The virus dataset shows the presence or absence (1/0) of 5 viruses (ABPV, BQCV, CBPV, DWV & SBV) in 621 colonies measured during Spring and Autumn in 2010 and 2011.
A summary table below showing five parameters: total number of cases, number of non-missing samples, number of negative samples, number of positive samples, percentage of positive samples (based on total) and percentage of positive samples (based on Measured).
The PPT can be thought of as the percentage of positive colonies out of total (621), while the PPA is the percentage of positive colonies out of number of colonies with data available (Measured). So, technically it is wrong to report just the PPT because most of those colonies were not measured or the data is missing.
The overall percentage of missing data is 93 %.
## Virus Year Season Total Measured Negatives Positives PPT PPA
## 1 ABPV 2010 Autumn 621 181 152 29 4.7 16.0
## 2 ABPV 2010 Spring 621 54 16 38 6.1 70.4
## 3 ABPV 2011 Autumn 621 30 30 0 0.0 0.0
## 4 ABPV 2011 Spring 621 36 36 0 0.0 0.0
## 5 BQCV 2010 Autumn 621 44 32 12 1.9 27.3
## 6 BQCV 2010 Spring 621 21 1 20 3.2 95.2
## 7 BQCV 2011 Autumn 621 0 0 0 0.0 NaN
## 8 BQCV 2011 Spring 621 0 0 0 0.0 NaN
## 9 CBPV 2010 Autumn 621 91 83 8 1.3 8.8
## 10 CBPV 2010 Spring 621 0 0 0 0.0 NaN
## 11 CBPV 2011 Autumn 621 0 0 0 0.0 NaN
## 12 CBPV 2011 Spring 621 35 32 3 0.5 8.6
## 13 DWV 2010 Autumn 621 180 69 111 17.9 61.7
## 14 DWV 2010 Spring 621 21 12 9 1.4 42.9
## 15 DWV 2011 Autumn 621 57 18 39 6.3 68.4
## 16 DWV 2011 Spring 621 36 13 23 3.7 63.9
## 17 SBV 2010 Autumn 621 44 32 12 1.9 27.3
## 18 SBV 2010 Spring 621 21 16 5 0.8 23.8
## 19 SBV 2011 Autumn 621 0 0 0 0.0 NaN
## 20 SBV 2011 Spring 621 0 0 0 0.0 NaN
This plot shows % of colonies out of number of colonies measured. Zeros are represented by the black line while the empty space represents missing data.
## Df Sum Sq Mean Sq F value Pr(>F)
## Virus 4 36.5 9.14 56.44 < 2e-16 ***
## Year 1 0.8 0.76 4.68 0.0307 *
## Season 1 5.8 5.77 35.65 3.5e-09 ***
## Region 1 3.2 3.23 19.95 9.1e-06 ***
## Surstatus 1 0.1 0.09 0.53 0.4649
## Originbreed 1 0.0 0.01 0.06 0.8118
## Subspecies 4 5.9 1.48 9.13 3.2e-07 ***
## Genotype 10 4.2 0.42 2.57 0.0046 **
## WeatherCluster1 1 3.5 3.45 21.33 4.5e-06 ***
## WeatherCluster2 1 3.3 3.33 20.59 6.5e-06 ***
## Residuals 825 133.5 0.16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 11569 observations deleted due to missingness
To view which is significantly different from other, we look at them seperately
## Virus by Virus
## Virus Measured Negatives Positives PPT PPA
## 1 ABPV 301 234 67 2.7 22.3
## 2 BQCV 65 33 32 1.3 49.2
## 3 CBPV 126 115 11 0.4 8.7
## 4 DWV 294 112 182 7.3 61.9
## 5 SBV 65 48 17 0.7 26.2
## Virus by Virus & Year
## Virus Year Measured Negatives Positives PPT PPA
## 1 ABPV 2010 235 168 67 5.4 28.5
## 2 ABPV 2011 66 66 0 0.0 0.0
## 3 BQCV 2010 65 33 32 2.6 49.2
## 4 BQCV 2011 0 0 0 0.0 NaN
## 5 CBPV 2010 91 83 8 0.6 8.8
## 6 CBPV 2011 35 32 3 0.2 8.6
## 7 DWV 2010 201 81 120 9.7 59.7
## 8 DWV 2011 93 31 62 5.0 66.7
## 9 SBV 2010 65 48 17 1.4 26.2
## 10 SBV 2011 0 0 0 0.0 NaN
## Virus by Virus & Season
## Virus Season Measured Negatives Positives PPT PPA
## 1 ABPV Autumn 211 182 29 2.3 13.7
## 2 ABPV Spring 90 52 38 3.1 42.2
## 3 BQCV Autumn 44 32 12 1.0 27.3
## 4 BQCV Spring 21 1 20 1.6 95.2
## 5 CBPV Autumn 91 83 8 0.6 8.8
## 6 CBPV Spring 35 32 3 0.2 8.6
## 7 DWV Autumn 237 87 150 12.1 63.3
## 8 DWV Spring 57 25 32 2.6 56.1
## 9 SBV Autumn 44 32 12 1.0 27.3
## 10 SBV Spring 21 16 5 0.4 23.8
## Virus by Virus & Region
## Virus Region Measured Negatives Positives PPT PPA
## 1 ABPV 1 27 13 14 1.3 51.9
## 2 ABPV 2 274 221 53 3.9 19.3
## 3 BQCV 1 26 15 11 1.0 42.3
## 4 BQCV 2 39 18 21 1.5 53.8
## 5 CBPV 1 0 0 0 0.0 NaN
## 6 CBPV 2 126 115 11 0.8 8.7
## 7 DWV 1 27 9 18 1.6 66.7
## 8 DWV 2 267 103 164 12.0 61.4
## 9 SBV 1 26 15 11 1.0 42.3
## 10 SBV 2 39 33 6 0.4 15.4
## Virus by Virus & Genotype
## Virus Genotype Measured Negatives Positives PPT PPA
## 1 ABPV 01 21 17 4 2.6 19.0
## 2 ABPV 02 17 12 5 3.1 29.4
## 3 ABPV 03 4 4 0 0.0 0.0
## 4 ABPV 04 15 12 3 2.0 20.0
## 5 ABPV 05 5 3 2 2.3 40.0
## 6 ABPV 06 9 8 1 0.9 11.1
## 7 ABPV 07 0 0 0 0.0 NaN
## 8 ABPV 08 13 3 10 4.9 76.9
## 9 ABPV 09 27 23 4 2.6 14.8
## 10 ABPV 10 31 25 6 4.5 19.4
## 11 ABPV 11 11 11 0 0.0 0.0
## 12 ABPV 12 62 52 10 3.1 16.1
## 13 ABPV 13 36 27 9 4.8 25.0
## 14 ABPV 14 31 22 9 6.8 29.0
## 15 ABPV 15 7 7 0 0.0 0.0
## 16 ABPV 16 12 8 4 2.2 33.3
## 17 BQCV 01 5 5 0 0.0 0.0
## 18 BQCV 02 0 0 0 0.0 NaN
## 19 BQCV 03 4 3 1 0.6 25.0
## 20 BQCV 04 0 0 0 0.0 NaN
## 21 BQCV 05 0 0 0 0.0 NaN
## 22 BQCV 06 0 0 0 0.0 NaN
## 23 BQCV 07 0 0 0 0.0 NaN
## 24 BQCV 08 12 4 8 3.9 66.7
## 25 BQCV 09 12 0 12 7.7 100.0
## 26 BQCV 10 0 0 0 0.0 NaN
## 27 BQCV 11 0 0 0 0.0 NaN
## 28 BQCV 12 0 0 0 0.0 NaN
## 29 BQCV 13 13 5 8 4.3 61.5
## 30 BQCV 14 0 0 0 0.0 NaN
## 31 BQCV 15 7 7 0 0.0 0.0
## 32 BQCV 16 12 9 3 1.6 25.0
## 33 CBPV 01 16 14 2 1.3 12.5
## 34 CBPV 02 0 0 0 0.0 NaN
## 35 CBPV 03 0 0 0 0.0 NaN
## 36 CBPV 04 15 15 0 0.0 0.0
## 37 CBPV 05 0 0 0 0.0 NaN
## 38 CBPV 06 9 6 3 2.6 33.3
## 39 CBPV 07 0 0 0 0.0 NaN
## 40 CBPV 08 0 0 0 0.0 NaN
## 41 CBPV 09 0 0 0 0.0 NaN
## 42 CBPV 10 8 7 1 0.8 12.5
## 43 CBPV 11 11 10 1 0.8 9.1
## 44 CBPV 12 35 31 4 1.2 11.4
## 45 CBPV 13 23 23 0 0.0 0.0
## 46 CBPV 14 9 9 0 0.0 0.0
## 47 CBPV 15 0 0 0 0.0 NaN
## 48 CBPV 16 0 0 0 0.0 NaN
## 49 DWV 01 21 7 14 9.0 66.7
## 50 DWV 02 11 4 7 4.4 63.6
## 51 DWV 03 4 4 0 0.0 0.0
## 52 DWV 04 15 1 14 9.2 93.3
## 53 DWV 05 3 3 0 0.0 0.0
## 54 DWV 06 9 3 6 5.2 66.7
## 55 DWV 07 0 0 0 0.0 NaN
## 56 DWV 08 13 3 10 4.9 76.9
## 57 DWV 09 31 18 13 8.3 41.9
## 58 DWV 10 26 12 14 10.6 53.8
## 59 DWV 11 16 8 8 6.5 50.0
## 60 DWV 12 59 17 42 13.0 71.2
## 61 DWV 13 45 15 30 16.0 66.7
## 62 DWV 14 22 6 16 12.1 72.7
## 63 DWV 15 7 7 0 0.0 0.0
## 64 DWV 16 12 4 8 4.3 66.7
## 65 SBV 01 5 5 0 0.0 0.0
## 66 SBV 02 0 0 0 0.0 NaN
## 67 SBV 03 4 4 0 0.0 0.0
## 68 SBV 04 0 0 0 0.0 NaN
## 69 SBV 05 0 0 0 0.0 NaN
## 70 SBV 06 0 0 0 0.0 NaN
## 71 SBV 07 0 0 0 0.0 NaN
## 72 SBV 08 12 4 8 3.9 66.7
## 73 SBV 09 12 9 3 1.9 25.0
## 74 SBV 10 0 0 0 0.0 NaN
## 75 SBV 11 0 0 0 0.0 NaN
## 76 SBV 12 0 0 0 0.0 NaN
## 77 SBV 13 13 11 2 1.1 15.4
## 78 SBV 14 0 0 0 0.0 NaN
## 79 SBV 15 7 6 1 0.7 14.3
## 80 SBV 16 12 9 3 1.6 25.0
## Virus by Virus & Subspecies
## Virus Subspecies Measured Negatives Positives PPT PPA
## 1 ABPV 1 219 177 42 3.5 19.2
## 2 ABPV 2 19 15 4 1.2 21.1
## 3 ABPV 3 14 11 3 1.2 21.4
## 4 ABPV 4 36 28 8 1.7 22.2
## 5 ABPV 5 13 3 10 4.9 76.9
## 6 BQCV 1 30 10 20 1.7 66.7
## 7 BQCV 2 19 16 3 0.9 15.8
## 8 BQCV 3 0 0 0 0.0 NaN
## 9 BQCV 4 4 3 1 0.2 25.0
## 10 BQCV 5 12 4 8 3.9 66.7
## 11 CBPV 1 102 94 8 0.7 7.8
## 12 CBPV 2 0 0 0 0.0 NaN
## 13 CBPV 3 9 6 3 1.2 33.3
## 14 CBPV 4 15 15 0 0.0 0.0
## 15 CBPV 5 0 0 0 0.0 NaN
## 16 DWV 1 220 83 137 11.3 62.3
## 17 DWV 2 19 11 8 2.4 42.1
## 18 DWV 3 12 6 6 2.3 50.0
## 19 DWV 4 30 9 21 4.4 70.0
## 20 DWV 5 13 3 10 4.9 76.9
## 21 SBV 1 30 25 5 0.4 16.7
## 22 SBV 2 19 15 4 1.2 21.1
## 23 SBV 3 0 0 0 0.0 NaN
## 24 SBV 4 4 4 0 0.0 0.0
## 25 SBV 5 12 4 8 3.9 66.7
## Virus by Virus & WeatherCluster1
## Virus WeatherCluster1 Measured Negatives Positives PPT PPA
## 1 ABPV 1 284 231 53 3.8 18.7
## 2 ABPV 2 17 3 14 1.3 82.4
## 3 BQCV 1 49 26 23 1.6 46.9
## 4 BQCV 2 16 7 9 0.8 56.2
## 5 CBPV 1 126 115 11 0.8 8.7
## 6 CBPV 2 0 0 0 0.0 NaN
## 7 DWV 1 277 109 168 11.9 60.6
## 8 DWV 2 17 3 14 1.3 82.4
## 9 SBV 1 49 43 6 0.4 12.2
## 10 SBV 2 16 5 11 1.0 68.8
## Virus by Virus & WeatherCluster2
## Virus WeatherCluster2 Measured Negatives Positives PPT PPA
## 1 ABPV 1 61 61 0 0.0 0.0
## 2 ABPV 2 0 0 0 0.0 NaN
## 3 ABPV 3 0 0 0 0.0 NaN
## 4 ABPV 4 0 0 0 0.0 NaN
## 5 ABPV 5 27 13 14 2.8 51.9
## 6 ABPV 6 213 160 53 5.7 24.9
## 7 BQCV 1 18 17 1 0.4 5.6
## 8 BQCV 2 0 0 0 0.0 NaN
## 9 BQCV 3 0 0 0 0.0 NaN
## 10 BQCV 4 0 0 0 0.0 NaN
## 11 BQCV 5 26 15 11 2.2 42.3
## 12 BQCV 6 21 1 20 2.1 95.2
## 13 CBPV 1 43 43 0 0.0 0.0
## 14 CBPV 2 0 0 0 0.0 NaN
## 15 CBPV 3 0 0 0 0.0 NaN
## 16 CBPV 4 0 0 0 0.0 NaN
## 17 CBPV 5 0 0 0 0.0 NaN
## 18 CBPV 6 83 72 11 1.2 13.3
## 19 DWV 1 61 22 39 14.1 63.9
## 20 DWV 2 0 0 0 0.0 NaN
## 21 DWV 3 0 0 0 0.0 NaN
## 22 DWV 4 0 0 0 0.0 NaN
## 23 DWV 5 27 9 18 3.5 66.7
## 24 DWV 6 206 81 125 13.4 60.7
## 25 SBV 1 18 17 1 0.4 5.6
## 26 SBV 2 0 0 0 0.0 NaN
## 27 SBV 3 0 0 0 0.0 NaN
## 28 SBV 4 0 0 0 0.0 NaN
## 29 SBV 5 26 15 11 2.2 42.3
## 30 SBV 6 21 16 5 0.5 23.8
GLM model for presence or absence of virus evaluated against list of variables: virus type, year, season, region, survival status, origin breed, subspecies, genotype, weather cluster 1 and weather cluster 2.
##
## Call:
## glm(formula = value ~ Virus + Year + Season + Region + Surstatus +
## Originbreed + Subspecies + Genotype + WeatherCluster1 + WeatherCluster2,
## family = binomial, data = v2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.469 -0.658 -0.368 0.840 2.976
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -15.0356 458.9609 -0.03 0.97387
## VirusBQCV 1.4187 0.3858 3.68 0.00024 ***
## VirusCBPV -0.8498 0.3649 -2.33 0.01986 *
## VirusDWV 2.3538 0.2227 10.57 < 2e-16 ***
## VirusSBV -0.0968 0.3971 -0.24 0.80735
## Year2011 -0.7647 0.2389 -3.20 0.00137 **
## SeasonSpring 1.4189 0.2416 5.87 4.3e-09 ***
## Region2 12.1261 458.9608 0.03 0.97892
## Surstatus1 0.2668 0.2342 1.14 0.25474
## Originbreed1 0.1881 0.2368 0.79 0.42705
## Subspecies2 11.9513 458.9613 0.03 0.97923
## Subspecies3 0.0139 0.6125 0.02 0.98191
## Subspecies4 0.7965 0.5161 1.54 0.12278
## Subspecies5 12.5679 458.9617 0.03 0.97815
## Genotype02 -0.4463 0.6048 -0.74 0.46055
## Genotype03 -2.0988 1.1498 -1.83 0.06794 .
## Genotype04 NA NA NA NA
## Genotype05 -1.2019 1.0401 -1.16 0.24788
## Genotype06 NA NA NA NA
## Genotype08 NA NA NA NA
## Genotype09 -0.9122 0.4603 -1.98 0.04750 *
## Genotype10 -0.3879 0.4573 -0.85 0.39639
## Genotype11 -1.0743 0.5736 -1.87 0.06110 .
## Genotype12 0.1817 0.4020 0.45 0.65130
## Genotype13 -0.0647 0.4107 -0.16 0.87476
## Genotype14 -0.0254 0.4572 -0.06 0.95571
## Genotype15 -13.8163 458.9598 -0.03 0.97598
## Genotype16 NA NA NA NA
## WeatherCluster12 2.6594 0.6765 3.93 8.5e-05 ***
## WeatherCluster25 NA NA NA NA
## WeatherCluster26 1.2142 0.2809 4.32 1.5e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1115.12 on 850 degrees of freedom
## Residual deviance: 802.77 on 825 degrees of freedom
## (11569 observations deleted due to missingness)
## AIC: 854.8
##
## Number of Fisher Scoring iterations: 14
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: value
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 850 1115
## Virus 4 165.8 846 949 < 2e-16 ***
## Year 1 4.2 845 945 0.0413 *
## Season 1 28.0 844 917 1.2e-07 ***
## Region 1 17.6 843 900 2.8e-05 ***
## Surstatus 1 0.5 842 899 0.4762
## Originbreed 1 0.1 841 899 0.8196
## Subspecies 4 32.3 837 867 1.7e-06 ***
## Genotype 10 26.2 827 841 0.0035 **
## WeatherCluster1 1 18.0 826 823 2.2e-05 ***
## WeatherCluster2 1 19.8 825 803 8.5e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = DWVAut10 ~ VWOct10, family = "binomial", data = v1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.706 -0.981 -0.813 1.093 1.661
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0887 0.4097 -2.66 0.0079 **
## VWOct10 0.1519 0.0557 2.73 0.0064 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 102.369 on 73 degrees of freedom
## Residual deviance: 90.807 on 72 degrees of freedom
## (547 observations deleted due to missingness)
## AIC: 94.81
##
## Number of Fisher Scoring iterations: 4
## 2.5 % 97.5 %
## (Intercept) 0.1424 0.7199
## VWOct10 1.0584 1.3174
## (Intercept) VWOct10
## 0.3367 1.1640
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: DWVAut10
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 73 102.4
## VWOct10 1 11.6 72 90.8 0.00067 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = DWVAut10 ~ NMFSpring10, family = "binomial", data = v1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.41 -1.32 1.03 1.04 1.04
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3230 0.1802 1.79 0.073 .
## NMFSpring10 0.0301 0.1235 0.24 0.808
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 215.81 on 158 degrees of freedom
## Residual deviance: 215.75 on 157 degrees of freedom
## (462 observations deleted due to missingness)
## AIC: 219.8
##
## Number of Fisher Scoring iterations: 4
## 2.5 % 97.5 %
## (Intercept) 0.9721 1.974
## NMFSpring10 0.8117 1.336
## (Intercept) NMFSpring10
## 1.381 1.031
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: DWVAut10
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 158 216
## NMFSpring10 1 0.0599 157 216 0.81
##
## Call:
## glm(formula = DWVAut10 ~ ObservedDays1000, family = "binomial",
## data = v1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.008 -1.233 0.758 0.936 1.281
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.08682 0.53084 3.93 8.5e-05 ***
## ObservedDays1000 -0.00233 0.00072 -3.24 0.0012 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 239.64 on 179 degrees of freedom
## Residual deviance: 228.62 on 178 degrees of freedom
## (441 observations deleted due to missingness)
## AIC: 232.6
##
## Number of Fisher Scoring iterations: 4
## 2.5 % 97.5 %
## (Intercept) 2.9254 23.5966
## ObservedDays1000 0.9962 0.9991
## (Intercept) ObservedDays1000
## 8.0592 0.9977
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: DWVAut10
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 179 240
## ObservedDays1000 1 11 178 229 9e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = DWVAut10 ~ Surstatus, family = "binomial", data = v1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.445 -1.445 0.932 0.932 1.177
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.24e-15 3.24e-01 0.00 1.000
## Surstatus1 6.10e-01 3.69e-01 1.65 0.098 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 239.64 on 179 degrees of freedom
## Residual deviance: 236.92 on 178 degrees of freedom
## (441 observations deleted due to missingness)
## AIC: 240.9
##
## Number of Fisher Scoring iterations: 4
## 2.5 % 97.5 %
## (Intercept) 0.5266 1.899
## Surstatus1 0.8906 3.811
## (Intercept) Surstatus1
## 1.00 1.84
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: DWVAut10
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 179 240
## Surstatus 1 2.72 178 237 0.099 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Here we see boxplots of viral titres for three viruses by months. This is taking both years into account. Months 3 and 10 were sampled in 2010 and months 4 and 9 were sampled in 2011.
Viral titres in each colony by Virus and Month aggregated by mean.
## Sample AKI_03 AKI_04 AKI_09 AKI_10 BQCV_03 BQCV_04 BQCV_09 BQCV_10 DWV_03 DWV_04 DWV_09 DWV_10
## 1 GR-03 1.1 NA NA NA 1.86 NA NA NA 4 NA NA NA
## 2 GR-06 NA 0.30 1.0 5.9 NA 3.482 0.5 2 NA 3 5 8
## 3 GR-07 0.3 3.74 NA 3.1 0.16 2.132 NA 3 3 4 NA 7
## 4 GR-08 0.0 1.15 NA 5.7 2.31 0.764 NA 2 2 6 NA 8
## 5 GR-09 0.0 NA 0.9 4.6 2.20 NA 2.0 2 3 NA 7 8
## 6 GR-10 0.0 NA NA NA 2.28 NA NA NA 3 NA NA NA
## 7 GR-11 0.0 0.00 1.1 0.8 0.08 1.045 1.9 3 2 6 8 7
## 8 GR-12 0.0 0.04 1.7 2.1 1.49 2.664 0.0 2 3 3 7 6
## 9 GR-13 0.0 NA NA NA 0.00 NA NA NA 7 NA NA NA
## 10 GR-14 NA 0.00 6.1 5.8 NA 2.767 0.6 3 NA 4 7 8
## 11 GR-15 0.0 1.04 0.9 6.0 0.45 3.922 1.2 2 3 4 7 8
## 12 GR-16 0.0 0.53 0.4 6.2 0.00 1.629 0.0 2 2 2 3 8
## 13 GR-17 0.0 NA NA NA 0.00 NA NA NA 2 NA NA NA
## 14 GR-18 0.2 0.00 1.1 1.8 0.00 0.000 0.0 2 3 6 5 7
## 15 GR-19 0.0 NA NA 4.9 1.42 NA NA 3 2 NA NA 8
## 16 GR-20 0.0 0.00 5.7 1.9 1.44 0.247 0.0 2 3 6 5 7
## 17 GR-21 0.0 NA NA NA 0.18 NA NA NA 2 NA NA NA
## 18 GR-22 0.0 NA NA 4.9 0.00 NA NA 2 4 NA NA 8
## 19 GR-23 0.0 NA NA NA 0.00 NA NA NA 5 NA NA NA
## 20 GR-24 1.2 NA NA 5.8 1.72 NA NA 2 3 NA NA 7
## 21 GR-25 1.2 NA NA NA 0.10 NA NA NA 4 NA NA NA
## 22 GR-26 0.0 NA NA NA 0.00 NA NA NA 2 NA NA NA
## 23 GR-31 0.0 1.00 NA 6.7 0.00 0.000 NA 2 7 3 NA 8
## 24 GR-32 2.0 NA 0.4 0.3 0.39 NA 1.2 2 6 NA 4 7
## 25 GR-33 1.0 NA NA 1.1 0.00 NA NA 3 7 NA NA 7
## 26 GR-34 0.0 NA NA NA 1.26 NA NA NA 7 NA NA NA
## 27 GR-35 0.8 0.37 NA 6.2 0.10 1.704 NA 3 3 7 NA 8
## 28 GR-36 0.2 NA NA NA 1.63 NA NA NA 5 NA NA NA
## 29 GR-37 0.4 0.00 NA 2.1 1.34 3.012 NA 2 6 6 NA 8
## 30 GR-38 0.0 0.00 NA 7.9 1.09 4.133 NA 0 6 7 NA 6
## 31 GR-38-Lim NA NA NA 1.5 NA NA NA 2 NA NA NA 7
## 32 GR-39 0.0 NA NA NA 2.14 NA NA NA 7 NA NA NA
## 33 GR-40 0.5 0.00 2.2 5.5 0.00 0.009 1.6 2 3 3 7 8
## 34 GR-ITALIK NA NA NA 2.9 NA NA NA 2 NA NA NA 7
Viral titres in each colony by Virus and Month and Year.
## Sample AKI_03_10 AKI_04_11 AKI_09_11 AKI_10_10 BQCV_03_10 BQCV_04_11 BQCV_09_11 BQCV_10_10 DWV_03_10 DWV_04_11 DWV_09_11 DWV_10_10
## 1 GR-03 1.1 NA NA NA 1.86 NA NA NA 4 NA NA NA
## 2 GR-06 NA 0.30 1.0 5.9 NA 3.482 0.5 2 NA 3 5 8
## 3 GR-07 0.3 3.74 NA 3.1 0.16 2.132 NA 3 3 4 NA 7
## 4 GR-08 0.0 1.15 NA 5.7 2.31 0.764 NA 2 2 6 NA 8
## 5 GR-09 0.0 NA 0.9 4.6 2.20 NA 2.0 2 3 NA 7 8
## 6 GR-10 0.0 NA NA NA 2.28 NA NA NA 3 NA NA NA
## 7 GR-11 0.0 0.00 1.1 0.8 0.08 1.045 1.9 3 2 6 8 7
## 8 GR-12 0.0 0.04 1.7 2.1 1.49 2.664 0.0 2 3 3 7 6
## 9 GR-13 0.0 NA NA NA 0.00 NA NA NA 7 NA NA NA
## 10 GR-14 NA 0.00 6.1 5.8 NA 2.767 0.6 3 NA 4 7 8
## 11 GR-15 0.0 1.04 0.9 6.0 0.45 3.922 1.2 2 3 4 7 8
## 12 GR-16 0.0 0.53 0.4 6.2 0.00 1.629 0.0 2 2 2 3 8
## 13 GR-17 0.0 NA NA NA 0.00 NA NA NA 2 NA NA NA
## 14 GR-18 0.2 0.00 1.1 1.8 0.00 0.000 0.0 2 3 6 5 7
## 15 GR-19 0.0 NA NA 4.9 1.42 NA NA 3 2 NA NA 8
## 16 GR-20 0.0 0.00 5.7 1.9 1.44 0.247 0.0 2 3 6 5 7
## 17 GR-21 0.0 NA NA NA 0.18 NA NA NA 2 NA NA NA
## 18 GR-22 0.0 NA NA 4.9 0.00 NA NA 2 4 NA NA 8
## 19 GR-23 0.0 NA NA NA 0.00 NA NA NA 5 NA NA NA
## 20 GR-24 1.2 NA NA 5.8 1.72 NA NA 2 3 NA NA 7
## 21 GR-25 1.2 NA NA NA 0.10 NA NA NA 4 NA NA NA
## 22 GR-26 0.0 NA NA NA 0.00 NA NA NA 2 NA NA NA
## 23 GR-31 0.0 1.00 NA 6.7 0.00 0.000 NA 2 7 3 NA 8
## 24 GR-32 2.0 NA 0.4 0.3 0.39 NA 1.2 2 6 NA 4 7
## 25 GR-33 1.0 NA NA 1.1 0.00 NA NA 3 7 NA NA 7
## 26 GR-34 0.0 NA NA NA 1.26 NA NA NA 7 NA NA NA
## 27 GR-35 0.8 0.37 NA 6.2 0.10 1.704 NA 3 3 7 NA 8
## 28 GR-36 0.2 NA NA NA 1.63 NA NA NA 5 NA NA NA
## 29 GR-37 0.4 0.00 NA 2.1 1.34 3.012 NA 2 6 6 NA 8
## 30 GR-38 0.0 0.00 NA 7.9 1.09 4.133 NA 0 6 7 NA 6
## 31 GR-38-Lim NA NA NA 1.5 NA NA NA 2 NA NA NA 7
## 32 GR-39 0.0 NA NA NA 2.14 NA NA NA 7 NA NA NA
## 33 GR-40 0.5 0.00 2.2 5.5 0.00 0.009 1.6 2 3 3 7 8
## 34 GR-ITALIK NA NA NA 2.9 NA NA NA 2 NA NA NA 7
Here, we look at sick bees where viral titre is greater than 7. Positives>7 shows number of colonies where titres>7 was observed. PP7 shows the percentage of colonies where titres>7 was observed.
## Virus Month Measured Negatives Positives Positives>7 PPA PP7 Mean SD Max Min
## 1 AKI 03 30 19 11 0 36.7 0 0.2944 0.5117 1.951 0.0000
## 2 AKI 04 15 7 8 0 53.3 0 0.5452 0.9812 3.743 0.0000
## 3 AKI 09 11 0 11 0 100.0 0 1.9527 2.0203 6.145 0.3791
## 4 AKI 10 23 0 23 1 100.0 4 4.0683 2.2421 7.880 0.2533
## 5 BQCV 03 30 10 20 0 66.7 0 0.7882 0.8653 2.306 0.0000
## 6 BQCV 04 15 2 13 0 86.7 0 1.8340 1.4590 4.133 0.0000
## 7 BQCV 09 11 4 7 0 63.6 0 0.8157 0.7942 2.040 0.0000
## 8 BQCV 10 23 1 22 0 95.7 0 2.1494 0.6221 3.087 0.0000
## 9 DWV 03 30 0 30 2 100.0 7 3.9990 1.8436 7.400 1.7000
## 10 DWV 04 15 0 15 1 100.0 7 4.6447 1.6044 7.490 1.8400
## 11 DWV 09 11 0 11 6 100.0 55 6.0464 1.5871 7.500 3.4300
## 12 DWV 10 23 0 23 18 100.0 78 7.3013 0.5788 8.000 5.5600
For AKI, we see 1 colony out of 23 has titres >7 in October. For DWV, we see a rise in colonies with titres >7. In October 78% of the colonies have titres >7.
31 colonies could be linked to the OID. One colony (13) present with virus data was not in the OID database.
Testing if the viral load is significantly different between levels of any categorical variables such as Location, region, genotype, Subspecies, Originbreed, Survival status or weathe r cluster.
Factors Location, Region, Country and weather clusters are same, so not used.
Combined DWV not significant for Survival status.
Combined DWV significant for Subspecies. Subspecies 4 (n=19) has more DWV than Subspecies 1 (n=6).
Combined DWV not significant for Originbreed.
Combined DWV significant for Genotype.Genotype 10 (n=10) has more DWV than Genotype 7 (n=6).
Combined BQCV not significant for Survival status.
Combined BQCV significant for Genotype.Genotype 10 (n=10) has more BQCV than Genotype 7 (n=6).
Combined BQCV significant for Subspecies. Subspecies 2 (n=6) has more BQCV than Subspecies 1 (n=6). Subspecies 4 (n=19) has more BQCV than Subspecies 1 (n=6).
Combined AKI not significant for any variable.
Significant for Supspecies but huge amount of missing data. Subspecies 2 (n=3) had more AKI than Subspecies 4 (n=12)
Singificant for Subspecies. Subspecies 2 (n=5) had more BQCV than Subspecies 1 (n=6). Subspecies 2 (n=5) had more BQCV than Subspecies 4 (n=18).
Significant for Genotype. Genotype 9 (n=5) had more BQCV than Genotype 7 (n=6).
Significant for Genotype. Genotype 10 (n=10) had more DWV than Genotype 7 (n=6), Genotype 9 (n=5) and Genotype 11 (n=8).
Significant for Originbreed. Originbreed 0 (n=21) had more DWV than Originbreed 1 (n=8).
This is a summary for the 31 colonies selected where quantitaive virus data was available. Shown by Pathogen, Year and Season.
## Pathogen Year Season Total Measured Negatives Positives PPT PPA Mean SD Max Min
## 1 AKI 2010 Autumn 31 21 0 21 67.7 100.0 4.2488 2.2557 7.880 0.2533
## 2 AKI 2010 Spring 31 29 18 11 35.5 37.9 0.3045 0.5177 1.951 0.0000
## 3 AKI 2011 Autumn 31 11 0 11 35.5 100.0 1.9527 2.0203 6.145 0.3791
## 4 AKI 2011 Spring 31 15 7 8 25.8 53.3 0.5452 0.9812 3.743 0.0000
## 5 BQCV 2010 Autumn 31 21 1 20 64.5 95.2 2.1860 0.6398 3.087 0.0000
## 6 BQCV 2010 Spring 31 29 9 20 64.5 69.0 0.8154 0.8675 2.306 0.0000
## 7 BQCV 2011 Autumn 31 11 4 7 22.6 63.6 0.8157 0.7942 2.040 0.0000
## 8 BQCV 2011 Spring 31 15 2 13 41.9 86.7 1.8340 1.4590 4.133 0.0000
## 9 DWV 2010 Autumn 31 21 0 21 67.7 100.0 7.3252 0.6013 8.000 5.5600
## 10 DWV 2010 Spring 31 29 0 29 93.5 100.0 3.9034 1.7990 7.400 1.7000
## 11 DWV 2011 Autumn 31 11 0 11 35.5 100.0 6.0464 1.5871 7.500 3.4300
## 12 DWV 2011 Spring 31 15 0 15 48.4 100.0 4.6447 1.6044 7.490 1.8400
## 13 NMF 2010 Spring 31 0 0 0 0.0 NaN NaN NA -Inf Inf
## 14 Nos 2010 Autumn 31 21 3 18 58.1 85.7 1.3333 0.9129 3.000 0.0000
## 15 Nos 2010 Spring 31 31 16 15 48.4 48.4 1.8387 4.0914 17.000 0.0000
## 16 Nos 2010 Summer 31 28 24 4 12.9 14.3 0.1429 0.3563 1.000 0.0000
## 17 Nos 2011 Autumn 31 11 7 4 12.9 36.4 2.7273 7.1567 24.000 0.0000
## 18 Nos 2011 Spring 31 13 5 8 25.8 61.5 6.8462 15.1154 55.000 0.0000
## 19 Nos 2011 Summer 31 11 11 0 0.0 0.0 0.0000 0.0000 0.000 0.0000
## 20 VW 2009 Autumn 31 31 5 26 83.9 83.9 2.0968 1.9036 7.000 0.0000
## 21 VW 2010 Autumn 93 59 4 55 59.1 93.2 5.5254 5.5191 29.000 0.0000
## 22 VW 2010 Summer 93 57 2 55 59.1 96.5 4.1930 3.6618 19.000 0.0000
## 23 VW 2011 Autumn 62 21 1 20 32.3 95.2 11.1905 9.9630 45.000 0.0000
## 24 VW 2011 Summer 93 33 17 16 17.2 48.5 0.5455 0.6170 2.000 0.0000
Since VWJuly10, NMFSpring10 and WI12 are completely missing, we remove them to do some correlations. NosSum11 was removed due to lack of useful data.
Here we see a heatmap based on the R2 values between variables. The large value on the figure represents R2 values while small values represent P values. Only P values <0.05 are shown in figure.The upper and lower triangles in the table are repeated values, therefore only one of the triangles are necessary. The diagonal can be ignored too.
From the above figure, here is a table of only those variable with significant correlation.
## X Y R2 PVal
## 1 NosSpr10 VWJune10 0.3720 4.693e-02
## 2 VWAut09 VWJune10 0.5123 4.490e-03
## 3 NosAut10 VWSep10 0.4895 2.430e-02
## 4 NosAut10 VWNov10 -0.6844 9.861e-03
## 5 NosSpr11 VWNov10 0.6619 1.372e-02
## 6 NosAut10 VWJune11 -0.7458 8.408e-03
## 7 VWNov10 VWJune11 1.0000 0.000e+00
## 8 NosAut11 VWSep11 0.9424 1.418e-05
## 9 VWNov10 VWOct11 0.6506 4.163e-02
## 10 VWJune11 VWOct11 0.6506 4.163e-02
## 11 VWAug11 VWOct11 0.7873 6.868e-03
## 12 NosSpr11 ObservedDays1000 -0.7454 3.450e-03
## 13 NosAut11 ObservedDays1000 -0.8848 2.957e-04
## 14 VWAut09 ObservedDays1000 -0.4019 2.502e-02
## 15 VWSep10 ObservedDays1000 -0.4021 4.631e-02
## 16 VWOct10 ObservedDays1000 -0.5438 1.083e-02
## 17 VWNov10 ObservedDays1000 -0.5894 3.401e-02
## 18 VWSep11 ObservedDays1000 -0.8745 4.279e-04
## 19 NosSum10 WI11 0.6946 1.770e-02
## 20 NosAut10 AKI_03_10 0.5565 1.334e-02
## 21 NosAut11 AKI_03_10 0.6712 4.776e-02
## 22 VWSep11 AKI_03_10 0.6885 4.029e-02
## 23 NosSum10 AKI_04_11 0.5533 3.238e-02
## 24 VWNov10 AKI_04_11 0.6439 1.755e-02
## 25 VWAug10 AKI_10_10 0.4351 4.870e-02
## 26 NosSpr11 BQCV_03_10 0.6790 2.159e-02
## 27 VWJune11 BQCV_03_10 0.7128 3.113e-02
## 28 VWAut09 BQCV_04_11 0.5430 3.647e-02
## 29 VWSep10 BQCV_09_11 0.6095 4.650e-02
## 30 WI11 BQCV_09_11 0.8457 4.073e-03
## 31 VWAut09 BQCV_10_10 -0.4797 2.776e-02
## 32 VWAut09 DWV_03_10 0.6408 1.804e-04
## 33 VWAug11 DWV_03_10 0.9660 2.305e-05
## 34 VWOct11 DWV_03_10 0.9641 1.121e-04
## 35 BQCV_10_10 DWV_03_10 -0.4733 4.068e-02
## 36 VWOct10 DWV_04_11 0.6621 7.171e-03
## 37 VWJuly11 DWV_04_11 0.6075 4.741e-02
## 38 WI11 DWV_09_11 0.6942 3.800e-02
## 39 VWAug10 DWV_10_10 0.4593 3.622e-02
## 40 BQCV_10_10 DWV_10_10 0.5871 5.139e-03
From the previous table, we select only the cases with R2 values greater than 0.6. And we must find out how many observations exist for variable (Xn and Yn). Yes, we find correlation with good R2 value and P values, but with such low n values, are they meaningful?
## X Y R2 PVal Xn Yn
## 1 NosAut10 VWNov10 -0.6844 9.861e-03 21 13
## 2 NosSpr11 VWNov10 0.6619 1.372e-02 13 13
## 3 NosAut10 VWJune11 -0.7458 8.408e-03 21 11
## 4 VWNov10 VWJune11 1.0000 0.000e+00 13 11
## 5 NosAut11 VWSep11 0.9424 1.418e-05 11 11
## 6 VWNov10 VWOct11 0.6506 4.163e-02 13 10
## 7 VWJune11 VWOct11 0.6506 4.163e-02 11 10
## 8 VWAug11 VWOct11 0.7873 6.868e-03 11 10
## 9 NosSpr11 ObservedDays1000 -0.7454 3.450e-03 13 31
## 10 NosAut11 ObservedDays1000 -0.8848 2.957e-04 11 31
## 11 VWSep11 ObservedDays1000 -0.8745 4.279e-04 11 31
## 12 NosSum10 WI11 0.6946 1.770e-02 28 11
## 13 NosAut11 AKI_03_10 0.6712 4.776e-02 11 29
## 14 VWSep11 AKI_03_10 0.6885 4.029e-02 11 29
## 15 VWNov10 AKI_04_11 0.6439 1.755e-02 13 15
## 16 NosSpr11 BQCV_03_10 0.6790 2.159e-02 13 29
## 17 VWJune11 BQCV_03_10 0.7128 3.113e-02 11 29
## 18 VWSep10 BQCV_09_11 0.6095 4.650e-02 25 11
## 19 WI11 BQCV_09_11 0.8457 4.073e-03 11 11
## 20 VWAut09 DWV_03_10 0.6408 1.804e-04 31 29
## 21 VWAug11 DWV_03_10 0.9660 2.305e-05 11 29
## 22 VWOct11 DWV_03_10 0.9641 1.121e-04 10 29
## 23 VWOct10 DWV_04_11 0.6621 7.171e-03 21 15
## 24 VWJuly11 DWV_04_11 0.6075 4.741e-02 11 15
## 25 WI11 DWV_09_11 0.6942 3.800e-02 11 11
Varroa growth rate is the ratio of NMF in Spring to VW at the end of season. Since we have VW from Sep, Oct and Nov, I made 3 VGRs. And an extra VGR combining Oct and Nov by taking their mean.
VGR equation in use is of the format VGR=10*log(1+147.41*((1+10*(v1$VWSep10))/(1+5*(v1$NMFSpring10)))) where log is natural log.
Here we see VGR for all colonies by month. Empty areas are missing data NA.
If we look at the histograms for VGR. We find that Sep & Oct have sort of normal distributions. Nov has a very strong negative skew. and thereby the Oct-Nov combined have a negative skew.
Significant variables for VGR (using VGROct) and pairwise significant levels based on ANOVA & Tukey's HSD
Location (too many to mention specifically)
Country (9-1, 9-2, 6-3, 7-3, 9-3, 9-6, 9-7)
Genotype (16-11, 8-11, 3-14, 3-16, 6-3, 8-3)
Subspecies (5-1, 4-2, 5-4)
WeatherCluster2 (5-2, 6-2, 5-3)
Not significant for Region, BroodRemoval2010, BroodRemoval2011, Originbreed, Survival status, WeatherCluster1
## The End