Package 'inferr'

Dummy data set for Cochran's Q test

Description

A dataset containing information about results of three exams.

Usage

exam

Format

A data frame with 15 rows and 3 variables:

exam1

result of exam1

exam2

result of exam2

exam3

result of exam3

Source

https://www.spss-tutorials.com/spss-cochran-q-test/

---

High School and Beyond Data Set

Description

A dataset containing demographic information and standardized test scores of high school students.

Usage

hsb

Format

A data frame with 200 rows and 10 variables:

id

id of the student

female

gender of the student

race

ethnic background of the student

ses

socio-economic status of the student

schtyp

school type

prog

program type

read

scores from test of reading

write

scores from test of writing

math

scores from test of math

science

scores from test of science

socst

scores from test of social studies

Source

https://nces.ed.gov/surveys/hsb/

---

Binomial Test

Description

Test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value.

Usage

ifr_binom_calc(n, success, prob = 0.5, ...)

ifr_binom_test(data, variable, prob = 0.5)

Arguments

n	number of observations
success	number of successes
prob	assumed probability of success on a trial
...	additional arguments passed to or from other methods
data	a data.frame or a tibble
variable	factor; column in data

Value

ifr_binom_test returns an object of class "ifr_binom_test". An object of class "ifr_binom_test" is a list containing the following components:

exp_k	expected number of successes
exp_p	expected probability of success
k	number of successes
n	number of observations
obs_p	assumed probability of success
pval_lower	lower one sided p value
pval_upper	upper one sided p value

Deprecated Functions

infer_binom_calc() and infer_binom_test() have been deprecated. Instead use ifr_binom_cal() and ifr_binom_test().

References

Hoel, P. G. 1984. Introduction to Mathematical Statistics. 5th ed. New York: Wiley.

See Also

binom.test

Examples

# using calculator
ifr_binom_calc(32, 13, prob = 0.5)

# using data set
ifr_binom_test(hsb, female, prob = 0.5)

---

Chi Square Test of Association

Description

Chi Square test of association to examine if there is a relationship between two categorical variables.

Usage

ifr_chisq_assoc_test(data, x, y)

Arguments

data	a data.frame or tibble
x	factor; column in data
y	factor; column in data

Value

ifr_chisq_assoc_test returns an object of class "ifr_chisq_assoc_test". An object of class "ifr_chisq_assoc_test" is a list containing the following components:

chisquare	chi square
chisquare_lr	likelihood ratio chi square
chisquare_mantel_haenszel	mantel haenszel chi square
chisquare_adjusted	continuity adjusted chi square
contingency_coefficient	contingency coefficient
cramers_v	cramer's v
df	degrees of freedom
ds	product of dimensions of the table of x and y
phi_coefficient	phi coefficient
pval_chisquare	p-value of chi square
pval_chisquare_adjusted	p-value of continuity adjusted chi square
pval_chisquare_lr	p-value of likelihood ratio chi square
pval_chisquare_mantel_haenszel	p-value of mantel haenszel chi square

Deprecated Function

infer_chisq_assoc_test() has been deprecated. Instead use ifr_chisq_assoc_test().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

chisq.test

Examples

ifr_chisq_assoc_test(hsb, female, schtyp)

ifr_chisq_assoc_test(hsb, female, ses)

---

Chi Square Goodness of Fit Test

Description

Test whether the observed proportions for a categorical variable differ from hypothesized proportions

Usage

ifr_chisq_gof_test(data, x, y, correct = FALSE)

Arguments

data	a data.frame or tibble
x	factor; column in data
y	expected proportions
correct	logical; if TRUE continuity correction is applied

Value

ifr_chisq_gof_test returns an object of class "ifr_chisq_gof_test". An object of class "ifr_chisq_gof_test" is a list containing the following components:

categories	levels of x
chisquare	chi square statistic
deviation	deviation of observed from frequency
degrees_of_freedom	chi square degrees of freedom
expected_frequency	expected frequency/proportion
n_levels	number of levels of x
observed_frequency	observed frequency/proportion
pvalue	p-value
sample_size	number of observations
std_residuals	standardized residuals
varname	name of categorical variable

Deprecated Function

infer_chisq_gof_test() has been deprecated. Instead use ifr_chisq_gof_test()

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

chisq.test

Examples

ifr_chisq_gof_test(hsb, race, c(20, 20, 20, 140))

# apply continuity correction
ifr_chisq_gof_test(hsb, race, c(20, 20, 20, 140), correct = TRUE)

---

Cochran Q Test

Description

Test if the proportions of 3 or more dichotomous variables are equal in the same population.

Usage

ifr_cochran_qtest(data, ...)

Arguments

data	a data.frame or tibble
...	columns in data

Value

ifr_cochran_qtest returns an object of class "ifr_cochran_qtest". An object of class "ifr_cochran_qtest" is a list containing the following components:

df	degrees of freedom
n	number of observations
pvalue	p value
q	cochran's q statistic

Deprecated Function

infer_cochran_test() has been deprecated. Instead use ifr_cochran_qtest().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

Examples

ifr_cochran_qtest(exam, exam1, exam2, exam3)

---

Launch Shiny App

Description

Launches shiny app

Usage

ifr_launch_shiny_app()

Deprecated Function

infer_launch_shiny_app() has been deprecated. Instead use ifr_launch_shiny_app().

Examples

## Not run: 
ifr_launch_shiny_app()

## End(Not run)

---

Levene's test for equality of variances

Description

ifr_levene_test reports Levene's robust test statistic for the equality of variances and the two statistics proposed by Brown and Forsythe that replace the mean in Levene's formula with alternative location estimators. The first alternative replaces the mean with the median. The second alternative replaces the mean with the 10

Usage

ifr_levene_test(data, ...)

## Default S3 method:
ifr_levene_test(data, ..., group_var = NULL, trim_mean = 0.1)

Arguments

data	a data.frame or tibble
...	numeric; columns in data
group_var	factor; column in data
trim_mean	trimmed mean

Value

ifr_levene_test returns an object of class "ifr_levene_test". An object of class "ifr_levene_test" is a list containing the following components:

bf	Brown and Forsythe f statistic
p_bf	p-value for Brown and Forsythe f statistic
lev	Levene's f statistic
p_lev	p-value for Levene's f statistic
bft	Brown and Forsythe f statistic using trimmed mean
p_bft	p-value for Brown and Forsythe f statistic using trimmed mean
avgs	mean for each level of the grouping variable
sds	standard deviations for each level of the grouping variable
avg	combined mean
sd	combined standard deviation
n	number of observations
n_df	numerator degrees of freedom
d_df	denominator degrees of freedom
levs	levels of the grouping variable
lens	number of observations for each level of the grouping variable
type	alternative hypothesis

Deprecated Function

infer_levene_test() has been deprecated. Instead use ifr_levene_test().

References

Bland, M. 2000. An Introduction to Medical Statistics. 3rd ed. Oxford: Oxford University Press.

Brown, M. B., and A. B. Forsythe. 1974. Robust tests for the equality of variances. Journal of the American Statistical Association 69: 364–367.

Carroll, R. J., and H. Schneider. 1985. A note on Levene’s tests for equality of variances. Statistics and Probability Letters 3: 191–194.

Examples

# using grouping variable
ifr_levene_test(hsb, read, group_var = race)

# using  variables
ifr_levene_test(hsb, read, write, socst)

---

McNemar Test

Description

Test if the proportions of two dichotomous variables are equal in the same population.

Usage

ifr_mcnemar_test(data, x = NULL, y = NULL)

Arguments

data	a data.frame or tibble
x	factor; column in data
y	factor; column in data

Value

ifr_mcnemar_test returns an object of class "ifr_mcnemar_test". An object of class "ifr_mcnemar_test" is a list containing the following components:

statistic	chi square statistic
df	degrees of freedom
pvalue	p-value
exactp	exact p-value
cstat	continuity correction chi square statistic
cpvalue	continuity correction p-value
kappa	kappa coefficient; measure of interrater agreement
std_err	asymptotic standard error
kappa_cil	95% kappa lower confidence limit
kappa_ciu	95% kappa upper confidence limit
cases	cases
controls	controls
ratio	ratio of proportion with factor
odratio	odds ratio
tbl	two way table

Deprecated Function

infer_mcnermar_test() has been deprecated. Instead use ifr_mcnemar_test().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

mcnemar.test

Examples

# using variables from data
hb <- hsb
hb$himath <- ifelse(hsb$math > 60, 1, 0)
hb$hiread <- ifelse(hsb$read > 60, 1, 0)
ifr_mcnemar_test(hb, himath, hiread)

# test if the proportion of students in himath and hiread group is same
himath <- ifelse(hsb$math > 60, 1, 0)
hiread <- ifelse(hsb$read > 60, 1, 0)
ifr_mcnemar_test(table(himath, hiread))

# using matrix
ifr_mcnemar_test(matrix(c(135, 18, 21, 26), nrow = 2))

---

One Way ANOVA

Description

One way analysis of variance

Usage

ifr_oneway_anova(data, x, y, ...)

Arguments

data	a data.frame or a tibble
x	numeric; column in data
y	factor; column in data
...	additional arguments passed to or from other methods

Value

ifr_oneway_anova returns an object of class "ifr_oneway_anova". An object of class "ifr_oneway_anova" is a list containing the following components:

adjusted_r2	adjusted r squared value
df_btw	between groups degress of freedom
df_within	within groups degress of freedom
df_total	total degress of freedom
fstat	f value
group_stats	group statistics
ms_btw	between groups mean square
ms_within	within groups mean square
obs	number of observations
pval	p value
r2	r squared value
rmse	root mean squared error
ss_between	between group sum of squares
ss_within	within group sum of squares
ss_total	total sum of squares

Deprecated Function

infer_oneway_anova() has been deprecated. Instead use ifr_oneway_anova()

References

Kutner, M. H., Nachtsheim, C., Neter, J., & Li, W. (2005). Applied linear statistical models. Boston: McGraw-Hill Irwin.

See Also

anova

Examples

ifr_oneway_anova(mtcars, mpg, cyl)
ifr_oneway_anova(hsb, write, prog)

---

One Sample Test of Proportion

Description

ifr_os_prop_test compares proportion in one group to a specified population proportion.

Usage

ifr_os_prop_test(
  data,
  variable = NULL,
  prob = 0.5,
  phat = 0.5,
  alternative = c("both", "less", "greater", "all")
)

## Default S3 method:
ifr_os_prop_test(
  data,
  variable = NULL,
  prob = 0.5,
  phat = 0.5,
  alternative = c("both", "less", "greater", "all")
)

Arguments

data	numeric vector of length 1 or a data.frame or tibble
variable	factor; column in data
prob	hypothesised proportion
phat	observed proportion
alternative	a character string specifying the alternative hypothesis, must be one of "both" (default), "greater", "less" or "all". You can specify just the initial letter.

Value

ifr_os_prop_test returns an object of class "ifr_os_prop_test". An object of class "ifr_os_prop_test" is a list containing the following components:

n	number of observations
phat	proportion of 1's
p	assumed probability of success
z	z statistic
sig	p-value for z statistic
alt	alternative hypothesis
obs	observed number of 0's and 1's
exp	expected number of 0's and 1's
deviation	deviation of observed from expected
std	standardized resiudals

Deprecated Function

infer_os_prop_test() has been deprecated. Instead use ifr_os_prop_test().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

prop.test binom.test

Examples

# use as a calculator
ifr_os_prop_test(200, prob = 0.5, phat = 0.3)

# using data set
ifr_os_prop_test(hsb, female, prob = 0.5)

---

One Sample t Test

Description

ifr_os_t_test performs t tests on the equality of means. It tests the hypothesis that a sample has a mean equal to a hypothesized value.

Usage

ifr_os_t_test(
  data,
  x,
  mu = 0,
  alpha = 0.05,
  alternative = c("both", "less", "greater", "all"),
  ...
)

Arguments

data	a data.frame or tibble
x	numeric; column in data
mu	a number indicating the true value of the mean
alpha	acceptable tolerance for type I error
alternative	a character string specifying the alternative hypothesis, must be one of "both" (default), "greater", "less" or "all". You can specify just the initial letter
...	additional arguments passed to or from other methods

Value

ifr_os_t_test returns an object of class "ifr_os_t_test". An object of class "ifr_os_t_test" is a list containing the following components:

mu	a number indicating the true value of the mean
n	number of observations
df	degrees of freedom
Mean	observed mean of x
stddev	standard deviation of x
std_err	estimate of standard error
test_stat	t statistic
confint	confidence interval for the mean
mean_diff	mean difference
mean_diff_l	lower confidence limit for mean difference
mean_diff_u	upper confidence limit for mean difference
p_l	lower one-sided p-value
p_u	upper one-sided p-value
p	two sided p-value
conf	confidence level
type	alternative hypothesis
var_name	name of x

Deprecated Function

infer_os_t_test() has been deprecated. Instead use ifr_os_t_test().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

t.test

Examples

# lower tail
ifr_os_t_test(hsb, write, mu = 50, alternative = 'less')

# upper tail
ifr_os_t_test(hsb, write, mu = 50, alternative = 'greater')

# both tails
ifr_os_t_test(hsb, write, mu = 50, alternative = 'both')

# all tails
ifr_os_t_test(hsb, write, mu = 50, alternative = 'all')

---

One Sample Variance Comparison Test

Description

ifr_os_var_test performs tests on the equality of standard deviations (variances).It tests that the standard deviation of a sample is equal to a hypothesized value.

Usage

ifr_os_var_test(
  data,
  x,
  sd,
  confint = 0.95,
  alternative = c("both", "less", "greater", "all"),
  ...
)

Arguments

data	a data.frame or tibble
x	numeric; column in data
sd	hypothesised standard deviation
confint	confidence level
alternative	a character string specifying the alternative hypothesis, must be one of "both" (default), "greater", "less" or "all". You can specify just the initial letter
...	additional arguments passed to or from other methods

Value

ifr_os_var_test returns an object of class "ifr_os_var_test". An object of class "ifr_os_var_test" is a list containing the following components:

n	number of observations
sd	hypothesised standard deviation of x
sigma	observed standard deviation
se	estimated standard error
chi	chi-square statistic
df	degrees of freedom
p_lower	lower one-sided p-value
p_upper	upper one-sided p-value
p_two	two-sided p-value
xbar	mean of x
c_lwr	lower confidence limit of standard deviation
c_upr	upper confidence limit of standard deviation
var_name	name of x
conf	confidence level
type	alternative hypothesis

Deprecated Function

infer_os_var_test() has been deprecated. Instead use ifr_os_var_test().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

var.test

Examples

# lower tail
ifr_os_var_test(mtcars, mpg, 5, alternative = 'less')

# upper tail
ifr_os_var_test(mtcars, mpg, 5, alternative = 'greater')

# both tails
ifr_os_var_test(mtcars, mpg, 5, alternative = 'both')

# all tails
ifr_os_var_test(mtcars, mpg, 5, alternative = 'all')

---

Test for Random Order

Description

runtest tests whether the observations of x are serially independent i.e. whether they occur in a random order, by counting how many runs there are above and below a threshold. By default, the median is used as the threshold. A small number of runs indicates positive serial correlation; a large number indicates negative serial correlation.

Usage

ifr_runs_test(
  data,
  x,
  drop = FALSE,
  split = FALSE,
  mean = FALSE,
  threshold = NA
)

Arguments

data	a data.frame or tibble
x	numeric; column in data
drop	logical; if TRUE, values equal to the threshold will be dropped from x
split	logical; if TRUE, data will be recoded in binary format
mean	logical; if TRUE, mean will be used as threshold
threshold	threshold to be used for counting runs, specify 0 if data is coded as a binary.

Value

infer_runs_test returns an object of class "ifr_runs_test". An object of class "ifr_runs_test" is a list containing the following components:

n	number of observations
threshold	within group sum of squares
n_below	number below the threshold
n_above	number above the threshold
mean	expected number of runs
var	variance of the number of runs
n_runs	number of runs
z	z statistic
p	p-value of z

Deprecated Function

runs_test() has been deprecated. Instead use ifr_runs_test().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

Edgington, E. S. 1961. Probability table for number of runs of signs of first differences in ordered series. Journal of the American Statistical Association 56: 156–159.

Madansky, A. 1988. Prescriptions for Working Statisticians. New York: Springer.

Swed, F. S., and C. Eisenhart. 1943. Tables for testing randomness of grouping in a sequence of alternatives. Annals of Mathematical Statistics 14: 66–87.

Examples

ifr_runs_test(hsb, read)

ifr_runs_test(hsb, read, drop = TRUE)

ifr_runs_test(hsb, read, split = TRUE)

ifr_runs_test(hsb, read, mean = TRUE)

ifr_runs_test(hsb, read, threshold = 0)

---

Two Independent Sample t Test

Description

ifr_ts_ind_ttest compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different.

Usage

ifr_ts_ind_ttest(
  data,
  x,
  y,
  confint = 0.95,
  alternative = c("both", "less", "greater", "all"),
  ...
)

Arguments

data	a data frame
x	factor; a column in data
y	numeric; a column in data
confint	confidence level
alternative	a character string specifying the alternative hypothesis, must be one of "both" (default), "greater", "less" or "all". You can specify just the initial letter
...	additional arguments passed to or from other methods

Value

ifr_ts_ind_ttest returns an object of class "ifr_ts_ind_ttest". An object of class "ifr_ts_ind_ttest" is a list containing the following components:

levels	levels of x
obs	number of observations of y for each level of x
n	total number of observations
mean	mean of y for each level of x
sd	standard deviation of y for each level of x
se	estimate of standard error of y for each level of x
lower	lower limit for the mean of y for each level of x
upper	upper limit for the mean of y for each level of x
combined	a data frame; mean, standard deviation, standard error and confidence limit of mean of y
mean_diff	difference in mean of y for the two groups of x
se_dif	estimate of the standard error for difference in mean of y for the two groups of x
sd_dif	degrees of freedom
conf_diff	confidence interval for mean_diff
df_pooled	degrees of freedom for the pooled method
df_satterthwaite	degrees of freedom for the Satterthwaite method
t_pooled	t statistic for the pooled method
t_satterthwaite	t statistic for the Satterthwaite method
sig_pooled	two-sided p-value for the pooled method
sig_pooled_l	lower one-sided p-value for the pooled method
sig_pooled_u	upper one-sided p-value for the pooled method
sig	two-sided p-value for the Satterthwaite method
sig_l	lower one-sided p-value for the Satterthwaite method
sig_u	upper one-sided p-value for the Satterthwaite method
num_df	numerator degrees of freedom for folded f test
den_df	denominator degrees of freedom for folded f test
f	f value for the equality of variances test
f_sig	p-value for the folded f test
var_y	name of y
confint	confidence level
alternative	alternative hypothesis

Deprecated Function

infer_ts_ind_ttest() has been deprecated. Instead use ifr_ts_ind_ttest().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

t.test

Examples

# lower tail
ifr_ts_ind_ttest(hsb, female, write, alternative = 'less')

# upper tail
ifr_ts_ind_ttest(hsb, female, write, alternative = 'greater')

# both tails
ifr_ts_ind_ttest(hsb, female, write, alternative = 'both')

# all tails
ifr_ts_ind_ttest(hsb, female, write, alternative = 'all')

---

Paired t test

Description

ifr_ts_paired_ttest tests that two samples have the same mean, assuming paired data.

Usage

ifr_ts_paired_ttest(
  data,
  x,
  y,
  confint = 0.95,
  alternative = c("both", "less", "greater", "all")
)

Arguments

data	a data.frame or tibble
x	numeric; column in data
y	numeric; column in data
confint	confidence level
alternative	a character string specifying the alternative hypothesis, must be one of "both" (default), "greater", "less" or "all". You can specify just the initial letter.

Value

ifr_ts_paired_ttest returns an object of class "ifr_ts_paired_ttest". An object of class "ifr_ts_paired_ttest" is a list containing the following components:

Obs	number of observations
b	mean, standard deviation and standard error of x, y and their difference
tstat	t statistic
p_lower	lower one-sided p-value
p_upper	upper one-sided p-value
p_two_tail	two sided p-value
corr	Correlation of x and y
corsig	p-value of correlation test
conf_int1	confidence interval for mean of x
conf_int2	confidence interval for mean of y
conf_int_diff	confidence interval for mean of difference of x and y
df	degrees of freedom
confint	confidence level
alternative	alternative hypothesis
var_names	names of x and y
xy	string used in printing results of the test

Deprecated Function

infer_ts_paired_ttest() has been deprecated. Instead use ifr_ts_paired_ttest().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

t.test

Examples

# lower tail
ifr_ts_paired_ttest(hsb, read, write, alternative = 'less')

# upper tail
ifr_ts_paired_ttest(hsb, read, write, alternative = 'greater')

# both tails
ifr_ts_paired_ttest(hsb, read, write, alternative = 'both')

# all tails
ifr_ts_paired_ttest(hsb, read, write, alternative = 'all')

---

Two Sample Test of Proportion

Description

Tests on the equality of proportions using large-sample statistics. It tests that a sample has the same proportion within two independent groups or two samples have the same proportion.

Usage

ifr_ts_prop_test(
  data,
  var1,
  var2,
  alternative = c("both", "less", "greater", "all"),
  ...
)

ifr_ts_prop_group(
  data,
  var,
  group,
  alternative = c("both", "less", "greater", "all")
)

ifr_ts_prop_calc(
  n1,
  n2,
  p1,
  p2,
  alternative = c("both", "less", "greater", "all"),
  ...
)

Arguments

data	a data.frame or tibble
var1	factor; column in data
var2	factor; column in data
alternative	a character string specifying the alternative hypothesis, must be one of "both" (default), "greater", "less" or "all". You can specify just the initial letter
...	additional arguments passed to or from other methods
var	factor; column in data
group	factor; column in data
n1	sample 1 size
n2	sample 2 size
p1	sample 1 proportion
p2	sample 2 proportion

Value

an object of class "ifr_ts_prop_test". An object of class "ifr_ts_prop_test" is a list containing the following components:

n1	sample 1 size
n2	sample 2 size
phat1	sample 1 proportion
phat2	sample 2 proportion
z	z statistic
sig	p-value for z statistic
alt	alternative hypothesis

Deprecated Functions

infer_ts_prop_test(), infer_ts_prop_grp() and infer_ts_prop_calc() have been deprecated. Instead use ifr_ts_prop_test(), ifr_ts_prop_group() and ifr_ts_prop_calc().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

prop.test

Examples

# using variables
# lower tail
ifr_ts_prop_test(treatment, treatment1, treatment2,
alternative = 'less')

# using groups
# lower tail
ifr_ts_prop_group(treatment2, outcome, female,
alternative = 'less')

# using sample size and proportions
# lower tail
ifr_ts_prop_calc(n1 = 30, n2 = 25, p1 = 0.3, p2 = 0.5, alternative = 'less')

---

Two Sample Variance Comparison Test

Description

ifr_ts_var_test performs tests on the equality of standard deviations (variances).

Usage

ifr_ts_var_test(
  data,
  ...,
  group_var = NULL,
  alternative = c("less", "greater", "all")
)

Arguments

data	a data.frame or tibble
...	numeric; column(s) in data
group_var	factor; column in data
alternative	a character string specifying the alternative hypothesis, must be one of "both" (default), "greater", "less" or "all". You can specify just the initial letter.

Value

ifr_ts_var_test returns an object of class "ifr_ts_var_test". An object of class "ifr_ts_var_test" is a list containing the following components:

f	f statistic
lower	lower one-sided p-value
upper	upper one-sided p-value
two_tail	two-sided p-value
vars	variances for each level of the grouping variable
avgs	means for each level of the grouping variable
sds	standard deviations for each level of the grouping variable
ses	standard errors for each level of the grouping variable
avg	combined mean
sd	combined standard deviation
se	estimated combined standard error
n1	numerator degrees of freedom
n2	denominator degrees of freedom
lens	number of observations for each level of grouping variable
len	number of observations
lev	levels of the grouping variable
type	alternative hypothesis

Deprecated Function

infer_ts_var_test() has been deprecated. Instead use ifr_ts_var_test().

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See Also

var.test

Examples

# using grouping variable
ifr_ts_var_test(hsb, read, group_var = female, alternative = 'less')

# using two variables
ifr_ts_var_test(hsb, read, write, alternative = 'less')

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Dummy data set for 2 Sample Proportion test

Description

A dataset containing information about two treatments

Usage

treatment

Format

A data frame with 50 rows and 2 variables:

treatment1

result of treatment type 1

treatment2

result of treatment type 2

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Dummy data set for 2 Sample Proportion test

Description

A dataset containing information about treatment outcomes

Usage

treatment2

Format

A data frame with 200 rows and 2 variables:

outcome

outcome of treatment

female

gender of patient, 0 for male and 1 for female

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