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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25 views

How to use RMSE when having data normalization?

I am new in machine learning and I am studying time series prediction using neural networks. Pseudocode 1: ...
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27 views

How does Stata calculate RMSE in regression with weights?

This problem came up because I was trying to replicate some results I was getting in Stata with R, and I was able to replicate everything except for the root mean squared error. When I run a ...
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1answer
43 views

RMSE is scale-dependent; is RMSE%?

I've got a graph of RMSE% vs. unit size and it declines nicely. Is this scale-dependence or does the "%" compensate for that? $$ \text{RMSE%} = 100\% \cdot \frac{\sqrt{\frac{1}{n}\Sigma_{i=1}^n (y_i ...
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32 views

compare different Imputation method by RMSE

My original dataset : ...
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18 views

Standard errors and confidence interval in cox regression model validation using RMS package

I am using RMS package of R to validate cox regression model with bootstrap. Please see the sample code below. I have three questions: (1) How do I request the standard errors and/or confidence ...
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1answer
30 views

Which denominator to use for cross-validated RMSE: $n$ or $n-k$?

I'm having a hard time understanding when I should take out the number of parameters from the denominator of the root mean squared error. From what I understood (other question, and wiki article), if ...
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12 views

Custom metric in Caret that puts more weight on bias

I am fitting a machine learning model that needs to have a low bias; variance is not as important. As such I would like to fit a model that places more weight on bias, than using the custom metric for ...
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12 views

Assessing a vector of errors in modeling

The quality of a model is often assessed based on a figure of merit such as RMSE. This reduces the individual errors in the model to a single number without assessing the errors as a population of ...
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60 views

How to interpret poor $R^2$ score but good RMSE value?

I split my data into training set and test set and am running linear regression on it. I am using Python's "scikit" library and I am getting an $R^2$ score of 0.31 and an RMSE value of 0.037. The ...
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1answer
23 views

How to apply a model from one data set to another

I'm still new to SAS, and I need to take a basic OLS regression from one data set, and find the RMSE when applied to a different set. This feels simple, but I haven't found anything. Help would be ...
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46 views

Shouldn't the root mean square error (RMSE) be called root mean square residual?

As far as I understand, estimating the error of a model, say an artificial neural network, requires to know the "true" model. Wikipedia says in its article "Errors and residuals": "The error (or ...
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1answer
31 views

How to request predicted points for each patient based on nomogram in RMS?

I am generating nomogram for a survival analysis project using RMS package of R. A PI of the project would like to have predicted points for each patient in the sample predicted by the nomogram. Is ...
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1answer
10 views

RMS and probability of steady values

Given an RMS value of a signal/waveform/list of numbers, I would like to construct an equation that calculates the probability that $n$ consecutive points are the same (or close). Suppose I have a ...
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67 views

How can I calculate the root mean square error (RMSE) for two covariance matrices?

I want to compare different methods of estimating the covariance matrices on the basis of RMSE and will recommend having the minimum RMSE. I have a sample of, say, 356 weekly observations of 10 ...
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32 views

Minimizing RMSE while using a log-transformation

I am trying to fit a linear model to some data, but the dependent variable y is clearly not normally distributed. It has a heavy tail on the right. A log-transformation helps to make the ...
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1answer
48 views

Is it possible to compare the RMSE of different models with different datasets?

I have 5 datasets and for each one of them I created 2 prediction models. For such task I divided each dataset in training and testing set (70/30%). Then I assessed the RMSE for each one and ...
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2answers
70 views

How to predict using ordered probit regression and calculate prediction accuracy?

I want to do an ordered probit regression, then cross-validate model prediction accuracy with 80% data for training and 20% for validation, and calculate RMSE for predictions. Consider this dataset: ...
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43 views

How to compare forecast accuracy of ordered probit and the equivalent Bayesian heierarchical model in R?

I have a dataset of a metric predictor variable $X$, and an ordered categorical predicted value $Y$ for several individuals. The dataset are from two groups $G_1$ and $G_2$. I want to estimate $Y$ ...
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127 views

How to discuss RMSE, MAE, $R^2$ results. What values are good/bad?

I have regression results for a study where I'm trying to develop a predictive model to estimate the value of a continuous output value for a physical process. My database has around 400 records and ...
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2answers
423 views

RMSE (Root Mean Squared Error) for logistic models

I have a question regarding the validity of using RMSE (Root Mean Squared Error) to compare different logistic models. The response is either 0 or ...
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24 views

Standard performance measure for regression?

I will perform time-series prediction and I will report the accuracy of my system with a measure like RMSE or MAE. However, the variables I will predict are in different ranges. So let's say one is ...
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2answers
104 views

How to change baseline patient in Predict function in rms package in R?

I am doing a time dependent Cox model using cph function in rms package. I use Predict and plot.Predict to plot the hazard ratio on y axis and a continuous covariate (e.g. LDL cholesterol) on X axis ...
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52 views

Root-Mean Squared Error for Bayesian Regression Models

I'm trying to get a sense of my prediction errors for a Bayesian regression model and I was using the Root-Mean-Squared Error. My question is, since are predictions are stochastic, would it make ...
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1answer
40 views

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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1answer
61 views

Visualizing nonlinear regression

I have following model using mtcars dataset: ...
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49 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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111 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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13 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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50 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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1answer
301 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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26 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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58 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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54 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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1answer
41 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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1answer
69 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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73 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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3answers
230 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 ...
4
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1answer
5k 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
145 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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144 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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40 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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81 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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55 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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81 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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20 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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2answers
973 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 ...
2
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2answers
85 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 ...
2
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1answer
226 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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62 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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18 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 ...