lourymigliorelli — Oct 15, 2013, 6:07 PM
install.packages("ggplot2")
Error: trying to use CRAN without setting a mirror
require(ggplot2)
Loading required package: ggplot2
env <- read.csv("rs_env_plot.csv")
head(env)
Study.Site NDVI.mean NDVI.CV Int.NDVI EVI.mean EVI.CV mean.sla.wt
1 Awaawapuhi Trail 0.81 7.23 182.9 0.38 12.41 8.649
2 Nualolo Trail 0.82 3.93 184.4 0.40 11.50 11.729
3 Milolii Ridge 0.79 8.05 179.8 0.40 13.71 12.489
4 Waimea Canyon 0.79 10.92 174.7 0.47 19.44 18.908
5 Honouliuli 0.80 9.62 178.3 0.47 21.05 28.624
6 Kaluakauila Gulch 0.66 17.75 149.2 0.43 28.95 8.825
mean.sla.new sum.sla.wt mean.sla.wt.dom mean.sla.dom sum.sla.wt.dom
1 82.22 95.14 12.085 81.17 48.340
2 76.88 93.83 13.670 77.79 68.352
3 76.68 87.42 22.829 86.98 68.487
4 96.90 132.36 9.428 63.52 18.856
5 99.39 143.12 3.288 63.99 6.576
6 98.76 70.60 20.157 71.95 60.472
mean.sub sum.sub sum.sub.wt Precip Temp Species.Richness Density
1 82.41 741.7 71.49 1823 21 21 245
2 75.57 453.4 71.08 1520 22 13 279
3 74.91 449.5 72.58 1314 20 9 313
4 100.38 501.9 114.15 1452 19 10 166
5 83.70 334.8 129.65 1020 21 12 161
6 85.17 596.2 69.95 1047 22 13 326
Basal.Area Canopy.Height NSR Substrate.Age D common not.singleton
1 4.5 7.11 9.52 4.7 0.8871 98 175
2 6.3 8.37 15.38 4.7 0.8318 189 268
3 2.5 8.88 22.22 4.7 0.6793 227 275
4 3.8 9.82 60.00 4.7 0.7876 1 109
5 3.6 9.44 33.33 3.0 0.7085 4 148
6 2.1 5.98 46.15 3.0 0.4328 249 303
pca.structure indiv comp Evenness H. Abun.Slope
1 1.1009 250 -0.8639 0.8142 2.479 -0.09163
2 2.4268 278 -0.7366 0.7860 2.016 -0.16200
3 0.9945 283 -0.9894 0.6881 1.512 -0.23000
4 0.9118 166 -0.3115 0.7691 1.771 -0.20930
5 0.7024 161 0.4703 0.6928 1.722 -0.14550
6 0.2292 326 0.1177 0.4168 1.069 -0.12439
mod = lm(env$Abun.Slope ~ env$Species.Richness)
modsum = summary(mod)
modsum
Call:
lm(formula = env$Abun.Slope ~ env$Species.Richness)
Residuals:
Min 1Q Median 3Q Max
-0.14406 -0.02776 0.00658 0.03215 0.10169
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.40013 0.04512 -8.87 1.3e-06 ***
env$Species.Richness 0.01796 0.00411 4.37 0.00091 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0647 on 12 degrees of freedom
Multiple R-squared: 0.614, Adjusted R-squared: 0.582
F-statistic: 19.1 on 1 and 12 DF, p-value: 0.000911
r2 = modsum$adj.r.squared
r2
[1] 0.5821
modsum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.40013 0.045115 -8.869 1.289e-06
env$Species.Richness 0.01796 0.004109 4.371 9.107e-04
my.p = modsum$coefficients[2,4]
my.p
[1] 0.0009107
ggplot(env, aes(x=env$Species.Richness, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("Species Richness") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = 0.58206", x = 15, y = 2) +
annotate("text", label = "P-value = 0.00091", x = 15, y = 1.8) +
annotate("text", label = "S", x = 15, y = 1.6)
lm(env$Abun.Slope ~ env$NDVI.mean)
Call:
lm(formula = env$Abun.Slope ~ env$NDVI.mean)
Coefficients:
(Intercept) env$NDVI.mean
-0.411 0.291
lm(env$Abun.Slope ~ env$Substrate.Age)
Call:
lm(formula = env$Abun.Slope ~ env$Substrate.Age)
Coefficients:
(Intercept) env$Substrate.Age
-0.2659 0.0206
lm(env$Abun.Slope ~ env$Precip)
Call:
lm(formula = env$Abun.Slope ~ env$Precip)
Coefficients:
(Intercept) env$Precip
-0.401982 0.000169
lm(env$Abun.Slope ~ env$Temp)
Call:
lm(formula = env$Abun.Slope ~ env$Temp)
Coefficients:
(Intercept) env$Temp
-0.09553 -0.00599
mod2 = lm(env$Abun.Slope ~ env$Substrate.Age)
mod2sum = summary(mod2)
mod2sum
Call:
lm(formula = env$Abun.Slope ~ env$Substrate.Age)
Residuals:
Min 1Q Median 3Q Max
-0.2194 -0.0437 0.0293 0.0660 0.1153
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2659 0.0434 -6.13 5.1e-05 ***
env$Substrate.Age 0.0206 0.0149 1.38 0.19
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0968 on 12 degrees of freedom
Multiple R-squared: 0.136, Adjusted R-squared: 0.0643
F-statistic: 1.89 on 1 and 12 DF, p-value: 0.194
r2 = mod2sum$adj.r.squared
r2
[1] 0.0643
mod2sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.26587 0.04336 -6.132 5.085e-05
env$Substrate.Age 0.02057 0.01495 1.376 1.940e-01
my.p = mod2sum$coefficients[2,4]
my.p
[1] 0.194
ggplot(env, aes(x=env$Substrate.Age, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("Substrate Age") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = 0.06430", x = 3, y = -0.36) +
annotate("text", label = "P-value = 0.19397", x = 3, y = -0.38) +
annotate("text", label = "NS", x = 3, y = -0.4)
mod3 = lm(env$Abun.Slope ~ env$Precip)
mod3sum = summary(mod3)
mod3sum
Call:
lm(formula = env$Abun.Slope ~ env$Precip)
Residuals:
Min 1Q Median 3Q Max
-0.16073 -0.05166 -0.00685 0.07005 0.10271
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.02e-01 7.51e-02 -5.35 0.00017 ***
env$Precip 1.69e-04 6.57e-05 2.57 0.02471 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0837 on 12 degrees of freedom
Multiple R-squared: 0.354, Adjusted R-squared: 0.301
F-statistic: 6.59 on 1 and 12 DF, p-value: 0.0247
r2 = mod3sum$adj.r.squared
r2
[1] 0.3006
mod3sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.4019816 7.510e-02 -5.353 0.0001729
env$Precip 0.0001687 6.574e-05 2.566 0.0247057
my.p = mod3sum$coefficients[2,4]
my.p
[1] 0.02471
ggplot(env, aes(x=env$Precip, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("Precipitation") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = 0.30058", x = 1500, y = -0.36) +
annotate("text", label = "P-value = 0.02471", x = 1500, y = -0.38) +
annotate("text", label = "S", x = 1500, y = -0.4)
mod4 = lm(env$Abun.Slope ~ env$Temp)
mod4sum = summary(mod4)
mod4sum
Call:
lm(formula = env$Abun.Slope ~ env$Temp)
Residuals:
Min 1Q Median 3Q Max
-0.2330 -0.0182 0.0182 0.0643 0.1298
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.09553 0.54103 -0.18 0.86
env$Temp -0.00599 0.02645 -0.23 0.82
Residual standard error: 0.104 on 12 degrees of freedom
Multiple R-squared: 0.00426, Adjusted R-squared: -0.0787
F-statistic: 0.0514 on 1 and 12 DF, p-value: 0.825
r2 = mod4sum$adj.r.squared
r2
[1] -0.07872
mod4sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.095528 0.54103 -0.1766 0.8628
env$Temp -0.005994 0.02645 -0.2266 0.8245
my.p = mod4sum$coefficients[2,4]
my.p
[1] 0.8245
ggplot(env, aes(x=env$Temp, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("Temp") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = -0.07872", x = 20, y = -0.36) +
annotate("text", label = "P-value = 0.82453", x = 20, y = -0.38) +
annotate("text", label = "NS", x = 20, y = -0.4)
mod5 = lm(env$Abun.Slope ~ env$NDVI.mean)
mod5sum = summary(mod5)
mod5sum
Call:
lm(formula = env$Abun.Slope ~ env$NDVI.mean)
Residuals:
Min 1Q Median 3Q Max
-0.2387 -0.0323 0.0221 0.0593 0.0942
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.411 0.110 -3.72 0.0029 **
env$NDVI.mean 0.291 0.162 1.79 0.0983 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0925 on 12 degrees of freedom
Multiple R-squared: 0.211, Adjusted R-squared: 0.145
F-statistic: 3.21 on 1 and 12 DF, p-value: 0.0983
r2 = mod5sum$adj.r.squared
r2
[1] 0.1454
mod5sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.4106 0.1103 -3.723 0.00291
env$NDVI.mean 0.2910 0.1623 1.792 0.09830
my.p = mod5sum$coefficients[2,4]
my.p
[1] 0.0983
ggplot(env, aes(x=env$NDVI.mean, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("NDVI mean") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = 0.14544", x = .45, y = -0.1) +
annotate("text", label = "P-value = 0.09830", x = .45, y = -0.12) +
annotate("text", label = "NS", x = .45, y = -0.14)
mod6 = lm(env$Abun.Slope ~ env$Basal.Area)
mod6sum = summary(mod6)
mod6sum
Call:
lm(formula = env$Abun.Slope ~ env$Basal.Area)
Residuals:
Min 1Q Median 3Q Max
-0.2230 -0.0263 0.0127 0.0681 0.1111
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2793 0.0623 -4.48 0.00075 ***
env$Basal.Area 0.0209 0.0192 1.09 0.29785
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0993 on 12 degrees of freedom
Multiple R-squared: 0.0898, Adjusted R-squared: 0.014
F-statistic: 1.18 on 1 and 12 DF, p-value: 0.298
r2 = mod6sum$adj.r.squared
r2
[1] 0.01398
mod6sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.27933 0.06231 -4.483 0.0007488
env$Basal.Area 0.02085 0.01916 1.088 0.2978452
my.p = mod6sum$coefficients[2,4]
my.p
[1] 0.2978
ggplot(env, aes(x=env$Basal.Area, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("Basal Area") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = 0.01398", x = 5, y = -0.36) +
annotate("text", label = "P-value = 0.29785", x = 5, y = -0.38) +
annotate("text", label = "NS", x = 5, y = -0.4)
mod7 = lm(env$Abun.Slope ~ env$Density)
mod7sum = summary(mod7)
mod7sum
Call:
lm(formula = env$Abun.Slope ~ env$Density)
Residuals:
Min 1Q Median 3Q Max
-0.2240 -0.0450 0.0239 0.0720 0.1082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.267901 0.059910 -4.47 0.00076 ***
env$Density 0.000278 0.000298 0.93 0.36956
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.101 on 12 degrees of freedom
Multiple R-squared: 0.0675, Adjusted R-squared: -0.0102
F-statistic: 0.869 on 1 and 12 DF, p-value: 0.37
r2 = mod7sum$adj.r.squared
r2
[1] -0.01016
mod7sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2679005 0.0599099 -4.4717 0.0007634
env$Density 0.0002779 0.0002981 0.9323 0.3695553
my.p = mod7sum$coefficients[2,4]
my.p
[1] 0.3696
ggplot(env, aes(x=env$Density, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("Density") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = -0.01016", x = 250, y = -0.36) +
annotate("text", label = "P-value = 0.36956", x = 250, y = -0.38) +
annotate("text", label = "NS", x = 250, y = -0.4)
mod8 = lm(env$Abun.Slope ~ env$Canopy.Height)
mod8sum = summary(mod8)
mod8sum
Call:
lm(formula = env$Abun.Slope ~ env$Canopy.Height)
Residuals:
Min 1Q Median 3Q Max
-0.17038 -0.04675 -0.00383 0.06644 0.12798
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.3669 0.0770 -4.76 0.00046 ***
env$Canopy.Height 0.0207 0.0102 2.03 0.06465 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0898 on 12 degrees of freedom
Multiple R-squared: 0.256, Adjusted R-squared: 0.194
F-statistic: 4.14 on 1 and 12 DF, p-value: 0.0647
r2 = mod8sum$adj.r.squared
r2
[1] 0.1944
mod8sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.36686 0.07702 -4.763 0.0004617
env$Canopy.Height 0.02071 0.01018 2.034 0.0646519
my.p = mod8sum$coefficients[2,4]
my.p
[1] 0.06465
ggplot(env, aes(x=env$Canopy.Height, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("Canopy Height") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = 0.19444", x = 10, y = -0.36) +
annotate("text", label = "P-value = 0.06465", x = 10, y = -0.38) +
annotate("text", label = "NS", x = 10, y = -0.4)
mod9 = lm(env$Abun.Slope ~ env$NSR)
mod9sum = summary(mod9)
mod9sum
Call:
lm(formula = env$Abun.Slope ~ env$NSR)
Residuals:
Min 1Q Median 3Q Max
-0.1526 -0.0378 0.0100 0.0602 0.1386
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.13376 0.04704 -2.84 0.015 *
env$NSR -0.00280 0.00135 -2.08 0.060 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0893 on 12 degrees of freedom
Multiple R-squared: 0.264, Adjusted R-squared: 0.203
F-statistic: 4.31 on 1 and 12 DF, p-value: 0.0599
r2 = mod9sum$adj.r.squared
r2
[1] 0.2032
mod9sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.133762 0.047044 -2.843 0.01481
env$NSR -0.002801 0.001349 -2.077 0.05992
my.p = mod9sum$coefficients[2,4]
my.p
[1] 0.05992
ggplot(env, aes(x=env$NSR, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("NSR") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = 0.20318", x = 20, y = -0.36) +
annotate("text", label = "P-value = 0.05992", x = 20, y = -0.38) +
annotate("text", label = "NS", x = 20, y = -0.4)
mod10 = lm(env$Abun.Slope ~ env$pca.structure)
mod10sum = summary(mod10)
mod10sum
Call:
lm(formula = env$Abun.Slope ~ env$pca.structure)
Residuals:
Min 1Q Median 3Q Max
-0.19757 -0.04523 0.00576 0.07788 0.09858
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2180 0.0251 -8.70 1.6e-06 ***
env$pca.structure 0.0300 0.0180 1.67 0.12
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0938 on 12 degrees of freedom
Multiple R-squared: 0.189, Adjusted R-squared: 0.121
F-statistic: 2.79 on 1 and 12 DF, p-value: 0.121
r2 = mod10sum$adj.r.squared
r2
[1] 0.1212
mod10sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.21798 0.02506 -8.698 1.581e-06
env$pca.structure 0.03001 0.01796 1.671 1.205e-01
my.p = mod10sum$coefficients[2,4]
my.p
[1] 0.1205
ggplot(env, aes(x=env$pca.structure, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("pca.structure") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = 0.12119", x = 1, y = -0.36) +
annotate("text", label = "P-value = 0.12055", x = 1, y = -0.38) +
annotate("text", label = "NS", x = 1, y = -0.4)
mod11 = lm(env$Abun.Slope ~ env$Evenness)
mod11sum = summary(mod11)
mod11sum
Call:
lm(formula = env$Abun.Slope ~ env$Evenness)
Residuals:
Min 1Q Median 3Q Max
-0.2311 -0.0174 0.0226 0.0657 0.1364
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1717 0.1511 -1.14 0.28
env$Evenness -0.0692 0.2221 -0.31 0.76
Residual standard error: 0.104 on 12 degrees of freedom
Multiple R-squared: 0.00802, Adjusted R-squared: -0.0746
F-statistic: 0.0971 on 1 and 12 DF, p-value: 0.761
r2 = mod11sum$adj.r.squared
r2
[1] -0.07464
mod11sum$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.17169 0.1511 -1.1359 0.2782
env$Evenness -0.06921 0.2221 -0.3116 0.7607
my.p = mod11sum$coefficients[2,4]
my.p
[1] 0.7607
ggplot(env, aes(x=env$Evenness, y=Abun.Slope)) +
geom_point(col="red") +
ylab("Slope") +
xlab("Evenness") + geom_smooth(method = "lm", se = FALSE) +
annotate("text", label = "Adjusted R-squared = -0.07464", x = 0.55, y = -0.36) +
annotate("text", label = "P-value = 0.76072", x = 0.55, y = -0.38) +
annotate("text", label = "NS", x = 0.55, y = -0.4)
