Entanglement Analysis
Rob Schick
12 May 2015
Entanglement Windows
We started with the windows. I’ll summarise the data a bit better than the piecemeal approach I took yesterday.
Reproductively Active Females
I’ll show the reproductively active females first.
Boxplot
## Warning in loop_apply(n, do.ply): Removed 723 rows containing missing
## values (geom_point).
Data Summary
Here are the summaries of the raw data:
##
## Attaching package: 'dplyr'
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Source: local data frame [4 x 4]
##
## group n m sd
## 1 unimpacted 28271 75.97280 11.374056
## 2 minor 47 71.00498 8.161709
## 3 moderate 19 70.63849 9.952520
## 4 severe 7 54.86305 17.492908GLM to compare differences
First we’ll run a glm using unimpacted as the reference level, and then I’ll compare the differences
dfglm <- transform(dfLong, group = factor(group))
dfglm <- within(dfglm, group <- relevel(group, ref = 'unimpacted'))
ft1 <- glm(value ~ group, data = dfglm)
summary(ft1)##
## Call:
## glm(formula = value ~ group, data = dfglm)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -75.769 -6.880 4.291 7.750 17.105
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.97280 0.06762 1123.450 < 2e-16 ***
## groupminor -4.96782 1.65992 -2.993 0.00277 **
## groupmoderate -5.33431 2.60942 -2.044 0.04094 *
## groupsevere -21.10975 4.29813 -4.911 9.09e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 129.2854)
##
## Null deviance: 3668761 on 28343 degrees of freedom
## Residual deviance: 3663949 on 28340 degrees of freedom
## AIC: 218252
##
## Number of Fisher Scoring iterations: 2We’ll run a simple multiple comparisons of the factors:
library(multcomp)## Loading required package: mvtnorm
## Loading required package: survival
## Loading required package: TH.datasummary(glht(ft1, mcp(group = "Tukey")))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glm(formula = value ~ group, data = dfglm)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## minor - unimpacted == 0 -4.9678 1.6599 -2.993 0.01259 *
## moderate - unimpacted == 0 -5.3343 2.6094 -2.044 0.15165
## severe - unimpacted == 0 -21.1097 4.2981 -4.911 < 0.001 ***
## moderate - minor == 0 -0.3665 3.0912 -0.119 0.99931
## severe - minor == 0 -16.1419 4.6065 -3.504 0.00212 **
## severe - moderate == 0 -15.7754 5.0273 -3.138 0.00783 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Non-Reproductively Active Animals
Ok, let’s repeat that all for the non-reproductively active animals
Boxplot
Data Summary
Here are the summaries of the raw data:
## Source: local data frame [7 x 4]
##
## variable n m sd
## 1 Severe...1 24 52.30306 24.00919
## 2 Severe...0 10 58.98385 22.11262
## 3 Moderate...1 14 72.78055 17.34964
## 4 Moderate...0 83 76.65288 11.41027
## 5 Minor...1 16 77.15186 21.04126
## 6 Minor...0 314 77.63647 10.52896
## 7 unimpacted 84899 77.67087 12.07031GLM to compare differences
First we’ll run a glm using unimpacted as the reference level, and then I’ll compare the differences
dfglm <- transform(dfLong, variable = factor(variable))
dfglm <- within(dfLong, variable <- relevel(variable, ref = 'unimpacted'))
ft1 <- glm(value ~ variable, data = dfglm)
summary(ft1)##
## Call:
## glm(formula = value ~ variable, data = dfglm)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -77.601 -4.203 4.679 8.074 32.609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 77.67087 0.04144 1874.405 < 2e-16 ***
## variableSevere...1 -25.36781 2.46491 -10.292 < 2e-16 ***
## variableSevere...0 -18.68702 3.81831 -4.894 9.9e-07 ***
## variableModerate...1 -4.89032 3.22714 -1.515 0.130
## variableModerate...0 -1.01798 1.32593 -0.768 0.443
## variableMinor...1 -0.51901 3.01875 -0.172 0.863
## variableMinor...0 -0.03440 0.68263 -0.050 0.960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 145.778)
##
## Null deviance: 12461939 on 85359 degrees of freedom
## Residual deviance: 12442587 on 85353 degrees of freedom
## AIC: 667521
##
## Number of Fisher Scoring iterations: 2We’ll run a simple multiple comparisons of the factors:
library(multcomp)
summary(glht(ft1, mcp(variable = "Tukey")))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glm(formula = value ~ variable, data = dfglm)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Severe...1 - unimpacted == 0 -25.3678 2.4649 -10.292 < 0.001 ***
## Severe...0 - unimpacted == 0 -18.6870 3.8183 -4.894 < 0.001 ***
## Moderate...1 - unimpacted == 0 -4.8903 3.2271 -1.515 0.68092
## Moderate...0 - unimpacted == 0 -1.0180 1.3259 -0.768 0.98297
## Minor...1 - unimpacted == 0 -0.5190 3.0187 -0.172 1.00000
## Minor...0 - unimpacted == 0 -0.0344 0.6826 -0.050 1.00000
## Severe...0 - Severe...1 == 0 6.6808 4.5444 1.470 0.71063
## Moderate...1 - Severe...1 == 0 20.4775 4.0604 5.043 < 0.001 ***
## Moderate...0 - Severe...1 == 0 24.3498 2.7983 8.702 < 0.001 ***
## Minor...1 - Severe...1 == 0 24.8488 3.8968 6.377 < 0.001 ***
## Minor...0 - Severe...1 == 0 25.3334 2.5570 9.907 < 0.001 ***
## Moderate...1 - Severe...0 == 0 13.7967 4.9991 2.760 0.06544 .
## Moderate...0 - Severe...0 == 0 17.6690 4.0415 4.372 < 0.001 ***
## Minor...1 - Severe...0 == 0 18.1680 4.8671 3.733 0.00254 **
## Minor...0 - Severe...0 == 0 18.6526 3.8784 4.809 < 0.001 ***
## Moderate...0 - Moderate...1 == 0 3.8723 3.4884 1.110 0.90147
## Minor...1 - Moderate...1 == 0 4.3713 4.4186 0.989 0.94119
## Minor...0 - Moderate...1 == 0 4.8559 3.2980 1.472 0.70944
## Minor...1 - Moderate...0 == 0 0.4990 3.2966 0.151 1.00000
## Minor...0 - Moderate...0 == 0 0.9836 1.4902 0.660 0.99232
## Minor...0 - Minor...1 == 0 0.4846 3.0944 0.157 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)