File:Datest Regression all on Tuberculosis.JPG
Original file (1,028 × 746 pixels, file size: 109 KB, MIME type: image/jpeg)
This is a file from the Wikimedia Commons. The description on its description page there is shown below. |
This file was moved to Wikimedia Commons from en.wikibooks using a bot script. All source information is still present. It requires review. Additionally, there may be errors in any or all of the information fields; information on this file should not be considered reliable and the file should not be used until it has been reviewed and any needed corrections have been made. Once the review has been completed, this template should be removed. For details about this file, see below. Check now! |
Contents
Summary
DescriptionDatest Regression all on Tuberculosis.JPG |
English: This picture has been created with MD*Tech XploRe.
Graphical Elements
InterpretationCheck en:Analysis of Tuberculosis. Program UsageIf you want to display the point clouds and regression lines of different parts of the dataset in one plot, the code has to be slightly adjusted as exemplary done for three of the twelve elements of the graphic (little red dots, dashed lines). Program CodeAttention! For repeating the computation a transformed dataset is needed! If you have not yet computated and saved the transformation, run the program for transformation on the wikipage en:Analysis of Tuberculosis first! library("xplore") library("stats") ; ----- Reading Data -------------------------------------------------------------------------- choose = "Read data from:" defaults = "C:\Dokumente und Einstellungen\All Users\Desktop\UN_data_ordered.csv" v1 = readvalue(choose, defaults) x = readcsvm(v1) x1 = readcsvm("C:\Dokumente und Einstellungen\All Users\Desktop\Cluster1.csv") x1 = x1.double data = x.double country = x.text ; ----- 1.1) Nonlinear Regression of Aids on Tuberculosis -------------------------------------- ta = (0.8*log(data[,4]))~((data[,6])) ta = paf(ta, isInf(ta[,1])==0) {beta,bse,bstan,bpval} = linreg(ta[,1], ta[,2]) rx = #(0:max(data[,4])) yq = beta[1] + beta[2]*log(rx) rdata = sort (rx~yq) rdata = setmask (rdata, "line", "red") ; ----- 4) Linear Regression of Sanitation on Tuberculosis ------------------------------------- gr4 = grlinreg(data[,8|6]) gr4 = setmask(gr4, "line", "red") linreg(data[,8],data[,6]) ; ----- 5) Linear Regression of Sanitation on Tuberculosis ------------------------------------- gr5 = grlinreg(data[,9|6]) gr5 = setmask(gr5, "line", "red") linreg(data[,9],data[,6]) ; ----- 5.1) Linear Regression (Cluster1) of Sanitation on Tuberculosis ------------------------ grcl5 = grlinreg(x1[,9|6]) grcl5 = setmask(grcl5, "line", "red", "size", "thin", "style", "dashed") lr5cl1 = linreg(x1[,9],x1[,6]) lr5cl1 ; ----- 6) Linear Regression of Sanitation on Tuberculosis ------------------------------------- gr6 = grlinreg(data[,10|6]) gr6 = setmask(gr6, "line", "red") linreg(data[,10],data[,6]) ; ----- 7) Linear Regression of Sanitation on Tuberculosis ------------------------------------- gr7 = grlinreg(data[,11|6]) gr7 = setmask(gr7, "line", "red") linreg(data[,11],data[,6]) ; ----- 8) Linear Regression of Sanitation on Tuberculosis ------------------------------------- gr8 = grlinreg(data[,12|6]) gr8 = setmask(gr8, "line", "red") linreg(data[,12],data[,6]) ; ----- 9) Linear Regression of 1/CO2^0.3 on Tuberculosis -------------------------------------- z9 = (data[,13]^(-0.3))~data[,6] gr9 = grlinreg(z9) gr9 = setmask(gr9, "line", "red") ; ----- 9.1) Nonlinear Regression -------------------------------------------------------------- {beta9,bse9,bstan9,bpval9} = linreg((data[,13]^(-0.3)), data[,6]) rx9 = #(0:max(data[,13])*100)/100 yq9 = beta9[1] + beta9[2]*rx9^(-0.3) rdata9 = sort (rx9~yq9) rdata9 = setmask (rdata9, "line", "red") ; ----- 10) Linear Regression of 1/Int^0.3 on Tuberculosis ------------------------------------- z10 = (data[,14]^(-0.3))~data[,6] gr10 = grlinreg(z10) gr10 = setmask(gr10, "line", "red") ; ----- 10.1) Nonlinear Regression ------------------------------------------------------------- {beta10,bse10,bstan10,bpval10} = linreg((data[,14]^(-0.3)), data[,6]) rx10 = #(0:max(data[,14])*1000)/1000 yq10 = beta10[1] + beta10[2]*rx10^(-0.3) rdata10 = sort (rx10~yq10) rdata10 = setmask (rdata10, "line", "red") ; ----- 10.2) Linear Regression (Cluster1) of Internet on Tuberculosis ------------------------- grcl10 = grlinreg(x1[,14|6]) grcl10 = setmask(grcl10, "line", "blue", "size", "thin", "style", "dashed") lr10cl1 = linreg(x1[,14],x1[,6]) lr10cl1 ; ----- 11) Linear Regression of 1/PC^0.3 on Tuberculosis -------------------------------------- z11 = (data[,15]^(-0.3))~data[,6] gr11 = grlinreg(z11) gr11 = setmask(gr11, "line", "red") ; ----- 11.1) Nonlinear Regression ------------------------------------------------------------- {beta11,bse11,bstan11,bpval11} = linreg((data[,15]^(-0.3)), data[,6]) rx11 = #(0:max(data[,15])*1000)/1000 yq11 = beta11[1] + beta11[2]*rx11^(-0.3) rdata11 = sort (rx11~yq11) rdata11 = setmask (rdata11, "line", "red") ; ----- 12) Linear Regression of 1/Tel^0.3 on Tuberculosis ------------------------------------- z12 = (data[,16]^(-0.3))~data[,6] gr12 = grlinreg(z12) gr12 = setmask(gr12, "line", "red") ; ----- 12.1) Nonlinear Regression ------------------------------------------------------------- {beta12,bse12,bstan12,bpval12} = linreg((data[,16]^(-0.3)), data[,6]) rx12 = #(0:max(data[,16])*300)/300 yq12 = beta12[1] + beta12[2]*rx12^(-0.3) rdata12 = sort (rx12~yq12) rdata12 = setmask (rdata12, "line", "red") ; ----- Create Colours ------------------------------------------------------------------------- randomize(25) bunt = uniform(10, 3)*255 createcolor(bunt) ; ----- Graphical Settings --------------------------------------------------------------------- setsize(800, 600) f = createdisplay (3, 4) ; ----- Plotting the Point Clouds -------------------------------------------------------------- i = 4 j = 6 t1 = data[,i|j] t2 = data[,i+1|j] t3 = data[,i+3|j] t4 = data[,i+4|j] t5 = data[,i+5|j] t6 = data[,i+6|j] t7 = data[,i+7|j] t8 = data[,i+8|j] t9 = data[,i+9|j] t10 = data[,i+10|j] t11 = data[,i+11|j] t12 = data[,i+12|j] ; ----- Example: Plotting the Cluster1 Point Cloud --------------------------------------------- cl5 = x1[,i+5|j] cl7 = x1[,i+7|j] cl10 = x1[,i+10|j] ; ----- Graphical Settings --------------------------------------------------------------------- setmaskp(t1, 0, 11) setmaskp(t2, 5, 11) setmaskp(t3, bunt[1], 11) setmaskp(t4, bunt[2], 11) setmaskp(t5, bunt[3], 11) setmaskp(t6, bunt[4], 11) setmaskp(t7, bunt[5], 11) setmaskp(t8, bunt[6], 11) setmaskp(t9, bunt[7], 11) setmaskp(t10, bunt[8], 11) setmaskp(t11, bunt[9], 11) setmaskp(t12, bunt[10], 11) setmaskp(cl5, 4, 1) setmaskp(cl7, 4, 1) setmaskp(cl10, 4, 1) show(f, 1, 1, rdata, t1) show(f, 1, 2, t2) show(f, 1, 3, t3) show(f, 1, 4, gr4, t4) show(f, 2, 1, gr5, grcl5, t5, cl5) show(f, 2, 2, gr6, t6) show(f, 2, 3, gr7, t7, cl7) show(f, 2, 4, gr8, t8) setxaxis(f, 2, 1, min(min(data[,9:12])'), max(max(data[,9:12])'), 0) setxaxis(f, 2, 2, min(min(data[,9:12])'), max(max(data[,9:12])'), 0) setxaxis(f, 2, 3, min(min(data[,9:12])'), max(max(data[,9:12])'), 0) setxaxis(f, 2, 4, min(min(data[,9:12])'), max(max(data[,9:12])'), 0) show(f, 3, 1, rdata9, t9) show(f, 3, 2, rdata10, grcl10, t10, cl10) show(f, 3, 3, rdata11, t11) show(f, 3, 4, rdata12, t12) setyaxis(f, 3, 2, -25, 500) |
Date | 30 March 2007 (original upload date) |
Source | Transferred from en.wikibooks to Commons. |
Author | Schtiwi at English Wikibooks |
Licensing
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled GNU Free Documentation License.http://www.gnu.org/copyleft/fdl.htmlGFDLGNU Free Documentation Licensetruetrue |
This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license. | ||
Attribution: Schtiwi at the English Wikipedia | ||
| ||
This licensing tag was added to this file as part of the GFDL licensing update.http://creativecommons.org/licenses/by-sa/3.0/CC BY-SA 3.0Creative Commons Attribution-Share Alike 3.0truetrue |
Original upload log
Date/Time | Dimensions | User | Comment |
---|---|---|---|
2007-03-30 11:55 | 1028×746× (111196 bytes) | Schtiwi | This picture has been created with MD*Tech XploRe. |
Items portrayed in this file
depicts
30 March 2007
image/jpeg
57e05d0547ba37dacdb907e955e1e94934ecfdfd
111,196 byte
746 pixel
1,028 pixel
File history
Click on a date/time to view the file as it appeared at that time.
Date/Time | Thumbnail | Dimensions | User | Comment | |
---|---|---|---|---|---|
current | 14:19, 19 August 2017 | 1,028 × 746 (109 KB) | JackPotte | {{BotMoveToCommons|en.wikibooks|year={{subst:CURRENTYEAR}}|month={{subst:CURRENTMONTHNAME}}|day={{subst:CURRENTDAY}}}} == {{int:filedesc}} == {{Information |Description={{en|This picture has been created with MD*Tech XploRe. == Graphical Elements ==... |
File usage
The following page uses this file: