| Type: | Package |
| Title: | Multivariate Menu for Radiant: Business Analytics using R and Shiny |
| Version: | 1.6.7 |
| Date: | 2025-3-9 |
| Description: | The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in 'radiant.data'. |
| Depends: | R (≥ 4.3.0), radiant.data (≥ 1.6.6) |
| Imports: | radiant.model (≥ 1.6.6), shiny (≥ 1.8.1), dplyr (≥ 1.0.7), rlang (≥ 0.4.10), ggplot2 (≥ 2.2.1), scales (≥ 0.4.0), magrittr (≥ 1.5), psych (≥ 1.8.4), GPArotation (≥ 2014.11-1), car (≥ 2.1.1), MASS (≥ 7.3), import (≥ 1.1.0), ggrepel (≥ 0.8), lubridate (≥ 1.7.4), polycor (≥ 0.7.10), gower (≥ 0.2.1), clustMixType (≥ 0.2.1), patchwork (≥ 1.0.0) |
| Suggests: | testthat (≥ 2.0.0), pkgdown (≥ 1.1.0) |
| URL: | https://github.com/radiant-rstats/radiant.multivariate/, https://radiant-rstats.github.io/radiant.multivariate/, https://radiant-rstats.github.io/docs/ |
| BugReports: | https://github.com/radiant-rstats/radiant.multivariate/issues/ |
| License: | AGPL-3 | file LICENSE |
| LazyData: | true |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-03-10 18:17:19 UTC; vnijs |
| Author: | Vincent Nijs [aut, cre] |
| Maintainer: | Vincent Nijs <radiant@rady.ucsd.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-03-10 18:40:02 UTC |
Carpet cleaners
Description
Carpet cleaners
Usage
data(carpet)
Format
A data frame with 18 rows and 5 variables
Details
Rankings reflect the evaluation of 18 alternative carpet cleaners by one respondent. Description provided in attr(carpet," description")
City distances
Description
City distances
Usage
data(city)
Format
A data frame with 45 rows and 3 variables
Details
Distance in miles between nine cities in the USA. The dataset is used to illustrate multi-dimensional scaling (MDS). Description provided in attr(city, "description")
City distances 2
Description
City distances 2
Usage
data(city2)
Format
A data frame with 78 rows and 3 variables
Details
Distance in miles between 12 cities in the USA. The dataset is used to illustrate multi-dimensional scaling (MDS). Description provided in attr(city2, "description")
Sort and clean loadings
Description
Sort and clean loadings
Usage
clean_loadings(floadings, cutoff = 0, fsort = FALSE, dec = 8, repl = NA)
Arguments
floadings |
Data frame with loadings |
cutoff |
Show only loadings with (absolute) values above cutoff (default = 0) |
fsort |
Sort factor loadings |
dec |
Number of decimals to show |
repl |
Replace loadings below the cutoff by NA (or "") |
Details
See https://radiant-rstats.github.io/docs/multivariate/full_factor.html for an example in Radiant
Examples
result <- full_factor(shopping, "v1:v6", nr_fact = 2)
clean_loadings(result$floadings, fsort = TRUE, cutoff = .5, dec = 2)
Perceptions of computer (re)sellers
Description
Perceptions of computer (re)sellers
Usage
data(computer)
Format
A data frame with 5 rows and 8 variables
Details
Perceptions of computer (re)sellers. The dataset is used to illustrate perceptual maps. Description provided in attr(computer, "description")
Conjoint analysis
Description
Conjoint analysis
Usage
conjoint(
dataset,
rvar,
evar,
int = "",
by = "none",
reverse = FALSE,
data_filter = "",
envir = parent.frame()
)
Arguments
dataset |
Dataset |
rvar |
The response variable (e.g., profile ratings) |
evar |
Explanatory variables in the regression |
int |
Interaction terms to include in the model |
by |
Variable to group data by before analysis (e.g., a respondent id) |
reverse |
Reverse the values of the response variable ('rvar') |
data_filter |
Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/multivariate/conjoint.html for an example in Radiant
Value
A list with all variables defined in the function as an object of class conjoint
See Also
summary.conjoint to summarize results
plot.conjoint to plot results
Examples
conjoint(mp3, rvar = "Rating", evar = "Memory:Shape") %>% str()
Factor analysis (PCA)
Description
Factor analysis (PCA)
Usage
full_factor(
dataset,
vars,
method = "PCA",
hcor = FALSE,
nr_fact = 1,
rotation = "varimax",
data_filter = "",
envir = parent.frame()
)
Arguments
dataset |
Dataset |
vars |
Variables to include in the analysis |
method |
Factor extraction method to use |
hcor |
Use polycor::hetcor to calculate the correlation matrix |
nr_fact |
Number of factors to extract |
rotation |
Apply varimax rotation or no rotation ("varimax" or "none") |
data_filter |
Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/multivariate/full_factor.html for an example in Radiant
Value
A list with all variables defined in the function as an object of class full_factor
See Also
summary.full_factor to summarize results
plot.full_factor to plot results
Examples
full_factor(shopping, "v1:v6") %>% str()
Hierarchical cluster analysis
Description
Hierarchical cluster analysis
Usage
hclus(
dataset,
vars,
labels = "none",
distance = "sq.euclidian",
method = "ward.D",
max_cases = 5000,
standardize = TRUE,
data_filter = "",
envir = parent.frame()
)
Arguments
dataset |
Dataset |
vars |
Vector of variables to include in the analysis |
labels |
A vector of labels for the leaves of the tree |
distance |
Distance |
method |
Method |
max_cases |
Maximum number of cases allowed (default is 1000). Set to avoid long-running analysis in the radiant web-interface |
standardize |
Standardized data (TRUE or FALSE) |
data_filter |
Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/multivariate/hclus.html for an example in Radiant
Value
A list of all variables used in hclus as an object of class hclus
See Also
summary.hclus to summarize results
plot.hclus to plot results
Examples
hclus(shopping, vars = "v1:v6") %>% str()
K-clustering
Description
K-clustering
Usage
kclus(
dataset,
vars,
fun = "kmeans",
hc_init = TRUE,
distance = "sq.euclidian",
method = "ward.D",
seed = 1234,
nr_clus = 2,
standardize = TRUE,
lambda = NULL,
data_filter = "",
envir = parent.frame()
)
Arguments
dataset |
Dataset |
vars |
Vector of variables to include in the analysis |
fun |
Use either "kmeans" or "kproto" for clustering |
hc_init |
Use centers from hclus as the starting point |
distance |
Distance for hclus |
method |
Method for hclus |
seed |
Random see to use for k-clustering if hc_init is FALSE |
nr_clus |
Number of clusters to extract |
standardize |
Standardize data (TRUE or FALSE) |
lambda |
Parameter > 0 to trade off between Euclidean distance of numeric variables and simple matching coefficient between categorical variables. Also a vector of variable specific factors is possible where the order must correspond to the order of the variables in the data. In this case all variables' distances will be multiplied by their corresponding lambda value. |
data_filter |
Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/multivariate/kclus.html for an example in Radiant
Value
A list of all variables used in kclus as an object of class kclus
See Also
summary.kclus to summarize results
plot.kclus to plot results
store.kclus to add cluster membership to the selected dataset
Examples
kclus(shopping, c("v1:v6"), nr_clus = 3) %>% str()
(Dis)similarity based brand maps (MDS)
Description
(Dis)similarity based brand maps (MDS)
Usage
mds(
dataset,
id1,
id2,
dis,
method = "metric",
nr_dim = 2,
seed = 1234,
data_filter = "",
envir = parent.frame()
)
Arguments
dataset |
Dataset |
id1 |
A character variable or factor with unique entries |
id2 |
A character variable or factor with unique entries |
dis |
A numeric measure of brand dissimilarity |
method |
Apply metric or non-metric MDS |
nr_dim |
Number of dimensions |
seed |
Random seed |
data_filter |
Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/multivariate/mds.html for an example in Radiant
Value
A list of all variables defined in the function as an object of class mds
See Also
summary.mds to summarize results
plot.mds to plot results
Examples
mds(city, "from", "to", "distance") %>% str()
mds(diamonds, "clarity", "cut", "price") %>% str()
Conjoint data for Movie theaters
Description
Conjoint data for Movie theaters
Usage
data(movie)
Format
A data frame with 18 rows and 6 variables
Details
Rankings reflect the evaluation of 18 alternative movie theaters by one respondent. Description provided in attr(movie, "description")
Conjoint data for MP3 players
Description
Conjoint data for MP3 players
Usage
data(mp3)
Format
A data frame with 18 rows and 6 variables
Details
Ratings reflect the evaluation of 18 alternative MP3 players by one respondent. Description provided in attr(mp3, "description")
Plot method for the conjoint function
Description
Plot method for the conjoint function
Usage
## S3 method for class 'conjoint'
plot(
x,
plots = "pw",
show = "",
scale_plot = FALSE,
shiny = FALSE,
custom = FALSE,
...
)
Arguments
x |
Return value from |
plots |
Show either the part-worth ("pw") or importance-weights ("iw") plot |
show |
Level in by variable to analyze (e.g., a specific respondent) |
scale_plot |
Scale the axes of the part-worth plots to the same range |
shiny |
Did the function call originate inside a shiny app |
custom |
Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org/ for options. |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/conjoint.html for an example in Radiant
See Also
conjoint to generate results
summary.conjoint to summarize results
Examples
result <- conjoint(mp3, rvar = "Rating", evar = "Memory:Shape")
plot(result, scale_plot = TRUE)
plot(result, plots = "iw")
Plot method for the full_factor function
Description
Plot method for the full_factor function
Usage
## S3 method for class 'full_factor'
plot(x, plots = "attr", shiny = FALSE, custom = FALSE, ...)
Arguments
x |
Return value from |
plots |
Include attribute ("attr"), respondents ("resp") or both in the plot |
shiny |
Did the function call originate inside a shiny app |
custom |
Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org/ for options. |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/full_factor.html for an example in Radiant
See Also
full_factor to calculate results
plot.full_factor to plot results
Examples
result <- full_factor(shopping, "v1:v6", nr_fact = 2)
plot(result)
Plot method for the hclus function
Description
Plot method for the hclus function
Usage
## S3 method for class 'hclus'
plot(
x,
plots = c("scree", "change"),
cutoff = 0.05,
shiny = FALSE,
custom = FALSE,
...
)
Arguments
x |
Return value from |
plots |
Plots to return. "change" shows the percentage change in within-cluster heterogeneity as respondents are grouped into different number of clusters, "dendro" shows the dendrogram, "scree" shows a scree plot of within-cluster heterogeneity |
cutoff |
For large datasets plots can take time to render and become hard to interpret. By selection a cutoff point (e.g., 0.05 percent) the initial steps in hierarchical cluster analysis are removed from the plot |
shiny |
Did the function call originate inside a shiny app |
custom |
Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org/ for options. |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/hclus.html for an example in Radiant
See Also
hclus to generate results
summary.hclus to summarize results
Examples
result <- hclus(shopping, vars = c("v1:v6"))
plot(result, plots = c("change", "scree"), cutoff = .05)
plot(result, plots = "dendro", cutoff = 0)
Plot method for kclus
Description
Plot method for kclus
Usage
## S3 method for class 'kclus'
plot(x, plots = "density", shiny = FALSE, custom = FALSE, ...)
Arguments
x |
Return value from |
plots |
One of "density", "bar", or "scatter") |
shiny |
Did the function call originate inside a shiny app |
custom |
Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org/ for options. |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/kclus.html for an example in Radiant
See Also
kclus to generate results
summary.kclus to summarize results
store.kclus to add cluster membership to the selected dataset
Examples
result <- kclus(shopping, vars = "v1:v6", nr_clus = 3)
plot(result)
Plot method for the mds function
Description
Plot method for the mds function
Usage
## S3 method for class 'mds'
plot(x, rev_dim = NULL, fontsz = 5, shiny = FALSE, custom = FALSE, ...)
Arguments
x |
Return value from |
rev_dim |
Flip the axes in plots |
fontsz |
Font size to use in plots |
shiny |
Did the function call originate inside a shiny app |
custom |
Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org/ for options. |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/mds.html for an example in Radiant
See Also
mds to calculate results
summary.mds to plot results
Examples
result <- mds(city, "from", "to", "distance")
plot(result, fontsz = 7)
plot(result, rev_dim = 1:2)
Plot method for the pre_factor function
Description
Plot method for the pre_factor function
Usage
## S3 method for class 'pre_factor'
plot(
x,
plots = c("scree", "change"),
cutoff = 0.2,
shiny = FALSE,
custom = FALSE,
...
)
Arguments
x |
Return value from |
plots |
Plots to return. "change" shows the change in eigenvalues as variables are grouped into different number of factors, "scree" shows a scree plot of eigenvalues |
cutoff |
For large datasets plots can take time to render and become hard to interpret. By selection a cutoff point (e.g., eigenvalues of .8 or higher) factors with the least explanatory power are removed from the plot |
shiny |
Did the function call originate inside a shiny app |
custom |
Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org/ for options. |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/pre_factor.html for an example in Radiant
See Also
pre_factor to calculate results
summary.pre_factor to summarize results
Examples
result <- pre_factor(shopping, "v1:v6")
plot(result, plots = c("change", "scree"), cutoff = .05)
Plot method for the prmap function
Description
Plot method for the prmap function
Usage
## S3 method for class 'prmap'
plot(
x,
plots = "",
scaling = 2,
fontsz = 5,
seed = 1234,
shiny = FALSE,
custom = FALSE,
...
)
Arguments
x |
Return value from |
plots |
Components to include in the plot ("brand", "attr"). If data on preferences is available use "pref" to add preference arrows to the plot |
scaling |
Arrow scaling in the brand map |
fontsz |
Font size to use in plots |
seed |
Random seed |
shiny |
Did the function call originate inside a shiny app |
custom |
Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org/ for options. |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/prmap.html for an example in Radiant
See Also
prmap to calculate results
summary.prmap to plot results
Examples
result <- prmap(computer, brand = "brand", attr = "high_end:business")
plot(result, plots = "brand")
plot(result, plots = c("brand", "attr"))
plot(result, scaling = 1, plots = c("brand", "attr"))
prmap(
retailers,
brand = "retailer",
attr = "good_value:cluttered",
pref = c("segment1", "segment2")
) %>% plot(plots = c("brand", "attr", "pref"))
Evaluate if data are appropriate for PCA / Factor analysis
Description
Evaluate if data are appropriate for PCA / Factor analysis
Usage
pre_factor(
dataset,
vars,
hcor = FALSE,
data_filter = "",
envir = parent.frame()
)
Arguments
dataset |
Dataset |
vars |
Variables to include in the analysis |
hcor |
Use polycor::hetcor to calculate the correlation matrix |
data_filter |
Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/multivariate/pre_factor.html for an example in Radiant
Value
A list with all variables defined in the function as an object of class pre_factor
See Also
summary.pre_factor to summarize results
plot.pre_factor to plot results
Examples
pre_factor(shopping, "v1:v6") %>% str()
Predict method for the conjoint function
Description
Predict method for the conjoint function
Usage
## S3 method for class 'conjoint'
predict(
object,
pred_data = NULL,
pred_cmd = "",
conf_lev = 0.95,
se = FALSE,
interval = "confidence",
dec = 3,
envir = parent.frame(),
...
)
Arguments
object |
Return value from |
pred_data |
Provide the dataframe to generate predictions. The dataset must contain all columns used in the estimation |
pred_cmd |
Command used to generate data for prediction |
conf_lev |
Confidence level used to estimate confidence intervals (.95 is the default) |
se |
Logical that indicates if prediction standard errors should be calculated (default = FALSE) |
interval |
Type of interval calculation ("confidence" or "prediction"). Set to "none" if se is FALSE |
dec |
Number of decimals to show |
envir |
Environment to extract data from |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/conjoint.html for an example in Radiant
See Also
conjoint to generate the result
summary.conjoint to summarize results
plot.conjoint to plot results
Examples
result <- conjoint(mp3, rvar = "Rating", evar = "Memory:Shape")
predict(result, pred_data = mp3)
Predict method for the conjoint function when a by variables is used
Description
Predict method for the conjoint function when a by variables is used
Usage
predict_conjoint_by(
object,
pfun,
pred_data = NULL,
pred_cmd = "",
conf_lev = 0.95,
se = FALSE,
dec = 3,
envir = parent.frame(),
...
)
Arguments
object |
Return value from |
pfun |
Function to use for prediction |
pred_data |
Name of the dataset to use for prediction |
pred_cmd |
Command used to generate data for prediction |
conf_lev |
Confidence level used to estimate confidence intervals (.95 is the default) |
se |
Logical that indicates if prediction standard errors should be calculated (default = FALSE) |
dec |
Number of decimals to show |
envir |
Environment to extract data from |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/conjoint.html for an example in Radiant
See Also
conjoint to generate the result
summary.conjoint to summarize results
plot.conjoint to plot results
Print method for predict.conjoint
Description
Print method for predict.conjoint
Usage
## S3 method for class 'conjoint.predict'
print(x, ..., n = 20)
Arguments
x |
Return value from prediction method |
... |
further arguments passed to or from other methods |
n |
Number of lines of prediction results to print. Use -1 to print all lines |
Attribute based brand maps
Description
Attribute based brand maps
Usage
prmap(
dataset,
brand,
attr,
pref = "",
nr_dim = 2,
hcor = FALSE,
data_filter = "",
envir = parent.frame()
)
Arguments
dataset |
Dataset |
brand |
A character variable with brand names |
attr |
Names of numeric variables |
pref |
Names of numeric brand preference measures |
nr_dim |
Number of dimensions |
hcor |
Use polycor::hetcor to calculate the correlation matrix |
data_filter |
Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/multivariate/prmap.html for an example in Radiant
Value
A list of all variables defined in the function as an object of class prmap
See Also
summary.prmap to summarize results
plot.prmap to plot results
Examples
prmap(computer, brand = "brand", attr = "high_end:business") %>% str()
radiant.multivariate
Description
Launch radiant.multivariate in the default web browser
Usage
radiant.multivariate(state, ...)
Arguments
state |
Path to state file to load |
... |
additional arguments to pass to shiny::runApp (e.g, port = 8080) |
Details
See https://radiant-rstats.github.io/docs/ for documentation and tutorials
Examples
## Not run:
radiant.multivariate()
## End(Not run)
Launch radiant.multivariate in the Rstudio viewer
Description
Launch radiant.multivariate in the Rstudio viewer
Usage
radiant.multivariate_viewer(state, ...)
Arguments
state |
Path to state file to load |
... |
additional arguments to pass to shiny::runApp (e.g, port = 8080) |
Details
See https://radiant-rstats.github.io/docs/ for documentation and tutorials
Examples
## Not run:
radiant.multivariate_viewer()
## End(Not run)
Launch radiant.multivariate in an Rstudio window
Description
Launch radiant.multivariate in an Rstudio window
Usage
radiant.multivariate_window(state, ...)
Arguments
state |
Path to state file to load |
... |
additional arguments to pass to shiny::runApp (e.g, port = 8080) |
Details
See https://radiant-rstats.github.io/docs/ for documentation and tutorials
Examples
## Not run:
radiant.multivariate_window()
## End(Not run)
Perceptions of retailers
Description
Perceptions of retailers
Usage
data(retailers)
Format
A data frame with 6 rows and 10 variables
Details
Consumer evaluations for a set of retailers in the Chicago area on 7 attributes. The dataset is used to illustrate perceptual maps. Description provided in attr(retailers, "description")
Shopping attitudes
Description
Shopping attitudes
Usage
data(shopping)
Format
A data frame with 20 rows and 7 variables
Details
Attitudinal data on shopping for 20 consumers. Description provided in attr(shopping, "description")
Store method for the Multivariate > Conjoint tab
Description
Store method for the Multivariate > Conjoint tab
Usage
## S3 method for class 'conjoint'
store(dataset, object, name, ...)
Arguments
dataset |
Dataset |
object |
Return value from conjoint |
name |
Variable name(s) assigned to predicted values |
... |
further arguments passed to or from other methods |
Details
Store data frame with PWs or IWs in Radiant r_data list if available
Store predicted values generated in predict.conjoint
Description
Store predicted values generated in predict.conjoint
Usage
## S3 method for class 'conjoint.predict'
store(dataset, object, name = "prediction", ...)
Arguments
dataset |
Dataset to add predictions to |
object |
Return value from model predict function |
name |
Variable name(s) assigned to predicted values |
... |
Additional arguments |
Details
See https://radiant-rstats.github.io/docs/multivariate/conjoint.html for an example in Radiant
Examples
conjoint(mp3, rvar = "Rating", evar = "Memory:Shape") %>%
predict(mp3) %>%
store(mp3, ., name = "pred_pref")
Store factor scores to active dataset
Description
Store factor scores to active dataset
Usage
## S3 method for class 'full_factor'
store(dataset, object, name = "", ...)
Arguments
dataset |
Dataset to append to factor scores to |
object |
Return value from |
name |
Name of factor score variables |
... |
Additional arguments |
Details
See https://radiant-rstats.github.io/docs/multivariate/full_factor.html for an example in Radiant
See Also
full_factor to generate results
summary.full_factor to summarize results
plot.full_factor to plot results
Examples
full_factor(shopping, "v1:v6", nr_fact = 3) %>%
store(shopping, .) %>%
head()
Add a cluster membership variable to the active dataset
Description
Add a cluster membership variable to the active dataset
Usage
## S3 method for class 'hclus'
store(dataset, object, nr_clus = 2, name = "", ...)
Arguments
dataset |
Dataset to append to cluster membership variable to |
object |
Return value from |
nr_clus |
Number of clusters to extract |
name |
Name of cluster membership variable |
... |
Additional arguments |
Details
See https://radiant-rstats.github.io/docs/multivariate/hclus.html for an example in Radiant
See Also
hclus to generate results
summary.hclus to summarize results
plot.hclus to plot results
Examples
hclus(shopping, vars = "v1:v6") %>%
store(shopping, ., nr_clus = 3) %>%
head()
Add a cluster membership variable to the active dataset
Description
Add a cluster membership variable to the active dataset
Usage
## S3 method for class 'kclus'
store(dataset, object, name = "", ...)
Arguments
dataset |
Dataset to append to cluster membership variable to |
object |
Return value from |
name |
Name of cluster membership variable |
... |
Additional arguments |
Details
See https://radiant-rstats.github.io/docs/multivariate/kclus.html for an example in Radiant
See Also
kclus to generate results
summary.kclus to summarize results
plot.kclus to plot results
Examples
kclus(shopping, vars = "v1:v6", nr_clus = 3) %>%
store(shopping, .) %>%
head()
Summary method for the conjoint function
Description
Summary method for the conjoint function
Usage
## S3 method for class 'conjoint'
summary(object, show = "", mc_diag = FALSE, additional = FALSE, dec = 3, ...)
Arguments
object |
Return value from |
show |
Level in by variable to analyze (e.g., a specific respondent) |
mc_diag |
Shows multicollinearity diagnostics. |
additional |
Show additional regression results |
dec |
Number of decimals to show |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/conjoint.html for an example in Radiant
See Also
conjoint to generate results
plot.conjoint to plot results
Examples
result <- conjoint(mp3, rvar = "Rating", evar = "Memory:Shape")
summary(result, mc_diag = TRUE)
Summary method for the full_factor function
Description
Summary method for the full_factor function
Usage
## S3 method for class 'full_factor'
summary(object, cutoff = 0, fsort = FALSE, dec = 2, ...)
Arguments
object |
Return value from |
cutoff |
Show only loadings with (absolute) values above cutoff (default = 0) |
fsort |
Sort factor loadings |
dec |
Number of decimals to show |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/full_factor.html for an example in Radiant
See Also
full_factor to calculate results
plot.full_factor to plot results
Examples
result <- full_factor(shopping, "v1:v6", nr_fact = 2)
summary(result)
summary(result, cutoff = .5, fsort = TRUE)
Summary method for the hclus function
Description
Summary method for the hclus function
Usage
## S3 method for class 'hclus'
summary(object, ...)
Arguments
object |
Return value from |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/hclus.html for an example in Radiant
See Also
hclus to generate results
plot.hclus to plot results
Examples
result <- hclus(shopping, vars = c("v1:v6"))
summary(result)
Summary method for kclus
Description
Summary method for kclus
Usage
## S3 method for class 'kclus'
summary(object, dec = 2, ...)
Arguments
object |
Return value from |
dec |
Number of decimals to show |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/kclus.html for an example in Radiant
See Also
kclus to generate results
plot.kclus to plot results
store.kclus to add cluster membership to the selected dataset
Examples
result <- kclus(shopping, vars = "v1:v6", nr_clus = 3)
summary(result)
Summary method for the mds function
Description
Summary method for the mds function
Usage
## S3 method for class 'mds'
summary(object, dec = 2, ...)
Arguments
object |
Return value from |
dec |
Rounding to use for output (default = 2). +1 used for stress measure |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/mds.html for an example in Radiant
See Also
mds to calculate results
plot.mds to plot results
Examples
result <- mds(city, "from", "to", "distance")
summary(result, dec = 1)
Summary method for the pre_factor function
Description
Summary method for the pre_factor function
Usage
## S3 method for class 'pre_factor'
summary(object, dec = 2, ...)
Arguments
object |
Return value from |
dec |
Rounding to use for output |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/pre_factor.html for an example in Radiant
See Also
pre_factor to calculate results
plot.pre_factor to plot results
Examples
result <- pre_factor(shopping, "v1:v6")
summary(result)
pre_factor(computer, "high_end:business") %>% summary()
Summary method for the prmap function
Description
Summary method for the prmap function
Usage
## S3 method for class 'prmap'
summary(object, cutoff = 0, dec = 2, ...)
Arguments
object |
Return value from |
cutoff |
Show only loadings with (absolute) values above cutoff (default = 0) |
dec |
Rounding to use for output |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/multivariate/prmap.html for an example in Radiant
See Also
prmap to calculate results
plot.prmap to plot results
Examples
result <- prmap(computer, brand = "brand", attr = "high_end:business")
summary(result)
summary(result, cutoff = .3)
prmap(
computer,
brand = "brand", attr = "high_end:dated",
pref = c("innovative", "business")
) %>% summary()
Function to calculate the PW and IW table for conjoint
Description
Function to calculate the PW and IW table for conjoint
Usage
the_table(model, dataset, evar)
Arguments
model |
Tidied model results (broom) output from |
dataset |
Conjoint data |
evar |
Explanatory variables used in the conjoint regression |
Details
See https://radiant-rstats.github.io/docs/multivariate/conjoint.html for an example in Radiant
See Also
conjoint to generate results
summary.conjoint to summarize results
plot.conjoint to plot results
Examples
result <- conjoint(mp3, rvar = "Rating", evar = "Memory:Shape")
the_table(tidy(result$model_list[[1]][["model"]]), result$dataset, result$evar)
Toothpaste attitudes
Description
Toothpaste attitudes
Usage
data(toothpaste)
Format
A data frame with 60 rows and 10 variables
Details
Attitudinal data on toothpaste for 60 consumers. Description provided in attr(toothpaste, "description")
Toothpaste brands
Description
Toothpaste brands
Usage
data(tpbrands)
Format
A data frame with 45 rows and 4 variables
Details
Perceived (dis)similarity of a set of toothpaste brands. The dataset is used to illustrate multi-dimensional scaling (MDS). Description provided in attr(tpbrands, "description")