is.vector(x)不是true。

问题描述 投票:0回答:1

我的数据库包括78个国家,但由于我需要将数据库的列 "代码 "与地图的列 "代码 "相匹配(r的世界地图包),我不得不将所有国家都包括在内,并在没有数据的地方写入新农合。

# A tibble: 241 x 12
   code  country sales2010      gdp  gdppc population   Export   Import tradecost skills
   <chr> <chr>       <dbl>    <dbl>  <dbl>      <dbl>    <dbl>    <dbl>     <dbl>  <dbl>
 1 ABW   Aruba          NA NA          NA          NA NA       NA           NA     NA   
 2 AFG   Afghan…        NA NA          NA          NA NA       NA           NA     NA   
 3 AGO   Angola         NA NA          NA          NA NA       NA           NA     NA   
 4 AIA   Anguil…        NA NA          NA          NA NA       NA           NA     NA   
 5 ALB   Albania        NA NA          NA          NA NA       NA           NA     NA   
 6 ALD   Aland          NA NA          NA          NA NA       NA           NA     NA   
 7 AND   Andorra        NA NA          NA          NA NA       NA           NA     NA   
 8 ARE   United…        NA NA          NA          NA NA       NA           NA     NA   
 9 ARG   Argent…     44287  4.24e11 10386.   40788453  8.11e10  6.88e10      2.83   9.48
10 ARM   Armenia         4  9.26e 9  3218.    2877319  2.21e 9  4.54e 9      1.37  10.9 
# … with 231 more rows, and 2 more variables: investmentcost <dbl>, distance <dbl>

然而,这给我带来了很多问题。我使用命令zero.policy=TRUE创建了一个加权矩阵,以考虑到没有邻国的国家,并使用na.省略()成功地进行了莫兰测试。然而,当我运行 moran.plot 命令时,它返回的错误是 na.省略(database$sales2010) 不是一个向量。

> moran.test(na.omit(database$sales2010), PPV3.w, zero.policy=TRUE)

   Moran I test under randomisation

data:  na.omit(database$sales2010)  
weights: PPV3.w 
omitted: 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 19, 21, 22, 23, 25, 27, 28, 30, 31, 34, 35, 36, 38, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 59, 60, 61, 63, 64, 66, 69, 71, 72, 74, 75, 78, 79, 81, 83, 84, 85, 86, 87, 89, 90, 91, 92, 98, 100, 101, 103, 105, 109, 112, 113, 115, 116, 117, 118, 120, 122, 123, 126, 127, 128, 129, 133, 134, 136, 137, 138, 139, 141, 142, 143, 145, 146, 147, 148, 149, 150, 151, 152, 155, 156, 157, 158, 159, 160, 161, 164, 165, 167, 168, 170, 173, 174, 176, 177, 180, 181, 182, 183, 185, 186, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 218, 220, 222, 223, 224, 225, 226, 227, 228, 229, 230, 232, 233, 235, 236, 237, 240 n reduced by no-neighbour observations


Moran I statistic standard deviate = 2.205, p-value = 0.01373
alternative hypothesis: greater
sample estimates:
Moran I statistic       Expectation          Variance 
      0.25571193       -0.01612903        0.01519935 

> ### Moran diagram
> moran.plot(na.omit(database$sales2010), PPV3.w, zero.policy = TRUE)
Error in moran.plot(na.omit(database$sales2010), PPV3.w,  : 
 is.vector(x) is not TRUE

这就是na.省略(database$sales2010)的样子。

[1]  44287      4 185329  20222   2019 130775   1123   6584     38    994 187351    312 600161 275761  32645 303281   1275   1642
[19]  24004   1781  18636 363995   5960  14952 100793    446  10643 211677    556 658153   1159      2   4762   5057    556    291
[37]  19039  34144  65621 271794    209  18539 131316    987    756 303618    658 107154   6531   1537   4311    316  28923    209
[55]   1955 227473   1411    469  57286 242155  51486  13592  10907  13722  21190  40124  13935   1790  41474  26593      1    152
[73]  32332  57996   7505  40122  23803   3069    940  36411    168
attr(,"na.action")
 [1]   1   2   3   4   5   6   7   8  11  12  13  14  15  19  21  22  23  25  27  28  30  31  34  35  36  38  44  46  47  49  50  51
[33]  52  53  54  55  59  60  61  63  64  66  69  71  72  74  75  78  79  81  83  84  85  86  87  89  90  91  92  98 100 101 103 105
[65] 109 112 113 115 116 117 118 120 122 123 126 127 128 129 133 134 136 137 138 139 141 142 143 145 146 147 148 149 150 151 152 155
[97] 156 157 158 159 160 161 164 165 167 168 170 173 174 176 177 180 181 182 183 185 186 189 191 192 193 194 195 196 197 198 199 200
[129] 201 202 203 204 205 207 208 209 210 211 212 213 215 216 217 218 220 222 223 224 225 226 227 228 229 230 232 233 235 236 237 240
attr(,"class")
[1] "omit"

如果我使用的数据库已经排除了NAs,它将返回x与权重矩阵的长度不一样,我应该如何解决这个问题?

谢谢您

r mapping correlation spatial
1个回答
1
投票

事实上 moran.plot 需要 is.vector 也许是没有必要的,还有其他的方法来处理它。说了这么多,你需要做的事情除了是 na.omit 来处理缺失的数值。

is.vector(na.omit(1))
# [1] TRUE
na.omit(1)
# [1] 1

is.vector(na.omit(c(1,NA)))
# [1] FALSE
na.omit(c(1,NA))
# [1] 1
# attr(,"na.action")
# [1] 2
# attr(,"class")
# [1] "omit"

也许

my.na.omit <- function(z) z[!is.na(z)]
is.vector(my.na.omit(1))
# [1] TRUE
is.vector(my.na.omit(c(1,NA)))
# [1] TRUE
my.na.omit(c(1,NA))
# [1] 1
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