Monday, November 3, 2014

11 3 2014 Brooder Proportion Stats Tests

reproanova.R
require(ggplot2)
## Loading required package: ggplot2
require(plyr)
## Loading required package: plyr
reproftable<-ftable(repro4$Date, repro4$Site, repro4$Pop, repro4$Brooders, exclude=c(NA, NaN,0))
summary(reproftable)
##        V1               V2               V3               V4        
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0.0000  
##  Mean   :0.0503   Mean   :0.0265   Mean   :0.0238   Mean   :0.0053  
##  3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##        V5               V6               V7               V8        
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0.0000  
##  Mean   :0.0079   Mean   :0.0079   Mean   :0.0053   Mean   :0.0053  
##  3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##        V9              V10              V11        
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.0000   Median :0.0000   Median :0.0000  
##  Mean   :0.0026   Mean   :0.0053   Mean   :0.0079  
##  3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000
reproaov<-aov(repro4$Brooders~repro4$Site+repro4$Pop+repro4$Site:repro4$Pop,repro4)
summary(reproaov)
##                         Df Sum Sq Mean Sq F value  Pr(>F)    
## repro4$Site              2    138    69.2   12.60 1.1e-05 ***
## repro4$Pop               2     99    49.6    9.03 0.00023 ***
## repro4$Site:repro4$Pop   4     31     7.7    1.41 0.23609    
## Residuals              117    642     5.5                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SitevPopTukey<-TukeyHSD(reproaov)
print(SitevPopTukey)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = repro4$Brooders ~ repro4$Site + repro4$Pop + repro4$Site:repro4$Pop, data = repro4)
## 
## $`repro4$Site`
##                          diff     lwr    upr  p adj
## Manchester-Fidalgo    -0.8571 -2.0710 0.3567 0.2186
## Oyster Bay-Fidalgo     1.6667  0.4529 2.8805 0.0041
## Oyster Bay-Manchester  2.5238  1.3100 3.7376 0.0000
## 
## $`repro4$Pop`
##         diff     lwr   upr  p adj
## N-H -0.04762 -1.2614 1.166 0.9952
## S-H  1.85714  0.6433 3.071 0.0012
## S-N  1.90476  0.6910 3.119 0.0009
## 
## $`repro4$Site:repro4$Pop`
##                                 diff      lwr    upr  p adj
## Manchester:H-Fidalgo:H    -7.143e-02 -2.87058 2.7277 1.0000
## Oyster Bay:H-Fidalgo:H     1.286e+00 -1.51344 4.0849 0.8749
## Fidalgo:N-Fidalgo:H       -4.330e-15 -2.79915 2.7992 1.0000
## Manchester:N-Fidalgo:H    -5.000e-01 -3.29915 2.2992 0.9997
## Oyster Bay:N-Fidalgo:H     1.571e+00 -1.22773 4.3706 0.6987
## Fidalgo:S-Fidalgo:H        2.214e+00 -0.58487 5.0134 0.2427
## Manchester:S-Fidalgo:H     2.143e-01 -2.58487 3.0134 1.0000
## Oyster Bay:S-Fidalgo:H     4.357e+00  1.55799 7.1563 0.0001
## Oyster Bay:H-Manchester:H  1.357e+00 -1.44201 4.1563 0.8379
## Fidalgo:N-Manchester:H     7.143e-02 -2.72773 2.8706 1.0000
## Manchester:N-Manchester:H -4.286e-01 -3.22773 2.3706 0.9999
## Oyster Bay:N-Manchester:H  1.643e+00 -1.15630 4.4420 0.6456
## Fidalgo:S-Manchester:H     2.286e+00 -0.51344 5.0849 0.2061
## Manchester:S-Manchester:H  2.857e-01 -2.51344 3.0849 1.0000
## Oyster Bay:S-Manchester:H  4.429e+00  1.62942 7.2277 0.0001
## Fidalgo:N-Oyster Bay:H    -1.286e+00 -4.08487 1.5134 0.8749
## Manchester:N-Oyster Bay:H -1.786e+00 -4.58487 1.0134 0.5353
## Oyster Bay:N-Oyster Bay:H  2.857e-01 -2.51344 3.0849 1.0000
## Fidalgo:S-Oyster Bay:H     9.286e-01 -1.87058 3.7277 0.9801
## Manchester:S-Oyster Bay:H -1.071e+00 -3.87058 1.7277 0.9529
## Oyster Bay:S-Oyster Bay:H  3.071e+00  0.27227 5.8706 0.0203
## Manchester:N-Fidalgo:N    -5.000e-01 -3.29915 2.2992 0.9997
## Oyster Bay:N-Fidalgo:N     1.571e+00 -1.22773 4.3706 0.6987
## Fidalgo:S-Fidalgo:N        2.214e+00 -0.58487 5.0134 0.2427
## Manchester:S-Fidalgo:N     2.143e-01 -2.58487 3.0134 1.0000
## Oyster Bay:S-Fidalgo:N     4.357e+00  1.55799 7.1563 0.0001
## Oyster Bay:N-Manchester:N  2.071e+00 -0.72773 4.8706 0.3279
## Fidalgo:S-Manchester:N     2.714e+00 -0.08487 5.5134 0.0649
## Manchester:S-Manchester:N  7.143e-01 -2.08487 3.5134 0.9965
## Oyster Bay:S-Manchester:N  4.857e+00  2.05799 7.6563 0.0000
## Fidalgo:S-Oyster Bay:N     6.429e-01 -2.15630 3.4420 0.9983
## Manchester:S-Oyster Bay:N -1.357e+00 -4.15630 1.4420 0.8379
## Oyster Bay:S-Oyster Bay:N  2.786e+00 -0.01344 5.5849 0.0521
## Manchester:S-Fidalgo:S    -2.000e+00 -4.79915 0.7992 0.3760
## Oyster Bay:S-Fidalgo:S     2.143e+00 -0.65630 4.9420 0.2834
## Oyster Bay:S-Manchester:S  4.143e+00  1.34370 6.9420 0.0003
Gapingsum<-ddply(repro4,.(Site,Pop),summarise,sum=sum(Gaping,na.rm=T))
Broodsum<-ddply(repro4,.(Site,Pop),summarise,sum=sum(Brooders,na.rm=T))
BroodGapingSum<-merge(Gapingsum,Broodsum,by=c("Site","Pop"))
BroodGapingSum<-rename(BroodGapingSum, c("Site"="Site","Pop"="Pop","sum.x"="Gaping","sum.y"="Brooder"))
BroodGapingSum$prop<-BroodGapingSum$Brooder/BroodGapingSum$Gaping
print(BroodGapingSum)
##         Site Pop Gaping Brooder     prop
## 1    Fidalgo   H    814       8 0.009828
## 2    Fidalgo   N    813       8 0.009840
## 3    Fidalgo   S    921      39 0.042345
## 4 Manchester   H    658       7 0.010638
## 5 Manchester   N    631       1 0.001585
## 6 Manchester   S    699      11 0.015737
## 7 Oyster Bay   H    931      26 0.027927
## 8 Oyster Bay   N    769      30 0.039012
## 9 Oyster Bay   S    883      69 0.078143
propaov<-aov(BroodGapingSum$prop~BroodGapingSum$Site+BroodGapingSum$Pop+BroodGapingSum$Site:BroodGapingSum$Pop,BroodGapingSum)
summary(propaov)
##                                        Df   Sum Sq  Mean Sq
## BroodGapingSum$Site                     2 0.002420 0.001210
## BroodGapingSum$Pop                      2 0.001675 0.000838
## BroodGapingSum$Site:BroodGapingSum$Pop  4 0.000524 0.000131
PropTukey<-TukeyHSD(propaov)
## Warning: NaNs produced
## Warning: NaNs produced
## Warning: NaNs produced
print(PropTukey)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = BroodGapingSum$prop ~ BroodGapingSum$Site + BroodGapingSum$Pop + BroodGapingSum$Site:BroodGapingSum$Pop, data = BroodGapingSum)
## 
## $`BroodGapingSum$Site`
##                           diff lwr upr p adj
## Manchester-Fidalgo    -0.01135 NaN NaN   NaN
## Oyster Bay-Fidalgo     0.02769 NaN NaN   NaN
## Oyster Bay-Manchester  0.03904 NaN NaN   NaN
## 
## $`BroodGapingSum$Pop`
##          diff lwr upr p adj
## N-H 0.0006811 NaN NaN   NaN
## S-H 0.0292772 NaN NaN   NaN
## S-N 0.0285961 NaN NaN   NaN
## 
## $`BroodGapingSum$Site:BroodGapingSum$Pop`
##                                 diff lwr upr p adj
## Manchester:H-Fidalgo:H     8.103e-04 NaN NaN   NaN
## Oyster Bay:H-Fidalgo:H     1.810e-02 NaN NaN   NaN
## Fidalgo:N-Fidalgo:H        1.209e-05 NaN NaN   NaN
## Manchester:N-Fidalgo:H    -8.243e-03 NaN NaN   NaN
## Oyster Bay:N-Fidalgo:H     2.918e-02 NaN NaN   NaN
## Fidalgo:S-Fidalgo:H        3.252e-02 NaN NaN   NaN
## Manchester:S-Fidalgo:H     5.909e-03 NaN NaN   NaN
## Oyster Bay:S-Fidalgo:H     6.831e-02 NaN NaN   NaN
## Oyster Bay:H-Manchester:H  1.729e-02 NaN NaN   NaN
## Fidalgo:N-Manchester:H    -7.982e-04 NaN NaN   NaN
## Manchester:N-Manchester:H -9.054e-03 NaN NaN   NaN
## Oyster Bay:N-Manchester:H  2.837e-02 NaN NaN   NaN
## Fidalgo:S-Manchester:H     3.171e-02 NaN NaN   NaN
## Manchester:S-Manchester:H  5.098e-03 NaN NaN   NaN
## Oyster Bay:S-Manchester:H  6.750e-02 NaN NaN   NaN
## Fidalgo:N-Oyster Bay:H    -1.809e-02 NaN NaN   NaN
## Manchester:N-Oyster Bay:H -2.634e-02 NaN NaN   NaN
## Oyster Bay:N-Oyster Bay:H  1.108e-02 NaN NaN   NaN
## Fidalgo:S-Oyster Bay:H     1.442e-02 NaN NaN   NaN
## Manchester:S-Oyster Bay:H -1.219e-02 NaN NaN   NaN
## Oyster Bay:S-Oyster Bay:H  5.022e-02 NaN NaN   NaN
## Manchester:N-Fidalgo:N    -8.255e-03 NaN NaN   NaN
## Oyster Bay:N-Fidalgo:N     2.917e-02 NaN NaN   NaN
## Fidalgo:S-Fidalgo:N        3.251e-02 NaN NaN   NaN
## Manchester:S-Fidalgo:N     5.897e-03 NaN NaN   NaN
## Oyster Bay:S-Fidalgo:N     6.830e-02 NaN NaN   NaN
## Oyster Bay:N-Manchester:N  3.743e-02 NaN NaN   NaN
## Fidalgo:S-Manchester:N     4.076e-02 NaN NaN   NaN
## Manchester:S-Manchester:N  1.415e-02 NaN NaN   NaN
## Oyster Bay:S-Manchester:N  7.656e-02 NaN NaN   NaN
## Fidalgo:S-Oyster Bay:N     3.334e-03 NaN NaN   NaN
## Manchester:S-Oyster Bay:N -2.327e-02 NaN NaN   NaN
## Oyster Bay:S-Oyster Bay:N  3.913e-02 NaN NaN   NaN
## Manchester:S-Fidalgo:S    -2.661e-02 NaN NaN   NaN
## Oyster Bay:S-Fidalgo:S     3.580e-02 NaN NaN   NaN
## Oyster Bay:S-Manchester:S  6.241e-02 NaN NaN   NaN
Proptest<-prop.test(BroodGapingSum$Brooder,BroodGapingSum$Gaping, conf.level=0.95)
print(Proptest)
## 
##  9-sample test for equality of proportions without continuity
##  correction
## 
## data:  BroodGapingSum$Brooder out of BroodGapingSum$Gaping
## X-squared = 139.2, df = 8, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
##   prop 1   prop 2   prop 3   prop 4   prop 5   prop 6   prop 7   prop 8 
## 0.009828 0.009840 0.042345 0.010638 0.001585 0.015737 0.027927 0.039012 
##   prop 9 
## 0.078143

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