| Title: | Generator of Experiments |
| Version: | 1.0-21 |
| Date: | 2023-06-19 |
| Author: | Ivan Bezerra Allaman <ivanalaman@gmail.com> and Jose Claudio Faria <joseclaudio.faria@gmail.com> |
| Maintainer: | Ivan Bezerra Allaman <ivanalaman@gmail.com> |
| Depends: | R (≥ 3.5.0) |
| Imports: | mvtnorm, tcltk, jpeg, png |
| Description: | Generates experiments - simulating structured or experimental data as: completely randomized design, randomized block design, latin square design, factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; Naes et al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985; Montgomery, 2001, ISBN:0471316490). |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/ivanalaman/gexp |
| Encoding: | UTF-8 |
| NeedsCompilation: | no |
| Packaged: | 2023-06-20 12:22:57 UTC; iballaman |
| Repository: | CRAN |
| Date/Publication: | 2023-06-21 20:30:06 UTC |
gexp: Generator of Experiments
Description
The package gexp was created to make it possible to plan, create and to model structured experiments, that is, under a experimental design. In the modeling it is possible to simulate results of experiments with possibility of user to report effects and random error(s). The designs are: Completely Randomized Design (CRD), Randomized Complete Block Design (RCBD) and Latin Squares Design (LSD). The types of experiments are: Simple (SIMPLE), Factorial Experiment (FE) and Split-plot Experiment (SPE).
The experiments can be generated with one or more response variables, in the latter case, a strict covariance structure can be imposed. It is also possible to plan experiments using the graphic functions for use in planning from pictures or pictures of the experimental area.
The possible uses are multiple: in the planning it makes possible to distribution and randomization of treatments and experimental units; in the data analysis allows to generate experiments for application in evaluations individual and can also be used to generate experiments for validations of new computational resources in the area of structured data analysis.
In summary, the package provides computational resources useful in planning and modeling of structured experiments in the R.
Details
In some situations, we are interested in simulating a variable randomized according to the experimental procedure where the differences between treatments are predetermined. In a completely randomized design with two treatments for example, we may have an interest in simulating a variable random whose treatment A will have a 1-deviation effect and treatment B a effect of 3 deviations from a given overall average. In addition, may be interested in imposing a pre-established error structure for purposes evaluation in the various analysis strategies.
Author(s)
Ivan Bezerra Allaman <ivanalaman@gmail.com> and Jose Claudio Faria <joseclaudio.faria@gmail.com>.
Maintainer: Ivan Bezerra Allaman <ivanalaman@gmail.com>
Generator of Experiments
Description
The package provides computational resources useful in planning and modeling of structured experiments in the R environment.
The generic function S3 gexp was created to enable plan, create and model structured experiments, that is, under a design experimental. In the modeling it is possible to simulate results of experiments with possibility of user informing the effects and the random error(s). The designs are: Completely Randomized Design (CRD), Randomized complete block design (RCBD) and Latin Squares Design (LSD). The types of experiments are: Factorial Experimentation (FE) and Split-plot experiment (SPE).
The experiments can be generated with one or more variable response, in the latter case, it may be important for a structure covariance between them. It is also possible to plan experiments with of graphic parts for use in planning from figures or pictures of the experimental area.
Usage
gexp(x, ...)
## Default S3 method:
gexp(
x = NULL,
mu = 26,
err = NULL,
errp = NULL,
r = 5L,
fl = NULL,
blkl = NULL,
rowl = NULL,
coll = NULL,
fe = NULL,
inte = NULL,
blke = NULL,
rowe = NULL,
cole = NULL,
contrasts = NULL,
type = c('SIMPLE','FE','SPE'),
design = c('CRD','RCBD','LSD'),
round = 2L,
...)
## S3 method for class 'simple_crd'
gexp(
x, ...)
## S3 method for class 'simple_rcbd'
gexp(
x, ...)
## S3 method for class 'simple_lsd'
gexp(
x, ...)
## S3 method for class 'fe_crd'
gexp(
x, ...)
## S3 method for class 'fe_rcbd'
gexp(
x, ...)
## S3 method for class 'fe_lsd'
gexp(
x, ...)
## S3 method for class 'spe_crd'
gexp(
x, ...)
## S3 method for class 'spe_rcbd'
gexp(
x, ...)
## S3 method for class 'spe_lsd'
gexp(
x, ...)
Arguments
x |
An object of gexp class. |
mu |
Is a |
err |
It is a vector, or |
errp |
It is a vector, or a |
r |
It is a scalar of the number of repetitions of the experiment. |
fl |
List of a vector of characters, or a |
blkl |
List of a vector of characters, or an array for MD, of block names. |
rowl |
List a vector of characters, or an |
coll |
List of a vector of characters, or an |
fe |
It is a numerical vector, or a |
inte |
It is a numerical vector, or a |
blke |
It is a numerical vector, or a |
rowe |
It is a numerical vector, or an |
cole |
Is a |
contrasts |
A |
type |
It is a vector of strings that contains the type of experiment to be used: Simple (SIMPLE), Factorial Experiment (FE) and Split-plot Experiment (SPE). SIMPLE is the default. |
design |
It is a vector of strings that contains the type of design to be used: Completely Randomized Design (CRD), Randomized Complete Block Design (RCBD), Latin Squares Design (LSD). CRD is the default. |
round |
This is a numeric scalar for rounding of the response variable. |
... |
Further arguments (required by generic). |
Value
The method gexp returns the list of class gexp with the slots:
X |
It is the incidence |
Z |
It is the incidence |
Y |
It is a vector, or a |
dfm |
It is a |
References
Ferreira, Daniel Furtado. 2008. Estatistica Multivariada. 1 ed. Lavras: Ed. UFLA.
Aquino, Luiz Henrique. Tecnica Experimental com Animais I. Apostila da disciplina “Tecnica Experimental com Animais” da Universidade Federal de Lavras, 1992.
Rencher, Alvin C. and Schaalje, Bruce G. 2007. Linear Models in Statistics, second edition. Hoboken: John Wiley and Sons.
Naes, T.; Aastveit, A.H.; Sahni, N.S. 2007. "Analysis of split-plot designs: An Overview and Comparison of Methods". Qual. Reliab. Engng. Int. 23, 801-820.
See Also
Examples
#++++++++++++++++++++++++++++#
# UNIVARIATE APPROACH #
#++++++++++++++++++++++++++++#
#!____________________________
#! Qualitative Factor(s) (QL)
#!____________________________
#! Completely Randomized Design (CRD)
#! 1 factor - CRD - QLF
# Nonsense(experimental error = 0)
# Yi = mu + fe + e
r <- 2 # (repet. number)
fln <- 3 # (factor levels number)
crd00 <- gexp(mu = 0,
r = r,
fe = list(f1 = c(1, 2, 3)),
err = matrix(0,
nrow = r*fln),
round = 0)
crd00$X
print(crd00)
summary(crd00)
str(crd00)
#! 1 factor - CRD - QL
# Nonsense(error is 0)
# Yi = mu + fe + e
r <- 3 # (repet. number)
fln <- 5 # (factor levels number)
crd01 <- gexp(mu = 1,
r = r,
fe = list(f1 = c(0, 2, 4, 6, 8)),
err = matrix(0,
nrow = r*fln),
round = 2)
summary(crd01)
#! 1 factor - CRD - QL
# Default error: rmvnorm(sigma = diag(ncol(as.matrix([[fe]]))))
crd_1f <- gexp(mu = 1,
r = 3,
fe = list(f1 = c(1, 1, 5, 1, 1)),
fl = list(Treat = LETTERS[1:5]),
round = 2)
crd_1f$X
summary(crd_1f)
#! Binomial error - CRD - QL
e_binom <- as.matrix(rbinom(n = 15,
size = 5,
prob = 0.1))
crd_bin <- gexp(mu = 20,
err = e_binom,
r = 5,
fe = list(f1 = c(1, 4, 1)))
summary(crd_bin)
mod <- aov(Y1 ~ X1,
data = crd_bin$dfm)
shapiro.test(mod$res)
#! Factorial Experiment (FE) - CRD - QL
fe_crd00 <- gexp(mu = 0,
r = 2,
fe = list(f1 = c(1, 1, 5),
f2 = c(1, 1),
f3 = c(2, 2, 1)),
fl = list(A = paste('a',
1:3,
sep = ''),
B = paste('b',
1:2,
sep = ''),
C = paste('c',
1:3,
sep = '')),
round = 0,
type = 'FE')
fe_crd00$X
summary(fe_crd00)
#! Factorial Experiment (FE) - With interaction - CRD - QL
fe_crd01 <- gexp(mu = 30,
fe = list(f1 = c(1, 1, 3),
f2 = c(1, 1)),
fl = list(A = paste('a',
1:3,
sep = ''),
B = paste('b',
1:2,
sep = '')),
inte = c(3, 1, 1, 1, 1, 5), # (3*2)
round = 1,
type = 'FE')
summary(fe_crd01)
#! Split-plot Experiment (SPE) - CRD - QL
split_crd <- gexp(mu = 30,
fe = list(f1 = c(1, 1),
f2 = c(2, 3)),
fl = list(P = paste('p',
1:2,
sep = ''),
SP = paste('sp',
1:2,
sep = '')),
inte = c(1, 15, 1, 1), # (2*2)
round = 1,
type = 'SPE',
design = 'CRD')
split_crd$X
split_crd$Z
summary(split_crd)
split_crd01 <- gexp(mu = 30,
r = 3,
fe = list(f1 = c(1, 1),
f2 = c(2, 3),
f3 = c(1, 1, 1)),
fl = list(P = paste('p',
1:2,
sep = ''),
A = paste('a',
1:2,
sep = ''),
B = paste('b',
1:3,
sep = '')),
round = 1,
type = 'SPE',
design = 'CRD')
split_crd01$X
split_crd01$Z
summary(split_crd01)
#! Randomized Complete Block Design (RCBD) - QL
# 1 factor, 3 blocks
rcbd <- gexp(mu = 0,
r = 2,
fe = list(f1 = c(5, 1, 1)),
fl = list(TR = LETTERS[1:3]),
blke = c(1, 2, 3),
blkl = list(BLK = paste('B',
1:3,
sep = '')),
round = 1,
design = 'RCBD')
rcbd$X
summary(rcbd)
#! Factorial Experiment (FE) - RCBD - QL
fe_rcbd <- gexp(mu = 30,
r = 2,
fe = list(f1 = c(1, 1, 1),
f2 = c(2, 3)),
blke = c(1, 3),
inte = c(1, 15, 1, 1, 5, 1), # (3*2)
round = 1,
type = 'FE',
design = 'RCBD')
summary(fe_rcbd)
#! Multivariated - RCBD - QL
rcbd_m <- gexp(mu = c(0, 2),
fe = list(f1 = matrix(c(1, 1,
5, 1,
1, 1),
ncol = 2,
byrow = TRUE)),
blke = matrix(c(2, 1,
1, 2,
1, 1),
ncol = 2,
byrow = TRUE),
round = 1,
design = 'RCBD')
summary(rcbd_m)
#! Split-plot Experiment (SPE) - RCBD - QL
split_rcbd <- gexp(mu = 30,
r = 2,
fe = list(f1 = c(1, 1),
f2 = c(2, 3),
f3 = c(1, 1, 1)),
fl = list(P = paste('p',
1:2,
sep = ''),
B = paste('b',
1:2,
sep = ''),
C = paste('c',
1:3,
sep = '')),
blke = c(1, 2),
blkl = list(BLK = paste('B',
1:2,
sep = '')),
round = 1,
type = 'SPE',
design = 'RCBD')
split_rcbd$Z
summary(split_rcbd)
#! Latin Square Design (LSD) - QL
#!. Warning!!!! r = 5 by default
lsd00 <- gexp(design = 'LSD')
#Set r = 1 to omiting warning
lsd01 <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1, 10)),
rowe = c(1, 1, 1),
cole = c(1, 1, 1),
rowl = list(Row = paste('r',
1:3,
sep = '')),
coll = list(Col = paste('c',
1:3,
sep = '')),
round = 0,
design = 'LSD')
summary(lsd01)
#! Factorial Experiment (FE) - LSD - QL
fe_lsd <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1),
f2 = c(2, 3)),
rowe = c(1, 3, 2, 1),
cole = c(2, 2, 1, 1),
rowl = list(Row = paste('r',
1:4,
sep = '')),
coll = list(Col = paste('c',
1:4,
sep = '')),
inte = c(1, 15, 1, 1), # (2*2)
round = 1,
type = 'FE',
design = 'LSD')
summary(fe_lsd)
#! Split-plot Experiment (SPE) - LSD - QL
split_lsd <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1, 2),
f2 = c(2, 3, 1)),
fl = list(P = paste('p',
1:3,
sep = ''),
SP = paste('sp',
1:3,
sep = '')),
inte = c(1, 15, 1, 1, 1, 1, 1, 1, 1), # (3*3)
rowe = c(1, 1, 1),
cole = c(1, 1, 1),
rowl = list(Row = paste('r',
1:3,
sep = '')),
coll = list(Col = paste('c',
1:3,
sep = '')),
round = 1,
type = 'SPE',
design = 'LSD')
summary(split_lsd)
#!_____________________________
#! Quantitative Factor(s) (QT)
#!_____________________________
#! CRD - Orthogonal polynomials
# Linear effect
# Nonsense(error is 0)
# Default contrasts: Orthogonal contrasts
r <- 4 # (repet. number)
fln <- 5 # (factor levels number)
level <- c(0, 10, 20, 30, 40)
crd_lo <- gexp(mu = 1, #in this case, mu=beta0 (intercept)
r = r,
fe = list(f1 = c(2, 0, 0, 0)), #b1 #b2 #b3 #b4
fl = list(Dose = level),
err = matrix(0,
nrow = r*fln),
round = 2)
crd_lo$X
summary(crd_lo)
plot(Y1 ~ Dose,
crd_lo$dfm)
# Quadratic effect
crd_qo <- gexp(mu = 2,
r = r,
fe = list(f1 = c(0, 3, 0, 0)), #b1 #b2 #b3 #b4
fl = list(Dose = level),
err = matrix(0,
nrow = r*fln))
summary(crd_qo)
plot(Y1 ~ Dose,
crd_qo$dfm)
# Cubic effect
crd_co <- gexp(mu = 2,
r = r,
fe = list(f1 = c(1, 1, 3, 0)), #b1 #b2 #b3 #b4
fl = list(Dose = level),
err = matrix(0,
nrow = r*fln))
summary(crd_co)
plot(Y1 ~ Dose,
crd_co$dfm)
# Not orthogonal polynomials
# Linear
cont_crd <- matrix(c(level,
level^2,
level^3,
level^4),
ncol = 4)
crd_l <- gexp(mu = 2,
r = 2,
fe = list(f1 = c(10, 0, 0, 0)), #b1 #b2 #b3 #b4
fl = list(Dose = level),
contrasts = list(Dose = cont_crd))
crd_l$X
summary(crd_l)
plot(Y1 ~ Dose,
crd_l$dfm)
reg <- lm(Y1 ~ Dose + I(Dose^2) + I(Dose^3) + I(Dose^4),
data = crd_l$dfm)
summary(reg)
# Linear and quadratic
level1 <- seq(0,30,by = 10)
cont_crd1 <- matrix(c(level1,
level1^2,
level1^3),
ncol = 3)
level2 <- 1:4
cont_crd2 <- matrix(c(level2,
level2^2,
level2^3),
ncol = 3)
crd_lq <- gexp(mu = 1,
r = 2,
fe = list(f1 = c(10, 0, 0), #b1 #b2 #b3
f2 = c(1, 8, 0)),
fl = list(P = level1,
N = level2),
contrasts = list(N = cont_crd2,
P = cont_crd1))
crd_lq$X
summary(crd_lq)
with(crd_lq$dfm,
plot(Y1 ~ P))
with(crd_lq$dfm,
plot(Y1 ~ N))
# Multivariated
crd_m <- gexp(mu = c(2, 10),
r = 4,
fe = list(f1 = matrix(c(10, 0, #L Q
0, 10,
0, 0),
ncol = 2,
byrow = TRUE)),
fl = list(Dose = level1),
contrasts = list(Dose = cont_crd1))
with(crd_m$dfm,
plot(Y1 ~ Dose))
with(crd_m$dfm,
plot(Y2 ~ Dose))
# RCBD - Orthogonal polynomios
level3 <- c(0, 2, 4, 6)
rcbd <- gexp(mu = 1,
fe = list(f1 = c(3, 0, 0)), #b1 #b2 #b3
blke = c(1, 2, 3),
r = 2,
fl = list(Dose = level3),
blkl = list(Blk = c('B1', 'B2', 'B3')),
design = 'RCBD')
rcbd$X
summary(rcbd)
# Not orthogonal
cont_crd3 <- matrix(c(level3, level3^2, level3^3),
ncol = 3)
rcbd_01 <- gexp(mu = 1,
fe = list(f1 = c(3, 0, 0)), #b1 #b2 #b3
blke = c(1, 2, 3),
r = 2,
fl = list(Dose = level3),
blkl = list(Blk = c('B1', 'B2', 'B3')),
contrasts = list(Dose = cont_crd3),
design = 'RCBD')
rcbd_01$X
summary(rcbd_01)
# Orthogonal polynomios - LSD
lsd <- gexp(mu = 1,
r = 1,
fe = list(f1 = c(3, 0, 0)), #b1 #b2 #b3
rowe = rep(1, 4),
cole = rep(1, 4),
fl = list(Dose = level1),
design = 'LSD')
lsd$X
summary(lsd)
lsd_01 <- gexp(mu = 1,
r = 1,
fe = list(f1 = c(3, 0, 0)), #b1 #b2 #b3
rowe = rep(1, 4),
cole = rep(1, 4),
rowl = list(row = paste('r',
1:4,
sep = '')),
fl = list(Dose = level1),
design = 'LSD')
lsd_01$X
summary(lsd_01)
# Not orthogonal
lsd_02 <- gexp(mu = 1,
r = 1,
fe = list(f1 = c(3, 0, 0)), #b1 #b2 #b3
rowe = rep(1, 4),
cole = rep(1, 4),
fl = list(Dose = level3),
contrasts = list(Dose = cont_crd3),
design = 'LSD')
lsd_02$X
str(lsd_02)
#!__________________________________________________________________________
#! Hibrid: qualitative and quantitative factors in the same experiment - HB
#!__________________________________________________________________________
#! CRD - HB
r <- 2 # (repet. number)
fl1 <- 4# (first factor levels number)
fl2 <- 3# (second factor levels number)
crd_hb <- gexp(mu = 1, #in this case, mu=beta0 (intercept)
r = r,
fe = list(f1 = c(2, 0, 0), #b1 #b2 #b3
f2 = c(1, 1, 3)),
fl = list(Dose = seq(0,30,
by = 10),
Trat = LETTERS[1:3]),
err = matrix(0,
nrow = r*fl1*fl2),
round = 2)
crd_hb$X
summary(crd_hb)
#Only one contrasts!
crd_hb2 <- gexp(mu = 1, #in this case, mu=beta0 (intercept)
r = r,
fe = list(f1 = c(2, 0, 0), #b1 #b2 #b3
f2 = c(1, 1, 3)),
fl = list(Dose = level1,
Trat = LETTERS[1:3]),
err = matrix(0,
nrow = r*fl1*fl2),
contrasts = list(Dose = cont_crd1),
round = 2)
crd_hb2$X
summary(crd_hb)
#! RCBD - HB
r <- 2
blke <- c(1, 2)
level <- c(0, 10, 20, 30)
(error <- matrix(rep(0,
4^1*3^1*r*length(blke)),
ncol=1))
rcbd_hb <- gexp(mu = 2,
err = error,
r = r,
fe = list(f1 = c(0, 1, 0), # Qualitative
f2 = c(1, 0, 0)), # Quantitative linear
fl = list(Var = LETTERS[1:3],
Dose = level),
blke = blke,
blkl = list(Blk = c('B1', 'B2')),
design = 'RCBD')
rcbd_hb$X
summary(rcbd_hb)
str(rcbd_hb)
#! LSD - QT
set.seed(3)
lsd <- gexp(mu = 100,
r = 1,
fe = list(f1 = c(10, # b1
20, # b2
0, # b3
0)), # b4
fl = list(tra = seq(0,
40,
by = 10)),
rowe = c(1, 2, 3, 4, 5),
rowl = list(row = paste('r',
1:5,
sep = '')),
cole = c(5, 4, 3, 2, 1),
coll = list(col = paste('c',
1:5,
sep = '')),
design = 'LSD')
summary(lsd)
plot(Y1 ~ tra, lsd$dfm)
#! FE - LSD - QT
fe_lsd <- gexp(mu = 10,
fe = list(f1 = c(2, 3),
f2 = c(5, # b1*
0, # b2
0, # b3
0)), # b4
rowe = rep(1, 10),
cole = rep(1, 10),
fl = list(var = paste('v',
1:2,
sep = ''),
tra = seq(0,
40,
by = 10)),
coll = list(col = paste('c',
1:10,
sep = '')),
rowl = list(row = paste('r',
1:10,
sep = '')),
type = 'FE',
design = 'LSD')
fe_lsd$X
str(fe_lsd)
summary(fe_lsd)
plot(Y1 ~ tra,
fe_lsd$dfm)
#! SPE - QL - QT
spe_lsd <- gexp(mu = 100,
r = 1,
fe = list(f1 = c(2, 3, 1),
f2 = c(1, # b1
5, # b2*
1)), # b3
fl = list(p = paste('p',
1:3,
sep = ''),
sp = seq(0,
30,
by = 10)),
rowe = c(1, 2, 3),
cole = c(3, 2, 1),
rowl = list(row = paste('r',
1:3,
sep = '')),
coll = list(col = paste('c',
1:3,
sep = '')),
round = 1,
type = 'SPE',
design = 'LSD')
summary(spe_lsd)
plot(spe_lsd)
#++++++++++++++++++++++++++++#
# MULTIVARIATE APPROACH #
#++++++++++++++++++++++++++++#
#! CRD - QL
# Error = 0 - Nonsense (you can easily undertand the effects)
r <- 2 # (repet. number)
fln <- 3 # (factor levels number)
crd_m01 <- gexp(mu = c(0,10),
r = r,
fe = list(f1 = matrix(c(1, 0, #Y1 Y2
2, 1,
3, 3),
ncol = 2,
byrow = TRUE)),
err = mvtnorm::rmvnorm(n = fln * r,
sigma = matrix(c(0, 0,
0, 0),
ncol = 2)),
round = 0)
summary(crd_m01)
#! FE - CRD - QL
r <- 2
crd_m_fe01 <- gexp(mu = c(0, 0),
r = r,
err = mvtnorm::rmvnorm(n = 3^1 * 2^1 * r,
sigma = matrix(c(0, 0,
0, 0),
ncol = 2)),
fe = list(f1 = matrix(c(0, 3, #X1 X1
1, 4, #X2 X2
2, 5), #X3 X3
ncol = 2,
byrow = TRUE),
f2 = matrix(c(0, 2, #X1 X1
1, 3), #X2 X2
ncol = 2,
byrow = TRUE)),
type = 'FE',
round = 1)
summary(crd_m_fe01)
#! FE - CRD - QL
# Using default error
set.seed(30)
crd_m_fe02 <- gexp(mu = c(0, 2),
r = 3,
fe = list(f1 = matrix(c(1, 1,
5, 1,
1, 1),
ncol = 2,
byrow = TRUE),
f2 = matrix(c(1, 3,
2, 2),
ncol = 2,
byrow = TRUE)),
type = 'FE',
round = 1)
summary(crd_m_fe02)
#! SPE - CRD - QL
# Using default error
crd_m_spe01 <- gexp(mu = c(0, 2),
r = 3,
fe = list(f1 = matrix(c(1, 1,
5, 1,
1, 1),
ncol = 2,
byrow = TRUE),
f2 = matrix(c(1, 3,
2, 2),
ncol = 2,
byrow = TRUE)),
type = 'SPE',
round = 1)
summary(crd_m_spe01)
#! RCBD - QL
r <- 2 # (repet. number)
fln <- 3 # (factor levels number)
bln <- 3 # (block levels number)
rcbd_m01 <- gexp(mu = c(0,10),
r = r,
fe = list(f1 = matrix(c(1, 0, #Y1 Y2
2, 1,
3, 3),
ncol = 2,
byrow = TRUE)),
blke = matrix(c(2, 1,
4, 1,
6, 1),
ncol = 2,
byrow = TRUE),
err = mvtnorm::rmvnorm(n = fln * r * bln,
sigma = matrix(c(0, 0,
0, 0),
ncol = 2)),
design = 'RCBD',
round = 0)
summary(rcbd_m01)
#! FE - RCBD - QL
rcbd_m_fe01 <- gexp(mu = c(0, 0),
r = r,
err = mvtnorm::rmvnorm(n = 3^1 * 2^1 * r * bln,
sigma = matrix(c(0, 0,
0, 0),
ncol = 2)),
fe = list(f1 = matrix(c(0, 3, #X1 X1
1, 4, #X2 X2
2, 5), #X3 X3
ncol = 2,
byrow = TRUE),
f2 = matrix(c(0, 2, #X1 X1
1, 3), #X2 X2
ncol = 2,
byrow = TRUE)),
blke = matrix(c(2, 1,
4, 1,
6, 1),
ncol = 2,
byrow = TRUE),
type = 'FE',
design = 'RCBD',
round = 1)
summary(rcbd_m_fe01)
#! SPE - RCBD - QL
rcbd_m_spe01 <- gexp(mu = c(0, 2),
r = 2,
fe = list(f1 = matrix(c(1, 1,
5, 1,
1, 1),
ncol = 2,
byrow = TRUE),
f2 = matrix(c(1, 3,
2, 2),
ncol = 2,
byrow = TRUE),
f3 = matrix(c(1, 3,
2, 2),
ncol = 2,
byrow = TRUE)),
blke = matrix(c(2, 1,
4, 1,
6, 1),
ncol = 2,
byrow = TRUE),
type = 'SPE',
design = 'RCBD',
round = 1)
summary(rcbd_m_spe01)
#! LSD - QL
lsd_m01 <- gexp(mu = c(0,10),
r = 1,
fe = list(f1 = matrix(c(1, 0,
2, 1,
3, 3),
ncol = 2,
byrow = TRUE)),
rowe = matrix(rep(1, 6),
ncol = 2),
cole = matrix(rep(1, 6),
ncol = 2),
err = mvtnorm::rmvnorm(n = 3^2,
sigma = matrix(c(0, 0,
0, 0),
ncol = 2)),
design = 'LSD',
round = 0)
summary(lsd_m01)
#! LSD/FE - QL
lsd_m_fe01 <- gexp(mu = c(0, 0),
r = 1,
err = mvtnorm::rmvnorm(n = 3^1 * 2^1 * 6,
sigma = matrix(c(0, 0,
0, 0),
ncol = 2)),
#Y1 Y2
fe = list(f1 = matrix(c(0, 3, #X1 X1
1, 4, #X2 X2
2, 5), #X3 X3
ncol = 2,
byrow = TRUE),
#Y1 Y2
f2 = matrix(c(0, 2, #X1 X1
1, 3), #X2 X2
ncol = 2,
byrow = TRUE)),
rowe = matrix(rep(1, 12),
ncol = 2),
cole = matrix(rep(1, 12),
ncol = 2),
type = 'FE',
design = 'LSD',
round = 1)
summary(lsd_m_fe01)
#! SPE - LSD - QL
# Using default error
lsd_m_spe01 <- gexp(mu = c(0, 2),
r = 1,
fe = list(f1 = matrix(c(1, 1,
5, 1,
1, 1),
ncol = 2,
byrow = TRUE),
f2 = matrix(c(1, 3,
2, 2),
ncol = 2,
byrow = TRUE)),
rowe = matrix(rep(1, 6),
ncol = 2),
cole = matrix(rep(1, 6),
ncol = 2),
type = 'SPE',
design = 'LSD',
round = 1)
summary(lsd_m_spe01)
#! FE - RCBD - QL
r = 1
bln = 3
fe_rcbd_m <- gexp(mu = c(0, 0),
r = 1,
err = mvtnorm::rmvnorm(n = 3^1 * 2^1 * r * bln,
sigma = matrix(c(0, 0,
0, 0),
ncol = 2)),
fe = list(f1 = matrix(c(0, 3, #X1 X1
1, 4, #X2 X2
2, 5), #X3 X3
ncol = 2,
byrow = TRUE),
f2 = matrix(c(0, 2, #X1 X1
1, 3), #X2 X2
ncol = 2,
byrow = TRUE)),
blke = matrix(c(2, 1,
4, 1,
6, 1),
ncol = 2,
byrow = TRUE),
type = 'FE',
design = 'RCBD')
str(fe_rcbd_m)
summary(fe_rcbd_m)
#! SPE - RCBD - QL
spe_rcbd_m <- gexp(mu = c(0, 2),
r = 3,
fe = list(f1 = matrix(c(1, 1,
5, 1,
1, 1),
ncol = 2,
byrow = TRUE),
f2 = matrix(c(1, 3,
2, 2),
ncol = 2,
byrow = TRUE),
f3 = matrix(c(1, 3,
2, 2),
ncol = 2,
byrow = TRUE)),
blke = matrix(c(2, 1,
4, 1,
6, 1),
ncol = 2,
byrow = TRUE),
type = 'SPE',
design = 'RCBD')
str(spe_rcbd_m)
summary(spe_rcbd_m)
Internal gexp functions
Description
Internal gexp functions.
Details
These are not to be called by the user and are undocumented.
Plot methods for gexp objects
Description
These are methods for objects of class
gexp.simple_crd - Completely Randomized Design (CRD),
gexp.simple_rcbd - Randomized Complete Block Design (RCBD),
gexp.simple_lsd - Latin Squares Design (LSD),
gexp.fe_crd - Factorial Experiment (FE) to CRD,
gexp.fe_rcbd - FE to RCBD and
gexp.fe_lsd - FE to LSD and
gexp.spe_crd - Split-plot Experiment (SPE) to CRD,
gexp.spe_rcbd - SPE to RCBD,
gexp.spe_lsd - SPE to LSD.
The main objective of these methods is to produce an experimental croqui with randomized treatments according with the design and type of experiment. It can be very useful in experiment planning.
Usage
## S3 method for class 'gexp.simple_crd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
## S3 method for class 'gexp.simple_rcbd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
## S3 method for class 'gexp.simple_lsd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
## S3 method for class 'gexp.fe_crd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
## S3 method for class 'gexp.fe_rcbd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
## S3 method for class 'gexp.fe_lsd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
## S3 method for class 'gexp.spe_crd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
srttext = 30,
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
## S3 method for class 'gexp.spe_rcbd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
srttext = 30,
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
## S3 method for class 'gexp.spe_lsd'
plot(x,
main = NULL,
sub = NULL,
colgrid = 'red',
coltext = 'blue',
srttext = 30,
ltygrid = 'dotted',
lwdgrid = par('lwd'),
xleftimg = par()$usr[1],
ybottomimg = par()$usr[3],
xrightimg = par()$usr[2],
ytopimg = par()$usr[4],
dynamic = FALSE,
random = TRUE, ...)
Arguments
x |
A class |
main |
An overall title for the |
sub |
An sub title for the |
coltext |
A color to the text on the |
srttext |
The string rotation in degrees. See |
colgrid |
A color to the grid on the |
ltygrid |
A |
lwdgrid |
A |
xleftimg |
A vector (or scalar) of left x positions. |
ybottomimg |
A vector (or scalar) of bottom y positions. |
xrightimg |
A vector (or scalar) of right x positions. |
ytopimg |
A vector (or scalar) of top y positions. |
dynamic |
A logical argument to plot experimental design using image. |
random |
It is a logical argument when the purpose is to plan experiments so that randomisation of treatments occurs in the experimental units. |
... |
Further arguments (required by generic). |
See Also
Examples
#! CRD
crd <- gexp()
plot(crd) # Default
plot(crd, # Changing some arguments
main = '',
sub = '',
coltext = 'black',
colgrid = 'darkred',
ltygrid = 'solid',
lwdgrid = 3)
#! FE - CRD
crd_fe <- gexp(type = 'FE')
plot(crd_fe)
#! SPE - CRD
split_crd <- gexp(type = 'SPE')
plot(split_crd)
#! RCBD
rcbd <- gexp(design = 'RCBD')
plot(rcbd)
#! FE - RCBD
fe_rcbd <- gexp(r = 2,
type = 'FE',
design = 'RCBD')
plot(fe_rcbd)
fe_rcbd1 <- gexp(r = 1,
blke = c(1, 2),
blkl = list(Blk = c('B1', 'B2')),
type = 'FE',
design = 'RCBD')
plot(fe_rcbd1)
#! SPE - RCBD
split_rcbd <- gexp(r = 2,
type = 'SPE',
design = 'RCBD')
plot(split_rcbd)
#! LSD
lsd <- gexp(r = 1,
design = 'LSD')
plot(lsd)
#! FE - LSD
fe_lsd <- gexp(r = 1,
fe = list(f1 = rep(1, 2),
f2 = rep(1, 2)),
rowe = c(1, 3, 2, 1),
cole = c(2, 2, 1, 1),
rowl = list(Row = paste('r',
1:4,
sep = '')),
coll = list(Col = paste('c',
1:4,
sep = '')),
type = 'FE',
design = 'LSD')
plot(fe_lsd)
#! SPE - LSD
split_lsd <- gexp(r = 1,
type = 'SPE',
design = 'LSD')
plot(split_lsd)
## Not run:
#! Using images in plannig
# CRD
# Dynamic
# Open picture 'crd.jpg' whem requested
crd_i <- gexp(r = 3,
fe = list(f1 = c(1, 1)),
fl = list(Treat = LETTERS[1:2]))
plot(crd_i) # Default
# dynamic plot require 'jpeg' package!
plot(crd_i,
dynamic = TRUE,
xleftimg = 0.6,
ybottomimg = .6,
xrightimg = 1.4,
ytopimg = 1.4)
# RCBD
rcbd_i <- gexp(fe = list(f1 = c(5, 1, 1)),
fl = list(TR = LETTERS[1:3]),
blke = c(1, 2, 3),
blkl = list(BLK = paste('B',
1:3,
sep = '')),
design = 'RCBD')
plot(rcbd_i,
dynamic=TRUE)
# LSD
lsd <- gexp(r = 1,
fe = list(f1 = c(1, 1, 10)),
rowe = c(1, 1, 1),
cole = c(1, 1, 1),
rowl = list(Row = paste('r',
1:3,
sep = '')),
coll = list(Col = paste('c',
1:3,
sep = '')),
design = 'LSD')
#TODO: dynamic plot require 'png' package!
plot(lsd,
dynamic=TRUE)
## End(Not run)
Print for gexp objects.
Description
Print gexp objects.
Usage
## S3 method for class 'gexp'
print(x,
digits=3L, ...)
Arguments
x |
A class |
digits |
Number of decimal digits in the results. The default is |
... |
Further arguments (required by generic). |
See Also
Examples
#! CRD
#! 1 factor - CRD
crd <- gexp(mu = 1,
r = 3,
fe = list(f1 = c(1, 1, 5, 1, 1)),
fl = list(Treat = LETTERS[1:5]),
round = 2)
print(crd)
crd
#! FE - CRD
# 5 factors (f1..f5, level^factor): 3^1 * 2^1 * 3^1 * 2^1 * 4^1 * 2 = 360 experimental units
# 5 factors (f1..f5, level^factor): 3^2 * 2^2 * 5^1 = 180 * 2 = 360 experimental units
crd_fe <- gexp(mu = 0,
r = 2,
fe = list(f1 = c(1, 1, 5),
f2 = c(1, 1),
f3 = c(2, 2, 1),
f4 = c(1, 5),
f5 = c(1, 2, 3, 4, 5)),
fl = list(A = paste('a',
1:3,
sep = ''),
B = paste('b',
1:2,
sep = ''),
C = paste('c',
1:3,
sep = ''),
D = paste('d',
1:2,
sep = ''),
E = paste('e',
1:5,
sep = '')),
round = 0,
type = 'FE')
print(crd_fe)
crd_fe
#! SPE - CRD
split_crd <- gexp(mu = 30,
fe = list(f1 = c(1, 1),
f2 = c(2, 3)),
fl = list(P = paste('p',
1:2,
sep = ''),
SP = paste('sp',
1:2,
sep = '')),
inte = c(1, 15, 1, 1),
round = 1,
type = 'SPE')
print(split_crd)
split_crd
#! RCBD
# 1 factor, 3 blocks
rcbd <- gexp(mu = 0,
fe = list(f1 = c(5, 1, 1)),
fl = list(TR = LETTERS[1:3]),
blke = c(1, 2, 3),
blkl = list(BLK = paste('B',
1:3,
sep = '')),
round = 1,
design = 'RCBD')
print(rcbd)
rcbd
#! FE - RCBD
fe_rcbd <- gexp(mu = 30,
fe = list(f1 = c(1, 1, 1),
f2 = c(2, 3)),
blke = c(1, 3),
round = 1,
type = 'FE',
design = 'RCBD')
print(fe_rcbd)
fe_rcbd
#! SPE - RCBD
split_rcbd <- gexp(mu = 30,
fe = list(f1 = c(1, 1),
f2 = c(2, 3),
f3 = c(1, 1, 1)),
fl = list(A = paste('a',
1:2,
sep = ''),
B = paste('b',
1:2,
sep = ''),
C = paste('c',
1:3,
sep = '')),
blke = c(1, 2),
blkl = list(BLK = paste('B',
1:2,
sep = '')),
inte = c(1, 15, 1, 1, 1, 3, 4, 2, 1, 1, 4, 1,
1, 2, 1, 1,
1, 1, 1, 1, 1, 1,
1, 1, 3, 3, 3, 3),
round = 1,
type = 'SPE',
design = 'RCBD')
print(split_rcbd)
split_rcbd
#! LSD
lsd <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1, 10)),
rowe = c(1, 1, 1),
cole = c(1, 1, 1),
rowl = list(Row = paste('r',
1:3,
sep = '')),
coll = list(Col = paste('c',
1:3,
sep = '')),
round = 1,
design = 'LSD')
print(lsd)
lsd
#! FE - LSD
fe_lsd <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1),
f2 = c(2, 3)),
rowe = c(1, 3, 2, 1),
cole = c(2, 2, 1, 1),
rowl = list(Row = paste('r',
1:4,
sep = '')),
coll = list(Col = paste('c',
1:4,
sep = '')),
inte = c(1, 15, 1, 1),
round = 1,
type = 'FE',
design = 'LSD')
print(fe_lsd)
fe_lsd
#! SPE - LSD
split_lsd <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1, 2),
f2 = c(2, 3, 1)),
fl = list(P = paste('p',
1:3,
sep = ''),
SP = paste('sp',
1:3,
sep = '')),
inte = c(1, 15, 1, 1, 1, 1, 1, 1, 1),
rowe = c(1, 1, 1),
cole = c(1, 1, 1),
rowl = list(Row = paste('r',
1:3,
sep = '')),
coll = list(Col = paste('c',
1:3,
sep = '')),
round = 1,
type = 'SPE',
design = 'LSD')
print(split_lsd)
split_lsd
Summary gexp objects.
Description
Summary gexp objects.
Usage
## S3 method for class 'gexp'
summary(object,
digits=3L, ...)
Arguments
object |
A class |
digits |
Number of decimal digits in the results. The default is |
... |
Further arguments (required by generic). |
Examples
#! CRD
#! 1 factor - CRD
crd <- gexp(mu = 1,
r = 3,
fe = list(f1 = c(1, 1, 5, 1, 1)),
fl = list(Treat = LETTERS[1:5]),
round = 2)
summary(crd)
#! FE - CRD
# 5 factors (f1..f5, level^factor): 3^1 * 2^1 * 3^1 * 2^1 * 4^1 * 2 = 360 experimental units
# 5 factors (f1..f5, level^factor): 3^2 * 2^2 * 5^1 = 180 * 2 = 360 experimental units
crd_fe <- gexp(mu = 0,
r = 2,
fe = list(f1 = c(1, 1, 5),
f2 = c(1, 1),
f3 = c(2, 2, 1),
f4 = c(1, 5),
f5 = c(1, 2, 3, 4, 5)),
fl = list(A = paste('a',
1:3,
sep = ''),
B = paste('b',
1:2,
sep = ''),
C = paste('c',
1:3,
sep = ''),
D = paste('d',
1:2,
sep = ''),
E = paste('e',
1:5,
sep = '')),
round = 0,
type = 'FE')
summary(crd_fe)
#! SPE - CRD
split_crd <- gexp(mu = 30,
fe = list(f1 = c(1, 1),
f2 = c(2, 3)),
fl = list(P = paste('p',
1:2,
sep = ''),
SP = paste('sp',
1:2,
sep = '')),
inte = c(1, 15, 1, 1),
round = 1,
type = 'SPE')
summary(split_crd)
#! RCBD
# 1 factor, 3 blocks
rcbd <- gexp(mu = 0,
fe = list(f1 = c(5, 1, 1)),
fl = list(TR = LETTERS[1:3]),
blke = c(1, 2, 3),
blkl = list(BLK = paste('B',
1:3,
sep = '')),
round = 1,
design = 'RCBD')
summary(rcbd)
#! FE - RCBD
fe_rcbd <- gexp(mu = 30,
fe = list(f1 = c(1, 1, 1),
f2 = c(2, 3)),
blke = c(1, 3),
round = 1,
type = 'FE',
design = 'RCBD')
summary(fe_rcbd)
#! SPE - RCBD
split_rcbd <- gexp(mu = 30,
fe = list(f1 = c(1, 1),
f2 = c(2, 3),
f3 = c(1, 1, 1)),
fl = list(A = paste('a',
1:2,
sep = ''),
B = paste('b',
1:2,
sep = ''),
C = paste('c',
1:3,
sep = '')),
blke = c(1, 2),
blkl = list(BLK = paste('B',
1:2,
sep = '')),
inte = c(1, 15, 1, 1, 1, 3, 4, 2, 1, 1, 4, 1,
1, 2, 1, 1,
1, 1, 1, 1, 1, 1,
1, 1, 3, 3, 3, 3),
round = 1,
type = 'SPE',
design = 'RCBD')
summary(split_rcbd)
#! LSD
lsd <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1, 10)),
rowe = c(1, 1, 1),
cole = c(1, 1, 1),
rowl = list(Row = paste('r',
1:3,
sep = '')),
coll = list(Col = paste('c',
1:3,
sep = '')),
round = 1,
design = 'LSD')
summary(lsd)
#! FE - LSD
fe_lsd <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1),
f2 = c(2, 3)),
rowe = c(1, 3, 2, 1),
cole = c(2, 2, 1, 1),
rowl = list(Row = paste('r',
1:4,
sep = '')),
coll = list(Col = paste('c',
1:4,
sep = '')),
inte = c(1, 15, 1, 1),
round = 1,
type = 'FE',
design = 'LSD')
summary(fe_lsd)
#! SPE - LSD
split_lsd <- gexp(mu = 30,
r = 1,
fe = list(f1 = c(1, 1, 2),
f2 = c(2, 3, 1)),
fl = list(P = paste('p',
1:3,
sep = ''),
SP = paste('sp',
1:3,
sep = '')),
inte = c(1, 15, 1, 1, 1, 1, 1, 1, 1),
rowe = c(1, 1, 1),
cole = c(1, 1, 1),
rowl = list(Row = paste('r',
1:3,
sep = '')),
coll = list(Col = paste('c',
1:3,
sep = '')),
round = 1,
type = 'SPE',
design = 'LSD')
summary(split_lsd)