Skip to main content
Cross Validated

Questions tagged [rms]

Ask Question

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)

342 questions
Newest Active Bountied Unanswered
Filter by
Sorted by
Tagged with
1 vote
1 answer
52 views

I edited the question to open it again because my main question is how to solve this issue statistically since I cannot interfere with the numerical analysis of the program. I’m working with ordinal ...
Kavalali's user avatar
  • 373
2 votes
2 answers
87 views

I trained a SVM multiple regression model and want to know how much each feature contributes to the prediction variance (quantified by the RMSE). I got the Shapley values for each feature on data from ...
n6r5's user avatar
  • 209
0 votes
0 answers
25 views

I have trained a multiregression model using non-linear SVM, and got quite good metrics, with no big differences between test (20% data) and train (80% data) metrics. The following are the test/train ...
n6r5's user avatar
  • 209
1 vote
0 answers
20 views

I am using the CatBoost model with 100 data points and I have done data augmentation, hyperparameter tuning,cross validation, added isolation forest, and randomizedsearchCV and at the end I could have ...
Beliz Yazici's user avatar
  • 11
0 votes
0 answers
51 views

If there is an ML model, the standard deviation (SD) of the root mean squared error (RMSE) can be calculated using time series splits by fitting the model on different training sets and evaluating it ...
Geek_Tech's user avatar
  • 329
0 votes
0 answers
50 views

I am working with Principal Component Analysis (PCA) and trying to evaluate reconstruction error. Specifically I am interested in being able to compare the results of PCA on differently scaled data (...
Zack's user avatar
  • 3
0 votes
1 answer
101 views

I am using "lmer" package to fit linear mixed-effects model to access the association of Y and X. The response variable of my data is continuous and repeated-measured which requires random ...
user456831's user avatar
  • 1
4 votes
2 answers
355 views

I am working on a project to externally validate a clinical prediction model. The original model coefficients were estimated using a Cox model. The model uses the baseline hazard and coefficients to ...
user167591's user avatar
  • 1,173
1 vote
1 answer
86 views

I am I am trying to fit a logistic regression to my dataset with the variable binary as the response variable of the date ...
user435931's user avatar
  • 13
0 votes
0 answers
67 views

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 (...
Sir Veza's user avatar
  • 1
0 votes
0 answers
118 views

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. ...
Xixuan Zhu's user avatar
  • 1
1 vote
0 answers
27 views

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 ...
Astral's user avatar
  • 133
2 votes
1 answer
74 views

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 ...
Astral's user avatar
  • 133
1 vote
1 answer
334 views

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 ...
user167591's user avatar
  • 1,173
4 votes
1 answer
305 views

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 ...
David's user avatar
  • 69
0 votes
0 answers
57 views

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 ...
Yuanyi Wu's user avatar
  • 101
6 votes
1 answer
291 views

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 ...
user167591's user avatar
  • 1,173
1 vote
0 answers
68 views

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 ...
user167591's user avatar
  • 1,173
6 votes
3 answers
539 views

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 ...
user167591's user avatar
  • 1,173
0 votes
0 answers
89 views

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 ...
noone's user avatar
  • 63
3 votes
1 answer
194 views

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(...
user167591's user avatar
  • 1,173
3 votes
2 answers
483 views

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, ...
S. Robinson's user avatar
  • 353
3 votes
1 answer
434 views

I'm kind of new in fitting rcs for cont. variables as a clinican, so I have a few questions: ...
sjg's user avatar
  • 33
1 vote
0 answers
91 views

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 ...
user23358153's user avatar
  • 11
1 vote
0 answers
41 views

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....
mmt1026's user avatar
  • 11
1 vote
1 answer
156 views

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 ...
Totti's user avatar
  • 87
1 vote
1 answer
74 views

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. ...
James's user avatar
  • 473
1 vote
1 answer
211 views

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 ...
Jim Worrall's user avatar
  • 111
0 votes
0 answers
75 views

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 ...
Jack789's user avatar
  • 1
4 votes
1 answer
275 views

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 ...
another_student's user avatar
  • 107
0 votes
0 answers
65 views

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, ...
geoweaser's user avatar
  • 1
1 vote
1 answer
1k views

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 ...
Simone's user avatar
  • 262
1 vote
0 answers
302 views

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 ...
user167591's user avatar
  • 1,173
0 votes
1 answer
135 views

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 ...
bad_at_stats's user avatar
  • 3
1 vote
1 answer
998 views

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 ...
Guilherme Parreira's user avatar
  • 381
3 votes
0 answers
198 views

I have data from an intervention study (10 clinics, 5 control, 5 treatment). The outcome is counts, and we have monthly data at baseline, treatment active phase, and post treatment phase. The number ...
user167591's user avatar
  • 1,173
1 vote
1 answer
119 views

I have 2 time series, say for instance, T1 and T2. T1 granger causes T2 at lag 2. Should this mean that if I make a VAR model with these two time series, and an autoregression model with just T2, the ...
Ritik P. Nayak's user avatar
  • 333
1 vote
1 answer
128 views

I have a couple of time series, say, T1 and T2. I have established (using the grangercausalitytest library of Statsmodels in ...
Ritik P. Nayak's user avatar
  • 333
0 votes
1 answer
267 views

I am working on a PV energy production forecasting problem. With various ML models (ANN, RNN, LSTM) I am trying to predict the energy for the following day, based on the historical data. The ...
GCMeccariello's user avatar
  • 101
1 vote
1 answer
325 views

As precipitation prediction models can only predict positive values, they won't be able to undershoot small values by much. When it comes to overshooting, there is no boundary. High precipitation ...
schefflaa's user avatar
  • 51
2 votes
1 answer
176 views

Let's say I train a model and it has an RMSE of 2.5. Does this mean, that on average, my prediction will be 2.5 away from the true value? Or does some scaling need to be done in oreder to get this ...
the man's user avatar
  • 275
0 votes
1 answer
1k views

I found the predicted hazard (the h(t) of Cox regression) through Predict() and cph() in rms package was different from common coxph(). ...
tumidou's user avatar
  • 75
0 votes
0 answers
42 views

I am attempting to recreate the results of the paper written by King, Stock and Watson in 1995: Temporal instability in the unemployment inflation relationship. The paper estimates a VAR model with 12 ...
Varun Sinha's user avatar
  • 1
1 vote
1 answer
316 views

We have cancer medical registry data, including information on date of diagnosis, treatment, and followup e.g. date of death etc. However, we only know type of treatment received for each person. We ...
user167591's user avatar
  • 1,173
2 votes
1 answer
1k views

I am analyzing a association between a frailty index and care needs using the cox model. I use R and use rms package to fit restricted cubic spline. This is my R code. ...
li jiaqi's user avatar
  • 49
0 votes
0 answers
190 views

I have a data with 2 variables: diagnosis- yes/no Score- numeric variable from 0-10. I need to do ROC analysis for this data and to find the best cut off values. The problem is the data is too small ...
Inbar Lavie's user avatar
  • 1
1 vote
2 answers
195 views

This might be a dumb question ! I built a model and I'm satisfied enough with the model, given that I have a dataset with categorical variables I wanted to see the R2/RMSE for each of those categories,...
Omar Sow's user avatar
  • 11
2 votes
1 answer
108 views

For example, if you have a R^2 of 0.95, you can explain this number to stakeholders in a presentation as: Our model explains 95% of the total variance within the data. However, if we have a RMSE of 11,...
Katsu's user avatar
  • 1,051
1 vote
1 answer
218 views

I need your help regarding the information inside the picture. As you know all the information will change with the change of NDVI axis from y-axis to X-axis, except R2 and p-value remain the same? ...
Hushiar Raheem's user avatar
  • 13
1 vote
1 answer
795 views

Because the discrete formula for RMS, $\displaystyle X_{RMS}=\sqrt{{1 \over N}(x[1]^2+x[2]^2+...+x[N]^2)}$, is almost the same as the formula for standard deviation (assuming mean zero), except for a ...
Homero Esmeraldo's user avatar
  • 211

1
2 3 4 5
…
7