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PulledStats
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# INFILTRATION
> model.I1a = lm (HC_mmh ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = a_Mou) # model 1
> Anova(model.I1a, type="II")
Anova Table (Type II tests)
Response: HC_mmh
Sum Sq Df F value Pr(>F)
psandMatrix 47.8 1 0.1360 0.7137
Vegetation 9702.9 1 27.5807 2.415e-06 ***
psandMatrix:Vegetation 266.0 1 0.7562 0.3882
Residuals 19700.9 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.I1b = lm (HC_mmh ~ psandMatrix + Vegetation, data = a_Mou) # model 2
> Anova(model.I1a, type="II")
Anova Table (Type II tests)
Response: HC_mmh
Sum Sq Df F value Pr(>F)
psandMatrix 47.8 1 0.1360 0.7137
Vegetation 9702.9 1 27.5807 2.415e-06 ***
psandMatrix:Vegetation 266.0 1 0.7562 0.3882
Residuals 19700.9 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> Anova(model.I1b, type="II")
Anova Table (Type II tests)
Response: HC_mmh
Sum Sq Df F value Pr(>F)
psandMatrix 47.8 1 0.1365 0.7131
Vegetation 9702.9 1 27.6992 2.232e-06 ***
Residuals 19966.9 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.I1b)
Call:
lm(formula = HC_mmh ~ psandMatrix + Vegetation, data = a_Mou)
Residuals:
Min 1Q Median 3Q Max
-27.146 -7.121 -2.615 3.086 64.140
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.68422 13.12920 -0.052 0.959
psandMatrix 0.06385 0.17279 0.370 0.713
VegetationGrassy 25.43355 4.83252 5.263 2.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 18.72 on 57 degrees of freedom
Multiple R-squared: 0.328, Adjusted R-squared: 0.3045
F-statistic: 13.91 on 2 and 57 DF, p-value: 1.2e-05
> model.I2a = lm (HC_mmh ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = b_Ped)
> Anova(model.I2a, type="II")
Anova Table (Type II tests)
Response: HC_mmh
Sum Sq Df F value Pr(>F)
psandMatrix 1572.0 1 2.9870 0.0894476 .
Vegetation 7410.0 1 14.0802 0.0004178 ***
psandMatrix:Vegetation 2045.1 1 3.8861 0.0536371 .
Residuals 29471.2 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.I2b = lm (HC_mmh ~ psandMatrix + Vegetation, data = b_Ped)
> Anova(model.I2b, type="II")
Anova Table (Type II tests)
Response: HC_mmh
Sum Sq Df F value Pr(>F)
psandMatrix 1572 1 2.843 0.0972382 .
Vegetation 7410 1 13.402 0.0005514 ***
Residuals 31516 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> Summary(model.I2b)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘Summary’ for signature ‘"lm"’
> summary(model.I2b)
Call:
lm(formula = HC_mmh ~ psandMatrix + Vegetation, data = b_Ped)
Residuals:
Min 1Q Median 3Q Max
-28.844 -14.344 -2.617 7.355 97.479
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -14.1814 16.4949 -0.860 0.393534
psandMatrix 0.3660 0.2171 1.686 0.097238 .
VegetationGrassy 22.2262 6.0714 3.661 0.000551 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.51 on 57 degrees of freedom
Multiple R-squared: 0.2215, Adjusted R-squared: 0.1942
F-statistic: 8.108 on 2 and 57 DF, p-value: 0.0007967
> model.I3a = lm (HC_mmh ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = c_Mat)
> Anova(model.I3a, type="II")
Anova Table (Type II tests)
Response: HC_mmh
Sum Sq Df F value Pr(>F)
psandMatrix 12300 1 8.1422 0.00605 **
Vegetation 0 1 0.0002 0.98823
psandMatrix:Vegetation 48 1 0.0318 0.85903
Residuals 84596 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.I3b = lm (HC_mmh ~ psandMatrix + Vegetation, data = c_Mat)
> Anova(model.I3b, type="II")
Anova Table (Type II tests)
Response: HC_mmh
Sum Sq Df F value Pr(>F)
psandMatrix 12300 1 8.2829 0.005624 **
Vegetation 0 1 0.0002 0.988129
Residuals 84644 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.I3b)
Call:
lm(formula = HC_mmh ~ psandMatrix + Vegetation, data = c_Mat)
Residuals:
Min 1Q Median 3Q Max
-41.55 -22.80 -13.18 11.73 128.78
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.5833 27.0322 -1.538 0.12951
psandMatrix 1.0239 0.3558 2.878 0.00562 **
VegetationGrassy -0.1487 9.9498 -0.015 0.98813
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 38.54 on 57 degrees of freedom
Multiple R-squared: 0.1269, Adjusted R-squared: 0.09625
F-statistic: 4.142 on 2 and 57 DF, p-value: 0.02092
> # MOISTURE
> model.M1a = lm (AvgMoist ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = a_Mou)
> Anova(model.M1a, type="II")
Anova Table (Type II tests)
Response: AvgMoist
Sum Sq Df F value Pr(>F)
psandMatrix 110.52 1 18.2370 7.621e-05 ***
Vegetation 46.80 1 7.7234 0.007407 **
psandMatrix:Vegetation 24.55 1 4.0510 0.048963 *
Residuals 339.36 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.M1a)
Call:
lm(formula = AvgMoist ~ psandMatrix + Vegetation + psandMatrix *
Vegetation, data = a_Mou)
Residuals:
Min 1Q Median 3Q Max
-4.1851 -1.4592 -0.3885 1.2963 6.3732
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.21295 2.43881 2.958 0.00453 **
psandMatrix -0.04981 0.03267 -1.524 0.13302
VegetationGrassy 8.47873 3.39499 2.497 0.01548 *
psandMatrix:VegetationGrassy -0.09153 0.04548 -2.013 0.04896 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.462 on 56 degrees of freedom
Multiple R-squared: 0.3494, Adjusted R-squared: 0.3145
F-statistic: 10.02 on 3 and 56 DF, p-value: 2.19e-05
> model.M2a = lm (AvgMoist ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = b_Ped)
> Anova(model.M2a, type="II")
Anova Table (Type II tests)
Response: AvgMoist
Sum Sq Df F value Pr(>F)
psandMatrix 176.97 1 16.0431 0.0001843 ***
Vegetation 55.64 1 5.0440 0.0286759 *
psandMatrix:Vegetation 11.73 1 1.0635 0.3068555
Residuals 617.74 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.M2b = lm (AvgMoist ~ psandMatrix + Vegetation, data = b_Ped)
> Anova(model.M2b, type="II")
Anova Table (Type II tests)
Response: AvgMoist
Sum Sq Df F value Pr(>F)
psandMatrix 176.97 1 16.0253 0.0001827 ***
Vegetation 55.64 1 5.0383 0.0286900 *
Residuals 629.48 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.M2b)
Call:
lm(formula = AvgMoist ~ psandMatrix + Vegetation, data = b_Ped)
Residuals:
Min 1Q Median 3Q Max
-5.2845 -2.2627 -0.4908 0.5664 12.0220
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.41280 2.33116 5.325 1.78e-06 ***
psandMatrix -0.12281 0.03068 -4.003 0.000183 ***
VegetationGrassy 1.92598 0.85804 2.245 0.028690 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.323 on 57 degrees of freedom
Multiple R-squared: 0.2702, Adjusted R-squared: 0.2446
F-statistic: 10.55 on 2 and 57 DF, p-value: 0.0001261
> model.M3a = lm (AvgMoist ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = c_Mat)
> Anova(model.M3a, type="II")
Anova Table (Type II tests)
Response: AvgMoist
Sum Sq Df F value Pr(>F)
psandMatrix 375.41 1 22.7086 1.379e-05 ***
Vegetation 0.04 1 0.0023 0.9621
psandMatrix:Vegetation 2.03 1 0.1229 0.7272
Residuals 925.77 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.M3b = lm (AvgMoist ~ psandMatrix + Vegetation, data = c_Mat)
> Anova(model.M3b, type="II")
Anova Table (Type II tests)
Response: AvgMoist
Sum Sq Df F value Pr(>F)
psandMatrix 375.41 1 23.0635 1.176e-05 ***
Vegetation 0.04 1 0.0023 0.9618
Residuals 927.80 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> # PERCENT CLAY
> model.C1a = lm (pclay ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = a_Mou)
> Anova(model.C1, type="II")
Anova Table (Type II tests)
Response: pclay
Sum Sq Df F value Pr(>F)
psandMatrix 1815.12 1 37.3458 9.453e-08 ***
Vegetation 12.87 1 0.2648 0.6089
Residuals 2770.38 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> Anova(model.C1a, type="II")
Anova Table (Type II tests)
Response: pclay
Sum Sq Df F value Pr(>F)
psandMatrix 1815.12 1 38.5313 6.977e-08 ***
Vegetation 12.87 1 0.2732 0.60328
psandMatrix:Vegetation 132.35 1 2.8095 0.09928 .
Residuals 2638.03 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.C1b = lm (pclay ~ psandMatrix + Vegetation, data = a_Mou)
> Anova(model.C1b, type="II")
Anova Table (Type II tests)
Response: pclay
Sum Sq Df F value Pr(>F)
psandMatrix 1815.12 1 37.3458 9.453e-08 ***
Vegetation 12.87 1 0.2648 0.6089
Residuals 2770.38 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.C1b)
Call:
lm(formula = pclay ~ psandMatrix + Vegetation, data = a_Mou)
Residuals:
Min 1Q Median 3Q Max
-13.931 -3.588 -1.351 2.271 20.716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.45640 4.89049 9.090 1.10e-12 ***
psandMatrix -0.39332 0.06436 -6.111 9.45e-08 ***
VegetationGrassy -0.92622 1.80006 -0.515 0.609
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.972 on 57 degrees of freedom
Multiple R-squared: 0.3974, Adjusted R-squared: 0.3763
F-statistic: 18.8 on 2 and 57 DF, p-value: 5.371e-07
> model.C2a = lm (pclay ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = b_Ped)
> Anova(model.C2a, type="II")
Anova Table (Type II tests)
Response: pclay
Sum Sq Df F value Pr(>F)
psandMatrix 1915.59 1 63.1194 9.531e-11 ***
Vegetation 55.70 1 1.8352 0.1810
psandMatrix:Vegetation 11.98 1 0.3948 0.5323
Residuals 1699.53 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.C2b = lm (pclay ~ psandMatrix + Vegetation, data = b_Ped)
> Anova(model.C2b, type="II")
Anova Table (Type II tests)
Response: pclay
Sum Sq Df F value Pr(>F)
psandMatrix 1915.6 1 63.7967 7.215e-11 ***
Vegetation 55.7 1 1.8549 0.1786
Residuals 1711.5 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.C2b)
Call:
lm(formula = pclay ~ psandMatrix + Vegetation, data = b_Ped)
Residuals:
Min 1Q Median 3Q Max
-12.6739 -4.4745 0.3829 3.8032 11.7829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.84426 3.84391 11.146 6.07e-16 ***
psandMatrix -0.40406 0.05059 -7.987 7.22e-11 ***
VegetationGrassy -1.92694 1.41484 -1.362 0.179
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.48 on 57 degrees of freedom
Multiple R-squared: 0.5351, Adjusted R-squared: 0.5188
F-statistic: 32.8 on 2 and 57 DF, p-value: 3.315e-10
> model.C3 = lm (pclay ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = c_Mat)
> model.C3a = lm (pclay ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = c_Mat)
> Anova(model.C3a, type="II")
Anova Table (Type II tests)
Response: pclay
Sum Sq Df F value Pr(>F)
psandMatrix 2231.34 1 107.7969 1.153e-14 ***
Vegetation 0.14 1 0.0066 0.9353
psandMatrix:Vegetation 6.75 1 0.3260 0.5703
Residuals 1159.17 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.C3b = lm (pclay ~ psandMatrix + Vegetation, data = c_Mat)
> Anova(model.C3b, type="II")
Anova Table (Type II tests)
Response: pclay
Sum Sq Df F value Pr(>F)
psandMatrix 2231.34 1 109.0867 7.456e-15 ***
Vegetation 0.14 1 0.0067 0.9349
Residuals 1165.92 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.C3b)
Call:
lm(formula = pclay ~ psandMatrix + Vegetation, data = c_Mat)
Residuals:
Min 1Q Median 3Q Max
-8.4514 -3.0887 -0.3472 2.0839 16.0599
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.36097 3.17262 13.352 < 2e-16 ***
psandMatrix -0.43609 0.04175 -10.444 7.46e-15 ***
VegetationGrassy -0.09574 1.16776 -0.082 0.935
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.523 on 57 degrees of freedom
Multiple R-squared: 0.6568, Adjusted R-squared: 0.6448
F-statistic: 54.54 on 2 and 57 DF, p-value: 5.79e-14
> # DEPTH AFTER 1 STRIKE
> model.D1a = lm (AvgDepth1S ~ psandMatrix + Vegetation +psandMatrix*Vegetation, data = a_Mou)
> Anova(model.D1a, type="II")
Anova Table (Type II tests)
Response: AvgDepth1S
Sum Sq Df F value Pr(>F)
psandMatrix 4.215 1 1.0320 0.3140
Vegetation 22.250 1 5.4481 0.0232 *
psandMatrix:Vegetation 2.422 1 0.5929 0.4445
Residuals 228.705 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.D1b)
Error in summary(model.D1b) : object 'model.D1b' not found
> model.D1b = lm (AvgDepth1S ~ psandMatrix + Vegetation, data = a_Mou)
> Anova(model.D1b, type="II")
Anova Table (Type II tests)
Response: AvgDepth1S
Sum Sq Df F value Pr(>F)
psandMatrix 4.215 1 1.0395 0.31225
Vegetation 22.250 1 5.4873 0.02267 *
Residuals 231.127 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.D1b)
Call:
lm(formula = AvgDepth1S ~ psandMatrix + Vegetation, data = a_Mou)
Residuals:
Min 1Q Median 3Q Max
-1.9591 -1.1202 -0.5906 0.7228 8.9651
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.44280 1.41256 0.313 0.7551
psandMatrix 0.01895 0.01859 1.020 0.3123
VegetationGrassy 1.21793 0.51993 2.342 0.0227 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.014 on 57 degrees of freedom
Multiple R-squared: 0.1026, Adjusted R-squared: 0.07109
F-statistic: 3.258 on 2 and 57 DF, p-value: 0.04575
> model.D2a = lm (AvgDepth1S ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = b_Ped)
> Anova(model.D2a, type="II")
Anova Table (Type II tests)
Response: AvgDepth1S
Sum Sq Df F value Pr(>F)
psandMatrix 0.0301 1 0.0778 0.78136
Vegetation 1.3510 1 3.4882 0.06704 .
psandMatrix:Vegetation 0.0318 1 0.0820 0.77567
Residuals 21.6881 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.D2b = lm (AvgDepth1S ~ psandMatrix + Vegetation , data = b_Ped)
> Anova(model.D2b, type="II")
Anova Table (Type II tests)
Response: AvgDepth1S
Sum Sq Df F value Pr(>F)
psandMatrix 0.0301 1 0.0791 0.77960
Vegetation 1.3510 1 3.5453 0.06482 .
Residuals 21.7199 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(D2b)
Error in summary(D2b) : object 'D2b' not found
> summary(model.D2b)
Call:
lm(formula = AvgDepth1S ~ psandMatrix + Vegetation, data = b_Ped)
Residuals:
Min 1Q Median 3Q Max
-1.52538 -0.41311 -0.04621 0.41734 1.46501
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.432446 0.433024 3.308 0.00163 **
psandMatrix 0.001602 0.005699 0.281 0.77960
VegetationGrassy 0.300107 0.159385 1.883 0.06482 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6173 on 57 degrees of freedom
Multiple R-squared: 0.05975, Adjusted R-squared: 0.02675
F-statistic: 1.811 on 2 and 57 DF, p-value: 0.1728
> model.D3a = lm (AvgDepth1S ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = c_Mat)
> Anova(model.D3a, type="II")
Anova Table (Type II tests)
Response: AvgDepth1S
Sum Sq Df F value Pr(>F)
psandMatrix 5.6895 1 16.1468 0.0001767 ***
Vegetation 0.0258 1 0.0732 0.7877245
psandMatrix:Vegetation 0.6435 1 1.8262 0.1820106
Residuals 19.7323 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.D3b = lm (AvgDepth1S ~ psandMatrix + Vegetation, data = c_Mat)
> Anova(model.Db, type="II")
Error in Anova(model.Db, type = "II") : object 'model.Db' not found
> Anova(model.D3b, type="II")
Anova Table (Type II tests)
Response: AvgDepth1S
Sum Sq Df F value Pr(>F)
psandMatrix 5.6895 1 15.9161 0.0001911 ***
Vegetation 0.0258 1 0.0722 0.7891922
Residuals 20.3758 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.M3a = lm (AvgMoist ~ psandMatrix + Vegetation + psandMatrix*Vegetation, data = c_Mat)
> Anova(model.M3a, type="II")
Anova Table (Type II tests)
Response: AvgMoist
Sum Sq Df F value Pr(>F)
psandMatrix 375.41 1 22.7086 1.379e-05 ***
Vegetation 0.04 1 0.0023 0.9621
psandMatrix:Vegetation 2.03 1 0.1229 0.7272
Residuals 925.77 56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> model.M3b = lm (AvgMoist ~ psandMatrix + Vegetation, data = c_Mat)
> Anova(model.M3b, type="II")
Anova Table (Type II tests)
Response: AvgMoist
Sum Sq Df F value Pr(>F)
psandMatrix 375.41 1 23.0635 1.176e-05 ***
Vegetation 0.04 1 0.0023 0.9618
Residuals 927.80 57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary(model.3b)
Error in summary(model.3b) : object 'model.3b' not found
> summary(model.M3b)
Call:
lm(formula = AvgMoist ~ psandMatrix + Vegetation, data = c_Mat)
Residuals:
Min 1Q Median 3Q Max
-5.077 -2.746 -1.132 2.498 11.420
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.33405 2.83016 6.478 2.34e-08 ***
psandMatrix -0.17887 0.03725 -4.802 1.18e-05 ***
VegetationGrassy 0.05008 1.04171 0.048 0.962
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.035 on 57 degrees of freedom
Multiple R-squared: 0.2881, Adjusted R-squared: 0.2631
F-statistic: 11.53 on 2 and 57 DF, p-value: 6.223e-05
> # tO Plot all data simultaneously IN 3X3X4 PLOT
> # fix structure of data so can label correctly
> go <- dplyr::select(Moist2, Geology:Component, psandMatrix, HC_mmh:pclay, AvgMoist, AvgDepth1S)
> go.l <- gather(go, Stat, Value, HC_mmh:AvgDepth1S)
> # tO Plot all data simultaneously IN 3X3X4 PLOT
> # fix structure of data so can label correctly
> go <- dplyr::select(Moist2, Geology:Component, psandMatrix, HC_mmh:pclay, AvgMoist, AvgDepth1S)
> go.l <- gather(go, Stat, Value, HC_mmh:AvgDepth1S)
> go.l$Stat <- as.factor(go.l$Stat)
> levels(go.l$Stat) <- c("Penentration Depth (cm)", "Avg. % Soil Moisture", "Infiltration (mm/h)", "Soil % Clay")
> go.l$Stat <- factor(go.l$Stat, levels =c( "Infiltration (mm/h)", "Penentration Depth (cm)", "Avg. % Soil Moisture", "Soil % Clay"))
> levels(go.l$Stat) <- c( "Infiltration (mm/h)", "Penentration Depth (cm)", "Avg. % Soil Moisture", "% Clay")
> go.l$Component <- factor(go.l$Component, levels = c("Mound", "Pediment", "Matrix"))
> ggplot(data=go.l, aes(x=psandMatrix, y=Value, color=Vegetation)) + geom_point()+ scale_color_manual(values=c("yellow2","seagreen3"))+
+ facet_grid(Stat~Component, scales="free")+(geom_smooth(method=lm))+labs(color = "Vegetation Cover", x = "Topsoil Texture: % Sand")+
+ theme_bw()+ theme(text = element_text(size = 20), axis.title.y = element_blank(), legend.position = "top")