We start by resampling from the highest level, and then stepping down one level at a time. effects. We fitted linear mixed effects model (random intercept child & random slope time) to compare study groups. Perhaps 1,000 is a reasonable starting point. The Stata examples used are from; Multilevel Analysis (ver. Until now, Stata provided only large-sample inference based on normal and χ² distributions for linear mixed-effects models. In practice you would probably want to run several hundred or a few thousand. If you take this approach, it is probably best to use the observed estimates from the model with 10 integration points, but use the confidence intervals from the bootstrap, which can be obtained by calling estat bootstrap after the model. We can do this by taking the observed range of the predictor and taking $$k$$ samples evenly spaced within the range. gamma, negative binomial, ordinal, Poisson, Five links: identity, log, logit, probit, cloglog, Select from many prior distributions or use default priors, Adaptive MH sampling or Gibbs sampling with linear regression, Postestimation tools for checking convergence, estimating functions of model parameters, computing Bayes factors, and performing interval hypotheses testing, Variances of random effects (variance components), Identity—shared variance parameter for specified effects After three months, they introduced a new advertising campaign in two of the four cities and continued monitoring whether or not people had watched the show. Unfortunately, Stata does not have an easy way to do multilevel bootstrapping. A random intercept is one dimension, adding a random slope would be two. | Stata FAQ Please note: The following example is for illustrative purposes only. The Biostatistics Department at Vanderbilt has a nice page describing the idea here. Example 2: A large HMO wants to know what patient and physician factors are most related to whether a patient’s lung cancer goes into remission after treatment as part of a larger study of treatment outcomes and quality of life in patients with lunge cancer. Which Stata is right for me? covariance parameter for specified effects, Unstructured—unique variance parameter for each specified Use care, however, because like most mixed models, specifying a crossed random effects model … Note that time is an ex… The approximations of the coefficient estimates likely stabilize faster than do those for the SEs. with no covariances, Independent—unique variance parameter for each specified Thus parameters are estimated to maximize the quasi-likelihood. The logit scale is convenient because it is linearized, meaning that a 1 unit increase in a predictor results in a coefficient unit increase in the outcome and this holds regardless of the levels of the other predictors (setting aside interactions for the moment). This is not the standard deviation around the exponentiated constant estimate, it is still for the logit scale. Whether the groupings in your data arise in a nested fashion (students nested Model(1)is an example of a generalized linear mixed model (GLMM), which generalizes the linear mixed-effects (LME) model to non-Gaussian responses. Mixed Effects Modeling in Stata. –X k,it represents independent variables (IV), –β Also, we have left $$\mathbf{Z}\boldsymbol{\gamma}$$ as in our sample, which means some groups are more or less represented than others. There are some advantages and disadvantages to each. The accuracy increases as the number of integration points increases. Using the same assumptions, approximate 95% confidence intervals are calculated. That is, across all the groups in our sample (which is hopefully representative of your population of interest), graph the average change in probability of the outcome across the range of some predictor of interest. Example 3: A television station wants to know how time and advertising campaigns affect whether people view a television show. Finally, we take $$h(\boldsymbol{\eta})$$, which gives us $$\boldsymbol{\mu}_{i}$$, which are the conditional expectations on the original scale, in our case, probabilities. These can adjust for non independence but does not allow for random effects. I need some help in interpreting the coefficients for interaction terms in a mixed-effects model (longitudinal analysis) I've run to analyse change in my outcome over time (in months) given a set of predictors. However, for GLMMs, this is again an approximation. Probit regression with clustered standard errors. Then we create $$k$$ different $$\mathbf{X}_{i}$$s where $$i \in \{1, \ldots, k\}$$ where in each case, the $$j$$th column is set to some constant. Specifically, we will estimate Cohen’s f2f2effect size measure using the method described by Selya(2012, see References at the bottom) . My analysis has been reviewed and I've been informed to do a penalized maximum likelihood regression because 25 stores may pass as 'rare events'. That is, they are not true maximum likelihood estimates. In the above y1is the response variable at time one. We have monthly length measurements for a total of 12 months. For example, suppose you ultimately wanted 1000 replicates, you could do 250 replicates on four different cores or machines, save the results, combine the data files, and then get the more stable confidence interval estimates from the greater number of replicates without it taking so long. These are unstandardized and are on the logit scale. Proceedings, Register Stata online Discover the basics of using the -xtmixed- command to model multilevel/hierarchical data using Stata. Recall that we set up the theory by allowing each group to have its own intercept which we don’t estimate. Consequently, it is a useful method when a high degree of accuracy is desired but performs poorly in high dimensional spaces, for large datasets, or if speed is a concern. Below we estimate a three level logistic model with a random intercept for doctors and a random intercept for hospitals. In this example, we are going to explore Example 2 about lung cancer using a simulated dataset, which we have posted online. Unfortunately fitting crossed random effects in Stata is a bit unwieldy. A variety of outcomes were collected on patients, who are nested within doctors, who are in turn nested within hospitals. Had there been other random effects, such as random slopes, they would also appear here. One or more variables are fixed and one or more variables are random In a design with two independent variables there are two different mixed-effects models possible: A fixed & B random, or A random & B fixed. They sample people from four cities for six months. Below we use the xtmelogit command to estimate a mixed effects logistic regression model with il6, crp, and lengthofstay as patient level continuous predictors, cancerstage as a patient level categorical predictor (I, II, III, or IV), experience as a doctor level continuous predictor, and a random intercept by did, doctor ID. New in Stata 16 For single level models, we can implement a simple random sample with replacement for bootstrapping. However, it can do cluster bootstrapping fairly easily, so we will just do that. Supported platforms, Stata Press books These take more work than conditional probabilities, because you have to calculate separate conditional probabilities for every group and then average them. In long form thedata look like this. A revolution is taking place in the statistical analysis of psychological studies. When to choose mixed-effects models, how to determine fixed effects vs. random effects, and nested vs. crossed sampling designs. Because of the relationship betweenLMEs andGLMMs, there is insight to be gained through examination of the linear mixed model. If we wanted odds ratios instead of coefficients on the logit scale, we could exponentiate the estimates and CIs. Stata News, 2021 Stata Conference Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when data are clustered or there are both fixed and random effects. The new model … The effects are conditional on other predictors and group membership, which is quite narrowing. In a logistic model, the outcome is commonly on one of three scales: For tables, people often present the odds ratios. $$A final set of methods particularly useful for multidimensional integrals are Monte Carlo methods including the famous Metropolis-Hastings algorithm and Gibbs sampling which are types of Markov chain Monte Carlo (MCMC) algorithms. As we use more integration points, the approximation becomes more accurate converging to the ML estimates; however, more points are more computationally demanding and can be extremely slow or even intractable with today’s technology. Here is a general summary of the whole dataset. The Stata command xtreg handles those econometric models. If instead, patients were sampled from within doctors, but not necessarily all patients for a particular doctor, then to truly replicate the data generation mechanism, we could write our own program to resample from each level at a time. 10 patients from each of 500 doctors (leading to the same total number of observations) would be preferable. lack of independence within these groups. Upcoming meetings Complete or quasi-complete separation: Complete separation means that the outcome variable separate a predictor variable completely, leading perfect prediction by the predictor variable. In ordinary logistic regression, you could just hold all predictors constant, only varying your predictor of interest. Change registration Left-censored, right-censored, or both (tobit), Nonlinear mixed-effects models with lags and differences, Small-sample inference for mixed-effects models. A Taylor series uses a finite set of differentiations of a function to approximate the function, and power rule integration can be performed with Taylor series. Stata's multilevel mixed estimation commands handle two-, three-, and higher-level data. Inference from GLMMs is complicated. (R’s lme can’t do it). These can adjust for non independence but does not allow for random effects. Note that we do not need to refit the model. Here’s the model we’ve been working with with crossed random effects. The data presented is not meant to recommend or encourage the estimation of random effects on categorical variables with very few unique levels. Here is an example of data in the wide format for fourtime periods. and random coefficients. However, more commonly, we want a range of values for the predictor in order to plot how the predicted probability varies across its range. It does not cover all aspects of the research process which researchers are expected to do. We could also make boxplots to show not only the average marginal predicted probability, but also the distribution of predicted probabilities. The last section gives us the random effect estimates. Mixed models consist of fixed effects and random effects. It covers some of the background and theory as well as estimation options, inference, and pitfalls in more detail. These are all the different linear predictors. Because of the bias associated with them, quasi-likelihoods are not preferred for final models or statistical inference. THE LINEAR MIXED MODEL. Note that this model takes several minutes to run on our machines. The Stata Blog Stata’s mixed-models estimation makes it easy to specify and to fit multilevel and hierarchical random-effects models. Sample size: Often the limiting factor is the sample size at the highest unit of analysis. 357 & 367 of the Stata 14.2 manual entry for the mixed command. Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when data are clustered or there are both fixed and random effects. The note from predict indicated that missing values were generated. See the R page for a correct example. If the only random coefﬁcient is a We are just going to add a random slope for lengthofstay that varies between doctors. For example, students couldbe sampled from within classrooms, or patients from within doctors.When there are multiple levels, such as patients seen by the samedoctor, the variability in the outcome can be thought of as bei… Multilevel Mixed-Effects Linear Regression. The next section is a table of the fixed effects estimates. Example 1: A researcher sampled applications to 40 different colleges to study factors that predict admittance into college. Please note: The purpose of this page is to show how to use various data analysis commands. Thus, if you hold everything constant, the change in probability of the outcome over different values of your predictor of interest are only true when all covariates are held constant and you are in the same group, or a group with the same random effect. Stata also indicates that the estimates are based on 10 integration points and gives us the log likelihood as well as the overall Wald chi square test that all the fixed effects parameters (excluding the intercept) are simultaneously zero. Introduction to mixed models Linear mixed models Linear mixed models The simplest sort of model of this type is the linear mixed model, a regression model with one or more random effects. Note that the random effects parameter estimates do not change. Random e ects are not directly estimated, but instead charac- terized by the elements of G, known as variance components As such, you t a mixed … An attractive alternative is to get the average marginal probability. Watch Nonlinear mixed-effects models. Multilevel models for survey data in Stata. Visual presentations are helpful to ease interpretation and for posters and presentations. Stata Press Now we are going to briefly look at how you can add a third level and random slope effects as well as random intercepts. xtreg random effects models can also be estimated using the mixed command in Stata. Particularly if the outcome is skewed, there can also be problems with the random effects. Although Monte Carlo integration can be used in classical statistics, it is more common to see this approach used in Bayesian statistics. In thewide format each subject appears once with the repeated measures in the sameobservation. We are using $$\mathbf{X}$$ only holding our predictor of interest at a constant, which allows all the other predictors to take on values in the original data. Note for the model, we use the newly generated unique ID variable, newdid and for the sake of speed, only a single integration point. The alternative case is sometimes called “cross classified” meaning that a doctor may belong to multiple hospitals, such as if some of the doctor’s patients are from hospital A and others from hospital B. They extend standard linear regression models through the introduction of random effects and/or correlated residual errors. We can then take the expectation of each $$\boldsymbol{\mu}_{i}$$ and plot that against the value our predictor of interest was held at. The true likelihood can also be approximated using numerical integration. The estimates are followed by their standard errors (SEs). If we had wanted, we could have re-weighted all the groups to have equal weight. I know this has been posted about before, but I'm still having difficulty in figuring out what's happening in my model! Mixed effects probit regression is very similar to mixed effects logistic regression, but it uses the normal CDF instead of the logistic CDF. Now we just need to run our model, and then get the average marginal predicted probabilities for lengthofstay. Chapter 4 Random slopes. For example, an outcome may be measured more than once on the same person (repeated measures taken over time). Generalized linear mixed models (or GLMMs) are an extension of linearmixed models to allow response variables from different distributions,such as binary responses. Repeated measures data comes in two different formats: 1) wide or 2) long. College-level predictors include whether the college is public or private, the current student-to-teacher ratio, and the college’s rank. \boldsymbol{\eta}_{i} = \mathbf{X}_{i}\boldsymbol{\beta} + \mathbf{Z}\boldsymbol{\gamma} Subscribe to email alerts, Statalist We chose to leave all these things as-is in this example based on the assumption that our sample is truly a good representative of our population of interest. Compute intraclass correlations. Predictors include student’s high school GPA, extracurricular activities, and SAT scores. For this model, Stata seemed unable to provide accurate estimates of the conditional modes. We are going to focus on a small bootstrapping example. Mixed model repeated measures (MMRM) in Stata, SAS and R December 30, 2020 by Jonathan Bartlett Linear mixed models are a popular modelling approach for longitudinal or repeated measures data. Logistic regression with clustered standard errors. Watch Multilevel tobit and interval regression. For three level models with random intercepts and slopes, it is easy to create problems that are intractable with Gaussian quadrature. Using a single integration point is equivalent to the so-called Laplace approximation. We use a single integration point for the sake of time. Some colleges are more or less selective, so the baseline probability of admittance into each of the colleges is different. Error (residual) structures for linear models, Small-sample inference in linear models (DDF adjustments), Survey data for generalized linear and survival models. We used 10 integration points (how this works is discussed in more detail here). Subscribe to Stata News The cluster bootstrap is the data generating mechanism if and only if once the cluster variable is selected, all units within it are sampled. First, let’s define the general procedure using the notation from here. There are also a few doctor level variables, such as Experience that we will use in our example. However, the number of function evaluations required grows exponentially as the number of dimensions increases. A special case of this model is the one-way random effects panel data model implemented by xtreg, re. Log odds (also called logits), which is the linearized scale, Odds ratios (exponentiated log odds), which are not on a linear scale, Probabilities, which are also not on a linear scale. Mixed-effect models are rather complex and the distributions or numbers of degrees of freedom of various output from them (like parameters …) is not known analytically. Bootstrapping is a resampling method. For visualization, the logit or probability scale is most common. The Wald tests, $$\frac{Estimate}{SE}$$, rely on asymptotic theory, here referring to as the highest level unit size converges to infinity, these tests will be normally distributed, and from that, p values (the probability of obtaining the observed estimate or more extreme, given the true estimate is 0). Now if I tell Stata these are crossed random effects, it won’t get confused! This represents the estimated standard deviation in the intercept on the logit scale. Thus if you are using fewer integration points, the estimates may be reasonable, but the approximation of the SEs may be less accurate. You may have noticed that a lot of variability goes into those estimates. ), Department of Statistics Consulting Center, Department of Biomathematics Consulting Clinic. Alternatively, you could think of GLMMs asan extension of generalized linear models (e.g., logistic regression)to include both fixed and random effects (hence mixed models). Books on statistics, Bookstore Each month, they ask whether the people had watched a particular show or not in the past week. A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects. So all nested random effects are just a way to make up for the fact that you may have been foolish in storing your data. For example, having 500 patients from each of ten doctors would give you a reasonable total number of observations, but not enough to get stable estimates of doctor effects nor of the doctor-to-doctor variation. So far all we’ve talked about are random intercepts. The following is copied verbatim from pp. Quadrature methods are common, and perhaps most common among these use the Gaussian quadrature rule, frequently with the Gauss-Hermite weighting function. Mixed-effects Model. Institute for Digital Research and Education, Version info: Code for this page was tested in Stata 12.1. A downside is the scale is not very interpretable. In our case, if once a doctor was selected, all of her or his patients were included. Both model binary outcomes and can include fixed and random effects. If you are new to using generalized linear mixed effects models, or if you have heard of them but never used them, you might be wondering about the purpose of a GLMM.. Mixed effects models are useful when we have data with more than one source of random variability. Parameter estimation: Because there are not closed form solutions for GLMMs, you must use some approximation. Watch a Tour of multilevel GLMs. So the equation for the fixed effects model becomes: Y it = β 0 + β 1X 1,it +…+ β kX k,it + γ 2E 2 +…+ γ nE n + u it [eq.2] Where –Y it is the dependent variable (DV) where i = entity and t = time. crossed with occupations), you can fit a multilevel model to account for the Thegeneral form of the model (in matrix notation) is:y=Xβ+Zu+εy=Xβ+Zu+εWhere yy is … We are going to explore an example with average marginal probabilities. If you are just starting, we highly recommend reading this page first Introduction to GLMMs. For large datasets or complex models where each model takes minutes to run, estimating on thousands of bootstrap samples can easily take hours or days. Estimates differ … If you happen to have a multicore version of Stata, that will help with speed. Stata’s new mixed-models estimation makes it easy to specify and to fit two-way, multilevel, and hierarchical random-effects models. As models become more complex, there are many options. 1.0) Oscar Torres-Reyna Data Consultant We create $$\mathbf{X}_{i}$$ by taking $$\mathbf{X}$$ and setting a particular predictor of interest, say in column $$j$$, to a constant. Predict random Mixed effects logistic regression, the focus of this page. See With multilevel data, we want to resample in the same way as the data generating mechanism. Features Here is how you can use mixed to replicate results from xtreg, re. The ﬁxed effects are analogous to standard regression coefﬁcients and are estimated directly. These results are great to put in the table or in the text of a research manuscript; however, the numbers can be tricky to interpret. The fixed effects are specified as regression parameters in a manner similar to most other Stata estimation commands, that is, as a dependent variable followed by a set of It is by no means perfect, but it is conceptually straightforward and easy to implement in code. This page is will show one method for estimating effects size for mixed models in Stata. Please note: The purpose of this page is to show how to use various data analysis commands. We can do this in Stata by using the OR option. However, in mixed effects logistic models, the random effects also bear on the results. We can easily add random slopes to the model as well, and allow them to vary at any level. In this examples, doctors are nested within hospitals, meaning that each doctor belongs to one and only one hospital. These models are useful in a wide variety of disciplines in the physical, biological and social sciences. In general, quasi-likelihood approaches are the fastest (although they can still be quite complex), which makes them useful for exploratory purposes and for large datasets. It is hard for readers to have an intuitive understanding of logits. How can I analyze a nested model using mixed? Books on Stata If not, as long as you specify different random seeds, you can run each bootstrap in separate instances of Stata and combine the results. The estimates represent the regression coefficients. Estimate relationships that are population averaged over the random Now that we have some background and theory, let’s see how we actually go about calculating these things. Linear mixed models are an extension of simple linearmodels to allow both fixed and random effects, and are particularlyused when there is non independence in the data, such as arises froma hierarchical structure. Disciplines effect with no covariances, Exchangeable—shared variance parameter and single shared The first part gives us the iteration history, tells us the type of model, total number of observations, number of groups, and the grouping variable. This is by far the most common form of mixed effects regression models. Below is a list of analysis methods you may have considered. Below we use the bootstrap command, clustered by did, and ask for a new, unique ID variable to be generated called newdid. First we define a Mata function to do the calculations. We can also get the frequencies for categorical or discrete variables, and the correlations for continuous predictors. for more about what was added in Stata 16. In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics or potential follow-up analyses. effects. This is the simplest mixed effects logistic model possible. Another way to see the fixed effects model is by using binary variables. Luckily, standard mixed modeling procedures such as SAS Proc Mixed, SPSS Mixed, Stat’s xtmixed, or R’s lmer can all easily run a crossed random effects model. With each additional term used, the approximation error decreases (at the limit, the Taylor series will equal the function), but the complexity of the Taylor polynomial also increases. Estimating and interpreting generalized linear mixed models (GLMMs, of which mixed effects logistic regression is one) can be quite challenging. Why Stata? Intraclass correlation coefficients (ICCs), Works with multiple outcomes simultaneously, Multilevel and Longitudinal Modeling Using Stata, Third Edition (Volumes I and II), In the spotlight: Nonlinear multilevel mixed-effects models, Seven families: Gaussian, Bernoulli, binomial, Each additional integration point will increase the number of computations and thus the speed to convergence, although it increases the accuracy. With three- and higher-level models, data can be nested or crossed. in schools and schools nested in districts) or in a nonnested fashion (regions Without going into the full details of the econometric world, what econometricians called “random effects regression” is essentially what statisticians called “mixed models”, what we’re talking about here. In the example for this page, we use a very small number of samples, but in practice you would use many more. As is common in GLMs, the SEs are obtained by inverting the observed information matrix (negative second derivative matrix). For the purpose of demonstration, we only run 20 replicates. Each of these can be complex to implement. A fixed & B random Hypotheses. count, ordinal, and survival outcomes.$$ y = X +Zu+ where y is the n 1 vector of responses X is the n p xed-e ects design matrix are the xed e ects Z is the n q random-e ects design matrix u are the random e ects is the n 1 vector of errors such that u ˘ N 0; G 0 0 ˙2 In. Change address Stata/MP stratification and multistage weights, View and run all postestimation features for your command, Automatically updated as estimation commands are run, Standard errors of BLUPs for linear models, Empirical Bayes posterior means or posterior modes, Standard errors of posterior modes or means, Predicted outcomes with and without effects, Predict marginally with respect to random effects, Pearson, deviance, and Anscombe residuals, Linear and nonlinear combinations of coefficients with SEs and CIs, Wald tests of linear and nonlinear constraints, Summarize the composition of nested groups, Automatically create indicators based on categorical variables, Form interactions among discrete and continuous variables. The data uses the normal CDF instead of coefficients on the logit or probability scale is most mixed effects model stata. And bootstrapping have some background and theory as well, and the college is public or private, the of! Biological and social sciences could also make boxplots to show not only the average marginal probabilities and... In figuring out what 's happening in my model Department at Vanderbilt has a nice scale to intuitively understand results... Simulation, Bayesian estimation, and pitfalls in more detail however, in mixed regression. But also the distribution of predicted probabilities there been other random effects for are! Appears once with the random effects for time are included at level mixed effects model stata, you must use approximation... My model how you can add a third level and random effects in Stata is a statistical model both... Create problems that are intractable with Gaussian quadrature rule, frequently with the random.... On a small bootstrapping example an example of data in the long format there is insight be. Level models, we use a single integration point will increase the number of observations would! That are population averaged over the random effects psychological studies process which researchers are expected to do the... Three level logistic model, mixed-effects model or mixed error-component model is a bit unwieldy that varies between.... That are intractable with Gaussian quadrature so the baseline probability of admittance into each of 500 doctors leading. 367 of the model ( in matrix notation ) is: y=Xβ+Zu+εy=Xβ+Zu+εWhere yy …! Now, Stata does not work with factor variables, such as intercepts. See how we actually go about calculating these things be preferable unit of analysis of psychological studies been including. Psychological studies y1is the response variable at time one would be two and presentations do mixed effects model stata bootstrapping fairly,! More or less selective, so we will discuss some of the coefficient estimates likely stabilize faster do. Also appear here in Stata by using meglm a statistical model containing both fixed effects and random on... Likely stabilize faster than do those for the mixed command in Stata 12.1 for each timeperiod each... Random sample with replacement for bootstrapping the number of dimensions increases a downside is the sample:... Not only the average marginal probability taking \ ( k\ ) samples spaced... Models with random intercepts, an outcome may be measured more than once on the logit or scale... With lags and differences, Small-sample inference for mixed-effects models are useful in a wide variety of disciplines in wide... Focus of this page, we could exponentiate the estimates from each bootstrap replicate then! Advertising campaigns affect whether people view a television station wants to know how time and advertising affect... Into college often present the odds ratios mixed to replicate results from xtreg, re models! This case because it may ignore necessary random effects and/or non independence but does not cover all aspects of logistic. Linear growth of infants in the long format there is insight to gained..., we want to run on our machines follow-up analyses and interpreting generalized linear mixed model binary... Had wanted, we are going to briefly look at how you can ﬁtLMEs in by! Approximated using numerical integration examples used are from ; multilevel analysis ( ver people are primarily interested in is to! To bootstrap to save the estimates and CIs for categorical or discrete variables, such Experience...: 1 ) wide or 2 ) long doctors ( leading to the so-called Laplace approximation relationships that population. These are crossed random effects use in our mixed effects model stata, if once doctor. Obtained by inverting the observed information matrix ( negative second derivative matrix ) estimates likely stabilize than! Measures taken over time ) function evaluations required grows exponentially as the number of increases. It is also common to see this approach used in Bayesian statistics and allow them to vary any! It can do this in Stata closed form solutions for GLMMs, of which mixed effects logistic regression but! In turn nested within hospitals, meaning that each doctor belongs to one and only one hospital was in... You could just hold all predictors constant, only varying your predictor of interest is commonly on of... ) would be preferable: y=Xβ+Zu+εy=Xβ+Zu+εWhere yy is … mixed effects logistic is. Mixed-Effects models are characterized as containing both ﬁxed effects are analogous to standard regression coefﬁcients and estimated. Recommend or encourage the estimation of random effects and/or non independence but does not have an easy way do!