Refactor rdiffnet #46
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…-test-discussion too.
… in rdiffnet function
…fnet_check_seed_graph fixed.
…ication following the merge of the 41... branch
…net_make_threshold. Some modification in rdiffnet too. Not expecting to work yet.
…ose functions are pass.
R/rdiffnet.r
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| } else if (class(seed.nodes) == "character") { | ||
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| stop("-character- class not supported for multi-diffusion. It must be a -list-.") | ||
| } |
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The following code is causing an error here:
z <- rdiffnet(100,10, seed.p.adopt = list(.1, .05), seed.nodes=c(1,2,3,4))
The option -copy.first- is set to TRUE. In this case, the first graph will be treated as a baseline, and thus, networks after T=1 will be replaced with T-1.TRUE
Message: Multi-diffusion behavior simulation selected. Number of behaviors: 2
Error in rdiffnet_validate_args(seed.p.adopt, seed.nodes, behavior) :
-character- class not supported for multi-diffusion. It must be a -list-.
> traceback()
3: stop("-character- class not supported for multi-diffusion. It must be a -list-.") at rdiffnet.r#567
2: rdiffnet_validate_args(seed.p.adopt, seed.nodes, behavior) at rdiffnet.r#381
1: rdiffnet(100, 10, seed.p.adopt = list(0.01, 0.03))|
Tagging @twvalente for a potential model of adoption/disadoption: We could also make disadoption to happen randomly. |
… from -list-. For example: rdiffnet(100,10, seed.p.adopt = list(.1, .05)), or adding seed.nodes=c(1,2,3,4), seed.nodes=random, or seed.nodes=c(random,central). Respective tests added.
… work to be done to display a line saying 'number of behaviors', and to fix the summary() function.
… work to be done to display a line saying 'number of behaviors', and to fix the summary() function.
… of behaviors-, and
… to something more simple later
R/adjmat.r
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| #' @include graph_data.r | ||
| #' @author George G. Vega Yon & Thomas W. Valente | ||
| toa_mat <- function(obj, labels=NULL, t0=NULL, t1=NULL) { | ||
| toa_mat <- function(obj, num_of_behaviors=1, labels=NULL, t0=NULL, t1=NULL) { |
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The number of behaviors should be implicit (so you can figure that out from diffnet object). So don't ask the user of it.
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I just deleted the 'num_of_behaviors' dependency in toa_mat and other functions.
R/diffnet-class.r
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| name = "Diffusion Network", | ||
| behavior = "Unspecified" | ||
| behavior = "Unspecified", | ||
| num_of_behaviors = 1 |
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The num of behaviors should be guessed from the data.
R/diffnet-class.r
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| "using -t0- and -t- (see ?toa_mat).") | ||
| } else { | ||
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| # This should be reviewed !! (here the graph becomes 'dynamic') |
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It took me a while realize that, at Step 1.1 in new_diffnet, a graph will never be 'static'. This is because, if I give as an input a single 'slice' of graph ('static'), that will be converted to a list of graph before this step anyway, so it become a 'dynamic' graph whatever the circumstance. This implies that the type of graph is shown as 'dynamic':
n <- 100
nper <- 5
static_graph <- rgraph_er(n = n, t = 1, p = 0.1)
diffnet <- rdiffnet(
seed.graph = static_graph,
t = 5,
seed.p.adopt = 0.1,
rewire = FALSE,
seed.nodes = 'central',
threshold.dist = runif(n, .1, .4)
)
str(diffnet) # always meta$type == dinamic !!
Since the code never really uses those lines (neither single diff nor multi diff), it's "working."
| class(rdiffnet_args$seed.p.adopt) == "list" | ||
| class(rdiffnet_args$seed.nodes) == "list" | ||
| class(rdiffnet_args$behavior) == "list" |
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| class(rdiffnet_args$seed.p.adopt) == "list" | |
| class(rdiffnet_args$seed.nodes) == "list" | |
| class(rdiffnet_args$behavior) == "list" |
Or add an expectation from testthat
| class(rdiffnet_args$seed.p.adopt) == "list" | ||
| class(rdiffnet_args$seed.nodes) == "list" | ||
| class(rdiffnet_args$behavior) == "list" |
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| class(rdiffnet_args$seed.p.adopt) == "list" | |
| class(rdiffnet_args$seed.nodes) == "list" | |
| class(rdiffnet_args$behavior) == "list" |
| class(rdiffnet_args$seed.p.adopt) == "list" | ||
| class(rdiffnet_args$seed.nodes) == "list" | ||
| class(rdiffnet_args$behavior) == "list" |
tests/testthat/test-rdiffnet.R
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| #single | ||
| rdiffnet(100, 5) | ||
| rdiffnet(100, 5, seed.p.adopt = 0.1) | ||
| rdiffnet(100, 5, seed.p.adopt = 0.1, seed.nodes = 'random') | ||
| rdiffnet(100, 5, seed.nodes = c(1,3,5)) | ||
| net_1 <- rdiffnet(100, 5, seed.nodes = c(1,3,5)) | ||
| summary(net_1) | ||
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| #multi | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08)) | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), behavior = c('tabacco', 'alcohol')) | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), seed.nodes = 'random') | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), seed.nodes = c('random', 'central')) | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), threshold.dist = 0.3) | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), threshold.dist = list(0.1,0.2)) | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), threshold.dist = rexp(100)) | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), threshold.dist = list(rexp(100),runif(100))) | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), threshold.dist = function(x) 0.3) | ||
| rdiffnet(100, 5, seed.p.adopt = list(0.1,0.08), threshold.dist = list(function(x) 0.3, function(x) 0.2)) |
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Perhaps, add expectations of of the sort of "expect class" something like that so you ensure it returns diffnet objects.
…d matrix objects. The same tests for single behavior were adapted.
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