model10 — Nov 14, 2013, 1:59 PM
env <- read.csv("rs_env_plot 2.csv")
install.packages("ggplot2")
Installing package into 'C:/Users/model10/Documents/R/win-library/3.0'
(as 'lib' is unspecified)
Error: trying to use CRAN without setting a mirror
require(ggplot2)
Loading required package: ggplot2
mod = lm(env$NDVI.mean ~ env$Precip)
summary(mod)
Call:
lm(formula = env$NDVI.mean ~ env$Precip)
Residuals:
Min 1Q Median 3Q Max
-0.3271 -0.0421 0.0152 0.0579 0.1590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.35e-01 1.10e-01 3.06 0.0099 **
env$Precip 3.00e-04 9.59e-05 3.13 0.0087 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.122 on 12 degrees of freedom
Multiple R-squared: 0.449, Adjusted R-squared: 0.403
F-statistic: 9.78 on 1 and 12 DF, p-value: 0.00872
ggplot(env, aes(x=env$Precip, y=env$NDVI.mean)) +
geom_point(col="red") +
ylab("mean NDVI") +
xlab("Precipitation") + geom_smooth(method = "lm", se = FALSE)+
annotate("text", label = "Adjusted R-squared = 0.4032", x = 1000, y = 2) +
annotate("text", label = "P-value = 0.008725", x = 1000, y = 1.8) +
annotate("text", label = "S", x = 1000, y = 1.6)
mod2 = lm(env$Species.Richness ~ env$Precip)
summary(mod2)
Call:
lm(formula = env$Species.Richness ~ env$Precip)
Residuals:
Min 1Q Median 3Q Max
-3.65 -2.00 -0.93 2.77 3.86
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.43403 2.53141 -0.17 0.8667
env$Precip 0.00970 0.00222 4.38 0.0009 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.82 on 12 degrees of freedom
Multiple R-squared: 0.615, Adjusted R-squared: 0.583
F-statistic: 19.2 on 1 and 12 DF, p-value: 0.000902
ggplot(env, aes(x=env$Precip, y=env$Species.Richness)) +
geom_point(col="red") +
ylab("Species Richness") +
xlab("Precipitation") + geom_smooth(method = "lm", se = FALSE)+
annotate("text", label = "Adjusted R-squared = 0.5827", x = 1000, y = 20) +
annotate("text", label = "P-value = 0.0009015", x = 1000, y = 19) +
annotate("text", label = "S", x = 1000, y = 18)
mod3 = lm(env$Density ~ env$Precip)
summary(mod3)
Call:
lm(formula = env$Density ~ env$Precip)
Residuals:
Min 1Q Median 3Q Max
-121.31 -59.19 2.09 50.42 152.62
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.8525 73.4331 0.31 0.761
env$Precip 0.1438 0.0643 2.24 0.045 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 81.8 on 12 degrees of freedom
Multiple R-squared: 0.294, Adjusted R-squared: 0.235
F-statistic: 5 on 1 and 12 DF, p-value: 0.0451
ggplot(env, aes(x=env$Precip, y=env$Density)) +
geom_point(col="red") +
ylab("Density") +
xlab("Precipitation") + geom_smooth(method = "lm", se = FALSE)+
annotate("text", label = "Adjusted R-squared = 0.2354", x = 1500, y = 100) +
annotate("text", label = "P-value = 0.04508", x = 1500, y = 80) +
annotate("text", label = "S", x = 1500, y = 60)
mod4 = lm(env$Canopy.Height ~ env$Precip)
summary(mod4)
Call:
lm(formula = env$Canopy.Height ~ env$Precip)
Residuals:
Min 1Q Median 3Q Max
-3.108 -1.264 -0.544 0.972 4.540
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.67820 1.83362 1.46 0.170
env$Precip 0.00414 0.00161 2.58 0.024 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.04 on 12 degrees of freedom
Multiple R-squared: 0.356, Adjusted R-squared: 0.303
F-statistic: 6.64 on 1 and 12 DF, p-value: 0.0242
ggplot(env, aes(x=env$Precip, y=env$Canopy.Height)) +
geom_point(col="red") +
ylab("Canopy Height") +
xlab("Precipitation") + geom_smooth(method = "lm", se = FALSE)+
annotate("text", label = "Adjusted R-squared = 0.3025", x =1500, y = 6) +
annotate("text", label = "P-value = 0.02425", x = 1500, y = 5.5) +
annotate("text", label = "S", x =1500, y = 5.0)
mod5 = lm(env$Basal.Area ~ env$Precip)
summary(mod5)
Call:
lm(formula = env$Basal.Area ~ env$Precip)
Residuals:
Min 1Q Median 3Q Max
-1.6701 -0.7246 -0.0175 0.6655 2.1040
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.24202 0.93684 -0.26 0.8005
env$Precip 0.00292 0.00082 3.56 0.0039 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.04 on 12 degrees of freedom
Multiple R-squared: 0.514, Adjusted R-squared: 0.473
F-statistic: 12.7 on 1 and 12 DF, p-value: 0.00392
ggplot(env, aes(x=env$Precip, y=env$Basal.Area)) +
geom_point(col="red") +
ylab("mean NDVI") +
xlab("Precipitation") + geom_smooth(method = "lm", se = FALSE)+
annotate("text", label = "Adjusted R-squared = 0.5827", x = 1000, y = 6) +
annotate("text", label = "P-value = 0.0009015", x = 1000, y = 5.7) +
annotate("text", label = "S", x = 1000, y = 5.4)
mod6 = lm(env$Substrate.Age ~ env$Precip)
summary(mod6)
Call:
lm(formula = env$Substrate.Age ~ env$Precip)
Residuals:
Min 1Q Median 3Q Max
-2.572 -0.108 0.550 0.881 1.637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.25556 1.28066 -0.98 0.346
env$Precip 0.00329 0.00112 2.93 0.013 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.43 on 12 degrees of freedom
Multiple R-squared: 0.417, Adjusted R-squared: 0.369
F-statistic: 8.59 on 1 and 12 DF, p-value: 0.0126
ggplot(env, aes(x=env$Precip, y=env$Substrate.Age)) +
geom_point(col="red") +
ylab("Substrate Age") +
xlab("Precipitation") + geom_smooth(method = "lm", se = FALSE)+
annotate("text", label = "Adjusted R-squared = 0.3688", x = 1500, y = 2) +
annotate("text", label = "P-value = 0.01257", x = 1500, y = 1.5) +
annotate("text", label = "S", x = 1500, y = 1)
mod6 = lm(env$pca.structure ~ env$Precip)
summary(mod6)
Call:
lm(formula = env$pca.structure ~ env$Precip)
Residuals:
Min 1Q Median 3Q Max
-1.922 -0.582 0.206 0.788 1.066
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.454470 0.860281 -4.02 0.0017 **
env$Precip 0.003168 0.000753 4.21 0.0012 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.958 on 12 degrees of freedom
Multiple R-squared: 0.596, Adjusted R-squared: 0.562
F-statistic: 17.7 on 1 and 12 DF, p-value: 0.00122
ggplot(env, aes(x=env$Precip, y=env$pca.structure)) +
geom_point(col="red") +
ylab("pca.structure") +
xlab("Precipitation") + geom_smooth(method = "lm", se = FALSE)+
annotate("text", label = "Adjusted R-squared = 0.5622", x = 1500, y = -.5) +
annotate("text", label = "P-value = 0.001218", x = 1500, y = -1) +
annotate("text", label = "S", x = 1500, y = -1.5)
