Generate summary or inference information for an iNZight plot
getPlotSummary(
x,
y = NULL,
g1 = NULL,
g1.level = NULL,
g2 = NULL,
g2.level = NULL,
varnames = list(),
colby = NULL,
sizeby = NULL,
data = NULL,
design = NULL,
freq = NULL,
missing.info = TRUE,
inzpars = inzpar(),
summary.type = "summary",
table.direction = c("horizontal", "vertical"),
hypothesis.value = 0,
hypothesis.alt = c("two.sided", "less", "greater"),
hypothesis.var.equal = FALSE,
hypothesis.use.exact = FALSE,
hypothesis.test = c("default", "t.test", "anova", "chi2", "proportion"),
hypothesis.simulated.p.value = FALSE,
hypothesis = list(value = hypothesis.value, alternative = match.arg(hypothesis.alt),
var.equal = hypothesis.var.equal, use.exact = hypothesis.use.exact, test =
match.arg(hypothesis.test), simulated.p.value = hypothesis.simulated.p.value),
survey.options = list(),
width = 100,
epi.out = FALSE,
privacy_controls = NULL,
html = FALSE,
...,
env = parent.frame()
)a vector (numeric or factor), or the name of a column in the supplied
data or design object
a vector (numeric or factor), or the name of a column in the supplied
data or design object
a vector (numeric or factor), or the name of a column in the supplied
data or design object. This variable acts as a subsetting variable.
the name (or numeric position) of the level of g1 that will be
used instead of the entire data set
a vector (numeric or factor), or the name of a column in the supplied
data or design object. This variable acts as a subsetting variable, similar to
g1
same as g1.level, however takes the additional value "_MULTI",
which produces a matrix of g1 by g2
a list of variable names, with the list named using the appropriate arguments
(i.e., list(x = "height", g1 = "gender"))
the name of a variable (numeric or factor) to colour points by. In the case of a numeric variable, a continuous colour scale is used, otherwise each level of the factor is assigned a colour
the name of a (numeric) variable, which controls the size of points
the name of a data set
the name of a survey object, obtained from the survey package
the name of a frequency variable if the data are frequencies
logical, if TRUE, information regarding missingness is
displayed in the plot
allows specification of iNZight plotting parameters over multiple plots
one of "summary" or "inference"
one of 'horizontal' (default) or 'vertical' (useful for many categories)
H0 value for hypothesis test
alternative hypothesis (!=, <, >)
use equal variance assumption for t-test?
logical, if TRUE the exact p-value will be calculated (if applicable)
in some cases (currently just two-samples) can perform multiple tests (t-test or ANOVA)
also calculate (where available) the simulated p-value
either NULL for no test, or missing (in which case above arguments are used)
additional options passed to survey methods
width for the output, default is 100 characters
logical, if TRUE, then odds/rate ratios and rate differences are printed when appropriate (y with 2 levels)
optional, pass in confidentialisation and privacy controls (e.g., random rounding, suppression) for microdata
logical, it TRUE output will be returned as an HTML page (if supported)
additional arguments, see inzpar
compatibility argument
an inzight.plotsummary object with a print method
Works much the same as iNZightPlot
getPlotSummary(Species, data = iris)
#> ====================================================================================================
#> iNZight Summary
#> ----------------------------------------------------------------------------------------------------
#> Primary variable of interest: Species (categorical)
#>
#> Total number of observations: 150
#> ====================================================================================================
#>
#> Summary of the distribution of Species:
#> ---------------------------------------
#>
#> setosa versicolor virginica Total
#> Count 50 50 50 150
#> Percent 33.33% 33.33% 33.33% 100%
#>
#>
#> ====================================================================================================
#>
#>
getPlotSummary(Species, data = iris,
summary.type = "inference", inference.type = "conf")
#> ====================================================================================================
#> iNZight Inference using Normal Theory
#> ----------------------------------------------------------------------------------------------------
#> Primary variable of interest: Species (categorical)
#>
#> Total number of observations: 150
#> ====================================================================================================
#>
#> Inference of the distribution of Species:
#> -----------------------------------------
#>
#> Estimated Proportions with 95% Confidence Interval
#>
#> Estimate Lower Upper
#> setosa 0.333 0.258 0.409
#> versicolor 0.333 0.258 0.409
#> virginica 0.333 0.258 0.409
#>
#> Chi-square test for equal proportions
#>
#> X^2 = 0, df = 2, p-value = 1
#>
#> Null Hypothesis: true proportions in each category are equal
#> Alternative Hypothesis: true proportions in each category are not equal
#>
#>
#> ### Difference in proportions of Species
#> with 95% Confidence Intervals (adjusted for multiple comparisons)
#>
#> Estimate Lower Upper
#> -----------------------------------------------------
#> setosa - versicolor 0 -0.1596 0.1596
#> setosa - virginica 0 -0.1596 0.1596
#>
#> versicolor - virginica 0 -0.1596 0.1596
#>
#>
#>
#> ====================================================================================================
#>
#>
# perform hypothesis testing
getPlotSummary(Sepal.Length, data = iris,
summary.type = "inference", inference.type = "conf",
hypothesis.value = 5)
#> ====================================================================================================
#> iNZight Inference using Normal Theory
#> ----------------------------------------------------------------------------------------------------
#> Primary variable of interest: Sepal.Length (numeric)
#>
#> Total number of observations: 150
#> ====================================================================================================
#>
#> Inference of Sepal.Length:
#> --------------------------
#>
#> Mean with 95% Confidence Interval
#>
#> Estimate Lower Upper
#> 5.843 5.71 5.977
#>
#> One Sample t-test
#>
#> t = 12.473, df = 149, p-value < 2.22e-16
#>
#> Null Hypothesis: true mean is equal to 5
#> Alternative Hypothesis: true mean is not equal to 5
#>
#>
#> ====================================================================================================
#>
#>
# if you prefer a formula interface
inzsummary(Sepal.Length ~ Species, data = iris)
#> ====================================================================================================
#> iNZight Summary
#> ----------------------------------------------------------------------------------------------------
#> Primary variable of interest: Sepal.Length (numeric)
#> Secondary variable: Species (categorical)
#>
#> Total number of observations: 150
#> ====================================================================================================
#>
#> Summary of Sepal.Length by Species:
#> -----------------------------------
#>
#> Estimates
#>
#> Min 25% Median 75% Max Mean SD Sample Size
#> setosa 4.3 4.800 5.0 5.2 5.8 5.006 0.3525 50
#> versicolor 4.9 5.600 5.9 6.3 7.0 5.936 0.5162 50
#> virginica 4.9 6.225 6.5 6.9 7.9 6.588 0.6359 50
#>
#>
#> ====================================================================================================
#>
#>
inzinference(Sepal.Length ~ Species, data = iris)
#> ====================================================================================================
#> iNZight Inference using Normal Theory
#> ----------------------------------------------------------------------------------------------------
#> Primary variable of interest: Sepal.Length (numeric)
#> Secondary variable: Species (categorical)
#>
#> Total number of observations: 150
#> ====================================================================================================
#>
#> Inference of Sepal.Length by Species:
#> -------------------------------------
#>
#> Group Means with 95% Confidence Intervals
#>
#> Estimate Lower Upper
#> setosa 5.006 4.906 5.106
#> versicolor 5.936 5.789 6.083
#> virginica 6.588 6.407 6.769
#>
#> One-way Analysis of Variance (ANOVA F-test)
#>
#> F = 119.26, df = 2 and 147, p-value < 2.22e-16
#>
#> Null Hypothesis: true group means are all equal
#> Alternative Hypothesis: true group means are not all equal
#>
#> Pairwise differences in group means with 95% Confidence Intervals and P-values
#> (The CIs and P-values have been adjusted for multiple comparisons)
#>
#> Estimate Lower Upper P-value
#> -------------------------------------------------------------------
#> setosa - versicolor -0.930 -1.1738 -0.6862 < 2.22e-16
#> setosa - virginica -1.582 -1.8258 -1.3382 < 2.22e-16
#>
#> versicolor - virginica -0.652 -0.8958 -0.4082 < 2.22e-16
#>
#>
#> Null Hypothesis: true difference in group means is zero
#> Alternative Hypothesis: true difference in group means is not zero
#>
#>
#> ====================================================================================================
#>
#>
## confidentialisation and privacy controls
# random rounding and suppression:
HairEyeColor_df <- as.data.frame(HairEyeColor)
inzsummary(Hair ~ Eye, data = HairEyeColor_df, freq = Freq)
#> ====================================================================================================
#> iNZight Summary
#> ----------------------------------------------------------------------------------------------------
#> Primary variable of interest: Hair (categorical)
#> Secondary variable: Eye (categorical)
#>
#> Total number of observations: 32
#> ====================================================================================================
#>
#> Summary of the distribution of Hair (columns) by Eye (rows):
#> ------------------------------------------------------------
#>
#> Table of Counts:
#>
#> Black Brown Red Blond Row Total
#> Brown 68 119 26 7 220
#> Blue 20 84 17 94 215
#> Hazel 15 54 14 10 93
#> Green 5 29 14 16 64
#>
#> Table of Percentages (within categories of Eye):
#>
#> Black Brown Red Blond Total Row N
#> Brown 30.91% 54.09% 11.82% 3.18% 100% 220
#> Blue 9.30% 39.07% 7.91% 43.72% 100% 215
#> Hazel 16.13% 58.06% 15.05% 10.75% 100% 93
#> Green 7.81% 45.31% 21.88% 25.00% 100% 64
#>
#>
#> ====================================================================================================
#>
#>
inzsummary(Hair ~ Eye, data = HairEyeColor_df, freq = Freq,
privacy_controls = list(
rounding = "RR3",
suppression = 10
)
)
#> ====================================================================================================
#> iNZight Summary
#> ----------------------------------------------------------------------------------------------------
#> Primary variable of interest: Hair (categorical)
#> Secondary variable: Eye (categorical)
#>
#> Total number of observations: 32
#> ====================================================================================================
#>
#> Privacy and confidentialisation information
#> -------------------------------------------
#>
#> * counts are rounded using RR3 (random rounding to base 3)
#> * suppression of counts smaller than 10, indicated by S, with secondary suppression where necessary
#> * suppression of totals and means where underlying unrounded count < 10
#>
#> NOTE: this feature is still experimental, and all output should be manually
#> checked before being made public. This is simply to aid that process.
#>
#> ====================================================================================================
#>
#> Summary of the distribution of Hair (columns) by Eye (rows):
#> ------------------------------------------------------------
#>
#> Table of Counts:
#>
#> Black Brown Red Blond Row Total
#> Brown 69 120 27 S S
#> Blue 21 84 18 96 219
#> Hazel 15 54 15 12 96
#> Green S 30 15 15 S
#>
#> Table of Percentages (within categories of Eye):
#>
#> Black Brown Red Blond Total Row N
#> Brown S S S S S S
#> Blue 9.59% 38.36% 8.22% 43.84% 100% 219
#> Hazel 15.62% 56.25% 15.62% 12.50% 100% 96
#> Green S S S S S S
#>
#>
#> ====================================================================================================
#>
#>