In this vignette, we take a look at how we can simplify many machine learning tasks using `manymodelr`

.

```
install.packages("manymodelr")
```

Once the package has been successfully installed, we can then proceed by loading the package and exploring some of the key functions.

**Loading the package**

```
library(manymodelr)
data("yields", package="manymodelr")
```

First, a word of caution. The examples shown in this section are meant to simply show what the functions do and not what the best model is. For a specific use case, please perform the necessary model checks, post-hoc analyses, and/or choose predictor variables and model types as appropriate based on domain knowledge.

With this in mind, let us look at how we can perform modeling tasks using `manymodelr`

.

`multi_model_1`

This is one of the core functions of the package. `multi_model_1`

aims to allow model fitting, prediction, and reporting with a single function. The `multi`

part of the function’s name reflects the fact that we can fit several model types with one function. An example follows next.

For purposes of this report, we create a simple dataset to use.

```
set.seed(520)
train_set<-createDataPartition(yields$normal,p=0.6,list=FALSE)
valid_set<-yields[-train_set,]
train_set<-yields[train_set,]
ctrl<-trainControl(method="cv",number=5)
m<-multi_model_1(train_set,"normal",".",c("knn","rpart"),
"Accuracy",ctrl,new_data =valid_set)
```

The above returns a list containing metrics, predictions, and a model summary. These can be extracted as shown below.

```
m$metric
#> # A tibble: 1 x 2
#> knn_accuracy rpart_accuracy
#> <dbl> <dbl>
#> 1 0.872 0.68
```

```
head(m$predictions)
#> # A tibble: 6 x 2
#> knn rpart
#> <chr> <chr>
#> 1 Yes Yes
#> 2 No Yes
#> 3 No No
#> 4 No Yes
#> 5 No No
#> 6 Yes Yes
```

**multi_model_2**

This is similar to `multi_model_1`

with one difference: it does not use metrics such as RMSE, accuracy and the like. This function is useful if one would like to fit and predict “simpler models” like generalized linear models or linear models. Let’s take a look:

```
# fit a linear model and get predictions
lin_model <- multi_model_2(mtcars[1:16,],mtcars[17:32,],"mpg","wt","lm")
lin_model[c("predicted", "mpg")]
#> predicted mpg
#> Mazda RX4 10.17314 21.0
#> Mazda RX4 Wag 24.32264 21.0
#> Datsun 710 26.95458 22.8
#> Hornet 4 Drive 25.96479 21.4
#> Hornet Sportabout 23.13039 18.7
#> Valiant 18.38390 18.1
#> Duster 360 18.76632 14.3
#> Merc 240D 16.94420 24.4
#> Merc 230 16.92171 22.8
#> Merc 280 25.51488 19.2
#> Merc 280C 24.59258 17.8
#> Merc 450SE 27.41348 16.4
#> Merc 450SL 19.95856 17.3
#> Merc 450SLC 21.75818 15.2
#> Cadillac Fleetwood 18.15895 10.4
#> Lincoln Continental 21.71319 10.4
```

From the above, we see that `wt`

alone may not be a great predictor for `mpg`

. We can fit a multi-linear model with other predictors. Let’s say `disp`

and `drat`

are important too, then we add those to the model.

```
multi_lin <- multi_model_2(mtcars[1:16, ], mtcars[17:32,],"mpg", "wt + disp + drat","lm")
multi_lin[,c("predicted", "mpg")]
#> predicted mpg
#> Mazda RX4 10.43041 21.0
#> Mazda RX4 Wag 24.39765 21.0
#> Datsun 710 25.56629 22.8
#> Hornet 4 Drive 25.38957 21.4
#> Hornet Sportabout 23.15234 18.7
#> Valiant 17.36908 18.1
#> Duster 360 17.67102 14.3
#> Merc 240D 15.59802 24.4
#> Merc 230 14.96161 22.8
#> Merc 280 25.05592 19.2
#> Merc 280C 23.66222 17.8
#> Merc 450SE 25.95326 16.4
#> Merc 450SL 17.05637 17.3
#> Merc 450SLC 21.97756 15.2
#> Cadillac Fleetwood 17.22593 10.4
#> Lincoln Continental 22.17872 10.4
```

`fit_model`

This function allows us to fit any kind of model without necessarily returning predictions.

```
lm_model <- fit_model(mtcars,"mpg","wt","lm")
lm_model
#>
#> Call:
#> lm(formula = mpg ~ wt, data = use_df)
#>
#> Coefficients:
#> (Intercept) wt
#> 37.285 -5.344
```

`fit_models`

This is similar to `fit_model`

with the ability to fit many models with many predictors at once. A simple linear model for instance:

```
models<-fit_models(df=yields,yname=c("height", "weight"),xname="yield",
modeltype="glm")
```

One can then use these models as one may wish. To add residuals from these models for example:

```
res_residuals <- lapply(models[[1]], add_model_residuals,yields)
res_predictions <- lapply(models[[1]], add_model_predictions, yields, yields)
# Get height predictions for the model height ~ yield
head(res_predictions[[1]])
#> normal height weight yield predicted
#> 1 Yes 0.2849090 0.13442312 520.2837 0.5028866
#> 2 No 0.2427826 0.37484971 504.4754 0.4943626
#> 3 Yes 0.2579432 0.47134828 515.6463 0.5003860
#> 4 No 0.5175604 0.50143592 522.2247 0.5039331
#> 5 Yes 0.4026023 0.47171755 502.6406 0.4933732
#> 6 No 0.9789886 0.04191937 509.4663 0.4970537
```

If one would like to drop non-numeric columns from the analysis, one can set `drop_non_numeric`

to `TRUE`

as follows. The same can be done for `fit_model`

above:

```
fit_models(df=yields,yname=c("height","weight"),
xname=".",modeltype=c("lm","glm"), drop_non_numeric = TRUE)
#> [[1]]
#> [[1]][[1]]
#>
#> Call:
#> lm(formula = height ~ ., data = use_df)
#>
#> Coefficients:
#> (Intercept) weight yield
#> 0.2176942 -0.2185572 0.0006712
#>
#>
#> [[1]][[2]]
#>
#> Call:
#> lm(formula = weight ~ ., data = use_df)
#>
#> Coefficients:
#> (Intercept) height yield
#> 0.0112753 -0.1463926 0.0006827
#>
#>
#>
#> [[2]]
#> [[2]][[1]]
#>
#> Call: glm(formula = height ~ ., data = use_df)
#>
#> Coefficients:
#> (Intercept) weight yield
#> 0.2176942 -0.2185572 0.0006712
#>
#> Degrees of Freedom: 999 Total (i.e. Null); 997 Residual
#> Null Deviance: 45.82
#> Residual Deviance: 44.32 AIC: -270.3
#>
#> [[2]][[2]]
#>
#> Call: glm(formula = weight ~ ., data = use_df)
#>
#> Coefficients:
#> (Intercept) height yield
#> 0.0112753 -0.1463926 0.0006827
#>
#> Degrees of Freedom: 999 Total (i.e. Null); 997 Residual
#> Null Deviance: 30.7
#> Residual Deviance: 29.69 AIC: -671.1
```

To extract information about a given model, we can use `extract_model_info`

as follows.

```
extract_model_info(lm_model, "r2")
#> [1] 0.7528328
```

To extract the adjusted R squared:

```
extract_model_info(lm_model, "adj_r2")
#> [1] 0.7445939
```

For the p value:

```
extract_model_info(lm_model, "p_value")
#> (Intercept) wt
#> 8.241799e-19 1.293959e-10
```

To extract multiple attributes:

```
extract_model_info(lm_model,c("p_value","response","call","predictors"))
#> $p_value
#> (Intercept) wt
#> 8.241799e-19 1.293959e-10
#>
#> $response
#> [1] "mpg"
#>
#> $call
#> lm(formula = mpg ~ wt, data = use_df)
#>
#> $predictors
#> [1] "wt"
```

This is not restricted to linear models but will work for most model types. See `help(extract_model_info)`

to see currently supported model types.

`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.

**Previously, one would set get_all to TRUE if they wanted to get correlations between all variables. This argument has been dropped in favor of simply supplying an optional other_vars vector if one does not want to get all correlations.**

Sample usage:

```
# getall correlations
# default pearson
head( corrs <- get_var_corr(mtcars,comparison_var="mpg") )
#> comparison_var other_var p.value correlation lower_ci upper_ci
#> 1 mpg cyl 6.112687e-10 -0.8521620 -0.92576936 -0.7163171
#> 2 mpg disp 9.380327e-10 -0.8475514 -0.92335937 -0.7081376
#> 3 mpg hp 1.787835e-07 -0.7761684 -0.88526861 -0.5860994
#> 4 mpg drat 1.776240e-05 0.6811719 0.43604838 0.8322010
#> 5 mpg wt 1.293959e-10 -0.8676594 -0.93382641 -0.7440872
#> 6 mpg qsec 1.708199e-02 0.4186840 0.08195487 0.6696186
```

**Previously, one would also set drop_columns to TRUE if they wanted to drop factor columns.** Now, a user simply provides a character vector specifying which column types(classes) should be dropped. It defaults to

`c("character","factor")`

.```
# purely demonstrative
get_var_corr(yields,"height",other_vars="weight",
drop_columns=c("factor","character"),method="spearman",
exact=FALSE)
#> Warning in get_var_corr.data.frame(yields, "height", other_vars = "weight", :
#> Columns with classes in drop_columns have been discarded. You can disable this
#> yourself by setting drop_columns to NULL.
#> comparison_var other_var p.value correlation
#> 1 height weight 4.204642e-07 -0.1591719
```

Similarly, `get_var_corr_`

(note the underscore at the end) provides a convenient way to get combination-wise correlations.

```
head(get_var_corr_(yields),6)
#> Warning in get_var_corr_.data.frame(yields): Columns with classes in
#> drop_columns were dropped.
#> comparison_var other_var p.value correlation lower_ci upper_ci
#> 1 height weight 1.470866e-08 -0.17793196 -0.23730741 -0.11723201
#> 2 height yield 4.473683e-01 0.02405390 -0.03799584 0.08591886
#> 3 weight yield 2.986171e-01 0.03290108 -0.02915146 0.09470100
```

To use only a subset of the data, we can use provide a list of columns to `subset_cols`

. By default, the first value(vector) in the list is mapped to `comparison_var`

and the other to `other_Var`

. The list is therefore of length 2.

```
head(get_var_corr_(mtcars,subset_cols=list(c("mpg","vs"),c("disp","wt")),
method="spearman",exact=FALSE))
#> comparison_var other_var p.value correlation
#> 2 mpg disp 6.370336e-13 -0.9088824
#> 5 mpg wt 1.487595e-11 -0.8864220
```

`plot_corr`

Obtaining correlations would mostly likely benefit from some form of visualization. `plot_corr`

aims to achieve just that. There are currently two plot styles, `squares`

and `circles`

. `circles`

has a `shape`

argument that can allow for more flexibility. It should be noted that the correlation matrix supplied to this function is an object produced by `get_var_corr_`

.

To modify the plot a bit, we can choose to switch the x and y values as shown below.

```
plot_corr(mtcars,show_which = "corr",
round_which = "correlation",decimals = 2,x="other_var", y="comparison_var",plot_style = "squares"
,width = 1.1,custom_cols = c("green","blue","red"),colour_by = "correlation")
#> Warning in plot_corr(mtcars, show_which = "corr", round_which = "correlation", :
#> Using colour_by for the legend title.
```

To show significance of the results instead of the correlations themselves, we can set `show_which`

to “signif” as shown below. By default, significance is set to 0.05. You can override this by supplying a different `signif_cutoff`

.

```
# color by p value
# change custom colors by supplying custom_cols
# significance is default
set.seed(233)
plot_corr(mtcars, x="other_var", y="comparison_var",plot_style = "circles",show_which = "signif", colour_by = "p.value", sample(colours(),3))
#> Warning in plot_corr(mtcars, x = "other_var", y = "comparison_var", plot_style =
#> "circles", : Using colour_by for the legend title.
```

To explore more options, please take a look at the documentation.

`agg_by_group`

As can be guessed from the name, this function provides an easy way to manipulate grouped data. We can for instance find the number of observations in the yields data set. The formula takes the form `x~y`

where `y`

is the grouping variable(in this case `normal`

). One can supply a formula as shown next.

```
head(agg_by_group(yields,.~normal,length))
#> Grouped By[1]: normal
#>
#> normal height weight yield
#> 1 No 500 500 500
#> 2 Yes 500 500 500
```

```
head(agg_by_group(mtcars,cyl~hp+vs,sum))
#> Grouped By[2]: hp vs
#>
#> hp vs cyl
#> 1 91 0 4
#> 2 110 0 12
#> 3 150 0 16
#> 4 175 0 22
#> 5 180 0 24
#> 6 205 0 8
```

`rowdiff`

This is useful when trying to find differences between rows. The `direction`

argument specifies how the subtractions are made while the `exclude`

argument is used to specify classes that should be removed before calculations are made. Using `direction="reverse"`

performs a subtraction akin to `x-(x-1)`

where `x`

is the row number.

```
head(rowdiff(yields,exclude = "factor",direction = "reverse"))
#> height weight yield
#> 1 NA NA NA
#> 2 -0.04212634 0.24042659 -15.808303
#> 3 0.01516059 0.09649856 11.170825
#> 4 0.25961718 0.03008764 6.578424
#> 5 -0.11495811 -0.02971837 -19.584090
#> 6 0.57638627 -0.42979818 6.825719
```

`na_replace`

This allows the user to conveniently replace missing values. Current options are `ffill`

which replaces with the next non-missing value, `samples`

that samples the data and does replacement, `value`

that allows one to fill `NA`

s with a specific value. Other common mathematical methods like `min`

, `max`

,`get_mode`

, `sd`

, etc are no longer supported. They are now available with more flexibility in standalone mde

```
head(na_replace(airquality, how="value", value="Missing"),8)
#> Ozone Solar.R Wind Temp Month Day
#> 1 41 190 7.4 67 5 1
#> 2 36 118 8.0 72 5 2
#> 3 12 149 12.6 74 5 3
#> 4 18 313 11.5 62 5 4
#> 5 Missing Missing 14.3 56 5 5
#> 6 28 Missing 14.9 66 5 6
#> 7 23 299 8.6 65 5 7
#> 8 19 99 13.8 59 5 8
```

`na_replace_grouped`

This provides a convenient way to replace values by group.

```
test_df <- data.frame(A=c(NA,1,2,3), B=c(1,5,6,NA),groups=c("A","A","B","B"))
# Replace NAs by group
# replace with the next non NA by group.
na_replace_grouped(df=test_df,group_by_cols = "groups",how="ffill")
#> groups A B
#> 1 A 1 1
#> 2 A 1 5
#> 3 B 2 6
#> 4 B 3 6
```

The use of `mean`

,`sd`

,etc is no longer supported. Use mde instead which is focused on missingness.

**Exploring Further**

The vignette has been short and therefore is non exhaustive. The best way to explore this and any package or language is to practise. For more examples, please use `?function_name`

and see a few implementations of the given function.

**Reporting Issues**

If you would like to contribute, report issues or improve any of these functions, please raise a pull request at (manymodelr)

“Programs must be written for people to read, and only incidentally for machines to execute.” - Harold Abelson (Reference)

**Thank You**