similar to: Obtaining the cross validation coefficient of determination

Hello all- I would appreciate any guidance that can be provided. I am new to R and am using it exclusively in a statistics program I am undertaking that mainly references Minitab. My focus is on data modeling and further more multivariate data analysis as much of my work in involves chemical measurements from custom sensors using all sorts of transduction methods. I am looking for a

Hello R users wiser than I - I am trying to produce a boxplot with quantiles defined by the type 6 algorithm used mainly by Minitab and SPSS for a comparison study. I found how to compare the results in tabular form using the quantile(x, type = 6) function, but am stuck on how to show the results in a summary boxplot form. Would someone be able the help with this situation? Many thanks, Matt

R users: I am trying to replicate the boxplot output I achieve with Minitab in R. I realize that R gives the user many more options on the algorithm used to calculate the IQR than Minitab, so I concentrated on type=6 when using the quantile() function in R. The problem I am having is setting the upper and lower limit of the whisker based on the nearest actual data that should be included. If

You should read the section on Indexing in the Introduction to R document that comes with R, regarding $ and `[[`. -- Sent from my phone. Please excuse my brevity. On August 1, 2017 2:44:18 AM PDT, Dimlak Gorkehgz <rain8dome9 at gmail.com> wrote: >You are right, maintainer does keep a list of model's packages. > >So how do I use a variable instead of $adaboost$? >

You are right, maintainer does keep a list of model's packages. So how do I use a variable instead of $adaboost$? getModelInfo()$adaboost$library Also, server not found: http://rwiki.sciviews.org/doku.php?id=getting-started:reference-cards:getting-help On Tue, Aug 1, 2017 at 11:46 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > I have provided you all the

I am curious as to the "lm" calculation of R2 (multiple coefficient of determination, I assume) when intercept is zero. I have 18 data points, two independent variables: First, a model with an intercept: > mod0=lm(Div~Rain+Evap,data=test) > summary(mod0)$r.squared [1] 0.6257541 > cor(predict(mod0),test$Div)^2 [1] 0.6257541 The $r.squared and the result from "cor"

Dear R-users, I used lm() to fit a standard linear regression model to a given data set, which led to a coefficient of determination (R^2) of about 0.96. After checking the residuals I realized that they follow an autoregressive process (AR) of order 1 (and therefore contradicting the i.i.d. assumption of the regression model). I then used gls() [library nlme] to fit a linear

Dear all, Is there a instruction that can help me obtain the coefficient of determination R^2 after doing linear/nonlinear regression using lm/nls? [[alternative HTML version deleted]]

Antonio, For linear regression we have the following identity total SS = regression SS + residual SS (*) where total SS is the sum of squares of observations around their mean, i.e. total SS = (n-1)*var(y) and residual SS is given by the deviance function. R-squared is defined as R^2 = regression SS/ total SS = 1 - residual SS/total SS. You can use this last formula

Hello everyone, I have estimated system of three linear equations with one non-linear restrictions with nlsystemfit. I was wondering how I can calculate the R-squared (or some alternative coefficient of determination) for the whole system. This is automatically given by linear systemfit but not by nlsystemfit. I can get the values for each of the equations separately, but apparently not for

As a long time nonlinear modeller, I always compute a quantity commonly referred to as R_squared or the coefficient of determination. However, I agree with other commentators, including those of several years ago, that one wants to be very careful about interpretation. In fact, I would say "DO NOT interpret". My usage of the quantity, which I tend to call "R_squared" but do

Dear, I want to compute coefficient of determination (R-squared) to complement AIC for model selection of multivariable GLM. However, I found this is not a built-in function in glm. neither is it available through reviewing the question in the R-help archive. Please kindly help and thanks a lot. Elaine [[alternative HTML version deleted]]

If I'm not mistaken, partial R-squared is the R^2 of the quantities plotted in a partial residual plot, so you can base the computation on that. Prof. Fox's `car' package on CRAN has a function for creating those plots, but you need to figure out the way to extract the quantities being plotted. [In any case, the basic tools for doing such computations are all in R, and it

Dear all, I'm modeling growth curve of some ecosystems with respect to their rainfall-productivity relationship using a simple linear regression (ANPP(t)=a+b*Rain(t)) and a modified version of the Brody Model ANPP(t)=a*(1-exp(-b*rain(t))) I would like to know why the "best model" is function of the criteria that I use (maximizing the fit using R2 or testing the Null hypothesis with

Dear Developers, I am a data science student. I wish to implement Neuro fuzzy Classifier, Adaboost, MLkNN Multi label algorithms for web document classification. I could not find packages and steps for above mention algorithms in mlr <https://mlr-org.github.io/mlr-tutorial/devel/html/multilabel/index.html>. Hence i request you to give your valuable suggestions. In need of your guidance,

Hi, Dirk: On 12/20/2016 10:56 AM, Dirk Eddelbuettel wrote: > On 20 December 2016 at 17:40, Martin Maechler wrote: > | >>>>> Steve Bronder <sbronder at stevebronder.com> > | >>>>> on Tue, 20 Dec 2016 01:34:31 -0500 writes: > | > | > Thanks Henrik this is very helpful! I will try this out on our tests and > | > see if gcDLLs()

Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get ?NA? in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached

>>>>> Steve Bronder <sbronder at stevebronder.com> >>>>> on Tue, 20 Dec 2016 01:34:31 -0500 writes: > Thanks Henrik this is very helpful! I will try this out on our tests and > see if gcDLLs() has a positive effect. > mlr currently has tests broken down by learner type such as classification, > regression, forecasting,

Hello, I'm with a conceptual doubt regarding Rsquared of both lm() and postResample(library caret). I've got a multiple regression linear model (lets say mlr) with anR² value of 67.52%. Then I use this model pro make predictions with predict() function using the same data as input , that is, use the generated model to predict the value associated with data that I used as input. Next, if

This is a request to increase MAX_NUM_DLLS in Rdynload.c in from 100 to 500. On line 131 of Rdynload.c, changing #define MAX_NUM_DLLS 100 to #define MAX_NUM_DLLS 500 In development of the mlr package, there have been several episodes in the past where we have had to break up unit tests because of the "maximum number of DLLs reached" error. This error has been an inconvenience that