|
|
|
||||
|
|
|||||
Please Note: Stata graph commands changed with version 8 and this page was developed before version 8 was released and uses Stata 7 graph commands. Please see How do I use version 7 graph commands in Stata version 8? for information on how to either run these Stata 7 graph commands in Stata version 8, or how you can covert these commands to use Stata 8 syntax.
.use crf * descriptive statistics by group or cell: sort b by b: summarize y sort a b by a b: summarize y tabulate a b,summarize(y) table a b, contents(mean y sd y) row col * graph interaction/plot means by cell: anova y a b a*b predict yhat sort a b graph yhat b,twoway ylabel xlabel c(L) sort b a graph yhat a,twoway ylabel xlabel c(L) * graph interaction with error bars: anova y a b a*b predict mean generate rmse=e(rmse) sort a b serrbar mean rmse b, ylabel xlabel c(L) sort b a serrbar mean rmse a, ylabel xlabel c(L) * Levene's test of heterogeneity of variance: egen m=mean y, by(b) generate d = abs(y - m) anova d b /* the F-ratio is Levene's test */ * check homogeneity of variance in factorial design: egen cell=group(a b) oneway y cell /* oneway includes Bartlett's test */ * check normality: sort b by b: graph y, normal pnorm y if b==1 /* pnorm does not allow by option */ pnorm y if b==2 /* etc */ * oneway anova: oneway y b, tabulate sidak /* with post-hoc comparisons */ anova y b * factorial anova: anova y a b a*b test b, error(a*b) /* using interaction as error term */ anova y a b / a*b / /* alternate method for using different error term */ * nested anova: anova y a b|a /* using residual as error term for both a and b|a */ anova y a / b|a / /* using b|a as error term for a and residual for b|a */ * reshape wide to long: reshape long y, i(s) j(b) * repeated measures anova: * data are long anova y b s, repeated(b) /* randomized block design */ reshape wide y, i(s) j(b) /* reshape so data are wide */ corr y1 y2 y3 y4, covariance /* check variance-covariance matrix */ anova y a / s|a b a*b, repeated(b) /* split-plot factorial */ * Tukeys test for additivity in randomized block designs: anova y a s predict yhat generate ystar = (yhat - 5.375)^2 /* 5.375 is the grand mean */ anova y a s ystar, continuous(ystar) * tests of simple main effects: * sme.ado written by Phil Ender anova y a b a*b sme a b sme b a * pairwise comparisons: * prcomp.ado written by John Gleason (sg-101) * in STB-47 downloaded from STATA prcomp y b, test prcomp y b, test tukey /* Tukey HSD */ prcomp y b, test tukey nu(24) sigma(.85) /* using a different dfe and rmse */ * contrasts: * contrast.ado written by Phil Ender anova y a b a*b contrast b, values(1 1 -1 -1) title(12 vs 34) * analysis of covariance: anova y b cov, continuous(cov) adjust cov, by(b) /* compute adjusted means */ anova y b cov b*cov, continuous(cov) /* check homogeneity of slopes */ * sequence of commands to make pairwise tests among adjusted means anova y b cov, continuous(cov) /* save dfe and rmse */ anova y b predict ybar summarize cov generate yhat = r(mean) anova cov b predict xbar regress y cov /* save slope coefficient b */ generate adj = ybar - b*(xbar - xhat) prcomp adj b,test tukey nu(dfe) sigma(rmse)
UCLA Researchers are invited to our Statistical Consulting Services
We recommend others to our list of Other Resources for Statistical Computing Help
These pages are Copyrighted (c) by UCLA Academic Technology Services