)
summary(SimData)
## Refuge_Time_Spent Food_Density Predators Species
## Min. :0.001 0.2: 64 three:128 LMB:192
## 1st Qu.:0.274 0.3: 64 two :256 SMB:192
## Median :0.475 0.5: 64
## Mean :0.474 0.6: 64
## 3rd Qu.:0.662 1 :128
## Max. :1.000
shapiro.test(SimData$Refuge_Time_Spent)
##
## Shapiro-Wilk normality test
##
## data: SimData$Refuge_Time_Spent
## W = 1, p-value = 0.000005
library(car)
## Loading required package: carData
leveneTest(SimData$Refuge_Time_Spent,SimData$Food_Density, center = mean)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 4 11.5 0.0000000079 ***
## 379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(SimData$Refuge_Time_Spent,SimData$Predators, center = mean)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 1 22.4 0.0000031 ***
## 382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hist(SimData$Refuge_Time_Spent)
SimData$sizelog<-log10(SimData$Refuge_Time_Spent)
shapiro.test(SimData$sizelog)
##
## Shapiro-Wilk normality test
##
## data: SimData$sizelog
## W = 0.8, p-value <0.0000000000000002
library(car)
leveneTest(SimData$sizelog,SimData$Food_Density, center = mean)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 4 9.24 0.00000039 ***
## 379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SimData$log<-log(SimData$Refuge_Time_Spent)
shapiro.test(SimData$log)
##
## Shapiro-Wilk normality test
##
## data: SimData$log
## W = 0.8, p-value <0.0000000000000002
library(car)
leveneTest(SimData$log,SimData$Food_Density, center = mean)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 4 9.24 0.00000039 ***
## 379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SimData$sqrt<-sqrt(SimData$Refuge_Time_Spent)
shapiro.test(SimData$sqrt)
##
## Shapiro-Wilk normality test
##
## data: SimData$sqrt
## W = 1, p-value = 0.000000007
library(car)
leveneTest(SimData$sqrt,SimData$Food_Density, center = mean)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 4 11.2 0.000000015 ***
## 379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
kruskal.test(Pred2LMB$Refuge_Time_Spent,Pred2LMB$Food_Density)
##
## Kruskal-Wallis rank sum test
##
## data: Pred2LMB$Refuge_Time_Spent and Pred2LMB$Food_Density
## Kruskal-Wallis chi-squared = 9, df = 3, p-value = 0.03
kruskal.test(Pred3LMB$Refuge_Time_Spent,Pred3LMB$Food_Density)
##
## Kruskal-Wallis rank sum test
##
## data: Pred3LMB$Refuge_Time_Spent and Pred3LMB$Food_Density
## Kruskal-Wallis chi-squared = 7, df = 1, p-value = 0.008
kruskal.test(Pred2SMB$Refuge_Time_Spent,Pred2SMB$Food_Density)
##
## Kruskal-Wallis rank sum test
##
## data: Pred2SMB$Refuge_Time_Spent and Pred2SMB$Food_Density
## Kruskal-Wallis chi-squared = 8, df = 3, p-value = 0.04
kruskal.test(Pred3SMB$Refuge_Time_Spent,Pred3SMB$Food_Density)
##
## Kruskal-Wallis rank sum test
##
## data: Pred3SMB$Refuge_Time_Spent and Pred3SMB$Food_Density
## Kruskal-Wallis chi-squared = 2, df = 1, p-value = 0.1
pairwise.wilcox.test(Pred2LMB$Refuge_Time_Spent,Pred2LMB$Food_Density)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: Pred2LMB$Refuge_Time_Spent and Pred2LMB$Food_Density
##
## 0.2 0.3 0.5
## 0.3 1.00 - -
## 0.5 0.23 0.07 -
## 1 1.00 1.00 0.05
##
## P value adjustment method: holm
pairwise.wilcox.test(Pred3LMB$Refuge_Time_Spent,Pred3LMB$Food_Density)
##
## Pairwise comparisons using Wilcoxon rank sum exact test
##
## data: Pred3LMB$Refuge_Time_Spent and Pred3LMB$Food_Density
##
## 0.6
## 1 0.007
##
## P value adjustment method: holm
pairwise.wilcox.test(Pred2SMB$Refuge_Time_Spent,Pred2SMB$Food_Density)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: Pred2SMB$Refuge_Time_Spent and Pred2SMB$Food_Density
##
## 0.2 0.3 0.5
## 0.3 1.00 - -
## 0.5 0.09 0.13 -
## 1 0.36 0.36 1.00
##
## P value adjustment method: holm
pairwise.wilcox.test(Pred3SMB$Refuge_Time_Spent,Pred3SMB$Food_Density)
##
## Pairwise comparisons using Wilcoxon rank sum exact test
##
## data: Pred3SMB$Refuge_Time_Spent and Pred3SMB$Food_Density
##
## 0.6
## 1 0.1
##
## P value adjustment method: holm
LMB_Sim$Food_Density<-as.factor(LMB_Sim$Food_Density)
library(RcmdrMisc)
## Loading required package: sandwich
plotMeans(LMB_Sim$Refuge_Time_Spent,LMB_Sim$Food_Density,LMB_Sim$Predators,error.bars = "sd",xlab = "Food density (#nymphes/cm2)", ylab = "Proportion of time spent in preferred habitat",desparse(substitute(LMB_Sim$Predators)),legend.lab = deparse(substitute(Predators)),legend.pos = "farright",main = "M.salmonid Habitat Use",pch = c(15,16),lty = 1,col = c("blue","purple"),connect = TRUE)
SMB_Sim$Food_Density<-as.factor(SMB_Sim$Food_Density)
library(RcmdrMisc)
plotMeans(SMB_Sim$Refuge_Time_Spent,SMB_Sim$Food_Density,SMB_Sim$Predators,error.bars = "sd",xlab = "Food density (#nymphes/cm2)", ylab = "Proportion of time spent in preferred habitat",desparse(substitute(SMB_Sim$Predators)),legend.lab = deparse(substitute(Predators)),legend.pos = "farright",main = "M.dolomiue Habitat Use",pch = c(15,16),lty = 1,col = c("blue","purple"),connect = TRUE)
