PNet for Dummies
Contents
1. Company Name DIN Think Tank BIN_BCAChrmsPni BIN_BCA4_ALL DIN A 0 5 156545 AWB Limited 001110000 0 113 O0 7272727 27272727 0 666666666666667 0 0 5 15 0 1 282793 APN News amp Media Limited 0001100000 11 3 0 818181818181818 10 0 1 282793 0 37 406 National Australia Bank Limited 1011110001103110 20 37 406 0 18766 Westfield Group 1111100110104110 3018766 0 1 551998 Queensland Sugar Limited 0001000000 10 3 10 666666666666667 0 0 1 551998 0 0 954586 Ten Network Holdings Limited 0001100110103110 0 0 954586 0 0 795892 ABB Grain Ltd 0001100000103110 0 0 795892 3 6 381376 Leighton Holdings Limited 001010010193 110 111111111111111 0 333333333333333 0 0 OO d OO OH Sa GA b i 0 33333333 et CH 3 003311 Woodside Petroleum Ltd 10111001019 3 O 666666666666667 1 01111111111110 3 2 2 Integrating Attribute and Network Data in a VNA file Attribute Data Select this column and press copy Open Notepad and press paste Type the follow text at the top of the file node data You should have a screen that looks like this A node data 178wNEDs Run4 3 FEB 07 txt Notepad File Edt Format View Help Frode data Company Name BIN_Think_Tank BIN_BCAChrmsPnl BIN_BCA_ALL B AWB Limited 0011100000001 00 11 3 0 727272727272 APN News amp Media Limited OO O1100001000001130 National Australia Bank Limited 1 0 00001 1 westfield Group 1 111100111 3 Queensland Sugar Limited 0001 Ten Netwo2. PNet for Dummies An introduction to estimating exponential random graph p models with PNet Version 1 04 Nicholas Harrigan To download the e atest copy of this manual go to www sna unimelb edu au pnet pnet html download INTRODUCTION EE 3 TERMINOLOGY gees EE 4 STEP 1 INSTALLING ENEE eeben 5 STEP 2 PREPARING MATRIX AND ATTRIBUTE FILES 6 2 1 Preparing Attributes in Excel c cceseeeseeeeeeeeeeeeeeeeeeeeeeeeeeneeeenees 6 2 2 Integrating Attribute and Network Data in a VNA file 00 8 2 3 Transforming VNA File into Raw Matrix and Attribute files 10 STEP 3 ESTIMATION IN PNET cscceeeeeesseeeeeeeeeeeeeeeeeneeees 12 3 1 Setting up PNet Estimation ccccce scence eee ee eee eeeeeeeeeennneeeeees 12 3 2 Preventing Model Degeneracy sececceeeneeeeeeeeeeeeeeeeeeeeeneneeeeees 18 3 3 Running an Estimation seu aeticacedcetsee Ee Seene Eege eege eg 21 3 4 Fitting an E e EE 22 STEP 4 GOODNESS OF EIN egegedhtueekrienbesegeddeAAE Eege ENen 25 4 1 Running a Goodness of Fit ccceccesseeeeeeeeeeee esse seer eeeeeeeeeneeenneeeeees 25 4 2 Interpreting GOF statistics ccceeeseeeeeeeeeeeeeeeeeeeeeeeeeeeeeeenneeeeees 25 APPENDICES HE 27 Appendix 1 Interpreting GOF statistics ssseeeeeeeeeeeeeeeeeeeeeeees 27 Appendix 2 Recommended Starting ParametersS ccccccceseeeeeees 27 Appendix 3 Running
3. 0 72629 Attribute10 0 603144 0 56737 0 01133 Attribute13 0 385550 0 59533 0 12868 Attributel 0 053504 0 27209 0 02081 Attribute2 0 042092 0 31588 0 06174 Attribute3 0 047976 0 29521 0 05286 Attribute4 0 640202 0 73629 0 01184 Attribute5 0 612086 0 77674 0 01725 Attribute6 0 STEET 0 237 SL D Le 5 After the title information you have a list of configurations and three values next to each one The first value is the parameter estimate the second is the standard error of that parameter estimate the third is the t statistic which compares the observed number of this configuration in your graph with the mean number of configurations in a sample of 500 graphs generated with these parameter estimates On some rows you will notice an asterisk This means that the parameter estimate is at least 1 96 essentially 2 standard errors away from zero and is thus indicates that there is a 95 or more chance that the parameter estimate is statistically significant commonly expressed as p lt 0 05 21 3 4 Fitting an Estimation What does a fitted estimation look like It is extremely unlikely that your first estimation run will be an adequate fit to your observed data An adequate fit is generally defined as 1 The parameter estimates and standard errors are within the bounds of a reasonable model 2 The t statistic for all the configurations in the model are less than 0 1 1 Reasonable parameter estimates
4. 2 Make an excluded configurations file to prevent degeneracy C Select parameters and set up estimation C Set Subphases to zero Press Start Open the file labelled start statistics your session name Select all the text after This graph contains and before Graph Density Copy into new Excel document Delete the blank row Save as Excluded Configurations experiment name Insert a row where configuration missing and type name of missing configuration C In PNet deselect the excluded configurations oooO Ou 3 Run an Estimation LC Reset the number of sub phases to 5 Press Start C If has t statistics lower than 2 for most values and lower than 4 for all values AND it is better that your current parameter estimates then press update L Repeat while L doubling the MF for each estimation run L Generally 200 600 is high enough to fit most models L If it is not fitting with runs of 500 or 1000 but is getting close say all numbers less that 0 2 0 5 then set the number of runs to say 20 and then leave the computer to run over night 4 Causes of unreasonable parameter estimates and standard errors L High values for all estimates SEs and t statistics L Rare configurations L Separation L Reference category for a set of dummy variables L Interactions L Other problems 5 Goodness of Fit L re enter all the data in the Goodness of Fit tab in PNet For large number of nodes L select all
5. GObaObaOObbOOOOObOOObOOboOtoOoOc Gtazbat tzbzbaboOkazbaotzoOkaotztzotataotztatz OR RPR REFER RORRFORORORKFERROREE SRRODOORKFOOOOOCOOCORKFEKEEKFOORKOO TEE GOOOOOOObbOOOOOObbbOOOOOOOc 11 Step 3 Estimation in PNet 3 1 Setting up PNet Estimation Before you open PNet take the raw matrix and attribute files that you have created and put them in a new folder on their own Make a copy of this folder and call it something like Original Network and Attribute files Then name the other folder the name of the session which you are going to run in PNet like to name my folders sessions experiments so give them a name like Exp 1 Top250_27Att 6 FEB 07 which means it s experiment 1 and it s of my top 250 corporations network with 27 attributes and it s on the 6 Feb 2007 am a bit obsessive with naming files You might prefer something simpler Make a mental or physical note of where this folder is Open PNet Click on the tab labelled Estimation You should have screen that looks like this File Help Session Name Session Folder Browse Simulation Estimation Goodness of fit Number of Actors jo Network File Browse Select Network Type Estimation Options No conditions C Fix out degree distribution Non directed Network Maximum Degree for Each Actor p a a Fix graph density i dech Li j ut egree tor act Je ctor i Directed Network J Maximu Deg E fp Number of Subphases
6. These appendices are designed to act as a quick reference guides compiling the most useful information in easy to read tables and check lists Appendix 1 Interpreting GOF statistics 1 If the parameter was estimated t statistic needs to be below 0 1 2 If the parameter was not estimated t statistic of less that 1 is a good fit more that 2 is a bad fit Appendix 2 Recommended Starting Configurations Structural Configurations Attribute Configurations Non directed graph Edge K star K triangle K 2 path Isolates Non directed graph Binary attributes e R e Rb Continuous attributes e sum e difference Directed graph Low density graph Arc Reciprocity Isolates K in star K out star AKT T e A2p T Higher density graph also include e AKT D e AKT U e AKT C e andsoon Directed graph Binary attributes e Rb e Rs e Rr Continuous attributes e sender e receiver 27 Appendix 3 Running an estimation Summary 1 Preparing matrix and attribute files Attribute Data C Create Excel file with attributes C Use Excel formulas to create VNA file Matrix Data C Make VNA file of matrix data TT Paste into attribute data VNA file Creating Raw Matrix and Attribute files C Import VNA file C Export as RAW Matrix and EXCEL Attribute Cut and paste Binary Continuous etc attributes into own RAW files C put files into their own folder and make a copy called originals
7. an estimation Summary seen 28 Introduction PNet for Dummies is intended to walk the new user through one complete estimation in PNet It is not a comprehensive guide to PNet Currently the most comprehensive guide to PNet is the PNet Users Manual PNet for Dummies exists to help get the new user started helping them overcome the most common initial barriers so that they can begin exploring and experimenting with PNet themselves To this end PNet for Dummies tries to emphasise solutions to some common problems through dealing with issues such as synchronising your network and attribute data using VNA files transforming your data into raw matrix and raw attribute formats deciding which configurations parameters to select for your model preventing degeneracy in your model identifying the causes of unreasonable parameter estimates in your model e fitting your model e interpreting goodness of fit statistics For the most part we have written PNet for Dummies as a way of documenting many of the heuristic that is rule of thumb solutions which we have come across as we have learnt to use PNet We hope you will find some of our solutions useful to your work The vast majority of the solutions documented in PNet for Dummies are techniques which have been developed by the staff and students of the Social Network s Laboratory at the University of Melbourne in particular Pip Pattison Garry Robins Peng Wang Dean Lusher Galina Dar
8. and standard errors The first of these conditions is not a scientific test but nonetheless it is important Below is a list of the main problems that give unreasonable parameter estimates High values for all estimates SEs and t statistics If you get high values for all or almost all of your parameter estimates standard errors and t statistics this is generally because your model has wandered into parameter space wilderness and can not get itself back This is not a major problem The best thing to do is to return all the parameter estimates to zero that is if you have updated them and they are not already zero and run the model again You may have many first runs up to perhaps 5 or 10 that end up in the wilderness but this is not generally a serious problem Rare configurations In the section on preventing model degeneracy we removed all of the configurations which were not found in the observed graph However we left all other configurations in the model Some of those configurations will have had values of just 1 or 2 and in a large graph these values may be very difficult to statistically analyse and give unrealistically large parameter values In this case it is best to remove these configurations from your model and to update your Excluded Configurations table accordingly Separation Another problem which can occur is complete separation or quasicomplete separation where the independent variables in this ca
9. other parameters except new parameters For low density networks L adjust number of itinerations to approx number of edges x 1 000 000 28 To download the latest copy of this manual go to http www sna unimelb edu au pnet pnet html download Any suggestions feedback comments or corrections would be greatly appreciated and can be emailed to nick_harrigan yahoo com or nick harrigan anu edu au 29
10. Aty J T3u Aty Clear All Select Al Press Browse and select your binary attribute file Non directed binary attributes For non directed binary attributes it is generally enough to select two parameters for each attribute e R e Rb Select the box next to each of these parameters for each variable and press OK 16 Directed binary attributes For directed binary attributes it is generally sufficient to select the following three parameters for each attribute e Rb e Rs e Rr Non directed continuous attributes For non directed continuous attributes it is generally enough to select the following parameters for each attribute e sum e difference Directed continuous attributes For directed continuous attributes it is generally enough to select the following parameters for each attribute e sender e receiver Of the Estimation Options the only ones that you will generally change are e Multiplication Factor e Number of Runs 17 3 2 Preventing Model Degeneracy In the early stages of running a model the main problem that you are most likely to run into is that of model degeneracy There are a number of reasons why this can occur but the main cause is when there are no instances of one or more graph configurations which we measure using parameters For example if there are no isolates in your graph and you have said you want to estimate the parameter for the isolates configuration called isolat
11. OOOOOOOOOOOOOOOOOOOOOOOOOOcOOOOcOOcH bOOcOOOOOOOOOOOOOOOOOOOOOOOOOOOcOOcH bcOcOOOOOcOOOOOOOOOOOOOOOOOOOOOOOOOtz bOOcOOOOOOOOOOOOOOOOOOOOOOOOOcOOOOcOOcH bOcOOOOOOOOOOOOOOOOOOOOOOOOOOOOcH bOcOOOOOOOOOOOOOOOOOOOOOOOOOOOcH bcOOcOOOOcOOOOOOOOOOOOOOOOOOOOOOOcH bOOOOOOOOOOOOOkzOOOOOOOOOOOOcH bOOOOOOOOOOOOOOOOOOOOOOOOOOcH bO0000000000000000000000000 10 Export the Attribute File as EXCEL data Data gt Export gt Excel Select your UCINET Attribute file press Ok Note if you have any unexplained problems with exporting to the Excel file or any other export file try deleting the entire path of the Output file or Output dataset and then just retyping in a name you want to call the file without the rest of the path i e without the bit that says C MyDocuments etc Open the Excel file you have created Select the cells containing the binary data Do not select the column or row headings Press copy Open Notepad Press paste Press save Save as a file with the title BIN_ and then the number of binary variables in this dataset and then some name you will remember eg BIN_14 NonExec250nodes txt This is your final Binary attribute file Repeat this process for your Continuous and Categorical attributes They should each look something like this E BIN_14 178wNEDs Run4 3 FEB 07 tt Notepad File Edit Format View Help 0 0 d 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 OOObOOObOOOOOOOOOOOOOOOOOCH
12. aganova and Johan Koskinen Any suggestions feedback comments or corrections would be greatly appreciated and can be emailed to nick_harrigan yahoo com or nick harrigan anu edu au Terminology You will notice certain indented text that is written in Courier font such as this Data gt Import gt VNA Such text is intended to emphasis that the text is referring to a piece of semi programming language text such as an excel formula or it is a direction to access a program or menu in a computer program The use of the gt symbol refers to opening of either folders in a computers desktop or the opening of menus inside a computer program For example the line above refers to a UCINET menu and asks the reader to select the Data menu then select the Import sub menu and then the VNA sub menu Step 1 Installing PNet Before running PNet you will need to install the program You can do this my going to the downloads part of the PNet section of the MelNet website which can be found here http www sna unimelb edu au pnet pnet html download Simply click on the PNet setup exe link under the heading PNet for Single Networks Follow the prompts selecting run ok and next as appropriate The default setting for PNet are all fine EXCEPT that you should not run PNet from the shortcuts If you do PNet will leave about 4 or 5 little files on your desktop containing starting statistics and update files Instead yo
13. data in any other format you can convert it to VNA file format by opening it in NETDRAW not UCINET and then selecting File gt Save Data As gt VNA gt Complete When you have done this select the matrix information which is located under the title tie data Alternatively you can prepare your own VNA matrix data using the directions contained in A Brief Guide to Using Netdraw A Brief Guide to Using Netdraw is a Word document which is included with UCINET and in Windows this can be found by navigating from your desktop to here Start gt All Program gt UCINET 6 gt A Brief Guide to Using Netdraw Once you have your matrix tie data in VNA format paste the section labelled tie data into the node data file prepared earlier You should paste it below the node data like this i node tie data 178wNEDs Run4 3 FEB 07 vna Notepad File Edit Format View Help Tyco International Pty Limited 00 0000000100 LG Electronics Australia Pty Limited 000000000 Tenix Pty Limited 000100011100000131 0 6 Robert Bosch Australia Pty ttd 00 000000000 canon Australia Pty Limited 0 000000001 McDonalds Australia Holdings Limited 00100000 Panasonic Australia Pty Limited 00 00000001000 CKI HEI Electricity Distribution Holdings Australia Pty L Amatek Industries Pty Limited 00 000000010000 schneider Electric Australia Holdings Pty Limited 0000 Tie data from to NumbLinks AMP Limi
14. es under structural parameters then your model will be degenerate because it will find it impossible to estimate a parameter for this value To prevent model degeneracy suggest that you undertake this procedure before attempting to run an estimation After selecting your parameters and setting up your estimation as described in the previous section set the number of Subphases to zero and press Start In less that 5 seconds a dialogue box should open that says Estimation Finished Press OK Notepad will open with a whole lots of statistics Close this file Go to your computers desktop and then navigate to your session folder Open the file labelled start statistics your session name This should open in Notepad Scroll to the very bottom of the file and then back up until you find a list of statistics starting with the line This graph contains e No of vertices P start statistics Run 4 Exp 1 178NEDs 27Ait 3 FEB 07 bt Notepad File Edt Format View Help 00000000000000 00000000000000 edges Isolates K star 2 00 K Triangel 2 00 AltTwoPath 2 00 for Attribute2 for Attribute3 for Attributed for Attributes for Attribute6 14 for Attribute8 11 for Attribute9 3 for AttributelO 47 for Attributel3 15 R for Attributel 32 R for Attribute2 36 R for Attribute3 134 R for Attribute4 203 R for Attribute5 208 18 Select all the text after This graph contain
15. irst estimation 20 3 3 Running an Estimation Reset the number of sub phases to 5 The other settings should be fine for a first run Press Start After a period of time most small networks will be almost instantaneous For one of my networks with 250 nodes and 88 ties a run with a MF of 10 takes about 30 seconds a dialogue box will appear Estimation Finished Press OK The estimation file will open in Notepad Scroll down to the very bottom of the file The part you are interested is the list of parameter values next to configuration names under the title Estimation Result for Network SUMMARY You should see something like this we L estimation Run 4 Exp 1 178NEDs 27Ait 3 FEB O tt Notepad Estimation Result for Network SUMMARY parameter standard error t NOTE t statistics observation sample mean standard error Edge 8 531393 3 11497 0 00141 Isolates 1 611401 0 79827 0 0077 K Star 2 00 0 903968 0 66223 0 00463 AKT T 2 00 1 172021 0 15187 0 01524 A2P T 2 00 0 109786 0 15107 0 02050 for for for for for for Attribute2 Attribute3 Attributed Attributes Attribute6 Attributes 294296 0 327114 0 930864 0 791082 QO 163366 0 0 49164 0 016413 77132 37907 85759 95367 45101 0 01205 0 05848 0 00308 0 0077 0 10496 0 00920 for 0 03711 for for R for R for R for R for R for R for Attribute9 0 778351
16. k Select Structural Parameters Gaining Factor a value ba JV Structural Parameters Select Parameters Multiplication Factor fio Select Dyadic Attribute Parameters Debt ka i il il b fi Select Parameters J Dyadic Attributes Number of attributes meter inher of Ectenation Rie i Select Actor Attribute Parameters JV Actor Attribute Parameters Start IV Binary Attributes Number of attributes 14 Select Parameters JV Continuous Attributes Number of attributes 13 Select Parameters J Categorical Attriubtes Number of attributes Select Parameters Type a session name in the top left hand corner box Select a session folder by clicking Browse then finding the folder where your raw matrix and attribute files are saved and then pressing open tend to name my session the same name as my session folder 12 Type in the number of actors in the Number of Actors box Select your Network file This is your raw matrix file and should be in the session folder you just selected Select your Network Type Generally you won t be fixing the maximum degree for each actor so leave this blank Tick the Structural Parameters box then press Select Parameters In the new form that opens click on the border in the bottom left hand corner and open the box so that you can see as many of the parameters as possible This will make it easier to navigate Note In each of these Select Parameter forms you wil
17. k you want to model so we will ignore the dyadic attributes parameter 15 If you have attributes select the box next to Actor Attributes Parameter and then select the boxes for the types of variables binary continuous categorical and then type the number of each type of variables you have into the designated box After doing this press Select Parameters If this does not open it is because you have not chosen a Session Folder at the top of the page Select a session folder and then the Select Parameters button should work These Select Parameters forms vary depending on the type of network directed or non directed and also the number of variables you have for your actors Binary Attribute Parameter Selection Binary Attribute File f harrigan Clubs Analysis Browse R attributet P CC Rb Attributet D I Otau Attributet P Otbu Attributet D J O2au ttribute1 kr O2bu attributet D I o3u attrbutet p I Tiu attrbutet TF T2u Attributet 7 IT T3u ttributet 7 J R Attribute2 fp J Rb Attribute2 p J Olau Attribute2 J Otbu Attribute2 J O2au Attribute2 J O2bu Attribute2 CC o3u Attributee p I Tiu sttribute2 0 oo T2u Attributez 0 I T3u sttribute2 0 R attributes TS CC Rb attributes TF I Otau attributes 7 ooo Otbu attributes 7 o I Ozau attributes fT ooo O2bu attributes E I osu attributes P Tiu attributes D ooo IT T2u Attributes D IT T3u Attributes TF J Tiu Aty J T2u
18. l be given a range of options depending on the network type and whether the form lists structural parameters or attribute parameters This PNet for Dummies guide includes suggestions about which parameters will be useful for many standard estimations However it doesn t include a complete list of parameters nor does it explain how to interpret each of these parameters For a complete list of the definitions of all the parameters used in PNet see Appendix B in the PNet User Manual For explanations of the meaning and interpretation of these parameters the reader will need to consult specific papers which interpret these parameters many of which are included on the MelNet website http sna unimelb edu au 13 Structural Parameters Non directed graphs If you have a non directed network you should have a screen that looks something like this Structural Parameter Selection Markoy Parameters New Parameters High Order Parameters TC pi fi 0 T d fo T K star jo lambda E I 2star 0 Te E I K star 2nd IR lambda Een I 3 star 0 Te kb IT K triangle f0 lambda 2 I Triangle jo 0 K triangle 2nd jo lambda 2 J Isolates Jo 0 J K 2 path 0 lambda E Clear All Select All If it is a non directed graph then generally you will want to select the following structural parameters e Edge e K star e K triangle e K 2 path For the higher order parameters the K parameters the default of lambda of 2 is fine If yo
19. me type in adjacent columns i e Binary attributes next to binary attributes Continuous next to continuous etc EI Microsoft Excel Top250Corp 17 Attributes ss File Edit View Insert Format Tools Data Window Help Dee SAY seas GERD MS Sans Serif 10 BZ U E go S 8 ES 5 Mi v 8 C D E z L CONT_Top4 BIN_Think_T BIN_BCAChr BIN_BCA_A BIN_Australi BusAssesEx CONT_N_No Company Name ank msPnl LL an ecs nExecDir AWB Limited APN News amp Media Limited National Australia Bank Limited Westfield Group Queensland Sugar Limited Ten Network Holdings Limited ABB Grain Ltd Leighton Holdings Limited Woodside Petroleum Ltd Mitre 10 Australia Ltd Coles Muer Ltd Commonwealth Bank of Austral Rio Tinto Ple Rio Tinto Limited Gantas Airways Limited Wesfarmers Limited Publishing and Broadcasting Li BHP Billiton Limited 1 E 3 4 5 6 7 8 a IEN ICH m O Le E IEN ICH IEN IEN IECH 0 i oioio O et a O et O a 1 EH 1 ECH 1 ECH ee es O 1 wei Lei d Leet Les Lei Lech Le Leg L Lei Le Leet et et amt 1O 191i IEN IEN EH 0 T Leen IEN IECH IECH IEN ln IEN lo OOOO OOOO a OOOO Lea Ol To attach these attributes to their network place the data in a VNA file which is a file type for UCINET It is similar to a DL file type To do this create a formula at the end of each row in Excel The formula use for the attributes i e all lines except for the first line which has the attribute names i
20. n it is SWI CAD GW s SC2 amp SD2 amp SE2 amp neben SG2 amp SH2 amp cJ2g SJ2 amp SK26 SL26 M26 SN2 amp wco2 g SP26 6026 SR2 amp 5S This basically just says put double inverted commas around the name of the node and then list the attribute values one at a time with a space between each one Write this formula out for the first line of data not the first row with headings and then copy and paste it down the rest of the row For the top line which contains the attribute labels use this formula SUE ALG ee ey tecie t SD1LE SELLE blem EGIS SH1E ST16 Wee EKL ELLE EMIL En EN1LE Olm TEPLE Olm GRIEG WEN This formula says put the first column heading in double inverted commas and then list the rest of the variable names with a comma between each one NOTE TO USE THIS FORUMULA YOUR ATTRIBUTE NAMES MUST NOT HAVE ANY SPACES IN THEM If you want to have spaces in your attribute names then you will have to place double inverted commas around each one of them as have done with the first column in my attribute heading list You should end up with a column in your dataset that looks like this E Microsoft Excel Top250Corp 17 Attributes xis File Edit View Insert Format Tools Data Window Help Dee elo BAS vo Br A 24 WW MS Sans Serif e 10 r BZU E ET AE AD40 R S CONT_Prop Execs_on_G CONT_Reve ovtBds nue_ bil
21. oposed digraphs observation sample mean standard error t statistic t statistics observation sample mean standard deviation Under these lines are the main GOF statistics you will be interested in The most important statistic is the t statistic for each configuration The rules for a good model are these 1 If the parameter was estimated and specified in your model that is if you had to type in the parameter estimate when you were setting up the GOF then the t statistic needs to be below 0 1 as it was in the estimation 2 If the parameter was not estimated and specified in your model that is if you didn t type the parameter estimate when you were setting up the 25 GOF but rather just selected the configuration and left the parameter value as zero then the t statistic should be below 2 for the model to not be a bad fit If you model doesn t fit these criteria then it s not a perfect fit There should be no reason why if the first criteria shouldn t be met if the estimation was run correctly since the estimation should have made sure that the t statistic is below 0 1 for those parameters However for the parameters that were not estimated it is highly likely that some of the t statistics will be greater than 2 Given that it is highly likely that one or more of these t statistics will be greater than 2 because no ERGM model is perfect then the decision as to whether to accept the model or to attempt to find anothe
22. ork file press Ok In Windows go to the folder in which you ve saved this file Find the raw Network Matrix file you have just created Change it s name so that it begins with MATRIX_ and then the number of nodes in the dataset This will help you remember this when you are using the file in PNet This is your final Matrix file for PNet The file you have created should look like this MATRIX 178wNEDs Run4 3 FEB 07 tt Notepad File Edit Format 0 bchcOtzOchOOOcOOOOOOOOOOOOOOOOOOOcOOOOcH bcOOcOOOOOcOOOOcOOOOOOOOOOOOOk OOOOOOcOOcH bOOcOOOOOcOOOOcOOOOOOOcOOOOOOOOOOOOcOOOcOOcH bOOcOOOOOcOOOOOcOOOOOOOOOOOOOOOOOOcOOOOcOOcH bOOcOOOOOcOOOOOcOOOOOOOOOOOOOOOOOcOOOcOOcH bOOcOOOOOOOOOOOOOOOOOOOOOOOOOOcOOOOOcH poococococococococococococococooco0o0o0o0o0o000o08o pocoecococoococococococococoe0ece0000 oro bOOcOOOOOOcOOOOOOOOOOOOOOOOOOOOOOOOOcOOOcOcH bOOcOOOOOOcOOOOOOOOOOOOOOOOOOOOOOOOcOOOcOcH bOOcOOOOOOcOOOOOOOOOOOOOOOOOOOOOOOcOcOOOcOcH bOOcOOOOOOcOOOOOOOOOOOOOOOOOOOOOOOcOcOOOcOcH bOOcOOOOOOcOOO OOOOOOOOOOOOOOOOOOOOOcOOOOcOcH bOOcOOOOOOcOOOOOOOOOOOOOOOcOOOOOOOOOOOOOcOOOcOcH bOOcOOOOOOcOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOcOOOOcOcH poOocoeococoococoococococococo0o0o0o0o0o0o0000o0 poOoceocococoocoococococococo0oco0o0o0o0oo000o0 poOcoeocococoococococococococo0oco0o0o0o0oo000o pocoecocoococococococoococo0oco0o0oeo0o00008o bccOOOcOOcOOOOOOOOOOOOOOkzOOOOOOOOOOOOOcOOcH bOOcOOOOOOOOOOOOOOOOOOOOOOOOOOOOcOOOOcOOcH bOOc
23. r model is one which is up the individual researcher and their interpretation of the nature of the poorly fit configurations in your model e gof Exp 11Top250ExsNEx_SATT txt Notepad File Edit Format View Help Receiver of Continuous Attr2 0 01481 Receiver of Continuous Attr3 0 18761 There are 10000000 proposed digraphs Statistic samples are picked at 1 per 10000 digraphs accepted 27932 proposed digraphs observation eg Mie mean standard error t statistic t statistics observation sample mean standard deviation Arc 88 0000 Mean 86 9550 12 6092 t 0 0829 Reciprocity 4 0000 Mean 3 7170 1 9659 t 0 1440 2 In Star 44 0000 Mean 41 9170 14 4555 t 0 1441 2 Out Star 34 0000 Mean 31 3210 10 1916 t 0 2629 3 In Star 18 0000 Mean 18 4280 13 6730 t 0 0313 3 Out Star 8 0000 Mean 9 7030 8 1168 t 0 2098 Mixed 2 Star 62 0000 Mean 66 2060 C 20 2322 t 0 2079 T1 0 0000 Mean 0 0110 0 1044 0 1054 T2 0 0000 Mean 0 1170 6766 0 1729 T3 0 0000 Mean 0 2440 8539 0 2857 T4 0 0000 Mean 0 2080 5666 0 3671 T5 0 0000 Mean 0 1780 5163 0 3447 T6 1 0000 Mean 0 5640 0015 0 4354 T7 4 0000 Mean 9 1370 8828 0 7463 T8 7 0000 Mean 7 0340 4117 0 0063 T9 030T 2 0000 Mean 1 2920 1 7555 t 0 4033 T10 030C 0 0000 Mean _ 0 2350 CO 5235 t 0 4489 wd lei DEER 26 Appendices
24. rk Holdings Limited 0 0 ABB Grain Ltd 00011000 Leighton Holdings Limited oo woodside Petroleum Ltd 101 Mitre 10 Australia Ltd 000 0 i of oo ki O oooe eoocr CH k Hoo CH Lu J ka oo he 0000193 coles Myer Ltd 001110 Commonwealth Bank of Australia Rio Tinto Plc Rio Tinto Limi 00 Qantas Airways Limited O 1 1 1 wesfarmers Limited 001110 Publishing and Broadcasting Limi BHP Billiton Limited 01111 Australia and New zealand Bankin Brambles Industries Limited O 1 Metcash Trading Limited 0001 J OO naneo 1100000001 w w Fo Ovw0w0 OO0OrF MOR H CH SO Ok Fooo ER roo LA 0 0 a 3 0 1 3 0 0 0 0 0 k hw OFWW OORO wor oO FF OF OOOO OO w O MO FOOF one OOit kW O OF ONAMN J OOil kW a 1 0 0 0 8 1 0 1 1 d ro oc FroOoo ro oc OOOO D oon D Ok D ooo CH Save this file As you can see from the title of my file at the top of the Notepad screen tend to label my files node data and then some descriptive information about the data set Go back to your Excel file and count the number of Binary Continuous and Categorical attributes you have in your dataset Note the order they should be grouped into their attribute types Write down these three numbers and the order we will use them later Matrix data prepare my matrix data in VNA file format as well If you have your matrix
25. s and before Graph Density Press copy Open a new Excel document select the first column and press paste A warning will appear but press OK The values should paste down the left hand column with one line of text per row Now read through the records one by one Delete the blank row that is between the structural parameters and the attribute parameters What you will notice is that some of the attribute parameters and even perhaps some of the structural parameters which you had selected to be in your model will be missing So in my example in the image above you can see that there is no Rb for Attribute1 or for Attribute 7 or Attributes 11 or 12 This is because there are no instances of these configurations in my network If these configurations are left in your model then the model will most likely be degenerate Thus the challenge is to clearly identify the configurations that are not in your network and remove them from your model do this by creating this Excel file generally save the file with the name Excluded Configurations experiment name Go through the file and where you find one of your configurations that is missing insert a row After creating rows for each missing configuration go back and type in the name of the missing configuration in the empty row and the value 0 next to it In the column next to the missing configuration place a large X Select this row and press Bold Insert a ro
26. s lower t statistics on average that your current parameter estimates then press update 3 Repeat For small datasets nodes lt 40 with few configurations then the default settings should be adequate For models with more configurations and a larger numbers of nodes the best way to get convergence is to slowly increase the multiplication factor The multiplication factor reflects the amount which PNet is able to experiment with different values for your parameter values When PNet runs and estimation it spends most of its time walking around the parameter space meaning that it is slowly changing the parameter values for each of the different configurations in your dataset and measuring whether these new estimates fit your model better or worse When you increase the multiplication factor you give PNet more time to explore different values for 23 your parameter values and thus more time to make a more accurate parameter estimate The problem with increasing the multiplication factor MF is that it increases the processing time At the moment we think that the processing time increases linearly with the increase in the multiplication factor so if a MF of 10 take 30 seconds a MF of 20 will take 1minute and a MF of 200 will take 20minutes So far we have experimented with MF of up to 1000 and have noticed dramatically improved results with higher MFs Our suggestion is however that you only increase the MF slo
27. se one of the configurations completely predicts the dependent variable in this case the formation of a tie When this occurs you will get large or very large parameter estimates with high standard errors How you treat these will depend on your dataset but in general it may be better to take them out or to find a better way of specifying this attribute in your model Make sure you mark this configuration on your Excluded Configurations table Reference category for a set of dummy variables A further problem which can occur is when you put in a set of dummy variables which are actually different values for a categorical variable for example variables that represent each state in a country In this case you must remove one of the variables which will then be used as a base or reference category For those who have done logistic regressions before this will be a familiar concept Make sure you mark this configuration on your Excluded Configurations table 22 Interactions Occasionally you can get strange interactions between different configurations in your model A classic sign of this is when you have two variables with large equal but opposite values eg 12 1275 and minus 12 1275 In this type of situation it is generally the case that the two variables are interacting strongly One solution is to look at the observed value of these configurations in your graph and the theoretical meaning of the two configurations and
28. t only select the configurations which you have values for in your estimation procedure but you also need to select the configurations which you want to use to check the goodness of fit for your model In practical terms this generally means pressing Select All when you are in the various Select Parameters windows You then enter the values for the parameters you have estimates for and leave the remained at zero The exception to this is when you are selecting structural parameters for larger networks n gt 40 In this case you generally want to leave out all or most of the parameters labelled New Parameters since they take an incredibly long time to calculate and the goodness of fit may take days or weeks or months to finish especially the new parameter C for 5 cliques For smaller networks n lt 40 the default settings for the Simulations Options are fine For larger networks you will need to adjust the Number of Itinerations For a network of 250 nodes with 88 ties instead of the normal 1 000 000 itinerations we ran 88 000 000 itinerations and left the number of samples to pick up as 1000 4 2 Interpreting GOF statistics When PNet has finished the GOF a dialogue box will open stating this Press OK the GOF statistics will open in Notepad Scroll down the document until you get to the following lines There are 10000000 proposed digraphs Statistic samples are picked up at 1 per 10000 digraphs Accepted 27932 pr
29. ted woolworths Limited Insurance Australia Group Limited woolworths Limited 1 woolworths Limited AMP Limited Insurance Australia Group Limited ap Limited 1 woolworths Limited Insurance Australia Group Limited 1 AMP Limited Insurance Australia Group Limited 1 Xstrata Coal Investments Australia Pty Limited Xstrata Queens xstrata Queensland Limited Xstrata Coal Investments Australi Ansell Limited National Foods Limited 1 National Foods Limited Ansell Limited 1 amcor Limited coles Myer Ltd coles Myer Ltd Amcor Limited O 1 0 Save this file tend to name this file node tie data 2 3 Transforming VNA File into Raw Matrix and Attribute files Open UCINET Set the default directory to the directory which your files are located in in UCINET 6 138 this is located at the bottom right hand side of the main screen of UCINET and is labelled with a filing cabinet draw symbol Import your complete VNA file the one that contains both node and tie data Data gt Import gt VNA Select the VNA file Press OK Then export each of the resulting UCINET files the Network and Attribute files labelled with a Net and a Att at the end of the UCINET file names respectively Export the Network file as RAW data Data gt Export gt Raw The default settings should be fine you want the output format to be FULLMATRIX Select your UCINET Netw
30. then choose one of the configurations to drop out of your model Make sure you mark this configuration on your Excluded Configurations table Other problems There are a number of other problems which will occur when attempting to specify a ERGM model If you are having trouble fitting a model or it is not having the expected results look over the results of the estimations and look for patterns in the movements of the parameter values Try to see which variables are moving together and see if these movements of the parameter values make sense or if instead they might be errors created by a poorly specified model or some form of interaction within the model itself If you find something like this choose the theoretically least important configuration and drop it out of your model and run it again Make sure you mark this configuration on your Excluded Configurations table 2 Reducing the t statistics to less than 0 1 Once you have got reasonable numbers for your parameter estimates and standard errors the major challenge is to reduce the t statistic for each configuration to less than 0 1 The general process for this is 1 Run an estimate 2 If this is o one of your first runs before you first press update then if the estimate has t statistics lower than 2 for most values and lower than 4 for all values for your first run then press update o after your first update then if the estimate has better t statistics that i
31. u have a significant number of isolates in your model you may want to select the structural parameter e Isolates 14 Structural parameters directed graphs If you have a directed graph then you should get a screen that looks like this Structural Parameter Selection Markov Parameters High Order Parameters IT are p J K in star 0 lambda pmi J Reciprocity kr J K out star 0 lambda kb E In 2 star KI IT K in star 2nd 0 lambda Ee I out 2 star e IT K out star 2nd 0 lambda ko Ee E T Kl star J0 lambda Ess IT Out 3 star kr Mixed 2 star Kc J Transitive Triad bp Say EE E es RS j T AKT D jo lambda rm fe J AKT U_ jo lambda EF J T2 fp J AKT TD 0 lambda E pe TC arm fp lambda TE Cub TC akt pu fa lambda 2 J K one star 0 lambda E J One L star 0 lambda E T AKT T 0 lambda B If it is a directed graph and it is of low density then generally you will want to select the following structural parameters Markov e Arc e Reciprocity e Isolates if you have a number of isolates in your dataset High Order Parameters e K in star K out star AKT T this is an alternating K triangle transitive A2p T alternating K 2 path transitive For more dense networks you may want to begin increasing the complexity of your model by start adding in more higher order triangle effects such as e AKT D e AKT U e AKT C e andso on For the moment we will assume you have only one networ
32. u should always run PNet from the folder it is installed in If you want to set up a short cut set up a short cut to that folder not to the PNet file itself If you attempt to run PNet and it does not work it may be because you do not have either Java Platform or the Microsoft NET framework installed These can both be downloaded from the PNet website under the heading Required Environment Step 2 Preparing matrix and attribute files Before using PNet we need to prepare matrix and attribute files which PNet will use as inputs At the moment PNet only accepts one form of input file Raw matrix and attribute data Most other network and attribute file types can be transformed to Raw data types by UCINET This section Step 2 provides a step by step guide to preparing your data in Excel creating an integrated network and attribute VNA file and then transforming this file into the raw data format using UCINET If you already have your data in raw matrix and raw attribute format and the rows and columns of these two raw data files match then you should feel free to skip this section 2 1 Preparing Attributes in Excel tend to prepare my attributes in Excel list each node down the left hand column and list the attributes across the top row label each attribute according to it s type either BIN_ for binary attributes and CONT_ for continuous attributes and CAT_ for categorical attributes group attributes of the sa
33. w above the first row and then in the first row of the second column type the heading Excluded Configurations Save this file It should look something like this 19 File Edit View Insert Format Tools Data Window Help Cee la A 2a Arial ID se BZU E Excluded Configurations _ of vertices 178 of edges 114 _ of Isolates 83 0 of K star 2 00 179 3 of K Triangel 2 00 58 5 of AltTwoPath 2 00 251 0 of Rb for Attribute1 0 _ of Rb for Attribute2 4 _ of Rb for Attribute3 44 _ of Rb for Attribute4 94 _ of Rb for Attribute5 99 _ of Rb for Attribute6 14 of Rb for Attribute7 0 _ of Rb for Attributes 11 _ of Rb for Attribute9 3 _ of Rb for Attribute10 47 of Rb for Attribute11 0 of Rb for Attribute12 0 _ of Rb for Attribute13 15 _ of R for Attribute1 32 1 2 3 4 5 6 T 8 9 There are two reasons for taking such care with creating this Excluded Configurations file Firstly we may need to exclude further configurations if there are other problems with the remaining parameters for example the problem of separation Secondly we may need to repeat the experiment on another day or after restarting PNet and in this case it is necessary to keep a log of which configurations have been excluded Go back to PNet Go through the structural and attribute parameters and deselect the excluded configurations parameters as listed on in your Excel table You are now ready to run your f
34. wly from one estimate to the next We generally start with the default setting of 10 and if that gives an estimate that has no t statistics more than 2 then we increase the MF to 20 If the estimates keep getting better we continually update our parameter values and approximately double the MF for each estimation run Generally 200 600 is high enough to fit most models Sometimes however you may have difficultly getting some of the t statistics to go below 0 1 even with a MF of 500 or 1000 In this case an option is to get a model that almost fits most t stats below 0 1 and 1 5 below 0 3 and then set the number of runs to say 20 and then leave the computer to run over night or for several days While this is running you can check the estimation file at any time to see the results of the estimations that PNet has run Checking the estimation file will not interfere with the running of PNet 24 Step 4 Goodness of Fit 4 1 Running a Goodness of Fit Once you have estimated a model which has t statistics for all configurations of less than 0 1 then you are ready to check the goodness of fit of the model To do this you press the Goodness of Fit tab in PNet You then need to re enter all the data which you have entered for the Estimation including the exact parameter values obtained in your fitted estimate The main difference with running a goodness of fit and an estimation is that when you Select Parameters you no
Download Pdf Manuals
Related Search