I have previously run mixed model analyses using glmer() in package lme4. I applied functions dredge() and get.models() in package MuMIn to quantify the top.models. I then used a model.avg() approach in package MuMIn to create a fitted object for function predict(). Finally, I created a newdata object called newdat, i.e. a new object for each predictor.
I then used newdatfinal <- predict(avModX, newdata = newdat, se.fit=TRUE, re.form=NA), where avModX presents the fitted model derived from subset.top.models <- c(top.models[[1]],top.models[[1]]) and avModX <- model.avg(subset.top.models). This all works fine.
I now need to use predict() on a segmented.lme() object. The code for function segmented.lme() can be found here: https://www.researchgate.net/publication/292986444_segmented_mixed_models_in_R_code_and_data. A reference working paper is available here: https://www.researchgate.net/publication/292629179_Segmented_mixed_models_with_random_changepoints_in_R. This function allows for detection of differences in slope and provides changepoint estimates, i.e. a test for breakpoint(s) in the data.
I first used the function
global.model.lme <- lme(response ~ predictor1*predictor2*predictor3*
predictor4 + covariate1 + covariate2 + covariate3,
data = mydat,
random = list(block = pdDiag(~ 1 + predictor1),
transect = pdDiag(~ 1 + predictor1)),
na.action="na.fail")
and followed by function
global.model.seg <- segmented.lme(global.model.lme,
Z = predictor1,
random = list(block = pdDiag(~ 1 + predictor1 + U + G0),
transect = pdDiag(~ 1 + predictor1 + U + G0)),
psi.link = "identity")
Z = the 'segmented' covariate having a segmented relationship with the response, U = slope difference, G0 = the formula of random effects for changepoints (changepoint estimate)
I would now like to use the segmented.lme() object in function predict(), i.e. something like newdatfinal <- predict(global.model.seg, newdata = newdat, se.fit=TRUE, re.form=NA)
I currently get the error message: Error in UseMethod("predict") : no applicable method for 'predict' applied to an object of class "segmented.lme"
This is a reproducible subset of the original data:
structure(list(block = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = c("B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8"), class = "factor"), transect = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = c("B1L", "B1M", "B1S", "B2L", "B2M", "B2S", "B3L", "B3M", "B3S", "B4L", "B4M", "B4S", "B5L", "B5M", "B5S", "B6L", "B6M", "B6S", "B7L", "B7M", "B7S", "B8L", "B8M", "B8S"), class = "factor"), predictor1 = c(28.63734661, 31.70995133, 27.40407982, 25.48842992, 21.81094637, 24.02032756), predictor2 = c(5.002945364, 6.85567854, 0, 22.470422, 0, 0), predictor3 = c(3.72, 3.55, 3.66, 3.65, 3.53, 3.66), predictor4 = c(504.8, 547.6, 499.7, 497.8, 473.8, 467.5), covariate1 = c(391L, 394L, 351L, 336L, 304L, 335L), covariate2 = c(0.96671086, 2.81939707, 0.899512367, 1.024730094, 1.641161861, 1.419433714), covariate3 = c(0.787505444, 0.641693911, 0.115804751, -0.041146951, 1.983567486, -0.451039179), response = c(0.81257636, 0.622662116, 0.490330786, 0.709929461, -0.156398286, -1.185175095)), .Names = c("block", "transect", "predictor1", "predictor2", "predictor3", "predictor4", "covariate1", "covariate2", "covariate3", "response"), row.names = c(NA, 6L), class = "data.frame")
and a reproducible subset of the newdat data:
structure(list(predictor1 = c(-0.441935, -0.433467318435754,0.424999636871508, -0.416531955307263, -0.408064273743017, -0.399596592178771), covariate1 = c(0L, 0L, 0L, 0L, 0L, 0L), covariate2 = c(0L, 0L, 0L, 0L, 0L, 0L), covariate3 = c(0L, 0L, 0L, 0L, 0L, 0L),
predictor2 = c(0L, 0L, 0L, 0L, 0L, 0L), predictor3 = c(0L,
0L, 0L, 0L, 0L, 0L), predictor4 = c(0L, 0L, 0L, 0L, 0L, 0L
)), .Names = c("predictor1", "covariate1", "covariate2", "covariate3", "predictor2", "predictor3", "predictor4"), row.names = c(NA, 6L), class = "data.frame")
Many thanks in advance for any advice.
