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How can I get p values from lmer?
You could use the package lmerTest. You just install/load it and the lmer models get extended. So e.g. would give you results with p-values. If p-values are the right indication is a little bit disputed, but if you want to have them, this is the way to get them.
What’s the best way to adjust the p value?
The simplest way to adjust your P values is to use the conservative Bonferroni correction method which multiplies the raw P values by the number of tests m (i.e. length of the vector P_values). Using the p.adjust function and the ‘method’ argument set to “bonferroni”, we get a vector of same length but with adjusted P values.
Is the p-value of an effect the right indication?
If p-values are the right indication is a little bit disputed, but if you want to have them, this is the way to get them. If you can handle abandoning p-values ( and you should ), you can compute a likelihood ratio that would represent the weight of evidence for the effect of status via:
Why do you use LME instead of lmer?
because you don’t need any of the stuff that lmer offers (higher speed, handling of crossed random effects, GLMMs …). lme should give you exactly the same coefficient and variance estimates but will also compute df and p-values for you (which do make sense in a “classical” design such as you appear to have).
Which is the best way to obtain the p-value?
You should really consider the Mike Lawrence answer because the whole idea of just sticking with a pass point for p-values as the final and most important information to extract from your data is generally misguided (but might not be in your case since we don’t really have enough information to know).
How do you know if a p value is statistically significant?
How do you know if a p-value is statistically significant? The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.
Why are there no p-values in mixed models?
It is tempting to believe that all statistical tech should provide a packaged results (e.g. p-values), but they do not. For example, you may have noted that summaries for model objects fitted with lmer list standard errors and t-statistics for the fixed effects, but no p-values.
How to obtain the p-value of an experiment?
You may also want to consider the random term ~status|experiment (allowing for variation of status effects across blocks, or equivalently including a status-by-experiment interaction). Posters above are also correct that your t statistics are so large that your p-value will definitely be <0.05, but I can imagine you would like “real” p-values.
Are there p values in the lme4 output?
The figure below shows the default output from lme4, which includes estimates, standard errors, and t values, but no p values. But current reporting standards are what they are in psychology, and people want p values.
How to get pvalues for fitted models in lme4?
The starred (*) suggestions provide finite-size corrections (important when the number of groups is <50); those marked (+) support GLMMs as well as LMMs. profile confidence intervals via profile.merMod and confint.merMod (CI,+)
Can a linear mixed model be included in lmer?
For the user of linear mixed effect models, such transparency is a boon. To illustrate, the figure below shows the output after loading the lmerTest package. A linear mixed model analyses using lmer will automatically include p values computed via the Satterthwaite approximation.
How to calculate p-values for estimated parameters?
One of the most frequently asked questions about lme4 is “how do I calculate p-values for estimated parameters?” Previous versions of lme4 provided the mcmcsamp function, which efficiently generated a Markov chain Monte Carlo sample from the posterior distribution of the parameters, assuming flat (scaled likelihood) priors.