Stata help file - University of Bristol
Contents
1. http www bristol ac uk cmm software support support fags errors html error gt message Examples IMPORTANT The following examples will only work on your computer once you have installed MLwiN and once you have told runmlwin what the mlwin exe file address is See Remarks on installing runmlwin above for more information a Continuous response models Two level models Setup use http www bristol ac uk cmm media runmlwin tutorial clear Two level random intercept model analogous to xtreg fitted using IGLS See Section 2 5 of the MLwiN User Manual runmlwin normexam cons standlrt level2 school cons levell student cons nopause Two level random intercept and random slope coefficient model fitted using IGLS See Section 4 4 of the MLwiN User Manual runmlwin normexam cons standlrt level2 school cons standlirt levell student cons nopause Refit the model where this time we additionally calculate the level 2 residuals fitted using IGLS See Section 4 4 of the MLwiN User Manual runmlwin normexam cons standlrt level2 school cons standlirt residuals u levell student cons nopause Two level random intercept and random slope coefficient model with a complex level 1 variance function fitted using IGLS See Section 7 3 of the MLwiN User Manual Matrix A 1 1 0 0 0 1 runmlwin normexam cons standlrt girl level2 school cons standlrt levell st2. he Bayesian Deviance Information Criterion DIC is an MCMC penalised goodness of fit measure It is equivalent to the Akaike Information Criterion AIC used in maximum likelihood estimation 4 he AIC is given by AIC 2 logL 2k Deviance 2k where L is the maximized value of the likelihood function i e the likelihood evaluated at the maximum likelihood point estimates of the model parameters k is the number of model parameters In MCMC estimation the DIC statistic has an analogous definition DIC Deviance 2 p_d Monday August 5 13 34 02 2013 Page 21 where The Deviance is evaluated at the posterior means of the model parameters p_dis th ffective number of model parameters The four statistics reported in the standard runmlwin model output are Dbar The average goodness of fit of the model over the iterations D thetabar The goodness of fit of the model evaluated at the posterior means of the model parameters pD Th ffective number of parameters summarises the complexity of the model pD Dbar D thetabar DIC The statistic of interest DIC Dbar pD D thetabar 2pD Remarks on using sampling weights Sampling weights are only available for estimation using R IGLS Sampling weights should therefore only be used for continuous response variables as the quasilikelihood procedures available for R IGLS estimation of discrete response variables
3. unstructured exchangeable areqvars eqcorrsindepvars arindepvars unstructured co variance matrix structured errors with a common correlation parameter and a common variance parameter AR1 errors with a common variance parameter structured errors with a common correlation parameter and independent variance parameters AR1 errors with independent variance parameters rppriors_spec MCMC only Description uniform gamma a b use the uniform prior distribution use the gamma prior distribution with shape a and scale b the default is gamma 0 001 0 001 mcmc_method MCMC only Description gibbs univariatemh multivariatemh Gibbs algorithm univariate Metropolis Hastings algorithm multivariate Metropolis Hastings algorithm savewinbugs_options MCMC only Description model filename replace inits filename replace data filename replace nofit save model as a WinBUGS model in filename txt save initial values in WinBUGS format in filename txt save data in WinBUGS format in filename txt do not fit the model in MLwiN mlwin_ settings R IGLS and MCMC Description size levels columns variables tempmat optimat specify maximum worksheet size allowed in MLwiN specify maximum number of levels allowed in MLwiN specify maximum number of data columns allowed in MLwiN specify maximum number of modelled variables allowed
4. R IGLS only Description nostandardised nofpsandwich norpsandwich no standardisation of the level specific weight variables no sandwich estimates for fixed part standard errors no sandwich estimates for random part standard errors residuals_options R IGLS and MCMC Description variances standardised leverage influence deletion sampling norecode reflated posterior variances standardised residuals leverage residuals influence residuals deletion residuals sampling variance covariance matrix do not recode residuals exceedingly close or equal to zero to missing unshrunken residuals residuals_options MCMC only Description savechains filename replace save MCMC residual estimates for each iteration in filename dta distname R IGLS and MCMC Description normal binomial multinomial poisson nbinomial normal distribution binomial distribution multinomial distribution Poisson distribution negative binomial distribution Monday August 5 13 34 02 2013 linkname R IGLS and MCMC Page 6 Description logit probit cloglog log ologit oprobit ocloglog mlogit logit link function probit link function complementary log log link function log link function ordered logit link function ordered probit link function ordered complementary log log link function unordered logit link function restype MCMC only Description
5. suppress random effects table do not calculate MCMC diagnostics report parameter estimates as chain modes report parameter medians report classical estimates as chain z ratios and p values simulates a new response variable newvar based on th stimated model parameters view the MLwiN macro for the fitted model save the MLwiN macro for model save the MLwiN worksheet model the fitted for the fitted Monday August 5 13 34 02 2013 Page 5 Other R IGLS and MCMC forcesort nosort forcerecast nodrop nomlwin mlwinpath string mlwinscriptpath string mlwinsettings mlwin settings nopause batch forces the data to be sorted according to the model hierarchy prevents checking that the data are sorted according to the model hierarchy forces a recast of all variables saved as long or double to float prevents variables that do not appear in the model from being dropped prior to sending the data to MLwiN prevent MLwiN from being run mlwin exe file address including the file name minscript exe file address including the file name advanced use only manually override MLwiN settings suppresses the two pause steps in MLwiN prevents any MLwiN GUI windows being displayed advanced use only resetname R IGLS only Description all variances none reset all co variances to zero reset only the variances to zero reset no co variances weights_options
6. estimates MOL1 MOL2 POL1 and POL2 Monday August 5 13 34 02 2013 Page 20 mgql1 mql2 pql1 and pql2 specify the linearization technique for fitting discrete response models by R IGLS All four quasilikelihood methods are approximate pql2 is the most accurate but the least stable and the slowest to converge mqll is the least accurate but the most stable and fastest to converge We recommend that model exploration is conducted using mqll For any final model we recommend fitting the model using pql2 or preferably MCMC as a two stage process First fit the model using mql1 then refit the model using pql2 where the initsprevious option or initsmodel name or initsb matrix is specified to use the mqll parameter estimates as the starting values for fitting the model using pql2 Remarks on MCMC Markov Chain Monte Carlo MCMC methods are Bayesian estimation techniques which can be used to estimate multilevel models MCMC works by drawing a random sample of values for each parameter from its probability distribution The mean and standard deviation of each random sample gives the point estimate and standard error for that parameter We start by specifying the model and our prior knowledge for each parameter we nearly always specify that we have no knowledge Next we specify initial values for the model parameters nearly always the IGLS estimates We then run the MCMC algorithm until each parameter distribution ha
7. expansion cannot be specified at level 1 b discrete response model options distribution distname specifies the distribution s of the depvar s normal is applicable for continuous response variables binomial for binary and proportion response variables multinomial for ordinal and nominal categorical response variables and poisson and nbinomial negative binomial are applicable for count responses In multivariate response models wher one or more response variables are discrete one distribution must be given for each response variable Monday August 5 13 34 02 2013 Page 9 link linkname specifies the link function logit probit and cloglog complementary log log are applicable when the binomial distribution has been specified log is applicable when either the poisson or nbinomial distributions have been specified and ologit ordered logit oprobit ordered probit ocloglog ordered complementary log log and mlogit unordered logit are applicable when the multinomial distribution has been specified Note that it is not possible to specify multivariate response models which have a mixture of different types of discrete response Thus only one link function can be specified for the discrete responses In multivariate response models where a mixture of continuous and discrete response variables are specified only the link function for the discrete responses is specified denominator varlist spec
8. including the file name For example mlwinpath C Program Files MLwiN v2 26 i386 mlwin exe mlwinscriptpath string is an advanced option which specifies the file address for mlnscript exe including the file name Monday August 5 13 34 02 2013 Page 16 For example mlwinscriptpath C Program Files MLwiN v2 26 i386 mlnscript exe mlnscript exe is a command line version of MLwiN which only runs scripts Note that runmlwin will only call mlilnscript exe when batch is specified See Remarks on running runmlwin in batch mode below for more information mlwinsettings mlwin_settings manually overrides MLwiN s default settings The default behaviour is that runmlwin will examine the specified model and then automatically override MLwiN s default size levels columns and variables settings with model specific settings However a potential issue is that runmlwin typically overrides MLwiN s default settings with settings that are conservative i e settings that are typically higher than the minimum required to fit the model Thus runmlwin will assign more RAM to MLwiN than is strictly necessary Users who run into error messages relating to a shortage of RAM on their computer may therefore wish to experiment with manually overriding these settings to attempt to get the model to fit mlwin_settings are size specifies the maximum worksheet size allowed in MLwiN levels specifies the maximum number of lev
9. lc1 lc 1 generate lc2 1c 2 generate lc3plus lc gt 3 Two level random intercepts unordered multinomial model fitted using IGLS MQL1 See Section 10 5 of the MLwiN User Manual runmlwin use4 cons lc1 1lc2 lc3plus level2 district cons levell woman discrete distribution multinomial link mlogit denom cons basecategory 4 nopause Ordered multinomial response models Setup use http www bristol ac uk cmm media runmlwin alevchem clear egen school group lea estab generate gcseav gcse tot gcse no egen gcseav_ rank rank gcseav generate gcseav uniform gcseav_ rank 0 5 N generate gcseavnormal invnorm gcseav_uniform Monday August 5 13 34 02 2013 Page 24 Two level random intercepts ordered multinomial model with common coefficients for predictor variable gcseavnormal fitted using IGLS PQL2 See Section 11 4 of the MLwiN User Manual runmlwin a_point cons gcseavnormal contrast 1 5 level2 school cons contrast 1 5 levell pupil discrete distribution multinomial link ologit denom cons base 6 pql2 nopause Click to run Two level random intercepts ordered multinomial model with separate coefficients for predictor variable gcseavnormal fitted using IGLS PQL2 See Section 12 3 of the MLwiN User Manual runmlwin a point cons gcseavnormal level2 school cons contrast 1 5 levell pupil discrete distribution multinomial l
10. modules starting from an introduction to quantitative research progressing to multilevel modelling of continuous and categorical data Modules include a description of concepts and models and instructions of how to carry out analyses in MLwiN Stata and R There is a also a user forum videos and interactive quiz questions for learners self assessment How to cite runmlwin and MLwiN runmlwin is not an official Stata command It is a free contribution to the research community like a paper Please cite it as such Leckie G and Charlton C 2013 runmlwin A Program to Run the MLwiN Multilevel Modelling Software from within Stata Journal of Statistical Software 52 11 1 40 http www jstatsoft org v52 ill Similarly please also cite the MLwiN software Rasbash J Charlton C Browne W J Healy M and Cameron B 2009 MLwiN Version 2 1 Centre for Multilevel Modelling University of Bristol For models fitted using MCMC estimation we ask that you additionally cite Browne W J 2012 MCMC Estimation in MLwiN v2 26 Centre for Multilevel Modelling University of Bristol The runmlwin user forum Please use the runmlwin user forum to post any questions you have about runmlwin We will try to answer your questions as quickly as possible but where you know the answer to another user s question please also reply to them http www cmm bristol ac uk forum Authors George Leckie Centre for Multile
11. on downloading runmlwin manually Some users will not be allowed to download Stata ado packages to their computer for example students in computer labs For these users we recommend that IT support at their institutions install runmlwin centrally for them However if this is not possible then we recommend users manually change their default Stata ado package download location to one where they are allowed to save files Users then need to instruct Stata where on their computer this location is This process can be semi automated by issuing the following five commands where users must change the directory path C temp to a path where they can save files 1 Store the original PLUS directory path in the global macro sysdir_plus global sysdir_plus c sysdir_plus 2 Change the PLUS directory path to a directory where you can save files sysdir set PLUS C temp 3 Install runmlwin to this new directory ssc install runmlwin 4 Revert back to the original PLUS directory path Monday August 5 13 34 02 2013 Page 18 sysdir set PLUS Ssysdir_plus 5 Add the runmlwin directory to the ado file directory path adopath C temp Note you will need to run the last command every time you open Stata Advanced users may wish do this by inserting the command into the profile do file profile do See profile Remarks on getting runmlwin working for the first time In order to get runmlwin working you must 1 install
12. only Description Model MCMC only logformulation cc msubscripts corresiduals restype me varlist variances numlist priormatrix matrix rppriors rppriors spec savewinbugs savewinbugs options Estimation MCMC only on burnin chain thinning refresh scale noadaptation acceptance tolerance cycles femethod mcmc_ method remethod mcmc_method levelonevarmethod mcmc_ method higherlevelsvarmethod mcme method smcmc smvn orthogonal hcentring seed Post estimation MCMC only savechains filename replace imputeiterations numlist imputesummaries specify log formulation for level 1 variance specify cross classified model se multiple subscript notation specify the correlation structure of the level 1 random effects i e the residual errors specify subset of independent variables with measurement error specify informative priors for fixed and random part parameters specify prior distributions for the random part priors the default is gamma 0 001 0 001 save specified model as a WinBUGS model G fit model using default MCMC options specify number of iterations for the burn in period default is 500 specify number of iterations for the monitoring chain period default is 5000 store every iteration default is 1 refresh the MLwiN equation window every iterations default is 50 set scale fa
13. runmlwin bang clear generate lc1 lc 1 generate lc2 1c 2 generate lc3plus lc gt 3 Two level random intercepts logit model fitted using IGLS MQL1 See Section 9 3 of the MLwiN User Manual runmlwin use cons lcl 1lc2 lc3plus age level2 district cons levell woman discrete distribution binomial link logit denominator cons nopause Two level random intercepts logit model fitted using IGLS PQ12 where IGLS MQL1 estimates from previous model are used as initial values See Section 9 3 of the MLwiN User Manual runmlwin use cons lc1 1c2 lc3plus age level2 district cons levell woman discrete distribution binomial link logit denominator cons pgl2 initsprevious nopause Two level random intercepts probit model fitted using IGLS PQL2 runmlwin use cons lcl 1lc2 lc3plus age level2 district cons levell woman discrete distribution binomial link probit denominator cons pql2 nopause Two level random intercepts probit model fitted using MCMC where IGLS PQL2 estimates from previous model are used as initial values See Section 10 2 of the MLwiN MCMC Manual runmlwin use cons lc1 1c2 lc3plus age level2 district cons levell woman discrete distribution binomial link probit denominator cons mcme on initsprevious nopause Unordered multinomial response models Setup use http www bristol ac uk cmm media runmlwin bang clear generate
14. the profile do file profile do See profile 32 bit users should point to the 32 bit version of mlnscript exe global MLwiNScript_path C Program Files MLwiN v2 26 i386 mlnscript exe 64 bit users should point to the 64 bit version of mlnscript exe global MLwiNScript_path C Program Files x86 MLwiN v2 26 x64 mlnscript exe Where in both cases you must substitute the MLwiN script path that is correct for your computer for the path given in quotes in the abov xampl Remarks on keeping runmlwin up to date Monday August 5 13 34 02 2013 Page 19 We are constantly improving runmlwin To check that you are using the latest version of runmlwin type the following command adoupdate runmlwin Remarks on how runmlwin works runmlwin carries out the following steps 1 Writes an MLwiN macro for the specified multilevel model 2 Opens MLwiN and runs the MLwiN macro 3 Pauses MLwiN once the model is specified This allows the user to check that the model is specified as expected If the model is specified correctly the user should click the Resume macro button otherwise the user should click the Abort macro button to return control to Stata 4 Fits the model in MLwiN 5 Pauses MLwiN once the model has been fitted i e converged This allows the user to examine the model results If the model has fitted correctly the user should click the Resume macro button otherwise the user shoul
15. to their initial values specify initial values for factor co variances constrain specific factor to their initial values where stub is the variable stub for factor scores and their standard errors at this level co variances parameter expansion reset co variances to zero when variances turn negative where stub is the variable stub for residuals and their standard errors at this level discrete_options R IGLS and MCMC Description Model R IGLS and MCMC distribution distname link linkname denominator varlist extra basecategory offset varname proportion varname Estimation mqll R IGLS only mql2 pqll pql2 distribution s link function denominator s of depvar s extra binomial variation set the value of the response to b as the base or reference category multinomial distributions only include varname in the model with coefficient constrained to 1 Poisson or negative binomial distributions only specify variable containing multinomial proportions of depvar s used fit model using first quasi likelihood linearization default fit model using second order marginal quasi likelihood linearization fit model using first order penalised quasi likelihood linearization fit model using second order penalised quasi likelihood linearization order marginal the Monday August 5 13 34 02 2013 Page 3 mcmc_options MCMC
16. upon replay nogroup suppresses the display of group summary information number of groups average group size minimum and maximum from the output header nogroup may be specified at estimation or when replaying previously estimated results nocontrast suppresses the display of contrast summary information number of contrasts description of each contrast from the output header This option is only relevant for multinomial response models nocontrast may be specified at estimation or when replaying previously estimated results nofetable suppresses the fixed effects table from the output either at estimation or upon replay noretable suppresses the random effects table from the output either at estimation or upon replay nodiagnostics prevents MCMC diagnostics from being calculated This is a helpful option to specify if you are running a simulation study using MCMC estimation as the calculation of the MCMC diagnostics can take some time for models fitted to large data sets Note that specifying this option will not allow the or irr rrr sd correlation mode and median options to be specified nodiagnostics may be specified at estimation or when replaying previously estimated results mode reports the parameter estimates as the modes of the MCMC chains rather than the means mode may be specified at estimation or when replaying previously estimated results median reports the parameter estimates as the media
17. variances numlist specifies the subset of independent variables with measurement error The corresponding measurement error variances are specified in variances numlist priormatrix matrix specifies informative priors for the fixed and random part parameters rppriors rppriors_spec specifies prior distributions for the random part priors uniform specifies the uniform distribution gamma a b specifies the gamma distribution The default rppriors_spec is gamma 0 001 0 001 savewinbugs savewinbugs_options saves the specified model as a WinBUGS model The model can then be fitted in WinBUGS using the winbugs suite of commands if these are installed savewinbugs_options are model filename replace saves the model as a WinBUGS model in filename txt inits filename replace saves the initial values in WinBUGS format in filename txt data filename replace saves the data in WinBUGS format in filename txt nofit do not fit the model in MLwi d general model options weights weights_options specifies overall options for any sampling weights variables included in the model This option can only be specified if the user has specified the weightvar option at one or more levels The nostandardisation option specifies that the level specific weight variables should not be standardised The nofpsandwich option specifies that sandwich estimates should not be used for the standard errors of the fixed par
18. Hastings algorithm and is the default for univariate discrete response models Monday August 5 13 34 02 2013 Page 12 multivariatemh uses the multivariate Metropolis Hastings algorithm and is the default for multivariate response models with at least one discret response remethod mcmc_method specifies the MCMC method for estimating the random effects The default depends on the specified model mcmc_method is defined under the femethod mcmc_method option levelonevarmethod mcmc_method specifies the MCMC method for estimating the level 1 variance The default depends on the specified model mcmc_method is defined under the femethod mcmc_method option higherlevelsvarmethod mcmc_method specifies the MCMC method for estimating the level 2 and higher variances The default depends on the specified model moemc_method is defined under the femethod mcmc_method option smemc uses structured MCMC methods smvn uses the structured multivariate normal MVN framework orthogonal uses orthogonal parameterisation Note that Browne 2012 recommends to always specify this option for discrete response models There is little advantage to specifying this option for continuous response models as in these models the fixed effects are generally blocked updated hcentring uses hierarchical centring at level seed specifies the initial value of the MCMC random number seed The default is seed 1 should
19. Monday August 5 13 34 02 2013 Page 1 JCIE Statistics Data Analysis help runmlwin Title runmlwin Run the MLwiN multilevel modelling software from within Stata Syntax runmlwin responses_and_fixed_part random part discrete discrete options meme mcmc_options general options where the syntax of responses_and_fixed_part is one of the following for univariate continuous binary proportion and count response models depvar indepvars if in for univariate ordered and unordered categorical response models depvar indepvarsi indepvars2 contrast numlist if in where indepvarsl1 are those independent variables which appear with separate coefficients in each of every log odds contrast while indepvars2 are those independent variables which appear with common coefficients for those log odds contrasts specified in contrast numlist Contrasts can be thought of as the separate subequations or arms of a multinomial response model Thes contrasts are indexed 1 2 up to the total number of contrasts included in the model The total number of contrasts will be one less than the number of response categories for multivariate response models depvarl indepvarsl equation numlist depvar2 indepvars2 equation numlist depvar3 indepvars3 equation numlist R if in where equation numlist specifies equation numbers Equation numbers are i
20. are only approximate See Remarks on quasilikelihood estimates MOLI MOL2 POL and POL2 above for more information We recommend that sampling weights should always be standardised and that sandwich estimates should always be used for the sampling estimates of both fixed part and random part parameter stimates These recommendations are implemented in the default settings for runmlwin but can be changed using the weights option Note also that if level 2 weights are specified then MLwiN requires the level 1 weights to be conditional level 1 weights Remarks on using multiple membership weights Consider a two level multiple membership model of students level 1 who are multiple members of schools level 2 In this example the number of multiple membership unit identifier variables specified should equal the maximum number of schools attended by any given student Suppose this maximum number of schools attended is three then there should be three multiple membership unit identifier variables Intuitively these can be thought of as corresponding to the first school attended the second school attended and the third school attended respectively However the order in which the potentially three school IDs appears is irrelevant other than it must correspond to the ordering of the associated multiple membership weight variables For students who attend three schools all three of the multiple membership uni
21. are used as the initial values S comments for initsprevious initsb matrix specifies the parameter initial values vector See comments for initsprevious initsv matrix specifies the parameter initial sampling variance covariance values matrix Note this option is only relevant when fitting the model using the Metropolis Hastings algorithm in MCMC i e for discrete response models See comments for initsprevious n E Robust fpsandwich specifies the robust or sandwich estimates for the fixed part standard errors rpsandwich specifies the robust or sandwich estimates for the random part standard errors Reporting level set confidence level credible level if using MCMC default is level 95 or reports the fixed effects coefficients transformed to odds ratios i e exp b rather than b Standard errors and confidence intervals are similarly transformed This option affects how results are displayed not how they ar stimated or may only be specified when modelling binary responses when using the binomial distribution and logit link function or when modelling ordinal categorical responses using the multinomial distribution or may be specified at estimation or when replaying previously estimated results irr reports the fixed effects coefficients transformed to incidence rate ratios i e exp b rather than b Standard errors and confidence intervals are similarly tran
22. atch mode Remarks on how runmlwin works Remarks on estimation procedures in MLwiN R IGLS and MCMC Remarks on IGLS vs RIGLS Remarks on quasilikelihood estimates MOL1 MOL2 POLI and POL2 Remarks on MCMC Remarks on Bayesian DIC Remarks on using sampling weights Remarks on using multiple membership weights Remarks on MLwiN estimation problems and error messages Remarks on alternative Stata commands for fitting multilevel models The multilevel models fitted by runmlwin are often considerably faster than those fitted by the Stata s XT xtmixed XT xtmelogit and XT xtmepoisson commands The range of models which can be fitted by runmlwin is also much wider than those available through those commands runmlwin also allows fast estimation on large data sets for many of the more complex multilevel models available through the user written gllamm command Rabe Hesketh and Skrondal 2012 is an outstanding resource for readers wanting to first familiarise themselves with each of these pre existing Stata commands The Stata manual help pages for these commands also provide much useful information Remarks on downloading runmlwin The recommended way to install runmlwin is to type the following from a net aware version of Stata ssc install runmlwin and this will install runmlwin from its official location on the Statistical Software Components SSC archive Remarks
23. aved results runmlwin saves the following in e Scalars e numlevels number of levels e N number of observations e k number of parameters e k_f number of FE parameters e k_r number of RE parameters e extrabinomial 1 if extra binomial variation is used 0 otherwise e 11 log restricted likelihood e deviance deviance 2 e 11 e iterations number of iterations e converged 1 if converged 0 otherwise e time estimation time seconds e burnin number of burn in iterations e chain number of chain iterations e thinning frequency with which successive values in the chain are stored e memcnofit 1 if MCMC model is not fitted 0 otherwise e mcmcdiagnostics 1 if MCMC diagnostics have been calculated 0 otherwise e dbar average deviance across the chain iterations Monday August 5 13 34 02 2013 Page 26 e dthetabar deviance at the mean values of the model parameters e pd ffective number of parameters e dic Bayesian deviance information criterion e size maximum worksheet size allowed in MLwiN e maxlevels maximum number of levels allowed in MLwiN e columns maximum number of data columns allowed in MLwiN e variables maximum number of modelled variables allowed in MLwiN e tempmat 1 if use memory allocated to the worksheet to store temporary matrices in MLwiN 0 otherwise Macros e cmd runmlwin e version runmlwin
24. be a positive integer Note that there are two random number seeds in MLwiN and therefore two seed options in runmlwin The IGLS random number seed and the MCMC random number seed In contrast to Stata commands both seeds in MLwiN have default initial values That is these seeds are the sam very time you call runmlwin The only time you will therefore need to set the IGLS or M seeds is if you wish to replicate MLwiN analyses carried out when using MLwiN in the traditional point and click way d General estimation options igls the default specifies that the model be fitted using iterative generalised least squares equivalent to maximum likelihood See Remarks on IGLS vs RIGLS below for more information rigls specifies that the model be fitted using restrictive iterative generalised least squares equivalent to maximum restricted likelihood also referred to as residual maximum likelihood maxiterations specifies the maximum number of R IGLS iterations The default is maxiterations 20 tolerance specifies the convergence tolerance for the IGLS algorithm The default is tolerance 2 as in a tolerance of 10e 2 IGLS iterations will be halted once every parameter changes by a relative amount less than seed specifies the initial value of the IGLS random number seed The default is seed 1 This option allows you to replicate your results if you use the simulate newvar option should b
25. ctor default is 5 8 do not use adaptation set Metropolis Hastings acceptance rate default is 0 5 set the tolerance default is 0 1 number of cycles default is 1 specify fixed effects method default depends on specified model specify random effects method default depends on specified model specify level 1 variance method default depends on specified model specify higher level variances method default depends on specified model use structured MCMC methods use structured multivariate normal MVN framework use orthogonal parameterisation use hierarchical centring at level set MCMC random number seed default is 1 save MCMC parameter estimates for each iteration in filename dta impute missing values at specified iterations for each missing value calculate the mean and the standard deviation of the chain for that missing value general_options R IGLS and MCMC Description Model R IGLS only weights weights options constraints numlist apply sampling weights options apply specified linear constraints Monday August 5 13 34 02 2013 n Reporting Estimation Estimation E Robust Page 4 R IGLS only igls rigls maxiterations tolerance seed R IGLS and MCMC initsprevious initsmodel name initsb matrix initsv matrix R IGLS only fpsandwich rpsandwich R IGLS and MCMC level or correlations noheader n
26. d click the Abort macro button to return control to Stata 6 Stores and displays the model results in Stata Note that advanced users can use the nopause option to suppress steps 3 and 5 This is essential when running simulation studies Remarks on estimation procedures in MLwiN R IGLS and MCMC MLwiN uses two principal estimation procedures 1 Iterative Generalised Least Squares IGLS equivalent to maximum likelihood under normality and Restrictive Iterative Generalised Least Squares RIGLS which is formally equivalent to residual maximum likelihood REML under normality See Remarks on IGLS vs RIGLS below for more information 2 Markov Chain Monte Carlo MCMC estimation See Remarks on MCMC below for more information In addition for discrete response models fitted by R IGLS quasilikelihood estimates are calculated See Remarks on quasilikelihood estimates MOL1 MOL2 POLI and POL2 below for more information Remarks on IGLS vs RIGLS igls and rigls will give almost identical results in models where the number of units at each level is high The methods give different results particularly for the random part parameters when the number of units at a given level are few For example the rigls estimate for the level 2 variance in a two level normal response model will be larger than the igls estimate when there are few level 2 units Remarks on quasilikelihood
27. e a positive integer Note that there are two random number seeds in MLwiN and therefore two seed options in runmlwin The IGLS random number seed and the MCMC random number seed In contrast to Stata commands both seeds in MLwiN have default initial values That is these seeds are the sam very time you call runmlwin The only time you will therefore need to set the IGLS or M seeds is if you wish to replicate MLwiN analyses carried out when using MLwiN in the traditional point and click way Monday August 5 13 34 02 2013 Page 13 initsprevious specifies that the parameter estimates from the previous model are used as the initial values This option is used 1 when building a series of increasingly complex models using IGLS 2 when moving from MQL1 estimation to PQL2 estimation 3 to specify initial values for MCMC estimation When the current model contains new parameters not specified in the previous model new fixed part parameters are set to zero random part variance parameters are set to one and random part covariances are set to zero Note that when this option is used to specify initial values for fitting the model using the Metropolis Hastings algorithm in MCMC i e for discrete response models this option will also feed the parameter sampling variance covariance values into the algorithm initsmodel name specifies that the parameter estimates from the model results saved under name
28. eing displayed This option is very useful if you want to perform a simulation study MbLwiN will automatically launch and exit once each specified model has been fitted but this will not be visible to the user A limitation of this option is that there is no way of monitoring a model s estimation progress For example it is not possible to see for how many iterations a model has been iterating for This option can also be used in conjunction with running Stata in batch mode in an environment without an interactive session Examples of this would be running Stata from a task scheduler see http www stata com support fagqs win batch html or submitting jobs to a cluster When this option is used any error messages produced by MLwiN are displayed in Stata after the model is run In addition if you have used mlwinscriptpath or the MLwiNScript_path global to specify the mlnscript exe file address then specifying batch will run the model using mlnscript exe rather than mlwin exe See Remarks on running runmlwin_ in batch mode below for more information Monday August 5 13 34 02 2013 Page 17 Remarks Remarks are presented under the following headings Remarks on alternative Stata commands for fitting multilevel models Remarks on downloading runmlwin Remarks on downloading runmlwin manuall Remarks on keeping runmlwin up to date Remarks on getting runmlwin working for the first time Remarks on running runmlwin in b
29. els allowed in MLwiN columns specifies the maximum number of data columns allowed in MLwiN variables specifies the maximum number of modelled variables allowed in MLwiN tempmat instructs MLwiN to use memory allocated to the worksheet to store temporary matrices used by the R IGLS algorithm optimat instructs MLwiN to limit the maximum matrix size that can be allocated by the R IGLS algorithm Specify this option if MLwiN gives the following error message Overflow allocating smatrix This error message arises if one more higher level units is extremely large contains more than 800 lower level units In this situation runmlwin s default behaviour is to instruct MLwiN to allocate a larger matrix size to the R IGLS algorithm than is currently possible Specifying optimat caps the maximum matrix size at 800 lower level units circumventing the MLwiN error message and allowing most MLwiN functionality nopause suppresses the two pause steps in MLwiN This option is very useful if you want to run a do file containing a series of runmlwin models MLwiN will automatically launch and exit onc ach specified model has been fitted MLwiN will not display the Equations window but estimation progress is indicated by the progress gauges in the bottom left hand corner of the MLwiN software See Examples for an example of this option batch suppresses the two pause steps in MLwiN and prevents the MLwiN software from b
30. for Multilevel Modelling University of Bristol http www bristol ac uk cmm software mlwin download manuals html Also see Manual XT xtmixed XT xtmelogit XT xtmepoisson XT xtreg Online XT xtmixed XT xtmelogit XT xtmepoisson XT xtreg mcmcsum usewsz savewsz reffadjust gllamm
31. ifies the variables containing the binomial denominator s i e the number of binomial trials for the response variable s This option is only applicable when modelling proportions i e counts with known totals For example to model school level data on the proportion of students who pass an exam the data should have one row per school the depvar is the proportion of students who passed the exam and the denominator is the number of students who took the exam Note that this implementation differs from that for XT xtmelogit where one would specify a numerator the number of children who passed the exam as the response variable rather than the proportion extra specifies an extra binomial variation parameter to allow for over or under dispersion basecategory specifies the value of the response to be used as the base or reference category This option is only applicable when modelling ordinal or nominal categorical response variables using the multinomial distribution When modelling ordinal responses the basecategory must be either the first or last response category offset varname includes varname in the fixed part of the model with coefficient constrained to one This option is only applicable when modelling count responses using the poisson or nbinomial distributions proportion varname specifies the variable containing multinomial proportions For example to model neighbourhood level data on the proportion of
32. in MLwiN use memory allocated to the worksheet to store temporary matrices in MLwiN limit the maximum matrix size allocated in MLwiN Monday August 5 13 34 02 2013 Page 7 Description runmlwin allows Stata users to run the powerful MLwiN multilevel modelling software from within Stata See Remarks on alternative Stata commands for fitting multilevel models below for more information MLwiN has the following features 1 Estimation of multilevel models for continuous binary count ordered categorical and unordered categorical data 2 Fast estimation via classical and Bayesian methods 3 Estimation of multilevel models for cross classified and multiple membership non hierarchical data structures 4 Estimation of multilevel multivariate response models multilevel spatial models multilevel measurement error models and multilevel multiple imputation models MLwiN is required to use runmlwin See Remarks on runmlwin_installation instructions below for more information A comprehensive range of runmlwin examples and a user forum are available at http www bristol ac uk cmm software runmlwin Options odel a random part model options All options reported in this sub section are specific to the level at which they are specified diagonal specifies a diagonal matrix for the random effects variance covariance matrix This implies zero correlations between the random effec
33. individuals in good average and poor health the data should have three rows per neighbourhood The first row should give the proportion of individuals in good health The second row should give the proportion of individuals in average health The third row should give the proportion of individuals in poor health The total number of individuals in each neighbourhood is then specified with the denominator option c MCMC model options logformulation specifies a log formulation model for the level 1 variance cc specifies that the model is a non hierarchical cross classified model rather than a hierarchal model In cross classified models the levels are often referred to as classifications msubscripts uses multiple subscript notation corresiduals restype specifies the correlation structure of the level 1 random effects i e the residual errors unstructured the default is the most general structure it estimates distinct variances for each residual error and distinct covariances for each residual error pair exchangeable fits structured errors with a common correlation parameter and a common variance parameter arleqvars fits AR1 errors with a common variance parameter eqcorrsindepvars fits structured errors with a common correlation parameter and independent variance parameters Monday August 5 13 34 02 2013 Page 10 arlindepvars fits AR1 errors with independent variance parameters me varlist
34. ink ologit denom cons base 6 pql2 nopause Count data model Setup use http www bristol ac uk cmm media runmlwin mmmec clear generate lnexpected 1n exp Three level random intercepts Poisson model fitted using RIGLS MQL1 See Section 12 3 of the MLwiN User Manual runmlwin obs cons uvbi level3 nation cons level2 region cons levell county discrete distribution poisson offset lnexpected rigls nopause Three level random intercepts Poisson model fitted using MCMC where IGLS MQL1 estimates from previous model are used as initial values See Section 11 3 of the MLwiN MCMC Manual runmlwin obs cons uvbi level3 nation cons level2 region cons levell county discrete distribution poisson offset lnexpected mcme burnin 5000 chain 50000 refresh 500 initsprevious nopause Click to run c Multivariate response models Multivariate discrete and mixed response models Setup use http www bristol ac uk cmm media runmlwin tutorial clear generate binexam normexam gt 0 generate binlrt standirt gt 0 Two level bivariate binary response probit model fitted using IGLS MQL1 See Section 14 5 of the MLwiN User Manual runmlwin binexam cons equation 1 binlrt cons equation 2 levell student discrete distribution binomial binomial link probit denominator cons cons nosort nopause Two level mixed bivariate continuous and binary response
35. le if there are two random effects terms in the model runmlwin would name the standardised residuals stub0std stublstd and stub2std Monday August 5 13 34 02 2013 Page 15 The sampling option calculates the sampling variance covariance matrix for the residuals For example if there are thr random effects terms in the m s odel runmlwin would name the standardised residuals stub0var stub0lcov tublvar stub02cov stubl2cov stub2var The norecode option prevents residuals with values exceedingly close or equal to zero from being recoded to missing 4 he reflate option returns unshrunken residuals savechains filename replace saves the MCMC parameter estimates for each iteration in filename dta imputeiterations numlist imputes missing values at specified iterations It is important to specify a sufficiently high number of iterations between imputations to reduce the correlation between the sets of imputed values imputesummaries for each missing value calculates the mean and the standard deviation of the chain for that missing value _ Export viewmacro view the MLwiN macro for the fitted model This option is useful if you wish to learn how to write your own MLwiN macros savemacro filename replace saves the MLwiN macro for the fitted model Th replace option overwrites the MLwiN macro if it already exists saveworksheet filename replace save
36. ltiple membership weights The number of variables should equal the maximum number of higher level units that any lower level unit belongs to The ordering of the variables must correspond to the ordering of the multiple membership unit identifier variables Zero values rather than missing values must be assigned for redundant weight variables carids varlist specifies the variables containing the conditional autoregressive CAR unit identifiers The number of variables should equal the maximum number of higher level units that any lower level unit belongs to The order in which the CAR unit identifier variables is specified is irrelevant other than it must correspond to the order in which the associated CAR weight variables is specified Zero values rather than missing values must be assigned for redundant identifier variables carweights varlist specifies the variables containing the conditional autoregressive CAR weights The number of variables should equal the maximum number of higher level units that any lower level unit belongs to The ordering of the variables must correspond to the ordering of the CAR unit identifier variables Zero values rather than missing values must be assigned for redundant weight variables flinits matrix specifies initial values for factor loadings The number of rows must equal the number of responses The number of columns must equal the number of factors flconstraints matrix constrain specific fac
37. ly apply to the thinned monitoring chains For example fitting a model for a monitoring chain length of 50 000 and setting thinning to 10 will result in 5 000 iterations being stored The parameter means and standard deviations will then be based on all 50 000 iterations while the ESS s and 95 credible intervals will be based on the 5 000 stored iterations refresh refreshes the MLwiN equation window every stored iterations The default is refresh 50 When the nopause option is not used refreshing the MLwiN equation window less frequently can speed up estimation time scale sets the scale factor The default is scale 5 8 noadaptation prevents adaptation from being used acceptance sets the Metropolis Hastings acceptance rate The default is acceptance 0 5 and permissible values range from zero to one tolerance sets the tolerance rate used for adapting The default is tolerance 0 1 and permissible values range from zero to one cycles sets the number of Metropolis Hastings cycles per MCMC iteration The default is cycles 1 The higher is the more likely a new proposed parameter value will be accepted on each iteration femethod mcmc_method specifies the MCMC method for estimating the fixed effects The default depends on the specified model gibbs uses the Gibbs algorithm and is the default for continuous response models univariatemh uses the univariate Metropolis
38. ndexed 1 2 up to the total number of equations i e response variables included in the model and the syntax of random part is level2 levelvar varlist random part options levell levelvar varlist random part options where levelvar is a variable identifying the groups or clusters for the random effects at each level varlist is the list of variables with random coefficients at each level random_part_options R IGLS and MCMC Description Model R IGLS and MCMC diagonal set all covariances to zero Model R IGLS only elements matrix set specific co variances to zero weightvar varname specify variable containing sampling weights Monday August 5 13 34 02 2013 Page 2 Model MCMC only mmids varlist mmweights varlist carids varlist carweights varlist flinits matrix flconstraints matrix fvinits matrix fvconstraints matrix scores stub arexpansion Estimation R IGLS only reset resetname R IGLS and MCMC residuals options Post estimation residuals stub specify variables containing multiple membership unit identifiers specify variables containing multiple membership weights specify variables containing conditional autoregressive CAR unit identifiers specify variables containing conditional autoregressive CAR weights specify initial values for factor loadings constrain specific factor loadings
39. ns of the MCMC chains rather than the means median may be specified at estimation or when replaying previously estimated results zratio reports classical z ratios and p values i e under the assumption that the chains are normally distributed zratio may be specified at estimation or when replaying previously estimated results Post estimation simulate newvar simulates a new response variable based on th stimated model parameters Make sure to specify the IGLS random number seed to be able to replicate the simulated responses residuals stub residuals_options calculates posterior estimates of the residuals and their associated standard errors for all random effects specified at the given level Posterior estimates are also known as empirical Bayes estimates or best linear unbiased predictions BLUPs of the random effects runmlwin will name the residuals For example if there ar three random effects terms in the model runmlwin would name the residuals stub0 stubl stub2 and would name their associated standard errors as stub0se stublse stub2se The variances option calculates the posterior variances instead of the posterior standard errors 4 he standardised leverage influence and deletion options calculate standardised leverage influence and deletion residuals respectively The postfix for these four types are std lev inf and del respectively For xamp
40. od linearization pqll specifies that the model be fitted using a first order penalised quasi likelihood linearization pql2 specifies that the model be fitted using a second order penalised quasi likelihood linearization c MCMC estimation options on fits the specified model using default MCMC options burnin specifies the number of iterations for the burn in period The default is burnin 500 This option specifies the number of iterations necessary for the MCMC to reach approximate stationary or equivalently to converge to a stationary distribution The required length of the burn in period will depend on the initial values chain specifies the number of iterations for the monitoring chain period The default is chain 5000 This is the number of iterations after the burn in period for which the chain is to be run Distributional summaries for the parameters are based on these iterations Parameter estimates ar given by the means of these chains while the standard errors are given by the standard deviation of these chains thinning stores every th iteration of the monitoring chains The default is thinning 1 Parameter means and standard deviations are based on the non thinned monitoring chains All other MCMC summary statistics e g ESS and 95 credible intervals are based on the thinned monitoring chain The or irr rrr sd correlation mode and median options also all on
41. ogroup nocontrast nofetable noretable Reporting MCMC only Post estimation Export nodiagnostics mode median zratio R IGLS and MCMC simulate newvar R IGLS and MCMC viewmacro savemacro filename replace saveworksheet filename replace fit model via iterative generalised least squares equivalent to maximum likelihood the default fit model via restrictive iterative generalised least squares equivalent to maximum restricted likelihood specifies the maximum number of R IGLS iterations default is 20 IGLS convergence tolerance as in 10e 2 set IGLS random number seed default is seed 1 default is 2 use parameter estimates from previous model as initial values use parameter estimates from specified model as initial values initial parameter values vector initial sampling co variance values matrix sandwich estimates for fixed part standard errors sandwich estimates for random part standard errors set confidence level default is level 95 report fixed effects coefficients as odds ratios report fixed effects coefficients as incidence rate ratios report fixed effects coefficients as relative rate ratios show random effects variance as standard deviations show random effects covariance as correlations suppress output header suppress table summarizing groups suppress table summarizing contrasts suppress fixed effects table
42. probit model fitted using IGLS MQL1 See Section 14 5 of the MLwiN User Manual runmlwin normexam cons equation 1 binlrt cons equation 2 levell student cons equation 1 cons equation 2 discrete distribution normal binomial link probit denominator cons cons nosort nopause Click to run A full range of runmlwin examples using both R IGLS and MCMC is available at http www bristol ac uk cmm software runmlwin examples These include do files which allow you to replicate all the analyses reported in the MLwiN User Manual Rasbash et al 2012 and the MCMC MLwiN Manual Browne 2012 The log files for these two manuals are presented below MLwiN MCMC Manual Monday August 5 13 34 02 2013 Page 25 MLwiN User Manual Introducing Multilevel Models Introduction to Multilevel Modelling Residuals Random Intercept and Random Slope Models Graphical Procedures for Exploring the Model Contextual Effects Modelling the Variance as a Function of Explanatory Variables Getting Started with your Data Logistic Models for Binary and Binomial ILO JOO oy Or HS Wo N Fe Responses Multinomial Logistic Models for Unordered Categorical Responses Fitting an Ordered Category Response Model Modelling Count Data Fitting Models to Repeated Measures Data Multivariate Response Models Diagno
43. s settled down to its stationary distribution i e the burnin period when the chains are converging to their posterior distribution We then run the MCMC algorithm for a further period the monitoring period in order to store a monitoring chain for each p s arameter Point estimates and standard errors are given by the means and tandard deviations of these monitoring chains An important aspect of MCMC is specifying initial values Users can specify that the initial values are the parameter estimates from the previous model initsprevious Alternatively they can specify that the initial values are the parameter estimates from some other previously stored model initsmodel name Or they can even manually specify any set of initial values they like initsb matrix A second important aspect of MCMC is the prior knowledge i e prior distribution that we specify for each parameter By default MLwiN sets diffuse or uninformative priors and these can be used to approximate maximum likelihood estimation Users can specify informative priors using the priormatrix matrix and rppriors rppriors_spec options We recommend users seeking further information examples and references to consult the comprehensive MLwiN MCMC manual by Browne 2012 and additionally the help system within MLwiN The MLwiN MCMC manual also gives lengthier explanations for all MCMC options implemented in runmlwin Remarks on Bayesian DIC
44. s the MLwiN worksheet for the fitted model The replace option overwrites the MLwiN worksheet if it already exists Other forcesort forces the data sent to MLwiN to be sorted according to the model hierarchy We recommend that users sort their data manually using the sort command prior to using runmlwin nosort prevents runmlwin from checking that the data are sorted according to the model hierarchy When this option is used runmlwin does not report the table summarizing groups and the residuals and fscores options are not allowed forcerecast forces a recast of all variables saved as long or double to float forcerecast should be used with caution forcerecast is for those instances where you have a variable saved as a long or double but would now be satisfied to have the variable stored as a float even though that would lead to a slight rounding of its values An important example of when this is inappropriate is when identifiers variables are saved as long or double A slight rounding of the values of identifiers will lead to a merging of units nodrop prevents variables that do not appear in the model from being dropped prior to sending the data to MLwiN nomlwin prevents MLwiN from being run When used in conjunction with the viewmacro option the user can examine the MLwiN macro that runmlwin writes without having to fit the associated model in MLwiN mlwinpath string specifies the file address for mlwin exe
45. sformed This option affects how results are displayed not how they ar stimated irr may only be specified when modelling count responses using the poisson or nbinomial distributions irr may be specified at estimation or when replaying previously estimated results rrr reports the fixed effects coefficients transformed to relative risk ratios i e exp b rather than b Standard errors and confidence intervals are similarly transformed This option affects how results are displayed not how they ar stimated rrr may only be specified when modelling nominal categorical responses using the multinomial distribution rrr may be specified at estimation or when replaying previously estimated results sd shows random effects variance parameter estimates as standard deviations Standard errors and confidence intervals are similarly transformed This option affects how results are displayed not how they ar stimated sd may be specified at estimation or when replaying previously estimated results Monday August 5 13 34 02 2013 Page 14 correlations shows random effects covariance parameter estimates as correlations Standard errors and confidence intervals are similarly transformed This option affects how results are displayed not how they are estimated correlations may be specified at estimation or when replaying previously estimated results noheader suppresses the output header either at estimation or
46. stics for Multilevel Models An Introduction to Simulation Methods o f Estimation Bootstrap Estimation Modelling Cross classified Data Multiple Membership Models COO SID B JW IN FF JO Introduction to MCMC Estimation and Baye sian Modelling Single Level Normal Response Modelling Variance Components Models Other Features of Variance Components Models Prior Distributions Starting Values and Random Number Seeds Random Slopes Regression Models Using the WinBUGS Interface in MLwiN Running a Simulation Study in MLwiN ILO JOO JWI Joy JOT BS QO N J Modelling Complex Variance at Level 1 Heteroscedasticity Modelling Binary Responses Poisson Response Modelling Unordered Categorical Responses Ordered Categorical Responses Adjusting for Measurement Errors in Predictor Variables Cross Classified Models Multiple Membership Models Modelling Spatial Data Multivariate Normal Response Models and Missing Data ILO JOO N Jo JOT PBS JO IN JR JO ixed Response Models and Correlated Re siduals 20 Multilevel Factor Analysis Modelling 21 Using Structured MCMC 22 Using the Structured MVN framework for models 23 Using Orthogonal fixed effect vectors 24 Parameter expansion 25 Hierarchical Centring S
47. t identifier variables should take different non zero values and these values should give the school IDs of the thr different schools attended or students who attend two schools two of the three multiple membership unit dentifier variables should take non zero values and these values should give he school IDs of the two different schools attended The third multiple embership unit identifier variable must take the value zero to indicate that no hird school was attended o other value than zero not even a missing value s permitted to indicate that a third school was not attended A consequence of his is that zero is the only invalid school identifier value no school in the data should have a zero value thet Bot ey Finally for students who attend a single school one of the thr multiple membership unit identifier variables should take a non zero value and this value should give the school ID of the single school attended The second and third multiple membership unit identifier variables should take zero values to indicate that no second or third schools were attended Remarks on MLwiN estimation problems and error messages Monday August 5 13 34 02 2013 Page 22 Multilevel models are complex often involving multiple sets of random effects at multiple levels Users may sometimes run into MLwiN error messages Help for a range of common MLwiN error messages are provided on the MLwiN website
48. t parameter estimates at any level The norpsandwich option specifies that sandwich estimates should not be used for the standard errors of the random part parameter estimates at any level See Remarks on using sampling weights below for more information constraints clist specifies the constraint numbers for the constraints to be applied to the model Constraints are specified using the R constraint command Only linear constraints can be specified S amp S Examples for an example of this option _ Estimation a random part estimation options All options reported in this sub section are specific to the level at which they are specified reset resetname specifies the action to be taken when during estimation a variance parameter at a particular iteration is estimated to be negativ all resets a negative variance to zero along with any associated covariances variances resets a negative variance to zero but not the associated covariances none ignores negative variances no parameters are reset to vero b discrete respons stimation options Monday August 5 13 34 02 2013 Page 11 mql1 the default specifies that the model be fitted using a first order marginal quasi likelihood linearization See Remarks on quasilikelihood estimates MOLI MOL2 POLI and POL2 below for more information mql2 specifies that the model be fitted using a second order marginal quasi likeliho
49. the latest version of MLwiN on your computer 2 set the full MLwiN path using mlwinpath string or a global macro called MLwiN_path If you don t have the latest version of MLwiN visit http www bristol ac uk cmm software mlwin MLwiN is free for UK academics thanks to support from the UK Economic and Social Research Council A fully unrestricted 30 day trial version is available for non UK academics Advanced users may wish to set the MLwiN path every time Stata is started by simply inserting the following line into the profile do file profile do See profile global MLwiN_path C Program Files MLwiN v2 26 i386 mlwin exe Where you must substitute the MLwiN path that is correct for your computer for the path given in quotes in the abov xampl Remarks on running runmlwin in batch mode Advanced users may wish to additionally specify the mlwinscriptpath string or a globa gt l macro called MLwiNScript_path in order to run runmlwin using batch The situations where this may be useful include 1 Faster loading times execution 2 Fitting models to very large datasets via the 64 bit version of milnscript exe 3 Running runmlwin in environments where no graphical user interface GUI is available for example when run as a scheduled task or on Unix Linux or Mac OSX type systems Users may wish to set the MLwiN script path every time Stata is started by simply inserting the following line into
50. tor loadings to their initial values The number of rows must equal the number of responses The number of columns must equal the number of factors The elements of this matrix must be 0 or 1 where 0 freely estimates the factor loading and 1 constrains the factor loading to its initial value fvinits matrix specifies initial values for factor co variances The number of rows and the number of columns must equal the number of factors The diagonal elements correspond to the factor variances The off diagonal elements correspond to the factor covariances Initial values for the factor variances must be positive Intial values for the covariances must correspond to correlations that lie between 1 and 1 fvconstraints matrix constrain specific factor co variances to their initial values The number of rows and the number of columns must equal the number of factors The elements of this matrix must be 0 or 1 where 0 freely estimates the co variance and 1 constrains the co variance to its initial value f scores stub calculates posterior estimates of the factor scores and their associated standard errors for all factors specified at the given level runmlwin will name the factors For example if there are thr factors in the model runmlwin would name the factors stubl stub2 stub3 and would name their associated standard errors as stublse stub2se stub3se parexpansion uses parameter expansion Note that parameter
51. ts at that level Note that should a Toeplitz or other banded structures be desired for example when modelling longitudinal data these can be implemented using the constraints options elements matrix sets specific co variances to zero Note that the matrix must be a row vector with one column for each co variance where the lower diagonal elements of the variance covariance matrix have been vectorised row by row If there are q random effects terms variables with random coefficients the unstructured covariance matrix has q q 1 2 unique parameters The elements of this vector must be 0 or 1 where 0 sets th relevant co variance to zero and 1 specifies the parameter to be freely stimated S Examples for an example of this option weightvar varname specifies the variable containing the sampling weights mmids varlist specifies the variables containing the multiple membership unit identifiers The number of variables should equal the maximum number of higher level units that any lower level unit belongs to The order in which the multiple membership unit identifier variables is specified is irrelevant other than it must correspond to the order in which the associated multiple membership weight variables is specified Zero values rather than missing values must be assigned for redundant identifier variables Monday August 5 13 34 02 2013 Page 8 mmweights varlist specifies the variables containing the mu
52. udent cons standlrt girl elements A nopause Two level random intercept and random slope coefficient model using MCMC where we first fit the model using IGLS to obtain initial values for the MCMC chains See Section 6 0 of the MLwiN MCMC Manual runmlwin normexam cons standlrt level2 school cons standlirt levell student cons nopause runmlwin normexam cons standlrt level2 school cons standlirt levell student cons mcme on initsprevious nopause Multivariate response models Setup use http www bristol ac uk cmm media runmlwin gcsemv1 clear Random intercept bivariate response model fitted using IGLS See Section 14 3 of the MLwiN User Manual runmlwin written cons female eq 1 csework cons female eq 2 level2 school cons eq 1 cons eq 2 levell student cons eq 1 cons eq 2 nopause Cross classified models Setup use http www bristol ac uk cmm media runmlwin xc clear Monday August 5 13 34 02 2013 Page 23 Two way cross classified model fitted using MCMC where starting values for the MCMC chains are manually specified by the user See Section 15 4 of the MLwiN MCMC Manual matrix b 0 33 33 33 runmlwin attain cons level3 sid cons level2 pid cons levell pupil cons mcme cc initsb b nopause b Discrete response models Binary response multilevel models Setup use http www bristol ac uk cmm media
53. utocorrelation function PACF for each chain e MCSE onte Carlo standard error MCSE for each chain e bd Brooks Draper diagnostic for mean of each chain e r11 Raftery Lewis diagnostic for each 2 5th quantile of each chain e r12 Raftery Lewis diagnostic for each 97 5th quantile of each chain e pvalmean one sided Bayesian p value for each chain where chain mean is treated as parameter estimate e pvalmode one sided Bayesian p value for each chain where chain mode is treated as parameter estimate e pvalmedian one sided Bayesian p value for each chain where chain median is treated as parameter estimate Functions e sample marks estimation sample About the Centre for Multilevel Modelling Monday August 5 13 34 03 2013 Page 27 The MLwiN software is developed at the Centre for Multilevel Modelling The Centre was established in 1986 and has been supported largely by project grants from the UK Economic and Social Research Council The Centre has been based at the University of Bristol since 2005 The Centre s websit http www bristol ac uk cmm contains much of interest including new developments and details of courses and workshops This website also contains the latest information about the MLwiN software including upgrade information maintenance downloads and documentation The Centre also runs a free online multilevel modelling course http www bristol ac uk cmm learning course html which contains
54. vel Modelling University of Bristol g leckie bristol ac uk Chris Charlton Centre for Multilevel Modelling University of Bristol Acknowledgments Monday August 5 13 34 03 2013 Page 28 We are very grateful to colleagues at the Centre for Multilevel Modelling and the University of Bristol for their useful comments The development of this command was funded under the LEMMA project a node of the UK Economic and Social Research Council s National Centre for Research Methods grant number RES 576 25 0003 Disclaimer runmlwin comes with no warranty We recommend that users check their results with those obtained through operating MLwiN by its graphical user interface Users are also encouraged to check their results with those produced by other statistical software packages References Browne W J 2012 MCMC Estimation in MLwiN v2 26 Centre for Multilevel Modelling University of Bristol http www bristol ac uk cmm software mlwin download manuals html Leckie G and Charlton C 2013 runmlwin A Program to Run the MLwiN Multilevel Modelling Software from within Stata Journal of Statistical Software 52 11 1 40 http www jstatsoft org v52 il1 Rabe Hesketh S and Skrondal A 2012 Multilevel and Longitudinal Modeling using Stata Third Edition College Station TX Stata Press Rasbash J Steele F Browne W J and Goldstein H 2012 A User s Guide to MLwiN v2 26 Centre
55. version e cmdline command as typed e title title in estimation output e depvars name s of dependent variable s e distribution distribution s e link link function e denominators denominator s e basecategory the value of depvar to be treated as the base category e respcategories the values of depvar e offsets offset s e ivars grouping variables e levellid level 1 identifier variable e weightvar sampling weights variables e weighttype sampling weights types e method estimation method IGLS RIGLS or MCMC e linearization linearization technique MQL1 MQL2 PQL1 or PQL2 e properties bv e chains MCMC chains for all parameters Matrices e b coefficient vector e V variance covariance matrix of the estimators e N_g group counts e g_min group size minimum e g_avg group size averages e g_max group size maximums e RP2 level 2 matrix of random part parameters e RP1 level 1 matrix of random part parameters e P priors e sd standard deviation for each chain e mode mode for each chain e meanmcse Monte Carlo standard error MCSE evaluated at the mean of each chain e ess ffective sample size ESS for each chain e quantiles quantiles for each chain e 1b lower credible interval bound for each chain e ub upper credible interval bound for each chain e KD1 kernel density for each chain e KD2 kernel density for each chain e ACF autocorrelation function ACF for each chain e PACF partial a
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