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...available before the end of the year. The new version of the car package has a number of new functions and data sets, along with many changes to old functions, including a change in naming conventions, using "camel-case" rather than period-separated names: for example, av.plots() is now avPlots(). For the time-being, old function names, such as av.plots(), are available as deprecated aliases. A few functions from the alr3 package have also been rewritten, renamed, and are now part of car. The alr3 package will be updated later in the year. Regards, John --------------------------------...

...available before the end of the year. The new version of the car package has a number of new functions and data sets, along with many changes to old functions, including a change in naming conventions, using "camel-case" rather than period-separated names: for example, av.plots() is now avPlots(). For the time-being, old function names, such as av.plots(), are available as deprecated aliases. A few functions from the alr3 package have also been rewritten, renamed, and are now part of car. The alr3 package will be updated later in the year. Regards, John --------------------------------...

Hello, R-Help answers questions on R code, your question is about statistics. You should try posting the question to https://stats.stackexchange.com/ Hope this helps, Rui Barradas Em 23-10-2017 18:54, kende jan via R-help escreveu: > Dear all, I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...). I have

Dear all,?I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...).?I have realised a sqrt(variable) transformation...?The results are great, but I don't know how to interprete the beta coefficients... Is it possible to do another transformation to get interpretable beta coefficients to express the variations in

Hello, I appreciate this is likely to be an easy question. I am trying to obtain the residuals from a linear regression where the line is forced to have a 1:1 relationship. An example of the data: A<-c(0.9803922, 1.3850416, 0.8241758, 0.0000000, 0.4672897, 1.1904762, 0.0000000, 0.9456265, 1.5151515) B<-c(1.3229572, 1.9471488, 1.3182674, 0.7007708, 1.0185740, 1.0268562, 0.8695652,

Muy buenas, quiero calcular correlaciones de Pearson entre dos variables (a,b) teniendo en cuenta una tercera (c). Para ello estoy usando una función llamada "pcor.test" (http://www.yilab.gatech.edu/pcor.html), que en realidad no está en ningún paquete de R, que yo sepa. ¿Alguien conoce una función similar en alguna librería de R? Por otro lado, para ver si me cuadraban los resultados,