Curso R - UFAC - Modelos Mistos e introdução à Seleção de Modelos
Ricardo Solar - UFMG (ricardosolar@ufmg.br)
December, 2022
© Ricardo Solar/UFMG - compartilhamento e utilização não-comercial livres. Não reproduzir sem autorização > DOI: http://doi.org/10.5281/zenodo.7392285
Até agora, vimos situações em que a premissa de independência das amostras se manteve íntegra, ou seja, sempre trabalhamos com réplicas verdadeiras. Mas e se essa premissa não for verdadeira? Vamos analisar conjuntos de dados onde a unidade de amostragem é diferente da unidade de réplicas. Essa avaliação sempre vai depender da escala da nossa perugunta, não havendo um limite predeterminado para determinar a partir de onde é uma réplica ou não.
Uma forma interessante de avaliar isso é se perguntar algumas coisas:
- há no meu banco de dados amostras que se parecem mais entre si do que com qualquer outra?
- meu banco de dados possui mais linhas do que aquilo que eu sei que são réplicas verdadeiras?
- há algum fator que possa agrupar as minhas amostras? (p.ex. uma população, um vaso de cultivo, um fragmento … etc.)
Se sua resposta foi sim para alguma dessas perguntas, é fato, você possui o que chamamos de *pseudoréplicas nos seus dados. O maior problema disso é que seus dados estão enganosamente** super-replicados, o que pode aumentar muito a chance de erro do tipo 1 (rejeitar uma H0 verdadeira). Uma das formas de solucionar esse problema é lançar mão da utlização de modelos mistos.
O nome modelo misto vem do fato de que além de \(y \sim x\), sendo x o que conhecemos por fator fixo (aquele que medimos e temos interesse direto), temos também o fator aleatório (que é um fator agrupador). Nosso novo modelo fica mais ou menos assim: \(y \sim x_{fixo} + x_{aleatório}\). O fator aleatório é necessariamente uma variável categórica, que, entre outras coisas, identifica a unidade de réplica verdadeira. Costumamos referir no texto que o fator aleatório é a identidade de alguma coisa. É importante todavia ter cuidado com o número de níveis dessa variável, sendo geralmente recomendado ter pelo menos 5 níveis da variável aleatória 1.
Ficará mais fácil entender essas questões com os exemplos abaixo, mas vale a pena mais uma observação aqui. Da mesma forma que tínhamos antes, podemos ter modelos mistos com distribuição Normal, chamados LMM (linear mixed models) e com todas distribuições possívels nos GLM, chamados GLMM (generalised linear mixed models). Então, as mesmas premissas que discutimos antes valem aqui!
1 Modelos Lineares Mistos (LMM)
Para esse primeiro exemplo, vamos usar um conjunto de dados que tem interesse de ver o efeito de um determinado fertilizante no crescimento da raiz de plantas. Já se sabe que o fertiilzante tem efeito em folhas, mas suspeita-se que ele pode também afetar o crescimento de raízes ao longo do tempo de vida da planta.
O experimento foi feito medindo-se o tamanho das raízes das plantas com e sem fertilizante a cada duas semanas, ao longo de 10 semanas. Veja que aqui temos o caso bem típico, a mesma planta foi medida várias vezes. Isso exemplifica exatamente o que vimos antes, as medidas na mesma planta são mais dependentes entre si. Para isso, iremos colocar a identidade da planta como fator aleatório no nosso modelo.
##Carregamento e exploração dos dados
dados <- read.table("fertilizacao.txt", h=T, stringsAsFactors = T)
summary(dados)## raiz semana planta fertilizante
## Min. : 0.800 Min. : 2 ID1 : 5 contole:30
## 1st Qu.: 2.550 1st Qu.: 4 ID10 : 5 sim :30
## Median : 4.900 Median : 6 ID11 : 5
## Mean : 5.024 Mean : 6 ID12 : 5
## 3rd Qu.: 7.350 3rd Qu.: 8 ID2 : 5
## Max. :11.100 Max. :10 ID3 : 5
## (Other):301.1 Construção e avaliação do modelo
library(lme4)## Loading required package: Matrixmod1 <- lmer(raiz~semana*fertilizante+(1|planta), data=dados)
DHARMa::simulateResiduals(mod1, plot=T)
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.216 0.316 0.984 0.84 0.8 0.7 0.308 0.372 0.164 0.2 0.5 0.156 0.596 0.376 0.388 0.944 0.896 0.852 0.424 1 ...anova(mod1, test="F") ##veja que não resolve## Warning in anova.merMod(mod1, test = "F"): additional arguments ignored: 'test'car::Anova(mod1)1.2 Plot dos resultados
Como todos os fatores foram significativos (incluindo a interação), eu posso fazer o plot direto usando a função geom_smooth.
library(ggplot2)
ggplot(dados, aes(x=semana, y=raiz, colour=fertilizante))+
geom_point()+
geom_smooth(method = "lm", se=F)+
scale_y_continuous("Tamanho da raiz (cm)")+
scale_x_continuous("Semanas de medição")## `geom_smooth()` using formula = 'y ~ x'
2 Modelos Lineares Generalizados Mistos (LMM)
Nossa pergunta agora é se o número de frutos de uma planta varia em função de estar sendo polinizada em locais só com espécies nativas, só invasoras e um mix de ambas. Agora temos dados de contagem como variável resposta (número de frutos). Isso nos pede para usar distribuição de Poisson. Vamos ver como fazer isso abaixo:
2.1 Carregar e inspecionar os dados
Para esse exemplo, vamos utilizar um conjunto de dados de plantas que foram amostradas em um experimento. Vários indivíduos foram amostrados no mesmo stand, o que pode gerar maior dependência entre as amostras. Vamos ver os dados:
dadosbees <- read.table("~/Dropbox/UFMG/Disciplinas/R UFAC/Aulas/Modelos Mistos/frutos_abelhas.txt", h=T, stringsAsFactors = T)
summary(dadosbees)## tratamento stand nfrutos tamanhofruto
## invasora :18 Min. :1.000 Min. : 1.00 Min. : 1.80
## natural :80 1st Qu.:2.000 1st Qu.: 7.00 1st Qu.:11.00
## natural_invasora:15 Median :4.000 Median :12.00 Median :24.50
## Mean :4.106 Mean :11.06 Mean :23.61
## 3rd Qu.:6.000 3rd Qu.:15.00 3rd Qu.:34.00
## Max. :7.000 Max. :21.00 Max. :50.00
## n_semente
## Min. : 1.00
## 1st Qu.: 9.10
## Median : 18.00
## Mean : 23.24
## 3rd Qu.: 28.10
## Max. :298.00head(dadosbees)Parece haver algo um pouco estranho, pois os dados dos vasos estão mostrados de forma contínua. Conversando com quem tabulou os dados, a pessoa me contou que numerou os stands de cada tratamento, transferindo assim para a tabela. Assim, os stands de natural, invasora e natural_invasora estão com número iguais, mesmo sendo stands diferentes. Eu vou ter que criar então uma variável que avisa que 1 de controle é diferente de 1 de isolada. Vou usar a função paste para isso, ela é similar à função concatenate do Excel, bem simples de usar.
dadosbees$random <- paste(dadosbees$tratamento, dadosbees$stand, sep="_")
summary(dadosbees)## tratamento stand nfrutos tamanhofruto
## invasora :18 Min. :1.000 Min. : 1.00 Min. : 1.80
## natural :80 1st Qu.:2.000 1st Qu.: 7.00 1st Qu.:11.00
## natural_invasora:15 Median :4.000 Median :12.00 Median :24.50
## Mean :4.106 Mean :11.06 Mean :23.61
## 3rd Qu.:6.000 3rd Qu.:15.00 3rd Qu.:34.00
## Max. :7.000 Max. :21.00 Max. :50.00
## n_semente random
## Min. : 1.00 Length:113
## 1st Qu.: 9.10 Class :character
## Median : 18.00 Mode :character
## Mean : 23.24
## 3rd Qu.: 28.10
## Max. :298.00O programa fez o que eu mandei, mas não o que eu queria, preciso agora forçar random para um factor.
dadosbees$random <- as.factor(dadosbees$random)
summary(dadosbees)## tratamento stand nfrutos tamanhofruto
## invasora :18 Min. :1.000 Min. : 1.00 Min. : 1.80
## natural :80 1st Qu.:2.000 1st Qu.: 7.00 1st Qu.:11.00
## natural_invasora:15 Median :4.000 Median :12.00 Median :24.50
## Mean :4.106 Mean :11.06 Mean :23.61
## 3rd Qu.:6.000 3rd Qu.:15.00 3rd Qu.:34.00
## Max. :7.000 Max. :21.00 Max. :50.00
##
## n_semente random
## Min. : 1.00 natural_5:14
## 1st Qu.: 9.10 natural_6:13
## Median : 18.00 natural_7:13
## Mean : 23.24 natural_3:11
## 3rd Qu.: 28.10 natural_4:11
## Max. :298.00 natural_2:10
## (Other) :41Funcionou! Posso fazer meu modelo.
2.2 Construção e avaliação do modelo
Antes usamos a função lmer, mas agora é um glm(er), então faremos a função glmer.
modelobee <- glmer(nfrutos~tratamento+(1|random), data=dadosbees, poisson)
DHARMa::simulateResiduals(modelobee, plot = T)
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.2342983 0.7515485 0.008623837 0.6840711 0.8231408 0.420344 0.4269619 0.3734597 0.105038 0.09447618 0.007371104 0.2707554 0.2023603 0.2141922 0.2600575 0.315343 0.4281296 0.4595107 0.8639486 0.8387564 ...Como o modelo está ok, posso partir para testá-lo. Isso é feito da mesma forma que fiz anteriormente. Para modelos mistos, via de regra usaremos o car::Anova.
car::Anova(modelobee)O modelo é singificativo, o que quer dizer que pelo menos um dos três níveis é diferente dos demais. Então não posso parar por aqui, preciso seguir verificando o modelo, e farei isso juntando níveis mais próximos e testando contrastes de modelos.
with(dadosbees, tapply(nfrutos, tratamento, mean))## invasora natural natural_invasora
## 5.555556 13.325000 5.600000tratamento2 <- dadosbees$tratamento
levels(tratamento2)## [1] "invasora" "natural" "natural_invasora"levels(tratamento2)[c(1,3)] <- "tem_invasora"
modelobee2 <- glmer(nfrutos~tratamento2+(1|random), data=dadosbees, poisson)
anova(modelobee, modelobee2)car::Anova(modelobee2)Temos então que os níveis que possuem espécies invasoras são diferentes do controle. Vamos aprender a fazer um gráfico com barras.
medias <- Rmisc::summarySE(dadosbees, measurevar = "nfrutos", groupvars = "tratamento")
ggplot(dadosbees, aes(x=tratamento, y=nfrutos, fill=tratamento))+
geom_bar(data = medias, aes(x=tratamento, y=nfrutos), stat="identity", alpha=.5)+
geom_errorbar(data = medias, aes(ymin=nfrutos-se, ymax=nfrutos+se), width=.5)+
geom_jitter(width=.3)+
scale_y_continuous("Número de frutos")+
scale_x_discrete("Tratamento")+
scale_fill_manual(values = c("red", "green", "red"))+
theme(legend.position = "null")
3 Seleção de modelos
A seleção de modelos consiste em uma lógica diferente de avaliar os nossos dados.
3.1 Carregamento dos dados
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.6-4library(MuMIn)dados <- read.table("~/Dropbox/UFMG/Disciplinas/R UFAC/Aulas/Modelos Mistos/dados_formigas.txt", h=T, stringsAsFactors = T)
summary(dados)## LU_FT_Code Trees PrimFor_500 MF_FCCP
## PA :41 Min. : 0.00 Min. : 0.00 Min. :-0.66954
## PFlogbur:39 1st Qu.: 1.25 1st Qu.: 6.13 1st Qu.:-0.06740
## PFlog :38 Median : 66.00 Median : 39.45 Median : 0.00000
## MA :12 Mean : 60.78 Mean : 45.22 Mean :-0.02028
## REF :11 3rd Qu.:112.00 3rd Qu.: 82.72 3rd Qu.: 0.03078
## SFinter :11 Max. :141.00 Max. :100.00 Max. : 0.45648
## (Other) :18
## Canopy Duff CWD_AGB FWD_AGB
## Min. :0.0000 Min. :0.0000 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0000 1st Qu.:0.4445 1st Qu.: 0.1958 1st Qu.: 0.5322
## Median :0.7621 Median :1.1590 Median : 15.6528 Median : 2.7266
## Mean :0.5224 Mean :1.4363 Mean : 25.6912 Mean : 3.4921
## 3rd Qu.:0.8651 3rd Qu.:2.1750 3rd Qu.: 43.3250 3rd Qu.: 5.7088
## Max. :0.9520 Max. :5.8960 Max. :132.2479 Max. :14.4978
##
## Litter_AGB SoilDens ant_sp_rich
## Min. :0.000 Min. : 83.35 Min. :21.00
## 1st Qu.:1.050 1st Qu.:108.10 1st Qu.:34.00
## Median :2.586 Median :126.89 Median :39.50
## Mean :2.427 Mean :125.34 Mean :39.32
## 3rd Qu.:3.549 3rd Qu.:140.40 3rd Qu.:45.00
## Max. :6.307 Max. :189.19 Max. :63.00
## 3.2 Construção e avaliação dos modelos
m1 <- glm(ant_sp_rich~LU_FT_Code+Trees+PrimFor_500+MF_FCCP+Canopy+Duff+CWD_AGB+FWD_AGB+Litter_AGB+SoilDens, data=dados, poisson)
DHARMa::simulateResiduals(m1, plot=T)
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.8409394 0.1531522 0.3261575 0.3848361 0.5123564 0.257287 0.4092167 0.1069182 0.1568893 0.3697526 0.1482651 0.4993616 0.2531791 0.8882501 0.5391196 0.09694753 0.7332633 0.2827136 0.4978872 0.04214996 ...3.3 Seleção dos modelos
options(na.action = "na.fail")
selec <- dredge(m1, extra = "R^2")## Fixed term is "(Intercept)"beepr::beep(9)
head(selec)averagemod <- model.avg(selec)
summary(averagemod)##
## Call:
## model.avg(object = selec)
##
## Component model call:
## glm(formula = ant_sp_rich ~ <1024 unique rhs>, family = poisson, data =
## dados)
##
## Component models:
## df logLik AICc delta weight
## 3+4+5+8 5 -552.89 1116.15 0.00 0.03
## 3+5+8 4 -554.14 1116.51 0.37 0.02
## 2+3+5+8 5 -553.42 1117.20 1.05 0.02
## 3+5+6 13 -544.44 1117.22 1.07 0.02
## 2+3+5+6 14 -543.38 1117.47 1.32 0.01
## 3+5+6+8 14 -543.47 1117.65 1.50 0.01
## 2+3+4+5+8 6 -552.58 1117.67 1.53 0.01
## 5+6 12 -545.90 1117.79 1.65 0.01
## 3+6 12 -545.98 1117.95 1.81 0.01
## 6 11 -547.22 1118.12 1.97 0.01
## 2+5+6 13 -544.90 1118.13 1.99 0.01
## 3+5+8+10 5 -553.90 1118.17 2.02 0.01
## 3+4+5+8+10 6 -552.86 1118.24 2.10 0.01
## 3+4+5+7+8 6 -552.88 1118.27 2.12 0.01
## 3+4+5+8+9 6 -552.88 1118.27 2.13 0.01
## 1+3+4+5+8 6 -552.89 1118.29 2.15 0.01
## 1+3+5+8 5 -554.00 1118.36 2.21 0.01
## 2+3+6 13 -545.05 1118.43 2.29 0.01
## 3+5+7+8 5 -554.12 1118.61 2.46 0.01
## 3+5+8+9 5 -554.13 1118.63 2.49 0.01
## 2+6 12 -546.33 1118.66 2.51 0.01
## 5+6+8 13 -545.19 1118.70 2.56 0.01
## 2+3+5+6+8 15 -542.80 1118.71 2.56 0.01
## 3+6+8 13 -545.26 1118.86 2.72 0.01
## 3+4+5+6 14 -544.12 1118.95 2.81 0.01
## 4+5+8 4 -555.44 1119.12 2.97 0.01
## 2+3+5+8+10 6 -553.32 1119.15 3.00 0.01
## 3+5+6+9 14 -544.24 1119.19 3.04 0.01
## 2+3+5+6+9 15 -543.05 1119.21 3.06 0.01
## 1+2+3+5+8 6 -553.35 1119.21 3.07 0.01
## 3+5+6+10 14 -544.28 1119.26 3.11 0.01
## 2+3+5+7+8 6 -553.40 1119.32 3.17 0.01
## 2+3+5+8+9 6 -553.42 1119.35 3.20 0.01
## 6+8 12 -546.69 1119.36 3.22 0.01
## 3+4+5+6+8 15 -543.19 1119.50 3.35 0.00
## 5+6+10 13 -545.62 1119.57 3.42 0.00
## 1+3+5+6 14 -544.44 1119.59 3.44 0.00
## 3+5+6+7 14 -544.44 1119.59 3.44 0.00
## 4+5+6 13 -545.64 1119.62 3.48 0.00
## 2+3+4+5+6 15 -543.26 1119.64 3.50 0.00
## 3+5+6+8+9 15 -543.27 1119.65 3.50 0.00
## 2+5+6+8 14 -544.51 1119.72 3.58 0.00
## 2+3+5+6+10 15 -543.30 1119.73 3.58 0.00
## 6+10 12 -546.88 1119.74 3.59 0.00
## 2+3+4+5+7+8 7 -552.57 1119.82 3.68 0.00
## 5+8 3 -556.84 1119.82 3.68 0.00
## 2+3+4+5+8+9 7 -552.57 1119.83 3.68 0.00
## 2+3+4+5+8+10 7 -552.57 1119.84 3.69 0.00
## 3+6+10 13 -545.75 1119.84 3.70 0.00
## 1+2+3+4+5+8 7 -552.58 1119.85 3.70 0.00
## 1+2+3+5+6 15 -543.38 1119.88 3.73 0.00
## 2+3+5+6+7 15 -543.38 1119.88 3.73 0.00
## 3+5+6+8+10 15 -543.39 1119.89 3.74 0.00
## 1+5+6 13 -545.83 1120.00 3.85 0.00
## 3+4+6 13 -545.84 1120.00 3.86 0.00
## 2+3+6+8 14 -544.65 1120.01 3.86 0.00
## 1+6 12 -547.01 1120.01 3.87 0.00
## 3+5+6+7+8 15 -543.46 1120.03 3.89 0.00
## 5+6+9 13 -545.85 1120.04 3.89 0.00
## 1+3+5+6+8 15 -543.46 1120.04 3.90 0.00
## 2+3+5+10 5 -554.85 1120.06 3.91 0.00
## 1+3+6 13 -545.90 1120.13 3.99 0.00
## 3+6+9 13 -545.90 1120.14 3.99 0.00
## 5+6+7 13 -545.90 1120.14 3.99 0.00
## 2+5+6+10 14 -544.73 1120.18 4.03 0.00
## 4+6 12 -547.11 1120.20 4.06 0.00
## 5+8+10 4 -556.00 1120.25 4.10 0.00
## 2+5+6+9 14 -544.78 1120.28 4.13 0.00
## 3+5+8+9+10 6 -553.88 1120.28 4.13 0.00
## 3+6+7 13 -545.98 1120.29 4.15 0.00
## 1+3+5+8+10 6 -553.90 1120.31 4.16 0.00
## 3+5+7+8+10 6 -553.90 1120.32 4.17 0.00
## 2+4+5+6 14 -544.82 1120.34 4.19 0.00
## 3+4+5+8+9+10 7 -552.84 1120.36 4.22 0.00
## 3+4+5+10 5 -555.00 1120.37 4.22 0.00
## 1+3+4+5+8+10 7 -552.86 1120.40 4.26 0.00
## 3+4+5+7+8+10 7 -552.86 1120.41 4.26 0.00
## 3+4+5+7+8+9 7 -552.86 1120.41 4.27 0.00
## 6+9 12 -547.22 1120.42 4.27 0.00
## 1+5+8 4 -556.09 1120.43 4.28 0.00
## 6+7 12 -547.22 1120.43 4.29 0.00
## 1+2+5+6 14 -544.86 1120.44 4.29 0.00
## 1+3+4+5+7+8 7 -552.88 1120.44 4.30 0.00
## 1+3+4+5+8+9 7 -552.88 1120.44 4.30 0.00
## 2+6+8 13 -546.06 1120.46 4.32 0.00
## 2+5+8 4 -556.11 1120.47 4.33 0.00
## 2+3+6+9 14 -544.90 1120.50 4.35 0.00
## 2+5+6+7 14 -544.90 1120.50 4.36 0.00
## 1+3+5+8+9 6 -554.00 1120.51 4.36 0.00
## 1+3+5+7+8 6 -554.00 1120.51 4.36 0.00
## 2+3+5+6+8+9 16 -542.49 1120.53 4.38 0.00
## 4+5+8+10 5 -555.09 1120.55 4.40 0.00
## 2+6+10 13 -546.11 1120.56 4.41 0.00
## 2+3+6+10 14 -544.93 1120.56 4.41 0.00
## 2+3+4+5 5 -555.11 1120.58 4.44 0.00
## 2+3+4+5+10 6 -554.06 1120.63 4.48 0.00
## 4+5+6+8 14 -544.96 1120.64 4.49 0.00
## 2+4+5+8 5 -555.15 1120.66 4.51 0.00
## 5+6+8+10 14 -544.99 1120.69 4.55 0.00
## 1+4+5+8 5 -555.16 1120.69 4.55 0.00
## 1+2+6 13 -546.18 1120.69 4.55 0.00
## 1+2+3+6 14 -545.00 1120.72 4.57 0.00
## 2+3+4+6 14 -545.02 1120.76 4.61 0.00
## 3+5+7+8+9 6 -554.12 1120.76 4.61 0.00
## 2+3+6+7 14 -545.05 1120.81 4.67 0.00
## 4+5+8+9 5 -555.24 1120.85 4.71 0.00
## 2+3+4+5+6+8 16 -542.67 1120.90 4.75 0.00
## 2+6+9 13 -546.30 1120.93 4.78 0.00
## 3+6+8+10 14 -545.12 1120.95 4.80 0.00
## 3+4+5+6+9 15 -543.92 1120.96 4.81 0.00
## 2+4+6 13 -546.32 1120.97 4.82 0.00
## 5+6+8+9 14 -545.14 1120.99 4.84 0.00
## 3+5+10 4 -556.38 1121.00 4.85 0.00
## 2+6+7 13 -546.33 1121.00 4.86 0.00
## 3+4+6+8 14 -545.15 1121.01 4.86 0.00
## 1+5+6+8 14 -545.15 1121.02 4.87 0.00
## 5+6+7+8 14 -545.17 1121.06 4.91 0.00
## 3+4+5+6+10 15 -543.97 1121.06 4.92 0.00
## 2+3+5+6+8+10 16 -542.76 1121.07 4.92 0.00
## 3+6+8+9 14 -545.19 1121.09 4.94 0.00
## 2+3+5+6+7+8 16 -542.78 1121.12 4.98 0.00
## 1+2+3+5+6+8 16 -542.78 1121.12 4.98 0.00
## 4+5+7+8 5 -555.39 1121.14 4.99 0.00
## 1+3+6+8 14 -545.22 1121.15 5.01 0.00
## 6+8+10 13 -546.42 1121.18 5.04 0.00
## 3+6+7+8 14 -545.26 1121.23 5.08 0.00
## 2+3+5+8+9+10 7 -553.31 1121.30 5.16 0.00
## 1+2+3+5+8+10 7 -553.31 1121.32 5.17 0.00
## 2+3+5+7+8+10 7 -553.31 1121.32 5.17 0.00
## 1+3+4+5+6 15 -544.12 1121.36 5.21 0.00
## 3+4+5+6+7 15 -544.12 1121.36 5.21 0.00
## 1+2+3+5+7+8 7 -553.35 1121.38 5.24 0.00
## 1+2+3+5+8+9 7 -553.35 1121.38 5.24 0.00
## 3+5+6+9+10 15 -544.14 1121.39 5.25 0.00
## 1+6+8 13 -546.53 1121.39 5.25 0.00
## 2+3+4+5+6+9 16 -542.94 1121.44 5.29 0.00
## 1+2+5+8 5 -555.54 1121.44 5.30 0.00
## 2+5+8+10 5 -555.54 1121.44 5.30 0.00
## 4+5+6+10 14 -545.38 1121.47 5.32 0.00
## 2+3+5+7+8+9 7 -553.40 1121.49 5.35 0.00
## 4+6+8 13 -546.60 1121.53 5.39 0.00
## 3+4+5+6+8+9 16 -542.99 1121.54 5.39 0.00
## 3+5+6+7+9 15 -544.23 1121.58 5.44 0.00
## 1+2+3+5 5 -555.61 1121.58 5.44 0.00
## 1+3+5+6+10 15 -544.23 1121.58 5.44 0.00
## 1+3+5+6+9 15 -544.24 1121.59 5.45 0.00
## 2+3+5+6+9+10 16 -543.03 1121.61 5.46 0.00
## 1+2+3+5+6+9 16 -543.04 1121.63 5.48 0.00
## 2+3+5+6+7+9 16 -543.04 1121.64 5.50 0.00
## 3+5+6+7+10 15 -544.27 1121.65 5.51 0.00
## 6+7+8 13 -546.68 1121.70 5.55 0.00
## 6+8+9 13 -546.68 1121.70 5.55 0.00
## 5+8+9+10 5 -555.67 1121.70 5.56 0.00
## 5+8+9 4 -556.76 1121.75 5.61 0.00
## 3+4+5+6+8+10 16 -543.12 1121.79 5.65 0.00
## 1+2+3+4+5 6 -554.64 1121.79 5.65 0.00
## 5+7+8 4 -556.79 1121.81 5.67 0.00
## 4+6+10 13 -546.77 1121.87 5.72 0.00
## 2+5+6+8+10 15 -544.38 1121.88 5.73 0.00
## 2+3+5 4 -556.83 1121.90 5.75 0.00
## 1+4+5+6 14 -545.59 1121.90 5.75 0.00
## 4+5+6+9 14 -545.60 1121.90 5.76 0.00
## 3+4+5+6+7+8 16 -543.17 1121.91 5.76 0.00
## 1+3+4+5+6+8 16 -543.18 1121.91 5.77 0.00
## 5+6+9+10 14 -545.60 1121.91 5.77 0.00
## 4+5+8+9+10 6 -554.71 1121.93 5.79 0.00
## 2+3+4+5+6+10 16 -543.19 1121.93 5.79 0.00
## 5+6+7+10 14 -545.61 1121.93 5.79 0.00
## 2+5+6+8+9 15 -544.41 1121.94 5.79 0.00
## 1+5+6+10 14 -545.62 1121.94 5.80 0.00
## 3+4+6+10 14 -545.62 1121.95 5.80 0.00
## 2+4+5+6+8 15 -544.42 1121.96 5.81 0.00
## 1+3+5+6+7 15 -544.44 1121.99 5.84 0.00
## 4+5+6+7 14 -545.64 1122.00 5.85 0.00
## 2+3+4+5+7+8+9 8 -552.55 1122.00 5.85 0.00
## 2+3+4+5+8+9+10 8 -552.55 1122.00 5.85 0.00
## 3+5+6+8+9+10 16 -543.23 1122.01 5.87 0.00
## 2+3+4+5+7+8+10 8 -552.56 1122.02 5.87 0.00
## 1+2+3+4+5+7+8 8 -552.57 1122.02 5.88 0.00
## 1+2+3+4+5+8+10 8 -552.57 1122.03 5.88 0.00
## 1+2+3+4+5+8+9 8 -552.57 1122.03 5.89 0.00
## 1+6+10 13 -546.85 1122.04 5.89 0.00
## 1+3+5+6+8+9 16 -543.25 1122.06 5.92 0.00
## 6+7+10 13 -546.86 1122.06 5.92 0.00
## 1+2+3+5+6+10 16 -543.26 1122.07 5.93 0.00
## 1+2+3+4+5+6 16 -543.26 1122.07 5.93 0.00
## 2+3+4+5+6+7 16 -543.26 1122.08 5.93 0.00
## 3+5+6+7+8+9 16 -543.26 1122.08 5.94 0.00
## 6+9+10 13 -546.88 1122.08 5.94 0.00
## 1+2+5+6+8 15 -544.49 1122.10 5.95 0.00
## 2+5+6+7+8 15 -544.49 1122.10 5.95 0.00
## 2+3+5+9+10 6 -554.81 1122.13 5.99 0.00
## 2+3+6+8+9 15 -544.51 1122.14 6.00 0.00
## 3+6+9+10 14 -545.72 1122.15 6.00 0.00
## 2+3+5+7+10 6 -554.82 1122.15 6.01 0.00
## 1+4+6 13 -546.91 1122.16 6.01 0.00
## 2+3+5+6+7+10 16 -543.30 1122.16 6.02 0.00
## 1+5+8+10 5 -555.91 1122.18 6.03 0.00
## 1+3+5+6+8+10 16 -543.31 1122.19 6.04 0.00
## 1+5+8+9 5 -555.91 1122.19 6.05 0.00
## 3+6+7+10 14 -545.74 1122.19 6.05 0.00
## 1+2+3+5+10 6 -554.84 1122.20 6.06 0.00
## 1+3+6+10 14 -545.75 1122.22 6.07 0.00
## 3+4+6+9 14 -545.76 1122.23 6.08 0.00
## 2+3+6+8+10 15 -544.56 1122.24 6.09 0.00
## 1+3+4+6 14 -545.76 1122.24 6.09 0.00
## 2+4+5+8+10 6 -554.87 1122.26 6.11 0.00
## 1+5+6+9 14 -545.79 1122.29 6.14 0.00
## 1+2+3+5+6+7 16 -543.38 1122.31 6.17 0.00
## 1+2+4+5+8 6 -554.90 1122.31 6.17 0.00
## 3+5+6+7+8+10 16 -543.38 1122.32 6.17 0.00
## 3+4+5+9+10 6 -554.91 1122.33 6.18 0.00
## 1+4+5+8+9 6 -554.91 1122.33 6.19 0.00
## 1+6+7 13 -547.00 1122.34 6.19 0.00
## 1+6+9 13 -547.01 1122.35 6.20 0.00
## 2+3+4+6+8 15 -544.62 1122.36 6.21 0.00
## 1+2+3+6+8 15 -544.62 1122.36 6.21 0.00
## 1+3+6+9 14 -545.83 1122.37 6.22 0.00
## 1+5+6+7 14 -545.83 1122.37 6.22 0.00
## 5+7+8+10 5 -556.00 1122.37 6.23 0.00
## 3+4+6+7 14 -545.83 1122.38 6.23 0.00
## 2+5+8+9 5 -556.01 1122.39 6.25 0.00
## 2+3+6+7+8 15 -544.64 1122.40 6.26 0.00
## 5+6+7+9 14 -545.85 1122.41 6.26 0.00
## 2+4+5+6+10 15 -544.65 1122.42 6.27 0.00
## 2+4+5+8+9 6 -554.95 1122.42 6.28 0.00
## 3+4+5+7+10 6 -554.97 1122.45 6.30 0.00
## 1+3+5+8+9+10 7 -553.88 1122.45 6.30 0.00
## 2+5+6+9+10 15 -544.67 1122.45 6.30 0.00
## 3+5+7+8+9+10 7 -553.88 1122.45 6.31 0.00
## 1+3+5+6+7+8 16 -543.45 1122.46 6.31 0.00
## 2+6+8+10 14 -545.88 1122.46 6.32 0.00
## 1+3+5+7+8+10 7 -553.90 1122.49 6.34 0.00
## 1+3+6+7 14 -545.89 1122.49 6.34 0.00
## 2+5+7+8 5 -556.06 1122.49 6.34 0.00
## 1+3+4+5+10 6 -554.99 1122.49 6.34 0.00
## 2+5+10 4 -557.13 1122.50 6.35 0.00
## 3+6+7+9 14 -545.89 1122.50 6.35 0.00
## 2+4+5+6+9 15 -544.71 1122.53 6.38 0.00
## 4+6+9 13 -547.10 1122.53 6.39 0.00
## 1+3+4+5+8+9+10 8 -552.83 1122.54 6.40 0.00
## 4+6+7 13 -547.11 1122.55 6.40 0.00
## 3+4+5+7+8+9+10 8 -552.83 1122.55 6.40 0.00
## 1+5+7+8 5 -556.09 1122.55 6.40 0.00
## 1+2+5+6+10 15 -544.73 1122.58 6.43 0.00
## 1+3+4+5+7+8+10 8 -552.84 1122.58 6.44 0.00
## 2+5+6+7+10 15 -544.73 1122.58 6.44 0.00
## 1+2+6+8 14 -545.94 1122.59 6.44 0.00
## 1+3+4+5+7+8+9 8 -552.86 1122.62 6.47 0.00
## 1+2+5+6+9 15 -544.76 1122.63 6.49 0.00
## 1+4+5+8+10 6 -555.06 1122.64 6.49 0.00
## 2+3+4+5+9 6 -555.08 1122.67 6.52 0.00
## 1+3+5+7+8+9 7 -553.99 1122.68 6.53 0.00
## 4+5+7+8+10 6 -555.08 1122.68 6.53 0.00
## 4+5+6+8+10 15 -544.78 1122.68 6.54 0.00
## 2+5+6+7+9 15 -544.78 1122.68 6.54 0.00
## 1+2+4+5+6 15 -544.79 1122.69 6.55 0.00
## 2+3+4+5+9+10 7 -554.00 1122.70 6.55 0.00
## 2+4+5+7+8 6 -555.10 1122.71 6.56 0.00
## 2+3+4+5+7 6 -555.11 1122.73 6.58 0.00
## 4+5+10 4 -557.25 1122.74 6.59 0.00
## 2+4+5+6+7 15 -544.81 1122.74 6.59 0.00
## 2+3+6+9+10 15 -544.82 1122.76 6.61 0.00
## 6+7+9 13 -547.21 1122.76 6.62 0.00
## 2+3+4+5+6+8+9 17 -542.38 1122.78 6.63 0.00
## 2+3+4+5+7+10 7 -554.04 1122.78 6.63 0.00
## 2+6+8+9 14 -546.04 1122.79 6.64 0.00
## 1+2+3+4+5+10 7 -554.05 1122.79 6.65 0.00
## 2+4+6+8 14 -546.05 1122.81 6.66 0.00
## 2+6+7+8 14 -546.06 1122.82 6.68 0.00
## 1+4+5+7+8 6 -555.16 1122.83 6.68 0.00
## 1+2+3+6+9 15 -544.86 1122.84 6.69 0.00
## 1+2+5+6+7 15 -544.86 1122.85 6.70 0.00
## 4+5+7+8+9 6 -555.17 1122.85 6.71 0.00
## 2+3+4+6+9 15 -544.87 1122.86 6.72 0.00
## 1+2+6+10 14 -546.09 1122.88 6.74 0.00
## 2+4+6+10 14 -546.09 1122.90 6.75 0.00
## 2+3+6+7+9 15 -544.89 1122.90 6.76 0.00
## 2+3+4+6+10 15 -544.90 1122.91 6.77 0.00
## 2+6+9+10 14 -546.10 1122.91 6.77 0.00
## 2+6+7+10 14 -546.11 1122.93 6.78 0.00
## 1+2+3+5+6+8+9 17 -542.46 1122.94 6.79 0.00
## 3+5+7+10 5 -556.30 1122.96 6.81 0.00
## 3+5+9+10 5 -556.30 1122.96 6.81 0.00
## 4+5+6+8+9 15 -544.92 1122.96 6.81 0.00
## 2+5+8+9+10 6 -555.22 1122.96 6.81 0.00
## 2+3+6+7+10 15 -544.92 1122.96 6.82 0.00
## 1+2+3+6+10 15 -544.92 1122.97 6.82 0.00
## 2+3+5+6+8+9+10 17 -542.48 1122.99 6.84 0.00
## 2+3+5+6+7+8+9 17 -542.48 1122.99 6.85 0.00
## 1+4+5+6+8 15 -544.94 1123.01 6.86 0.00
## 4+5+6+7+8 15 -544.95 1123.01 6.86 0.00
## 1+2+6+9 14 -546.15 1123.02 6.87 0.00
## 1+3+4+5 5 -556.32 1123.02 6.87 0.00
## 1+2+4+6 14 -546.17 1123.05 6.90 0.00
## 1+2+6+7 14 -546.18 1123.07 6.92 0.00
## 5+6+8+9+10 15 -544.98 1123.07 6.92 0.00
## 1+2+3+4+6 15 -544.98 1123.07 6.93 0.00
## 1+5+6+8+10 15 -544.98 1123.09 6.94 0.00
## 5+6+7+8+10 15 -544.99 1123.10 6.95 0.00
## 5+10 3 -558.48 1123.10 6.95 0.00
## 1+3+5+10 5 -556.38 1123.12 6.97 0.00
## 1+2+3+6+7 15 -545.00 1123.12 6.97 0.00
## 3+4+6+8+10 15 -545.01 1123.14 6.99 0.00
## 2+3+4+6+7 15 -545.02 1123.16 7.02 0.00
## 2+4+5+10 5 -556.42 1123.21 7.06 0.00
## 1+2+5+8+9 6 -555.35 1123.21 7.06 0.00
## 3+4+5+6+9+10 16 -543.83 1123.22 7.08 0.00
## 3+4+6+8+9 15 -545.08 1123.28 7.13 0.00
## 3+6+8+9+10 15 -545.08 1123.28 7.13 0.00
## 2+4+6+9 14 -546.28 1123.28 7.13 0.00
## 2+3+4+5+6+8+10 17 -542.63 1123.29 7.15 0.00
## 2+6+7+9 14 -546.30 1123.31 7.16 0.00
## 1+2+3+4+5+6+8 17 -542.65 1123.33 7.19 0.00
## 2+3+4+5+6+7+8 17 -542.65 1123.33 7.19 0.00
## 2+4+6+7 14 -546.32 1123.34 7.20 0.00
## 1+5+6+8+9 15 -545.11 1123.35 7.20 0.00
## 1+3+4+6+8 15 -545.11 1123.35 7.20 0.00
## 1+3+6+8+10 15 -545.12 1123.36 7.21 0.00
## 3+6+7+8+10 15 -545.12 1123.36 7.21 0.00
## 3+4+5 4 -557.56 1123.37 7.23 0.00
## 1+3+4+5+6+10 16 -543.91 1123.38 7.23 0.00
## 5+6+7+8+9 15 -545.13 1123.38 7.24 0.00
## 1+2+5+8+10 6 -555.43 1123.38 7.24 0.00
## 4+6+8+10 14 -546.34 1123.39 7.24 0.00
## 3+4+5+6+7+9 16 -543.92 1123.39 7.25 0.00
## 1+3+4+5+6+9 16 -543.92 1123.40 7.25 0.00
## 1+2+3+5+6+8+10 17 -542.69 1123.40 7.25 0.00
## 3+4+6+7+8 15 -545.14 1123.40 7.26 0.00
## 1+5+6+7+8 15 -545.15 1123.41 7.27 0.00
## 1+3+6+8+9 15 -545.16 1123.43 7.28 0.00
## 3+6+7+8+9 15 -545.19 1123.49 7.35 0.00
## 3+4+5+6+7+10 16 -543.97 1123.50 7.35 0.00
## 1+2+3+5+8+9+10 8 -553.30 1123.50 7.35 0.00
## 2+3+5+7+8+9+10 8 -553.30 1123.50 7.35 0.00
## 1+2+3+5+7+8+10 8 -553.31 1123.52 7.37 0.00
## 2+3+5+6+7+8+10 17 -542.75 1123.52 7.38 0.00
## 1+6+8+10 14 -546.41 1123.52 7.38 0.00
## 1+2+3+5+6+7+8 17 -542.76 1123.56 7.41 0.00
## 6+8+9+10 14 -546.42 1123.56 7.41 0.00
## 6+7+8+10 14 -546.42 1123.56 7.41 0.00
## 1+3+6+7+8 15 -545.22 1123.56 7.41 0.00
## 1+2+3+5+7+8+9 8 -553.34 1123.58 7.44 0.00
## 2+5+7+8+10 6 -555.54 1123.59 7.44 0.00
## 1+2+5+7+8 6 -555.54 1123.59 7.44 0.00
## 1+4+6+8 14 -546.45 1123.61 7.47 0.00
## 1+2+3+5+7 6 -555.56 1123.64 7.50 0.00
## 2+3+5+9 5 -556.66 1123.69 7.55 0.00
## 1+5+8+9+10 6 -555.60 1123.71 7.56 0.00
## 2+4+5+8+9+10 7 -554.51 1123.71 7.56 0.00
## 1+2+3+5+9 6 -555.60 1123.72 7.57 0.00
## 4+5+9+10 5 -556.68 1123.73 7.58 0.00
## 5+7+8+9 5 -556.69 1123.74 7.59 0.00
## 1+3+5+6+9+10 16 -544.10 1123.75 7.61 0.00
## 2+5+9+10 5 -556.70 1123.76 7.62 0.00
## 1+6+8+9 14 -546.53 1123.77 7.62 0.00
## 1+6+7+8 14 -546.53 1123.77 7.62 0.00
## 1+3+4+5+6+7 16 -544.12 1123.80 7.65 0.00
## 3+5+6+7+9+10 16 -544.12 1123.80 7.66 0.00
## 4+5+6+9+10 15 -545.36 1123.84 7.70 0.00
## 5+7+8+9+10 6 -555.67 1123.85 7.70 0.00
## 1+4+5+6+10 15 -545.37 1123.86 7.72 0.00
## 1+3+8 4 -557.81 1123.86 7.72 0.00
## 2+3+4+5+6+9+10 17 -542.92 1123.87 7.72 0.00
## 4+5+6+7+10 15 -545.38 1123.87 7.72 0.00
## 1+2+3+4+5+6+9 17 -542.93 1123.88 7.73 0.00
## 4+6+7+8 14 -546.59 1123.89 7.74 0.00
## 4+6+8+9 14 -546.60 1123.90 7.75 0.00
## 2+3+4+5+6+7+9 17 -542.94 1123.91 7.76 0.00
## 1+2+3+4+5+7 7 -554.62 1123.94 7.79 0.00
## 3+4+5+6+8+9+10 17 -542.96 1123.95 7.80 0.00
## 1+3+4+5+6+8+9 17 -542.97 1123.96 7.81 0.00
## 1+2+3+4+5+9 7 -554.64 1123.97 7.82 0.00
## 1+2+4+5+8+9 7 -554.65 1123.98 7.84 0.00
## 1+2+3+5+6+9+10 17 -542.98 1123.99 7.84 0.00
## 3+4+5+6+7+8+9 17 -542.98 1123.99 7.85 0.00
## 1+3+5+6+7+10 16 -544.23 1124.01 7.87 0.00
## 2+3+5+7 5 -556.83 1124.02 7.87 0.00
## 1+3+5+6+7+9 16 -544.23 1124.02 7.87 0.00
## 1+8 3 -558.96 1124.06 7.91 0.00
## 6+7+8+9 14 -546.68 1124.07 7.92 0.00
## 2+3+5+6+7+9+10 17 -543.02 1124.07 7.93 0.00
## 1+3+4+5+6+8+10 17 -543.02 1124.07 7.93 0.00
## 1+4+5+8+9+10 7 -554.69 1124.08 7.93 0.00
## 4+5+7+8+9+10 7 -554.69 1124.08 7.93 0.00
## 1+2+3+5+6+7+9 17 -543.04 1124.10 7.95 0.00
## 1+3+4+8 5 -556.89 1124.14 7.99 0.00
## 2+4+5+6+8+10 16 -544.29 1124.14 8.00 0.00
## 5+9+10 4 -557.96 1124.17 8.02 0.00
## 2+5+6+8+9+10 16 -544.32 1124.20 8.05 0.00
## 2+3+4+5+7+8+9+10 9 -552.54 1124.21 8.06 0.00
## 1+4+6+10 14 -546.75 1124.21 8.06 0.00
## 2+4+5+6+8+9 16 -544.33 1124.21 8.07 0.00
## 1+2+3+4+5+8+9+10 9 -552.55 1124.22 8.07 0.00
## 1+4+5+6+9 15 -545.55 1124.22 8.08 0.00
## 1+2+3+4+5+7+8+9 9 -552.55 1124.23 8.08 0.00
## 1+2+3+4+5+7+8+10 9 -552.55 1124.23 8.09 0.00
## 4+6+7+10 14 -546.76 1124.23 8.09 0.00
## 4+6+9+10 14 -546.77 1124.25 8.10 0.00
## 3+4+5+6+7+8+10 17 -543.11 1124.25 8.10 0.00
## 2+3+5+7+9+10 7 -554.79 1124.26 8.12 0.00
## 3+4+6+9+10 15 -545.58 1124.28 8.14 0.00
## 1+2+3+4+5+6+10 17 -543.13 1124.29 8.14 0.00
## 1+2+3+5+9+10 7 -554.81 1124.30 8.16 0.00
## 1+2+5+6+8+10 16 -544.37 1124.30 8.16 0.00
## 1+4+5+6+7 15 -545.59 1124.31 8.16 0.00
## 2+5+6+7+8+10 16 -544.38 1124.31 8.16 0.00
## 5+6+7+9+10 15 -545.59 1124.31 8.16 0.00
## 4+5+6+7+9 15 -545.60 1124.31 8.16 0.00
## 1+2+3+5+7+10 7 -554.81 1124.31 8.17 0.00
## 1+5+6+9+10 15 -545.60 1124.32 8.17 0.00
## 1+5+7+8+10 6 -555.90 1124.32 8.18 0.00
## 3+4+6+7+10 15 -545.61 1124.34 8.19 0.00
## 1+3+4+5+6+7+8 17 -543.16 1124.34 8.19 0.00
## 1+5+6+7+10 15 -545.61 1124.34 8.19 0.00
## 1+5+7+8+9 6 -555.91 1124.34 8.19 0.00
## 1+3+5+6+8+9+10 17 -543.16 1124.35 8.20 0.00
## 1+2+5+6+8+9 16 -544.40 1124.35 8.21 0.00
## 1+3+4+6+10 15 -545.62 1124.35 8.21 0.00
## 2+4+5+6+7+8 16 -544.40 1124.35 8.21 0.00
## 2+5+6+7+8+9 16 -544.40 1124.35 8.21 0.00
## 1+2+4+5+8+10 7 -554.83 1124.36 8.21 0.00
## 1+2+4+5+6+8 16 -544.41 1124.37 8.22 0.00
## 1+6+7+10 14 -546.84 1124.38 8.24 0.00
## 2+4+5+9+10 6 -555.94 1124.39 8.24 0.00
## 2+5+7+8+9 6 -555.94 1124.40 8.26 0.00
## 2+3+4+5+6+7+10 17 -543.19 1124.40 8.26 0.00
## 1+2+5+10 5 -557.02 1124.41 8.27 0.00
## 1+6+9+10 14 -546.85 1124.41 8.27 0.00
## 2+4+5+7+8+10 7 -554.86 1124.41 8.27 0.00
## 6+7+9+10 14 -546.86 1124.44 8.29 0.00
## 3+4+5+7+9+10 7 -554.88 1124.45 8.30 0.00
## 2+4+5+7+8+9 7 -554.88 1124.45 8.30 0.00
## 1+3+4+5+9+10 7 -554.89 1124.47 8.32 0.00
## 1+2+4+5+7+8 7 -554.89 1124.47 8.33 0.00
## 1+2+5 4 -558.12 1124.48 8.33 0.00
## 1+4+5+7+8+9 7 -554.89 1124.48 8.33 0.00
## 3+5+6+7+8+9+10 17 -543.23 1124.48 8.34 0.00
## 2+3+6+8+9+10 16 -544.46 1124.48 8.34 0.00
## 1+2+5+6+7+8 16 -544.48 1124.51 8.37 0.00
## 1+3+4+6+9 15 -545.70 1124.51 8.37 0.00
## 3+6+7+9+10 15 -545.70 1124.52 8.37 0.00
## 1+3+5+6+7+8+9 17 -543.25 1124.52 8.37 0.00
## 1+4+6+7 14 -546.91 1124.52 8.37 0.00
## 1+4+6+9 14 -546.91 1124.53 8.38 0.00
## 1+4+8 4 -558.14 1124.53 8.38 0.00
## 2+3+4+6+8+9 16 -544.49 1124.54 8.39 0.00
## 1+2+3+4+5+6+7 17 -543.26 1124.54 8.40 0.00
## 1+2+3+5+6+7+10 17 -543.26 1124.54 8.40 0.00
## 1+2+3+8 5 -557.09 1124.54 8.40 0.00
## 1+2+3+6+8+9 16 -544.50 1124.55 8.40 0.00
## 1+3+6+9+10 15 -545.72 1124.55 8.40 0.00
## 2+5+7+10 5 -557.10 1124.57 8.42 0.00
## 2+3+6+7+8+9 16 -544.51 1124.58 8.43 0.00
## 1+3+6+7+10 15 -545.74 1124.60 8.45 0.00
## 1+3+4+5+7+10 7 -554.96 1124.61 8.46 0.00
## 2+3+4+6+8+10 16 -544.53 1124.62 8.47 0.00
## 3+4+6+7+9 15 -545.76 1124.63 8.48 0.00
## 1+3+4+6+7 15 -545.76 1124.63 8.49 0.00
## 1+3+5+6+7+8+10 17 -543.31 1124.64 8.49 0.00
## 1+3+5+7+8+9+10 8 -553.88 1124.65 8.50 0.00
## 2+3+6+7+8+10 16 -544.56 1124.67 8.53 0.00
## 1+2+3+6+8+10 16 -544.56 1124.67 8.53 0.00
## 1+2+3 4 -558.22 1124.68 8.54 0.00
## 1+5+6+7+9 15 -545.78 1124.69 8.54 0.00
## 1+6+7+9 14 -547.00 1124.70 8.56 0.00
## 2+4+5+6+9+10 16 -544.59 1124.73 8.59 0.00
## 1+3+6+7+9 15 -545.81 1124.74 8.60 0.00
## 1+2+3+4+6+8 16 -544.60 1124.75 8.60 0.00
## 1+3+4+5+7+8+9+10 9 -552.81 1124.75 8.60 0.00
## 2+3+4+6+7+8 16 -544.61 1124.78 8.64 0.00
## 4+5+7+10 5 -557.21 1124.79 8.64 0.00
## 1+2+3+6+7+8 16 -544.62 1124.79 8.65 0.00
## 1+4+5+7+8+10 7 -555.06 1124.81 8.66 0.00
## 1+4+5+10 5 -557.22 1124.81 8.67 0.00
## 1+2+6+8+10 15 -545.86 1124.83 8.69 0.00
## 2+4+6+8+10 15 -545.86 1124.84 8.69 0.00
## 2+3+4+5+7+9 7 -555.08 1124.84 8.70 0.00
## 1+2+4+5+6+10 16 -544.65 1124.85 8.70 0.00
## 2+4+5+6+7+10 16 -544.65 1124.86 8.71 0.00
## 2+6+8+9+10 15 -545.87 1124.86 8.71 0.00
## 2+6+7+8+10 15 -545.88 1124.87 8.72 0.00
## 2+3+4+5+7+9+10 8 -553.99 1124.88 8.74 0.00
## 1+2+5+6+9+10 16 -544.66 1124.88 8.74 0.00
## 2+5+6+7+9+10 16 -544.66 1124.89 8.74 0.00
## 1+2+3+4+5+9+10 8 -554.00 1124.89 8.74 0.00
## 1+2+8 4 -558.32 1124.89 8.74 0.00
## 4+6+7+9 14 -547.10 1124.91 8.76 0.00
## 1+2+4+5+6+9 16 -544.69 1124.93 8.78 0.00
## 1+2+6+8+9 15 -545.92 1124.95 8.81 0.00
## 1+2+4+5 5 -557.30 1124.96 8.81 0.00
## 3+5+7+9+10 6 -556.22 1124.96 8.82 0.00
## 2+4+5+6+7+9 16 -544.71 1124.97 8.82 0.00
## 1+2+4+6+8 15 -545.93 1124.97 8.82 0.00
## 1+2+3+4+5+7+10 8 -554.04 1124.98 8.83 0.00
## 1+2+5+8+9+10 7 -555.14 1124.98 8.83 0.00
## 1+2+6+7+8 15 -545.94 1124.99 8.85 0.00
## 1+5+10 4 -558.38 1125.01 8.86 0.00
## 1+2+5+6+7+10 16 -544.73 1125.02 8.87 0.00
## 5+7+10 4 -558.40 1125.04 8.90 0.00
## 1+3+4+5+7 6 -556.27 1125.06 8.91 0.00
## 1+2+5+6+7+9 16 -544.76 1125.07 8.93 0.00
## 1+3+5+7+10 6 -556.28 1125.08 8.94 0.00
## 4+5+6+8+9+10 16 -544.77 1125.09 8.94 0.00
## 1+4+5+6+8+10 16 -544.77 1125.09 8.95 0.00
## 1+3+5+9+10 6 -556.29 1125.10 8.96 0.00
## 4+5+6+7+8+10 16 -544.78 1125.11 8.96 0.00
## 2+5+7+8+9+10 7 -555.22 1125.12 8.98 0.00
## 1+2+4+5+6+7 16 -544.79 1125.13 8.98 0.00
## 2+3+4+6+9+10 16 -544.80 1125.15 9.01 0.00
## 1+3+4+5+9 6 -556.32 1125.16 9.02 0.00
## 2+4+6+8+9 15 -546.03 1125.17 9.02 0.00
## 2+6+7+8+9 15 -546.03 1125.18 9.04 0.00
## 2+3+6+7+9+10 16 -544.81 1125.18 9.04 0.00
## 1+2+3+6+9+10 16 -544.82 1125.19 9.05 0.00
## 2+4+6+7+8 15 -546.04 1125.20 9.05 0.00
## 1+2+3+4+5+6+8+9 18 -542.33 1125.20 9.05 0.00
## 1+3+5 4 -558.50 1125.24 9.09 0.00
## 1+2+3+4+6+9 16 -544.84 1125.24 9.09 0.00
## 1+2+4+6+10 15 -546.07 1125.26 9.12 0.00
## 2+3+4+5+6+7+8+9 18 -542.37 1125.27 9.12 0.00
## 1+2+3+6+7+9 16 -544.86 1125.27 9.12 0.00
## 1+2+6+9+10 15 -546.08 1125.27 9.12 0.00
## 2+3+4+5+6+8+9+10 18 -542.37 1125.27 9.12 0.00
## 1+2+4+5+10 6 -556.38 1125.28 9.13 0.00
## 1+2+6+7+10 15 -546.08 1125.28 9.14 0.00
## 2+4+6+9+10 15 -546.09 1125.29 9.14 0.00
## 3+4+5+9 5 -557.46 1125.29 9.14 0.00
## 2+3+4+6+7+9 16 -544.87 1125.30 9.15 0.00
## 2+4+6+7+10 15 -546.09 1125.30 9.16 0.00
## 1+2+3+4 5 -557.47 1125.31 9.16 0.00
## 2+6+7+9+10 15 -546.10 1125.31 9.17 0.00
## 2+4+5+7+10 6 -556.41 1125.33 9.19 0.00
## 2+3+4+6+7+10 16 -544.90 1125.35 9.20 0.00
## 1+2+3+4+6+10 16 -544.90 1125.35 9.20 0.00
## 1+2+3+5+6+8+9+10 18 -542.42 1125.36 9.21 0.00
## 1+4+5+6+8+9 16 -544.91 1125.37 9.22 0.00
## 4+5+6+7+8+9 16 -544.91 1125.37 9.23 0.00
## 1+2+5+7+8+9 7 -555.34 1125.38 9.24 0.00
## 1+2+3+6+7+10 16 -544.92 1125.40 9.25 0.00
## 1+2+4+6+9 15 -546.14 1125.40 9.26 0.00
## 1+2+6+7+9 15 -546.15 1125.42 9.27 0.00
## 1+4+5+6+7+8 16 -544.93 1125.42 9.27 0.00
## 1+2+3+5+6+7+8+9 18 -542.45 1125.42 9.28 0.00
## 1+2+4+6+7 15 -546.17 1125.45 9.30 0.00
## 2+3+5+6+7+8+9+10 18 -542.48 1125.49 9.34 0.00
## 1+5+6+8+9+10 16 -544.97 1125.49 9.35 0.00
## 3+4+5+7 5 -557.56 1125.49 9.35 0.00
## 3+4+6+8+9+10 16 -544.97 1125.50 9.36 0.00
## 5+6+7+8+9+10 16 -544.97 1125.50 9.36 0.00
## 1+2+3+4+8 6 -556.49 1125.50 9.36 0.00
## 1+2+3+4+6+7 16 -544.98 1125.51 9.36 0.00
## 1+5+6+7+8+10 16 -544.98 1125.52 9.37 0.00
## 1+2+5+7+8+10 7 -555.43 1125.56 9.41 0.00
## 3+4+6+7+8+10 16 -545.01 1125.58 9.43 0.00
## 1+3+4+5+6+9+10 17 -543.77 1125.58 9.43 0.00
## 1+3+4+6+8+10 16 -545.01 1125.58 9.43 0.00
## 1+2+3+4+5+6+8+10 18 -542.55 1125.62 9.48 0.00
## 1+3+4+6+8+9 16 -545.05 1125.66 9.51 0.00
## 1+8+9 4 -558.71 1125.67 9.53 0.00
## 3+4+5+6+7+9+10 17 -543.83 1125.68 9.54 0.00
## 2+4+6+7+9 15 -546.28 1125.69 9.54 0.00
## 3+4+6+7+8+9 16 -545.08 1125.71 9.56 0.00
## 3+6+7+8+9+10 16 -545.08 1125.71 9.57 0.00
## 1+3+6+8+9+10 16 -545.08 1125.71 9.57 0.00
## 1+2+3+5+7+8+9+10 9 -553.30 1125.72 9.58 0.00
## 1+2 3 -559.79 1125.73 9.58 0.00
## 1+3+8+10 5 -557.69 1125.75 9.60 0.00
## 1+2+5+9+10 6 -556.62 1125.76 9.62 0.00
## 1+4+6+8+10 15 -546.33 1125.77 9.62 0.00
## 2+3+4+5+6+7+8+10 18 -542.62 1125.77 9.63 0.00
## 1+8+10 4 -558.77 1125.78 9.63 0.00
## 1+5+6+7+8+9 16 -545.11 1125.78 9.63 0.00
## 1+3+4+6+7+8 16 -545.11 1125.78 9.64 0.00
## 1+2+3+4+5+6+7+8 18 -542.63 1125.78 9.64 0.00
## 1+3+6+7+8+10 16 -545.12 1125.79 9.65 0.00
## 4+6+8+9+10 15 -546.34 1125.80 9.65 0.00
## 4+6+7+8+10 15 -546.34 1125.80 9.65 0.00
## 1+2+3+5+7+9 7 -555.56 1125.80 9.66 0.00
## 4+5+7+9+10 6 -556.66 1125.84 9.69 0.00
## 1+2+3+10 5 -557.74 1125.84 9.70 0.00
## 2+3+5+7+9 6 -556.66 1125.84 9.70 0.00
## 1+3+4+5+6+7+10 17 -543.91 1125.85 9.70 0.00
## 1+3+4+5+6+7+9 17 -543.92 1125.86 9.72 0.00
## 1+4+5+9+10 6 -556.67 1125.86 9.72 0.00
## 1+3+6+7+8+9 16 -545.16 1125.87 9.72 0.00
## 1+2+4+5+8+9+10 8 -554.49 1125.87 9.72 0.00
## 1+2+3+5+6+7+8+10 18 -542.67 1125.87 9.73 0.00
## 2+4+5+7+8+9+10 8 -554.49 1125.88 9.73 0.00
## 2+5+7+9+10 6 -556.68 1125.88 9.73 0.00
## 1+5+7+8+9+10 7 -555.60 1125.88 9.74 0.00
## 1+3+8+9 5 -557.76 1125.89 9.74 0.00
## 1+3+4 4 -558.84 1125.91 9.77 0.00
## 1+3+7+8 5 -557.78 1125.93 9.78 0.00
## 1+6+7+8+10 15 -546.41 1125.93 9.78 0.00
## 1+6+8+9+10 15 -546.41 1125.93 9.78 0.00
## 6+7+8+9+10 15 -546.42 1125.96 9.82 0.00
## 1+2+4+8 5 -557.80 1125.97 9.82 0.00
## 1+4+8+9 5 -557.82 1126.00 9.86 0.00
## 1+4+6+8+9 15 -546.45 1126.02 9.87 0.00
## 1+4+6+7+8 15 -546.45 1126.02 9.87 0.00
## 3+4+10 4 -558.92 1126.08 9.93 0.00
## 1+7+8 4 -558.93 1126.10 9.95 0.00
## 1+3+4+8+9 6 -556.80 1126.12 9.97 0.00
## 1+2+3+4+5+7+9 8 -554.62 1126.14 10.00 0.00
## 1+2+4+5+7+8+9 8 -554.63 1126.16 10.01 0.00
## 1+5+9+10 5 -557.90 1126.17 10.02 0.00
## 1+6+7+8+9 15 -546.53 1126.17 10.03 0.00
## 5+7+9+10 5 -557.91 1126.18 10.03 0.00
## 1+3+4+8+10 6 -556.84 1126.19 10.04 0.00
## 1+3+5+6+7+9+10 17 -544.09 1126.21 10.06 0.00
## 1+3+4+10 5 -557.93 1126.22 10.07 0.00
## 1+2+3+4+5+6+9+10 18 -542.86 1126.26 10.11 0.00
## 1+4+5+7+8+9+10 8 -554.68 1126.26 10.12 0.00
## 1+3+4+5+6+8+9+10 18 -542.87 1126.27 10.12 0.00
## 1+3+4+7+8 6 -556.88 1126.27 10.12 0.00
## 1+4+5+6+9+10 16 -545.36 1126.27 10.13 0.00
## 4+5+6+7+9+10 16 -545.36 1126.28 10.13 0.00
## 1+4+5 4 -559.02 1126.29 10.14 0.00
## 4+6+7+8+9 15 -546.59 1126.29 10.15 0.00
## 1+4+5+6+7+10 16 -545.37 1126.30 10.15 0.00
## 1+2+10 4 -559.04 1126.33 10.18 0.00
## 1+3+10 4 -559.04 1126.33 10.18 0.00
## 2+3+4+5+6+7+9+10 18 -542.92 1126.37 10.22 0.00
## 1+2+5+9 5 -558.01 1126.38 10.23 0.00
## 1+2+3+4+5+6+7+9 18 -542.93 1126.38 10.24 0.00
## 1+4+8+10 5 -558.03 1126.43 10.29 0.00
## 1+3+4+5+6+7+8+9 18 -542.95 1126.43 10.29 0.00
## 3+4+5+6+7+8+9+10 18 -542.96 1126.44 10.30 0.00
## 1+2+3+4+5+7+8+9+10 10 -552.53 1126.45 10.30 0.00
## 1+2+3+5+7+9+10 8 -554.78 1126.45 10.30 0.00
## 1+2+5+7+10 6 -556.98 1126.47 10.32 0.00
## 1+2+4 4 -559.11 1126.47 10.33 0.00
## 1+2+5+7 5 -558.05 1126.47 10.33 0.00
## 1+2+3+5+6+7+9+10 18 -542.98 1126.49 10.35 0.00
## 1+2+8+9 5 -558.07 1126.50 10.35 0.00
## 2+4+5+6+8+9+10 17 -544.24 1126.51 10.36 0.00
## 1+2+4+5+9+10 7 -555.92 1126.53 10.38 0.00
## 1+3+4+5+6+7+8+10 18 -543.01 1126.54 10.40 0.00
## 1+2+4+5+7+8+10 8 -554.83 1126.55 10.40 0.00
## 2+4+5+7+9+10 7 -555.93 1126.55 10.41 0.00
## 2+3+10 4 -559.17 1126.58 10.44 0.00
## 1+2+3+8+9 6 -557.04 1126.59 10.44 0.00
## 1+2+4+5+6+8+10 17 -544.28 1126.59 10.45 0.00
## 1+4+6+7+10 15 -546.74 1126.60 10.45 0.00
## 2+4+5+6+7+8+10 17 -544.29 1126.60 10.45 0.00
## 1+2+3+8+10 6 -557.05 1126.61 10.46 0.00
## 1+2+3+7 5 -558.12 1126.61 10.47 0.00
## 1+4+6+9+10 15 -546.75 1126.62 10.47 0.00
## 1+10 3 -560.24 1126.63 10.48 0.00
## 1+3+4+5+7+9+10 8 -554.87 1126.63 10.48 0.00
## 1+4+7+8 5 -558.13 1126.63 10.49 0.00
## 4+6+7+9+10 15 -546.76 1126.64 10.49 0.00
## 1+2+3+7+8 6 -557.07 1126.65 10.51 0.00
## 1+4+5+6+7+9 16 -545.55 1126.66 10.51 0.00
## 2+4+5+6+7+8+9 17 -544.32 1126.66 10.51 0.00
## 1+2+5+6+8+9+10 17 -544.32 1126.66 10.51 0.00
## 2+5+6+7+8+9+10 17 -544.32 1126.66 10.52 0.00
## 1+2+4+5+6+8+9 17 -544.32 1126.67 10.52 0.00
## 3+4+8+10 5 -558.16 1126.68 10.54 0.00
## 3+4+6+7+9+10 16 -545.57 1126.70 10.55 0.00
## 1+3+4+6+9+10 16 -545.58 1126.72 10.58 0.00
## 1+5+6+7+9+10 16 -545.59 1126.74 10.60 0.00
## 1+2+3+4+10 6 -557.12 1126.75 10.61 0.00
## 1+4+10 4 -559.26 1126.76 10.61 0.00
## 1+2+5+6+7+8+10 17 -544.37 1126.76 10.61 0.00
## 1+2+4+5+9 6 -557.12 1126.76 10.62 0.00
## 1+3+4+6+7+10 16 -545.61 1126.77 10.63 0.00
## 1+2+3+9 5 -558.21 1126.78 10.63 0.00
## 1+2+3+4+5+6+7+10 18 -543.13 1126.79 10.64 0.00
## 1+6+7+9+10 15 -546.84 1126.79 10.65 0.00
## 2+4+5 4 -559.28 1126.80 10.65 0.00
## 1+2+5+6+7+8+9 17 -544.39 1126.81 10.66 0.00
## 1+2+4+5+6+7+8 17 -544.39 1126.81 10.66 0.00
## 1+2+8+10 5 -558.22 1126.81 10.67 0.00
## 1+3+5+6+7+8+9+10 18 -543.16 1126.84 10.70 0.00
## 2+3+4+10 5 -558.25 1126.87 10.72 0.00
## 1+4+5+7+10 6 -557.18 1126.87 10.73 0.00
## 3+10 3 -560.37 1126.88 10.74 0.00
## 2+3+4+6+8+9+10 17 -544.44 1126.91 10.76 0.00
## 1+5+7+10 5 -558.27 1126.91 10.76 0.00
## 1+4+6+7+9 15 -546.90 1126.92 10.77 0.00
## 1+3+4+6+7+9 16 -545.69 1126.93 10.78 0.00
## 1+3+6+7+9+10 16 -545.70 1126.95 10.81 0.00
## 1+2+3+6+8+9+10 17 -544.46 1126.95 10.81 0.00
## 2+3+6+7+8+9+10 17 -544.46 1126.95 10.81 0.00
## 1+2+7+8 5 -558.30 1126.97 10.82 0.00
## 1+2+3+4+6+8+9 17 -544.48 1126.98 10.83 0.00
## 4+10 3 -560.43 1127.00 10.85 0.00
## 2+3+4+6+7+8+9 17 -544.49 1127.00 10.86 0.00
## 1+3+5+7 5 -558.33 1127.02 10.87 0.00
## 1+2+3+6+7+8+9 17 -544.50 1127.02 10.87 0.00
## 3+8+10 4 -559.40 1127.04 10.90 0.00
## 1+2+4+5+7 6 -557.27 1127.05 10.90 0.00
## 2+3+4+6+7+8+10 17 -544.53 1127.09 10.94 0.00
## 1+2+3+4+6+8+10 17 -544.53 1127.09 10.94 0.00
## 1+2+3+4+5+7+9+10 9 -553.99 1127.10 10.96 0.00
## 1+3+5+7+9+10 7 -556.21 1127.12 10.97 0.00
## 1+8+9+10 5 -558.38 1127.12 10.97 0.00
## 1+2+3+6+7+8+10 17 -544.56 1127.14 11.00 0.00
## 1+2+5+7+8+9+10 8 -555.14 1127.18 11.03 0.00
## 1+2+4+5+6+9+10 17 -544.58 1127.20 11.05 0.00
## 2+4+5+6+7+9+10 17 -544.59 1127.20 11.06 0.00
## 1+2+3+4+6+7+8 17 -544.59 1127.21 11.07 0.00
## 1+3+4+5+7+9 7 -556.27 1127.23 11.08 0.00
## 1+2+4+6+8+10 16 -545.84 1127.24 11.10 0.00
## 1+2+6+8+9+10 16 -545.85 1127.26 11.11 0.00
## 1+4 3 -560.56 1127.27 11.12 0.00
## 2+4+6+8+9+10 16 -545.86 1127.27 11.12 0.00
## 1+2+6+7+8+10 16 -545.86 1127.27 11.13 0.00
## 2+4+6+7+8+10 16 -545.86 1127.28 11.13 0.00
## 1+3 3 -560.57 1127.29 11.15 0.00
## 2+6+7+8+9+10 16 -545.87 1127.30 11.15 0.00
## 1+3+5+9 5 -558.47 1127.31 11.16 0.00
## 1+2+4+5+6+7+10 17 -544.65 1127.32 11.17 0.00
## 1+2+3+4+7 6 -557.41 1127.34 11.20 0.00
## 1+2+5+6+7+9+10 17 -544.66 1127.35 11.21 0.00
## 1+2+4+6+8+9 16 -545.91 1127.37 11.22 0.00
## 4+8+10 4 -559.57 1127.38 11.24 0.00
## 1+2+3+4+9 6 -557.44 1127.39 11.24 0.00
## 1+2+4+10 5 -558.51 1127.39 11.24 0.00
## 1+2+6+7+8+9 16 -545.92 1127.39 11.25 0.00
## 1+2+4+5+6+7+9 17 -544.69 1127.40 11.25 0.00
## 1+2+4+6+7+8 16 -545.92 1127.40 11.26 0.00
## 1+2+4+5+7+10 7 -556.36 1127.41 11.27 0.00
## 3+4+5+7+9 6 -557.46 1127.44 11.29 0.00
## 2+10 3 -560.66 1127.46 11.32 0.00
## 1+2+4+8+9 6 -557.48 1127.47 11.33 0.00
## 1+2+9 4 -559.62 1127.49 11.34 0.00
## 8+10 3 -560.68 1127.51 11.36 0.00
## 1+2+3+4+8+9 7 -556.41 1127.51 11.37 0.00
## 1+4+5+6+8+9+10 17 -544.75 1127.53 11.39 0.00
## 1+4+5+6+7+8+10 17 -544.76 1127.55 11.40 0.00
## 4+5+6+7+8+9+10 17 -544.76 1127.55 11.40 0.00
## 4+9+10 4 -559.66 1127.55 11.41 0.00
## 10 2 -561.74 1127.56 11.41 0.00
## 1+2+9+10 5 -558.60 1127.56 11.42 0.00
## 2+4+6+7+8+9 16 -546.02 1127.59 11.45 0.00
## 1+2+7 4 -559.68 1127.61 11.47 0.00
## 2+3+4+6+7+9+10 17 -544.79 1127.61 11.47 0.00
## 1+2+3+4+6+9+10 17 -544.80 1127.62 11.47 0.00
## 3+4+9+10 5 -558.63 1127.62 11.48 0.00
## 1+2+3+4+8+10 7 -556.47 1127.64 11.49 0.00
## 1+2+3+4+5+6+8+9+10 19 -542.29 1127.64 11.50 0.00
## 1+2+3+6+7+9+10 17 -544.81 1127.65 11.50 0.00
## 1+2+3+4+7+8 7 -556.49 1127.66 11.52 0.00
## 1+3+8+9+10 6 -557.58 1127.67 11.52 0.00
## 1+4+8+9+10 6 -557.58 1127.68 11.53 0.00
## 1+2+4+6+9+10 16 -546.06 1127.68 11.53 0.00
## 1+9+10 4 -559.73 1127.69 11.55 0.00
## 1+2+4+6+7+10 16 -546.07 1127.69 11.55 0.00
## 1+2+6+7+9+10 16 -546.07 1127.70 11.55 0.00
## 1+2+3+4+6+7+9 17 -544.84 1127.70 11.56 0.00
## 1+2+3+4+5+6+7+8+9 19 -542.32 1127.71 11.56 0.00
## 1+4+9+10 5 -558.68 1127.72 11.57 0.00
## 2+4+6+7+9+10 16 -546.08 1127.72 11.58 0.00
## 1+2+3+9+10 6 -557.62 1127.75 11.61 0.00
## 1+7+8+9 5 -558.70 1127.76 11.61 0.00
## 2+3+4+5+6+7+8+9+10 19 -542.36 1127.80 11.65 0.00
## 1+2+3+4+6+7+10 17 -544.89 1127.82 11.67 0.00
## 1+4+5+6+7+8+9 17 -544.90 1127.82 11.67 0.00
## 1+3+4+7 5 -558.73 1127.82 11.67 0.00
## 1+2+3+7+10 6 -557.65 1127.82 11.68 0.00
## 1+3+7+8+10 6 -557.66 1127.83 11.68 0.00
## 1+7+8+10 5 -558.73 1127.83 11.68 0.00
## 1+2+4+6+7+9 16 -546.14 1127.83 11.69 0.00
## 2+3+8+10 5 -558.73 1127.84 11.69 0.00
## 1+2+5+7+9+10 7 -556.59 1127.88 11.73 0.00
## 1+2+3+5+6+7+8+9+10 19 -542.41 1127.88 11.74 0.00
## 2+4+10 4 -559.83 1127.89 11.75 0.00
## 1+5+6+7+8+9+10 17 -544.97 1127.96 11.81 0.00
## 1+3+4+9+10 6 -557.72 1127.96 11.82 0.00
## 1+3+4+6+8+9+10 17 -544.97 1127.97 11.83 0.00
## 3+4+6+7+8+9+10 17 -544.97 1127.97 11.83 0.00
## 1+2+4+8+10 6 -557.74 1127.99 11.84 0.00
## 1+3+7+8+9 6 -557.74 1127.99 11.84 0.00
## 1+4+5+7+9+10 7 -556.65 1127.99 11.84 0.00
## 1+3+4+9 5 -558.81 1127.99 11.85 0.00
## 1+3+4+6+7+8+10 17 -545.01 1128.04 11.90 0.00
## 1+3+4+5+6+7+9+10 18 -543.77 1128.07 11.93 0.00
## 1+2+4+5+7+8+9+10 9 -554.48 1128.08 11.93 0.00
## 1+3+4+8+9+10 7 -556.70 1128.08 11.94 0.00
## 1+2+4+7+8 6 -557.79 1128.10 11.95 0.00
## 1+3+9+10 5 -558.87 1128.10 11.95 0.00
## 1+5 3 -560.98 1128.10 11.96 0.00
## 1+4+5+9 5 -558.87 1128.11 11.96 0.00
## 1+2+4+9 5 -558.88 1128.12 11.97 0.00
## 1+2+3+4+5+6+7+8+10 19 -542.53 1128.12 11.97 0.00
## 1+3+4+6+7+8+9 17 -545.05 1128.13 11.98 0.00
## 1+3+7+10 5 -558.89 1128.14 11.99 0.00
## 1+4+7+8+9 6 -557.82 1128.15 12.00 0.00
## 1+5+7+9+10 6 -557.82 1128.16 12.01 0.00
## 4+8+9+10 5 -558.90 1128.17 12.03 0.00
## 3+4+7+10 5 -558.91 1128.18 12.03 0.00
## 2+3+4+8+10 6 -557.83 1128.18 12.04 0.00
## 1+3+6+7+8+9+10 17 -545.08 1128.19 12.04 0.00
## 1+3+4+7+10 6 -557.84 1128.20 12.06 0.00
## 9+10 3 -561.03 1128.20 12.06 0.00
## 1+4+6+8+9+10 16 -546.33 1128.21 12.06 0.00
## 1+4+6+7+8+10 16 -546.33 1128.21 12.06 0.00
## 1+2+8+9+10 6 -557.85 1128.22 12.08 0.00
## 4+6+7+8+9+10 16 -546.34 1128.23 12.09 0.00
## 1+4+5+7 5 -558.94 1128.24 12.10 0.00
## 1+2+7+10 5 -558.95 1128.27 12.12 0.00
## 2+9+10 4 -560.02 1128.28 12.13 0.00
## 1+3+4+7+8+9 7 -556.79 1128.28 12.13 0.00
## 2+3+9+10 5 -558.96 1128.29 12.15 0.00
## 1+3+4+7+8+10 7 -556.82 1128.34 12.19 0.00
## 3+4+8+9+10 6 -557.92 1128.35 12.21 0.00
## 1+6+7+8+9+10 16 -546.41 1128.37 12.22 0.00
## 8+9+10 4 -560.08 1128.39 12.25 0.00
## 1+7+10 4 -560.08 1128.40 12.25 0.00
## 1+2+5+7+9 6 -557.95 1128.42 12.27 0.00
## 3+9+10 4 -560.10 1128.44 12.29 0.00
## 1+4+6+7+8+9 16 -546.45 1128.46 12.31 0.00
## 1+2+4+7 5 -559.05 1128.46 12.31 0.00
## 1 2 -562.22 1128.50 12.36 0.00
## 2+8+10 4 -560.13 1128.50 12.36 0.00
## 1+2+4+9+10 6 -558.00 1128.52 12.38 0.00
## 2+3+4+9+10 6 -558.01 1128.54 12.40 0.00
## 1+4+7+8+10 6 -558.02 1128.56 12.41 0.00
## 1+2+3+8+9+10 7 -556.95 1128.60 12.45 0.00
## 2+4+9+10 5 -559.12 1128.61 12.46 0.00
## 1+2+7+8+9 6 -558.05 1128.62 12.48 0.00
## 1+2+3+4+9+10 7 -556.97 1128.64 12.49 0.00
## 2+3+7+10 5 -559.16 1128.69 12.54 0.00
## 1+4+7+10 5 -559.17 1128.70 12.55 0.00
## 1+2+4+5+7+9+10 8 -555.91 1128.71 12.56 0.00
## 1+2+3+7+8+9 7 -557.02 1128.73 12.58 0.00
## 3+8+9+10 5 -559.18 1128.73 12.59 0.00
## 1+2+3+7+8+10 7 -557.02 1128.73 12.59 0.00
## 1+4+5+6+7+9+10 17 -545.36 1128.74 12.59 0.00
## 1+2+3+7+9 6 -558.12 1128.75 12.60 0.00
## 1+3+4+5+6+7+8+9+10 19 -542.86 1128.78 12.64 0.00
## 1+2+3+4+5+6+7+9+10 19 -542.86 1128.80 12.65 0.00
## 3+4+7+8+10 6 -558.16 1128.83 12.68 0.00
## 1+2+3+4+7+10 7 -557.07 1128.83 12.68 0.00
## 2+4+5+9 5 -559.24 1128.85 12.71 0.00
## 2+4+5+7 5 -559.25 1128.87 12.73 0.00
## 1+2+4+5+7+9 7 -557.10 1128.89 12.75 0.00
## 3+7+10 4 -560.33 1128.89 12.75 0.00
## 1+2+7+8+10 6 -558.20 1128.91 12.77 0.00
## 1+3+7 4 -560.34 1128.93 12.78 0.00
## 1+4+9 4 -560.36 1128.95 12.81 0.00
## 2+4+8+10 5 -559.31 1128.99 12.85 0.00
## 1+2+4+5+6+8+9+10 18 -544.23 1128.99 12.85 0.00
## 2+4+5+6+7+8+9+10 18 -544.24 1129.00 12.85 0.00
## 2+3+4+7+10 6 -558.25 1129.01 12.86 0.00
## 1+4+6+7+9+10 16 -546.74 1129.04 12.89 0.00
## 4+7+10 4 -560.41 1129.07 12.92 0.00
## 1+2+4+5+6+7+8+10 18 -544.27 1129.08 12.93 0.00
## 1+3+5+7+9 6 -558.29 1129.10 12.95 0.00
## 1+4+7 4 -560.43 1129.11 12.97 0.00
## 1+2+4+5+6+7+8+9 18 -544.31 1129.15 13.01 0.00
## 1+2+5+6+7+8+9+10 18 -544.31 1129.15 13.01 0.00
## 3+7+8+10 5 -559.40 1129.17 13.02 0.00
## 1+3+4+6+7+9+10 17 -545.57 1129.17 13.02 0.00
## 1+7+8+9+10 6 -558.35 1129.22 13.08 0.00
## 1+2+4+8+9+10 7 -557.31 1129.31 13.16 0.00
## 2+5 3 -561.60 1129.35 13.20 0.00
## 1+3+9 4 -560.57 1129.39 13.25 0.00
## 2+3+4+6+7+8+9+10 18 -544.44 1129.41 13.26 0.00
## 1+2+3+4+6+8+9+10 18 -544.44 1129.41 13.26 0.00
## 1+2+4+7+10 6 -558.45 1129.42 13.27 0.00
## 1+2+7+9 5 -559.53 1129.42 13.28 0.00
## 1+2+3+6+7+8+9+10 18 -544.46 1129.46 13.31 0.00
## 1+2+3+4+7+9 7 -557.39 1129.46 13.32 0.00
## 2+8+9+10 5 -559.55 1129.46 13.32 0.00
## 1+2+3+4+6+7+8+9 18 -544.47 1129.48 13.33 0.00
## 4+7+8+10 5 -559.57 1129.50 13.35 0.00
## 2+7+10 4 -560.65 1129.54 13.39 0.00
## 7+10 3 -561.70 1129.54 13.40 0.00
## 1+7+9+10 5 -559.60 1129.57 13.42 0.00
## 1+2+7+9+10 6 -558.53 1129.58 13.43 0.00
## 1+2+3+4+6+7+8+10 18 -544.53 1129.59 13.44 0.00
## 1+2+3+4+8+9+10 8 -556.36 1129.61 13.46 0.00
## 7+8+10 4 -560.68 1129.61 13.46 0.00
## 2+3+8+9+10 6 -558.55 1129.61 13.47 0.00
## 1+2+4+7+8+9 7 -557.48 1129.64 13.50 0.00
## 4+7+9+10 5 -559.65 1129.67 13.52 0.00
## 1+2+4+5+6+7+9+10 18 -544.58 1129.70 13.55 0.00
## 1+2+4+6+8+9+10 17 -545.84 1129.70 13.56 0.00
## 1+2+3+4+7+8+9 8 -556.41 1129.71 13.56 0.00
## 1+2+4+6+7+8+10 17 -545.84 1129.71 13.57 0.00
## 1+2+6+7+8+9+10 17 -545.85 1129.73 13.58 0.00
## 2+4+6+7+8+9+10 17 -545.86 1129.74 13.59 0.00
## 1+4+7+9+10 6 -558.62 1129.75 13.60 0.00
## 3+4+7+9+10 6 -558.62 1129.76 13.61 0.00
## 1+5+7 4 -560.77 1129.78 13.63 0.00
## 1+3+7+8+9+10 7 -557.55 1129.79 13.64 0.00
## 1+2+3+7+9+10 7 -557.55 1129.79 13.64 0.00
## 1+2+3+4+7+8+10 8 -556.46 1129.82 13.68 0.00
## 1+2+4+6+7+8+9 17 -545.91 1129.84 13.69 0.00
## 1+4+7+8+9+10 7 -557.58 1129.84 13.70 0.00
## 2+4+8+9+10 6 -558.67 1129.86 13.71 0.00
## 2+3+4+8+9+10 7 -557.62 1129.93 13.79 0.00
## 1+3+4+7+9 6 -558.71 1129.94 13.79 0.00
## 1+3+7+9+10 6 -558.73 1129.98 13.83 0.00
## 2+3+7+8+10 6 -558.73 1129.98 13.84 0.00
## 2+4+7+10 5 -559.82 1130.01 13.86 0.00
## 1+3+4+7+9+10 7 -557.66 1130.01 13.87 0.00
## 1+4+5+6+7+8+9+10 18 -544.75 1130.02 13.88 0.00
## 1+7 3 -561.96 1130.06 13.92 0.00
## 1+5+9 4 -560.92 1130.08 13.93 0.00
## 1+2+3+4+6+7+9+10 18 -544.79 1130.11 13.97 0.00
## 1+4+5+7+9 6 -558.80 1130.12 13.97 0.00
## 1+2+4+7+8+10 7 -557.72 1130.14 13.99 0.00
## 1+2+4+6+7+9+10 17 -546.06 1130.14 14.00 0.00
## 1+2+4+7+9 6 -558.82 1130.16 14.02 0.00
## 1+2+3+4+5+6+7+8+9+10 20 -542.27 1130.19 14.04 0.00
## 7+9+10 4 -561.00 1130.25 14.10 0.00
## 1+3+4+7+8+9+10 8 -556.69 1130.27 14.12 0.00
## 4+7+8+9+10 6 -558.89 1130.30 14.16 0.00
## 2+3+4+7+8+10 7 -557.83 1130.35 14.20 0.00
## 1+2+7+8+9+10 7 -557.84 1130.37 14.22 0.00
## 1+9 3 -562.12 1130.38 14.23 0.00
## 2+7+9+10 5 -560.01 1130.39 14.25 0.00
## 2+3+7+9+10 6 -558.96 1130.43 14.28 0.00
## 1+3+4+6+7+8+9+10 18 -544.97 1130.48 14.33 0.00
## 3+7+9+10 5 -560.07 1130.50 14.35 0.00
## 7+8+9+10 5 -560.07 1130.52 14.37 0.00
## 3+4+7+8+9+10 7 -557.91 1130.52 14.37 0.00
## 3+4+8 4 -561.18 1130.61 14.47 0.00
## 2+7+8+10 5 -560.13 1130.63 14.48 0.00
## 1+2+4+7+9+10 7 -557.97 1130.63 14.48 0.00
## 1+4+6+7+8+9+10 17 -546.33 1130.68 14.53 0.00
## 2+3+4+7+9+10 7 -558.01 1130.72 14.57 0.00
## 2+4+7+9+10 6 -559.12 1130.76 14.61 0.00
## 1+2+3+7+8+9+10 8 -556.93 1130.76 14.61 0.00
## 1+2+3+4+7+9+10 8 -556.93 1130.76 14.62 0.00
## 1+4+7+9 5 -560.25 1130.86 14.72 0.00
## 3+7+8+9+10 6 -559.18 1130.88 14.73 0.00
## 2+4+5+7+9 6 -559.21 1130.94 14.80 0.00
## 1+3+7+9 5 -560.34 1131.05 14.90 0.00
## 2+4+7+8+10 6 -559.31 1131.13 14.98 0.00
## 2+3+4+8 5 -560.42 1131.21 15.07 0.00
## 2+5+7 4 -561.58 1131.40 15.25 0.00
## 2+5+9 4 -561.60 1131.44 15.29 0.00
## 1+2+4+7+8+9+10 8 -557.30 1131.50 15.36 0.00
## 1+2+4+5+6+7+8+9+10 19 -544.22 1131.52 15.37 0.00
## 2+7+8+9+10 6 -559.55 1131.61 15.46 0.00
## 4+5 3 -562.76 1131.66 15.51 0.00
## 2+3+7+8+9+10 7 -558.55 1131.79 15.64 0.00
## 1+5+7+9 5 -560.72 1131.80 15.65 0.00
## 1+2+3+4+7+8+9+10 9 -556.35 1131.83 15.68 0.00
## 1+2+3+4+6+7+8+9+10 19 -544.44 1131.95 15.80 0.00
## 1+7+9 4 -561.87 1131.99 15.85 0.00
## 2+4+7+8+9+10 7 -558.66 1132.01 15.86 0.00
## 3+5 3 -562.95 1132.04 15.89 0.00
## 2+3+4+7+8+9+10 8 -557.61 1132.12 15.97 0.00
## 1+2+4+6+7+8+9+10 18 -545.84 1132.20 16.06 0.00
## 3+4+7+8 5 -560.94 1132.24 16.09 0.00
## 3+5+9 4 -562.19 1132.62 16.47 0.00
## 3+4+8+9 5 -561.18 1132.72 16.57 0.00
## 2+3+4+7+8 6 -560.20 1132.91 16.76 0.00
## 2+3+4+8+9 6 -560.42 1133.35 17.20 0.00
## 2+5+7+9 5 -561.58 1133.52 17.37 0.00
## 4+5+7 4 -562.74 1133.72 17.58 0.00
## 4+5+9 4 -562.76 1133.75 17.61 0.00
## 2+3+8 4 -562.87 1133.98 17.84 0.00
## 4+8 3 -563.93 1134.00 17.86 0.00
## 3+5+7 4 -562.95 1134.13 17.99 0.00
## 3+4+7+8+9 6 -560.94 1134.39 18.24 0.00
## 2+4+8 4 -563.20 1134.65 18.50 0.00
## 3+5+7+9 5 -562.17 1134.71 18.57 0.00
## 2+3+4+7+8+9 7 -560.20 1135.08 18.94 0.00
## 4+7+8 4 -563.55 1135.35 19.20 0.00
## 2+3+7+8 5 -562.59 1135.55 19.40 0.00
## 4+5+7+9 5 -562.74 1135.84 19.69 0.00
## 2+3+8+9 5 -562.78 1135.92 19.77 0.00
## 4+8+9 4 -563.86 1135.96 19.81 0.00
## 3+8 3 -564.95 1136.04 19.89 0.00
## 2+4+7+8 5 -562.85 1136.07 19.92 0.00
## 2+3+4 4 -563.98 1136.21 20.06 0.00
## 2+4+8+9 5 -563.13 1136.62 20.47 0.00
## 4+7+8+9 5 -563.44 1137.25 21.10 0.00
## 3+7+8 4 -564.61 1137.47 21.32 0.00
## 2+3+7+8+9 6 -562.53 1137.58 21.44 0.00
## 3+8+9 4 -564.80 1137.84 21.69 0.00
## 2+8 3 -565.90 1137.95 21.81 0.00
## 2+3+4+9 5 -563.80 1137.97 21.83 0.00
## 2+4+7+8+9 6 -562.73 1137.98 21.83 0.00
## 2+3+4+7 5 -563.83 1138.02 21.87 0.00
## 2+7+8 4 -565.48 1139.20 23.06 0.00
## 3+7+8+9 5 -564.51 1139.39 23.25 0.00
## 2+3+4+7+9 6 -563.68 1139.88 23.73 0.00
## 2+8+9 4 -565.90 1140.04 23.90 0.00
## 8 2 -568.00 1140.07 23.92 0.00
## 7+8 3 -567.50 1141.15 25.01 0.00
## 2+7+8+9 5 -565.46 1141.29 25.14 0.00
## 8+9 3 -568.00 1142.14 25.99 0.00
## 7+8+9 4 -567.50 1143.24 27.10 0.00
## 2+3 3 -568.56 1143.26 27.12 0.00
## 3+4 3 -568.61 1143.36 27.22 0.00
## 2+4 3 -568.84 1143.83 27.68 0.00
## 2+3+9 4 -567.86 1143.97 27.82 0.00
## 3+4+9 4 -568.17 1144.58 28.43 0.00
## 2+3+7 4 -568.36 1144.97 28.82 0.00
## 3+4+7 4 -568.44 1145.13 28.98 0.00
## 2+4+7 4 -568.55 1145.35 29.20 0.00
## 5 2 -570.66 1145.39 29.24 0.00
## 2+3+7+9 5 -567.74 1145.84 29.70 0.00
## 2+4+9 4 -568.84 1145.92 29.78 0.00
## 3+4+7+9 5 -568.06 1146.48 30.34 0.00
## 5+9 3 -570.34 1146.83 30.69 0.00
## 5+7 3 -570.65 1147.44 31.30 0.00
## 2+4+7+9 5 -568.55 1147.47 31.33 0.00
## 5+7+9 4 -570.34 1148.93 32.78 0.00
## 2 2 -574.68 1153.44 37.29 0.00
## 4 2 -575.12 1154.31 38.17 0.00
## 2+7 3 -574.32 1154.78 38.63 0.00
## 2+9 3 -574.48 1155.11 38.96 0.00
## 4+7 3 -574.80 1155.74 39.59 0.00
## 4+9 3 -575.06 1156.26 40.12 0.00
## 2+7+9 4 -574.17 1156.58 40.43 0.00
## 4+7+9 4 -574.76 1157.76 41.62 0.00
## 3+9 3 -581.05 1168.25 52.10 0.00
## 3+7+9 4 -580.95 1170.14 54.00 0.00
## 3 2 -584.30 1172.68 56.53 0.00
## 3+7 3 -584.03 1174.21 58.07 0.00
## 9 2 -595.40 1194.87 78.73 0.00
## 7+9 3 -595.02 1196.18 80.03 0.00
## (Null) 1 -597.63 1197.29 81.14 0.00
## 7 2 -597.04 1198.15 82.01 0.00
##
## Term codes:
## Canopy CWD_AGB Duff FWD_AGB Litter_AGB LU_FT_Code
## 1 2 3 4 5 6
## MF_FCCP PrimFor_500 SoilDens Trees
## 7 8 9 10
##
## Model-averaged coefficients:
## (full average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 3.4320272 0.1591203 0.1602430 21.418 <2e-16 ***
## Duff 0.0144140 0.0141597 0.0142060 1.015 0.310
## FWD_AGB 0.0018147 0.0040311 0.0040472 0.448 0.654
## Litter_AGB 0.0243333 0.0189551 0.0190072 1.280 0.200
## PrimFor_500 0.0006019 0.0007257 0.0007277 0.827 0.408
## CWD_AGB 0.0002946 0.0005265 0.0005285 0.557 0.577
## LU_FT_CodeMA -0.0013133 0.1512660 0.1523855 0.009 0.993
## LU_FT_CodePA 0.0635148 0.1543328 0.1553546 0.409 0.683
## LU_FT_CodePFlog 0.1166728 0.1736924 0.1745915 0.668 0.504
## LU_FT_CodePFlogbur 0.1522615 0.1891404 0.1899364 0.802 0.423
## LU_FT_CodePFund 0.1659877 0.2038392 0.2046599 0.811 0.417
## LU_FT_CodeREF 0.0612725 0.1520268 0.1530604 0.400 0.689
## LU_FT_CodeSFinter 0.1531857 0.1894235 0.1902264 0.805 0.421
## LU_FT_CodeSFold 0.0608570 0.1560067 0.1570857 0.387 0.698
## LU_FT_CodeSFyoung 0.0628221 0.1729060 0.1741164 0.361 0.718
## LU_FT_CodeSHA 0.2810634 0.2965756 0.2974175 0.945 0.345
## Trees 0.0001394 0.0004511 0.0004534 0.307 0.759
## MF_FCCP 0.0007256 0.0382827 0.0385711 0.019 0.985
## SoilDens -0.0000321 0.0004119 0.0004144 0.077 0.938
## Canopy 0.0075676 0.0577191 0.0580711 0.130 0.896
##
## (conditional average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 3.4320272 0.1591204 0.1602430 21.418 <2e-16 ***
## Duff 0.0221392 0.0117014 0.0117873 1.878 0.0604 .
## FWD_AGB 0.0050997 0.0053771 0.0054110 0.942 0.3460
## Litter_AGB 0.0326128 0.0145440 0.0146348 2.228 0.0259 *
## PrimFor_500 0.0010869 0.0006509 0.0006551 1.659 0.0971 .
## CWD_AGB 0.0007311 0.0006072 0.0006115 1.196 0.2319
## LU_FT_CodeMA -0.0022278 0.1970062 0.1984643 0.011 0.9910
## LU_FT_CodePA 0.1077395 0.1887817 0.1901980 0.566 0.5711
## LU_FT_CodePFlog 0.1979107 0.1873435 0.1887558 1.049 0.2944
## LU_FT_CodePFlogbur 0.2582795 0.1824853 0.1838825 1.405 0.1601
## LU_FT_CodePFund 0.2815631 0.1947812 0.1962357 1.435 0.1513
## LU_FT_CodeREF 0.1039358 0.1864689 0.1878978 0.553 0.5802
## LU_FT_CodeSFinter 0.2598473 0.1820695 0.1834839 1.416 0.1567
## LU_FT_CodeSFold 0.1032311 0.1921201 0.1936057 0.533 0.5939
## LU_FT_CodeSFyoung 0.1065645 0.2145966 0.2162504 0.493 0.6222
## LU_FT_CodeSHA 0.4767649 0.2364257 0.2382128 2.001 0.0453 *
## Trees 0.0004673 0.0007273 0.0007321 0.638 0.5233
## MF_FCCP 0.0029965 0.0777536 0.0783400 0.038 0.9695
## SoilDens -0.0001233 0.0008002 0.0008053 0.153 0.8783
## Canopy 0.0283403 0.1090303 0.1097280 0.258 0.7962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1confset.95p <- get.models(selec, cumsum(weight) <= .95)
avgmod.95p <- model.avg(confset.95p)
avgmod.95p$coefficients## (Intercept) Duff FWD_AGB Litter_AGB PrimFor_500 CWD_AGB
## full 3.430584 0.01465205 0.001780807 0.02499268 0.0006127082 0.0002887040
## subset 3.430584 0.02219668 0.005191741 0.03270330 0.0011005938 0.0007304337
## LU_FT_CodeMA LU_FT_CodePA LU_FT_CodePFlog LU_FT_CodePFlogbur
## full -0.0008568164 0.06415575 0.1182567 0.1537818
## subset -0.0014469374 0.10834218 0.1997044 0.2596970
## LU_FT_CodePFund LU_FT_CodeREF LU_FT_CodeSFinter LU_FT_CodeSFold
## full 0.1677946 0.06168069 0.1541881 0.06138779
## subset 0.2833610 0.10416246 0.2603831 0.10366782
## LU_FT_CodeSFyoung LU_FT_CodeSHA Trees MF_FCCP SoilDens
## full 0.06349272 0.2829572 0.0001270356 0.0008858057 -3.280297e-05
## subset 0.10722249 0.4778403 0.0004504761 0.0039389910 -1.350626e-04
## Canopy
## full 0.005651748
## subset 0.023120115confint(avgmod.95p)## 2.5 % 97.5 %
## (Intercept) 3.1175357125 3.743631734
## Duff -0.0008585784 0.045251942
## FWD_AGB -0.0053282389 0.015711721
## Litter_AGB 0.0040560111 0.061350580
## PrimFor_500 -0.0001727379 0.002373926
## CWD_AGB -0.0004671555 0.001928023
## LU_FT_CodeMA -0.3897889356 0.386895061
## LU_FT_CodePA -0.2636954849 0.480379846
## LU_FT_CodePFlog -0.1693283879 0.568737224
## LU_FT_CodePFlogbur -0.1000889250 0.619482945
## LU_FT_CodePFund -0.1002431388 0.666965137
## LU_FT_CodeREF -0.2639376391 0.472262552
## LU_FT_CodeSFinter -0.0990527090 0.619818809
## LU_FT_CodeSFold -0.2755769562 0.482912598
## LU_FT_CodeSFyoung -0.3164483323 0.530893320
## LU_FT_CodeSHA 0.0117367528 0.943943884
## Trees -0.0009521465 0.001853099
## MF_FCCP -0.1492247236 0.157102706
## SoilDens -0.0017033059 0.001433181
## Canopy -0.1840157623 0.230255992De forma geral, esse é o básico de como se executa uma seleção de modelos.