-
Notifications
You must be signed in to change notification settings - Fork 0
/
statsPractice.py
180 lines (117 loc) · 4.91 KB
/
statsPractice.py
1
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
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as stats
import statsmodels as models
#import statsmodels.stats.multitest as m
import numpy as npy #has :
#npy.mean (mean)
#npy.std (standard deviation)
#npy.var (variance)
#npy.sem (standard error)
#stats.norm.interval (CI for mean) parameters: confidence = (confidence), loc = (mean), scale = (standard error)
#note : standard error formula : std/m.sqrt(n)
import math as m
import csv
import statsmodels
import pyFunctions as s
data0 = [301, 305, 312, 315, 318, 319, 310, 318, 305, 313, 305, 305, 305, 311]
data1 = pd.read_csv("anova.csv") # opens the csv file
data2 = pd.read_csv("anova2.csv")
univ = data2[data2["Group"] == "University students"]
anova1 = [[3.7,1.2,4.1,5.4,2.5], [5.1,2.1,4.5,4.3], [1.1,0.8,2.3]]
anova2 = [[3.7,1.2,4.1,5.4,2.5], [5.1,2.1,4.5,4.3,4.2], [1.1,0.8,2.3,2.2,1.9]]
anova3 = [[3.7,1.2,4.1,5.4,2.5], [5.1,2.1,4.5,4.3,4.2], [1.1,0.8,2.3,2.2,1.9], [1.2,2.5,3.4,1.1,1.5]]
anova2Way = [[3.7,1.2,4.1,5.4,2.5], [5.1,2.1,4.5,4.3,4.2], [1.1,0.8,2.3,2.2,1.9], [1.2,2.5,3.4,1.1,1.5], [3.2,2.1,4.5,4.3,4.2], [5.1,1.9,4.5,2.2,4.2]]
#anovaDF = pd.DataFrame({'sample 1': anova1[0], 'sample 2': anova1[1], 'sample 3': anova1[2]})
dataframe1 = pd.DataFrame({'Fertilizer': npy.repeat(['daily', 'weekly'], 15),
'Watering': npy.repeat(['daily', 'weekly'], 15),
'height': [14, 16, 15, 15, 16, 13, 12, 11, 14,
15, 16, 16, 17, 18, 14, 13, 14, 14,
14, 15, 16, 16, 17, 18, 14, 13, 14,
14, 14, 15]})
dataframe2 = pd.DataFrame({
'video': npy.repeat(['Violent', 'Non-violent'], 30),
'student type': npy.repeat(['Volunteer', 'Psychology', 'Volunteer', 'Psychology'], 15),
'rating':
[4.1,3.5,3.4,4.1,3.7,2.8,3.4,4.0,2.5,3.0,3.4,3.5,3.2,3.1,2.4, # violent, volunteer
3.4,3.9,4.2,3.2,4.3,3.3,3.1,3.2,3.8,3.1,3.8,4.1,3.3,3.8,4.5, # violent physcology
2.4,2.4,2.5,2.6,3.6,4.0,3.3,3.7,2.8,2.9,3.2,2.5,2.9,3.0,2.4, #non violent volunteer
2.5,2.9,2.9,3.0,2.6,2.4,3.5,3.3,3.7,3.3,2.8,2.5,2.8,2.0,3.1] #non violent psychology
})
# 2 WAY ANOVAAAAAAAAAAAAAAAAAAAAAAA
print(dataframe1)
model = statsmodels.formula.api.ols('height ~ C(Fertilizer) + C(Watering) + C(Fertilizer):C(Watering)', data = dataframe1).fit()
result = statsmodels.stats.anova.anova_lm(model, type=2) #p vals: 0.9133, .99, .904
pVals = result.loc[:, 'PR(>F)'].values.tolist()
pVals.pop()
print(result, '\n', pVals)
#-------------------
print(dataframe2)
final = []
final.append([])
data3 = pd.read_csv("anova.csv").values.tolist()
#print(data3)
#key = data3[0][0]
#value = 0
#for rowName, data in data3:
# if key != rowName:
# value = value +1
# final.append([])
# key = rowName
#
# print(key)
# final[value] += [data]
#
#print(final)
with open("anova2.csv", 'r') as f:
iterator = csv.reader(f) #csv.writer to write into the file
next(iterator) # iterates over the column name for the loop
#for line in iterator: # iterator cycles through each row in tghe csv file
# print(line)
#for line in iterator:
#print(line[0], end=", ") # iterates bjy column, if left blank iterates by row
#makes a new csv file and adds a tab delimiter
#with open("pStat/anova2.csv", 'r') as f:
# iterator = csv.reader(f)
#
# next(iterator)
#
# with open("new.csv", 'w') as file:
# writeIt = csv.writer(file, delimiter='\t')
#
# for line in iterator:
# writeIt.writerow(line)
#prints the boxplot out !!
#plt.show() top 5 lines, can be changed to any value
#plt.tail() bottom lines, can be changed to any value
#print(univ)
#
#print(data2.head())
#print(data2.tail())
#data2.boxplot("Stress_score", by="Group") # .boxplot(y axis, x axis)
#data2.plot.pie(y="Stress_score")
#plt.show()
#F STATISTIC = VARIANCE 1 /VARIANCE 2
print(2*(1-stats.f.cdf(1.083646869,24,24))) # two tail f test
#f.oneway method. only gives fstatistic and p value
fStat, pVal = stats.f_oneway(*anova1) #asterisk specifies unlimited args, can input a list and itll open lists within that list
print(f"f stat:{fStat} \t p-value: {pVal}")
print(stats.tukey_hsd(*anova2))
#print(data1.values.tolist())
#stats.false_discovery_control()
dict = {}
for i, item in enumerate(anova2Way):
dict.update({i: item})
df = pd.DataFrame(dict)
print(df)
#2 WAY ANOVA TEST
print(npy.repeat(['Violent', 'Non-violent'], 30))
data = pd.DataFrame({0: [1.2,1.3,2.1,1.1,2.3], 1: [2.2,2.2,1.2,1.4,2.1], 2: [0.4,0.9,0.2,1.1,1.4], 3: [2.2,1.4,1.1,1.2,1.5], 4: [2.2,3.2,1.5,1.9,2.4]})
#out = stats.tukey_hsd(data[0:4])
#print(stats.f_oneway(data), out, out.confidence_interval(), sep='\n')
#tuke = npy.array(out.confidence_interval())
#
#print(tuke[0], '\n')
#print(tuke[0,2, :])
data.boxplot()
plt.show()