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Questions tagged [r-squared]

The coefficient of determination, usually symbolized by $R^2$, is the proportion of the total response variance explained by a regression model.

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Is it possible to get too small McFadden R^2 while at least one variable is significant?

I am quite new in logistic regression. I tried to apply a logistic regression on a dataset with 3 independent variables: Gender (Categorical- either male or female), Freq_A and Freq_B (continious ...
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Help with saving stats in rms bootcov [migrated]

I'm trying to save the distribution of R2 values as I bootstrap a model, using the ols and bootcov functions in the rms package. ...
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Justification for and optimality of $R^2_{adj.}$ as a model selection criterion

In a recent thread, use of adjusted $R^2$ ($R^2_{adj.}$) is mentioned in the context of model selection, e.g. The adjustment was invented as a solution to problems caused by variable selection ...
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Adjusted R2 for model with only one independent variable?

Adjusted R2 is said to be more unbiased than ordinary R2 as it takes the number of explanatory variables into account. Can adjusted R2 be used in a model with only an intercept and one independent ...
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adjusted R squared for multiple exact same input variables

I was trying to understand how adjusted $R^2$ in a simple linear regression behaves when there exists multicolinearity. And realized I could not replicate the adjusted $R^2$ provided by excel data ...
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R-square for regression using multiple imputation

I'm using multiple imputation to see how confidently we can apply the regression coefficients found for a sample to the whole population. Does it make sense to have an R-square for the model made ...
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41 views

Standardized MSE-style metric for nonlinear regression, chiefly neural networks

I am interested in neural network regression and if I have a model that has a level of performance that I deem acceptable. I am comfortable using MSE as a loss function, but I am not keen to use MSE ...
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Regression analysis question on model selection and reduced model

I am doing a regression project on some medical data using SAS. I used forward selection, backward selection, stepwise selection, and the LASSO, and all procedures gave me the same subset of variables....
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How does one interpret r-sq (adj) versus deviance explained in GAMM creation?

I am running some models for my master's dissertation using backwards stepwise regression of GAMMs. I have six total models. I have a base model with several significant variables; the r-sq (adj) = -0....
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20 views

goodness-of-fit for logistic regression with a ratio dependent variable

My dependent variable is number of days in a week a certain activity occurs, so I figured I would express it as a percentage out of 7 (days) and model it using logistic regression. I would like to ...
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lagged dependent variable and R-squared

I have these two regressions below: $$ y_t = \beta_1+\beta_2 x_t + \beta_3 y_{t-1} + u_t \\ \Delta y_t = \alpha_1+\alpha_2 x_t + \alpha_3 y_{t-1} + v_t $$ How are they different in $SSR$ and $R^2$?
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Adjusted R^2 for regression with dummy variables

I'm familiar with R^2 and adj R^2 for penalizing the addition of predictive variables in a regression. I just want to double check that If I have three continuous predictor variables and 30 dummy ...
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I ran an ANN model and got an extremely low R2 but a pretty good MSE, what does this mean? [closed]

I ran an artificial neuron network on data with about 2,000 rows and 3 features. I got a R2 of .06 which is really low, but a good MSE of .41. Why are these performance evaluators of this model ...
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Is it possible for a logistic regression to have negative r squared on its training dataset?

Let's say I train a logistic model on xs and ys, and then use that model to back-predict ys from the original xs and compute an $r^2$ value as $1-rss/tss$. Is it possible for that value to be ...
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High AUC but low R squared in a random forest classifier

I have been looking for an answer on this website and on Google but I can't seem to find a clear explanation anywhere. The problem is the following. I built a Random Forest model (using Python's ...
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Assumption testing for a large amount of individual regression models and averaging R-squared?

I am running the following model for my thesis, a simple regression: \begin{equation} y_{i,t} = \alpha_t+\beta_tx_{p,t}+\varepsilon_{i,t} \end{equation} where Y is an observed variable (...
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Convergence of R-squared and Predicted R-squared

We know for a fact that R-squared would reach 1 when more X's are added to the model. Many have suggested the use of Predicted R-squared as a measure of predictive power, and we know that its value ...
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test if significantly different from each other

I have a set of values of r squared from different robust estimators. The r squared values fall not fall from each other. I want to test if they are significantly different from each other. Is it ...
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Binary predictors and their effect on the coefficient of determination $R^2$

I found a question on here asking about the effect of binary responses and the lack of validity in using the coefficient of determination to evaluate model adequacy. However, I'm wondering if this ...
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1answer
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Adjusted R Square For Binomial [duplicate]

mtcars=data.frame(mtcars) m=glm(vs~mpg+cyl+disp+hp+drat,family="binomial",data=mtcars) m=glm(vs~wt,family="binomial",data=mtcars) I seek to estimate pseudo-r-...
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R-squared vs MSE, why the discrepancy?

I am carrying out a project where I am imputing missing data. I am trying to compare an imputed dataset with a baseline dataset by measuring MSE and R-squared. These metrics are measured by ...
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Which R-squared value to report while using a fixed effects model - within, between or overall?

I am using a fixed effects model with household fixed effects. I just added a year dummy for year fixed effects. Here below is the Stata result screenshot from running the regression. In the ...
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Why R is not significant (and very low), while all predictors are significant? [duplicate]

I used a network logistic regression to regress five predictors against a dependent. They are all significant, instead the R is not significant and it's even very low. I can understand that it may be ...
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Decomposing R^2 into independent variables

Consider a linear regression model: $$y = β_0 + β_1X_1 + β_2X_2 + ... + β_kX_k + ε$$ where $R^2 = 1 - (SSR/SST)$. I would like to determine the contribution of a factor $i$ (call it $R^2_i$) into ...
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Meaning of centered vs uncentered coefficient of determination

The coefficient of determination, $R^2$, can be expressed as $$R^2=\frac{ESS}{TSS}=\frac{\hat y'M_{[1]}\hat y}{y'M_{[1]}y},$$ where $M_{[1]}$ is the residual maker for the unity vector, ESS is ...
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what is the formula for r2 when using H20 GBM H2OGradientBoostingEstimator

I used a H2OGradientBoostingEstimator to do a classification (n features into a binary 0/1). What does the reported R^2 mean? Should I look more into AUC or into R^2? And for classification, what ...
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Correlation formula for a Quadratic

I have used quadratic regression on a dataset to find the graph of best fit, that is, finding the coefficients a, b and c in the general formula of y = ax^2 + bx + c. Having done that I would now ...
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Coefficient of determination in time series models

Nagelkerke's (1991) generalized $R^{2}$ (below) is a modification of the Cox Snell (1989) generalized $R^{2}$ (the numerator in the below) which is a coefficient of determination based on the log-...
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random effect variance as pseudo-rsquared in GLMM

Suppose I have data on the abundance of a species across multiple sites that differ in some covariate of interest. Suppose that the logarithm of the abundance (logAbun) meets assumptions for linear ...
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I have an insignificant beta weight of a predictor, which the only predictor in a step with significant R-square change and significant F-value

I am running a hierarchichal multiple linear regression with 4 steps containing theoretically justifyable variables: Outcome: pain rating Step 1: demographic variables (age, gender) Step 2: Pain ...
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Goodness of fit test for any regression model?

Is there a general goodness-of-fit test for any kind of regression model? My problem is that I have a deep neural network that tries to predict some real value labels using high-dimensional input. The ...
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Calculating R-squared using standard errors

I have the following estimated model: $\hat{y} = 0.2857 + 0.8019x_1 - 0.0741x_2$ (the $t$-statistics are $1.8959$, $8.4198$, and $-3.7017$, respectively). Furthermore, I know the sample size $N = 92$,...
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How to interpret low R^2 value when we have the whole population

I am predicting the performance in a subject given the percentage of a gender that is in a group, for example, a group might be 70% female and 30% male. There is a significant relationship (p < 0....
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1answer
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Excellent model fit but high VIF

I want to use a predictive model for a time series variable M that is related to an other variable X. I can generate independent scenarios for X and I need to generate corresponding values for M. ...
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Should I report the pseudo $R^2$ value for full or final logistic regression model after removing NA's & running stepwise selection?

I'm working with a logistic regression model in r. model <- glm(response~., family="binomial", data) and I'm using ...
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What does it mean when I add a new variable to my linear model and the R^2 stays the same?

I'm inclined to think that the new variable is not correlated to the response. But could the new variable be correlated to another variable in the model?
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R-squared from Backward elimination doesn't match that from linear model

I am trying to pick features using Backward Elimination on the Housing Prices dataset in Kaggle using the following function. ...
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1answer
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Coefficient of multiple correlation for multiple linear regression with degree > 2 and interaction terms

I want to calculate the Coefficient of Multiple Correlation $R^2$ for a multiple linear regression with polynomial features of degree >= 2 (with interaction terms). Let's say I want to obtain the ...
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1answer
34 views

How exactly do I calculate the power in my SEM?

I am trying to calculate the power in my SEM analysis post-hoc. How exactly should I do this? What is the power for the R-squared result of IT-T2 and IT-T3? Background info: Sample size is 255. IT ...
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Interpretation of singularities in AICc and adjusted r-square

Wikipedia states the small sample size AIC for an univariate, linear in paramters mode with normal residuals as: $$ AICc=AIC+2\tfrac{k^2+k}{n-k-1}, $$ where $n$ denotes sample size and $k$ the number ...
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How are R2 and adjusted R2 mathematically related to the idea of explained variance?

I am trying to understand in what sense, $R^2$ and $R_{adj}^2$ represent the "explained variance." I can't find any similar question that explores the connection in mathematical detail. My current ...
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Bayes R2 computation

I am working on evaluating the performance of a Bayesian network. One of the metrics I'm considering is the Bayes R-squared. On going through this publication, http://www.stat.columbia.edu/~gelman/...
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Does R2 (R square) of 100% means over fitting in machine learning? [closed]

( Its an Interview Question.) Is there a straight yes/no type of answer? Or this should be answered more diplomatic way? Kindly help!
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Why R square getting Negative value? [duplicate]

Why do we getting Negative R square value when working on MLR model. How do we interpretation?
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Mathematically, what are the drawbacks of R-squared in evaluation a regression model?

I kept seeing articles about the drawbacks of R-squared (and that's why we need to have adjusted R-squared). One drawback is that: "Every time you add a predictor to a model, the R-squared increases,...
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Correlation between Likert scale question and binary question?

I am writing a report and I need to find correlation between question: how much do you trust banks (from 1-I dont trust them at all and 5-i trust them completely) and question: are you a member of ...
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Explained variance of incremental feature?

Suppose I have two features, and I know the explained variance of feature A for feature B. I build a linear model on feature A only, and I have a the explained variance of my target using this model. ...
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CAPM Very Low R-squared Meaning

When running a CAPM on a portfolio I get a R-squared of 0.000964 which just seems impossible given the used portfolio, index and observed fit. What could be an error leading to such a result ? (I ...
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How to infer the bounds on the R-squared value given the relationship between individual features?

Let say you have three variables X1, X2, and Y, all normally distributed, zero mean, unit variance. When you build a simple linear regression using: ...
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What is the meaning of r-squared values in confirmatory factor analysis?

When all fit indices are acceptable, are r-squared values still important? Some of my r-squared values are very low (such as $0.10$).