![]() Null) 7 Residual #> Null Deviance: 3.119 #> Residual Deviance: 0.01267 AIC: 27.03 summary( glm_5) #> Analysis of Deviance Table #> #> Model: Gamma, link: inverse #> #> Response: lot1 #> #> Terms added sequentially (first to last) #> #> #> Df Deviance Resid. Null) 7 Residual #> Null Deviance: 3.513 #> Residual Deviance: 0.01673 AIC: 37.99 summary( glm_4) 300-2) clotting #> Call: glm(formula = lot1 ~ log(u), family = Gamma, data = clotting) #> #> Coefficients: #> (Intercept) log(u) #> -0.01655 0.01534 #> #> Degrees of Freedom: 8 Total (i.e. # A Gamma example, from McCullagh & Nelder (1989, pp. #> Analysis of Deviance Table #> #> Model: poisson, link: log #> #> Response: counts #> #> Terms added sequentially (first to last) #> #> #> Df Deviance Resid. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for poisson family taken to be 1) #> #> Null deviance: 10.5814 on 8 degrees of freedom #> Residual deviance: 5.1291 on 4 degrees of freedom #> AIC: 56.761 #> #> Number of Fisher Scoring iterations: 4 anova( glm_1) ![]() Null) 4 Residual #> Null Deviance: 10.58 #> Residual Deviance: 5.129 AIC: 56.76 summary( glm_1) ![]() # From ?glm # Dobson (1990) : Randomized Controlled Trial : ad #> Call: glm(formula = counts ~ outcome + treatment, family = poisson(), #> data = ad) #> #> Coefficients: #> (Intercept) outcome2 outcome3 treatment2 treatment3 #> 3.045e+00 -4.543e-01 -2.930e-01 1.338e-15 1.421e-15 #> #> Degrees of Freedom: 8 Total (i.e. Just load the package (and perhaps also ‘data.io’, or even the whole SciViews::R suite): So, the new generic function form() creates formatted tables or other objects (lists, …) to be directly integrated in an R Markdown document, or to be used at the R Console. Moreover, none of kable() or pander() functions allow for the translation of the result in a different language, and they also do not take into account attributes like label or units (see the ‘data.io’ package). However, the syntax is not always easy, and code eventually differ depending on the situation among the four possible ones here above. In a knitted R markdown file, HTML format,īoth ‘knitr’ with kable(), and the ‘kableExtra’ functions, and ‘pander’ with the pander() functions do that.Inside an R Markdown/Notebook inline area just beneath a chunk,. ![]() Here, we reuse existing functions like knitr::kable() or pander::pander(), but we make them simpler to use and working in all cases being: Among the ‘SciViews’ R packages, ‘form.io’ would be devoted to the input and especially output of textual data (input of tabular data being managed by ‘data.io’). ![]()
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