The sim_design() function creates a dataset with a
specific between- and within-subjects design.
Quick example
For example, the following creates a 2w*2b design with 100
observations in each cell. The between-subject factor is
pet with two levels (cat and
dog). The within-subject factor is time with
two levels (day and night).
The data is sampled from a population where the mean for the
cat_day cell is 10, the mean for the cat_night
cell is 20, the mean for the dog_day cell is 15, and the
mean for the dog_night cell is 25. All cells have a SD of 5
and all within-subject cells are correlated r = 0.5.
between <- list(pet = c(cat = "Cat Owners",
dog = "Dog Owners"))
within <- list(time = c("morning", "noon", "evening", "night"))
mu <- data.frame(
cat = c(10, 12, 14, 16),
dog = c(10, 15, 20, 25),
row.names = within$time
)
# add factor labels for plotting
vardesc <- c(pet = "Type of Pet",
time = "Time of Day")
df <- sim_design(within, between,
n = 100, mu = mu, sd = 5, r = .5,
empirical = TRUE, vardesc = vardesc, plot = TRUE)
Specification of design parameters
Factor and level names
Anonymous
If you don’t feel like naming your factors and levels, you can just put in a vector of levels. So you can make a quick 2w*3w*2b with the following code.
df <- sim_design(within = c(2,3), between = c(2),
n = 10, mu = 1:12, sd = 1, r = 0.5)
List of vectors
You can specify between-subject and within-subject factors as a list of vectors where the item names are the factor labels and the vectors are the level labels.
between <- list(
pet = c("cat", "dog")
)
within <- list(
time = c("day", "night")
)
df <- sim_design(within, between, mu = 1:4)
Levels with underscores
In wide format, faux uses an underscore to separate level names. Therefore, any underscores in factor level names will generate an error.
# with default sep = _
within <- list(
A = c("A_1", "A_2"),
B = c("B_1", "B_2")
)
sim_design(within, n = 5, plot = FALSE)
#> Error in check_design(within = within, between = between, n = n, mu = mu, : These level names have the separator '_' in them: A_1, A_2, B_1, B_2
#> Please change the names (see fix_name_labels) or choose another separator character using the sep argument or faux_options(sep = '~')
#> safe separators for your factor labels are: _You can change this default with faux_options().
faux_options(sep = ".")
sim_design(within, n = 5, plot = FALSE)
#> id A_1.B_1 A_1.B_2 A_2.B_1 A_2.B_2
#> 1 S1 0.26758568 -1.0427328 -0.76117907 -1.4205876
#> 2 S2 -1.26342118 -0.6933384 -0.27144145 -0.3073318
#> 3 S3 -1.05053275 0.9666903 1.63505565 0.6926959
#> 4 S4 0.65104622 0.2780749 0.77626277 -0.9197527
#> 5 S5 0.01020941 -0.1250620 -0.04939468 0.4310845
# put the separator back to _ for the rest of this vignette
faux_options(sep = "_")List of named vectors/lists
You can also specify factors as a list of named vectors or lists where the item names are the factor labels, the vector names are the level labels that are used in the data table, and the vector items are the long labels for a codebook or plot.
between <- list(
pet = c(cat = "Is a cat person", dog = "Is a dog person")
)
within <- list(
time = c(day = "Tested during the day", night = "Tested at night")
)
df <- sim_design(within, between, mu = 1:4)
#### Factor labels
Add factor labels with the vardesc argument. You can
also customise the ID and DV column names (they default to
c(id = "id") and c(y = "value")).
vardesc <- c(pet = "Type of Pet",
time = "Time of Day")
df <- sim_design(within, between, mu = 1:4,
id = c(pet_id = "Pet ID"),
dv = c(score = "Score on the Test"),
vardesc = vardesc)
If you have any within-subjects factors and your table is in wide format, you will only see the DV name in plots. But it will be used if you convert the table from wide to long.
Specifying N
You can specify the Ns for each between-subject cell as a single number, named list, or data frame.
Single number
You usually want to specify n as a single number. This
is N per cell, not total sample size.
n <- 20 # n per cell, not total
design <- check_design(2, c(2,2), n = n, plot = FALSE)
str(design$n)
#> List of 4
#> $ B1a_B2a: num 20
#> $ B1a_B2b: num 20
#> $ B1b_B2a: num 20
#> $ B1b_B2b: num 20Named list
You can also specify n as a named list of Ns per
between-subject cell.
n <- list(
B1a_B2a = 10,
B1a_B2b = 20,
B1b_B2a = 30,
B1b_B2b = 40
)
design <- check_design(2, c(2,2), n = n, plot = FALSE)
str(design$n)
#> List of 4
#> $ B1a_B2a: num 10
#> $ B1a_B2b: num 20
#> $ B1b_B2a: num 30
#> $ B1b_B2b: num 40Dataframe
Or as a data frame. You just need to get the row or column names right, but they don’t have to be in the right order.
n <- data.frame(
B1b_B2b = 40,
B1a_B2a = 10,
B1a_B2b = 20,
B1b_B2a = 30
)
design <- check_design(2, c(2,2), n = n, plot = FALSE)
str(design$n)
#> List of 4
#> $ B1a_B2a: num 10
#> $ B1a_B2b: num 20
#> $ B1b_B2a: num 30
#> $ B1b_B2b: num 40You can specify the cells as row names or column names and
check_design() will fix them. Since n
has to be the same for each within-subject factor, you
can specify n as a single column with any name.
n <- data.frame(n = c(10, 20, 30, 40),
row.names = c("B1a_B2a", "B1a_B2b", "B1b_B2a", "B1b_B2b"))
design <- check_design(2, c(2,2), n = n, plot = FALSE)
str(design$n)
#> List of 4
#> $ B1a_B2a: num 10
#> $ B1a_B2b: num 20
#> $ B1b_B2a: num 30
#> $ B1b_B2b: num 40Mu and SD
The specifications for mu and sd need both
within-subject and between-subject cells. You can specify these as a
single numbers, a vector, a named list of named vectors or a data
frame.
Unnamed vector
An unnamed vector is a quick way to specify means and SDs, but the order relative to your between- and within-subject cells can be confusing.
between <- list(pet = c("cat", "dog"),
condition = c("A", "B"))
within <- list(time = c("day", "night"))
mu <- c(10, 20, 30, 40, 50, 60, 70, 80)
design <- check_design(within, between, mu = mu, plot = FALSE)
str(design$mu)
#> List of 4
#> $ cat_A:List of 2
#> ..$ day : num 10
#> ..$ night: num 20
#> $ cat_B:List of 2
#> ..$ day : num 30
#> ..$ night: num 40
#> $ dog_A:List of 2
#> ..$ day : num 50
#> ..$ night: num 60
#> $ dog_B:List of 2
#> ..$ day : num 70
#> ..$ night: num 80Named list of named vectors
A named list of named vectors prevents confusion due to order. The levels of the between-subject factors are the list names and the levels of the within-subject factors are the vector names, but their order doesn’t matter.
mu <- list(
cat_B = c(night = 40, day = 30),
cat_A = c(day = 10, night = 20),
dog_A = c(day = 50, night = 60),
dog_B = c(day = 70, night = 80)
)
design <- check_design(within, between, mu = mu, sd = 1, plot = FALSE)
str(design$mu)
#> List of 4
#> $ cat_A:List of 2
#> ..$ day : num 10
#> ..$ night: num 20
#> $ cat_B:List of 2
#> ..$ day : num 30
#> ..$ night: num 40
#> $ dog_A:List of 2
#> ..$ day : num 50
#> ..$ night: num 60
#> $ dog_B:List of 2
#> ..$ day : num 70
#> ..$ night: num 80Alternatively, you can specify them as data frames.
mu <- data.frame(
cat_A = c(10, 20),
cat_B = c(30, 40),
dog_A = c(50, 60),
dog_B = c(70, 80),
row.names = c("day", "night")
)
design <- check_design(within, between, mu = mu, plot = FALSE)
str(design$mu)
#> List of 4
#> $ cat_A:List of 2
#> ..$ day : num 10
#> ..$ night: num 20
#> $ cat_B:List of 2
#> ..$ day : num 30
#> ..$ night: num 40
#> $ dog_A:List of 2
#> ..$ day : num 50
#> ..$ night: num 60
#> $ dog_B:List of 2
#> ..$ day : num 70
#> ..$ night: num 80If you transpose the dataframe, this works out fine unless your within- and between-subject cells have identical names.
mu <- data.frame(
day = c(10, 30, 50, 70),
night = c(20, 40, 60, 80),
row.names = c("cat_A", "cat_B", "dog_A", "dog_B")
)
design <- check_design(within, between, mu = mu, plot = FALSE)
str(design$mu)
#> List of 4
#> $ cat_A:List of 2
#> ..$ day : num 10
#> ..$ night: num 20
#> $ cat_B:List of 2
#> ..$ day : num 30
#> ..$ night: num 40
#> $ dog_A:List of 2
#> ..$ day : num 50
#> ..$ night: num 60
#> $ dog_B:List of 2
#> ..$ day : num 70
#> ..$ night: num 80Correlations
If you have any within-subject factors, you need to set the correlation for each between-cell. Here, we only have two levels of one within-subject factor, so can only set one correlation per between-cell.
r <- list(
cat_A = .5,
cat_B = .5,
dog_A = .6,
dog_B = .4
)
design <- check_design(within, between, r = r, plot = FALSE)
design$r
#> $cat_A
#> day night
#> day 1.0 0.5
#> night 0.5 1.0
#>
#> $cat_B
#> day night
#> day 1.0 0.5
#> night 0.5 1.0
#>
#> $dog_A
#> day night
#> day 1.0 0.6
#> night 0.6 1.0
#>
#> $dog_B
#> day night
#> day 1.0 0.4
#> night 0.4 1.0Upper right triangle
If you have more than 2 within-subject cells, you can specify each specific correlation in the upper right triangle of the correlation matrix as a vector.
r <- list(
B1a = c(.10, .20, .30,
.40, .50,
.60),
B1b = c(.15, .25, .35,
.45, .55,
.65)
)
design <- check_design(4, 2, r = r, plot = FALSE)
design$r
#> $B1a
#> W1a W1b W1c W1d
#> W1a 1.0 0.1 0.2 0.3
#> W1b 0.1 1.0 0.4 0.5
#> W1c 0.2 0.4 1.0 0.6
#> W1d 0.3 0.5 0.6 1.0
#>
#> $B1b
#> W1a W1b W1c W1d
#> W1a 1.00 0.15 0.25 0.35
#> W1b 0.15 1.00 0.45 0.55
#> W1c 0.25 0.45 1.00 0.65
#> W1d 0.35 0.55 0.65 1.00Correlation matrix
You can also enter the correlation matrix from
cor().
within <- list(cars = c("speed", "dist"))
between <- list(half = c("first", "last"))
r <- list(
first = cor(cars[1:25,]),
last = cor(cars[26:50,])
)
design <- check_design(within, between, r = r, plot = FALSE)
design$r
#> $first
#> speed dist
#> speed 1.0000000 0.6090282
#> dist 0.6090282 1.0000000
#>
#> $last
#> speed dist
#> speed 1.0000000 0.6687654
#> dist 0.6687654 1.0000000Empirical
If you set empirical = TRUE, you will get the
exact means, SDs and correlations you specified. If you set
empirical = FALSE or omit that argument, your data will be
sampled from a population with those parameters, but your dataset will
not have exactly those values (just on average).
between <- list(pet = c("cat", "dog"))
within <- list(time = c("day", "night"))
mu <- list(
cat = c(day = 10, night = 20),
dog = c(day = 30, night = 40)
)
sd <- list(
cat = c(day = 5, night = 10),
dog = c(day = 15, night = 20)
)
r <- list(cat = .5, dog = .6)
df <- sim_design(within, between, n = 100,
mu = mu, sd = sd, r = r,
empirical = TRUE)
| pet | n | var | day | night | mean | sd |
|---|---|---|---|---|---|---|
| cat | 100 | day | 1.0 | 0.5 | 10 | 5 |
| cat | 100 | night | 0.5 | 1.0 | 20 | 10 |
| dog | 100 | day | 1.0 | 0.6 | 30 | 15 |
| dog | 100 | night | 0.6 | 1.0 | 40 | 20 |
More factors
Here is a 2w*3w*2b*2b example. When you have multiple within or
between factors, you need to specify parameters by cell. Cell names are
the level names, in the order they are listed in the within
or between arguments, separated by underscores.
Foe example, if you have one within-subject factor of condition with
levels con and inc, and another within-subject
factor of version with levels easy, med, and
hard, your cell labels will be: con_easy,
inc_easy, con_med, inc_med,
con_hard, and inc_hard.
If you have any characters in your level names except letters and
numbers, they will be replaced by a full stop (e.g.,
my super-good level_name will become
my.super.good.level.name).
within <- list(
condition = c(con = "Mean of congruent trials",
inc = "Mean of incongruent trials"),
version = c(easy = "Easy",
med = "Medium",
hard = "Difficult")
)
between <- list(
experience = c(novice = "Novice", expert = "Expert"),
time = c(day = "Before 5pm", night = "After 5pm")
)
mu <- data.frame(
row.names = c("con_easy", "con_med", "con_hard",
"inc_easy", "inc_med", "inc_hard"),
novice_day = 10:15,
novice_night = 11:16,
expert_day = 9:14,
expert_night = 10:15
)You can set the correlation for each between-cell to a single number.
r <- list(
novice_day = 0.3,
novice_night = 0.2,
expert_day = 0.5,
expert_night = 0.4
)Or you can set the full correlation matrix with a vector or matrix. Since we have 6 within-cells, this is a 6x6 matrix or a vector of the upper right 15 values.
# upper right triangle correlation specification
# inc and con have r = 0.5 within each difficultly level, 0.2 otherwise
# ce, ie, cm, im, ch, ih
triangle <- c(0.5, 0.2, 0.2, 0.2, 0.2, #con_easy
0.2, 0.2, 0.2, 0.2, #inc_easy
0.5, 0.2, 0.2, #con_med
0.2, 0.2, #inc_med
0.5) #con_hard
#inc_hard
r <- list(
novice_day = triangle,
novice_night = triangle,
expert_day = triangle,
expert_night = triangle
)You can set long = TRUE to return the data frame in long
format, which is usually easier for plotting.
df <- sim_design(within, between, n = 100,
mu = mu, sd = 2, r = r,
dv = c(rt = "Reaction Time"),
plot = FALSE, long = TRUE)
head(df)
#> id experience time condition version rt
#> 1 S001 novice day con easy 9.612842
#> 2 S002 novice day con easy 9.339860
#> 3 S003 novice day con easy 8.146942
#> 4 S004 novice day con easy 11.587107
#> 5 S005 novice day con easy 7.536527
#> 6 S006 novice day con easy 11.726081Multiple datasets
You might want to make multiple datasets with the same design for
simulations. It can be inefficient to just put sim_design()
in an apply function or a for loop (especially if you leave plot =
TRUE). The patterns below make this faster.
rep argument
You can simulate multiple datasets by setting the rep
argument of sim_design() to a number greater than 1. This
will return a nested data frame with one column called rep
and another column called data, that contains each
individual data frame. This method is faster than repeatedly calling
sim_design(), which will check the syntax of your design
each time, and returns a nested dataset that makes it easy to apply the
same analysis to each replicate.
The code below creates 5 data frames with a 2W*2B design.
df <- sim_design(within = 2, between = 2,
n = 50, mu = c(1, 1, 1, 1.5),
sd = 1, r = 0.5, plot = FALSE,
long = TRUE, rep = 5)
df
#> rep
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5
#> data
#> 1 S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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#> 4 S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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#> 5 S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1.13210993968314, 1.76896305566814, 3.48572911798513, 1.41233612186743, 2.82461287595284, 0.766380639932241, 2.04150889429403You can access an individual data frame using
df$data[[1]] or run the same function on each data frame
using a pattern like below:
# using tidyverse functions
analyse <- function(data) {
stats::aov(y ~ B1 * W1 + Error(id/W1), data = data) %>%
broom::tidy()
}
df %>%
dplyr::mutate(analysis = lapply(data, analyse)) %>%
dplyr::select(-data) %>%
tidyr::unnest(analysis)
#> # A tibble: 25 × 8
#> rep stratum term df sumsq meansq statistic p.value
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 id B1 1 11.9 11.9 8.52 0.00436
#> 2 1 id Residuals 98 137. 1.40 NA NA
#> 3 1 id:W1 W1 1 0.550 0.550 1.01 0.316
#> 4 1 id:W1 B1:W1 1 0.464 0.464 0.855 0.357
#> 5 1 id:W1 Residuals 98 53.2 0.542 NA NA
#> 6 2 id B1 1 12.4 12.4 8.03 0.00558
#> 7 2 id Residuals 98 151. 1.54 NA NA
#> 8 2 id:W1 W1 1 1.39 1.39 3.18 0.0776
#> 9 2 id:W1 B1:W1 1 0.266 0.266 0.611 0.436
#> 10 2 id:W1 Residuals 98 42.8 0.436 NA NA
#> # … with 15 more rowsUnnested reps
You can also get an unnested data frame by setting
nested = FALSE. This is a bit faster to return, but less
useful for the pattern above.
df <- sim_design(within = 2, between = 2,
n = 2, mu = c(1, 1, 1, 1.5),
sd = 1, r = 0.5, plot = FALSE,
long = TRUE, rep = 2, nested = FALSE)
df
#> rep id B1 W1 y
#> 1 1 S1 B1a W1a -0.6145438
#> 2 1 S2 B1a W1a 1.5815508
#> 3 1 S3 B1b W1a -0.9296229
#> 4 1 S4 B1b W1a 0.9573383
#> 5 1 S1 B1a W1b -0.2703416
#> 6 1 S2 B1a W1b 1.4611508
#> 7 1 S3 B1b W1b 2.0628431
#> 8 1 S4 B1b W1b 2.5993118
#> 9 2 S1 B1a W1a 0.7399179
#> 10 2 S2 B1a W1a 1.5328323
#> 11 2 S3 B1b W1a 0.2531167
#> 12 2 S4 B1b W1a 2.1176671
#> 13 2 S1 B1a W1b -0.3890839
#> 14 2 S2 B1a W1b 1.6844656
#> 15 2 S3 B1b W1b 1.9668646
#> 16 2 S4 B1b W1b 2.5087846Simulate by design
The check_design() function converts any abbreviated
design specification to the fully expanded version; it checks your
design specification when you run sim_design() or you can
run it on its own to create a validated design list.
between <- list(pet = c("cat", "dog"))
within <- list(time = c("day", "night"))
vardesc <- c(pet = "Type of Pet",
time = "Time of Day")
design <- check_design(within, between, n = 10,
mu = 1:4, sd = 1:4, r = 0.5,
vardesc = vardesc, plot = FALSE)
design- [DV] y: value
- [ID] id: id
- Within-subject variables:
- time: Time of Day:
- day: day
- night: night
- time: Time of Day:
- Between-subject variables:
- pet: Type of Pet:
- cat: cat
- dog: dog
- pet: Type of Pet:
- Parameters: |pet |time | day| night| n| mu| sd| |:—|:—–|—:|—–:|–:|–:|–:| |cat |day | 1.0| 0.5| 10| 1| 1| |cat |night | 0.5| 1.0| 10| 2| 2| |dog |day | 1.0| 0.5| 10| 3| 3| |dog |night | 0.5| 1.0| 10| 4| 4|
You can then use the design object to create simulated datasets. If
you set the design for sim_design() with the
design argument instead of specifying parameters, it will
skip the time-consuming checks, which can speed up simulations by 30% or
more.
data <- sim_design(design = design)