Displaying 9 results from an estimated 9 matches for "cut_number".
2018 Feb 26
3
Random Seed Location
...654321)
B. Install and load the caret, ggplot2 and e1071 packages.
> install.packages(?caret?)
> install.packages(?ggplot2?)
> install.packages(?e1071?)
> library(caret)
> library(ggplot2)
> library(e1071)
C. Bin the predictor variables with approximately equal counts using
the cut_number function from the ggplot2 package. We will use 20 bins.
> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
> outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20)
> outOfSample[, 2] <- cut_number(outOfSampl...
2018 Feb 27
0
Random Seed Location
...1071 packages.
>
> > install.packages(?caret?)
> > install.packages(?ggplot2?)
> > install.packages(?e1071?)
> > library(caret)
> > library(ggplot2)
> > library(e1071)
>
> C. Bin the predictor variables with approximately equal counts using
> the cut_number function from the ggplot2 package. We will use 20 bins.
>
> > InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
> > InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
> > outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20)
> > outOfSample[, 2] <...
2018 Mar 04
3
Random Seed Location
...packages.
>
>> install.packages(?caret?)
>> install.packages(?ggplot2?)
>> install.packages(?e1071?)
>> library(caret)
>> library(ggplot2)
>> library(e1071)
>
> C. Bin the predictor variables with approximately equal counts using
> the cut_number function from the ggplot2 package. We will use 20 bins.
>
>> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
>> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
>> outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20)
>> outOfSample[, 2] <...
2018 Mar 04
0
Random Seed Location
...ll.packages(?caret?)
> >> install.packages(?ggplot2?)
> >> install.packages(?e1071?)
> >> library(caret)
> >> library(ggplot2)
> >> library(e1071)
> >
> > C. Bin the predictor variables with approximately equal counts using
> > the cut_number function from the ggplot2 package. We will use 20 bins.
> >
> >> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
> >> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
> >> outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20)
> >>...
2018 Mar 04
2
Random Seed Location
...t;> install.packages(?ggplot2?)
>> >> install.packages(?e1071?)
>> >> library(caret)
>> >> library(ggplot2)
>> >> library(e1071)
>> >
>> > C. Bin the predictor variables with approximately equal counts using
>> > the cut_number function from the ggplot2 package. We will use 20 bins.
>> >
>> >> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
>> >> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
>> >> outOfSample[, 1] <- cut_number(outOfSample[, 1], n = 20...
2018 Mar 04
0
Random Seed Location
...es(?ggplot2?)
>>>>> install.packages(?e1071?)
>>>>> library(caret)
>>>>> library(ggplot2)
>>>>> library(e1071)
>>>>
>>>> C. Bin the predictor variables with approximately equal counts using
>>>> the cut_number function from the ggplot2 package. We will use 20 bins.
>>>>
>>>>> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
>>>>> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
>>>>> outOfSample[, 1] <- cut_number(outOfSample[...
2018 Mar 05
1
Random Seed Location
...gt;>>>> library(caret)
>>>>>> library(ggplot2)
>>>>>> library(e1071)
>>>>>
>>>>>
>>>>> C. Bin the predictor variables with approximately equal counts
>>>>> using
>>>>> the cut_number function from the ggplot2 package. We will use 20 bins.
>>>>>
>>>>>> InvestTech[, 1] <- cut_number(InvestTech[, 1], n = 20)
>>>>>> InvestTech[, 2] <- cut_number(InvestTech[, 2], n = 20)
>>>>>> outOfSample[, 1] <- cut_num...
2009 Feb 25
0
ggplot2 0.8.2
...at http://groups.google.com/group/ggplot2, or track
development at http://github.com/hadley/ggplot2
ggplot2 0.8.2 (2008-02-23)
----------------------------------------
New features
* borders, fortify.map and map_data to make it easier to draw map
borders and choropleth maps
* cut_interval and cut_number utility functions to discretise
continuous variables
* stat_summary has reparameterised to make it easier to specify
different summary functions. It now has four parameters: fun.y,
fun.ymin and fun.ymax; and fun.data. See the documentation for
stat_summary for more details
Minor improvements
*...
2009 Feb 25
0
ggplot2 0.8.2
...at http://groups.google.com/group/ggplot2, or track
development at http://github.com/hadley/ggplot2
ggplot2 0.8.2 (2008-02-23)
----------------------------------------
New features
* borders, fortify.map and map_data to make it easier to draw map
borders and choropleth maps
* cut_interval and cut_number utility functions to discretise
continuous variables
* stat_summary has reparameterised to make it easier to specify
different summary functions. It now has four parameters: fun.y,
fun.ymin and fun.ymax; and fun.data. See the documentation for
stat_summary for more details
Minor improvements
*...