According to the web search results, TAM stands for Test Analysis Modules and it is a package for R that includes marginal maximum likelihood estimation and joint maximum likelihood estimation for unidimensional and multidimensional item response models. The package functionality covers various models such as the Rasch model, 2PL model, 3PL model, generalized partial credit model, multi-faceted Rasch model, nominal item response model, structured latent class model, mixture distribution IRT models, and located latent class models. Latent regression models and plausible value imputation are also supported.

To interpret your findings, you may want to look at the documentation of the TAM package (1) which provides detailed information on the functions and parameters of the package, as well as some examples and tutorials. You may also want to check the references cited in the documentation for more theoretical background on the models and methods implemented in TAM.

(1) TAM: Test Analysis Modules - The Comprehensive R Archive Network. https://cran.r-project.org/web/packages/TAM/TAM.pdf

Amin Kordi yoosefinejad, at their core, models are grounded in the estimation of model parameters and the accompanying fit statistics which can help to determine the appropriateness of the estimates for the model parameters. In the case of simple Rasch analysis, these elements are being estimated: person ability and item difficulty. The beauty of the Rasch approach is that both forms of these estimates are on the same scale.

The output of the TAM package should allow you to review all of the person ability statistics as well as their accompanying model fit statistics. In addition, there should be an output that displays the item difficulty statistics as well as their accompanying model fit statistics. After you review those two sets of results, you should be able to make decisions about your instrument.