Seasonal adjustment package (Census Bureau) in R only adjusting half the data - r
I've managed to get the library(seasonal) package to run perfectly on half my data (columns 2 and 8) but it refuses to seasonally adjust the other half. I'm really confused as the code runs fine and no error shows up, so I'm wondering if this is a feature of my data (I'm new to time-series) or are there hidden errors in my code?
head(x)
Date European Maori Pacific Peoples Asian MELAA Other Ethnicity Total
1 2004-09-30 7.9 17.9 14.8 15.4 13.4 14.1 9.7
2 2004-12-31 7.9 18.6 13.3 20.9 14.3 14.9 9.9
3 2005-03-31 7.8 17.8 14.6 21.1 12.6 16.1 9.8
4 2005-06-30 7.6 18.1 11.9 20.4 12.6 13.7 9.4
5 2005-09-30 7.1 17.4 10.5 16.1 9.4 19.2 9.0
6 2005-12-31 7.6 15.9 9.8 16.6 11.1 12.0 9.3
Code as follows:
*Note the seasonal adjustment runs smoothly in sub1 and sub2. No seasonal adjustment occurs in sub3 and sub4
library(tidyverse)
library(seasonal)
x <- read.csv("HLF524501_20191112_083700_50.csv", header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
x <- x %>%
mutate(Date = ifelse(substring(Date, 5, nchar(x)) == "Q3", paste(substring(Date, 0, 4), "09-30", sep = "-"),
ifelse(substring(Date, 5, nchar(x)) == "Q4", paste(substring(Date, 0, 4), "12-31", sep = "-"),
ifelse(substring(Date, 5, nchar(x)) == "Q1", paste(substring(Date, 0, 4), "03-31", sep = "-"),
paste(substring(Date, 0, 4), "06-30", sep = "-")))))
x$Date <- as.Date(x$Date)
sub1 <- x[,c(1,8)]
season0<-ts(sub1[,-1],frequency=4,start=c(2004,3))
sea1 <- seas(season0)
plot(sea1)
sub2 <- x[,c(1,2)]
season0<-ts(sub2[,-1],frequency=4,start=c(2004,3))
sea2 <- seas(season0)
plot(sea2)
sub3 <- x[,c(1,3)]
season0<-ts(sub3[,-1],frequency=4,start=c(2004,3))
sea3 <- seas(season0)
plot(sea3)
sub4 <- x[,c(1,4)]
season0<-ts(sub4[,-1],frequency=4,start=c(2004,3))
sea4 <- seas(season0)
plot(sea4)
Data as follows:
dput(x)
structure(list(Date = c("2004-09-30", "2004-12-31", "2005-03-31",
"2005-06-30", "2005-09-30", "2005-12-31", "2006-03-31", "2006-06-30",
"2006-09-30", "2006-12-31", "2007-03-31", "2007-06-30", "2007-09-30",
"2007-12-31", "2008-03-31", "2008-06-30", "2008-09-30", "2008-12-31",
"2009-03-31", "2009-06-30", "2009-09-30", "2009-12-31", "2010-03-31",
"2010-06-30", "2010-09-30", "2010-12-31", "2011-03-31", "2011-06-30",
"2011-09-30", "2011-12-31", "2012-03-31", "2012-06-30", "2012-09-30",
"2012-12-31", "2013-03-31", "2013-06-30", "2013-09-30", "2013-12-31",
"2014-03-31", "2014-06-30", "2014-09-30", "2014-12-31", "2015-03-31",
"2015-06-30", "2015-09-30", "2015-12-31", "2016-03-31", "2016-06-30",
"2016-09-30", "2016-12-31", "2017-03-31", "2017-06-30", "2017-09-30",
"2017-12-31", "2018-03-31", "2018-06-30", "2018-09-30", "2018-12-31",
"2019-03-31", "2019-06-30", "2019-09-30"), European = c(7.9,
7.9, 7.8, 7.6, 7.1, 7.6, 7.7, 6.7, 7.2, 8.5, 8.1, 7.8, 7.2, 7.7,
9, 8.1, 8.9, 9.6, 10.8, 11.5, 12.1, 12.5, 11.4, 11.3, 10.9, 11.4,
11.8, 11.7, 10.9, 11, 11.6, 11.8, 12.6, 12.4, 11.1, 10.7, 11.2,
12.2, 11.2, 10.8, 10.3, 11.7, 11.3, 10.9, 11, 10.7, 11.4, 10.9,
10.4, 11.1, 10.6, 10.1, 10, 11.1, 11, 10.4, 9.5, 11.4, 10.2,
9.5, 8.7), Maori = c(17.9, 18.6, 17.8, 18.1, 17.4, 15.9, 17.3,
16.1, 15.6, 18.1, 19.3, 17, 16.8, 15.9, 18.9, 16.2, 17.5, 18.9,
21.7, 22.4, 24.9, 26.3, 25, 25.5, 25.6, 25.1, 25.8, 25.5, 25.1,
24.5, 25, 25, 28.1, 28.4, 24, 24.7, 24.8, 25.7, 24.5, 22.9, 23.1,
25.1, 24.4, 24, 25.1, 23.5, 25.2, 22.8, 22.9, 23.1, 22.3, 21.4,
20.6, 21.7, 20.9, 20.3, 19, 20.9, 19.3, 17.6, 17.6), `Pacific Peoples` = c(14.8,
13.3, 14.6, 11.9, 10.5, 9.8, 15, 12.3, 12.3, 12.6, 11.6, 15,
9.5, 12.8, 17.6, 13.2, 15.6, 16.1, 21.3, 23.4, 25.1, 25.8, 24.4,
25.8, 26.1, 24.8, 24.2, 25.1, 25.2, 24.2, 27.6, 27.8, 29.6, 29.2,
27.7, 27.7, 27.2, 26.2, 24.8, 21.6, 22.4, 22.9, 23.7, 20.8, 23.9,
21.7, 21.8, 18.8, 18.7, 19.8, 19.9, 18.9, 18.4, 18.5, 16.9, 17.4,
14.8, 20.4, 18, 15.5, 14.1), Asian = c(15.4, 20.9, 21.1, 20.4,
16.1, 16.6, 11.1, 12.5, 8.9, 19.6, 17.3, 14.6, 15.5, 13.3, 13.9,
14.6, 13.4, 15.2, 16, 17, 20.6, 19.4, 18.2, 18.7, 17.9, 17.8,
18.2, 15.2, 15.3, 19, 18.4, 18.3, 18.5, 17.8, 14, 14.7, 14.4,
14.5, 14.9, 14.9, 13, 13.7, 15.6, 14.9, 13.7, 14.5, 17.5, 14.6,
13.4, 12.5, 13.1, 11.3, 11.1, 13.1, 11.9, 11.1, 11.1, 12.5, 11.1,
10.5, 9.5), MELAA = c(13.4, 14.3, 12.6, 12.6, 9.4, 11.1, 18.6,
7, 13.7, 15.4, 19.7, 20.5, 12.1, 15.9, 25.8, 21.7, 25.6, 26.4,
26.5, 26.9, 19.1, 28.5, 23, 21.4, 24.2, 14.5, 19.8, 25, 27.1,
14.2, 23.7, 22.4, 23.8, 21.8, 18.3, 14.5, 21.7, 20.7, 25.5, 21.4,
20.9, 26.2, 21.9, 25.9, 19.9, 18.7, 21.7, 19, 15.8, 23.9, 17.4,
18.3, 17, 20.3, 17.5, 17.9, 10.9, 16.1, 14.9, 22.6, 13.5), `Other Ethnicity` = c(14.1,
14.9, 16.1, 13.7, 19.2, 12, 9.8, 13.4, 10.1, 16.6, 10, 16.1,
18.5, 9.2, 9.2, 13, 8.2, 9.8, 6.7, 8.6, 10.2, 9.1, 12.6, 10.6,
8.8, 10.6, 11.1, 12.5, 12, 13.7, 15, 16.8, 12.2, 12.7, 7.9, 10.8,
12.5, 13.2, 12.1, 12.4, 8.5, 9.5, 13.1, 12.6, 10.2, 7.8, 9.4,
11.8, 11.9, 14.2, 13, 13.4, 12.3, 12.6, 13.4, 11.6, 13.7, 11.4,
11.2, 13.6, 9.3), Total = c(9.7, 9.9, 9.8, 9.4, 9, 9.3, 9.4,
8.5, 8.6, 10.3, 10.3, 9.7, 8.8, 9.2, 10.8, 9.8, 10.3, 11.2, 12.6,
13.4, 14.6, 14.9, 13.9, 13.8, 13.7, 13.9, 14.3, 13.8, 13.4, 13.6,
14.3, 14.4, 15.3, 14.9, 13.2, 13, 13.5, 14.4, 13.4, 12.9, 12.3,
13.7, 13.8, 13.2, 13.2, 12.8, 13.8, 12.7, 12.2, 12.8, 12.5, 11.6,
11.7, 12.6, 12.1, 11.7, 10.9, 12.8, 11.5, 10.8, 9.9)), class = "data.frame", row.names = c(NA,
-61L))
Would really appreciate any help :)
Thanks!
The seas function doesn't include seasonality if it doesn't detect seasonality in the data. This is based on QS statistics, which is discussed in detail here: https://stats.stackexchange.com/questions/148573/the-results-and-specifics-from-the-qs-function-in-r
You can use summary(sea1) and qs(sea1) vs. summary(sea3) and qs(sea3) to see ARIMA modeling details and the tests for seasonality on your data. More broadly, would recommend using summary on all 4 of your models to see the modeling details for each part of your data.
Related
Count the number of EVENTS which "var" is between "level1" and "level2" for "n" hours
Hi I have a question some of you could help me out. My vector is temperature collected hourly: a<-c(7.95, 7.8, 7.85, 7.6, 7.1, 5.55, 4.35, 4.1, 7.35, 10.7, 14.2, 17.25, 19.1, 19.8, 20.1, 20.15, 19.9, 18.95, 16.7, 14.4, 13.75, 12.1, 12.3, 11.4, 10.3, 8.55, 7.45, 7.05, 5.6, 5.95, 4.85, 5.3, 9.35, 12.7, 16.15, 19.1, 20.5, 21.05, 21.4, 21.4, 21.35, 20.1, 16.95, 15.8, 15.6, 14.95, 14.15, 13.85) I want to determine how many events there are which a is above 20 and below 10 for a certain period of time. Pictorially, this is what I am looking for. Here there are two events (blue and green) where temperature amplitude threshold was achieved. Result should be 2. ============ Another example: Here temperature was below 10 & above 20 for at least 1 hour, two times (or two events). Result should be 2. Data for example 2: b<-c(20.2, 20.55, 20.85, 21.7, 20.7, 18.7, 17.5, 17.4, 16.65, 17.15, 15.8, 13.85, 12.55, 11.45, 10.2, 9.3, 8.2, 7.4, 7.25, 6.65, 5.9, 4.75, 4.5, 4.15, 4.4, 6.25, 8.1, 10.35, 12.4, 14.3, 15.3, 16.3, 17.25, 17.25, 16.85, 14.45, 12.85, 11.35, 10.2, 9.1, 8.6, 7.35, 5.9, 4.85, 3.65, 3.3, 2.95, 2.65, 2.45, 4.85, 6.45, 8.25, 9.95, 11.1, 12.3, 13.2, 13.95, 14.05, 13.15, 10.35, 8.15, 6.6, 6.3, 6, 7.55, 5.85, 5.05, 4.75, 4.5, 4.75, 4.75, 4.55, 5.15, 8.45, 12.05, 16.35, 18.9, 20.55, 21.6, 21.45, 21.75, 21.15, 20.05, 17.75, 16.5, 18.2, 18.05, 17.95, 17.8, 17.55, 17.25, 16.95, 16.6, 16.35, 16.1, 16.25, 16.4, 17.1, 17.8)
I update my answer using your new example b. I found a solution based on: Find a numeric pattern R . # Get out of range (10,20) x = ifelse(b<=10,1,0) # Don't need nested ifelse # Specify if 2 for upper limit [20,inf) x[b>=20]=2 z = rle(x) > z Run Length Encoding lengths: int [1:10] 5 10 12 12 14 7 14 3 6 16 values : num [1:10] 2 0 1 0 1 0 1 0 2 0 Like your plot, there is two combinations of going lower limit to upper limit or the other way round: 2-0-1 or 1-0-2. You can do: ranges = z$values # This line looks for 0 -groups of T in range (10,20)- and the looks if the group before is 1 and the next 2 (low to up) or 2 and 1 (up to low) x = as.integer(ranges == 0 & ( (lag(ranges)==2 & lead(ranges)==1) | (lag(ranges)==1 & lead(ranges)==2) ) ) x [1] 0 1 0 0 0 0 0 1 0 NA You can sum, omitting the NAs, to return 2: sum(x, na.rm=TRUE) You could remove 0s and just look for 1 followed by 2 or the other way round but it is the same concept. If you want to keep the z$lengths to work with them later, you could transform rle() output to a dataframe and adapt the code to mutate a new column.
R cut function: how to cut data which can include the right lowest and highest boundaries
I am a beginner in R. I used cut function in R to bin my data. My data starts from 0 but after cutting the lower boundary has a negative result and I have no idea why this happened. My code is: cancer_rtcl$cancer_rate_cut=cut(cancer_rtcl$rate,6) The statistical summary of my data is: > summary(cancer_rtcl$rate) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0 13.3 16.5 16.4 18.8 63.5 > dput(cancer_rtcl$rate) c(63.5, 41.5, 36, 33.9, 29.7, 27.2, 27.2, 26, 25.9, 25.9, 25.3, 25.1, 24.6, 24.3, 23.6, 23.3, 22.8, 22.7, 22.5, 22.4, 22.3, 22.3, 21.9, 21.9, 21.7, 21.6, 21.5, 21.4, 21.3, 21.2, 21.2, 20.9, 20.8, 20.7, 20.5, 20.5, 20.3, 20.2, 20, 19.7, 19.7, 19.6, 19.6, 19.5, 19.4, 19.1, 19, 19, 19, 18.9, 18.9, 18.8, 18.8, 18.8, 18.8, 18.8, 18.7, 18.5, 18.5, 18.5, 18.4, 18.3, 18.3, 18.2, 18.2, 18.2, 18.1, 18.1, 18, 17.9, 17.9, 17.9, 17.8, 17.8, 17.8, 17.7, 17.7, 17.6, 17.6, 17.6, 17.5, 17.4, 17.4, 17.3, 17.3, 17.3, 17.3, 17.3, 17.2, 17.2, 17.1, 17.1, 17.1, 17, 17, 16.9, 16.9, 16.9, 16.8, 16.8, 16.7, 16.6, 16.6, 16.6, 16.5, 16.5, 16.5, 16.5, 16.5, 16.4, 16.4, 16.4, 16.4, 16.2, 16.1, 16, 16, 16, 16, 15.9, 15.9, 15.8, 15.8, 15.7, 15.7, 15.7, 15.7, 15.6, 15.6, 15.6, 15.6, 15.6, 15.5, 15.4, 15.4, 15.4, 15.3, 15.3, 15.3, 15.3, 15.2, 15.1, 15.1, 15, 15, 14.8, 14.6, 14.6, 14.4, 14.2, 14.2, 14.1, 14.1, 14.1, 14.1, 14, 13.9, 13.8, 13.7, 13.6, 13.6, 13.6, 13.3, 13.2, 13.2, 13.1, 13.1, 13, 12.9, 12.9, 12.7, 12.6, 12.5, 12.4, 12.3, 12.3, 12.2, 12, 11.9, 11.8, 11.6, 11.6, 11.4, 11.4, 11.3, 11, 10.8, 10.8, 10.7, 10.6, 10.5, 10.2, 9.9, 9.8, 9.7, 9.7, 9.6, 9.6, 9.5, 9.3, 9.2, 9.2, 9, 9, 8, 7.9, 7.3, 7.1, 7, 6.9, 6.3, 4.6, 0, 0, 0, 0, 0) But the cutting result is: 6 Levels: (-0.0635,10.6] (10.6,21.2] (21.2,31.8] (31.8,42.3] ... (52.9,63.6] As you can see, the lowest boundary is a negative result, which is not ideal because I need to make a map based on the binned data. I also tried another type of coding: cancer_rtcl$rate_cut=cut(cancer_rtcl$rate,c(5,10,15,20,25)) But in this way, I lost the data larger than 25. Can anyone help to figure out how to bin the data and get the exact lowest and highest boundaries? Thanks!
Does this work to capture data larger than 25? cancer_rtcl$rate_cut1=cut(cancer_rtcl$rate,c(5,10,15,20,25,Inf))
How can I order two vectors in R on the sorting of one?
My question is about the two following vectors: list_1 <- c(17.5, 19.3, 17.0, 16.5, 19.4, 19.0, 19.6, 20.7, 18.5, 19.0, 20.2, 21.0, 19.7, 19.5, 17.0, 20.2, 19.3, 19.6, 19.9, 18.4, 18.6, 19.5) list_2 <- c(24.5, 24.5, 24.0, 23.9, 25.8, 27.5, 29.1, 26.5, 28.1, 26.5, 29.2, 30.5, 27.0, 28.5, 25.2, 28.5, 29.1, 29.1, 26.8, 27.8, 26.5, 26.8) The values in both vectors are correlated, that means that for example 17.5 (list_1) and 24.5 (list_2) belong together. I want to order list_1 from small to large, and order list_2 relative to the new placement of the value it correlates with in list_1, so keeping 17.5 and 24.5 together on the same placement in both lists.
list_1 <- c(17.5, 19.3, 17.0, 16.5, 19.4, 19.0, 19.6, 20.7, 18.5, 19.0, 20.2, 21.0, 19.7, 19.5, 17.0, 20.2, 19.3, 19.6, 19.9, 18.4, 18.6, 19.5) list_2 <- c(24.5, 24.5, 24.0, 23.9, 25.8, 27.5, 29.1, 26.5, 28.1, 26.5, 29.2, 30.5, 27.0, 28.5, 25.2, 28.5, 29.1, 29.1, 26.8, 27.8, 26.5, 26.8) df <- data.frame(list_1, list_2) df <- df[order(df$list_1),] list2 <- df$list_2 Demo
What is the expansion of this model formula in R
I have a set of data points that can be fit with the following model:
temp <- structure(list(x = 1:119, y = c(50, 50, 43.1, 39.2, 34.3, 34.9,
34.3, 32.5, 31.8, 30.1, 30.5, 29.7, 28.6, 28.5, 28.5, 28.6, 27.8,
27.7, 26, 27, 26.9, 25.6, 25.8, 25.5, 25.1, 24.3, 24.1, 24.3,
24.1, 24.8, 24.7, 24.5, 23.9, 23.8, 23.7, 23.7, 23, 23.3, 23.7,
23.2, 22.8, 23.5, 22.7, 22.3, 22.7, 22.1, 22.1, 22.4, 22.5, 22.3,
22, 22.2, 22.1, 22.3, 21.9, 23.5, 21.5, 21.4, 22.3, 21.9, 21.7,
21.2, 21.6, 21.5, 21.5, 21.5, 21.7, 21.3, 20.9, 20.9, 21.2, 20.9,
20.8, 21, 21.3, 21.3, 20.7, 21, 20.8, 20.8, 20.8, 20.9, 20.7,
20.7, 20.1, 20.8, 20.8, 22.3, 20.8, 20.6, 20.4, 20.5, 20.2, 20.6,
20.1, 20.5, 20.5, 22, 20.5, 20.3, 20.3, 20.3, 20.1, 20.1, 20.1,
20.2, 20, 20.2, 20.2, 20.1, 20.1, 20, 20.1, 20.1, 20, 19.9, 20,
19.8, 19.9)), .Names = c("x", "y"), row.names = c(NA, -119L), class = "data.frame")
mod <- glm(y ~ log(x)*atan(x), data=temp)
plot(temp)
lines(predict(mod), col="red")
The question is, which is the resulting expansion of the model formula?
R levelplot and interpolation [closed]
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Closed 10 years ago.
some days ago I managed to make levelplots, with interpolation, with the following command within a script:
levelplot(jan~lon*lat,APM,main="Jan",panel=panel.levelplot.raster,interpolate=T)
I accidentaly did not keep that session, only the script, so it is not possible for me to go through the history and recover all the commands I had used.
Now, after loading latticeExtra, the same command produces an empty plot. On the other hand, leaving out the last two elements of the instruction, i.e.:
levelplot(jan~lon*lat,APM,main="Jan")
The graph is drawn.
I'd like to have interpolation at the surface, but something is going wrong with
panel=panel.levelplot.raster
the same behaviour happens when rasterVis is loaded.
I guess I am missing something... any help?
The data has the following structure:
> head(APM)
lat lon jan feb mar apr may jun jul aug sep oct nov dec
1 -18.5 10.5 29.7 28.8 25.6 25.6 26.8 29.9 35.5 46.8 35.5 27.5 27.5 27.9
2 -17.5 10.5 28.8 29.8 26.3 26.2 27.8 31.6 39.7 63.1 40.4 27.6 27.6 28.3
3 -16.5 10.5 28.7 30.0 26.9 26.8 28.6 32.1 41.1 109.4 42.8 29.7 28.9 29.4
4 -15.5 10.5 28.4 29.5 27.5 26.9 29.1 34.2 46.4 109.5 40.8 29.7 29.7 28.5
5 -14.5 10.5 28.2 29.3 27.4 27.8 27.8 42.8 60.7 104.3 49.1 29.4 28.8 28.6
6 -13.5 10.5 27.8 28.4 27.7 28.3 29.8 41.2 102.8 105.7 47.8 29.5 28.5 28.0
totalling 224 cells listed by latitude and longitude:
> dput(APM)
structure(list(lat = c(-18.5, -17.5, -16.5, -15.5, -14.5, -13.5,
-12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -18.5, -17.5,
-16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5,
-7.5, -6.5, -5.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5,
-11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -18.5, -17.5, -16.5,
-15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5,
-5.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5,
-10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -18.5, -17.5, -16.5, -15.5,
-14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5,
-18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
-9.5, -8.5, -7.5, -6.5, -5.5, -18.5, -17.5, -16.5, -15.5, -14.5,
-13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -18.5,
-17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5,
-8.5, -7.5, -6.5, -5.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5,
-12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -18.5, -17.5,
-16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5,
-7.5, -6.5, -5.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5,
-11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -18.5, -17.5, -16.5,
-15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5,
-5.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5,
-10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -18.5, -17.5, -16.5, -15.5,
-14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5,
-18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
-9.5, -8.5, -7.5, -6.5, -5.5), lon = c(10.5, 10.5, 10.5, 10.5,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 11.5,
11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5,
11.5, 11.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5,
12.5, 12.5, 12.5, 12.5, 12.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5,
13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 14.5, 14.5, 14.5,
14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5,
15.5, 15.5, 15.5, 15.5, 15.5, 15.5, 15.5, 15.5, 15.5, 15.5, 15.5,
15.5, 15.5, 15.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5,
16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 17.5, 17.5, 17.5, 17.5, 17.5,
17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 18.5, 18.5,
18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5,
18.5, 19.5, 19.5, 19.5, 19.5, 19.5, 19.5, 19.5, 19.5, 19.5, 19.5,
19.5, 19.5, 19.5, 19.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5,
20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 21.5, 21.5, 21.5, 21.5,
21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 22.5,
22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5,
22.5, 22.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5,
23.5, 23.5, 23.5, 23.5, 23.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5,
24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 25.5, 25.5, 25.5,
25.5, 25.5, 25.5, 25.5, 25.5, 25.5, 25.5, 25.5, 25.5, 25.5, 25.5
), jan = c(29.7, 28.8, 28.7, 28.4, 28.2, 27.8, 28, 29.4, 30.3,
32.5, 33.2, 33.5, 33.1, 34.3, 28.4, 29.4, 29.6, 29, 28.9, 28.8,
28.5, 28.9, 29.9, 31, 32.2, 32.9, 35.8, 37.4, 30, 27.4, 29.6,
30, 28.4, 30.6, 30.4, 29.9, 30.2, 31.3, 33.9, 35.5, 35.8, 34.5,
30.3, 23.8, 25.2, 23.2, 22.9, 24, 27.2, 29.3, 31.6, 32.3, 31,
31.4, 37.3, 37.4, 24.9, 25.1, 23.4, 21.2, 23.5, 22.8, 23.5, 24.5,
26, 27.7, 28.6, 33.3, 37.3, 40.7, 37.9, 38.5, 27.1, 22.5, 24.7,
23.3, 24.7, 24.8, 26.6, 27.4, 30.7, 33.9, 35.9, 37.6, 30.7, 29.5,
26.6, 24.1, 24.1, 25.4, 25.4, 25.7, 28, 28.2, 32.9, 36.3, 35.2,
40.4, 22.7, 25.5, 26.5, 24.6, 24.3, 24, 25.8, 26.7, 29.4, 31.8,
35.4, 38, 37.4, 41.3, 22.2, 23.7, 26.8, 25.8, 25.3, 24, 25.1,
26.9, 29.6, 31.8, 34.4, 35.6, 39.3, 40.1, 26.2, 26.2, 26, 25.8,
25.3, 23.2, 24.5, 25.9, 26.9, 30.8, 33.3, 38.3, 40.2, 41.2, 26.9,
26.4, 27.1, 23.1, 22.9, 24, 28.5, 26.9, 27.1, 31, 32.7, 36.6,
38, 41.6, 26.6, 27.2, 27.3, 26, 23.6, 25.8, 33.2, 33.8, 25.8,
28.1, 31.6, 34.7, 35.3, 38.5, 27.3, 28.1, 28.6, 27, 31.5, 31.8,
29.9, 27.6, 25.4, 28.4, 29.8, 32.2, 36.4, 36.8, 28.2, 26.2, 27.3,
27, 27.1, 23.7, 23.5, 25.3, 26.1, 29, 29.3, 29.3, 36.4, 35.1,
24.2, 25.1, 23.5, 23.1, 24, 24.2, 24.3, 26.7, 26.7, 26.8, 29.3,
30.4, 33.3, 33.4, 24.8, 24.3, 24.5, 24.8, 26.1, 24.6, 25, 25.3,
27.2, 26.8, 28.9, 30.5, 31.2, 33.1), feb = c(28.8, 29.8, 30,
29.5, 29.3, 28.4, 28.6, 28.7, 28.7, 28.5, 29.4, 32.2, 34.4, 37.4,
29.4, 30.5, 30.8, 30.7, 29.6, 28.3, 28, 28.4, 28.4, 29, 29.5,
31.4, 33.3, 37.1, 31.7, 26.5, 29.8, 30.4, 28.4, 28.9, 27.7, 27.8,
29.3, 30, 30.4, 31.7, 32.9, 34.7, 29.1, 23.5, 24.2, 23.3, 22.7,
23.6, 25.4, 26.4, 27.7, 28.1, 26.9, 27.5, 30.4, 31.6, 22.3, 24.7,
23.7, 21.2, 23.4, 22.5, 22.3, 21.7, 23.2, 23.3, 23.2, 27.5, 31.6,
34, 33, 33.3, 26.3, 22.5, 23.9, 24, 24.5, 23.7, 24.2, 24.5, 25.4,
27.8, 31, 33.4, 29.3, 27.5, 24.7, 23.2, 23.5, 24.7, 25.7, 25.4,
25.8, 25.6, 27.2, 30.7, 30.4, 35, 21.4, 23.3, 24.1, 24.7, 25.2,
25.2, 25.6, 25.9, 27.7, 26.9, 30.1, 31.5, 32, 34.5, 20.2, 22.4,
24.9, 25.1, 25.3, 25.5, 24.1, 25.8, 26.2, 28.4, 29.3, 30.9, 33.5,
35.6, 24.3, 24.2, 24, 24.5, 24.9, 24.7, 23.8, 25, 26.2, 27.1,
29.9, 31.3, 34.6, 34.7, 25.3, 23.6, 24.9, 22.6, 23.1, 23.1, 25.3,
26.8, 24.9, 25.1, 28.1, 30.7, 32.1, 34.2, 24, 23.4, 24.6, 25.6,
24.9, 26.4, 30.6, 31.2, 22.2, 23.9, 27.6, 30.8, 29.1, 29.7, 24,
23.6, 25.4, 25.8, 29.9, 29.5, 27.2, 26.3, 23.5, 24.5, 25.6, 27,
28.9, 28.3, 24.4, 22, 23.4, 24.9, 25.4, 22.7, 23, 23, 22.4, 23.7,
24.6, 24.9, 29.8, 28.5, 21.3, 22.8, 21.3, 21.1, 21.7, 22.8, 24.4,
23.2, 23.3, 22.4, 25.4, 26.3, 27.3, 27.1, 22.8, 22.1, 21.4, 21.9,
24.1, 24.1, 26, 23.5, 23.8, 22.5, 23, 24.9, 26.5, 28)), .Names = c("lat",
"lon", "jan", "feb"), row.names = c(NA,
-224L))
The following produces a plot in a fresh interactive session of R APM <- results of dput(APM) from the OP library(latticeExtra) levelplot(jan~lon*lat,APM,main="Jan",panel=panel.levelplot.raster,interpolate=T) or alternatively levelplot(jan~lon*lat,APM,main="Jan",interpolate=TRUE, useRaster = TRUE) If this is not working for you, check to see if you have the latest versions of the packages in question; for example, R version 2.15.2 (2012-10-26) latticeExtra_0.6-24 lattice_0.20-10 RColorBrewer_1.0-5 If the problem still is not resolved, there's something else that hasn't been mentioned in this post.