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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_mlregression.wasp

Title produced by software

Multiple Regression Machine Learning

Date of computation

Thu, 10 Sep 2020 22:29:07 +0200

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Sep/10/t1599769755s9trzjbaslwfheq.htm/, Retrieved Sat, 20 Apr 2024 13:07:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319240, Retrieved Sat, 20 Apr 2024 13:07:19 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

132

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression Machine Learning] [] [2020-09-10 20:29:07] [63a9f0ea7bb98050796b649e85481845] [Current]

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Dataseries X:

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Histogram

Boxplots

99.2	96.7	101.0
99.0	98.1	100.1
100.0	100.0	100.0
111.6	104.9	90.6
122.2	104.9	86.5
117.6	109.5	89.7
121.1	110.8	90.6
136.0	112.3	82.8
154.2	109.3	70.1
153.6	105.3	65.4
158.5	101.7	61.3
140.6	95.4	62.5
136.2	96.4	63.6
168.0	97.6	52.6
154.3	102.4	59.7
149.0	101.6	59.5
165.5	103.8	61.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319240&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

3 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319240&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319240&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Regression
> lm_via_caret$results intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD 1 FALSE 10.231392 0.8812081 8.531534 2.256155 0.06895833 1.930088 2 TRUE 6.569702 0.9554811 5.526304 1.438897 0.03747540 1.480363
> lm_via_caret$finalModel$tuneValue intercept 2 TRUE
> varImp(lm_via_caret) lm variable importance Overall c 100 b 0
> lm_via_caret$finalModel Call: lm(formula = .outcome ~ ., data = dat) Coefficients: (Intercept) b c 130.707 1.062 -1.383

\begin{tabular}{lllllllll}
\hline
Multiple Regression \tabularnewline
> lm_via_caret$results
  intercept      RMSE  Rsquared      MAE   RMSESD RsquaredSD    MAESD
1     FALSE 10.231392 0.8812081 8.531534 2.256155 0.06895833 1.930088
2      TRUE  6.569702 0.9554811 5.526304 1.438897 0.03747540 1.480363
 \tabularnewline
> lm_via_caret$finalModel$tuneValue
  intercept
2      TRUE
 \tabularnewline
> varImp(lm_via_caret)
lm variable importance
  Overall
c     100
b       0
 \tabularnewline
> lm_via_caret$finalModel
Call:
lm(formula = .outcome ~ ., data = dat)
Coefficients:
(Intercept)            b            c  
    130.707        1.062       -1.383  
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319240&T=1

[TABLE]
[ROW][C]Multiple Regression[/C][/ROW]
[ROW][C]> lm_via_caret$results
  intercept      RMSE  Rsquared      MAE   RMSESD RsquaredSD    MAESD
1     FALSE 10.231392 0.8812081 8.531534 2.256155 0.06895833 1.930088
2      TRUE  6.569702 0.9554811 5.526304 1.438897 0.03747540 1.480363
[/C][/ROW]
[ROW][C]> lm_via_caret$finalModel$tuneValue
  intercept
2      TRUE
[/C][/ROW]
[ROW][C]> varImp(lm_via_caret)
lm variable importance
  Overall
c     100
b       0
[/C][/ROW]
[ROW][C]> lm_via_caret$finalModel
Call:
lm(formula = .outcome ~ ., data = dat)
Coefficients:
(Intercept)            b            c  
    130.707        1.062       -1.383  
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319240&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319240&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Regression
> lm_via_caret$results intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD 1 FALSE 10.231392 0.8812081 8.531534 2.256155 0.06895833 1.930088 2 TRUE 6.569702 0.9554811 5.526304 1.438897 0.03747540 1.480363
> lm_via_caret$finalModel$tuneValue intercept 2 TRUE
> varImp(lm_via_caret) lm variable importance Overall c 100 b 0
> lm_via_caret$finalModel Call: lm(formula = .outcome ~ ., data = dat) Coefficients: (Intercept) b c 130.707 1.062 -1.383

Parameters (Session):

par1 = 1 ; par2 = no ;

Parameters (R input):

par1 = 1 ; par2 = no ;

R code (references can be found in the software module):

par2 <- 'no'
par1 <- '1'
library(caret)
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the target! The first column was selected by default.'
}
x <- na.omit(data.frame(t(x)))
k <- length(x[1,])
n <- length(x[,1])
x <- as.data.frame(x)
for(ii in 1:k) {
x[,ii] <- as.numeric(x[,ii])
}
myf <- formula(paste(colnames(x)[par1],' ~ .',sep=''))
myf
lm_grid <-   expand.grid(intercept = c(TRUE, FALSE))
fitControl <- trainControl(method = 'repeatedcv', number = 10, repeats = 5)
if(par2=='no') {
lm_via_caret <- train(myf, data = x, method = 'lm', tuneGrid = lm_grid)
}
if(par2=='yes') {
lm_via_caret <- train(myf, data = x, method = 'lm', tuneGrid = lm_grid, trControl = fitControl)
}
lm_via_caret
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Regression',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('lm_via_caret$results'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('lm_via_caret$finalModel$tuneValue'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('varImp(lm_via_caret )'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('lm_via_caret$finalModel'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')

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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code