@@ -107,9 +107,35 @@ test_that("Simulation study", {
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})
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- # Test for multi diffusion ---
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+ # Testing diffnet class across several inputs (single)
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+ test_that(" rdiffnet must run across several inputs (single)" , {
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+ expect_s3_class(rdiffnet(100 , 5 ), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = 0.1 ), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = 0.1 , seed.nodes = ' random' ), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.nodes = c(1 , 3 , 5 )), " diffnet" )
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+
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+ # summary
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+ net_1 <- rdiffnet(100 , 5 , seed.nodes = c(1 ,3 ,5 ))
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+ expect_s3_class(summary(net_1 ), " data.frame" )
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+ })
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+
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+ # Testing diffnet class across several inputs (multiple)
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+ test_that(" rdiffnet must run across several inputs (multiple)" , {
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 )), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), behavior = c(' tabacco' , ' alcohol' )), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), seed.nodes = ' random' ), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), seed.nodes = c(' random' , ' central' )), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), threshold.dist = 0.3 ), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), threshold.dist = list (0.1 , 0.2 )), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), threshold.dist = rexp(100 )), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), threshold.dist = list (rexp(100 ), runif(100 ))), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), threshold.dist = function (x ) 0.3 ), " diffnet" )
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+ expect_s3_class(rdiffnet(100 , 5 , seed.p.adopt = list (0.1 , 0.08 ), threshold.dist = list (function (x ) 0.3 , function (x ) 0.2 )), " diffnet" )
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+
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+ net_2 <- rdiffnet(100 , 5 , seed.p.adopt = list (0.05 ,0.05 ), seed.nodes = c(1 ,3 ,5 ))
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+ expect_s3_class(summary(net_2 ), " data.frame" )
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+ })
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- # Seed of first adopters
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test_that(" All should be equal! (multiple)" , {
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set.seed(12131 )
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n <- 50
@@ -131,31 +157,6 @@ test_that("All should be equal! (multiple)", {
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})
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- # single
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- rdiffnet(100 , 5 )
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- rdiffnet(100 , 5 , seed.p.adopt = 0.1 )
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- rdiffnet(100 , 5 , seed.p.adopt = 0.1 , seed.nodes = ' random' )
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- rdiffnet(100 , 5 , seed.nodes = c(1 ,3 ,5 ))
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- net_1 <- rdiffnet(100 , 5 , seed.nodes = c(1 ,3 ,5 ))
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- summary(net_1 )
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-
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- # multi
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ))
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ), behavior = c(' tabacco' , ' alcohol' ))
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ), seed.nodes = ' random' )
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ), seed.nodes = c(' random' , ' central' ))
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ), threshold.dist = 0.3 )
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ), threshold.dist = list (0.1 ,0.2 ))
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ), threshold.dist = rexp(100 ))
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ), threshold.dist = list (rexp(100 ),runif(100 )))
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- rdiffnet(100 , 5 , seed.p.adopt = list (0.1 ,0.08 ), threshold.dist = function (x ) 0.3 )
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- 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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-
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- net_1 <- rdiffnet(100 , 5 , seed.nodes = c(1 ,3 ,5 ))
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- summary(net_1 )
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- net_2 <- rdiffnet(100 , 5 , seed.p.adopt = list (0.05 ,0.05 ), seed.nodes = c(1 ,3 ,5 ))
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- summary(net_2 )
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-
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# rdiffnet(100, 5, seed.p.adopt = 0.9, threshold.dist = 2, exposure.args = list(normalized=FALSE))
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# set.seed(1234)
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