setwd("C:/Users/Nikol/Documents/Ciguatara Project/Data/CXT Data")
All = read.csv("CTX_All_NR.csv")
dim(All) #examine data## [1] 197 23head(All,2)# examine data## ï..Site Tag Species TL Age site_coral herbivore CTX3 EC50 CXT1B Buffer
## 1 Anaehoom AB02 CEAR 35 10 14.23 1827.46 0 0 0 40
## 2 Anaehoom AB05 CEAR 47 22 14.23 1827.46 0 0 0 40
## boulder hard_bottom soft_bottom coral unknown wave effluent fishing
## 1 0.000 0 0 1.000 0 2.62089 27.8167
## 2 0.128 0 0 0.872 0 2.64007 35.3690
## mean_depth BAA3 BAA5 BAA7
## 1 -5.9999 67 69 121
## 2 -4.9986 67 69 121library(psych)
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphalibrary(ggpubr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(AICcmodavg)CTST = subset(All, Species =="CTST")
dim(CTST) #examine data## [1] 109 23headTail(CTST,2)# examine data## ï..Site Tag Species TL Age site_coral herbivore CTX3 EC50 CXT1B Buffer
## 89 Anaehoom AB01 CTST 13 6 14.23 1827.46 0 0 0 20
## 90 Anaehoom AB03 CTST 13 4 14.23 1827.46 1 1 0.4 20
## ... <NA> <NA> <NA> <NA> <NA> ... ... <NA> <NA> <NA> ...
## 194 Puako PU14 CTST 13 3 4.13 2827.84 0 0 0 20
## 195 Puako PU15 CTST 15 5 4.13 2827.84 . . . 20
## 196 Puako PU22 CTST 18 7 4.13 2827.84 . . . 20
## 197 Puako PU28 CTST 15 6 4.13 2827.84 0 0 0 20
## boulder hard_bottom soft_bottom coral unknown wave effluent fishing
## 89 0 0 0 1 0 2.61 37.6
## 90 0 0 0 1 0 2.61 37.36
## ... ... ... ... ... ... ... <NA> ...
## 194 0 0 0 1 0 1.99 13035.25 28.52
## 195 0 0 0 1 0 2.04 5192.94 38.49
## 196 0 0 0 0.96 0.04 1.91 9700.96 39.16
## 197 0 0 0 0.96 0.04 1.91 9718.17 39.24
## mean_depth BAA3 BAA5 BAA7
## 89 -4.1565 67 69 .
## 90 -4.0627 67 . .
## ... <NA> <NA> <NA> <NA>
## 194 -5.8475 67 . .
## 195 -4.808 67 68 .
## 196 -5.5423 67 68 119
## 197 -5.0679 67 68 .CEAR = subset(All, Species =="CEAR")
dim(CEAR) #examine data## [1] 88 23headTail(CEAR,2)# examine data## ï..Site Tag Species TL Age site_coral herbivore CTX3 EC50 CXT1B
## 1 Anaehoom AB02 CEAR 35 10 14.23 1827.46 0 0 0
## 2 Anaehoom AB05 CEAR 47 22 14.23 1827.46 0 0 0
## ... <NA> <NA> <NA> <NA> <NA> ... ... <NA> <NA> <NA>
## 85 Puako PU37 CEAR 32 8.1148 4.13 2827.84 1 3 0.13333
## 86 Puako PU38 CEAR 38 9 4.13 2827.84 1 5.5 0.07273
## 87 Puako PU39 CEAR 34 8 4.13 2827.84 1 2 0.2
## 88 Puako PU40 CEAR 48.5 21.6393 4.13 2827.84 1 0.3 1.33333
## Buffer boulder hard_bottom soft_bottom coral unknown wave effluent fishing
## 1 40 0 0 0 1 0 2.62 27.82
## 2 40 0.13 0 0 0.87 0 2.64 35.37
## ... ... ... ... ... ... ... ... <NA> ...
## 85 40 0 0 0.14 0.84 0.02 2.25 1302.29 27.05
## 86 40 0 0 0.14 0.84 0.02 2.25 1302.29 27.05
## 87 40 0 0 0.14 0.84 0.02 2.25 1302.29 27.05
## 88 40 0 0 0.14 0.84 0.02 2.25 1302.29 27.05
## mean_depth BAA3 BAA5 BAA7
## 1 -5.9999 67 69 121
## 2 -4.9986 67 69 121
## ... <NA> <NA> <NA> <NA>
## 85 -5.7854 61 67 101
## 86 -5.7854 61 67 101
## 87 -5.7854 61 67 101
## 88 . 61 67 101##Check structure of data and change character to numeric
#convert column 'a' from character to numeric
str(CTST)## 'data.frame': 109 obs. of 23 variables:
## $ ï..Site : chr "Anaehoom" "Anaehoom" "Anaehoom" "Anaehoom" ...
## $ Tag : chr "AB01" "AB03" "AB04" "AB08" ...
## $ Species : chr "CTST" "CTST" "CTST" "CTST" ...
## $ TL : chr "13" "13" "13.5" "12" ...
## $ Age : chr "6" "4" "3" "2" ...
## $ site_coral : num 14.2 14.2 14.2 14.2 14.2 ...
## $ herbivore : num 1827 1827 1827 1827 1827 ...
## $ CTX3 : chr "0" "1" "0" "1" ...
## $ EC50 : chr "0" "1" "0" "0.75" ...
## $ CXT1B : chr "0" "0.4" "0" "0.53333" ...
## $ Buffer : int 20 20 20 20 20 20 20 20 20 20 ...
## $ boulder : num 0 0 1 0 0 0 1 0.128 0 0.128 ...
## $ hard_bottom: num 0 0 0 0 0 0 0 0 0 0 ...
## $ soft_bottom: num 0 0 0 0 0 0 0 0 0 0 ...
## $ coral : num 1 1 0 1 1 1 0 0.872 1 0.872 ...
## $ unknown : num 0 0 0 0 0 0 0 0 0 0 ...
## $ wave : num 2.61 2.61 2.61 2.61 2.61 ...
## $ effluent : chr "" "" "" "" ...
## $ fishing : num 37.6 37.4 39.9 37.7 37.3 ...
## $ mean_depth : chr "-4.1565" "-4.0627" "-1.3427" "-3.1513" ...
## $ BAA3 : chr "67" "67" "67" "." ...
## $ BAA5 : chr "69" "." "." "." ...
## $ BAA7 : chr "." "." "." "." ...CTST$boulder = as.numeric(CTST$boulder)
CTST$hard_bottom = as.numeric(CTST$hard_bottom)
CTST$soft_bottom = as.numeric(CTST$soft_bottom)
CTST$coral = as.numeric(CTST$coral)
CTST$unknown = as.numeric(CTST$unknown)
CTST$wave = as.numeric(CTST$wave)
CTST$effluent = as.numeric(CTST$effluent)
CTST$fishing = as.numeric(CTST$fishing)
CTST$mean_depth = as.numeric(CTST$mean_depth)
CTST$BAA3 = as.numeric(CTST$BAA3)## Warning: NAs introduced by coercionCTST$TL = as.numeric(CTST$TL)## Warning: NAs introduced by coercionCTST$Age = as.numeric(CTST$Age)## Warning: NAs introduced by coercionstr(CTST)## 'data.frame': 109 obs. of 23 variables:
## $ ï..Site : chr "Anaehoom" "Anaehoom" "Anaehoom" "Anaehoom" ...
## $ Tag : chr "AB01" "AB03" "AB04" "AB08" ...
## $ Species : chr "CTST" "CTST" "CTST" "CTST" ...
## $ TL : num 13 13 13.5 12 13.5 14.5 12.5 13 12.5 15.5 ...
## $ Age : num 6 4 3 2 8 ...
## $ site_coral : num 14.2 14.2 14.2 14.2 14.2 ...
## $ herbivore : num 1827 1827 1827 1827 1827 ...
## $ CTX3 : chr "0" "1" "0" "1" ...
## $ EC50 : chr "0" "1" "0" "0.75" ...
## $ CXT1B : chr "0" "0.4" "0" "0.53333" ...
## $ Buffer : int 20 20 20 20 20 20 20 20 20 20 ...
## $ boulder : num 0 0 1 0 0 0 1 0.128 0 0.128 ...
## $ hard_bottom: num 0 0 0 0 0 0 0 0 0 0 ...
## $ soft_bottom: num 0 0 0 0 0 0 0 0 0 0 ...
## $ coral : num 1 1 0 1 1 1 0 0.872 1 0.872 ...
## $ unknown : num 0 0 0 0 0 0 0 0 0 0 ...
## $ wave : num 2.61 2.61 2.61 2.61 2.61 ...
## $ effluent : num NA NA NA NA NA NA NA NA NA NA ...
## $ fishing : num 37.6 37.4 39.9 37.7 37.3 ...
## $ mean_depth : num -4.16 -4.06 -1.34 -3.15 -4.25 ...
## $ BAA3 : num 67 67 67 NA 67 NA 67 67 67 67 ...
## $ BAA5 : chr "69" "." "." "." ...
## $ BAA7 : chr "." "." "." "." ...#standerize data in data frame and put 0 into missing values
# Standardize all numeric columns (excluding factors, character, etc.)
numeric_cols <- sapply(CTST, is.numeric)
CTST[numeric_cols] <- scale(CTST[numeric_cols])
headTail(CTST)## ï..Site Tag Species TL Age site_coral herbivore CTX3 EC50 CXT1B
## 89 Anaehoom AB01 CTST -0.85 1.49 0.07 -0.91 0 0 0
## 90 Anaehoom AB03 CTST -0.85 -0.09 0.07 -0.91 1 1 0.4
## 91 Anaehoom AB04 CTST -0.57 -0.88 0.07 -0.91 0 0 0
## 92 Anaehoom AB08 CTST -1.42 -1.67 0.07 -0.91 1 0.75 0.53333
## ... <NA> <NA> <NA> ... ... ... ... <NA> <NA> <NA>
## 194 Puako PU14 CTST -0.85 -0.88 -2.46 -0.59 0 0 0
## 195 Puako PU15 CTST 0.28 0.7 -2.46 -0.59 . . .
## 196 Puako PU22 CTST 1.99 2.28 -2.46 -0.59 . . .
## 197 Puako PU28 CTST 0.28 1.49 -2.46 -0.59 0 0 0
## Buffer boulder hard_bottom soft_bottom coral unknown wave effluent fishing
## 89 NaN -0.36 -0.1 -0.17 0.44 -0.25 -0.74 <NA> 0.69
## 90 NaN -0.36 -0.1 -0.17 0.44 -0.25 -0.74 <NA> 0.66
## 91 NaN 5.51 -0.1 -0.17 -3.29 -0.25 -0.74 <NA> 0.95
## 92 NaN -0.36 -0.1 -0.17 0.44 -0.25 -0.74 <NA> 0.7
## ... ... ... ... ... ... ... ... ... ...
## 194 NaN -0.36 -0.1 -0.17 0.44 -0.25 -1.41 2.03 -0.36
## 195 NaN -0.36 -0.1 -0.17 0.44 -0.25 -1.36 -0.09 0.79
## 196 NaN -0.36 -0.1 -0.17 0.29 -0.06 -1.5 1.13 0.87
## 197 NaN -0.36 -0.1 -0.17 0.29 -0.06 -1.5 1.13 0.88
## mean_depth BAA3 BAA5 BAA7
## 89 0.99 0.97 69 .
## 90 1.02 0.97 . .
## 91 1.77 0.97 . .
## 92 1.27 <NA> . .
## ... ... ... <NA> <NA>
## 194 0.53 0.97 . .
## 195 0.81 0.97 68 .
## 196 0.61 0.97 68 119
## 197 0.74 0.97 68 .# Replace missing values (NA) with 0
CTST[is.na(CTST)] <- 0
headTail(CTST)## ï..Site Tag Species TL Age site_coral herbivore CTX3 EC50 CXT1B
## 89 Anaehoom AB01 CTST -0.85 1.49 0.07 -0.91 0 0 0
## 90 Anaehoom AB03 CTST -0.85 -0.09 0.07 -0.91 1 1 0.4
## 91 Anaehoom AB04 CTST -0.57 -0.88 0.07 -0.91 0 0 0
## 92 Anaehoom AB08 CTST -1.42 -1.67 0.07 -0.91 1 0.75 0.53333
## ... <NA> <NA> <NA> ... ... ... ... <NA> <NA> <NA>
## 194 Puako PU14 CTST -0.85 -0.88 -2.46 -0.59 0 0 0
## 195 Puako PU15 CTST 0.28 0.7 -2.46 -0.59 . . .
## 196 Puako PU22 CTST 1.99 2.28 -2.46 -0.59 . . .
## 197 Puako PU28 CTST 0.28 1.49 -2.46 -0.59 0 0 0
## Buffer boulder hard_bottom soft_bottom coral unknown wave effluent fishing
## 89 0 -0.36 -0.1 -0.17 0.44 -0.25 -0.74 0 0.69
## 90 0 -0.36 -0.1 -0.17 0.44 -0.25 -0.74 0 0.66
## 91 0 5.51 -0.1 -0.17 -3.29 -0.25 -0.74 0 0.95
## 92 0 -0.36 -0.1 -0.17 0.44 -0.25 -0.74 0 0.7
## ... ... ... ... ... ... ... ... ... ...
## 194 0 -0.36 -0.1 -0.17 0.44 -0.25 -1.41 2.03 -0.36
## 195 0 -0.36 -0.1 -0.17 0.44 -0.25 -1.36 -0.09 0.79
## 196 0 -0.36 -0.1 -0.17 0.29 -0.06 -1.5 1.13 0.87
## 197 0 -0.36 -0.1 -0.17 0.29 -0.06 -1.5 1.13 0.88
## mean_depth BAA3 BAA5 BAA7
## 89 0.99 0.97 69 .
## 90 1.02 0.97 . .
## 91 1.77 0.97 . .
## 92 1.27 0 . .
## ... ... ... <NA> <NA>
## 194 0.53 0.97 . .
## 195 0.81 0.97 68 .
## 196 0.61 0.97 68 119
## 197 0.74 0.97 68 .CTST$CTX3= as.numeric(CTST$CTX3)## Warning: NAs introduced by coercionstr(CTST)## 'data.frame': 109 obs. of 23 variables:
## $ ï..Site : chr "Anaehoom" "Anaehoom" "Anaehoom" "Anaehoom" ...
## $ Tag : chr "AB01" "AB03" "AB04" "AB08" ...
## $ Species : chr "CTST" "CTST" "CTST" "CTST" ...
## $ TL : num -0.851 -0.851 -0.568 -1.419 -0.568 ...
## $ Age : num 1.4859 -0.0935 -0.8832 -1.6729 3.0653 ...
## $ site_coral : num 0.074 0.074 0.074 0.074 0.074 ...
## $ herbivore : num -0.908 -0.908 -0.908 -0.908 -0.908 ...
## $ CTX3 : num 0 1 0 1 1 1 1 1 0 1 ...
## $ EC50 : chr "0" "1" "0" "0.75" ...
## $ CXT1B : chr "0" "0.4" "0" "0.53333" ...
## $ Buffer : num 0 0 0 0 0 0 0 0 0 0 ...
## $ boulder : num -0.364 -0.364 5.506 -0.364 -0.364 ...
## $ hard_bottom: num -0.102 -0.102 -0.102 -0.102 -0.102 ...
## $ soft_bottom: num -0.169 -0.169 -0.169 -0.169 -0.169 ...
## $ coral : num 0.443 0.443 -3.293 0.443 0.443 ...
## $ unknown : num -0.25 -0.25 -0.25 -0.25 -0.25 ...
## $ wave : num -0.741 -0.737 -0.742 -0.742 -0.736 ...
## $ effluent : num 0 0 0 0 0 0 0 0 0 0 ...
## $ fishing : num 0.689 0.662 0.949 0.703 0.653 ...
## $ mean_depth : num 0.993 1.018 1.766 1.269 0.967 ...
## $ BAA3 : num 0.972 0.972 0.972 0 0.972 ...
## $ BAA5 : chr "69" "." "." "." ...
## $ BAA7 : chr "." "." "." "." ...#fit models
model1 <- glm(CTX3 ~ TL + BAA3, family="binomial", data = CTST)
model2 <- glm(CTX3 ~ TL + BAA3 + fishing, family="binomial", data = CTST)
model3 <- glm(CTX3 ~ TL + fishing, family="binomial", data = CTST)
model4 = glm(CTX3 ~ TL + fishing + effluent, family="binomial", data = CTST)
model5 = glm(CTX3 ~ herbivore + BAA3, family="binomial", data = CTST)
model6 = glm(CTX3 ~ TL + effluent, family="binomial", data = CTST)
model7 = glm(CTX3~ TL + mean_depth, family="binomial", data = CTST)
model8 = glm(CTX3 ~ coral + boulder + TL + BAA3, family="binomial", data = CTST)
model9 = glm(CTX3 ~ TL, family="binomial", data = CTST)
model10 = glm(CTX3 ~ TL + coral + boulder + herbivore, family="binomial", data = CTST)
model11 = glm(CTX3 ~ TL + wave, family="binomial", data = CTST)
model12 = glm(CTX3 ~ TL + Age, family="binomial", data = CTST)
model13 = glm(CTX3 ~ TL + coral + boulder, family="binomial", data = CTST)
model14 = glm(CTX3 ~ TL + site_coral + herbivore, family="binomial", data = CTST)
model15 = glm(CTX3 ~ BAA3, family="binomial", data = CTST)
model16 = glm(CTX3 ~ herbivore + fishing, family="binomial", data = CTST)
model17 = glm(CTX3 ~ site_coral + fishing, family="binomial", data = CTST)
model18 = glm(CTX3 ~ herbivore + fishing + mean_depth, family="binomial", data = CTST)
model19 = glm(CTX3 ~ coral + boulder + hard_bottom + soft_bottom, family="binomial", data = CTST)
model20 = glm(CTX3 ~ site_coral + effluent, family="binomial", data = CTST)
model21 = glm(CTX3 ~ site_coral, family="binomial", data = CTST)
model22 = glm(CTX3 ~ herbivore, family="binomial", data = CTST)
model23 = glm(CTX3 ~ wave, family="binomial", data = CTST)
model24 = glm(CTX3 ~ TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom, family="binomial", data = CTST)
model25 = glm(CTX3~1,family="binomial", data = CTST)#define list of models
models <- list(model1, model2, model3, model4, model5, model6, model7, model8, model9, model10, model11, model12, model13, model14, model15, model16, model17, model18, model19, model20, model21, model22, model23, model24, model25)#specify model names
mod.names <- c('TL + BAA3', 'TL + BAA3 + fishing', 'TL + fishing', 'TL + fishing + effluent', 'herbivore + BAA3', 'TL + effluent', 'TL + mean_depth', 'coral + boulder + TL + BAA3', 'TL, data', 'TL + coral + boulder + herbivore', 'TL + wave', 'TL + Age', 'TL + coral + boulder', 'TL + site_coral + herbivore_biomass', 'BAA3', 'herbivore_biomass + fishing', 'site_coral + fishing', 'herbivore + fishing + mean_depth', 'coral + boulder + hard_bottom + soft_bottom', 'site_coral + effluent', 'site_coral', 'herbivore', 'wave', 'TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom', '1')
#calculate AIC of each model
aictab(cand.set = models, modnames = mod.names)##
## Model selection based on AICc:
##
## K
## site_coral + fishing 3
## TL + BAA3 + fishing 4
## TL + fishing + effluent 4
## TL + BAA3 3
## TL + effluent 3
## TL + fishing 3
## site_coral + effluent 3
## coral + boulder + TL + BAA3 5
## TL + site_coral + herbivore_biomass 4
## site_coral 2
## herbivore_biomass + fishing 3
## BAA3 2
## herbivore + fishing + mean_depth 4
## TL + wave 3
## herbivore + BAA3 3
## TL + mean_depth 3
## TL, data 2
## TL + Age 3
## 1 1
## TL + coral + boulder 4
## wave 2
## TL + coral + boulder + herbivore 5
## herbivore 2
## TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom 14
## coral + boulder + hard_bottom + soft_bottom 5
## AICc
## site_coral + fishing 88.33
## TL + BAA3 + fishing 89.42
## TL + fishing + effluent 90.35
## TL + BAA3 91.48
## TL + effluent 91.52
## TL + fishing 91.60
## site_coral + effluent 91.92
## coral + boulder + TL + BAA3 95.26
## TL + site_coral + herbivore_biomass 96.49
## site_coral 96.57
## herbivore_biomass + fishing 96.95
## BAA3 97.19
## herbivore + fishing + mean_depth 99.08
## TL + wave 99.31
## herbivore + BAA3 99.34
## TL + mean_depth 99.47
## TL, data 100.95
## TL + Age 102.89
## 1 103.52
## TL + coral + boulder 103.62
## wave 103.79
## TL + coral + boulder + herbivore 104.95
## herbivore 105.53
## TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom 105.87
## coral + boulder + hard_bottom + soft_bottom 110.43
## Delta_AICc
## site_coral + fishing 0.00
## TL + BAA3 + fishing 1.09
## TL + fishing + effluent 2.02
## TL + BAA3 3.15
## TL + effluent 3.19
## TL + fishing 3.27
## site_coral + effluent 3.59
## coral + boulder + TL + BAA3 6.93
## TL + site_coral + herbivore_biomass 8.16
## site_coral 8.24
## herbivore_biomass + fishing 8.62
## BAA3 8.86
## herbivore + fishing + mean_depth 10.75
## TL + wave 10.98
## herbivore + BAA3 11.01
## TL + mean_depth 11.14
## TL, data 12.62
## TL + Age 14.56
## 1 15.19
## TL + coral + boulder 15.29
## wave 15.46
## TL + coral + boulder + herbivore 16.62
## herbivore 17.20
## TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom 17.54
## coral + boulder + hard_bottom + soft_bottom 22.10
## AICcWt
## site_coral + fishing 0.35
## TL + BAA3 + fishing 0.21
## TL + fishing + effluent 0.13
## TL + BAA3 0.07
## TL + effluent 0.07
## TL + fishing 0.07
## site_coral + effluent 0.06
## coral + boulder + TL + BAA3 0.01
## TL + site_coral + herbivore_biomass 0.01
## site_coral 0.01
## herbivore_biomass + fishing 0.00
## BAA3 0.00
## herbivore + fishing + mean_depth 0.00
## TL + wave 0.00
## herbivore + BAA3 0.00
## TL + mean_depth 0.00
## TL, data 0.00
## TL + Age 0.00
## 1 0.00
## TL + coral + boulder 0.00
## wave 0.00
## TL + coral + boulder + herbivore 0.00
## herbivore 0.00
## TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom 0.00
## coral + boulder + hard_bottom + soft_bottom 0.00
## Cum.Wt
## site_coral + fishing 0.35
## TL + BAA3 + fishing 0.56
## TL + fishing + effluent 0.69
## TL + BAA3 0.76
## TL + effluent 0.83
## TL + fishing 0.90
## site_coral + effluent 0.96
## coral + boulder + TL + BAA3 0.97
## TL + site_coral + herbivore_biomass 0.98
## site_coral 0.98
## herbivore_biomass + fishing 0.99
## BAA3 0.99
## herbivore + fishing + mean_depth 0.99
## TL + wave 1.00
## herbivore + BAA3 1.00
## TL + mean_depth 1.00
## TL, data 1.00
## TL + Age 1.00
## 1 1.00
## TL + coral + boulder 1.00
## wave 1.00
## TL + coral + boulder + herbivore 1.00
## herbivore 1.00
## TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom 1.00
## coral + boulder + hard_bottom + soft_bottom 1.00
## LL
## site_coral + fishing -41.01
## TL + BAA3 + fishing -40.44
## TL + fishing + effluent -40.91
## TL + BAA3 -42.58
## TL + effluent -42.60
## TL + fishing -42.64
## site_coral + effluent -42.80
## coral + boulder + TL + BAA3 -42.22
## TL + site_coral + herbivore_biomass -43.98
## site_coral -46.21
## herbivore_biomass + fishing -45.32
## BAA3 -46.52
## herbivore + fishing + mean_depth -45.27
## TL + wave -46.49
## herbivore + BAA3 -46.51
## TL + mean_depth -46.58
## TL, data -48.40
## TL + Age -48.29
## 1 -50.73
## TL + coral + boulder -47.54
## wave -49.81
## TL + coral + boulder + herbivore -47.06
## herbivore -50.69
## TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom -35.66
## coral + boulder + hard_bottom + soft_bottom -49.81model1 <- glm(CTX3 ~ TL + BAA3, family="binomial", data = CTST)
summary(model1)##
## Call:
## glm(formula = CTX3 ~ TL + BAA3, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0284 -0.9713 0.5458 0.7650 1.7769
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7249 0.2705 2.680 0.00737 **
## TL -0.8091 0.3066 -2.639 0.00831 **
## BAA3 -1.1179 0.3633 -3.077 0.00209 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 85.16 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 91.16
##
## Number of Fisher Scoring iterations: 4model2 <- glm(CTX3 ~ TL + BAA3 + fishing, family="binomial", data = CTST)
summary(model2)##
## Call:
## glm(formula = CTX3 ~ TL + BAA3 + fishing, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3611 -0.8974 0.4277 0.8113 1.6634
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.8172 0.2972 2.749 0.00597 **
## TL -0.8022 0.3118 -2.573 0.01009 *
## BAA3 -0.7766 0.3870 -2.007 0.04478 *
## fishing -0.8665 0.4668 -1.857 0.06338 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 80.882 on 75 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 88.882
##
## Number of Fisher Scoring iterations: 5model3 <- glm(CTX3 ~ TL + fishing, family="binomial", data = CTST)
summary(model3)##
## Call:
## glm(formula = CTX3 ~ TL + fishing, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2820 -1.0491 0.4089 0.9265 1.4920
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.8438 0.2924 2.886 0.00390 **
## TL -0.6538 0.2938 -2.225 0.02607 *
## fishing -1.1706 0.4398 -2.662 0.00777 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 85.28 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 91.28
##
## Number of Fisher Scoring iterations: 5model4 = glm(CTX3 ~ TL + fishing + effluent, family="binomial", data = CTST)
summary(model4)##
## Call:
## glm(formula = CTX3 ~ TL + fishing + effluent, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9960 -0.9998 0.3629 0.8919 1.5794
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7512 0.2973 2.527 0.0115 *
## TL -0.6963 0.3013 -2.311 0.0208 *
## fishing -0.7818 0.4670 -1.674 0.0941 .
## effluent -0.9800 0.5484 -1.787 0.0739 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 81.812 on 75 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 89.812
##
## Number of Fisher Scoring iterations: 5model5 = glm(CTX3 ~ herbivore + BAA3, family="binomial", data = CTST)
summary(model5)##
## Call:
## glm(formula = CTX3 ~ herbivore + BAA3, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0425 -1.0794 0.5812 0.9371 1.2787
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.63368 0.25566 2.479 0.01319 *
## herbivore -0.02527 0.22707 -0.111 0.91139
## BAA3 -0.91719 0.34820 -2.634 0.00844 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 93.019 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 99.019
##
## Number of Fisher Scoring iterations: 4model6 = glm(CTX3 ~ TL + effluent, family="binomial", data = CTST)
summary(model6)##
## Call:
## glm(formula = CTX3 ~ TL + effluent, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0356 -1.0532 0.4226 0.8735 1.5840
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6116 0.2690 2.273 0.02300 *
## TL -0.6420 0.2931 -2.191 0.02848 *
## effluent -1.4113 0.5021 -2.811 0.00494 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 85.20 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 91.2
##
## Number of Fisher Scoring iterations: 5model7 = glm(CTX3~ TL + mean_depth, family="binomial", data = CTST)
summary(model7)##
## Call:
## glm(formula = CTX3 ~ TL + mean_depth, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2377 -1.1751 0.6480 0.9324 1.5083
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.8143 0.2658 3.063 0.00219 **
## TL -0.6566 0.2905 -2.260 0.02379 *
## mean_depth -0.5807 0.3178 -1.827 0.06767 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 93.151 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 99.151
##
## Number of Fisher Scoring iterations: 4model8 = glm(CTX3 ~ coral + boulder + TL + BAA3, family="binomial", data = CTST)
summary(model8)##
## Call:
## glm(formula = CTX3 ~ coral + boulder + TL + BAA3, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0112 -0.9598 0.5325 0.7454 1.7844
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7392 0.2772 2.667 0.00766 **
## coral -0.1802 0.3207 -0.562 0.57421
## boulder -0.2405 0.2947 -0.816 0.41444
## TL -0.8427 0.3148 -2.677 0.00744 **
## BAA3 -1.0887 0.3669 -2.967 0.00300 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 84.443 on 74 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 94.443
##
## Number of Fisher Scoring iterations: 4model9 = glm(CTX3 ~ TL, family="binomial", data = CTST)
summary(model9)##
## Call:
## glm(formula = CTX3 ~ TL, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8039 -1.2411 0.7122 0.9433 1.4620
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7450 0.2516 2.96 0.00307 **
## TL -0.5843 0.2783 -2.10 0.03574 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 96.793 on 77 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 100.79
##
## Number of Fisher Scoring iterations: 4model10 = glm(CTX3 ~ TL + coral + boulder + herbivore, family="binomial", data = CTST)
summary(model10)##
## Call:
## glm(formula = CTX3 ~ TL + coral + boulder + herbivore, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9624 -1.1984 0.6786 0.9026 1.4617
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.77310 0.26088 2.963 0.00304 **
## TL -0.74662 0.31069 -2.403 0.01626 *
## coral -0.03327 0.32188 -0.103 0.91768
## boulder -0.32243 0.29071 -1.109 0.26738
## herbivore 0.23806 0.24806 0.960 0.33720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 94.13 on 74 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 104.13
##
## Number of Fisher Scoring iterations: 4model11 = glm(CTX3 ~ TL + wave, family="binomial", data = CTST)
summary(model11)##
## Call:
## glm(formula = CTX3 ~ TL + wave, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9537 -1.1249 0.6520 0.9081 1.6267
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7600 0.2591 2.934 0.00335 **
## TL -0.7440 0.3044 -2.444 0.01452 *
## wave 0.5218 0.2779 1.878 0.06042 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 92.986 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 98.986
##
## Number of Fisher Scoring iterations: 4model12 = glm(CTX3 ~ TL + Age, family="binomial", data = CTST)
summary(model12)##
## Call:
## glm(formula = CTX3 ~ TL + Age, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8243 -1.2509 0.7092 0.9382 1.4356
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7420 0.2523 2.941 0.00327 **
## TL -0.5170 0.3120 -1.657 0.09749 .
## Age -0.1418 0.3021 -0.469 0.63873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 96.575 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 102.57
##
## Number of Fisher Scoring iterations: 4model13 = glm(CTX3 ~ TL + coral + boulder, family="binomial", data = CTST)
summary(model13)##
## Call:
## glm(formula = CTX3 ~ TL + coral + boulder, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8864 -1.1349 0.6945 0.9075 1.5147
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7846 0.2594 3.025 0.00249 **
## TL -0.6501 0.2886 -2.252 0.02429 *
## coral -0.1315 0.3044 -0.432 0.66577
## boulder -0.3572 0.2850 -1.253 0.21007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 95.077 on 75 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 103.08
##
## Number of Fisher Scoring iterations: 4model14 = glm(CTX3 ~ TL + site_coral + herbivore, family="binomial", data = CTST)
summary(model14)##
## Call:
## glm(formula = CTX3 ~ TL + site_coral + herbivore, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2128 -1.2298 0.6406 0.8811 1.3963
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6598 0.2787 2.367 0.0179 *
## TL -0.6463 0.3180 -2.032 0.0421 *
## site_coral 1.2291 0.5925 2.074 0.0381 *
## herbivore 0.1014 0.2385 0.425 0.6707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 87.954 on 75 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 95.954
##
## Number of Fisher Scoring iterations: 5model15 = glm(CTX3 ~ BAA3, family="binomial", data = CTST)
summary(model15)##
## Call:
## glm(formula = CTX3 ~ BAA3, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0525 -1.0707 0.5907 0.9245 1.2880
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6288 0.2515 2.500 0.01241 *
## BAA3 -0.9106 0.3422 -2.661 0.00779 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 93.032 on 77 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 97.032
##
## Number of Fisher Scoring iterations: 3model16 = glm(CTX3 ~ herbivore + fishing, family="binomial", data = CTST)
summary(model16)##
## Call:
## glm(formula = CTX3 ~ herbivore + fishing, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2867 -1.1571 0.5431 0.9395 1.3014
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7044 0.2749 2.562 0.01040 *
## herbivore 0.0242 0.2331 0.104 0.91732
## fishing -1.0209 0.3926 -2.600 0.00933 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 90.633 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 96.633
##
## Number of Fisher Scoring iterations: 5model17 = glm(CTX3 ~ site_coral + fishing, family="binomial", data = CTST)
summary(model17)##
## Call:
## glm(formula = CTX3 ~ site_coral + fishing, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3100 -1.1291 0.4813 0.8567 1.3308
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.5543 0.3078 1.801 0.07172 .
## site_coral 1.4052 0.7148 1.966 0.04932 *
## fishing -1.0658 0.4119 -2.587 0.00968 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 82.01 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 88.01
##
## Number of Fisher Scoring iterations: 5model18 = glm(CTX3 ~ herbivore + fishing + mean_depth, family="binomial", data = CTST)
summary(model18)##
## Call:
## glm(formula = CTX3 ~ herbivore + fishing + mean_depth, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2898 -1.1494 0.5482 0.9397 1.3486
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.72109 0.28123 2.564 0.0103 *
## herbivore -0.01531 0.26687 -0.057 0.9542
## fishing -0.98973 0.40825 -2.424 0.0153 *
## mean_depth -0.10583 0.34684 -0.305 0.7603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 90.539 on 75 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 98.539
##
## Number of Fisher Scoring iterations: 5model19 = glm(CTX3 ~ coral + boulder + hard_bottom + soft_bottom, family="binomial", data = CTST)
summary(model19)##
## Call:
## glm(formula = CTX3 ~ coral + boulder + hard_bottom + soft_bottom,
## family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7334 -1.2672 0.8516 0.8777 1.3046
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.18232 0.60553 0.301 0.763
## coral -0.11315 0.29958 -0.378 0.706
## boulder -0.26309 0.27805 -0.946 0.344
## hard_bottom -5.72108 6.47606 -0.883 0.377
## soft_bottom -0.09371 0.20831 -0.450 0.653
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 99.61 on 74 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 109.61
##
## Number of Fisher Scoring iterations: 4model20 = glm(CTX3 ~ site_coral + effluent, family="binomial", data = CTST)
summary(model20)##
## Call:
## glm(formula = CTX3 ~ site_coral + effluent, family = "binomial",
## data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1343 -1.3583 0.4974 0.9531 1.0067
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3233 0.3229 1.001 0.317
## site_coral 1.2487 0.8028 1.555 0.120
## effluent -1.2181 0.5473 -2.226 0.026 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 85.597 on 76 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 91.597
##
## Number of Fisher Scoring iterations: 6model21 = glm(CTX3 ~ site_coral, family="binomial", data = CTST)
summary(model21)##
## Call:
## glm(formula = CTX3 ~ site_coral, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8921 -1.4713 0.8603 0.9096 0.9096
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.5786 0.2618 2.210 0.0271 *
## site_coral 1.2189 0.5531 2.204 0.0275 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 92.413 on 77 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 96.413
##
## Number of Fisher Scoring iterations: 4model22 = glm(CTX3 ~ herbivore, family="binomial", data = CTST)
summary(model22)##
## Call:
## glm(formula = CTX3 ~ herbivore, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5044 -1.4351 0.8825 0.9399 0.9399
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.64594 0.23907 2.702 0.00689 **
## herbivore 0.06379 0.21158 0.301 0.76305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 101.38 on 77 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 105.38
##
## Number of Fisher Scoring iterations: 4model23 = glm(CTX3 ~ wave, family="binomial", data = CTST)
summary(model23)##
## Call:
## glm(formula = CTX3 ~ wave, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7821 -1.3491 0.7925 1.0109 1.0150
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6510 0.2402 2.710 0.00672 **
## wave 0.3451 0.2603 1.326 0.18494
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 99.63 on 77 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 103.63
##
## Number of Fisher Scoring iterations: 4model24 = glm(CTX3 ~ TL + BAA3 + fishing + effluent + herbivore + mean_depth + site_coral + wave + Age + coral + boulder + hard_bottom + soft_bottom, family="binomial", data = CTST)
summary(model24)##
## Call:
## glm(formula = CTX3 ~ TL + BAA3 + fishing + effluent + herbivore +
## mean_depth + site_coral + wave + Age + coral + boulder +
## hard_bottom + soft_bottom, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0058 -0.7644 0.2950 0.7580 1.8163
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.27337 1.01368 0.270 0.7874
## TL -1.00191 0.43194 -2.320 0.0204 *
## BAA3 -0.46293 0.48859 -0.947 0.3434
## fishing -0.85276 0.68529 -1.244 0.2134
## effluent -1.08272 1.19833 -0.904 0.3662
## herbivore 0.18874 0.63296 0.298 0.7656
## mean_depth 0.44625 0.58527 0.762 0.4458
## site_coral 1.58342 1.29745 1.220 0.2223
## wave 0.25800 0.81770 0.316 0.7524
## Age 0.14648 0.38768 0.378 0.7056
## coral 0.27709 0.43017 0.644 0.5195
## boulder -0.09633 0.35855 -0.269 0.7882
## hard_bottom -1.72563 10.71512 -0.161 0.8721
## soft_bottom -0.27337 0.37262 -0.734 0.4632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.468 on 78 degrees of freedom
## Residual deviance: 71.312 on 65 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 99.312
##
## Number of Fisher Scoring iterations: 7model25 = glm(CTX3~1, family="binomial", data = CTST)
summary(model25)##
## Call:
## glm(formula = CTX3 ~ 1, family = "binomial", data = CTST)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4653 -1.4653 0.9145 0.9145 0.9145
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6554 0.2372 2.763 0.00573 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.47 on 78 degrees of freedom
## Residual deviance: 101.47 on 78 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 103.47
##
## Number of Fisher Scoring iterations: 4