goodness-of-fit | 易学教程

R-squared within for a regression with multiple fixed effects [closed]

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问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 10 months ago . Improve this question I would like to get the R-squared within for a fixed effect regression with multiple fixed effects (let's say Country, Year, Trimester). The least squared dummy variable (LSDV) model ( lm in R/ reg in Stata) would only provide the overall R-squared. The same is

How to create a search for common fit distribution of two Goodness-to-fit tests list?

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问题 I looked into the question Best fit Distribution plots and found out that answers used the Kolmogorov-Smirnov Test to find the best fit distribution. I also found out that there is an Anderson-Darling test that is also used to get the best fit distribution based on given data. So, I have a few questions: Question 1: If I want to combine both tests, how can I do that where it searches for the maximum p-value of both tests(find the highest p-value and is common in both tests then I extract the

Assesing the goodness of fit for the multinomial logit in R with the nnet package

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问题 I use the multinom() function from the nnet package to run the multinomial logistic regression in R. The nnet package does not include p-value calculation and t-statistic calculation. I found a way to calculate the p-values using the two tailed z-test from this page. To give one example of calculating a test statistic for a multinom logit (not really a t-stat, but an equivalent) I calculate the Wald's statistic: mm<-multinom(Empst ~ Agegroup + Marst + Education + State, data = temp,weight

Assesing the goodness of fit for the multinomial logit in R with the nnet package

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Goodness-of-fit for fixed effect logit model using 'bife' package

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问题 I am using the 'bife' package to run the fixed effect logit model in R. However, I cannot compute any goodness-of-fit to measure the model's overall fit given the result I have below. I would appreciate if I can know how to measure the goodness-of-fit given this limited information. I prefer chi-square test but still cannot find a way to implement this either. --------------------------------------------------------------- Fixed effects logit model with analytical bias-correction Estimated

Goodness of fit in CCA in R

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问题 The following are the datasets mm <- read.csv("https://stats.idre.ucla.edu/stat/data/mmreg.csv") colnames(mm) <- c("Control", "Concept", "Motivation", "Read", "Write", "Math", "Science", "Sex") psych <- mm[, 1:3] # dataset A acad <- mm[, 4:8] # dataset B For these datasets psych and acad,I wanted to do the canonical correlation analysis and obtained the canonical correlation coefficients and canonical loadings as follows: require(CCA) cc1 <- cc(psych, acad) I would like to know if there is a

How to create a null model for conditional logistic regression in R?

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问题 I would like to see if including some covariates will give me an AIC which is smaller than the null model, while working with conditional logistic regression in R (discrete choice). I realize I can build a model, such as mymodel <- clogit(choice ~ dark+soft+nuts + strata(ID), data=candy) and get the AIC by using extractAIC(mymodel)[2] But I am a little unsure as to how I can create a null model to compare this against. Any advice would be greatly appreciated. 回答1: Just fit a model without

Discrete Kolmogorov-Smirnov testing: getting wrong value when using rpy2 compared to pure R

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问题 I am trying to use the dgof module from R, in Python 3 via rpy2 . I use it inside python as so: # import rpy2's package module import rpy2.robjects.packages as rpackages # Import R's utility package utils = rpackages.importr('utils') # Select a mirror for R packages utils.chooseCRANmirror(ind=1) # select the first mirror in the list # R vector of strings from rpy2.robjects.vectors import StrVector # Install R package name: 'dgof' (discrete goodness-of-fit) is what we're interested in if

How to create a null model for conditional logistic regression in R?

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I would like to see if including some covariates will give me an AIC which is smaller than the null model, while working with conditional logistic regression in R (discrete choice). I realize I can build a model, such as mymodel <- clogit(choice ~ dark+soft+nuts + strata(ID), data=candy) and get the AIC by using extractAIC(mymodel)[2] But I am a little unsure as to how I can create a null model to compare this against. Any advice would be greatly appreciated. Just fit a model without covariates nullmodel <- clogit(choice ~ 1 + strata(ID), data=candy) That's assuming that your null is that dark

Chi-squared goodness of fit test in R

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问题 I have a vector of observed values and also a vector of values calculated with model: actual <- c(1411,439,214,100,62,38,29,64) expected <- c(1425.3,399.5,201.6,116.9,72.2,46.3,30.4,64.8) Now I'm using the Chi-squared goodness of fit test to see how well my model performs. I wrote the following: chisq.test(expected,actual) but it doesn't work. Can you help me with this? 回答1: X^2 = 10.2 at 7 degrees of freedom will give you a p ~ 0.18 . > 1-pchisq(10.2, df = 7) [1] 0.1775201 You should pass on