The main purpose of the package ExpDes is to analyze simple experiments under completely randomized designs (crd()), randomized block designs (cbd()) and Latin square designs (latsd()). Also enables the analysis of treatments in a factorial design with 2 and 3 factors (fat2.crd(), fat3.crd(), fat2.rbd(), fat3.rbd()) and also the analysis of split-plot designs (split2.crd(), split2.rbd()). Other functionality is analyzing experiments with one additional treatments on completely randomized design and randomized blocks design with 2 or 3 factors (fat2.ad.crd(), fat2.ad.rbd(), fat3.ad.crd() and fat3.ad.crd()).
After loading the package and reading and attaching the data, a single command is required to analyze any situation. For instance, consider a completely randomized design for a qualitative factor.
crd(treat, resp, quali = TRUE, mcomp = "tukey")
For multiple comparison used Tukey´s test. Other tests where implements in package. For factor quantitative is possible used models regression linear and non linear.
crd(trat, resp, quali = FALSE, sigF = 0.05)
Output:
--------------------------------------------------------------- Analysis of Variance Table --------------------------------------------------------------- DF SS MS Fc Pr>Fc Treatament 3 214.88 71.626 6.5212 0.0029622 Residuals 20 219.67 10.984 Total 23 434.55 --------------------------------------------------------------- CV = 3.41 % --------------------------------------------------------------- Shapiro-Wilk normality test p-value: 0.91697 According to Shapiro-Wilk normality test at 5% of significance, residuals can be considered normal. --------------------------------------------------------------- --------------------------------------------------------------- Homogeneity of variances test p-value: 0.1863216 According to the test of bartlett at 5% of significance, residuals can be considered homocedastic. --------------------------------------------------------------- Tukey's test --------------------------------------------------------------- Groups Treatments Means a A 102.1983 b B 96.74333 b C 95.05833 b D 94.74333 ---------------------------------------------------------------
a <- crd(treat, resp) plotres(a)
The object “a” contains information about residuals, means, among others. In this way, graphs of averages and other analyzes can be performed. Grafical ExpDes (GExpDes) expansions are being worked on and new features are in progress. Information in 10.4236/am.2014.519280 and https://cran.r-project.org/web/packages/ExpDes/index.html.[contact-form][contact-field label=”Name” type=”name” required=”true” /][contact-field label=”Email” type=”email” required=”true” /][contact-field label=”Website” type=”url” /][contact-field label=”Message” type=”textarea” /][/contact-form]