From 9e8edc681ed5587e5a7e964032ee036e44c462c9 Mon Sep 17 00:00:00 2001 From: James Hollway Date: Tue, 5 Nov 2024 18:01:20 +0100 Subject: [PATCH 01/11] Added test_configuration() --- NAMESPACE | 2 ++ R/model_tests.R | 44 ++++++++++++++++++++++++++ man/tests.Rd | 10 ++++++ tests/testthat/test-model_regression.R | 4 +-- 4 files changed, 58 insertions(+), 2 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 073ce071..3a7158af 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -19,6 +19,7 @@ export(labs) export(net_regression) export(network_reg) export(scale_y_discrete) +export(test_configuration) export(test_distribution) export(test_fit) export(test_gof) @@ -45,6 +46,7 @@ importFrom(ggplot2,scale_y_discrete) importFrom(ggplot2,xlab) importFrom(ggplot2,ylab) importFrom(manynet,bind_node_attributes) +importFrom(manynet,generate_configuration) importFrom(manynet,generate_random) importFrom(manynet,is_complex) importFrom(manynet,is_directed) diff --git a/R/model_tests.R b/R/model_tests.R index 9ad1fa15..cb0883d1 100644 --- a/R/model_tests.R +++ b/R/model_tests.R @@ -71,6 +71,50 @@ test_random <- function(.data, FUN, ..., out } +#' @rdname tests +#' @importFrom manynet generate_configuration +#' @export +test_configuration <- function(.data, FUN, ..., + times = 1000, + strategy = "sequential", + verbose = FALSE){ + args <- unlist(list(...)) + if (!is.null(args)) { + obsd <- FUN(.data, args) + } else { + obsd <- FUN(.data) + } + oplan <- future::plan(strategy) + on.exit(future::plan(oplan), add = TRUE) + rands <- furrr::future_map(1:times, manynet::generate_configuration, n = .data, + .progress = verbose, + .options = furrr::furrr_options(seed = T)) + if (length(args) > 0) { + rands <- furrr::future_map(rands, + manynet::bind_node_attributes, object2 = .data, + .progress = verbose, + .options = furrr::furrr_options(seed = T)) + } + if (!is.null(args)) { + simd <- furrr::future_map_dbl(rands, + FUN, args) + } else { + simd <- furrr::future_map_dbl(rands, + FUN) + } + out <- list(test = "configuration", + testval = obsd, + testdist = simd, + mode = manynet::is_directed(.data), + diag = manynet::is_complex(.data), + cmode = "edges", + plteobs = mean(simd <= obsd), + pgteobs = mean(simd >= obsd), + reps = times) + class(out) <- "network_test" + out +} + #' @rdname tests #' @examples #' # (qaptest <- test_permutation(marvel_friends, diff --git a/man/tests.Rd b/man/tests.Rd index e464d12a..2cfc99cd 100644 --- a/man/tests.Rd +++ b/man/tests.Rd @@ -3,6 +3,7 @@ \name{tests} \alias{tests} \alias{test_random} +\alias{test_configuration} \alias{test_permutation} \title{Tests of network measures} \usage{ @@ -15,6 +16,15 @@ test_random( verbose = FALSE ) +test_configuration( + .data, + FUN, + ..., + times = 1000, + strategy = "sequential", + verbose = FALSE +) + test_permutation( .data, FUN, diff --git a/tests/testthat/test-model_regression.R b/tests/testthat/test-model_regression.R index 28af43ff..5511f92e 100644 --- a/tests/testthat/test-model_regression.R +++ b/tests/testthat/test-model_regression.R @@ -2,9 +2,9 @@ set.seed(123) networkers <- manynet::ison_networkers %>% manynet::to_subgraph(Discipline == "Sociology") netsenders <- manynet::to_unweighted(networkers) -test <- network_reg(weight ~ ego(Citations), +test <- net_regression(weight ~ ego(Citations), networkers, times = 50) -test_logit <- network_reg(. ~ ego(Citations), +test_logit <- net_regression(. ~ ego(Citations), netsenders, times = 50) test_that("network_reg estimates correctly",{ From 3af352f940d1c828111fb4f026edb068a8f2274a Mon Sep 17 00:00:00 2001 From: James Hollway Date: Tue, 5 Nov 2024 18:01:45 +0100 Subject: [PATCH 02/11] Updated mpn_bristol with info --- data/mpn_bristol.rda | Bin 10726 -> 10839 bytes man/mpn_bristol.Rd | 4 +++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/data/mpn_bristol.rda b/data/mpn_bristol.rda index 2e5d71a3c6742bbdca0d7dabd3f6362a3137b2dc..ad78184a4c33f7f3dd45ac46b3baa3306a9da112 100644 GIT binary patch literal 10839 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}}\preformatted{#> # Bristol protest event network +#> # A labelled, two-mode network of 150 individuals and 1496 affiliation and +#> participation ties #> # A tibble: 264 x 3 #> name type lvl #> From d4fae3c8411856cf213d07dedda76dd7557fa47e Mon Sep 17 00:00:00 2001 From: James Hollway Date: Tue, 5 Nov 2024 18:02:06 +0100 Subject: [PATCH 03/11] Updated mpn_ryanair with info --- data/mpn_ryanair.rda | Bin 831 -> 919 bytes man/mpn_ryanair.Rd | 4 +++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/data/mpn_ryanair.rda b/data/mpn_ryanair.rda index 5ca54665d1a839a76ac1f2daf4bdaad44417b060..dddcd09f1435e5df883f8352862666bce299f9f5 100644 GIT binary patch literal 919 zcmV;I18Dq0T4*^jL0KkKS(zc#695nt7;2ACjX0i#U-83RB8QY1|fn-EfYCiOKu zQ!-5r>PAK~5cNE#phG9>VoaKEQgAx26`Z(SmhY}u`w4BfQ-Ni5Msw{a4j3S?_i(>1eqQd1=s?;%B)S!_Lolr2Kr zh{D<}sfHL~OeRkHQkJo1)KZkRr7YeSv)oBLJGoD3qehBbV>ZT2*|4_)m}+i*F5A26 zyHWaERaHtIYAh8UiklqmW*ukwulq{$eY$;l z=dW2d%57IwZLO{8_vq`RrjuPa)PD+_s!XzO*O9iIG72`_h>1N!tv?Gvoh3DMa^ouV zDV`x3>MDsOY`c(2CH87@`%j_uGLMy0lAKM{uJ(r~LfWOg%fk=uvs=c=oZSZcNNymY zi>M^HD5{lG5+u43N~vm9BtKyVNg@1S?$RC#g9>DlDJ0aCu64{LqLC_yl|nZ)K1cd< zyw9DRmD2sp-baz{sl@Uy$=OVqI2#(?M+2?v?&KnJ(h&2!99Rce);*1##%N9L?f6LR z=w{93e`k&U9{cA0Zz`S_HyTOb<8ip&XNcI`?rcQSqn`_5$6fCW*1>NE%see!-%j># zMtpxO_o767>K5ijy~QY$wG=I2rL3~lQr4DQB`GA5Pj%AszQ@j;DMR9V54Gt|r|oKM zdDwWJEZj_%TAfN5nv|B~P8N=@I}F;GJW0ZOUL%oC6r3zMSZr%qqA5iq4R4TSaTqo^ z8i!%&Q<1`Ka3w9+IG$Q)pBmY;8Z%2|(Y2F>lQxW;28D$*7+4U#&{n#+rAlVg>lU4x zK!+;9HC;OzwixR+K>Q_YT@Jtdcm!_VnFj8O#Q33sy@BAXz6n#}nUse)3PKTDppdX6 zz8)U6O#)vb6jg+57R-7$)>PDzsYx*6Rah|qU_nOuLkKguwdHLQ5GR5J5I~g}LMYb? tnejpbajrDDey)c;ts42d6Rjdh@!0iL3!%_@l;4-;F64@Ep&>FuswOZ~q{aXM literal 831 zcmV-F1Hk-3T4*^jL0KkKSq*0TDF6@Pe~AD8eMVJR|KR`c-r&FI-Jl=KMKlfS z6DFUkZ6G|1L^h+;?L9<^8^KQlxbU*=p`y|)`BIXd(M6LCHY8DMD8s_RY%=!Hqbf;m z@b94vzRSsi6riEbDslNtyH$=jm)%Y!rRotV~f=2cm0=i_MMd}N>aQE zZ1%|!C%cuv|Jt_nWZ5XBQjp4v1x{LRdksuK->!M8d9isXB&P*=`EQlkPsdlTj}<1` za@2V0aj7$wxJ}YYMM#K~w;ruAAk7rx#_?W7XA%u*Dx^en$^?>8@4u`2uzv+ODZJ3y z?yZ}77+d))*!r1lazxumIpj$~7Nm&pR2Y&_r4b@bU5J-OQq)7>AgF`md2J#2=_VA) zNT^MTO;s?Gib)Eg9{XeGf0sMR{*4^(6XIy=b-ZeFI@o30OtRkG+Usv|B6Cs@^g4H7 zA9s{@`WZrSaB#v$vdb>F(*7^z{co@Ky43j{u2hrW;Nal;-A3mZdvYeNUmh02g?pV( zYX!VJuFv-YZ+~#Z?uS%TG!MVLDZc~xhO;hMwMx$1VY_)b-b{S~ZmiFe2%^Drv*5vtr z=he{0I{JQJ22Wqun{%jDr9GO8dZX}u_w<>ql;we}G>=}}\preformatted{#> # A labelled, weighted, directed network with 20 nodes and 177 arcs +\if{html}{\out{
}}\preformatted{#> # EU policy influence network +#> # A labelled, weighted, directed network of 20 policy actors and 177 +#> interaction arcs #> # A tibble: 20 x 1 #> name #> From 98c0e1e1edfde08b914f14317c570b75c5213896 Mon Sep 17 00:00:00 2001 From: James Hollway Date: Tue, 5 Nov 2024 18:12:14 +0100 Subject: [PATCH 04/11] Updated mpn_elite_mex with info --- data/mpn_elite_mex.rda | Bin 1964 -> 2062 bytes man/mpn_elite_mex.Rd | 4 +++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/data/mpn_elite_mex.rda b/data/mpn_elite_mex.rda index df8a93bc152439fc098f736e4e0abc335d17ee2f..efcba763a38a09379c9b6398df0f66c25c40ea8d 100644 GIT binary patch literal 2062 zcmZ9Mdpy$%AIClFaVe=G!b&z&1I7e1EIbTEy63|^^%uKBi_}#+f@q_j zbgw`*XmFr&DR=LuD-*TvVxb?mA5PRVtp2XETx>xc%D9ZqXFDc3D()OGN=w2MC#43Y zy=%CPp=;h+cakb$lqAAdkw(^Q$s{I?^k^k@Hg5!L@W;VHJJ5XxJ7((CMg&=Rw@Ljk$y3mf zi+$32*!aCE6kXV6_g2Uzr?Le!G}E;M5hHCFj*Q9K*40H2+F&RdNNoCi2-Q$3+J6)I z<^EGOb=({~6gBwdq5u%2UQr7W~b z7rFJ$VU!(}8VzLWJ_?+J} z{CEN_LxtOGOj??Y57bh4z=69_`g8^v?Xt6j4y5tT_`C*CM;r+2CV*H5U2@y)F?{YE z1y0w*D>vyVqpdt$Tq^58ePo+^znC-Yv)`xY3L-{maiM0})^uqtwPU3O_%XJ+YBKg; z;T|9OfKt@cBL>OR-CQ%gJ0Sq68$vm69 z>Rtk0yu1D|=S^I|mk~P4Q*9fww6LwUTPBX(`bxc{ZnKTuz_^cSb&%qh1>gCCz*3#+ zpWh_^@zU=jH{i?@jb0%f^yTtGfhOg}LJE@^CFJE2wiiLS=f{M`=%x0seA{WGRF8L8 z5(2eZ^$cN=me(6{qIKho)@b_^<<9=*wz=KPnwBXrxi8Wx5`Ou3sjQbFgH|Us(k?u^ zBz`+t5*3~!`1$D8rn=5+_ul)g2s{UFoQBQB&)#4>>M?eSs{!bNDfs)In-)Oi!-p2* zKQ)V+J~P9e>c7P8csJBJo2jOa=v(QkO<<)>#BqmlM#bls_vrAVi!$0j6^vajt8y(h zqcWjdn*Bp>Kq~`Xow+T96AamQnY22x0a-R#Tjx3J&F4bHzNy-4vnIFc;;da$FTWH!K><+ar=w~@h%sgo%8ab>i9D*oX zjVSTNeZP(@;P9bw)8dZ)1?k~oljQ6z*Qdio@oK4naT^umW7nU%<(VZ$m`iy9Uxoyz zP78jT=MI_8bJH;k4n}`u0f|Rz9(B&uU5YN5R!WCyEmg6i-Hhct6!|s06bfV=i%;V- zLjon)Eh$99mzYnBji>GBN7Ws&o93SCXV;9|!S4enWFz6B2v&7u!GLQIFc#vF>0*Gh z!7=46ALSm)N%vEZcyJ;tbwa?9BVYY)U&49O`IPQg6fz;0Fv*yvHqnCYW-6{ibOzjp z*nk;Th$$0bQZy(Fk49;Oy&NnS4|L|v@UO^wW+INtZLs!T+%awMa$R3)g#cfMz#c#j z^j`69iy%#_y1&s2!<3&xm~Wi9H%|b<@;BO)h>=kIql%Op-|XFwxg4MiuQ!ECou6fbn2Z_2+j3rrKSe)2;d<`o$!kRrPpFhVrwqfh1$g@&`)=0^F_#=(2(?qHeVRQ)+B* z(>uaJfC${`i?7K*Db{u;$uFoWzos%(79)M`_xMU_eM@XxhU`1nq-b#e@k{V+b|eZOTVF+4L|`0UzPl zhwnxuF?m#jMVp$9I%;BSWN1`sjyaZ(%*=c=>$>*G{=fH}dw%zxbI&D*o$%S=$96l+ zCVjE$f?4C1zp5|d`-{uV^8>-Z%`YcXVEg?q!%ShY{V)mz#_=0k8@T|(tX*{p_Eqpk z7;OCtNih9al2+p873*Ozll?1QzXBNSo5ufR1OpG0uZMm9-^6xy(7K^T)E0r+50=8%j3%2mX79*0 z#|^pXO)r7FcI~@9>0bw-K!5^ZK!!mk#!%#T-k=zqQT`MWyMu08s;V;}vsvtzhcp&r z+SgRw8rasFwmKh&Su9jHk?qbJ>zy-OI8AS(XMI1#X`M4Wj4HF&+cXB_Bt5UTB|nIK z#8ZpudC7Dqn0NH|>uWmD;>R`Y7?Z8sNyDx?r#OqCuEeeP5Fa3TMOPgxU}xniY8z_t zM>O0VyrXR_cgWk8QT$%)DfdUwj_e5<{;2kwo^|@RAhoBfyKY@g@FF<#a9+{_{w(-e zw4af(MkxFQ@LK%lmfDuxGvL-V1{Q9|D;NH|Lrtz0RPH`e>9G0K@D~;(P&HmKS(bY@ zm$kjWb@f7_%P15ukn8plqYXu;^==)8qHv=DZ*X?N!kcu0z#>&50B`ssIPv5ySJMM+ zv22z^m}X&7h(tQ+C`i+}z6;Gho3m`tyt~9d0ga}pIJ*~9k(Xv}?BenCve1j44p&57 zVY5uNkSCGJ4B@G4zo#Gdk5#F|ue)KnDzf?rzLgrHd|%ceZhcP6W_yIzh+TJ`Wf8`+ zWV(R1o0c!_FN~%a;FVQ0wKO}K;-!O|<6W!)>&uQPln<(L=S~+Cc(&VAkV*77JtPSH zRp4eVOn0mn_)bbvz?x%w&~W&?a}|$;+zt8zSWe|SI7SGeEp04ql`>yfQ9GjnbIP01 z8D3MV)=&|^He8&P!wrTiF1Kz(aL^WLlp3s5GB2r$vR1oU+ORjtNafH$364z&Si2)> zK+FD>6U)`%3h^=pgC&J%x{cgrlYW8gtMc95GsGn4z@pnk!l;?aH$PZ3>mlT_6Wns2 z&AL;vEv&uclF4Sl-i?1<%O_iHQ*5VCDV0IZ5EYJ@M%%Ed2(f&Orr=WY1K$P9&{zNz zqY+>T0-}Y0Ar?TQM0jotiGbh;z=9T%Z^|oI1`CxwWhNgBfAjyKTneg~_RYMuWNFQh zNRmr*?;?6MH)A#on@=zkN%yLUnDRwihWp#&X88e8kL~FF=4tblTw3a^I_F1Q>pL_d`(~8GpjpSQ!SNjo*63RAP(qGX-i$H# z+5Ikpy`%q}Cib)s_(#0=CU1B{QOUV##{QtMY>m;9MPz=1R|(UwD_=J*yp#FOy7I(vgCdS^bA(@}jgE8;3S+sb0gm|wzCzu6N69d&&+LLt&T z>1qbtyFK>x+%D7FeDu!Cs4|>lJ6Y_$lGL8vKYBbT>ospmgAt}23;uB<{kgl)pfyedR($sM(k_D8QxhQCX8jP!n^_uLyjn+P(x&+O8I_4nf(AmpP5 z`gpsP_AbM-n4I?wRc-u8@8TlQen9SCJr+pL9SM1h>{9QODZ!r#jXvpt1*3&&0H<{1{b6O6TrVeGW z@O=?QS!SI^(%l{WXk{cd;#tL}Umn}%jR4w24*%rII1*Ak?fN1xc)}50uIJS1@%#uv zRaJClK~fGsNpO9Qg2F_MqKjQ{d&YKBl=C`&lm7UEI6PF>xklEO-eJd+ifG|DXAYvcOuCE{5Vs!nnIfl zbr|{3Fe*K0_5zcC-LGx1;PmId}}\preformatted{#> # A labelled, undirected network with 35 nodes and 117 ties +\if{html}{\out{
}}\preformatted{#> # Mexican power elite network +#> # A labelled, undirected network of 35 elites and 117 common belonging or +#> interest ties #> # A tibble: 35 x 8 #> name full_name entry_year military in_mpn PlaceOfBirth state region #> From 9ef1a13c277bed8795732875d9f842bd74b083c8 Mon Sep 17 00:00:00 2001 From: James Hollway Date: Tue, 5 Nov 2024 18:18:22 +0100 Subject: [PATCH 05/11] Updated mpn_cow --- data/mpn_cow_igo.rda | Bin 2912 -> 2912 bytes data/mpn_cow_trade.rda | Bin 42667 -> 42671 bytes man/mpn_cow.Rd | 4 ++-- 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/data/mpn_cow_igo.rda b/data/mpn_cow_igo.rda index e469f14e865df167d50a6b5496149b12a976af20..273f3caf53f9f9fe1498215067cdeb15c73779ce 100644 GIT binary patch delta 2674 zcmV-&3XS#P7T^{ULRx4!F+o`-Q(0+ehm-(6dyx^A1OYJ(G?A-Wf68Ho6@<$T4HcC| zWuU6d4HT+Y2+ESGs;N~~N>)s=Ob_POgZTKAoR)ON&g~xh{O{VL zzr@1|RaH$9e>5rnBLyZH3Z*2KRVp9!OfVHGQdE+PugLdbm502;^B7m=eumAPHevc3 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}}\preformatted{#> # A labelled, weighted, directed network with 116 nodes and 11489 arcs +\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, directed network of 116 nodes and 11489 arcs #> # A tibble: 116 x 1 #> name #> @@ -30,7 +30,7 @@ #> # i 11,483 more rows }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network with 152 nodes and 839 ties +\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 152 nodes and 839 ties #> # A tibble: 152 x 3 #> name type polity2 #> From 4cb300db31e46bb24caf712b68413676029c6c7f Mon Sep 17 00:00:00 2001 From: James Hollway Date: Tue, 5 Nov 2024 18:18:32 +0100 Subject: [PATCH 06/11] Updated mpn_elite_usa --- data/mpn_elite_usa_advice.rda | Bin 742 -> 772 bytes data/mpn_elite_usa_money.rda | Bin 854 -> 858 bytes man/mpn_elite_usa.Rd | 4 ++-- 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/data/mpn_elite_usa_advice.rda b/data/mpn_elite_usa_advice.rda index 7eacbe35d59b8eab6fdf93375151c841dfb02583..6f5dc5add1bdd125b92351a7bd8b2482881d6161 100644 GIT binary patch delta 741 zcmVLRx4!F+o`-Q(4P!c9H-EkC71;6wm`6002-8QW|KJ4hK2~x( zf8=UCuQJ7l?zQf2zAT!{*py4HVVNy6ps)&&g`O-seBqCND zZu0|y;7hjjJnuqSm`O@qObjYLEO#pPI@sK-3sSk2>t#`uW9xqkurs}mh0Lqc=Umln zFs)`@ms|OLOPX3z#?tA?$k}@tniOYMp(-^QnHdeM9{t$hy0%WsWh?JP-D=bPRS9PP%{8}~1{R^uC4Wf|KnsKt%8 zj)IL=vRIg!R>jH5Ee$JdTN!gOGrJq?3fSjO88o?^?cMeoceFFJuWatNo;Uc1N8bkW XVG%^5xzjD1Kk;`YQ-ui(w+Cq;d){ID delta 711 zcmV;&0yzDI2Id73LRx4!F+o`-Q(4^n#v=d)i;)o)7Qh1>4w3^y4N@9t&;T@OXbk`~ z8fe;)I~#upq>2DGpfmsg000000000LM1*Oe6Df@&De7p`)OaIMdW}6G)714EHldTr z2GriE?PZSSVMb(ULqO6tQM8S;YDVHa=PQNAaXB0(h2l}l;CyQl!jg_9Nhp;`FBg<@ zs&Y8k*p@H39ya&BqCOqw`^fS&Dr9cBmk@EwfVqDp@HrV(7wdu$R;7VYQjB`|GD%s-1Adfj+EPGb_wqVOrNG(I~oJDTh49X^5^-Y5@c&iDtuC zKmjpD5+>UvJjks~0T7A~^hk(DL!nnwrPQG+-gXzU)U8?jtb1FmI}EGpCD^RSmX)W4 zp{;-1l^^R8*x7_i0b7+sgjo;>8U!jBu&m%g2?{{~3Qs8r5}HV%UluUx&OZ#=Kc1xzGKcml6w|C5`8Ts-}O8&|h{gvICyAX`k zQI*F;;G&WvGfDEh%GD@|5f7atH6(;evr~WIUT{7{ybnvz^rdR~DNCVweMegb%ATiV z1C@DVS24Y8YB8&QZ{bq&E49wKjhQZG&4$YQ#zthjAIs`o($ba&mzNV0Y1+unqbjC! zsKUs^#BTX}?;ke*#ZMrzD{v=0L~1i2>=6ow1rQYFaN25YL7>3H#W|z!fLVcma&>>~ zytD3p40+p)p8Cf^>+BzMZN$pN%Gqo~B5P(xxs{W0$AT^9Xm3aS(A)24Yi%`gt6imP zRUFfq=Hb;FbZ^|iqj76Ox-vGHk)s1rIm$K~618he$hkPBouy@KBOXQew)*n7GHhbf t@;RHk>+-|0DHMtdccsC&dFGxp^IAQvCQ){5!p_& zLW=7Hf6msrV|*bVc)HUCBH>d8K3kWhqL8H|i`gz*=+4;g_xo+_rPbXTKMf5xXl^$c zDYVJmN6$&3n{^(ln(9mGGsz-Sq(U}IJG5C6_RW^MuvPaYhH4c=6Mbt)NgtE_ujO*S zm7eoEZO4t9Oc+1n_0NKti!7(+`<={KH&n&GfB(z)B9svqR7qRbNJ~*vT~LRNP)YSY)*hORn6MWU6;hWea#E61qAH0+5$XLa z)o}Oyc1N=hSFZ7wuT{aX!N+niw%x;Ok@RQB3)1>e(uwraM4w}P?QkzHSrkN$B9u~x ze-epeQi@7RD551PN+l^uDM?78iWIz0$^M*cf_WL4FH6#Oz04RJuW9$aDt-^orMAq$ zsgnnZor0g?iL}n#eA|o-1?K~8se)bT@oV+h>bue4{rr#F=IZmVYT#VyZOjhm3;3~O zp5>bL!*za@?3R-E s4zt2=6rQClS!7tHkw~NQN6*&|bwrj@bb4vtzyFK5BAh5l9tc*ofUh%yN&o-= delta 758 zcmVcNbILk zrA2iCf9Gmk&x9c!c&Un!VN(`Xahw%*!ayQ3%JrL%o4#^V(> z89S)?=`_=7qt#PYNrfhWl9eJ6u2k;RWK0_M+VzUCyhvuDRS-@2NJ$@){jbR6eJefY zhSN1Puweg;7km`VS!F*r-0o*$)~0sz|33DEf1$*|lWn1?p{8&|y%HVyB#M$tD1)U^ zM@2-20+kgalo1$O#Fo9H64X@^9#K+ApHtmo>8QIi5prf#DRnNCq?IU&qESS8e@gXS zd;Ys4*^jH&fXmmnaMj}Cx-quZ#>FG((VrX>3Qy9B^wLD1V|?v!FfOtvi5x{Jr4A($ zf5fE}l#)?IN>Y?cQj}7Xkwp|KfS;57JPU;LGcsV9BkfwbFVm-Kv^y>6mcSCAocQ{^#c69eF z*Gs=1cU`iBCpJRB-9SM>nly?hz(|lJf9W&B=`hlRcxaBuLqjJqkI<&hwx)GnI2Qt#@OQPA2_j0HuL@wEzGB diff --git a/man/mpn_elite_usa.Rd b/man/mpn_elite_usa.Rd index fca620f1..b6b114f5 100644 --- a/man/mpn_elite_usa.Rd +++ b/man/mpn_elite_usa.Rd @@ -7,7 +7,7 @@ \alias{mpn_elite_usa_money} \title{Two-mode and three-mode American power elite database (Domhoff 2016)} \format{ -\if{html}{\out{
}}\preformatted{#> # A labelled, two-mode network with 34 nodes and 46 ties +\if{html}{\out{
}}\preformatted{#> # A labelled, two-mode network of 34 nodes and 46 ties #> # A tibble: 34 x 2 #> type name #> @@ -30,7 +30,7 @@ #> # i 40 more rows }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{#> # A labelled, two-mode network with 38 nodes and 103 ties +\if{html}{\out{
}}\preformatted{#> # A labelled, two-mode network of 38 nodes and 103 ties #> # A tibble: 38 x 2 #> type name #> From 8338028ba6dcbe86d07543c1680d21fdbb0fe1e8 Mon Sep 17 00:00:00 2001 From: James Hollway Date: Tue, 5 Nov 2024 18:40:32 +0100 Subject: [PATCH 07/11] Updated mpn_senate --- R/data_mpn.R | 18 +++++++++--------- data/mpn_DemSxP.rda | Bin 2108 -> 0 bytes data/mpn_OverSxP.rda | Bin 1046 -> 0 bytes data/mpn_RepSxP.rda | Bin 2535 -> 0 bytes data/mpn_senate_dem.rda | Bin 0 -> 2119 bytes data/mpn_senate_over.rda | Bin 0 -> 1052 bytes data/mpn_senate_rep.rda | Bin 0 -> 2539 bytes man/mpn_senate112.Rd | 18 +++++++++--------- 8 files changed, 18 insertions(+), 18 deletions(-) delete mode 100644 data/mpn_DemSxP.rda delete mode 100644 data/mpn_OverSxP.rda delete mode 100644 data/mpn_RepSxP.rda create mode 100644 data/mpn_senate_dem.rda create mode 100644 data/mpn_senate_over.rda create mode 100644 data/mpn_senate_rep.rda diff --git a/R/data_mpn.R b/R/data_mpn.R index 89e41a94..a108d8be 100644 --- a/R/data_mpn.R +++ b/R/data_mpn.R @@ -204,32 +204,32 @@ #' @docType data #' @keywords datasets #' @name mpn_senate112 -#' @usage data(mpn_DemSxP) +#' @usage data(mpn_senate_dem) #' @references #' Knoke, David, Mario Diani, James Hollway, and Dimitris C Christopoulos. 2021. #' \href{https://www.cambridge.org/core/books/multimodal-political-networks/43EE8C192A1B0DCD65B4D9B9A7842128}{\emph{Multimodal Political Networks}}. #' Cambridge University Press. Cambridge University Press. #' @format #' ```{r, echo = FALSE} -#' mpn_DemSxP +#' mpn_senate_dem #' ``` -"mpn_DemSxP" +"mpn_senate_dem" #' @rdname mpn_senate112 -#' @usage data(mpn_RepSxP) +#' @usage data(mpn_senate_rep) #' @format #' ```{r, echo = FALSE} -#' mpn_RepSxP +#' mpn_senate_rep #' ``` -"mpn_RepSxP" +"mpn_senate_rep" #' @rdname mpn_senate112 -#' @usage data(mpn_OverSxP) +#' @usage data(mpn_senate_over) #' @format #' ```{r, echo = FALSE} -#' mpn_OverSxP +#' mpn_senate_over #' ``` -"mpn_OverSxP" +"mpn_senate_over" # EVS #### diff --git a/data/mpn_DemSxP.rda b/data/mpn_DemSxP.rda deleted file mode 100644 index c73a36ce75cdf5ec051274ab4c4a03d3d0eb4458..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2108 zcmWlbeLR!<8^>?S$_|w=KY2)dK)Y8RNgi?t&Dxg9=Dz8P?2t3k145F{(a*%9xMzhS zv}OA$q9l$wp+h-EriZ;)CsBDSI_gj=e*0a2ey;0veXc*Q_v^ZRcMIJ&`0TO|+EwZ5 z7zFh*e*JUdE0>9e{JNlvZ+}d^{NN3FFNIEp8x9)pgP;?JtMl}hLXe*K8i=FU?(Ga& z>U#?z2v_X<80QV?GmCI;LfmgU%@~59K4vj=AA*E^t2>RM$B@uiuN;Cbg?-*DA;{P= z(&AOQC8X1wAxNKVDb!yL>0R>wuK|p%y2QIF;bltv3;lO+{hxGjoj+YBH7r*bM|yfg zkn>}_*jMOn$*%X0iFT<{6&FWWfrzI@pJ;REkYmN^e!YqGTLPz7-<4 zP_UE42iJ^fBDxU)2$+T_L1l*o8=@lAha+XRqIeZv-iq4sQIw5}2teL$WXD5LolRx1 z9_%Y1IZ|~a0l*}(00W?$%ZtVGHmU&5N2Gir%2Q9Lo2hAWy?fJx%c~2pH`1`e_2Flu z=}$OKT^Brgz3^>b2#bE1KW=cm>pYM?MH;ZM<}+7RiCw#2*M>_q_;=Xv-p%BzBJ=wp zv5FgLGG6v`%{bl3xb;W!)Y7R+PeYUG#XyV())q+cnAgv~D*9YrTefJ4g0RKWVT3%Z zE_xz+u;aH&%UA5k27gHGdx=hpKUF`817HqgP`xha zgc@>j(7*wOl5x(BdB8Pq%l#>&cX^D=hIV;-$Jo3lS{ds$%Cv&$PoXyHmzNq8Re zesivQ)L-l?A6;ys`a%^&_kxoo8#f9@H#*y1k1LpT^7!$^mwg@|?Nw-JTbNeD94m9jNr=4H#dh@Hz?AD_0*tnM3J29=?+Iiu$`yLr8 z%fIn8NA|R%=6pLP0zaH=YvnNC65us-=Mz_$_0g9NGuj?d=eMu+G^gg}Jw&leWwr_B zCjrUS>{htR70ODDNe|9-%xC z%aXQcE(KX2pJ<0jc}PK}5+JBA3}+~G`y0psK0FRUQ5chwRX!+Q4u~kxS1DF1Q6dke z@em*)z(f`-&eBn-Ao5j2M8P_RsdQja0gVc>3Hj(El@484o&f^I9How3Oot2%@;Ru< z8YxSPU;v;501Oe9iiq-27VtAbL#V2GuG3KEJEIVy&(gwK$YkZhC>SHc)auA4=d;lY@SxEN%UMaU^|gKR(hsdRbK z{En;YtHduQheeVl6=4nW)l6Fo{=ry`VA@^HDt4iKFcV&$d26-jWj5!1O#l2c5+SL? zW_@PObB|@)=!%E&UVYMPmqQ4~9HAs!cvKypF&^#4eI^@A(l(Ze>)itAVy|tLuR!n_ z3J)W@)^F}M?oPVLD)y|>S6yOYmL5HOsMMJLw~-iYW)y9aDD$(#u=eygSmm02@~lIltB12Su_)xcsCa4I&@t7f8Lp`&&6;NMgA zODAGuG?S}_n)ccT3@$fK4T*5h(~fNpjaBQvNUIWQ#?u0VmHGFkT#HCJ4&x|bN#fW#(72up2hGfK1=K=Dinsm4lxft->aDr1Tvbc<9-_ZunS#a~KX+(=jW`7@gV_ zGkUzFNBXc=J>U7yi5F`xdW7IQPoK;!@h`4Feupv|rL93zz1}@~m&Oh2`&#-urTb&c z@`#WKmuI$Cnr{w&Ou5PWo6DA1HE5&8V^jW~V0J2+{%x{(UwBfdX=bzs&t6EM+!T4e ztHt5XF^yCk`82fFG+{8RS2Jb(Xw_uR{*+&~L>!nnBB;|$-5#O1COd>s6q;VAfX1Ns z!v{`V7CXBYCfm&B7rlt;=)Abq)>gJPVH4A`%X+WC1_S>w%5@E2#))ICp#mGLHUb14DtPnrJjMG%_>;APo%xri_397)(HDa5U3R z28M=)Mu21ip`bL;kN^V-hz$f%l=TLH0ibAlfuH~Y0000000l`w8WTo_iK9bQ^Fvhj ziR9GUPYN|2D0)o+>VBwX@@gKZYS}vZlRBc7_(FNg^bogpvugm8L%oR>(#4Q)6PwYn4}9Bt|+)s3aRwh)D_Ld>LL= zXIt=aGJKef9#{2R`6UA|(Eq}JQ!)?<2Yu=W#64pg9INPnane<-YQ?2x5 zW_;Aj%GW_uPG&|}6iO(o0)&Jq{6K@|LP6=MK`#`kDHNduCH7c}63QyYi4o8RNQ0@r zVBDfwxg?S!OfXZCND9FKs}LxpK!Uk9b(7@-=ha~(pS|K|J@*n!jcd~N98Vr1NptO| z<&v%;pv7&XFJmkbih?f3;?afTFltzWOihwO49XHtbu_OLV!}y50ozJqSczdNL{gF> zN+Crkic%<)q)H)3QiUlLB7{;xl2U}B6p*46r68gdr6egOD2hQz3S3Bh-sjZ%&y$5r ztcrJ*K1-J}q=yR=c%;UrC;D%&U`TPZKXT?a3=FS|_CSqB3~aX~g*jU5QbUoI>{^It zWp7z+#EITYUG=Zr-ezSz@t8vqvPjt=X5x@uH?||zw^77 z?PbKKKW9I!k?Y%F?DtFEjkXVK^6Yf2ZaQ0zH;J;;YP37;ezjeV+J4vA%XNpdaf&!q zoj*gdO}4Xh2I`(!m+xx4>)0u;+sM>fv9PxiVrX~PDdB1Gtmfy$#+ci6bH|M{tbObp z42Ju8<9UrM_gANNV^N(qv@^rB@1^b4&9$YS3e}~|>)E`H;@h3()AcgD-NvH^H=%oB zx9{-UZ>gUv(AjQjZ?iX$dtT8UDGvb;0FGpbLPL!tr6&lC5EG8^Pz2W95`hY`5V;#L Q6HERs8$XDcowSD2(#2-XxEyJRB$u=dCNr*coOim|Xd)}QloF|}R#Ya$aN3Z~ z*g2YW#&yvRiHR;t`DwCp+3sc4kCxK5D!;S8_x-$|&-eYl@AEw0=a1*h-yG-e{p zd=q$@x=d$j)!f^;*Kjy^i` z)KL_`{WiMdoC!^PRK z6|aEGqF}7kC)*@bRMA;q#ZXalDQaULAO)z(Tpt76a9xT|VpI`Bx)G+qR*ezhDYDIh z1Qy{(WFi8=0Yak1I067qrGhsi^_`3}LI#YgCXSwIqGcT zz&L}6RKN#|6~ZWQz=!HsQ~Q@4-hy1bwcvv-H2y9H1^n-raVO-@pqjHR;m11Kh{Y*AsggmNys1`40Q< z4f7DGcOZX~kxzU_b+niV74qw{Zc}mY)@XChL-fjX$%DZ9%D)XS>O18;fDa;)%%9>; 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}}\preformatted{#> # A labelled, weighted, two-mode network with 114 nodes and 2791 ties +\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 114 nodes and 2791 ties #> # A tibble: 114 x 2 #> type name #> @@ -31,7 +31,7 @@ #> # i 2,785 more rows }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network with 134 nodes and 3675 ties +\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 134 nodes and 3675 ties #> # A tibble: 134 x 2 #> type name #> @@ -54,7 +54,7 @@ #> # i 3,669 more rows }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network with 52 nodes and 614 ties +\if{html}{\out{
}}\preformatted{#> # A labelled, weighted, two-mode network of 52 nodes and 614 ties #> # A tibble: 52 x 2 #> type name #> @@ -78,11 +78,11 @@ }\if{html}{\out{
}} } \usage{ -data(mpn_DemSxP) +data(mpn_senate_dem) -data(mpn_RepSxP) +data(mpn_senate_rep) -data(mpn_OverSxP) +data(mpn_senate_over) } \description{ These datasets list the U.S. Senators who served in the 112th Congress, From 019221805aeaa2b611b856341d06eb8fd00cd9a6 Mon Sep 17 00:00:00 2001 From: James Hollway Date: Wed, 6 Nov 2024 11:11:51 +0100 Subject: [PATCH 08/11] Updated diversity tutorial figure width --- inst/tutorials/tutorial8/diversity.Rmd | 29 ++++++++++++++------------ 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/inst/tutorials/tutorial8/diversity.Rmd b/inst/tutorials/tutorial8/diversity.Rmd index 0e1d5515..d50b2b0a 100644 --- a/inst/tutorials/tutorial8/diversity.Rmd +++ b/inst/tutorials/tutorial8/diversity.Rmd @@ -82,7 +82,7 @@ Recall that this data has several nodal attributes. Let's explore a couple of these attributes, "Gender" and "PowerOrigin", visually using `graphr()`. -```{r plotfriends, exercise=TRUE, purl = FALSE} +```{r plotfriends, exercise=TRUE, purl = FALSE, fig.width=9} ``` @@ -98,8 +98,8 @@ graphr(____, ```{r plotfriends-solution} graphr(marvel_friends, - node_shape = "Gender", - node_color = "PowerOrigin") + node_shape = "Gender", + node_color = "PowerOrigin") ``` These variables seem to be distributed unevenly across the network. @@ -218,12 +218,12 @@ It looks like some origin stories are more gender diverse than others. Gods (just Thor here) and humans are all men, whereas those with mutant or radiation origin stories are more gender diverse. There doesn't appear to be much difference in intellect -across gender categories however. +across gender categories in this data. Ok, this tells us about how (un)even the distribution of these variables is in this network, but it doesn't necessarily tell us whether ties are appearing more frequently -between nodes of similar (or different) categories. -For that we need to look at homophily/heterophily. +_between_ nodes of similar (or different) categories. +For that we need to look at _homophily_/heterophily. ## Measuring heterophily @@ -240,6 +240,7 @@ As such, an EI index of -1 suggests perfect homophily, whereas an EI index of +1 Check how homophilic three variables in the network are, "Gender", "PowerOrigin", and "Attractive". +Please assign the results so that we can use them later. ```{r ei, exercise=TRUE, purl = FALSE} @@ -290,7 +291,7 @@ Plot the results of running this function with respect to the EI index on each of the three variables. You can specify that one thousand simulations should be used using `times = 1000`. -```{r rando, exercise=TRUE, purl = FALSE} +```{r rando, exercise=TRUE, purl = FALSE, exercise.timelimit = 120} ``` @@ -337,8 +338,10 @@ a red vertical line for the observed score, and a density plot of the scores from the randomly generated networks. The grey tails of the distribution are a visual aid indicating the most extreme 5% of scores from the distribution. - The results are _really_ interesting. + +gif of ironman explosion + Despite being the larger coefficients (in absolute terms), it looks like we cannot reject the null hypothesis that there is no homophily for gender nor for attractiveness. @@ -365,7 +368,7 @@ Permuting the network retains the structure of the network because the ties are kept and only the labels (variables) are reassigned randomly. Let's first plot the observed data and some permuted data next to each other. -```{r perm, exercise=TRUE, purl = FALSE} +```{r perm, exercise=TRUE, purl = FALSE, fig.width=9} ``` @@ -388,7 +391,7 @@ old + new ```{r cupqap-qn, echo=FALSE, purl = FALSE} question("Which of the following is true?", answer("Random networks retain the structure of the original network.", - message = learnr::random_encouragement()), + message = paste(learnr::random_encouragement(), 'gif of thor frustration')), answer("Permuted networks retain the structure of the original network.", correct = TRUE, message = learnr::random_praise()), @@ -408,7 +411,7 @@ Let's try a test that runs this over a succession of permutations, just as we did with random graphs. Plot the results for gender and power according to the random and permutation baselines. -```{r testperm, exercise=TRUE, exercise.setup = "rando-solution", purl = FALSE} +```{r testperm, exercise=TRUE, exercise.setup = "rando-solution", purl = FALSE, exercise.timelimit = 120, fig.width=9} ``` @@ -466,7 +469,7 @@ You may recognise some of the names. The main network consists of 32 scholars with directed ties weighted by the total number of messages sent from $i$ to $j$ over the period of the study. Nodal attributes collected include the primary discipline and number of citations in the social science citation index at the start of the study. -```{r introeies, exercise=TRUE, purl = FALSE} +```{r introeies, exercise=TRUE, purl = FALSE, fig.width=9} ison_networkers graphr(ison_networkers, node_color = "Discipline") ``` @@ -507,7 +510,7 @@ We can use tidy methods (e.g. `tidy()`, `glance()`) to get the salient informati and `{migraph}` includes also a plot method for these results to facilitate the quick interpretation of these results. -```{r qapinterp, exercise=TRUE, exercise.setup = "qapmax-solution", purl = FALSE} +```{r qapinterp, exercise=TRUE, exercise.setup = "qapmax-solution", purl = FALSE, fig.width=9} ``` From 0c0a037d4a21e6167b74ce2f19b21508ec5f508e Mon Sep 17 00:00:00 2001 From: James Hollway Date: Wed, 6 Nov 2024 11:12:16 +0100 Subject: [PATCH 09/11] More elaborate start to diversity tutorial --- inst/tutorials/tutorial8/diversity.Rmd | 40 +++++++++++++++++++------- 1 file changed, 29 insertions(+), 11 deletions(-) diff --git a/inst/tutorials/tutorial8/diversity.Rmd b/inst/tutorials/tutorial8/diversity.Rmd index d50b2b0a..f2c93752 100644 --- a/inst/tutorials/tutorial8/diversity.Rmd +++ b/inst/tutorials/tutorial8/diversity.Rmd @@ -17,12 +17,29 @@ library(manynet) library(migraph) library(patchwork) library(ggplot2) +clear_glossary() knitr::opts_chunk$set(echo = FALSE) marvel_friends <- to_unsigned(ison_marvel_relationships, keep = "positive") marvel_friends <- to_giant(marvel_friends) marvel_friends <- marvel_friends %>% to_subgraph(Appearances >= mean(Appearances)) ``` + +## This tutorial + +gif of deadpool greeting + +Within a network, nodes and ties vary. +But how much do they vary and are there associations in this variance? +By the end of this tutorial, you will be able to: + +- [ ]   measure the richness of a network in terms of attribute types +- [ ]   measure how diverse a network is +- [ ]   measure how heterophilous the ties in a network are +- [ ]   test how heterophilous the ties in a network are compared to random networks +- [ ]   test how heterophilous the ties in a network are compared to permutations of the original network +- [ ]   regress a network's ties on other ties and/or nodal or dyadic attributes + ## Initial visualisation For this session, we'll explore a couple of different datasets. @@ -113,15 +130,16 @@ We therefore need to establish how diverse this network really is. ## Measuring richness -We can begin by measuring the number of different categories there are. -Here we might assume that the more different categories there are, -the more diverse the network is. -The measure of 'richness' is inherited from the study of biodiversity, -and calculates the number of different categories are presented in a -dataset for a given variable. +Some categorical variables have more categories than others, +and this can be thought of as indicating greater diversity than e.g. a binary +categorical variable. +A measure of this 'richness' is inherited from the study of biodiversity. +_Richness_ calculates the number of different categories that appear in +the data for a given variable. +Let's calculate the richness of all of the variables in our Marvel data. ```{r rich, exercise=TRUE, purl = FALSE} - +net_node_attributes(marvel_friends) ``` ```{r rich-hint, purl = FALSE} @@ -166,12 +184,12 @@ Recall that the Blau index for any given variable is: $$1 - \sum p_i^2$$ -where $p$ represents the proportion belonging to any given category, -and $i$ indexes each of the given categories. -A perfectly homogeneous group would receive a score of 0, +where $p_i$ represents the proportion of observations holding $i$, +which indexes each of the given categories. +A perfectly homogeneous group (all the same category) would receive a score of 0, while a perfectly heterogeneous group (with members spread evenly over the maximum categories) would receive a score of 1. -Obtain the network diversity scores for our five attributes. +Obtain the network diversity scores for the five attributes used above. ```{r blau, exercise=TRUE, purl = FALSE} From 851b3768c459e408946920a1331bbbb50371540a Mon Sep 17 00:00:00 2001 From: James Hollway Date: Wed, 6 Nov 2024 11:12:39 +0100 Subject: [PATCH 10/11] Dropped alter(Discipline) from net_regression call in diversity tutorial --- inst/tutorials/tutorial8/diversity.Rmd | 19 +- inst/tutorials/tutorial8/diversity.html | 338 +++++++++++++++--------- 2 files changed, 228 insertions(+), 129 deletions(-) diff --git a/inst/tutorials/tutorial8/diversity.Rmd b/inst/tutorials/tutorial8/diversity.Rmd index f2c93752..ff32587c 100644 --- a/inst/tutorials/tutorial8/diversity.Rmd +++ b/inst/tutorials/tutorial8/diversity.Rmd @@ -509,18 +509,18 @@ simulations or more. ``` ```{r qapmax-hint-1, purl = FALSE} -network_reg(____, ison_networkers, times = 200) +net_regression(____, ison_networkers, times = 200) # If the model runs into a timeout error, please reduce the number of 'times' in the function above. ``` ```{r qapmax-hint-2, purl = FALSE} -weight ~ alter(Citations) + sim(Citations) + - alter(Discipline) + same(Discipline) +weight ~ ego(Citations) + alter(Citations) + sim(Citations) + + ego(Discipline) + same(Discipline) ``` ```{r qapmax-solution} -model1 <- network_reg(weight ~ ego(Citations) + alter(Citations) + sim(Citations) + - ego(Discipline) + alter(Discipline) + same(Discipline), +model1 <- net_regression(weight ~ ego(Citations) + alter(Citations) + sim(Citations) + + ego(Discipline) + same(Discipline), ison_networkers, times = 200) ``` @@ -560,8 +560,17 @@ question("What can we say from the results from model 1?", ) ``` + +## Free play + While these are the conclusions from this 'play' data, you may have more and more interesting data at hand. How would you go about specifying such a model? Why is such an approach more appropriate for network data than linear or logistic regression? + +```{r freeplay, exercise = TRUE, fig.width=9} + +``` + + diff --git a/inst/tutorials/tutorial8/diversity.html b/inst/tutorials/tutorial8/diversity.html index 770a6bb5..7c8a4b07 100644 --- a/inst/tutorials/tutorial8/diversity.html +++ b/inst/tutorials/tutorial8/diversity.html @@ -110,6 +110,28 @@
+
+

This tutorial

+

gif of deadpool greeting

+

Within a network, nodes and ties vary. But how much do they vary and +are there associations in this variance? By the end of this tutorial, +you will be able to:

+
    +
  • +
  • +
  • +
  • +
  • +
  • +
+

Initial visualisation

For this session, we’ll explore a couple of different datasets. @@ -197,8 +219,8 @@

Initial visualisation

data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0" data-pipe="|>">
graphr(marvel_friends, 
-           node_shape = "Gender",
-           node_color = "PowerOrigin")
+ node_shape = "Gender", + node_color = "PowerOrigin")

These variables seem to be distributed unevenly across the network. There seems to be some homophily – or like choosing like – operating @@ -211,14 +233,16 @@

Initial visualisation

Measuring richness

-

We can begin by measuring the number of different categories there -are. Here we might assume that the more different categories there are, -the more diverse the network is. The measure of ‘richness’ is inherited -from the study of biodiversity, and calculates the number of different -categories are presented in a dataset for a given variable.

+

Some categorical variables have more categories than others, and this +can be thought of as indicating greater diversity than e.g. a binary +categorical variable. A measure of this ‘richness’ is inherited from the +study of biodiversity. Richness calculates the number of +different categories that appear in the data for a given variable. Let’s +calculate the richness of all of the variables in our Marvel data.

+
net_node_attributes(marvel_friends)
Measuring diversity attribute is the Blau Index. Recall that the Blau index for any given variable is:

\[1 - \sum p_i^2\]

-

where \(p\) represents the -proportion belonging to any given category, and \(i\) indexes each of the given categories. A -perfectly homogeneous group would receive a score of 0, while a -perfectly heterogeneous group (with members spread evenly over the -maximum categories) would receive a score of 1. Obtain the network -diversity scores for our five attributes.

+

where \(p_i\) represents the +proportion of observations holding \(i\), which indexes each of the given +categories. A perfectly homogeneous group (all the same category) would +receive a score of 0, while a perfectly heterogeneous group (with +members spread evenly over the maximum categories) would receive a score +of 1. Obtain the network diversity scores for the five attributes used +above.

@@ -312,12 +337,12 @@

Measuring diversity

stories are more gender diverse than others. Gods (just Thor here) and humans are all men, whereas those with mutant or radiation origin stories are more gender diverse. There doesn’t appear to be much -difference in intellect across gender categories however.

+difference in intellect across gender categories in this data.

Ok, this tells us about how (un)even the distribution of these variables is in this network, but it doesn’t necessarily tell us whether -ties are appearing more frequently between nodes of similar (or +ties are appearing more frequently between nodes of similar (or different) categories. For that we need to look at -homophily/heterophily.

+homophily/heterophily.


    @@ -342,7 +367,8 @@

    Measuring heterophily

    heterophily. (This is why the function is called net_heterophily()).

    Check how homophilic three variables in the network are, “Gender”, -“PowerOrigin”, and “Attractive”.

    +“PowerOrigin”, and “Attractive”. Please assign the results so that we +can use them later.

    @@ -426,13 +452,14 @@

    CUG tests

    this is in bounds, a red vertical line for the observed score, and a density plot of the scores from the randomly generated networks. The grey tails of the distribution are a visual aid indicating the most -extreme 5% of scores from the distribution.

    -

    The results are really interesting. Despite being the larger -coefficients (in absolute terms), it looks like we cannot reject the -null hypothesis that there is no homophily for gender nor for -attractiveness. Both observed scores fall within the range of scores we -would expect from randomly generated networks with the same distribution -of that variable.

    +extreme 5% of scores from the distribution. The results are +really interesting.

    +

    gif of ironman explosion

    +

    Despite being the larger coefficients (in absolute terms), it looks +like we cannot reject the null hypothesis that there is no homophily for +gender nor for attractiveness. Both observed scores fall within the +range of scores we would expect from randomly generated networks with +the same distribution of that variable.

    However, we can reject the null hypothesis with respect to their power origin story. While the coefficient itself is close to 0 (neither strong homophily nor heterophily), all the random networks generated @@ -579,14 +606,14 @@

    Network regression

    -
    network_reg(____, ison_networkers, times = 200)
    +
    net_regression(____, ison_networkers, times = 200)
     # If the model runs into a timeout error, please reduce the number of 'times' in the function above.
    -
    weight ~ alter(Citations) + sim(Citations) + 
    -                      alter(Discipline) + same(Discipline)
    +
    weight ~ ego(Citations) + alter(Citations) + sim(Citations) + 
    +                      ego(Discipline) + same(Discipline)
    Network regression

    While these are the conclusions from this ‘play’ data, you may have more and more interesting data at hand. How would you go about specifying such a model? Why is such an approach more appropriate for -network data than linear or logistic regression? - +network data than linear or logistic regression?

    +
    +
    +

    Free play

    +
    + +
    +

    @@ -791,20 +830,20 @@

    Network regression

    @@ -830,7 +869,8 @@

    Network regression

    @@ -989,13 +1031,14 @@

    Network regression

    @@ -1125,15 +1169,17 @@

    Network regression

    @@ -1218,14 +1264,15 @@

    Network regression

    + + + + +

    From b141046a45a5f5944d4540d80148a00e23762bd4 Mon Sep 17 00:00:00 2001 From: James Hollway Date: Wed, 6 Nov 2024 11:15:42 +0100 Subject: [PATCH 11/11] #patch bump --- DESCRIPTION | 4 ++-- NEWS.md | 22 +++++++++++++++++++++- 2 files changed, 23 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index b3f4e70e..9814d138 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: migraph Title: Univariate and Multivariate Tests for Multimodal and Other Networks -Version: 1.4.2 -Date: 2024-09-04 +Version: 1.4.3 +Date: 2024-11-06 Description: A set of tools for testing networks. It includes functions for univariate and multivariate conditional uniform graph and quadratic assignment procedure testing, diff --git a/NEWS.md b/NEWS.md index 753b76db..fec7d32d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,12 +1,32 @@ # migraph 1.4.3 -2024-09-04 +2024-11-06 ## Package - Using different github actions for renaming and releasing binaries (thanks @auzaheta) - Updated manynet dependence to >=1.0.5 +## Models + +- Added `test_configuration()` + +## Data + +- Updated `mpn_bristol` with info +- Updated `mpn_ryanair` with info +- Updated `mpn_elite_mex` with info +- Updated `mpn_elite_usa` +- Updated `mpn_senate` +- Updated `mpn_cow` + +## Tutorial + +- Updated diversity tutorial + - More elaborate start + - Broader figure widths + - Dropped `alter(Discipline)` from `net_regression()` call + # migraph 1.4.2 2024-09-03