# Declare functions fn0: the actual value
fn0 <- function(x) x
# fn1: mean value
fn1 <- function(x) mean(x, na.rm = T)
# fn2: standard deviation
fn2 <- function(x) sd(x, na.rm = T)
# fn2.1: standard error
fn2.1 <- function(x) sd(x, na.rm = T)/sqrt(length(x))
# fn3: number of non-missing/measured samples
fn3 <- function(x) length(which(!is.na(x)))
# fn3.1: number of total samples
fn3.1 <- function(x) length((x))
# fn4: number of samples where nosema is greater than zero/positive
fn4 <- function(x) length(which(x > 0))
# fn5: number of samples where nosema is zero/negative
fn5 <- function(x) length(which(x == 0))
# fn6: % of samples where nosema is present out of total number of samples
fn6 <- function(x) round((length(which(x > 0))/length(x)) * 100, 1)
# fn6.1: % of samples where nosema is present out of measured samples
fn6.1 <- function(x) round((length(which(x > 0))/length(which(!is.na(x)))) * 100, 1)
# fn7: highest nosema count
fn7 <- function(x) max(x, na.rm = T)
# fn8: lowest nosema count
fn8 <- function(x) min(x, na.rm = T)
# fn9: sum of nosema count
fn9 <- function(x) c(sum = sum(x, na.rm = T))
Nosema Prevalence
1.1 Nosema prevalence by Genotype
1.2 Nosema prevalence by Genotype & Year
1.3 Nosema prevalence by Genotype & Season
1.4 Nosema prevalence by Location & Season
1.5 Nosema prevalence by Subspecies & Season
1.6 Nosema prevalence by Country & Season
1.7 Nosema prevalence by WeatherCluster1 & Season
1.8 Nosema prevalence by WeatherCluster2 & Season
1.9 Nosema prevalence by Originbreed & Season
Nosema titres
2.1 Nosema spores by Genotype & Year & Season
2.2 Nosema spores by Location & Year & Season
2.3 Nosema spores by Country & Year & Season
2.4 Nosema spores by WeatherCluster2 & Year & Season
2.5 Nosema spores by Subspecies & Season
Histogram
ANOVA
4.1 Significance values for overall factors
4.2 Pairwise Tukey's HSD
GLM
5.1 GLM Summary
5.2 GLM ANOVA Chi-Sq Test
Individual Variable Comparisons
We have 621 colonies for which there is data for two years and three seasons. Therefore, 621x2x3 produces 3726 datapoints. The data looks something like this
## code 2010_Autumn 2010_Spring 2010_Summer 2011_Autumn 2011_Spring 2011_Summer
## 1 1 0 0 NA NA NA NA
## 2 2 0 0 NA NA NA NA
## 3 3 0 0 NA NA NA NA
## 4 4 0 0 NA NA NA NA
## 5 5 0 0 NA NA NA NA
## 6 6 0 0 NA NA NA NA
Here we see by Year and season, the total number of colonies available, measured (data availbale) and the percentage of colonies measured (percent data available of total).
## Year Season Total Measured Percent
## 1 2010 Autumn 621 315 50.7
## 2 2010 Spring 621 575 92.6
## 3 2010 Summer 621 163 26.2
## 4 2011 Autumn 621 85 13.7
## 5 2011 Spring 621 147 23.7
## 6 2011 Summer 621 117 18.8
Now, we take out only the samples with nosema data into a new table.
Originally we had 3726 datapoints and now we have 1402 datapoints. So, 37.6% is the amount of available nosema data.
All through, PPA is percentage of positive samples out of measured samples. PPT is the percentage of positive samples out of all possible samples. In this doesn't make any sense, ignore PPT and only consider PPA.
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 312 312 41.66 1.5e-10 ***
## Season 2 826 413 55.13 < 2e-16 ***
## Region 1 13 13 1.67 0.20
## Surstatus 1 8 8 1.05 0.31
## Orignbreed 1 19 19 2.49 0.11
## Genotype 15 517 34 4.60 1.2e-08 ***
## Location 18 1291 72 9.57 < 2e-16 ***
## Residuals 1362 10202 7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2324 observations deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## Country 9 888 98.7 11.25 <2e-16 ***
## Subspecies 4 107 26.7 3.04 0.017 *
## WeatherCluster1 1 25 25.2 2.87 0.090 .
## Residuals 1387 12167 8.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2324 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Year + Season + Region + Surstatus + Orignbreed + Genotype + Location, data = nos1)
##
## $Year
## diff lwr upr p adj
## 2011-2010 1.091 0.7595 1.423 0
##
## $Season
## diff lwr upr p adj
## Spring-Autumn 1.55682 1.1566 1.9571 0.0000
## Summer-Autumn 0.07344 -0.4269 0.5738 0.9367
## Summer-Spring -1.48338 -1.9355 -1.0313 0.0000
##
## $Region
## diff lwr upr p adj
## 2-1 0.1923 -0.1051 0.4898 0.2049
##
## $Surstatus
## diff lwr upr p adj
## 1-0 -0.1684 -0.5016 0.1648 0.3217
##
## $Orignbreed
## diff lwr upr p adj
## 1-0 0.2476 -0.06138 0.5566 0.1162
##
## $Genotype
## diff lwr upr p adj
## 2-1 1.94040 0.484508 3.3963 0.0006
## 3-1 0.97201 -0.554990 2.4990 0.7078
## 4-1 1.49122 -0.039643 3.0221 0.0658
## 5-1 1.63925 0.328351 2.9501 0.0020
## 6-1 1.06646 -0.334074 2.4670 0.3896
## 7-1 0.69276 -0.854359 2.2399 0.9781
## 8-1 1.81951 0.254647 3.3844 0.0069
## 9-1 2.46068 0.941159 3.9802 0.0000
## 10-1 2.10683 0.645845 3.5678 0.0001
## 11-1 1.45375 -0.007239 2.9147 0.0528
## 12-1 0.79401 -0.766265 2.3543 0.9339
## 13-1 2.42480 0.075325 4.7743 0.0350
## 14-1 1.14863 -0.560748 2.8580 0.6190
## 15-1 1.25779 -1.425700 3.9413 0.9669
## 16-1 1.18942 -0.480378 2.8592 0.5136
## 3-2 -0.96839 -2.316273 0.3795 0.4976
## 4-2 -0.44918 -1.801437 0.9031 0.9991
## 5-2 -0.30115 -1.398199 0.7959 0.9999
## 6-2 -0.87394 -2.076668 0.3288 0.4765
## 7-2 -1.24763 -2.618274 0.1230 0.1233
## 8-2 -0.12089 -1.511521 1.2697 1.0000
## 9-2 0.52028 -0.819124 1.8597 0.9946
## 10-2 0.16643 -1.106180 1.4390 1.0000
## 11-2 -0.48665 -1.759265 0.7860 0.9955
## 12-2 -1.14639 -2.531857 0.2391 0.2475
## 13-2 0.48440 -1.752806 2.7216 1.0000
## 14-2 -0.79177 -2.343231 0.7597 0.9324
## 15-2 -0.68261 -3.268374 1.9032 0.9999
## 16-2 -0.75097 -2.258722 0.7568 0.9444
## 4-3 0.51920 -0.909328 1.9477 0.9974
## 5-3 0.66724 -0.522565 1.8570 0.8643
## 6-3 0.09445 -1.193445 1.3823 1.0000
## 7-3 -0.27925 -1.725196 1.1667 1.0000
## 8-3 0.84749 -0.617415 2.3124 0.8331
## 9-3 1.48867 0.072293 2.9050 0.0281
## 10-3 1.13482 -0.218567 2.4882 0.2273
## 11-3 0.48173 -0.871652 1.8351 0.9979
## 12-3 -0.17800 -1.638013 1.2820 1.0000
## 13-3 1.45279 -0.831332 3.7369 0.7090
## 14-3 0.17661 -1.441760 1.7950 1.0000
## 15-3 0.28578 -2.340682 2.9122 1.0000
## 16-3 0.21741 -1.359107 1.7939 1.0000
## 5-4 0.14803 -1.046717 1.3428 1.0000
## 6-4 -0.42476 -1.717221 0.8677 0.9992
## 7-4 -0.79845 -2.248473 0.6516 0.8804
## 8-4 0.32829 -1.140640 1.7972 1.0000
## 9-4 0.96946 -0.451070 2.3900 0.5917
## 10-4 0.61561 -0.742123 1.9734 0.9754
## 11-4 -0.03747 -1.395207 1.3203 1.0000
## 12-4 -0.69720 -2.161252 0.7668 0.9619
## 13-4 0.93358 -1.353118 3.2203 0.9911
## 14-4 -0.34259 -1.964605 1.2794 1.0000
## 15-4 -0.23343 -2.862131 2.3953 1.0000
## 16-4 -0.30179 -1.882048 1.2785 1.0000
## 6-5 -0.57279 -1.595228 0.4497 0.8652
## 7-5 -0.94648 -2.162000 0.2690 0.3493
## 8-5 0.18026 -1.057754 1.4183 1.0000
## 9-5 0.82143 -0.358751 2.0016 0.5562
## 10-5 0.46758 -0.636215 1.5714 0.9871
## 11-5 -0.18550 -1.289299 0.9183 1.0000
## 12-5 -0.84524 -2.077451 0.3870 0.5826
## 13-5 0.78555 -1.360122 2.9312 0.9972
## 14-5 -0.49062 -1.906902 0.9257 0.9985
## 15-5 -0.38146 -2.888449 2.1255 1.0000
## 16-5 -0.44982 -1.818081 0.9184 0.9992
## 7-6 -0.37370 -1.685384 0.9380 0.9999
## 8-6 0.75305 -0.579516 2.0856 0.8570
## 9-6 1.39422 0.115205 2.6732 0.0176
## 10-6 1.04037 -0.168520 2.2493 0.1903
## 11-6 0.38729 -0.821604 1.5962 0.9994
## 12-6 -0.27245 -1.599626 1.0547 1.0000
## 13-6 1.35834 -0.843243 3.5599 0.7535
## 14-6 0.08217 -1.417466 1.5818 1.0000
## 15-6 0.19133 -2.363676 2.7463 1.0000
## 16-6 0.12296 -1.331400 1.5773 1.0000
## 8-7 1.12674 -0.359130 2.6126 0.3972
## 9-7 1.76791 0.329870 3.2060 0.0027
## 10-7 1.41407 0.038017 2.7901 0.0368
## 11-7 0.76098 -0.615067 2.1370 0.8768
## 12-7 0.10125 -1.379798 1.5823 1.0000
## 13-7 1.73203 -0.565585 4.0297 0.4081
## 14-7 0.45586 -1.181512 2.0932 0.9999
## 15-7 0.56502 -2.073184 3.2032 1.0000
## 16-7 0.49666 -1.099357 2.0927 0.9996
## 9-8 0.64117 -0.815937 2.0983 0.9814
## 10-8 0.28732 -1.108637 1.6833 1.0000
## 11-8 -0.36576 -1.761721 1.0302 0.9999
## 12-8 -1.02549 -2.525058 0.4741 0.5880
## 13-8 0.60529 -1.704308 2.9149 0.9999
## 14-8 -0.67088 -2.325023 0.9833 0.9916
## 15-8 -0.56172 -3.210366 2.0869 1.0000
## 16-8 -0.63008 -2.243298 0.9831 0.9943
## 10-9 -0.35385 -1.698784 0.9911 0.9999
## 11-9 -1.00693 -2.351869 0.3380 0.4208
## 12-9 -1.66667 -3.118851 -0.2145 0.0084
## 13-9 -0.03588 -2.315002 2.2432 1.0000
## 14-9 -1.31205 -2.923367 0.2993 0.2730
## 15-9 -1.20289 -3.825004 1.4192 0.9726
## 16-9 -1.27125 -2.840526 0.2980 0.2814
## 11-10 -0.65308 -1.931520 0.6254 0.9318
## 12-10 -1.31282 -2.703640 0.0780 0.0894
## 13-10 0.31797 -1.922555 2.5585 1.0000
## 14-10 -0.95820 -2.514443 0.5980 0.7564
## 15-10 -0.84904 -3.437677 1.7396 0.9992
## 16-10 -0.91741 -2.430072 0.5953 0.7767
## 12-11 -0.65973 -2.050556 0.7311 0.9632
## 13-11 0.97105 -1.269471 3.2116 0.9839
## 14-11 -0.30512 -1.861359 1.2511 1.0000
## 15-11 -0.19596 -2.784592 2.3927 1.0000
## 16-11 -0.26432 -1.776988 1.2483 1.0000
## 13-12 1.63079 -0.675710 3.9373 0.5273
## 14-12 0.35461 -1.295193 2.0044 1.0000
## 15-12 0.46378 -2.182166 3.1097 1.0000
## 16-12 0.39541 -1.213358 2.0042 1.0000
## 14-13 -1.27617 -3.686030 1.1337 0.9100
## 15-13 -1.16701 -4.342945 2.0089 0.9971
## 16-13 -1.23537 -3.617326 1.1466 0.9232
## 15-14 0.10916 -2.627353 2.8457 1.0000
## 16-14 0.04080 -1.712953 1.7945 1.0000
## 16-15 -0.06837 -2.780337 2.6436 1.0000
##
## $Location
## diff lwr upr p adj
## 2-1 1.94658 -0.203051 4.09620 0.1356
## 3-1 0.06836 -2.440940 2.57767 1.0000
## 4-1 -0.33086 -2.714272 2.05254 1.0000
## 5-1 1.49596 0.007114 2.98480 0.0473
## 6-1 2.21925 0.694864 3.74363 0.0000
## 7-1 0.02405 -2.323193 2.37128 1.0000
## 8-1 1.45986 -1.049441 3.96916 0.8736
## 9-1 -0.54496 -3.864458 2.77453 1.0000
## 10-1 3.23687 0.727566 5.74617 0.0009
## 11-1 2.57459 0.958406 4.19077 0.0000
## 12-1 0.53689 -1.304439 2.37821 1.0000
## 13-1 -0.16464 -2.314261 1.98499 1.0000
## 14-1 2.40073 0.880197 3.92127 0.0000
## 15-1 1.87044 0.210689 3.53018 0.0101
## 16-1 1.32940 -0.397982 3.05678 0.4047
## 17-1 1.42867 -0.037639 2.89498 0.0667
## 18-1 2.27220 0.759036 3.78537 0.0000
## 19-1 2.34898 0.873946 3.82402 0.0000
## 21-1 0.65141 -1.906940 3.20976 1.0000
## 3-2 -1.87821 -4.665540 0.90911 0.6669
## 4-2 -2.27744 -4.951991 0.39711 0.2204
## 5-2 -0.45062 -2.371359 1.47013 1.0000
## 6-2 0.27267 -1.675747 2.22109 1.0000
## 7-2 -1.92253 -4.564900 0.71984 0.5184
## 8-2 -0.48671 -3.274040 2.30061 1.0000
## 9-2 -2.49154 -6.025889 1.04281 0.5816
## 10-2 1.29029 -1.497034 4.07762 0.9855
## 11-2 0.62801 -1.393035 2.64906 0.9999
## 12-2 -1.40969 -3.614926 0.79555 0.7551
## 13-2 -2.11121 -4.579709 0.35729 0.2136
## 14-2 0.45416 -1.491253 2.39957 1.0000
## 15-2 -0.07614 -2.132192 1.97991 1.0000
## 16-2 -0.61718 -2.728204 1.49385 1.0000
## 17-2 -0.51791 -2.421232 1.38542 1.0000
## 18-2 0.32563 -1.614028 2.26528 1.0000
## 19-2 0.40241 -1.507651 2.31247 1.0000
## 21-2 -1.29517 -4.126728 1.53640 0.9873
## 4-3 -0.39923 -3.370579 2.57212 1.0000
## 5-3 1.42760 -0.888642 3.74383 0.8073
## 6-3 2.15089 -0.188355 4.49013 0.1184
## 7-3 -0.04432 -2.986736 2.89810 1.0000
## 8-3 1.39150 -1.681756 4.46476 0.9887
## 9-3 -0.61332 -4.377279 3.15063 1.0000
## 10-3 3.16851 0.095250 6.24176 0.0347
## 11-3 2.50623 0.106154 4.90630 0.0295
## 12-3 0.46852 -2.088580 3.02563 1.0000
## 13-3 -0.23300 -3.020325 2.55433 1.0000
## 14-3 2.33237 -0.004364 4.66911 0.0511
## 15-3 1.80207 -0.627548 4.23170 0.4794
## 16-3 1.26103 -1.215281 3.73735 0.9608
## 17-3 1.36031 -0.941510 3.66212 0.8575
## 18-3 2.20384 -0.128106 4.53579 0.0913
## 19-3 2.28062 -0.026766 4.58801 0.0571
## 21-3 0.58305 -2.530384 3.69648 1.0000
## 5-4 1.82682 -0.352394 4.00604 0.2458
## 6-4 2.55011 0.346462 4.75376 0.0066
## 7-4 0.35491 -2.480906 3.19073 1.0000
## 8-4 1.79073 -1.180625 4.76208 0.8354
## 9-4 -0.21410 -3.895317 3.46712 1.0000
## 10-4 3.56773 0.596382 6.53908 0.0035
## 11-4 2.90545 0.637332 5.17358 0.0010
## 12-4 0.86775 -1.565931 3.30143 0.9995
## 13-4 0.16623 -2.508321 2.84078 1.0000
## 14-4 2.73160 0.530607 4.93259 0.0019
## 15-4 2.20130 -0.098066 4.50067 0.0802
## 16-4 1.66026 -0.688390 4.00891 0.5761
## 17-4 1.75953 -0.404349 3.92342 0.2990
## 18-4 2.60307 0.407162 4.79897 0.0044
## 19-4 2.67985 0.510041 4.84966 0.0021
## 21-4 0.98227 -2.030612 3.99516 0.9998
## 6-5 0.72329 -0.456587 1.90316 0.8143
## 7-5 -1.47191 -3.611513 0.66769 0.6287
## 8-5 -0.03610 -2.352335 2.28014 1.0000
## 9-5 -2.04092 -5.216989 1.13515 0.7471
## 10-5 1.74091 -0.575329 4.05715 0.4524
## 11-5 1.07863 -0.217676 2.37494 0.2583
## 12-5 -0.95907 -2.527141 0.60900 0.8172
## 13-5 -1.66059 -3.581337 0.26015 0.1972
## 14-5 0.90477 -0.270127 2.07968 0.4034
## 15-5 0.37448 -0.975753 1.72471 1.0000
## 16-5 -0.16656 -1.599112 1.26599 1.0000
## 17-5 -0.06729 -1.171110 1.03653 1.0000
## 18-5 0.77624 -0.389103 1.94159 0.6874
## 19-5 0.85302 -0.262366 1.96842 0.4173
## 21-5 -0.84455 -3.213835 1.52473 0.9995
## 7-6 -2.19520 -4.359682 -0.03072 0.0424
## 8-6 -0.75939 -3.098626 1.57985 0.9999
## 9-6 -2.76421 -5.957092 0.42867 0.1952
## 10-6 1.01762 -1.321619 3.35686 0.9929
## 11-6 0.35534 -0.981631 1.69232 1.0000
## 12-6 -1.68236 -3.284211 -0.08051 0.0274
## 13-6 -2.38388 -4.332302 -0.43546 0.0025
## 14-6 0.18149 -1.038136 1.40111 1.0000
## 15-6 -0.34881 -1.738130 1.04051 1.0000
## 16-6 -0.88985 -2.359301 0.57960 0.8296
## 17-6 -0.79058 -1.941884 0.36073 0.6321
## 18-6 0.05295 -1.157466 1.26338 1.0000
## 19-6 0.12974 -1.032667 1.29214 1.0000
## 21-6 -1.56784 -3.959615 0.82394 0.7149
## 8-7 1.43582 -1.506602 4.37824 0.9745
## 9-7 -0.56901 -4.226913 3.08890 1.0000
## 10-7 3.21282 0.270405 6.15524 0.0161
## 11-7 2.55054 0.320460 4.78063 0.0080
## 12-7 0.51284 -1.885430 2.91111 1.0000
## 13-7 -0.18868 -2.831050 2.45369 1.0000
## 14-7 2.37669 0.214916 4.53846 0.0146
## 15-7 1.84639 -0.415464 4.10825 0.2919
## 16-7 1.30535 -1.006588 3.61729 0.9006
## 17-7 1.40462 -0.719355 3.52860 0.6999
## 18-7 2.24816 0.091563 4.40475 0.0302
## 19-7 2.32494 0.194924 4.45495 0.0162
## 21-7 0.62736 -2.356991 3.61172 1.0000
## 9-8 -2.00482 -5.768778 1.75913 0.9401
## 10-8 1.77701 -1.296249 4.85026 0.8796
## 11-8 1.11473 -1.285345 3.51480 0.9850
## 12-8 -0.92298 -3.480079 1.63413 0.9994
## 13-8 -1.62450 -4.411824 1.16283 0.8719
## 14-8 0.94087 -1.395864 3.27761 0.9973
## 15-8 0.41057 -2.019047 2.84020 1.0000
## 16-8 -0.13047 -2.606781 2.34585 1.0000
## 17-8 -0.03119 -2.333009 2.27062 1.0000
## 18-8 0.81234 -1.519606 3.14429 0.9996
## 19-8 0.88912 -1.418266 3.19651 0.9984
## 21-8 -0.80845 -3.921884 2.30498 1.0000
## 10-9 3.78183 0.017876 7.54578 0.0473
## 11-9 3.11955 -0.118164 6.35727 0.0751
## 12-9 1.08185 -2.273927 4.43762 0.9999
## 13-9 0.38033 -3.154025 3.91468 1.0000
## 14-9 2.94570 -0.245352 6.13674 0.1142
## 15-9 2.41540 -0.844282 5.67508 0.4813
## 16-9 1.87436 -1.420272 5.16899 0.8942
## 17-9 1.97363 -1.191935 5.13920 0.7917
## 18-9 2.81716 -0.370378 6.00471 0.1662
## 19-9 2.89395 -0.275674 6.06356 0.1261
## 21-9 1.19637 -2.600457 4.99320 0.9999
## 11-10 -0.66228 -3.062352 1.73779 1.0000
## 12-10 -2.69998 -5.257086 -0.14288 0.0256
## 13-10 -3.40150 -6.188831 -0.61418 0.0026
## 14-10 -0.83613 -3.172870 1.50060 0.9994
## 15-10 -1.36643 -3.796054 1.06319 0.9037
## 16-10 -1.90747 -4.383787 0.56884 0.4029
## 17-10 -1.80820 -4.110016 0.49362 0.3642
## 18-10 -0.96467 -3.296612 1.36728 0.9961
## 19-10 -0.88788 -3.195272 1.41950 0.9985
## 21-10 -2.58546 -5.698890 0.52797 0.2618
## 12-11 -2.03770 -3.727150 -0.34826 0.0032
## 13-11 -2.73922 -4.760275 -0.71817 0.0003
## 14-11 -0.17386 -1.506442 1.15873 1.0000
## 15-11 -0.70415 -2.193621 0.78531 0.9818
## 16-11 -1.24519 -2.809670 0.31928 0.3389
## 17-11 -1.14592 -2.416279 0.12444 0.1402
## 18-11 -0.30239 -1.626556 1.02178 1.0000
## 19-11 -0.22561 -1.506031 1.05482 1.0000
## 21-11 -1.92318 -4.374487 0.52813 0.3666
## 13-12 -0.70152 -2.906758 1.50372 0.9999
## 14-12 1.86385 0.265658 3.46204 0.0058
## 15-12 1.33355 -0.397617 3.06472 0.4028
## 16-12 0.79251 -1.003600 2.58862 0.9916
## 17-12 0.89178 -0.654902 2.43847 0.8822
## 18-12 1.73532 0.144138 3.32650 0.0164
## 19-12 1.81210 0.257133 3.36706 0.0059
## 21-12 0.11452 -2.490727 2.71977 1.0000
## 14-13 2.56537 0.619957 4.51078 0.0005
## 15-13 2.03507 -0.020982 4.09112 0.0562
## 16-13 1.49403 -0.616994 3.60506 0.5738
## 17-13 1.59330 -0.310022 3.49663 0.2481
## 18-13 2.43684 0.497182 4.37649 0.0015
## 19-13 2.51362 0.603559 4.42368 0.0006
## 21-13 0.81604 -2.015518 3.64761 1.0000
## 15-14 -0.53030 -1.915395 0.85480 0.9986
## 16-14 -1.07134 -2.536796 0.39412 0.5088
## 17-14 -0.97206 -2.118272 0.17414 0.2269
## 18-14 -0.12853 -1.334104 1.07704 1.0000
## 19-14 -0.05175 -1.209104 1.10560 1.0000
## 21-14 -1.74932 -4.138652 0.64000 0.5058
## 16-15 -0.54104 -2.150481 1.06840 0.9998
## 17-15 -0.44177 -1.767106 0.88357 0.9998
## 18-15 0.40177 -0.975236 1.77877 1.0000
## 19-15 0.47855 -0.856443 1.81354 0.9994
## 21-15 -1.21903 -3.699272 1.26122 0.9725
## 17-16 0.09927 -1.309839 1.50838 1.0000
## 18-16 0.94281 -0.515004 2.40062 0.7369
## 19-16 1.01959 -0.398606 2.43778 0.5425
## 21-16 -0.67799 -3.203990 1.84801 1.0000
## 18-17 0.84353 -0.292878 1.97995 0.4778
## 19-17 0.92031 -0.164810 2.00544 0.2268
## 21-17 -0.77726 -3.132448 1.57793 0.9998
## 19-18 0.07678 -1.070873 1.22443 1.0000
## 21-18 -1.62079 -4.005437 0.76385 0.6512
## 21-19 -1.69757 -4.058207 0.66306 0.5420
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ +Country + Subspecies + WeatherCluster1 + WeatherCluster2, data = nos1)
##
## $Country
## diff lwr upr p adj
## 2-1 -1.080e-02 -1.06224 1.040641 1.0000
## 3-1 1.256e+00 0.41849 2.092686 0.0001
## 4-1 -1.458e+00 -2.86363 -0.053333 0.0345
## 5-1 1.209e+00 0.11118 2.307000 0.0179
## 6-1 9.986e-01 -0.05286 2.050017 0.0792
## 7-1 1.150e-01 -1.18757 1.417572 1.0000
## 8-1 1.198e+00 0.10548 2.291324 0.0188
## 9-1 2.073e-01 -1.29873 1.713307 1.0000
## 10-1 -1.458e+00 -3.05689 0.139919 0.1088
## 3-2 1.266e+00 0.37548 2.157297 0.0003
## 4-2 -1.448e+00 -2.88554 -0.009826 0.0468
## 5-2 1.220e+00 0.08042 2.359360 0.0247
## 6-2 1.009e+00 -0.08539 2.104143 0.1005
## 7-2 1.258e-01 -1.21198 1.463589 1.0000
## 8-2 1.209e+00 0.07454 2.343867 0.0260
## 9-2 2.181e-01 -1.31849 1.754668 1.0000
## 10-2 -1.448e+00 -3.07492 0.179546 0.1309
## 4-3 -2.714e+00 -4.00351 -1.424639 0.0000
## 5-3 -4.650e-02 -0.99180 0.898803 1.0000
## 6-3 -2.570e-01 -1.14792 0.633896 0.9960
## 7-3 -1.141e+00 -2.31739 0.036222 0.0665
## 8-3 -5.719e-02 -0.99669 0.882318 1.0000
## 9-3 -1.048e+00 -2.44697 0.350370 0.3417
## 10-3 -2.714e+00 -4.21177 -1.216380 0.0000
## 5-4 2.668e+00 1.19540 4.139755 0.0000
## 6-4 2.457e+00 1.01920 3.894919 0.0000
## 7-4 1.573e+00 -0.05703 3.204007 0.0691
## 8-4 2.657e+00 1.18842 4.125351 0.0000
## 9-4 1.666e+00 -0.13144 3.462993 0.0965
## 10-4 1.013e-14 -1.87531 1.875314 1.0000
## 6-5 -2.105e-01 -1.34998 0.928955 0.9999
## 7-5 -1.094e+00 -2.46870 0.280520 0.2573
## 8-5 -1.069e-02 -1.18854 1.167166 1.0000
## 9-5 -1.002e+00 -2.57054 0.566940 0.5823
## 10-5 -2.668e+00 -4.32521 -1.009940 0.0000
## 7-6 -8.836e-01 -2.22136 0.454213 0.5328
## 8-6 1.998e-01 -0.93484 1.334491 0.9999
## 9-6 -7.913e-01 -2.32787 0.745291 0.8325
## 10-6 -2.457e+00 -4.08429 -0.829830 0.0001
## 8-7 1.083e+00 -0.28723 2.454029 0.2666
## 9-7 9.229e-02 -1.62592 1.810493 1.0000
## 10-7 -1.573e+00 -3.37322 0.226246 0.1476
## 9-8 -9.911e-01 -2.55637 0.574144 0.5945
## 10-8 -2.657e+00 -4.31122 -1.002548 0.0000
## 10-9 -1.666e+00 -3.61781 0.286261 0.1728
##
## $Subspecies
## diff lwr upr p adj
## 2-1 0.56998 -0.1191 1.2591 0.1589
## 3-1 0.62266 -0.2730 1.5183 0.3183
## 4-1 0.25194 -0.3001 0.8040 0.7240
## 5-1 -0.43945 -1.5228 0.6439 0.8024
## 3-2 0.05269 -1.0014 1.1068 0.9999
## 4-2 -0.31804 -1.1015 0.4654 0.8020
## 5-2 -1.00942 -2.2271 0.2082 0.1572
## 4-3 -0.37072 -1.3408 0.5993 0.8349
## 5-3 -1.06211 -2.4075 0.2832 0.1972
## 5-4 -0.69139 -1.8371 0.4543 0.4667
##
## $WeatherCluster1
## diff lwr upr p adj
## 2-1 -0.09274 -0.4154 0.2299 0.573
##
## Call:
## glm(formula = nosema1 ~ Year + Season + Region + Surstatus +
## Orignbreed + Subspecies + Genotype + Location, family = "gaussian",
## data = nos1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.898 -2.211 -0.678 2.566 6.857
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.25666 0.85307 -0.30 0.7636
## Year2011 0.55217 0.19284 2.86 0.0043 **
## SeasonSpring 1.70030 0.17671 9.62 < 2e-16 ***
## SeasonSummer -0.53926 0.23676 -2.28 0.0229 *
## Region2 -1.27540 0.83846 -1.52 0.1285
## Surstatus1 -0.12485 0.19330 -0.65 0.5185
## Orignbreed1 0.23621 0.16677 1.42 0.1569
## Subspecies2 1.25475 0.61458 2.04 0.0414 *
## Subspecies3 0.90857 0.85866 1.06 0.2902
## Subspecies4 1.04966 0.51872 2.02 0.0432 *
## Subspecies5 0.15985 0.71680 0.22 0.8236
## Genotype2 0.98468 0.50850 1.94 0.0530 .
## Genotype3 0.04064 0.54936 0.07 0.9410
## Genotype4 -0.05950 0.54276 -0.11 0.9127
## Genotype5 0.67726 0.47076 1.44 0.1505
## Genotype6 0.45714 0.44922 1.02 0.3090
## Genotype7 -0.00592 0.54487 -0.01 0.9913
## Genotype8 -0.46188 0.49424 -0.93 0.3502
## Genotype9 NA NA NA NA
## Genotype10 -0.03367 0.52012 -0.06 0.9484
## Genotype11 -0.76607 0.48727 -1.57 0.1161
## Genotype12 NA NA NA NA
## Genotype13 0.26728 1.04776 0.26 0.7987
## Genotype14 -0.77746 0.84383 -0.92 0.3570
## Genotype15 NA NA NA NA
## Genotype16 NA NA NA NA
## Location2 2.66248 0.64790 4.11 4.2e-05 ***
## Location3 0.84080 0.75226 1.12 0.2639
## Location4 0.41843 0.71243 0.59 0.5571
## Location5 0.05599 0.78115 0.07 0.9429
## Location6 2.83876 0.49037 5.79 8.8e-09 ***
## Location7 -1.76594 0.92664 -1.91 0.0569 .
## Location8 -0.10070 0.97527 -0.10 0.9178
## Location9 -2.18351 1.15789 -1.89 0.0595 .
## Location10 1.67622 0.97703 1.72 0.0865 .
## Location11 3.61639 0.52405 6.90 7.9e-12 ***
## Location12 0.21785 0.54692 0.40 0.6905
## Location13 -0.13822 0.67721 -0.20 0.8383
## Location14 1.60499 0.76695 2.09 0.0366 *
## Location15 1.04429 0.89383 1.17 0.2429
## Location16 0.34232 0.92417 0.37 0.7111
## Location17 2.22144 0.49358 4.50 7.4e-06 ***
## Location18 3.23244 0.52349 6.17 8.7e-10 ***
## Location19 3.38911 0.46559 7.28 5.7e-13 ***
## Location21 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7.491)
##
## Null deviance: 13187 on 1401 degrees of freedom
## Residual deviance: 10202 on 1362 degrees of freedom
## (2324 observations deleted due to missingness)
## AIC: 6843
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = nosema1 ~ Year + Season + Country + WeatherCluster1 +
## WeatherCluster2, family = "gaussian", data = nos1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.15 -2.23 -1.23 3.01 6.58
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.680 0.252 2.70 0.00711 **
## Year2011 0.723 0.195 3.71 0.00022 ***
## SeasonSpring 1.510 0.180 8.37 < 2e-16 ***
## SeasonSummer -0.214 0.239 -0.89 0.37109
## Country2 0.503 0.576 0.87 0.38320
## Country3 1.103 0.266 4.15 3.5e-05 ***
## Country4 -1.435 0.431 -3.33 0.00091 ***
## Country5 0.974 0.343 2.84 0.00456 **
## Country6 0.960 0.390 2.46 0.01386 *
## Country7 0.773 0.620 1.25 0.21278
## Country8 1.976 0.586 3.37 0.00076 ***
## Country9 0.816 0.661 1.24 0.21687
## Country10 -0.691 0.684 -1.01 0.31211
## WeatherCluster12 -0.736 0.476 -1.55 0.12200
## WeatherCluster22 NA NA NA NA
## WeatherCluster23 NA NA NA NA
## WeatherCluster24 NA NA NA NA
## WeatherCluster25 NA NA NA NA
## WeatherCluster26 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 8.167)
##
## Null deviance: 13187 on 1401 degrees of freedom
## Residual deviance: 11336 on 1388 degrees of freedom
## (2324 observations deleted due to missingness)
## AIC: 6939
##
## Number of Fisher Scoring iterations: 2
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: nosema1
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1401 13187
## Year 1 312 1400 12875 1.1e-10 ***
## Season 2 826 1398 12049 < 2e-16 ***
## Region 1 13 1397 12037 0.19569
## Surstatus 1 8 1396 12029 0.30609
## Orignbreed 1 19 1395 12010 0.11450
## Subspecies 4 148 1391 11862 0.00056 ***
## Genotype 11 369 1380 11493 8.4e-07 ***
## Location 18 1291 1362 10202 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: nosema1
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1401 13187
## Year 1 312 1400 12875 6.4e-10 ***
## Season 2 826 1398 12049 < 2e-16 ***
## Country 9 693 1389 11356 1.7e-14 ***
## WeatherCluster1 1 20 1388 11336 0.12
## WeatherCluster2 0 0 1388 11336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Summary, Boxplot and ANOVA results
## Year
## Year Mean SE Max Min Measured Positives Negatives PPA
## 1 2010 1.821 0.09079 7.699 0 1053 293 760 27.8
## 2 2011 2.912 0.17556 7.881 0 349 155 194 44.4
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Year, data = nos2)
##
## $Year
## diff lwr upr p adj
## 2011-2010 1.091 0.7236 1.459 0
## Season
## Season Mean SE Max Min Measured Positives Negatives PPA
## 1 Autumn 1.236 0.1261 7.380 0 400 78 322 19.5
## 2 Spring 2.783 0.1236 7.881 0 722 300 422 41.6
## 3 Summer 1.533 0.1592 6.954 0 280 70 210 25.0
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Season, data = nos2)
##
## $Season
## diff lwr upr p adj
## Spring-Autumn 1.5471 1.1106 1.9836 0.0000
## Summer-Autumn 0.2975 -0.2481 0.8431 0.4071
## Summer-Spring -1.2496 -1.7426 -0.7566 0.0000
## Survival Status
## Surstatus Mean SE Max Min Measured Positives Negatives PPA
## 1 0 2.210 0.16804 7.881 0 344 116 228 33.7
## 2 1 2.054 0.09385 7.869 0 1058 332 726 31.4
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Surstatus, data = nos2)
##
## $Surstatus
## diff lwr upr p adj
## 1-0 -0.1557 -0.5293 0.2179 0.4138
## Origin Breed
## Orignbreed Mean SE Max Min Measured Positives Negatives PPA
## 1 0 2.022 0.09777 7.869 0 962 298 664 31.0
## 2 1 2.247 0.14981 7.881 0 440 150 290 34.1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Orignbreed, data = nos2)
##
## $Orignbreed
## diff lwr upr p adj
## 1-0 0.225 -0.1213 0.5713 0.2027
## Genotype
## Genotype Mean SE Max Min Measured Positives Negatives PPA
## 1 1 0.8741 0.2737 7.146 0 67 9 58 13.4
## 2 2 2.6765 0.3095 7.519 0 110 45 65 40.9
## 3 3 1.8095 0.3272 7.881 0 87 23 64 26.4
## 4 4 2.0544 0.3214 7.079 0 86 28 58 32.6
## 5 5 2.3678 0.2126 7.431 0 220 80 140 36.4
## 6 6 1.8082 0.2511 7.792 0 137 38 99 27.7
## 7 7 1.2962 0.2821 6.954 0 82 17 65 20.7
## 8 8 2.6239 0.3802 7.740 0 78 30 48 38.5
## 9 9 3.0247 0.3518 7.740 0 89 41 48 46.1
## 10 10 2.6335 0.3147 7.633 0 108 43 65 39.8
## 11 11 2.1089 0.2957 7.869 0 108 35 73 32.4
## 12 12 1.2974 0.2919 6.954 0 79 16 63 20.3
## 13 13 2.9637 0.7675 7.301 0 21 9 12 42.9
## 14 14 1.4811 0.3626 6.699 0 55 13 42 23.6
## 15 15 1.7601 0.7817 6.954 0 15 4 11 26.7
## 16 16 1.8751 0.3912 7.699 0 60 17 43 28.3
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Genotype, data = nos2)
##
## $Genotype
## diff lwr upr p adj
## 2-1 1.802388 0.18958 3.4152 0.0125
## 3-1 0.935384 -0.75620 2.6270 0.8769
## 4-1 1.180264 -0.51559 2.8761 0.5564
## 5-1 1.493655 0.04147 2.9458 0.0364
## 6-1 0.934083 -0.61740 2.4856 0.7862
## 7-1 0.422058 -1.29181 2.1359 1.0000
## 8-1 1.749724 0.01620 3.4832 0.0451
## 9-1 2.150549 0.46726 3.8338 0.0013
## 10-1 1.759361 0.14091 3.3778 0.0182
## 11-1 1.234778 -0.38367 2.8532 0.3861
## 12-1 0.423300 -1.30514 2.1517 1.0000
## 13-1 2.089568 -0.51313 4.6923 0.2964
## 14-1 0.606935 -1.28668 2.5005 0.9994
## 15-1 0.885948 -2.08677 3.8587 0.9997
## 16-1 1.000954 -0.84882 2.8507 0.8945
## 3-2 -0.867004 -2.36017 0.6262 0.8291
## 4-2 -0.622124 -2.12013 0.8759 0.9894
## 5-2 -0.308733 -1.52402 0.9066 1.0000
## 6-2 -0.868306 -2.20067 0.4641 0.6706
## 7-2 -1.380330 -2.89870 0.1380 0.1246
## 8-2 -0.052665 -1.59318 1.4878 1.0000
## 9-2 0.348161 -1.13560 1.8319 1.0000
## 10-2 -0.043028 -1.45280 1.3667 1.0000
## 11-2 -0.567610 -1.97739 0.8422 0.9922
## 12-2 -1.379088 -2.91388 0.1557 0.1372
## 13-2 0.287180 -2.19115 2.7655 1.0000
## 14-2 -1.195454 -2.91413 0.5232 0.5574
## 15-2 -0.916440 -3.78090 1.9480 0.9994
## 16-2 -0.801434 -2.47169 0.8688 0.9594
## 4-3 0.244880 -1.33762 1.8274 1.0000
## 5-3 0.558271 -0.75977 1.8763 0.9871
## 6-3 -0.001301 -1.42801 1.4254 1.0000
## 7-3 -0.513326 -2.11512 1.0885 0.9994
## 8-3 0.814340 -0.80846 2.4371 0.9409
## 9-3 1.215165 -0.35387 2.7842 0.3590
## 10-3 0.823977 -0.67528 2.3232 0.8821
## 11-3 0.299394 -1.19986 1.7986 1.0000
## 12-3 -0.512084 -2.12946 1.1053 0.9995
## 13-3 1.154184 -1.37612 3.6845 0.9740
## 14-3 -0.328449 -2.12125 1.4644 1.0000
## 15-3 -0.049436 -2.95898 2.8601 1.0000
## 16-3 0.065570 -1.68087 1.8120 1.0000
## 5-4 0.313391 -1.01013 1.6369 1.0000
## 6-4 -0.246182 -1.67795 1.1856 1.0000
## 7-4 -0.758206 -2.36451 0.8481 0.9648
## 8-4 0.569459 -1.05779 2.1967 0.9983
## 9-4 0.970285 -0.60335 2.5439 0.7545
## 10-4 0.579096 -0.92498 2.0832 0.9951
## 11-4 0.054514 -1.44956 1.5586 1.0000
## 12-4 -0.756964 -2.37881 0.8649 0.9681
## 13-4 0.909304 -1.62386 3.4425 0.9977
## 14-4 -0.573330 -2.37017 1.2235 0.9994
## 15-4 -0.294316 -3.20635 2.6177 1.0000
## 16-4 -0.179310 -1.92989 1.5713 1.0000
## 6-5 -0.559572 -1.69221 0.5731 0.9480
## 7-5 -1.071597 -2.41812 0.2749 0.3113
## 8-5 0.256069 -1.11538 1.6275 1.0000
## 9-5 0.656894 -0.65049 1.9643 0.9403
## 10-5 0.265706 -0.95706 1.4885 1.0000
## 11-5 -0.258877 -1.48164 0.9639 1.0000
## 12-5 -1.070354 -2.43538 0.2947 0.3370
## 13-5 0.595913 -1.78102 2.9728 1.0000
## 14-5 -0.886720 -2.45565 0.6822 0.8569
## 15-5 -0.607707 -3.38490 2.1695 1.0000
## 16-5 -0.492701 -2.00843 1.0230 0.9993
## 7-6 -0.512024 -1.96509 0.9410 0.9981
## 8-6 0.815641 -0.66055 2.2918 0.8776
## 9-6 1.216467 -0.20040 2.6333 0.1936
## 10-6 0.825278 -0.51391 2.1645 0.7552
## 11-6 0.300695 -1.03849 1.6399 1.0000
## 12-6 -0.510782 -1.98100 0.9594 0.9984
## 13-6 1.155485 -1.28338 3.5944 0.9636
## 14-6 -0.327148 -1.98841 1.3341 1.0000
## 15-6 -0.048134 -2.87852 2.7823 1.0000
## 16-6 0.066872 -1.54425 1.6780 1.0000
## 8-7 1.327665 -0.31836 2.9737 0.2886
## 9-7 1.728491 0.13545 3.3215 0.0187
## 10-7 1.337302 -0.18706 2.8617 0.1654
## 11-7 0.812720 -0.71164 2.3371 0.9054
## 12-7 0.001242 -1.63943 1.6419 1.0000
## 13-7 1.667510 -0.87775 4.2128 0.6620
## 14-7 0.184876 -1.62898 1.9987 1.0000
## 15-7 0.463890 -2.45867 3.3864 1.0000
## 16-7 0.578896 -1.18914 2.3469 0.9992
## 9-8 0.400826 -1.21333 2.0150 1.0000
## 10-8 0.009637 -1.53678 1.5561 1.0000
## 11-8 -0.514946 -2.06136 1.0315 0.9990
## 12-8 -1.326423 -2.98761 0.3348 0.3056
## 13-8 0.339844 -2.21869 2.8984 1.0000
## 14-8 -1.142789 -2.97522 0.6896 0.7384
## 15-8 -0.863775 -3.79790 2.0703 0.9998
## 16-8 -0.748769 -2.53586 1.0383 0.9884
## 10-9 -0.391189 -1.88109 1.0987 0.9999
## 11-9 -0.915771 -2.40567 0.5741 0.7588
## 12-9 -1.727249 -3.33595 -0.1185 0.0215
## 13-9 -0.060981 -2.58575 2.4638 1.0000
## 14-9 -1.543615 -3.32860 0.2414 0.1839
## 15-9 -1.264601 -4.16933 1.6401 0.9832
## 16-9 -1.149595 -2.88801 0.5888 0.6464
## 11-10 -0.524583 -1.94081 0.8916 0.9968
## 12-10 -1.336060 -2.87679 0.2047 0.1802
## 13-10 0.330207 -2.15180 2.8122 1.0000
## 14-10 -1.152426 -2.87640 0.5715 0.6280
## 15-10 -0.873412 -3.74105 1.9942 0.9997
## 16-10 -0.758406 -2.43411 0.9173 0.9758
## 12-11 -0.811478 -2.35220 0.7292 0.9137
## 13-11 0.854790 -1.62722 3.3368 0.9986
## 14-11 -0.627843 -2.35182 1.0961 0.9973
## 15-11 -0.348830 -3.21647 2.5188 1.0000
## 16-11 -0.233824 -1.90953 1.4419 1.0000
## 13-12 1.666268 -0.88883 4.2214 0.6695
## 14-12 0.183634 -1.64399 2.0113 1.0000
## 15-12 0.462648 -2.46848 3.3938 1.0000
## 16-12 0.577654 -1.20451 2.3598 0.9993
## 14-13 -1.482633 -4.15223 1.1870 0.8731
## 15-13 -1.203620 -4.72186 2.3146 0.9987
## 16-13 -1.088614 -3.72730 1.5501 0.9901
## 15-14 0.279013 -2.75245 3.3105 1.0000
## 16-14 0.394020 -1.54875 2.3368 1.0000
## 16-15 0.115006 -2.88927 3.1193 1.0000
## Location
## Location Mean SE Max Min Measured Positives Negatives PPA
## 1 1 0.0000 0.0000 0.000 0 60 0 60 0.0
## 2 2 2.6947 0.6197 7.301 0 31 12 19 38.7
## 3 3 1.0151 0.5551 7.000 0 20 3 17 15.0
## 4 4 0.5479 0.3785 6.301 0 23 2 21 8.7
## 5 5 1.4477 0.2230 6.954 0 147 33 114 22.4
## 6 6 2.6676 0.2956 7.869 0 126 50 76 39.7
## 7 7 0.0000 0.0000 0.000 0 24 0 24 0.0
## 8 8 1.2000 0.5506 6.000 0 20 4 16 20.0
## 9 9 0.0000 0.0000 0.000 0 10 0 10 0.0
## 10 10 2.9644 0.7530 6.954 0 20 9 11 45.0
## 11 11 2.7228 0.3245 6.778 0 91 40 51 44.0
## 12 12 0.1154 0.1154 6.000 0 52 1 51 1.9
## 13 13 0.0000 0.0000 0.000 0 31 0 31 0.0
## 14 14 2.6569 0.2770 7.740 0 128 54 74 42.2
## 15 15 2.8901 0.3905 7.699 0 80 33 47 41.2
## 16 16 1.9400 0.3544 7.580 0 67 21 46 31.3
## 17 17 1.7842 0.2245 7.785 0 164 46 118 28.0
## 18 18 3.1576 0.3035 7.881 0 132 60 72 45.5
## 19 19 3.3125 0.2615 7.519 0 157 80 77 51.0
## 20 21 0.0000 0.0000 0.000 0 19 0 19 0.0
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Location, data = nos2)
##
## $Location
## diff lwr upr p adj
## 2-1 2.695e+00 0.41906 4.97029 0.0045
## 3-1 1.015e+00 -1.64132 3.67143 0.9986
## 4-1 5.479e-01 -1.97518 3.07102 1.0000
## 5-1 1.448e+00 -0.12842 3.02379 0.1195
## 6-1 2.668e+00 1.05385 4.28130 0.0000
## 7-1 1.567e-14 -2.48481 2.48481 1.0000
## 8-1 1.200e+00 -1.45637 3.85637 0.9890
## 9-1 1.107e-13 -3.51405 3.51405 1.0000
## 10-1 2.964e+00 0.30806 5.62081 0.0117
## 11-1 2.723e+00 1.01191 4.43372 0.0000
## 12-1 1.154e-01 -1.83386 2.06463 1.0000
## 13-1 4.409e-14 -2.27562 2.27562 1.0000
## 14-1 2.657e+00 1.04723 4.26654 0.0000
## 15-1 2.890e+00 1.13306 4.64711 0.0000
## 16-1 1.940e+00 0.11140 3.76864 0.0240
## 17-1 1.784e+00 0.23196 3.33646 0.0073
## 18-1 3.158e+00 1.55579 4.75950 0.0000
## 19-1 3.312e+00 1.75096 4.87395 0.0000
## 21-1 -1.320e-14 -2.70830 2.70830 1.0000
## 3-2 -1.680e+00 -4.63032 1.27107 0.8938
## 4-2 -2.147e+00 -4.97807 0.68455 0.4347
## 5-2 -1.247e+00 -3.28031 0.78633 0.8138
## 6-2 -2.710e-02 -2.08972 2.03552 1.0000
## 7-2 -2.695e+00 -5.49192 0.10256 0.0752
## 8-2 -1.495e+00 -4.44537 1.45602 0.9628
## 9-2 -2.695e+00 -6.43618 1.04683 0.5390
## 10-2 2.698e-01 -2.68093 3.22045 1.0000
## 11-2 2.814e-02 -2.11137 2.16764 1.0000
## 12-2 -2.579e+00 -4.91378 -0.24480 0.0136
## 13-2 -2.695e+00 -5.30785 -0.08150 0.0346
## 14-2 -3.779e-02 -2.09722 2.02164 1.0000
## 15-2 1.954e-01 -1.98115 2.37197 1.0000
## 16-2 -7.547e-01 -2.98941 1.48010 0.9997
## 17-2 -9.105e-01 -2.92535 1.10442 0.9890
## 18-2 4.630e-01 -1.59037 2.51631 1.0000
## 19-2 6.178e-01 -1.40423 2.63979 0.9999
## 21-2 -2.695e+00 -5.69220 0.30285 0.1444
## 4-3 -4.671e-01 -3.61264 2.67837 1.0000
## 5-3 4.326e-01 -2.01936 2.88463 1.0000
## 6-3 1.653e+00 -0.82382 4.12887 0.6843
## 7-3 -1.015e+00 -4.12993 2.09982 0.9999
## 8-3 1.849e-01 -3.06843 3.43833 1.0000
## 9-3 -1.015e+00 -4.99961 2.96951 1.0000
## 10-3 1.949e+00 -1.30400 5.20276 0.8422
## 11-3 1.708e+00 -0.83298 4.24851 0.6715
## 12-3 -8.997e-01 -3.60664 1.80731 0.9998
## 13-3 -1.015e+00 -3.96574 1.93564 0.9997
## 14-3 1.642e+00 -0.83186 4.11553 0.6937
## 15-3 1.875e+00 -0.69699 4.44706 0.5145
## 16-3 9.250e-01 -1.69649 3.54642 0.9995
## 17-3 7.692e-01 -1.66757 3.20589 0.9999
## 18-3 2.143e+00 -0.32603 4.61122 0.1915
## 19-3 2.297e+00 -0.14522 4.74003 0.0957
## 21-3 -1.015e+00 -4.31096 2.28086 0.9999
## 5-4 8.998e-01 -1.40717 3.20671 0.9982
## 6-4 2.120e+00 -0.21315 4.45247 0.1316
## 7-4 -5.479e-01 -3.54994 2.45411 1.0000
## 8-4 6.521e-01 -2.49342 3.79759 1.0000
## 9-4 -5.479e-01 -4.44489 3.34906 1.0000
## 10-4 2.417e+00 -0.72898 5.56202 0.4083
## 11-4 2.175e+00 -0.22616 4.57596 0.1353
## 12-4 -4.325e-01 -3.00885 2.14379 1.0000
## 13-4 -5.479e-01 -3.37922 2.28339 1.0000
## 14-4 2.109e+00 -0.22102 4.43896 0.1361
## 15-4 2.342e+00 -0.09197 4.77630 0.0761
## 16-4 1.392e+00 -1.09421 3.87841 0.9073
## 17-4 1.236e+00 -1.05441 3.52700 0.9325
## 18-4 2.610e+00 0.28512 4.93434 0.0107
## 19-4 2.765e+00 0.46756 5.06152 0.0034
## 21-4 -5.479e-01 -3.73739 2.64156 1.0000
## 6-5 1.220e+00 -0.02914 2.46892 0.0650
## 7-5 -1.448e+00 -3.71269 0.81732 0.7554
## 8-5 -2.477e-01 -2.69968 2.20431 1.0000
## 9-5 -1.448e+00 -4.80990 1.91453 0.9937
## 10-5 1.517e+00 -0.93524 3.96874 0.8025
## 11-5 1.275e+00 -0.09715 2.64741 0.1074
## 12-5 -1.332e+00 -2.99227 0.32767 0.3231
## 13-5 -1.448e+00 -3.48100 0.58563 0.5620
## 14-5 1.209e+00 -0.03456 2.45296 0.0683
## 15-5 1.442e+00 0.01303 2.87177 0.0450
## 16-5 4.923e-01 -1.02418 2.00884 0.9999
## 17-5 3.365e-01 -0.83199 1.50504 1.0000
## 18-5 1.710e+00 0.47631 2.94361 0.0002
## 19-5 1.865e+00 0.68401 3.04553 0.0000
## 21-5 -1.448e+00 -3.95583 1.06046 0.8813
## 7-6 -2.668e+00 -4.95892 -0.37624 0.0060
## 8-6 -1.468e+00 -3.94392 1.00877 0.8545
## 9-6 -2.668e+00 -6.04759 0.71244 0.3552
## 10-6 2.969e-01 -2.17948 2.77320 1.0000
## 11-6 5.524e-02 -1.36009 1.47057 1.0000
## 12-6 -2.552e+00 -4.24793 -0.85646 0.0000
## 13-6 -2.668e+00 -4.73019 -0.60496 0.0008
## 14-6 -1.069e-02 -1.30179 1.28042 1.0000
## 15-6 2.225e-01 -1.24824 1.69326 1.0000
## 16-6 -7.276e-01 -2.28313 0.82802 0.9838
## 17-6 -8.834e-01 -2.10215 0.33542 0.5262
## 18-6 4.901e-01 -0.79129 1.77143 0.9986
## 19-6 6.449e-01 -0.58565 1.87541 0.9484
## 21-6 -2.668e+00 -5.19953 -0.13562 0.0263
## 8-7 1.200e+00 -1.91487 4.31487 0.9984
## 9-7 9.504e-14 -3.87230 3.87230 1.0000
## 10-7 2.964e+00 -0.15044 6.07931 0.0852
## 11-7 2.723e+00 0.36202 5.08361 0.0070
## 12-7 1.154e-01 -2.42345 2.65422 1.0000
## 13-7 2.842e-14 -2.79724 2.79724 1.0000
## 14-7 2.657e+00 0.36841 4.94536 0.0062
## 15-7 2.890e+00 0.49566 5.28451 0.0032
## 16-7 1.940e+00 -0.50742 4.38746 0.3467
## 17-7 1.784e+00 -0.46426 4.03268 0.3447
## 18-7 3.158e+00 0.87465 5.44064 0.0002
## 19-7 3.312e+00 1.05760 5.56731 0.0000
## 21-7 -2.887e-14 -3.15927 3.15927 1.0000
## 9-8 -1.200e+00 -5.18456 2.78456 1.0000
## 10-8 1.764e+00 -1.48895 5.01782 0.9295
## 11-8 1.523e+00 -1.01793 4.06356 0.8419
## 12-8 -1.085e+00 -3.79159 1.62236 0.9974
## 13-8 -1.200e+00 -4.15069 1.75069 0.9969
## 14-8 1.457e+00 -1.01681 3.93058 0.8612
## 15-8 1.690e+00 -0.88194 4.26211 0.7108
## 16-8 7.400e-01 -1.88144 3.36147 1.0000
## 17-8 5.842e-01 -1.85252 3.02094 1.0000
## 18-8 1.958e+00 -0.51098 4.42627 0.3459
## 19-8 2.112e+00 -0.33017 4.55508 0.1968
## 21-8 -1.200e+00 -4.49591 2.09591 0.9993
## 10-9 2.964e+00 -1.02013 6.94900 0.4732
## 11-9 2.723e+00 -0.70466 6.15029 0.3425
## 12-9 1.154e-01 -3.43707 3.66784 1.0000
## 13-9 -6.661e-14 -3.74150 3.74150 1.0000
## 14-9 2.657e+00 -0.72119 6.03496 0.3618
## 15-9 2.890e+00 -0.56065 6.34082 0.2473
## 16-9 1.940e+00 -1.54771 5.42775 0.9123
## 17-9 1.784e+00 -1.56689 5.13531 0.9403
## 18-9 3.158e+00 -0.21672 6.53201 0.1005
## 19-9 3.312e+00 -0.04294 6.66785 0.0578
## 21-9 -1.239e-13 -4.01936 4.01936 1.0000
## 11-10 -2.416e-01 -2.78236 2.29912 1.0000
## 12-10 -2.849e+00 -5.55603 -0.14207 0.0267
## 13-10 -2.964e+00 -5.91513 -0.01374 0.0474
## 14-10 -3.075e-01 -2.78124 2.16614 1.0000
## 15-10 -7.435e-02 -2.64637 2.49767 1.0000
## 16-10 -1.024e+00 -3.64587 1.59704 0.9981
## 17-10 -1.180e+00 -3.61695 1.25650 0.9764
## 18-10 1.932e-01 -2.27541 2.66183 1.0000
## 19-10 3.480e-01 -2.09460 2.79064 1.0000
## 21-10 -2.964e+00 -6.26034 0.33148 0.1437
## 12-11 -2.607e+00 -4.39590 -0.81896 0.0000
## 13-11 -2.723e+00 -4.86232 -0.58331 0.0012
## 14-11 -6.593e-02 -1.47662 1.34476 1.0000
## 15-11 1.673e-01 -1.40950 1.74404 1.0000
## 16-11 -7.828e-01 -2.43897 0.87337 0.9818
## 17-11 -9.386e-01 -2.28342 0.40621 0.6011
## 18-11 4.348e-01 -0.96695 1.83661 0.9999
## 19-11 5.896e-01 -0.76583 1.94511 0.9929
## 21-11 -2.723e+00 -5.31779 -0.12784 0.0277
## 13-12 -1.154e-01 -2.44987 2.21910 1.0000
## 14-12 2.542e+00 0.84964 4.23336 0.0000
## 15-12 2.775e+00 0.94207 4.60733 0.0000
## 16-12 1.825e+00 -0.07675 3.72601 0.0783
## 17-12 1.669e+00 0.03149 3.30616 0.0399
## 18-12 3.042e+00 1.35782 4.72670 0.0000
## 19-12 3.197e+00 1.55097 4.84317 0.0000
## 21-12 -1.154e-01 -2.87333 2.64256 1.0000
## 14-13 2.657e+00 0.59745 4.71632 0.0009
## 15-13 2.890e+00 0.71353 5.06664 0.0005
## 16-13 1.940e+00 -0.29474 4.17477 0.1912
## 17-13 1.784e+00 -0.23067 3.79909 0.1636
## 18-13 3.158e+00 1.10431 5.21099 0.0000
## 19-13 3.312e+00 1.29045 5.33446 0.0000
## 21-13 -5.729e-14 -2.99752 2.99752 1.0000
## 15-14 2.332e-01 -1.23308 1.69948 1.0000
## 16-14 -7.169e-01 -2.26822 0.83448 0.9858
## 17-14 -8.727e-01 -2.08606 0.34071 0.5418
## 18-14 5.008e-01 -0.77547 1.77699 0.9980
## 19-14 6.556e-01 -0.56962 1.88076 0.9375
## 21-14 -2.657e+00 -5.18625 -0.12752 0.0274
## 16-15 -9.501e-01 -2.65384 0.75370 0.9104
## 17-15 -1.106e+00 -2.50889 0.29714 0.3576
## 18-15 2.676e-01 -1.19015 1.72527 1.0000
## 19-15 4.224e-01 -0.99086 1.83560 1.0000
## 21-15 -2.890e+00 -5.51570 -0.26447 0.0143
## 17-16 -1.558e-01 -1.64751 1.33589 1.0000
## 18-16 1.218e+00 -0.32562 2.76088 0.3557
## 19-16 1.372e+00 -0.12888 2.87375 0.1248
## 21-16 -1.940e+00 -4.61407 0.73403 0.5243
## 18-17 1.373e+00 0.17042 2.57645 0.0082
## 19-17 1.528e+00 0.37952 2.67697 0.0004
## 21-17 -1.784e+00 -4.27744 0.70902 0.5518
## 19-18 1.548e-01 -1.06011 1.36973 1.0000
## 21-18 -3.158e+00 -5.68205 -0.63324 0.0016
## 21-19 -3.312e+00 -5.81145 -0.81346 0.0005
## Country
## Country Mean SE Max Min Measured Positives Negatives PPA
## 1 1 1.458 0.1999 6.778 0 174 41 133 23.6
## 2 2 1.448 0.2230 6.954 0 147 33 114 22.4
## 3 3 2.714 0.1537 7.881 0 453 186 267 41.1
## 4 4 0.000 0.0000 0.000 0 60 0 60 0.0
## 5 5 2.668 0.2956 7.869 0 126 50 76 39.7
## 6 6 2.457 0.2689 7.699 0 147 54 93 36.7
## 7 7 1.573 0.3377 7.301 0 74 17 57 23.0
## 8 8 2.657 0.2770 7.740 0 128 54 74 42.2
## 9 9 1.666 0.4023 6.954 0 50 13 37 26.0
## 10 10 0.000 0.0000 0.000 0 43 0 43 0.0
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Country, data = nos2)
##
## $Country
## diff lwr upr p adj
## 2-1 -1.080e-02 -1.06601 1.044415 1.0000
## 3-1 1.256e+00 0.41549 2.095690 0.0001
## 4-1 -1.458e+00 -2.86868 -0.048290 0.0358
## 5-1 1.209e+00 0.10724 2.310940 0.0186
## 6-1 9.986e-01 -0.05664 2.053791 0.0816
## 7-1 1.150e-01 -1.19224 1.422247 1.0000
## 8-1 1.198e+00 0.10156 2.295247 0.0196
## 9-1 2.073e-01 -1.30413 1.718712 1.0000
## 10-1 -1.458e+00 -3.06262 0.145656 0.1118
## 3-2 1.266e+00 0.37228 2.160495 0.0003
## 4-2 -1.448e+00 -2.89070 -0.004666 0.0485
## 5-2 1.220e+00 0.07633 2.363450 0.0258
## 6-2 1.009e+00 -0.08932 2.108072 0.1033
## 7-2 1.258e-01 -1.21679 1.468390 1.0000
## 8-2 1.209e+00 0.07046 2.347939 0.0271
## 9-2 2.181e-01 -1.32400 1.760182 1.0000
## 10-2 -1.448e+00 -3.08076 0.185386 0.1342
## 4-3 -2.714e+00 -4.00814 -1.420011 0.0000
## 5-3 -4.650e-02 -0.99519 0.902196 1.0000
## 6-3 -2.570e-01 -1.15112 0.637094 0.9962
## 7-3 -1.141e+00 -2.32162 0.040445 0.0687
## 8-3 -5.719e-02 -1.00006 0.885690 1.0000
## 9-3 -1.048e+00 -2.45199 0.355390 0.3470
## 10-3 -2.714e+00 -4.21714 -1.211005 0.0000
## 5-4 2.668e+00 1.19011 4.145039 0.0000
## 6-4 2.457e+00 1.01404 3.900080 0.0000
## 7-4 1.573e+00 -0.06289 3.209859 0.0713
## 8-4 2.657e+00 1.18315 4.130621 0.0000
## 9-4 1.666e+00 -0.13790 3.469443 0.0992
## 10-4 1.013e-14 -1.88204 1.882044 1.0000
## 6-5 -2.105e-01 -1.35407 0.933044 0.9999
## 7-5 -1.094e+00 -2.47363 0.285454 0.2621
## 8-5 -1.069e-02 -1.19277 1.171393 1.0000
## 9-5 -1.002e+00 -2.57617 0.572570 0.5875
## 10-5 -2.668e+00 -4.33116 -1.003990 0.0000
## 7-6 -8.836e-01 -2.22616 0.459014 0.5381
## 8-6 1.998e-01 -0.93891 1.338563 0.9999
## 9-6 -7.913e-01 -2.33338 0.750806 0.8354
## 10-6 -2.457e+00 -4.09013 -0.823990 0.0001
## 8-7 1.083e+00 -0.29215 2.458948 0.2714
## 9-7 9.229e-02 -1.63209 1.816660 1.0000
## 10-7 -1.573e+00 -3.37968 0.232705 0.1512
## 9-8 -9.911e-01 -2.56199 0.579761 0.5997
## 10-8 -2.657e+00 -4.31716 -0.996611 0.0000
## 10-9 -1.666e+00 -3.62481 0.293266 0.1768
## Subspecies
## Subspecies Mean SE Max Min Measured Positives Negatives PPA
## 1 1 1.980 0.1071 7.881 0 789 240 549 30.4
## 2 2 2.837 0.2579 7.740 0 167 71 96 42.5
## 3 3 1.869 0.3125 7.301 0 91 26 65 28.6
## 4 4 2.084 0.1784 7.869 0 295 94 201 31.9
## 5 5 1.875 0.3912 7.699 0 60 17 43 28.3
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ Subspecies, data = nos2)
##
## $Subspecies
## diff lwr upr p adj
## 2-1 0.857728 0.1459 1.56954 0.0090
## 3-1 -0.110538 -1.0357 0.81462 0.9976
## 4-1 0.103908 -0.4664 0.67420 0.9876
## 5-1 -0.104655 -1.2238 1.01446 0.9991
## 3-2 -0.968265 -2.0571 0.12058 0.1082
## 4-2 -0.753820 -1.5631 0.05544 0.0817
## 5-2 -0.962383 -2.2202 0.29543 0.2250
## 4-3 0.214446 -0.7876 1.21651 0.9774
## 5-3 0.005882 -1.3838 1.39560 1.0000
## 5-4 -0.208563 -1.3920 0.97492 0.9891
## Weather Cluster 1
## WeatherCluster1 Mean SE Max Min Measured Positives Negatives PPA
## 1 1 2.296 0.1060 7.881 0 893 310 583 34.7
## 2 2 1.734 0.1265 7.740 0 509 138 371 27.1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ WeatherCluster1, data = nos2)
##
## $WeatherCluster1
## diff lwr upr p adj
## 2-1 -0.5623 -0.8954 -0.2292 0.001
## Weather Cluster 2
## WeatherCluster2 Mean SE Max Min Measured Positives Negatives PPA
## 1 1 1.807 0.2200 7.869 0 186 50 136 26.9
## 2 2 1.573 0.3377 7.301 0 74 17 57 23.0
## 3 3 2.011 0.1790 7.740 0 275 87 188 31.6
## 4 4 0.000 0.0000 0.000 0 43 0 43 0.0
## 5 5 2.256 0.2260 7.699 0 197 67 130 34.0
## 6 6 2.366 0.1261 7.881 0 627 227 400 36.2
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nosema1 ~ WeatherCluster2, data = nos2)
##
## $WeatherCluster2
## diff lwr upr p adj
## 2-1 -0.2336 -1.4263 0.95914 0.9936
## 3-1 0.2034 -0.6204 1.02731 0.9814
## 4-1 -1.8071 -3.2755 -0.33864 0.0061
## 5-1 0.4492 -0.4381 1.33639 0.6995
## 6-1 0.5586 -0.1660 1.28314 0.2383
## 3-2 0.4370 -0.6994 1.57349 0.8825
## 4-2 -1.5735 -3.2375 0.09057 0.0762
## 5-2 0.6827 -0.5005 1.86595 0.5676
## 6-2 0.7921 -0.2745 1.85883 0.2778
## 4-3 -2.0105 -3.4336 -0.58740 0.0008
## 5-3 0.2457 -0.5643 1.05574 0.9546
## 6-3 0.3551 -0.2725 0.98279 0.5890
## 5-4 2.2562 0.7955 3.71694 0.0002
## 6-4 2.3656 0.9976 3.73366 0.0000
## 6-5 0.1094 -0.5994 0.81820 0.9979
The End