RMS stands for 'root-mean-square' is a measure of the typical size of a varying quantity. It occurs in the n-denominator form of standard deviation (the RMS deviation from the mean)

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Using {rms} package for multinomial logit

Is it possible to use the rms package to model multinomials logits, or elsewise to model several binary logits to achieve the same effect? I am aware that there are many other packages specifically ...
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47 views

Visualizing nonlinear regression

I have following model using mtcars dataset: ...
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32 views

What measure to use in finding the best linear model

I have a bunch of linear models (say 20 of them), and a bunch of datasets (e.g. 400). I wrote a code in R so that each dataset is exposed to each model, and the goal is to select the best model that ...
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43 views

KS, RMSE tests in R

I have a set of yearly peak river flows and I am trying to use the KS test to conclude if the data is coming from a generalized extreme value (GEV) distribution. I am also trying to calculate a root ...
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8 views

How do you compare RMSE after logging the y (the dependant variable)?

In my intro stat class, I'm told that it is difficult to compare RMSE after logging the y. However, after checking the sktest, the data appears to not be normal. So, in order to normalize the y ...
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27 views

Can there be a situation where one regression model gives lower RMSE than the other but also lower R-squared?

Consider the following scenario where you use the same data X (the same number of predictors p, same number of observations ...
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59 views

what is the meaning of RMSE in caret::train [duplicate]

I'm confused by the exercise solutions of the book Applied Predictive Modeling. In https://github.com/topepo/APM_Exercises/blob/master/Ch_06.pdf at the beginning of page 4 ...
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19 views

Why is the Multivariate RMSD “normalised” differently to the NMB?

For the multivariate case in regression, and also in other model predictions, the Root Mean Squared Deviation (RMSD or RMSE) is normalised by $n-p-1$, giving, $$RMSD = \sqrt{\frac{\sum_{i=1}^n ...
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30 views

Leave one out cross validation error term interpretation

I have a dataset that involved 70 participants and 7 variables (1 y variable and 6 explanatory variable). I have used leave one out cross validation to assess the model and have resulted in an answer ...
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37 views

Recommendation system and baseline predictors

I'm participating in programming contest, where I have a data, and where the first number is a user, second number is a movie, and the third is a number in then-points rating. ...
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34 views

RMSE normalisation, what method prefer?

According to this article on wikipedia http://en.wikipedia.org/wiki/Root-mean-square_deviation, two methods are widely used to normalise the RMSE. The first is dividing by the range: $$NRMSE = ...
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49 views

Comparing linear and nonlinear models

Is it possible to compare between these two types of model? I have a set of data that involves 6 independent variables and 1 dependent variable. It is based of a questionnaire for social science ...
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60 views

Coefficient of determination of two time series with RMSE

I have two time series $ts_{1}$ and $ts_{2}$ of length $N$. $ts_{1}$ is obtained from a statistical model and has an estimated RMSE computed using cross-validation. $ts_{2}$ is independant from ...
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192 views

Smaller standard errors *after* multiple imputation?

I have 1771 observations, with 30% missing data for x1 (Yes:No), and no other missing values from 26 other variables (mix of continuous and factor). I am using quantile regression in R, with and ...
2
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1answer
767 views

How to interpret error measures in Weka output?

I am running the classify in Weka for a certain dataset and I've noticed that if I'm trying to predict a nominal value the output specifically shows the correctly and incorrectly predicted values. ...
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1answer
82 views

MSEP and R2pred for Linear Model

I have two set of data 1-Training (Calibrating) 2-Test. With these datasets, I Fit the model using first dataset. predict using the second dataset x-variables I have to test the closeness of the ...
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115 views

Multiple Imputation - Help Needed

These multiple imputation results relate to data I have previously described and shown here - Skewed Distributions for Logistic Regression Three variables I am using have missing data. Their names, ...
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29 views

Absolute Loss in R

I have been asked to compared between Robustness of absolute lost regression and its variants compared to least squares. I have done the least squares should I use Lasso now? ...
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66 views

Calculation of RMSE for Weibull distribution model

I'm currently studying Weibull distribution and I have questions regarding 'statistical tests' (RMSE for now) for Weibull distribution. First, I want to be clear on one thing here. What does 'actual ...
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42 views

What is the “pdm” stat in the “rms” R package?

I am new to the world of Regression in statistics and I have been doing a research in which I am building an ordinal logistic regression model (ORM). In order to fit my ORM model, I am using the 'orm' ...
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64 views

Coefficient of determination contradicts the normalized RMSE

I have a model that allows me to estimate the values of a signal and I’m testing it under different environmental conditions. To compare my estimated signal to the real (reference) signal I’m using ...
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18 views

How to separate model errors from measurement errors?

I've got data measured with errors that have known measured_RMS. I am testing a model that has model_RMS differences between model prediction and measured data. What would be a reasonable estimate of ...
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17 views

Using confidence level for error evaluation

I want to evaluate performances of a forecasting system. I have some samples of the whole population. But what i actually have is the mean value of the sample and the forecast for the mean of the ...
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10 views

Accuracy Assessment: Do I still have to use any other test statistics?

I have a training set and a separate test set. In both sets, I have extracted two different parameters and I have compared the predicted values of these parameters to the actual values. So, I have ...
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16 views

multimodel inference when using rms package

I would be glad to have some advise about how to proceed with multimodel inference to obtain weighted estimates based on AICc after running ordinal logistic analyzes with "rms' package. I used the ...
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2answers
468 views

Evaluating predicted vs observed - RMSE vs. Pearson's R interpretation

I'm evaluating the error in three cross-validated models plotting observations against predictions. To do so, I'm comparing the RMSE (root-mean-squared-error) and the Pearson's R between predictions ...
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2answers
61 views

Is it possible for a predictor to have low correlation but low rmse as well

I have this strange condition. I have two predictors. One of the predictors has low correlation with the target but less rmse. On the other hand another predictor has high correlation but high rmse as ...
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1answer
131 views

Normalized RMSE

I have several time-series in a VAR(1) and, due to some of them haven't the same unit of measure, I'd like to estimate the RMSE in percentage. I know that it could be done in several ways (see below) ...
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42 views

Equivalence in model performance metric like RMSE

I calculated the root mean squared error (RMSE) to compare the simulated values of a hydrologic model with the corresponding observations for three observational datasets.The number of data points in ...
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327 views

how to calculate Root Mean Square Error (RMSE) for predicted Probability Density Function (PDF) in Matlab

I have used Mixture Density Networks for probability density function prediction. I am wondering how I can calculate Root Mean Square Error (RMSE) of predicted pdf in MATLAB. Thanks.
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17 views

error on truncated rms

I am computing the RMS of a sample to estimate the standar error $\sigma$ of the underlying distribution (for simplicity let say a normal distribution $N[\mu$, $\sigma$]). $ \text{RMS} = \sum_{i=1}^N ...
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61 views

Constant RMSE margin between training and teseting set

I have a large number of independent datasets of varying size but same feature meaning. Features and outcome are both binary. I am trying to fit logistic regression to the data. I estimate ...
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1answer
207 views

Individual and overall RMSE for multivariate data

I have a dataset which contains missing values, and I'm using imputation packages (Rs mi and ...
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1answer
40 views

Inconsistency between RMSE and 95% CI Coverage

I am running simulations to compare different weighting methods to estimate the mean of y (with missing values). I use bias, RMSE and 95% CI Coverage as my performance metrics. However, looking at ...
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79 views

Average of root mean square error

Is taking the average of different rmse valid? for example average rmse = (rmse1+rmse2+rmse3)/3 Thank you for your help!
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1answer
252 views

Looking for help with ITSM software (or other comparable software)

I'm looking for someone who is familiar with the ITSM software. I have some data that needs to be fit with an ARIMA/SARIMA model and then forecast using Holt-Winters/Seasonal method. I then need to ...
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109 views

Forecast accuracy – can we use correlation and $t$-tests?

Does it make sense to compare actual vs. forecast using correlation analysis / see how close $R^2$ is to 1? Does it make sense to use a paired t-test to test actual vs. forecast to get accuracy of ...
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1answer
56 views

Confusion related to variance and mse

I was reading this wikipedia article and it states that MSE of a predictor is equivalent to variance of the error. To test it I did something like this ...
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2answers
138 views

rms validate on models with a predict function such as coxph and glmnet

I would like to use bootstrapping to evaluate models generated by coxph and glmnet. Would that be somehow possible with rms validate ? From the documentation it seems limited to rms functions (cph, ...
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1answer
548 views

How to use rfe object with function pickSizeTolerance in R package caret

I run caret's recursive feature selection with randomForest. While running rfe function with method repeatedcv, I had parameter ...
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1answer
364 views

What are RMSE SD and Rsquared SD metrics in resampling results using R package:caret?

I've been doing predictive modelling with R package caret. When resampling regression models, I get the traditional RMSE and Rsquared metrics, but also RMSE SD and ...
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32 views

Calculate RMS for regression line

The RMS for the regression line is: $\sqrt{(1-r^2)}\cdot sd(y)$ where $sd(y)$ is the standard deviation of $y$ -- let's call this eq 1. Another way of calculating it is: ...
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2answers
513 views

What is the RMSE of k-Fold Cross Validation?

I am testing a neural net to predict numeric values. For that i am using a Training,Validation and Test split. I made a manual 4-Fold CV, this means i am getting 4 RMSE error, each one is the error ...
4
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1answer
306 views

Is there a computationally less expensive way to calculate RMS error between two signals?

Root-mean-square error (RMS error) between two signals can be calculated as given: ${\text{RMS}(x_\textrm{ actual}(t)-x_\textrm{ reference}(t))}$ When you want to calculate within a sliding window, ...
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2answers
2k views

Calibration of Cox regression survival analysis

To perform calibration of a Cox regression model (i.e. assessing for the agreement between the predicted and the observed outcome), what is the best method to present the accuracy of the model in ...
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121 views

RMSE, MAD of vectors

I have a set of $N$ high-dimensional vectors. I use some approximation routine to make my output faster. Now I would like to evaluate the error of the approximation. Typically I use the RMSE to ...
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249 views

SE of RMSE in R

I have crossvalidated my models and measured RMSE between the modelled values and reality: RMSE <- function(err) sqrt(mean(err^2)) RMSE(predicted - reality) I ...
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0answers
272 views

Error in validation of a Cox PH model using rms package in R

I am trying to do validation of extended cox model with time-varying covariates in R using rms package. Here is a toy data that looks similar to my data. It has 385 ...
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1answer
1k views

fastbw with rule=“p” in R's rms package: why do results depend on number of covariates?

I've been trying to use the fastbw function from the rms package in R to perform logistic regression with backward selection, with p-values as exclusion criterion (I am well aware of the arguments ...
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2answers
2k views

Computing c-index for an external validation of a Cox PH model with R

First off, I'll state that I'm aware many questions get asked about the c-index. I've searched this site and others, and I haven't found an answer for my situation. I can successfully use ...