R2MLwiN Using the multilevel modelling software package MLwiN from R
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1 Using the multilevel modelling software package MLwiN from R Richard Parker Zhengzheng Zhang Chris Charlton George Leckie Bill Browne Centre for Multilevel Modelling (CMM) University of Bristol
2 Using the multilevel modelling software package MLwiN from R
3 Using the multilevel modelling software package MLwiN from R R2MLwiN
4 Using the multilevel modelling software package MLwiN from R R2MLwiN runmlwin Stata
5 Using the multilevel modelling software package MLwiN from R R2MLwiN SPSS BUGS runmlwin Stata gretl MATLAB JAGS aml SAS
6 Using the multilevel modelling software package MLwiN from R R2MLwiN SPSS BUGS runmlwin Stata gretl MATLAB JAGS aml SAS
7 Why operate one application from another? A B
8 Why operate one application from another? A Perhaps this package (B) has functionality / facilities of value to user that this package (A) doesn t B
9 Why operate one application from another? A E.g. package B can: Perhaps this package (B) has functionality / facilities of value to user that this package (A) doesn t B
10 Why operate one application from another? A E.g. package B can: Perhaps this package (B) has functionality / facilities of value to user that this package (A) doesn t fit a certain type of statistical model B
11 Why operate one application from another? A E.g. package B can: Perhaps this package (B) has functionality / facilities of value to user that this package (A) doesn t fit a certain type of statistical model use a quicker / less-biased / etc. means of estimation B
12 Why operate one application from another? A E.g. package B can: Perhaps this package (B) has functionality / facilities of value to user that this package (A) doesn t fit a certain type of statistical model use a quicker / less-biased / etc. means of estimation produce a certain type of plot, table, statistic, etc. B
13 Why operate one application from another? A E.g. package B can: fit a certain type of statistical model use a quicker / less-biased / etc. means of estimation produce a certain type of plot, table, statistic, etc. or perhaps package B is: Perhaps this package (B) has functionality / facilities of value to user that this package (A) doesn t B
14 Why operate one application from another? E.g. package B can: fit a certain type of statistical model use a quicker / less-biased / etc. means of estimation produce a certain type of plot, table, statistic, etc. or perhaps package B is: more familiar A Perhaps this package (B) has functionality / facilities of value to user that this package (A) doesn t B
15 Why operate one application from another? E.g. package B can: fit a certain type of statistical model use a quicker / less-biased / etc. means of estimation produce a certain type of plot, table, statistic, etc. or perhaps package B is: more familiar A Perhaps this package (B) has functionality / facilities of value to user that this package (A) doesn t has useful supporting resources B
16 Why operate one application from another? A B
17 Why operate one application from another? A B The user could (in principle) operate package B directly
18 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf B
19 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf The user might not know how to operate Package B B
20 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf The user might not know how to operate Package B and it s not realistic to learn how to do so given time, etc., available B
21 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf The user might not know how to operate Package B and it s not realistic to learn how to do so given time, etc., available or the user wants to use A as a means of learning how to use B directly him/herself B
22 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf B
23 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf Or perhaps the user does know how to directly operate package B, but it s more convenient if A does so for him/her, because e.g.: B
24 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf Or perhaps the user does know how to directly operate package B, but it s more convenient if A does so for him/her, because e.g.: data s already in A and it s time-consuming to translate it, oneself, into package B s format B
25 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf Or perhaps the user does know how to directly operate package B, but it s more convenient if A does so for him/her, because e.g.: data s already in A and it s time-consuming to translate it, oneself, into package B s format can more efficiently fit many models / produce many plots / etc. in B if run it from A B
26 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf Or perhaps the user does know how to directly operate package B, but it s more convenient if A does so for him/her, because e.g.: data s already in A and it s time-consuming to translate it, oneself, into package B s format can more efficiently fit many models / produce many plots / etc. in B if run it from A wish to use A s functionality to post-process the results from B B
27 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf Or perhaps the user does know how to directly operate package B, but it s more convenient if A does so for him/her, because e.g.: data s already in A and it s time-consuming to translate it, oneself, into package B s format can more efficiently fit many models / produce many plots / etc. in B if run it from A wish to use A s functionality to post-process the results from B may be able to better document analyses in A B
28 Why operate one application from another? A data & commands results The user could (in principle) operate package B directly but wants package A to do so on their behalf Or perhaps the user does know how to directly operate package B, but it s more convenient if A does so for him/her, because e.g.: data s already in A and it s time-consuming to translate it, oneself, into package B s format can more efficiently fit many models / produce many plots / etc. in B if run it from A wish to use A s functionality to post-process the results from B may be able to better document analyses in A may want to compare model fits from many packages B
29 Using the multilevel modelling software package MLwiN from R R2MLwiN SPSS BUGS runmlwin Stata gretl MATLAB JAGS aml SAS
30 Using the multilevel modelling software Stan package MLwiN from R R2MLwiN SPSS BUGS runmlwin Stata gretl MATLAB JAGS aml SAS
31 First released (on CRAN) December 2012 Most recent release (0.8-0) March 2015 this has a number of new features / syntactical changes but back-compatibility maintained where possible R2MLwiN Using the multilevel modelling software package MLwiN from R
32 MLwiN R2MLwiN Developed & maintained by Centre for Multilevel Modelling (now at Bristol) Estimated 18,000 users worldwide Large body of supporting documentation, examples, workshops, etc. As well as Windows, native versions of MLwiN engine for Mac OS X and Linux now available too
33 MLwiN Allows for a variety of response types to be modelled, including: continuous binary count ordinal nominal multivariate combinations (i.e., simultaneous equations) Estimation available via: IGLS (iterative generalised least squares), which yields maximum likelihood estimates MCMC (Markov chain Monte Carlo ) estimation for Bayesian inference Supported data structures: nested, cross-classified and/or multiple membership Other features include: fitting of complex level 1 variance (heteroskedastic) models multilevel factor analysis (MCMC only) adjustments for measurement errors in predictors spatial conditional auto regressive (CAR) models autoregressive structures at level 1 a selection of MCMC algorithms to increase efficiency.
34 MLwiN R2MLwiN GUI (graphical user interface) has number of innovative features, e.g.: Interactive equations window:
35 MLwiN R2MLwiN GUI (graphical user interface) has number of innovative features, e.g.: Interactive equations window:
36 MLwiN R2MLwiN GUI (graphical user interface) has number of innovative features, e.g.: Interactive equations window:
37 MLwiN R2MLwiN GUI (graphical user interface) has number of innovative features, e.g.: Interactive equations window:
38 MLwiN GUI (graphical user interface) has number of innovative features, e.g.: Interactive graphs: R2MLwiN
39 MLwiN R2MLwiN Most users will likely operate MLwiN via GUI Macro language can be unwieldy
40 Using the multilevel modelling software package MLwiN from R
41 Using the multilevel modelling software package MLwiN from R
42 Using the multilevel modelling software package MLwiN from R
43 Using the multilevel modelling software package MLwiN from R Tells user where it s expecting to find MLwiN; easy to change path if it s elsewhere
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45 All sample datasets released with MLwiN available with R2MLwiN
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48 R> F1 <- normexam ~ 1 + (1 school) + + (1 student)
49 R> F1 <- normexam ~ 1 + (1 school) + + (1 student) normexam ij = β 0 + u j + e ij u j ~N(0, σ u 2 ) e ij ~N(0, σ e 2 )
50 R> F1 <- normexam ~ 1 + (1 school) + + (1 student) normexam ij = β 0 + u j + e ij u j ~N(0, σ u 2 ) e ij ~N(0, σ e 2 ) Note: need to explicitly add intercept (as in MLwiN) Specify random part of model in order of hierarchy
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54 R> F1 <- normexam ~ 1 + (1 school) + + (1 student) R> (VarCompModel <- runmlwin( + Formula = F1, data = tutorial))
55 R> F1 <- normexam ~ 1 + (1 school) + + (1 student) R> (VarCompModel <- runmlwin( + Formula = F1, data = tutorial))
56 R> F1 <- normexam ~ 1 + (1 school) + + (1 student) R> (VarCompModel <- runmlwin( + Formula = F1, data = tutorial)) runmlwin function: 1. takes input & creates MLwiN macro file 2. calls MLwiN and executes macro script 3. output is returned to R for post-processing
57 R> F1 <- normexam ~ 1 + (1 school) + + (1 student) R> (VarCompModel <- runmlwin( + Formula = F1, data = tutorial)) runmlwin function Arguments include: Formula data D since we don t specify here, using default: D = Normal estoptions again using default, which is IGLS: estoptions = list(estm = 0) See?runMLwiN for full list of arguments
58 R> F1 <- normexam ~ 1 + (1 school) + + (1 student) R> (VarCompModel <- runmlwin( + Formula = F1, data = tutorial)) runmlwin function Arguments include: Formula data D since we don t specify here, using default: D = Normal estoptions again using default, which is IGLS: estoptions = list(estm = 0) See?runMLwiN for full list of arguments
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66 R> F2 <- normexam ~ 1 + (1 student)
67 R> F2 <- normexam ~ 1 + (1 student) R> OneLevelModel <- runmlwin( + Formula = F2, data = tutorial)
68 R> F2 <- normexam ~ 1 + (1 student) R> OneLevelModel <- runmlwin( + Formula = F2, data = tutorial) R> library("lmtest") R> lrtest(onelevelmodel, VarCompModel) Model objects returned by R2MLwiN contain some generic S4 methods e.g. has a method for the function loglik which allows us to conduct a likelihood ratio test using the lrtest function (part of the lmtest package)
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75 R> F3 <- normexam ~ 1 + standlrt + + (1 + standlrt school) + (1 student) R> (RandomSlopeModel <- runmlwin( + Formula = F3, estoptions = list( + resi.store = TRUE), data = tutorial))
76 R> F3 <- normexam ~ 1 + standlrt + + (1 + standlrt school) + (1 student) R> (RandomSlopeModel <- runmlwin( + Formula = F3, estoptions = list( + resi.store = TRUE), data = tutorial))
77 R> F3 <- normexam ~ 1 + standlrt + + (1 + standlrt school) + (1 student) R> (RandomSlopeModel <- runmlwin( + Formula = F3, estoptions = list( + resi.store = TRUE), data = tutorial)) R> predlines(randomslopemodel, + xname="standlrt", lev = 2, legend = F)
78 R> F3 <- normexam ~ 1 + standlrt + + (1 + standlrt school) + (1 student) R> (RandomSlopeModel <- runmlwin( + Formula = F3, estoptions = list( + resi.store = TRUE), data = tutorial)) R> predlines(randomslopemodel, + xname="standlrt", lev = 2, legend = F)
79 R> F3 <- normexam ~ 1 + standlrt + + (1 + standlrt school) + (1 student) R> (RandomSlopeModel <- runmlwin( + Formula = F3, estoptions = list( + resi.store = TRUE), data = tutorial)) R> predlines(randomslopemodel, + xname="standlrt", lev = 2, legend = F) R> predlines(randomslopemodel, + xname = "standlrt", lev = 2, + selected = c(30, 44, 53, 59))
80 R> F3 <- normexam ~ 1 + standlrt + + (1 + standlrt school) + (1 student) R> (RandomSlopeModel <- runmlwin( + Formula = F3, estoptions = list( + resi.store = TRUE), data = tutorial)) R> predlines(randomslopemodel, + xname="standlrt", lev = 2, legend = F) R> predlines(randomslopemodel, + xname = "standlrt", lev = 2, + selected = c(30, 44, 53, 59))
81 R> F4 <- normexam ~ 1 + standlrt + + (1 + standlrt school) + + (1 + standlrt student) R> ComplexLevOneModel <- runmlwin( + Formula = F4, data = tutorial, + estoptions = list(debugmode = TRUE))
82 R> F4 <- normexam ~ 1 + standlrt + + (1 + standlrt school) + + (1 + standlrt student) R> ComplexLevOneModel <- runmlwin( + Formula = F4, data = tutorial, + estoptions = list(debugmode = TRUE))
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85 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1)))
86 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1)))
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88 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1)))
89 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1))) R> print(binomialmcmc, z.ratio = FALSE)
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92 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1))) R> print(binomialmcmc, z.ratio = FALSE)
93 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1))) R> print(binomialmcmc, z.ratio = FALSE) R> sixway(binomialmcmc["chains"] + [, "FP_Intercept", drop = FALSE], + "beta_0")
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95 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1))) R> print(binomialmcmc, z.ratio = FALSE) R> sixway(binomialmcmc["chains"] + [, "FP_Intercept", drop = FALSE], + "beta_0")
96 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1))) R> print(binomialmcmc, z.ratio = FALSE) R> sixway(binomialmcmc["chains"] + [, "FP_Intercept", drop = FALSE], + "beta_0") R> trajectories(binomialmcmc["chains"] + [, "FP_Intercept", drop = FALSE])
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98 R> data( bang1 ) R> F5 = logit(use, cons) ~ 1 + age + + lc + urban + (1 + urban district) R> (binomialmcmc <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1))) R> print(binomialmcmc, z.ratio = FALSE) R> sixway(binomialmcmc["chains"] + [, "FP_Intercept", drop = FALSE], + "beta_0") R> trajectories(binomialmcmc["chains"] + [, "FP_Intercept", drop = FALSE])
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100 R> (OrthogbinomialMCMC <- runmlwin( + Formula = F5, D = "Binomial", + data = bang1, estoptions = list(estm = 1, + mcmcoptions = list(orth = 1)))) R> trajectories(orthogbinomialmcmc["chains"] + [,"FP_Intercept", drop = FALSE])
101 R> (OrthogbinomialMCMC <- runmlwin( + Formula = F5, D = "Binomial", + data = bang1, estoptions = list(estm = 1, + mcmcoptions = list(orth = 1)))) R> trajectories(orthogbinomialmcmc["chains"] + [,"FP_Intercept", drop = FALSE])
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103 As well as using MLwiN s own MCMC estimation engine, R2MLwiN can fit models in WinBUGS / OpenBUGS
104 As well as using MLwiN s own MCMC estimation engine, R2MLwiN can fit models in WinBUGS / OpenBUGS With the aid of the rbugs package (Yan and Prates 2013), user can employ single runmlwin function call to: obtain starting values from an IGLS run in MLwiN, automatically generate necessary BUGS model code, initial values, data files, and script, fit the model in BUGS R2MLwiN
105 R> WinBUGS <- "C:/WinBUGS14/WinBUGS14.exe" R> BUGSmodel <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1), + BUGO = c(version = 4, n.chains = 1, + seed = 1, bugs = WinBUGS, + OpenBugs = FALSE))
106 R> WinBUGS <- "C:/WinBUGS14/WinBUGS14.exe" R> BUGSmodel <- runmlwin(formula = F5, + D = "Binomial", data = bang1, + estoptions = list(estm = 1), + BUGO = c(version = 4, n.chains = 1, + seed = 1, bugs = WinBUGS, + OpenBugs = FALSE))
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109 As well as usual help files R2MLwiN comes with demos as well these replicate all the examples in: MLwiN User Manual (IGLS) MLwiN MCMC Manual
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111 To list demos: R> demo(package = "R2MLwiN")
112 To list demos: R> demo(package = "R2MLwiN")
113 To list demos: R> demo(package = "R2MLwiN")
114 To list demos: R> demo(package = "R2MLwiN")
115 To list demos: R> demo(package = "R2MLwiN") To run a specific demo: R> demo(mcmcguide03)
116 To list demos: R> demo(package = "R2MLwiN") To run a specific demo: R> demo(mcmcguide03) To show demo script: R> file.show(system.file("demo", "MCMCGuide03.R", package = "R2MLwiN"))
117 To list demos: R> demo(package = "R2MLwiN") To run a specific demo: R> demo(mcmcguide03) To show demo script: R> file.show(system.file("demo", "MCMCGuide03.R", package = "R2MLwiN"))
118 Obtaining R2MLwiN & finding out more:
119 Obtaining R2MLwiN & finding out more: R2MLwiN available on CRAN, e.g. via: R> install.packages("r2mlwin", + repos = "
120 Obtaining R2MLwiN & finding out more: R2MLwiN available on CRAN, e.g. via: R> install.packages("r2mlwin", + repos = " & supporting info on Centre for Multilevel Modelling website: includes link to log files of demo runs
121 Obtaining R2MLwiN & finding out more: R2MLwiN available on CRAN, e.g. via: R> install.packages("r2mlwin", + repos = " & supporting info on Centre for Multilevel Modelling website: includes link to log files of demo runs Leaflets available at the front too
122 Obtaining R2MLwiN & finding out more: Forum on Centre for Multilevel Modelling website : R2MLwiN available on CRAN, e.g. via: R> install.packages("r2mlwin", + repos = " & supporting info on Centre for Multilevel Modelling website: includes link to log files of demo runs Leaflets available at the front too
123 Obtaining R2MLwiN & finding out more: R2MLwiN available on CRAN, e.g. via: R> install.packages("r2mlwin", + repos = " & supporting info on Centre for Multilevel Modelling website: includes link to log files of demo runs Leaflets available at the front too
124 Obtaining R2MLwiN & finding out more: R2MLwiN available on CRAN, e.g. via: R> install.packages("r2mlwin", + repos = " & supporting info on Centre for Multilevel Modelling website: includes link to log files of demo runs Leaflets available at the front too
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