Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/insilico_standard.r
This function implements InSilicoVA model. The InSilicoVA model is fitted with MCMC implemented in Java. For more detail, see the paper on https://arxiv.org/abs/1411.3042.
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  insilico(
data,
data.type = c("WHO2012", "WHO2016")[1],
sci = NULL,
isNumeric = FALSE,
updateCondProb = TRUE,
keepProbbase.level = TRUE,
CondProb = NULL,
CondProbNum = NULL,
datacheck = TRUE,
datacheck.missing = TRUE,
warning.write = FALSE,
directory = NULL,
external.sep = TRUE,
Nsim = 4000,
thin = 10,
burnin = 2000,
auto.length = TRUE,
conv.csmf = 0.02,
jump.scale = 0.1,
levels.prior = NULL,
levels.strength = 1,
trunc.min = 1e04,
trunc.max = 0.9999,
subpop = NULL,
java_option = "Xmx1g",
seed = 1,
phy.code = NULL,
phy.cat = NULL,
phy.unknown = NULL,
phy.external = NULL,
phy.debias = NULL,
exclude.impossible.cause = c("subset2", "subset", "all", "InterVA", "none")[1],
impossible.combination = NULL,
no.is.missing = FALSE,
indiv.CI = NULL,
groupcode = FALSE,
...
)

data 
The original data to be used. It is suggested to use similar
input as InterVA4, with the first column being death IDs and 245 symptoms.
The only difference in input is InsilicoVA takes three levels: “present”,
“absent”, and “missing (no data)”. Similar to InterVA software,
“present” symptoms takes value “Y”; “absent” symptoms take take value
“NA” or “”. For missing symptoms, e.g., questions not asked or answered
in the original interview, corrupted data, etc., the input should be coded
by “.” to distinguish from “absent” category. The order of the columns does
not matter as long as the column names are correct. It can also include more
unused columns than the standard InterVA4 input. But the first column should be
the death ID. For example input data format, see 
data.type 
Type of questionnaire. “WHO2012” corresponds to the standard input of InterVA4, and “WHO2016” corresponds to the standard input of InterVA5. 
sci 
A data frame that contains the symptomcauseinformation (aka Probbase) that InterVA uses to assign a cause of death. 
isNumeric 
Indicator if the input is already in numeric form. If the input is coded numerically such that 1 for “present”, 0 for “absent”, and 1 for “missing”, this indicator could be set to True to avoid conversion to standard InterVA format. 
updateCondProb 
Logical indicator. If FALSE, then fit InSilicoVA model without reestimating conditional probabilities. 
keepProbbase.level 
Logical indicator when 
CondProb 
Customized conditional probability matrix to use.It should be strict the same configuration as InterVA4 software. That is, it should be a matrix of 245 rows of symptoms and 60 columns of causes, arranged in the same order as in InterVA4 specification. The elements in the matrix should be the conditional probability of corresponding symptom given the corresponding cause, represented in alphabetic form indicating levels. For example input, see 
CondProbNum 
Customized conditional probability matrix to use if specified fully by numerical values between 0 and 1. If it is specified, reestimation of conditional probabilities will not be performed, i.e., 
datacheck 
Logical indicator for whether to check the data satisfying
InterVA rules. Default set to be TRUE. If 
datacheck.missing 
Logical indicator for whether to perform data check before deleting complete missing symptoms. Default to TRUE. 
warning.write 
Logical indicator for whether to save the changes made
to data input by 
directory 
The directory to store the output from. It should be an valid existing directory or a folder to be created. 
external.sep 
Logical indicator for whether to separate out external causes first. Default set to be TRUE. If set to TRUE, the algorithm will estimate external causes, e.g., traffic accident, accidental fall, suicide, etc., by checking the corresponding indicator only without considering other medical symptoms. It is strongly suggested to set to be TRUE. 
Nsim 
Number of iterations to run. Default to be 4000. 
thin 
Proportion of thinning for storing parameters. For example, if thin = k, the output parameters will only be saved every k iterations. Default to be 10 
burnin 
Number of iterations as burnin period. Parameters sampled in burnin period will not be saved. 
auto.length 
Logical indicator of whether to automatically increase chain length if convergence not reached. 
conv.csmf 
Minimum CSMF value to check for convergence if auto.length is set to TRUE. For example, under the default value 0.02, all causes with mean CSMF at least 0.02 will be checked for convergence. 
jump.scale 
The scale of Metropolis proposal in the Normal model. Default to be 0.1. 
levels.prior 
Vector of prior expectation of conditional probability
levels. They do not have to be scaled. The algorithm internally calibrate
the scale to the working scale through 
levels.strength 
Scaling factor for the strength of prior beliefs in
the conditional probability levels. Larger value constrain the posterior
estimates to be closer to prior expectation. Defult value 1 scales

trunc.min 
Minimum possible value for estimated conditional probability table. Default to be 0.0001 
trunc.max 
Maximum possible value for estimated conditional probability table. Default to be 0.9999 
subpop 
This could be the column name of the variable in data that is to be used as subpopulation indicator, or a list of column names if more than one variable are to be used. Or it could be a vector of subpopulation assignments of the same length of death records. It could be numerical indicators or character vectors of names. 
java_option 
Option to initialize java JVM. Default to “Xmx1g”,
which sets the maximum heap size to be 1GB. If R produces
“java.lang.OutOfMemoryError: Java heap space” error message, consider
increasing heap size using this option, or one of the following: (1)
decreasing 
seed 
Seed used for initializing sampler. The algorithm will produce the same outcome with the same seed in each machine. 
phy.code 
A matrix of physician assigned cause distribution. The
physician assigned causes need not be the same as the list of causes used in
InSilicoVA and InterVA4. The cause list used could be a higher level
aggregation of the InSilicoVA causes. See 
phy.cat 
A two column matrix describing the correspondence between
InSilicoVA causes and the physician assigned causes. Note each InSilicoVA
cause (see 
phy.unknown 
The name of the physician assigned cause that correspond to unknown COD. 
phy.external 
The name of the physician assigned cause that correspond
to external causes. This will only be used if 
phy.debias 
Fitted object from physician coding debias function (see

exclude.impossible.cause 
option to exclude impossible causes at the individual level. The following rules are implemented: 
impossible.combination 
matrix indicating additional impossible symptomcause combinations in addition to the ones specified by 
no.is.missing 
logical indicator to treat all absence of symptoms as missing. Default to FALSE. If set to TRUE, InSilicoVA will perform calculations similar to InterVA4 w.r.t treating absent symptoms. It is highly recommended to set this argument to FALSE. 
indiv.CI 
credible interval for individual probabilities. If set to NULL, individual COD distributions will not be calculated to accelerate model fitting time. See 
groupcode 
logical indicator of including the group code in the output causes 
... 
not used 
For Windows user, this function will produce a popup window showing the progress. For Mac and Unix user, this function will print progress messages on the console. Special notice for users using default R GUI for mac, the output will not be printed on console while the function is running, and will only be printed out after it is completed. Thus if you use a Mac, we suggest using either RStudio for mac, or running R from terminal.
The chains could be set to run automatically longer. If set
auto.length
to be TRUE, the chain will assess convergence after
finishing the length K chain input by user using Heidelberger and Welch's
convergence diagnostic. If convergence is not reached, the chain will run
another K iterations and use the first K iterations as burnin. If the chain
is still not converged after 2K iterations, it will proceed to another 2K
iterations and again use the first 2K iterations as burnin. If convergence
is still not reached by the end, it will not double the length again to
avoid heavy memory use. A warning will be given in that case. The extended
chains will be thinned in the same way.
For more detail of model specification, see the paper on https://arxiv.org/abs/1411.3042.
id 
A vector of death ID. Note the order of the ID is in
general different from the input file. See 
data 
Cleaned numerical data. 
indiv.prob 
Matrix of individual mean cause of death distribution. Each row corresponds to one death with the corresponding ID. 
csmf 
Matrix of CSMF vector at each iterations after burnin and thinning. Each column corresponds to one cause. 
conditional.probs 
If the model is estimated with

missing.symptoms 
Vector of symptoms missing from all input data. 
external 
Logical indicator of whether the model is fitted with external causes separated calculated. 
impossible.causes 
Impossible causesymptom pairs, if any. 
indiv.CI 
The posterior credible interval to compute for individual COD probability distributions. If set to NULL, only the posterior mean of the individual COD probabilities will be produced. Default to be 0.95. 
indiv.prob.median 
median probability of each cause of death for each individual death. 
indiv.prob.lower 
lower CI bound for the probability of each cause of death for each individual death. 
indiv.prob.upper 
upper CI bound for the probability of each cause of death for each individual death. 
errors 
Logs of deleted observations and reasons of deletion. 
Zehang Li, Tyler McCormick, Sam Clark
Maintainer: Zehang Li <lizehang@uw.edu>
Tyler H. McCormick, Zehang R. Li, Clara Calvert, Amelia C.
Crampin, Kathleen Kahn and Samuel J. Clark(2014) Probabilistic
causeofdeath assignment using verbal autopsies,
https://arxiv.org/abs/1411.3042
Working paper no. 147, Center
for Statistics and the Social Sciences, University of Washington
plot.insilico
, summary.insilico
, physician_debias
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 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160  ## Not run:
data(RandomVA1)
fit0< insilico(RandomVA1, subpop = NULL,
Nsim = 20, burnin = 10, thin = 1 , seed = 1,
auto.length = FALSE)
summary(fit0)
summary(fit0, id = "d199")
##
## Scenario 1: standard input without subpopulation specification
##
fit1< insilico(RandomVA1, subpop = NULL,
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
auto.length = FALSE)
summary(fit1)
plot(fit1)
##
## Scenario 2: standard input with subpopulation specification
##
data(RandomVA2)
fit2< insilico(RandomVA2, subpop = list("sex"),
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
auto.length = FALSE)
summary(fit2)
plot(fit2, type = "compare")
plot(fit2, which.sub = "Men")
##
## Scenario 3: standard input with multiple subpopulation specification
##
fit3< insilico(RandomVA2, subpop = list("sex", "age"),
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
auto.length = FALSE)
summary(fit3)
##
## Scenario 3: standard input with multiple subpopulation specification
##
fit3< insilico(RandomVA2, subpop = list("sex", "age"),
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
auto.length = FALSE)
summary(fit3)
##
## Scenario 5  7 are special situations rarely needed in practice,
## but included here for completeness.
## The below examples use no subpopulation or physician codes,
## but specifying subpopulation is still possible as in Scenario 2  4.
##
##
## Scenario 5: skipping reestimation of conditional probabilities
##
# Though in practice the need for this situation is very unlikely,
# use only the default conditional probabilities without reestimation
fit5< insilico(RandomVA1, subpop = NULL,
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
updateCondProb = FALSE,
auto.length = FALSE)
summary(fit5)
##
## Scenario 6: modify default conditional probability matrix
##
# Load the default conditional probability matrix
data(condprob)
# The conditional probabilities are given in levels such as I, A+, A, A, etc.
condprob[1:5, 1:5]
# To modify certain cells
new_cond_prob < condprob
new_cond_prob["elder", "HIV/AIDS related death"] < "C"
# or equivalently
new_cond_prob[1, 3] < "C"
fit6< insilico(RandomVA1, subpop = NULL,
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
CondProb = new_cond_prob,
auto.length = FALSE)
# note: compare this with fit1 above to see the change induced
# by changing Pr(elder  HIV) from "C+" to "C".
summary(fit6)
##
## Scenario 7: modify default numerical values in conditional probabilities directly
##
# Load the default conditional probability matrix
data(condprobnum)
# The conditional probabilities are given in numerical values in this dataset
condprobnum[1:5, 1:5]
# To modify certain cells, into any numerical values you want
new_cond_prob_num < condprobnum
new_cond_prob_num["elder", "HIV/AIDS related death"] < 0.004
# or equivalently
new_cond_prob_num[1, 3] < 0.005
fit7< insilico(RandomVA1, subpop = NULL,
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
CondProbNum = new_cond_prob_num,
auto.length = FALSE)
# note: compare this with fit1, fit5, and fit6
summary(fit7)
##
## Scenario 8: physician coding
## see also the examples in physician_debias() function section
##
# Load sample input for physicians
data(RandomPhysician)
# The symptom section looks the same as standard input
head(RandomPhysician[, 1:5])
# At the end of file, including a few more columns of physician id and coded cause
head(RandomPhysician[, 245:250])
# load Cause Grouping (if physiciancoded causes are in larger categories)
data(SampleCategory)
head(SampleCategory)
# existing doctor codes in the sample dataset
doctors < paste0("doc", c(1:15))
causelist < c("Communicable", "TB/AIDS", "Maternal",
"NCD", "External", "Unknown")
phydebias < physician_debias(RandomPhysician,
phy.id = c("rev1", "rev2"), phy.code = c("code1", "code2"),
phylist = doctors, causelist = causelist,
tol = 0.0001, max.itr = 100)
fit8 < insilico(RandomVA1, subpop = NULL,
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
phy.debias = phydebias,
phy.cat = SampleCategory,
phy.external = "External", phy.unknown = "Unknown",
auto.length = FALSE)
summary(fit8)
# example to fit WHO2016 data
data(RandomVA5)
fit1a < insilico(RandomVA5, data.type="WHO2016", subpop = NULL,
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
auto.length = FALSE)
summary(fit1a)
plot(fit1)
# example to change directory for error files
fit1b < insilico(RandomVA5[1:50, ], data.type="WHO2016",
Nsim = 1000, burnin = 500, thin = 10 ,
seed = 1, warning.write = T, auto.length=F)
fit1c < insilico(RandomVA5[1:50, ], data.type="WHO2016",
Nsim = 1000, burnin = 500, thin = 10 ,
seed = 1, warning.write = T,
directory = "fit1b_errorfolder", auto.length=F)
# similarly for WHO 2012 version
fit1< insilico(RandomVA1, subpop = NULL,
Nsim = 1000, burnin = 500, thin = 10 , seed = 1,
auto.length = FALSE,
warning.write = T, directory = "fit1_errorfolder")
## End(Not run)

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