Data collected by Rick Linthurst (1979) at North Carolina State University for the purpose of identifying the important soil characteristics influencing aerial biomass production of the marsh grass Spartina alterniflora in the Cape Fear Estuary of North Carolina. Three types of Spartina vegetation areas (devegetated “dead” areas, “short” Spartina areas, and “tall” Spartina areas) were sampled in each of three locations (Oak Island, Smith Island, and Snows Marsh)
Samples of the soil substrate from 5 random sites within each location–vegetation type (giving 45 total samples) were analyzed for 14 soil physico-chemical characteristics each month for several months.
Format
A data frame with 45 observations on the following 17 variables.
loclocation, a factor with levels
OISISMtypearea type, a factor with levels
DVEGSHRTTALLbiomassaerial biomass in \(gm^{-2}\), a numeric vector
H2Shydrogen sulfide ppm, a numeric vector
salpercent salinity, a numeric vector
Eh7ester-hydrolase, a numeric vector
pHacidity as measured in water, a numeric vector
bufa numeric vector
Pphosphorus ppm, a numeric vector
Kpotassium ppm, a numeric vector
Cacalcium ppm, a numeric vector
Mgmagnesium ppm, a numeric vector
Nasodium ppm, a numeric vector
Mnmanganese ppm, a numeric vector
Znzinc ppm, a numeric vector
Cucopper ppm, a numeric vector
NH4ammonium ion ppm, a numeric vector
Source
Rawlings, J. O., Pantula, S. G., & Dickey, D. A. (2001). Applied Regression Analysis: A Research Tool, 2nd Ed., Springer New York. Table 5.1.
References
R. A. Linthurst. Aeration, nitrogen, pH and salinity as factors affecting Spartina Alterniflora growth and dieback. PhD thesis, North Carolina State University, 1979.
Examples
data(biomass)
str(biomass)
#> 'data.frame': 45 obs. of 17 variables:
#> $ loc : Factor w/ 3 levels "OI","SI","SM": 1 1 1 1 1 1 1 1 1 1 ...
#> $ type : Factor w/ 3 levels "DVEG","SHRT",..: 1 1 1 1 1 2 2 2 2 2 ...
#> $ biomass: int 676 516 1052 868 1008 436 544 680 640 492 ...
#> $ H2S : int -610 -570 -610 -560 -610 -620 -590 -610 -580 -610 ...
#> $ sal : int 33 35 32 30 33 33 36 30 38 30 ...
#> $ Eh7 : int -290 -268 -282 -232 -318 -308 -264 -340 -252 -288 ...
#> $ pH : num 5 4.75 4.2 4.4 5.55 5.05 4.25 4.45 4.75 4.6 ...
#> $ buf : num 2.34 2.66 4.18 3.6 1.9 3.22 4.5 3.5 2.62 3.04 ...
#> $ P : num 20.2 15.6 18.7 22.8 37.8 ...
#> $ K : num 1442 1299 1154 1045 522 ...
#> $ Ca : num 2150 1845 1750 1674 3360 ...
#> $ Mg : num 5169 4358 4041 3966 4609 ...
#> $ Na : num 35185 28170 26455 25073 31664 ...
#> $ Mn : num 14.29 7.73 17.81 49.15 30.52 ...
#> $ Zn : num 16.5 14 15.3 17.3 22.3 ...
#> $ Cu : num 5.02 4.19 4.79 4.09 4.6 ...
#> $ NH4 : num 59.5 51.4 68.8 82.3 70.9 ...
biomass.mod <- lm (biomass ~ H2S + sal + Eh7 + pH + buf + P + K + Ca + Mg + Na +
Mn + Zn + Cu + NH4,
data=biomass)
car::vif(biomass.mod)
#> H2S sal Eh7 pH buf P K Ca
#> 3.027456 3.387615 1.977447 62.080846 34.431748 1.895804 7.367110 16.662146
#> Mg Na Mn Zn Cu NH4
#> 23.764229 10.351043 6.185628 11.626479 4.829203 8.376506
(cd <- colldiag(biomass.mod, add.intercept=FALSE, center=TRUE))
#> Condition
#> Index Variance Decomposition Proportions
#> H2S sal Eh7 pH buf P K Ca Mg Na Mn
#> 1 1.000 0.002 0.001 0.002 0.001 0.001 0.008 0.000 0.002 0.000 0.000 0.003
#> 2 1.154 0.000 0.000 0.007 0.000 0.000 0.002 0.009 0.001 0.003 0.006 0.001
#> 3 1.750 0.011 0.067 0.066 0.001 0.001 0.008 0.000 0.002 0.000 0.000 0.000
#> 4 1.921 0.118 0.016 0.034 0.000 0.001 0.016 0.000 0.000 0.000 0.000 0.028
#> 5 2.668 0.000 0.110 0.020 0.000 0.001 0.426 0.001 0.004 0.001 0.008 0.000
#> 6 3.136 0.116 0.000 0.360 0.001 0.000 0.000 0.000 0.022 0.000 0.001 0.029
#> 7 3.574 0.077 0.113 0.116 0.000 0.001 0.155 0.002 0.002 0.000 0.008 0.007
#> 8 3.596 0.005 0.008 0.130 0.000 0.002 0.220 0.005 0.005 0.003 0.051 0.120
#> 9 5.447 0.056 0.055 0.156 0.000 0.002 0.019 0.256 0.013 0.000 0.005 0.007
#> 10 5.868 0.202 0.107 0.025 0.002 0.002 0.001 0.292 0.002 0.004 0.130 0.148
#> 11 7.529 0.189 0.027 0.003 0.000 0.040 0.007 0.074 0.181 0.007 0.052 0.136
#> 12 10.427 0.002 0.159 0.034 0.039 0.133 0.049 0.003 0.159 0.147 0.282 0.022
#> 13 12.843 0.001 0.089 0.019 0.000 0.120 0.076 0.283 0.013 0.671 0.247 0.162
#> 14 22.775 0.222 0.248 0.028 0.955 0.697 0.012 0.076 0.596 0.164 0.210 0.338
#> Zn Cu NH4
#> 1 0.003 0.000 0.004
#> 2 0.000 0.009 0.000
#> 3 0.002 0.018 0.000
#> 4 0.001 0.002 0.001
#> 5 0.000 0.001 0.000
#> 6 0.000 0.003 0.035
#> 7 0.001 0.170 0.048
#> 8 0.010 0.042 0.007
#> 9 0.100 0.177 0.127
#> 10 0.000 0.024 0.129
#> 11 0.320 0.089 0.023
#> 12 0.094 0.426 0.240
#> 13 0.453 0.000 0.213
#> 14 0.015 0.041 0.173
# simplified display
print(cd, fuzz=.3)
#> Condition
#> Index Variance Decomposition Proportions
#> H2S sal Eh7 pH buf P K Ca Mg Na Mn Zn Cu
#> 1 1.000 . . . . . . . . . . . . .
#> 2 1.154 . . . . . . . . . . . . .
#> 3 1.750 . . . . . . . . . . . . .
#> 4 1.921 . . . . . . . . . . . . .
#> 5 2.668 . . . . . 0.426 . . . . . . .
#> 6 3.136 . . 0.360 . . . . . . . . . .
#> 7 3.574 . . . . . . . . . . . . .
#> 8 3.596 . . . . . . . . . . . . .
#> 9 5.447 . . . . . . . . . . . . .
#> 10 5.868 . . . . . . . . . . . . .
#> 11 7.529 . . . . . . . . . . . 0.320 .
#> 12 10.427 . . . . . . . . . . . . 0.426
#> 13 12.843 . . . . . . . . 0.671 . . 0.453 .
#> 14 22.775 . . . 0.955 0.697 . . 0.596 . . 0.338 . .
#> NH4
#> 1 .
#> 2 .
#> 3 .
#> 4 .
#> 5 .
#> 6 .
#> 7 .
#> 8 .
#> 9 .
#> 10 .
#> 11 .
#> 12 .
#> 13 .
#> 14 .