![]() ![]() Restricted permutations using the permute package Vegan (≥ 2.0-0), testthat (≥ 0.5), parallel, knitr, rmarkdown, bookdown, sessioninfo Usage numPerms(object, control how()) Arguments Details Function numPermsreturns the number of permutations for the passed objectand the selected permutation scheme. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak. numPermscalculates the maximum number of permutations possible under the current permutation scheme. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. It seems that with poor models R is more restrictive and does not show standard errors.Permute: Functions for Generating Restricted Permutations of DataĪ set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. allPerms returns a matrix con- taining all possible permutations, possibly containing the observed ordering (if argument observed is TRUE). ![]() ![]() So if your starting values are around R 1.5 and k 1.5, your code should find the maximum likelihood estimates (the green point) and not go near. If you have any suggestions on how I could provide a replicable example (maybe with some sample panel data, any suggestions?), I will gladly do so.Ī smaller model produces identical results in R and Stata. For your example with test1, the log likelihood surface looks like the following: You were examining R 0.02 and k 0.25 (the point in red) which is far away from the maximum likelihood solution. Provides the generic function and methods for permuting the order of various objects including vectors, lists, dendrograms (also hclust objects), the order of observations in a dist object, the rows and columns of a matrix or ame, and all dimensions of an array given a suitable serpermutation object. it possible to interpret the R statistic directly as an absolute measure of the strength of. Group variable: id Number of groups = 15,945 A google search suggests that the Rmpfr::igamma function might be what you want if you cannot work with the log of the gamma function: Rmpfr::igamma (171, 0) R> 1 'mpfr' number of precision 53 bits R> 1 7.257415615307999e+306 Rmpfr. ![]() The returned object contains component control, an object of class 'how'suitably modified if checkidentifies a problem. The issue is: why do I get standard errors in Stata but "Inf" in R? Conditional fixed-effects Poisson regression Number of obs = 110,233 Otherwise you will need to use a third party library which has higher precision than R's floating points. It calculates the maximum number of possible permutations for the number of observations in objectand the permutation scheme described by control. The results in Stata (see below) yield standard errors and where the standard errors are significant the coefficients are very much the same as in R, so I assume in both cases the same model was calculated. Xtpoisson x1 x3 x3_lag x3_lag2 season x4 x1#x3_lag x1#x3_lag2 x1#x4, fe performance data and returns reports to help customer manage system. My code in R is (formula shortened for illustration): library(pglm) Disk Busy KBPS TPS KB-R ART MRT KB-W AWT MWT AQW AQD. Unfortunately I cannot upload and share the data due to legal restrictions. I'm running a fixed-effects Poisson regression and get different results in Stata and R. ![]()
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