blocks|key|76660|text|当你不知道如何去做时，这里有一种方法可以在R中获得关于新主题/包的帮助：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|76661|library(help=package.name)|code-block|syntax|javascript|76662|这将为您提供在该语言中定义的所有函数和数据集的概述，并给出每个函数和数据集的简短标题。在确定了所需的函数之后，您可以像这样查阅感兴趣的函数的文档：|76663|?function.name|76664|在文档中，还要注意See+Also部分，该部分通常列出了与所考虑的函数结合使用的有用函数。此外，还可以使用示例。您还可以使用|offset|length|style|CODE|76665|example(function.name)|76666|以演示该函数的用法和使用它的常见习惯用法。|76667|最后，如果您幸运的话，包的作者可能已经为包编写了一个vignette。您可以搜索包中的所有小插图，如下所示：|76668|vignette(package="package.name")|76669|希望这能让您开始使用rminer和nnet包。|76670|entityMap^0|0|0|0|0|9|8|0|0|0|Q|8|0|0|A|6|H|4|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|15|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|16|8|@$M|17|N|18|O|P]]|9|@]|A|$]]|$1|Q|3|R|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|S|3|T|5|6|7|1A|8|@]|9|@]|A|$]]|$1|U|3|V|5|6|7|1B|8|@$M|1C|N|1D|O|P]]|9|@]|A|$]]|$1|W|3|X|5|D|7|1E|8|@]|9|@]|A|$E|F]]|$1|Y|3|Z|5|6|7|1F|8|@$M|1G|N|1H|O|P]|$M|1I|N|1J|O|P]]|9|@]|A|$]]|$1|10|3|-4|5|6|7|1K|8|@]|9|@]|A|$]]]|11|$]]

Here is a way to get help on a new topic / package in R when you don't know how to go about it:

<pre><code>library(help=package.name)
</code></pre>

This will give you an overview of all the functions and data sets defined in the language with a brief title of each. After you have identified the functions that you need, you can consult the documentation of the functions of interest like so:

<pre><code>?function.name
</code></pre>

In the documentation, also pay attention to the <code>See Also</code> section which typically lists functions that are useful in conjunction with the function being considered. Also, work the examples. You can also use

<pre><code>example(function.name)
</code></pre>

for a demonstration of the function's use and common idioms using it.

Lastly, if you are lucky, the package author may have written a <code>vignette</code> for the package. You can search for all vignettes in a package like this:

<pre><code>vignette(package="package.name")
</code></pre>

Hopefully, this will get you started with the <code>rminer</code> and <code>nnet</code> packages.

blocks|key|293893|text|也许太晚了，但我在寻找我的问题的答案时发现了这个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|293894|++++#+Splitting+in+training,+Cross-Validation+and+test+datasets
++++++++#The+entire+dataset+has+100%25+of+the+observations.+The+training+dataset+will+have+60%25,+the+Cross-Validation+(CV)+will+have+20%25+and+the+testing+dataset+will+have+20%25.++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++train_ind+<-+sample(seq_len(nrow(DF.mergedPredModels)),+size+=+floor(0.6+*+nrow(DF.mergedPredModels)))
++++++++trainDF.mergedPredModels+<-+DF.mergedPredModels[train_ind,+]

++++++++#+The+CV+and+testing+datasets'+observations+will+be+built+from+the+observations+from+the+initial+dataset+excepting+the+ones+from+the+training+dataset
++++++++#+Cross-Validation+dataset
++++++++#+The+CV's+number+of+observations+can+be+changed+simply+by+changing+"0.5"+to+a+fraction+of+your+choice+but+the+CV+and+testing+dataset's+fractions+must+add+up+to+1.
++++++++cvDF.mergedPredModels+<-+DF.mergedPredModels[-train_ind,+][sample(seq_len(nrow(DF.mergedPredModels[-train_ind,+])),+size+=+floor(0.5+*+nrow(DF.mergedPredModels[-train_ind,+]))),]

++++++++#+Testing+dataset
++++++++testDF.mergedPredModels+<-+DF.mergedPredModels[-train_ind,+][-sample(seq_len(nrow(DF.mergedPredModels[-train_ind,+])),+size+=+floor(0.5+*+nrow(DF.mergedPredModels[-train_ind,+]))),]

++++++++#temporal+data+and+other+will+be+added+after+the+predictions+are+made+because+I+don't+need+the+models+to+be+built+on+the+dates.+Additionally,+you+can+add+these+columns+to+the+training,+CV+and+testing+datasets+and+plot+the+real+values+of+your+predicted+parameter+and+the+respective+predicitons+over+your+time+variables+(half-hour,+hour,+day,+week,+month,+quarter,+season,+year,+etc.).
++++++++#+aa+=+Explicitly+specify+the+columns+to+be+used+in+the+temporal+datasets
++++++++aa+<-+c("date",+"period",+"publish_date",+"quarter",+"month",+"Season")
++++++++temporaltrainDF.mergedPredModels+<-+trainDF.mergedPredModels[,+c(aa)]
++++++++temporalcvDF.mergedPredModels+<-+cvDF.mergedPredModels[,+c(aa)]
++++++++temporaltestDF.mergedPredModels+<-+testDF.mergedPredModels[,+c(aa)]

++++++++#+bb+=+Explicitly+specify+the+columns+to+be+used+in+the+training,+CV+and+testing+datasets
++++++++bb+<-+c("quarter",+"month",+"Season",+"period",+"temp.mean",+"wind_speed.mean",+"solar_radiation",+"realValue")
++++++++trainDF.mergedPredModels.Orig+<-+trainDF.mergedPredModels[,+c(bb)]
++++++++trainDF.mergedPredModels+<-+trainDF.mergedPredModels[,+c(bb)]
++++++++smalltrainDF.mergedPredModels.Orig+<-+trainDF.mergedPredModels.Orig[1:10,]+#see+if+the+models+converge+without+errors
++++++++cvDF.mergedPredModels+<-+cvDF.mergedPredModels[,+c(bb)]
++++++++testDF.mergedPredModels+<-+testDF.mergedPredModels[,+c(bb)]
#+/Splitting+in+training,+Cross-Validation+and+test+datasets|code-block|syntax|javascript|293895|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

It's maybe too late, but I found this Q while I was looking for an answer to my Q.
You can use something like this

<pre><code> # Splitting in training, Cross-Validation and test datasets
 #The entire dataset has 100% of the observations. The training dataset will have 60%, the Cross-Validation (CV) will have 20% and the testing dataset will have 20%. 
 train_ind &lt;- sample(seq_len(nrow(DF.mergedPredModels)), size = floor(0.6 * nrow(DF.mergedPredModels)))
 trainDF.mergedPredModels &lt;- DF.mergedPredModels[train_ind, ]

 # The CV and testing datasets' observations will be built from the observations from the initial dataset excepting the ones from the training dataset
 # Cross-Validation dataset
 # The CV's number of observations can be changed simply by changing "0.5" to a fraction of your choice but the CV and testing dataset's fractions must add up to 1.
 cvDF.mergedPredModels &lt;- DF.mergedPredModels[-train_ind, ][sample(seq_len(nrow(DF.mergedPredModels[-train_ind, ])), size = floor(0.5 * nrow(DF.mergedPredModels[-train_ind, ]))),]

 # Testing dataset
 testDF.mergedPredModels &lt;- DF.mergedPredModels[-train_ind, ][-sample(seq_len(nrow(DF.mergedPredModels[-train_ind, ])), size = floor(0.5 * nrow(DF.mergedPredModels[-train_ind, ]))),]

 #temporal data and other will be added after the predictions are made because I don't need the models to be built on the dates. Additionally, you can add these columns to the training, CV and testing datasets and plot the real values of your predicted parameter and the respective predicitons over your time variables (half-hour, hour, day, week, month, quarter, season, year, etc.).
 # aa = Explicitly specify the columns to be used in the temporal datasets
 aa &lt;- c("date", "period", "publish_date", "quarter", "month", "Season")
 temporaltrainDF.mergedPredModels &lt;- trainDF.mergedPredModels[, c(aa)]
 temporalcvDF.mergedPredModels &lt;- cvDF.mergedPredModels[, c(aa)]
 temporaltestDF.mergedPredModels &lt;- testDF.mergedPredModels[, c(aa)]

 # bb = Explicitly specify the columns to be used in the training, CV and testing datasets
 bb &lt;- c("quarter", "month", "Season", "period", "temp.mean", "wind_speed.mean", "solar_radiation", "realValue")
 trainDF.mergedPredModels.Orig &lt;- trainDF.mergedPredModels[, c(bb)]
 trainDF.mergedPredModels &lt;- trainDF.mergedPredModels[, c(bb)]
 smalltrainDF.mergedPredModels.Orig &lt;- trainDF.mergedPredModels.Orig[1:10,] #see if the models converge without errors
 cvDF.mergedPredModels &lt;- cvDF.mergedPredModels[, c(bb)]
 testDF.mergedPredModels &lt;- testDF.mergedPredModels[, c(bb)]
# /Splitting in training, Cross-Validation and test datasets
</code></pre>

I'm new programmer in R and i'm writing my thesis for training a neural network.
First i use rminer for datamining and after nnet for training.
Now i don't know which function use for divide data-set in training set and validation set, therefore k-fold cross validation, and after use nnet for each of this.
sorry for my english.
Thanks in advance

How use Rminer and nnet

数据挖掘

我是R的新程序员，我正在写一篇关于训练神经网络的论文。首先，我使用rminer进行数据挖掘，然后使用nnet进行训练。现在我不知道哪个函数用于划分训练集和验证集的数据集，因此k折交叉验证，并在使用set后对每个数据集进行交叉验证。对不起，我的英语不好。提前感谢

问如何使用Rminer和nnet
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