Add Names
Description
Add names to columns from naming list
Usage
AddNames(
par,
job.names,
job.group = NULL,
keep.par = TRUE,
names.only = FALSE,
...
)
Arguments
par
defined parameter to analyze (e.g., "cor[1,2]")
job.names
names of all parameters in analysis, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
keep.par
logical, indicating whether or not to keep parameter name (e.g., "cor[1,2]"), Default: TRUE
names.only
logical, indicating whether or not to return vector (TRUE) or string with separator (e.g., "cor[1,2]: A vs. B"), Default: FALSE
...
further arguments passed to or from other methods
Examples
par <- "cor[1,2]"
job.names <- c("A","B")
AddNames(par, job.names, keep.par = TRUE)
# [1] "cor[1,2]: A vs. B"
AddNames(par, job.names, keep.par = FALSE)
# [1] "A vs. B"
AddNames(par, job.names, names.only = TRUE)
# [1] "A" "B"
Capitalize Words
Description
capitalize the first letter in each words in a string
Usage
CapWords(s, strict = FALSE)
Arguments
s
string
strict
logical, indicating whether or not string it set to title case , Default: FALSE
Value
returns capitalized string
Examples
CapWords("example eXAMPLE", FALSE)
# [1] "Example EXAMPLE"
CapWords("example eXAMPLE", TRUE)
# [1] "Example Example"
Dataset with Cats
Description
Shamelessly adapted from Field (2017).
Usage
Cats
Format
A data frame with 2000 rows and 4 variables:
Rewardinteger Food or Affection
Danceinteger Yes or No
Alignmentinteger Good or Evil
Ratingsdouble Cats rate their owners (average of multiple seven-point Likert-type scale (1 = Hate ... 7 = Love)
Details
Example data for BFW
Change Names
Description
Change names, colnames or rownames of single items or a list of items
Usage
ChangeNames(
x,
names,
single.items = FALSE,
row.names = FALSE,
param = NULL,
where = NULL,
environment = NULL
)
Arguments
x
list, vector, matrix, dataframe or a list of such items
names
names to insert
single.items
logical, indicating whether or not to use names rather than colnames or rownames, Default: FALSE
row.names
logical, indicating whether or not to use rownames rather than colnames, Default: FALSE
param
Variable name, Default: NULL
where
select parents, Default: NULL
environment
select reference environment, Default: NULL
Value
returns Named items # ABC <- c("1","2","3") # "1" "2" "3" # ChangeNames(ABC, names = c("A","B","C") , single.items = TRUE) # A B C # "1" "2" "3"
Compute HDI
Description
Compute highest density interval (HDI) from posterior output
Usage
ComputeHDI(data, credible.region)
Arguments
data
data to compute HDI from
credible.region
summarize uncertainty by defining a region of most credible values (e.g., 95 percent of the distribution), Default: 0.95
Details
values within the HDI have higher probability density than values outside the HDI, and the values inside the HDI have a total probability equal to the credible region (e.g., 95 percent).
Value
Return HDI
Examples
set.seed(1)
data <-rnorm(100,0,1)
credible.region <- 0.95
ComputeHDI(data,credible.region)
# HDIlo HDIhi
# -1.99 1.60
Contrast Names
Description
utilize the AddNames function to create contrast names
Usage
ContrastNames(items, job.names, col.names)
Arguments
items
items to create names for
job.names
names of all parameters in analysis, Default: NULL
col.names
columns in MCMC to create names from
Diagnose MCMC
Description
MCMC convergence diagnostics
Usage
DiagMCMC(
data.MCMC,
par.name,
job.names,
job.group,
credible.region = 0.95,
monochrome = TRUE,
plot.colors = c("#495054", "#e3e8ea")
)
Arguments
data.MCMC
MCMC chains to diagnose
par.name
parameter to analyze
job.names
names of all parameters in analysis, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
credible.region
summarize uncertainty by defining a region of most credible values (e.g., 95 percent of the distribution), Default: 0.95
monochrome
logical, indicating whether or not to use monochrome colors, else use DistinctColors, Default: TRUE
plot.colors
range of color to use, Default: c("#495054", "#e3e8ea")
Value
list of diagnostic plots
See Also
dev.new ,colorRampPalette ,recordPlot ,graphics.off ,dev.list ,dev.off
par ,layout ,plot.new ,matplot ,abline ,text ,points ,mtext
traceplot ,gelman.plot ,effectiveSize
sd ,acf ,density
Distinct Colors
Description
create vector containing Hex color codes
Usage
DistinctColors(range, random = FALSE)
Arguments
range
number of colors as sequence
random
logical, indicating whether or not to provide random colors, Default: FALSE
Examples
DistinctColors(1:3)
# [1] "#FFFF00" "#1CE6FF" "#FF34FF"
set.seed(1)
DistinctColors(1:3, TRUE)
# [1] "#575329" "#CB7E98" "#D86A78"
ETA
Description
Print estimated time for arrival (ETA)
Usage
ETA(start.time, i, total, results = NULL)
Arguments
start.time
start time (preset variable with Sys.time())
i
incremental steps towards total
total
total number of steps
results
message to display, Default: NULL
See Also
File Name
Description
simple function to construct a file name for data
Usage
FileName(
project = "Project",
subset = NULL,
type = NULL,
name = NULL,
unix = TRUE,
...
)
Arguments
project
name of project, Default: 'Project'
subset
define subset of data, Default: NULL
type
type of data, Default: NULL
name
save name, Default: NULL
unix
logical, indicating whether or not to append unix timestamp, Default: TRUE
...
further arguments passed to or from other methods
Examples
FileName()
# [1] "Project-Name-1528834963"
FileName(project = "Project" ,
subset = "subset" ,
type = "longitudinal" ,
name = "cheese",
unix = FALSE)
# [1] "Projectsubset-longitudinal-cheese"
Find Environment
Description
Find the environment of a selected variable.
Usage
FindEnvironment(x, where = NULL)
Arguments
x
any type of named object
where
select reference environment, Default: NULL
Value
returns Found environment, Default: R_GlobalEnv.
Flatten List
Description
flatten a nested list into a single list
Usage
FlattenList(li, rm.duplicated = TRUE, unname.li = TRUE, rm.empty = TRUE)
Arguments
li
list to flatten
rm.duplicated
logical, indicating whether or not to remove duplicated lists, Default: TRUE
unname.li
logical, indicating whether or not to unname lists, Default: TRUE
rm.empty
logical, indicating whether or not to remove empty lists, Default: TRUE
Examples
li <- list(LETTERS[1:3],
list(letters[1:3],
list(LETTERS[4:6])),
DEF = letters[4:6],
LETTERS[1:3],
list() # Emtpy list
)
print(li)
# [[1]]
# [1] "A" "B" "C"
#
# [[2]]
# [[2]][[1]]
# [1] "a" "b" "c"
#
# [[2]][[2]]
# [[2]][[2]][[1]]
# [1] "D" "E" "F"
#
#
#
# $DEF
# [1] "d" "e" "f"
#
# [[4]]
# [1] "A" "B" "C"
#
# [[5]]
# list()
FlattenList(li)
# [[1]]
# [1] "A" "B" "C"
#
# [[2]]
# [1] "a" "b" "c"
#
# [[3]]
# [1] "D" "E" "F"
#
# [[4]]
# [1] "d" "e" "f"
Gamma Distribution
Description
compute gamma distribution (shape and rate) from mode and standard deviation
Usage
GammaDist(mode, sd)
Arguments
mode
mode from data
sd
standard deviation from data
Examples
GammaDist(1,0.5)
# $shape
# [1] 5.828427
# $rate
# [1] 4.828427
Get Range
Description
simple function to extract columns from data frame
Usage
GetRange(var, range = 1:8, df)
Arguments
var
variable of interest (e.g., V)
range
range of variables with same stem name (e.g., V1, V2, ..., V8) , Default: 1:8
df
data to extract from
Examples
data <- as.data.frame(matrix(1:80,ncol=8))
GetRange("V", c(1,4), data)
# V1 V4
# 1 1 31
# 2 2 32
# 3 3 33
# 4 4 34
# 5 5 35
# 6 6 36
# 7 7 37
# 8 8 38
# 9 9 39
# 10 10 40
Interleave
Description
mix vectors by alternating between them
Usage
Interleave(a, b)
Arguments
a
first vector
b
second vector
Value
mixed vector
Examples
a <- 1:3
b <- LETTERS[1:3]
Interleave(a,b)
# [1] "1" "A" "2" "B" "3" "C"
Compute Inverse HDI
Description
Compute inverse cumulative density function of the distribution
Usage
InverseHDI(
beta,
shape1,
shape2,
credible.region = 0.95,
tolerance = 0.00000001
)
Arguments
beta
density, distribution function, quantile function and random generation for the Beta distribution with parameters shape1 and shape2
shape1
non-negative parameter of the Beta distribution.
shape2
non-negative parameter of the Beta distribution.
credible.region
summarize uncertainty by defining a region of most credible values (e.g., 95 percent of the distribution), Default: 0.95
tolerance
the desired accuracy, Default: 1e-8
Details
values within the HDI have higher probability density than values outside the HDI, and the values inside the HDI have a total probability equal to the credible region (e.g., 95 percent).
Value
Return HDI
See Also
Examples
InverseHDI( qbeta , 554 , 149 )
# HDIlo HDIhi
# 0.758 0.818
Layout
Description
collection of layout sizes
Usage
Layout(x = "a4", layout.inverse = FALSE)
Arguments
x
type of layout, Default: 'a4'
layout.inverse
logical, indicating whether or not to inverse layout (e.g., landscape) , Default: FALSE
Value
width and height of select medium
Examples
Layout()
# [1] 8.3 11.7
Matrix Combinations
Description
Create matrices from combinations of columns
Usage
MatrixCombn(
matrix,
first.stem,
last.stem = NULL,
q.levels,
rm.last = TRUE,
row.means = TRUE
)
Arguments
matrix
matrix to combine
first.stem
first name of columns to use (e.g., "m" for mean)
last.stem
optional last name of columns to use (e.g., "p" for proportions) , Default: NONE
q.levels
number of levels per column
rm.last
logical, indicating whether or not to remove last combination (i.e., m1m2m3m4) , Default: TRUE
row.means
logical, indicating whether or not to compute row means from combined columns, else use row sums, Default: TRUE
Merge MCMC
Description
Merge two or more MCMC simulations
Usage
MergeMCMC(pat, project.dir = "Results/", data.sets)
Arguments
pat
pattern to select MCMC chain from
project.dir
define where to save data, Default: 'Results/'
data.sets
data sets to combine
Value
Merged MCMC chains
See Also
Multi Grep
Description
Use multiple patterns from vector to find element in another vector, with option to remove certain patterns
Usage
MultiGrep(find, from, remove = NULL, value = TRUE)
Arguments
find
vector to find
from
vector to find from
remove
variables to remove, Default: NULL
value
logical, if TRUE returns value, Default: TRUE
Normalize
Description
simple function to normalize data
Usage
Normalize(x)
Arguments
x
numeric vector to normalize
Examples
Normalize(1:10)
# [1] 0.0182 0.0364 0.0545 0.0727 0.0909
# 0.1091 0.1273 0.1455 0.1636 0.1818
Pad Vector
Description
Pad a numeric vector according to the highest value
Usage
PadVector(v)
Arguments
v
numeric vector to pad
Examples
PadVector(1:10)
# [1] "01" "02" "03" "04" "05" "06" "07" "08" "09" "10"
Parse Numbers
Description
simple function to extract numbers from string/vector
Usage
ParseNumber(x, digits = FALSE)
Arguments
x
string or vector
digits
logical, indicating whether or not to extract decimals, Default: FALSE
See Also
Examples
ParseNumber("String1WithNumbers2")
# [1] 1 2
Parse Plot
Description
Display and/or save plots
Usage
ParsePlot(
plot.data,
project.dir = "Results/",
project.name = FileName(name = "Print"),
graphic.type = "pdf",
plot.size = "15,10",
scaling = 100,
plot.aspect = NULL,
save.data = FALSE,
vector.graphic = FALSE,
point.size = 12,
font.type = "serif",
one.file = TRUE,
ppi = 300,
units = "in",
layout = "a4",
layout.inverse = FALSE,
return.files = FALSE,
...
)
Arguments
plot.data
a list of plots
project.dir
define where to save data, Default: 'Results/'
project.name
define name of project, Default: 'FileName(name="Print")'
graphic.type
type of graphics to use (e.g., pdf, png, ps), Default: 'pdf'
plot.size
size of plot, Default: '15,10'
scaling
scale size of plot, Default: 100
plot.aspect
aspect of plot, Default: NULL
save.data
logical, indicating whether or not to save data, Default: FALSE
vector.graphic
logical, indicating whether or not visualizations should be vector or raster graphics, Default: FALSE
point.size
point size used for visualizations, Default: 12
font.type
font type used for visualizations, Default: 'serif'
one.file
logical, indicating whether or not visualizations should be placed in one or several files, Default: TRUE
ppi
define pixel per inch used for visualizations, Default: 300
units
define unit of length used for visualizations, Default: 'in'
layout
define a layout size for visualizations, Default: 'a4'
layout.inverse
logical, indicating whether or not to inverse layout (e.g., landscape) , Default: FALSE
return.files
logical, indicating whether or not to return saved file names
...
further arguments passed to or from other methods
See Also
dev ,
png ,
ps.options ,
recordPlot
head
readPNG
par ,
plot ,
rasterImage
read_pptx ,
add_slide ,
ph_with
dml
Examples
# Create three plots
plot.data <- lapply(1:3, function (i) {
# Open new device
grDevices::dev.new()
# Print plot
plot(1:i)
# Record plot
p <- grDevices::recordPlot()
# Turn off graphics device drive
grDevices::dev.off()
return (p)
} )
# Print plots
ParsePlot(plot.data)
Circlize Plot
Description
Create a circlize plot
Usage
PlotCirclize(
data,
category.spacing = 1.2,
category.inset = c(-0.4, 0),
monochrome = TRUE,
plot.colors = c("#CCCCCC", "#DEDEDE"),
font.type = "serif"
)
Arguments
data
data for circlize plot
category.spacing
spacing between category items , Default: 1.25
category.inset
inset of category items form plot , Default: c(-0.5, 0)
monochrome
logical, indicating whether or not to use monochrome colors, else use DistinctColors, Default: TRUE
plot.colors
range of color to use, Default: c("#CCCCCC", "#DEDEDE")
font.type
font type used for visualizations, Default: 'serif'
See Also
dev ,
recordPlot
legend
circos.par ,
chordDiagram ,
circos.trackPlotRegion ,
circos.clear
Plot Data
Description
Plot data as violin plot visualizing density, box plots to display HDI, whiskers to display standard deviation
Usage
PlotData(data, data.type = "Mean", ...)
Arguments
data
data to plot data from
data.type
define what kind of data is being used, Default: 'Mean'
...
further arguments passed to or from other methods
Plot Mean
Description
Create a (repeated) mean plot
Usage
PlotMean(
data,
monochrome = TRUE,
plot.colors = c("#495054", "#e3e8ea"),
font.type = "serif",
run.repeated = FALSE,
run.split = FALSE,
y.split = FALSE,
ribbon.plot = TRUE,
y.text = "Score",
x.text = NULL,
remove.x = FALSE
)
Arguments
data
MCMC data to plot
monochrome
logical, indicating whether or not to use monochrome colors, else use DistinctColors, Default: TRUE
plot.colors
range of color to use, Default: c("#495054", "#e3e8ea")
font.type
font type used for visualizations, Default: 'serif'
run.repeated
logical, indicating whether or not to use repeated measures plot, Default: FALSE
run.split
logical, indicating whether or not to use split violin plot and compare distribution between groups, Default: FALSE
y.split
logical, indicating whether or not to split within (TRUE) or between groups, Default: FALSE
ribbon.plot
logical, indicating whether or not to use ribbon plot for HDI, Default: TRUE
y.text
label on y axis, Default: 'Score'
x.text
label on x axis, Default: NULL
remove.x
logical, indicating whether or not to show x.axis information, Default: FALSE
See Also
ggproto ,
ggplot2-ggproto ,
aes ,
margin ,
geom_boxplot ,
geom_crossbar ,
geom_path ,
geom_ribbon ,
geom_violin ,
ggplot ,
scale_manual ,
scale_x_discrete ,
theme ,
layer ,
labs
arrange ,
rbind.fill
zero_range
grid.grob ,
grobName ,
unit
approxfun
colorRamp
Plot Nominal
Description
Create a nominal plot
Usage
PlotNominal(
data,
monochrome = TRUE,
plot.colors = c("#CCCCCC", "#DEDEDE"),
font.type = "serif",
bar.dodge = 0.6,
bar.alpha = 0.7,
bar.width = 0.4,
bar.extras.dodge = 0,
bar.border = "black",
bar.label = FALSE,
bar.error = TRUE,
use.cutoff = FALSE,
diff.cutoff = 1,
q.items = NULL
)
Arguments
data
MCMC data to plot
monochrome
logical, indicating whether or not to use monochrome colors, else use DistinctColors, Default: TRUE
plot.colors
range of color to use, Default: c("#CCCCCC", "#DEDEDE")
font.type
font type used for visualizations, Default: 'serif'
bar.dodge
distance between within bar plots, Default: 0.6
bar.alpha
transparency for bar plot, Default: 0.7
bar.width
width of bar plot, Default: 0.4
bar.extras.dodge
dodge of error bar and label, Default: 0
bar.border
color of the bar border, Default: 'black'
bar.label
logical, indicating whether or not to show bar labels, Default: TRUE
bar.error
logical, indicating whether or not to show error bars, Default: TRUE
use.cutoff
logical, indicating whether or not to use a cutoff for keeping plots, Default: FALSE
diff.cutoff
if using a cutoff, determine the percentage that expected and observed values should differ, Default: 1
q.items
which variables should be used in the plot. Defaults to all , Default: NULL
See Also
aes ,margin ,geom_crossbar ,ggplot ,scale_manual ,theme
Plot Param
Description
Create a density plot with parameter values
Usage
PlotParam(
data,
param,
ROPE = FALSE,
monochrome = TRUE,
plot.colors = c("#495054", "#e3e8ea"),
font.type = "serif",
font.size = 4.5,
rope.line = -0.2,
rope.tick = -0.1,
rope.label = -0.35,
line.size = 0.5,
dens.zero.col = "black",
dens.mean.col = "white",
dens.median.col = "white",
dens.mode.col = "black",
dens.rope.col = "black",
scale = FALSE,
y.limits = NULL,
y.breaks = NULL,
x.limits = NULL,
x.breaks = NULL,
plot.title = NULL
)
Arguments
data
MCMC data to plot
param
parameter of interest
ROPE
plot ROPE values, Default: FALSE
monochrome
logical, indicating whether or not to use monochrome colors, else use DistinctColors, Default: TRUE
plot.colors
range of color to use, Default: c("#495054", "#e3e8ea")
font.type
font type used for visualizations, Default: 'serif'
font.size
font size, Default: 4.5
rope.line
size of ROPE lien, Default: -0.2
rope.tick
distance to ROPE tick, Default: -0.1
rope.label
distance to ROPE label, Default: -0.35
line.size
overall line size, Default: 0.5
dens.zero.col
colour of line indicating zero, Default: 'black'
dens.mean.col
colour of line indicating mean value, Default: 'white'
dens.median.col
colour of line indicating median value, Default: 'white'
dens.mode.col
colour of line indicating mode value, Default: 'black'
dens.rope.col
colour of line indicating ROPE value, Default: 'black'
scale
scale x and y axis, Default: FALSE
y.limits
vector of y limits, Default: NULL
y.breaks
vector of y breaks, Default: NULL
x.limits
= vector of x limits, Default: NULL
x.breaks
= vector of x breaks, Default: NULL
plot.title
= title of plot, Default: NULL
Value
Density plot of parameter values
See Also
mutate ,group_by ,join ,select ,slice ,filter
approxfun
aes ,margin ,geom_density ,geom_polygon ,geom_segment ,geom_label ,ggplot ,ggplot_build ,scale_continuous ,theme ,labs
Read File
Description
opens connection to a file
Usage
ReadFile(
file = NULL,
path = "models/",
package = "bfw",
type = "string",
sep = ",",
data.format = "txt",
custom = FALSE
)
Arguments
file
name of file, Default: NULL
path
path to file, Default: 'models/'
package
choose package to open from, Default: 'bfw'
type
Type of file (i.e., text or data), Default: 'string'
sep
symbol to separate data (e.g., comma-delimited), Default: ','
data.format
define what data format is being used, Default: 'csv'
custom
logical, indicating whether or not to use custom file, , Default: FALSE
See Also
Examples
# Print JAGS model for bernoulli trials
cat(ReadFile("stats_bernoulli"))
# model {
# for (i in 1:n){
# x[i] ~ dbern(theta)
# }
# theta ~ dunif(0,1)
# }
Remove Empty
Description
Remove empty elements in vector
Usage
RemoveEmpty(x)
Arguments
x
vector to eliminate NA and blanks
Examples
RemoveEmpty( c("",NA,"","Remains") )
# [1] "Remains"
Remove Garbage
Description
Remove variable(s) and remove garbage from memory
Usage
RemoveGarbage(v)
Arguments
v
variables to remove
Remove Spaces
Description
simple function to remove whitespace
Usage
RemoveSpaces(x)
Arguments
x
string
Examples
RemoveSpaces(" No More S p a c e s")
# [1] "NoMoreSpaces"
Run Contrasts
Description
Compute contrasts from mean and standard deviation (Cohen's d) or frequencies (odds ratio)
Usage
RunContrasts(contrast.type, q.levels, use.contrast, contrasts, data, job.names)
Arguments
contrast.type
type of contrast: "m" indicate means and standard deviations, "o" indicate frequency
q.levels
Number of levels of each variable/column
use.contrast
choose from "between", "within" and "mixed". Between compare groups at different conditions. Within compare a group at different conditions. Mixed compute all comparisons
contrasts
specified contrasts columns
data
data to compute contrasts from
job.names
names of all parameters in analysis, Default: NULL
See Also
Run MCMC
Description
Conduct MCMC simulations using JAGS
Usage
RunMCMC(
jags.model,
params = NULL,
name.list,
data.list,
initial.list = list(),
run.contrasts = FALSE,
use.contrast = "between",
contrasts = NULL,
custom.contrast = NULL,
run.ppp = FALSE,
k.ppp = 10,
n.data,
credible.region = 0.95,
save.data = FALSE,
ROPE = NULL,
merge.MCMC = FALSE,
run.diag = FALSE,
param.diag = NULL,
sep = ",",
monochrome = TRUE,
plot.colors = c("#495054", "#e3e8ea"),
graphic.type = "pdf",
plot.size = "15,10",
scaling = 100,
plot.aspect = NULL,
vector.graphic = FALSE,
point.size = 12,
font.type = "serif",
one.file = TRUE,
ppi = 300,
units = "in",
layout = "a4",
layout.inverse = FALSE,
...
)
Arguments
jags.model
specify which module to use
params
define parameters to observe, Default: NULL
name.list
list of names
data.list
list of data
initial.list
initial values for analysis, Default: list()
run.contrasts
logical, indicating whether or not to run contrasts, Default: FALSE
use.contrast
choose from "between", "within" and "mixed". Between compare groups at different conditions. Within compare a group at different conditions. Mixed compute all comparisons, Default: "between",
contrasts
define contrasts to use for analysis (defaults to all) , Default: NULL
custom.contrast
define contrasts for custom models , Default: NULL
run.ppp
logical, indicating whether or not to conduct ppp analysis, Default: FALSE
k.ppp
run ppp for every kth length of MCMC chains, Default: 10
n.data
sample size for each parameter
credible.region
summarize uncertainty by defining a region of most credible values (e.g., 95 percent of the distribution), Default: 0.95
save.data
logical, indicating whether or not to save data, Default: FALSE
ROPE
define range for region of practical equivalence (e.g., c(-0.05 , 0.05), Default: NULL
merge.MCMC
logical, indicating whether or not to merge MCMC chains, Default: FALSE
run.diag
logical, indicating whether or not to run diagnostics, Default: FALSE
param.diag
define parameters to use for diagnostics, default equals all parameters, Default: NULL
sep
symbol to separate data (e.g., comma-delimited), Default: ','
monochrome
logical, indicating whether or not to use monochrome colors, else use DistinctColors, Default: TRUE
plot.colors
range of color to use, Default: c("#495054", "#e3e8ea")
graphic.type
type of graphics to use (e.g., pdf, png, ps), Default: 'pdf'
plot.size
size of plot, Default: '15,10'
scaling
scale size of plot, Default: 100
plot.aspect
aspect of plot, Default: NULL
vector.graphic
logical, indicating whether or not visualizations should be vector or raster graphics, Default: FALSE
point.size
point size used for visualizations, Default: 12
font.type
font type used for visualizations, Default: 'serif'
one.file
logical, indicating whether or not visualizations should be placed in one or several files, Default: TRUE
ppi
define pixel per inch used for visualizations, Default: 300
units
define unit of length used for visualizations, Default: 'in'
layout
define a layout size for visualizations, Default: 'a4'
layout.inverse
logical, indicating whether or not to inverse layout (e.g., landscape) , Default: FALSE
...
further arguments passed to or from other methods
Value
list containing MCMC chains , MCMC chains as matrix , summary of MCMC, list of name used, list of data, the jags model, running time of analysis and names of saved files
See Also
runjags.options ,run.jags
detectCores
as.mcmc.list ,varnames
rbind.fill
cor ,cov ,sd
mvrnorm
write.table
Single String
Description
determine whether input is a single string
Usage
SingleString(x)
Arguments
x
string
Value
true or false
Examples
A <- "This is a single string"
SingleString(A)
# [1] TRUE
is.character(A)
# [1] TRUE
B <- c("This is a vector" , "containing two strings")
SingleString(B)
# [1] FALSE
is.character(B)
# [1] TRUE
Bernoulli Trials
Description
Conduct bernoulli trials
Usage
StatsBernoulli(
x = NULL,
x.names = NULL,
DF,
params = NULL,
initial.list = list(),
...
)
Arguments
x
predictor variable(s), Default: NULL
x.names
optional names for predictor variable(s), Default: NULL
DF
data for analysis
params
define parameters to observe, Default: NULL
initial.list
initial values for analysis, Default: list()
...
further arguments passed to or from other methods
See Also
Examples
## Create coin toss data: heads = 50 and tails = 50
#fair.coin<- as.matrix(c(rep("Heads",50),rep("Tails",50)))
#colnames(fair.coin) <- "X"
#fair.coin <- bfw(project.data = fair.coin,
# x = "X",
# saved.steps = 50000,
# jags.model = "bernoulli",
# jags.seed = 100,
# ROPE = c(0.4,0.6),
# silent = TRUE)
#fair.coin.freq <- binom.test( 50000 * 0.5, 50000)
## Create coin toss data: heads = 20 and tails = 80
#biased.coin <- as.matrix(c(rep("Heads",20),rep("Tails",80)))
#colnames(biased.coin) <- "X"
#biased.coin <- bfw(project.data = biased.coin,
# x = "X",
# saved.steps = 50000,
# jags.model = "bernoulli",
# jags.seed = 101,
# initial.list = list(theta = 0.7),
# ROPE = c(0.4,0.6),
# silent = TRUE)
#biased.coin.freq <- binom.test( 50000 * 0.8, 50000)
## Print Bayesian and frequentist results of fair coin
#fair.coin$summary.MCMC[,c(3:6,9:12)]
## Mode ESS HDIlo HDIhi ROPElo ROPEhi ROPEin n
## 0.505 50480.000 0.405 0.597 2.070 2.044 95.886 100.00
#sprintf("Frequentist: %.3f [%.3f , %.3f], p = %.3f" ,
# fair.coin.freq$estimate ,
# fair.coin.freq$conf.int[1] ,
# fair.coin.freq$conf.int[2] ,
# fair.coin.freq$p.value)
## [1] "Frequentist: 0.500 [0.496 , 0.504], p = 1.000"
## Print Bayesian and frequentist results of biased coin
#biased.coin$summary.MCMC[,c(3:6,9:12)]
## Mode ESS HDIlo HDIhi ROPElo ROPEhi ROPEin n
## 0.803 50000.000 0.715 0.870 0.000 99.996 0.004 100.000
#sprintf("Frequentist: %.3f [%.3f , %.3f], p = %.3f" ,
# biased.coin.freq$estimate ,
# biased.coin.freq$conf.int[1] ,
# biased.coin.freq$conf.int[2] ,
# biased.coin.freq$p.value)
## [1] "Frequentist: 0.800 [0.796 , 0.803], p = 0.000"
Covariate
Description
Covariate estimations (including correlation and Cronbach's alpha)
Usage
StatsCovariate(
y = NULL,
y.names = NULL,
x = NULL,
x.names = NULL,
DF,
params = NULL,
job.group = NULL,
initial.list = list(),
jags.model,
...
)
Arguments
y
criterion variable(s), Default: NULL
y.names
optional names for criterion variable(s), Default: NULL
x
predictor variable(s), Default: NULL
x.names
optional names for predictor variable(s), Default: NULL
DF
data to analyze
params
define parameters to observe, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
initial.list
initial values for analysis, Default: list()
jags.model
specify which module to use
...
further arguments passed to or from other methods
Value
covariate, correlation and (optional) Cronbach's alpha
See Also
Examples
## Create normal distributed data with mean = 0 and standard deviation = 1
### r = 0.5
#data <- MASS::mvrnorm(n=100,
# mu=c(0, 0),
# Sigma=matrix(c(1, 0.5, 0.5, 1), 2),
# empirical=TRUE)
## Add names
#colnames(data) <- c("X","Y")
## Create noise with mean = 10 / -10 and sd = 1
### r = -1.0
#noise <- MASS::mvrnorm(n=2,
# mu=c(10, -10),
# Sigma=matrix(c(1, -1, -1, 1), 2),
# empirical=TRUE)
## Combine noise and data
#biased.data <- rbind(data,noise)
#
#
## Run analysis on normal distributed data
#mcmc <- bfw(project.data = data,
# y = "X,Y",
# saved.steps = 50000,
# jags.model = "covariate",
# jags.seed = 100,
# silent = TRUE)
## Run robust analysis on normal distributed data
#mcmc.robust <- bfw(project.data = data,
# y = "X,Y",
# saved.steps = 50000,
# jags.model = "covariate",
# run.robust = TRUE,
# jags.seed = 101,
# silent = TRUE)
## Run analysis on data with outliers
#biased.mcmc <- bfw(project.data = biased.data,
# y = "X,Y",
# saved.steps = 50000,
# jags.model = "covariate",
# jags.seed = 102,
# silent = TRUE)
## Run robust analysis on data with outliers
#biased.mcmc.robust <- bfw(project.data = biased.data,
# y = "X,Y",
# saved.steps = 50000,
# jags.model = "covariate",
# run.robust = TRUE,
# jags.seed = 103,
# silent = TRUE)
## Print frequentist results
#stats::cor(data)[2]
## [1] 0.5
#stats::cor(noise)[2]
## [1] -1
#stats::cor(biased.data)[2]
## [1] -0.498
## Print Bayesian results
#mcmc$summary.MCMC
## Mean Median Mode ESS HDIlo HDIhi n
## cor[1,1]: X vs. X 1.000 1.000 0.999 0 1.000 1.000 100
## cor[2,1]: Y vs. X 0.488 0.491 0.496 19411 0.337 0.633 100
## cor[1,2]: X vs. Y 0.488 0.491 0.496 19411 0.337 0.633 100
## cor[2,2]: Y vs. Y 1.000 1.000 0.999 0 1.000 1.000 100
#mcmc.robust$summary.MCMC
## Mean Median Mode ESS HDIlo HDIhi n
## cor[1,1]: X vs. X 1.00 1.000 0.999 0 1.000 1.000 100
## cor[2,1]: Y vs. X 0.47 0.474 0.491 18626 0.311 0.626 100
## cor[1,2]: X vs. Y 0.47 0.474 0.491 18626 0.311 0.626 100
## cor[2,2]: Y vs. Y 1.00 1.000 0.999 0 1.000 1.000 100
#biased.mcmc$summary.MCMC
## Mean Median Mode ESS HDIlo HDIhi n
## cor[1,1]: X vs. X 1.000 1.000 0.999 0 1.000 1.000 102
## cor[2,1]: Y vs. X -0.486 -0.489 -0.505 19340 -0.627 -0.335 102
## cor[1,2]: X vs. Y -0.486 -0.489 -0.505 19340 -0.627 -0.335 102
## cor[2,2]: Y vs. Y 1.000 1.000 0.999 0 1.000 1.000 102
#biased.mcmc.robust$summary.MCMC
## Mean Median Mode ESS HDIlo HDIhi n
## cor[1,1]: X vs. X 1.000 1.000 0.999 0 1.000 1.000 102
## cor[2,1]: Y vs. X 0.338 0.343 0.356 23450 0.125 0.538 102
## cor[1,2]: X vs. Y 0.338 0.343 0.356 23450 0.125 0.538 102
Fit Data
Description
Apply latent or observed models to fit data (e.g., SEM, CFA, mediation)
Usage
StatsFit(
latent = NULL,
latent.names = NULL,
observed = NULL,
observed.names = NULL,
additional = NULL,
additional.names = NULL,
DF,
params = NULL,
job.group = NULL,
initial.list = list(),
model.name,
jags.model,
custom.model = NULL,
run.ppp = FALSE,
run.robust = FALSE,
...
)
Arguments
latent
latenr variables, Default: NULL
latent.names
optional names for for latent variables, Default: NULL
observed
observed variable(s), Default: NULL
observed.names
optional names for for observed variable(s), Default: NULL
additional
supplemental parameters for fitted data (e.g., indirect pathways and total effect), Default: NULL
additional.names
optional names for supplemental parameters, Default: NULL
DF
data to analyze
params
define parameters to observe, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
initial.list
initial values for analysis, Default: list()
model.name
name of model used
jags.model
specify which module to use
custom.model
define a custom model to use (e.g., string or text file (.txt), Default: NULL
run.ppp
logical, indicating whether or not to conduct ppp analysis, Default: FALSE
run.robust
logical, indicating whether or not robust analysis, Default: FALSE
...
further arguments passed to or from other methods
See Also
Cohen's Kappa
Description
Bayesian alternative to Cohen's kappa
Usage
StatsKappa(
x = NULL,
x.names = NULL,
DF,
params = NULL,
initial.list = list(),
...
)
Arguments
x
predictor variable(s), Default: NULL
x.names
optional names for predictor variable(s), Default: NULL
DF
data to analyze
params
define parameters to observe, Default: NULL
initial.list
initial values for analysis, Default: list()
...
further arguments passed to or from other methods
See Also
Examples
## Simulate rater data
#Rater1 <- c(rep(0,20),rep(1,80))
#set.seed(100)
#Rater2 <- c(rbinom(20,1,0.1), rbinom(80,1,0.9))
#data <- data.frame(Rater1,Rater2)
#mcmc <- bfw(project.data = data,
# x = "Rater1,Rater2",
# saved.steps = 50000,
# jags.model = "kappa",
# jags.seed = 100,
# silent = TRUE)
## Print frequentist and Bayesian kappa
#library(psych)
#psych::cohen.kappa(data)$confid[1,]
## lower estimate upper
## 0.6137906 0.7593583 0.9049260
##' mcmc$summary.MCMC
## Mean Median Mode ESS HDIlo HDIhi n
## Kappa[1]: 0.739176 0.7472905 0.7634503 50657 0.578132 0.886647 100
Mean Data
Description
Compute means and standard deviations.
Usage
StatsMean(
y = NULL,
y.names = NULL,
x = NULL,
x.names = NULL,
DF,
params = NULL,
initial.list = list(),
...
)
Arguments
y
criterion variable(s), Default: NULL
y.names
optional names for criterion variable(s), Default: NULL
x
categorical variable(s), Default: NULL
x.names
optional names for categorical variable(s), Default: NULL
DF
User defined data frame, Default: NULL
params
define parameters to observe, Default: NULL
initial.list
Initial values for simulations, Default: list()
...
further arguments passed to or from other methods
Value
mean and standard deviation
Predict Metric
Description
Bayesian alternative to ANOVA
Usage
StatsMetric(
y = NULL,
y.names = NULL,
x = NULL,
x.names = NULL,
DF,
params = NULL,
job.group = NULL,
initial.list = list(),
model.name,
jags.model,
custom.model = NULL,
run.robust = FALSE,
...
)
Arguments
y
criterion variable(s), Default: NULL
y.names
optional names for criterion variable(s), Default: NULL
x
categorical variable(s), Default: NULL
x.names
optional names for categorical variable(s), Default: NULL
DF
data to analyze
params
define parameters to observe, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
initial.list
initial values for analysis, Default: list()
model.name
name of model used
jags.model
specify which module to use
custom.model
define a custom model to use (e.g., string or text file (.txt), Default: NULL
run.robust
logical, indicating whether or not robust analysis, Default: FALSE
...
further arguments passed to or from other methods
See Also
complete.cases ,
sd ,
aggregate ,
median
head
Predict Nominal
Description
Bayesian alternative to chi-square test
Usage
StatsNominal(
x = NULL,
x.names = NULL,
DF,
params = NULL,
job.group = NULL,
initial.list = list(),
model.name,
jags.model,
custom.model = NULL,
...
)
Arguments
x
categorical variable(s), Default: NULL
x.names
optional names for categorical variable(s), Default: NULL
DF
data to analyze
params
define parameters to observe, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
initial.list
initial values for analysis, Default: list()
model.name
name of model used
jags.model
specify which module to use
custom.model
define a custom model to use (e.g., string or text file (.txt), Default: NULL
...
further arguments passed to or from other methods
Examples
## Use cats data
# mcmc <- bfw(project.data = bfw::Cats,
# x = "Reward,Dance,Alignment",
# saved.steps = 50000,
# jags.model = "nominal",
# run.contrasts = TRUE,
# jags.seed = 100)
## Print only odds-ratio and effect sizes
# mcmc$summary.MCMC[ grep("Odds ratio|Effect",
# rownames(mcmc$summary.MCMC)) , c(3:7) ]
## Mode ESS HDIlo HDIhi n
## Effect size: Affection/Food vs. Evil/Good 0.12844 45222 0.00115 0.25510 2000
## Effect size: Affection/Food vs. No/Yes 1.05346 44304 0.90825 1.18519 2000
## Effect size: Affection/Food vs. No/Yes @ Evil 2.58578 30734 2.35471 2.85450 1299
## Effect size: Affection/Food vs. No/Yes @ Good -0.51934 35316 -0.73443 -0.30726 701
## Effect size: Food/Affection vs. Evil/Good -0.12844 45222 -0.25510 -0.00115 2000
## Effect size: Food/Affection vs. No/Yes -1.05346 44304 -1.18519 -0.90825 2000
## Effect size: Food/Affection vs. No/Yes @ Evil -2.58578 30734 -2.85450 -2.35471 1299
## Effect size: Food/Affection vs. No/Yes @ Good 0.51934 35316 0.30726 0.73443 701
## Effect size: No/Yes vs. Evil/Good 1.43361 43603 1.30715 1.55020 2000
## Effect size: Yes/No vs. Evil/Good -1.43361 43603 -1.55020 -1.30715 2000
## Odds ratio: Affection/Food vs. Evil/Good 1.25432 45225 0.99311 1.57765 2000
## Odds ratio: Affection/Food vs. No/Yes 6.49442 44215 5.10392 8.46668 2000
## Odds ratio: Affection/Food vs. No/Yes @ Evil 104.20109 30523 66.55346 169.12331 1299
## Odds ratio: Affection/Food vs. No/Yes @ Good 0.36685 35417 0.25478 0.55982 701
## Odds ratio: Food/Affection vs. Evil/Good 0.77604 45245 0.62328 0.98904 2000
## Odds ratio: Food/Affection vs. No/Yes 0.14586 44452 0.11426 0.18982 2000
## Odds ratio: Food/Affection vs. No/Yes @ Evil 0.00848 31117 0.00527 0.01336 1299
## Odds ratio: Food/Affection vs. No/Yes @ Good 2.44193 35397 1.65204 3.63743 701
## Odds ratio: No/Yes vs. Evil/Good 13.12995 43500 10.58859 16.49207 2000
## Odds ratio: Yes/No vs. Evil/Good 0.07393 43739 0.05909 0.09221 2000
#
## The results indicate that evil cats are 13.13 times more likely than good cats to decline dancing
## Furthermore, when offered affection, evil cats are 104.20 times more likely to decline dancing,
## relative to evil cats that are offered food.
Regression
Description
Simple, multiple and hierarchical regression
Usage
StatsRegression(
y = NULL,
y.names = NULL,
x = NULL,
x.names = NULL,
x.steps = NULL,
x.blocks = NULL,
DF,
params = NULL,
job.group = NULL,
initial.list = list(),
...
)
Arguments
y
criterion variable(s), Default: NULL
y.names
optional names for criterion variable(s), Default: NULL
x
predictor variable(s), Default: NULL
x.names
optional names for predictor variable(s), Default: NULL
x.steps
define number of steps in hierarchical regression , Default: NULL
x.blocks
define which predictors are included in each step (e.g., for three steps "1,2,3") , Default: NULL
DF
data to analyze
params
define parameters to observe, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
initial.list
initial values for analysis, Default: list()
...
further arguments passed to or from other methods
See Also
Softmax Regression
Description
Perform softmax regression (i.e., multinomial logistic regression)
Usage
StatsSoftmax(
y = NULL,
y.names = NULL,
x = NULL,
x.names = NULL,
DF,
params = NULL,
job.group = NULL,
initial.list = NULL,
run.robust = FALSE,
...
)
Arguments
y
criterion variable(s), Default: NULL
y.names
optional names for criterion variable(s), Default: NULL
x
predictor variable(s), Default: NULL
x.names
optional names for predictor variable(s), Default: NULL
DF
data to analyze
params
define parameters to observe, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
initial.list
initial values for analysis, Default: list()
run.robust
logical, indicating whether or not robust analysis, Default: FALSE
...
further arguments passed to or from other methods
See Also
Examples
## Conduct softmax regression on Cats data
### Reward is 0 = Food and 1 = Dance
### Sample 100 datapoints from Cats data
#mcmc <- bfw(project.data = bfw::Cats,
# y = "Alignment",
# x = "Ratings,Reward",
# saved.steps = 50000,
# jags.model = "softmax",
# jags.seed = 100)
## Conduct binominal generalized linear model
#model <- glm(Alignment ~ Ratings + Reward, data=bfw::Cats, family = binomial(link="logit"))
## Print output from softmax
#mcmc$summary.MCMC
#
## Mean Median Mode ESS HDIlo HDIhi n
##beta[1,1]: Evil vs. Ratings 0.000 0.00 -0.000607 0 0.000 0.000 2000
##beta[1,2]: Evil vs. Reward 0.000 0.00 -0.000607 0 0.000 0.000 2000
##beta[2,1]: Good vs. Ratings 1.289 1.29 1.283403 19614 1.187 1.387 2000
##beta[2,2]: Good vs. Reward 1.276 1.27 1.279209 20807 0.961 1.597 2000
##beta0[1]: Intercept: Evil 0.000 0.00 -0.000607 0 0.000 0.000 2000
##beta0[2]: Intercept: Good -7.690 -7.68 -7.659198 17693 -8.472 -6.918 2000
##zbeta[1,1]: Evil vs. Ratings 0.000 0.00 -0.000607 0 0.000 0.000 2000
##zbeta[1,2]: Evil vs. Reward 0.000 0.00 -0.000607 0 0.000 0.000 2000
##zbeta[2,1]: Good vs. Ratings 2.476 2.47 2.464586 19614 2.280 2.664 2000
##zbeta[2,2]: Good vs. Reward 0.501 0.50 0.501960 20807 0.377 0.626 2000
##zbeta0[1]: Intercept: Evil 0.000 0.00 -0.000607 0 0.000 0.000 2000
##zbeta0[2]: Intercept: Good -1.031 -1.03 -1.024178 22812 -1.185 -0.870 2000
#
## Print (truncated) output from GML
## Estimate Std. Error z value Pr(>|z|)
##(Intercept) -6.39328 0.27255 -23.457 < 2e-16 ***
##Ratings 1.28480 0.05136 25.014 < 2e-16 ***
##RewardAffection 1.26975 0.16381 7.751 9.1e-15 ***
Summarize MCMC
Description
The function provide a summary of each parameter of interest (mean, median, mode, effective sample size (ESS), HDI and n)
Usage
SumMCMC(
par,
par.names,
job.names = NULL,
job.group = NULL,
credible.region = 0.95,
ROPE = NULL,
n.data,
...
)
Arguments
par
defined parameter
par.names
parameter names
job.names
names of all parameters in analysis, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
credible.region
summarize uncertainty by defining a region of most credible values (e.g., 95 percent of the distribution), Default: 0.95
ROPE
define range for region of practical equivalence (e.g., c(-0.05 , 0.05), Default: NULL
n.data
sample size for each parameter
...
further arguments passed to or from other methods
See Also
Sum to Zero
Description
Compute sum to zero values across all levels of a data matrix
Usage
SumToZero(q.levels, data, contrasts)
Arguments
q.levels
number of levels of each variable/column
data
data matrix to combine from
contrasts
specified contrasts columns
Examples
data <- matrix(c(1,2),ncol=2)
colnames(data) <- c("m1[1]","m1[2]")
SumToZero( 2 , data , contrasts = NULL )
# b0[1] b1[1] b1[2]
# m1[1] 1.5 -0.5 0.5
Tidy Code
Description
Small function that clears up messy code
Usage
TidyCode(tidy.code, jags = TRUE)
Arguments
tidy.code
Messy code that needs cleaning
jags
logical, if TRUE run code as JAGS model, Default: TRUE
Value
(Somewhat) tidy code
Examples
messy <- "code <- function( x ) {
print (x ) }"
cat(messy)
code <- function( x ) {
print (x ) }
cat ( TidyCode(messy, jags = FALSE) )
code <- function(x) {
print(x)
}
Trim
Description
remove excess whitespace from string
Usage
Trim(s, multi = TRUE)
Arguments
s
string
multi
logical, indicating whether or not to remove excess whitespace between characters, Default: TRUE
Examples
Trim(" Trimmed string")
# [1] "Trimmed string"
Trim(" Trimmed string", FALSE)
# [1] "Trimmed string"
Trim Split
Description
Extends strsplit by trimming and unlisting string
Usage
TrimSplit(
x,
sep = ",",
fixed = FALSE,
perl = FALSE,
useBytes = FALSE,
rm.empty = TRUE
)
Arguments
x
string
sep
symbol to separate data (e.g., comma-delimited), Default: ','
fixed
logical, if TRUE match split exactly, otherwise use regular expressions. Has priority over perl, Default: FALSE
perl
logical, indicating whether or not to use Perl-compatible regexps, Default: FALSE
useBytes
logical, if TRUE the matching is done byte-by-byte rather than character-by-character, Default: FALSE
rm.empty
logical. indicating whether or not to remove empty elements, Default: TRUE
Details
Examples
TrimSplit("Data 1, Data2, Data3")
# [1] "Data 1" "Data2" "Data3"
Pattern Matching and Replacement From Vectors
Description
extending gsub by matching pattern and replacement from two vectors
Usage
VectorSub(pattern, replacement, string)
Arguments
pattern
vector containing words to match
replacement
vector containing words to replace existing words.
string
string to replace from
Value
modified string with replaced values
Examples
pattern <- c("A","B","C")
replacement <- 1:3
string <- "A went to B went to C"
VectorSub(pattern,replacement,string)
# [1] "1 went to 2 went to 3"
Settings
Description
main settings for bfw
Usage
bfw(
job.title = NULL,
job.group = NULL,
jags.model,
jags.seed = NULL,
jags.method = NULL,
jags.chains = NULL,
custom.function = NULL,
custom.model = NULL,
params = NULL,
saved.steps = 10000,
thinned.steps = 1,
adapt.steps = NULL,
burnin.steps = NULL,
initial.list = list(),
custom.name = NULL,
project.name = "Project",
project.dir = "Results/",
project.data = NULL,
time.stamp = TRUE,
save.data = FALSE,
data.set = "AllData",
data.format = "csv",
raw.data = FALSE,
run.robust = FALSE,
merge.MCMC = FALSE,
run.diag = FALSE,
sep = ",",
silent = FALSE,
...
)
Arguments
job.title
title of analysis, Default: NULL
job.group
for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
jags.model
specify which module to use
jags.seed
specify seed to replicate a analysis, Default: NULL
jags.method
specify method for JAGS (e.g., parallel or simple), Default: NULL
jags.chains
specify specify number of chains for JAGS, Default: NULL
custom.function
custom function to use (e.g., defined function, external R file or string with function), Default: NULL
custom.model
define a custom model to use (e.g., string or text file (.txt), Default: NULL
params
define parameters to observe, Default: NULL
saved.steps
define the number of iterations/steps/chains in the MCMC simulations, Default: 10000
thinned.steps
save every kth step of the original saved.steps, Default: 1
adapt.steps
the number of adaptive iterations to use at the start of each simulation, Default: NULL
burnin.steps
the number of burnin iterations, NOT including the adaptive iterations to use for the simulation, Default: NULL
initial.list
initial values for analysis, Default: list()
custom.name
custom name of project, Default: NULL
project.name
name of project, Default: 'Project'
project.dir
define where to save data, Default: 'Results/'
project.data
define data to use for analysis (e.g., csv, rda, custom data.frame or matrix, or data included in package, Default: NULL
time.stamp
logical, indicating whether or not to append unix time stamp to file name, Default: TRUE
save.data
logical, indicating whether or not to save data, Default: FALSE
data.set
define subset of data, Default: 'AllData'
data.format
define what data format is being used, Default: 'csv'
raw.data
logical, indicating whether or not to use unprocessed data, Default: FALSE
run.robust
logical, indicating whether or not robust analysis, Default: FALSE
merge.MCMC
logical, indicating whether or not to merge MCMC chains, Default: FALSE
run.diag
logical, indicating whether or not to run diagnostics, Default: FALSE
sep
symbol to separate data (e.g., comma-delimited), Default: ','
silent
logical, indicating whether or not to run analysis without output, Default: FALSE
...
further arguments passed to or from other methods
Details
Settings act like the main framework for bfw, connecting function, model and JAGS.
Value
data from MCMC RunMCMC