CAC PHlTONG PHAP PHAN TICH THONG KE DA BIEN SO LIEU NGHIEN CUtJ LAM NGHIEP BANG SAS Biii Manh Hung
Truang Bgi hgc Ldm nghigp T O M T A T
Phan tich da bien da va dang chung minh dugc nhilu mi diem ndi trgi nhu: khai thac tri?t de s6 lieu, kit qua ph3n tidi toan di?n va khach quan hon. SAS c6 thi th\rc h i ^ duqrc nhieu phan tich da bi&i khac nhaa D5u tien phai ke den la phan tidi thanh phan diuih. Phuong phip nay co the dugc ap dung Sh phan tich m6i quan h?
giiia cac loai trong rimg tir nhien. Cac loai se AVQC phSn thanh 3 nhdm chinh: doi khang, doi khang it va khong doi khang. Phan tich thii hai la tirong quan chmh t5c. PhSn tich nay c6 thi phan ti'di dugc moi tuong quan giiia hai nhom bien (nhdm X, nhdm Y). Difiu nay vugt tr§i hon bin cac phan tich tuong quan don biin thuong dugc ap dung trudc day. Phan tich ihu ba la phan tich tuong dong. Phan tich tuong d6ng co the tim ra cac IOM UU the d moi 6, d&ig thoi phan loai cac 6 cd miic tuong dong ve miic do da dang sinh hgc loai thanh cac nhom. Day la CO so quan trgng de dieu tiet to thanh va nang cao da dang sinh hpc tgi khu vuc nghioi cihi. Phan tich cuoi cimg la phan tich phSn nhom. Phan tich niy se t^o thanh cac nhdm loai tuong dong, it ddi khang. Ngoai ra n6 se cho biet phiic dg bien dgng co the dugc gi^ thidi boi c^c nhdm. D6 la co sd tit d i Idi^g dinh do tin cay cua cdc nhdm.
Tir khoa: PhSn tich nhdm, phan ticb thanh phan chinh, phSn tfch tuvng ddng, Sas, tinmg quan chinh t a c I . D A T V A N D E
Viec xii ly so lieu trong nghien cuu noi chung va trong Ldm nghiep noi rieng la dieu cue ky quan trpng. Bai le, phan tich so lieu la CO so de giup cac nha nghien cuu co nhiing ket luan diing ddn, chinh xac, tir do co nhiing nhan dinh, cdch nhin va de xudt phii hop trong viec qudn ly va phdt trien tai nguyen rimg mot each bSn vung (B.M. Himg, 2016;
S. Wagner, 2016).
Trong nhiing nam gdn day, co nhieu phdn tich thong ke da biin da ducfc dp dung nhu:
phan tich tuong quan da bien, phdn tich thanh phan chinh, phan tich he so duong dnh hudng, phan tich tucmg dong, phan tich phdn nhom...
dd duoc dp dyng nhieu trong cdc Biih vuc nghien cuu sinh thdi hoc noi chung, trong do CO lam nghiep (S. Wagner, 2014; S. Wagner, 2016; U. Berger, 2008). Tuy nhi6n, tai Viet Nam, viec iing dyug cac phuong phdp phdn tich nay trong Enh vuc lam nghiep con rat han ch6. Mot nguyen nhdn chinh ddn den han che nay la thiSu cdc tai liSu huong ddn khai thdc va ling dung cac phan mem thong ke manh cho phan tich so lieu nghien ciiu lam nghiep (B.M.
Hirag va cong s\r, 2013; B.M. Himg va cong sv, 2017).
Phdn tich da bien da va dang chiing minh dupe nhiing uu diem n6i troi hon cac phuang phap don bi6n thuong duoc dp dung trudc kia trong cac nghien cuu lam nghiep. Trudc hit, no khai thac dupe tong hpp toan bp cac biSn, cac so lieu ma chung ta co, tranh viec iang phi s6 iieu va cong siic thu thdp. Thii hai, ket qua phan tich phan anh toan di8n va khdeh quan ban doi tuong md cdc nhd nghien ciiu cdn phan tich. Vd vi the, no ddn d6n mpt tm diem cudi cung Id cac de xudt, k6t lu|ln se trd len chinh xac vd hieu qud hon.
Trong phdn tich s6 li?u noi chung, co nhi6u phan m6m tin hpc h6 tra rdt m^nh cho viec xu ly s6 lieu nghien ciiu noi chung vd sd lieu ldm nghig) ndi rieng nhu: SPSS, Stata, R, M.S.
Excel, Irristat, Minitab, Statgraphics... Tuy nhien, qua qud trinh nghi8n ciiu va sii dung phan mem SAS da chiing minh duae nhieu chuc nang mdi cd gia tri cao trong phdn tich sd lieu nghien cmi ldm nghiep nhu: lap phdn bd thue nghi8m cho dai lupng liSn tuc, he thdng tiSu chuan phi tham sd de so sanh cac man, h?
thing phdn tich tuong quan phi tuyin va ddc biet la phan tich da bien, da mau (M.
Marasinghe, 2008; C Y . Joanne Peng, 2009;
L.Q. Hung, 2009; B.M. Hung, 2011). Mpt mi TAP CHI KHOA HQC VA CONG NGHfi LAM NGHEpP SO 1-2018 43
Ldm hpc
diim npi troi khdc cua SAS la viec viet va tao lap cac ddng l?ch de phdn tich so li?u. Dieu nay se giiip viec phan tich so lieu ldn tiep theo, hoac lap ]^\ a mpt 6 tieu chuan khac duoc thue hi?n mpt each rat de dang vd nhanh chong.
Vdi nhirng ly do nhu tren, bai bdo nay se trinh bay mpt cdch cu thi cac phuang phap phdn tich thing k% da biin vdi sir hd trp bdi SAS; qua do cho thdy sir can thiet va hiiu ich trong viec iing diing phan mem nay trong phan tich so lieu nghien cihi ldm nghidp, giiip viSc phan tich so li8u dupe hieu qua, nhanh chong vd chinh xdc. Phuang phdp phan tich thong ke da bien se khac phuc dupe nhung nhugc di8m ciia Excel va mpt so phdn mem khac.
U. PHirONG P H A P NGHIEN CUtJ 2.1. Phutmg phap nghiSn eiru tai lieu va so lieu chpn I9C
Mpt s6 tai li?u hudng dan su dung SAS cung nhu phan tich thong ke da bien trong SAS duoc thu theip, phdn tich mpt each co chpn lpc.
Cdc tai lipu phan tich ve liiih vuc ldm nghiep dupe uu tiSn hdng ddu. Sau do tdi cdc linh vuc gan giii ban nhu qudn ly tai nguyen rimg, quan ly moi trudng, che bien gd vd kinh te lam nghiep. Cac tai liSu duoc tdp hpp va phan tich theo ca sd ly thuyet ve phan tich l^ng SAS, thdnh tuu vd nhung kit qua da dsit dupe trong liiih vuc phdn tich sd lieu nghiSn ciiu lam nghiep bdng SAS (V.C. Ddm, 1999).
Sd lieu dupe ke thira tir nhung nghi6n ciiu trudc, vdi su ddng y cua cdc tdc gid giu quySn sd hiiu cdc bd so li?u do. So lieu t^p trung chu yeu ve cac lmh vuc trong Idm nghiep nhu: Dieu tra quy ho^ch. Lam hpc, Ldm nghiep xa hdi...
2.2. Phiromg phap thir nghiem va so saob Tir vipc thdng ke, phan tich cac trinh 18nh,
quy trmh duoc su dung de phdn tich da biin vdi su ho trp cua SAS, cdc trmh lenh cho phan tich so lipu lam nghiep dupe xdy dung mot each ti mi, chmh xdc. Tiip dd, cac trinh lenh dupe ch^y thu vdi cdc bp sd lieu ldm nghi?p, Sau dd, kit qua xudt ra duoc kiem tra, danh gid va so sdnh vdi ket qua xuat ra ciia cac ph5n mim khac nhu Spss, Stata vd R. Tir dd, chpn ra duoc quy trinh chmh xdc, hieu qud cho phan tich da bien sd liSu lam nghiep (B.M. Hung va cdng sir, 2013).
IH. KET QUA NGHIEN ClJtJ
3.1. Phan tich thanh phao chinh (Principal Component Analysis)
Phan tich thanh phan chinh (PCA) la mot phdn tich da biin rdt quan trpng trong phan tich sd liSu. Day Id phuong phap nhom cdc doi tuang phdn tich. Phdn tich thdnh phdn chmh rat hihi ich khi bang du lieu cd nhieu bien tham gia. Phuang phdp nay se giiip tim ra duoc cac thdnh phdn nao la chinh trong bang dir !i?u.
Nhung nhan td ndy se ddng gop phdn ldn vao sir bien ddng cua tap du lieu. Nguyen ly ciia PCA kha dan gian, trudc h6t PCA se do ra hudi^ nao cd biin ddng nhieu nhat trong tap dli lieu. Sau dd PCA se xoay true hoanh theo hudng dd va true tung theo hudng vudng goc con lai (A.M.C. Davies vd cpng sir, 2017). Day la ca sd de chiing ta co the loai bdt cdc bien, cac nhdn td khdng can thiit, khdng quan tnji^
trong tap dir lipu. Ddng thdi phdn loai dupe nhdm cac nhan td ddi khang, it ddi khang va ddi khdng manh.
PCA cd nhieu ling dung, tuy nhien mpt iing dung kha phd bien Id de phdn tich quan he gifia cac loai trong rimg tu nhien. D i chay dupe ling dung ndy, cdc lenh sau dupe thue hien:
proc princomp data=W0RK.IMP0RT5 plots(only nconip=2)=(pattem);
var"Ten biin cua cdc lodi";
T ^ P CHi KHOA HQC VA CONG NGHi: LAM NGHIEP SO 1-2018
Ung dung sau day cho thdy PCA co thi phan loai dupe cdc l o a i ^ y ra thanh cdc nhdm:
ddi khdng, ddi khang ft vd doi khang m^nh. Vi du nhu Chd xdt va De da ning thudng chung sdng cimg nhau va khdng doi khdng. Chung ddi khang it vdi Da cua, Bdi ldi trung bd, Chdi mdi niii vd Cdm Fleury. Tuy nhien, chiing rat
Ldm hpc ddi khdng vdi cdc lodi: Cdi rdo, Bau mit, Mac cua hay Trdm rdng... Vi vdy, khi gdy tao rimg trdng vdi cac loai tu nhien, can trdnh cac lodi ddi khang va cdn tap trung vao cdc loai khdng ddi khang, dd la co sd smh 1;^ tu nhien nit ra dupe tir cdc qudn the thue vdt. Dieu nay dupe thi hien trong biiu dd PCA (hmh 01).
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-10 .06 .06 .04 .02 ao 02 04 06 Component 1 (1263%)
H i n h 0 1 . Bieu do p h a n tich P C A cho cac loai r u n g tiJT nhien 3 . 2 . P h a n t i c h t i r o n g q u a n c h i n h t d c
( C a n o D i c a l C o r r e l a t i o n )
P h a n t i c h t u o n g q u a n c h i n h t d c ( C C ) d u p e sii d u n g d e p h d n tfch m d i q u a n h e giira h a i t a p b i e n . T u y n h i d n , C C k h d n g xdc djnh d a u la t a p b i e n d p c lap, d d u la t ^ bien p h u t h u p c . C C s e lap mdt t a p bien c h i n h tac (canonical variates).
D a y Id t a p h p p t u y e n t m h c a c bien d e gidi t h i c h tdt nhat c h o m d i q u a n h e gifia h a i t a p bien, t a p g p i Id t a p b i e n X v a t a p b i e n Y . C C se t a o ra hai bien c h i n h t d c d d u tidn, t h u d n g k y hieu la W i v a V l . T r o n g d d : W | la t o h p p t u y e n t i n h ciia c d c b i e n t r o n g n h d m X vd V i la t d h o p t u y i n t i n h ciia c d c b i e n t r o n g n h d m Y . S a u d d C C s e t a o tiep c a c b i e n c h i n h t a c tiep t h e o . Sd l u g n g b i e n c h i n h tdc b a n g v d i s d h r p n g bien t r o n g t a p b i i n n h o h o n . K i t q u a phdn t i c h
t u a n g q u a n chinh t a c se c h o chiing t a t h i y m o i q u a n hp c h ? t h a y k h d n g chat gifia h a i n h d m b i e n X vd Y n h d v a o h e sd t u a n g q u a n b i n h p h u a n g g i u a W i v a V i , d d n g thdi k i e m d i n h sir t d n tai ciia m d hinh t h d n g q u a tieu chudn F . B i e u d o t u o n g q u a n g i u a b i e n c h i n h tac W ] v a V l Cling d u p e t a o r a d e co cai nhin t r u e q u a n b a n v e m d i q u a n hp g i u a h a i t a p bien X v a Y ( R o b e r t M . T h o m d i k e , 2 0 0 0 ) . N g o a i ra, C C c d n cho chiing t a thdy d u p e m d i q u a n hp g i u a c a c b i i n t r o n g timg n h d m b i i n vd g i u a c d c n h d m b i e n khdc n h a u ( R o d r i g o Loureiro M a l a c a m e , 2 0 1 4 ; R i c h a r d A . J o h n s o n a n d D e a n W . W i c h e m , 2 0 0 7 ) .
Q u y t r m h thirc hipn t r o n g S A S d e thirc h i e n ptidn tich t u a n g q u a n c h i n h tdc n h u sau:
T A P C H I K H O A H Q C V A C O N G N G H ? L A M N G H I E P S O 1-2018
Ldm hpc
proc cancorr data-W0RK.IMP0RT4 out=Work._tempout;
/*** The VAR statement defines Variable set 1 ***/
var dtnn dtln tuoi songuoi;
/*** The WITH statement defines Variable set 2 ***/
with thunhap hocluc;
run;
proc sgrender data^Work.tempout template="squareplot";
run;
proc delete data=Work._tempout;
run;
Trong ling dung dudi ddy, tir s6 lieu diiu tra ndng nghidp, dien tich ddt ldm nghiep, dp tudi xa hdi hpc cua cdc hpi gia dinh, mudn phan va sd ngudi lao ddng trong gia dinh. Kit qua tich moi quan he gifia tap biin Y bao gdm: thu phdn tich moi quan he gifia hai nhom bien nhap bmh quan cua hd gia dinh va trinh dd hpc duae nhu bang 01.
van cua hd vdi tdp biin X gdm: dien tich dat
Bang 01. Ket qua phdo tich hfii quy chinh tac giira hai nhom bien X, Y
~ ~ . , Adjusted Approximate Squared Eigenvalues of Inv(E)*H C r T T i n Canonical Standard Canonical = CaDRsq/(l-CanRsq)
Correlation Error Correlation Eigenvalue DiiTerence Proportion Cumulath>e I 0,343989 0,295846 0,082941 0,118329 0,1342 0,1136 0,8667 0,8667
> 0,142187 0,092902 0,092170 0,020217 0,0206 0,1333 1,0000 Kit qua bai^ tren cho thdy tuong quan gifia giua dien tich dat ndng nghiep va sd ngudi hai nhom bien X va Y khdng chat. Ket qua R2 trong gia dmh la tuong ddi ldn (R = -0,4247).
la 0,11. Tiic la chi 11% bien ddng cua nhdm Y Tuy nhien, quan he giua hai biin nay Iai duoc diln ta bdi nhom X. nghich biin, tiic Id niu sd ngudi tang len trong
Kit qua phan tich mdi quan he gifia cac moi gia dinh thi dien tich ddt ndng nghiep lai bien thupc nhdm X dupe trinh bay trong bdng giam di. Ly do cho ket qud nay Id nhieu lao sau. Ket qua bdng sau cho thdy rang mdi tuong dpng trong cac hp gia dinh khdng lam nong quan gifia cac biin la rat long leo. Chi duy nhdt nghiep ma lam cac ngdnh nghi khdc.
Bdng 02. Ket qua phSn tich hoi qui giira cdc biln thuoc nhom X _^ Correlations Among the Regression Coefficient Estimates
dtnn dtla tuoi songuoi dtnn
dtln tuoi songuoi
1,0000 -0,0025 -0,2617 -0,4247
-0,0025 1,0000 -0,0292 -0,0669
-0,2617 -0,0292 1,0000 0,0008
-0,4247 -0,0669 0,0008 1,0000
Bieu do tuang quan gifia hai bien chinh tac Idng leo, khdng thue su chat. Bdi le, cac diem ddu tien dupe tao ra trong cdc nhdm X va Y Idm rai rac, khdng tap trung vd hmh thdnh mpt duoc trinh bay nhu sau. Bieu dd mdt ldn nua xu hudng nao ca.
cho thdy tuong quan giua hai nhom bien Id
46 T ^ CHI KHOA HQC v A CONG NGHE L A M NGHIEP SO 1-2018
Ldm hoc
Hinh 02. Bieu do the hif n moi tirong quan giira hai bien chfnh tdc dau 3.3. Phan tich tuong dong (Correspondence
Analysis)
Phan tich tuong ddng (CA) la mdt phuang phap phan tich da bien. Phuang phap nay dupe phat trien bdi Hirechfeld, sau dd dupe ke thiia va phat trien tiep bdi Jean-Paul Benzecri. CA thudng dupe ap dung cho cdc bien rdi rac, thii bac, han la cdc bien lien tuc.
Cac budc ca ban ciia phdn tich tuong ddng la (P.M. Yelland, 2010; J.C. Epidemiol, 2010):
- Budc 1: Thanh lap bang sd lieu bao gdm hai nhdm bien X vd Y. Sau do se tmh todn gid tri tdn sd d mdi td ciia nhdm bien X va nhdm biin Y.
- Budc 2: Tinh todn gia tri khoang each gifia hai biin cho timg d, theo ddng, tao nen ma tr^n khodng each Mng cdng thiic sau:
ic(xK)= k . ( f c ^ )
(1) Trong dd:K(X,Y) la gid tri khodng each giiia hai nhdm bien X va Y;
Fij la gia tri luy tich tuong iing dong thii i va cdt j ;
Fij la gid tri luy tich tuong iing ddng thu i' vdcptj;
Fj la tdng gia tri tuong iing d cot thu j . - Budc 3: Tmh diem cho cac ddng. Phan tich tuong ddng se sir dung phuang phdp bieu dd d i thi hipn ma tr|n khoang each tinh toan a budc 2. Trong do, cdc ddng bieu thi bdi cac
diem. Vi vdy, khodng each giiia cac diem chmh la gia tri khoang cdch giua cac ddng. Sau do, tir tpa dd ciia cac diem se tinh todn dupe diem cho mdi ddng.
- Budc 4: Ve hidu dd. Hai thanh phdn dau tien ciia mdi ddng diem dupe sfi dung de ve bieu dd dimg 2 chiiu. Bieu dd se phdn xac bien trong nhdm X vd Y thanh 4 nhdm, nam tai 4 cung phan tu. Tu thdng tin thu dupe d 4 cung phan tu, cho phep ket luan vi mdi quan he gifia cdc bien trong nhdm X vdi tung biin trong nhdm Y, ciing nhu cac bien trdi trong nhdm X tuang ling vdi timg biin trong nhdm Y. Ddng thdi, cd thS ket ludn ve cac bien trong tiing nhdm X va Y cd tinh tuong ddng cao ban.
^k thue hien phdn tich tuong dong thi cac lenh sau can dupe th\rc hien trong SAS:
proc corresp data=WORK.IMPORTl dimens=2 plots;
varTSn cac bi8n;
idTen bien loai;
run;
Vi du dudi ddy dupe dp dyng cho viec phdn tich mdi quan he gifia hai nhdm bien la d tieu chudn I (OTC) va nhdm biin ten lodi. Tu do cd the tim duoc lodi uu the tai mdi d, ciing nhu phan nhdm duoc cdc d cd miic dp tuong ddng vi da dang sinh hpc cao hon.
Phuang phap nay uu diem hon nhimg phdn tich truyen thdng d chd kit qud se phan dnh toan mpt cdnh todn dien he trang thdi, vi dua TAP CHI KHOA HQC VA CONG NGH|: L A M NGHIEP SO 1-2018 47
vdo sd lieu ciia nhiiu d. Ngodi ra, cac phan tich truyin thdng dira vao so cdy va ty 1? so cay cua mdi loai khdng phan loai dupe cdc d co miic dp da dang sinh hpc tuang tir nhau (Pabner, 2017; Murtagh, 2016).
Bdng 03. Ket qua tinh toan tieu chu^n y^
Ket qud tinh todn ti8u chuan y^ d i phdn tich mdi quan he giua hai biin OTC va loai cay dupe the hien trong bdng sau. Ket qud cho thdy rdng gifia hai biin thue sfi t6n t^i mdi quan h£.
Singular Prinafd
iiiiiiiiiiii i
5.i6»7
1 nertia aid a«-Square O i i - Cumulaue Sc^iBre PwEBit Pacent
£7903
«5«13 12.7G
&e5
100DO 431M
Deccnfiasition 00 25 S.0 75
" / . , - : ' ~ " - " l
Degrees of Fieedom iao
= 2?10 125
Ngoai ra, CA con cung cdp bieu dd phan d nhu trong hinh 03.
loai cdc loai, cdc d va tuong quan gifia loai va
1..
I-
•2
CoiespcnlEnDe Aidyss
. 2 - 1 0 1 2
Diirensoi 1(1256%)
Hioh 03. Kit qua phan nhom Kit qud trong hmh 03 cho thdy rdng cdc Ioai uu thi cua trying thdi lib chu yiu la Sung trd, Bdng lang di, Bdi ldi trung bp. Da cu, Bdi ldi cudng ngdn, D^ dd ning... trong khi dd, vdi rimg III loai uu thi chu ySu Id: Mac cua, Cdi trao. Ma tra, Cdm dak Idk...
v i muc dp da dgng smh hpc khdng thue su cd sir khdc biet giua hai trang thdi, kit luan nay mdt ldn nfia dupe khdng djnh, bdi le cd nhieu d cua rimg lib va rimg III cd cimg loai uu the vd td thanh lodi cung tuang tu nhau, do vay chung
Ioai, d va cac 6 tuvng dSng
cimg dupe phan vao mpt nhdm, diSu nay co the thdy dupe trong gdc phdn tu thii IL
Tuy nhi£n, niu x6t d nhdm nhd hon, chi tiet, dira vao loai uu the vd td thanh lodi cua cac 6 thi muc dp dong nhat gifia cac d cimg trang thai ldn hom nhieu so vdi cdc d khdc trgng thai.
Dieu nay dupe chiing minh trong 3 gdc phdn hi I, HI va IV trong hmh tren. Nhu vay, cd the thdy rang, viec Iua chpn vj tri diiu tra cua cac 6 trong ciing mpt trgng thai la tuong ddi tdt ve nat Io£u cay va vi the sd lieu thu thap dat dp tin cay cao.
TAP CHI KHOA HQC VA CONG NGHE LAM NGHIEP SO 1-2018
3.4. Phan tich nhdm (Cluster Variables) Phuong phap phdn tich phdn nhom (CV) dua vao ma trdn khoang each gifia cdc ddng d tuang ling d timg cdt. Cdc budc ca bdn nhu sau (T. Lee vd cOng sir, 2008):
- Budc 1: Tmh toan ma tran khoang each cua cac bien;
- Budc 2: Ap dung thuat todn phdn nhdm cho ma trdn khoang each vua thu dupe;
- Budc 3: Phdn thanh cac nhdm. Mdi nhom sg bao gdm cac biin ddng nhdt vdi nhau;
- Budc 4: Tinh todn thanh phdn nhdm thii nhat cho mdi nhdm.
Phdn tich nhdm thii bde cd the dupe su dung de phan tich mdi quan he gifia cdc Ioai.
Ve nguyen ly, phdn tich thii bac se phan cdc lodi xudt hien ciing nhau vd cd sd luang ca the tucmg duang nhau vao cimg mot nhdm. Dua
Ldm hpc vao sd lieu ca the ciia mdi loai d cac d, phan tich thu bgc se tao ra ma trdn khoang each, cac loai cd khoang each tnmg binh vdi cdc loai khac nhd thi dupe xep vao mdt nhdm, cac loai cd khodng trung bmh ldn thi se tdeh thanh mpt nhdm khac (Oksanen va cdc cdng su, 2016).
De thirc hien npi dung phdn tich ndy trong SAS thi cdc lenh sau duae thue hien.
Lpnh chay trong SAS:
proc varclus dala=WORKIMPORT hierarchy pbts;
var Tdn cac Ioai;
run;
Vdi so lieu dau vao la cac loai trong rung t\i nhien, thi ket qua phdn nhdm vd ty le bien cd the dupe gidi thich bdi cdc nhdm nhu bang 04.
Nhu vdy, vdi 28 nhdm cd the giai thich tdi 88,63% so lieu thue can kiem tra, do vay dp tin cay ciia cac nhdm Id rat cao.
Bang 04. Ket qua tfnh toan phuvng sai dirpc giai thich bdng cac nh6m Number
of Clusters
I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Total Variation Explained by Clusters 21.598738 37.962014 51.202034 62.767409 73.931556 82.806384 92.927573 100.831495 108.406836 115.645378 122.323184 126.430594 131.343500 134.306853 135.956806 137.753808 139.176678 140.687956 142.214079 143.444178 144.711209 145.716423 146.780875 147.696386 148.670995 149.609593 150.578857 151.552876
Proportion of Variation Explained by Clusters
0.1263 0.2220 0.2994 0.3671 0.4323 0.4842 0.5434 0.5897 0.6340 0.6763 0.7153 0.7394 0.7681 0.7854 0.7951 0.8056 0.8139 0.8227 0.8317 0.8389 0.8463 0.8521 0.8584 0.8637 0.8694 0.8749 0.8806 0.8863
Minimum Proportion Explained by a Cluster 0.1263 0.2150 0.2150 0.2636 0.3138 0.3995 0.3995 0.4114 0.4973 0.5218 0.5218 0.6399 0.6533 0.6769 0.6769 0.6408 0.6408 0.6408 0.6408 0.6408 0.6408 0.7081 0.7081 0.7081 0.7081 0.7081 0.7081 0.7222
Maximum Second Eigenvalue
in a Cluster 18.192509 14.349161 13.282871 12.750986 10.362658 10.250255 8.161210 8.045070 7.395710 6.738627 5.272939 5.034271 3.202249 2.137728 2.046481 1.781546 1.777455 1.728825 1.480002 1.442020 1.430204 1.242435 1.179482 1.110132 1.091138 1.081336 1.055805 0.986217
Minimum R-squared for a Variable
0.0000 0.0015 0.0042 0.0011 0.0011 0.0160 0.0159 0.0159 0.0159 0.0159 0.0159 0.0159 0.0159 0.0283 0.0283 0.0283 0.0283 0.0283 0.0271 0.0271 0.0271 0.0271 0.0271 0.0271 0.0314 0.0314 0.0314 0.0821
Maximum 1-R**2
Ratio for a Variable
0.9990 1.0326 1.0359 1.2108 1.3147 1.3147 1.6954 7.2717 8.1875 14.603 14.685 14685 24.278 24278 24278 14.685 14685 14.685 M.6S5 14685 14685 14685 14.685 14685 14685 14685 8.1650 Bieu do phdn nhdm cac loai cay dupe the hipn trong hinh 04.
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Hinh 04 cho thdy cdc loai dupe sdp xep thanh cdc nhdm nhd. Cdc lodi trong cung mpt nhdm nhd la cac loai khdng ddi khdng. Chiing hd tra nhau cimg phdt trien vd cung xuat hien trong mdt d. Vi du: Vang nhua, Manh sanh, Bui trdn, Trudng, De da ning. Sen mat va Sam Id mdt nhdm thudng xuat hien cung nhau. Hay Thanh ngach nam, Gang cao, Sdi do, Mdp la dep va Cdm cd mgt la mot nhdm nhd khdc thudng chung sdng ciing nhau. Do vdy, khi phuc hdi rimg vdi muc dich ndng cao da dgng sinh hpc thi can tap trung chpn cdc loai tai cdc nhdm khdc nhau, do Id ca sd tdt cho phuc hdi rimg, ndng cao da dang sinh hpc.
IV. KET LUAN
Trong nhiing ndm gdn ddy, rdt nhieu cdc phuang phap phan tich da bien da dupe ap dung nhieu trong cdc linh vuc nghien cim smh thdi hpc ndi chung, trong dd cd lam nghiep (S.
Wagner, 2014; S. Wagner, 2016; U. Berger, 2008). Bdi le phan tich da bien da chiing minh dupe nhieu uu diim ndi trdi nhu: khai thac tridt d i sd lieu, ket qua phan tich todn dien va khach quan han, vi vdy nhfing de xuat se hieu qud va chmh xdc han. Tuy nhien, tai Viet Nam, viec ling dung cdc phuang phdp phdn tich nay trong lihb vuc ldm nghidp cdn rdt hgn che. Nguyen nhdn chinh la cdn han chd trong hudng ddn va khai thac sfi dimg cac phdn mem phan tich sd lieu manh hien nay.
Trong phdn tich sd lidu ndi chung, cd nhiiu phdn mem tin hpc hd tra rat manh cho viec xir ly sd lieu nghidn ciiu ndi chung vd sd lieu lam nghiep ndi rieng nhu: Spss, Stata, R, M.S.
Excel, Irristat, Minitab, Statgraphics... Tuy nhien, qua qua trinh nghien ciiu vd sfi dung phdn mim SAS da chung minh dupe nhieu chuc nang mdi cd gia tri cao trong phdn tich sd lieu nghidn cuu lam nghipp, ddc biet Id phan tich da biin, da mlu (M. Marasinghe, 2008;
C Y . Joanne Peng, 2009; L.Q. Hung, 2009;
B.M. Hung, 2011).
Ldm hpc Ket qua nghien ciiu da cho thay rdng SAS cd the thyic hipn dupe phan ldn cac phuong phap phan tich da bien hien nay. Tnidc het, SAS cd the thirc hien phan tich thdnh phan chinh. Phuang phdp ndy cd the dupe dp dung dd phan tich mdi quan he gifia cac loai trong rimg tu nhien. Cdc lodi se duoc phdn thdnh 3 nhdm chinh: ddi khang, ddi khdng it vd khdng doi khdng. Phan tich thii hai cd thd thue hien trong SAS la tuong quan chinh tdc. Phan tich nay cd the phan tfch dupe mdi tuong quan giua hai nhdm bien (nhdm X, nhdm Y). Dieu nay vupt trpi hon han cdc phdn tich tuong quan dan bien thudng dupe ap dung trudc ddy. Phdn tich thu ba la phan tich tuong ddng. Phdn tich nay cd kha nang ung dung cao trong phdn tich sd lieu rimg tu nhien. Cu the, phdn tich tuong ddng cd the tim ra cac loai uu the d moi d, dong thdi phdn loai cdc d cd muc tuong ddng ve miic dp da dang sinh hpc loai thanh cdc nhdm. Ddy Id co sd quan trpng d i dieu tiet td thdnh vd ndng cao da dgng sinh hpc tai khu virc nghien cihi. Phdn tich cudi cimg dupe trinh bay trong bdi bdo ndy la phdn tich phan nhdm.
Phan tich'phdn nhdm se tao thdnh cdc nhdm lodi tuong ddng, it ddi khdng. Ngodi ra nd se cho biit phiic dp bien ddng cd the dupe gidi thich bdi cac nhdm. Dd Id ca sd tdt de khdng dinh dp tin cdy cua cac nhdm.
V. T A I LIEU THAM K H A O
1. Bui Manh Hung and Bui The Doi (2017).
Applying Imear mixed model (LMM) to analyze forestry data, checking autoccHTelati<xi and random effects, using R Journal of Forestry Science and technology, 2(2017):
p. 17-26.
2. L.Q. Hung (2009). U'ng dgng SAS phdn tich sd ligu thi nghiim. Dai hoc Nong LSm TP. Ho Chi Minh.
3. Ngo Dang Phong, Huynh T. Thiiy Trang, Nguyen Duy Nang, Tran Van MJ, Trin Hoai Thanh (2013).
Hudng ddn sir dung Mslatc, Sas vd Excel 2007 trong xir ly thi nghiim cho ngdnh nong nghiep vd qudn ly nu&c.
Truong D?i hoc Nong LSm TP. Uh Chi Minh.
4. Vu Cao Dam (1999). Phuang phdp nghiin cuu khoa hgc. NXB. Khoa hpc va KJ thuat
5. A. M. C. Davies and Tom Feam (2017). 6ack to
T 4 P CHI KHOA HQC v A CONG NGHE LAM NGHIEP SO 1-2018 51
Ldm hpc _ ^
basics: the principles of principal corr^onent analysis. 7. Rodriga Lourdnj Malacame (2014) Canoricd Spectroscopy Europe and Asia, pp. 20-23. ConvlctiatAnciyss TheM^hemctKaJomxi, 16(2014): p 1-22.
6. Robert M. Thomdike (2000). Canonical 8. Richard A. Johnson and Dean W. Wichem correlation analysis, in Handbook of Applied (2007). Applied Multivariate Statistical Analysis.
Multivariate Statistics and Mathematical Modeling. Pearson Educatim, hic.
Academic Press,
MULTIVARIATE ANALYSIS METHODS FOR FORESTRY RESEARCH DATA, USING SAS
B u i M a n h Hung Vietnam National University of Forestry S U M M A R Y
MuUivariate analysis has beai shown to have many outstanding advantages such as fiill exploitation of data, more comprehensive analysis and more objective results, SAS can pa-form a varied of multivariate analyzes.
First of all, it is the principal component analysis. This method can be applied to analyze relati(»iships among species in natural forests. The species will be classified into three main groups: resistance, minor resistance and non-resistance. The second analysis is a canonical correlation. This analysis can analyze the correlation bdween two groups of variables (group X, group Y). TTiis surpasses the previous regression analysis. The third analysis is the corresp widen ce analysis. The correspondence analysis can identify dominant species in each plot and classify plots vnth similar levels of species biodiversity into groups. This is an important basis for regulating and enhancing biodiversity in a r^ion. The final analysis is the cluster analysis. TTiis analysis will form similar, less antagonistic ^oups. In addition, it will indicate the variaticxi tiiat can be explained by the clusters. That is an excellent basis for asserting the significance of groups.
Keywords: Canonical correlation, cluster analysis, correspondence analysis, principle component analysis, Sas.
Ngdy n h a n bdi Ngay p h a n bif n Ngdy quyet dinh d a n g
02/8/2017 30/8/2017 08/9/2017
T ^ CHi KHOA HQC VA CONG NGHE LAM NGHIEP SO 1-2018