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From |
Solveig Hillesund <solveig.hillesund@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Problem with ci_marg_mu with nominal dependent variable |

Date |
Fri, 11 Oct 2013 08:22:06 +0200 |

Problem with ci_marg_mu with nominal dependent variable I am attempting to make figures with confidence intervals around predicted probabilities for a two-level multinominal logistic regression model with a random intercept. The problem is creating the confidence intervals. If y was my dependent variable (with categories 0, 1, 2 and 3), x and z my independent variables and g the grouping variable, I would create the figure with predicted probabilities for x (when z=0) like this (following an version of the description in Rabe-Hesketh and Skrondal (2012: 673-676) adapted to population-averaged expectation with the mu marginal option): gllamm y x z, i(g) lin(mlogit) family(binom) base(1) from(a) skip adapt save temp, replace drop _all set obs 50 generate x = -2 + _n*0.005 /*to fit range of original x*/ generate set = 3000 + _n expand 4 by set, sort: generate y=_n generate z=0 recode z (4=0) replace p0607d=. append using temp generate preddata = p0607d ==. gllapred probs0, mu marginal fsample outcome(0) gllapred probs1, mu marginal fsample outcome(1) gllapred probs2, mu marginal fsample outcome(2) gllapred probs3, mu marginal fsample outcome(3) twoway (line probs2 z_HI_ostby, sort lpatt(solid)) (line probs3 z_HI_ostby, sort lpatt(longdash)) (line probs0 z_HI_ostby, sort lpatt(shortdash)) (line probs1 z_HI_ostby, sort lpatt(dash)) if preddata==1 According to Skrondal “Approximate confidence intervals for predicted marginal expectations can be obtained by simulating parameters from their estimated asymptotic sampling distribution” (Skrondal and Rabe Hesketh 2009:16). More specifically, “To produce the confidence bands, we randomly drew 1000 parameter vectors from a multivariate normal distribution with mean vector ˆϑ and covariance matrix côv(ϑ), the estimated asymptotic sampling distribution of the estimates. For each randomly drawn parameter vector ϑk, k=1, . . . , 1000, we computed the predicted marginal mean μ-k.(x0 j ) for each school and then identified the 25th- and 976th-largest values for each school.” (Skrondal and Rabe Hesketh 2009:17-18). I have tried to use ci_marg_mu to accomplish this, but run into problems. First, the fsample option is not allowed (so not sure whether full sample or only estimation sample will be used). Second, because of the nominal dependent variable Stata tells me I need to “specify outcome() option if data is not in expanded form”, but when I specify outcome I get the error “outcome() option not allowed”. I guess it might work to use expanded data, but I am having some problems specifying the model in this form, so I am hoping there is some other way to get around the problem. According to a powerpoint presentation I have found, written by the author of the program, the operation is supposed to work with nominal data. References: Rabe-Hesketh, S., & Skrondal, A. (2012). Multilevel and longitudinal modeling using Stata. Categorical responses, counts, and survival. (Third ed. Vol. 2). College Station, Tex.: Stata Press Skrondal, A. and S. Rabe-Hesketh (2009). “Prediction in multilevel generalized linear models”. In J.R. Statst. Soc. A. 172(3). * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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