Description Usage Arguments Details Value Note References Examples
Estimate the conditional (in)dependence with either an analytic solution or efficiently
sampling from the posterior distribution. These methods were introduced in \insertCiteWilliams2019;textualBGGM.
The graph is selected with select.estimate
and then plotted with plot.select
.
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Y 
Matrix (or data frame) of dimensions n (observations) by p (variables). 
formula 
An object of class 
type 
Character string. Which type of data for 
mixed_type 
Numeric vector. An indicator of length p for which variables should be treated as ranks.
(1 for rank and 0 to assume normality). The default is currently to treat all integer variables as ranks
when 
analytic 
Logical. Should the analytic solution be computed (default is 
prior_sd 
Scale of the prior distribution, approximately the standard deviation of a beta distribution (defaults to 0.50). 
iter 
Number of iterations (posterior samples; defaults to 5000). 
impute 
Logical. Should the missing values ( 
progress 
Logical. Should a progress bar be included (defaults to 
seed 
An integer for the random seed. 
... 
Currently ignored. 
The default is to draw samples from the posterior distribution (analytic = FALSE
). The samples are
required for computing edge differences (see ggm_compare_estimate
), Bayesian R2 introduced in
\insertCitegelman_r2_2019;textualBGGM (see predictability
), etc. If the goal is
to *only* determine the nonzero effects, this can be accomplished by setting analytic = TRUE
.
This is particularly useful when a fast solution is needed (see the examples in ggm_compare_ppc
)
Controlling for Variables:
When controlling for variables, it is assumed that Y
includes only
the nodes in the GGM and the control variables. Internally, only
the predictors
that are included in formula
are removed from Y
. This is not behavior of, say,
lm
, but was adopted to ensure users do not have to write out each variable that
should be included in the GGM. An example is provided below.
Mixed Type:
The term "mixed" is somewhat of a misnomer, because the method can be used for data including only continuous or only discrete variables. This is based on the ranked likelihood which requires sampling the ranks for each variable (i.e., the data is not merely transformed to ranks). This is computationally expensive when there are many levels. For example, with continuous data, there are as many ranks as data points!
The option mixed_type
allows the user to determine which variable should be treated as ranks
and the "emprical" distribution is used otherwise \insertCitehoff2007extendingBGGM. This is
accomplished by specifying an indicator vector of length p. A one indicates to use the ranks,
whereas a zero indicates to "ignore" that variable. By default all integer variables are treated as ranks.
Dealing with Errors:
An error is most likely to arise when type = "ordinal"
. The are two common errors (although still rare):
The first is due to sampling the thresholds, especially when the data is heavily skewed.
This can result in an illdefined matrix. If this occurs, we recommend to first try
decreasing prior_sd
(i.e., a more informative prior). If that does not work, then
change the data type to type = mixed
which then estimates a copula GGM
(this method can be used for data containing only ordinal variable). This should
work without a problem.
The second is due to how the ordinal data are categorized. For example, if the error states
that the index is out of bounds, this indicates that the first category is a zero. This is not allowed, as
the first category must be one. This is addressed by adding one (e.g., Y + 1
) to the data matrix.
Imputing Missing Values:
Missing values are imputed with the approach described in \insertCitehoff2009first;textualBGGM.
The basic idea is to impute the missing values with the respective posterior pedictive distribution,
given the observed data, as the model is being estimated. Note that the default is TRUE
,
but this ignored when there are no missing values. If set to FALSE
, and there are missing
values, listwise deletion is performed with na.omit
.
The returned object of class estimate
contains a lot of information that
is used for printing and plotting the results. For users of BGGM, the following
are the useful objects:
pcor_mat
Partial correltion matrix (posterior mean).
post_samp
An object containing the posterior samples.
Posterior Uncertainty:
A key feature of BGGM is that there is a posterior distribution for each partial correlation.
This readily allows for visiualizing uncertainty in the estimates. This feature works
with all data types and is accomplished by plotting the summary of the estimate
object
(i.e., plot(summary(fit))
). Several examples are provided below.
Interpretation of Conditional (In)dependence Models for Latent Data:
See BGGMpackage
for details about interpreting GGMs based on latent data
(i.e, all data types besides "continuous"
)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67  # note: iter = 250 for demonstrative purposes
#########################################
### example 1: continuous and ordinal ###
#########################################
# data
Y < ptsd
# continuous
# fit model
fit < estimate(Y, type = "continuous",
iter = 250)
# summarize the partial correlations
summ < summary(fit)
# plot the summary
plt_summ < plot(summary(fit))
# select the graph
E < select(fit)
# plot the selected graph
plt_E < plot(select(fit))
# ordinal
# fit model (note + 1, due to zeros)
fit < estimate(Y + 1, type = "ordinal",
iter = 250)
# summarize the partial correlations
summ < summary(fit)
# plot the summary
plt < plot(summary(fit))
# select the graph
E < select(fit)
# plot the selected graph
plt_E < plot(select(fit))
##################################
## example 2: analytic solution ##
##################################
# (only continuous)
# data
Y < ptsd
# fit model
fit < estimate(Y, analytic = TRUE)
# summarize the partial correlations
summ < summary(fit)
# plot summary
plt_summ < plot(summary(fit))
# select graph
E < select(fit)
# plot the selected graph
plt_E < plot(select(fit))

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