DATA fitness
INPUT VEK VAHA DOBA_CV POC_PULZ CV_PULZ MAX_PULZ SPOTR_KYSL SKUPINA POHLAVI $ ID;
DATALINES;
57	73.37	12.63	58	174	176	39.407	2	M	1
54	79.38	11.17	62	156	165	46.08	2	M	2
52	76.32	9.63	48	164	166	45.441	2	M	3
50	70.87	8.92	48	146	155	54.625	2	M	4
51	67.25	11.08	48	172	172	45.118	2	M	5
54	91.63	12.88	44	168	172	39.203	2	M	6
51	73.71	10.47	59	186	188	45.79	2	M	7
57	59.08	9.93	49	148	155	50.545	2	M	8
49	76.32	9.4	56	186	188	48.673	2	M	9
48	61.24	11.5	52	170	176	47.92	2	M	10
52	82.78	10.5	53	170	172	47.467	2	M	11
44	73.03	10.13	45	168	168	50.541	1	M	12
45	87.66	14.03	56	186	192	37.388	1	M	13
45	66.45	11.12	51	176	176	44.754	1	M	14
47	79.15	10.6	47	162	164	47.273	1	M	15
54	83.12	10.33	50	166	170	51.855	1	M	16
49	81.42	8.95	44	180	185	49.156	1	M	17
51	69.63	10.95	57	168	172	40.836	1	M	18
51	77.91	10	48	162	168	46.672	1	Z	19
48	91.63	10.25	48	162	164	46.774	1	Z	20
49	73.37	10.08	76	168	168	50.388	1	Z	21
44	89.47	11.37	62	178	182	44.609	0	Z	22
40	75.07	10.07	62	185	185	45.313	0	Z	23
44	85.84	8.65	45	156	168	54.297	0	Z	24
42	68.15	8.17	40	166	172	59.571	0	Z	25
38	89.02	9.22	55	178	180	49.874	0	Z	26
47	77.45	11.63	58	176	176	44.811	0	Z	27
40	75.98	11.95	70	176	180	45.681	0	Z	28
43	81.19	10.85	64	162	170	49.091	0	Z	29
44	81.42	13.08	63	174	176	39.442	0	Z	30
38	81.87	8.63	48	170	186	60.055	0	Z	31
;
PROC PRINT; RUN;

p<0,05 - zamitame H0
H0: ma normal

velke soubory

data work.prestupky2;
 
 
proc univariate data=work.prestupky2 normal plot;
histogram pokuta/kernel normal;
qqplot pokuta/normal (mu=est sigma=est);
var pokuta;
run;
 
 
proc boxplot data=work.prestupky2;
plot pokuta*pohlavi;
plot pokuta*pohlavi / boxstyle=schematic;
plot pokuta*pohlavi / notches;
run;
 
 
proc univariate data=work.prestupky2 winsor=2;
var pokuta;
run;
 
 
proc means data=work.prestupky2 mean clm maxdec=2;
var pokuta;
run;
 
 
proc univariate data=work.prestupky2 cibasic;
var pokuta;
run;
 
proc univariate data=work.prestupky2 cibasic mu0=1335;
var pokuta;
run;

male soubory

data stat;
input vyuka @@;
datalines
;
90 85 98 87 65 88 93 85 97 103
;
 
 
proc univariate data=stat normal plot;
histogram vyuka/kernel normal;
qqplot vyuka/normal (mu=est sigma=est);
var vyuka;
run;

3. cv

proc reg data=fitness
model spotr_kysl = doba_cv;
plot spotr_kysl*doba_cv;
plot r.*p.;
symbol v=star;
run;
 
proc reg data=work.fitness;
model spotr_kysl = doba_cv/r influence spec;
plot spotr_kysl*doba_cv;
plot r.*p.;
symbol v=star;
run;
 
proc reg data=work.fitness;
model spotr_kysl = doba_cv vek/r vif influence spec;
plot r.*p.;
symbol v=star;
run;

4. cv

proc boxplot data=work.teploty;
plot vysledky*mesic;
run;
 
proc glm data=work.teploty;
class mesic;
model vysledky=mesic;
means mesic/hovtest tukey scheffe lsd sidak;
run;
 
proc npar1way data=work.teploty wilcoxon;
class mesic;
var vysledky;
run;

5. cv

## pearson chi squared
 
data souhlas;
input vzdelani $ prirazka $ pocet @@;
datalines;
ano ano 50 ano ne 7 ano nevim 11
ne ano 14 ne ne 23 ne nevim 20
;
 
proc freq data=souhlas;
tables vzdelani*prirazka /expected chisq measures norow nocol nopercent;
weight pocet;
run;
 
 
 
 
## Fischer (pokud pearson varuje, ze 33% cetnosti je < 5)
 
 
data zakon;
input zmena $ nakup $ pocet @@;
datalines;
ano denne 27 ano nekolikrat_t 79 ano jednou_t 13 ano jednou_14 2
ne denne 38 ne nekolikrat_t 79 ne jednou_t 24 ne jednou_14 3
;
 
proc freq data=zakon;
tables zmena*nakup/expected chisq measures norow nocol nopercent exact;
weight pocet;
run;
 
 
 
 
chisq p-value H0: neexistuje zavislost
lambda asymetric C|R = %
 
 
 
data zkouska;
input skola $ splneno $ pocet @@;
datalines;
gympl ano 45 ss ano 22 uc ano 7
gympl ne 7 ss ne 10 uc ne 9
;
 
proc freq data=zkouska;
tables skola*splneno/expected chisq measures norow nocol nopercent;
weight pocet;
run;
 
 
 
 
 
 
data semena;
input osetreno $ vyklicilo $ pocet @@;
datalines;
ne ano 70 ne ne 30
ano ano 130 ano ne 130
;
 
proc freq data=semena;
tables osetreno*vyklicilo /expected chisq measures norow nocol nopercent;
weight pocet;
run;

t-test

proc ttest data=mesta h0=1335;
run;

data work.prestupky2;
proc univariate data= work.prestupky2 normal plot;
histogram body/kernel normal;
qqplot body/normal (mu= est sigma= est);
var body;
run;
 
data work.prestupky2;
proc boxplot data= work.prestupky2;
plot body*pohlavi/boxstyle=schematic;
plot body*pohlavi/notches;
run;
 
data work prestupky2;
proc univariate data= work.prestupky2 trimmed=2;
var body;
run;
 
data work prestupky2;
proc univariate data= work.prestupky2 winsorized=2;
var body;
run;
 
data work prestupky2;
proc means data= work.prestupky2 mean cv clm maxdec=2;
var body;
title "Interval spolehlivosti pro průměr";
run;
 
data work prestupky2;
proc univariate data= work.prestupky2 cibasic;
var body;
title "Basic Confidence Limits Assuming Normality - IS pro základní popisné statistiky";
run;
 
data stat;
input vyuka @@;
datalines;
98 79 88 64 80 92 67 88 90 60 63 67
;
proc univariate data= stat normal plot;
histogram vyuka/kernel normal;
qqplot vyuka/normal (mu= est sigma= est);
var vyuka;
run;

data fitness;
input vek doba_cv spotr_kysl;
datalines;
57	12.63	39.407		
54	11.17	46.08		
52	9.63	45.441		
50	8.92	54.625		
51	11.08	45.118		
54	12.88	39.203		
51	10.47	45.79		
57	9.93	50.545		
49	9.4	48.673		
48	11.5	47.92		
52	10.5	47.467		
44	10.13	50.541		
45	14.03	37.388		
45	11.12	44.754		
47	10.6	47.273		
54	10.33	51.855		
49	8.95	49.156		
51	10.95	40.836		
51	10	46.672		
48	10.25	46.774		
49	10.08	50.388		
44	11.37	44.609		
40	10.07	45.313		
44	8.65	54.297		
42	8.17	59.571		
38	9.22	49.874		
47	11.63	44.811		
40	11.95	45.681		
43	10.85	49.091		
44	13.08	39.442		
38	8.63	60.055		
;
 
proc means data= fitness n mean cv median min max std skewness kurtosis max maxdec= 2;
var vek doba_cv spotr_kysl;
run;
 
kurt: 1+ = spicaty (light tail), -1- = placaty (heavy tail); +-3 silne\\
skew: 0.8 +vpravo -vlevo
 
proc univariate data= fitness normal plot;
var vek doba_cv spotr_kysl;
run;
 
proc corr data= fitness plots= matrix (histogram);
run;
 
proc corr data= fitness nosimple fisher;
run;
 
proc corr data= fitness nosimple;
var doba_cv spotr_kysl;
partial vek;
run;
 
proc corr data= fitness nosimple;
var vek spotr_kysl;
partial doba_cv;
run;
 
proc corr data= fitness nosimple pearson spearman;
run;
 
proc reg data= fitness;
model spotr_kysl = doba_cv;
plot spotr_kysl*doba_cv;
symbol v=star;
run;
 
proc reg data= fitness;
model spotr_kysl = doba_cv/r influence spec;
plot spotr_kysl*doba_cv;
plot r.*p.;
symbol v= star;
run;

proc reg data= fitness;
model spotr_kysl = doba_cv/r influence spec;
plot spotr_kysl*doba_cv;
plot r.*p.;
symbol v= star;
run;

proc reg data= work.fitness;
model spotr_kysl = doba_cv/r influence spec;
plot spotr_kysl*doba_cv;
plot r.*p.;
symbol v= star;
run;
proc reg data= work.fitness;
model spotr_kysl = doba_cv vek/r vif influence spec;
plot r.*p.;
symbol v= star;
run;
quit;
proc reg data= work.fitness;
model spotr_kysl = doba_cv vek vaha/r vif influence spec;
plot r.*p.;
symbol v= star;
run;
quit;
proc reg data= work.fitness;
Forward:model spotr_kysl = vek vaha doba_cv max_pulz/selection=f; 
Backward:model spotr_kysl = vek vaha doba_cv max_pulz/selection=b; 
Stepwise:model spotr_kysl = vek vaha doba_cv max_pulz/selection=stepwise; 
R_square:model spotr_kysl = vek vaha doba_cv max_pulz/selection=rsquare; 
run;
quit;
proc reg data= work.fitness;
Forward:model spotr_kysl = vek vaha doba_cv max_pulz/selection=f details= summary; 
Backward:model spotr_kysl = vek vaha doba_cv max_pulz/selection=b details= summary; 
Stepwise:model spotr_kysl = vek vaha doba_cv max_pulz/selection=stepwise details= summary; 
R_square:model spotr_kysl = vek vaha doba_cv max_pulz/selection=rsquare details= summary; 
run;
quit;

data teplota;
input mesic $ vysledky @@;
 
datalines;
leden 1.00 leden 0.64 leden 1.22 leden 1.19 leden 0.62 leden 0.87 leden 1.23 leden 0.96 leden 0.92 leden 1.11
unor 1.06 unor 0.88 unor 1.04 unor 1.66 unor 1.06 unor 1.07 unor 0.87 unor 0.97 unor 2.00 unor 1.09
brezen 1.19 brezen 1.77 brezen 1.46 brezen 1.58 brezen 1.55 brezen 1.22 brezen 1.64 brezen 1.35 brezen 1.29 brezen 1.41
;
 proc boxplot data= teplota;
 plot vysledky*mesic/boxstyle = schematic;
 plot vysledky*mesic/notches;
 run;
 proc means data= teplota n mean median min max std cv skewness   kurtosis maxdec=2;
 class mesic;
 var vysledky;
 run;
 proc univariate data= teplota noprint;
 class mesic;
 histogram vysledky/normal;
 qqplot vysledky/normal (mu= est sigma= est)nrows =3;
 run;
 proc glm data= teplota;
 class mesic;
 model vysledky= mesic;
 means mesic/hovtest tukey;
 run;
 proc npar1way data= teplota wilcoxon;
 class mesic;
 var vysledky;
 run;

proc boxplot data= work.trzby;
 plot trzby*prodejna/boxstyle = schematic;
 plot trzby*prodejna/notches;
 run;
 proc means data= work.trzby n mean median min max std cv skewness   kurtosis maxdec=2;
 class prodejna;
 var trzby;
 run;
 proc univariate data= work.trzby noprint;
 class prodejna;
 histogram trzby/normal;
 qqplot trzby/normal (mu= est sigma= est)nrows =3;
 run;
 proc glm data= work.trzby;
 class prodejna;
 model trzby= prodejna;
 means prodejna/hovtest tukey;
 run;
 proc npar1way data= work.trzby wilcoxon;
 class prodejna;
 var trzby;
 run;

1. prikad

data souhlas;
input vzdelani $ prirazka $ pocet @@;
datalines;
ano ano 50 ano ne 7 ano nevim 11
ne ano 14 ne ne 23 ne nevim 20
;
proc freq data = souhlas;
tables vzdelani*prirazka/expected chisq measures norow nocol nopercent;
weight pocet;
run;

2. priklad

data zakon;
input zmena $ nakup $ pocet @@;
datalines;
ano denne 27 ano nekolik_t 79 ano jednou_t 13 ano jednou_14 2
ne denne 38 ne nekolik_t 79 ne jednou_t 24 ne jednou_14 3
;
proc freq data = zakon;
tables zmena*nakup/expected chisq measures norow nocol nopercent exact;
weight pocet;
run;

3. priklad

data zkouska;
input slozeni_zk $ skola $ pocet @@;
datalines;
ano gymnazium 45 ano stredni_od 22 ano uciliste 7
ne gymnazium 7 ne stredni_od 10 ne uciliste 9
;
proc freq data = zkouska;
tables slozeni_zk*skola/expected chisq measures norow nocol nopercent exact;
weight pocet;
run;

data work.kriminalita;
proc princomp data= work.kriminalita out= components plots=score (ellipse ncomp=2);
var kriminalita_celkem obecna_kriminalita hospodarska_kriminalita loupeze vloupani vrazdy;
id kraj;
run;
proc gplot data=components;
plot prin2*prin1/vref=0 href=0;
symbol v=star pointlabel= (j=r position=middle "#kraj");
run;
proc cluster data= work.kriminalita method=ave std;
id kraj;
run;

> setwd("C:/Users/C24-11/Desktop")
> read.csv("mzdy.csv");
     rok.mzda
1   1992;4644
2   1993;5904
3   1994;7004
4   1995;8307
5   1996;9825
6  1997;10802
7  1998;11801
8  1999;12797
9  2000;13219
10 2001;14378
11 2002;15524
12 2003;16430
13 2004;17466
14 2005;18344
15 2006;19546
16 2007;20957
17 2008;22592
18 2009;23344
19 2010;23797
20 2011;24319
> data = read.csv("mzdy.csv");
> View(data)
> View(data)
> data = read.csv("mzdy.csv", header=T, sep=";");
> y = data$mzda;
> x = data$rok;
> View(data)
> View(y);
> boxplot(y);
> plot(y~x)
> summary(data)
      rok            mzda      
 Min.   :1992   Min.   : 4644  
 1st Qu.:1997   1st Qu.:10558  
 Median :2002   Median :14951  
 Mean   :2002   Mean   :15050  
 3rd Qu.:2006   3rd Qu.:19899  
 Max.   :2011   Max.   :24319  
> summary(y)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   4644   10560   14950   15050   19900   24320 
> install.packages("outliers")
Installing package into ‘C:/Users/C24-11/Documents/R/win-library/3.2’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/outliers_0.14.zip'
Content type 'application/zip' length 52663 bytes (51 KB)
downloaded 51 KB
 
package ‘outliers’ successfully unpacked and MD5 sums checked
 
The downloaded binary packages are in
	C:\Users\C24-11\AppData\Local\Temp\Rtmpw9vBez\downloaded_packages
> outliers(y)
Error: could not find function "outliers"
> outliers::outlier(y)
> y[y<4645] <-NA
> data$dummy <- ifelse(data$rok > 2003, c(1), c(0))

# zpožděné proměnné
shift<-function(x,shift_by){
  stopifnot(is.numeric(shift_by))
  stopifnot(is.numeric(x))
 
  if (length(shift_by)>1)
    return(sapply(shift_by,shift, x=x))
 
  out<-NULL
  abs_shift_by=abs(shift_by)
  if (shift_by > 0 )
    out<-c(tail(x,-abs_shift_by),rep(NA,abs_shift_by))
  else if (shift_by < 0 )
    out<-c(rep(NA,abs_shift_by), head(x,-abs_shift_by))
  else
    out<-x
  out
}

setwd("C:/Users/C24-21.ADDS.002/Desktop")
data = read.csv("mzdy.csv", header=T, sep=";");
 
y = data$mzda;
x = data$rok;
data <- data.frame(y,x)
data$yl <- shift(data$y, -1)
data$yl3 <- shift(data$y, -3)
data$yd <- (data$y - data$yl)
data$yr <- (data$y / data$yl)
 
 
spotreba = read.csv("spotreba.csv", header=T, sep=";");
y = spotreba$Sp_VM
x1 = spotreba$SpC_VM
x2 = spotreba$SpC_HM
x3 = spotreba$SpC_DM
x4 = spotreba$Prijem
x0 = matrix(1,11,1)
 
X = cbind(x0, x1, x2, x3, x4)
A = t(X)%*%X
B = solve(A)
C = t(X)%*%y
coef = B%*%C
View(coef)
 
fit = lm(formula = y~x1+x2+x3+x4, data = spotreba)
summary(fit)

#LRM verifikace
data = read.csv("spotreba.csv", header = T, sep = ";")
 
data$konst <- c(1)
 
y = data$Sp_VM
x0 = data$konst
x1 = data$SpC_VM
x2 = data$SpC_HM
x3 = data$SpC_DM
x4 = data$Prijem
 
X = cbind(x0, x1, x2, x3, x4)
K = solve(t(X)%*%X)
coef = K%*%t(X)%*%y
View (coef)
 
teor = X%*%coef # vektor teoretickych hodnot zavisle promenne
res = y - teor # vektor rezidui
 
RSS = sum(res*res)
a = y-mean(y)
TSS = sum(a*a)
KD = 1-(RSS/TSS)
 
n = length(y)
k = length(coef)
KKD = 1 - (1 - KD) * ((n - 1)*(n - k))
 
KRR = RSS/(n-k)
Rg1 = KRR*K[1,1]
SCHg1 = sqrt(Rg1)
tg1 = coef[1,1]/SCHg1
 
fit = lm(y~x1+x2+x3+x4)
summary(fit)
 
tseries::jarque.bera.test(res)
lmtest::dwtest(y~x1+x2+x3+x4)

#Dalsi triky s R:
print(cbind(rbind(1,2,3),rbind(4,5,6)))
print(rbind(cbind(1,2,3),cbind(4,5,6)))
 
#rbind() = sklada prvky pod sebe (rows)
#cbind() = sklada prvky vedle sebe (columns)
#takze lze jejich kombinaci zadat matici po sloupcich i po radkach podle potreby..
#print() = jako View() ale vypisuje do stavajici konzole misto otvirani novyho okna

cvicebnice do strany 28

#odhad logaritmickyho modelu ala gretl
 
setwd("C:/Users/C24-17.ADDS.001/Desktop")
data    = read.csv("spotreba.csv", header = T, sep = ";")
data$konst <- c(1)
 
ly      = log(data$Sp_VM)
x0      = data$konst
lx1     = log(data$SpC_VM)
lx2     = log(data$SpC_HM)
lx3     = log(data$Prijem)
 
 
#prvni zpusob vypoctu odhadu
fit = lm(ly~lx1+lx2+lx3)
summary(fit)
 
coef = fit$coefficients
gama0 = exp(coef[1])
 
#druhej zpusob vypoctu odhadu
X = cbind(x0, lx1, lx2, lx3)
A = t(X)%*%X
B = solve(A)
C = t(X)%*%ly
coef2 = B%*%C
View(coef2)

#nejsem si jistej co to pocita, ale dela se to takhle :-D
setwd("C:/Users/C24-17.ADDS.001/Desktop")
data    = read.csv("tq.csv", header = T, sep = ";")
 
data$konst <- c(1)
 
y     = 1/data$y1
x     = 1/data$x1
#c     = data$konst
 
fit = lm(y~x)
summary(fit)
 
#vypocet parametru
coef = fit$coefficients
gama0 = 1/coef[1]
gama1 = coef[2]*gama0
 
#testy
#install.packages("lmtest") #staci nainstalovat jednou
lmtest::reset(y~x) #ramseyuv RESET test
lmtest::bptest(y~x) #Breusch–Pagan test - vypocet heteroskedasticity

Gretl → nastroje → spustit GNU R. otevre se vam novy okno s R a jsou v nem prednacteny data z gretlu v objektu gretldata prvni promenna z gretlu je tam teda jako gretldata[,1], dalsi jako gretldata[,2], atd… muzete si to zkusit vypsat/vykreslit

• print(gretldata);
• plot(gretldata);
• print(gretldata[,1]);
• plot(gretldata[,1]);

dejme tomu, ze chcem sezonne ocistit prvni promennou z gretlu. pouzijem tyhle prikazy:

fit <- stl(gretldata[,1], s.window=12)
print(fit)
plot(fit)

gretldata[,1] je vstupni vektor (nactenej z gretlu), s.window=12 udava, ze mame 12 udaju rocne (data po mesicich).