VSGFS | R Documentation |
VSGFS: an experiment using an optimized orthogonal array in 72 runs
VSGFS
VSGFS is a data frame of class design
with seven experimental factors and
three response variables. The data have been published in Vasilev et al. (2014).
The experimental factors, all stored as R factors, with their levels are
[,1] | Light | Lght-, Lght+ |
[,2] | ShakFreq | SF-, SF+ |
[,3] | InocSize | IS-, IS+ |
[,4] | FilledVol | FV-, FV0, FV+ |
[,5] | CM | CM-, CM+ |
[,6] | Carbo | Suc, Gluc, Mannit (Sucrose, Glucose, Mannitol) |
[,7] | Cyclodextrin | CD1, CD2, CD3, CD4 (beta, methyl-beta, triacetyl-beta, none) |
The response variables, all stored as numerical variables, are
[,8] | Biomass | fresh weight in g |
[,9] | Content | geraniol content in \mu g per g fresh weight |
[,10] | Yield | geraniol yield in \mu g per flask
|
The data set comes from an experiment that was created with function
oa.design
using the array L72.2.43.3.8.4.1.6.1
.
Column selection within the array was done with option columns="min34"
that picks the first set of columns obtained by function oa.min34
.
(Optimization takes quite a while, so that the design was reconstructed later
by explicitly requesting the optimum set of columns.)
Design creation and the experiment itself were conducted at the Fraunhofer IME in Aachen by Nikolay Vasilev and colleagues. More detail on the experiment and the variables can be found in Vasilev et al. (2014).
The design was created under an R version before 3.6.0. For reproducing its
creation under R 3.6.0 and later, it is therefore necessary to switch
to the previous version of random number generation
(using the RNGkind
function, see examples section). Note that the
previous discrete random uniform random number generator was not perfectly
uniform, especially for very large samples; for randomizing experiments
of typical sizes (like this one), this problem can be neglected.
Ulrike Groemping
Vasilev, N., Schmidt, C., Groemping, U., Fischer, R. and Schillberg, S. (2014). Assessment of Cultivation Factors that Affect Biomass and Geraniol Production in Transgenic Tobacco Cell Suspension Cultures. PLoS ONE 9(8): e104620. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0104620.
See also oacat
, show.oas
, oa.min34
,
oa.design
## code used for creating the data frame
## option levordold is needed, because the level ordering
## changed (improved) with version 0.27
## and the design was originally created with an earlier version
## Not run:
if (getRversion()>='3.6.0') RNGkind(sample.kind="Rounding")
VSGFS <- oa.design(ID=L72.2.43.3.8.4.1.6.1,
nlevels=c(2,2,2,3,2,3,4),
columns=c(4,22,37,46,41,48,52),
factor.names=list(Light=c("Lght-","Lght+"),
ShakFreq=c("SF-","SF+"),
InocSize=c("IS-","IS+"),
FilledVol=c("FV-","FV0", "FV+"),
CM=c("CM-","CM+"),
Sugar=c("Suc", "Gluc", "Mannit"),
CDs=c("CD1","CD2","CD3","CD4")),
seed = 9, randomize=TRUE, levordold=TRUE)
if (getRversion()>='3.6.0') RNGkind(sample.kind="default")
response <- as.data.frame(scan(what=list(Biomass=0, Content=0, Yield=0), sep=" "))
5.80 24.13 139.98
4.97 16.96 84.28
1.28 21.08 26.99
6.83 17.71 120.95
0.86 21.28 18.30
4.09 18.86 77.14
2.39 17.08 40.81
4.05 17.84 72.23
5.84 17.74 103.61
3.38 18.08 61.11
0.40 24.82 9.93
3.86 18.10 69.88
4.58 21.29 97.49
6.29 17.32 108.91
4.85 15.50 75.17
1.25 23.14 28.92
2.09 18.43 38.51
4.26 17.75 75.62
4.78 18.53 88.57
6.63 17.82 118.14
0.77 18.79 14.47
4.89 18.23 89.15
4.53 17.69 80.11
4.27 18.05 77.07
3.90 15.84 61.77
4.15 18.73 77.74
3.95 17.12 67.63
6.92 16.86 116.68
5.00 16.96 84.80
0.37 21.79 8.06
2.36 19.57 46.18
5.11 18.13 92.66
4.69 17.38 81.50
1.20 19.57 23.49
1.76 17.98 31.65
6.21 17.03 105.76
5.63 15.71 88.43
3.98 18.42 73.32
2.31 19.38 44.76
1.86 18.41 34.25
4.22 17.93 75.68
2.77 17.17 47.55
0.40 23.10 9.24
1.42 18.89 26.83
1.54 17.44 26.86
5.03 17.40 87.53
8.70 14.41 125.38
3.21 19.29 61.92
5.36 18.46 98.93
3.87 16.89 65.35
7.70 18.60 143.20
1.71 17.67 30.22
4.38 16.79 73.54
2.24 19.61 43.92
3.79 19.35 73.35
3.09 18.67 57.70
1.57 17.64 27.70
5.43 18.45 100.19
3.86 17.09 65.96
7.44 19.07 141.85
5.87 17.13 100.53
2.65 17.51 46.39
6.14 15.85 97.34
6.32 14.80 93.56
5.19 16.53 85.78
5.09 17.30 88.04
4.40 17.52 77.08
1.68 21.89 36.78
0.93 23.06 21.45
1.79 22.88 40.95
2.64 18.38 48.52
7.78 16.22 126.19
VSGFS <- add.response(VSGFS, response)
VSGFS$Sugar <- relevel(VSGFS$Sugar, "Suc")
VSGFS$FilledVol <- relevel(VSGFS$FilledVol, "FV0")
VSGFS$FilledVol <- relevel(VSGFS$FilledVol, "FV-")
## End(Not run)
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