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Latent profile analysis in r

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latent profile analysis in r

Search forums. Log in. For a better experience, please enable JavaScript in your browser before proceeding. Latent Profile Analysis in R - Is it possible? Thread starter Samstats Start date Aug 28, Tags latent profile anaysis lpa polca. Samstats New Member Aug 28, Hello Forum, I am trying to run latent profile analysis for a work project. The measure includes 8 continuous variables 5-point Likert scale composites and I am having trouble finding an appropriate statistical package for R.

I have seen many posts referencing poLCA as a great package for doing latent class analysis, however my measures are not polytomous in the sense that they have a great number of possible responses since they are continuous. Is there a stats package for R that can do LPA? Thanks, Sam.

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Lazar Phineas Packard Aug 28, I assume openMX can do it but I am not sure how. PS- poLCA is pretty awesome to work with but i'm also a fan of flexmix. Does poLCA and flexmix require the variances to be invariant across classes? If so I am not sure you would want to use them. You must log in or register to reply here.Where questions were not asked they need to be excluded from any analysis as the whole point of techniques like latent class analysis and cluster analysis is to identify respondents with similar data and using variables only asked of some of the respondents is at odds with this.

The other two types of missing data can, in theory, be addressed by both cluster analysis and latent class analysis but, in practice, only latent class analysis programs can reliably be used to form segments in data containing missing values.

All of the well-known cluster analysis algorithms assume that there are no missing values.

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As this is often not the case a variety of solutions have been developed for addressing instances where there are missing values. Imputation is surprisingly often used to impute replacement values for the missing values. This is 'surprising' in that it is extremely dangerous.

The whole point of cluster analysis is to find respondents that are similar and the assumptions of imputation creates artificial similarities. When missing values are replaced with averages the result is that respondents with higher numbers of missing values are guaranteed to be more similar to each other i.

Where predictive models and many standard imputation techniques are used the same problem occurs, albeit to a lessor extent, due to regression to the mean.

latent profile analysis in r

When stochastic models are used for imputation there is the reverse problem, with randomization being added to the data and thus more randomization for people with more missing data making them less likely to be grouped together. Better practice than imputation is to assign observations to the most similar cluster based using the non-missing data.

This is the approach that is built into most cluster analysis algorithms that purport to deal with missing values e. Although preferable to imputation, this approach implicitly makes the assumption that the data is Missing Completely At Random when forming the segments and then makes a different assumption, that of Missing At Randomwhen assigning respondents to the clusters. The problems that this leads to are best appreciated by examining the following data, in which a.

If carefully examining this data you will likely conclude that there are three clusters: the first two observations are in one cluster with means on the four variables of 1, 2, 3 and 4. The second cluster consists of observations 3 and 4 with means of 4, 3, 2 and 1, while the third cluster consists of observations 5, 6, 7 and 8 and has means of 1, 2, 2 and 1. However, if using the nearest neighbor imputation approach this is not what will be uncovered.

For example, when using SPSS to do the cluster analysis it is only able to find the first two clusters and it ends up assigning observations 5 and 7 to the first cluster and observations 6 and 8 to the second cluster.

This occurs because the cluster analysis forms the clusters using only the data that is complete and this contains no observations from the third cluster. And, consequently, the observations that should be in this third cluster can only be assigned to the first two clusters. This approach involves forming the clusters using the observations with complete data and then using a predictive model, such as Linear Discriminant Analysis to predict the segments for observations that have some missing values.

In terms of the assumptions regarding missing data, this approach is identical to using nearest neighbor assignment. Nevertheless, this method is inferior to nearest neighbor assignment as generally the predictive models make different assumptions to cluster analysis and this leads to a compounding of errors. Whereas cluster analysis is technically only valid in the presence of data that is Missing Completely At Randomlatent class models can, in principle if not practice, be applied with any type of data.

Due to certain features of the underlying maths of latent class analysis it is standard practice to program software to make the Missing At Random assumption. The consequence of this is that it will generally do a substantially better job at addressing missing values than can be achieve by cluster analysis.

For example, considering the data set used above to illustrate the problems of nearest neighbor assignmentwhen this same data set is analyzed using latent class software e. Latent class models can even be used in some situations when the missing values are Nonignorable. This is done by treating the variables containing missing values as being categorical variables and treating the missing values as being another category.

This approach is not always effective, particularly where the variables are truly numeric.

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Find and Replace. Learning Displayr.All the other ways and programs might be frustrating, but are helpful if your purposes happen to coincide with the specific R package. For example, I found at least 15 packages involving latent class models, of which only six perform latent class analysis in the form of classification based on indicators, and only two of them allow including nominal indicators, and none allows including ordinal indicators.

The meaning of the latent classes here is different as they are based not on the responses of respondents, but on the effects of one variables on other. I do not discuss them. These models look for homogeneous in terms of responses classes which differ by responses. Optionally, one can add some predictors and distal outcomes variables that depend on classes but not their indicators. R packages capabilities. NONE can apply 3-step approach in adding covariates.

Latent classes based on nominal responses onlymay add predictors of all latent classes in one stage. Nice features:. I use WVS round 5 data from Canada and three items on trust: generalized interpersonal point trust scale, and two 4-point scales of trust to family and trust to strangers. The input commands are very simple, excluding an odd need to cbind the LCA indicators.

All the other functions worked smoothly. And here is a solution from Daniel Oberski slide 66called Entropy R The package allows to fit various kinds of hidden Markov models, to which LCA model is also generalized. Nice features. With a little messy function I could plot these conditional probabilities:. Expanding example to mixed distributions. The second indicator is continuous, so it has two parameters Re2.

I hope there will be some progress and they will find a way to keep developing lcca. Example: WVS data from Canada, three trust items.

The basic input commands are similar to poLCA:. When adding predictors make sure to change the function to lca. A specific post about multiple groups LCA in different software will follow. Thanks for pointing it to me, Aline, I will definitely have a look and make an update to my post. You must be logged in to post a comment.

Nice features: simple input. Classes are called states. Some basic functions are needed to be specified manually: There is no easy way to plot probabilities; no G2 or X2 values, no entropy.In statisticsa mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs.

Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information.

Some ways of implementing mixture models involve steps that attribute postulated sub-population-identities to individual observations or weights towards such sub-populationsin which case these can be regarded as types of unsupervised learning or clustering procedures.

However, not all inference procedures involve such steps. Mixture models should not be confused with models for compositional datai. However, compositional models can be thought of as mixture models, where members of the population are sampled at random. Conversely, mixture models can be thought of as compositional models, where the total size reading population has been normalized to 1.

A typical finite-dimensional mixture model is a hierarchical model consisting of the following components:. In addition, in a Bayesian settingthe mixture weights and parameters will themselves be random variables, and prior distributions will be placed over the variables. In such a case, the weights are typically viewed as a K -dimensional random vector drawn from a Dirichlet distribution the conjugate prior of the categorical distributionand the parameters will be distributed according to their respective conjugate priors.

This characterization uses F and H to describe arbitrary distributions over observations and parameters, respectively. Typically H will be the conjugate prior of F. The two most common choices of F are Gaussian aka " normal " for real-valued observations and categorical for discrete observations. Other common possibilities for the distribution of the mixture components are:. A typical non-Bayesian Gaussian mixture model looks like this:. A Bayesian version of a Gaussian mixture model is as follows:.

A Bayesian Gaussian mixture model is commonly extended to fit a vector of unknown parameters denoted in boldor multivariate normal distributions. In a multivariate distribution i. Note that this formulation yields a closed-form solution to the complete posterior distribution. Such distributions are useful for assuming patch-wise shapes of images and clusters, for example.

The Relationship Between Cluster Analysis, Latent Class Analysis and Self-Organizing Maps

One Gaussian distribution of the set is fit to each patch usually of size 8x8 pixels in the image. A typical non-Bayesian mixture model with categorical observations looks like this:. A typical Bayesian mixture model with categorical observations looks like this:. Financial returns often behave differently in normal situations and during crisis times.

A mixture model [3] for return data seems reasonable. Sometimes the model used is a jump-diffusion modelor as a mixture of two normal distributions. See Financial economics Challenges and criticism for further context. Assume that we observe the prices of N different houses. Different types of houses in different neighborhoods will have vastly different prices, but the price of a particular type of house in a particular neighborhood e.

Fitting this model to observed prices, e. Note that for values such as prices or incomes that are guaranteed to be positive and which tend to grow exponentiallya log-normal distribution might actually be a better model than a normal distribution. Assume that a document is composed of N different words from a total vocabulary of size Vwhere each word corresponds to one of K possible topics.

Missing Values in Cluster Analysis and Latent Class Analysis

The distribution of such words could be modelled as a mixture of K different V -dimensional categorical distributions. A model of this sort is commonly termed a topic model. Note that expectation maximization applied to such a model will typically fail to produce realistic results, due among other things to the excessive number of parameters.

Some sorts of additional assumptions are typically necessary to get good results.

latent profile analysis in r

Typically two sorts of additional components are added to the model:.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. My dataset contains the manifest variables used to derive the clusters as in any other LCA modelwhich are categorical.

Found packages that do the trick in deriving the classes and that can also incorporate covariates in a regression to see whether they are related to the classes i. My problem is that I haven't found a way to incorporate distal outcomes in any R packages that deal with LCA.

Does anyone have any experience in implementing distal outcomes in any existing R packages or any way to get around to this? I don't know of many statistical software packages in general that implement latent class analysis with distal outcomes. However, sometimes we aren't interested in that. Estimating that association isn't a trivial problem, because the latent class model doesn't estimate that directly. The paper also states how to generalize this to categorical or count outcomes.

I'm not sure how to implement that in R, and I'm not sure which if any latent class packages implement this. Frequently, you will see people determine which class each observation is most likely to belong to i. I would suggest that if your latent classes are well-separated and the model is fairly certain about which class each observation belongs to e.

Alternatively, you i. However, at minimum, you want to make sure you have enough quasi-imputations to cover the number of classes you estimated, accounting for their prevalences as well. What if you have 6 latent classes? What if one is very small e. Lanza et al suggest a minimum of 20 imputations, but in some contexts, I have wondered if you would need more.

MPlus implements the multiple imputation-like framework no personal experience, but Lanza et al state this. I believe Latent Gold commercial software may implement quasi-MI also.There are four main types of algorithms in use for cluster-based segmentation :. Where the goal is to form segments, latent class analysis is almost always preferable to any of the other algorithms.

Indeed, the other algorithms should generally be regarded as "plan B" algorithms, only used when latent class analysis cannot be used. This is because latent class analysis has important strengths relative to the other algorithms, whereas the other algorithms have no substantive advantages over latent class analysis. However, as ultimately segmentation is part art and part science, it is often the case that the other algorithms can lead to useful and even superior solutions to those obtained from latent class analysis, so the best approach is to use latent class analysis if in a rush but to consider multiple different segmentation where time permits.

As discussed below, k -means cluster analysis can be viewed as a variant of latent class analysis. Its only advantage over latent class analysis is that it is much faster to compute which means that with huge database k -means can be preferable.

Hierarchical cluster analysis can produce a dendrogram i. Self-organizing maps create clusters that are ordered on a two dimensional "map" and, where a large number of clusters are created, this can be beneficial from a presentation perspective. While each of these advantages can be relevant in some circumstances they are, by and large, irrelevant in most segmentation studies which is why latent class is, in general, superior. Each of latent class analysis, k -means cluster analysis and self-organizing map algorithms have an almost identical structure: [note 1].

Step 1: Initialization. Observations are assigned to a pre-determined number of clusters. Most commonly this is done randomly either by randomly assigning observations to clusters or by randomly generating parameters. However, it can involve assigning respondents to pre-existing groups e. In the case of self-organizing maps, each cluster is assigned a location on a grid e.

Step 2: Initial cluster description. A statistical summary is prepared of each cluster. With k -means and self-organizing maps this involves computing the mean value for each variable in each cluster. Latent class analysis also typically involves computation of the means, occasionally measures of variation e. Step 3: Computing the distance between each observation and each cluster. A measure of the distance between each observation and each cluster is computed.

latent profile analysis in r

With latent class analysis, a probability of cluster membership is computed; this probability takes into account both the distance from of each observation from each cluster and the size of the cluster.

Step 4: Revising the cluster descriptions. Using the result of Step 3 the cluster descriptions are updated.

Ways to do Latent Class Analysis in R

This occurs in slightly different ways for each of the algorithms:. Step 5: Iteration to convergence'. Step 3 and 4 are referred to jointly as an iteration. They are repeated in a continuous cycle until the descriptions stabilize which is referred to as convergence. In the case of cluster analysis, this may take only a few iterations.

In the case of latent class analysis and self-organizing maps, it can take hundreds or thousands of observations.Mplus Home. Search Help. I have a question regarding Latent Profile Analysis. I have several measures of child "executive function" that include behavior e.

These measures are popular in the field and are measured on different scales and thus have different variances. Is it a requirement that all items are measured on the same scale and have similar variances?? Thanks in advance.

Linda K. No, it is not a requirement that all items be measured on the same scale and have the similar variances. Putting items on the same scale may, however, help convergence. Hi again. I tested the adequacy of my three class latent profile model by giving each class different start values to make sure the solution I got was the "right" one. The model appeared stable results and log likelihood.

Next, I simply changed the order of class 1 and 2 and kept the same start values so, in my mind the results shouldn't have changed just the order of the results--what were class "2" results should have become class "1" results. Is there something I have overlooked or should be concerned about given the changes in results? My operating assumption was that the start values were important, not the order of the classes by the way, I am using actual start values, instead of 1 and -1, for convergence, it helps because the items I am using are on different scales.

I would need to see both of the outputs to answer this question. Please send them to support statmodel. Thanks for the offer to look at my output. However, I discovered that the issue I raised was a mistake on my part.


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