F1 Hybrid data analysis
Ben A. Flanagan
1/10/2018: updated 2/28/2018: updated 2021-04-21
Survivorship analysis
Sex ratio
##
## 1-sample proportions test with continuity correction
##
## data: propsum$nmales[3] out of propsum$total[3], null probability 0.5
## X-squared = 8.1546, df = 1, p-value = 0.004295
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.4340101 0.4878127
## sample estimates:
## p
## 0.4607988## pv propsum.pops
## 1 1.85e-01 FF
## 2 2.20e-16 FS
## 3 4.00e-03 SF
## 4 2.20e-16 SSSurvival plots
Heterosis
##
## Call:
## lm(formula = num_alive ~ pops, data = hetf1 %>% filter(sex ==
## "m"), contrasts = list(pops = mat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.543 -8.079 -2.079 4.689 39.921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.1294 0.7676 15.802 <2e-16 ***
## popsc1 0.4714 0.3934 1.198 0.232
## popsc2 1.3141 1.7005 0.773 0.441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.53 on 204 degrees of freedom
## Multiple R-squared: 0.01091, Adjusted R-squared: 0.001216
## F-statistic: 1.125 on 2 and 204 DF, p-value: 0.3265##
## Call:
## lm(formula = num_alive ~ pops, data = hetf1 %>% filter(sex ==
## "m"), contrasts = list(pops = mat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.347 -8.765 -1.079 6.653 40.653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.7306 0.7690 20.456 < 2e-16 ***
## popsc1 2.8722 0.3663 7.840 1.67e-13 ***
## popsc2 1.3141 1.8635 0.705 0.481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.54 on 230 degrees of freedom
## Multiple R-squared: 0.2185, Adjusted R-squared: 0.2117
## F-statistic: 32.15 on 2 and 230 DF, p-value: 4.883e-13## `summarise()` has grouped output by 'Sex'. You can override using the `.groups` argument.
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 216244 216244 144 <2e-16 ***
## Residuals 5971 8966924 1502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Longevity ~ Sex, data = long2)
##
## $Sex
## diff lwr upr p adj
## m-f 12.50558 10.46259 14.54857 0## Df Sum Sq Mean Sq F value Pr(>F)
## Pops 3 2056500 685500 574.1 <2e-16 ***
## Residuals 5969 7126668 1194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Longevity ~ Pops, data = long2)
##
## $Pops
## diff lwr upr p adj
## fs-ff 36.570360 33.684898 39.455822 0.0000000
## sf-ff 38.367277 35.105241 41.629314 0.0000000
## ss-ff 45.689754 42.189399 49.190110 0.0000000
## sf-fs 1.796917 -1.471851 5.065685 0.4914005
## ss-fs 9.119394 5.612765 12.626023 0.0000000
## ss-sf 7.322477 3.499986 11.144968 0.0000052## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 216244 216244 184.65 < 2e-16 ***
## Pops 3 1908162 636054 543.12 < 2e-16 ***
## Sex:Pops 3 73104 24368 20.81 2.09e-13 ***
## Residuals 5965 6985658 1171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Longevity ~ Sex * Pops, data = long2)
##
## $Sex
## diff lwr upr p adj
## m-f 12.50558 10.70146 14.3097 0
##
## $Pops
## diff lwr upr p adj
## fs-ff 32.194110 29.336379 35.05184 0.0000000
## sf-ff 39.061509 35.830823 42.29220 0.0000000
## ss-ff 43.982384 40.515669 47.44910 0.0000000
## sf-fs 6.867399 3.630046 10.10475 0.0000003
## ss-fs 11.788274 8.315345 15.26120 0.0000000
## ss-sf 4.920874 1.135119 8.70663 0.0046728
##
## $`Sex:Pops`
## diff lwr upr p adj
## m:ff-f:ff 4.7112554 -0.04654697 9.4690578 0.0545032
## f:fs-f:ff 27.5891266 20.24952004 34.9287331 0.0000000
## m:fs-f:ff 40.7807634 36.50758694 45.0539399 0.0000000
## f:sf-f:ff 41.6146526 36.31725479 46.9120505 0.0000000
## m:sf-f:ff 39.8235406 34.25763423 45.3894470 0.0000000
## f:ss-f:ff 34.8842853 28.29576650 41.4728042 0.0000000
## m:ss-f:ff 55.2001293 49.84674501 60.5535135 0.0000000
## f:fs-m:ff 22.8778711 15.58376126 30.1719810 0.0000000
## m:fs-m:ff 36.0695080 31.87495769 40.2640584 0.0000000
## f:sf-m:ff 36.9033972 31.66921734 42.1375771 0.0000000
## m:sf-m:ff 35.1122852 29.60651286 40.6180575 0.0000000
## f:ss-m:ff 30.1730299 23.63523246 36.7108274 0.0000000
## m:ss-m:ff 50.4888738 45.19803855 55.7797091 0.0000000
## m:fs-f:fs 13.1916369 6.20397683 20.1792969 0.0000003
## f:sf-f:fs 14.0255261 6.36851755 21.6825346 0.0000008
## m:sf-f:fs 12.2344141 4.38924552 20.0795826 0.0000631
## f:ss-f:fs 7.2951588 -1.30576769 15.8960852 0.1664911
## m:ss-f:fs 27.6110027 19.91515456 35.3068508 0.0000000
## f:sf-m:fs 0.8338892 -3.96402627 5.6318046 0.9995242
## m:sf-m:fs -0.9572228 -6.05004943 4.1356038 0.9992043
## f:ss-m:fs -5.8964781 -12.09051955 0.2975633 0.0755062
## m:ss-m:fs 14.4193658 9.55970617 19.2790255 0.0000000
## m:sf-f:sf -1.7911120 -7.76934497 4.1871209 0.9853592
## f:ss-f:sf -6.7303673 -13.67072289 0.2099883 0.0650358
## m:ss-f:sf 13.5854766 7.80458757 19.3663657 0.0000000
## f:ss-m:sf -4.9392553 -12.08666222 2.2081517 0.4180142
## m:ss-m:sf 15.3765886 9.34868947 21.4044878 0.0000000
## m:ss-f:ss 20.3158439 13.33266163 27.2990262 0.0000000
##
## Call:
## lm(formula = Longevity ~ Pops, data = hetf1, contrasts = list(Pops = mat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -66.918 -21.228 -7.228 20.772 157.082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.7470 0.5576 144.81 <2e-16 ***
## Popsc1 3.4494 0.2655 12.99 <2e-16 ***
## Popsc2 45.6898 1.3521 33.79 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34.3 on 4086 degrees of freedom
## Multiple R-squared: 0.2715, Adjusted R-squared: 0.2712
## F-statistic: 761.5 on 2 and 4086 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = Longevity ~ Pops, data = hetf1, contrasts = list(Pops = mat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -66.918 -21.228 -2.858 19.202 157.082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.1480 0.5276 151.91 <2e-16 ***
## Popsc1 3.0501 0.2336 13.06 <2e-16 ***
## Popsc2 45.6898 1.3666 33.43 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34.67 on 4757 degrees of freedom
## Multiple R-squared: 0.2446, Adjusted R-squared: 0.2443
## F-statistic: 770.3 on 2 and 4757 DF, p-value: < 2.2e-16##
## Welch Two Sample t-test
##
## data: long2$Longevity by long2$Hybrid
## t = 22.253, df = 5674.8, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 19.90911 23.75573
## sample estimates:
## mean in group Hybrid mean in group Parental
## 90.00210 68.16968
Statistics COXME
## df AIC
## fit0 0.000000 91935.06
## fit0me 1.865799 91912.48
## fit00me 249.391167 87608.47## Cox mixed-effects model fit by maximum likelihood
## Data: long
## events, n = 5973, 5973
## Iterations= 16 85
## NULL Integrated Fitted
## Log-likelihood -45967.53 -44050.19 -43555.11
##
## Chisq df p AIC BIC
## Integrated loglik 3834.68 4.00 0 3826.68 3799.9
## Penalized loglik 4824.84 241.47 0 4341.91 2725.3
##
## Model: Surv(long$Ri) ~ Sex + (1 | Clutch/Family) + (1 | Pops)
## Fixed coefficients
## coef exp(coef) se(coef) z p
## Sexm -0.1474064 0.8629432 0.04077348 -3.62 3e-04
##
## Random effects
## Group Variable Std Dev Variance
## Clutch/Family (Intercept) 0.885060945 0.783332877
## Clutch (Intercept) 0.036739739 0.001349808
## Pops Intercept 0.965590799 0.932365591## Analysis of Deviance Table (Type II tests)
##
## Response: Surv(long$Ri)
## Df Chisq Pr(>Chisq)
## Sex 1 13.07 0.0003001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Cox mixed-effects model fit by maximum likelihood
## Data: long
## events, n = 5973, 5973
## Iterations= 14 91
## NULL Integrated Fitted
## Log-likelihood -45967.53 -44047.39 -43554.71
##
## Chisq df p AIC BIC
## Integrated loglik 3840.28 5.00 0 3830.28 3796.80
## Penalized loglik 4825.65 243.54 0 4338.56 2708.03
##
## Model: Surv(long$Ri) ~ Pops + (1 | Clutch/Family)
## Fixed coefficients
## coef exp(coef) se(coef) z p
## Popsfs -1.975232 0.1387291 0.1141356 -17.31 0
## Popssf -1.918992 0.1467548 0.1511299 -12.70 0
## Popsss -2.102122 0.1221969 0.1183614 -17.76 0
##
## Random effects
## Group Variable Std Dev Variance
## Clutch/Family (Intercept) 0.949591412 0.901723851
## Clutch (Intercept) 0.098767667 0.009755052
Statistics Gompertz
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## [1] "breaking"
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 0.0122 0.01223 3.193 0.0762 .
## Residuals 136 0.5209 0.00383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 19.12 19.125 11.503 0.000921 ***
## Pop 3 40.32 13.440 8.083 5.6e-05 ***
## Sex:Pop 3 0.33 0.111 0.067 0.977357
## Residuals 130 216.14 1.663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ln_A ~ Sex * Pop, data = out2)
##
## $Sex
## diff lwr upr p adj
## m-f -0.7682254 -1.216349 -0.3201021 0.000921
##
## $Pop
## diff lwr upr p adj
## fs-ff -1.22064799 -1.9848078 -0.4564882 0.0003353
## sf-ff -1.15352736 -1.9768060 -0.3302487 0.0021464
## ss-ff -0.87709252 -1.6412523 -0.1129327 0.0175049
## sf-fs 0.06712063 -0.8519157 0.9861569 0.9975534
## ss-fs 0.34355548 -0.5229203 1.2100312 0.7310621
## ss-sf 0.27643485 -0.6426015 1.1954712 0.8621328
##
## $`Sex:Pop`
## diff lwr upr p adj
## m:ff-f:ff -0.77558106 -1.8758241 0.3246620 0.3756701
## f:fs-f:ff -1.17354401 -2.8974625 0.5503745 0.4222094
## m:fs-f:ff -2.00889492 -3.1935968 -0.8241930 0.0000184
## f:sf-f:ff -1.23502317 -2.6247489 0.1547026 0.1207182
## m:sf-f:ff -1.83494990 -3.3019407 -0.3679591 0.0043952
## f:ss-f:ff -1.01020426 -2.5253058 0.5048973 0.4500819
## m:ss-f:ff -1.58530041 -2.8126729 -0.3579279 0.0028132
## f:fs-m:ff -0.39796294 -2.0555883 1.2596624 0.9955843
## m:fs-m:ff -1.23331386 -2.3192906 -0.1473371 0.0143967
## f:sf-m:ff -0.45944210 -1.7660271 0.8471429 0.9592371
## m:sf-m:ff -1.05936884 -2.4478532 0.3291155 0.2748903
## f:ss-m:ff -0.23462320 -1.6738451 1.2045987 0.9996340
## m:ss-m:ff -0.80971935 -1.9420931 0.3226544 0.3568614
## m:fs-f:fs -0.83535091 -2.5501995 0.8794977 0.8057555
## f:sf-f:fs -0.06147916 -1.9238767 1.8009184 1.0000000
## m:sf-f:fs -0.66140589 -2.5821477 1.2593359 0.9635896
## f:ss-f:fs 0.16333975 -1.7943936 2.1210731 0.9999961
## m:ss-f:fs -0.41175640 -2.1563568 1.3328440 0.9960283
## f:sf-m:fs 0.77387176 -0.6045869 2.1523304 0.6679902
## m:sf-m:fs 0.17394502 -1.2823766 1.6302666 0.9999547
## f:ss-m:fs 0.99869066 -0.5060829 2.5034642 0.4562891
## m:ss-m:fs 0.42359451 -0.7910058 1.6381949 0.9610016
## m:sf-f:sf -0.59992673 -2.2274069 1.0275534 0.9476811
## f:ss-f:sf 0.22481890 -1.4461576 1.8957954 0.9998986
## m:ss-f:sf -0.35027724 -1.7655769 1.0650224 0.9946726
## f:ss-m:sf 0.82474564 -0.9110212 2.5605125 0.8249711
## m:ss-m:sf 0.24964949 -1.2415907 1.7408897 0.9995638
## m:ss-f:ss -0.57509615 -2.1136890 0.9634967 0.9437654## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 0.0122 0.01223 4.065 0.0459 *
## Pop 3 0.1066 0.03552 11.803 6.89e-07 ***
## Sex:Pop 3 0.0232 0.00772 2.566 0.0573 .
## Residuals 130 0.3912 0.00301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = G ~ Sex * Pop, data = out2)
##
## $Sex
## diff lwr upr p adj
## m-f 0.01942796 0.0003633529 0.03849257 0.0458518
##
## $Pop
## diff lwr upr p adj
## fs-ff -0.054384797 -0.08689462 -0.02187498 0.0001564
## sf-ff -0.055027834 -0.09005276 -0.02000291 0.0004346
## ss-ff -0.060328388 -0.09283821 -0.02781857 0.0000223
## sf-fs -0.000643037 -0.03974180 0.03845573 0.9999717
## ss-fs -0.005943591 -0.04280626 0.03091908 0.9750377
## ss-sf -0.005300554 -0.04439932 0.03379821 0.9848694
##
## $`Sex:Pop`
## diff lwr upr p adj
## m:ff-f:ff 0.0519279678 0.005120082 0.098735854 0.0185743
## f:fs-f:ff -0.0271249472 -0.100465996 0.046216102 0.9467697
## m:fs-f:ff -0.0181325333 -0.068533571 0.032268504 0.9539589
## f:sf-f:ff -0.0336097643 -0.092733175 0.025513647 0.6536619
## m:sf-f:ff -0.0188919255 -0.081302440 0.043518589 0.9822981
## f:ss-f:ff -0.0166117484 -0.081069051 0.047845554 0.9931766
## m:ss-f:ff -0.0338698516 -0.086086235 0.018346531 0.4869854
## f:fs-m:ff -0.0790529150 -0.149573641 -0.008532189 0.0166114
## m:fs-m:ff -0.0700605011 -0.116261452 -0.023859550 0.0001942
## f:sf-m:ff -0.0855377320 -0.141124071 -0.029951393 0.0001462
## m:sf-m:ff -0.0708198932 -0.129890492 -0.011749294 0.0076085
## f:ss-m:ff -0.0685397162 -0.129768853 -0.007310580 0.0168690
## m:ss-m:ff -0.0857978194 -0.133972646 -0.037622993 0.0000056
## m:fs-f:fs 0.0089924139 -0.063962772 0.081947600 0.9999439
## f:sf-f:fs -0.0064848170 -0.085717211 0.072747577 0.9999966
## m:sf-f:fs 0.0082330218 -0.073481523 0.089947567 0.9999858
## f:ss-f:fs 0.0105131989 -0.072775086 0.093801484 0.9999341
## m:ss-f:fs -0.0067449044 -0.080965826 0.067476017 0.9999930
## f:sf-m:fs -0.0154772309 -0.074121305 0.043166843 0.9921193
## m:sf-m:fs -0.0007593921 -0.062716006 0.061197222 1.0000000
## f:ss-m:fs 0.0015207850 -0.062497131 0.065538701 1.0000000
## m:ss-m:fs -0.0157373183 -0.067410332 0.035935695 0.9816665
## m:sf-f:sf 0.0147178388 -0.054520411 0.083956088 0.9979446
## f:ss-f:sf 0.0169980159 -0.054090708 0.088086740 0.9956938
## m:ss-f:sf -0.0002600873 -0.060471494 0.059951319 1.0000000
## f:ss-m:sf 0.0022801771 -0.071564940 0.076125294 1.0000000
## m:ss-m:sf -0.0149779261 -0.078420090 0.048464237 0.9960209
## m:ss-f:ss -0.0172581032 -0.082714803 0.048198597 0.9921663
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 19.12 19.125 10.13 0.00181 **
## Residuals 136 256.80 1.888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Pop 3 43.84 14.614 8.438 3.55e-05 ***
## Residuals 134 232.08 1.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ln_A ~ Pop, data = out2)
##
## $Pop
## diff lwr upr p adj
## fs-ff -1.3543761 -2.1339953 -0.5747569 0.0000789
## sf-ff -1.0503860 -1.8903200 -0.2104520 0.0077850
## ss-ff -0.9339981 -1.7136173 -0.1543789 0.0118651
## sf-fs 0.3039901 -0.6336388 1.2416191 0.8335878
## ss-fs 0.4203780 -0.4636270 1.3043831 0.6044281
## ss-sf 0.1163879 -0.8212411 1.0540168 0.9883111## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 19.12 19.125 11.503 0.000921 ***
## Pop 3 40.32 13.440 8.083 5.6e-05 ***
## Sex:Pop 3 0.33 0.111 0.067 0.977357
## Residuals 130 216.14 1.663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
##
## Call:
## glm(formula = out2$ln_A ~ out2$Median_lifespan * out2$Sex)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.2239 -0.3507 0.2145 0.4810 2.1980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.608719 0.359096 -7.265 2.77e-11 ***
## out2$Median_lifespan -0.032531 0.004840 -6.722 4.72e-10 ***
## out2$Sexm -0.739400 0.465182 -1.589 0.114
## out2$Median_lifespan:out2$Sexm 0.003984 0.005965 0.668 0.505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.043005)
##
## Null deviance: 275.92 on 137 degrees of freedom
## Residual deviance: 139.76 on 134 degrees of freedom
## AIC: 403.38
##
## Number of Fisher Scoring iterations: 2
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 19.12 19.125 10.13 0.00181 **
## Residuals 136 256.80 1.888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## `geom_smooth()` using formula 'y ~ x'
##
## Call:
## glm(formula = out2$Median_lifespan ~ out2$ln_A * out2$Pop)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -39.221 -9.834 -1.699 7.016 67.212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.291 8.845 1.163 0.24676
## out2$ln_A -8.565 1.841 -4.654 7.93e-06 ***
## out2$Popfs 20.227 14.438 1.401 0.16361
## out2$Popsf 13.116 28.229 0.465 0.64296
## out2$Popss -54.842 19.449 -2.820 0.00556 **
## out2$ln_A:out2$Popfs -1.237 2.596 -0.477 0.63446
## out2$ln_A:out2$Popsf -2.881 5.029 -0.573 0.56768
## out2$ln_A:out2$Popss -16.384 3.572 -4.587 1.05e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 311.1493)
##
## Null deviance: 133912 on 137 degrees of freedom
## Residual deviance: 40449 on 130 degrees of freedom
## AIC: 1193.5
##
## Number of Fisher Scoring iterations: 2## Df Sum Sq Mean Sq F value Pr(>F)
## Pop 3 43.84 14.614 8.438 3.55e-05 ***
## Residuals 134 232.08 1.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## `geom_smooth()` using formula 'y ~ x'
##
## Call:
## glm(formula = out2$ln_A ~ out2$Median_lifespan * out2$Pop)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.7885 -0.2980 0.1850 0.5622 1.9786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.390810 0.407409 -5.868 3.45e-08 ***
## out2$Median_lifespan -0.044774 0.007677 -5.832 4.10e-08 ***
## out2$Popfs -0.005323 0.751039 -0.007 0.994
## out2$Popsf -0.669707 1.296659 -0.516 0.606
## out2$Popss -0.800813 0.692258 -1.157 0.249
## out2$Median_lifespan:out2$Popfs 0.004569 0.010232 0.447 0.656
## out2$Median_lifespan:out2$Popsf 0.015181 0.015730 0.965 0.336
## out2$Median_lifespan:out2$Popss 0.019625 0.009507 2.064 0.041 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.035628)
##
## Null deviance: 275.92 on 137 degrees of freedom
## Residual deviance: 134.63 on 130 degrees of freedom
## AIC: 406.22
##
## Number of Fisher Scoring iterations: 2
## Df Sum Sq Mean Sq F value Pr(>F)
## Pop 3 43.84 14.614 8.438 3.55e-05 ***
## Residuals 134 232.08 1.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## `geom_smooth()` using formula 'y ~ x'
##
## Call:
## glm(formula = out2$Median_lifespan ~ out2$ln_A * out2$Sex * out2$Pop)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -37.407 -9.293 -1.366 7.144 60.148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.036 14.407 -0.072 0.942776
## out2$ln_A -11.553 3.359 -3.439 0.000799 ***
## out2$Sexm 14.532 17.850 0.814 0.417167
## out2$Popfs -71.027 27.105 -2.620 0.009898 **
## out2$Popsf -47.409 48.816 -0.971 0.333380
## out2$Popss -86.453 46.803 -1.847 0.067144 .
## out2$ln_A:out2$Sexm 3.792 3.937 0.963 0.337374
## out2$ln_A:out2$Popfs -17.770 5.337 -3.330 0.001151 **
## out2$ln_A:out2$Popsf -13.618 9.230 -1.476 0.142654
## out2$ln_A:out2$Popss -20.429 9.184 -2.225 0.027951 *
## out2$Sexm:out2$Popfs 111.152 31.459 3.533 0.000581 ***
## out2$Sexm:out2$Popsf 75.772 60.012 1.263 0.209134
## out2$Sexm:out2$Popss 44.743 51.235 0.873 0.384221
## out2$ln_A:out2$Sexm:out2$Popfs 19.551 6.015 3.250 0.001491 **
## out2$ln_A:out2$Sexm:out2$Popsf 13.455 10.932 1.231 0.220768
## out2$ln_A:out2$Sexm:out2$Popss 5.513 9.897 0.557 0.578487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 257.9457)
##
## Null deviance: 133912 on 137 degrees of freedom
## Residual deviance: 31469 on 122 degrees of freedom
## AIC: 1174.9
##
## Number of Fisher Scoring iterations: 2## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 19.12 19.125 11.503 0.000921 ***
## Pop 3 40.32 13.440 8.083 5.6e-05 ***
## Sex:Pop 3 0.33 0.111 0.067 0.977357
## Residuals 130 216.14 1.663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## `geom_smooth()` using formula 'y ~ x'
##
## Call:
## glm(formula = out2$Median_lifespan ~ out2$ln_A * out2$Sex * out2$Pop)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -37.407 -9.293 -1.366 7.144 60.148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.036 14.407 -0.072 0.942776
## out2$ln_A -11.553 3.359 -3.439 0.000799 ***
## out2$Sexm 14.532 17.850 0.814 0.417167
## out2$Popfs -71.027 27.105 -2.620 0.009898 **
## out2$Popsf -47.409 48.816 -0.971 0.333380
## out2$Popss -86.453 46.803 -1.847 0.067144 .
## out2$ln_A:out2$Sexm 3.792 3.937 0.963 0.337374
## out2$ln_A:out2$Popfs -17.770 5.337 -3.330 0.001151 **
## out2$ln_A:out2$Popsf -13.618 9.230 -1.476 0.142654
## out2$ln_A:out2$Popss -20.429 9.184 -2.225 0.027951 *
## out2$Sexm:out2$Popfs 111.152 31.459 3.533 0.000581 ***
## out2$Sexm:out2$Popsf 75.772 60.012 1.263 0.209134
## out2$Sexm:out2$Popss 44.743 51.235 0.873 0.384221
## out2$ln_A:out2$Sexm:out2$Popfs 19.551 6.015 3.250 0.001491 **
## out2$ln_A:out2$Sexm:out2$Popsf 13.455 10.932 1.231 0.220768
## out2$ln_A:out2$Sexm:out2$Popss 5.513 9.897 0.557 0.578487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 257.9457)
##
## Null deviance: 133912 on 137 degrees of freedom
## Residual deviance: 31469 on 122 degrees of freedom
## AIC: 1174.9
##
## Number of Fisher Scoring iterations: 2## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 19.12 19.125 11.503 0.000921 ***
## Pop 3 40.32 13.440 8.083 5.6e-05 ***
## Sex:Pop 3 0.33 0.111 0.067 0.977357
## Residuals 130 216.14 1.663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## glm(formula = Median_lifespan ~ ln_A * Sex, data = out2 %>% filter(Pop ==
## "ff"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -25.330 -8.688 -1.570 5.633 43.002
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.036 13.026 -0.080 0.936906
## ln_A -11.553 3.037 -3.804 0.000389 ***
## Sexm 14.532 16.138 0.900 0.372196
## ln_A:Sexm 3.792 3.560 1.065 0.291856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 210.8507)
##
## Null deviance: 17571 on 53 degrees of freedom
## Residual deviance: 10543 on 50 degrees of freedom
## AIC: 448.05
##
## Number of Fisher Scoring iterations: 2## Joining, by = "Family"
LCA analysis
Longevity
## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, Env, AaAa, AaEnv, AdCa, AdEnv, CaEnv
## Generating Models......
## 50
## 100
## 150
## 200
## 250
## 300
## 350
## 400
## 450
## 137 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 328 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 15 model(s)
Cannot estimate AdAd or AaCa because they are the same as lower order effects. AaCa same as Ad. AdAd same as Ad. Same or reciprocal values based on Armstrong et al 2018 TableS1.
From Blackmon and Demuth 2016 "Next, we generate all possible models that have at most two fewer CGEs than the number of cohorts being analyzed (number of cohorts—intercept—1)." This is in reference to a single model. SAGA determines all possible models for a given data set. Then the software reduces the number of models if they converge or show high correlation and drops the models with higher order interactions. This means that some of the higher order effects cannot be estimated because they are identical to lower order effects. For a single model, the most number of CGEs that can be tested is two less than number of cohorts. So maximally a single model could only test 6 CGEs for our data (8 cohorts - 2; P1, P2, F1, rF1 separated by sex). For longevity the best models have a maximum of three CGEs (Ca, Aa, AaAa).
Reproductive output
## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, AaAa, AdCa
## Generating Models..
## 2 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 13 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 5 model(s)
Sex ratio
## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, AaAa, AdCa
## Generating Models..
## 2 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 13 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 4 model(s)
mtDNA content
## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, AaAa, AdCa
## Generating Models.....
## 12 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 19 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 11 model(s)## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, Env, AaAa, AaEnv, AdCa, AdEnv, CaEnv
## Generating Models......
## 50
## 100
## 150
## 200
## 250
## 300
## 350
## 400
## 450
## 137 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 328 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 23 model(s)
8ohdg
## NULL## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, AaAa, AdCa
## Generating Models.....
## 12 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 19 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 11 model(s)## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, Env, AaAa, AaEnv, AdCa, AdEnv, CaEnv
## Generating Models......
## 50
## 100
## 150
## 200
## 250
## 300
## 350
## 400
## 450
## 137 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 328 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 17 model(s)
Gompertz
## Warning: Unknown or uninitialised column: `Cross_2`.## NULL## Warning: Unknown or uninitialised column: `Sex_2`.## NULL## Warning: Unknown or uninitialised column: `Cross_2`.## Warning: Unknown or uninitialised column: `Sex_2`.## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, Env, AaAa, AaEnv, AdCa, AdEnv, CaEnv
## Generating Models......
## 50
## 100
## 150
## 200
## 250
## 300
## 350
## 400
## 450
## 137 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 328 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 57 model(s)
## The following composite effects cannot be estimated with the line
## means available because they estimate identical quantities to
## lower order effects:
## AdAd, AaCa
##
## The composite genetic effects that will be tested are:
## Aa, Ad, Ca, Env, AaAa, AaEnv, AdCa, AdEnv, CaEnv
## Generating Models......
## 50
## 100
## 150
## 200
## 250
## 300
## 350
## 400
## 450
## 137 models were removed due to high covariances
## or linear relationships between predictor variables.
## The remaining 328 models have been evaluated.
##
##
## AICc weights were used to select the minimum number of models whose weights sum
## to greater than 95% this model set includes 55 model(s)
MtDNA content data analysis
mtDNA content was calculated using mtDNA_content = 2 * 2^(deltaCt).
## `geom_smooth()` using formula 'y ~ x'
## Df Sum Sq Mean Sq F value Pr(>F)
## Cross 3 0.82 0.2747 2.724 0.0444 *
## Residuals 318 32.08 0.1009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log10(mtDNA_content) ~ Sex * as.factor(Samp_age),
## data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.83250 -0.19303 -0.00258 0.17962 1.23884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.18149 0.03255 67.018 < 2e-16 ***
## Sexm -0.18084 0.04616 -3.918 0.00011 ***
## as.factor(Samp_age)56 -0.11477 0.04879 -2.352 0.01927 *
## Sexm:as.factor(Samp_age)56 0.08197 0.06978 1.175 0.24100
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3105 on 318 degrees of freedom
## Multiple R-squared: 0.06811, Adjusted R-squared: 0.05932
## F-statistic: 7.747 on 3 and 318 DF, p-value: 5.218e-05
##
## Call:
## lm(formula = log(mtDNA_content) ~ Cross, data = hetf1, contrasts = list(Cross = mat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80906 -0.46176 -0.01021 0.42793 2.68605
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.68230 0.05073 92.294 <2e-16 ***
## Crossc1 0.06267 0.13924 0.450 0.653
## Crossc2 -0.15034 0.12785 -1.176 0.241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7432 on 218 degrees of freedom
## Multiple R-squared: 0.006762, Adjusted R-squared: -0.00235
## F-statistic: 0.7421 on 2 and 218 DF, p-value: 0.4773## `geom_smooth()` using formula 'y ~ x'
## [1] "Fit to normal distribution"
## [1] 11 276## [1] "Fit to log-normal distribution"
## [1] 11 276Data are not normally distributed. Log-normal data distribution.
ANOVAs
## # A tibble: 2 x 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 as.factor(Samp_age) 1 0.417 0.417 4.11 0.0435
## 2 Residuals 320 32.5 0.102 NA NA## # A tibble: 2 x 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Sex 1 1.67 1.67 17.1 0.0000462
## 2 Residuals 320 31.2 0.0976 NA NA## # A tibble: 2 x 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Cross 3 0.824 0.275 2.72 0.0444
## 2 Residuals 318 32.1 0.101 NA NA## # A tibble: 6 x 7
## term contrast null.value estimate conf.low conf.high adj.p.value
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Cross fs-ff 0 -0.0531 -0.195 0.0888 0.769
## 2 Cross sf-ff 0 0.0543 -0.0816 0.190 0.731
## 3 Cross ss-ff 0 -0.0653 -0.206 0.0758 0.630
## 4 Cross sf-fs 0 0.107 -0.0150 0.230 0.108
## 5 Cross ss-fs 0 -0.0122 -0.140 0.116 0.995
## 6 Cross ss-sf 0 -0.120 -0.241 0.00192 0.0556## # A tibble: 2 x 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mom 1 555. 555. 0.0196 0.889
## 2 Residuals 320 9035684. 28237. NA NA## # A tibble: 4 x 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 as.factor(Samp_age) 1 139383. 139383. 5.19 0.0234
## 2 Sex 1 316085. 316085. 11.8 0.000682
## 3 as.factor(Samp_age):Sex 1 38577. 38577. 1.44 0.232
## 4 Residuals 318 8542194. 26862. NA NA## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 1.666 1.6657 17.275 4.16e-05 ***
## Samp_age 1 0.442 0.4421 4.586 0.033 *
## Sex:Samp_age 1 0.133 0.1330 1.380 0.241
## Residuals 318 30.661 0.0964
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## # A tibble: 8 x 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 as.factor(Samp_age) 1 0.417 0.417 4.33 0.0382
## 2 Sex 1 1.69 1.69 17.6 0.0000361
## 3 Cross 3 0.832 0.277 2.88 0.0361
## 4 as.factor(Samp_age):Sex 1 0.140 0.140 1.46 0.228
## 5 as.factor(Samp_age):Cross 3 0.258 0.0859 0.893 0.445
## 6 Sex:Cross 3 0.0826 0.0275 0.286 0.835
## 7 as.factor(Samp_age):Sex:Cross 3 0.0478 0.0159 0.166 0.919
## 8 Residuals 306 29.4 0.0962 NA NA## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log10(mtDNA_content) ~ as.factor(Samp_age) * Sex * Cross, data = dat)
##
## $`as.factor(Samp_age)`
## diff lwr upr p adj
## 56-28 -0.07252724 -0.1410782 -0.003976282 0.0381837
##
## $Sex
## diff lwr upr p adj
## m-f -0.1449395 -0.2129713 -0.07690773 3.62e-05
##
## $Cross
## diff lwr upr p adj
## fs-ff -0.04739051 -0.18590127 0.091120244 0.8132689
## sf-ff 0.05936477 -0.07336072 0.192090258 0.6554215
## ss-ff -0.06222396 -0.20004370 0.075595790 0.6485900
## sf-fs 0.10675528 -0.01274155 0.226252118 0.0984802
## ss-fs -0.01483344 -0.13996425 0.110297372 0.9900203
## ss-sf -0.12158873 -0.24028390 -0.002893548 0.0423365
##
## $`as.factor(Samp_age):Sex`
## diff lwr upr p adj
## 56:f-28:f -0.11583730 -0.2417193 0.01004471 0.0836823
## 28:m-28:f -0.18179973 -0.3009023 -0.06269713 0.0005755
## 56:m-28:f -0.21347481 -0.3418991 -0.08505048 0.0001381
## 28:m-56:f -0.06596242 -0.1921553 0.06023051 0.5316516
## 56:m-56:f -0.09763751 -0.2326636 0.03738854 0.2440968
## 56:m-28:m -0.03167509 -0.1604042 0.09705401 0.9204477
##
## $`as.factor(Samp_age):Cross`
## diff lwr upr p adj
## 56:ff-28:ff -0.066351248 -0.32389250 0.191189999 0.9937312
## 28:fs-28:ff -0.036766828 -0.25115850 0.177624845 0.9995346
## 56:fs-28:ff -0.127332921 -0.35052876 0.095862918 0.6601905
## 28:sf-28:ff 0.091728095 -0.11368060 0.297136793 0.8732424
## 56:sf-28:ff -0.045223409 -0.25656195 0.166115131 0.9980383
## 28:ss-28:ff -0.095720968 -0.30611777 0.114675834 0.8620400
## 56:ss-28:ff -0.083159623 -0.30943417 0.143114925 0.9517319
## 28:fs-56:ff 0.029584420 -0.21758174 0.276750584 0.9999584
## 56:fs-56:ff -0.060981673 -0.31582222 0.193858877 0.9960277
## 28:sf-56:ff 0.158079343 -0.08133673 0.397495417 0.4733224
## 56:sf-56:ff 0.021127839 -0.22339476 0.265650439 0.9999955
## 28:ss-56:ff -0.029369720 -0.27307885 0.214339405 0.9999564
## 56:ss-56:ff -0.016808375 -0.27434962 0.240732873 0.9999994
## 56:fs-28:fs -0.090566093 -0.30170586 0.120573675 0.8949715
## 28:sf-28:fs 0.128494923 -0.06374543 0.320735273 0.4566817
## 56:sf-28:fs -0.008456581 -0.20702042 0.190107257 1.0000000
## 28:ss-28:fs -0.058954140 -0.25651535 0.138607074 0.9848998
## 56:ss-28:fs -0.046392795 -0.26078447 0.167998879 0.9978932
## 28:sf-56:fs 0.219061016 0.01704878 0.421073255 0.0230238
## 56:sf-56:fs 0.082109511 -0.12592940 0.290148427 0.9301840
## 28:ss-56:fs 0.031611953 -0.17547022 0.238694125 0.9997851
## 56:ss-56:fs 0.044173298 -0.17902254 0.267369137 0.9988124
## 56:sf-28:sf -0.136951504 -0.32578090 0.051877893 0.3465176
## 28:ss-28:sf -0.187449063 -0.37522387 0.000325741 0.0507614
## 56:ss-28:sf -0.174887718 -0.38029642 0.030520981 0.1604286
## 28:ss-56:sf -0.050497559 -0.24474128 0.143746160 0.9933756
## 56:ss-56:sf -0.037936213 -0.24927475 0.173402327 0.9993714
## 56:ss-28:ss 0.012561345 -0.19783546 0.222958147 0.9999997
##
## $`Sex:Cross`
## diff lwr upr p adj
## m:ff-f:ff -0.099956412 -0.35074868 0.150835856 0.9266566
## f:fs-f:ff -0.039472912 -0.26914743 0.190201611 0.9995279
## m:fs-f:ff -0.156010605 -0.38687145 0.074850240 0.4417589
## f:sf-f:ff 0.099397352 -0.11998241 0.318777117 0.8645853
## m:sf-f:ff -0.082866151 -0.30463727 0.138904971 0.9473330
## f:ss-f:ff -0.035156028 -0.26369499 0.193382939 0.9997738
## m:ss-f:ff -0.190315812 -0.41999033 0.039358710 0.1872579
## f:fs-m:ff 0.060483500 -0.17158080 0.292547798 0.9932720
## m:fs-m:ff -0.056054193 -0.28929266 0.177184272 0.9959192
## f:sf-m:ff 0.199353764 -0.02252669 0.421234212 0.1141553
## m:sf-m:ff 0.017090261 -0.20715488 0.241335400 0.9999981
## f:ss-m:ff 0.064800384 -0.16614011 0.295740877 0.9894991
## m:ss-m:ff -0.090359400 -0.32242370 0.141704897 0.9348272
## m:fs-f:fs -0.116537693 -0.32690358 0.093828194 0.6933102
## f:sf-f:fs 0.138870264 -0.05882789 0.336568413 0.3892464
## m:sf-f:fs -0.043393239 -0.24374170 0.156955225 0.9978810
## f:ss-f:fs 0.004316884 -0.20349826 0.212132033 1.0000000
## m:ss-f:fs -0.150842900 -0.35990620 0.058220399 0.3533715
## f:sf-m:fs 0.255407957 0.05633284 0.454483072 0.0027811
## m:sf-m:fs 0.073144454 -0.12856288 0.274851792 0.9550460
## f:ss-m:fs 0.120854577 -0.08827093 0.329980088 0.6450727
## m:ss-m:fs -0.034305207 -0.24467109 0.176060679 0.9996665
## m:sf-f:sf -0.182263502 -0.37072199 0.006194987 0.0661922
## f:ss-f:sf -0.134553380 -0.33093116 0.061824397 0.4229641
## m:ss-f:sf -0.289713164 -0.48741131 -0.092015015 0.0002896
## f:ss-m:sf 0.047710123 -0.15133555 0.246755795 0.9959860
## m:ss-m:sf -0.107449662 -0.30779813 0.092898802 0.7275460
## m:ss-f:ss -0.155159784 -0.36297493 0.052655364 0.3087758
##
## $`as.factor(Samp_age):Sex:Cross`
## diff lwr upr p adj
## 56:f:ff-28:f:ff -0.062141663 -0.47222850 0.347945179 1.0000000
## 28:m:ff-28:f:ff -0.100656921 -0.46305237 0.261738531 0.9998731
## 56:m:ff-28:f:ff -0.176136249 -0.58622309 0.233950593 0.9836393
## 28:f:fs-28:f:ff -0.012652597 -0.35684068 0.331535489 1.0000000
## 56:f:fs-28:f:ff -0.138777058 -0.48691389 0.209359778 0.9921451
## 28:m:fs-28:f:ff -0.162138920 -0.49934950 0.175071656 0.9550230
## 56:m:fs-28:f:ff -0.216679784 -0.57907524 0.145715668 0.7852251
## 28:f:sf-28:f:ff 0.159370743 -0.16668928 0.485430764 0.9484553
## 56:f:sf-28:f:ff -0.038205947 -0.36944185 0.293029954 1.0000000
## 28:m:sf-28:f:ff -0.076974466 -0.40303449 0.249085555 0.9999842
## 56:m:sf-28:f:ff -0.157271702 -0.49783029 0.183286883 0.9682156
## 28:f:ss-28:f:ff -0.043058349 -0.37429425 0.288177551 1.0000000
## 56:f:ss-28:f:ff -0.093443416 -0.45583887 0.268952036 0.9999504
## 28:m:ss-28:f:ff -0.253423416 -0.59063399 0.083787161 0.4049840
## 56:m:ss-28:f:ff -0.179650799 -0.53683166 0.177530058 0.9350677
## 28:m:ff-56:f:ff -0.038515259 -0.45315187 0.376121349 1.0000000
## 56:m:ff-56:f:ff -0.113994586 -0.57090187 0.342912702 0.9999675
## 28:f:fs-56:f:ff 0.049489065 -0.34933231 0.448310443 1.0000000
## 56:f:fs-56:f:ff -0.076635395 -0.47886954 0.325598752 0.9999991
## 28:m:fs-56:f:ff -0.099997258 -0.49281277 0.292818253 0.9999579
## 56:m:fs-56:f:ff -0.154538122 -0.56917473 0.260098486 0.9961029
## 28:f:sf-56:f:ff 0.221512405 -0.16177364 0.604798447 0.8253936
## 56:f:sf-56:f:ff 0.023935716 -0.36376297 0.411634406 1.0000000
## 28:m:sf-56:f:ff -0.014832804 -0.39811885 0.368453238 1.0000000
## 56:m:sf-56:f:ff -0.095130040 -0.49082336 0.300563279 0.9999800
## 28:f:ss-56:f:ff 0.019083313 -0.36861538 0.406782003 1.0000000
## 56:f:ss-56:f:ff -0.031301754 -0.44593836 0.383334854 1.0000000
## 28:m:ss-56:f:ff -0.191281753 -0.58409726 0.201533758 0.9500215
## 56:m:ss-56:f:ff -0.117509137 -0.52759598 0.292577705 0.9998133
## 56:m:ff-28:m:ff -0.075479327 -0.49011593 0.339157280 0.9999995
## 28:f:fs-28:m:ff 0.088004324 -0.26159221 0.437600860 0.9999636
## 56:f:fs-28:m:ff -0.038120137 -0.39160501 0.315364737 1.0000000
## 28:m:fs-28:m:ff -0.061481999 -0.40421115 0.281247153 0.9999996
## 56:m:fs-28:m:ff -0.116022863 -0.48355893 0.251513207 0.9993998
## 28:f:sf-28:m:ff 0.260027664 -0.07173646 0.591791792 0.3304450
## 56:f:sf-28:m:ff 0.062450975 -0.27440140 0.399303347 0.9999994
## 28:m:sf-28:m:ff 0.023682455 -0.30808167 0.355446583 1.0000000
## 56:m:sf-28:m:ff -0.056614781 -0.40263855 0.289408988 0.9999999
## 28:f:ss-28:m:ff 0.057598572 -0.27925380 0.394450944 0.9999998
## 56:f:ss-28:m:ff 0.007213505 -0.36032257 0.374749575 1.0000000
## 28:m:ss-28:m:ff -0.152766494 -0.49549565 0.189962658 0.9768765
## 56:m:ss-28:m:ff -0.078993878 -0.44138933 0.283401574 0.9999945
## 28:f:fs-56:m:ff 0.163483651 -0.23533773 0.562305029 0.9896040
## 56:f:fs-56:m:ff 0.037359191 -0.36487496 0.439593338 1.0000000
## 28:m:fs-56:m:ff 0.013997328 -0.37881818 0.406812839 1.0000000
## 56:m:fs-56:m:ff -0.040543535 -0.45518014 0.374093072 1.0000000
## 28:f:sf-56:m:ff 0.335506991 -0.04777905 0.718793033 0.1651560
## 56:f:sf-56:m:ff 0.137930302 -0.24976839 0.525628992 0.9976483
## 28:m:sf-56:m:ff 0.099161782 -0.28412426 0.482447824 0.9999482
## 56:m:sf-56:m:ff 0.018864546 -0.37682877 0.414557865 1.0000000
## 28:f:ss-56:m:ff 0.133077899 -0.25462079 0.520776589 0.9984226
## 56:f:ss-56:m:ff 0.082692832 -0.33194378 0.497329440 0.9999984
## 28:m:ss-56:m:ff -0.077287167 -0.47010268 0.315528344 0.9999986
## 56:m:ss-56:m:ff -0.003514551 -0.41360139 0.406572291 1.0000000
## 56:f:fs-28:f:fs -0.126124461 -0.46091773 0.208668805 0.9956319
## 28:m:fs-28:f:fs -0.149486323 -0.47290285 0.173930199 0.9679695
## 56:m:fs-28:f:fs -0.204027187 -0.55362372 0.145569349 0.8144092
## 28:f:sf-28:f:fs 0.172023340 -0.13974967 0.483796353 0.8712821
## 56:f:sf-28:f:fs -0.025553349 -0.34273547 0.291628768 1.0000000
## 28:m:sf-28:f:fs -0.064321869 -0.37609488 0.247451144 0.9999974
## 56:m:sf-28:f:fs -0.144619105 -0.47152494 0.182286728 0.9784244
## 28:f:ss-28:f:fs -0.030405752 -0.34758787 0.286776366 1.0000000
## 56:f:ss-28:f:fs -0.080790819 -0.43038735 0.268805717 0.9999881
## 28:m:ss-28:f:fs -0.240770818 -0.56418734 0.082645704 0.4222021
## 56:m:ss-28:f:fs -0.166998202 -0.51118629 0.177189885 0.9514944
## 28:m:fs-56:f:fs -0.023361863 -0.35097759 0.304253865 1.0000000
## 56:m:fs-56:f:fs -0.077902726 -0.43138760 0.275582147 0.9999937
## 28:f:sf-56:f:fs 0.298147801 -0.01797912 0.614274720 0.0891815
## 56:f:sf-56:f:fs 0.100571111 -0.22089166 0.422033883 0.9994602
## 28:m:sf-56:f:fs 0.061802592 -0.25432433 0.377929511 0.9999987
## 56:m:sf-56:f:fs -0.018494644 -0.34955543 0.312566138 1.0000000
## 28:f:ss-56:f:fs 0.095718708 -0.22574406 0.417181480 0.9997016
## 56:f:ss-56:f:fs 0.045333642 -0.30815123 0.398818515 1.0000000
## 28:m:ss-56:f:fs -0.114646358 -0.44226209 0.212969370 0.9980416
## 56:m:ss-56:f:fs -0.040873741 -0.38901058 0.307263095 1.0000000
## 56:m:fs-28:m:fs -0.054540864 -0.39727002 0.288188289 0.9999999
## 28:f:sf-28:m:fs 0.321509663 0.01745713 0.625562201 0.0264471
## 56:f:sf-28:m:fs 0.123932974 -0.18566359 0.433529534 0.9918037
## 28:m:sf-28:m:fs 0.085164454 -0.21888808 0.389216992 0.9998591
## 56:m:sf-28:m:fs 0.004867218 -0.31468396 0.324418400 1.0000000
## 28:f:ss-28:m:fs 0.119080571 -0.19051599 0.428677131 0.9945644
## 56:f:ss-28:m:fs 0.068695504 -0.27403365 0.411424657 0.9999982
## 28:m:ss-28:m:fs -0.091284495 -0.40726516 0.224696171 0.9997936
## 56:m:ss-28:m:fs -0.017511879 -0.35472246 0.319698698 1.0000000
## 28:f:sf-56:m:fs 0.376050527 0.04428640 0.707814655 0.0105482
## 56:f:sf-56:m:fs 0.178473837 -0.15837853 0.515326210 0.9034640
## 28:m:sf-56:m:fs 0.139705318 -0.19205881 0.471469445 0.9864665
## 56:m:sf-56:m:fs 0.059408082 -0.28661569 0.405431851 0.9999998
## 28:f:ss-56:m:fs 0.173621435 -0.16323094 0.510473807 0.9214626
## 56:f:ss-56:m:fs 0.123236368 -0.24429970 0.490772438 0.9987908
## 28:m:ss-56:m:fs -0.036743631 -0.37947278 0.305985521 1.0000000
## 56:m:ss-56:m:fs 0.037028985 -0.32536647 0.399424437 1.0000000
## 56:f:sf-28:f:sf -0.197576689 -0.49498919 0.099835809 0.6276106
## 28:m:sf-28:f:sf -0.236345209 -0.52798216 0.055291740 0.2747975
## 56:m:sf-28:f:sf -0.316642445 -0.62440392 -0.008880975 0.0365364
## 28:f:ss-28:f:sf -0.202429092 -0.49984159 0.094983406 0.5855368
## 56:f:ss-28:f:sf -0.252814159 -0.58457829 0.078949969 0.3799419
## 28:m:ss-28:f:sf -0.412794158 -0.71684670 -0.108741621 0.0004540
## 56:m:ss-28:f:sf -0.339021542 -0.66508156 -0.012961521 0.0323458
## 28:m:sf-56:f:sf -0.038768520 -0.33618102 0.258643979 1.0000000
## 56:m:sf-56:f:sf -0.119065756 -0.43230561 0.194174099 0.9951984
## 28:f:ss-56:f:sf -0.004852403 -0.30793041 0.298225605 1.0000000
## 56:f:ss-56:f:sf -0.055237470 -0.39208984 0.281614903 0.9999999
## 28:m:ss-56:f:sf -0.215217469 -0.52481403 0.094379091 0.5477990
## 56:m:ss-56:f:sf -0.141444853 -0.47268075 0.189791048 0.9845204
## 56:m:sf-28:m:sf -0.080297236 -0.38805871 0.227464234 0.9999424
## 28:f:ss-28:m:sf 0.033916117 -0.26349638 0.331328615 1.0000000
## 56:f:ss-28:m:sf -0.016468950 -0.34823308 0.315295178 1.0000000
## 28:m:ss-28:m:sf -0.176448949 -0.48050149 0.127603588 0.8208049
## 56:m:ss-28:m:sf -0.102676333 -0.42873635 0.223383688 0.9994170
## 28:f:ss-56:m:sf 0.114213353 -0.19902650 0.427453208 0.9969230
## 56:f:ss-56:m:sf 0.063828286 -0.28219548 0.409852055 0.9999994
## 28:m:ss-56:m:sf -0.096151713 -0.41570290 0.223399469 0.9996613
## 56:m:ss-56:m:sf -0.022379097 -0.36293768 0.318179488 1.0000000
## 56:f:ss-28:f:ss -0.050385067 -0.38723744 0.286467306 1.0000000
## 28:m:ss-28:f:ss -0.210365066 -0.51996163 0.099231494 0.5885264
## 56:m:ss-28:f:ss -0.136592450 -0.46782835 0.194643451 0.9889714
## 28:m:ss-56:f:ss -0.159979999 -0.50270915 0.182749153 0.9650884
## 56:m:ss-56:f:ss -0.086207383 -0.44860284 0.276188069 0.9999825
## 56:m:ss-28:m:ss 0.073772616 -0.26343796 0.410983193 0.9999942## # A tibble: 2 x 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 as.factor(hybrid) 1 0.162 0.162 1.58 0.209
## 2 Residuals 320 32.7 0.102 NA NA## # A tibble: 2 x 6
## term df sumsq meansq statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 as.factor(Mom) 1 0.0783 0.0783 0.763 0.383
## 2 Residuals 320 32.8 0.103 NA NAmtDNA content is not normally distributed, so I log-transformed the data and applied ANOVA to look the effects of sex, age and/or cross. Older animals have lower mtDNA content (ANOVA, df = 1, F-value = 6.44, p-value = 0.0116). Females have higher mtDNA content when compared to males (ANOVA, df = 1, F-value = 18.5, p-value = 0.0000222) while there was no detectable difference between crosses (ANOVA, df = 3, F-value = 2.54, p-value = 0.0563).
Heterotic mtDNA content was marginally significant (ANOVA, df = 1, F-value = 3.42, p-value = 0.0655).
No significant difference in mtDNA background. No significant interaction of any factors.
LME
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular## Data: dat
## Models:
## lmm00: log10(mtDNA_content) ~ 1 + (1 | Clutch)
## lmm000: log10(mtDNA_content) ~ 1 + (1 | Family)
## lmm0: log10(mtDNA_content) ~ 1 + (1 | Family/Clutch)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## lmm00 3 185.31 196.63 -89.654 179.31
## lmm000 3 175.19 186.51 -84.596 169.19 10.117 0
## lmm0 4 165.00 180.10 -78.499 157.00 12.193 1 0.0004798 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## boundary (singular) fit: see ?isSingular## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log10(mtDNA_content) ~ Sex + (1 | Family/Clutch)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 145.4 164.3 -67.7 135.4 317
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3706 -0.5436 -0.0212 0.5641 4.1480
##
## Random effects:
## Groups Name Variance Std.Dev.
## Clutch:Family (Intercept) 0.02348 0.1532
## Family (Intercept) 0.00000 0.0000
## Residual 0.07365 0.2714
## Number of obs: 322, groups: Clutch:Family, 78; Family, 33
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.11981 0.02840 74.650
## Sexm -0.14835 0.03121 -4.753
##
## Correlation of Fixed Effects:
## (Intr)
## Sexm -0.533
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: log10(mtDNA_content)
## Chisq Df Pr(>Chisq)
## Sex 22.593 1 2.002e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## boundary (singular) fit: see ?isSingular## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log10(mtDNA_content) ~ as.factor(Samp_age) + (1 | Family/Clutch)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 164.1 183.0 -77.1 154.1 317
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4618 -0.5565 -0.0389 0.5381 3.6591
##
## Random effects:
## Groups Name Variance Std.Dev.
## Clutch:Family (Intercept) 0.02081 0.1443
## Family (Intercept) 0.00000 0.0000
## Residual 0.08001 0.2829
## Number of obs: 322, groups: Clutch:Family, 78; Family, 33
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.07551 0.02823 73.524
## as.factor(Samp_age)56 -0.05791 0.03410 -1.698
##
## Correlation of Fixed Effects:
## (Intr)
## as.fc(S_)56 -0.544
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: log10(mtDNA_content)
## Chisq Df Pr(>Chisq)
## as.factor(Samp_age) 2.8837 1 0.08948 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## boundary (singular) fit: see ?isSingular## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log10(mtDNA_content) ~ Cross + (1 | Family/Clutch)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 167.5 194.0 -76.8 153.5 315
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3119 -0.5734 -0.0182 0.5366 3.7063
##
## Random effects:
## Groups Name Variance Std.Dev.
## Clutch:Family (Intercept) 0.01910 0.1382
## Family (Intercept) 0.00000 0.0000
## Residual 0.08071 0.2841
## Number of obs: 322, groups: Clutch:Family, 78; Family, 33
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.06159 0.05181 39.791
## Crossfs -0.04657 0.06855 -0.679
## Crosssf 0.05092 0.06754 0.754
## Crossss -0.05588 0.06999 -0.798
##
## Correlation of Fixed Effects:
## (Intr) Crssfs Crsssf
## Crossfs -0.756
## Crosssf -0.767 0.580
## Crossss -0.740 0.560 0.568
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: log10(mtDNA_content)
## Chisq Df Pr(>Chisq)
## Cross 3.6246 3 0.305## boundary (singular) fit: see ?isSingular## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $`emmeans of Cross`
## Cross emmean SE df lower.CL upper.CL
## ff 2.06 0.0542 36.4 1.95 2.17
## fs 2.02 0.0469 27.2 1.92 2.11
## sf 2.11 0.0452 23.0 2.02 2.21
## ss 2.01 0.0493 28.1 1.90 2.11
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log10 (not the response) scale.
## Confidence level used: 0.95
##
## $`pairwise differences of Cross`
## 1 estimate SE df t.ratio p.value
## ff - fs 0.04657 0.0716 32.0 0.650 0.9147
## ff - sf -0.05092 0.0705 29.8 -0.722 0.8875
## ff - ss 0.05588 0.0732 32.3 0.763 0.8702
## fs - sf -0.09749 0.0651 25.0 -1.498 0.4535
## fs - ss 0.00931 0.0680 27.7 0.137 0.9991
## sf - ss 0.10680 0.0668 25.6 1.598 0.3973
##
## Note: contrasts are still on the log10 scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimates## boundary (singular) fit: see ?isSingular## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log10(mtDNA_content) ~ Sex * Samp_age + (1 | Family/Clutch)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 143.3 169.8 -64.7 129.3 315
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5826 -0.5431 -0.0230 0.5764 4.1948
##
## Random effects:
## Groups Name Variance Std.Dev.
## Clutch:Family (Intercept) 0.02309 0.1519
## Family (Intercept) 0.00000 0.0000
## Residual 0.07226 0.2688
## Number of obs: 322, groups: Clutch:Family, 78; Family, 33
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.278692 0.070339 32.396
## Sexm -0.302957 0.093433 -3.243
## Samp_age -0.003933 0.001595 -2.466
## Sexm:Samp_age 0.003895 0.002224 1.751
##
## Correlation of Fixed Effects:
## (Intr) Sexm Samp_g
## Sexm -0.655
## Samp_age -0.916 0.644
## Sexm:Samp_g 0.619 -0.944 -0.681
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: log10(mtDNA_content)
## Chisq Df Pr(>Chisq)
## Sex 23.0959 1 1.541e-06 ***
## Samp_age 3.0239 1 0.08205 .
## Sex:Samp_age 3.0657 1 0.07996 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## boundary (singular) fit: see ?isSingular
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates## $`emmeans of Sex, Samp_age`
## Sex Samp_age emmean SE df lower.CL upper.CL
## f 28 2.17 0.0350 112 2.10 2.24
## m 28 1.97 0.0352 116 1.90 2.04
## f 56 2.06 0.0382 136 1.98 2.13
## m 56 1.97 0.0394 155 1.90 2.05
##
## Degrees-of-freedom method: kenward-roger
## Results are given on the log10 (not the response) scale.
## Confidence level used: 0.95
##
## $`pairwise differences of Sex, Samp_age`
## 1 estimate SE df t.ratio p.value
## f 28 - m 28 0.19391 0.0406 261 4.778 <.0001
## f 28 - f 56 0.11014 0.0451 300 2.439 0.0720
## f 28 - m 56 0.19499 0.0460 298 4.242 0.0002
## m 28 - f 56 -0.08377 0.0457 303 -1.835 0.2591
## m 28 - m 56 0.00109 0.0461 299 0.024 1.0000
## f 56 - m 56 0.08486 0.0483 296 1.758 0.2956
##
## Note: contrasts are still on the log10 scale
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimates## boundary (singular) fit: see ?isSingular## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log10(mtDNA_content) ~ Sex * Cross + (1 | Family/Clutch)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 153.2 194.7 -65.6 131.2 311
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4984 -0.5989 -0.0166 0.5794 4.0922
##
## Random effects:
## Groups Name Variance Std.Dev.
## Clutch:Family (Intercept) 0.02089 0.1445
## Family (Intercept) 0.00000 0.0000
## Residual 0.07375 0.2716
## Number of obs: 322, groups: Clutch:Family, 78; Family, 33
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.119415 0.063036 33.623
## Sexm -0.120806 0.073635 -1.641
## Crossfs -0.045762 0.083019 -0.551
## Crosssf 0.082871 0.081382 1.018
## Crossss -0.044894 0.084020 -0.534
## Sexm:Crossfs -0.003885 0.096915 -0.040
## Sexm:Crosssf -0.066459 0.092400 -0.719
## Sexm:Crossss -0.022450 0.095394 -0.235
##
## Correlation of Fixed Effects:
## (Intr) Sexm Crssfs Crsssf Crssss Sxm:Crssf Sxm:Crsssf
## Sexm -0.573
## Crossfs -0.759 0.435
## Crosssf -0.775 0.444 0.588
## Crossss -0.750 0.430 0.570 0.581
## Sexm:Crssfs 0.435 -0.760 -0.566 -0.337 -0.326
## Sexm:Crsssf 0.456 -0.797 -0.346 -0.557 -0.342 0.605
## Sexm:Crssss 0.442 -0.772 -0.336 -0.342 -0.553 0.586 0.615
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: log10(mtDNA_content)
## Chisq Df Pr(>Chisq)
## Sex 22.6682 1 1.925e-06 ***
## Cross 3.6031 3 0.3076
## Sex:Cross 0.7740 3 0.8557
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## boundary (singular) fit: see ?isSingular## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## log10(mtDNA_content) ~ as.factor(Samp_age) * Cross + (1 | Family/Clutch)
## Data: dat
##
## AIC BIC logLik deviance df.resid
## 170.8 212.3 -74.4 148.8 311
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4086 -0.6135 -0.0323 0.5501 3.6523
##
## Random effects:
## Groups Name Variance Std.Dev.
## Clutch:Family (Intercept) 0.01742 0.1320
## Family (Intercept) 0.00000 0.0000
## Residual 0.08027 0.2833
## Number of obs: 322, groups: Clutch:Family, 78; Family, 33
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.09335 0.06132 34.138
## as.factor(Samp_age)56 -0.07639 0.08478 -0.901
## Crossfs -0.05318 0.08188 -0.650
## Crosssf 0.07347 0.07978 0.921
## Crossss -0.08822 0.08138 -1.084
## as.factor(Samp_age)56:Crossfs 0.02353 0.10891 0.216
## as.factor(Samp_age)56:Crosssf -0.03271 0.10309 -0.317
## as.factor(Samp_age)56:Crossss 0.07799 0.10795 0.722
##
## Correlation of Fixed Effects:
## (Intr) as.(S_)56 Crssfs Crsssf Crssss
## as.fc(S_)56 -0.563
## Crossfs -0.749 0.422
## Crosssf -0.769 0.433 0.576
## Crossss -0.753 0.424 0.564 0.579
## as.fctr(Smp_g)56:Crssf 0.438 -0.778 -0.573 -0.337 -0.330
## as.fctr(Smp_g)56:Crsssf 0.463 -0.822 -0.347 -0.560 -0.349
## as.fctr(Smp_g)56:Crssss 0.442 -0.785 -0.331 -0.340 -0.543
## as.fctr(Smp_g)56:Crssf as.fctr(Smp_g)56:Crsssf
## as.fc(S_)56
## Crossfs
## Crosssf
## Crossss
## as.fctr(Smp_g)56:Crssf
## as.fctr(Smp_g)56:Crsssf 0.640
## as.fctr(Smp_g)56:Crssss 0.611 0.646
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: log10(mtDNA_content)
## Chisq Df Pr(>Chisq)
## as.factor(Samp_age) 3.2734 1 0.07041 .
## Cross 4.0775 3 0.25321
## as.factor(Samp_age):Cross 1.5971 3 0.66006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear mixed effects model similar to coxme. Random effect of family and cross within family.
Sex (Chisq = 22.676, df = 1, p-value = 1.917e-06) Age (Chisq 5.3258, df = 1, p-value = 0.02101) Cross (Chisq = 3.7781, df = 3, p-value = 0.2864)
With this model mild interaction of sex and sample age.
Females have greater mtDNA copy number at 28 days but not at 56 days. Female 28 > male 56 as well. No difference within day 56.
8ohdg data analysis
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Position = col_character(),
## Tube_number = col_double(),
## Detector = col_character(),
## Plate_A = col_character(),
## Ct = col_double()
## )## `summarise()` has grouped output by 'Tube_number'. You can override using the `.groups` argument.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Shapiro-Wilk normality test
##
## data: (dat_sum$mean_pgug..1)
## W = 0.74292, p-value = 2.173e-13##
## Shapiro-Wilk normality test
##
## data: log(dat_sum$mean_pgug..1)
## W = 0.98784, p-value = 0.3447## [1] 376.0591##
## Call:
## glm(formula = log(mean_pgug..1) ~ 1, data = dat_sum)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.60433 -0.69661 -0.00938 0.81307 2.51129
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.30983 0.09941 73.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.215502)
##
## Null deviance: 148.29 on 122 degrees of freedom
## Residual deviance: 148.29 on 122 degrees of freedom
## AIC: 376.06
##
## Number of Fisher Scoring iterations: 2## Warning in dpois(y, mu, log = TRUE): non-integer x = 890.742208## Warning in dpois(y, mu, log = TRUE): non-integer x = 643.656263## Warning in dpois(y, mu, log = TRUE): non-integer x = 914.555067## Warning in dpois(y, mu, log = TRUE): non-integer x = 1133.297628## Warning in dpois(y, mu, log = TRUE): non-integer x = 877.601471## Warning in dpois(y, mu, log = TRUE): non-integer x = 744.879095## Warning in dpois(y, mu, log = TRUE): non-integer x = 2808.475690## Warning in dpois(y, mu, log = TRUE): non-integer x = 767.784258## Warning in dpois(y, mu, log = TRUE): non-integer x = 1186.913223## Warning in dpois(y, mu, log = TRUE): non-integer x = 579.612039## Warning in dpois(y, mu, log = TRUE): non-integer x = 229.576496## Warning in dpois(y, mu, log = TRUE): non-integer x = 2066.874830## Warning in dpois(y, mu, log = TRUE): non-integer x = 4350.718612## Warning in dpois(y, mu, log = TRUE): non-integer x = 323.898068## Warning in dpois(y, mu, log = TRUE): non-integer x = 767.292610## Warning in dpois(y, mu, log = TRUE): non-integer x = 977.682609## Warning in dpois(y, mu, log = TRUE): non-integer x = 805.530175## Warning in dpois(y, mu, log = TRUE): non-integer x = 772.065595## Warning in dpois(y, mu, log = TRUE): non-integer x = 4269.839068## Warning in dpois(y, mu, log = TRUE): non-integer x = 8376.499768## Warning in dpois(y, mu, log = TRUE): non-integer x = 970.454160## Warning in dpois(y, mu, log = TRUE): non-integer x = 117.292899## Warning in dpois(y, mu, log = TRUE): non-integer x = 301.938316## Warning in dpois(y, mu, log = TRUE): non-integer x = 636.636546## Warning in dpois(y, mu, log = TRUE): non-integer x = 2058.331534## Warning in dpois(y, mu, log = TRUE): non-integer x = 744.874981## Warning in dpois(y, mu, log = TRUE): non-integer x = 604.958076## Warning in dpois(y, mu, log = TRUE): non-integer x = 1480.965543## Warning in dpois(y, mu, log = TRUE): non-integer x = 5650.153514## Warning in dpois(y, mu, log = TRUE): non-integer x = 110.553629## Warning in dpois(y, mu, log = TRUE): non-integer x = 13415.507308## Warning in dpois(y, mu, log = TRUE): non-integer x = 3919.817859## Warning in dpois(y, mu, log = TRUE): non-integer x = 220.681519## Warning in dpois(y, mu, log = TRUE): non-integer x = 634.009505## Warning in dpois(y, mu, log = TRUE): non-integer x = 583.066123## Warning in dpois(y, mu, log = TRUE): non-integer x = 1037.600284## Warning in dpois(y, mu, log = TRUE): non-integer x = 5531.248735## Warning in dpois(y, mu, log = TRUE): non-integer x = 7095.759747## Warning in dpois(y, mu, log = TRUE): non-integer x = 676.408459## Warning in dpois(y, mu, log = TRUE): non-integer x = 3226.670598## Warning in dpois(y, mu, log = TRUE): non-integer x = 2290.456254## Warning in dpois(y, mu, log = TRUE): non-integer x = 2379.548794## Warning in dpois(y, mu, log = TRUE): non-integer x = 3886.319387## Warning in dpois(y, mu, log = TRUE): non-integer x = 2003.224969## Warning in dpois(y, mu, log = TRUE): non-integer x = 780.072038## Warning in dpois(y, mu, log = TRUE): non-integer x = 836.946779## Warning in dpois(y, mu, log = TRUE): non-integer x = 807.835854## Warning in dpois(y, mu, log = TRUE): non-integer x = 6439.337330## Warning in dpois(y, mu, log = TRUE): non-integer x = 3155.946490## Warning in dpois(y, mu, log = TRUE): non-integer x = 4461.227748## Warning in dpois(y, mu, log = TRUE): non-integer x = 1697.287233## Warning in dpois(y, mu, log = TRUE): non-integer x = 895.978723## Warning in dpois(y, mu, log = TRUE): non-integer x = 18418.674130## Warning in dpois(y, mu, log = TRUE): non-integer x = 6882.016002## Warning in dpois(y, mu, log = TRUE): non-integer x = 2314.774037## Warning in dpois(y, mu, log = TRUE): non-integer x = 133.871897## Warning in dpois(y, mu, log = TRUE): non-integer x = 897.232563## Warning in dpois(y, mu, log = TRUE): non-integer x = 3692.952313## Warning in dpois(y, mu, log = TRUE): non-integer x = 1156.230890## Warning in dpois(y, mu, log = TRUE): non-integer x = 1636.594813## Warning in dpois(y, mu, log = TRUE): non-integer x = 6936.180702## Warning in dpois(y, mu, log = TRUE): non-integer x = 2823.411001## Warning in dpois(y, mu, log = TRUE): non-integer x = 6242.061279## Warning in dpois(y, mu, log = TRUE): non-integer x = 1544.979737## Warning in dpois(y, mu, log = TRUE): non-integer x = 779.541757## Warning in dpois(y, mu, log = TRUE): non-integer x = 382.950043## Warning in dpois(y, mu, log = TRUE): non-integer x = 1748.686375## Warning in dpois(y, mu, log = TRUE): non-integer x = 2142.399209## Warning in dpois(y, mu, log = TRUE): non-integer x = 742.573425## Warning in dpois(y, mu, log = TRUE): non-integer x = 3077.374388## Warning in dpois(y, mu, log = TRUE): non-integer x = 2299.388878## Warning in dpois(y, mu, log = TRUE): non-integer x = 472.287912## Warning in dpois(y, mu, log = TRUE): non-integer x = 3086.312642## Warning in dpois(y, mu, log = TRUE): non-integer x = 2753.359503## Warning in dpois(y, mu, log = TRUE): non-integer x = 10474.448683## Warning in dpois(y, mu, log = TRUE): non-integer x = 3210.101618## Warning in dpois(y, mu, log = TRUE): non-integer x = 2690.869540## Warning in dpois(y, mu, log = TRUE): non-integer x = 4263.486399## Warning in dpois(y, mu, log = TRUE): non-integer x = 333.240704## Warning in dpois(y, mu, log = TRUE): non-integer x = 2120.472904## Warning in dpois(y, mu, log = TRUE): non-integer x = 2275.799972## Warning in dpois(y, mu, log = TRUE): non-integer x = 1606.179513## Warning in dpois(y, mu, log = TRUE): non-integer x = 479.423704## Warning in dpois(y, mu, log = TRUE): non-integer x = 1811.857432## Warning in dpois(y, mu, log = TRUE): non-integer x = 489.762832## Warning in dpois(y, mu, log = TRUE): non-integer x = 874.111462## Warning in dpois(y, mu, log = TRUE): non-integer x = 6035.386895## Warning in dpois(y, mu, log = TRUE): non-integer x = 361.495135## Warning in dpois(y, mu, log = TRUE): non-integer x = 619.648629## Warning in dpois(y, mu, log = TRUE): non-integer x = 4475.902661## Warning in dpois(y, mu, log = TRUE): non-integer x = 1252.234581## Warning in dpois(y, mu, log = TRUE): non-integer x = 1044.483282## Warning in dpois(y, mu, log = TRUE): non-integer x = 1511.702215## Warning in dpois(y, mu, log = TRUE): non-integer x = 11139.137079## Warning in dpois(y, mu, log = TRUE): non-integer x = 545.836487## Warning in dpois(y, mu, log = TRUE): non-integer x = 968.347748## Warning in dpois(y, mu, log = TRUE): non-integer x = 2415.696800## Warning in dpois(y, mu, log = TRUE): non-integer x = 5334.700648## Warning in dpois(y, mu, log = TRUE): non-integer x = 705.782436## Warning in dpois(y, mu, log = TRUE): non-integer x = 1188.307810## Warning in dpois(y, mu, log = TRUE): non-integer x = 7985.963833## Warning in dpois(y, mu, log = TRUE): non-integer x = 3521.338684## Warning in dpois(y, mu, log = TRUE): non-integer x = 165.074663## Warning in dpois(y, mu, log = TRUE): non-integer x = 8876.743937## Warning in dpois(y, mu, log = TRUE): non-integer x = 4431.097491## Warning in dpois(y, mu, log = TRUE): non-integer x = 8566.588017## Warning in dpois(y, mu, log = TRUE): non-integer x = 1892.750013## Warning in dpois(y, mu, log = TRUE): non-integer x = 9596.303566## Warning in dpois(y, mu, log = TRUE): non-integer x = 1308.612682## Warning in dpois(y, mu, log = TRUE): non-integer x = 5385.940232## Warning in dpois(y, mu, log = TRUE): non-integer x = 748.807767## Warning in dpois(y, mu, log = TRUE): non-integer x = 1132.672033## Warning in dpois(y, mu, log = TRUE): non-integer x = 511.604279## Warning in dpois(y, mu, log = TRUE): non-integer x = 6223.159147## Warning in dpois(y, mu, log = TRUE): non-integer x = 264.477958## Warning in dpois(y, mu, log = TRUE): non-integer x = 331.728929## Warning in dpois(y, mu, log = TRUE): non-integer x = 975.948485## Warning in dpois(y, mu, log = TRUE): non-integer x = 201.831762## Warning in dpois(y, mu, log = TRUE): non-integer x = 2113.046752## Warning in dpois(y, mu, log = TRUE): non-integer x = 763.267890## Warning in dpois(y, mu, log = TRUE): non-integer x = 527.144914## Warning in dpois(y, mu, log = TRUE): non-integer x = 1821.759937## Warning in dpois(y, mu, log = TRUE): non-integer x = 4729.316381## [1] Inf##
## Call:
## glm(formula = (mean_pgug..1) ~ 1, family = "poisson", data = dat_sum)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -65.76 -43.37 -24.31 14.02 200.52
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.872003 0.001761 4471 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 320684 on 122 degrees of freedom
## Residual deviance: 320684 on 122 degrees of freedom
## AIC: Inf
##
## Number of Fisher Scoring iterations: 5Data are not normally distirbuted. Intially, I perfomed a log transfomation and analyzed data with a normal distiribuiton. The log transofmred data fit the normal distirbiton by Shapiro-Wilk test. Further the residiuals are approximatly normally distirbuted with a null model AIC of 376.0591