Introduction Data Outreg Plots Free Lunch Conclusions Guessing rockchalk package Paul E. Johnson 1 2 < pauljohn@ku.edu > 1 Department of Political Science 2 Center for Research Methods and Data Analysis, University of Kansas 2013 rockchalk 1 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Outline Introduction 1 Data 2 Outreg 3 Plots 4 Categorical modx Numeric moderator Free Lunch 5 Conclusions 6 Guessing 7 rockchalk 2 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Outline Introduction 1 Data 2 Outreg 3 Plots 4 Categorical modx Numeric moderator Free Lunch 5 Conclusions 6 Guessing 7 rockchalk 3 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Thanks for Joining Thanks to Ray DiGiacomo, Jr & OC RUG for organizing Downloads: http://pj.freefaculty.org/guides { all my lectures on anything } .../Rcourse/rockchalk-2013 { this lecture, source code, L YX doc, etc } http://pj.freefaculty.org/R : Rtips, links to other R stuff rockchalk 4 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Why Make a Package? Avoid a riot after an influx of 40 MA-bound behavioral scientists into my regression class Honestly, I’d rather teach R programming, but I can understand the view that statistics exists apart from R Package has“convenience”functions for Me preparing lectures Them doing papers (with nice looking graphs!) I had distributed functions before, but never made a package rockchalk 5 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing What do you expect in rockchalk? Functions for difficult/tedious/hard-to-teach chores Verbose documentation, (too) many examples vignettes “rockchalk” : Dicussion & demonstration of package “Rchaeology” : Deep insights into R programming I accumulate while working on the package “ Rstyle” : The style manual I wish R Core would adopt Hidden value added: the examples folder in the package install directory includes some special educational R examples (look for noWords-001.R and centeredRegression.R ) rockchalk 6 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Where is the hard work in version 1.8? predictOMatic() . Flexible way to demonstrate marginal effects of predictors. Goal: make it easy to understand regression as a translation of inputs into predicted values (and uncertainty) Scan fitted regressions, create newdata objects with possible predictor values ( “divider”algorithms to create focal values for consideration). rockchalk 7 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Outline Introduction 1 Data 2 Outreg 3 Plots 4 Categorical modx Numeric moderator Free Lunch 5 Conclusions 6 Guessing 7 rockchalk 8 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Make a Presentable Table Describing The Data Assignment: create a summary table for your research article R’s summary () does not include diversity estimates does not separate numeric from factor variables in the report does not provide output in a usable format rockchalk summarize() does rockchalk 9 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Example ( datsum < − summary ( dat ) ) income educ age r e l i g i o n gender Min. : − 56816 Min. : 2 .00 Min. : 9 .00 cath :177 female :532 1 s t Qu. : − 2225 1 s t Qu. : 8 .00 1 s t Qu. :19 .00 j e w i s h : 87 male :468 Median : 10565 Median :10 .00 Median :22 .00 muslem : 94 Mean : 10473 Mean :10 .02 Mean :22 .04 other :294 3 rd Qu. : 23772 3 rd Qu. :12 .00 3 rd Qu. :25 .00 prot :169 Max. : 77189 Max. :21 .00 Max. :37 .00 roman :105 NA ✬ s :80 NA ✬ s :40 NA ✬ s : 74 Can you wrestle that into a paper? I can’t! It has text and values combined datsum [ , 1 ] ”Min. : − 56816 ” ”1 s t Qu. : − 2225 ” ”Median : 10565 ” ”Mean : 10473 ” ”3 rd Qu. : 23772 ” ”Max. : 77189 ” ”NA ✬ s :80 ” Default output from summarize separates numerics & factors, alphabetizes rockchalk 10 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Example ... datsum2 < − summarize ( dat ) The result object datsum2 is a list with 2 parts, a numeric matrix part and a factor variable display. The numerics are a matrix, easy to take rows or columns to put into a paper datsum2$ numerics age educ income 0% 9 .000 2 .000 − 56820 25% 19 .000 8 .000 − 2225 50% 22 .000 10 .000 10570 75% 25 .000 12 .000 23770 100% 37 .000 21 .000 77190 mean 22 .040 10 .020 10470 sd 4 .556 3 .056 19630 var 20 .760 9 .337 385400000 NA ✬ s 0 .000 40 .000 80 N 1000 .000 1000 .000 1000 rockchalk 11 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Example ... The factors are a separate list datsum2$ f a c t o r s gender r e l i g i o n female : 532 .000 other : 294 .0000 male : 468 .000 cath : 177 .0000 NA ✬ s : 0 .000 prot : 169 .0000 entropy : 0 .997 roman : 105 .0000 normedEntropy : 0 .997 ( A l l Others ) : 181 .0000 N :1000 .000 NA ✬ s : 74 .0000 entropy : 2 .4414 normedEntropy : 0 .9445 N :1000 .0000 Indicators of central tendency and dispersion are included in both displays Try summarizeNumerics() and summarizeFactors() to get just one or the other. rockchalk 12 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Sidenote: recoding a factor Note the religion variable has levels“cath”and“roman” , which was a data entry error. Catholic and Roman Catholic represent the same idea Did you ever try to write R code to fix that (without killing yourself)? Try rockchalk::combineLevels() : dat $ r e l i g i o n 2 < − combineLevels ( dat $ r e l i g i o n , c ( ”cath ” , ”roman ”) , ”cath ”) The o r i g i n a l l e v e l s cath j e w i s h muslem other prot roman have been r e p l a c e d by j e w i s h muslem other prot cath rockchalk 13 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Sidenote: recoding a factor ... t a b l e ( dat $ r e l i g i o n 2 , dat $ r e l i g i o n , dnn = c ( ” r e l i g i o n 2 ” , ” r e l i g i o n ”) ) r e l i g i o n r e l i g i o n 2 cath j e w i s h muslem other prot roman j e w i s h 0 87 0 0 0 0 muslem 0 0 94 0 0 0 other 0 0 0 294 0 0 prot 0 0 0 0 169 0 cath 177 0 0 0 0 105 rockchalk 14 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Outline Introduction 1 Data 2 Outreg 3 Plots 4 Categorical modx Numeric moderator Free Lunch 5 Conclusions 6 Guessing 7 rockchalk 15 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Need a Nice Looking Regression Table? Each student should not invent a unique report format for regressions. MS Word users especially tempted to“finger paint”with fonts and formats. Solution: provide usable L T EX tables (added benefit: bait to A get them to use L A T EX) rockchalk-1.8 provides HTML backend as well (compromise with reality) rockchalk 16 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing For many years, outreg was a function in search of a package Dave Armstrong (then at U. Maryland student) gave me the outreg idea 10 years ago I wrote up a function that more-or-less worked, distributed it, revised it as my R programming skills improved I didn’t know there was ” outreg”module for Stata . . . . rockchalk 17 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing outreg example usage I fit a regression using a subset of the American National Election Study 2004 (ICPSR), which I called“mydta1” mod1age < − lm ( t h . b u s h . k e r r y ∼ V043250 , data = mydta1 ) outreg ( mod1age , t i g h t = FALSE , modelLabels = c ( ”Age as P r e d i c t o r ”) ) rockchalk 18 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Produces this LaTeX Markup \ begin { t a b u l a r }{ ✯ { 3 }{ l }} \ h l i n e & \ multicolumn { 2 }{ c }{ Age as P r e d i c t o r } \\ &Estimate &( S.E. ) \\ \ h l i n e \ h l i n e ( I n t e r c e p t ) & − 6.841 & (4 .596 ) \\ V043250 & 0 .184 ✯ & (0 .092 ) \\ \ h l i n e N &1191 & \\ RMSE &53 .885 & \\ $ R 2 $ &0 .003 & \\ \ h l i n e \ h l i n e \ multicolumn { 2 }{ l }{ $ { ✯ } p \ l e 0 .05 $ }\\ \ end { t a b u l a r } rockchalk 19 / 81 K.U.
Introduction Data Outreg Plots Free Lunch Conclusions Guessing Which LaTeX Renders as Age as Predictor Estimate (S.E.) (Intercept) -6.841 (4.596) V043250 0.184* (0.092) N 1191 RMSE 53.885 R 2 0.003 ∗ p ≤ 0 . 05 My terminology: tight = FALSE ⇒ ˆ β and std . err (ˆ β ) are side by side tight = TRUE ⇒ ˆ β and std . err (ˆ β ) are vertically aligned. rockchalk 20 / 81 K.U.
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