# Questions tagged [rms]

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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### Is there any value in models that have a larger out of sample RMSE than a standard deviation?

I am predicting y values from x values using various regression models, elastic net and partial least squares regression (PLSR). To quantify performance of models we utilize root mean squared error (...
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### rms::val.surv function estimated the same survival probability for all cases

I have fit a cox regression model, and used val.surv function to plot calibration plot to compare observed survival probability with predicted survival probability. ...
1 vote
20 views

### Testing the difference between two Root Mean Square Error values for statistical significance [duplicate]

I would like to compare the predictive power of 2 models. The models are meant to model count data and respective probabilities. I am using two metrics as means of comparison: Root Mean Square Error ...
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### Is Mean Square Prediction Error acceptable to use if predicted values are continuous but actual observed values are discrete?

I would like to compare the predictive power of 2 models. The models are meant to model count data, so the actual observed values are discrete. However both models are designed such that they output ...
• 133
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### How to determine the ideal number of components for PLSR using RMSE?

I would like to determine a non-visual, numeric based approach to determine the ideal number of components for my PLSR model. There are 10 components in the model for 1 target variable. If I simply ...
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1 vote
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### Regression spline for time to allow for slope changes

Suppose we have a regression / survival model where we would like to model follow-up time using a regression spline. Follow-up time has two phases (first treatment active, and second treatment ...
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### Standard error of RMSE and differences in RMSE

I have a set of models $M = \{1, ..., m, ..., K\}$, and for each I am calculating RMSE on out-of-sample data as standard: \mathrm{RMSE_{m}} = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (\...
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### Approximation function for MLP and LSTM

I have total of 6300 samples, 5800 of which are training data, and 500 of which are testing data. We compare the performance of LSTM and multilayer perceptron (MLP) with one hidden layer in terms of ...
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### How to compute the confidence interval of root mean square (e.g., ci of the RMS of the alternating current signal)

If $\bf x$ are $N$ measurement of alternating current signal (sinusoidal), its root mean square is computed using $RMS=\sqrt{\frac{\sum{x_i^2}}{N}}$. My question is: Does the confidence interval exist ...
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### Valid confidence intervals in GAM’s using shrinkage estimation

In this blog article: https://www.fharrell.com/post/improve-research/ it states: “The frequentist paradigm does not provide confidence intervals or p-values when parameters are penalized”. I was ...
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1 vote
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### Observational pre post design and confounder adjustment

I have observational data with baseline, and two follow-up measures with a binary treatment. Dependant variable is questionnaire scale score ***. I am planning on fitting a linear mixed effects model ...
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### Cox model - unsure of time unit of analysis

I am running a survival analysis (Cox model) on time to event in cancer patients. The start of followup is end of treatment. Tests for the event (recurrence) are performed every 6 months from cancer ...
• 859
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### Question about LASSO, RMSE, and Standardization

I have a question about doing LASSO in R using glmnet. It's kind of a conceptual question; I learned that we should interpret RMSE after performing ...
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### in ann model statistical calculation can mae be greater than rmse? [duplicate]

in ann modelling creation can mae be greater than rmse? i am using the LMNN algorithm for creting the ann model.i am having the values as said above in the performance evaluation of the ann model.can ...
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### Shrinkage of covariates in the Cox model

In a regression model (e.g Cox model) when there are too few events to support modeling all desired covariates / confounders, a possible solution is to apply shrinkage / penalise all but the exposure(...
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### Machine learning benchmarks: MAE, RMSE, and R-squared

I'm working on a machine learning problem, and I'm having trouble interpreting different measures of model performance. I have a single dependent variable (proportion change between two treatments, ...
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### Restricted cubic splines for time-to-event data

I'm kind of new in fitting rcs for cont. variables as a clinican, so I have a few questions: ...
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1 vote
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### How to compute relative error of multi-dimensional time-series?

I have written a python script that uses a variety of different integrators to simulate the gravitational N-body problem. I would like to compare the positions obtained from my simulation to the ...
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### Why is taking the mean RMSE sometimes so far off overall RMSE?

I'm working with a multi-threaded program, which splits a dataset into N chunks, and evaluates some regression model's performance, predicting a score for each item in each chunk. I'm using RMSD as ...
1 vote
17 views

### Poor RMSEA/Fit for Simple Poisson Regression

I am running a simple Poisson regression. $X$ = time, $Y$ = count data. This is a huge dataset with many years. There is significance between $X$ and $Y$. But model shows poor fit via high RMSEA value....
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### how to calculate overall RMSE accumulated during several processing steps

I have a digital terrain model (DTM) downloaded from NASA's SRTM dataset at a resolution of 1 arc second covering Spain and France. This has a stated RMSE of 9.73m [output 1] I projected this to ...
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### How to construct an optimal spline model when two continuous independent variables are included

I am interested in evaluating the relationship between age, BMI and lipid level. The lipid level is an outcome in my study. I think that the relationship between lipid level and age and the ...
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1 vote
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### Comparing families of classifiers on large datasets using mixed effect logistic regression models on individual questions

I have a testing dataset of about 6000 images which I am going to try about 25 different neural networks on in a multi-class classification problem. Each network will belong to around 5 families (e.g. ...
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1 vote
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### Can you compare regression models using RMSE when samples have different proportions of zeros?

I am using the ranger package (which implements random forests) in R to build regression models of tree species' basal area, a continuous measure of abundance and ...
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### Compare Root Mean Square Values

I'm trying to compare a regression neural network to a commonly used equation. I have an 80:20 split for my training:test, and I get the root mean square error on the test set from the neural network ...
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### Why isn't there a square root version of the Brier score similar to how RMSE complements MSE?

When computing the mean squared error of a regression model, we get a metric in square units. For ease of interpretation, we can therefore instead compute the root mean squared error, which are in ...
22 views

### RMS residual normalized by standard-deviation

Is there a proper name for the following misfit quantification? misfit=√{∑[(xi−xi')^2/(n*𝜎i^2)]} where n is the number of data points xi−xi' is the ith residual, ...
1 vote
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### How to interpret interaction effect in ordinal (logistic) regression?

I ran an ordinal regression in R with the polr function from the MASS package as described in this tutorial, which is very good. However, the tutorial does not ...
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### Normalization of log-returns or normalization of cumulative log-returns

This questions seeks for discussion to find theoretical support for normalizing cumulative log-returns vs normalizing log-returns By "normalizing" (also known as standarizing) I mean it in ...
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1 vote
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### Cox model with ridge term: how to choose value of theta?

The coxph() function in R package "survival" is used to fit the Cox proportional hazards model. This function allows a ridge() term in the formula to penalise selected terms, which requires ...
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### Root Mean Square Error of the addition of two measurements whose RMS Error is known

I am working on a measurement system which tries to measure the distance between two values i.e $\Delta F=F_1-F_2$. Where $F_1, F_2$ are the values I actually measure. I have set up a Monte Carlo ...
1 vote
323 views

### Can I say that the relative root mean squared error is the averaged percentage error?

RMSE is an error metric in which the mean of the data minimizes its loss function: $\text{RMSE} = \sqrt{\frac{\sum_{t=1}^{n}(y_t - \hat{y_t})^2}{n}}$ But it gives ...
116 views

1 vote