Questions tagged [intercept]
The intercept in regression-type models is the value of the Y variable when all X variables are 0.
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Independent variable is correlated with intercept, creating singularities
I am building a logistic model with about 20 variables. I have used the following code:
`fullmod = glm(cancer ~ B_SEX+BLINE_AGE_AT_BASELINE+B_BMI+B_chro+B_fdrc+B_hrt+B_LMET+
B_MET+B_FV+B_EDU+B_INC+...
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3answers
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Intercept term in logistic regression
Suppose we have the following logistic regression model:
$$\text{logit}(p) = \beta_0+\beta_{1}x_{1} + \beta_{2}x_{2}$$
Is $\beta_0$ the odds of the event when $x_1 = 0$ and $x_2=0$? In other words, ...
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Why does using the pnorm function on the intercept estimate of a binomial regression recover the mean of the dependent variable?
I've fit a series of mixed effect models with the lme4 package in R on a set of binomial outcome variables (1: the event happened, 2: it did not). The fixed effect ...
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Interpreting estimates in a Poisson regression
I know similar questions have already been answered but in this particular case I need some additional help.
I am working with this data set: https://github.com/proback/BeyondMLR/blob/master/data/...
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1answer
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Two intercept coefficients in glmnet output
I am not sure I understand why glmnet returns 2 intercepts in its result.
If model matrix already has intercept column, why do we need another intercept for the model?
...
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2answers
44 views
When forcing intercept to zero, how R-squared is changed? [duplicate]
I have some questions.
In a linear model, I want to force intercept to zero.
The program (I used JMP) does not provide R-squared when intercept becomes zero.
So, I calculated R-squared by myself by ...
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0answers
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Understanding the intercept in an unadjusted logistic regression
I have performed an unadjusted logistic regression using weights (obtained via genetic matching) as below. I am using the survey package to make working with the ...
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0answers
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Intercept in a dynamic panel model
I have been taught that including fixed/random effects in a dynamic panel model yields inconsistent estimates when using OLS and hence motivates the usage of other estimation methods.
However, does ...
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1answer
388 views
Calculating variance components and ICC of a random intercept model by hand
When I really want to understand a measure or parameter, I tend to do the calculation by hand with simplified data. Today I have attempted to do the same with the ICC, but somehow keep failing. I was ...
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1answer
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Is the intercept fit differently for each regressor in Multiple Linear Regression?
is the intercept B0 in y = B0 + B1X1 + .... fit differently for every feature x1.
Is it different for every feature coefficient or the same for all feature coefficients and why so?
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1answer
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A queston regarding the meaning of the intercept in regression
Suppose we have a dataset where the indepedent variable $x$ is the work experience in years of an employee and $y$ is his salary in dollars. Such a dataset could consist of the following elements
$$(...
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1answer
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Adjusted $R^2$ and regression without an intercept
I am using $R^2$ and then computing the adjusted $R^2$ in cases like linear regression that use an intercept and the regression line does not necessarily passes through the origin. Lately, I've been ...
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22 views
Variance of intercept in multiple regression
I'm calculating a variance of coefficients in a multiple regression with three predictors. I have built matrices necessary for calculation of variance of independent variables, for example, for b1 I ...
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2answers
486 views
'Size' of intercept at linear regression
I have a question about this table.
Why does the constant (intercept) change so dramatically from Model 1 to Model 2?
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1answer
211 views
multiple regression coefficients - Standard error of intercept
I am implementing an R-type summary() function in python with the restriction to exclude use of scientific libraries. (assignment)
I found this https://www.nd.edu/~rwilliam/stats1/x91.pdf material ...
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1answer
45 views
Interpreting the intercept in logistic regression with a binary variable
I am using R to run a logistic regression to analyze how a categorical variable ("population") correlates with a binary variable ("response") and am having some trouble ...
gung - Reinstate Monica
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Logistic Regression with dummy variables? [duplicate]
I am working on a problem where response variable is binary and my features are dummy variables. I observed when I include intercept to model all the dummy variables' p-values are equal to 1. When I ...
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1answer
485 views
How to interptet Jensen Alpha statistical significance?
When you regress portfolio excess returns against relative benchmark excess return you get a model in which the beta (slope) could be interpreted as the one you get from the CAPM, that is systemic ...
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2answers
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How do I interpret the regression output using Artificial Neural Network in R? [closed]
I'm working on a covid-19 dataset, and I'm interested in using the Artificial Neural Network (ANN) to measure the effect of some independent variables namely: confirmed cases, new cases, and total ...
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1answer
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Weighted average, then adding an intercept term
I am working with a problem where we are calculating the weighted average of DNA modifications. Modified sites are placed into four different groups and the average weight is calculated from there. ...
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1answer
45 views
How does the interpretation of a binomial-logit GLM change when an offset term is included?
My GLM is as follows:
...
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1answer
361 views
Re-calibrating Intercept on logistic regression models for unbalance data
I have data-set that Iām modelling using logistic regression as land.cover~H1+H2+H3+H4+H6+H8+H14. My response and categorical variables are binary. However the number of 0 and 1 in my response ...
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Removing intercept from GLM for multiple factorial predictors only works for first factor in model
I am running a binomial logistic regression with a logit link function in R. My response is factorial [0/1] and I have two multilevel factorial predictors - let's call them $a$ and $b$ where $a$ has 4 ...
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0answers
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Why the OLS parameters of a linear regression are correlated? [duplicate]
I found that in a simple regression model
y=Ī²0+Ī²1x+Īµ,
the parameters Ī²0 and Ī²1 are negatively correlated.
Is there a explanation for this correlation?
I found this property here, but I would like to ...
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0answers
7 views
Mixed Effects Logit Intercept
When I run a simple model using the ordinal package from R.
model1 <- clmm(A~ B*C+ (1| Subject), data = df, threshold = "flexible")
summary(model1)
I ...
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0answers
36 views
Likelihood Ratio Test for Intercept in Simple Linear Regression?
Iām trying to construct a test statistic for the likelihood ratio test for the following hypotheses about the intercept of a simple linear regression:
$$y_i = \alpha + x_i\beta$$
$$H_0: \alpha=0$$
$$...
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0answers
29 views
Logistic Regression with unbalanced data, intercept effect
In several papers I have read that when doing a logistic regression with unbalanced data, the entire effect of the imbalance is carried by the intercept. However, I cannot understand why this occurs, ...
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0answers
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Should I give up the intercept when calculating general trend of time series?
I have a time series of customers. Say, every customer has 30 observations of how may items he purchased over a period of 30 months. I would like to add a feature indicating what the general trend of ...
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2answers
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How to interpret insignificant intercept in logistic regression with orthogonal polynomial encoding
I have conducted a logistic regression.
Model <- logistf(A ~ C, family ="binomial"(link = "logit"), data=Data)
The dependent variable is ...
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0answers
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Why does level means coding only work for one (dummy) variable?
Consider the following reproducible example:
...
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0answers
7 views
Ability of neural network for regression to learn to predict input 0 where functional form doesn't hold for the data at input 0
Hopefully the title to this question wasn't too confusing.
I have a feed-forward neural network with $tanh$ activation functions on each of the (four) hidden layers. I'm performing a one-dimensional ...
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plot probabilities from mixed effect logistic regression with random intercept and fixed continuous and categorical covariate
I have created a mixed-effect logistic regression model with a random intercept, a ...
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2answers
2k views
Machine learning - use of intercept in regression? [closed]
what is the purpose of adding the intercept in regression. why we are adding the bias.
How we can predict if we have only dependent variable not any independent variable.
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What is the role of an intercept in testing the linear hypothesis b1 = -b2?
EDIT2: Possibly the short version of the question is just
how would I test cat2 = cat3 in the STATA example in the link below
I would like to test the hypothesis that $\beta_{typeprof} = -\beta_{...
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0answers
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Count Time Series Following Generalized Linear Models - Questionable Intercept
I am attempting to appropriately examine the relationship between a count time series variable (outcome) and a continuous, mean-centered time series variable (predictor).
...
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1answer
237 views
How to report variance components of random intercept model?
I have used:
model1 <- glmer(binary~ X1 + X2 +(1|MAINCATEGORY/YEAR), data = mydata, family = binomial(link = 'logit')
To get the variance components of the ...
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How to fit the intercept
I'm practising using R and I'd like to do this task:
So I fitted the model:
...
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0answers
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How to interpret the intercept of a multiple regression analysis after accounting for continuous and categorical variables
I'm attempting to run a regression analysis on a continuous outcome variable y, using a primary predictor variable that represents a treatment (...
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1answer
314 views
The definition of a constant term in a fixed effects model
Is it considered necessary to include a constant term in the definition of a panel data model which. among other regressors, includes a variable representing fixed effects?
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1answer
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How to interpret an insignificant intercept in a model with significant coefficients?
Imagine a regression with one dependent variable (y) and two explanatory variables (x1 and x2).
Both coefficients of x1 and x2 are significant. But the intercept is around 0.1 is not significant. How ...
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How do we explain together the results of paired t-tests and simple linear regression?
Description of my experiment:
I have 2000 samples.
For each sample I am measuring a metric Mi using four different methods A,B,C and D. These methods of measurement are completely independent.
Ideally,...
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0answers
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Does mean centering help with intercept estimation?
Suppose a model is built to model the sales of 20 beer brands for thousands of stores across the U.S. The response variable is sales and the independent variables include marketing activity and ...
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1answer
52 views
Effect of treatment in a model with random effects
I am working with Eyes (volume), each person has two eyes and I am using random effects to account for this in my model. (linear mixed effects model)
The problem with nlme is that the output needs ...
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0answers
38 views
How if the coefficient constant Insignificant when Estimate ARMA?
I'm doing my thesis about estimate Volatility where I use ARMA for Mean Equation.
when I'm trying to estimate the ARMA, I get Insignificant for coefficient constant but the AR and MA both are ...
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1answer
240 views
How is mean calculated in ARIMA models?
I am currently working with ARIMA models and I am a little confused about the way they are formulated. I found Rob J. Hyndman's blog post "Constants and ARIMA models in R" explaining it.
But still, I'...
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0answers
69 views
Extremely high intercept from Elastic Net
I am using the ElasticNet library from sklearn. I am using one predictor which takes values in the range [827.559, 827.5625].
When I fit the model using this ...
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0answers
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Why are the intercept values of my QR non-monotonic?
I was led to believe that when conducting quantile regression you would expect intercept values to increase as you go up quantiles. However, I have run a quantile regression and the intercepts are as ...
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0answers
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Formula for type III sum of squares of the intercept term in linear multiple regression
assume we have the regression model:
$$Y = b_0 + b_1 x_1 + \dots + b_k x_k + \varepsilon $$
I know the formulas for all type III sum of squares for the regression terms except the formula for SS of ...
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0answers
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Coefficients in a Randomized Block Design
If, for example, I am running a GLM with Poisson distribution (it could be any distribution) and I have a Randomized Block Design (RBD) [Note: I don't wanna run a GLMM just to put the blocks as a ...
