| Type: | Package |
| Title: | Exploratory Regression 'Shiny' App |
| Version: | 0.1.4 |
| Date: | 2023-8-21 |
| Author: | Catherine B. Hurley |
| Maintainer: | Catherine B. Hurley <catherine.hurley@mu.ie> |
| Description: | Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| Encoding: | UTF-8 |
| Imports: | shiny, miniUI,RColorBrewer, ggplot2, car, leaps, broom, dplyr, tidyr, purrr, combinat,stats, methods, rlang |
| RoxygenNote: | 7.2.3 |
| Suggests: | knitr, rmarkdown, testthat |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2023-08-21 11:49:44 UTC; catherine |
| Repository: | CRAN |
| Date/Publication: | 2023-08-21 12:20:02 UTC |
ERSA: A package exploring regressions with a Shiny app
Description
The Exploratory Regression Shiny App (ERSA) package consists of a collection of functions for displaying the results of a regression calculation, which are then packaged together as a shiny app function.
Constructs a list of fits by adding predictors sequentially
Description
Constructs a list of fits by adding predictors sequentially
Usage
add1_models(model, preds, data = NULL)
Arguments
model |
A linear model |
preds |
Predictors to be added sequentially |
data |
The dataset (optional) |
Value
A list of linear fits
A function which returns a shiny server for Exploratory Regression
Description
A function which returns a shiny server for Exploratory Regression
Usage
createERServer(
ERfit,
ERdata = NULL,
ERbarcols = RColorBrewer::brewer.pal(4, "Set2"),
ERnpcpCols = 4,
pvalOrder = F
)
Arguments
ERfit |
the lm fit to be explored |
ERdata |
the data used to fit the model. If NULL, attempts to extract from ERfit. |
ERbarcols |
a vector of colours, one per term in lm. Will be expanded via colorRampPalette if not the correct length. |
ERnpcpCols |
number of colours for the PCP |
pvalOrder |
if TRUE, re-arranges predictors in order of p-value |
Value
a function
Constructs UI for Exploratory Regression app
Description
Constructs UI for Exploratory Regression app
Usage
createERUI(tablesOnly = F, gadget = TRUE)
Arguments
tablesOnly |
if TRUE, shows Plots 1-3 only. |
gadget |
If TRUE, constructs a gadget, otherwise a shinyApp |
Value
the UI
Constructs a list of fits by dropping predictors from the supplied model
Description
Constructs a list of fits by dropping predictors from the supplied model
Usage
drop1_models(model, preds, data = NULL)
Arguments
model |
A linear model |
preds |
Predictors to be dropped |
data |
The dataset (optional) |
Value
A list of linear fits
A function to launch the Exploratory Regression Shiny App
Description
A function to launch the Exploratory Regression Shiny App
Usage
exploreReg(
ERmfull,
ERdata = NULL,
ERbarcols = RColorBrewer::brewer.pal(4, "Set2"),
npcpCols = 4,
pvalOrder = F,
tablesOnly = F,
displayHeight = NULL,
gadget = TRUE,
viewer = "dialogViewer"
)
Arguments
ERmfull |
the lm fit to be explored |
ERdata |
the data used to fit the model. If NULL, attempts to extract from ERmfull. |
ERbarcols |
a vector of colours, one per term in lm. Will be expanded via colorRampPalette if not the correct length. |
npcpCols |
number of colours for the PCP |
pvalOrder |
if TRUE, re-arranges predictors in order of p-value |
tablesOnly |
if TRUE, shows Plots 1-3 only. |
displayHeight |
supply a value for the display height |
gadget |
If TRUE, constructs a gadget, otherwise a shinyApp. |
viewer |
For gadget, defaults to "dialogViewer". May be "paneViewer" or "browserViewer" |
Value
the result
Examples
f <- lm(mpg ~ hp+wt+disp, data=mtcars)
## Not run: exploreReg(f)
A PCP plot of the data, residuals or hat values from regression fits
Description
A PCP plot of the data, residuals or hat values from regression fits
Usage
pcpPlot(
data,
fit,
type = "Variables",
npcpCols = 4,
resDiff = F,
absResid = F,
sequential = F,
selnum = NULL
)
Arguments
data |
a data frame |
fit |
a lm for the data frame |
type |
one of "Variables", "Residuals", "Hatvalues" |
npcpCols |
number of colours |
resDiff |
difference residuals, TRUE or FALSE |
absResid |
absolute residuals, TRUE or FALSE |
sequential |
use sequential fits (TRUE) or drop1 fits (FALSE) |
selnum |
row numbers of cases to be highlighted |
Value
ggplot
Examples
f <- lm(mpg ~ wt+hp+disp, data=mtcars)
pcpPlot(mtcars, f, type="Residuals")
Plots barcharts of sequential sums of squares of lm
Description
Plots barcharts of sequential sums of squares of lm
Usage
plotSeqSS(fits, barcols = NULL, legend = F)
Arguments
fits |
list of lm objects |
barcols |
a vector of colours, one per term in lms |
legend |
TRUE or FALSE |
Value
a ggplot
Examples
plotSeqSS(list(fit1= lm(mpg ~ wt+hp+disp, data=mtcars),
fit2=lm(mpg ~ wt*hp*disp, data=mtcars)))
Plots of model summaries
Description
Plots of model summaries
Usage
plotAnovaStats(
fit0,
barcols = NULL,
preds = NULL,
alpha = 0.05,
type = "SS",
width = 0.3
)
plottStats(fit0, barcols = NULL, preds = NULL, alpha = 0.05, width = 0.3)
plotCIStats(
fit0,
barcols = NULL,
preds = NULL,
alpha = 0.05,
stdunits = FALSE,
width = 0.3
)
Arguments
fit0 |
is an lm object |
barcols |
a vector of colours, one per term in lm |
preds |
terms to include in plot |
alpha |
significance level |
type |
"SS" or "F", from type 3 Anova |
width |
bar width |
stdunits |
TRUE or FALSE. If TRUE, coefficients refer to standardised predictor units. |
Value
a ggplot
Functions
-
plotAnovaStats(): Plots barchart of F or SS from lm -
plottStats(): Plots barchart of t stats from lm -
plotCIStats(): Plots confidence intervals from lm
Examples
plotAnovaStats(lm(mpg ~ wt+hp+disp, data=mtcars))
plottStats(lm(mpg ~ wt+hp+disp, data=mtcars))
plotCIStats(lm(mpg ~ wt+hp+disp, data=mtcars))
Re-order model terms
Description
Re-order model terms
Usage
pvalOrder(m, d = NULL, refit = TRUE)
bselOrder(m, d = NULL, refit = TRUE, maxNPred = NULL)
fselOrder(m, d = NULL, refit = TRUE, maxNPred = NULL)
revPredOrder(m, d = NULL, refit = TRUE)
randomPredOrder(m, d = NULL, refit = TRUE)
regsubsetsOrder(m, d = NULL, refit = TRUE, collapse = TRUE)
Arguments
m |
an lm objecct |
d |
the data frame. If NULL, attempts to extract from m. |
refit |
TRUE or FALSE |
maxNPred |
maximum number of predictors to use, defaults to all. |
collapse |
TRUE or FALSE |
Value
a vector of terms in order last to first, or an lm if refit=TRUE. regsubsetsOrder returns a list of predictor vectors, or a list of fits
Functions
-
pvalOrder(): Arranges model terms in order of increasing p-value -
bselOrder(): Arranges model terms using backwards selection -
fselOrder(): Forwards selection -
revPredOrder(): Reverses order of terms in a fit -
randomPredOrder(): Reorders terms in a fit randomly -
regsubsetsOrder(): Best subsets regression.
Examples
bselOrder(lm(mpg~wt+hp+disp, data=mtcars))
fselOrder(lm(mpg~wt+hp+disp, data=mtcars))
revPredOrder(lm(mpg~wt+hp+disp, data=mtcars))
randomPredOrder(lm(mpg~wt+hp+disp, data=mtcars))
regsubsetsOrder(lm(mpg~wt+hp+disp, data=mtcars))
Constructs colour vector for model terms
Description
Constructs colour vector for model terms
Usage
termColours(f, pal = RColorBrewer::brewer.pal(4, "Set2"))
Arguments
f |
a model fit with term labels |
pal |
use this palette |
Value
a vector of colours. Residuals are given a grey color
Examples
termColours(lm(mpg ~ wt+hp, data=mtcars))