setwd("C:/Users/Nikol/Documents/Ciguatara Project/Data/CXT Data")
All = read.csv("CTX_Z_CEAR.csv")
dim(All) #examine data## [1] 191 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 0 0 1 0 2.62089 27.8167
## 2 0.128 0 0 0.872 0 2.64007 35.369
## 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
## 83 Anaehoom AB01 CTST 13 6 4.13 1827.46 0 0 0 20
## 84 Anaehoom AB03 CTST 13 4 4.13 1827.46 . 0 0 20
## ... <NA> <NA> <NA> <NA> <NA> ... ... <NA> ... ... ...
## 188 Puako PU14 CTST 13 3 2827.84 4.13 . 0 0 20
## 189 Puako PU15 CTST 15 5 2827.84 4.13 . 0 0 20
## 190 Puako PU22 CTST 18 7 2827.84 4.13 . 0 0 20
## 191 Puako PU28 CTST 15 6 2827.84 4.13 . 0 0 20
## boulder hard_bottom soft_bottom coral unknown Wave Effluent Fishing
## 83 0 0 0 1 0 2.60907 37.6015
## 84 0 0 0 1 0 2.61244 37.3647
## ... <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 188 0 0 0 1 0 1.98688 13035.25 28.5239
## 189 0 0 0 1 0 2.03578 5192.94 38.4889
## 190 0 0 0 0.958 0.042 1.91096 9700.96 39.1557
## 191 0 0 0 0.958 0.042 1.90795 9718.17 39.2377
## mean_depth BAA3 BAA5 BAA7
## 83 -4.1565 67 69 .
## 84 -4.0627 67 . .
## ... <NA> <NA> <NA> <NA>
## 188 -5.8475 67 . .
## 189 -4.808 67 68 .
## 190 -5.5423 67 68 119
## 191 -5.0679 67 68 .CEAR = subset(All, Species =="CEAR")
dim(CEAR) #examine data## [1] 82 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> ... ...
## 79 Puako PU37 CEAR 32 8.1148 4.13 2827.84 1 3 0.13
## 80 Puako PU38 CEAR 38 9 4.13 2827.84 1 5.5 0.07
## 81 Puako PU39 CEAR 34 8 4.13 2827.84 1 2 0.2
## 82 Puako PU40 CEAR 48.5 21.6393 4.13 2827.84 1 0.3 1.33
## Buffer boulder hard_bottom soft_bottom coral unknown Wave Effluent
## 1 40 0 0 0 1 0 2.62089
## 2 40 0.128 0 0 0.872 0 2.64007
## ... ... <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 79 40 0 0 0.143 0.837 0.02 2.24847 1302.29
## 80 40 0 0 0.143 0.837 0.02 2.24847 1302.29
## 81 40 0 0 0.143 0.837 0.02 2.24847 1302.29
## 82 40 0 0 0.143 0.837 0.02 2.24847 1302.29
## Fishing mean_depth BAA3 BAA5 BAA7
## 1 27.8167 -5.9999 67 69 121
## 2 35.369 -4.9986 67 69 121
## ... <NA> <NA> <NA> <NA> <NA>
## 79 27.0478 -5.7854 61 67 101
## 80 27.0478 -5.7854 61 67 101
## 81 27.0478 -5.7854 61 67 101
## 82 27.0478 -5.7854 61 67 101##Check structure of data and change character to numeric
#convert column 'a' from character to numeric
str(CEAR)## 'data.frame': 82 obs. of 23 variables:
## $ ï..Site : chr "Anaehoom" "Anaehoom" "Anaehoom" "Anaehoom" ...
## $ Tag : chr "AB02" "AB05" "AB06" "AB07" ...
## $ Species : chr "CEAR" "CEAR" "CEAR" "CEAR" ...
## $ TL : chr "35" "47" "29.5" "31.5" ...
## $ Age : chr "10" "22" "7" "9" ...
## $ site_coral : num 14.2 14.2 14.2 14.2 14.2 ...
## $ herbivore : num 1827 1827 1827 1827 1827 ...
## $ CTX3 : chr "0" "0" "0" "0" ...
## $ EC50 : num 0 0 0 0 0 0 0 3 0 1.5 ...
## $ CXT1B : num 0 0 0 0 0 ...
## $ Buffer : int 40 40 40 40 40 40 40 40 40 40 ...
## $ boulder : chr "0" "0.128" "0" "0" ...
## $ hard_bottom: chr "0" "0" "0" "0" ...
## $ soft_bottom: chr "0" "0" "0" "0" ...
## $ coral : chr "1" "0.872" "1" "1" ...
## $ unknown : chr "0" "0" "0" "0" ...
## $ Wave : chr "2.62089" "2.64007" "2.62772" "2.62876" ...
## $ Effluent : chr "" "" "" "" ...
## $ Fishing : chr "27.8167" "35.369" "26.8266" "34.4041" ...
## $ mean_depth : chr "-5.9999" "-4.9986" "-6.6052" "-5.5554" ...
## $ BAA3 : chr "67" "67" "67" "67" ...
## $ BAA5 : chr "69" "69" "69" "69" ...
## $ BAA7 : chr "121" "121" "121" "121" ...CEAR$CTX3= as.character(CEAR$CTX3)
CEAR$boulder = as.numeric(CEAR$boulder)
CEAR$hard_bottom = as.numeric(CEAR$hard_bottom)
CEAR$soft_bottom = as.numeric(CEAR$soft_bottom)
CEAR$coral = as.numeric(CEAR$coral)
CEAR$unknown = as.numeric(CEAR$unknown)
CEAR$Wave = as.numeric(CEAR$Wave)
CEAR$Effluent = as.numeric(CEAR$Effluent)
CEAR$Fishing = as.numeric(CEAR$Fishing)
CEAR$mean_depth = as.numeric(CEAR$mean_depth)
CEAR$BAA3 = as.numeric(CEAR$BAA3)
CEAR$TL = as.numeric(CEAR$TL)
CEAR$Age = as.numeric(CEAR$Age)
str(CEAR)## 'data.frame': 82 obs. of 23 variables:
## $ ï..Site : chr "Anaehoom" "Anaehoom" "Anaehoom" "Anaehoom" ...
## $ Tag : chr "AB02" "AB05" "AB06" "AB07" ...
## $ Species : chr "CEAR" "CEAR" "CEAR" "CEAR" ...
## $ TL : num 35 47 29.5 31.5 22.5 29 41.5 27.5 24 49.8 ...
## $ Age : num 10 22 7 9 10 5 12 3 4 9 ...
## $ site_coral : num 14.2 14.2 14.2 14.2 14.2 ...
## $ herbivore : num 1827 1827 1827 1827 1827 ...
## $ CTX3 : chr "0" "0" "0" "0" ...
## $ EC50 : num 0 0 0 0 0 0 0 3 0 1.5 ...
## $ CXT1B : num 0 0 0 0 0 ...
## $ Buffer : int 40 40 40 40 40 40 40 40 40 40 ...
## $ boulder : num 0 0.128 0 0 0 0.369 0 0 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 0.872 1 1 1 0.631 1 1 1 0.872 ...
## $ unknown : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Wave : num 2.62 2.64 2.63 2.63 2.62 ...
## $ Effluent : num NA NA NA NA NA ...
## $ Fishing : num 27.8 35.4 26.8 34.4 25.9 ...
## $ mean_depth : num -6 -5 -6.61 -5.56 -6 ...
## $ BAA3 : num 67 67 67 67 67 67 67 67 67 61 ...
## $ BAA5 : chr "69" "69" "69" "69" ...
## $ BAA7 : chr "121" "121" "121" "121" ...#standerize data in data frame and put 0 into missing values
# Standardize all numeric columns (excluding factors, character, etc.)
numeric_cols <- sapply(CEAR, is.numeric)
CEAR[numeric_cols] <- scale(CEAR[numeric_cols])
headTail(CEAR)## ï..Site Tag Species TL Age site_coral herbivore CTX3 EC50 CXT1B
## 1 Anaehoom AB02 CEAR 0.25 0.19 0.27 -0.78 0 -0.39 -0.33
## 2 Anaehoom AB05 CEAR 1.86 2.9 0.27 -0.78 0 -0.39 -0.33
## 3 Anaehoom AB06 CEAR -0.49 -0.48 0.27 -0.78 0 -0.39 -0.33
## 4 Anaehoom AB07 CEAR -0.22 -0.03 0.27 -0.78 0 -0.39 -0.33
## ... <NA> <NA> <NA> ... ... ... ... <NA> ... ...
## 79 Puako PU37 CEAR -0.15 -0.23 -1.81 -0.41 1 0.84 0.09
## 80 Puako PU38 CEAR 0.65 -0.03 -1.81 -0.41 1 1.87 -0.1
## 81 Puako PU39 CEAR 0.12 -0.26 -1.81 -0.41 1 0.43 0.3
## 82 Puako PU40 CEAR 2.06 2.82 -1.81 -0.41 1 -0.27 3.91
## Buffer boulder hard_bottom soft_bottom coral unknown Wave Effluent Fishing
## 1 NaN -0.32 -0.17 -0.44 0.77 -0.56 -0.54 <NA> 0.13
## 2 NaN 0.34 -0.17 -0.44 0.46 -0.56 -0.51 <NA> 0.8
## 3 NaN -0.32 -0.17 -0.44 0.77 -0.56 -0.53 <NA> 0.04
## 4 NaN -0.32 -0.17 -0.44 0.77 -0.56 -0.53 <NA> 0.71
## ... ... ... ... ... ... ... ... ... ...
## 79 NaN -0.32 -0.17 2.38 0.38 -0.52 -0.97 -0.77 0.06
## 80 NaN -0.32 -0.17 2.38 0.38 -0.52 -0.97 -0.77 0.06
## 81 NaN -0.32 -0.17 2.38 0.38 -0.52 -0.97 -0.77 0.06
## 82 NaN -0.32 -0.17 2.38 0.38 -0.52 -0.97 -0.77 0.06
## mean_depth BAA3 BAA5 BAA7
## 1 0.53 1.21 69 121
## 2 0.69 1.21 69 121
## 3 0.42 1.21 69 121
## 4 0.6 1.21 69 121
## ... ... ... <NA> <NA>
## 79 0.56 -1.21 67 101
## 80 0.56 -1.21 67 101
## 81 0.56 -1.21 67 101
## 82 0.56 -1.21 67 101# Replace missing values (NA) with 0
CEAR[is.na(CEAR)] <- 0
headTail(CEAR)## ï..Site Tag Species TL Age site_coral herbivore CTX3 EC50 CXT1B
## 1 Anaehoom AB02 CEAR 0.25 0.19 0.27 -0.78 0 -0.39 -0.33
## 2 Anaehoom AB05 CEAR 1.86 2.9 0.27 -0.78 0 -0.39 -0.33
## 3 Anaehoom AB06 CEAR -0.49 -0.48 0.27 -0.78 0 -0.39 -0.33
## 4 Anaehoom AB07 CEAR -0.22 -0.03 0.27 -0.78 0 -0.39 -0.33
## ... <NA> <NA> <NA> ... ... ... ... <NA> ... ...
## 79 Puako PU37 CEAR -0.15 -0.23 -1.81 -0.41 1 0.84 0.09
## 80 Puako PU38 CEAR 0.65 -0.03 -1.81 -0.41 1 1.87 -0.1
## 81 Puako PU39 CEAR 0.12 -0.26 -1.81 -0.41 1 0.43 0.3
## 82 Puako PU40 CEAR 2.06 2.82 -1.81 -0.41 1 -0.27 3.91
## Buffer boulder hard_bottom soft_bottom coral unknown Wave Effluent Fishing
## 1 0 -0.32 -0.17 -0.44 0.77 -0.56 -0.54 0 0.13
## 2 0 0.34 -0.17 -0.44 0.46 -0.56 -0.51 0 0.8
## 3 0 -0.32 -0.17 -0.44 0.77 -0.56 -0.53 0 0.04
## 4 0 -0.32 -0.17 -0.44 0.77 -0.56 -0.53 0 0.71
## ... ... ... ... ... ... ... ... ... ...
## 79 0 -0.32 -0.17 2.38 0.38 -0.52 -0.97 -0.77 0.06
## 80 0 -0.32 -0.17 2.38 0.38 -0.52 -0.97 -0.77 0.06
## 81 0 -0.32 -0.17 2.38 0.38 -0.52 -0.97 -0.77 0.06
## 82 0 -0.32 -0.17 2.38 0.38 -0.52 -0.97 -0.77 0.06
## mean_depth BAA3 BAA5 BAA7
## 1 0.53 1.21 69 121
## 2 0.69 1.21 69 121
## 3 0.42 1.21 69 121
## 4 0.6 1.21 69 121
## ... ... ... <NA> <NA>
## 79 0.56 -1.21 67 101
## 80 0.56 -1.21 67 101
## 81 0.56 -1.21 67 101
## 82 0.56 -1.21 67 101CEAR$CTX3= as.numeric(CEAR$CTX3)## Warning: NAs introduced by coercionstr(CEAR)## 'data.frame': 82 obs. of 23 variables:
## $ ï..Site : chr "Anaehoom" "Anaehoom" "Anaehoom" "Anaehoom" ...
## $ Tag : chr "AB02" "AB05" "AB06" "AB07" ...
## $ Species : chr "CEAR" "CEAR" "CEAR" "CEAR" ...
## $ TL : num 0.25 1.861 -0.488 -0.219 -1.427 ...
## $ Age : num 0.1946 2.9013 -0.482 -0.0309 0.1946 ...
## $ site_coral : num 0.27 0.27 0.27 0.27 0.27 ...
## $ herbivore : num -0.776 -0.776 -0.776 -0.776 -0.776 ...
## $ CTX3 : num 0 0 0 0 0 0 0 1 0 1 ...
## $ EC50 : num -0.392 -0.392 -0.392 -0.392 -0.392 ...
## $ CXT1B : num -0.335 -0.335 -0.335 -0.335 -0.335 ...
## $ Buffer : num 0 0 0 0 0 0 0 0 0 0 ...
## $ boulder : num -0.321 0.335 -0.321 -0.321 -0.321 ...
## $ hard_bottom: num -0.173 -0.173 -0.173 -0.173 -0.173 ...
## $ soft_bottom: num -0.439 -0.439 -0.439 -0.439 -0.439 ...
## $ coral : num 0.775 0.463 0.775 0.775 0.775 ...
## $ unknown : num -0.564 -0.564 -0.564 -0.564 -0.564 ...
## $ Wave : num -0.536 -0.514 -0.528 -0.527 -0.54 ...
## $ Effluent : num 0 0 0 0 0 ...
## $ Fishing : num 0.1285 0.799 0.0406 0.7133 -0.0446 ...
## $ mean_depth : num 0.525 0.692 0.424 0.599 0.525 ...
## $ BAA3 : num 1.21 1.21 1.21 1.21 1.21 ...
## $ BAA5 : chr "69" "69" "69" "69" ...
## $ BAA7 : chr "121" "121" "121" "121" ...#fit models
model1 <- glm(CTX3 ~ TL + BAA3, family="binomial", data = CEAR)
model2 <- glm(CTX3 ~ TL + BAA3 + Fishing, family="binomial", data = CEAR)
model3 <- glm(CTX3 ~ TL + Fishing, family="binomial", data = CEAR)
model4 = glm(CTX3 ~ TL + Fishing + Effluent, family="binomial", data = CEAR)
model5 = glm(CTX3 ~ herbivore + BAA3, family="binomial", data = CEAR)
model6 = glm(CTX3 ~ TL + Effluent, family="binomial", data = CEAR)
model7 = glm(CTX3~ TL + mean_depth, family="binomial", data = CEAR)
model8 = glm(CTX3 ~ coral + boulder + TL + BAA3, family="binomial", data = CEAR)
model9 = glm(CTX3 ~ TL, family="binomial", data = CEAR)
model10 = glm(CTX3 ~ TL + coral + boulder + herbivore, family="binomial", data = CEAR)
model11 = glm(CTX3 ~ TL + Wave, family="binomial", data = CEAR)
model12 = glm(CTX3 ~ TL + Age, family="binomial", data = CEAR)
model13 = glm(CTX3 ~ TL + coral + boulder, family="binomial", data = CEAR)
model14 = glm(CTX3 ~ TL + site_coral + herbivore, family="binomial", data = CEAR)
model15 = glm(CTX3 ~ BAA3, family="binomial", data = CEAR)
model16 = glm(CTX3 ~ herbivore + Fishing, family="binomial", data = CEAR)
model17 = glm(CTX3 ~ site_coral + Fishing, family="binomial", data = CEAR)
model18 = glm(CTX3 ~ herbivore + Fishing + mean_depth, family="binomial", data = CEAR)
model19 = glm(CTX3 ~ coral + boulder + hard_bottom + soft_bottom, family="binomial", data = CEAR)
model20 = glm(CTX3 ~ site_coral + Effluent, family="binomial", data = CEAR)
model21 = glm(CTX3 ~ site_coral, family="binomial", data = CEAR)
model22 = glm(CTX3 ~ herbivore, family="binomial", data = CEAR)
model23 = glm(CTX3 ~ Wave, family="binomial", data = CEAR)
model24 = glm(CTX3 ~ TL + BAA3 + Fishing + Effluent + herbivore + mean_depth + site_coral + Wave + Age + coral + boulder + hard_bottom + soft_bottom, family="binomial", data = CEAR)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred#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)
#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 + mean_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')
#calculate AIC of each model
aictab(cand.set = models, modnames = mod.names)##
## Model selection based on AICc:
##
## K
## TL + BAA3 3
## TL + BAA3 + Fishing 4
## coral + boulder + TL + BAA3 5
## TL + Fishing 3
## TL + Fishing + Effluent 4
## TL + Effluent 3
## TL, data 2
## TL + mean_depth 3
## TL + Age 3
## TL + Wave 3
## BAA3 2
## TL + coral + boulder 4
## TL + mean_coral + herbivore_biomass 4
## herbivore + BAA3 3
## TL + coral + boulder + herbivore 5
## site_coral + Fishing 3
## coral + boulder + hard_bottom + soft_bottom 5
## site_coral + Effluent 3
## site_coral 2
## herbivore_biomass + fishing 3
## Wave 2
## herbivore + Fishing + mean_depth 4
## TL + BAA3 + Fishing + Effluent + herbivore + mean_depth + site_coral + Wave + Age + coral + boulder + hard_bottom + soft_bottom 14
## herbivore 2
## AICc
## TL + BAA3 72.99
## TL + BAA3 + Fishing 74.91
## coral + boulder + TL + BAA3 76.70
## TL + Fishing 77.14
## TL + Fishing + Effluent 78.11
## TL + Effluent 79.87
## TL, data 80.29
## TL + mean_depth 81.66
## TL + Age 82.24
## TL + Wave 82.28
## BAA3 82.95
## TL + coral + boulder 83.22
## TL + mean_coral + herbivore_biomass 84.02
## herbivore + BAA3 84.68
## TL + coral + boulder + herbivore 85.32
## site_coral + Fishing 87.48
## coral + boulder + hard_bottom + soft_bottom 90.87
## site_coral + Effluent 91.96
## site_coral 93.20
## herbivore_biomass + fishing 93.62
## Wave 94.18
## herbivore + Fishing + mean_depth 94.55
## TL + BAA3 + Fishing + Effluent + herbivore + mean_depth + site_coral + Wave + Age + coral + boulder + hard_bottom + soft_bottom 94.99
## herbivore 95.56
## Delta_AICc
## TL + BAA3 0.00
## TL + BAA3 + Fishing 1.92
## coral + boulder + TL + BAA3 3.72
## TL + Fishing 4.15
## TL + Fishing + Effluent 5.13
## TL + Effluent 6.88
## TL, data 7.30
## TL + mean_depth 8.67
## TL + Age 9.26
## TL + Wave 9.30
## BAA3 9.97
## TL + coral + boulder 10.24
## TL + mean_coral + herbivore_biomass 11.04
## herbivore + BAA3 11.70
## TL + coral + boulder + herbivore 12.33
## site_coral + Fishing 14.50
## coral + boulder + hard_bottom + soft_bottom 17.89
## site_coral + Effluent 18.98
## site_coral 20.21
## herbivore_biomass + fishing 20.63
## Wave 21.19
## herbivore + Fishing + mean_depth 21.57
## TL + BAA3 + Fishing + Effluent + herbivore + mean_depth + site_coral + Wave + Age + coral + boulder + hard_bottom + soft_bottom 22.00
## herbivore 22.58
## AICcWt
## TL + BAA3 0.54
## TL + BAA3 + Fishing 0.21
## coral + boulder + TL + BAA3 0.08
## TL + Fishing 0.07
## TL + Fishing + Effluent 0.04
## TL + Effluent 0.02
## TL, data 0.01
## TL + mean_depth 0.01
## TL + Age 0.01
## TL + Wave 0.01
## BAA3 0.00
## TL + coral + boulder 0.00
## TL + mean_coral + herbivore_biomass 0.00
## herbivore + BAA3 0.00
## TL + coral + boulder + herbivore 0.00
## site_coral + Fishing 0.00
## coral + boulder + hard_bottom + soft_bottom 0.00
## site_coral + Effluent 0.00
## site_coral 0.00
## herbivore_biomass + fishing 0.00
## Wave 0.00
## herbivore + Fishing + mean_depth 0.00
## TL + BAA3 + Fishing + Effluent + herbivore + mean_depth + site_coral + Wave + Age + coral + boulder + hard_bottom + soft_bottom 0.00
## herbivore 0.00
## Cum.Wt
## TL + BAA3 0.54
## TL + BAA3 + Fishing 0.75
## coral + boulder + TL + BAA3 0.83
## TL + Fishing 0.90
## TL + Fishing + Effluent 0.94
## TL + Effluent 0.96
## TL, data 0.97
## TL + mean_depth 0.98
## TL + Age 0.98
## TL + Wave 0.99
## BAA3 0.99
## TL + coral + boulder 0.99
## TL + mean_coral + herbivore_biomass 1.00
## herbivore + BAA3 1.00
## TL + coral + boulder + herbivore 1.00
## site_coral + Fishing 1.00
## coral + boulder + hard_bottom + soft_bottom 1.00
## site_coral + Effluent 1.00
## site_coral 1.00
## herbivore_biomass + fishing 1.00
## Wave 1.00
## herbivore + Fishing + mean_depth 1.00
## TL + BAA3 + Fishing + Effluent + herbivore + mean_depth + site_coral + Wave + Age + coral + boulder + hard_bottom + soft_bottom 1.00
## herbivore 1.00
## LL
## TL + BAA3 -33.33
## TL + BAA3 + Fishing -33.17
## coral + boulder + TL + BAA3 -32.92
## TL + Fishing -35.40
## TL + Fishing + Effluent -34.78
## TL + Effluent -36.77
## TL, data -38.06
## TL + mean_depth -37.66
## TL + Age -37.95
## TL + Wave -37.98
## BAA3 -39.39
## TL + coral + boulder -37.33
## TL + mean_coral + herbivore_biomass -37.73
## herbivore + BAA3 -39.17
## TL + coral + boulder + herbivore -37.23
## site_coral + Fishing -40.58
## coral + boulder + hard_bottom + soft_bottom -40.01
## site_coral + Effluent -42.82
## site_coral -44.52
## herbivore_biomass + fishing -43.64
## Wave -45.01
## herbivore + Fishing + mean_depth -42.99
## TL + BAA3 + Fishing + Effluent + herbivore + mean_depth + site_coral + Wave + Age + coral + boulder + hard_bottom + soft_bottom -30.05
## herbivore -45.70model1 <- glm(CTX3 ~ TL + BAA3, family="binomial", data = CEAR)
summary(model1)##
## Call:
## glm(formula = CTX3 ~ TL + BAA3, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7498 -0.6096 -0.3858 0.5652 2.4903
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1746 0.3293 -3.567 0.000361 ***
## TL 1.0236 0.3184 3.215 0.001304 **
## BAA3 -0.9125 0.3136 -2.910 0.003613 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 66.652 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 72.652
##
## Number of Fisher Scoring iterations: 5model2 <- glm(CTX3 ~ TL + BAA3 + Fishing, family="binomial", data = CEAR)
summary(model2)##
## Call:
## glm(formula = CTX3 ~ TL + BAA3 + Fishing, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6997 -0.6099 -0.3819 0.5220 2.4399
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1636 0.3306 -3.520 0.000432 ***
## TL 1.0556 0.3278 3.220 0.001283 **
## BAA3 -0.7893 0.3824 -2.064 0.039008 *
## Fishing -0.2613 0.4753 -0.550 0.582428
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 66.344 on 72 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 74.344
##
## Number of Fisher Scoring iterations: 5model3 <- glm(CTX3 ~ TL + Fishing, family="binomial", data = CEAR)
summary(model3)##
## Call:
## glm(formula = CTX3 ~ TL + Fishing, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5150 -0.6653 -0.4485 0.7246 2.0497
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0770 0.3099 -3.475 0.000511 ***
## TL 1.1570 0.3199 3.617 0.000299 ***
## Fishing -0.8619 0.3948 -2.183 0.029029 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 70.807 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 76.807
##
## Number of Fisher Scoring iterations: 5model4 = glm(CTX3 ~ TL + Fishing + Effluent, family="binomial", data = CEAR)
summary(model4)##
## Call:
## glm(formula = CTX3 ~ TL + Fishing + Effluent, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5992 -0.6563 -0.4586 0.6485 2.2324
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1599 0.3291 -3.525 0.000424 ***
## TL 1.1339 0.3187 3.558 0.000374 ***
## Fishing -0.7694 0.4024 -1.912 0.055859 .
## Effluent -0.4238 0.3864 -1.097 0.272678
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 69.551 on 72 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 77.551
##
## Number of Fisher Scoring iterations: 5model5 = glm(CTX3 ~ herbivore + BAA3, family="binomial", data = CEAR)
summary(model5)##
## Call:
## glm(formula = CTX3 ~ herbivore + BAA3, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5857 -0.7651 -0.4243 1.0981 2.2429
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0278 0.2952 -3.481 0.000499 ***
## herbivore 0.1878 0.2814 0.667 0.504597
## BAA3 -1.0374 0.3206 -3.236 0.001212 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 78.349 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 84.349
##
## Number of Fisher Scoring iterations: 4model6 = glm(CTX3 ~ TL + Effluent, family="binomial", data = CEAR)
summary(model6)##
## Call:
## glm(formula = CTX3 ~ TL + Effluent, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6500 -0.6983 -0.4687 0.8031 2.2001
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1578 0.3131 -3.698 0.000218 ***
## TL 1.0624 0.3029 3.507 0.000453 ***
## Effluent -0.5453 0.3526 -1.547 0.121983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 73.537 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 79.537
##
## Number of Fisher Scoring iterations: 4model7 = glm(CTX3~ TL + mean_depth, family="binomial", data = CEAR)
summary(model7)##
## Call:
## glm(formula = CTX3 ~ TL + mean_depth, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4978 -0.7116 -0.5433 0.7513 2.0728
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0520 0.2973 -3.539 0.000402 ***
## TL 1.0686 0.3011 3.549 0.000387 ***
## mean_depth -0.3232 0.3627 -0.891 0.372894
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 75.325 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 81.325
##
## Number of Fisher Scoring iterations: 4model8 = glm(CTX3 ~ coral + boulder + TL + BAA3, family="binomial", data = CEAR)
summary(model8)##
## Call:
## glm(formula = CTX3 ~ coral + boulder + TL + BAA3, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7792 -0.5862 -0.4004 0.5949 2.3857
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2311 0.3580 -3.439 0.000584 ***
## coral 0.1658 0.3990 0.416 0.677662
## boulder -0.4216 0.7917 -0.533 0.594362
## TL 0.9978 0.3204 3.114 0.001843 **
## BAA3 -0.8780 0.3125 -2.810 0.004957 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 65.848 on 71 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 75.848
##
## Number of Fisher Scoring iterations: 6model9 = glm(CTX3 ~ TL, family="binomial", data = CEAR)
summary(model9)##
## Call:
## glm(formula = CTX3 ~ TL, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5688 -0.6961 -0.5614 0.7796 2.0085
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0770 0.2959 -3.640 0.000273 ***
## TL 1.0542 0.2967 3.553 0.000381 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 76.123 on 74 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 80.123
##
## Number of Fisher Scoring iterations: 4model10 = glm(CTX3 ~ TL + coral + boulder + herbivore, family="binomial", data = CEAR)
summary(model10)##
## Call:
## glm(formula = CTX3 ~ TL + coral + boulder + herbivore, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5039 -0.7039 -0.5275 0.7917 2.1096
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2053 0.3618 -3.331 0.000865 ***
## TL 1.0581 0.3009 3.516 0.000438 ***
## coral 0.2346 0.4236 0.554 0.579653
## boulder -0.7350 0.9673 -0.760 0.447359
## herbivore 0.1507 0.3380 0.446 0.655746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 74.463 on 71 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 84.463
##
## Number of Fisher Scoring iterations: 6model11 = glm(CTX3 ~ TL + Wave, family="binomial", data = CEAR)
summary(model11)##
## Call:
## glm(formula = CTX3 ~ TL + Wave, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5736 -0.6998 -0.5393 0.7857 2.0190
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0947 0.3011 -3.635 0.000278 ***
## TL 1.0324 0.3003 3.438 0.000586 ***
## Wave -0.1343 0.3242 -0.414 0.678669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 75.950 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 81.95
##
## Number of Fisher Scoring iterations: 4model12 = glm(CTX3 ~ TL + Age, family="binomial", data = CEAR)
summary(model12)##
## Call:
## glm(formula = CTX3 ~ TL + Age, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7054 -0.6874 -0.5733 0.7544 2.0710
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0646 0.2965 -3.590 0.00033 ***
## TL 0.9068 0.4274 2.122 0.03387 *
## Age 0.1948 0.4224 0.461 0.64471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 75.909 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 81.909
##
## Number of Fisher Scoring iterations: 4model13 = glm(CTX3 ~ TL + coral + boulder, family="binomial", data = CEAR)
summary(model13)##
## Call:
## glm(formula = CTX3 ~ TL + coral + boulder, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4944 -0.7055 -0.5269 0.8135 2.0207
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1856 0.3509 -3.379 0.000728 ***
## TL 1.0429 0.2976 3.504 0.000458 ***
## coral 0.1341 0.3603 0.372 0.709707
## boulder -0.6986 0.9112 -0.767 0.443288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 74.660 on 72 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 82.66
##
## Number of Fisher Scoring iterations: 6model14 = glm(CTX3 ~ TL + site_coral + herbivore, family="binomial", data = CEAR)
summary(model14)##
## Call:
## glm(formula = CTX3 ~ TL + site_coral + herbivore, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5374 -0.7067 -0.5502 0.8113 2.0771
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.11339 0.30354 -3.668 0.000244 ***
## TL 1.02100 0.30005 3.403 0.000667 ***
## site_coral -0.22549 0.27931 -0.807 0.419481
## herbivore 0.09132 0.29067 0.314 0.753399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 75.459 on 72 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 83.459
##
## Number of Fisher Scoring iterations: 4model15 = glm(CTX3 ~ BAA3, family="binomial", data = CEAR)
summary(model15)##
## Call:
## glm(formula = CTX3 ~ BAA3, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.598 -0.697 -0.464 1.107 2.137
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0030 0.2878 -3.484 0.000493 ***
## BAA3 -0.9665 0.2909 -3.322 0.000893 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 78.787 on 74 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 82.787
##
## Number of Fisher Scoring iterations: 4model16 = glm(CTX3 ~ herbivore + Fishing, family="binomial", data = CEAR)
summary(model16)##
## Call:
## glm(formula = CTX3 ~ herbivore + Fishing, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3095 -0.8482 -0.6522 1.1186 1.8812
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.87731 0.26060 -3.367 0.000761 ***
## herbivore -0.05184 0.25316 -0.205 0.837734
## Fishing -0.64107 0.32371 -1.980 0.047661 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 87.283 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 93.283
##
## Number of Fisher Scoring iterations: 4model17 = glm(CTX3 ~ site_coral + Fishing, family="binomial", data = CEAR)
summary(model17)##
## Call:
## glm(formula = CTX3 ~ site_coral + Fishing, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4474 -0.8001 -0.5444 1.0408 2.3050
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.9964 0.2844 -3.504 0.000458 ***
## site_coral -0.7073 0.2936 -2.409 0.015976 *
## Fishing -1.0116 0.3805 -2.659 0.007849 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 81.151 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 87.151
##
## Number of Fisher Scoring iterations: 4model18 = glm(CTX3 ~ herbivore + Fishing + mean_depth, family="binomial", data = CEAR)
summary(model18)##
## Call:
## glm(formula = CTX3 ~ herbivore + Fishing + mean_depth, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.426 -0.858 -0.629 1.249 2.223
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.92778 0.27000 -3.436 0.00059 ***
## herbivore 0.09269 0.28428 0.326 0.74438
## Fishing -1.16307 0.58824 -1.977 0.04802 *
## mean_depth 0.66067 0.59824 1.104 0.26944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 85.988 on 72 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 93.988
##
## Number of Fisher Scoring iterations: 4model19 = glm(CTX3 ~ coral + boulder + hard_bottom + soft_bottom, family="binomial", data = CEAR)
summary(model19)##
## Call:
## glm(formula = CTX3 ~ coral + boulder + hard_bottom + soft_bottom,
## family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6013 -0.7638 -0.6945 0.8062 1.7763
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.29930 0.65117 -1.995 0.0460 *
## coral 0.02326 0.32907 0.071 0.9437
## boulder -0.39425 0.54580 -0.722 0.4701
## hard_bottom -2.27539 4.19604 -0.542 0.5876
## soft_bottom 0.72614 0.28465 2.551 0.0107 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 80.014 on 71 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 90.014
##
## Number of Fisher Scoring iterations: 8model20 = glm(CTX3 ~ site_coral + Effluent, family="binomial", data = CEAR)
summary(model20)##
## Call:
## glm(formula = CTX3 ~ site_coral + Effluent, family = "binomial",
## data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2617 -0.7601 -0.7408 1.0954 2.0616
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0344 0.2776 -3.726 0.000194 ***
## site_coral -0.4388 0.2616 -1.677 0.093494 .
## Effluent -0.5636 0.3164 -1.782 0.074807 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 85.630 on 73 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 91.63
##
## Number of Fisher Scoring iterations: 4model21 = glm(CTX3 ~ site_coral, family="binomial", data = CEAR)
summary(model21)##
## Call:
## glm(formula = CTX3 ~ site_coral, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0685 -0.7727 -0.7613 1.2904 1.7543
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.9529 0.2622 -3.634 0.000279 ***
## site_coral -0.3814 0.2438 -1.564 0.117745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 89.036 on 74 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 93.036
##
## Number of Fisher Scoring iterations: 4model22 = glm(CTX3 ~ herbivore, family="binomial", data = CEAR)
summary(model22)##
## Call:
## glm(formula = CTX3 ~ herbivore, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8426 -0.8426 -0.8298 1.5542 1.6262
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.89802 0.25305 -3.549 0.000387 ***
## herbivore -0.05827 0.24946 -0.234 0.815320
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 91.400 on 74 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 95.4
##
## Number of Fisher Scoring iterations: 4model23 = glm(CTX3 ~ Wave, family="binomial", data = CEAR)
summary(model23)##
## Call:
## glm(formula = CTX3 ~ Wave, family = "binomial", data = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9891 -0.8759 -0.7276 1.4447 1.7901
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.9406 0.2609 -3.606 0.000311 ***
## Wave -0.3422 0.2895 -1.182 0.237215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 90.011 on 74 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 94.011
##
## 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 = CEAR)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(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 = CEAR)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9061 -0.5359 -0.3367 0.5413 2.4093
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2479 1.1286 -1.992 0.0464 *
## TL 1.0848 0.5834 1.859 0.0630 .
## BAA3 -0.5707 0.5756 -0.991 0.3214
## Fishing -0.3203 0.9644 -0.332 0.7398
## Effluent 0.4149 0.6557 0.633 0.5269
## herbivore 1.1816 0.6612 1.787 0.0739 .
## mean_depth -0.1498 1.1431 -0.131 0.8958
## site_coral 1.5498 1.1560 1.341 0.1800
## Wave -1.8868 1.3694 -1.378 0.1682
## Age -0.2319 0.5936 -0.391 0.6961
## coral 0.2005 0.5208 0.385 0.7003
## boulder -0.1656 0.7710 -0.215 0.8299
## hard_bottom -7.4774 7.3464 -1.018 0.3088
## soft_bottom 1.0215 0.7471 1.367 0.1715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.455 on 75 degrees of freedom
## Residual deviance: 60.102 on 62 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 88.102
##
## Number of Fisher Scoring iterations: 8