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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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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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33 views

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
25 views

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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32 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 $0.10$).
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Is it wrong to choose a regressor based on MSE?

I see many people on the web assuming that R² is not an appropriate metric to select a regressor instead of another, suggesting AIC or BIC to do so. From my view, it means that its almost preferable ...
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Regression - variance of predictions much lower than variance of target

I am using non-negative lasso(sklearn) on a dataset with 1.5MM data points and 120 features. It is a low R2 environment (working with noisy financial data), so $R^2$ is about 10%. What I am more ...
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Generating R squared statistics when carrying out a Firth Logistic Regression

I am using the logistf package available for SPPS to carry out a firth logistic regression, and have results relating to the coefficents, standard errors and p-values associated with each predictor. I ...
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Can Adjusted R squared be equal to 1?

I have a dataset with around 15 independent variables. I am using a multi-regression model to fit the dataset. For model selection, I am using a backward elimination procedure based on the p-values. ...
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How to report Logistic Regression Pseudo-R^2 in publication or reports?

I have a Logisitc Regression model with a McFadden Pseudo-$R^2=0.7113$. Based on the answers to this question: McFadden's Pseudo-R2 Interpretation, it seems my model has a good fit. However if I ...
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Does the size of original dataset influence measurment error in bootstrap? [closed]

As above. So for example would the measurment error of model's r-squared be higher if the bootstrap sample (1000) was drawn from original sample of 20 observation than if the bootstrap sample (1000) ...
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Is there an alternative to R squared to compare goodness of fits of different datasets? Slope makes them incomparable

I'm fitting the degradation of a signal. Some instruments degrade faster than others, so the slope varies a bit. This makes it difficult to compare how good the fits are relative to eachother. See ...
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1answer
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Is conditional r-squared ever zero?

I am using multivariate auto regressive modeling (MAR) to assess a complex data set (MAR is a form of vector auto regressive modeling, VAR). The output of the MAR method is >1 response variables and >...
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36 views

Comparing the output and fit of an OLS and a Tobit model, and comparing Tobit models

I am trying to estimate a quite complicated model (many variables with different structures), with a limited dependent variable, which ranges from 0-100% with about 45% of the sample having an ...
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1answer
77 views

What type of regression should be used in predicting Click Through Rate?

I'm looking for a model to predict CTR (click-through-rate) I have the following data: For each ad I know the number of impressions, clicks and some other attributes (which are mainly dummy variables)...
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R squared / deviance explained for elastic net glmnet

I am using R glmnet function for the elastic net for logistic regression with binary outcome and would like to calculate the R-square value. I am getting different results when I use the dev.ratio ...
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R^square for a pre-determined linear regression

I would like to produce the R^square goodness-of-fit statistics for a predictive model. I have the base data (10, 000 number of x-values) which are the true values given by an analytic/deterministic ...
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What does small R squared mean in linear regression [duplicate]

I'm applying linear regression on. problem and getting very small R squared, e.g. 0.087 and adjusted R squared 0.057. This is very small for R squared. What does this mean for my regression? Does it ...
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Computation of R-squared with lm() in R [duplicate]

Confusion on difference between the $R^2$ results from the lm() function in R and from the Equation $1-rss/sst$ (1) Ref: https://onlinecourses.science.psu....
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Does the $R^2$ depend on sample size?

It's well known that adding more regressors can only improve the $R^2$. What about the number of observations? Say you have a sample of size $N$, and you draw a random subsample of size $n < N$. ...
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Should I stack models or extract more features for a tiny, but hard gain in R2?

I heard that stacking models is only worth it doing it in a Kaggle competition as everyone is dealing with the same training data, and due to time limit, feature engineering only helps a little with ...
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1answer
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What does “regression of predictor onto all of the other predictors” mean?

I encountered a lot of references that talk about R squared but I can't understand what the difference is between the R squared in regression of the response on the predictors and the R squared that ...
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R2 - contribution of explanatory variables x1, x2 and x3 to the variance of the explained variable y

I wish to look at the contribution of explanatory variables x1, x2 and x3 to the variance of the explained variable y. To summarize the contribution of the explanatory variables alone to the ...
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101 views

Why are conditional and marginal pseudo R^2 the same in this example?

Here is a reproducible example: The problem is that when I calculate Pseudo R^squared for the null model it gives 0.18 to conditional R^2, the variance explained by the random factor is not 0. However ...
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Pearson and R^2 Correlation between three variables

Get it from someone else but don't quite know how to answer. If $\rho_{X,Z}=0.4$, $\rho_{Y,Z}=0.3$, what is the range of $\rho_{X,Y}$? Here $\rho$ is the Pearson correlation coefficient. We run a ...
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2answers
38 views

How do I select right features

I am working on Boston Dataset in which the aim is to predict the MEDV which is median value of owner-occupied homes in $1000s. ...
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1answer
39 views

Mediation, low R^2

I am currently working on my model in which I would like to test mediation effects. In my model following applies Y-company attractiveness X-type of application (digital vs nondigital) and M-...
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2answers
182 views

Why is R squared zero when the best-fit line is horizontal?

Why is it so that the regression line is horizontal to the x-axis when r-squared = 0? I do understand that when r-squared = 1, estimated Y and actual Y are equal.
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1answer
53 views

Compute a measure of explained variance for hurdle models in R

I am working with a dataset df which comprises count data count and a number of categorical variables. ...
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Discussing R-squared of log-log model with a non-technical audience

I have been asked to report on the relationship between two right-skew financial variables using R-squared e.g. "market cap explain ?% of the variance in CEO compensation". The purpose of the ...
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Why is individual R-squared higher than overall R-squared?

I ran a ridge regression model on a set of data across 6 groups. As you can see, the overall R-squared is low. Because groups A and B make up the most of the data, I would think the overall R-...
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What statistics can I use to compare OLS to an ordered probit

I am trying to justify to the use of an ordered probit, my dependent variable is a survey response on a likert scale so is likely ordinal, but I wanted to provide a goodness of fit stat to back up my ...
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450 views

Calculating the Shared Variance from a Correlation Coefficient?

We often square coefficients like the R coefficient in simple/multiple linear regression or standardized factor loadings to get a percentage of variance accounted for by predictor variables. Can the ...