This paper uses Monte Carlo methods to demonstrate that aggregated variables produce spurious relationships with other dependent and independent variables in a model even when there are no underlying relationships among those variables. The size of the spurious relationships (or postulated effects) ...
aggregate在时间序列数据上的应用请参考R的函数说明文档。 Example2 ## Compute the averages for the variables in 'state.x77', grouped## according to the region (Northeast, South, North Central, West) that## each state belongs to.aggregate(state.x77, list(Region = state.region), mean)## Comp...
上面的公式 cbind(mpg,hp) ~ cyl+gear 表示使用 cyl 和 gear 的因子组合对 cbind(mpg,hp) 数据进行操作。aggregate在时间序列数据上的应用请参考R的函数说明文档。 Example2 ## Compute the averages for the variables in 'state.x77', grouped ## according to the region (Northeast, South, North Cent...
x:指定R 对象by:指定分组元素列表FUN:指定函数来计算统计汇总 要了解更多可选参数,请在控制台中使用以下命令: help("aggregate") 范例1: # Using state.x77 and state.region dataset # Compute the mean of the states in state.x77 # grouped by state.region variables aggregate(state.x77, list(region...
Grouping variable(s) and variables to be aggregated can be specified with R’s formula notation. Setting drop = TRUE means that any groups with zero count are removed. na.action controls the treatment of missing values within the data. TRY IT OUT The analysis in this post was performed in...
you’ll learn how to group your data by variables. Next, you’ll discover how to apply summary functions to calculate basic statistics such as averages and sums. Finally, you’ll explore how to use grouping and nested summaries to perform multi-level aggregations and identify further insights....
上面的公式 cbind(mpg,hp) ~ cyl+gear 表示使用 cyl 和 gear 的因子组合对 cbind(mpg,hp) 数据进行操作。aggregate在时间序列数据上的应用请参考R的函数说明文档。 Example2 ## Compute the averages for the variables in ‘state.x77‘, grouped ...
上面的公式 cbind(mpg,hp) ~ cyl+gear 表示使用 cyl 和 gear 的因子组合对 cbind(mpg,hp) 数据进行操作。aggregate在时间序列数据上的应用请参考R的函数说明文档。 Example2 ## Compute the averages for the variables in 'state.x77', grouped
In R, I have changed the data to a star schema representation (when all metadata are represented row-wise and every value gets its own row) using reshape2 package and melt then used aggregate along different variables to get the different totals. The less variables you use in by the more...
In R, I have changed the data to a star schema representation (when all metadata are represented row-wise and every value gets its own row) using reshape2 package and melt then used aggregate along different variables to get the different totals. The less variables you use in by the more...