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1 R version ( ) -- "World-Famous Astronaut" Copyright (C) 2015 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. rm(list=ls()) library(nlme) setwd("/users/paulino/downloads/diana") clima=read.csv(row.names=1,file="clima.csv",header=true) suelo=read.csv(row.names=1,file="suelo.csv",header=true) pca.suelo=prcomp(suelo,center=true,scale=true) plot(pca.suelo) summary(pca.suelo) plot(pca.suelo$x[,1:2],pch=19,xlab="pc1 (49%)",ylab="PC2 (21%)") text(pca.suelo$x[,1:2],rownames(suelo),pos=1,cex=0.50) pca.clima=prcomp(clima,center=true,scale=true) plot(pca.clima) summary(pca.clima) plot(pca.clima$x[,1:2],pch=19,xlab="pc1 (90%)",ylab="PC2 (9%)") text(pca.clima$x[,1:2],rownames(clima),pos=1,cex=0.50) Total=c(17,15,9,14,20,14,6,97,0, 6, 54, 24, 27, 6, 42, 21, 30, 5, 12,48) sites=c(rep("ac",5),rep("n",5),rep("ic",5),rep("lv",5)) sites=as.factor(sites)
2 attach(suelo) fm=lm(total~sites+ CC + PMP + ph + MO + N + P + K + cl + sand + fm=lm(total~pca.clima$x[,1]+ CC + PMP + ph + MO + N + P + K + cl + sand + ltotal=log(total+1) fm=lm(ltotal~sites+ CC + PMP + ph + MO + N + P + K + cl + sand + fm=lm(ltotal~pca.clima$x[,1]+ CC + PMP + ph + MO + N + P + K + cl + sand + ceni summary(pca.suelo) Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 Standard deviation Proportion of Variance Cumulative Proportion PC8 PC9 PC10 PC11 PC12 PC13 Standard deviation e-16 Proportion of Variance e+00 Cumulative Proportion e+00 plot(pca.suelo$x[,1:2],pch=19,xlab="pc1 (49%)",ylab="PC2 (21%)") text(pca.suelo$x[,1:2],rownames(suelo),pos=1,cex=0.50) pca.clima=prcomp(clima,center=true,scale=true) plot(pca.clima) summary(pca.clima) plot(pca.clima$x[,1:2],pch=19,xlab="pc1 (90%)",ylab="PC2 (9%)") text(pca.clima$x[,1:2],rownames(clima),pos=1,cex=0.50) Total=c(17,15,9,14,20,14,6,97,0, 6, 54, 24, 27, 6, 42, 21, 30, 5, 12,48)
3 sites=c(rep("ac",5),rep("n",5),rep("ic",5),rep("lv",5)) sites=as.factor(sites) attach(suelo) fm=lm(total~sites+ CC + PMP + ph + MO + N + P + K + cl + sand + fm=lm(total~pca.clima$x[,1]+ CC + PMP + ph + MO + N + P + K + cl + sand + ltotal=log(total+1) fm=lm(ltotal~sites+ CC + PMP + ph + MO + N + P + K + cl + sand + fm=lm(ltotal~pca.clima$x[,1]+ CC + PMP + ph + MO + N + P + K + cl + sand + fm_mixed=lme(ltotal~cc + PMP + ph + MO + N + P + K + cl + sand + cenizas + CT + N_grass,random=~1 sites) Pontox=c(0,0,0,0,0,6,3,26,0,1,25,24,27,6,21,0,5,0,0,44) sites=c(rep("ac",5),rep("n",5 summary(pca.clima) Importance of components: PC1 PC2 PC3 PC4 PC5 Standard deviation e e-16 Proportion of Variance e e+00 Cumulative Proportion e e+00 plot(pca.clima$x[,1:2],pch=19,xlab="pc1 (90%)",ylab="PC2 (9%)") text(pca.clima$x[,1:2],rownames(clima),pos=1,cex=0.50) Total=c(17,15,9,14,20,14,6,97,0, + 6, 54, 24, 27, 6, 42, 21, + 30, 5, 12,48) sites=c(rep("ac",5),rep("n",5),rep("ic",5),rep("lv",5))
4 sites=as.factor(sites) attach(suelo) fm=lm(total~sites+ CC + PMP + ph + MO + N + P + K + cl + sand + Call: lm(formula = Total ~ sites + CC + PMP + ph + MO + N + P + K + cl + sand + Residuals: Coefficients: Estimate Std. Error t value Pr( t ) (Intercept) sitesic siteslv sitesn CC * PMP * ph MO * N * P ** K ** cl sand cenizas CT N_grass * Residual standard error: on 4 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 15 and 4 DF, p-value:
5 Response: Total Df Sum Sq Mean Sq F value Pr(F) sites CC * PMP ph * MO N P ** K cl sand cenizas CT N_grass * Residuals fm=lm(total~pca.clima$x[,1]+ CC + PMP + ph + MO + N + P + K + cl + sand + ltotal=log(total+1) fm=lm(ltotal~sites+ CC + PMP + ph + MO + N + P + K + cl + sand + fm=lm(ltotal~pca.clima$x[,1]+ CC + PMP + ph + MO + N + P + K + cl + sand + fm_mixed=lme(ltotal~cc + PMP + ph + MO + N + P + K + cl + sand + cenizas + CT + N_grass,random=~1 sites) Pontox=c(0,0,0,0,0,6,3,26,0,1,25,24,27,6,21,0,5,0,0,44) sites=c(rep("ac",5),rep("n",5),rep("ic",5),rep("lv",5)) sites=as.factor(sites) lm(pontox~sites) anova(lm(pontox~sites)) boxplot(pontox~sites)
6 lpontox=log(pontox+1) anova(lm(lpontox~sites)) #Kruskal-Wallis kruskal.test(pontox,sites) Call: lm(formula = ltotal ~ sites + CC + PMP + ph + MO + N + P + K + cl + sand + Residuals: Coefficients: Estimate Std. Error t value Pr( t ) (Intercept) 4.402e e sitesic 1.020e e siteslv 3.936e e sitesn 7.004e e CC e e * PMP 4.840e e * ph 9.200e e * MO 1.563e e * N e e * P 1.844e e * K e e * cl e e sand 1.951e e cenizas e e CT e e N_grass 3.559e e ** Residual standard error: on 4 degrees of freedom Multiple R-squared: , Adjusted R-squared:
7 F-statistic: on 15 and 4 DF, p-value: Response: ltotal Df Sum Sq Mean Sq F value Pr(F) sites CC * PMP ph * MO N P ** K cl * sand cenizas CT N_grass ** Residuals fm=lm(ltotal~pca.clima$x[,1]+ CC + PMP + ph + MO + N + P + K + cl + sand + Response: ltotal Df Sum Sq Mean Sq F value Pr(F) pca.clima$x[, 1] CC ** PMP ph * MO N P ** K cl * sand cenizas CT N_grass ** Residuals fm_mixed=lme(ltotal~cc + PMP + ph + MO + N + P + K + cl + sand + cenizas + CT + N_grass,random=~1 sites)
8 Pontox=c(0,0,0,0,0,6,3,26,0,1,25,24,27,6,21,0,5,0,0,44) sites=c(rep("ac",5),rep("n",5),rep("ic",5),rep("lv",5)) sites=as.factor(sites) lm(pontox~sites) Call: lm(formula = Pontox ~ sites) Coefficients: (Intercept) sitesic siteslv sitesn e e e e+00 anova(lm(pontox~sites)) Response: Pontox Df Sum Sq Mean Sq F value Pr(F) sites Residuals boxplot(pontox~sites) lpontox=log(pontox+1) anova(lm(lpontox~sites)) Response: lpontox Df Sum Sq Mean Sq F value Pr(F) sites ** Residuals #Kruskal-Wallis kruskal.test(pontox,sites) Kruskal-Wallis rank sum test data: Pontox and sites Kruskal-Wallis chi-squared = 10.03, df = 3, p-value =
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