regression

Plotting the predictions of a mixed model as a line in R

由 三世轮回 提交于 2021-02-07 10:51:39
问题 I'm trying to plot the predictions ( predict() ) of my mixed model below such that I can obtain my conceptually desired plot as a line below. I have tried to plot my model's predictions, but I don't achieve my desired plot. Is there a better way to define predict() so I can achieve my desired plot? library(lme4) dat3 <- read.csv('https://raw.githubusercontent.com/rnorouzian/e/master/dat3.csv') m4 <- lmer(math~pc1+pc2+discon+(pc1+pc2+discon|id), data=dat3) newdata <- with(dat3, expand.grid(pc1

R - Extract ns spline object from lmer model and predict on new data

由 纵然是瞬间 提交于 2021-02-07 10:31:19
问题 I'm looking to predict 'terms', especially ns splines, from an lmer model. I've replicated the problem with the mtcars dataset (technically poor example, but works to get the point across). Here is what I'm trying to do with a linear model: data(mtcars) mtcarsmodel <- lm(wt ~ ns(drat,2) + hp + as.factor(gear), data= mtcars) summary(mtcarsmodel) coef(mtcarsmodel) test <- predict(mtcarsmodel, type = "terms") Perfect. However, there is no equivalent 'terms' option for lmer predict (unresolved

R - Extract ns spline object from lmer model and predict on new data

由 馋奶兔 提交于 2021-02-07 10:31:04
问题 I'm looking to predict 'terms', especially ns splines, from an lmer model. I've replicated the problem with the mtcars dataset (technically poor example, but works to get the point across). Here is what I'm trying to do with a linear model: data(mtcars) mtcarsmodel <- lm(wt ~ ns(drat,2) + hp + as.factor(gear), data= mtcars) summary(mtcarsmodel) coef(mtcarsmodel) test <- predict(mtcarsmodel, type = "terms") Perfect. However, there is no equivalent 'terms' option for lmer predict (unresolved

how to work with prais.winsten results in stargazer and broom (r)

由 |▌冷眼眸甩不掉的悲伤 提交于 2021-02-07 09:13:58
问题 The package "prais" contains the function prais.winsten to run a regression models with Prais Winsten estimator. However, neither stargazer nor broom packages seem to work with the results from the prais.winsten function. After fitting a model of the form pw<- prais.winsten(speed ~ dist, cars) When I try to report the results with stargazer I get the following error: stargazer(pw, out = "pw.html") Error: $ operator is invalid for atomic vectors and likewise tidying the model with the broom

r for loop for regression lm(y~x)

由 情到浓时终转凉″ 提交于 2021-02-07 08:47:48
问题 Example: df <- data.frame(A=1:5, B=2:6, C=3:7,D=4:8,E=5:9,F=6:10) I want make a regression loop lm(y,x) using like y the col 1 and 2 and like x the rest of the cols. my idea: lmf <- function (y,x) { f <- lm(y ~ x, data=df) cbind(summary(f)$r.squared,summary(f)$coefficients) } for(y in 1:3) { R<- apply(df[,3:6], 2, lmf(y,x)); R } error: Error in model.frame.default(formula = y ~ x, data = df, drop.unused.levels = TRUE) : variable lengths differ (found for 'x') I give this example very small

linearmodels panelOLS: Regression output with stars

由 时光总嘲笑我的痴心妄想 提交于 2021-02-07 03:12:45
问题 I'm using the linearmodels package to estimate a Panel-OLS. As an example see: import numpy as np from statsmodels.datasets import grunfeld data = grunfeld.load_pandas().data data.year = data.year.astype(np.int64) # MultiIndex, entity - time data = data.set_index(['firm','year']) from linearmodels import PanelOLS mod = PanelOLS(data.invest, data[['value','capital']], entity_effect=True) res = mod.fit(cov_type='clustered', cluster_entity=True) I want to export the regression's output in a .tex

How to get comparable and reproducible results from LogisticRegressionCV and GridSearchCV

由 这一生的挚爱 提交于 2021-02-06 08:58:24
问题 I want to score different classifiers with different parameters. For speedup on LogisticRegression I use LogisticRegressionCV (which at least 2x faster) and plan use GridSearchCV for others. But problem while it give me equal C parameters, but not the AUC ROC scoring. I'll try fix many parameters like scorer , random_state , solver , max_iter , tol ... Please look at example (real data have no mater): Test data and common part: from sklearn import datasets boston = datasets.load_boston() X =

How to get comparable and reproducible results from LogisticRegressionCV and GridSearchCV

由 那年仲夏 提交于 2021-02-06 08:58:00
问题 I want to score different classifiers with different parameters. For speedup on LogisticRegression I use LogisticRegressionCV (which at least 2x faster) and plan use GridSearchCV for others. But problem while it give me equal C parameters, but not the AUC ROC scoring. I'll try fix many parameters like scorer , random_state , solver , max_iter , tol ... Please look at example (real data have no mater): Test data and common part: from sklearn import datasets boston = datasets.load_boston() X =

译——人工智能是否是测试的未来？

由 爷，独闯天下 提交于 2021-02-04 22:59:41
Software development has already undergone an important journey from its beginnings to test automation and continuous testing. As time progresses, however, it is certain that testing will have to evolve as well. With digital transformations and the drive towards DevOps, automated testing is now at the heart of software testing and has taken the lead in the development process. 软件的发展从开始到自动化测试和持续测试已经走过了一个重要的旅程。时间的流逝，测试也会同样的演变是毋庸置疑的。随着数据的变革和开发运维驱动，自动化测试现在已经是软件测试的核心，并且在发展的进程中处于领先地位。 Artificial Intelligence (AI) seems to be the future of testing. AI has created high hopes in software testing and

How to extract the goodness-of-fit statistics from lmer() model for msummary from modelsummary package

由 给你一囗甜甜゛ 提交于 2021-02-04 21:06:17
问题 I am using lmerTest::lmer() to perform linear regression with repeated measures data. My model contains a fixed effect (factor with 5 levels) and a random effect (subject): library(lmerTest) model_lm <- lmer(likertscore ~ task.f + (1 | subject), data = df_long) I would like to include the total number of observations, the number of subjects, total R^2, and the R^2 of the fixed effects in the regression table which I generate with modelsummary() . I tried to extract these and build a gof_map
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