lmtest: Interfaces for lmtest package for data science pipelines.
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Interfaces to lmtest functions that can be used
in a pipeline implemented by magrittr.
Usage
1
2
3
4
5
6
7
8
9
10
11
12
ntbt_bgtest(data, ...)
ntbt_bptest(data, ...)
ntbt_coxtest(data, ...)
ntbt_dwtest(data, ...)
ntbt_encomptest(data, ...)
ntbt_gqtest(data, ...)
ntbt_grangertest(data, ...)
ntbt_harvtest(data, ...)
ntbt_hmctest(data, ...)
ntbt_jtest(data, ...)
ntbt_raintest(data, ...)
ntbt_resettest(data, ...)
Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
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
181
## Not run:
library(intubate)
library(magrittr)
library(lmtest)
## ntbt_bgtest: Breusch-Godfrey Test for higher-order serial correlation
x <- rep(c(1, -1), 50)
y1 <- 1 + x + rnorm(100)
dta <- data.frame(x, y1)
## or for fourth-order serial correlation
## Original function to interface
bgtest(y1 ~ x, order = 4, data = dta)
## The interface puts data as first parameter
ntbt_bgtest(dta, y1 ~ x, order = 4)
## so it can be used easily in a pipeline.
dta %>%
ntbt_bgtest(y1 ~ x, order = 4)
## ntbt_bptest: Breusch-Pagan test against heteroskedasticity
## ntbt_gqtest: Goldfeld-Quandt test against heteroskedasticity
## ntbt_hmctest: Harrison-McCabe test for heteroskedasticity
x <- rep(c(-1,1), 50)
err1 <- c(rnorm(50, sd=1), rnorm(50, sd=2))
err2 <- rnorm(100)
y1 <- 1 + x + err1
y2 <- 1 + x + err2
dtah <- data.frame(x, y1, y2)
## Original function to interface
bptest(y1 ~ x, data = dtah)
gqtest(y1 ~ x, data = dtah)
hmctest(y1 ~ x, data = dtah)
bptest(y2 ~ x, data = dtah)
gqtest(y2 ~ x, data = dtah)
hmctest(y2 ~ x, data = dtah)
## The interface puts data as first parameter
ntbt_bptest(dtah, y1 ~ x)
ntbt_gqtest(dtah, y1 ~ x)
ntbt_hmctest(dtah, y1 ~ x)
ntbt_bptest(dtah, y2 ~ x)
ntbt_gqtest(dtah, y2 ~ x)
ntbt_hmctest(dtah, y2 ~ x)
## so it can be used easily in a pipeline.
dtah %>%
ntbt_bptest(y1 ~ x)
dtah %>%
ntbt_gqtest(y1 ~ x)
dtah %>%
ntbt_hmctest(y1 ~ x)
dtah %>%
ntbt_bptest(y2 ~ x)
dtah %>%
ntbt_gqtest(y2 ~ x)
dtah %>%
ntbt_hmctest(y2 ~ x)
## ntbt_coxtest: Cox Test for Comparing Non-Nested Models
## ntbt_encomptest: encompassing test of Davidson & MacKinnon for comparing non-nested models
## ntbt_jtest: Davidson-MacKinnon J test for comparing non-nested models
data(USDistLag)
usdl <- na.contiguous(cbind(USDistLag, lag(USDistLag, k = -1)))
colnames(usdl) <- c("con", "gnp", "con1", "gnp1")
## Original function to interface
coxtest(con ~ gnp + con1, con ~ gnp + gnp1, data = usdl)
encomptest(con ~ gnp + con1, con ~ gnp + gnp1, data = usdl)
jtest(con ~ gnp + con1, con ~ gnp + gnp1, data = usdl)
## The interface puts data as first parameter
ntbt_coxtest(usdl, con ~ gnp + con1, con ~ gnp + gnp1)
ntbt_encomptest(usdl, con ~ gnp + con1, con ~ gnp + gnp1)
ntbt_jtest(usdl, con ~ gnp + con1, con ~ gnp + gnp1)
## so it can be used easily in a pipeline.
usdl %>%
ntbt_coxtest(con ~ gnp + con1, con ~ gnp + gnp1)
usdl %>%
ntbt_encomptest(con ~ gnp + con1, con ~ gnp + gnp1)
usdl %>%
ntbt_jtest(con ~ gnp + con1, con ~ gnp + gnp1)
## ntbt_dwtest: Durbin-Watson test for autocorrelation of disturbances
err1 <- rnorm(100)
x <- rep(c(-1,1), 50)
y1 <- 1 + x + err1
err2 <- filter(err1, 0.9, method="recursive")
y2 <- 1 + x + err2
dta <- data.frame(y1, y2, x)
## Original function to interface
dwtest(y1 ~ x, data = dta)
dwtest(y2 ~ x, data = dta)
## The interface puts data as first parameter
ntbt_dwtest(dta, y1 ~ x)
ntbt_dwtest(dta, y2 ~ x)
## so it can be used easily in a pipeline.
dta %>%
ntbt_dwtest(y1 ~ x)
dta %>%
ntbt_dwtest(y2 ~ x)
## ntbt_grangertest: Test for Granger Causality
data(ChickEgg)
## Original function to interface
grangertest(egg ~ chicken, order = 3, data = ChickEgg)
grangertest(chicken ~ egg, order = 3, data = ChickEgg)
## The interface puts data as first parameter
ntbt_grangertest(ChickEgg, egg ~ chicken, order = 3)
ntbt_grangertest(ChickEgg, chicken ~ egg, order = 3)
## so it can be used easily in a pipeline.
ChickEgg %>%
ntbt_grangertest(egg ~ chicken, order = 3)
ChickEgg %>%
ntbt_grangertest(chicken ~ egg, order = 3)
## ntbt_harvtest: Harvey-Collier test for linearity
x <- 1:50
y1 <- 1 + x + rnorm(50)
y2 <- y1 + 0.3*x^2
dta <- data.frame(y1, x)
## Original function to interface
harvtest(y1 ~ x, data = dta)
## The interface puts data as first parameter
ntbt_harvtest(dta, y1 ~ x)
## so it can be used easily in a pipeline.
dta %>%
ntbt_harvtest(y1 ~ x)
## ntbt_raintest: Rainbow test for linearity
x <- c(1:30)
y <- x^2 + rnorm(30,0,2)
dta <- data.frame(x, y)
## Original function to interface
raintest(y ~ x, data = dta)
## The interface puts data as first parameter
ntbt_raintest(dta, y ~ x)
## so it can be used easily in a pipeline.
dta %>%
ntbt_raintest(y ~ x)
## ntbt_resettest: Ramsey's RESET test for functional form
x <- c(1:30)
y1 <- 1 + x + x^2 + rnorm(30)
y2 <- 1 + x + rnorm(30)
dta <- data.frame(x, y1, y2)
## Original function to interface
resettest(y1 ~ x , power=2, type="regressor", data = dta)
resettest(y2 ~ x , power=2, type="regressor", data = dta)
## The interface puts data as first parameter
ntbt_resettest(dta, y1 ~ x , power=2, type="regressor")
ntbt_resettest(dta, y2 ~ x , power=2, type="regressor")
## so it can be used easily in a pipeline.
dta %>%
ntbt_resettest(y1 ~ x , power=2, type="regressor")
dta %>%
ntbt_resettest(y2 ~ x , power=2, type="regressor")
## End(Not run)
Example output
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Breusch-Godfrey test for serial correlation of order up to 4
data: y1 ~ x
LM test = 2.5801, df = 4, p-value = 0.6304
Breusch-Godfrey test for serial correlation of order up to 4
data: y1 ~ x
LM test = 2.5801, df = 4, p-value = 0.6304
Breusch-Godfrey test for serial correlation of order up to 4
data: y1 ~ x
LM test = 2.5801, df = 4, p-value = 0.6304
studentized Breusch-Pagan test
data: y1 ~ x
BP = 0.019081, df = 1, p-value = 0.8901
Goldfeld-Quandt test
data: y1 ~ x
GQ = 6.4967, df1 = 48, df2 = 48, p-value = 7.012e-10
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y1 ~ x
HMC = 0.13722, p-value < 2.2e-16
studentized Breusch-Pagan test
data: y2 ~ x
BP = 0.0031537, df = 1, p-value = 0.9552
Goldfeld-Quandt test
data: y2 ~ x
GQ = 0.91157, df1 = 48, df2 = 48, p-value = 0.6251
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y2 ~ x
HMC = 0.523, p-value = 0.647
studentized Breusch-Pagan test
data: y1 ~ x
BP = 0.019081, df = 1, p-value = 0.8901
Goldfeld-Quandt test
data: y1 ~ x
GQ = 6.4967, df1 = 48, df2 = 48, p-value = 7.012e-10
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y1 ~ x
HMC = 0.13722, p-value < 2.2e-16
studentized Breusch-Pagan test
data: y2 ~ x
BP = 0.0031537, df = 1, p-value = 0.9552
Goldfeld-Quandt test
data: y2 ~ x
GQ = 0.91157, df1 = 48, df2 = 48, p-value = 0.6251
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y2 ~ x
HMC = 0.523, p-value = 0.636
studentized Breusch-Pagan test
data: y1 ~ x
BP = 0.019081, df = 1, p-value = 0.8901
Goldfeld-Quandt test
data: y1 ~ x
GQ = 6.4967, df1 = 48, df2 = 48, p-value = 7.012e-10
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y1 ~ x
HMC = 0.13722, p-value < 2.2e-16
studentized Breusch-Pagan test
data: y2 ~ x
BP = 0.0031537, df = 1, p-value = 0.9552
Goldfeld-Quandt test
data: y2 ~ x
GQ = 0.91157, df1 = 48, df2 = 48, p-value = 0.6251
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y2 ~ x
HMC = 0.523, p-value = 0.62
Cox test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error z value Pr(>|z|)
fitted(M1) ~ M2 2.8543 1.29978 2.1960 0.02809 *
fitted(M2) ~ M1 -4.4003 0.78961 -5.5727 2.508e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Encompassing test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Model E: con ~ gnp + con1 + gnp1
Res.Df Df F Pr(>F)
M1 vs. ME 15 -1 12.569 0.0029371 **
M2 vs. ME 15 -1 27.093 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
J test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error t value Pr(>|t|)
M1 + fitted(M2) -2.7041 0.76273 -3.5454 0.0029371 **
M2 + fitted(M1) 2.7436 0.52710 5.2051 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Cox test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error z value Pr(>|z|)
fitted(M1) ~ M2 2.8543 1.29978 2.1960 0.02809 *
fitted(M2) ~ M1 -4.4003 0.78961 -5.5727 2.508e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Encompassing test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Model E: con ~ gnp + con1 + gnp1
Res.Df Df F Pr(>F)
M1 vs. ME 15 -1 12.569 0.0029371 **
M2 vs. ME 15 -1 27.093 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
J test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error t value Pr(>|t|)
M1 + fitted(M2) -2.7041 0.76273 -3.5454 0.0029371 **
M2 + fitted(M1) 2.7436 0.52710 5.2051 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Cox test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error z value Pr(>|z|)
fitted(M1) ~ M2 2.8543 1.29978 2.1960 0.02809 *
fitted(M2) ~ M1 -4.4003 0.78961 -5.5727 2.508e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Encompassing test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Model E: con ~ gnp + con1 + gnp1
Res.Df Df F Pr(>F)
M1 vs. ME 15 -1 12.569 0.0029371 **
M2 vs. ME 15 -1 27.093 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
J test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error t value Pr(>|t|)
M1 + fitted(M2) -2.7041 0.76273 -3.5454 0.0029371 **
M2 + fitted(M1) 2.7436 0.52710 5.2051 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Durbin-Watson test
data: y1 ~ x
DW = 1.8977, p-value = 0.3388
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y2 ~ x
DW = 0.23539, p-value < 2.2e-16
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y1 ~ x
DW = 1.8977, p-value = 0.3388
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y2 ~ x
DW = 0.23539, p-value < 2.2e-16
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y1 ~ x
DW = 1.8977, p-value = 0.3388
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y2 ~ x
DW = 0.23539, p-value < 2.2e-16
alternative hypothesis: true autocorrelation is greater than 0
Granger causality test
Model 1: egg ~ Lags(egg, 1:3) + Lags(chicken, 1:3)
Model 2: egg ~ Lags(egg, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 0.5916 0.6238
Granger causality test
Model 1: chicken ~ Lags(chicken, 1:3) + Lags(egg, 1:3)
Model 2: chicken ~ Lags(chicken, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 5.405 0.002966 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Granger causality test
Model 1: egg ~ Lags(egg, 1:3) + Lags(chicken, 1:3)
Model 2: egg ~ Lags(egg, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 0.5916 0.6238
Granger causality test
Model 1: chicken ~ Lags(chicken, 1:3) + Lags(egg, 1:3)
Model 2: chicken ~ Lags(chicken, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 5.405 0.002966 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Granger causality test
Model 1: egg ~ Lags(egg, 1:3) + Lags(chicken, 1:3)
Model 2: egg ~ Lags(egg, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 0.5916 0.6238
Granger causality test
Model 1: chicken ~ Lags(chicken, 1:3) + Lags(egg, 1:3)
Model 2: chicken ~ Lags(chicken, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 5.405 0.002966 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Harvey-Collier test
data: y1 ~ x
HC = 0.45652, df = 47, p-value = 0.6501
Harvey-Collier test
data: y1 ~ x
HC = 0.45652, df = 47, p-value = 0.6501
Harvey-Collier test
data: y1 ~ x
HC = 0.45652, df = 47, p-value = 0.6501
Rainbow test
data: y ~ x
Rain = 28.868, df1 = 15, df2 = 13, p-value = 1.577e-07
Rainbow test
data: y ~ x
Rain = 28.868, df1 = 15, df2 = 13, p-value = 1.577e-07
Rainbow test
data: y ~ x
Rain = 28.868, df1 = 15, df2 = 13, p-value = 1.577e-07
RESET test
data: y1 ~ x
RESET = 123550, df1 = 1, df2 = 27, p-value < 2.2e-16
RESET test
data: y2 ~ x
RESET = 0.47418, df1 = 1, df2 = 27, p-value = 0.4969
RESET test
data: y1 ~ x
RESET = 123550, df1 = 1, df2 = 27, p-value < 2.2e-16
RESET test
data: y2 ~ x
RESET = 0.47418, df1 = 1, df2 = 27, p-value = 0.4969
RESET test
data: y1 ~ x
RESET = 123550, df1 = 1, df2 = 27, p-value < 2.2e-16
RESET test
data: y2 ~ x
RESET = 0.47418, df1 = 1, df2 = 27, p-value = 0.4969
intubate documentation built on May 2, 2019, 2:46 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Author(s) Examples
Description
Interfaces to lmtest functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 | ntbt_bgtest(data, ...)
ntbt_bptest(data, ...)
ntbt_coxtest(data, ...)
ntbt_dwtest(data, ...)
ntbt_encomptest(data, ...)
ntbt_gqtest(data, ...)
ntbt_grangertest(data, ...)
ntbt_harvtest(data, ...)
ntbt_hmctest(data, ...)
ntbt_jtest(data, ...)
ntbt_raintest(data, ...)
ntbt_resettest(data, ...)
|
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | ## Not run:
library(intubate)
library(magrittr)
library(lmtest)
## ntbt_bgtest: Breusch-Godfrey Test for higher-order serial correlation
x <- rep(c(1, -1), 50)
y1 <- 1 + x + rnorm(100)
dta <- data.frame(x, y1)
## or for fourth-order serial correlation
## Original function to interface
bgtest(y1 ~ x, order = 4, data = dta)
## The interface puts data as first parameter
ntbt_bgtest(dta, y1 ~ x, order = 4)
## so it can be used easily in a pipeline.
dta %>%
ntbt_bgtest(y1 ~ x, order = 4)
## ntbt_bptest: Breusch-Pagan test against heteroskedasticity
## ntbt_gqtest: Goldfeld-Quandt test against heteroskedasticity
## ntbt_hmctest: Harrison-McCabe test for heteroskedasticity
x <- rep(c(-1,1), 50)
err1 <- c(rnorm(50, sd=1), rnorm(50, sd=2))
err2 <- rnorm(100)
y1 <- 1 + x + err1
y2 <- 1 + x + err2
dtah <- data.frame(x, y1, y2)
## Original function to interface
bptest(y1 ~ x, data = dtah)
gqtest(y1 ~ x, data = dtah)
hmctest(y1 ~ x, data = dtah)
bptest(y2 ~ x, data = dtah)
gqtest(y2 ~ x, data = dtah)
hmctest(y2 ~ x, data = dtah)
## The interface puts data as first parameter
ntbt_bptest(dtah, y1 ~ x)
ntbt_gqtest(dtah, y1 ~ x)
ntbt_hmctest(dtah, y1 ~ x)
ntbt_bptest(dtah, y2 ~ x)
ntbt_gqtest(dtah, y2 ~ x)
ntbt_hmctest(dtah, y2 ~ x)
## so it can be used easily in a pipeline.
dtah %>%
ntbt_bptest(y1 ~ x)
dtah %>%
ntbt_gqtest(y1 ~ x)
dtah %>%
ntbt_hmctest(y1 ~ x)
dtah %>%
ntbt_bptest(y2 ~ x)
dtah %>%
ntbt_gqtest(y2 ~ x)
dtah %>%
ntbt_hmctest(y2 ~ x)
## ntbt_coxtest: Cox Test for Comparing Non-Nested Models
## ntbt_encomptest: encompassing test of Davidson & MacKinnon for comparing non-nested models
## ntbt_jtest: Davidson-MacKinnon J test for comparing non-nested models
data(USDistLag)
usdl <- na.contiguous(cbind(USDistLag, lag(USDistLag, k = -1)))
colnames(usdl) <- c("con", "gnp", "con1", "gnp1")
## Original function to interface
coxtest(con ~ gnp + con1, con ~ gnp + gnp1, data = usdl)
encomptest(con ~ gnp + con1, con ~ gnp + gnp1, data = usdl)
jtest(con ~ gnp + con1, con ~ gnp + gnp1, data = usdl)
## The interface puts data as first parameter
ntbt_coxtest(usdl, con ~ gnp + con1, con ~ gnp + gnp1)
ntbt_encomptest(usdl, con ~ gnp + con1, con ~ gnp + gnp1)
ntbt_jtest(usdl, con ~ gnp + con1, con ~ gnp + gnp1)
## so it can be used easily in a pipeline.
usdl %>%
ntbt_coxtest(con ~ gnp + con1, con ~ gnp + gnp1)
usdl %>%
ntbt_encomptest(con ~ gnp + con1, con ~ gnp + gnp1)
usdl %>%
ntbt_jtest(con ~ gnp + con1, con ~ gnp + gnp1)
## ntbt_dwtest: Durbin-Watson test for autocorrelation of disturbances
err1 <- rnorm(100)
x <- rep(c(-1,1), 50)
y1 <- 1 + x + err1
err2 <- filter(err1, 0.9, method="recursive")
y2 <- 1 + x + err2
dta <- data.frame(y1, y2, x)
## Original function to interface
dwtest(y1 ~ x, data = dta)
dwtest(y2 ~ x, data = dta)
## The interface puts data as first parameter
ntbt_dwtest(dta, y1 ~ x)
ntbt_dwtest(dta, y2 ~ x)
## so it can be used easily in a pipeline.
dta %>%
ntbt_dwtest(y1 ~ x)
dta %>%
ntbt_dwtest(y2 ~ x)
## ntbt_grangertest: Test for Granger Causality
data(ChickEgg)
## Original function to interface
grangertest(egg ~ chicken, order = 3, data = ChickEgg)
grangertest(chicken ~ egg, order = 3, data = ChickEgg)
## The interface puts data as first parameter
ntbt_grangertest(ChickEgg, egg ~ chicken, order = 3)
ntbt_grangertest(ChickEgg, chicken ~ egg, order = 3)
## so it can be used easily in a pipeline.
ChickEgg %>%
ntbt_grangertest(egg ~ chicken, order = 3)
ChickEgg %>%
ntbt_grangertest(chicken ~ egg, order = 3)
## ntbt_harvtest: Harvey-Collier test for linearity
x <- 1:50
y1 <- 1 + x + rnorm(50)
y2 <- y1 + 0.3*x^2
dta <- data.frame(y1, x)
## Original function to interface
harvtest(y1 ~ x, data = dta)
## The interface puts data as first parameter
ntbt_harvtest(dta, y1 ~ x)
## so it can be used easily in a pipeline.
dta %>%
ntbt_harvtest(y1 ~ x)
## ntbt_raintest: Rainbow test for linearity
x <- c(1:30)
y <- x^2 + rnorm(30,0,2)
dta <- data.frame(x, y)
## Original function to interface
raintest(y ~ x, data = dta)
## The interface puts data as first parameter
ntbt_raintest(dta, y ~ x)
## so it can be used easily in a pipeline.
dta %>%
ntbt_raintest(y ~ x)
## ntbt_resettest: Ramsey's RESET test for functional form
x <- c(1:30)
y1 <- 1 + x + x^2 + rnorm(30)
y2 <- 1 + x + rnorm(30)
dta <- data.frame(x, y1, y2)
## Original function to interface
resettest(y1 ~ x , power=2, type="regressor", data = dta)
resettest(y2 ~ x , power=2, type="regressor", data = dta)
## The interface puts data as first parameter
ntbt_resettest(dta, y1 ~ x , power=2, type="regressor")
ntbt_resettest(dta, y2 ~ x , power=2, type="regressor")
## so it can be used easily in a pipeline.
dta %>%
ntbt_resettest(y1 ~ x , power=2, type="regressor")
dta %>%
ntbt_resettest(y2 ~ x , power=2, type="regressor")
## End(Not run)
|
Example output
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Breusch-Godfrey test for serial correlation of order up to 4
data: y1 ~ x
LM test = 2.5801, df = 4, p-value = 0.6304
Breusch-Godfrey test for serial correlation of order up to 4
data: y1 ~ x
LM test = 2.5801, df = 4, p-value = 0.6304
Breusch-Godfrey test for serial correlation of order up to 4
data: y1 ~ x
LM test = 2.5801, df = 4, p-value = 0.6304
studentized Breusch-Pagan test
data: y1 ~ x
BP = 0.019081, df = 1, p-value = 0.8901
Goldfeld-Quandt test
data: y1 ~ x
GQ = 6.4967, df1 = 48, df2 = 48, p-value = 7.012e-10
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y1 ~ x
HMC = 0.13722, p-value < 2.2e-16
studentized Breusch-Pagan test
data: y2 ~ x
BP = 0.0031537, df = 1, p-value = 0.9552
Goldfeld-Quandt test
data: y2 ~ x
GQ = 0.91157, df1 = 48, df2 = 48, p-value = 0.6251
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y2 ~ x
HMC = 0.523, p-value = 0.647
studentized Breusch-Pagan test
data: y1 ~ x
BP = 0.019081, df = 1, p-value = 0.8901
Goldfeld-Quandt test
data: y1 ~ x
GQ = 6.4967, df1 = 48, df2 = 48, p-value = 7.012e-10
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y1 ~ x
HMC = 0.13722, p-value < 2.2e-16
studentized Breusch-Pagan test
data: y2 ~ x
BP = 0.0031537, df = 1, p-value = 0.9552
Goldfeld-Quandt test
data: y2 ~ x
GQ = 0.91157, df1 = 48, df2 = 48, p-value = 0.6251
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y2 ~ x
HMC = 0.523, p-value = 0.636
studentized Breusch-Pagan test
data: y1 ~ x
BP = 0.019081, df = 1, p-value = 0.8901
Goldfeld-Quandt test
data: y1 ~ x
GQ = 6.4967, df1 = 48, df2 = 48, p-value = 7.012e-10
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y1 ~ x
HMC = 0.13722, p-value < 2.2e-16
studentized Breusch-Pagan test
data: y2 ~ x
BP = 0.0031537, df = 1, p-value = 0.9552
Goldfeld-Quandt test
data: y2 ~ x
GQ = 0.91157, df1 = 48, df2 = 48, p-value = 0.6251
alternative hypothesis: variance increases from segment 1 to 2
Harrison-McCabe test
data: y2 ~ x
HMC = 0.523, p-value = 0.62
Cox test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error z value Pr(>|z|)
fitted(M1) ~ M2 2.8543 1.29978 2.1960 0.02809 *
fitted(M2) ~ M1 -4.4003 0.78961 -5.5727 2.508e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Encompassing test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Model E: con ~ gnp + con1 + gnp1
Res.Df Df F Pr(>F)
M1 vs. ME 15 -1 12.569 0.0029371 **
M2 vs. ME 15 -1 27.093 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
J test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error t value Pr(>|t|)
M1 + fitted(M2) -2.7041 0.76273 -3.5454 0.0029371 **
M2 + fitted(M1) 2.7436 0.52710 5.2051 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Cox test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error z value Pr(>|z|)
fitted(M1) ~ M2 2.8543 1.29978 2.1960 0.02809 *
fitted(M2) ~ M1 -4.4003 0.78961 -5.5727 2.508e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Encompassing test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Model E: con ~ gnp + con1 + gnp1
Res.Df Df F Pr(>F)
M1 vs. ME 15 -1 12.569 0.0029371 **
M2 vs. ME 15 -1 27.093 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
J test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error t value Pr(>|t|)
M1 + fitted(M2) -2.7041 0.76273 -3.5454 0.0029371 **
M2 + fitted(M1) 2.7436 0.52710 5.2051 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Cox test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error z value Pr(>|z|)
fitted(M1) ~ M2 2.8543 1.29978 2.1960 0.02809 *
fitted(M2) ~ M1 -4.4003 0.78961 -5.5727 2.508e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Encompassing test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Model E: con ~ gnp + con1 + gnp1
Res.Df Df F Pr(>F)
M1 vs. ME 15 -1 12.569 0.0029371 **
M2 vs. ME 15 -1 27.093 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
J test
Model 1: con ~ gnp + con1
Model 2: con ~ gnp + gnp1
Estimate Std. Error t value Pr(>|t|)
M1 + fitted(M2) -2.7041 0.76273 -3.5454 0.0029371 **
M2 + fitted(M1) 2.7436 0.52710 5.2051 0.0001067 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Durbin-Watson test
data: y1 ~ x
DW = 1.8977, p-value = 0.3388
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y2 ~ x
DW = 0.23539, p-value < 2.2e-16
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y1 ~ x
DW = 1.8977, p-value = 0.3388
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y2 ~ x
DW = 0.23539, p-value < 2.2e-16
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y1 ~ x
DW = 1.8977, p-value = 0.3388
alternative hypothesis: true autocorrelation is greater than 0
Durbin-Watson test
data: y2 ~ x
DW = 0.23539, p-value < 2.2e-16
alternative hypothesis: true autocorrelation is greater than 0
Granger causality test
Model 1: egg ~ Lags(egg, 1:3) + Lags(chicken, 1:3)
Model 2: egg ~ Lags(egg, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 0.5916 0.6238
Granger causality test
Model 1: chicken ~ Lags(chicken, 1:3) + Lags(egg, 1:3)
Model 2: chicken ~ Lags(chicken, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 5.405 0.002966 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Granger causality test
Model 1: egg ~ Lags(egg, 1:3) + Lags(chicken, 1:3)
Model 2: egg ~ Lags(egg, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 0.5916 0.6238
Granger causality test
Model 1: chicken ~ Lags(chicken, 1:3) + Lags(egg, 1:3)
Model 2: chicken ~ Lags(chicken, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 5.405 0.002966 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Granger causality test
Model 1: egg ~ Lags(egg, 1:3) + Lags(chicken, 1:3)
Model 2: egg ~ Lags(egg, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 0.5916 0.6238
Granger causality test
Model 1: chicken ~ Lags(chicken, 1:3) + Lags(egg, 1:3)
Model 2: chicken ~ Lags(chicken, 1:3)
Res.Df Df F Pr(>F)
1 44
2 47 -3 5.405 0.002966 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Harvey-Collier test
data: y1 ~ x
HC = 0.45652, df = 47, p-value = 0.6501
Harvey-Collier test
data: y1 ~ x
HC = 0.45652, df = 47, p-value = 0.6501
Harvey-Collier test
data: y1 ~ x
HC = 0.45652, df = 47, p-value = 0.6501
Rainbow test
data: y ~ x
Rain = 28.868, df1 = 15, df2 = 13, p-value = 1.577e-07
Rainbow test
data: y ~ x
Rain = 28.868, df1 = 15, df2 = 13, p-value = 1.577e-07
Rainbow test
data: y ~ x
Rain = 28.868, df1 = 15, df2 = 13, p-value = 1.577e-07
RESET test
data: y1 ~ x
RESET = 123550, df1 = 1, df2 = 27, p-value < 2.2e-16
RESET test
data: y2 ~ x
RESET = 0.47418, df1 = 1, df2 = 27, p-value = 0.4969
RESET test
data: y1 ~ x
RESET = 123550, df1 = 1, df2 = 27, p-value < 2.2e-16
RESET test
data: y2 ~ x
RESET = 0.47418, df1 = 1, df2 = 27, p-value = 0.4969
RESET test
data: y1 ~ x
RESET = 123550, df1 = 1, df2 = 27, p-value < 2.2e-16
RESET test
data: y2 ~ x
RESET = 0.47418, df1 = 1, df2 = 27, p-value = 0.4969
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.