Warning messages:1:In install.packages("ggplot2"):installation of package ‘mgcv’ had non-zero exit status2:In install.packages("ggplot2"):installation of package ‘ggplot2’ had non-zero exit status 而自己去安装依赖的话,也是安装不上的。 > install.packages("MASS") Warning message: package ...
p-value: < 2.2e-16那么需要估计的模型参数会很多,在这里不太适合
SmoothingparametersarechosentominimizetheGCVUBRE/AIC,GACVorREMLscoresforthemodel,andthemaincomputationalchallengesolvedbythemgcvpackageistodothisefficientlyandreliably.Variousalternativenumericalmethods 39、areprovidedwhichcanbesetbyargumentoptimizer平滑化参数的选择,以尽量减少GCV,UBRE/AIC,GACV或模型REML分数,和求解...
Generalized additive models(GAM)是一种自动拟合spline回归的技术。这可以使用mgcv R package: library(mgcv) # Build the model model <- gam(medv ~ s(lstat), data = train.data) # Make predictions predictions <- model %>% predict(test.data) # Model performance data.frame( RMSE = RMSE(predicti...
·Generalized additive models (GAMs)(广义加性模型): mgcv, gam, gamlss and VGAM. ·Extreme bounds analysis(极值边界分析): ExtremeBounds. ·Miscellaneous(其他): The packages VGAM, rms and Hmisc provide several tools for extended handling of (generalized) linear regression models. Zelig is a unifie...
一旦您发现数据中的非线性关系,多项式项可能不足以捕获这种关系,并且spline项需要指定knots。Generalized additive models(GAM)是一种自动拟合spline回归的技术。这可以使用mgcv R package: library(mgcv) # Build the model model <- gam(medv ~ s(lstat), data = train.data) ...
2)输入findFn指令查找。 代码:findFn("gam") 搜索结果: 运行指令后,在操作台会显示搜索的情况。 接着会弹出一个网页显示详细的搜索结果。这里给出了每项搜索结果的得分。 如下图所示,最佳的就是mgcv包,我们就可以通过下载mgcv包实现广义线性模型啦。
· Generalized additive models (GAMs) (广义加性模型): mgcv, gam, gamlss and VGAM. · Extreme bounds analysis(极值边界分析) : ExtremeBounds. · Miscellaneous(其他) : The packages VGAM, rms and Hmisc provide several tools for extended handling of (generalized) linear regression models. Zelig is...
## Loading required package: mgcv ## Loading required package: nlme ## This is mgcv 1.8-33. For overview type 'help("mgcv-package")'. ##apply a tensor product on CreditAmount and a spline on Age creditGam <- gam(Credit ~ te(CreditAmount)+s(Age)+CreditHistory+Employment, data=credit...
Smoothing parameters are chosen to minimize the GCV, UBRE/AIC, GACV or REML scores for the model, and the main computational challenge solved by the mgcv package is to do this efficiently and reliably. Various alternative numerical methods are provided which can be set by argument optimizer. ...