chisqtestGC: Chi-Square Test (GC version)

In tigerstats: R Functions for Elementary Statistics

Description

Perform chi-square test, either goodness of fit or test for association. Enter either formula-data input or a summary table. Simulation is optional.

Usage

chisqtestGC(x, data = parent.frame(), p = NULL, graph = FALSE,
             simulate.p.value = FALSE, B = 2000, verbose = TRUE)

Arguments

x	Could be a formula. If so, either ~var (for goodness of fit) or ~var1+var2 (for test for association). Otherwise either a table, matrix or vector of summary data.
data	dataframe supplying variables for formula x. If variables in x are not found in the data, then they will be searched for in the parent environment.
p	For goodness of fit, a vector of probabilities. This will be automatically scaled so as to sum to 1. Negative elements result in an error message.
graph	produce relevant graph for P-value (chi-square curve or histogram of simulation results).
simulate.p.value	If FALSE, use a chi-square distribution to estimate the P-value. Other possible values are "random" and "fixed" and TRUE. Random effects are suitable for resampling when the data are a random sample from a population. Fixed effects assume that the values of the explanatory variable (row variable for table, var1 in formula ~var1+var2) remain fixed in resampling, and values of response variable are random with null distribution estimated from the data. When set to TRUE, we implement an equivalent to R's routine. In our view procedure is most suitable when the data come from a randomized experiment in which the treatment groups are the values of the explanatory variable.
B	number of resamples to take.
verbose	If TRUE, include lots of information in the output.

Value

an object of class GCchisqtest

Author(s)

Homer White [e-mail]

Examples

#Goodness of fit test for one factor variable:
chisqtestGC(~seat,data=m111survey,p=c(1/3,1/3,1/3))

#Test for relationship between two factor variables:
chisqtestGC(~sex+seat,data=m111survey)

#You can input a two-way table directly into chisqtestGC():
SexSeat <- xtabs(~sex+seat,data=m111survey)
chisqtestGC(SexSeat)

#Several types of simulation are possible, e.g.:
chisqtestGC(~weather+crowd.behavior,data=ledgejump,simulate.p.value="fixed",B=2500)

#For less ouptut, set argument verbose to FALSE:
chisqtestGC(~sex+seat,data=m111survey,verbose=FALSE)

Example output

Loading required package: abd
Loading required package: nlme
Loading required package: lattice
Loading required package: grid
Loading required package: mosaic
Loading required package: dplyr

Attaching package: 'dplyr'

The following object is masked from 'package:nlme':

    collapse

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: ggformula
Loading required package: ggplot2
Loading required package: ggstance

Attaching package: 'ggstance'

The following objects are masked from 'package:ggplot2':

    GeomErrorbarh, geom_errorbarh


New to ggformula?  Try the tutorials: 
	learnr::run_tutorial("introduction", package = "ggformula")
	learnr::run_tutorial("refining", package = "ggformula")
Loading required package: mosaicData
Loading required package: Matrix

The 'mosaic' package masks several functions from core packages in order to add 
additional features.  The original behavior of these functions should not be affected by this.

Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.

Attaching package: 'mosaic'

The following object is masked from 'package:Matrix':

    mean

The following object is masked from 'package:ggplot2':

    stat

The following objects are masked from 'package:dplyr':

    count, do, tally

The following objects are masked from 'package:stats':

    IQR, binom.test, cor, cor.test, cov, fivenum, median, prop.test,
    quantile, sd, t.test, var

The following objects are masked from 'package:base':

    max, mean, min, prod, range, sample, sum

Welcome to tigerstats!
To learn more about this package, consult its website:
	http://homerhanumat.github.io/tigerstats
Chi-squared test for given probabilities 

         Observed counts Expected by Null Contr to chisq stat
1_front               27            23.67                0.47
2_middle              32            23.67                2.93
3_back                12            23.67                5.75


Chi-Square Statistic = 9.1549 
Degrees of Freedom of the table = 2 
P-Value = 0.0103 

Pearson's Chi-squared test 

Observed Counts:
        seat
sex      1_front 2_middle 3_back
  female      19       16      5
  male         8       16      7

Counts Expected by Null:
        seat
sex      1_front 2_middle 3_back
  female   15.21    18.03   6.76
  male     11.79    13.97   5.24

Contributions to the chi-square statistic:
        seat
sex      1_front 2_middle 3_back
  female    0.94     0.23   0.46
  male      1.22     0.29   0.59


Chi-Square Statistic = 3.734 
Degrees of Freedom of the table = 2 
P-Value = 0.1546 

Pearson's Chi-squared test 

Observed Counts:
        seat
sex      1_front 2_middle 3_back
  female      19       16      5
  male         8       16      7

Counts Expected by Null:
        seat
sex      1_front 2_middle 3_back
  female   15.21    18.03   6.76
  male     11.79    13.97   5.24

Contributions to the chi-square statistic:
        seat
sex      1_front 2_middle 3_back
  female    0.94     0.23   0.46
  male      1.22     0.29   0.59


Chi-Square Statistic = 3.734 
Degrees of Freedom of the table = 2 
P-Value = 0.1546 

Pearson's chi-squared test with simulated p-value, fixed row sums
	 (based on 2500 resamples) 

Observed Counts:
       crowd.behavior
weather baiting polite
   cool       2      7
   warm       8      4

Counts Expected by Null:
       crowd.behavior
weather baiting polite
   cool    4.29   4.71
   warm    5.71   6.29

Contributions to the chi-square statistic:
       crowd.behavior
weather baiting polite
   cool    1.22   1.11
   warm    0.91   0.83


Chi-Square Statistic = 4.0727 
Degrees of Freedom of the table = 1 
P-Value = 0.0604 

Pearson's Chi-squared test 

Chi-Square Statistic = 3.734 
Degrees of Freedom of the table = 2 
P-Value = 0.1546 

tigerstats documentation built on July 2, 2020, 2:32 a.m.