Tune and build several Machine Learning models.
-
Installing the package
-
From CRAN(0.2.2)
install.packages("manymodelr")
- From GitHub
remotes::install_github("Nelson-Gon/manymodelr")
devtools::install_github("Nelson-Gon/manymodelr")
devtools::install_github("Nelson-Gon/manymodelr",build_vignettes=TRUE) #Builds vignettes
For the current(unstable) developer version, please see develop.
- Loading the package
library(manymodelr)
Example usage of major functions
multi_model_1
suppressMessages(library(caret))
set.seed(520)
train_set<-createDataPartition(iris$Species,p=0.8,list=FALSE)
valid_set<-iris[-train_set,]
train_set<-iris[train_set,]
ctrl<-trainControl(method="cv",number=5)
m<-multi_model_1(train_set,"Species",".",c("knn","rpart"),
"Accuracy",ctrl,newdata =valid_set,valid=TRUE)
In the above we have trained and also got predictions(validated) on our data.
Results:
To get the metrics for all our models, we can proceed as follows:
m$Metrics
# A tibble: 1 x 2
knn rpart
<dbl> <dbl>
1 0.933 0.967
To obtain the predicted values(validation in this case):
head(m$Predictions)
# A tibble: 6 x 2
knn rpart
<fct> <fct>
1 setosa setosa
2 setosa setosa
3 setosa setosa
4 setosa setosa
5 setosa setosa
6 setosa setosa
One can also get all the corresponding model statistics as follows:
m$modelInfo
modeleR
This provides a convenient way to build linear models, generalised linear models and carry out analysis of variance(currently). Example usage is as shown below:
iris1<-iris[1:60,]
iris2<-iris[60:nrow(iris),]
m1<-modeleR(iris1,Sepal.Length,Petal.Length,
lm,na.rm=TRUE,iris2)
We can get the predicted values as shown below:
head(m1$Predictions)
Predicted
60 5.985141
61 5.821972
62 6.107518
63 6.025933
64 6.311478
65 5.862764
get_var_corr
As can probably(hopefully) be guessed from the name, this provides a convenient way to get variable correlations. It enables one to get correlation between one variable and all other variables in the data set if get_all is set to TRUE or with specific variables if get_all is set to FALSE
Sample usage:
corrs <- get_var_corr(mtcars,comparison_var="mpg",
get_all=TRUE)
The result is as follows(default pearson):
head(corrs)
Comparison_Var Other_Var p_value Correlation lower_ci
1 mpg cyl 6.112687e-10 -0.8521620 -0.92576936
2 mpg disp 9.380327e-10 -0.8475514 -0.92335937
3 mpg hp 1.787835e-07 -0.7761684 -0.88526861
4 mpg drat 1.776240e-05 0.6811719 0.43604838
5 mpg wt 1.293959e-10 -0.8676594 -0.93382641
6 mpg qsec 1.708199e-02 0.4186840 0.08195487
upper_ci
1 -0.7163171
2 -0.7081376
3 -0.5860994
4 0.8322010
5 -0.7440872
6 0.6696186
- A closely related function is
get_var_corr_(note the underscore) that enables finer control over which correlations to obtain with the ability to perform combination wise correlations. To get correlations formpgandvs"against"cylanddispl, one could do:
head(get_var_corr_(mtcars, comparison_var=c("mpg","vs"), other_var=c("cyl",displ),method="kendall"))
The above gives us(strictly kendall is used for demonstration purposes):
Comparison_Var Other_Var p.value Correlation lower_ci
1 mpg cyl 6.112687e-10 -0.8521620 -0.92576936
2 mpg disp 9.380327e-10 -0.8475514 -0.92335937
3 mpg hp 1.787835e-07 -0.7761684 -0.88526861
4 mpg drat 1.776240e-05 0.6811719 0.43604838
5 mpg wt 1.293959e-10 -0.8676594 -0.93382641
6 mpg qsec 1.708199e-02 0.4186840 0.08195487
upper_ci
1 -0.7163171
2 -0.7081376
3 -0.5860994
4 0.8322010
5 -0.7440872
6 0.6696186
rowdiff
If one needs to obtain differences between rows, rowdiff is designed to do exactly that.
head(rowdiff(iris,direction="reverse", exclude="non_numeric"))
This gives us the following result:
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 NA NA NA NA
2 -0.2 -0.5 0.0 0.0
3 -0.2 0.2 -0.1 0.0
4 -0.1 -0.1 0.2 0.0
5 0.4 0.5 -0.1 0.0
6 0.4 0.3 0.3 0.2
The NAs can simply be dealt with as necessary. An NA simply serves to show the direction in which the differences were performed. See the documentation for more details.
Space constraints mean that a detailed exploration of the package cannot be made. A more thorough walkthrough is provided in the vignettes that can be opened as shown below:
browseVignettes("manymodelr")
For previous users, please see the NEWS.md file for a list of changes and/or additions. For a complete list of available functions, please use:
help(package="manymodelr")
Thank You and Happy Coding!